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Atta L, Clifton K, Anant M, Aihara G, Fan J. Gene count normalization in single-cell imaging-based spatially resolved transcriptomics. bioRxiv 2024:2023.08.30.555624. [PMID: 37693542 PMCID: PMC10491191 DOI: 10.1101/2023.08.30.555624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Recent advances in imaging-based spatially resolved transcriptomics (im-SRT) technologies now enable high-throughput profiling of targeted genes and their locations in fixed tissues. Normalization of gene expression data is often needed to account for technical factors that may confound underlying biological signals. Here, we investigate the potential impact of different gene count normalization methods with different targeted gene panels in the analysis and interpretation of im-SRT data. Using different simulated gene panels that overrepresent genes expressed in specific tissue regions or cell types, we demonstrate how normalization methods based on detected gene counts per cell differentially impact normalized gene expression magnitudes in a region- or cell type-specific manner. We show that these normalization-induced effects may reduce the reliability of downstream analyses including differential gene expression, gene fold change, and spatially variable gene analysis, introducing false positive and false negative results when compared to results obtained from gene panels that are more representative of the gene expression of the tissue's component cell types. These effects are not observed with normalization approaches that do not use detected gene counts for gene expression magnitude adjustment, such as with cell volume or cell area normalization. We recommend using non-gene count-based normalization approaches when feasible and evaluating gene panel representativeness before using gene count-based normalization methods if necessary. Overall, we caution that the choice of normalization method and gene panel may impact the biological interpretation of the im-SRT data.
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Affiliation(s)
- Lyla Atta
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Kalen Clifton
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Manjari Anant
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
- Department of Neuroscience, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Gohta Aihara
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21211, USA
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2
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de Souza N, Zhao S, Bodenmiller B. Multiplex protein imaging in tumour biology. Nat Rev Cancer 2024; 24:171-191. [PMID: 38316945 DOI: 10.1038/s41568-023-00657-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 02/07/2024]
Abstract
Tissue imaging has become much more colourful in the past decade. Advances in both experimental and analytical methods now make it possible to image protein markers in tissue samples in high multiplex. The ability to routinely image 40-50 markers simultaneously, at single-cell or subcellular resolution, has opened up new vistas in the study of tumour biology. Cellular phenotypes, interaction, communication and spatial organization have become amenable to molecular-level analysis, and application to patient cohorts has identified clinically relevant cellular and tissue features in several cancer types. Here, we review the use of multiplex protein imaging methods to study tumour biology, discuss ongoing attempts to combine these approaches with other forms of spatial omics, and highlight challenges in the field.
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Affiliation(s)
- Natalie de Souza
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Systems Biology, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Shan Zhao
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland
| | - Bernd Bodenmiller
- University of Zurich, Department of Quantitative Biomedicine, Zurich, Switzerland.
- ETH Zurich, Institute of Molecular Health Sciences, Zurich, Switzerland.
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3
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Fang Z, Liu T, Zheng R, A J, Yin M, Li M. stAA: adversarial graph autoencoder for spatial clustering task of spatially resolved transcriptomics. Brief Bioinform 2023; 25:bbad500. [PMID: 38189544 PMCID: PMC10772985 DOI: 10.1093/bib/bbad500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/22/2023] [Accepted: 12/11/2023] [Indexed: 01/09/2024] Open
Abstract
With the development of spatially resolved transcriptomics technologies, it is now possible to explore the gene expression profiles of single cells while preserving their spatial context. Spatial clustering plays a key role in spatial transcriptome data analysis. In the past 2 years, several graph neural network-based methods have emerged, which significantly improved the accuracy of spatial clustering. However, accurately identifying the boundaries of spatial domains remains a challenging task. In this article, we propose stAA, an adversarial variational graph autoencoder, to identify spatial domain. stAA generates cell embedding by leveraging gene expression and spatial information using graph neural networks and enforces the distribution of cell embeddings to a prior distribution through Wasserstein distance. The adversarial training process can make cell embeddings better capture spatial domain information and more robust. Moreover, stAA incorporates global graph information into cell embeddings using labels generated by pre-clustering. Our experimental results show that stAA outperforms the state-of-the-art methods and achieves better clustering results across different profiling platforms and various resolutions. We also conducted numerous biological analyses and found that stAA can identify fine-grained structures in tissues, recognize different functional subtypes within tumors and accurately identify developmental trajectories.
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Affiliation(s)
- Zhaoyu Fang
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Teng Liu
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing 404031, China
- Translational Medicine Research Center (TMRC), School of Medicine, Chongqing University, Chongqing 401331, China
| | - Ruiqing Zheng
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Jin A
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
| | - Mingzhu Yin
- Clinical Research Center (CRC), Medical Pathology Center (MPC), Cancer Early Detection and Treatment Center (CEDTC), Chongqing University Three Gorges Hospital, Chongqing University, Chongqing 404031, China
- Translational Medicine Research Center (TMRC), School of Medicine, Chongqing University, Chongqing 401331, China
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 106] [Impact Index Per Article: 106.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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5
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Luo D, Robertson S, Zhan Y, Rong R, Wang S, Jiang X, Yang S, Palmer S, Jia L, Li Q, Xiao G, Zhan X. ScopeViewer: A Browser-Based Solution for Visualizing Spatial Transcriptomics Data. bioRxiv 2023:2023.07.24.549256. [PMID: 37546786 PMCID: PMC10401999 DOI: 10.1101/2023.07.24.549256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Motivation Spatial transcriptomics (ST) enables a high-resolution interrogation of molecular characteristics within specific spatial contexts and tissue morphology. Despite its potential, visualization of ST data is a challenging task due to the complexities in handling, sharing and visualizing large image datasets together with molecular information. Results We introduce ScopeViewer, a browser-based software designed to overcome these challenges. ScopeViewer offers the following functionalities: (1) It visualizes large image data and associated annotations at various zoom levels, allowing for intricate exploration of the data; (2) It enables dual interactive viewing of the original images along with their annotations, providing a comprehensive understanding of the context; (3) It displays spatial molecular features with optimized bandwidth, ensuring a smooth user experience; and (4) It bolsters data security by circumventing data transfers. Availability ScopeViewer is available at: https://datacommons.swmed.edu/scopeviewer.
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Affiliation(s)
- Danni Luo
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Sophie Robertson
- Paul Allen School of Computer Science & Engineering, University of Washington
| | - Yuanchun Zhan
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Ruichen Rong
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Shidan Wang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Xi Jiang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Sen Yang
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Suzette Palmer
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Liwei Jia
- Department of Pathology, UT Southwestern Medical Center
| | - Qiwei Li
- Department of Mathematics Sciences, University of Texas at Dallas
| | - Guanghua Xiao
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
| | - Xiaowei Zhan
- Quantitative Biomedical Research Center, Peter O’Donnell Jr. School of Public Health, UT Southwestern Medical Center
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6
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Jiang X, Luo D, Fern Ndez E, Yang J, Li H, Jin KW, Zhan Y, Yao B, Bedi S, Xiao G, Zhan X, Li Q, Xie Y. Spatial Transcriptomics Arena (STAr): an Integrated Platform for Spatial Transcriptomics Methodology Research. bioRxiv 2023:2023.03.10.532127. [PMID: 36945650 PMCID: PMC10028992 DOI: 10.1101/2023.03.10.532127] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2023]
Abstract
The emerging field of spatially resolved transcriptomics (SRT) has revolutionized biomedical research. SRT quantifies expression levels at different spatial locations, providing a new and powerful tool to interrogate novel biological insights. An essential question in the analysis of SRT data is to identify spatially variable (SV) genes; the expression levels of such genes have spatial variation across different tissues. SV genes usually play an important role in underlying biological mechanisms and tissue heterogeneity. Currently, several computational methods have been developed to detect such genes; however, there is a lack of unbiased assessment of these approaches to guide researchers in selecting the appropriate methods for their specific biomedical applications. In addition, it is difficult for researchers to implement different existing methods for either biological study or methodology development. Furthermore, currently available public SRT datasets are scattered across different websites and preprocessed in different ways, posing additional obstacles for quantitative researchers developing computational methods for SRT data analysis. To address these challenges, we designed Spatial Transcriptomics Arena (STAr), an open platform comprising 193 curated datasets from seven technologies, seven statistical methods, and analysis results. This resource allows users to retrieve high-quality datasets, apply or develop spatial gene detection methods, as well as browse and compare spatial gene analysis results. It also enables researchers to comprehensively evaluate SRT methodology research in both simulated and real datasets. Altogether, STAr is an integrated research resource intended to promote reproducible research and accelerate rigorous methodology development, which can eventually lead to an improved understanding of biological processes and diseases. STAr can be accessed at https://lce.biohpc.swmed.edu/star/ .
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7
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Li B, Zhang F, Niu Q, Liu J, Yu Y, Wang P, Zhang S, Zhang H, Wang Z. A molecular classification of gastric cancer associated with distinct clinical outcomes and validated by an XGBoost-based prediction model. Mol Ther Nucleic Acids 2022; 31:224-240. [PMID: 36700042 PMCID: PMC9843270 DOI: 10.1016/j.omtn.2022.12.014] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/10/2022] [Accepted: 12/22/2022] [Indexed: 12/28/2022]
Abstract
Gastric cancer (GC) is a heterogeneous disease and a leading cause of cancer-related deaths. Discovering robust, clinically relevant molecular classifications is critical for guiding personalized therapies for GC. Here, we propose a refined molecular classification scheme for GC using integrated optimal algorithms and multi-omics data. Based on the important features of mRNA, microRNA, and DNA methylation data selected by the multivariate Cox regression model, three subtypes linked to distinct clinical outcomes were identified by combining similarity network fusion and consensus clustering methods. Three subtypes were validated by an extreme gradient boosting machine learning prediction model with 125 differentially expressed genes in multiple independent cohorts. The molecular characteristics of mutation signatures, characteristic gene sets, driver genes, and chemotherapy sensitivity for each subtype were also identified: subtype 1 was associated with favorable prognosis and characterized by high ARID1A and PIK3CA mutations, subtype 2 was associated with a poor prognosis and harbored high recurrent TP53 mutations, and subtype 3 was associated with high CHD1, APOA1 mutations, and a poor prognosis. The proposed three-subtype scheme achieved a better clinical prediction performance (area under the curve value = 0.71) than The Cancer Genome Atlas classification, which may provide a practical subtyping framework to improve the treatment of GC.
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Affiliation(s)
- Bing Li
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Fengbin Zhang
- Department of Gastroenterology and Hepatology, The Fourth Hospital of Hebei Medical University, Shijiazhuang 050011, China
| | - Qikai Niu
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Yanan Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Pengqian Wang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Siqi Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China
| | - Huamin Zhang
- Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China,Corresponding author: Huamin Zhang, Institute of Chinese Materia Medica, China Academy of Chinese Medical Sciences, Beijing 100700, China.
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China,Corresponding author: Zhong Wang, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China.
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8
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Abstract
Spatial proteomics is an interdisciplinary field that investigates the localization and dynamics of proteins, and it has gained extensive attention in recent years, especially the subcellular proteomics. Numerous evidence indicate that the subcellular localization of proteins is associated with various cellular processes and disease progression. Mass spectrometry (MS)-based and imaging-based experimental approaches have been developed to acquire large-scale spatial proteomic data. To allow the reliable analysis of increasingly complex spatial proteomics data, machine learning (ML) methods have been widely used in both MS-based and imaging-based spatial proteomic data analysis pipelines. Here, we comprehensively survey the applications of ML in spatial proteomics from following aspects: (1) data resources for spatial proteome are comprehensively introduced; (2) the roles of different ML algorithms in data analysis pipelines are elaborated; (3) successful applications of spatial proteomics and several analytical tools integrating ML methods are presented; (4) challenges existing in modern ML-based spatial proteomics studies are discussed. This review provides guidelines for researchers seeking to apply ML methods to analyze spatial proteomic data and can facilitate insightful understanding of cell biology as well as the future research in medical and drug discovery communities.
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Affiliation(s)
- Minjie Mou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Ziqi Pan
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Mingkun Lu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Huaicheng Sun
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yunxia Wang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Yongchao Luo
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, China
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Fonseca-Montaño MA, Blancas S, Herrera-Montalvo LA, Hidalgo-Miranda A. Cancer Genomics. Arch Med Res 2022; 53:723-731. [PMID: 36460546 DOI: 10.1016/j.arcmed.2022.11.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 12/04/2022]
Abstract
In the past decade, genomics has fundamentally changed our view of cancer biology, allowing comprehensive analyses of mutations, copy number alterations, structural variants, gene expression and DNA methylation profiles in large-scale studies across different cancer types. Efforts like The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) have fostered international collaborations for cancer genomic analyses and have generated public databases that give scientists around the world access to thoroughly curated data, which have been extensively used as a tool for further hypothesis driven research on several aspects of cancer biology. In parallel, some of these findings are being translated into specific clinical benefits for cancer patients. In this review, we provide a brief historical description of the evolution of international public cancer genome projects and related databases, as well as we discuss about their impact on general cancer research.
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Affiliation(s)
- Marco A Fonseca-Montaño
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México
| | - Susana Blancas
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Cátedras Consejo Nacional de Ciencia y Tecnología, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México
| | | | - Alfredo Hidalgo-Miranda
- Instituto Nacional de Medicina Genómica, Ciudad de México, México; Laboratorio de Genómica del Cáncer, Instituto Nacional de Medicina Genómica, Ciudad de México, México.
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10
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Shao X, Li C, Yang H, Lu X, Liao J, Qian J, Wang K, Cheng J, Yang P, Chen H, Xu X, Fan X. Knowledge-graph-based cell-cell communication inference for spatially resolved transcriptomic data with SpaTalk. Nat Commun 2022; 13:4429. [PMID: 35908020 DOI: 10.1038/s41467-022-32111-8] [Citation(s) in RCA: 30] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 07/18/2022] [Indexed: 12/19/2022] Open
Abstract
Spatially resolved transcriptomics provides genetic information in space toward elucidation of the spatial architecture in intact organs and the spatially resolved cell-cell communications mediating tissue homeostasis, development, and disease. To facilitate inference of spatially resolved cell-cell communications, we here present SpaTalk, which relies on a graph network and knowledge graph to model and score the ligand-receptor-target signaling network between spatially proximal cells by dissecting cell-type composition through a non-negative linear model and spatial mapping between single-cell transcriptomic and spatially resolved transcriptomic data. The benchmarked performance of SpaTalk on public single-cell spatial transcriptomic datasets is superior to that of existing inference methods. Then we apply SpaTalk to STARmap, Slide-seq, and 10X Visium data, revealing the in-depth communicative mechanisms underlying normal and disease tissues with spatial structure. SpaTalk can uncover spatially resolved cell-cell communications for single-cell and spot-based spatially resolved transcriptomic data universally, providing valuable insights into spatial inter-cellular tissue dynamics. Cell-cell communication is a vital feature involving numerous biological processes. Here, the authors develop SpaTalk, a cell-cell communication inference method using knowledge graph for spatially resolved transcriptomic data, providing valuable insights into spatial intercellular tissue dynamics.
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11
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Hoft SG, Pherson MD, DiPaolo RJ. Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing. Front Immunol 2022; 13:902017. [PMID: 35757757 PMCID: PMC9231461 DOI: 10.3389/fimmu.2022.902017] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/27/2022] [Indexed: 12/17/2022] Open
Abstract
Single-cell RNA sequencing (scRNAseq) technology is still relatively new in the field of gastric cancer immunology but gaining significant traction. This technology now provides unprecedented insights into the intratumoral and intertumoral heterogeneities at the immunological, cellular, and molecular levels. Within the last few years, a volume of publications reported the usefulness of scRNAseq technology in identifying thus far elusive immunological mechanisms that may promote and impede gastric cancer development. These studies analyzed datasets generated from primary human gastric cancer tissues, metastatic ascites fluid from gastric cancer patients, and laboratory-generated data from in vitro and in vivo models of gastric diseases. In this review, we overview the exciting findings from scRNAseq datasets that uncovered the role of critical immune cells, including T cells, B cells, myeloid cells, mast cells, ILC2s, and other inflammatory stromal cells, like fibroblasts and endothelial cells. In addition, we also provide a synopsis of the initial scRNAseq findings on the interesting epithelial cell responses to inflammation. In summary, these new studies have implicated roles for T and B cells and subsets like NKT cells in tumor development and progression. The current studies identified diverse subsets of macrophages and mast cells in the tumor microenvironment, however, additional studies to determine their roles in promoting cancer growth are needed. Some groups specifically focus on the less prevalent ILC2 cell type that may contribute to early cancer development. ScRNAseq analysis also reveals that stromal cells, e.g., fibroblasts and endothelial cells, regulate inflammation and promote metastasis, making them key targets for future investigations. While evaluating the outcomes, we also highlight the gaps in the current findings and provide an assessment of what this technology holds for gastric cancer research in the coming years. With scRNAseq technology expanding rapidly, we stress the need for periodic review of the findings and assess the available scRNAseq analytical tools to guide future work on immunological mechanisms of gastric carcinogenesis.
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Affiliation(s)
- Stella G Hoft
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Michelle D Pherson
- Department of Biochemistry and Molecular Biology, Saint Louis University School of Medicine, St. Louis, MO, United States.,Genomics Core Facility, Saint Louis University School of Medicine, St. Louis, MO, United States
| | - Richard J DiPaolo
- Department of Molecular Microbiology and Immunology, Saint Louis University School of Medicine, St. Louis, MO, United States
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12
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Giannikopoulos P, Parham DM. Pediatric Sarcomas: The Next Generation of Molecular Studies. Cancers (Basel) 2022; 14:2515. [PMID: 35626119 PMCID: PMC9139929 DOI: 10.3390/cancers14102515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Revised: 05/13/2022] [Accepted: 05/13/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary There has been an incredible amount of discovery in pediatric sarcomas, but much remains to be accomplished. Clinical challenges include diagnostic heterogeneity and the poor outcome of patients with high risk, metastatic, and relapsed disease. The emergence of single cell sequencing has allowed the ability to document tumor cell heterogeneity in amazing detail, but it does not allow the ability to visualize spatial orientation. This problem has been solved by spatial multi-omics, which can be used to map tumors and visualize the distribution of critical transcripts, mutations, and proteins. However, these tools only offer observational data. High-throughput functional genomics provides a powerful way to highlight oncogenic drivers and potential therapy opportunities. Research has been hamstrung by a need for annotated specimens, particularly in post-therapy, relapsed, and metastatic disease, and initial biopsies offer only limited data opportunities. Data complexity, variability, and inconsistency present problems best approached with AI/machine learning. We stand on the threshold of a revolution in cancer cell biology that has the potential for translation into more effective and more directed therapies, particularly for previously recalcitrant diseases. Abstract Pediatric sarcomas constitute one of the largest groups of childhood cancers, following hematopoietic, neural, and renal lesions. Partly because of their diversity, they continue to offer challenges in diagnosis and treatment. In spite of the diagnostic, nosologic, and therapeutic gains made with genetic technology, newer means for investigation are needed. This article reviews emerging technology being used to study human neoplasia and how these methods might be applicable to pediatric sarcomas. Methods reviewed include single cell RNA sequencing (scRNAseq), spatial multi-omics, high-throughput functional genomics, and clustered regularly interspersed short palindromic sequence-Cas9 (CRISPR-Cas9) technology. In spite of these advances, the field continues to be challenged by a dearth of properly annotated materials, particularly from recurrences and metastases and pre- and post-treatment samples.
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Jiang Y, Hao S, Chen X, Cheng M, Xu J, Li C, Zheng H, Volpe G, Chen A, Liao S, Liu C, Liu L, Xu X. Spatial Transcriptome Uncovers the Mouse Lung Architectures and Functions. Front Genet 2022; 13:858808. [PMID: 35391793 PMCID: PMC8982079 DOI: 10.3389/fgene.2022.858808] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2022] [Accepted: 02/21/2022] [Indexed: 11/21/2022] Open
Affiliation(s)
- Yujia Jiang
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,BGI-Shenzhen, Shenzhen, China
| | - Shijie Hao
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Xi Chen
- BGI-Shenzhen, Shenzhen, China
| | - Mengnan Cheng
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | - Jiangshan Xu
- BGI-Shenzhen, Shenzhen, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China
| | | | - Huiwen Zheng
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,BGI-Shenzhen, Shenzhen, China
| | - Giacomo Volpe
- Hematology and Cell Therapy Unit, IRCCS Istituto Tumori "Giovanni Paolo II", Bari, Italy
| | - Ao Chen
- BGI-Shenzhen, Shenzhen, China
| | | | | | | | - Xun Xu
- BGI College and Henan Institute of Medical and Pharmaceutical Sciences, Zhengzhou University, Zhengzhou, China.,BGI-Shenzhen, Shenzhen, China
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Ospina OE, Wilson CM, Soupir AC, Berglund A, Smalley I, Tsai KY, Fridley BL. spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics. Bioinformatics 2022; 38:2645-2647. [PMID: 35258565 PMCID: PMC9890305 DOI: 10.1093/bioinformatics/btac145] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 02/04/2022] [Accepted: 03/07/2022] [Indexed: 02/05/2023] Open
Abstract
SUMMARY Spatially resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics that can be compared against clinical information, spot-level cell deconvolution and spatially informed clustering, all using a new data object to store data and resulting analyses simultaneously. AVAILABILITY AND IMPLEMENTATION The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE. A script to reproduce the analyses in this manuscript is available in Supplementary information. The Thrane study data included in spatialGE was made available from the public available from the website https://www.spatialresearch.org/resources-published-datasets/doi-10-1158-0008-5472-can-18-0747/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Christopher M Wilson
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Alex C Soupir
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Anders Berglund
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Inna Smalley
- Department of Tumor Biology, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Kenneth Y Tsai
- Department of Anatomic Pathology, Moffitt Cancer Center, Tampa, FL 33612, USA
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Abstract
Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.
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Levy JJ, Bobak CA, Nasir-Moin M, Veziroglu EM, Palisoul SM, Barney RE, Salas LA, Christensen BC, Tsongalis GJ, Vaickus LJ. Mixed Effects Machine Learning Models for Colon Cancer Metastasis Prediction using Spatially Localized Immuno-Oncology Markers. Pac Symp Biocomput 2022; 27:175-186. [PMID: 34890147 PMCID: PMC8669762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Spatially resolved characterization of the transcriptome and proteome promises to provide further clarity on cancer pathogenesis and etiology, which may inform future clinical practice through classifier development for clinical outcomes. However, batch effects may potentially obscure the ability of machine learning methods to derive complex associations within spatial omics data. Profiling thirty-five stage three colon cancer patients using the GeoMX Digital Spatial Profiler, we found that mixed-effects machine learning (MEML) methods† may provide utility for overcoming significant batch effects to communicate key and complex disease associations from spatial information. These results point to further exploration and application of MEML methods within the spatial omics algorithm development life cycle for clinical deployment.
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Affiliation(s)
- Joshua J Levy
- Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, NH 03756, USA,
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Chen Y, Qian W, Lin L, Cai L, Yin K, Jiang S, Song J, Han RPS, Yang C. Mapping Gene Expression in the Spatial Dimension. Small Methods 2021; 5:e2100722. [PMID: 34927963 DOI: 10.1002/smtd.202100722] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/25/2021] [Indexed: 06/14/2023]
Abstract
The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.
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Affiliation(s)
- Yingwen Chen
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Weizhou Qian
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Li Lin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Linfeng Cai
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Kun Yin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Shaowei Jiang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Ray P S Han
- Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, 33004, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
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Lanzer P, Hannan FM, Lanzer JD, Janzen J, Raggi P, Furniss D, Schuchardt M, Thakker R, Fok PW, Saez-Rodriguez J, Millan A, Sato Y, Ferraresi R, Virmani R, St Hilaire C. Medial Arterial Calcification: JACC State-of-the-Art Review. J Am Coll Cardiol 2021; 78:1145-1165. [PMID: 34503684 PMCID: PMC8439554 DOI: 10.1016/j.jacc.2021.06.049] [Citation(s) in RCA: 87] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 06/23/2021] [Accepted: 06/28/2021] [Indexed: 01/07/2023]
Abstract
Medial arterial calcification (MAC) is a chronic systemic vascular disorder distinct from atherosclerosis that is frequently but not always associated with diabetes mellitus, chronic kidney disease, and aging. MAC is also a part of more complex phenotypes in numerous less common diseases. The hallmarks of MAC include disseminated and progressive precipitation of calcium phosphate within the medial layer, a prolonged and clinically silent course, and compromise of hemodynamics associated with chronic limb-threatening ischemia. MAC increases the risk of complications during vascular interventions and mitigates their outcomes. With the exception of rare monogenetic defects affecting adenosine triphosphate metabolism, MAC pathogenesis remains unknown, and causal therapy is not available. Implementation of genetics and omics-based approaches in research recognizing the critical importance of calcium phosphate thermodynamics holds promise to unravel MAC molecular pathogenesis and to provide guidance for therapy. The current state of knowledge concerning MAC is reviewed, and future perspectives are outlined.
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Affiliation(s)
- Peter Lanzer
- Middle German Heart Center-Bitterfeld, Bitterfeld-Wolfen Health Care Center, Bitterfeld, Germany.
| | - Fadil M Hannan
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, United Kingdom
| | - Jan D Lanzer
- Institute for Computational Biomedicine, Bioquant, Faculty of Medicine, Heidelberg University, Heidelberg, Germany; Department of Internal Medicine II, Heidelberg University Hospital, Heidelberg, Germany; Faculty of Biosciences, Heidelberg University, Heidelberg, Heidelberg, Germany
| | | | - Paolo Raggi
- Division of Cardiology, Department of Medicine, University of Alberta, Edmonton, Alberta, Canada
| | - Dominic Furniss
- Botnar Research Centre, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, United Kingdom
| | - Mirjam Schuchardt
- Department of Nephrology and Medical Intensive Care, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität and Humboldt Universität Berlin, Campus Benjamin Franklin, Berlin, Germany
| | - Rajesh Thakker
- Academic Endocrine Unit, Radcliffe Department of Medicine, University of Oxford, Oxford, United Kingdom
| | - Pak-Wing Fok
- Department of Mathematical Sciences, University of Delaware, Newark, Delaware, USA
| | - Julio Saez-Rodriguez
- Institute for Computational Biomedicine, Bioquant, Faculty of Medicine, Heidelberg University, Heidelberg, Germany
| | - Angel Millan
- Institute of Materials Science, University of Zaragoza, Zaragoza, Spain
| | - Yu Sato
- CVPath Institute, Gaithersburg, Maryland, USA
| | | | | | - Cynthia St Hilaire
- Division of Cardiology, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; Pittsburgh Heart, Lung, and Blood Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, Pennsylvania, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Li Q, Zhang M, Xie Y, Xiao G. Bayesian Modeling of Spatial Molecular Profiling Data via Gaussian Process. Bioinformatics 2021; 37:4129-4136. [PMID: 34146105 PMCID: PMC9502169 DOI: 10.1093/bioinformatics/btab455] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 05/29/2021] [Accepted: 06/16/2021] [Indexed: 12/13/2022] Open
Abstract
MOTIVATION The location, timing, and abundance of gene expression (both mRNA and proteins) within a tissue define the molecular mechanisms of cell functions. Recent technology breakthroughs in spatial molecular profiling, including imaging-based technologies and sequencing-based technologies, have enabled the comprehensive molecular characterization of single cells while preserving their spatial and morphological contexts. This new bioinformatics scenario calls for effective and robust computational methods to identify genes with spatial patterns. RESULTS We represent a novel Bayesian hierarchical model to analyze spatial transcriptomics data, with several unique characteristics. It models the zero-inflated and over-dispersed counts by deploying a zero-inflated negative binomial model that greatly increases model stability and robustness. Besides, the Bayesian inference framework allows us to borrow strength in parameter estimation in a de novo fashion. As a result, the proposed model shows competitive performances in accuracy and robustness over existing methods in both simulation studies and two real data applications. AVAILABILITY The related R/C ++ source code is available at https://github.com/Minzhe/BOOST-GP. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Qiwei Li
- Department of Mathematical Sciences, The University of Texas at Dallas, Richardson, TX 75080, USA
| | - Minzhe Zhang
- Quantitative Biology Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Yang Xie
- Quantitative Biology Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Guanghua Xiao
- Quantitative Biology Research Center, Department of Population and Data Sciences, The University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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Yu J, Luo X. Recovering Spatially-Varying Cell-Specific Gene Co-expression Networks for Single-Cell Spatial Expression Data. Front Genet 2021; 12:656637. [PMID: 33981332 PMCID: PMC8107398 DOI: 10.3389/fgene.2021.656637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Accepted: 03/18/2021] [Indexed: 11/23/2022] Open
Abstract
Recent advances in single-cell technologies enable spatial expression profiling at the cell level, making it possible to elucidate spatial changes of cell-specific genomic features. The gene co-expression network is an important feature that encodes the gene-gene marginal dependence structure and allows for the functional annotation of highly connected genes. In this paper, we design a simple and computationally efficient two-step algorithm to recover spatially-varying cell-specific gene co-expression networks for single-cell spatial expression data. The algorithm first estimates the gene expression covariance matrix for each cell type and then leverages the spatial locations of cells to construct cell-specific networks. The second step uses expression covariance matrices estimated in step one and label information from neighboring cells as an empirical prior to obtain thresholded Bayesian posterior estimates. After completing estimates for each cell, this algorithm can further predict or interpolate gene co-expression networks on tissue positions where cells are not captured. In the simulation study, the comparison against the traditional cell-type-specific network algorithms and the cell-specific network method but without incorporating spatial information highlights the advantages of the proposed algorithm in estimation accuracy. We also applied our algorithm to real-world datasets and found some meaningful biological results. The accompanied software is available on https://github.com/jingeyu/CSSN.
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Affiliation(s)
- Jinge Yu
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Xiangyu Luo
- Institute of Statistics and Big Data, Renmin University of China, Beijing, China
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