1
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Kiemen AL, Dequiedt L, Shen Y, Zhu Y, Matos-Romero V, Forjaz A, Campbell K, Dhana W, Cornish T, Braxton AM, Wu PH, Fishman EK, Wood LD, Wirtz D, Hruban RH. PanIN or IPMN? Redefining Lesion Size in 3 Dimensions. Am J Surg Pathol 2024:00000478-990000000-00353. [PMID: 38764379 DOI: 10.1097/pas.0000000000002245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2024]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) develops from 2 known precursor lesions: a majority (∼85%) develops from pancreatic intraepithelial neoplasia (PanIN), and a minority develops from intraductal papillary mucinous neoplasms (IPMNs). Clinical classification of PanIN and IPMN relies on a combination of low-resolution, 3-dimensional (D) imaging (computed tomography, CT), and high-resolution, 2D imaging (histology). The definitions of PanIN and IPMN currently rely heavily on size. IPMNs are defined as macroscopic: generally >1.0 cm and visible in CT, and PanINs are defined as microscopic: generally <0.5 cm and not identifiable in CT. As 2D evaluation fails to take into account 3D structures, we hypothesized that this classification would fail in evaluation of high-resolution, 3D images. To characterize the size and prevalence of PanINs in 3D, 47 thick slabs of pancreas were harvested from grossly normal areas of pancreatic resections, excluding samples from individuals with a diagnosis of an IPMN. All patients but one underwent preoperative CT scans. Through construction of cellular resolution 3D maps, we identified >1400 ductal precursor lesions that met the 2D histologic size criteria of PanINs. We show that, when 3D space is considered, 25 of these lesions can be digitally sectioned to meet the 2D histologic size criterion of IPMN. Re-evaluation of the preoperative CT images of individuals found to possess these large precursor lesions showed that nearly half are visible on imaging. These findings demonstrate that the clinical classification of PanIN and IPMN fails in evaluation of high-resolution, 3D images, emphasizing the need for re-evaluation of classification guidelines that place significant weight on 2D assessment of 3D structures.
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Affiliation(s)
| | - Lucie Dequiedt
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Yu Shen
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Yutong Zhu
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Valentina Matos-Romero
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - André Forjaz
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | | | - Will Dhana
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Toby Cornish
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Alicia M Braxton
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Pei-Hsun Wu
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Elliot K Fishman
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | | | - Denis Wirtz
- Departments of Pathology
- Oncology
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
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2
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Li Y, Huang M, Wang M, Wang Y, Deng P, Li C, Huang J, Chen H, Wei Z, Ouyang Q, Zhao J, Lu Y, Su S. Tumor cells impair immunological synapse formation via central nervous system-enriched metabolite. Cancer Cell 2024:S1535-6108(24)00169-7. [PMID: 38821061 DOI: 10.1016/j.ccell.2024.05.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Revised: 02/22/2024] [Accepted: 05/06/2024] [Indexed: 06/02/2024]
Abstract
Tumors employ various strategies to evade immune surveillance. Central nervous system (CNS) has multiple features to restrain immune response. Whether tumors and CNS share similar programs of immunosuppression is elusive. Here, we analyze multi-omics data of tumors from HER2+ breast cancer patients receiving trastuzumab and anti-PD-L1 antibody and find that CNS-enriched N-acetyltransferase 8-like (NAT8L) and its metabolite N-acetylaspartate (NAA) are overexpressed in resistant tumors. In CNS, NAA is released during brain inflammation. NAT8L attenuates brain inflammation and impairs anti-tumor immunity by inhibiting cytotoxicity of natural killer (NK) cells and CD8+ T cells via NAA. NAA disrupts the formation of immunological synapse by promoting PCAF-induced acetylation of lamin A-K542, which inhibits the integration between lamin A and SUN2 and impairs polarization of lytic granules. We uncover that tumor cells mimic the anti-inflammatory mechanism of CNS to evade anti-tumor immunity and NAT8L is a potential target to enhance efficacy of anti-cancer agents.
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Affiliation(s)
- Yihong Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Min Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Minger Wang
- School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Yi Wang
- School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Peng Deng
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Chunni Li
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Jingying Huang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Hui Chen
- Guangdong Provincial Key Laboratory of Liver Disease Research the Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou 510630, China
| | - Zhihao Wei
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Qian Ouyang
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Jinghua Zhao
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China
| | - Yiwen Lu
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
| | - Shicheng Su
- Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Medical Research Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Breast Tumor Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China; Department of Immunology, Zhongshan School of Medicine, Sun Yat-Sen University, Guangzhou 510080, China; Biotherapy Center, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou 510120, China.
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3
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Song AH, Williams M, Williamson DFK, Chow SSL, Jaume G, Gao G, Zhang A, Chen B, Baras AS, Serafin R, Colling R, Downes MR, Farré X, Humphrey P, Verrill C, True LD, Parwani AV, Liu JTC, Mahmood F. Analysis of 3D pathology samples using weakly supervised AI. Cell 2024; 187:2502-2520.e17. [PMID: 38729110 DOI: 10.1016/j.cell.2024.03.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 01/15/2024] [Accepted: 03/25/2024] [Indexed: 05/12/2024]
Abstract
Human tissue, which is inherently three-dimensional (3D), is traditionally examined through standard-of-care histopathology as limited two-dimensional (2D) cross-sections that can insufficiently represent the tissue due to sampling bias. To holistically characterize histomorphology, 3D imaging modalities have been developed, but clinical translation is hampered by complex manual evaluation and lack of computational platforms to distill clinical insights from large, high-resolution datasets. We present TriPath, a deep-learning platform for processing tissue volumes and efficiently predicting clinical outcomes based on 3D morphological features. Recurrence risk-stratification models were trained on prostate cancer specimens imaged with open-top light-sheet microscopy or microcomputed tomography. By comprehensively capturing 3D morphologies, 3D volume-based prognostication achieves superior performance to traditional 2D slice-based approaches, including clinical/histopathological baselines from six certified genitourinary pathologists. Incorporating greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, further emphasizing the value of capturing larger extents of heterogeneous morphology.
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Affiliation(s)
- Andrew H Song
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Mane Williams
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Drew F K Williamson
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sarah S L Chow
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Guillaume Jaume
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gan Gao
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Andrew Zhang
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bowen Chen
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Alexander S Baras
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Robert Serafin
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA
| | - Richard Colling
- Nuffield Department of Surgical Sciences, University of Oxford, UK; Department of Cellular Pathology, Oxford University Hospitals NHS Foundations Trust, John Radcliffe Hospital, Oxford, UK
| | - Michelle R Downes
- Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada
| | - Xavier Farré
- Public Health Agency of Catalonia, Lleida, Spain
| | - Peter Humphrey
- Department of Pathology, Yale School of Medicine, New Haven, CT, USA
| | - Clare Verrill
- Nuffield Department of Surgical Sciences, University of Oxford, UK; Department of Cellular Pathology, Oxford University Hospitals NHS Foundations Trust, John Radcliffe Hospital, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Lawrence D True
- Department of Laboratory Medicine & Pathology, University of Washington School of Medicine, Seattle, WA, USA
| | - Anil V Parwani
- Department of Pathology, The Ohio State University, Columbus, OH, USA
| | - Jonathan T C Liu
- Department of Mechanical Engineering, Bioengineering, and Laboratory Medicine & Pathology, University of Washington, Seattle, WA, USA.
| | - Faisal Mahmood
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Cancer Program, Broad Institute of Harvard and MIT, Cambridge, MA, USA; Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
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4
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Li J, Wang Y, Raina MA, Xu C, Su L, Guo Q, Ma Q, Wang J, Xu D. scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592851. [PMID: 38765956 PMCID: PMC11100755 DOI: 10.1101/2024.05.06.592851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.
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5
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Zhou FY, Yapp C, Shang Z, Daetwyler S, Marin Z, Islam MT, Nanes B, Jenkins E, Gihana GM, Chang BJ, Weems A, Dustin M, Morrison S, Fiolka R, Dean K, Jamieson A, Sorger PK, Danuser G. A general algorithm for consensus 3D cell segmentation from 2D segmented stacks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.03.592249. [PMID: 38766074 PMCID: PMC11100681 DOI: 10.1101/2024.05.03.592249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Cell segmentation is the fundamental task. Only by segmenting, can we define the quantitative spatial unit for collecting measurements to draw biological conclusions. Deep learning has revolutionized 2D cell segmentation, enabling generalized solutions across cell types and imaging modalities. This has been driven by the ease of scaling up image acquisition, annotation and computation. However 3D cell segmentation, which requires dense annotation of 2D slices still poses significant challenges. Labelling every cell in every 2D slice is prohibitive. Moreover it is ambiguous, necessitating cross-referencing with other orthoviews. Lastly, there is limited ability to unambiguously record and visualize 1000's of annotated cells. Here we develop a theory and toolbox, u-Segment3D for 2D-to-3D segmentation, compatible with any 2D segmentation method. Given optimal 2D segmentations, u-Segment3D generates the optimal 3D segmentation without data training, as demonstrated on 11 real life datasets, >70,000 cells, spanning single cells, cell aggregates and tissue.
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Affiliation(s)
- Felix Y. Zhou
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Clarence Yapp
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, 02115, USA
| | - Zhiguo Shang
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Stephan Daetwyler
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Zach Marin
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Md Torikul Islam
- Children’s Research Institute and Department of Pediatrics, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Benjamin Nanes
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Edward Jenkins
- Kennedy Institute of Rheumatology, University of Oxford, OX3 7FY UK
| | - Gabriel M. Gihana
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Bo-Jui Chang
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Andrew Weems
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Michael Dustin
- Kennedy Institute of Rheumatology, University of Oxford, OX3 7FY UK
| | - Sean Morrison
- Children’s Research Institute and Department of Pediatrics, Howard Hughes Medical Institute, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Reto Fiolka
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Kevin Dean
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Andrew Jamieson
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Gaudenz Danuser
- Lyda Hill Department of Bioinformatics, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Cecil H. & Ida Green Center for System Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA
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6
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Zheng J, Wu YC, Phillips EH, Cai X, Wang X, Seung-Young Lee S. Increased Multiplexity in Optical Tissue Clearing-Based Three-Dimensional Immunofluorescence Microscopy of the Tumor Microenvironment by Light-Emitting Diode Photobleaching. J Transl Med 2024; 104:102072. [PMID: 38679160 DOI: 10.1016/j.labinv.2024.102072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/29/2024] [Accepted: 04/19/2024] [Indexed: 05/01/2024] Open
Abstract
Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy is transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only 3 or 4 cellular and noncellular TME components can be localized in cleared tumor tissue. Here we report a light-emitting diode (LED) photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through 3 work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.
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Affiliation(s)
- Jingtian Zheng
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Yi-Chien Wu
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Evan H Phillips
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Xiaoying Cai
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Xu Wang
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois
| | - Steve Seung-Young Lee
- Department of Pharmaceutical Sciences, University of Illinois, Chicago, Chicago, Illinois; University of Illinois Cancer Center, University of Illinois Chicago, Chicago, Illinois.
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7
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Elst L, Philips G, Vandermaesen K, Bassez A, Lodi F, Vreeburg MTA, Brouwer OR, Schepers R, Van Brussel T, Mohanty SK, Parwani AV, Spans L, Vanden Bempt I, Jacomen G, Baldewijns M, Lambrechts D, Albersen M. Single-cell Atlas of Penile Cancer Reveals TP53 Mutations as a Driver of an Aggressive Phenotype, Irrespective of Human Papillomavirus Status, and Provides Clues for Treatment Personalization. Eur Urol 2024:S0302-2838(24)02266-8. [PMID: 38670879 DOI: 10.1016/j.eururo.2024.03.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 03/11/2024] [Accepted: 03/25/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND AND OBJECTIVE TP53 loss-of-function (TP53LOF) mutations might be a driver of poor prognosis and chemoresistance in both human papillomavirus (HPV)-independent (HPV-) and HPV-associated (HPV+) penile squamous cell carcinoma (PSCC). Here, we aim to describe transcriptomic differences in the PSCC microenvironment stratified by TP53LOF and HPV status. METHODS We used single-cell RNA sequencing (scRNA-seq) and T-cell receptor sequencing to obtain a comprehensive atlas of the cellular architecture of PSCC. TP53LOF and HPV status were determined by targeted next-generation sequencing and sequencing HPV-DNA reads. Six HPV+ TP53 wild type (WT), six HPV- TP53WT, and four TP53LOF PSCC samples and six controls were included. Immunohistochemistry and hematoxylin-eosin confirmed the morphological context of the observed signatures. Prognostic differences between patient groups were validated in 541 PSCC patients using Kaplan-Meier survival estimates. KEY FINDINGS AND LIMITATIONS Patients with aberrant p53 staining fare much worse than patients with either HPV- or HPV+ tumors and WT p53 expression. Using scRNA-seq, we revealed 65 cell subtypes within 83 682 cells. TP53LOF tumors exhibit a partial epithelial-to-mesenchymal transition, immune-excluded, angiogenic, and morphologically invasive environment, underlying their aggressive phenotype. HPV- TP53WT tumors show stemness and immune exhaustion. HPV+ TP53WT tumors mirror normal epithelial maturation with upregulation of antibody-drug-conjugate targets and activation of innate immunity. Inherent to the scRNA-seq analysis, low sample size is a limitation and validation of signatures in large PSCC cohorts is needed. CONCLUSIONS AND CLINICAL IMPLICATIONS This first scRNA-seq atlas offers unprecedented in-depth insights into PSCC biology underlying prognostic differences based on TP53 and HPV status. Our findings provide clues for testing novel biomarker-driven therapies in PSCC. PATIENT SUMMARY Here, we analyzed tissues of penile cancer at the level of individual cells, which helps us understand why patients who harbor a deactivating mutation in the TP53 gene do much worse than patients lacking such a mutation. Such an analysis may help us tailor future therapies based on TP53 gene mutations and human papillomavirus status of these tumors.
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Affiliation(s)
- Laura Elst
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium; Department of Urology, University Hospitals Leuven, Leuven, Belgium; Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Gino Philips
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium
| | - Kaat Vandermaesen
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium; Department of Urology, University Hospitals Leuven, Leuven, Belgium; Department of Development and Regeneration, KU Leuven, Leuven, Belgium
| | - Ayse Bassez
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium
| | - Francesca Lodi
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium
| | - Manon T A Vreeburg
- Department of Urology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Oscar R Brouwer
- Department of Urology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, The Netherlands
| | - Rogier Schepers
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium
| | - Thomas Van Brussel
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium
| | - Sambit K Mohanty
- Department of Pathology and Laboratory Medicine, Advanced Medical Research Institute, Bhubaneswar, India; Department of Pathology and Laboratory Medicine, CORE Diagnostics, Gurgaon, India
| | - Anil V Parwani
- Department of Pathology, Wexner Medical Center, Columbus, OH, USA
| | - Lien Spans
- Department of Human Genetics, University Hospitals Leuven, Leuven, Belgium
| | | | - Gerd Jacomen
- Laboratory of Pathological Anatomy, AZ Sint-Maarten, Mechelen, Belgium
| | | | - Diether Lambrechts
- Center for Cancer Biology, Laboratory of Translational Genetics, VIB-KU Leuven, Leuven, Belgium
| | - Maarten Albersen
- Department of Urology, University Hospitals Leuven, Leuven, Belgium; Department of Development and Regeneration, KU Leuven, Leuven, Belgium.
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8
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Cadinu P, Sivanathan KN, Misra A, Xu RJ, Mangani D, Yang E, Rone JM, Tooley K, Kye YC, Bod L, Geistlinger L, Lee T, Mertens RT, Ono N, Wang G, Sanmarco L, Quintana FJ, Anderson AC, Kuchroo VK, Moffitt JR, Nowarski R. Charting the cellular biogeography in colitis reveals fibroblast trajectories and coordinated spatial remodeling. Cell 2024; 187:2010-2028.e30. [PMID: 38569542 PMCID: PMC11017707 DOI: 10.1016/j.cell.2024.03.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 11/20/2023] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
Gut inflammation involves contributions from immune and non-immune cells, whose interactions are shaped by the spatial organization of the healthy gut and its remodeling during inflammation. The crosstalk between fibroblasts and immune cells is an important axis in this process, but our understanding has been challenged by incomplete cell-type definition and biogeography. To address this challenge, we used multiplexed error-robust fluorescence in situ hybridization (MERFISH) to profile the expression of 940 genes in 1.35 million cells imaged across the onset and recovery from a mouse colitis model. We identified diverse cell populations, charted their spatial organization, and revealed their polarization or recruitment in inflammation. We found a staged progression of inflammation-associated tissue neighborhoods defined, in part, by multiple inflammation-associated fibroblasts, with unique expression profiles, spatial localization, cell-cell interactions, and healthy fibroblast origins. Similar signatures in ulcerative colitis suggest conserved human processes. Broadly, we provide a framework for understanding inflammation-induced remodeling in the gut and other tissues.
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Affiliation(s)
- Paolo Cadinu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Kisha N Sivanathan
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Aditya Misra
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Rosalind J Xu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA 02138, USA
| | - Davide Mangani
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Evan Yang
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Joseph M Rone
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Katherine Tooley
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Yoon-Chul Kye
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Lloyd Bod
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ludwig Geistlinger
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA
| | - Tyrone Lee
- Center for Computational Biomedicine, Harvard Medical School, Boston, MA 02115, USA
| | - Randall T Mertens
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Noriaki Ono
- University of Texas Health Science Center at Houston School of Dentistry, Houston, TX 77030, USA
| | - Gang Wang
- Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA
| | - Liliana Sanmarco
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Francisco J Quintana
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Ana C Anderson
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Vijay K Kuchroo
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
| | - Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA 02115, USA; Department of Microbiology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
| | - Roni Nowarski
- Gene Lay Institute of Immunology and Inflammation, Brigham and Women's Hospital, Mass General Hospital, and Harvard Medical School, Boston, MA 02115, USA; Ann Romney Center for Neurologic Diseases, Harvard Medical School and Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Immunology, Blavatnik Institute, Harvard Medical School, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA.
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9
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Seal S, Neelon B, Angel PM, O’Quinn EC, Hill E, Vu T, Ghosh D, Mehta AS, Wallace K, Alekseyenko AV. SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA. J Proteome Res 2024; 23:1131-1143. [PMID: 38417823 PMCID: PMC11002919 DOI: 10.1021/acs.jproteome.3c00462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 03/01/2024]
Abstract
Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.
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Affiliation(s)
- Souvik Seal
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Brian Neelon
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Peggi M. Angel
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth C. O’Quinn
- Translational
Science Laboratory, Hollings Cancer Center, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Elizabeth Hill
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Thao Vu
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Debashis Ghosh
- Department
of Biostatistics and Informatics, University
of Colorado CU Anschutz Medical Campus Aurora, Colorado 80045, United States
| | - Anand S. Mehta
- Department
of Cell and Molecular Pharmacology and Experimental Therapeutics, Medical University of South Carolina Charleston, South Carolina 29425, United States
| | - Kristin Wallace
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
| | - Alexander V. Alekseyenko
- Department
of Public Health Sciences, Medical University
of South Carolina Charleston, South Carolina 29425, United States
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10
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Wang L, Li M, Hwang TH. The 3D Revolution in Cancer Discovery. Cancer Discov 2024; 14:625-629. [PMID: 38571426 DOI: 10.1158/2159-8290.cd-23-1499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
SUMMARY The transition from 2D to 3D spatial profiling marks a revolutionary era in cancer research, offering unprecedented potential to enhance cancer diagnosis and treatment. This commentary outlines the experimental and computational advancements and challenges in 3D spatial molecular profiling, underscoring the innovation needed in imaging tools, software, artificial intelligence, and machine learning to overcome implementation hurdles and harness the full potential of 3D analysis in the field.
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Affiliation(s)
- Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
- UTHealth Houston Graduate School of Biomedical Sciences, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Tae Hyun Hwang
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, Florida
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11
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Dequiedt L, Forjaz A, Lo JO, McCarty O, Wu PH, Rosenberg A, Wirtz D, Kiemen A. Three-dimensional reconstruction of fetal rhesus macaque kidneys at single-cell resolution reveals complex inter-relation of structures. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.07.570622. [PMID: 38106004 PMCID: PMC10723390 DOI: 10.1101/2023.12.07.570622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Kidneys are among the most structurally complex organs in the body. Their architecture is critical to ensure proper function and is often impacted by diseases such as diabetes and hypertension. Understanding the spatial interplay between the different structures of the nephron and renal vasculature is crucial. Recent efforts have demonstrated the value of three-dimensional (3D) imaging in revealing new insights into the various components of the kidney; however, these studies used antibodies or autofluorescence to detect structures and so were limited in their ability to compare the many subtle structures of the kidney at once. Here, through 3D reconstruction of fetal rhesus macaque kidneys at cellular resolution, we demonstrate the power of deep learning in exhaustively labelling seventeen microstructures of the kidney. Using these tissue maps, we interrogate the spatial distribution and spatial correlation of the glomeruli, renal arteries, and the nephron. This work demonstrates the power of deep learning applied to 3D tissue images to improve our ability to compare many microanatomical structures at once, paving the way for further works investigating renal pathologies.
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Affiliation(s)
- Lucie Dequiedt
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
| | - Jamie O Lo
- Department of Obstetrics and Gynecology, Oregon Health and Sciences University
- Division of Reproductive and Developmental Sciences, Oregon National Primate Research Center
| | - Owen McCarty
- Department of Biomedical Engineering, Oregon Health and Sciences University
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
- Institute for NanoBioTechnology, Johns Hopkins University
| | - Avi Rosenberg
- Department of Pathology, Johns Hopkins School of Medicine
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University
- Institute for NanoBioTechnology, Johns Hopkins University
- Department of Pathology, Johns Hopkins School of Medicine
- Department of Oncology, Johns Hopkins School of Medicine
| | - Ashley Kiemen
- Institute for NanoBioTechnology, Johns Hopkins University
- Department of Pathology, Johns Hopkins School of Medicine
- Department of Oncology, Johns Hopkins School of Medicine
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12
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Sims Z, Mills GB, Chang YH. MIM-CyCIF: masked imaging modeling for enhancing cyclic immunofluorescence (CyCIF) with panel reduction and imputation. Commun Biol 2024; 7:409. [PMID: 38570598 PMCID: PMC10991424 DOI: 10.1038/s42003-024-06110-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 03/26/2024] [Indexed: 04/05/2024] Open
Abstract
Cyclic Immunofluorescence (CyCIF) can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.
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Affiliation(s)
- Zachary Sims
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, USA
| | - Gordon B Mills
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, USA.
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA.
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13
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Haddox CL, Nathenson MJ, Mazzola E, Lin JR, Baginska J, Nau A, Weirather JL, Choy E, Marino-Enriquez A, Morgan JA, Cote GM, Merriam P, Wagner AJ, Sorger PK, Santagata S, George S. Phase II Study of Eribulin plus Pembrolizumab in Metastatic Soft-tissue Sarcomas: Clinical Outcomes and Biological Correlates. Clin Cancer Res 2024; 30:1281-1292. [PMID: 38236580 PMCID: PMC10982640 DOI: 10.1158/1078-0432.ccr-23-2250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 10/19/2023] [Accepted: 01/12/2024] [Indexed: 01/19/2024]
Abstract
PURPOSE Eribulin modulates the tumor-immune microenvironment via cGAS-STING signaling in preclinical models. This non-randomized phase II trial evaluated the combination of eribulin and pembrolizumab in patients with soft-tissue sarcomas (STS). PATIENTS AND METHODS Patients enrolled in one of three cohorts: leiomyosarcoma (LMS), liposarcomas (LPS), or other STS that may benefit from PD-1 inhibitors, including undifferentiated pleomorphic sarcoma (UPS). Eribulin was administered at 1.4 mg/m2 i.v. (days 1 and 8) with fixed-dose pembrolizumab 200 mg i.v. (day 1) of each 21-day cycle, until progression, unacceptable toxicity, or completion of 2 years of treatment. The primary endpoint was the 12-week progression-free survival rate (PFS-12) in each cohort. Secondary endpoints included the objective response rate, median PFS, safety profile, and overall survival (OS). Pretreatment and on-treatment blood specimens were evaluated in patients who achieved durable disease control (DDC) or progression within 12 weeks [early progression (EP)]. Multiplexed immunofluorescence was performed on archival LPS samples from patients with DDC or EP. RESULTS Fifty-seven patients enrolled (LMS, n = 19; LPS, n = 20; UPS/Other, n = 18). The PFS-12 was 36.8% (90% confidence interval: 22.5-60.4) for LMS, 69.6% (54.5-89.0) for LPS, and 52.6% (36.8-75.3) for UPS/Other cohorts. All 3 patients in the UPS/Other cohort with angiosarcoma achieved RECIST responses. Toxicity was manageable. Higher IFNα and IL4 serum levels were associated with clinical benefit. Immune aggregates expressing PD-1 and PD-L1 were observed in a patient that completed 2 years of treatment. CONCLUSIONS The combination of eribulin and pembrolizumab demonstrated promising activity in LPS and angiosarcoma.
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Affiliation(s)
- Candace L. Haddox
- Sarcoma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Michael J. Nathenson
- Sarcoma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Emanuele Mazzola
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jia-Ren Lin
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts
| | - Joanna Baginska
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Allison Nau
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jason L. Weirather
- Department of Data Science, Dana-Farber Cancer Institute, Boston, Massachusetts
- Center for Immuno-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Edwin Choy
- Division of Hematology Oncology, Massachusetts General Cancer Center, Boston, Massachusetts
| | | | - Jeffrey A. Morgan
- Sarcoma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Gregory M. Cote
- Division of Hematology Oncology, Massachusetts General Cancer Center, Boston, Massachusetts
| | - Priscilla Merriam
- Sarcoma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Andrew J. Wagner
- Sarcoma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Peter K. Sorger
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts
| | - Sandro Santagata
- Department of Systems Biology, Harvard Medical School, Boston, Massachusetts
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, Massachusetts
- Department of Pathology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Suzanne George
- Sarcoma Center, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
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14
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Yapp C, Nirmal AJ, Zhou F, Maliga Z, Tefft JB, Llopis PM, Murphy GF, Lian CG, Danuser G, Santagata S, Sorger PK. Multiplexed 3D Analysis of Immune States and Niches in Human Tissue. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.10.566670. [PMID: 38014052 PMCID: PMC10680601 DOI: 10.1101/2023.11.10.566670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Tissue homeostasis and the emergence of disease are controlled by changes in the proportions of resident and recruited cells, their organization into cellular neighbourhoods, and their interactions with acellular tissue components. Highly multiplexed tissue profiling (spatial omics)1 makes it possible to study this microenvironment in situ, usually in 4-5 micron thick sections (the standard histopathology format)2. Microscopy-based tissue profiling is commonly performed at a resolution sufficient to determine cell types but not to detect subtle morphological features associated with cytoskeletal reorganisation, juxtracrine signalling, or membrane trafficking3. Here we describe a high-resolution 3D imaging approach able to characterize a wide variety of organelles and structures at sub-micron scale while simultaneously quantifying millimetre-scale spatial features. This approach combines cyclic immunofluorescence (CyCIF) imaging4 of over 50 markers with confocal microscopy of archival human tissue thick enough (30-40 microns) to fully encompass two or more layers of intact cells. 3D imaging of entire cell volumes substantially improves the accuracy of cell phenotyping and allows cell proximity to be scored using plasma membrane apposition, not just nuclear position. In pre-invasive melanoma in situ5, precise phenotyping shows that adjacent melanocytic cells are plastic in state and participate in tightly localised niches of interferon signalling near sites of initial invasion into the underlying dermis. In this and metastatic melanoma, mature and precursor T cells engage in an unexpectedly diverse array of juxtracrine and membrane-membrane interactions as well as looser "neighbourhood" associations6 whose morphologies reveal functional states. These data provide new insight into the transitions occurring during early tumour formation and immunoediting and demonstrate the potential for phenotyping of tissues at a level of detail previously restricted to cultured cells and organoids.
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Affiliation(s)
- Clarence Yapp
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
| | - Ajit J. Nirmal
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Felix Zhou
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
| | - Juliann B. Tefft
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
| | - Paula Montero Llopis
- Microscopy Resources on the North Quad (MicRoN), Harvard Medical School, Boston, MA 02115, USA
| | - George F. Murphy
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Christine G. Lian
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Gaudenz Danuser
- Lyda Hill Department of Bioinformatics, UT Southwestern Medical Center, Dallas, TX, 75390, USA
| | - Sandro Santagata
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, 02115, USA
- Ludwig Centre at Harvard, Harvard Medical School, Boston, MA, 02115, USA
- Department of Systems Biology, Harvard Medical School, 200 Longwood Avenue, Boston, MA 02115, USA
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15
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Joshi S, Forjaz A, Han KS, Shen Y, Queiroga V, Xenes D, Matelsk J, Wester B, Barrutia AM, Kiemen AL, Wu PH, Wirtz D. Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.07.583909. [PMID: 38496512 PMCID: PMC10942457 DOI: 10.1101/2024.03.07.583909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 μm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.
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Affiliation(s)
- Saurabh Joshi
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - André Forjaz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Kyu Sang Han
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Yu Shen
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
- Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Vasco Queiroga
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Daniel Xenes
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD
| | - Jordan Matelsk
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD
| | - Brock Wester
- Research and Exploratory Development Department, Johns Hopkins Applied Physics Laboratory, Laurel, MD
| | - Arrate Munoz Barrutia
- Bioengineering Department, Universidad Carlos III de Madrid and Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain
| | - Ashley L. Kiemen
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
- Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
| | - Denis Wirtz
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore MD
- The Johns Hopkins Institute for NanoBioTechnology, Johns Hopkins University, Baltimore, MD
- Departments of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins School of Medicine, Baltimore, MD
- Department of Oncology, Johns Hopkins School of Medicine, Johns Hopkins University, Baltimore, MD
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16
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Wan G, Maliga Z, Yan B, Vallius T, Shi Y, Khattab S, Chang C, Nirmal AJ, Yu KH, Liu D, Lian CG, DeSimone MS, Sorger PK, Semenov YR. SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data. Brief Bioinform 2024; 25:bbae189. [PMID: 38701421 PMCID: PMC11066940 DOI: 10.1093/bib/bbae189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 03/01/2024] [Accepted: 04/12/2024] [Indexed: 05/05/2024] Open
Abstract
Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.
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Affiliation(s)
- Guihong Wan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Boshen Yan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tuulia Vallius
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
| | - Yingxiao Shi
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Khattab
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Crystal Chang
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ajit J Nirmal
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David Liu
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine G Lian
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Mia S DeSimone
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter K Sorger
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
| | - Yevgeniy R Semenov
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA, USA
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17
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Ma J, Xie R, Ayyadhury S, Ge C, Gupta A, Gupta R, Gu S, Zhang Y, Lee G, Kim J, Lou W, Li H, Upschulte E, Dickscheid T, de Almeida JG, Wang Y, Han L, Yang X, Labagnara M, Gligorovski V, Scheder M, Rahi SJ, Kempster C, Pollitt A, Espinosa L, Mignot T, Middeke JM, Eckardt JN, Li W, Li Z, Cai X, Bai B, Greenwald NF, Van Valen D, Weisbart E, Cimini BA, Cheung T, Brück O, Bader GD, Wang B. The multimodality cell segmentation challenge: toward universal solutions. Nat Methods 2024:10.1038/s41592-024-02233-6. [PMID: 38532015 DOI: 10.1038/s41592-024-02233-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multimodality cell segmentation benchmark, comprising more than 1,500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging.
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Affiliation(s)
- Jun Ma
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Ronald Xie
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Shamini Ayyadhury
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Cheng Ge
- School of Medicine and Pharmacy, Ocean University of China, Qingdao, China
| | - Anubha Gupta
- Department of Electronics and Communications Engineering, Indraprastha Institute of Information Technology Delhi (IIITD), New Delhi, India
| | - Ritu Gupta
- Laboratory Oncology Unit, Dr. BRAIRCH, All India Institute of Medical Sciences, New Delhi, India
| | - Song Gu
- Department of Image Reconstruction, Nanjing Anke Medical Technology Co., Nanjing, China
| | - Yao Zhang
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Gihun Lee
- Graduate School of AI, KAIST, Seoul, South Korea
| | - Joonkee Kim
- Graduate School of AI, KAIST, Seoul, South Korea
| | - Wei Lou
- Shenzhen Research Institute of Big Data, Shenzhen, China
- Chinese University of Hong Kong (Shenzhen), Shenzhen, China
| | - Haofeng Li
- Shenzhen Research Institute of Big Data, Shenzhen, China
| | - Eric Upschulte
- Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1) and Helmholtz AI, Research Center Jülich, Jülich, Germany
- Faculty of Mathematics and Natural Sciences - Institute of Computer Science, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - José Guilherme de Almeida
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Hinxton, UK
- Champalimaud Foundation - Centre for the Unknown, Lisbon, Portugal
| | - Yixin Wang
- Department of Bioengineering, Stanford University, Palo Alto, CA, USA
| | - Lin Han
- Tandon School of Engineering, New York University, New York, NY, USA
| | - Xin Yang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Marco Labagnara
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Vojislav Gligorovski
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maxime Scheder
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Sahand Jamal Rahi
- Laboratory of the Physics of Biological Systems, Institute of Physics, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Carly Kempster
- School of Biological Sciences, University of Reading, Reading, UK
| | - Alice Pollitt
- School of Biological Sciences, University of Reading, Reading, UK
| | - Leon Espinosa
- Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France
| | - Tâm Mignot
- Laboratoire de Chimie Bactérienne, CNRS-Université Aix-Marseille UMR, Institut de Microbiologie de la Méditerranée, Marseille, France
| | - Jan Moritz Middeke
- Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Jan-Niklas Eckardt
- Department of Internal Medicine I, University Hospital Dresden, Technical University Dresden, Dresden, Germany
- Else Kroener Fresenius Center for Digital Health, Technical University Dresden, Dresden, Germany
| | - Wangkai Li
- Department of Automation, University of Science and Technology of China, Hefei, China
| | - Zhaoyang Li
- Institute of Advanced Technology, University of Science and Technology of China, Hefei, China
| | - Xiaochen Cai
- Department of Computer Science and Technology, Nanjing University, Nanjing, China
| | - Bizhe Bai
- School of EECS, The University of Queensland, Brisbane, Queensland, Australia
| | | | - David Van Valen
- Division of Computing and Mathematical Science, Caltech, Pasadena, CA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Erin Weisbart
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beth A Cimini
- Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Trevor Cheung
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Oscar Brück
- Hematoscope Laboratory, Comprehensive Cancer Center & Center of Diagnostics, Helsinki University Hospital, Helsinki, Finland
- Department of Oncology, University of Helsinki, Helsinki, Finland
| | - Gary D Bader
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
- Donnelly Centre, University of Toronto, Toronto, Ontario, Canada
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Ontario, Canada
- CIFAR Multiscale Human Program, CIFAR, Toronto, Ontario, Canada
| | - Bo Wang
- Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.
- Vector Institute, Toronto, Ontario, Canada.
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada.
- UHN AI Hub, University Health Network, Toronto, Ontario, Canada.
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18
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Baker GJ, Novikov E, Zhao Z, Vallius T, Davis JA, Lin JR, Muhlich JL, Mittendorf EA, Santagata S, Guerriero JL, Sorger PK. Quality Control for Single Cell Analysis of High-plex Tissue Profiles using CyLinter. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.01.565120. [PMID: 37961235 PMCID: PMC10634977 DOI: 10.1101/2023.11.01.565120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Tumors are complex assemblies of cellular and acellular structures patterned on spatial scales from microns to centimeters. Study of these assemblies has advanced dramatically with the introduction of high-plex spatial profiling. Image-based profiling methods reveal the intensities and spatial distributions of 20-100 proteins at subcellular resolution in 103-107 cells per specimen. Despite extensive work on methods for extracting single-cell data from these images, all tissue images contain artefacts such as folds, debris, antibody aggregates, optical aberrations and image processing errors that arise from imperfections in specimen preparation, data acquisition, image assembly, and feature extraction. We show that these artefacts dramatically impact single-cell data analysis, obscuring meaningful biological interpretation. We describe an interactive quality control software tool, CyLinter, that identifies and removes data associated with imaging artefacts. CyLinter greatly improves single-cell analysis, especially for archival specimens sectioned many years prior to data collection, such as those from clinical trials.
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Affiliation(s)
- Gregory J. Baker
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
| | - Edward Novikov
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Ziyuan Zhao
- Systems, Synthetic, and Quantitative Biology Program, Harvard University, Cambridge, MA
| | - Tuulia Vallius
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Janae A. Davis
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
| | - Jia-Ren Lin
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Jeremy L. Muhlich
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Elizabeth A. Mittendorf
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Sandro Santagata
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA
| | - Jennifer L. Guerriero
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA
- Breast Oncology Program, Dana-Farber/Brigham and Women’s Cancer Center, Boston, MA
- Division of Breast Surgery, Department of Surgery, Brigham and Women’s Hospital, Boston, MA
| | - Peter K. Sorger
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Systems Biology, Harvard Medical School, Boston, MA
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19
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Kurz A, Müller H, Kather JN, Schneider L, Bucher TC, Brinker TJ. 3-Dimensional Reconstruction From Histopathological Sections: A Systematic Review. J Transl Med 2024; 104:102049. [PMID: 38513977 DOI: 10.1016/j.labinv.2024.102049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 02/18/2024] [Accepted: 03/14/2024] [Indexed: 03/23/2024] Open
Abstract
Although pathological tissue analysis is typically performed on single 2-dimensional (2D) histologic reference slides, 3-dimensional (3D) reconstruction from a sequence of histologic sections could provide novel opportunities for spatial analysis of the extracted tissue. In this review, we analyze recent works published after 2018 and report information on the extracted tissue types, the section thickness, and the number of sections used for reconstruction. By analyzing the technological requirements for 3D reconstruction, we observe that software tools exist, both free and commercial, which include the functionality to perform 3D reconstruction from a sequence of histologic images. Through the analysis of the most recent works, we provide an overview of the workflows and tools that are currently used for 3D reconstruction from histologic sections and address points for future work, such as a missing common file format or computer-aided analysis of the reconstructed model.
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Affiliation(s)
- Alexander Kurz
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Heimo Müller
- Diagnostics and Research Institute for Pathology, Medical University of Graz, Graz, Austria
| | - Jakob N Kather
- Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany
| | - Lucas Schneider
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Tabea-C Bucher
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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20
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Chen H, Murphy RF. 3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.08.584082. [PMID: 38559093 PMCID: PMC10979887 DOI: 10.1101/2024.03.08.584082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Background Cell segmentation is crucial in bioimage informatics, as its accuracy directly impacts conclusions drawn from cellular analyses. While many approaches to 2D cell segmentation have been described, 3D cell segmentation has received much less attention. 3D segmentation faces significant challenges, including limited training data availability due to the difficulty of the task for human annotators, and inherent three-dimensional complexity. As a result, existing 3D cell segmentation methods often lack broad applicability across different imaging modalities. Results To address this, we developed a generalizable approach for using 2D cell segmentation methods to produce accurate 3D cell segmentations. We implemented this approach in 3DCellComposer, a versatile, open-source package that allows users to choose any existing 2D segmentation model appropriate for their tissue or cell type(s) without requiring any additional training. Importantly, we have enhanced our open source CellSegmentationEvaluator quality evaluation tool to support 3D images. It provides metrics that allow selection of the best approach for a given imaging source and modality, without the need for human annotations to assess performance. Using these metrics, we demonstrated that our approach produced high-quality 3D segmentations of tissue images, and that it could outperform an existing 3D segmentation method on the cell culture images with which it was trained. Conclusions 3DCellComposer, when paired with well-trained 2D segmentation models, provides an important alternative to acquiring human-annotated 3D images for new sample types or imaging modalities and then training 3D segmentation models using them. It is expected to be of significant value for large scale projects such as the Human BioMolecular Atlas Program.
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Affiliation(s)
- Haoran Chen
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA 15213, USA
| | - Robert F Murphy
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh PA 15213, USA
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21
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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22
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Schäfer PSL, Dimitrov D, Villablanca EJ, Saez-Rodriguez J. Integrating single-cell multi-omics and prior biological knowledge for a functional characterization of the immune system. Nat Immunol 2024; 25:405-417. [PMID: 38413722 DOI: 10.1038/s41590-024-01768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 01/16/2024] [Indexed: 02/29/2024]
Abstract
The immune system comprises diverse specialized cell types that cooperate to defend the host against a wide range of pathogenic threats. Recent advancements in single-cell and spatial multi-omics technologies provide rich information about the molecular state of immune cells. Here, we review how the integration of single-cell and spatial multi-omics data with prior knowledge-gathered from decades of detailed biochemical studies-allows us to obtain functional insights, focusing on gene regulatory processes and cell-cell interactions. We present diverse applications in immunology and critically assess underlying assumptions and limitations. Finally, we offer a perspective on the ongoing technological and algorithmic developments that promise to get us closer to a systemic mechanistic understanding of the immune system.
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Affiliation(s)
- Philipp Sven Lars Schäfer
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Daniel Dimitrov
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany
| | - Eduardo J Villablanca
- Division of Immunology and Allergy, Department of Medicine Solna, Karolinska Institute and Karolinska University Hospital, Stockholm, Sweden
- Center of Molecular Medicine, Stockholm, Sweden
| | - Julio Saez-Rodriguez
- Institute for Computational Bioscience, Faculty of Medicine and Heidelberg University Hospital, Heidelberg University, Heidelberg, Germany.
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23
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Wolf D, Salcher S, Pircher A. The multivisceral landscape of colorectal cancer metastasis: implications for targeted therapies. J Clin Invest 2024; 134:e178331. [PMID: 38426495 PMCID: PMC10904034 DOI: 10.1172/jci178331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/02/2024] Open
Abstract
Colorectal cancer (CRC) is among the most common cancer types and the second deadliest malignancy for both sexes. Metastatic disease poses substantial therapeutic challenges, and peritoneal spread, in particular, reduces quality of life and has a dismal outcome. In this issue of the JCI, Berlin and authors have made considerable advancements in understanding the cellular and molecular composition of multivisceral CRC metastasis in a sophisticated murine orthotopic organoid model and in humans. The study provides unprecedented insights into the complex biology of the disease and points toward the development of compartmentalized immune-therapeutic strategies.
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Affiliation(s)
- Dominik Wolf
- Internal Medicine V, Department of Hematology and Oncology, Comprehensive Cancer Center Innsbruck (CCCI), Medical University of Innsbruck (MUI), Innsbruck, Austria
- Tyrolean Cancer Research Institute (TKFI), Innsbruck, Austria
| | - Stefan Salcher
- Internal Medicine V, Department of Hematology and Oncology, Comprehensive Cancer Center Innsbruck (CCCI), Medical University of Innsbruck (MUI), Innsbruck, Austria
- Tyrolean Cancer Research Institute (TKFI), Innsbruck, Austria
| | - Andreas Pircher
- Internal Medicine V, Department of Hematology and Oncology, Comprehensive Cancer Center Innsbruck (CCCI), Medical University of Innsbruck (MUI), Innsbruck, Austria
- Tyrolean Cancer Research Institute (TKFI), Innsbruck, Austria
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24
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Goto N, Westcott PMK, Goto S, Imada S, Taylor MS, Eng G, Braverman J, Deshpande V, Jacks T, Agudo J, Yilmaz ÖH. SOX17 enables immune evasion of early colorectal adenomas and cancers. Nature 2024; 627:636-645. [PMID: 38418875 DOI: 10.1038/s41586-024-07135-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 01/30/2024] [Indexed: 03/02/2024]
Abstract
A hallmark of cancer is the avoidance of immune destruction. This process has been primarily investigated in locally advanced or metastatic cancer1-3; however, much less is known about how pre-malignant or early invasive tumours evade immune detection. Here, to understand this process in early colorectal cancers (CRCs), we investigated how naive colon cancer organoids that were engineered in vitro to harbour Apc-null, KrasG12D and Trp53-null (AKP) mutations adapted to the in vivo native colonic environment. Comprehensive transcriptomic and chromatin analyses revealed that the endoderm-specifying transcription factor SOX17 became strongly upregulated in vivo. Notably, whereas SOX17 loss did not affect AKP organoid propagation in vitro, its loss markedly reduced the ability of AKP tumours to persist in vivo. The small fraction of SOX17-null tumours that grew displayed notable interferon-γ (IFNγ)-producing effector-like CD8+ T cell infiltrates in contrast to the immune-suppressive microenvironment in wild-type counterparts. Mechanistically, in both endogenous Apc-null pre-malignant adenomas and transplanted organoid-derived AKP CRCs, SOX17 suppresses the ability of tumour cells to sense and respond to IFNγ, preventing anti-tumour T cell responses. Finally, SOX17 engages a fetal intestinal programme that drives differentiation away from LGR5+ tumour cells to produce immune-evasive LGR5- tumour cells with lower expression of major histocompatibility complex class I (MHC-I). We propose that SOX17 is a transcription factor that is engaged during the early steps of colon cancer to orchestrate an immune-evasive programme that permits CRC initiation and progression.
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Affiliation(s)
- Norihiro Goto
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Peter M K Westcott
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Saori Goto
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Shinya Imada
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin S Taylor
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - George Eng
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jonathan Braverman
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
- Innovative Genomics Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Vikram Deshpande
- Department of Pathology, Beth Israel Deaconess Hospital and Harvard Medical School, Boston, MA, USA
| | - Tyler Jacks
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Judith Agudo
- Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA, USA.
- Department of Immunology, Harvard Medical School, Boston, MA, USA.
- Ludwig Center at Harvard, Boston, MA, USA.
- Parker Institute for Cancer Immunotherapy at Dana-Farber Cancer Institute, Boston, MA, USA.
- New York Stem Cell Foundation-Robertson Investigator, New York, NY, USA.
| | - Ömer H Yilmaz
- Department of Biology, The David H. Koch Institute for Integrative Cancer Research at MIT, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Beth Israel Deaconess Hospital and Harvard Medical School, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Celià-Terrassa T, Kang Y. How important is EMT for cancer metastasis? PLoS Biol 2024; 22:e3002487. [PMID: 38324529 PMCID: PMC10849258 DOI: 10.1371/journal.pbio.3002487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024] Open
Abstract
Epithelial-to-mesenchymal transition (EMT), a biological phenomenon of cellular plasticity initially reported in embryonic development, has been increasingly recognized for its importance in cancer progression and metastasis. Despite tremendous progress being made in the past 2 decades in our understanding of the molecular mechanism and functional importance of EMT in cancer, there are several mysteries around EMT that remain unresolved. In this Unsolved Mystery, we focus on the variety of EMT types in metastasis, cooperative and collective EMT behaviors, spatiotemporal characterization of EMT, and strategies of therapeutically targeting EMT. We also highlight new technical advances that will facilitate the efforts to elucidate the unsolved mysteries of EMT in metastasis.
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Affiliation(s)
- Toni Celià-Terrassa
- Cancer Research Program, Hospital del Mar Research Institute, Barcelona, Spain
| | - Yibin Kang
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, United States of America
- Ludwig Institute for Cancer Research Princeton Branch, Princeton, New Jersey, United States of America
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Chandel SS, Mishra A, Dubey G, Singh RP, Singh M, Agarwal M, Chawra HS, Kukreti N. Unravelling the role of long non-coding RNAs in modulating the Hedgehog pathway in cancer. Pathol Res Pract 2024; 254:155156. [PMID: 38309021 DOI: 10.1016/j.prp.2024.155156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/14/2024] [Accepted: 01/18/2024] [Indexed: 02/05/2024]
Abstract
Cancer is a multifactorial pathological condition characterized by uncontrolled cellular proliferation, genomic instability, and evasion of regulatory mechanisms. It arises from the accumulation of genetic mutations confer selective growth advantages, leading to malignant transformation and tumor formation. The intricate interplay between LncRNAs and the Hedgehog pathway has emerged as a captivating frontier in cancer research. The Hedgehog pathway, known for its fundamental roles in embryonic development and tissue homeostasis, is frequently dysregulated in various cancers, contributing to aberrant cellular proliferation, survival, and differentiation. The Hh pathway is crucial in organizing growth and maturation processes in multicellular organisms. It plays a pivotal role in the initiation of tumors as well as in conferring resistance to conventional therapeutic approaches. The crosstalk among the Hh pathway and lncRNAs affects the expression of Hh signaling components through various transcriptional and post-transcriptional processes. Numerous pathogenic processes, including both non-malignant and malignant illnesses, have been identified to be induced by this interaction. The dysregulation of lncRNAs has been associated with the activation or inhibition of the Hh pathway, making it a potential therapeutic target against tumorigenesis. Insights into the functional significance of LncRNAs in Hedgehog pathway modulation provide promising avenues for diagnostic and therapeutic interventions. The dysregulation of LncRNAs in various cancer types underscores their potential as biomarkers for early detection and prognostication. Additionally, targeting LncRNAs associated with the Hedgehog pathway presents an innovative strategy for developing precision therapeutics to restore pathway homeostasis and impede cancer progression. This review aims to elucidate the complex regulatory network orchestrated by LncRNAs, unravelling their pivotal roles in modulating the Hedgehog pathway and influencing cancer progression.
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Affiliation(s)
| | - Anurag Mishra
- NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, India
| | - Gaurav Dubey
- NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, India
| | | | - Mithilesh Singh
- NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, India
| | - Mohit Agarwal
- NIMS Institute of Pharmacy, NIMS University Rajasthan, Jaipur, India.
| | | | - Neelima Kukreti
- School of Pharmacy, Graphic Era Hill University, Dehradun 248007, India
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Femel J, Hill C, Illa Bochaca I, Booth JL, Asnaashari TG, Steele MM, Moshiri AS, Do H, Zhong J, Osman I, Leachman SA, Tsujikawa T, White KP, Chang YH, Lund AW. Quantitative multiplex immunohistochemistry reveals inter-patient lymphovascular and immune heterogeneity in primary cutaneous melanoma. Front Immunol 2024; 15:1328602. [PMID: 38361951 PMCID: PMC10867179 DOI: 10.3389/fimmu.2024.1328602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/15/2024] [Indexed: 02/17/2024] Open
Abstract
Introduction Quantitative, multiplexed imaging is revealing complex spatial relationships between phenotypically diverse tumor infiltrating leukocyte populations and their prognostic implications. The underlying mechanisms and tissue structures that determine leukocyte distribution within and around tumor nests, however, remain poorly understood. While presumed players in metastatic dissemination, new preclinical data demonstrates that blood and lymphatic vessels (lymphovasculature) also dictate leukocyte trafficking within tumor microenvironments and thereby impact anti-tumor immunity. Here we interrogate these relationships in primary human cutaneous melanoma. Methods We established a quantitative, multiplexed imaging platform to simultaneously detect immune infiltrates and tumor-associated vessels in formalin-fixed paraffin embedded patient samples. We performed a discovery, retrospective analysis of 28 treatment-naïve, primary cutaneous melanomas. Results Here we find that the lymphvasculature and immune infiltrate is heterogenous across patients in treatment naïve, primary melanoma. We categorized five lymphovascular subtypes that differ by functionality and morphology and mapped their localization in and around primary tumors. Interestingly, the localization of specific vessel subtypes, but not overall vessel density, significantly associated with the presence of lymphoid aggregates, regional progression, and intratumoral T cell infiltrates. Discussion We describe a quantitative platform to enable simultaneous lymphovascular and immune infiltrate analysis and map their spatial relationships in primary melanoma. Our data indicate that tumor-associated vessels exist in different states and that their localization may determine potential for metastasis or immune infiltration. This platform will support future efforts to map tumor-associated lymphovascular evolution across stage, assess its prognostic value, and stratify patients for adjuvant therapy.
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Affiliation(s)
- Julia Femel
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
| | - Cameron Hill
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Irineu Illa Bochaca
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Jamie L. Booth
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
| | - Tina G. Asnaashari
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, United States
| | - Maria M. Steele
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Ata S. Moshiri
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Hyungrok Do
- Department of Population Health, New York University (NYU) Grossman School of Medicine, New York, NY, United States
| | - Judy Zhong
- Department of Population Health, New York University (NYU) Grossman School of Medicine, New York, NY, United States
- Laura and Isaac Perlmutter Cancer Center, New York University (NYU) Langone Health, New York, NY, United States
| | - Iman Osman
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
- Laura and Isaac Perlmutter Cancer Center, New York University (NYU) Langone Health, New York, NY, United States
| | - Sancy A. Leachman
- Department of Dermatology, Oregon Health & Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Takahiro Tsujikawa
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
- Department of Otolaryngology-Head and Neck Surgery, Kyoto Prefectural University of Medicine, Kyoto, Japan
| | - Kevin P. White
- Department of Dermatology, Oregon Health & Science University, Portland, OR, United States
| | - Young H. Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
| | - Amanda W. Lund
- Department of Cell, Developmental, & Cancer Biology, Oregon Health & Science University, Portland, OR, United States
- Ronald O. Perelman Department of Dermatology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health & Science University, Portland, OR, United States
- Laura and Isaac Perlmutter Cancer Center, New York University (NYU) Langone Health, New York, NY, United States
- Department of Dermatology, Oregon Health & Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, United States
- Department of Pathology, New York University (NYU) Grossman School of Medicine, New York, NY, United States
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28
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Lee S, Kim G, Lee J, Lee AC, Kwon S. Mapping cancer biology in space: applications and perspectives on spatial omics for oncology. Mol Cancer 2024; 23:26. [PMID: 38291400 PMCID: PMC10826015 DOI: 10.1186/s12943-024-01941-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 01/12/2024] [Indexed: 02/01/2024] Open
Abstract
Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.
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Affiliation(s)
- Sumin Lee
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea
| | - Gyeongjun Kim
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea
| | - JinYoung Lee
- Division of Engineering Science, University of Toronto, Toronto, Ontario, ON, M5S 3H6, Canada
| | - Amos C Lee
- Meteor Biotech,, Co. Ltd, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
| | - Sunghoon Kwon
- Department of Electrical and Computer Engineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Interdisciplinary Program in Bioengineering, Seoul National University, Seoul, 08826, Republic of Korea.
- Bio-MAX Institute, Seoul National University, Seoul, 08826, Republic of Korea.
- Institutes of Entrepreneurial BioConvergence, Seoul National University, Seoul, 08826, Republic of Korea.
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, 03080, Republic of Korea.
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29
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Guerriero JL, Lin JR, Pastorello RG, Du Z, Chen YA, Townsend MG, Shimada K, Hughes ME, Ren S, Tayob N, Zheng K, Mei S, Patterson A, Taneja KL, Metzger O, Tolaney SM, Lin NU, Dillon DA, Schnitt SJ, Sorger PK, Mittendorf EA, Santagata S. Qualification of a multiplexed tissue imaging assay and detection of novel patterns of HER2 heterogeneity in breast cancer. NPJ Breast Cancer 2024; 10:2. [PMID: 38167908 PMCID: PMC10761880 DOI: 10.1038/s41523-023-00605-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Accepted: 12/02/2023] [Indexed: 01/05/2024] Open
Abstract
Emerging data suggests that HER2 intratumoral heterogeneity (ITH) is associated with therapy resistance, highlighting the need for new strategies to assess HER2 ITH. A promising approach is leveraging multiplexed tissue analysis techniques such as cyclic immunofluorescence (CyCIF), which enable visualization and quantification of 10-60 antigens at single-cell resolution from individual tissue sections. In this study, we qualified a breast cancer-specific antibody panel, including HER2, ER, and PR, for multiplexed tissue imaging. We then compared the performance of these antibodies against established clinical standards using pixel-, cell- and tissue-level analyses, utilizing 866 tissue cores (representing 294 patients). To ensure reliability, the CyCIF antibodies were qualified against HER2 immunohistochemistry (IHC) and fluorescence in situ hybridization (FISH) data from the same samples. Our findings demonstrate the successful qualification of a breast cancer antibody panel for CyCIF, showing high concordance with established clinical antibodies. Subsequently, we employed the qualified antibodies, along with antibodies for CD45, CD68, PD-L1, p53, Ki67, pRB, and AR, to characterize 567 HER2+ invasive breast cancer samples from 189 patients. Through single-cell analysis, we identified four distinct cell clusters within HER2+ breast cancer exhibiting heterogeneous HER2 expression. Furthermore, these clusters displayed variations in ER, PR, p53, AR, and PD-L1 expression. To quantify the extent of heterogeneity, we calculated heterogeneity scores based on the diversity among these clusters. Our analysis revealed expression patterns that are relevant to breast cancer biology, with correlations to HER2 ITH and potential relevance to clinical outcomes.
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Affiliation(s)
- Jennifer L Guerriero
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, 02115, USA.
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, 02215, USA.
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02215, USA.
| | - Jia-Ren Lin
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, 02215, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02215, USA
| | - Ricardo G Pastorello
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Department of Pathology, Hospital Sírio Libanês, São Paulo, SP, 01308-050, Brazil
| | - Ziming Du
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Molecular Diagnostics, Sun Yat-sen University Cancer Center, Guangzhou, China
| | - Yu-An Chen
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02215, USA
| | - Madeline G Townsend
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Kenichi Shimada
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, 02215, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02215, USA
| | - Melissa E Hughes
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, 02215, USA
| | - Siyang Ren
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Nabihah Tayob
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
| | - Kelly Zheng
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Shaolin Mei
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02215, USA
| | - Alyssa Patterson
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, 02215, USA
| | - Krishan L Taneja
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Otto Metzger
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, 02215, USA
| | - Sara M Tolaney
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, 02215, USA
| | - Nancy U Lin
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, 02215, USA
| | - Deborah A Dillon
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Stuart J Schnitt
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Peter K Sorger
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, 02215, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02215, USA
| | - Elizabeth A Mittendorf
- Division of Breast Surgery, Department of Surgery, Brigham and Women's Hospital, Boston, MA, 02115, USA
- Breast Tumor Immunology Laboratory, Dana-Farber Cancer Institute, Boston, MA, 02215, USA
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, 02215, USA
- Breast Oncology Program, Dana-Farber/Brigham and Women's Cancer Center, Boston, MA, 02215, USA
| | - Sandro Santagata
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, 02215, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02215, USA
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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30
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Zhang D, Schroeder A, Yan H, Yang H, Hu J, Lee MYY, Cho KS, Susztak K, Xu GX, Feldman MD, Lee EB, Furth EE, Wang L, Li M. Inferring super-resolution tissue architecture by integrating spatial transcriptomics with histology. Nat Biotechnol 2024:10.1038/s41587-023-02019-9. [PMID: 38168986 DOI: 10.1038/s41587-023-02019-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/04/2023] [Indexed: 01/05/2024]
Abstract
Spatial transcriptomics (ST) has demonstrated enormous potential for generating intricate molecular maps of cells within tissues. Here we present iStar, a method based on hierarchical image feature extraction that integrates ST data and high-resolution histology images to predict spatial gene expression with super-resolution. Our method enhances gene expression resolution to near-single-cell levels in ST and enables gene expression prediction in tissue sections where only histology images are available.
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Affiliation(s)
- Daiwei Zhang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Amelia Schroeder
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Hanying Yan
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Haochen Yang
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jian Hu
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA, USA
| | - Michelle Y Y Lee
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kyung S Cho
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Katalin Susztak
- Renal, Electrolyte, and Hypertension Division, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - George X Xu
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael D Feldman
- Department of Pathology and Laboratory Medicine, School of Medicine, Indiana University, Indianapolis, IN, USA
| | - Edward B Lee
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Emma E Furth
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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31
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Herzberger L, Hadwiger M, Kruger R, Sorger P, Pfister H, Groller E, Beyer J. Residency Octree: A Hybrid Approach for Scalable Web-Based Multi-Volume Rendering. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2024; 30:1380-1390. [PMID: 37889813 PMCID: PMC10840607 DOI: 10.1109/tvcg.2023.3327193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/29/2023]
Abstract
We present a hybrid multi-volume rendering approach based on a novel Residency Octree that combines the advantages of out-of-core volume rendering using page tables with those of standard octrees. Octree approaches work by performing hierarchical tree traversal. However, in octree volume rendering, tree traversal and the selection of data resolution are intrinsically coupled. This makes fine-grained empty-space skipping costly. Page tables, on the other hand, allow access to any cached brick from any resolution. However, they do not offer a clear and efficient strategy for substituting missing high-resolution data with lower-resolution data. We enable flexible mixed-resolution out-of-core multi-volume rendering by decoupling the cache residency of multi-resolution data from a resolution-independent spatial subdivision determined by the tree. Instead of one-to-one node-to-brick correspondences, each residency octree node is mapped to a set of bricks from different resolution levels. This makes it possible to efficiently and adaptively choose and mix resolutions, adapt sampling rates, and compensate for cache misses. At the same time, residency octrees support fine-grained empty-space skipping, independent of the data subdivision used for caching. Finally, to facilitate collaboration and outreach, and to eliminate local data storage, our implementation is a web-based, pure client-side renderer using WebGPU and WebAssembly. Our method is faster than prior approaches and efficient for many data channels with a flexible and adaptive choice of data resolution.
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32
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Liu JTC, Chow SSL, Colling R, Downes MR, Farré X, Humphrey P, Janowczyk A, Mirtti T, Verrill C, Zlobec I, True LD. Engineering the future of 3D pathology. J Pathol Clin Res 2024; 10:e347. [PMID: 37919231 PMCID: PMC10807588 DOI: 10.1002/cjp2.347] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 10/06/2023] [Accepted: 10/15/2023] [Indexed: 11/04/2023]
Abstract
In recent years, technological advances in tissue preparation, high-throughput volumetric microscopy, and computational infrastructure have enabled rapid developments in nondestructive 3D pathology, in which high-resolution histologic datasets are obtained from thick tissue specimens, such as whole biopsies, without the need for physical sectioning onto glass slides. While 3D pathology generates massive datasets that are attractive for automated computational analysis, there is also a desire to use 3D pathology to improve the visual assessment of tissue histology. In this perspective, we discuss and provide examples of potential advantages of 3D pathology for the visual assessment of clinical specimens and the challenges of dealing with large 3D datasets (of individual or multiple specimens) that pathologists have not been trained to interpret. We discuss the need for artificial intelligence triaging algorithms and explainable analysis methods to assist pathologists or other domain experts in the interpretation of these novel, often complex, large datasets.
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Affiliation(s)
- Jonathan TC Liu
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
- Department of BioengineeringUniversity of WashingtonSeattleUSA
| | - Sarah SL Chow
- Department of Mechanical EngineeringUniversity of WashingtonSeattleWAUSA
| | | | | | | | - Peter Humphrey
- Department of UrologyYale School of MedicineNew HavenCTUSA
| | - Andrew Janowczyk
- Wallace H Coulter Department of Biomedical EngineeringEmory University and Georgia Institute of TechnologyAtlantaGAUSA
- Geneva University HospitalsGenevaSwitzerland
| | - Tuomas Mirtti
- Helsinki University Hospital and University of HelsinkiHelsinkiFinland
- Emory University School of MedicineAtlantaGAUSA
| | - Clare Verrill
- John Radcliffe HospitalUniversity of OxfordOxfordUK
- NIHR Oxford Biomedical Research CentreOxford University Hospitals NHS Foundation TrustOxfordUK
| | - Inti Zlobec
- Institute for Tissue Medicine and PathologyUniversity of BernBernSwitzerland
| | - Lawrence D True
- Department of Laboratory Medicine & PathologyUniversity of Washington School of MedicineSeattleUSA
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33
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Torborg SR, Grbovic-Huezo O, Singhal A, Holm M, Wu K, Han X, Ho YJ, Haglund C, Mitchell MJ, Lowe SW, Dow LE, Pitter KL, Sanchez-Rivera FJ, Levchenko A, Tammela T. Solid tumor growth depends on an intricate equilibrium of malignant cell states. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.30.573100. [PMID: 38234855 PMCID: PMC10793475 DOI: 10.1101/2023.12.30.573100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Control of cell identity and number is central to tissue function, yet principles governing organization of malignant cells in tumor tissues remain poorly understood. Using mathematical modeling and candidate-based analysis, we discover primary and metastatic pancreatic ductal adenocarcinoma (PDAC) organize in a stereotypic pattern whereby PDAC cells responding to WNT signals (WNT-R) neighbor WNT-secreting cancer cells (WNT-S). Leveraging lineage-tracing, we reveal the WNT-R state is transient and gives rise to the WNT-S state that is highly stable and committed to organizing malignant tissue. We further show that a subset of WNT-S cells expressing the Notch ligand DLL1 form a functional niche for WNT-R cells. Genetic inactivation of WNT secretion or Notch pathway components, or cytoablation of the WNT-S state disrupts PDAC tissue organization, suppressing tumor growth and metastasis. This work indicates PDAC growth depends on an intricately controlled equilibrium of functionally distinct cancer cell states, uncovering a fundamental principle governing solid tumor growth and revealing new opportunities for therapeutic intervention.
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34
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Wrenn ED, Apfelbaum AA, Rudzinski ER, Deng X, Jiang W, Sud S, Van Noord RA, Newman EA, Garcia NM, Miyaki A, Hoglund VJ, Bhise SS, Kanaan SB, Waltner OG, Furlan SN, Lawlor ER. Cancer-Associated Fibroblast-Like Tumor Cells Remodel the Ewing Sarcoma Tumor Microenvironment. Clin Cancer Res 2023; 29:5140-5154. [PMID: 37471463 PMCID: PMC10801911 DOI: 10.1158/1078-0432.ccr-23-1111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 06/07/2023] [Accepted: 07/18/2023] [Indexed: 07/22/2023]
Abstract
PURPOSE Despite limited genetic and histologic heterogeneity, Ewing sarcoma (EwS) tumor cells are transcriptionally heterogeneous and display varying degrees of mesenchymal lineage specification in vitro. In this study, we investigated if and how transcriptional heterogeneity of EwS cells contributes to heterogeneity of tumor phenotypes in vivo. EXPERIMENTAL DESIGN Single-cell proteogenomic-sequencing of EwS cell lines was performed and integrated with patient tumor transcriptomic data. Cell subpopulations were isolated by FACS for assessment of gene expression and phenotype. Digital spatial profiling and human whole transcriptome analysis interrogated transcriptomic heterogeneity in EwS xenografts. Tumor cell subpopulations and matrix protein deposition were evaluated in xenografts and patient tumors using multiplex immunofluorescence staining. RESULTS We identified CD73 as a biomarker of highly mesenchymal EwS cell subpopulations in tumor models and patient biopsies. CD73+ tumor cells displayed distinct transcriptional and phenotypic properties, including selective upregulation of genes that are repressed by EWS::FLI1, and increased migratory potential. CD73+ cells were distinguished in vitro and in vivo by increased expression of matrisomal genes and abundant deposition of extracellular matrix (ECM) proteins. In epithelial-derived malignancies, ECM is largely deposited by cancer-associated fibroblasts (CAF), and we thus labeled CD73+ EwS cells, CAF-like tumor cells. Marked heterogeneity of CD73+ EwS cell frequency and distribution was detected in tumors in situ, and CAF-like tumor cells and associated ECM were observed in peri-necrotic regions and invasive foci. CONCLUSIONS EwS tumor cells can adopt CAF-like properties, and these distinct cell subpopulations contribute to tumor heterogeneity by remodeling the tumor microenvironment. See related commentary by Kuo and Amatruda, p. 5002.
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Affiliation(s)
- Emma D. Wrenn
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA
| | - April A. Apfelbaum
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA
- Cancer Biology PhD Program, University of Michigan, Ann Arbor, Michigan
| | - Erin R. Rudzinski
- Pathology Department, Seattle Children’s Hospital, Seattle, Washington
| | - Xuemei Deng
- Pathology Department, Seattle Children’s Hospital, Seattle, Washington
| | - Wei Jiang
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Sudha Sud
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | | | - Erika A. Newman
- Rogel Cancer Center, University of Michigan, Ann Arbor, Michigan
| | - Nicolas M. Garcia
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA
| | - Aya Miyaki
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA
| | - Virginia J. Hoglund
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA
| | - Shruti S. Bhise
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Sami B. Kanaan
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Olivia G. Waltner
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington
| | - Scott N. Furlan
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington
- Department of Pediatrics, University of Washington, Seattle, WA
| | - Elizabeth R. Lawlor
- Ben Towne Center for Childhood Cancer Research, Seattle Children’s Research Institute, Seattle, WA
- Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, Washington
- Department of Pediatrics, University of Washington, Seattle, WA
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You X, Koop K, Weigert A. Heterogeneity of tertiary lymphoid structures in cancer. Front Immunol 2023; 14:1286850. [PMID: 38111571 PMCID: PMC10725932 DOI: 10.3389/fimmu.2023.1286850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 11/20/2023] [Indexed: 12/20/2023] Open
Abstract
The success of immunotherapy approaches, such as immune checkpoint blockade and cellular immunotherapy with genetically modified lymphocytes, has firmly embedded the immune system in the roadmap for combating cancer. Unfortunately, the majority of cancer patients do not yet benefit from these therapeutic approaches, even when the prognostic relevance of the immune response in their tumor entity has been demonstrated. Therefore, there is a justified need to explore new strategies for inducing anti-tumor immunity. The recent connection between the formation of ectopic lymphoid aggregates at tumor sites and patient prognosis, along with an effective anti-tumor response, suggests that manipulating the occurrence of these tertiary lymphoid structures (TLS) may play a critical role in activating the immune system against a growing tumor. However, mechanisms governing TLS formation and a clear understanding of their substantial heterogeneity are still lacking. Here, we briefly summarize the current state of knowledge regarding the mechanisms driving TLS development, outline the impact of TLS heterogeneity on clinical outcomes in cancer patients, and discuss appropriate systems for modeling TLS heterogeneity that may help identify new strategies for inducing protective TLS formation in cancer patients.
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Affiliation(s)
- Xin You
- Goethe-University Frankfurt, Faculty of Medicine, Institute of Biochemistry I, Frankfurt, Germany
| | - Kristina Koop
- First Department of Medicine, Universitätsklinikum Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Andreas Weigert
- Goethe-University Frankfurt, Faculty of Medicine, Institute of Biochemistry I, Frankfurt, Germany
- Frankfurt Cancer Institute, Goethe-University Frankfurt, Frankfurt, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt, Frankfurt, Germany
- Cardiopulmonary Institute (CPI), Frankfurt, Germany
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36
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Xiong D, Zhang L, Sun ZJ. Targeting the epigenome to reinvigorate T cells for cancer immunotherapy. Mil Med Res 2023; 10:59. [PMID: 38044445 PMCID: PMC10694991 DOI: 10.1186/s40779-023-00496-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 11/14/2023] [Indexed: 12/05/2023] Open
Abstract
Cancer immunotherapy using immune-checkpoint inhibitors (ICIs) has revolutionized the field of cancer treatment; however, ICI efficacy is constrained by progressive dysfunction of CD8+ tumor-infiltrating lymphocytes (TILs), which is termed T cell exhaustion. This process is driven by diverse extrinsic factors across heterogeneous tumor immune microenvironment (TIME). Simultaneously, tumorigenesis entails robust reshaping of the epigenetic landscape, potentially instigating T cell exhaustion. In this review, we summarize the epigenetic mechanisms governing tumor microenvironmental cues leading to T cell exhaustion, and discuss therapeutic potential of targeting epigenetic regulators for immunotherapies. Finally, we outline conceptual and technical advances in developing potential treatment paradigms involving immunostimulatory agents and epigenetic therapies.
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Affiliation(s)
- Dian Xiong
- State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, 430079, China
| | - Lu Zhang
- State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, 430079, China.
| | - Zhi-Jun Sun
- State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Frontier Science Center for Immunology and Metabolism, Wuhan University, Wuhan, 430079, China.
- Department of Oral Maxillofacial-Head Neck Oncology, School and and Hospital of Stomatology, Wuhan University, Wuhan, 430079, China.
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Kiemen AL, Wu PH, Braxton AM, Cornish TC, Hruban RH, Wood L, Wirtz D, Zwicker D. Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.01.569633. [PMID: 38105957 PMCID: PMC10723372 DOI: 10.1101/2023.12.01.569633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Pancreatic ductal adenocarcinoma is a rare but lethal cancer. Recent evidence reveals that pancreatic intraepithelial neoplasms (PanINs), the microscopic precursor lesions in the pancreatic ducts that can give rise to invasive pancreatic cancer, are significantly larger and more prevalent than previously believed. Better understanding of the growth law dynamics of PanINs may improve our ability to understand how a miniscule fraction of these lesions makes the transition to invasive cancer. Here, using artificial intelligence (AI)-based three-dimensional (3D) tissue mapping method, we measured the volumes of >1,000 PanIN and found that lesion size is distributed according to a power law with a fitted exponent of -1.7 over > 3 orders of magnitude. Our data also suggest that PanIN growth is not very sensitive to the pancreatic microenvironment or an individual's age, family history, and lifestyle, and is rather shaped by general growth behavior. We analyze several models of PanIN growth and fit the predicted size distributions to the observed data. The best fitting models suggest that both intraductal spread of PanIN lesions and fusing of multiple lesions into large, highly branched structures drive PanIN growth patterns. This work lays the groundwork for future mathematical modeling efforts integrating PanIN incidence, morphology, genomic, and transcriptomic features to understand pancreas tumorigenesis, and demonstrates the utility of combining experimental measurement of human tissues with dynamic modeling for understanding cancer tumorigenesis.
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Affiliation(s)
- Ashley L. Kiemen
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Pei-Hsun Wu
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Alicia M. Braxton
- Department of Comparative Medicine, Medical University of South Carolina, Charleston, SC
| | - Toby C. Cornish
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Ralph H. Hruban
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Laura Wood
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Denis Wirtz
- Department of Pathology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Oncology, The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - David Zwicker
- Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
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Zheng J, Wu YC, Phillips EH, Wang X, Lee SSY. Increased multiplexity in optical tissue clearing-based 3D immunofluorescence microscopy of the tumor microenvironment by LED photobleaching. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.29.569277. [PMID: 38076864 PMCID: PMC10705380 DOI: 10.1101/2023.11.29.569277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Optical tissue clearing and three-dimensional (3D) immunofluorescence (IF) microscopy have been transforming imaging of the complex tumor microenvironment (TME). However, current 3D IF microscopy has restricted multiplexity; only three or four cellular and non-cellular TME components can be localized in a cleared tumor tissue. Here we report a LED photobleaching method and its application for 3D multiplexed optical mapping of the TME. We built a high-power LED light irradiation device and temperature-controlled chamber for completely bleaching fluorescent signals throughout optically cleared tumor tissues without compromise of tissue and protein antigen integrity. With newly developed tissue mounting and selected region-tracking methods, we established a cyclic workflow involving IF staining, tissue clearing, 3D confocal microscopy, and LED photobleaching. By registering microscope channel images generated through three work cycles, we produced 8-plex image data from individual 400 μm-thick tumor macrosections that visualize various vascular, immune, and cancer cells in the same TME at tissue-wide and cellular levels in 3D. Our method was also validated for quantitative 3D spatial analysis of cellular remodeling in the TME after immunotherapy. These results demonstrate that our LED photobleaching system and its workflow offer a novel approach to increase the multiplexing power of 3D IF microscopy for studying tumor heterogeneity and response to therapy.
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Walsh LA, Quail DF. Decoding the tumor microenvironment with spatial technologies. Nat Immunol 2023; 24:1982-1993. [PMID: 38012408 DOI: 10.1038/s41590-023-01678-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Accepted: 10/10/2023] [Indexed: 11/29/2023]
Abstract
Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.
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Affiliation(s)
- Logan A Walsh
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Human Genetics, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
| | - Daniela F Quail
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montreal, Quebec, Canada.
- Department of Physiology, Faculty of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, Division of Experimental Medicine, McGill University, Montreal, Quebec, Canada.
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Nirmal AJ, Yapp C, Santagata S, Sorger PK. Cell Spotter (CSPOT): A machine-learning approach to automated cell spotting and quantification of highly multiplexed tissue images. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.15.567196. [PMID: 38014110 PMCID: PMC10680730 DOI: 10.1101/2023.11.15.567196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Highly multiplexed tissue imaging and in situ spatial profiling aim to extract single-cell data from specimens containing closely packed cells of diverse morphology. This is challenging due to the difficulty of accurately assigning boundaries between cells (segmentation) and then generating per-cell staining intensities. Existing methods use gating to convert per-cell intensity data to positive and negative scores; this is a common approach in flow cytometry, but one that is problematic in imaging. In contrast, human experts identify cells in crowded environments using morphological, neighborhood, and intensity information. Here we describe a computational approach (Cell Spotter or CSPOT) that uses supervised machine learning in combination with classical segmentation to perform automated cell type calling. CSPOT is robust to artifacts that commonly afflict tissue imaging and can replace conventional gating. The end-to-end Python implementation of CSPOT can be integrated into cloud-based image processing pipelines to substantially improve the speed, accuracy, and reproducibility of single-cell spatial data.
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Affiliation(s)
- Ajit J. Nirmal
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clarence Yapp
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
| | - Sandro Santagata
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Peter K. Sorger
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02115, USA
- Laboratory of Systems Pharmacology, Department of Systems Biology, Harvard Medical School, Boston, MA, 02115, USA
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41
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Wan G, Maliga Z, Yan B, Vallius T, Shi Y, Khattab S, Chang C, Nirmal AJ, Yu KH, Liu D, Lian CG, DeSimone MS, Sorger PK, Semenov YR. SpatialCells: Automated Profiling of Tumor Microenvironments with Spatially Resolved Multiplexed Single-Cell Data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.10.566378. [PMID: 38014067 PMCID: PMC10680639 DOI: 10.1101/2023.11.10.566378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Background Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal, and other cells within the tumor microenvironment. Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize the molecular, cellular, and spatial properties of tumor microenvironments for various malignancies. Results This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of tumor microenvironments using multiplexed single-cell data. Conclusions SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion, and metastasis.
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Affiliation(s)
- Guihong Wan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Zoltan Maliga
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Boshen Yan
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Tuulia Vallius
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA
| | - Yingxiao Shi
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Khattab
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Crystal Chang
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ajit J. Nirmal
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
- Department of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Kun-Hsing Yu
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - David Liu
- Department of Medicine, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Christine G. Lian
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Mia S. DeSimone
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter K. Sorger
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
| | - Yevgeniy R. Semenov
- Department of Dermatology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Program in Therapeutic Science, Harvard Medical School, Boston, MA
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Wang J, Li J, Kramer ST, Su L, Chang Y, Xu C, Eadon MT, Kiryluk K, Ma Q, Xu D. Dimension-agnostic and granularity-based spatially variable gene identification using BSP. Nat Commun 2023; 14:7367. [PMID: 37963892 PMCID: PMC10645821 DOI: 10.1038/s41467-023-43256-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/03/2023] [Indexed: 11/16/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
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Affiliation(s)
- Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
| | - Jinpu Li
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Skyler T Kramer
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Li Su
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Chunhui Xu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA.
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
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Coy S, Cheng B, Lee JS, Rashid R, Browning L, Xu Y, Chakrabarty SS, Yapp C, Chan S, Tefft JB, Scott E, Spektor A, Ligon KL, Baker GJ, Pellman D, Sorger PK, Santagata S. 2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.07.566063. [PMID: 37986801 PMCID: PMC10659270 DOI: 10.1101/2023.11.07.566063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2023]
Abstract
Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.
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Affiliation(s)
- Shannon Coy
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Brian Cheng
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Jong Suk Lee
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Rumana Rashid
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Lindsay Browning
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Yilin Xu
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Sankha S. Chakrabarty
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Clarence Yapp
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Sabrina Chan
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Juliann B. Tefft
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Emily Scott
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Alexander Spektor
- Department of Radiation Oncology, Brigham and Women’s Hospital and Dana Farber Cancer Institute, Boston, MA, USA
| | - Keith L. Ligon
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gregory J. Baker
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
| | - David Pellman
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
| | - Peter K. Sorger
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Sandro Santagata
- Ludwig Center at Harvard, Harvard Medical School, Boston, MA, USA
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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Burlingame E, Ternes L, Lin JR, Chen YA, Kim EN, Gray JW, Chang YH. 3D multiplexed tissue imaging reconstruction and optimized region of interest (ROI) selection through deep learning model of channels embedding. FRONTIERS IN BIOINFORMATICS 2023; 3:1275402. [PMID: 37928169 PMCID: PMC10620917 DOI: 10.3389/fbinf.2023.1275402] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 10/05/2023] [Indexed: 11/07/2023] Open
Abstract
Introduction: Tissue-based sampling and diagnosis are defined as the extraction of information from certain limited spaces and its diagnostic significance of a certain object. Pathologists deal with issues related to tumor heterogeneity since analyzing a single sample does not necessarily capture a representative depiction of cancer, and a tissue biopsy usually only presents a small fraction of the tumor. Many multiplex tissue imaging platforms (MTIs) make the assumption that tissue microarrays (TMAs) containing small core samples of 2-dimensional (2D) tissue sections are a good approximation of bulk tumors although tumors are not 2D. However, emerging whole slide imaging (WSI) or 3D tumor atlases that use MTIs like cyclic immunofluorescence (CyCIF) strongly challenge this assumption. In spite of the additional insight gathered by measuring the tumor microenvironment in WSI or 3D, it can be prohibitively expensive and time-consuming to process tens or hundreds of tissue sections with CyCIF. Even when resources are not limited, the criteria for region of interest (ROI) selection in tissues for downstream analysis remain largely qualitative and subjective as stratified sampling requires the knowledge of objects and evaluates their features. Despite the fact TMAs fail to adequately approximate whole tissue features, a theoretical subsampling of tissue exists that can best represent the tumor in the whole slide image. Methods: To address these challenges, we propose deep learning approaches to learn multi-modal image translation tasks from two aspects: 1) generative modeling approach to reconstruct 3D CyCIF representation and 2) co-embedding CyCIF image and Hematoxylin and Eosin (H&E) section to learn multi-modal mappings by a cross-domain translation for minimum representative ROI selection. Results and discussion: We demonstrate that generative modeling enables a 3D virtual CyCIF reconstruction of a colorectal cancer specimen given a small subset of the imaging data at training time. By co-embedding histology and MTI features, we propose a simple convex optimization for objective ROI selection. We demonstrate the potential application of ROI selection and the efficiency of its performance with respect to cellular heterogeneity.
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Affiliation(s)
- Erik Burlingame
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Luke Ternes
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Jia-Ren Lin
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Yu-An Chen
- Ludwig Center for Cancer Research at Harvard, Harvard Medical School, Boston, MA, United States
- Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, United States
| | - Eun Na Kim
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
| | - Joe W. Gray
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
| | - Young Hwan Chang
- Department of Biomedical Engineering and Computational Biology Program, Oregon Health and Science University, Portland, OR, United States
- Knight Cancer Institute, Oregon Health and Science University, Portland, OR, United States
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Cammareri P, Myant KB. Be like water, my cells: cell plasticity and the art of transformation. Front Cell Dev Biol 2023; 11:1272730. [PMID: 37886398 PMCID: PMC10598658 DOI: 10.3389/fcell.2023.1272730] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023] Open
Abstract
Cellular plasticity defines the capacity of cells to adopt distinct identities during development, tissue homeostasis and regeneration. Dynamic fluctuations between different states, within or across lineages, are regulated by changes in chromatin accessibility and in gene expression. When deregulated, cellular plasticity can contribute to cancer initiation and progression. Cancer cells are remarkably plastic which contributes to phenotypic and functional heterogeneity within tumours as well as resistance to targeted therapies. It is for these reasons that the scientific community has become increasingly interested in understanding the molecular mechanisms governing cancer cell plasticity. The purpose of this mini-review is to discuss different examples of cellular plasticity associated with metaplasia and epithelial-mesenchymal transition with a focus on therapy resistance.
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Affiliation(s)
| | - Kevin B. Myant
- Cancer Research UK Scotland Centre, Institute of Genetics and Cancer, The University of Edinburgh, Edinburgh, United Kingdom
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Kulasinghe A, Wood F, Belz G. The seductive allure of spatial biology: accelerating new discoveries in the life sciences. Immunol Cell Biol 2023; 101:798-804. [PMID: 37572002 DOI: 10.1111/imcb.12669] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/30/2023] [Accepted: 06/30/2023] [Indexed: 08/14/2023]
Abstract
Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic assay developments. Immediate applications are in precision medicine, such as being able to match patients to optimal therapies through better understanding the tumor microenvironment. However, it also has ramifications for many other disciplines (e.g. immunology, cancer, infectious disease and digital pathology). With increasingly massive data sets being generated, data storage, curation, analysis and sharing require more computational approaches and artificial intelligence-powered tools to fully utilize spatial tools. Here, we discuss spatial biology as an important convergent science approach to tackling complex global challenges in areas such as health.
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Affiliation(s)
- Arutha Kulasinghe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
| | - Fiona Wood
- Independent Innovation Strategy Analyst, NSW, Australia
| | - Gabrielle Belz
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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Kasi PM, Hidalgo M, Jafari MD, Yeo H, Lowenfeld L, Khan U, Nguyen ATH, Siolas D, Swed B, Hyun J, Khan S, Wood M, Samstein B, Rocca JP, Ocean AJ, Popa EC, Hunt DH, Uppal NP, Garrett KA, Pigazzi A, Zhou XK, Shah MA, Hissong E. Neoadjuvant botensilimab plus balstilimab response pattern in locally advanced mismatch repair proficient colorectal cancer. Oncogene 2023; 42:3252-3259. [PMID: 37731056 PMCID: PMC10611560 DOI: 10.1038/s41388-023-02835-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 08/25/2023] [Accepted: 09/04/2023] [Indexed: 09/22/2023]
Abstract
In patients with locally advanced cancer without distant metastases, the neoadjuvant setting presents a platform to evaluate new drugs. For mismatch repair proficient/microsatellite stable (pMMR/MSS) colon and rectal cancer, immunotherapy has shown limited efficacy. Herein, we report exceptional responses observed with neoadjuvant botensilimab (BOT), an Fc-enhanced next-generation anti-CTLA-4 antibody, alongside balstilimab (BAL; an anti-PD-1 antibody) in two patients with pMMR/MSS colon and rectal cancer. The histological pattern of rapid immune response observed ("inside-out" (serosa-to-mucosa) tumor regression) has not been described previously in this setting. Spatial biology analyses (RareCyte Inc.) reveal mechanisms of actions of BOT, a novel innate-adaptive immune activator. These observations have downstream implications for clinical trial designs using neoadjuvant immunotherapy and potentially sparing patients chemotherapy.
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Affiliation(s)
- Pashtoon Murtaza Kasi
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA.
| | - Manuel Hidalgo
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Mehraneh D Jafari
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Heather Yeo
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Lea Lowenfeld
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Uqba Khan
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Alana T H Nguyen
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Despina Siolas
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Brandon Swed
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Jini Hyun
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Sahrish Khan
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Madeleine Wood
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Benjamin Samstein
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Juan P Rocca
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Allyson J Ocean
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Elizabeta C Popa
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Daniel H Hunt
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Nikhil P Uppal
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Kelly A Garrett
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Alessio Pigazzi
- Department of Surgery, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Xi Kathy Zhou
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY, 10021, USA
| | - Manish A Shah
- Department of Oncology/Hematology, New York Presbyterian/Weill Cornell Medicine New York, New York, NY, 10021, USA
| | - Erika Hissong
- Department of Pathology and Laboratory Medicine, New York Presbyterian/Weill Cornell Medicine, New York, NY, 10021, USA
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Xiong J, Kaur H, Heiser CN, McKinley ET, Roland JT, Coffey RJ, Shrubsole MJ, Wrobel J, Ma S, Lau KS, Vandekar S. GammaGateR: semi-automated marker gating for single-cell multiplexed imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.20.558645. [PMID: 37781604 PMCID: PMC10541135 DOI: 10.1101/2023.09.20.558645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
Abstract
Motivation Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data. Results To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation. Availability and Implementation The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.
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Affiliation(s)
| | - Harsimran Kaur
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
| | - Cody N Heiser
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Regeneron Pharmaceuticals, USA
| | - Eliot T McKinley
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- GlaxoSmithKline, USA
| | - Joseph T Roland
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Surgery, Vanderbilt University Medical Center, USA
| | - Robert J Coffey
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Medicine, Vanderbilt University Medical Center, USA
| | | | - Julia Wrobel
- Department of Biostatistics and Bioinformatics, Emory University, USA
| | - Siyuan Ma
- Department of Biostatistics, Vanderbilt University, USA
| | - Ken S Lau
- Program of Chemical and Physical Biology, Vanderbilt University School of Medicine, USA
- Epithelial Biology Center, Vanderbilt University Medical Center, USA
- Department of Surgery, Vanderbilt University Medical Center, USA
- Department of Cell and Developmental Biology, Vanderbilt University School of Medicine, USA
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49
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Chang YH, Sims Z, Mills G. MIM-CyCIF: Masked Imaging Modeling for Enhancing Cyclic Immunofluorescence (CyCIF) with Panel Reduction and Imputation. RESEARCH SQUARE 2023:rs.3.rs-3270272. [PMID: 37790506 PMCID: PMC10543389 DOI: 10.21203/rs.3.rs-3270272/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
CyCIF can quantify multiple biomarkers, but panel capacity is limited by technical challenges. We propose a computational panel reduction approach that can impute the information content from 25 markers using only 9 markers, learning co-expression and morphological patterns while concurrently increasing speed and panel content and decreasing cost. We demonstrate strong correlations in predictions and generalizability across breast and colorectal cancer, illustrating applicability of our approach to diverse tissue types.
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50
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Du J, An ZJ, Huang ZF, Yang YC, Zhang MH, Fu XH, Shi WY, Hou J. Novel insights from spatial transcriptome analysis in solid tumors. Int J Biol Sci 2023; 19:4778-4792. [PMID: 37781515 PMCID: PMC10539699 DOI: 10.7150/ijbs.83098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 08/03/2023] [Indexed: 10/03/2023] Open
Abstract
Since its first application in 2016, spatial transcriptomics has become a rapidly evolving technology in recent years. Spatial transcriptomics enables transcriptomic data to be acquired from intact tissue sections and provides spatial distribution information and remedies the disadvantage of single-cell RNA sequencing (scRNA-seq), whose data lack spatially resolved information. Presently, spatial transcriptomics has been widely applied to various tissue types, especially for the study of tumor heterogeneity. In this review, we provide a summary of the research progress in utilizing spatial transcriptomics to investigate tumor heterogeneity and the microenvironment with a focus on solid tumors. We summarize the research breakthroughs in various fields and perspectives due to the application of spatial transcriptomics, including cell clustering and interaction, cellular metabolism, gene expression, immune cell programs and combination with other techniques. As a combination of multiple transcriptomics, single-cell multiomics shows its superiority and validity in single-cell analysis. We also discuss the application prospect of single-cell multiomics, and we believe that with the progress of data integration from various transcriptomics, a multilayered subcellular landscape will be revealed.
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Affiliation(s)
- Jun Du
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
| | - Zhi-Jie An
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Zou-Fang Huang
- Ganzhou Key Laboratory of Hematology, Department of Hematology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, Jiangxi, 341000, China
| | - Yu-Chen Yang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Ming-Hui Zhang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025, China
| | - Xue-Hang Fu
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
| | - Wei-Yang Shi
- Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao 266003, China
| | - Jian Hou
- Department of Hematology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127, China
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