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Yuan D, Chen W, Jin S, Li W, Liu W, Liu L, Wu Y, Zhang Y, He X, Jiang J, Sun H, Liu X, Liu J. Co-expression of immune checkpoints in glioblastoma revealed by single-nucleus RNA sequencing and spatial transcriptomics. Comput Struct Biotechnol J 2024; 23:1534-1546. [PMID: 38633388 PMCID: PMC11021796 DOI: 10.1016/j.csbj.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 04/06/2024] [Accepted: 04/09/2024] [Indexed: 04/19/2024] Open
Abstract
Glioblastoma (GBM) is one of the most malignant tumors of the central nervous system. The pattern of immune checkpoint expression in GBM remains largely unknown. We performed snRNA-Seq and spatial transcriptomic (ST) analyses on untreated GBM samples. 8 major cell types were found in both tumor and adjacent normal tissues, with variations in infiltration grade. Neoplastic cells_6 was identified in malignant cells with high expression of invasion and proliferator-related genes, and analyzed its interactions with microglia, MDM cells and T cells. Significant alterations in ligand-receptor interactions were observed, particularly between Neoplastic cells_6 and microglia, and found prominent expression of VISTA/VSIG3, suggesting a potential mechanism for evading immune system attacks. High expression of TIM-3, VISTA, PSGL-1 and VSIG-3 with similar expression patterns in GBM, may have potential as therapeutic targets. The prognostic value of VISTA expression was cross-validated in 180 glioma patients, and it was observed that patients with high VISTA expression had a poorer prognosis. In addition, multimodal cross analysis integrated SnRNA-seq and ST, revealing complex intracellular communication and mapping the GBM tumor microenvironment. This study reveals novel molecular characteristics of GBM, co-expression of immune checkpoints, and potential therapeutic targets, contributing to improving the understanding and treatment of GBM.
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Affiliation(s)
- Dingyi Yuan
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Wenting Chen
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Shasha Jin
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Wei Li
- Department of Neurosurgery, the Affiliated Drum Tower Hospital, School of Medicine, Nanjing University, Nanjing, China
| | - Wanmei Liu
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Liu Liu
- Jiangsu Key Laboratory of Drug Discovery for Metabolic Disease, China Pharmaceutical University, Nanjing, China
| | - Yinhao Wu
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Yuxin Zhang
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Xiaoyu He
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Jingwei Jiang
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
| | - Hongbin Sun
- Jiangsu Key Laboratory of Drug Discovery for Metabolic Disease, China Pharmaceutical University, Nanjing, China
| | - Xiangyu Liu
- Department of Neurosurgery, the Affiliated Drum Tower Hospital, School of Medicine, Nanjing University, Nanjing, China
| | - Jun Liu
- New Drug Screening and Pharmacodynamics Evaluation Center, China Pharmaceutical University, Nanjing, China
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2
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Cilento MA, Sweeney CJ, Butler LM. Spatial transcriptomics in cancer research and potential clinical impact: a narrative review. J Cancer Res Clin Oncol 2024; 150:296. [PMID: 38850363 PMCID: PMC11162383 DOI: 10.1007/s00432-024-05816-0] [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: 03/19/2024] [Accepted: 05/22/2024] [Indexed: 06/10/2024]
Abstract
Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?
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Affiliation(s)
- Michael A Cilento
- South Australian Immunogenomics Cancer Institute, The University of Adelaide, Adelaide, SA, Australia.
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia.
- The Queen Elizabeth Hospital, Woodville South, SA, Australia.
| | - Christopher J Sweeney
- South Australian Immunogenomics Cancer Institute, The University of Adelaide, Adelaide, SA, Australia
| | - Lisa M Butler
- South Australian Immunogenomics Cancer Institute, The University of Adelaide, Adelaide, SA, Australia
- South Australian Health and Medical Research Institute, Adelaide, SA, Australia
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3
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Schepps S, Xu J, Yang H, Mandel J, Mehta J, Tolotta J, Baker N, Tekmen V, Nikbakht N, Fortina P, Fuentes I, LaFleur B, Cho RJ, South AP. Skin in the game: a review of single-cell and spatial transcriptomics in dermatological research. Clin Chem Lab Med 2024; 0:cclm-2023-1245. [PMID: 38656304 DOI: 10.1515/cclm-2023-1245] [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: 11/03/2023] [Accepted: 02/29/2024] [Indexed: 04/26/2024]
Abstract
Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) are two emerging research technologies that uniquely characterize gene expression microenvironments on a cellular or subcellular level. The skin, a clinically accessible tissue composed of diverse, essential cell populations, serves as an ideal target for these high-resolution investigative approaches. Using these tools, researchers are assembling a compendium of data and discoveries in healthy skin as well as a range of dermatologic pathophysiologies, including atopic dermatitis, psoriasis, and cutaneous malignancies. The ongoing advancement of single-cell approaches, coupled with anticipated decreases in cost with increased adoption, will reshape dermatologic research, profoundly influencing disease characterization, prognosis, and ultimately clinical practice.
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Affiliation(s)
- Samuel Schepps
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Jonathan Xu
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Henry Yang
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Jenna Mandel
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Jaanvi Mehta
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Julianna Tolotta
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Nicole Baker
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Volkan Tekmen
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Neda Nikbakht
- Department of Dermatology and Cutaneous Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
- Department of Pharmacology, Physiology and Cancer Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
| | - Paolo Fortina
- Department of Pharmacology, Physiology and Cancer Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
- International Federation of Clinical Chemistry Working Group on Single Cell and Spatial Transcriptomics, Milan, Italy
| | - Ignacia Fuentes
- International Federation of Clinical Chemistry Working Group on Single Cell and Spatial Transcriptomics, Milan, Italy
- Departamento de Biología Celular y Molecular, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Directora de Investigación Fundación DEBRA Chile, Santiago, Chile
| | - Bonnie LaFleur
- International Federation of Clinical Chemistry Working Group on Single Cell and Spatial Transcriptomics, Milan, Italy
- R. Ken Coit College of Pharmacy, University of Arizona, University of Arizona Cancer Center, Tucson, AZ, USA
| | - Raymond J Cho
- International Federation of Clinical Chemistry Working Group on Single Cell and Spatial Transcriptomics, Milan, Italy
- Department of Dermatology, University of San Francisco, San Francisco, CA, USA
| | - Andrew P South
- Department of Pharmacology, Physiology and Cancer Biology, 6559 Thomas Jefferson University , Philadelphia, PA, USA
- International Federation of Clinical Chemistry Working Group on Single Cell and Spatial Transcriptomics, Milan, Italy
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4
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Wang J, Li B, Luo M, Huang J, Zhang K, Zheng S, Zhang S, Zhou J. Progression from ductal carcinoma in situ to invasive breast cancer: molecular features and clinical significance. Signal Transduct Target Ther 2024; 9:83. [PMID: 38570490 PMCID: PMC10991592 DOI: 10.1038/s41392-024-01779-3] [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: 06/16/2023] [Revised: 02/14/2024] [Accepted: 02/26/2024] [Indexed: 04/05/2024] Open
Abstract
Ductal carcinoma in situ (DCIS) represents pre-invasive breast carcinoma. In untreated cases, 25-60% DCIS progress to invasive ductal carcinoma (IDC). The challenge lies in distinguishing between non-progressive and progressive DCIS, often resulting in over- or under-treatment in many cases. With increasing screen-detected DCIS in these years, the nature of DCIS has aroused worldwide attention. A deeper understanding of the biological nature of DCIS and the molecular journey of the DCIS-IDC transition is crucial for more effective clinical management. Here, we reviewed the key signaling pathways in breast cancer that may contribute to DCIS initiation and progression. We also explored the molecular features of DCIS and IDC, shedding light on the progression of DCIS through both inherent changes within tumor cells and alterations in the tumor microenvironment. In addition, valuable research tools utilized in studying DCIS including preclinical models and newer advanced technologies such as single-cell sequencing, spatial transcriptomics and artificial intelligence, have been systematically summarized. Further, we thoroughly discussed the clinical advancements in DCIS and IDC, including prognostic biomarkers and clinical managements, with the aim of facilitating more personalized treatment strategies in the future. Research on DCIS has already yielded significant insights into breast carcinogenesis and will continue to pave the way for practical clinical applications.
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Affiliation(s)
- Jing Wang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Baizhou Li
- Department of Pathology, the Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, China
| | - Meng Luo
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
- Department of Plastic Surgery, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jia Huang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Kun Zhang
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shu Zheng
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China
| | - Suzhan Zhang
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
| | - Jiaojiao Zhou
- The Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Department of Breast Surgery and Oncology, the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- Zhejiang Provincial Clinical Research Center for Cancer, Hangzhou, China.
- Cancer Center, Zhejiang University, Hangzhou, China.
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5
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Imodoye SO, Adedokun KA, Bello IO. From complexity to clarity: unravelling tumor heterogeneity through the lens of tumor microenvironment for innovative cancer therapy. Histochem Cell Biol 2024; 161:299-323. [PMID: 38189822 DOI: 10.1007/s00418-023-02258-6] [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] [Accepted: 12/06/2023] [Indexed: 01/09/2024]
Abstract
Despite the tremendous clinical successes recorded in the landscape of cancer therapy, tumor heterogeneity remains a formidable challenge to successful cancer treatment. In recent years, the emergence of high-throughput technologies has advanced our understanding of the variables influencing tumor heterogeneity beyond intrinsic tumor characteristics. Emerging knowledge shows that drivers of tumor heterogeneity are not only intrinsic to cancer cells but can also emanate from their microenvironment, which significantly favors tumor progression and impairs therapeutic response. Although much has been explored to understand the fundamentals of the influence of innate tumor factors on cancer diversity, the roles of the tumor microenvironment (TME) are often undervalued. It is therefore imperative that a clear understanding of the interactions between the TME and other tumor intrinsic factors underlying the plastic molecular behaviors of cancers be identified to develop patient-specific treatment strategies. This review highlights the roles of the TME as an emerging factor in tumor heterogeneity. More particularly, we discuss the role of the TME in the context of tumor heterogeneity and explore the cutting-edge diagnostic and therapeutic approaches that could be used to resolve this recurring clinical conundrum. We conclude by speculating on exciting research questions that can advance our understanding of tumor heterogeneity with the goal of developing customized therapeutic solutions.
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Affiliation(s)
- Sikiru O Imodoye
- Department of Oncological Sciences, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA.
| | - Kamoru A Adedokun
- Department of Immunology, Roswell Park Comprehensive Cancer Center, Buffalo, NY, 14263, USA
| | - Ibrahim O Bello
- Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, Riyadh, Saudi Arabia.
- Department of Pathology, University of Helsinki, Haartmaninkatu 3, 00014, Helsinki, Finland.
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6
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Jiang W, Caruana DL, Back J, Lee FY. Unique Spatial Transcriptomic Profiling of the Murine Femoral Fracture Callus: A Preliminary Report. Cells 2024; 13:522. [PMID: 38534368 DOI: 10.3390/cells13060522] [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: 02/03/2024] [Revised: 03/07/2024] [Accepted: 03/14/2024] [Indexed: 03/28/2024] Open
Abstract
Fracture callus formation is a dynamic stage of bone activity and repair with precise, spatially localized gene expression. Metastatic breast cancer impairs fracture healing by disrupting bone homeostasis and imparting an altered genomic profile. Previous sequencing techniques such as single-cell RNA and in situ hybridization are limited by missing spatial context and low throughput, respectively. We present a preliminary approach using the Visium CytAssist spatial transcriptomics platform to provide the first spatially intact characterization of genetic expression changes within an orthopedic model of impaired fracture healing. Tissue slides prepared from BALB/c mice with or without MDA-MB-231 metastatic breast cancer cells were used. Both unsupervised clustering and histology-based annotations were performed to identify the hard callus, soft callus, and interzone for differential gene expression between the wild-type and pathological fracture model. The spatial transcriptomics platform successfully localized validated genes of the hard (Dmp1, Sost) and soft callus (Acan, Col2a1). The fibrous interzone was identified as a region of extensive genomic heterogeneity. MDA-MB-231 samples demonstrated downregulation of the critical bone matrix and structural regulators that may explain the weakened bone structure of pathological fractures. Spatial transcriptomics may represent a valuable tool in orthopedic research by providing temporal and spatial context.
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Affiliation(s)
- Will Jiang
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Dennis L Caruana
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Jungho Back
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
| | - Francis Y Lee
- Department of Orthopaedics & Rehabilitation, Yale School of Medicine, 47 College Place, New Haven, CT 06510, USA
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7
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Jose A, Kulkarni P, Thilakan J, Munisamy M, Malhotra AG, Singh J, Kumar A, Rangnekar VM, Arya N, Rao M. Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine. Mol Cancer 2024; 23:50. [PMID: 38461268 PMCID: PMC10924370 DOI: 10.1186/s12943-023-01916-6] [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: 08/05/2023] [Accepted: 12/15/2023] [Indexed: 03/11/2024] Open
Abstract
Despite advancements in treatment protocols, cancer is one of the leading cause of deaths worldwide. Therefore, there is a need to identify newer and personalized therapeutic targets along with screening technologies to combat cancer. With the advent of pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, and lipidomics, the scientific community has witnessed an improved molecular and metabolomic understanding of various diseases, including cancer. In addition, three-dimensional (3-D) disease models have been efficiently utilized for understanding disease pathophysiology and as screening tools in drug discovery. An integrated approach utilizing pan-omics technologies and 3-D in vitro tumor models has led to improved understanding of the intricate network encompassing various signalling pathways and molecular cross-talk in solid tumors. In the present review, we underscore the current trends in omics technologies and highlight their role in understanding genotypic-phenotypic co-relation in cancer with respect to 3-D in vitro tumor models. We further discuss the challenges associated with omics technologies and provide our outlook on the future applications of these technologies in drug discovery and precision medicine for improved management of cancer.
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Affiliation(s)
- Anmi Jose
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Pallavi Kulkarni
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Jaya Thilakan
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Murali Munisamy
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Anvita Gupta Malhotra
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Jitendra Singh
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Ashok Kumar
- Department of Biochemistry, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India
| | - Vivek M Rangnekar
- Markey Cancer Center and Department of Radiation Medicine, University of Kentucky, Lexington, KY, 40536, USA
| | - Neha Arya
- Department of Translational Medicine, All India Institute of Medical Sciences Bhopal, Bhopal, Madhya Pradesh, 462020, India.
| | - Mahadev Rao
- Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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8
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Pang JMB, Byrne DJ, Bergin ART, Caramia F, Loi S, Gorringe KL, Fox SB. Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology. Histopathology 2024; 84:577-586. [PMID: 37991396 DOI: 10.1111/his.15093] [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: 06/18/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/23/2023]
Abstract
In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.
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Affiliation(s)
- Jia-Min B Pang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - David J Byrne
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Alice R T Bergin
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Franco Caramia
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Sherene Loi
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Kylie L Gorringe
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Stephen B Fox
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
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Cai P, Robinson MD, Tiberi S. DESpace: spatially variable gene detection via differential expression testing of spatial clusters. Bioinformatics 2024; 40:btae027. [PMID: 38243704 PMCID: PMC10868334 DOI: 10.1093/bioinformatics/btae027] [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: 06/06/2023] [Revised: 12/23/2023] [Accepted: 01/15/2024] [Indexed: 01/21/2024] Open
Abstract
MOTIVATION Spatially resolved transcriptomics (SRT) enables scientists to investigate spatial context of mRNA abundance, including identifying spatially variable genes (SVGs), i.e. genes whose expression varies across the tissue. Although several methods have been proposed for this task, native SVG tools cannot jointly model biological replicates, or identify the key areas of the tissue affected by spatial variability. RESULTS Here, we introduce DESpace, a framework, based on an original application of existing methods, to discover SVGs. In particular, our approach inputs all types of SRT data, summarizes spatial information via spatial clusters, and identifies spatially variable genes by performing differential gene expression testing between clusters. Furthermore, our framework can identify (and test) the main cluster of the tissue affected by spatial variability; this allows scientists to investigate spatial expression changes in specific areas of interest. Additionally, DESpace enables joint modeling of multiple samples (i.e. biological replicates); compared to inference based on individual samples, this approach increases statistical power, and targets SVGs with consistent spatial patterns across replicates. Overall, in our benchmarks, DESpace displays good true positive rates, controls for false positive and false discovery rates, and is computationally efficient. AVAILABILITY AND IMPLEMENTATION DESpace is freely distributed as a Bioconductor R package at https://bioconductor.org/packages/DESpace.
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Affiliation(s)
- Peiying Cai
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich 8057, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich 8057, Switzerland
| | - Simone Tiberi
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich 8057, Switzerland
- Department of Statistical Sciences, University of Bologna, Bologna 40126, Italy
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10
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Zhao C, Xu Z, Wang X, Tao S, MacDonald WA, He K, Poholek AC, Chen K, Huang H, Chen W. Innovative super-resolution in spatial transcriptomics: a transformer model exploiting histology images and spatial gene expression. Brief Bioinform 2024; 25:bbae052. [PMID: 38436557 PMCID: PMC10939304 DOI: 10.1093/bib/bbae052] [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: 11/21/2023] [Revised: 01/26/2024] [Accepted: 01/27/2024] [Indexed: 03/05/2024] Open
Abstract
Spatial transcriptomics technologies have shed light on the complexities of tissue structures by accurately mapping spatial microenvironments. Nonetheless, a myriad of methods, especially those utilized in platforms like Visium, often relinquish spatial details owing to intrinsic resolution limitations. In response, we introduce TransformerST, an innovative, unsupervised model anchored in the Transformer architecture, which operates independently of references, thereby ensuring cost-efficiency by circumventing the need for single-cell RNA sequencing. TransformerST not only elevates Visium data from a multicellular level to a single-cell granularity but also showcases adaptability across diverse spatial transcriptomics platforms. By employing a vision transformer-based encoder, it discerns latent image-gene expression co-representations and is further enhanced by spatial correlations, derived from an adaptive graph Transformer module. The sophisticated cross-scale graph network, utilized in super-resolution, significantly boosts the model's accuracy, unveiling complex structure-functional relationships within histology images. Empirical evaluations validate its adeptness in revealing tissue subtleties at the single-cell scale. Crucially, TransformerST adeptly navigates through image-gene co-representation, maximizing the synergistic utility of gene expression and histology images, thereby emerging as a pioneering tool in spatial transcriptomics. It not only enhances resolution to a single-cell level but also introduces a novel approach that optimally utilizes histology images alongside gene expression, providing a refined lens for investigating spatial transcriptomics.
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Affiliation(s)
- Chongyue Zhao
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Zhongli Xu
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- School of Medicine, Tsinghua University, Beijing, 100084, Beijing, China
| | - Xinjun Wang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, 10065, New York, USA
| | - Shiyue Tao
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, 15261, Pennsylvania, USA
| | - William A MacDonald
- Health Sciences Sequencing Core at UPMC Children’s Hospital of Pittsburgh, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Kun He
- Division of Pediatric Rheumatology, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Amanda C Poholek
- Division of Pediatric Rheumatology, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Department of Immunology , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Health Sciences Sequencing Core at UPMC Children’s Hospital of Pittsburgh, Department of Pediatrics , University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
| | - Kong Chen
- Department of Medicine, University of Pittsburgh, Pittsburgh, 15213, Pennsylvania, USA
| | - Heng Huang
- Department of Computer Science, University of Maryland, College Park, 20742, Maryland, USA
| | - Wei Chen
- Department of Pediatrics, University of Pittsburgh, Pittsburgh, 15224, Pennsylvania, USA
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, 15261, Pennsylvania, USA
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11
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Al Hmada Y, Brodell RT, Kharouf N, Flanagan TW, Alamodi AA, Hassan SY, Shalaby H, Hassan SL, Haikel Y, Megahed M, Santourlidis S, Hassan M. Mechanisms of Melanoma Progression and Treatment Resistance: Role of Cancer Stem-like Cells. Cancers (Basel) 2024; 16:470. [PMID: 38275910 PMCID: PMC10814963 DOI: 10.3390/cancers16020470] [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: 12/05/2023] [Revised: 01/18/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024] Open
Abstract
Melanoma is the third most common type of skin cancer, characterized by its heterogeneity and propensity to metastasize to distant organs. Melanoma is a heterogeneous tumor, composed of genetically divergent subpopulations, including a small fraction of melanoma-initiating cancer stem-like cells (CSCs) and many non-cancer stem cells (non-CSCs). CSCs are characterized by their unique surface proteins associated with aberrant signaling pathways with a causal or consequential relationship with tumor progression, drug resistance, and recurrence. Melanomas also harbor significant alterations in functional genes (BRAF, CDKN2A, NRAS, TP53, and NF1). Of these, the most common are the BRAF and NRAS oncogenes, with 50% of melanomas demonstrating the BRAF mutation (BRAFV600E). While the successful targeting of BRAFV600E does improve overall survival, the long-term efficacy of available therapeutic options is limited due to adverse side effects and reduced clinical efficacy. Additionally, drug resistance develops rapidly via mechanisms involving fast feedback re-activation of MAPK signaling pathways. This article updates information relevant to the mechanisms of melanoma progression and resistance and particularly the mechanistic role of CSCs in melanoma progression, drug resistance, and recurrence.
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Affiliation(s)
- Youssef Al Hmada
- Department of Pathology, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216, USA; (Y.A.H.); (R.T.B.)
| | - Robert T. Brodell
- Department of Pathology, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216, USA; (Y.A.H.); (R.T.B.)
| | - Naji Kharouf
- Institut National de la Santé et de la Recherche Médicale, University of Strasbourg, 67000 Strasbourg, France; (N.K.); (Y.H.)
- Department of Operative Dentistry and Endodontics, Dental Faculty, University of Strasbourg, 67000 Strasbourg, France
| | - Thomas W. Flanagan
- Department of Pharmacology and Experimental Therapeutics, LSU Health Sciences Center, New Orleans, LA 70112, USA;
| | - Abdulhadi A. Alamodi
- College of Health Sciences, Jackson State University, 310 W Woodrow Wilson Ave Ste 300, Jackson, MS 39213, USA;
| | - Sofie-Yasmin Hassan
- Department of Pharmacy, Faculty of Science, Heinrich-Heine University Duesseldorf, 40225 Dusseldorf, Germany;
| | - Hosam Shalaby
- Department of Urology, Tulane University School of Medicine, New Orleans, LA 70112, USA;
| | - Sarah-Lilly Hassan
- Department of Chemistry, Faculty of Science, Heinrich-Heine University Duesseldorf, 40225 Dusseldorf, Germany;
| | - Youssef Haikel
- Institut National de la Santé et de la Recherche Médicale, University of Strasbourg, 67000 Strasbourg, France; (N.K.); (Y.H.)
- Department of Operative Dentistry and Endodontics, Dental Faculty, University of Strasbourg, 67000 Strasbourg, France
- Pôle de Médecine et Chirurgie Bucco-Dentaire, Hôpital Civil, Hôpitaux Universitaire de Strasbourg, 67000 Strasbourg, France
| | - Mosaad Megahed
- Clinic of Dermatology, University Hospital of Aachen, 52074 Aachen, Germany;
| | - Simeon Santourlidis
- Epigenetics Core Laboratory, Medical Faculty, Institute of Transplantation Diagnostics and Cell Therapeutics, Heinrich Heine University Düsseldorf, 40225 Dusseldorf, Germany;
| | - Mohamed Hassan
- Institut National de la Santé et de la Recherche Médicale, University of Strasbourg, 67000 Strasbourg, France; (N.K.); (Y.H.)
- Department of Operative Dentistry and Endodontics, Dental Faculty, University of Strasbourg, 67000 Strasbourg, France
- Research Laboratory of Surgery-Oncology, Department of Surgery, Tulane University School of Medicine, New Orleans, LA 70112, USA
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12
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Grujčić V, Saarenpää S, Sundh J, Sennblad B, Norgren B, Latz M, Giacomello S, Foster RA, Andersson AF. Towards high-throughput parallel imaging and single-cell transcriptomics of microbial eukaryotic plankton. PLoS One 2024; 19:e0296672. [PMID: 38241213 PMCID: PMC10798536 DOI: 10.1371/journal.pone.0296672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 12/13/2023] [Indexed: 01/21/2024] Open
Abstract
Single-cell transcriptomics has the potential to provide novel insights into poorly studied microbial eukaryotes. Although several such technologies are available and benchmarked on mammalian cells, few have been tested on protists. Here, we applied a microarray single-cell sequencing (MASC-seq) technology, that generates microscope images of cells in parallel with capturing their transcriptomes, on three species representing important plankton groups with different cell structures; the ciliate Tetrahymena thermophila, the diatom Phaeodactylum tricornutum, and the dinoflagellate Heterocapsa sp. Both the cell fixation and permeabilization steps were adjusted. For the ciliate and dinoflagellate, the number of transcripts of microarray spots with single cells were significantly higher than for background spots, and the overall expression patterns were correlated with that of bulk RNA, while for the much smaller diatom cells, it was not possible to separate single-cell transcripts from background. The MASC-seq method holds promise for investigating "microbial dark matter", although further optimizations are necessary to increase the signal-to-noise ratio.
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Affiliation(s)
- Vesna Grujčić
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Sami Saarenpää
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - John Sundh
- Science for Life Laboratory, Department of Biochemistry and Biophysics, National Bioinformatics Infrastructure Sweden, Stockholm University, Solna, Sweden
| | - Bengt Sennblad
- Science for Life Laboratory, Dept of Cell and Molecular Biology, National Bioinformatics Infrastructure Sweden, Uppsala University, Uppsala, Sweden
| | - Benjamin Norgren
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Meike Latz
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Stefania Giacomello
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Rachel A. Foster
- Department of Ecology, Environment and Plant Sciences, Stockholm University, Stockholm, Sweden
| | - Anders F. Andersson
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
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13
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Chen W, He Y, Zhou G, Chen X, Ye Y, Zhang G, Liu H. Multiomics characterization of pyroptosis in the tumor microenvironment and therapeutic relevance in metastatic melanoma. BMC Med 2024; 22:24. [PMID: 38229080 PMCID: PMC10792919 DOI: 10.1186/s12916-023-03175-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 11/14/2023] [Indexed: 01/18/2024] Open
Abstract
BACKGROUND Pyroptosis, mediated by gasdermins with the release of multiple inflammatory cytokines, has emerged as playing an important role in targeted therapy and immunotherapy due to its effectiveness at inhibiting tumor growth. Melanoma is one of the most commonly used models for immunotherapy development, though an inadequate immune response can occur. Moreover, the development of pyroptosis-related therapy and combinations with other therapeutic strategies is limited due to insufficient understanding of the role of pyroptosis in the context of different tumor immune microenvironments (TMEs). METHODS Here, we present a computational model (pyroptosis-related gene score, PScore) to assess the pyroptosis status. We applied PScore to 1388 melanoma samples in our in-house cohort and eight other publicly available independent cohorts and then calculated its prognostic power of and potential as a predictive marker of immunotherapy efficacy. Furthermore, we performed association analysis for PScore and the characteristics of the TME by using bulk, single-cell, and spatial transcriptomics and assessed the association of PScore with mutation status, which contributes to targeted therapy. RESULTS Pyroptosis-related genes (PRGs) showed distinct expression patterns and prognostic predictive ability in melanoma. Most PRGs were associated with better survival in metastatic melanoma. Our PScore model based on genes associated with prognosis exhibits robust performance in survival prediction in multiple metastatic melanoma cohorts. We also found PScore to be associated with BRAF mutation and correlate positively with multiple molecular signatures, such as KRAS signaling and the IFN gamma response pathway. Based on our data, melanoma with an immune-enriched TME had a higher PScore than melanoma with an immune-depleted or fibrotic TME. Additionally, monocytes had the highest PScore and malignant cells and fibroblasts the lowest PScore based on single-cell and spatial transcriptome analyses. Finally, a higher PScore was associated with better therapeutic efficacy of immune checkpoint blockade, suggesting the potential of pyroptosis to serve as a marker of immunotherapy response. CONCLUSIONS Collectively, our findings indicate that pyroptosis is a prognostic factor and is associated with the immune response in metastatic melanoma, as based on multiomics data. Our results provide a theoretical basis for drug combination and reveal potential immunotherapy response markers.
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Affiliation(s)
- Wenqiong Chen
- The Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Yi He
- The Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Guowei Zhou
- The Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China
| | - Xiang Chen
- The Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China.
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China.
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.
- Furong Laboratory, Changsha, Hunan, China.
| | - Youqiong Ye
- Department of Immunology and Microbiology, Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
| | - Guanxiong Zhang
- The Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China.
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China.
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.
- Furong Laboratory, Changsha, Hunan, China.
| | - Hong Liu
- The Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China.
- Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Center of Skin Health and Disease, Xiangya Hospital, Changsha, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Changsha, China.
- Xiangya Clinical Research Center for Cancer Immunotherapy, Central South University, Changsha, China.
- National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Changsha, China.
- Furong Laboratory, Changsha, Hunan, China.
- Research Center of Molecular Metabolomics, Xiangya Hospital, Central South University, Changsha, China.
- Big Data Institute, Central South University, Changsha, 410083, China.
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14
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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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15
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Xu Z, Wang W, Yang T, Li L, Ma X, Chen J, Wang J, Huang Y, Gould J, Lu H, Du W, Sahu SK, Yang F, Li Z, Hu Q, Hua C, Hu S, Liu Y, Cai J, You L, Zhang Y, Li Y, Zeng W, Chen A, Wang B, Liu L, Chen F, Ma K, Xu X, Wei X. STOmicsDB: a comprehensive database for spatial transcriptomics data sharing, analysis and visualization. Nucleic Acids Res 2024; 52:D1053-D1061. [PMID: 37953328 PMCID: PMC10767841 DOI: 10.1093/nar/gkad933] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/20/2023] [Accepted: 10/10/2023] [Indexed: 11/14/2023] Open
Abstract
Recent technological developments in spatial transcriptomics allow researchers to measure gene expression of cells and their spatial locations at the single-cell level, generating detailed biological insight into biological processes. A comprehensive database could facilitate the sharing of spatial transcriptomic data and streamline the data acquisition process for researchers. Here, we present the Spatial TranscriptOmics DataBase (STOmicsDB), a database that serves as a one-stop hub for spatial transcriptomics. STOmicsDB integrates 218 manually curated datasets representing 17 species. We annotated cell types, identified spatial regions and genes, and performed cell-cell interaction analysis for these datasets. STOmicsDB features a user-friendly interface for the rapid visualization of millions of cells. To further facilitate the reusability and interoperability of spatial transcriptomic data, we developed standards for spatial transcriptomic data archiving and constructed a spatial transcriptomic data archiving system. Additionally, we offer a distinctive capability of customizing dedicated sub-databases in STOmicsDB for researchers, assisting them in visualizing their spatial transcriptomic analyses. We believe that STOmicsDB could contribute to research insights in the spatial transcriptomics field, including data archiving, sharing, visualization and analysis. STOmicsDB is freely accessible at https://db.cngb.org/stomics/.
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Affiliation(s)
- Zhicheng Xu
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Weiwen Wang
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Tao Yang
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Ling Li
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Xizheng Ma
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Jing Chen
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Jieyu Wang
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Yan Huang
- BGI Research, Shenzhen 518083, China
| | - Joshua Gould
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Wensi Du
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | | | - Fan Yang
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | | | - Qingjiang Hu
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Cong Hua
- BGI Research, Wuhan 430074, China
| | - Shoujie Hu
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Yiqun Liu
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Jia Cai
- BGI Research, Wuhan 430074, China
| | - Lijin You
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | | | | | - Wenjun Zeng
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | - Bo Wang
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | | | | | - Kailong Ma
- China National GeneBank, BGI Research, Shenzhen 518120, China
| | - Xun Xu
- BGI Research, Shenzhen 518083, China
- Guangdong Provincial Key Laboratory of Genome Read and Write, BGI research, Shenzhen 518120, China
| | - Xiaofeng Wei
- China National GeneBank, BGI Research, Shenzhen 518120, China
- Guangdong Provincial Genomics Data Center, BGI research, Shenzhen 518120, China
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16
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Manicardi V, Gugnoni M, Sauta E, Donati B, Vitale E, Torricelli F, Manzotti G, Piana S, Longo C, Ghini F, Ciarrocchi A. Ex vivo mapping of enhancer networks that define the transcriptional program driving melanoma metastasis. Mol Oncol 2023; 17:2728-2742. [PMID: 37408506 DOI: 10.1002/1878-0261.13485] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 04/20/2023] [Accepted: 07/04/2023] [Indexed: 07/07/2023] Open
Abstract
Mortality from vmelanoma is associated with metastatic disease, but the mechanisms leading to spreading of the cancer cells remain obscure. Spatial profiling revealed that melanoma is characterized by a high degree of heterogeneity, which is established by the ability of melanoma cells to switch between different phenotypical stages. This plasticity, likely a heritage from embryonic pathways, accounts for a relevant part of the metastatic potential of these lesions, and requires the rapid and efficient reorganization of the transcriptional landscape of melanoma cells. A large part of the non-coding genome cooperates to control gene expression, specifically through the activity of enhancers (ENHs). In this study, we aimed to identify ex vivo the network of active ENHs and to outline their cooperative interactions in supporting transcriptional adaptation during melanoma metastatic progression. We conducted a genome-wide analysis to map active ENHs distribution in a retrospective cohort of 39 melanoma patients, comparing the profiles obtained in primary (N = 19) and metastatic (N = 20) melanoma lesions. Unsupervised clustering showed that the profile for acetylated histone H3 at lysine 27 (H3K27ac) efficiently segregates lesions into three different clusters corresponding to progressive stages of the disease. We reconstructed the map of super-ENHs (SEs) and cooperative ENHs that associate with metastatic progression in melanoma, which showed that cooperation among regulatory elements is a mandatory requirement for transcriptional plasticity. We also showed that these elements carry out specialized and non-redundant functions, and indicated the existence of a hierarchical organization, with SEs on top as masterminds of the entire transcriptional program and classical ENHs as executors. By providing an innovative vision of how the chromatin landscape of melanoma works during metastatic spreading, our data also point out the need to integrate functional profiling in the analysis of cancer lesions to increase definition and improve interpretation of tumor heterogeneity.
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Affiliation(s)
- Veronica Manicardi
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Italy
| | - Mila Gugnoni
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
| | | | - Benedetta Donati
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
| | - Emanuele Vitale
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
- Clinical and Experimental Medicine PhD Program, University of Modena and Reggio Emilia, Italy
| | - Federica Torricelli
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
| | - Gloria Manzotti
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
| | | | - Caterina Longo
- Skin Cancer Unit, Azienda USL-IRCCS di Reggio Emilia, Italy
| | - Francesco Ghini
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
| | - Alessia Ciarrocchi
- Laboratory of Translational Research, Azienda USL-IRCCS di Reggio Emilia, Italy
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17
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Davis S, Scott C, Oetjen J, Charles PD, Kessler BM, Ansorge O, Fischer R. Deep topographic proteomics of a human brain tumour. Nat Commun 2023; 14:7710. [PMID: 38001067 PMCID: PMC10673928 DOI: 10.1038/s41467-023-43520-8] [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: 03/01/2023] [Accepted: 11/13/2023] [Indexed: 11/26/2023] Open
Abstract
The spatial organisation of cellular protein expression profiles within tissue determines cellular function and is key to understanding disease pathology. To define molecular phenotypes in the spatial context of tissue, there is a need for unbiased, quantitative technology capable of mapping proteomes within tissue structures. Here, we present a workflow for spatially-resolved, quantitative proteomics of tissue that generates maps of protein abundance across tissue slices derived from a human atypical teratoid-rhabdoid tumour at three spatial resolutions, the highest being 40 µm, to reveal distinct abundance patterns of thousands of proteins. We employ spatially-aware algorithms that do not require prior knowledge of the fine tissue structure to detect proteins and pathways with spatial abundance patterns and correlate proteins in the context of tissue heterogeneity and cellular features such as extracellular matrix or proximity to blood vessels. We identify PYGL, ASPH and CD45 as spatial markers for tumour boundary and reveal immune response-driven, spatially-organised protein networks of the extracellular tumour matrix. Overall, we demonstrate spatially-aware deep proteo-phenotyping of tissue heterogeneity, to re-define understanding tissue biology and pathology at the molecular level.
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Affiliation(s)
- Simon Davis
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Connor Scott
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Janina Oetjen
- Bruker Daltonics GmbH & Co. KG, Fahrenheitstraße 4, 28359, Bremen, Germany
| | - Philip D Charles
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Big Data Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Benedikt M Kessler
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK
| | - Olaf Ansorge
- Academic Unit of Neuropathology, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, OX3 9DU, UK
| | - Roman Fischer
- Target Discovery Institute, Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK.
- Chinese Academy for Medical Sciences Oxford Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, UK.
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18
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Zhang T, Zhang Z, Li L, Dong B, Wang G, Zhang D. GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data. Brief Bioinform 2023; 25:bbad469. [PMID: 38127088 PMCID: PMC10734610 DOI: 10.1093/bib/bbad469] [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: 10/21/2023] [Revised: 11/20/2023] [Accepted: 11/28/2023] [Indexed: 12/23/2023] Open
Abstract
With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.
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Affiliation(s)
- Tianjiao Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Ziheng Zhang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Liangyu Li
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Benzhi Dong
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Guohua Wang
- College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China
| | - Dandan Zhang
- Department of Obstetrics and Gynecology, the First Affiliated Hospital of Harbin Medical University, Harbin 150001, China
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19
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Zhong C, Tian T, Wei Z. Hidden Markov random field models for cell-type assignment of spatially resolved transcriptomics. Bioinformatics 2023; 39:btad641. [PMID: 37944045 PMCID: PMC10640398 DOI: 10.1093/bioinformatics/btad641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 08/07/2023] [Accepted: 11/06/2023] [Indexed: 11/12/2023] Open
Abstract
MOTIVATION The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data. RESULTS We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments. AVAILABILITY AND IMPLEMENTATION https://github.com/ChengZ352/SPAN.
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Affiliation(s)
- Cheng Zhong
- Department of Computer Science, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, United States
| | - Tian Tian
- Center for Applied Genomics, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, United States
| | - Zhi Wei
- Department of Computer Science, Ying Wu College of Computing, New Jersey Institute of Technology, Newark, NJ 07102, United States
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20
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Vahid MR, Brown EL, Steen CB, Zhang W, Jeon HS, Kang M, Gentles AJ, Newman AM. High-resolution alignment of single-cell and spatial transcriptomes with CytoSPACE. Nat Biotechnol 2023; 41:1543-1548. [PMID: 36879008 PMCID: PMC10635828 DOI: 10.1038/s41587-023-01697-9] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 01/25/2023] [Indexed: 03/08/2023]
Abstract
Recent studies have emphasized the importance of single-cell spatial biology, yet available assays for spatial transcriptomics have limited gene recovery or low spatial resolution. Here we introduce CytoSPACE, an optimization method for mapping individual cells from a single-cell RNA sequencing atlas to spatial expression profiles. Across diverse platforms and tissue types, we show that CytoSPACE outperforms previous methods with respect to noise tolerance and accuracy, enabling tissue cartography at single-cell resolution.
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Affiliation(s)
- Milad R Vahid
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Erin L Brown
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Chloé B Steen
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Department of Cancer Immunology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
- Department of Medical Genetics, Oslo University Hospital, Oslo, Norway
| | - Wubing Zhang
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Hyun Soo Jeon
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Minji Kang
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Andrew J Gentles
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
- Department of Medicine, Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
| | - Aaron M Newman
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
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21
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Saqib J, Park B, Jin Y, Seo J, Mo J, Kim J. Identification of Niche-Specific Gene Signatures between Malignant Tumor Microenvironments by Integrating Single Cell and Spatial Transcriptomics Data. Genes (Basel) 2023; 14:2033. [PMID: 38002976 PMCID: PMC10671538 DOI: 10.3390/genes14112033] [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: 10/05/2023] [Revised: 10/29/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
The tumor microenvironment significantly affects the transcriptomic states of tumor cells. Single-cell RNA sequencing (scRNA-seq) helps elucidate the transcriptomes of individual cancer cells and their neighboring cells. However, cell dissociation results in the loss of information on neighboring cells. To address this challenge and comprehensively assess the gene activity in tissue samples, it is imperative to integrate scRNA-seq with spatial transcriptomics. In our previous study on physically interacting cell sequencing (PIC-seq), we demonstrated that gene expression in single cells is affected by neighboring cell information. In the present study, we proposed a strategy to identify niche-specific gene signatures by harmonizing scRNA-seq and spatial transcriptomic data. This approach was applied to the paired or matched scRNA-seq and Visium platform data of five cancer types: breast cancer, gastrointestinal stromal tumor, liver hepatocellular carcinoma, uterine corpus endometrial carcinoma, and ovarian cancer. We observed distinct gene signatures specific to cellular niches and their neighboring counterparts. Intriguingly, these niche-specific genes display considerable dissimilarity to cell type markers and exhibit unique functional attributes independent of the cancer types. Collectively, these results demonstrate the potential of this integrative approach for identifying novel marker genes and their spatial relationships.
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Affiliation(s)
| | | | | | | | | | - Junil Kim
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-Ro, Dongjak-Gu, Seoul 06978, Republic of Korea; (J.S.); (Y.J.); (J.M.)
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22
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Rahman MN, Noman AA, Turza AM, Abrar MA, Samee MAH, Rahman MS. ScribbleDom: using scribble-annotated histology images to identify domains in spatial transcriptomics data. Bioinformatics 2023; 39:btad594. [PMID: 37756699 PMCID: PMC10564617 DOI: 10.1093/bioinformatics/btad594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 09/03/2023] [Accepted: 09/22/2023] [Indexed: 09/29/2023] Open
Abstract
MOTIVATION Spatial domain identification is a very important problem in the field of spatial transcriptomics. The state-of-the-art solutions to this problem focus on unsupervised methods, as there is lack of data for a supervised learning formulation. The results obtained from these methods highlight significant opportunities for improvement. RESULTS In this article, we propose a potential avenue for enhancement through the development of a semi-supervised convolutional neural network based approach. Named "ScribbleDom", our method leverages human expert's input as a form of semi-supervision, thereby seamlessly combines the cognitive abilities of human experts with the computational power of machines. ScribbleDom incorporates a loss function that integrates two crucial components: similarity in gene expression profiles and adherence to the valuable input of a human annotator through scribbles on histology images, providing prior knowledge about spot labels. The spatial continuity of the tissue domains is taken into account by extracting information on the spot microenvironment through convolution filters of varying sizes, in the form of "Inception" blocks. By leveraging this semi-supervised approach, ScribbleDom significantly improves the quality of spatial domains, yielding superior results both quantitatively and qualitatively. Our experiments on several benchmark datasets demonstrate the clear edge of ScribbleDom over state-of-the-art methods-between 1.82% to 169.38% improvements in adjusted Rand index for 9 of the 12 human dorsolateral prefrontal cortex samples, and 15.54% improvement in the melanoma cancer dataset. Notably, when the expert input is absent, ScribbleDom can still operate, in a fully unsupervised manner like the state-of-the-art methods, and produces results that remain competitive. AVAILABILITY AND IMPLEMENTATION Source code is available at Github (https://github.com/1alnoman/ScribbleDom) and Zenodo (https://zenodo.org/badge/latestdoi/681572669).
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Affiliation(s)
- Mohammad Nuwaisir Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Abdullah Al Noman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Abir Mohammad Turza
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
| | - Mohammed Abid Abrar
- Department of Computer Science and Engineering, Brac University, Dhaka 1212, Bangladesh
| | - Md Abul Hassan Samee
- Department of Integrative Physiology, Baylor College of Medicine, Houston, TX 77030, United States
| | - M Saifur Rahman
- Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
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23
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. PLoS Genet 2023; 19:e1010983. [PMID: 37862362 PMCID: PMC10619839 DOI: 10.1371/journal.pgen.1010983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 11/01/2023] [Accepted: 09/19/2023] [Indexed: 10/22/2023] Open
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
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Wang Q, Zhi Y, Zi M, Mo Y, Wang Y, Liao Q, Zhang S, Gong Z, Wang F, Zeng Z, Guo C, Xiong W. Spatially Resolved Transcriptomics Technology Facilitates Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2302558. [PMID: 37632718 PMCID: PMC10602551 DOI: 10.1002/advs.202302558] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/16/2023] [Indexed: 08/28/2023]
Abstract
Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.
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Affiliation(s)
- Qian Wang
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Yuan Zhi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Moxin Zi
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Yongzhen Mo
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Qianjin Liao
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
| | - Shanshan Zhang
- Department of Otolaryngology Head and Neck SurgeryXiangya HospitalCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaojian Gong
- Department of Oral and Maxillofacial SurgeryThe Second Xiangya Hospital of Central South UniversityChangshaHunan410012P. R. China
| | - Fuyan Wang
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Zhaoyang Zeng
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Can Guo
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
| | - Wei Xiong
- NHC Key Laboratory of Carcinogenesis and Hunan Key Laboratory of Cancer MetabolismHunan Cancer Hospital and the Affiliated Cancer Hospital of Xiangya School of MedicineCentral South UniversityChangshaHunan410008P. R. China
- Key Laboratory of Carcinogenesis and Cancer Invasion of the Chinese Ministry of EducationCancer Research InstituteCentral South UniversityChangshaHunan410008P. R. China
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25
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Hounye AH, Hu B, Wang Z, Wang J, Cao C, Zhang J, Hou M, Qi M. Evaluation of drug sensitivity, immunological characteristics, and prognosis in melanoma patients using an endoplasmic reticulum stress-associated signature based on bioinformatics and pan-cancer analysis. J Mol Med (Berl) 2023; 101:1267-1287. [PMID: 37653150 DOI: 10.1007/s00109-023-02365-w] [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: 07/17/2022] [Revised: 05/27/2023] [Accepted: 08/21/2023] [Indexed: 09/02/2023]
Abstract
We aimed to develop endoplasmic reticulum (ER) stress-related risk signature to predict the prognosis of melanoma and elucidate the immune characteristics and benefit of immunotherapy in ER-related risk score-defined subgroups of melanoma based on a machine learning algorithm. Based on The Cancer Genome Atlas (TCGA) melanoma dataset (n = 471) and GTEx database (n = 813), 365 differentially expressed ER-associated genes were selected using the univariate Cox model and LASSO penalty Cox model. Ten genes impacting OS were identified to construct an ER-related signature by using the multivariate Cox regression method and validated with the Gene Expression Omnibus (GEO) dataset. Thereafter, the immune features, CNV, methylation, drug sensitivity, and the clinical benefit of anticancer immune checkpoint inhibitor (ICI) therapy in risk score subgroups, were analyzed. We further validated the gene signature using pan-cancer analysis by comparing it to other tumor types. The ER-related risk score was constructed based on the ARNTL, AGO1, TXN, SORL1, CHD7, EGFR, KIT, HLA-DRB1 KCNA2, and EDNRB genes. The high ER stress-related risk score group patients had a poorer overall survival (OS) than the low-risk score group patients, consistent with the results in the GEO cohort. The combined results suggested that a high ER stress-related risk score was associated with cell adhesion, gamma phagocytosis, cation transport, cell surface cell adhesion, KRAS signalling, CD4 T cells, M1 macrophages, naive B cells, natural killer (NK) cells, and eosinophils and less benefitted from ICI therapy. Based on the expression patterns of ER stress-related genes, we created an appropriate predictive model, which can also help distinguish the immune characteristics, CNV, methylation, and the clinical benefit of ICI therapy. KEY MESSAGES: Melanoma is the cutaneous tumor with a high degree of malignancy, the highest fatality rate, and extremely poor prognosis. Model usefulness should be considered when using models that contained more features. We constructed the Endoplasmic Reticulum stress-associated signature using TCGA and GEO database based on machine learning algorithm. ER stress-associated signature has excellent ability for predicting prognosis for melanoma.
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Affiliation(s)
| | - Bingqian Hu
- Department of Dermatology, Xiangya Hospital of Central South University, Changsha, 410000, China
| | - Zheng Wang
- School of Computer Science, Hunan First Normal University, Changsha, 410205, China
| | - Jiaoju Wang
- School of Mathematics and Statistics, Central South University, Changsha, 410083, China
| | - Cong Cao
- School of Mathematics and Statistics, Central South University, Changsha, 410083, China
| | - Jianglin Zhang
- Department of Dermatology, The Second Clinical Medical College, Shenzhen People's Hospital Jinan University, The First Affiliated Hospital, Southern University of Science and Technology, Shenzhen, Guangdong, 518020, China
| | - Muzhou Hou
- School of Mathematics and Statistics, Central South University, Changsha, 410083, China.
| | - Min Qi
- Department of Plastic Surgery, Xiangya Hospital, Central South University, Changsha, 410008, China.
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26
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Tang Z, Li Z, Hou T, Zhang T, Yang B, Su J, Song Q. SiGra: single-cell spatial elucidation through an image-augmented graph transformer. Nat Commun 2023; 14:5618. [PMID: 37699885 PMCID: PMC10497630 DOI: 10.1038/s41467-023-41437-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: 09/09/2022] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27-50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tieying Hou
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA.
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA.
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA.
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Florida, USA.
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27
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Liu Y, Li N, Qi J, Xu G, Zhao J, Wang N, Huang X, Jiang W, Justet A, Adams TS, Homer R, Amei A, Rosas IO, Kaminski N, Wang Z, Yan X. A hybrid machine learning and regression method for cell type deconvolution of spatial barcoding-based transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.24.554722. [PMID: 37662370 PMCID: PMC10473707 DOI: 10.1101/2023.08.24.554722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Spatial barcoding-based transcriptomic (ST) data require cell type deconvolution for cellular-level downstream analysis. Here we present SDePER, a hybrid machine learning and regression method, to deconvolve ST data using reference single-cell RNA sequencing (scRNA-seq) data. SDePER uses a machine learning approach to remove the systematic difference between ST and scRNA-seq data (platform effects) explicitly and efficiently to ensure the linear relationship between ST data and cell type-specific expression profile. It also considers sparsity of cell types per capture spot and across-spots spatial correlation in cell type compositions. Based on the estimated cell type proportions, SDePER imputes cell type compositions and gene expression at unmeasured locations in a tissue map with enhanced resolution. Applications to coarse-grained simulated data and four real datasets showed that SDePER achieved more accurate and robust results than existing methods, suggesting the importance of considering platform effects, sparsity and spatial correlation in cell type deconvolution.
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28
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Zhou Y, Jiang X, Wang X, Huang J, Li T, Jin H, He J. Promise of spatially resolved omics for tumor research. J Pharm Anal 2023; 13:851-861. [PMID: 37719191 PMCID: PMC10499658 DOI: 10.1016/j.jpha.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 07/01/2023] [Accepted: 07/06/2023] [Indexed: 09/19/2023] Open
Abstract
Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.
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Affiliation(s)
- Yanhe Zhou
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xinyi Jiang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Xiangyi Wang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Jianpeng Huang
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Tong Li
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
| | - Hongtao Jin
- New Drug Safety Evaluation Center, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
| | - Jiuming He
- State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100050, China
- NMPA Key Laboratory for Safety Research and Evaluation of Innovative Drug, Beijing, 10050, China
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29
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Zhang H, Hunter MV, Chou J, Quinn JF, Zhou M, White RM, Tansey W. BayesTME: An end-to-end method for multiscale spatial transcriptional profiling of the tissue microenvironment. Cell Syst 2023; 14:605-619.e7. [PMID: 37473731 PMCID: PMC10368078 DOI: 10.1016/j.cels.2023.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/09/2023] [Accepted: 06/09/2023] [Indexed: 07/22/2023]
Abstract
Spatial variation in cellular phenotypes underlies heterogeneity in immune recognition and response to therapy in cancer and many other diseases. Spatial transcriptomics holds the potential to quantify such variation, but existing analysis methods are limited by their focus on individual tasks such as spot deconvolution. We present BayesTME, an end-to-end Bayesian method for analyzing spatial transcriptomics data. BayesTME unifies several previously distinct analysis goals under a single, holistic generative model. This unified approach enables BayesTME to deconvolve spots into cell phenotypes without any need for paired single-cell RNA-seq. BayesTME then goes beyond spot deconvolution to uncover spatial expression patterns among coordinated subsets of genes within phenotypes, which we term spatial transcriptional programs. BayesTME achieves state-of-the-art performance across myriad benchmarks. On human and zebrafish melanoma tissues, BayesTME identifies spatial transcriptional programs that capture fundamental biological phenomena such as bilateral symmetry and tumor-associated fibroblast and macrophage reprogramming. BayesTME is open source.
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Affiliation(s)
- Haoran Zhang
- Department of Computer Science, University of Texas at Austin, Austin, TX 78712, USA
| | - Miranda V Hunter
- Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Jacqueline Chou
- Department of Physiology, Biophysics, & Systems Biology, Weill Cornell Medical College, New York, NY 10065, USA
| | - Jeffrey F Quinn
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - Mingyuan Zhou
- McCombs School of Business, University of Texas at Austin, Austin, TX 78712, USA
| | - Richard M White
- Ludwig Institute for Cancer Research, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7DQ, UK
| | - Wesley Tansey
- Computational Oncology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA.
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30
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Li Z, Wang T, Liu P, Huang Y. SpatialDM for rapid identification of spatially co-expressed ligand-receptor and revealing cell-cell communication patterns. Nat Commun 2023; 14:3995. [PMID: 37414760 PMCID: PMC10325966 DOI: 10.1038/s41467-023-39608-w] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 06/21/2023] [Indexed: 07/08/2023] Open
Abstract
Cell-cell communication is a key aspect of dissecting the complex cellular microenvironment. Existing single-cell and spatial transcriptomics-based methods primarily focus on identifying cell-type pairs for a specific interaction, while less attention has been paid to the prioritisation of interaction features or the identification of interaction spots in the spatial context. Here, we introduce SpatialDM, a statistical model and toolbox leveraging a bivariant Moran's statistic to detect spatially co-expressed ligand and receptor pairs, their local interacting spots (single-spot resolution), and communication patterns. By deriving an analytical null distribution, this method is scalable to millions of spots and shows accurate and robust performance in various simulations. On multiple datasets including melanoma, Ventricular-Subventricular Zone, and intestine, SpatialDM reveals promising communication patterns and identifies differential interactions between conditions, hence enabling the discovery of context-specific cell cooperation and signalling.
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Affiliation(s)
- Zhuoxuan Li
- School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China
| | - Tianjie Wang
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China
| | - Pentao Liu
- School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China.
- Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China.
| | - Yuanhua Huang
- School of Biomedical Sciences, University of Hong Kong, Hong Kong SAR, China.
- Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China.
- Center for Translational Stem Cell Biology, Hong Kong Science and Technology Park, Hong Kong SAR, China.
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Li Y, Luo Y. Spatial Transcriptomic Cell-type Deconvolution Using Graph Neural Networks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.10.532112. [PMID: 37333198 PMCID: PMC10274700 DOI: 10.1101/2023.03.10.532112] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Spatially resolved transcriptomics performs high-throughput measurement of transcriptomes while preserving spatial information about the cellular organizations. However, many spatially resolved transcriptomic technologies can only distinguish spots consisting of a mixture of cells instead of working at single-cell resolution. Here, we present STdGCN, a graph neural network model designed for cell type deconvolution of spatial transcriptomic (ST) data that can leverage abundant single-cell RNA sequencing (scRNA-seq) data as reference. STdGCN is the first model incorporating the expression profiles from single cell data as well as the spatial localization information from the ST data for cell type deconvolution. Extensive benchmarking experiments on multiple ST datasets showed that STdGCN outperformed 14 published state-of-the-art models. Applied to a human breast cancer Visium dataset, STdGCN discerned spatial distributions between stroma, lymphocytes and cancer cells for tumor microenvironment dissection. In a human heart ST dataset, STdGCN detected the changes of potential endothelial-cardiomyocyte communications during tissue development.
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Affiliation(s)
- Yawei Li
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
- Center for Collaborative AI in Healthcare, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA
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Rájová J, Davidsson M, Avallone M, Hartnor M, Aldrin-Kirk P, Cardoso T, Nolbrant S, Mollbrink A, Storm P, Heuer A, Parmar M, Björklund T. Deconvolution of spatial sequencing provides accurate characterization of hESC-derived DA transplants in vivo. Mol Ther Methods Clin Dev 2023; 29:381-394. [PMID: 37251982 PMCID: PMC10209706 DOI: 10.1016/j.omtm.2023.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 04/28/2023] [Indexed: 05/31/2023]
Abstract
Cell therapy for Parkinson's disease has experienced substantial growth in the past decades with several ongoing clinical trials. Despite increasing refinement of differentiation protocols and standardization of the transplanted neural precursors, the transcriptomic analysis of cells in the transplant after its full maturation in vivo has not been thoroughly investigated. Here, we present spatial transcriptomics analysis of fully differentiated grafts in their host tissue. Unlike earlier transcriptomics analyses using single-cell technologies, we observe that cells derived from human embryonic stem cells (hESCs) in the grafts adopt mature dopaminergic signatures. We show that the presence of phenotypic dopaminergic genes, which were found to be differentially expressed in the transplants, is concentrated toward the edges of the grafts, in agreement with the immunohistochemical analyses. Deconvolution shows dopamine neurons being the dominating cell type in many features beneath the graft area. These findings further support the preferred environmental niche of TH-positive cells and confirm their dopaminergic phenotype through the presence of multiple dopaminergic markers.
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Affiliation(s)
- Jana Rájová
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Marcus Davidsson
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Martino Avallone
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Morgan Hartnor
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Patrick Aldrin-Kirk
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Tiago Cardoso
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Sara Nolbrant
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Annelie Mollbrink
- Science for Life Laboratory, Division of Gene Technology, KTH Royal Institute of Technology, 106 91 Stockholm, Sweden
| | - Petter Storm
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Andreas Heuer
- Behavioural Neuroscience Laboratory, Department of Experimental Medical Sciences, Lund University, 221 84 Lund, Sweden
| | - Malin Parmar
- Developmental and Regenerative Neurobiology, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
| | - Tomas Björklund
- Molecular Neuromodulation, Department of Experimental Medical Science, Lund University, 221 84 Lund, Sweden
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33
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Yang L, Cao ZJ, Zhang Y, Zhou JK, Tian J. Disulfidptosis-related classification patterns and tumor microenvironment characterization in skin cutaneous melanoma. Melanoma Manag 2023; 10:MMT65. [PMID: 38230203 PMCID: PMC10789442 DOI: 10.2217/mmt-2023-0006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/28/2023] [Indexed: 01/18/2024] Open
Abstract
Aim To identify distinct disulfidptosis-molecular subtypes and develop a novel prognostic signature. Methods/materials We integrated into this study multiple SKCM transcriptomic datasets from the Cancer Genome Atlas database and Gene Expression Omnibus dataset. The consensus clustering algorithm was applied to categorize SKCM patients into different DRG subtypes. Results Three distinct DRG subtypes were identified, which were correlated to different clinical outcomes and signaling pathways. Then, a disulfidptosis-relaed signature and nomogram were constructed, which could accurately predict the individual OS of patients with SKCM. The high-risk group was less sensitive to immunotherapy than the low-risk group. Conclusion The signature can assist healthcare professionals in making more accurate and individualized treatment choices for patients with SKCM.
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Affiliation(s)
- Li Yang
- Department of Dermatology, Shaanxi Provincial People's Hospital, Xi'an 710068, China
| | - Zi-jian Cao
- Department of Dermatology, The 63600 Hospital of PLA, Lanzhou, 732750, China
| | - Yuan Zhang
- Department of Oncology, Shaanxi Provincial People's Hospital, Xi'an 710068, China
| | - Jin-ke Zhou
- Department of Dermatology, The 63600 Hospital of PLA, Lanzhou, 732750, China
| | - Jun Tian
- Department of Dermatology, Shaanxi Provincial People's Hospital, Xi'an 710068, China
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Chu Y, Dai E, Li Y, Han G, Pei G, Ingram DR, Thakkar K, Qin JJ, Dang M, Le X, Hu C, Deng Q, Sinjab A, Gupta P, Wang R, Hao D, Peng F, Yan X, Liu Y, Song S, Zhang S, Heymach JV, Reuben A, Elamin YY, Pizzi MP, Lu Y, Lazcano R, Hu J, Li M, Curran M, Futreal A, Maitra A, Jazaeri AA, Ajani JA, Swanton C, Cheng XD, Abbas HA, Gillison M, Bhat K, Lazar AJ, Green M, Litchfield K, Kadara H, Yee C, Wang L. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat Med 2023; 29:1550-1562. [PMID: 37248301 DOI: 10.1038/s41591-023-02371-y] [Citation(s) in RCA: 44] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 04/26/2023] [Indexed: 05/31/2023]
Abstract
Tumor-infiltrating T cells offer a promising avenue for cancer treatment, yet their states remain to be fully characterized. Here we present a single-cell atlas of T cells from 308,048 transcriptomes across 16 cancer types, uncovering previously undescribed T cell states and heterogeneous subpopulations of follicular helper, regulatory and proliferative T cells. We identified a unique stress response state, TSTR, characterized by heat shock gene expression. TSTR cells are detectable in situ in the tumor microenvironment across various cancer types, mostly within lymphocyte aggregates or potential tertiary lymphoid structures in tumor beds or surrounding tumor edges. T cell states/compositions correlated with genomic, pathological and clinical features in 375 patients from 23 cohorts, including 171 patients who received immune checkpoint blockade therapy. We also found significantly upregulated heat shock gene expression in intratumoral CD4/CD8+ cells following immune checkpoint blockade treatment, particularly in nonresponsive tumors, suggesting a potential role of TSTR cells in immunotherapy resistance. Our well-annotated T cell reference maps, web portal and automatic alignment/annotation tool could provide valuable resources for T cell therapy optimization and biomarker discovery.
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Affiliation(s)
- Yanshuo Chu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Enyu Dai
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yating Li
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangchun Han
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Guangsheng Pei
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Davis R Ingram
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Krupa Thakkar
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
| | - Jiang-Jiang Qin
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Minghao Dang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xiuning Le
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Can Hu
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Qing Deng
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ansam Sinjab
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Pravesh Gupta
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ruiping Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Dapeng Hao
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Fuduan Peng
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Xinmiao Yan
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shumei Song
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Shaojun Zhang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John V Heymach
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexandre Reuben
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yasir Y Elamin
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Melissa P Pizzi
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yang Lu
- Department of Nuclear Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rossana Lazcano
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jian Hu
- Department of Human Genetics, Emory School of Medicine, Atlanta, GA, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michael Curran
- Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Andrew Futreal
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Anirban Maitra
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amir A Jazaeri
- Department of Gynecologic Oncology and Reproductive Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jaffer A Ajani
- Department of Gastrointestinal Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Charles Swanton
- Cancer Evolution and Genome Instability Laboratory, The Francis Crick Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Xiang-Dong Cheng
- Department of Gastric Surgery, Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Hangzhou, China
- Institute of Basic Medicine and Cancer, Chinese Academy of Sciences, Hangzhou, China
| | - Hussein A Abbas
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Maura Gillison
- Department of Thoracic/Head and Neck Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Krishna Bhat
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Alexander J Lazar
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Michael Green
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Lymphoma and Myeloma, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Kevin Litchfield
- Tumour Immunogenomics and Immunosurveillance Laboratory, University College London Cancer Institute, London, UK
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, London, UK
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Cassian Yee
- Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- Department of Immunology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA.
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Freeman TL, Zhao C, Schrode N, Fortune T, Shroff S, Tweel B, Beaumont KG, Swartz TH. HIV-1 activates oxidative phosphorylation in infected CD4 T cells in a human tonsil explant model. Front Immunol 2023; 14:1172938. [PMID: 37325659 PMCID: PMC10266353 DOI: 10.3389/fimmu.2023.1172938] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 05/16/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Human immunodeficiency virus type 1 (HIV-1) causes a chronic, incurable infection leading to immune activation and chronic inflammation in people with HIV-1 (PWH), even with virologic suppression on antiretroviral therapy (ART). The role of lymphoid structures as reservoirs for viral latency and immune activation has been implicated in chronic inflammation mechanisms. Still, the specific transcriptomic changes induced by HIV-1 infection in different cell types within lymphoid tissue remain unexplored. Methods In this study, we utilized human tonsil explants from healthy human donors and infected them with HIV-1 ex vivo. We performed single-cell RNA sequencing (scRNA-seq) to analyze the cell types represented in the tissue and to investigate the impact of infection on gene expression profiles and inflammatory signaling pathways. Results Our analysis revealed that infected CD4+ T cells exhibited upregulation of genes associated with oxidative phosphorylation. Furthermore, macrophages exposed to the virus but uninfected showed increased expression of genes associated with the NLRP3 inflammasome pathway. Discussion These findings provide valuable insights into the specific transcriptomic changes induced by HIV-1 infection in different cell types within lymphoid tissue. The activation of oxidative phosphorylation in infected CD4+ T cells and the proinflammatory response in macrophages may contribute to the chronic inflammation observed in PWH despite ART. Understanding these mechanisms is crucial for developing targeted therapeutic strategies to eradicate HIV-1 infection in PWH.
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Affiliation(s)
- Tracey L. Freeman
- Medical Scientist Training Program, University of Pittsburgh-Carnegie Mellon University, Pittsburgh, PA, United States
| | - Connie Zhao
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Nadine Schrode
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Trinisia Fortune
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Sanjana Shroff
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Benjamin Tweel
- Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kristin G. Beaumont
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Talia H. Swartz
- Division of Infectious Diseases, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Cao R, Ling Y, Meng J, Jiang A, Luo R, He Q, Li A, Chen Y, Zhang Z, Liu F, Li Y, Zhang G. SMDB: a Spatial Multimodal Data Browser. Nucleic Acids Res 2023:7175352. [PMID: 37216588 DOI: 10.1093/nar/gkad413] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Revised: 05/01/2023] [Accepted: 05/05/2023] [Indexed: 05/24/2023] Open
Abstract
Understanding the relationship between fine-scale spatial organization and biological function necessitates a tool that effectively combines spatial positions, morphological information, and spatial transcriptomics (ST) data. We introduce the Spatial Multimodal Data Browser (SMDB, https://www.biosino.org/smdb), a robust visualization web service for interactively exploring ST data. By integrating multimodal data, such as hematoxylin and eosin (H&E) images, gene expression-based molecular clusters, and more, SMDB facilitates the analysis of tissue composition through the dissociation of two-dimensional (2D) sections and the identification of gene expression-profiled boundaries. In a digital three-dimensional (3D) space, SMDB allows researchers to reconstruct morphology visualizations based on manually filtered spots or expand anatomical structures using high-resolution molecular subtypes. To enhance user experience, it offers customizable workspaces for interactive exploration of ST spots in tissues, providing features like smooth zooming, panning, 360-degree rotation in 3D and adjustable spot scaling. SMDB is particularly valuable in neuroscience and spatial histology studies, as it incorporates Allen's mouse brain anatomy atlas for reference in morphological research. This powerful tool provides a comprehensive and efficient solution for examining the intricate relationships between spatial morphology, and biological function in various tissues.
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Affiliation(s)
- Ruifang Cao
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Yunchao Ling
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Jiayue Meng
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Ao Jiang
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Ruijin Luo
- Shanghai Southgene Technology Co., Ltd., Shanghai 201203, China
| | - Qinwen He
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou 215123, China
| | - Yujie Chen
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
| | - Zoutao Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Feng Liu
- School of Computer Science, Wuhan University, Wuhan 430072, China
| | - Yixue Li
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
- Guangzhou Laboratory, Guangzhou 510005, China
| | - Guoqing Zhang
- National Genomics Data Center& Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Science, Shanghai 200031, China
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37
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Du J, Yang YC, An ZJ, Zhang MH, Fu XH, Huang ZF, Yuan Y, Hou J. Advances in spatial transcriptomics and related data analysis strategies. J Transl Med 2023; 21:330. [PMID: 37202762 PMCID: PMC10193345 DOI: 10.1186/s12967-023-04150-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 04/25/2023] [Indexed: 05/20/2023] Open
Abstract
Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.
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Affiliation(s)
- Jun Du
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Yu-Chen Yang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Zhi-Jie An
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Ming-Hui Zhang
- School of Medicine, Shanghai Jiao Tong University, Shanghai, 200025 China
| | - Xue-Hang Fu
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
| | - Zou-Fang Huang
- Ganzhou Key Laboratory of Hematology, Department of Hematology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, 341000 Jiangxi China
| | - Ye Yuan
- Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240 China
- Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai, 200240 China
| | - Jian Hou
- Department of Hematology, School of Medicine, Renji Hospital, Shanghai Jiao Tong University, 160 Pujiang Road, Shanghai, 200127 China
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Lian W, Zheng X. Identification and validation of TME-related signatures to predict prognosis and response to anti-tumor therapies in skin cutaneous melanoma. Funct Integr Genomics 2023; 23:153. [PMID: 37160578 DOI: 10.1007/s10142-023-01051-x] [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: 12/17/2022] [Revised: 04/05/2023] [Accepted: 04/06/2023] [Indexed: 05/11/2023]
Abstract
The tumor microenvironment (TME) dynamically regulates cancer progression and affects clinical outcomes. This study aimed to identify molecular subtypes and construct a prognostic risk model based on TME-related signatures in skin cutaneous melanoma (SKCM) patients. We categorized SKCM patients based on transcriptome data of SKCM from The Cancer Genome Atlas (TCGA) database and 29 TME-related gene signatures. Differentially expressed genes were identified using univariate Cox regression and Lasso regression analysis, which were used for risk model construction. The robustness of this model was validated in independent external cohorts. Genetic landscape alterations, immune characteristics, and responsiveness to immunotherapy/chemotherapy were evaluated. Three TME-related subtypes were identified, and subtype C3 exhibited the most favorable prognosis, had enriched immune-related pathways, and possessed more infiltration of T_cells_CD8, T_cells_CD4_memory_activated, and Macrophages_M1 but a lower TumorPurity, whereas Macrophages_M2 were increased in subtype C1 and subtype C2. Subtype C1 was more sensitive to Cisplatin, subtype C2 was more sensitive to Temozolomide, and subtype C3 was more sensitive to Paclitaxel; 8 TME-related genes (NOTCH3, HEYL, ZNF703, ABCC2, PAEP, CCL8, HAPLN3, and HPDL) were screened for risk model construction. High-risk patients had dismal prognosis with good prediction performance. Moreover, low-risk patients were more sensitive to Paclitaxel and Temozolomide, whereas high-risk patients were more sensitive to Cisplatin. This risk model had robustness in predicting prognosis in SKCM patients. The results facilitate the understanding of TME-related genes in SKCM and provide a TME-related genes-based predictive model in prognosis and direction of personalized options for SKCM patients.
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Affiliation(s)
- Wenqin Lian
- Department of Burns and Plastic & Wound Repair Surgery, Xiang'an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, 361100, China
| | - Xiao Zheng
- Department of General Surgery, Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, 200437, China.
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Hu J, Coleman K, Zhang D, Lee EB, Kadara H, Wang L, Li M. Deciphering tumor ecosystems at super resolution from spatial transcriptomics with TESLA. Cell Syst 2023; 14:404-417.e4. [PMID: 37164011 DOI: 10.1016/j.cels.2023.03.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 02/06/2023] [Accepted: 03/30/2023] [Indexed: 05/12/2023]
Abstract
Cell populations in the tumor microenvironment (TME), including their abundance, composition, and spatial location, are critical determinants of patient response to therapy. Recent advances in spatial transcriptomics (ST) have enabled the comprehensive characterization of gene expression in the TME. However, popular ST platforms, such as Visium, only measure expression in low-resolution spots and have large tissue areas that are not covered by any spots, which limits their usefulness in studying the detailed structure of TME. Here, we present TESLA, a machine learning framework for tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique TME features such as tertiary lymphoid structures, which represents a promising avenue for understanding the spatial architecture of the TME. Although we mainly illustrated the applications in cancer, TESLA can also be applied to other diseases.
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Affiliation(s)
- Jian Hu
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, GA 30322, USA.
| | - Kyle Coleman
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daiwei Zhang
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Humam Kadara
- Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences (GSBS), Houston, TX 77030, USA.
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
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40
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Tekkela S, Theocharidis G, McGrath JA, Onoufriadis A. Spatial transcriptomics in human skin research. Exp Dermatol 2023. [PMID: 37150587 DOI: 10.1111/exd.14827] [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: 02/28/2023] [Revised: 04/17/2023] [Accepted: 04/21/2023] [Indexed: 05/09/2023]
Abstract
Spatial transcriptomics is a revolutionary technique that enables researchers to characterise tissue architecture and localisation of gene expression. A plethora of technologies that map gene expression are currently being developed, aiming to facilitate spatially resolved, high-dimensional assessment of gene transcription in the context of human skin research. Knowing which gene is expressed by which cell and in which location within skin, facilitates understanding of skin function and dysfunction in both health and disease. In this review, we summarise the available spatial transcriptomic methods and we describe their application to a broad spectrum of dermatological diseases.
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Affiliation(s)
- Stavroula Tekkela
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Georgios Theocharidis
- Joslin-Beth Israel Deaconess Foot Center and The Rongxiang Xu, MD, Center for Regenerative Therapeutics, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
| | - John A McGrath
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
| | - Alexandros Onoufriadis
- St John's Institute of Dermatology, School of Basic and Medical Biosciences, King's College London, London, UK
- Laboratory of Medical Biology and Genetics, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece
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41
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Qian J, Liao J, Liu Z, Chi Y, Fang Y, Zheng Y, Shao X, Liu B, Cui Y, Guo W, Hu Y, Bao H, Yang P, Chen Q, Li M, Zhang B, Fan X. Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace. Nat Commun 2023; 14:2484. [PMID: 37120608 PMCID: PMC10148590 DOI: 10.1038/s41467-023-38121-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 04/17/2023] [Indexed: 05/01/2023] Open
Abstract
Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.
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Affiliation(s)
- Jingyang Qian
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Jie Liao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China.
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China.
| | - Ziqi Liu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Ying Chi
- DAMO Academy, Alibaba group, 310052, Hangzhou, China
| | - Yin Fang
- College of Computer Science and Technology, Zhejiang University, 310013, Hangzhou, China
| | - Yanrong Zheng
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Key Laboratory of Neuropharmacology and Translational Medicine of Zhejiang Province, School of Pharmaceutical Sciences, Zhejiang Chinese Medical University, 310053, Hangzhou, China
| | - Xin Shao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
- Key Laboratory of Integrated Oncology and Intelligent Medicine of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, 310006, Hangzhou, China
| | - Bingqi Liu
- School of Mathematical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Yongjin Cui
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Wenbo Guo
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Yining Hu
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Hudong Bao
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
| | - Penghui Yang
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Qian Chen
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China
| | - Mingxiao Li
- Institute of Microelectronics of the Chinese Academy of Sciences, 100029, Beijing, China
| | - Bing Zhang
- DAMO Academy, Alibaba group, 310052, Hangzhou, China.
- iMedicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Healthcare, 310058, Hangzhou, China.
- Alibaba Cloud, Alibaba Group, 310052, Hangzhou, China.
| | - Xiaohui Fan
- College of Pharmaceutical Sciences, Zhejiang University, 310058, Hangzhou, China.
- Future Health Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, 314102, Jiaxing, China.
- National Key Laboratory of Modern Chinese Medicine Innovation and Manufacturing, 310058, Hangzhou, China.
- iMedicine Lab, Alibaba-Zhejiang University Joint Research Center for Future Digital Healthcare, 310058, Hangzhou, China.
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Coleman K, Hu J, Schroeder A, Lee EB, Li M. SpaDecon: cell-type deconvolution in spatial transcriptomics with semi-supervised learning. Commun Biol 2023; 6:378. [PMID: 37029267 PMCID: PMC10082183 DOI: 10.1038/s42003-023-04761-x] [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: 05/03/2022] [Accepted: 03/24/2023] [Indexed: 04/09/2023] Open
Abstract
Spatially resolved transcriptomics (SRT) has advanced our understanding of the spatial patterns of gene expression, but the lack of single-cell resolution in spatial barcoding-based SRT hinders the inference of specific locations of individual cells. To determine the spatial distribution of cell types in SRT, we present SpaDecon, a semi-supervised learning approach that incorporates gene expression, spatial location, and histology information for cell-type deconvolution. SpaDecon was evaluated through analyses of four real SRT datasets using knowledge of the expected distributions of cell types. Quantitative evaluations were performed for four pseudo-SRT datasets constructed according to benchmark proportions. Using mean squared error and Jensen-Shannon divergence with the benchmark proportions as evaluation criteria, we show that SpaDecon performance surpasses that of published cell-type deconvolution methods. Given the accuracy and computational speed of SpaDecon, we anticipate it will be valuable for SRT data analysis and will facilitate the integration of genomics and digital pathology.
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Affiliation(s)
- Kyle Coleman
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
| | - Jian Hu
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Amelia Schroeder
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Edward B Lee
- Translational Neuropathology Research Laboratory, Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Mingyao Li
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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43
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.23.533980. [PMID: 36993287 PMCID: PMC10055313 DOI: 10.1101/2023.03.23.533980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, USA
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver - Anschutz Medical Campus, Aurora, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver - Anschutz Medical Campus, Aurora, USA
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44
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Zhao Y, Wang K, Hu G. DIST: spatial transcriptomics enhancement using deep learning. Brief Bioinform 2023; 24:6991203. [PMID: 36653906 DOI: 10.1093/bib/bbad013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/19/2022] [Accepted: 01/03/2023] [Indexed: 01/20/2023] Open
Abstract
Spatially resolved transcriptomics technologies enable comprehensive measurement of gene expression patterns in the context of intact tissues. However, existing technologies suffer from either low resolution or shallow sequencing depth. Here, we present DIST, a deep learning-based method that imputes the gene expression profiles on unmeasured locations and enhances the gene expression for both original measured spots and imputed spots by self-supervised learning and transfer learning. We evaluate the performance of DIST for imputation, clustering, differential expression analysis and functional enrichment analysis. The results show that DIST can impute the gene expression accurately, enhance the gene expression for low-quality data, help detect more biological meaningful differentially expressed genes and pathways, therefore allow for deeper insights into the biological processes.
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Affiliation(s)
- Yanping Zhao
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Kui Wang
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
| | - Gang Hu
- School of Statistics and Data Science, LPMC and KLMDASR, Nankai University, Tianjin, China
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45
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Wang M, Zhuo L, Ma W, Wu Q, Zhuo Y, Wang X. AllenDigger, a Tool for Spatial Expression Data Visualization, Spatial Heterogeneity Delineation, and Single-Cell Registration Based on the Allen Brain Atlas. J Phys Chem A 2023; 127:2864-2872. [PMID: 36926884 PMCID: PMC10068737 DOI: 10.1021/acs.jpca.3c00145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
Spatial transcriptomics can be used to capture cellular spatial organization and has facilitated new insights into different biological contexts, including developmental biology, cancer, and neuroscience. However, its wide application is still hindered by its technical challenges and immature data analysis methods. Allen Brain Atlas (ABA) provides a great source for spatial gene expression throughout the mouse brain at various developmental stages with in situ hybridization image data. To the best of our knowledge, the portal developed to access spatial expression data is not very useful to biologists. Here, we developed a toolkit to collect and preprocess expression data from the ABA and allow a friendlier query to visualize the spatial distribution of genes of interest, characterize the spatial heterogeneity of the brain, and register cells from single-cell transcriptomics data to fine anatomical brain regions via machine learning methods with high accuracy. AllenDigger will be very helpful to the community in precise spatial gene expression queries and add extra spatial information to further interpret the scRNA-seq data in a cost-effective manner.
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Affiliation(s)
- Mengdi Wang
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,Changping Laboratory, Beijing 102206, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Liangchen Zhuo
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wenji Ma
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Qian Wu
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,Changping Laboratory, Beijing 102206, China
| | - Yan Zhuo
- State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiaoqun Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.,State Key Laboratory of Brain and Cognitive Science, CAS Center for Excellence in Brain Science and Intelligence Technology (Shanghai), Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,Changping Laboratory, Beijing 102206, China
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46
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Elhanani O, Ben-Uri R, Keren L. Spatial profiling technologies illuminate the tumor microenvironment. Cancer Cell 2023; 41:404-420. [PMID: 36800999 DOI: 10.1016/j.ccell.2023.01.010] [Citation(s) in RCA: 40] [Impact Index Per Article: 40.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Revised: 12/01/2022] [Accepted: 01/26/2023] [Indexed: 02/18/2023]
Abstract
The tumor microenvironment (TME) is composed of many different cellular and acellular components that together drive tumor growth, invasion, metastasis, and response to therapies. Increasing realization of the significance of the TME in cancer biology has shifted cancer research from a cancer-centric model to one that considers the TME as a whole. Recent technological advancements in spatial profiling methodologies provide a systematic view and illuminate the physical localization of the components of the TME. In this review, we provide an overview of major spatial profiling technologies. We present the types of information that can be extracted from these data and describe their applications, findings and challenges in cancer research. Finally, we provide a future perspective of how spatial profiling could be integrated into cancer research to improve patient diagnosis, prognosis, stratification to treatment and development of novel therapeutics.
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Affiliation(s)
- Ofer Elhanani
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Raz Ben-Uri
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Leeat Keren
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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47
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Amini P, Hajihosseini M, Pyne S, Dinu I. Geographically weighted linear combination test for gene-set analysis of a continuous spatial phenotype as applied to intratumor heterogeneity. Front Cell Dev Biol 2023; 11:1065586. [PMID: 36998245 PMCID: PMC10044624 DOI: 10.3389/fcell.2023.1065586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2022] [Accepted: 02/22/2023] [Indexed: 03/11/2023] Open
Abstract
Background: The impact of gene-sets on a spatial phenotype is not necessarily uniform across different locations of cancer tissue. This study introduces a computational platform, GWLCT, for combining gene set analysis with spatial data modeling to provide a new statistical test for location-specific association of phenotypes and molecular pathways in spatial single-cell RNA-seq data collected from an input tumor sample.Methods: The main advantage of GWLCT consists of an analysis beyond global significance, allowing the association between the gene-set and the phenotype to vary across the tumor space. At each location, the most significant linear combination is found using a geographically weighted shrunken covariance matrix and kernel function. Whether a fixed or adaptive bandwidth is determined based on a cross-validation cross procedure. Our proposed method is compared to the global version of linear combination test (LCT), bulk and random-forest based gene-set enrichment analyses using data created by the Visium Spatial Gene Expression technique on an invasive breast cancer tissue sample, as well as 144 different simulation scenarios.Results: In an illustrative example, the new geographically weighted linear combination test, GWLCT, identifies the cancer hallmark gene-sets that are significantly associated at each location with the five spatially continuous phenotypic contexts in the tumors defined by different well-known markers of cancer-associated fibroblasts. Scan statistics revealed clustering in the number of significant gene-sets. A spatial heatmap of combined significance over all selected gene-sets is also produced. Extensive simulation studies demonstrate that our proposed approach outperforms other methods in the considered scenarios, especially when the spatial association increases.Conclusion: Our proposed approach considers the spatial covariance of gene expression to detect the most significant gene-sets affecting a continuous phenotype. It reveals spatially detailed information in tissue space and can thus play a key role in understanding the contextual heterogeneity of cancer cells.
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Affiliation(s)
- Payam Amini
- Department of Biostatistics, School of Public Health, Iran University of Medical Sciences, Tehran, Iran
- School of Medicine, Keele University, Keele, Staffordshire, United Kingdom
| | - Morteza Hajihosseini
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- Stanford Department of Urology, Center for Academic Medicine, Palo Alto, CA, United States
| | - Saumyadipta Pyne
- Health Analytics Network, Pittsburgh, PA, United States
- University of California, Santa Barbara, Santa Barbara, CA, United States
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
| | - Irina Dinu
- School of Public Health, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Saumyadipta Pyne, ; Irina Dinu,
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Guo C, Qu X, Tang X, Song Y, Wang J, Hua K, Qiu J. Spatiotemporally deciphering the mysterious mechanism of persistent HPV-induced malignant transition and immune remodelling from HPV-infected normal cervix, precancer to cervical cancer: Integrating single-cell RNA-sequencing and spatial transcriptome. Clin Transl Med 2023; 13:e1219. [PMID: 36967539 PMCID: PMC10040725 DOI: 10.1002/ctm2.1219] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/29/2023] Open
Abstract
BACKGROUND The mechanism underlying cervical carcinogenesis that is mediated by persistent human papillomavirus (HPV) infection remains elusive. AIMS Here, for the first time, we deciphered both the temporal transition and spatial distribution of cellular subsets during disease progression from normal cervix tissues to precursor lesions to cervical cancer. MATERIALS & METHODS We generated scRNA-seq profiles and spatial transcriptomics data from nine patient samples, including two HPV-negative normal, two HPV-positive normal, two HPV-positive HSIL and three HPV-positive cancer samples. RESULTS We not only identified three 'HPV-related epithelial clusters' that are unique to normal, high-grade squamous intraepithelial lesions (HSIL) and cervical cancer tissues but also discovered node genes that potentially regulate disease progression. Moreover, we observed the gradual transition of multiple immune cells that exhibited positive immune responses, followed by dysregulation and exhaustion, and ultimately established an immune-suppressive microenvironment during the malignant program. In addition, analysis of cellular interactions further verified that a 'homeostasis-balance-malignancy' change occurred within the cervical microenvironment during disease progression. DISCUSSION We for the first time presented a spatiotemporal atlas that systematically described the cellular heterogeneity and spatial map along the four developmental steps of HPV-related cervical oncogenesis, including normal, HPV-positive normal, HSIL and cancer. We identified three unique HPV-related clusters, discovered critical node genes that determined the cell fate and uncovered the immune remodeling during disease escalation. CONCLUSION Together, these findings provided novel possibilities for accurate diagnosis, precise treatment and prognosis evaluation of patients with precancer and cervical cancer.
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Affiliation(s)
- Chenyan Guo
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Xinyu Qu
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Xiaoyan Tang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Yu Song
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Jue Wang
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Keqin Hua
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
| | - Junjun Qiu
- Department of Gynecology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, China
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Fang Y, Peng Z, Wang Y, Yuan X, Gao K, Fan R, Liu R, Liu Y, Zhang H, Xie Z, Jiang W. Improvements and challenges of tissue preparation for spatial transcriptome analysis of skull base tumors. Heliyon 2023; 9:e14133. [PMID: 36938455 PMCID: PMC10018477 DOI: 10.1016/j.heliyon.2023.e14133] [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: 12/13/2022] [Revised: 02/21/2023] [Accepted: 02/22/2023] [Indexed: 03/09/2023] Open
Abstract
Background Spatial transcriptome (ST) provides molecular profiles of tumor cells at the spatial level, which brings new progress to the research of tumors and the tumor microenvironment. This study summarizes the experiences and lessons learned in the spatial section preparation of two different pathological types of nose and skull base tumors at our institution, with the aim of offering guidelines to researchers to avoid wasting precious samples and provide a basis for the application of ST in clinical practice. Methods Frozen tissue blocks from patients with squamous cell carcinoma and adenocarcinoma of the nose and skull base diagnosed at our institution were prepared. The effects of different procedures and pathological tissue types on slide quality were explored and evaluated using RNA integrity number (RIN) and HE scores as criteria. The effects of different RIN values on ST sequencing data were explored. Results A total of 43 samples were obtained from 26 patients, including 22 with squamous carcinomas and 21 with adenocarcinomas. Thirteen samples with satisfactory RNA quality control and good histological morphology were sequenced for ST. Sample isolation time <15 min and abandonment of snap-frozen isopentane significantly improved RNA quality (p = 0.004, p < 0.0001) and histomorphological integrity (p = 0.02, p = 0.02). Selection of a suitable tissue RNA extraction kit was critical for RNA quality (p < 0.0001). No difference between 6 ≤ RIN <7 and RIN >7 in ST sequencing results was found, indicating that RIN ≥6 can be used as a criterion for qualified RNA quality control. Therefore, fresh tissues washed as soon as possible with cold PBS and then dried using OCT for snap freezing are currently the best method for preparing spatial sections of nose and skull base tumor tissues of different pathological types. Conclusion This study is the first to investigate the feasibility of applying ST to different pathological types of nose and skull base tumors and to demonstrate the widespread application of ST in tumors. Rational optimization of spatial slide preparation procedures and exploration of individualized pre-sequencing protocols are used as the first stage to ensure the quality of spatial sequencing and lay the foundation for subsequent spatial analysis.
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Affiliation(s)
- Yan Fang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Zhouying Peng
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yumin Wang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Xiaotian Yuan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Kelei Gao
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Ruohao Fan
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Ruijie Liu
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Yalan Liu
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Hua Zhang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Zhihai Xie
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
| | - Weihong Jiang
- Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Otolaryngology Major Disease Research Key Laboratory of Hunan Province, Changsha, Hunan, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Anatomy Laboratory of Division of Nose and Cranial Base, Clinical Anatomy Center of Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China
- Corresponding author. Department of Otolaryngology Head and Neck Surgery, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
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50
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Lyu L, Li X, Feng R, Zhou X, Guha TK, Yu X, Chen GQ, Yao Y, Su B, Zou D, Snyder MP, Chen L. Simultaneous profiling of host expression and microbial abundance by spatial metatranscriptome sequencing. Genome Res 2023; 33:401-411. [PMID: 37310927 PMCID: PMC10078289 DOI: 10.1101/gr.277178.122] [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: 08/04/2022] [Accepted: 01/31/2023] [Indexed: 03/29/2023]
Abstract
We developed an analysis pipeline that can extract microbial sequences from spatial transcriptomic (ST) data and assign taxonomic labels, generating a spatial microbial abundance matrix in addition to the default host expression matrix, enabling simultaneous analysis of host expression and microbial distribution. We called the pipeline spatial metatranscriptome (SMT) and applied it on both human and murine intestinal sections and validated the spatial microbial abundance information with alternative assays. Biological insights were gained from these novel data that showed host-microbe interaction at various spatial scales. Finally, we tested experimental modification that can increase microbial capture while preserving host spatial expression quality and, by use of positive controls, quantitatively showed the capture efficiency and recall of our methods. This proof-of-concept work shows the feasibility of SMT analysis and paves the way for further experimental optimization and application.
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Affiliation(s)
- Lin Lyu
- Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xue Li
- Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Ru Feng
- Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Xin Zhou
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Tuhin K Guha
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA
| | - Xiaofei Yu
- State Key Laboratory of Genetic Engineering, School of Life Sciences, Shanghai Engineering Research Center of Industrial Microorganisms, Fudan University, Shanghai 20082, China
| | - Guo Qiang Chen
- Department of Pathophysiology, Key Laboratory of Cell Differentiation and Apoptosis of Chinese Ministry of Education, State Key Laboratory of Oncogenes and Related Genes and Chinese Academy of Medical Sciences, Shanghai Cancer Institute, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Yufeng Yao
- Laboratory of Bacterial Pathogenesis, Department of Microbiology and Immunology, Institutes of Medical Sciences, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Bing Su
- Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Duowu Zou
- Department of Gastroenterology, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
| | - Michael P Snyder
- Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA;
| | - Lei Chen
- Shanghai Institute of Immunology, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China;
- Renji Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China
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