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Castro DC, Chan-Andersen P, Romanova EV, Sweedler JV. Probe-based mass spectrometry approaches for single-cell and single-organelle measurements. MASS SPECTROMETRY REVIEWS 2024; 43:888-912. [PMID: 37010120 PMCID: PMC10545815 DOI: 10.1002/mas.21841] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Revised: 02/09/2023] [Accepted: 03/01/2023] [Indexed: 06/19/2023]
Abstract
Exploring the chemical content of individual cells not only reveals underlying cell-to-cell chemical heterogeneity but is also a key component in understanding how cells combine to form emergent properties of cellular networks and tissues. Recent technological advances in many analytical techniques including mass spectrometry (MS) have improved instrumental limits of detection and laser/ion probe dimensions, allowing the analysis of micron and submicron sized areas. In the case of MS, these improvements combined with MS's broad analyte detection capabilities have enabled the rise of single-cell and single-organelle chemical characterization. As the chemical coverage and throughput of single-cell measurements increase, more advanced statistical and data analysis methods have aided in data visualization and interpretation. This review focuses on secondary ion MS and matrix-assisted laser desorption/ionization MS approaches for single-cell and single-organelle characterization, which is followed by advances in mass spectral data visualization and analysis.
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Affiliation(s)
- Daniel C. Castro
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Peter Chan-Andersen
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Elena V. Romanova
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
| | - Jonathan V. Sweedler
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Chemistry, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL USA
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL USA
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2
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Liao X, Scheidereit E, Kuppe C. New tools to study renal fibrogenesis. Curr Opin Nephrol Hypertens 2024; 33:420-426. [PMID: 38587103 PMCID: PMC11139246 DOI: 10.1097/mnh.0000000000000988] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
PURPOSE OF REVIEW Kidney fibrosis is a key pathological aspect and outcome of chronic kidney disease (CKD). The advent of multiomic analyses using human kidney tissue, enabled by technological advances, marks a new chapter of discovery in fibrosis research of the kidney. This review highlights the rapid advancements of single-cell and spatial multiomic techniques that offer new avenues for exploring research questions related to human kidney fibrosis development. RECENT FINDINGS We recently focused on understanding the origin and transition of myofibroblasts in kidney fibrosis using single-cell RNA sequencing (scRNA-seq) [1] . We analysed cells from healthy human kidneys and compared them to patient samples with CKD. We identified PDGFRα+/PDGFRβ+ mesenchymal cells as the primary cellular source of extracellular matrix (ECM) in human kidney fibrosis. We found several commonly shared cell states of fibroblasts and myofibroblasts and provided insights into molecular regulators. Novel single-cell and spatial multiomics tools are now available to shed light on cell lineages, the plasticity of kidney cells and cell-cell communication in fibrosis. SUMMARY As further single-cell and spatial multiomic approaches are being developed, opportunities to apply these methods to human kidney tissues expand similarly. Careful design and optimisation of the multiomic experiments are needed to answer questions related to cell lineages, plasticity and cell-cell communication in kidney fibrosis.
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Affiliation(s)
- Xian Liao
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, Aachen, Germany
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3
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Rietjens RG, Wang G, van den Berg BM, Rabelink TJ. Spatial metabolomics in tissue injury and regeneration. Curr Opin Genet Dev 2024; 87:102223. [PMID: 38901101 DOI: 10.1016/j.gde.2024.102223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2024] [Revised: 06/04/2024] [Accepted: 06/04/2024] [Indexed: 06/22/2024]
Abstract
Tissue homeostasis is intricately linked to cellular metabolism and metabolite exchange within the tissue microenvironment. The orchestration of adaptive cellular responses during injury and repair depends critically upon metabolic adaptation. This adaptation, in turn, shapes cell fate decisions required for the restoration of tissue homeostasis. Understanding the nuances of metabolic processes within the tissue context and comprehending the intricate communication between cells is therefore imperative for unraveling the complexity of tissue homeostasis and the processes of injury and repair. In this review, we focus on mass spectrometry imaging as an advanced platform with the potential to provide such comprehensive insights into the metabolic instruction governing tissue function. Recent advances in this technology allow to decipher the intricate metabolic networks that determine cellular behavior in the context of tissue resilience, injury, and repair. These insights not only advance our fundamental understanding of tissue biology but also hold implications for therapeutic interventions by targeting metabolic pathways critical for maintaining tissue homeostasis.
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Affiliation(s)
- Rosalie Gj Rietjens
- Department of Internal Medicine (Nephrology) & Einthoven Laboratory of Vascular and Regenerative Medicine & The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands. https://twitter.com/@RietjensRosalie
| | - Gangqi Wang
- Department of Internal Medicine (Nephrology) & Einthoven Laboratory of Vascular and Regenerative Medicine & The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands. https://twitter.com/@GangqiW
| | - Bernard M van den Berg
- Department of Internal Medicine (Nephrology) & Einthoven Laboratory of Vascular and Regenerative Medicine & The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands
| | - Ton J Rabelink
- Department of Internal Medicine (Nephrology) & Einthoven Laboratory of Vascular and Regenerative Medicine & The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, The Netherlands.
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4
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VanderDoes J, Marceaux C, Yokote K, Asselin-Labat ML, Rice G, Hywood JD. Using random forests to uncover the predictive power of distance-varying cell interactions in tumor microenvironments. PLoS Comput Biol 2024; 20:e1011361. [PMID: 38875302 DOI: 10.1371/journal.pcbi.1011361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 05/31/2024] [Indexed: 06/16/2024] Open
Abstract
Tumor microenvironments (TMEs) contain vast amounts of information on patient's cancer through their cellular composition and the spatial distribution of tumor cells and immune cell populations. Exploring variations in TMEs between patient groups, as well as determining the extent to which this information can predict outcomes such as patient survival or treatment success with emerging immunotherapies, is of great interest. Moreover, in the face of a large number of cell interactions to consider, we often wish to identify specific interactions that are useful in making such predictions. We present an approach to achieve these goals based on summarizing spatial relationships in the TME using spatial K functions, and then applying functional data analysis and random forest models to both predict outcomes of interest and identify important spatial relationships. This approach is shown to be effective in simulation experiments at both identifying important spatial interactions while also controlling the false discovery rate. We further used the proposed approach to interrogate two real data sets of Multiplexed Ion Beam Images of TMEs in triple negative breast cancer and lung cancer patients. The methods proposed are publicly available in a companion R package funkycells.
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Affiliation(s)
- Jeremy VanderDoes
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
| | - Claire Marceaux
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Kenta Yokote
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
| | - Marie-Liesse Asselin-Labat
- Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia
- Department of Medical Biology, The University of Melbourne, Parkville, Australia
| | - Gregory Rice
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada
| | - Jack D Hywood
- Department of Anatomical Pathology, Royal Melbourne Hospital, Melbourne, Australia
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5
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Kumar R, Zemaitis KJ, Fulcher JM, Paša-Tolić L. Advances in mass spectrometry-enabled multiomics at single-cell resolution. Curr Opin Biotechnol 2024; 87:103096. [PMID: 38432187 DOI: 10.1016/j.copbio.2024.103096] [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: 01/15/2024] [Accepted: 02/06/2024] [Indexed: 03/05/2024]
Abstract
Biological organisms are multifaceted, intricate systems where slight perturbations can result in extensive changes in gene expression, protein abundance and/or activity, and metabolic flux. These changes occur at different timescales, spatially across cells of heterogeneous origins, and within single-cells. Hence, multimodal measurements at the smallest biological scales are necessary to capture dynamic changes in heterogeneous biological systems. Of the analytical techniques used to measure biomolecules, mass spectrometry (MS) has proven to be a powerful option due to its sensitivity, robustness, and flexibility with regard to the breadth of biomolecules that can be analyzed. Recently, many studies have coupled MS to other analytical techniques with the goal of measuring multiple modalities from the same single-cell. It is with these concepts in mind that we focus this review on MS-enabled multiomic measurements at single-cell or near-single- cell resolution.
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Affiliation(s)
- Rashmi Kumar
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - Kevin J Zemaitis
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA
| | - James M Fulcher
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
| | - Ljiljana Paša-Tolić
- Environmental Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, USA.
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Schrom EC, McCaffrey EF, Sreejithkumar V, Radtke AJ, Ichise H, Arroyo-Mejias A, Speranza E, Arakkal L, Thakur N, Grant S, Germain RN. Spatial Patterning Analysis of Cellular Ensembles (SPACE) discovers complex spatial organization at the cell and tissue levels. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.08.570837. [PMID: 38168288 PMCID: PMC10760187 DOI: 10.1101/2023.12.08.570837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
Spatial patterns of cells and other biological elements drive both physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co-occurrence but also context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements - single elements, pairs, triplets, and so on - and ranks the most strongly patterned ensembles. For single images, rankings reflect patterns that differ from random assortment. For sets of images, rankings reflect patterns that differ across sample groups (e.g., genotypes, treatments, timepoints, etc.). Further tools then thoroughly characterize the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then use SPACE to detect new patterns across varied datasets, including tumors and tuberculosis granulomas.
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Affiliation(s)
- Edward C. Schrom
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Erin F. McCaffrey
- Spatial Immunology Unit, T-Lymphocyte Biology Section, Laboratory of Parasitic Diseases, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Vivek Sreejithkumar
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Andrea J. Radtke
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Hiroshi Ichise
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Armando Arroyo-Mejias
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Emily Speranza
- Florida Research and Innovation Center, Cleveland Clinic Lerner Research Institute, Port Saint Lucie, FL 34987, USA
| | - Leanne Arakkal
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Nishant Thakur
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Spencer Grant
- Center for Alzheimer’s and Related Dementias, National Institute on Aging, National Institutes of Health, Bethesda, MD 20892-1892, USA
| | - Ronald N. Germain
- Lymphocyte Biology Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
- Center for Advanced Tissue Imaging, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892-1892, USA
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7
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Chen JJ, Vincent MY, Shepard D, Peereboom D, Mahalingam D, Battiste J, Patel MR, Juric D, Wen PY, Bullock A, Selfridge JE, Pant S, Liu J, Li W, Fyfe S, Wang S, Zota V, Mahoney J, Watnick RS, Cieslewicz M, Watnick J. Phase 1 dose expansion and biomarker study assessing first-in-class tumor microenvironment modulator VT1021 in patients with advanced solid tumors. COMMUNICATIONS MEDICINE 2024; 4:95. [PMID: 38773224 PMCID: PMC11109328 DOI: 10.1038/s43856-024-00520-z] [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: 02/23/2023] [Accepted: 05/03/2024] [Indexed: 05/23/2024] Open
Abstract
BACKGROUND Preclinical studies have demonstrated that VT1021, a first-in-class therapeutic agent, inhibits tumor growth via stimulation of thrombospondin-1 (TSP-1) and reprograms the tumor microenvironment. We recently reported data from the dose escalation part of a phase I study of VT1021 in solid tumors. Here, we report findings from the dose expansion phase of the same study. METHODS We analyzed the safety and tolerability, clinical response, and biomarker profile of VT1021 in the expansion portion of the phase I study (NCT03364400). Safety/tolerability is determined by adverse events related to the treatment. Clinical response is determined by RECIST v1.1 and iRECIST. Biomarkers are measured by multiplexed ion beam imaging and enzyme-linked immunoassay (ELISA). RESULTS First, we report the safety and tolerability data as the primary outcome of this study. Adverse events (AE) suspected to be related to the study treatment (RTEAEs) are mostly grade 1-2. There are no grade 4 or 5 adverse events. VT1021 is safe and well tolerated in patients with solid tumors in this study. We report clinical responses as a secondary efficacy outcome. VT1021 demonstrates promising single-agent clinical activity in recurrent GBM (rGBM) in this study. Among 22 patients with rGBM, the overall disease control rate (DCR) is 45% (95% confidence interval, 0.24-0.67). Finally, we report the exploratory outcomes of this study. We show the clinical confirmation of TSP-1 induction and TME remodeling by VT1021. Our biomarker analysis identifies several plasmatic cytokines as potential biomarkers for future clinical studies. CONCLUSIONS VT1021 is safe and well-tolerated in patients with solid tumors in a phase I expansion study. VT1021 has advanced to a phase II/III clinical study in glioblastoma (NCT03970447).
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Affiliation(s)
| | | | | | | | | | | | - Manish R Patel
- Florida Cancer Specialists/Sarah Cannon Research Institute, Sarasota, FL, USA
| | - Dejan Juric
- Massachusetts General Hospital, Boston, MA, USA
| | | | | | | | - Shubham Pant
- The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Joyce Liu
- Dana Farber Cancer Institute, Boston, MA, USA
| | - Wendy Li
- Vigeo Therapeutics, Cambridge, MA, USA
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8
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Peters-Clarke TM, Coon JJ, Riley NM. Instrumentation at the Leading Edge of Proteomics. Anal Chem 2024; 96:7976-8010. [PMID: 38738990 DOI: 10.1021/acs.analchem.3c04497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/14/2024]
Affiliation(s)
- Trenton M Peters-Clarke
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
| | - Joshua J Coon
- Department of Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Department of Biomolecular Chemistry, University of Wisconsin─Madison, Madison, Wisconsin 53706, United States
- Morgridge Institute for Research, Madison, Wisconsin 53715, United States
| | - Nicholas M Riley
- Department of Chemistry, University of Washington, Seattle, Washington 98195, United States
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9
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Yang L, Yang Q, Lin L, Zhang C, Dong L, Gao X, Zhang Z, Zeng C, Wang PG. LectoScape: A Highly Multiplexed Imaging Platform for Glycome Analysis and Biomedical Diagnosis. Anal Chem 2024; 96:6558-6565. [PMID: 38632928 DOI: 10.1021/acs.analchem.3c04925] [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: 04/19/2024]
Abstract
Glycosylation, a fundamental biological process, involves the attachment of glycans to proteins, lipids, and RNA, and it plays a crucial role in various biological pathways. It is of great significance to obtain the precise spatial distribution of glycosylation modifications at the cellular and tissue levels. Here, we introduce LectoScape, an innovative method enabling detailed imaging of tissue glycomes with up to 1 μm resolution through image mass cytometry (IMC). This method utilizes 12 distinct, nonoverlapping lectins selected via microarray technology, enabling the multiplexed detection of a wide array of glycans. Furthermore, we developed an efficient labeling strategy for these lectins. Crucially, our approach facilitates the concurrent imaging of diverse glycan motifs, including N-glycan and O-glycan, surpassing the capabilities of existing technologies. Using LectoScape, we have successfully delineated unique glycan structures in various cell types, enhancing our understanding of the glycan distribution across human tissues. Our method has identified specific glycan markers, such as α2,3-sialylated Galβ1, 3GalNAc in O-glycan, and terminal GalNAc, as diagnostic indicators for cervical intraepithelial neoplasia. This highlights the potential of LectoScape in cancer diagnostics through the detection of abnormal glycosylation patterns.
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Affiliation(s)
- Lujie Yang
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Qianting Yang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital; The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
- Shenzhen Clinical Research Center for Tuberculosis, Shenzhen, 518020, Guangdong, China
| | - Ling Lin
- Shanghai Institute of Cardiovascular Diseases, Zhongshan Hospital, Fudan University, Shanghai, 200032, China
| | - Chi Zhang
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Lingkai Dong
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Xiang Gao
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital; The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
- Shenzhen Clinical Research Center for Tuberculosis, Shenzhen, 518020, Guangdong, China
| | - Zheng Zhang
- Institute for Hepatology, National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital; The Second Affiliated Hospital, School of Medicine, Southern University of Science and Technology, Shenzhen, 518020, Guangdong, China
- Shenzhen Clinical Research Center for Tuberculosis, Shenzhen, 518020, Guangdong, China
| | - Chen Zeng
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
- Institute of Advanced Biotechnology and School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Peng George Wang
- Department of Pharmacology, School of Medicine, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
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McMahon NP, Solanki A, Wang LG, Montaño AR, Jones JA, Samkoe KS, Tichauer KM, Gibbs SL. In situ single-cell therapeutic response imaging facilitated by the TRIPODD fluorescence imaging platform. Theranostics 2024; 14:2816-2834. [PMID: 38773974 PMCID: PMC11103495 DOI: 10.7150/thno.93256] [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: 12/15/2023] [Accepted: 03/21/2024] [Indexed: 05/24/2024] Open
Abstract
Purpose: Small molecule drugs such as tyrosine kinase inhibitors (TKIs) targeting tumoral molecular dependencies have become standard of care for numerous cancer types. Notably, epidermal growth factor receptor (EGFR) TKIs (e.g., erlotinib, afatinib, osimertinib) are the current first-line treatment for non-small cell lung cancer (NSCLC) due to their improved therapeutic outcomes for EGFR mutated and overexpressing disease over traditional platinum-based chemotherapy. However, many NSCLC tumors develop resistance to EGFR TKI therapy causing disease progression. Currently, the relationship between in situ drug target availability (DTA), local protein expression and therapeutic response cannot be accurately assessed using existing analytical tools despite being crucial to understanding the mechanism of therapeutic efficacy. Procedure: We have previously reported development of our fluorescence imaging platform termed TRIPODD (Therapeutic Response Imaging through Proteomic and Optical Drug Distribution) that is capable of simultaneous quantification of single-cell DTA and protein expression with preserved spatial context within a tumor. TRIPODD combines two complementary fluorescence imaging techniques: intracellular paired agent imaging (iPAI) to measure DTA and cyclic immunofluorescence (cyCIF), which utilizes oligonucleotide conjugated antibodies (Ab-oligos) for spatial proteomic expression profiling on tissue samples. Herein, TRIPODD was modified and optimized to provide a downstream analysis of therapeutic response through single-cell DTA and proteomic response imaging. Results: We successfully performed sequential imaging of iPAI and cyCIF resulting in high dimensional imaging and biomarker assessment to quantify single-cell DTA and local protein expression on erlotinib treated NSCLC models. Pharmacodynamic and pharmacokinetic studies of the erlotinib iPAI probes revealed that administration of 2.5 mg/kg each of the targeted and untargeted probe 4 h prior to tumor collection enabled calculation of DTA values with high Pearson correlation to EGFR, the erlotinib molecular target, expression in the tumors. Analysis of single-cell biomarker expression revealed that a single erlotinib dose was insufficient to enact a measurable decrease in the EGFR signaling cascade protein expression, where only the DTA metric detected the presence of bound erlotinib. Conclusion: We demonstrated the capability of TRIPODD to evaluate therapeutic response imaging to erlotinib treatment as it relates to signaling inhibition, DTA, proliferation, and apoptosis with preserved spatial context.
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Affiliation(s)
- Nathan P. McMahon
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Allison Solanki
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Lei G. Wang
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
| | - Antonio R. Montaño
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Jocelyn A. Jones
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
| | - Kimberley S. Samkoe
- Thayer School of Engineering, Dartmouth College, Hanover, NH 03755, USA
- Geisel School of Medicine, Dartmouth College, Hanover, NH 03755, USA
| | - Kenneth M. Tichauer
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA
| | - Summer L. Gibbs
- Biomedical Engineering Department, Oregon Health & Science University, Portland, OR 97201, USA
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR 97201, USA
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11
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Wang Z, Santa-Maria CA, Popel AS, Sulam J. Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns from Breast Multiplexed Digital Pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590118. [PMID: 38712207 PMCID: PMC11071347 DOI: 10.1101/2024.04.22.590118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. Despite extensive research efforts dedicated to characterizing this complex and heterogeneous environment, considerable challenges persist. In this study, we introduce a data-driven approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients, where the identified tumor microenvironment patterns result in a risk stratification system that provides complementary, new information with respect to alternative standards. Our results, which are validated in a completely independent cohort, allow for new insights into the prognostic implications of the breast tumor microenvironment, and this methodology could be applied to other cancer types more generally.
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Affiliation(s)
- Zhenzhen Wang
- Department of Biomedical Engineering, Johns Hopkins University
- Mathematical Institute for Data Science, Johns Hopkins University
| | - Cesar A Santa-Maria
- Department of Oncology, Johns Hopkins University
- Sidney Kimmel Comprehensive Cancer Center
| | | | - Jeremias Sulam
- Department of Biomedical Engineering, Johns Hopkins University
- Mathematical Institute for Data Science, Johns Hopkins University
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12
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Ng SWK. Characterization of relapse-supportive monocytic cells in acute myeloid leukemia. Cell Rep Med 2024; 5:101507. [PMID: 38631289 PMCID: PMC11031415 DOI: 10.1016/j.xcrm.2024.101507] [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: 03/19/2024] [Revised: 03/19/2024] [Accepted: 03/19/2024] [Indexed: 04/19/2024]
Abstract
The precise identities of bone marrow resident cells contributing to AML relapse are not fully known. Hollands et al. report early evidence to support the existence of an aberrant monocytic cell population that appears to promote LSC expansion after cytarabine treatment.
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Affiliation(s)
- Stanley W K Ng
- Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA 92697, USA.
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13
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Hollands CG, Boyd AL, Zhao X, Reid JC, Henly C, ElRafie A, Boylan D, Broder E, Kalau O, Johnson P, Mark A, McNicol J, Xenocostas A, Berg T, Foley R, Trus M, Leber B, Garcia-Horton A, Campbell C, Bhatia M. Identification of cells of leukemic stem cell origin with non-canonical regenerative properties. Cell Rep Med 2024; 5:101485. [PMID: 38582086 PMCID: PMC11031376 DOI: 10.1016/j.xcrm.2024.101485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 10/19/2023] [Accepted: 03/04/2024] [Indexed: 04/08/2024]
Abstract
Despite most acute myeloid leukemia (AML) patients entering remission following chemotherapy, outcomes remain poor due to surviving leukemic cells that contribute to relapse. The nature of these enduring cells is poorly understood. Here, through temporal single-cell transcriptomic characterization of AML hierarchical regeneration in response to chemotherapy, we reveal a cell population: AML regeneration enriched cells (RECs). RECs are defined by CD74/CD68 expression, and although derived from leukemic stem cells (LSCs), are devoid of stem/progenitor capacity. Based on REC in situ proximity to CD34-expressing cells identified using spatial transcriptomics on AML patient bone marrow samples, RECs demonstrate the ability to augment or reduce leukemic regeneration in vivo based on transfusion or depletion, respectively. Furthermore, RECs are prognostic for patient survival as well as predictive of treatment failure in AML cohorts. Our study reveals RECs as a previously unknown functional catalyst of LSC-driven regeneration contributing to the non-canonical framework of AML regeneration.
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Affiliation(s)
- Cameron G Hollands
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Allison L Boyd
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Xueli Zhao
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Jennifer C Reid
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Charisa Henly
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Amro ElRafie
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - David Boylan
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Emily Broder
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Olivia Kalau
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Paige Johnson
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Alyssa Mark
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Jamie McNicol
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada
| | - Anargyros Xenocostas
- Department of Medicine, Division of Hematology, Schulich School of Medicine, University of Western Ontario, London, Ontario N6A 3K7, Canada; Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada
| | - Tobias Berg
- Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada; Department of Oncology, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Ronan Foley
- Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada; Department of Oncology, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Michael Trus
- Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Brian Leber
- Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Alejandro Garcia-Horton
- Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada; Department of Oncology, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Clinton Campbell
- Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada; Department of Pathology and Molecular Medicine, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Mickie Bhatia
- Michael G. DeGroote School of Medicine, Department of Biochemistry and Biomedical Sciences, McMaster University, Hamilton, ON L8N 3Z5, Canada; Hematology Exploration and Applications in Leukemia (HEAL) Program, Hamilton, ON, Canada.
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14
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Watson SS, Duc B, Kang Z, de Tonnac A, Eling N, Font L, Whitmarsh T, Massara M, Bodenmiller B, Hausser J, Joyce JA. Microenvironmental reorganization in brain tumors following radiotherapy and recurrence revealed by hyperplexed immunofluorescence imaging. Nat Commun 2024; 15:3226. [PMID: 38622132 PMCID: PMC11018859 DOI: 10.1038/s41467-024-47185-9] [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: 01/30/2023] [Accepted: 03/22/2024] [Indexed: 04/17/2024] Open
Abstract
The tumor microenvironment plays a crucial role in determining response to treatment. This involves a series of interconnected changes in the cellular landscape, spatial organization, and extracellular matrix composition. However, assessing these alterations simultaneously is challenging from a spatial perspective, due to the limitations of current high-dimensional imaging techniques and the extent of intratumoral heterogeneity over large lesion areas. In this study, we introduce a spatial proteomic workflow termed Hyperplexed Immunofluorescence Imaging (HIFI) that overcomes these limitations. HIFI allows for the simultaneous analysis of > 45 markers in fragile tissue sections at high magnification, using a cost-effective high-throughput workflow. We integrate HIFI with machine learning feature detection, graph-based network analysis, and cluster-based neighborhood analysis to analyze the microenvironment response to radiation therapy in a preclinical model of glioblastoma, and compare this response to a mouse model of breast-to-brain metastasis. Here we show that glioblastomas undergo extensive spatial reorganization of immune cell populations and structural architecture in response to treatment, while brain metastases show no comparable reorganization. Our integrated spatial analyses reveal highly divergent responses to radiation therapy between brain tumor models, despite equivalent radiotherapy benefit.
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Affiliation(s)
- Spencer S Watson
- Department of Oncology, University of Lausanne, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, 1011, Switzerland.
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
| | - Benoit Duc
- Department of Oncology, University of Lausanne, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, 1011, Switzerland
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland
| | - Ziqi Kang
- Department of Cellular and Molecular Biology, Karolinska Institutet and SciLifeLab, Stockholm, Sweden
| | - Axel de Tonnac
- Department of Cellular and Molecular Biology, Karolinska Institutet and SciLifeLab, Stockholm, Sweden
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Laure Font
- Department of Oncology, University of Lausanne, Lausanne, Switzerland
- École Polytechnique Fédérale Lausanne, Lausanne, Switzerland
| | - Tristan Whitmarsh
- Machine Intelligence Laboratory, Department of Engineering, University of Cambridge, Cambridge, UK
| | - Matteo Massara
- Department of Oncology, University of Lausanne, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland
- Agora Cancer Research Center, Lausanne, 1011, Switzerland
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Jean Hausser
- Department of Cellular and Molecular Biology, Karolinska Institutet and SciLifeLab, Stockholm, Sweden
| | - Johanna A Joyce
- Department of Oncology, University of Lausanne, Lausanne, Switzerland.
- Ludwig Institute for Cancer Research, University of Lausanne, Lausanne, Switzerland.
- Agora Cancer Research Center, Lausanne, 1011, Switzerland.
- L. Lundin and Family Brain Tumor Research Center, Departments of Oncology and Clinical Neurosciences, Centre Hospitalier Universitaire Vaudois, Lausanne, 1011, Switzerland.
- Cancer Research UK, Cancer Grand Challenges iMAXT Consortium, University of Cambridge, Cambridge, UK.
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15
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Ahn S, Lee HS. Applicability of Spatial Technology in Cancer Research. Cancer Res Treat 2024; 56:343-356. [PMID: 38291743 PMCID: PMC11016655 DOI: 10.4143/crt.2023.1302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 01/29/2024] [Indexed: 02/01/2024] Open
Abstract
This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.
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Affiliation(s)
- Sangjeong Ahn
- Department of Pathology, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Artificial Intelligence Center, Korea University Anam Hospital, Korea University College of Medicine, Seoul, Korea
- Department of Medical Informatics, Korea University College of Medicine, Seoul, Korea
| | - Hye Seung Lee
- Department of Pathology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Korea
- Cancer Research Institute, Seoul National University College of Medicine, Seoul, Korea
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16
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Ranek JS, Stallaert W, Milner JJ, Redick M, Wolff SC, Beltran AS, Stanley N, Purvis JE. DELVE: feature selection for preserving biological trajectories in single-cell data. Nat Commun 2024; 15:2765. [PMID: 38553455 PMCID: PMC10980758 DOI: 10.1038/s41467-024-46773-z] [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: 05/18/2023] [Accepted: 03/07/2024] [Indexed: 04/02/2024] Open
Abstract
Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .
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Affiliation(s)
- Jolene S Ranek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Wayne Stallaert
- Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | - J Justin Milner
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Margaret Redick
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Samuel C Wolff
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Adriana S Beltran
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Human Pluripotent Cell Core, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, NC, USA
| | - Natalie Stanley
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Jeremy E Purvis
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- Computational Medicine Program, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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17
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De Biasi S, Gigan JP, Borella R, Santacroce E, Lo Tartaro D, Neroni A, Paschalidis N, Piwocka K, Argüello RJ, Gibellini L, Cossarizza A. Cell metabolism: Functional and phenotypic single cell approaches. Methods Cell Biol 2024; 186:151-187. [PMID: 38705598 DOI: 10.1016/bs.mcb.2024.02.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Several metabolic pathways are essential for the physiological regulation of immune cells, but their dysregulation can cause immune dysfunction. Hypermetabolic and hypometabolic states represent deviations in the magnitude and flexibility of effector cells in different contexts, for example in autoimmunity, infections or cancer. To study immunometabolism, most methods focus on bulk populations and rely on in vitro activation assays. Nowadays, thanks to the development of single-cell technologies, including multiparameter flow cytometry, mass cytometry, RNA cytometry, among others, the metabolic state of individual immune cells can be measured in a variety of samples obtained in basic, translational and clinical studies. Here, we provide an overview of different single-cell approaches that are employed to investigate both mitochondrial functions and cell dependence from mitochondria metabolism. Moreover, besides the description of the appropriate experimental settings, we discuss the strengths and weaknesses of different approaches with the aim to suggest how to study cell metabolism in the settings of interest.
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Affiliation(s)
- Sara De Biasi
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy.
| | - Julien Paul Gigan
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France
| | - Rebecca Borella
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Elena Santacroce
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Domenico Lo Tartaro
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Anita Neroni
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | | | - Katarzyna Piwocka
- Nencki Institute of Experimental Biology, Polish Academy of Sciences, Warsaw, Poland
| | - Rafael José Argüello
- Aix Marseille University, CNRS, INSERM, CIML, Centre d'Immunologie de Marseille-Luminy, Marseille, France
| | - Lara Gibellini
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
| | - Andrea Cossarizza
- Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, Modena, Italy
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18
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Scheuermann S, Kristmann B, Engelmann F, Nuernbergk A, Scheuermann D, Koloseus M, Abed T, Solass W, Seitz CM. Unveiling spatial complexity in solid tumor immune microenvironments through multiplexed imaging. Front Immunol 2024; 15:1383932. [PMID: 38566984 PMCID: PMC10985204 DOI: 10.3389/fimmu.2024.1383932] [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: 02/08/2024] [Accepted: 02/29/2024] [Indexed: 04/04/2024] Open
Abstract
Deciphering cellular components and the spatial interaction network of the tumor immune microenvironment (TIME) of solid tumors is pivotal for understanding biologically relevant cross-talks and, ultimately, advancing therapies. Multiplexed tissue imaging provides a powerful tool to elucidate spatial complexity in a holistic manner. We established and cross-validated a comprehensive immunophenotyping panel comprising over 121 markers for multiplexed tissue imaging using MACSima™ imaging cyclic staining (MICS) alongside an end-to-end analysis workflow. Applying this panel and workflow to primary cancer tissues, we characterized tumor heterogeneity, investigated potential therapeutical targets, conducted in-depth profiling of cell types and states, sub-phenotyped T cells within the TIME, and scrutinized cellular neighborhoods of diverse T cell subsets. Our findings highlight the advantage of spatial profiling, revealing immunosuppressive molecular signatures of tumor-associated myeloid cells interacting with neighboring exhausted, PD1high T cells in the TIME of hepatocellular carcinoma (HCC). This study establishes a robust framework for spatial exploration of TIMEs in solid tumors and underscores the potency of multiplexed tissue imaging and ultra-deep cell phenotyping in unraveling clinically relevant tumor components.
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Affiliation(s)
- Sophia Scheuermann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence EXC 2180 ‘Image-Guided and Functionally Instructed Tumor Therapies’, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), partner site Tuebingen, a partnership between German Cancer Research Center (DKFZ) and University Hospital Tuebingen, Tuebingen, Germany
| | - Beate Kristmann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Fabienne Engelmann
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Alice Nuernbergk
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - David Scheuermann
- School of Business and Economics, Faculty of Economics and Social Sciences, University of Tuebingen, Tuebingen, Germany
| | - Marie Koloseus
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
| | - Tayeb Abed
- Institute of Pathology and Neuropathology, University Hospital Tuebingen and Comprehensive Cancer Center, Tuebingen, Germany
| | - Wiebke Solass
- Institute of Tissue Medicine and Pathology (ITMP), University of Bern, Bern, Switzerland
| | - Christian M. Seitz
- Department of Haematology, Oncology, Gastroenterology, Nephrology, Rheumatology, University Children’s Hospital Tuebingen, Tuebingen, Germany
- iFIT Cluster of Excellence EXC 2180 ‘Image-Guided and Functionally Instructed Tumor Therapies’, University of Tuebingen, Tuebingen, Germany
- German Cancer Consortium (DKTK), partner site Tuebingen, a partnership between German Cancer Research Center (DKFZ) and University Hospital Tuebingen, Tuebingen, Germany
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19
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Yeo YY, Cramer P, Deisher A, Bai Y, Zhu B, Yeo WJ, Shipp MA, Rodig SJ, Jiang S. A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging. Methods Cell Biol 2024; 186:213-231. [PMID: 38705600 DOI: 10.1016/bs.mcb.2024.02.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/07/2024]
Abstract
Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section. Here, we describe an optimized MIBI workflow for high-plex analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues following antigen retrieval, metal isotope-conjugated antibody staining, imaging using the MIBI instrument, and subsequent data processing and analysis. While this workflow is focused on imaging human FFPE samples using the MIBI, this workflow can be easily extended to model systems, biological questions, and multiplexed imaging modalities.
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Affiliation(s)
- Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, United States
| | - Precious Cramer
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Addison Deisher
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Yunhao Bai
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, United States
| | - Bokai Zhu
- Department of Microbiology & Immunology, Stanford University School of Medicine, Stanford, CA, United States
| | - Wan-Jin Yeo
- Department of Physics, Institute of Learning and Brain Sciences, University of Washington, Seattle, WA, United States
| | - Margaret A Shipp
- Department of Medical Oncology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States; Program in Virology, Division of Medical Sciences, Harvard Medical School, Boston, MA, United States; Department of Pathology, Dana Farber Cancer Institute, Harvard Medical School, Boston, MA, United States; Broad Institute of MIT and Harvard, Cambridge, MA, United States.
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20
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Bhushan V, Nita-Lazar A. Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology. J Proteome Res 2024. [PMID: 38451675 DOI: 10.1021/acs.jproteome.3c00839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.
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Affiliation(s)
- Vanya Bhushan
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
| | - Aleksandra Nita-Lazar
- Functional Cellular Networks Section, Laboratory of Immune System Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, United States
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21
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Yeo YY, Qiu H, Bai Y, Zhu B, Chang Y, Yeung J, Michel HA, Wright K, Shaban M, Sadigh S, Nkosi D, Shanmugam V, Rock P, Tung Yiu SP, Cramer P, Paczkowska J, Stephan P, Liao G, Huang AY, Wang H, Chen H, Frauenfeld L, Mitra B, Gewurz BE, Schürch CM, Zhao B, Nolan GP, Zhang B, Shalek AK, Angelo M, Mahmood F, Ma Q, Burack WR, Shipp MA, Rodig SJ, Jiang S. Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.05.583586. [PMID: 38496566 PMCID: PMC10942289 DOI: 10.1101/2024.03.05.583586] [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
Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.
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22
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Zhu B, Bai Y, Yeo YY, Lu X, Rovira-Clavé X, Chen H, Yeung J, Gerber GK, Angelo M, Shalek AK, Nolan GP, Jiang S. A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583400. [PMID: 38496402 PMCID: PMC10942342 DOI: 10.1101/2024.03.04.583400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.
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Affiliation(s)
- Bokai Zhu
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yunhao Bai
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiaowei Lu
- Mass Spectrometry Core Facility, Stanford University, Stanford, CA, United States
| | - Xavier Rovira-Clavé
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Institute for Bioengineering of Catalonia (IBEC), Barcelona Institute of Science and Technology (BIST), Barcelona, Spain
| | - Han Chen
- Department of Microbiology and Immunology, Stanford University, Stanford, CA, United States
- Biological and Medical Informatics program, UCSF, San Francisco, CA, United States
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Georg K Gerber
- Division of Computational Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Health Sciences and Technology, Harvard University and MIT, Cambridge, MA, USA
| | - Mike Angelo
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Alex K Shalek
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, United States
| | - Sizun Jiang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- Department of Microbiology, Harvard Medical School, Boston, MA, USA
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23
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Kwon Y, Woo J, Yu F, Williams SM, Markillie LM, Moore RJ, Nakayasu ES, Chen J, Campbell-Thompson M, Mathews CE, Nesvizhskii AI, Qia WJ, Zhu Y. Proteome-scale tissue mapping using mass spectrometry based on label-free and multiplexed workflows. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.04.583367. [PMID: 38496682 PMCID: PMC10942300 DOI: 10.1101/2024.03.04.583367] [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
Multiplexed bimolecular profiling of tissue microenvironment, or spatial omics, can provide deep insight into cellular compositions and interactions in both normal and diseased tissues. Proteome-scale tissue mapping, which aims to unbiasedly visualize all the proteins in whole tissue section or region of interest, has attracted significant interest because it holds great potential to directly reveal diagnostic biomarkers and therapeutic targets. While many approaches are available, however, proteome mapping still exhibits significant technical challenges in both protein coverage and analytical throughput. Since many of these existing challenges are associated with mass spectrometry-based protein identification and quantification, we performed a detailed benchmarking study of three protein quantification methods for spatial proteome mapping, including label-free, TMT-MS2, and TMT-MS3. Our study indicates label-free method provided the deepest coverages of ~3500 proteins at a spatial resolution of 50 μm and the largest quantification dynamic range, while TMT-MS2 method holds great benefit in mapping throughput at >125 pixels per day. The evaluation also indicates both label-free and TMT-MS2 provide robust protein quantifications in terms of identifying differentially abundant proteins and spatially co-variable clusters. In the study of pancreatic islet microenvironment, we demonstrated deep proteome mapping not only enables to identify protein markers specific to different cell types, but more importantly, it also reveals unknown or hidden protein patterns by spatial co-expression analysis.
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Affiliation(s)
- Yumi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jongmin Woo
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
| | - Sarah M. Williams
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Lye Meng Markillie
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ronald J. Moore
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ernesto S. Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Jing Chen
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Martha Campbell-Thompson
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Clayton E. Mathews
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL 32610, United States
| | - Alexey I. Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, United States
| | - Wei-Jun Qia
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99354, United States
| | - Ying Zhu
- Department of Microchemistry, Proteomics, Lipidomics and Next Generation Sequencing, Genentech, 1 DNA Way, South San Francisco, CA 94080, United States
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24
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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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25
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Macdonald JK, Mehta AS, Drake RR, Angel PM. Molecular analysis of the extracellular microenvironment: from form to function. FEBS Lett 2024; 598:602-620. [PMID: 38509768 PMCID: PMC11049795 DOI: 10.1002/1873-3468.14852] [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: 11/29/2023] [Revised: 02/26/2024] [Accepted: 02/28/2024] [Indexed: 03/22/2024]
Abstract
The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.
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Affiliation(s)
- Jade K Macdonald
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
| | - Anand S Mehta
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
| | - Richard R Drake
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
| | - Peggi M. Angel
- Department of Cell and Molecular Pharmacology & Experimental Therapeutics, Medical University of South Carolina, Charleston, SC
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26
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Aung TN, Bates KM, Rimm DL. High-Plex Assessment of Biomarkers in Tumors. Mod Pathol 2024; 37:100425. [PMID: 38219953 DOI: 10.1016/j.modpat.2024.100425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 01/02/2024] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
The assessment of biomarkers plays a critical role in the diagnosis and treatment of many cancers. Biomarkers not only provide diagnostic, prognostic, or predictive information but also can act as effective targets for new pharmaceutical therapies. As the utility of biomarkers increases, it becomes more important to utilize accurate and efficient methods for biomarker discovery and, ultimately, clinical assessment. High-plex imaging studies, defined here as assessment of 8 or more biomarkers on a single slide, have become the method of choice for biomarker discovery and assessment of biomarker spatial context. In this review, we discuss methods of measuring biomarkers in slide-mounted tissue samples, detail the various high-plex methods that allow for the simultaneous assessment of multiple biomarkers in situ, and describe the impact of high-plex biomarker assessment on the future of anatomic pathology.
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Affiliation(s)
- Thazin N Aung
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - Katherine M Bates
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut
| | - David L Rimm
- Department of Pathology, Yale University School of Medicine, New Haven, Connecticut; Department of Internal Medicine (Medical Oncology), Yale University School of Medicine, New Haven, Connecticut.
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27
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Lin S, Zhao F, Wu Z, Yao J, Zhao Y, Yuan Z. Streamlining spatial omics data analysis with Pysodb. Nat Protoc 2024; 19:831-895. [PMID: 38135744 DOI: 10.1038/s41596-023-00925-5] [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: 04/04/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023]
Abstract
Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | | | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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28
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Fan X, Sun AR, Young RSE, Afara IO, Hamilton BR, Ong LJY, Crawford R, Prasadam I. Spatial analysis of the osteoarthritis microenvironment: techniques, insights, and applications. Bone Res 2024; 12:7. [PMID: 38311627 PMCID: PMC10838951 DOI: 10.1038/s41413-023-00304-6] [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] [Revised: 11/21/2023] [Accepted: 11/27/2023] [Indexed: 02/06/2024] Open
Abstract
Osteoarthritis (OA) is a debilitating degenerative disease affecting multiple joint tissues, including cartilage, bone, synovium, and adipose tissues. OA presents diverse clinical phenotypes and distinct molecular endotypes, including inflammatory, metabolic, mechanical, genetic, and synovial variants. Consequently, innovative technologies are needed to support the development of effective diagnostic and precision therapeutic approaches. Traditional analysis of bulk OA tissue extracts has limitations due to technical constraints, causing challenges in the differentiation between various physiological and pathological phenotypes in joint tissues. This issue has led to standardization difficulties and hindered the success of clinical trials. Gaining insights into the spatial variations of the cellular and molecular structures in OA tissues, encompassing DNA, RNA, metabolites, and proteins, as well as their chemical properties, elemental composition, and mechanical attributes, can contribute to a more comprehensive understanding of the disease subtypes. Spatially resolved biology enables biologists to investigate cells within the context of their tissue microenvironment, providing a more holistic view of cellular function. Recent advances in innovative spatial biology techniques now allow intact tissue sections to be examined using various -omics lenses, such as genomics, transcriptomics, proteomics, and metabolomics, with spatial data. This fusion of approaches provides researchers with critical insights into the molecular composition and functions of the cells and tissues at precise spatial coordinates. Furthermore, advanced imaging techniques, including high-resolution microscopy, hyperspectral imaging, and mass spectrometry imaging, enable the visualization and analysis of the spatial distribution of biomolecules, cells, and tissues. Linking these molecular imaging outputs to conventional tissue histology can facilitate a more comprehensive characterization of disease phenotypes. This review summarizes the recent advancements in the molecular imaging modalities and methodologies for in-depth spatial analysis. It explores their applications, challenges, and potential opportunities in the field of OA. Additionally, this review provides a perspective on the potential research directions for these contemporary approaches that can meet the requirements of clinical diagnoses and the establishment of therapeutic targets for OA.
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Affiliation(s)
- Xiwei Fan
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Antonia Rujia Sun
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Reuben S E Young
- Central Analytical Research Facility, Queensland University of Technology, Brisbane, QLD, Australia
- Molecular Horizons, University of Wollongong, Wollongong, NSW, Australia
| | - Isaac O Afara
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland
- School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology, University of Queensland, Brisbane, QLD, Australia
| | - Brett R Hamilton
- Centre for Microscopy and Microanalysis, University of Queensland, Brisbane, QLD, Australia
| | - Louis Jun Ye Ong
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ross Crawford
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia
- The Prince Charles Hospital, Brisbane, QLD, Australia
| | - Indira Prasadam
- Centre for Biomedical Technologies, Queensland University of Technology, Brisbane, QLD, Australia.
- School of Mechanical, Medical & Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
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29
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Ligon JA, Ramakrishna S, Ceppi F, Calkoen FGJ, Diorio C, Davis KL, Jacoby E, Gottschalk S, Schultz LM, Capitini CM. INSPIRED Symposium Part 4B: Chimeric Antigen Receptor T Cell Correlative Studies-Established Findings and Future Priorities. Transplant Cell Ther 2024; 30:155-170. [PMID: 37863355 DOI: 10.1016/j.jtct.2023.10.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 10/22/2023]
Abstract
Chimeric antigen receptor (CAR) T cell therapy has revolutionized the treatment of B cell malignancies, with multiple CAR T cell products approved for numerous indications by regulatory agencies worldwide. However, significant work remains to be done to enhance these treatments. In March 2023, a group of experts in CAR T cell therapy assembled at the National Institutes of Health in Bethesda, Maryland at the Insights in Pediatric CAR T Cell Immunotherapy: Recent Advances and Future Directions (INSPIRED) Symposium to identify key areas for research for the coming years. In session 4B, correlative studies to be incorporated into future clinical trials and real-world settings were discussed. Active areas of research identified included (1) optimizing CAR T cell product manufacturing; (2) ensuring adequate lymphodepletion prior to CAR T cell administration; (3) overcoming immunoregulatory cells and tumor stroma present in the tumor microenvironment, particularly in solid tumors; (4) understanding tumor intrinsic properties that lead to CAR T cell immunotherapy resistance; and (5) uncovering biomarkers predictive of treatment resistance, treatment durability, or immune-related adverse events. Here we review the results of previously published clinical trials and real-world studies to summarize what is currently known about each of these topics. We then outline priorities for future research that we believe will be important for improving our understanding of CAR T cell therapy and ultimately leading to better outcomes for patients.
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Affiliation(s)
- John A Ligon
- Department of Pediatrics, Division of Hematology/Oncology, University of Florida, Gainesville, Florida; University of Florida Health Cancer Center, Gainesville, Florida.
| | - Sneha Ramakrishna
- Stanford Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, California; Department of Pediatrics, Stanford University, Stanford, California
| | - Francesco Ceppi
- Division of Pediatrics, Department of Woman-Mother-Child, Pediatric Hematology-Oncology Unit, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland
| | - Friso G J Calkoen
- Division of Pediatric Oncology, Princess Maxima Center, Utrecht, The Netherlands
| | - Caroline Diorio
- Division of Oncology and Center for Childhood Cancer Research, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Kara L Davis
- Stanford Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, California; Department of Pediatrics, Stanford University, Stanford, California
| | - Elad Jacoby
- Pediatric Hemato-Oncology, Sheba Medical Center and Tel Aviv University, Tel Aviv, Israel
| | - Stephen Gottschalk
- Department of Bone Marrow Transplantation and Cellular Therapy, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Liora M Schultz
- Stanford Center for Cancer Cell Therapy, Stanford University School of Medicine, Stanford, California; Department of Pediatrics, Stanford University, Stanford, California
| | - Christian M Capitini
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin; University of Wisconsin Carbone Cancer Center, Madison, Wisconsin
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30
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Gorman BL, Torti SV, Torti FM, Anderton CR. Mass spectrometry imaging of metals in tissues and cells: Methods and biological applications. Biochim Biophys Acta Gen Subj 2024; 1868:130329. [PMID: 36791830 PMCID: PMC10423302 DOI: 10.1016/j.bbagen.2023.130329] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/24/2023] [Accepted: 02/08/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Metals are pervasive throughout biological processes, where they play essential structural and catalytic roles. Metals can also exhibit deleterious effects on human health. Powerful analytical techniques, such as mass spectrometry imaging (MSI), are required to map metals due to their low concentrations within biological tissue. SCOPE OF REVIEW This Mini Review focuses on key MSI technology that can image metal distributions in situ, describing considerations for each technique (e.g., resolution, sensitivity, etc.). We highlight recent work using MSI for mapping trace metals in tissues, detecting metal-based drugs, and simultaneously imaging metals and biomolecules. MAJOR CONCLUSIONS MSI has enabled significant advances in locating bioactive metals at high spatial resolution and correlating their distributions with that of biomolecules. The use of metal-based immunochemistry has enabled simultaneous high-throughput protein and biomolecule imaging. GENERAL SIGNIFICANCE The techniques and examples described herein can be applied to many biological questions concerning the important biological roles of metals, metal toxicity, and localization of metal-based drugs.
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Affiliation(s)
- Brittney L Gorman
- Environmental Molecular Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, United States of America
| | - Suzy V Torti
- Department of Molecular Biology and Biophysics, University of Connecticut Health Center, Farmington, CT 06030, United States of America
| | - Frank M Torti
- Department of Medicine, University of Connecticut Health Center, Farmington, CT 06030, United States of America
| | - Christopher R Anderton
- Environmental Molecular Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, United States of America.
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31
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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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32
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Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [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: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
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Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
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33
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Patel AG, Ashenberg O, Collins NB, Segerstolpe Å, Jiang S, Slyper M, Huang X, Caraccio C, Jin H, Sheppard H, Xu K, Chang TC, Orr BA, Shirinifard A, Chapple RH, Shen A, Clay MR, Tatevossian RG, Reilly C, Patel J, Lupo M, Cline C, Dionne D, Porter CBM, Waldman J, Bai Y, Zhu B, Barrera I, Murray E, Vigneau S, Napolitano S, Wakiro I, Wu J, Grimaldi G, Dellostritto L, Helvie K, Rotem A, Lako A, Cullen N, Pfaff KL, Karlström Å, Jané-Valbuena J, Todres E, Thorner A, Geeleher P, Rodig SJ, Zhou X, Stewart E, Johnson BE, Wu G, Chen F, Yu J, Goltsev Y, Nolan GP, Rozenblatt-Rosen O, Regev A, Dyer MA. A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574538. [PMID: 38260392 PMCID: PMC10802404 DOI: 10.1101/2024.01.07.574538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.
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Affiliation(s)
- Anand G Patel
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- These authors contributed equally
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally
| | - Natalie B Collins
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- These authors contributed equally
| | - Åsa Segerstolpe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Huang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chiara Caraccio
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Heather Sheppard
- Comparative Pathology Core, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ke Xu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ti-Cheng Chang
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Brent A Orr
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Abbas Shirinifard
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Amber Shen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael R Clay
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ruth G Tatevossian
- Cancer Biomarkers Laboratory, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Colleen Reilly
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jaimin Patel
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Marybeth Lupo
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Cynthia Cline
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Caroline B M Porter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sébastien Vigneau
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Napolitano
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Isaac Wakiro
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jingyi Wu
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Grace Grimaldi
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Dellostritto
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Asaf Rotem
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kathleen L Pfaff
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Åsa Karlström
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Judit Jané-Valbuena
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ellen Todres
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron Thorner
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Elizabeth Stewart
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gang Wu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Jiyang Yu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yury Goltsev
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
- Lead contacts
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Lead contacts
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Mahalingam D, Harb W, Patnaik A, Bullock A, Watnick RS, Vincent MY, Chen JJ, Wang S, Pestana H, Chao J, Mahoney J, Cieslewicz M, Watnick J. First-in-human phase I dose escalation trial of the first-in-class tumor microenvironment modulator VT1021 in advanced solid tumors. COMMUNICATIONS MEDICINE 2024; 4:10. [PMID: 38218979 PMCID: PMC10787778 DOI: 10.1038/s43856-024-00433-x] [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: 03/31/2023] [Accepted: 01/04/2024] [Indexed: 01/15/2024] Open
Abstract
BACKGROUND VT1021 is a cyclic peptide that induces the expression of thrombospondin-1 (TSP-1) in myeloid-derived suppressor cells (MDSCs) recruited to the tumor microenvironment (TME). TSP-1 reprograms the TME via binding to CD36 and CD47 to induce tumor and endothelial cell apoptosis as well as immune modulation in the TME. METHODS Study VT1021-01 (ClinicalTrials.gov ID NCT03364400) used a modified 3 + 3 design. The primary objective was to determine the recommended Phase 2 dose (RP2D) in patients with advanced solid tumors. Safety, tolerability, and pharmacokinetics (PK) were assessed. Patients were dosed twice weekly intravenously in 9 cohorts (0.5-15.6 mg/kg). Safety was evaluated using CTCAE version 5.0 and the anti-tumor activity was evaluated by RECIST version 1.1. RESULTS The RP2D of VT1021 is established at 11.8 mg/kg. VT1021 is well tolerated with no dose-limiting toxicities reported (0/38). The most frequent drug-related adverse events are fatigue (15.8%), nausea (10.5%), and infusion-related reactions (10.5%). Exposure increases proportionally from 0.5 to 8.8 mg/kg. The disease control rate (DCR) is 42.9% with 12 of 28 patients deriving clinical benefit including a partial response (PR) in one thymoma patient (504 days). CONCLUSIONS VT1021 is safe and well-tolerated across all doses tested. RP2D has been selected for future clinical studies. PR and SD with tumor shrinkage are observed in multiple patients underscoring the single-agent potential of VT1021. Expansion studies in GBM, pancreatic cancer and other solid tumors at the RP2D have been completed and results will be communicated in a separate report.
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Affiliation(s)
| | - Wael Harb
- Horizon Oncology Center, Lafayette, IN, USA
| | - Amita Patnaik
- South Texas Accelerated Research Therapeutics, San Antonio, TX, USA
| | | | | | | | | | | | | | - Judy Chao
- Vigeo Therapeutics, Cambridge, MA, USA
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35
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Magrill J, Moldoveanu D, Gu J, Lajoie M, Watson IR. Mapping the single cell spatial immune landscapes of the melanoma microenvironment. Clin Exp Metastasis 2024:10.1007/s10585-023-10252-4. [PMID: 38217840 DOI: 10.1007/s10585-023-10252-4] [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: 07/15/2023] [Accepted: 11/27/2023] [Indexed: 01/15/2024]
Abstract
Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.
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Affiliation(s)
- Jamie Magrill
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Dan Moldoveanu
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Jiayao Gu
- Department of Human Genetics, McGill University, Montréal, QC, Canada
| | - Mathieu Lajoie
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada
| | - Ian R Watson
- Rosalind and Morris Goodman Cancer Institute, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
- Department of Biochemistry, McGill University, Montréal, QC, Canada.
- Research Institute of the McGill University Health Centre, Montréal, QC, Canada.
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36
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Mennillo E, Kim YJ, Lee G, Rusu I, Patel RK, Dorman LC, Flynn E, Li S, Bain JL, Andersen C, Rao A, Tamaki S, Tsui J, Shen A, Lotstein ML, Rahim M, Naser M, Bernard-Vazquez F, Eckalbar W, Cho SJ, Beck K, El-Nachef N, Lewin S, Selvig DR, Terdiman JP, Mahadevan U, Oh DY, Fragiadakis GK, Pisco A, Combes AJ, Kattah MG. Single-cell and spatial multi-omics highlight effects of anti-integrin therapy across cellular compartments in ulcerative colitis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.01.21.525036. [PMID: 36711576 PMCID: PMC9882264 DOI: 10.1101/2023.01.21.525036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Ulcerative colitis (UC) is driven by immune and stromal subsets, culminating in epithelial injury. Vedolizumab (VDZ) is an anti-integrin antibody that is effective for treating UC. VDZ is known to inhibit lymphocyte trafficking to the intestine, but its broader effects on other cell subsets are less defined. To identify the inflammatory cells that contribute to colitis and are affected by VDZ, we performed single-cell transcriptomic and proteomic analyses of peripheral blood and colonic biopsies in healthy controls and patients with UC on VDZ or other therapies. Here we show that VDZ treatment is associated with alterations in circulating and tissue mononuclear phagocyte (MNP) subsets, along with modest shifts in lymphocytes. Spatial multi-omics of formalin-fixed biopsies demonstrates trends towards increased abundance and proximity of MNP and fibroblast subsets in active colitis. Spatial transcriptomics of archived specimens pre-treatment identifies epithelial-, MNP-, and fibroblast-enriched genes related to VDZ responsiveness, highlighting important roles for these subsets in UC.
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37
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Liu T, Fang Z, Li X, Zhang L, Cao DS, Li M, Yin M. Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep. Bioinformatics 2024; 40:btae023. [PMID: 38243703 PMCID: PMC10832355 DOI: 10.1093/bioinformatics/btae023] [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: 08/14/2023] [Revised: 11/24/2023] [Accepted: 01/13/2024] [Indexed: 01/21/2024] Open
Abstract
MOTIVATION Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation. RESULTS In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering. AVAILABILITY AND IMPLEMENTATION The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.
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Affiliation(s)
- Teng Liu
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
| | - Zhaoyu Fang
- Department of Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, 410083, China
| | - Xin Li
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
| | - Lining Zhang
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
| | - Dong-Sheng Cao
- Department of Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410003, P.R. China
| | - Min Li
- Department of Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering at Central South University, Hunan, 410083, China
| | - Mingzhu Yin
- Department of Clinical Research Center (CRC), Clinical Pathology Center (CPC), Cancer Early Detection and Treatment Center (CEDTC) and Translational Medicine Research Center (TMRC), Chongqing University Three Gorges Hospital, Chongqing University, Wanzhou, Chongqing, 404000, China
- Department of Chongqing Technical Innovation Center for Quality Evaluation and Identification of Authentic Medicinal Herbs, Chongqing, 400044, China
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Shaban M, Bai Y, Qiu H, Mao S, Yeung J, Yeo YY, Shanmugam V, Chen H, Zhu B, Weirather JL, Nolan GP, Shipp MA, Rodig SJ, Jiang S, Mahmood F. MAPS: pathologist-level cell type annotation from tissue images through machine learning. Nat Commun 2024; 15:28. [PMID: 38167832 PMCID: PMC10761896 DOI: 10.1038/s41467-023-44188-w] [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: 05/15/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024] Open
Abstract
Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.
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Affiliation(s)
- Muhammad Shaban
- 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 Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Huaying Qiu
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Shulin Mao
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Jason Yeung
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Vignesh Shanmugam
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Han Chen
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Jason L Weirather
- Cancer Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA
- Center for Immuno-oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA
| | - Margaret A Shipp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Sizun Jiang
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA.
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA, 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 Data Science Program, Dana-Farber Cancer Institute, Boston, MA, USA.
- Broad Institute of Harvard and MIT, Cambridge, MA, USA.
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Weeratunga P, Moller DR, Ho LP. Immune mechanisms of granuloma formation in sarcoidosis and tuberculosis. J Clin Invest 2024; 134:e175264. [PMID: 38165044 PMCID: PMC10760966 DOI: 10.1172/jci175264] [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: 01/03/2024] Open
Abstract
Sarcoidosis is a complex immune-mediated disease characterized by clusters of immune cells called granulomas. Despite major steps in understanding the cause of this disease, many questions remain. In this Review, we perform a mechanistic interrogation of the immune activities that contribute to granuloma formation in sarcoidosis and compare these processes with its closest mimic, tuberculosis, highlighting shared and divergent immune activities. We examine how Mycobacterium tuberculosis is sensed by the immune system; how the granuloma is initiated, formed, and perpetuated in tuberculosis compared with sarcoidosis; and the role of major innate and adaptive immune cells in shaping these processes. Finally, we draw these findings together around several recent high-resolution studies of the granuloma in situ that utilized the latest advances in single-cell technology combined with spatial methods to analyze plausible disease mechanisms. We conclude with an overall view of granuloma formation in sarcoidosis.
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Affiliation(s)
- Praveen Weeratunga
- MRC Translational Immunology Discovery Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
| | | | - Ling-Pei Ho
- MRC Translational Immunology Discovery Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom
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40
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Rodríguez-Bejarano OH, Roa L, Vargas-Hernández G, Botero-Espinosa L, Parra-López C, Patarroyo MA. Strategies for studying immune and non-immune human and canine mammary gland cancer tumour infiltrate. Biochim Biophys Acta Rev Cancer 2024; 1879:189064. [PMID: 38158026 DOI: 10.1016/j.bbcan.2023.189064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 12/11/2023] [Accepted: 12/20/2023] [Indexed: 01/03/2024]
Abstract
The tumour microenvironment (TME) is usually defined as a cell environment associated with tumours or cancerous stem cells where conditions are established affecting tumour development and progression through malignant cell interaction with non-malignant cells. The TME is made up of endothelial, immune and non-immune cells, extracellular matrix (ECM) components and signalling molecules acting specifically on tumour and non-tumour cells. Breast cancer (BC) is the commonest malignant neoplasm worldwide and the main cause of mortality in women globally; advances regarding BC study and understanding it are relevant for acquiring novel, personalised therapeutic tools. Studying canine mammary gland tumours (CMGT) is one of the most relevant options for understanding BC using animal models as they share common epidemiological, clinical, pathological, biological, environmental, genetic and molecular characteristics with human BC. In-depth, detailed investigation regarding knowledge of human BC-related TME and in its canine model is considered extremely relevant for understanding changes in TME composition during tumour development. This review addresses important aspects concerned with different methods used for studying BC- and CMGT-related TME that are important for developing new and more effective therapeutic strategies for attacking a tumour during specific evolutionary stages.
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Affiliation(s)
- Oscar Hernán Rodríguez-Bejarano
- Health Sciences Faculty, Universidad de Ciencias Aplicadas y Ambientales (U.D.C.A), Calle 222#55-37, Bogotá 111166, Colombia; Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; PhD Programme in Biotechnology, Faculty of Sciences, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Leonardo Roa
- Veterinary Clinic, Faculty of Agricultural Sciences, Universidad de La Salle, Carrera 7 #179-03, Bogotá 110141, Colombia
| | - Giovanni Vargas-Hernández
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Lucía Botero-Espinosa
- Animal Health Department, Faculty of Veterinary Medicine and Zootechnics, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia
| | - Carlos Parra-López
- Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
| | - Manuel Alfonso Patarroyo
- Molecular Biology and Immunology Department, Fundacion Instituto de Inmunología de Colombia (FIDIC), Carrera 50#26-20, Bogotá 111321, Colombia; Microbiology Department, Faculty of Medicine, Universidad Nacional de Colombia, Carrera 45#26-85, Bogotá 111321, Colombia.
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Pandey A, Bhutani N. Profiling joint tissues at single-cell resolution: advances and insights. Nat Rev Rheumatol 2024; 20:7-20. [PMID: 38057475 DOI: 10.1038/s41584-023-01052-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/25/2023] [Indexed: 12/08/2023]
Abstract
Advances in the profiling of human joint tissues at single-cell resolution have provided unique insights into the organization and function of these tissues in health and disease. Data generated by various single-cell technologies, including single-cell RNA sequencing and cytometry by time-of-flight, have identified the distinct subpopulations that constitute these tissues. These timely studies have provided the building blocks for the construction of single-cell atlases of joint tissues including cartilage, bone and synovium, leading to the identification of developmental trajectories, deciphering of crosstalk between cells and discovery of rare populations such as stem and progenitor cells. In addition, these studies have revealed unique pathogenetic populations that are potential therapeutic targets. The use of these approaches in synovial tissues has helped to identify how distinct cell subpopulations can orchestrate disease initiation and progression and be responsible for distinct pathological outcomes. Additionally, repair of tissues such as cartilage and meniscus remains an unmet medical need, and single-cell methodologies can be invaluable in providing a blueprint for both effective tissue-engineering strategies and therapeutic interventions for chronic joint diseases such as osteoarthritis and rheumatoid arthritis.
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Affiliation(s)
- Akshay Pandey
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA, USA
| | - Nidhi Bhutani
- Department of Orthopaedic Surgery, School of Medicine, Stanford University, Stanford, CA, USA.
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42
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Mi H, Varadhan R, Cimino-Mathews AM, Emens LA, Santa-Maria CA, Popel AS. Spatial and Compositional Biomarkers in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.18.572234. [PMID: 38187696 PMCID: PMC10769235 DOI: 10.1101/2023.12.18.572234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between anti-tumor immunity and clinical outcomes, yet such connections remain underexplored. Here we employed a dataset derived from imaging mass cytometry of 58 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multi-scale computational algorithms. We detected distinct cell distribution patterns among clinical subgroups, potentially stemming from different infiltration related to tumor vasculature and fibroblast heterogeneity. Spatial analysis also identified ten recurrent cellular neighborhoods (CNs) - a collection of local TME characteristics with unique cell components. Coupling of the prevalence of pan-immune and perivasculature immune hotspot CNs, enrichment of inter-CN interactions was associated with improved survival. Using a deep learning model trained on engineered spatial data, we can with high accuracy (mean AUC of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles, and suggest novel imaging-based biomarkers for treatment development in the context of TNBC.
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Affiliation(s)
- Haoyang Mi
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ravi Varadhan
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ashley M. Cimino-Mathews
- Bloomberg-Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, MD, United States
| | | | - Cesar A. Santa-Maria
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Aleksander S. Popel
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
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43
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Liu Y, Jin Y, Azizi E, Blumberg AJ. Cellstitch: 3D cellular anisotropic image segmentation via optimal transport. BMC Bioinformatics 2023; 24:480. [PMID: 38102537 PMCID: PMC10724925 DOI: 10.1186/s12859-023-05608-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 12/07/2023] [Indexed: 12/17/2023] Open
Abstract
BACKGROUND Spatial mapping of transcriptional states provides valuable biological insights into cellular functions and interactions in the context of the tissue. Accurate 3D cell segmentation is a critical step in the analysis of this data towards understanding diseases and normal development in situ. Current approaches designed to automate 3D segmentation include stitching masks along one dimension, training a 3D neural network architecture from scratch, and reconstructing a 3D volume from 2D segmentations on all dimensions. However, the applicability of existing methods is hampered by inaccurate segmentations along the non-stitching dimensions, the lack of high-quality diverse 3D training data, and inhomogeneity of image resolution along orthogonal directions due to acquisition constraints; as a result, they have not been widely used in practice. METHODS To address these challenges, we formulate the problem of finding cell correspondence across layers with a novel optimal transport (OT) approach. We propose CellStitch, a flexible pipeline that segments cells from 3D images without requiring large amounts of 3D training data. We further extend our method to interpolate internal slices from highly anisotropic cell images to recover isotropic cell morphology. RESULTS We evaluated the performance of CellStitch through eight 3D plant microscopic datasets with diverse anisotropic levels and cell shapes. CellStitch substantially outperforms the state-of-the art methods on anisotropic images, and achieves comparable segmentation quality against competing methods in isotropic setting. We benchmarked and reported 3D segmentation results of all the methods with instance-level precision, recall and average precision (AP) metrics. CONCLUSIONS The proposed OT-based 3D segmentation pipeline outperformed the existing state-of-the-art methods on different datasets with nonzero anisotropy, providing high fidelity recovery of 3D cell morphology from microscopic images.
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Affiliation(s)
- Yining Liu
- Department of Computer Science, Columbia University, New York, USA
- Irving Institute for Cancer Dynamics, New York, USA
| | - Yinuo Jin
- Department of Biomedical Engineering, Columbia University, New York, USA
- Irving Institute for Cancer Dynamics, New York, USA
| | - Elham Azizi
- Department of Computer Science, Columbia University, New York, USA.
- Department of Biomedical Engineering, Columbia University, New York, USA.
- Data Science Institute, Columbia University, New York, USA.
- Irving Institute for Cancer Dynamics, New York, USA.
| | - Andrew J Blumberg
- Department of Computer Science, Columbia University, New York, USA.
- Department of Mathematics, Columbia University, New York, USA.
- Irving Institute for Cancer Dynamics, New York, USA.
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Song YC, Das D, Zhang Y, Chen MX, Fernie AR, Zhu FY, Han J. Proteogenomics-based functional genome research: approaches, applications, and perspectives in plants. Trends Biotechnol 2023; 41:1532-1548. [PMID: 37365082 DOI: 10.1016/j.tibtech.2023.05.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/17/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023]
Abstract
Proteogenomics (PG) integrates the proteome with the genome and transcriptome to refine gene models and annotation. Coupled with single-cell (SC) assays, PG effectively distinguishes heterogeneity among cell groups. Affiliating spatial information to PG reveals the high-resolution circuitry within SC atlases. Additionally, PG can investigate dynamic changes in protein-coding genes in plants across growth and development as well as stress and external stimulation, significantly contributing to the functional genome. Here we summarize existing PG research in plants and introduce the technical features of various methods. Combining PG with other omics, such as metabolomics and peptidomics, can offer even deeper insights into gene functions. We argue that the application of PG will represent an important font of foundational knowledge for plants.
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Affiliation(s)
- Yu-Chen Song
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China
| | - Debatosh Das
- College of Agriculture, Food and Natural Resources (CAFNR), Division of Plant Sciences and Technology, 52 Agricultural Building, University of Missouri-Columbia, MO 65201, USA
| | - Youjun Zhang
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria
| | - Mo-Xian Chen
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - Alisdair R Fernie
- Max-Planck-Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany; Center of Plant Systems Biology and Biotechnology, Plovdiv, Bulgaria.
| | - Fu-Yuan Zhu
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
| | - Jiangang Han
- State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of Tree Genetics and Biotechnology of Educational Department of China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China; College of Biology and Environment, Nanjing Forestry University, Nanjing 210037, China.
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45
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Barut I, Fletcher JS. Cell and tissue imaging by secondary ion mass spectrometry. Biointerphases 2023; 18:061202. [PMID: 38108477 DOI: 10.1116/6.0003140] [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: 09/14/2023] [Accepted: 11/20/2023] [Indexed: 12/19/2023] Open
Abstract
This Tutorial focuses on the use of secondary ion mass spectrometry for the analysis of cellular and tissue samples. The Tutorial aims to cover the considerations in sample preparation analytical set up and some specific aspects of data interpretation associated with such analysis.
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Affiliation(s)
- Inci Barut
- Department of Pharmacy, Basic Pharmaceutical Sciences, Gazi University, Ankara 06330, Turkey
| | - John S Fletcher
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg 413 90, Sweden
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46
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Phipps WS, Kilgore MR, Kennedy JJ, Whiteaker JR, Hoofnagle AN, Paulovich AG. Clinical Proteomics for Solid Organ Tissues. Mol Cell Proteomics 2023; 22:100648. [PMID: 37730181 PMCID: PMC10692389 DOI: 10.1016/j.mcpro.2023.100648] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 09/07/2023] [Accepted: 09/12/2023] [Indexed: 09/22/2023] Open
Abstract
The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.
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Affiliation(s)
- William S Phipps
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Mark R Kilgore
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Jacob J Kennedy
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Jeffrey R Whiteaker
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA
| | - Andrew N Hoofnagle
- Department of Laboratory Medicine and Pathology, University of Washington School of Medicine, Seattle, Washington, USA; Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA.
| | - Amanda G Paulovich
- Clinical Research Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA; Department of Medicine, University of Washington School of Medicine, Seattle, Washington, USA.
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47
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Behera RN, Bisht VS, Giri K, Ambatipudi K. Realm of proteomics in breast cancer management and drug repurposing to alleviate intricacies of treatment. Proteomics Clin Appl 2023; 17:e2300016. [PMID: 37259687 DOI: 10.1002/prca.202300016] [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: 02/16/2023] [Revised: 05/10/2023] [Accepted: 05/15/2023] [Indexed: 06/02/2023]
Abstract
Breast cancer, a multi-networking heterogeneous disease, has emerged as a serious impediment to progress in clinical oncology. Although technological advancements and emerging cancer research studies have mitigated breast cancer lethality, a precision cancer-oriented solution has not been achieved. Thus, this review will persuade the acquiescence of proteomics-based diagnostic and therapeutic options in breast cancer management. Recently, the evidence of breast cancer health surveillance through imaging proteomics, single-cell proteomics, interactomics, and post-translational modification (PTM) tracking, to construct proteome maps and proteotyping for stage-specific and sample-specific cancer subtyping have outperformed conventional ways of dealing with breast cancer by increasing diagnostic efficiency, prognostic value, and predictive response. Additionally, the paradigm shift in applied proteomics for designing a chemotherapy regimen to identify novel drug targets with minor adverse effects has been elaborated. Finally, the potential of proteomics in alleviating the occurrence of chemoresistance and enhancing reprofiled drugs' effectiveness to combat therapeutic obstacles has been discussed. Owing to the enormous potential of proteomics techniques, the clinical recognition of proteomics in breast cancer management can be achievable and therapeutic intricacies can be surmountable.
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Affiliation(s)
- Rama N Behera
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Vinod S Bisht
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Kuldeep Giri
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
| | - Kiran Ambatipudi
- Department of Biosciences and Bioengineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
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48
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Sender R, Weiss Y, Navon Y, Milo I, Azulay N, Keren L, Fuchs S, Ben-Zvi D, Noor E, Milo R. The total mass, number, and distribution of immune cells in the human body. Proc Natl Acad Sci U S A 2023; 120:e2308511120. [PMID: 37871201 PMCID: PMC10623016 DOI: 10.1073/pnas.2308511120] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Accepted: 09/11/2023] [Indexed: 10/25/2023] Open
Abstract
The immune system is a complex network of cells with critical functions in health and disease. However, a comprehensive census of the cells comprising the immune system is lacking. Here, we estimated the abundance of the primary immune cell types throughout all tissues in the human body. We conducted a literature survey and integrated data from multiplexed imaging and methylome-based deconvolution. We also considered cellular mass to determine the distribution of immune cells in terms of both number and total mass. Our results indicate that the immune system of a reference 73 kg man consists of 1.8 × 1012 cells (95% CI 1.5-2.3 × 1012), weighing 1.2 kg (95% CI 0.8-1.9). Lymphocytes constitute 40% of the total number of immune cells and 15% of the mass and are mainly located in the lymph nodes and spleen. Neutrophils account for similar proportions of both the number and total mass of immune cells, with most neutrophils residing in the bone marrow. Macrophages, present in most tissues, account for 10% of immune cells but contribute nearly 50% of the total cellular mass due to their large size. The quantification of immune cells within the human body presented here can serve to understand the immune function better and facilitate quantitative modeling of this vital system.
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Affiliation(s)
- Ron Sender
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yarden Weiss
- Department of Molecular Genetics, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Yoav Navon
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Idan Milo
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Nofar Azulay
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Shai Fuchs
- Pediatric Endocrine and Diabetes Unit, Edmond and Lily Safra Children's Hospital, Sheba Medical Center, Ramat Gan 52621, Israel
| | - Danny Ben-Zvi
- Department of Developmental Biology and Cancer Research, Institute for Medical Research Israel-Canada, The Hebrew University-Hadassah Medical School, Jerusalem 91120, Israel
| | - Elad Noor
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
| | - Ron Milo
- Department of Plant and Environmental Sciences, Weizmann Institute of Science, Rehovot 76100, Israel
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49
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Cohn DE, Forder A, Marshall EA, Vucic EA, Stewart GL, Noureddine K, Lockwood WW, MacAulay CE, Guillaud M, Lam WL. Delineating spatial cell-cell interactions in the solid tumour microenvironment through the lens of highly multiplexed imaging. Front Immunol 2023; 14:1275890. [PMID: 37936700 PMCID: PMC10627006 DOI: 10.3389/fimmu.2023.1275890] [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: 08/10/2023] [Accepted: 10/11/2023] [Indexed: 11/09/2023] Open
Abstract
The growth and metastasis of solid tumours is known to be facilitated by the tumour microenvironment (TME), which is composed of a highly diverse collection of cell types that interact and communicate with one another extensively. Many of these interactions involve the immune cell population within the TME, referred to as the tumour immune microenvironment (TIME). These non-cell autonomous interactions exert substantial influence over cell behaviour and contribute to the reprogramming of immune and stromal cells into numerous pro-tumourigenic phenotypes. The study of some of these interactions, such as the PD-1/PD-L1 axis that induces CD8+ T cell exhaustion, has led to the development of breakthrough therapeutic advances. Yet many common analyses of the TME either do not retain the spatial data necessary to assess cell-cell interactions, or interrogate few (<10) markers, limiting the capacity for cell phenotyping. Recently developed digital pathology technologies, together with sophisticated bioimage analysis programs, now enable the high-resolution, highly-multiplexed analysis of diverse immune and stromal cell markers within the TME of clinical specimens. In this article, we review the tumour-promoting non-cell autonomous interactions in the TME and their impact on tumour behaviour. We additionally survey commonly used image analysis programs and highly-multiplexed spatial imaging technologies, and we discuss their relative advantages and limitations. The spatial organization of the TME varies enormously between patients, and so leveraging these technologies in future studies to further characterize how non-cell autonomous interactions impact tumour behaviour may inform the personalization of cancer treatment..
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Affiliation(s)
- David E. Cohn
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Aisling Forder
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Erin A. Marshall
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Emily A. Vucic
- Department of Biochemistry and Molecular Pharmacology, New York University (NYU) Langone Medical Center, New York, NY, United States
| | - Greg L. Stewart
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Kouther Noureddine
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - William W. Lockwood
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Calum E. MacAulay
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Martial Guillaud
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
| | - Wan L. Lam
- Department of Integrative Oncology, British Columbia Cancer Research Centre, Vancouver, BC, Canada
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50
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Willie E, Yang P, Patrick E. The impact of similarity metrics on cell-type clustering in highly multiplexed in situ imaging cytometry data. BIOINFORMATICS ADVANCES 2023; 3:vbad141. [PMID: 37928340 PMCID: PMC10625459 DOI: 10.1093/bioadv/vbad141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 08/23/2023] [Accepted: 10/07/2023] [Indexed: 11/07/2023]
Abstract
Motivation The advent of highly multiplexed in situ imaging cytometry assays has revolutionized the study of cellular systems, offering unparalleled detail in observing cellular activities and characteristics. These assays provide comprehensive insights by concurrently profiling the spatial distribution and molecular features of numerous cells. In navigating this complex data landscape, unsupervised machine learning techniques, particularly clustering algorithms, have become essential tools. They enable the identification and categorization of cell types and subsets based on their molecular characteristics. Despite their widespread adoption, most clustering algorithms in use were initially developed for cell suspension technologies, leading to a potential mismatch in application. There is a critical gap in the systematic evaluation of these methods, particularly in determining the properties that make them optimal for in situ imaging assays. Addressing this gap is vital for ensuring accurate, reliable analyses and fostering advancements in cellular biology research. Results In our extensive investigation, we evaluated a range of similarity metrics, which are crucial in determining the relationships between cells during the clustering process. Our findings reveal substantial variations in clustering performance, contingent on the similarity metric employed. These variations underscore the importance of selecting appropriate metrics to ensure accurate cell type and subset identification. In response to these challenges, we introduce FuseSOM, a novel ensemble clustering algorithm that integrates hierarchical multiview learning of similarity metrics with self-organizing maps. Through a rigorous stratified subsampling analysis framework, we demonstrate that FuseSOM outperforms existing best-practice clustering methods specifically tailored for in situ imaging cytometry data. Our work not only provides critical insights into the performance of clustering algorithms in this novel context but also offers a robust solution, paving the way for more accurate and reliable in situ imaging cytometry data analysis. Availability and implementation The FuseSOM R package is available on Bioconductor and is available under the GPL-3 license. All the codes for the analysis performed can be found at Github.
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Affiliation(s)
- Elijah Willie
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Pengyi Yang
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong, China
- Computational Systems Biology Group, Children’s Medical Research Institute, The University of Sydney, Westmead, NSW 2145, Australia
| | - Ellis Patrick
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong, China
- Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia
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