1
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Zhang C, Wang L, Shi Q. Computational modeling for deciphering tissue microenvironment heterogeneity from spatially resolved transcriptomics. Comput Struct Biotechnol J 2024; 23:2109-2115. [PMID: 38800634 PMCID: PMC11126885 DOI: 10.1016/j.csbj.2024.05.028] [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: 03/30/2024] [Revised: 05/15/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024] Open
Abstract
Spatial transcriptomics techniques, while measuring gene expression, retain spatial location information, aiding in situ studies of organismal tissue architecture and the progression of pathological processes. These techniques generate vast amounts of omics data, necessitating the development of computational methods to reveal the underlying tissue microenvironment heterogeneity. The main directions in spatial transcriptomics data analysis are spatial domain detection and spatial deconvolution, which can identify spatial functional regions and parse the distribution of cell types in spatial transcriptomics data by integrating single-cell transcriptomics data. In these two research directions, many computational methods have been successively proposed. This article will categorize them into three types: machine learning-based methods, probabilistic models-based methods, and deep learning-based methods. It will list and discuss the representative algorithms of each type along with their advantages and disadvantages and describe the datasets and evaluation metrics used to assess these computational methods, facilitating researchers in selecting suitable computational methods according to their research needs. Finally, combining the latest technological developments and the advantages and disadvantages of current algorithms, this article will look forward to the future directions of computational method development.
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Affiliation(s)
- Chuanchao Zhang
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, Hangzhou 310024; University of Chinese Academy of Sciences, China
| | - Lequn Wang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, Shanghai 200031, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Qianqian Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China
- Hubei Engineering Technology Research Center of Agricultural Big Data, Huazhong Agricultural University, Wuhan 430070, Hubei, China
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2
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Swain AK, Pandit V, Sharma J, Yadav P. SpatialPrompt: spatially aware scalable and accurate tool for spot deconvolution and domain identification in spatial transcriptomics. Commun Biol 2024; 7:639. [PMID: 38796505 PMCID: PMC11127982 DOI: 10.1038/s42003-024-06349-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Accepted: 05/17/2024] [Indexed: 05/28/2024] Open
Abstract
Efficiently mapping of cell types in situ remains a major challenge in spatial transcriptomics. Most spot deconvolution tools ignore spatial coordinate information and perform extremely slow on large datasets. Here, we introduce SpatialPrompt, a spatially aware and scalable tool for spot deconvolution and domain identification. SpatialPrompt integrates gene expression, spatial location, and single-cell RNA sequencing (scRNA-seq) dataset as reference to accurately infer cell-type proportions of spatial spots. SpatialPrompt uses non-negative ridge regression and graph neural network to efficiently capture local microenvironment information. Our extensive benchmarking analysis on Visium, Slide-seq, and MERFISH datasets demonstrated superior performance of SpatialPrompt over 15 existing tools. On mouse hippocampus dataset, SpatialPrompt achieves spot deconvolution and domain identification within 2 minutes for 50,000 spots. Overall, domain identification using SpatialPrompt was 44 to 150 times faster than existing methods. We build a database housing 40 plus curated scRNA-seq datasets for seamless integration with SpatialPrompt for spot deconvolution.
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Affiliation(s)
- Asish Kumar Swain
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India
| | - Vrushali Pandit
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India
| | - Jyoti Sharma
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India
| | - Pankaj Yadav
- Department of Bioscience & Bioengineering, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India.
- School of Artificial Intelligence and Data Science, Indian Institute of Technology, Jodhpur, Rajasthan, 342030, India.
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3
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Huuki-Myers LA, Spangler A, Eagles NJ, Montgomery KD, Kwon SH, Guo B, Grant-Peters M, Divecha HR, Tippani M, Sriworarat C, Nguyen AB, Ravichandran P, Tran MN, Seyedian A, Hyde TM, Kleinman JE, Battle A, Page SC, Ryten M, Hicks SC, Martinowich K, Collado-Torres L, Maynard KR. A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 2024; 384:eadh1938. [PMID: 38781370 DOI: 10.1126/science.adh1938] [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: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 05/25/2024]
Abstract
The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
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Affiliation(s)
- Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Kelsey D Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Heena R Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Chaichontat Sriworarat
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Annie B Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Prashanthi Ravichandran
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
| | - Matthew N Tran
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Arta Seyedian
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Stephanie C Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London WC1N 1EH, UK
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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4
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Li CC, Liu M, Lee HP, Wu W, Ma L. Heterogeneity in Liver Cancer Immune Microenvironment: Emerging Single-Cell and Spatial Perspectives. Semin Liver Dis 2024. [PMID: 38788780 DOI: 10.1055/s-0044-1787152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/26/2024]
Abstract
Primary liver cancer is a solid malignancy with a high mortality rate. The success of immunotherapy has shown great promise in improving patient care and highlights a crucial need to understand the complexity of the liver tumor immune microenvironment (TIME). Recent advances in single-cell and spatial omics technologies, coupled with the development of systems biology approaches, are rapidly transforming the landscape of tumor immunology. Here we review the cellular landscape of liver TIME from single-cell and spatial perspectives. We also discuss the cellular interaction networks within the tumor cell community in regulating immune responses. We further highlight the challenges and opportunities with implications for biomarker discovery, patient stratification, and combination immunotherapies.
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Affiliation(s)
- Caiyi Cherry Li
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Meng Liu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Hsin-Pei Lee
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Wenqi Wu
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
| | - Lichun Ma
- Cancer Data Science Laboratory, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
- Liver Cancer Program, Center for Cancer Research, National Cancer Institute, Bethesda, Maryland
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5
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Sang-aram C, Browaeys R, Seurinck R, Saeys Y. Spotless, a reproducible pipeline for benchmarking cell type deconvolution in spatial transcriptomics. eLife 2024; 12:RP88431. [PMID: 38787371 PMCID: PMC11126312 DOI: 10.7554/elife.88431] [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] [Indexed: 05/25/2024] Open
Abstract
Spatial transcriptomics (ST) technologies allow the profiling of the transcriptome of cells while keeping their spatial context. Since most commercial untargeted ST technologies do not yet operate at single-cell resolution, computational methods such as deconvolution are often used to infer the cell type composition of each sequenced spot. We benchmarked 11 deconvolution methods using 63 silver standards, 3 gold standards, and 2 case studies on liver and melanoma tissues. We developed a simulation engine called synthspot to generate silver standards from single-cell RNA-sequencing data, while gold standards are generated by pooling single cells from targeted ST data. We evaluated methods based on their performance, stability across different reference datasets, and scalability. We found that cell2location and RCTD are the top-performing methods, but surprisingly, a simple regression model outperforms almost half of the dedicated spatial deconvolution methods. Furthermore, we observe that the performance of all methods significantly decreased in datasets with highly abundant or rare cell types. Our results are reproducible in a Nextflow pipeline, which also allows users to generate synthetic data, run deconvolution methods and optionally benchmark them on their dataset (https://github.com/saeyslab/spotless-benchmark).
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Affiliation(s)
- Chananchida Sang-aram
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Robin Browaeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Ruth Seurinck
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine, VIB Center for Inflammation ResearchGhentBelgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent UniversityGhentBelgium
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6
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Tao Q, Xu Y, He Y, Luo T, Li X, Han L. Benchmarking mapping algorithms for cell-type annotating in mouse brain by integrating single-nucleus RNA-seq and Stereo-seq data. Brief Bioinform 2024; 25:bbae250. [PMID: 38796691 PMCID: PMC11128029 DOI: 10.1093/bib/bbae250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Revised: 04/17/2024] [Accepted: 05/08/2024] [Indexed: 05/28/2024] Open
Abstract
Limited gene capture efficiency and spot size of spatial transcriptome (ST) data pose significant challenges in cell-type characterization. The heterogeneity and complexity of cell composition in the mammalian brain make it more challenging to accurately annotate ST data from brain. Many algorithms attempt to characterize subtypes of neuron by integrating ST data with single-nucleus RNA sequencing (snRNA-seq) or single-cell RNA sequencing. However, assessing the accuracy of these algorithms on Stereo-seq ST data remains unresolved. Here, we benchmarked 9 mapping algorithms using 10 ST datasets from four mouse brain regions in two different resolutions and 24 pseudo-ST datasets from snRNA-seq. Both actual ST data and pseudo-ST data were mapped using snRNA-seq datasets from the corresponding brain regions as reference data. After comparing the performance across different areas and resolutions of the mouse brain, we have reached the conclusion that both robust cell-type decomposition and SpatialDWLS demonstrated superior robustness and accuracy in cell-type annotation. Testing with publicly available snRNA-seq data from another sequencing platform in the cortex region further validated our conclusions. Altogether, we developed a workflow for assessing suitability of mapping algorithm that fits for ST datasets, which can improve the efficiency and accuracy of spatial data annotation.
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Affiliation(s)
- Quyuan Tao
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Hangzhou 310012, China
| | - Yiheng Xu
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
| | - Youzhe He
- College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- BGI Research, Hangzhou 310012, China
| | - Ting Luo
- BGI Research, Hangzhou 310012, China
- BGI Research, Shenzhen 518103, China
| | - Xiaoming Li
- Department of Neurobiology and Department of Neurology of Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310058, China
- NHC and CAMS Key Laboratory of Medical Neurobiology, MOE Frontier Center of Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou 310058, China
- Research Units for Emotion and Emotion disorders, Chinese Academy of Medical Sciences, Beijing 100730, China
| | - Lei Han
- BGI Research, Hangzhou 310012, China
- BGI Research, Shenzhen 518103, China
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7
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Li S, Gai K, Dong K, Zhang Y, Zhang S. High-density generation of spatial transcriptomics with STAGE. Nucleic Acids Res 2024; 52:4843-4856. [PMID: 38647109 PMCID: PMC11109953 DOI: 10.1093/nar/gkae294] [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: 01/15/2024] [Revised: 03/06/2024] [Accepted: 04/06/2024] [Indexed: 04/25/2024] Open
Abstract
Spatial transcriptome technologies have enabled the measurement of gene expression while maintaining spatial location information for deciphering the spatial heterogeneity of biological tissues. However, they were heavily limited by the sparse spatial resolution and low data quality. To this end, we develop a spatial location-supervised auto-encoder generator STAGE for generating high-density spatial transcriptomics (ST). STAGE takes advantage of the customized supervised auto-encoder to learn continuous patterns of gene expression in space and generate high-resolution expressions for given spatial coordinates. STAGE can improve the low quality of spatial transcriptome data and smooth the generated manifold of gene expression through the de-noising function on the latent codes of the auto-encoder. Applications to four ST datasets, STAGE has shown better recovery performance for down-sampled data than existing methods, revealed significant tissue structure specificity, and enabled robust identification of spatially informative genes and patterns. In addition, STAGE can be extended to three-dimensional (3D) stacked ST data for generating gene expression at any position between consecutive sections for shaping high-density 3D ST configuration.
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Affiliation(s)
- Shang Li
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kuo Gai
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kangning Dong
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yiyang Zhang
- School of Software, Yunnan University, Kunming 650091, China
| | - Shihua Zhang
- NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
- School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou 310024, China
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8
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Schmidt M, Avagyan S, Reiche K, Binder H, Loeffler-Wirth H. A Spatial Transcriptomics Browser for Discovering Gene Expression Landscapes across Microscopic Tissue Sections. Curr Issues Mol Biol 2024; 46:4701-4720. [PMID: 38785552 PMCID: PMC11119626 DOI: 10.3390/cimb46050284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/30/2024] [Accepted: 05/03/2024] [Indexed: 05/25/2024] Open
Abstract
A crucial feature of life is its spatial organization and compartmentalization on the molecular, cellular, and tissue levels. Spatial transcriptomics (ST) technology has opened a new chapter of the sequencing revolution, emerging rapidly with transformative effects across biology. This technique produces extensive and complex sequencing data, raising the need for computational methods for their comprehensive analysis and interpretation. We developed the ST browser web tool for the interactive discovery of ST images, focusing on different functional aspects such as single gene expression, the expression of functional gene sets, as well as the inspection of the spatial patterns of cell-cell interactions. As a unique feature, our tool applies self-organizing map (SOM) machine learning to the ST data. Our SOM data portrayal method generates individual gene expression landscapes for each spot in the ST image, enabling its downstream analysis with high resolution. The performance of the spatial browser is demonstrated by disentangling the intra-tumoral heterogeneity of melanoma and the microarchitecture of the mouse brain. The integration of machine-learning-based SOM portrayal into an interactive ST analysis environment opens novel perspectives for the comprehensive knowledge mining of the organization and interactions of cellular ecosystems.
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Affiliation(s)
- Maria Schmidt
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
| | - Susanna Avagyan
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Kristin Reiche
- Department of Diagnostics, Fraunhofer Institute for Cell Therapy and Immunology (IZI), Perlickstrasse 1, 04103 Leipzig, Germany
- Institute for Clinical Immunology, University Hospital of Leipzig, 04103 Leipzig, Germany
| | - Hans Binder
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
- Armenian Bioinformatics Institute, 3/6 Nelson Stepanyan Str., Yerevan 0062, Armenia
| | - Henry Loeffler-Wirth
- Interdisciplinary Centre for Bioinformatics (IZBI), Leipzig University, Härtelstr. 16-18, 04107 Leipzig, Germany; (M.S.); (H.B.)
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9
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Yan W, Sharif R, Sohail H, Zhu Y, Chen X, Xu X. Surviving a Double-Edged Sword: Response of Horticultural Crops to Multiple Abiotic Stressors. Int J Mol Sci 2024; 25:5199. [PMID: 38791235 PMCID: PMC11121501 DOI: 10.3390/ijms25105199] [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: 03/31/2024] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Climate change-induced weather events, such as extreme temperatures, prolonged drought spells, or flooding, pose an enormous risk to crop productivity. Studies on the implications of multiple stresses may vary from those on a single stress. Usually, these stresses coincide, amplifying the extent of collateral damage and contributing to significant financial losses. The breadth of investigations focusing on the response of horticultural crops to a single abiotic stress is immense. However, the tolerance mechanisms of horticultural crops to multiple abiotic stresses remain poorly understood. In this review, we described the most prevalent types of abiotic stresses that occur simultaneously and discussed them in in-depth detail regarding the physiological and molecular responses of horticultural crops. In particular, we discussed the transcriptional, posttranscriptional, and metabolic responses of horticultural crops to multiple abiotic stresses. Strategies to breed multi-stress-resilient lines have been presented. Our manuscript presents an interesting amount of proposed knowledge that could be valuable in generating resilient genotypes for multiple stressors.
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Affiliation(s)
- Wenjing Yan
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Rahat Sharif
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Hamza Sohail
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Yu Zhu
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Xuehao Chen
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
| | - Xuewen Xu
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
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10
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Fujiwara N, Kimura G, Nakagawa H. Emerging Roles of Spatial Transcriptomics in Liver Research. Semin Liver Dis 2024. [PMID: 38574750 DOI: 10.1055/a-2299-7880] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
Spatial transcriptomics, leveraging sequencing- and imaging-based techniques, has emerged as a groundbreaking technology for mapping gene expression within the complex architectures of tissues. This approach provides an in-depth understanding of cellular and molecular dynamics across various states of healthy and diseased livers. Through the integration of sophisticated bioinformatics strategies, it enables detailed exploration of cellular heterogeneity, transitions in cell states, and intricate cell-cell interactions with remarkable precision. In liver research, spatial transcriptomics has been particularly revelatory, identifying distinct zonated functions of hepatocytes that are crucial for understanding the metabolic and detoxification processes of the liver. Moreover, this technology has unveiled new insights into the pathogenesis of liver diseases, such as the role of lipid-associated macrophages in steatosis and endothelial cell signals in liver regeneration and repair. In the domain of liver cancer, spatial transcriptomics has proven instrumental in delineating intratumor heterogeneity, identifying supportive microenvironmental niches and revealing the complex interplay between tumor cells and the immune system as well as susceptibility to immune checkpoint inhibitors. In conclusion, spatial transcriptomics represents a significant advance in hepatology, promising to enhance our understanding and treatment of liver diseases.
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Affiliation(s)
- Naoto Fujiwara
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Genki Kimura
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
| | - Hayato Nakagawa
- Department of Gastroenterology and Hepatology, Graduate School of Medicine, Mie University, Mie, Japan
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11
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Nelson ED, Tippani M, Ramnauth AD, Divecha HR, Miller RA, Eagles NJ, Pattie EA, Kwon SH, Bach SV, Kaipa UM, Yao J, Kleinman JE, Collado-Torres L, Han S, Maynard KR, Hyde TM, Martinowich K, Page SC, Hicks SC. An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590643. [PMID: 38712198 PMCID: PMC11071618 DOI: 10.1101/2024.04.26.590643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. With this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.
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12
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Daly AC, Cambuli F, Äijö T, Lötstedt B, Marjanovic N, Kuksenko O, Smith-Erb M, Fernandez S, Domovic D, Van Wittenberghe N, Drokhlyansky E, Griffin GK, Phatnani H, Bonneau R, Regev A, Vickovic S. Tissue and cellular spatiotemporal dynamics in colon aging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.22.590125. [PMID: 38712088 PMCID: PMC11071407 DOI: 10.1101/2024.04.22.590125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Tissue structure and molecular circuitry in the colon can be profoundly impacted by systemic age-related effects, but many of the underlying molecular cues remain unclear. Here, we built a cellular and spatial atlas of the colon across three anatomical regions and 11 age groups, encompassing ~1,500 mouse gut tissues profiled by spatial transcriptomics and ~400,000 single nucleus RNA-seq profiles. We developed a new computational framework, cSplotch, which learns a hierarchical Bayesian model of spatially resolved cellular expression associated with age, tissue region, and sex, by leveraging histological features to share information across tissue samples and data modalities. Using this model, we identified cellular and molecular gradients along the adult colonic tract and across the main crypt axis, and multicellular programs associated with aging in the large intestine. Our multi-modal framework for the investigation of cell and tissue organization can aid in the understanding of cellular roles in tissue-level pathology.
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Affiliation(s)
- Aidan C. Daly
- New York Genome Center, New York, NY, USA
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | | | - Tarmo Äijö
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
| | - Britta Lötstedt
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden
| | - Nemanja Marjanovic
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Olena Kuksenko
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | | | | | | | | | - Eugene Drokhlyansky
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gabriel K Griffin
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, New York, NY, USA
| | - Richard Bonneau
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Center for Data Science, New York University, New York, NY, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Aviv Regev
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Genentech, 1 DNA Way, South San Francisco, CA, USA
| | - Sanja Vickovic
- New York Genome Center, New York, NY, USA
- Klarman Cell Observatory Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biomedical Engineering and Herbert Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Science for Life Laboratory, Department of Immunology, Genetics and Pathology, Beijer Laboratory for Gene and Neuro Research, Uppsala University, Uppsala, Sweden
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13
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Stouffer KM, Trouvé A, Younes L, Kunst M, Ng L, Zeng H, Anant M, Fan J, Kim Y, Chen X, Rue M, Miller MI. Cross-modality mapping using image varifolds to align tissue-scale atlases to molecular-scale measures with application to 2D brain sections. Nat Commun 2024; 15:3530. [PMID: 38664422 PMCID: PMC11045777 DOI: 10.1038/s41467-024-47883-4] [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: 04/13/2023] [Accepted: 04/10/2024] [Indexed: 04/28/2024] Open
Abstract
This paper explicates a solution to building correspondences between molecular-scale transcriptomics and tissue-scale atlases. This problem arises in atlas construction and cross-specimen/technology alignment where specimens per emerging technology remain sparse and conventional image representations cannot efficiently model the high dimensions from subcellular detection of thousands of genes. We address these challenges by representing spatial transcriptomics data as generalized functions encoding position and high-dimensional feature (gene, cell type) identity. We map onto low-dimensional atlas ontologies by modeling regions as homogeneous random fields with unknown transcriptomic feature distribution. We solve simultaneously for the minimizing geodesic diffeomorphism of coordinates through LDDMM and for these latent feature densities. We map tissue-scale mouse brain atlases to gene-based and cell-based transcriptomics data from MERFISH and BARseq technologies and to histopathology and cross-species atlases to illustrate integration of diverse molecular and cellular datasets into a single coordinate system as a means of comparison and further atlas construction.
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Affiliation(s)
- Kaitlin M Stouffer
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France.
| | - Alain Trouvé
- Centre Borelli, ENS Paris-Saclay, Gif-sur-yvette, France
| | - Laurent Younes
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, MD, USA
| | | | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Manjari Anant
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Jean Fan
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, Penn State University, College of Medicine, State College, PA, USA
| | - Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Mara Rue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Michael I Miller
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
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14
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Kukanja P, Langseth CM, Rubio Rodríguez-Kirby LA, Agirre E, Zheng C, Raman A, Yokota C, Avenel C, Tiklová K, Guerreiro-Cacais AO, Olsson T, Hilscher MM, Nilsson M, Castelo-Branco G. Cellular architecture of evolving neuroinflammatory lesions and multiple sclerosis pathology. Cell 2024; 187:1990-2009.e19. [PMID: 38513664 DOI: 10.1016/j.cell.2024.02.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Revised: 12/13/2023] [Accepted: 02/23/2024] [Indexed: 03/23/2024]
Abstract
Multiple sclerosis (MS) is a neurological disease characterized by multifocal lesions and smoldering pathology. Although single-cell analyses provided insights into cytopathology, evolving cellular processes underlying MS remain poorly understood. We investigated the cellular dynamics of MS by modeling temporal and regional rates of disease progression in mouse experimental autoimmune encephalomyelitis (EAE). By performing single-cell spatial expression profiling using in situ sequencing (ISS), we annotated disease neighborhoods and found centrifugal evolution of active lesions. We demonstrated that disease-associated (DA)-glia arise independently of lesions and are dynamically induced and resolved over the disease course. Single-cell spatial mapping of human archival MS spinal cords confirmed the differential distribution of homeostatic and DA-glia, enabled deconvolution of active and inactive lesions into sub-compartments, and identified new lesion areas. By establishing a spatial resource of mouse and human MS neuropathology at a single-cell resolution, our study unveils the intricate cellular dynamics underlying MS.
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Affiliation(s)
- Petra Kukanja
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden.
| | - Christoffer M Langseth
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden.
| | - Leslie A Rubio Rodríguez-Kirby
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Eneritz Agirre
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Chao Zheng
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden
| | - Amitha Raman
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Chika Yokota
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Christophe Avenel
- Department of Information Technology, Uppsala University, 752 37 Uppsala, Sweden; BioImage Informatics Facility, Science for Life Laboratory, SciLifeLab, 751 05 Uppsala, Sweden
| | - Katarina Tiklová
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - André O Guerreiro-Cacais
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, 171 76 Solna, Sweden
| | - Tomas Olsson
- Center for Molecular Medicine, Department of Clinical Neuroscience, Karolinska Institutet, Karolinska University Hospital, 171 76 Solna, Sweden
| | - Markus M Hilscher
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden
| | - Mats Nilsson
- Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, 17154 Stockholm, Sweden.
| | - Gonçalo Castelo-Branco
- Laboratory of Molecular Neurobiology, Department of Medical Biochemistry and Biophysics, Biomedicum, Karolinska Institutet, 17177 Stockholm, Sweden.
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15
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Diao B, Luo J, Guo Y. A comprehensive survey on deep learning-based identification and predicting the interaction mechanism of long non-coding RNAs. Brief Funct Genomics 2024:elae010. [PMID: 38576205 DOI: 10.1093/bfgp/elae010] [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: 12/06/2023] [Revised: 02/25/2024] [Accepted: 03/14/2024] [Indexed: 04/06/2024] Open
Abstract
Long noncoding RNAs (lncRNAs) have been discovered to be extensively involved in eukaryotic epigenetic, transcriptional, and post-transcriptional regulatory processes with the advancements in sequencing technology and genomics research. Therefore, they play crucial roles in the body's normal physiology and various disease outcomes. Presently, numerous unknown lncRNA sequencing data require exploration. Establishing deep learning-based prediction models for lncRNAs provides valuable insights for researchers, substantially reducing time and costs associated with trial and error and facilitating the disease-relevant lncRNA identification for prognosis analysis and targeted drug development as the era of artificial intelligence progresses. However, most lncRNA-related researchers lack awareness of the latest advancements in deep learning models and model selection and application in functional research on lncRNAs. Thus, we elucidate the concept of deep learning models, explore several prevalent deep learning algorithms and their data preferences, conduct a comprehensive review of recent literature studies with exemplary predictive performance over the past 5 years in conjunction with diverse prediction functions, critically analyze and discuss the merits and limitations of current deep learning models and solutions, while also proposing prospects based on cutting-edge advancements in lncRNA research.
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Affiliation(s)
- Biyu Diao
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Jin Luo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
| | - Yu Guo
- Department of Breast Surgery, The First Affiliated Hospital of Ningbo University, No. 59, Liuting Street, Haishu District, Ningbo 315000, China
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16
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Koupourtidou C, Schwarz V, Aliee H, Frerich S, Fischer-Sternjak J, Bocchi R, Simon-Ebert T, Bai X, Sirko S, Kirchhoff F, Dichgans M, Götz M, Theis FJ, Ninkovic J. Shared inflammatory glial cell signature after stab wound injury, revealed by spatial, temporal, and cell-type-specific profiling of the murine cerebral cortex. Nat Commun 2024; 15:2866. [PMID: 38570482 PMCID: PMC10991294 DOI: 10.1038/s41467-024-46625-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: 03/03/2023] [Accepted: 02/29/2024] [Indexed: 04/05/2024] Open
Abstract
Traumatic brain injury leads to a highly orchestrated immune- and glial cell response partially responsible for long-lasting disability and the development of secondary neurodegenerative diseases. A holistic understanding of the mechanisms controlling the responses of specific cell types and their crosstalk is required to develop an efficient strategy for better regeneration. Here, we combine spatial and single-cell transcriptomics to chart the transcriptomic signature of the injured male murine cerebral cortex, and identify specific states of different glial cells contributing to this signature. Interestingly, distinct glial cells share a large fraction of injury-regulated genes, including inflammatory programs downstream of the innate immune-associated pathways Cxcr3 and Tlr1/2. Systemic manipulation of these pathways decreases the reactivity state of glial cells associated with poor regeneration. The functional relevance of the discovered shared signature of glial cells highlights the importance of our resource enabling comprehensive analysis of early events after brain injury.
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Affiliation(s)
- Christina Koupourtidou
- Chair of Cell Biology and Anatomy, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
- Graduate School of Systemic Neurosciences, LMU Munich, Munich, Germany
| | - Veronika Schwarz
- Chair of Cell Biology and Anatomy, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
- Graduate School of Systemic Neurosciences, LMU Munich, Munich, Germany
| | - Hananeh Aliee
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Simon Frerich
- Graduate School of Systemic Neurosciences, LMU Munich, Munich, Germany
- Institute for Stroke and Dementia Research, LMU University Hospital, LMU Munich, Munich, Germany
| | - Judith Fischer-Sternjak
- Chair of Physiological Genomics, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Riccardo Bocchi
- Chair of Physiological Genomics, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Basic Neurosciences, University of Geneva, Geneva, Switzerland
| | - Tatiana Simon-Ebert
- Chair of Physiological Genomics, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Xianshu Bai
- Molecular Physiology, Center for Integrative Physiology and Molecular Medicine, University of Saarland, Homburg, Germany
- Center for Gender-specific Biology and Medicine (CGBM), University of Saarland, Homburg, Germany
| | - Swetlana Sirko
- Chair of Physiological Genomics, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
| | - Frank Kirchhoff
- Molecular Physiology, Center for Integrative Physiology and Molecular Medicine, University of Saarland, Homburg, Germany
- Center for Gender-specific Biology and Medicine (CGBM), University of Saarland, Homburg, Germany
- Experimental Research Center for Normal and Pathological Aging, University of Medicine and Pharmacy of Craiova, 200349, Craiova, Romania
| | - Martin Dichgans
- Institute for Stroke and Dementia Research, LMU University Hospital, LMU Munich, Munich, Germany
- Munich Cluster for Systems Neurology SYNERGY, LMU Munich, Munich, Germany
- German Centre for Neurodegenerative Diseases, Munich, Germany
| | - Magdalena Götz
- Chair of Physiological Genomics, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany
- Institute of Stem Cell Research, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Munich Cluster for Systems Neurology SYNERGY, LMU Munich, Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany
- Department of Mathematics, Technical University of Munich, Munich, Germany
| | - Jovica Ninkovic
- Chair of Cell Biology and Anatomy, Biomedical Center (BMC), Faculty of Medicine, LMU Munich, Planegg-Martinsried, Germany.
- Institute of Stem Cell Research, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
- Munich Cluster for Systems Neurology SYNERGY, LMU Munich, Munich, Germany.
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17
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Zhou Y, He W, Hou W, Zhu Y. Pianno: a probabilistic framework automating semantic annotation for spatial transcriptomics. Nat Commun 2024; 15:2848. [PMID: 38565531 DOI: 10.1038/s41467-024-47152-4] [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: 09/12/2023] [Accepted: 03/20/2024] [Indexed: 04/04/2024] Open
Abstract
Spatial transcriptomics has revolutionized the study of gene expression within tissues, while preserving spatial context. However, annotating spatial spots' biological identity remains a challenge. To tackle this, we introduce Pianno, a Bayesian framework automating structural semantics annotation based on marker genes. Comprehensive evaluations underscore Pianno's remarkable prowess in precisely annotating a wide array of spatial semantics, ranging from diverse anatomical structures to intricate tumor microenvironments, as well as in estimating cell type distributions, across data generated from various spatial transcriptomics platforms. Furthermore, Pianno, in conjunction with clustering approaches, uncovers a region- and species-specific excitatory neuron subtype in the deep layer 3 of the human neocortex, shedding light on cellular evolution in the human neocortex. Overall, Pianno equips researchers with a robust and efficient tool for annotating diverse biological structures, offering new perspectives on spatial transcriptomics data.
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Affiliation(s)
- Yuqiu Zhou
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei He
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Weizhen Hou
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Ying Zhu
- State Key Laboratory of Medical Neurobiology, MOE Frontiers Center for Brain Science, Institutes of Brain Science and Department of Neurosurgery, Huashan Hospital, Fudan University, Shanghai, China.
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18
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Haviv D, Remšík J, Gatie M, Snopkowski C, Takizawa M, Pereira N, Bashkin J, Jovanovich S, Nawy T, Chaligne R, Boire A, Hadjantonakis AK, Pe'er D. The covariance environment defines cellular niches for spatial inference. Nat Biotechnol 2024:10.1038/s41587-024-02193-4. [PMID: 38565973 DOI: 10.1038/s41587-024-02193-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 02/28/2024] [Indexed: 04/04/2024]
Abstract
A key challenge of analyzing data from high-resolution spatial profiling technologies is to suitably represent the features of cellular neighborhoods or niches. Here we introduce the covariance environment (COVET), a representation that leverages the gene-gene covariate structure across cells in the niche to capture the multivariate nature of cellular interactions within it. We define a principled optimal transport-based distance metric between COVET niches that scales to millions of cells. Using COVET to encode spatial context, we developed environmental variational inference (ENVI), a conditional variational autoencoder that jointly embeds spatial and single-cell RNA sequencing data into a latent space. ENVI includes two decoders: one to impute gene expression across the spatial modality and a second to project spatial information onto single-cell data. ENVI can confer spatial context to genomics data from single dissociated cells and outperforms alternatives for imputing gene expression on diverse spatial datasets.
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Affiliation(s)
- Doron Haviv
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Tri-Institutional Training Program in Computational Biology and Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Ján Remšík
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Mohamed Gatie
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Catherine Snopkowski
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meril Takizawa
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | | | - Tal Nawy
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ronan Chaligne
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Adrienne Boire
- Human Oncology & Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Neurology, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Brain Tumor Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Anna-Katerina Hadjantonakis
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Dana Pe'er
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, New York, NY, USA.
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19
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Zhou Z, Zhang J, Zheng X, Pan Z, Zhao F, Gao Y. CIRI-Deep Enables Single-Cell and Spatial Transcriptomic Analysis of Circular RNAs with Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2308115. [PMID: 38308181 PMCID: PMC11005702 DOI: 10.1002/advs.202308115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/03/2024] [Indexed: 02/04/2024]
Abstract
Circular RNAs (circRNAs) are a crucial yet relatively unexplored class of transcripts known for their tissue- and cell-type-specific expression patterns. Despite the advances in single-cell and spatial transcriptomics, these technologies face difficulties in effectively profiling circRNAs due to inherent limitations in circRNA sequencing efficiency. To address this gap, a deep learning model, CIRI-deep, is presented for comprehensive prediction of circRNA regulation on diverse types of RNA-seq data. CIRI-deep is trained on an extensive dataset of 25 million high-confidence circRNA regulation events and achieved high performances on both test and leave-out data, ensuring its accuracy in inferring differential events from RNA-seq data. It is demonstrated that CIRI-deep and its adapted version enable various circRNA analyses, including cluster- or region-specific circRNA detection, BSJ ratio map visualization, and trans and cis feature importance evaluation. Collectively, CIRI-deep's adaptability extends to all major types of RNA-seq datasets including single-cell and spatial transcriptomic data, which will undoubtedly broaden the horizons of circRNA research.
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Affiliation(s)
- Zihan Zhou
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Jinyang Zhang
- Beijing Institutes of Life ScienceChinese Academy of SciencesBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Xin Zheng
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Zhicheng Pan
- Center for Computational Biology Flatiron InstituteNew York10010USA
| | - Fangqing Zhao
- Beijing Institutes of Life ScienceChinese Academy of SciencesBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
| | - Yuan Gao
- National Genomics Data Center & CAS Key Laboratory of Genome Sciences and Information Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- University of Chinese Academy of SciencesBeijing100101China
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20
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Yin W, Wan Y, Zhou Y. SpatialcoGCN: deconvolution and spatial information-aware simulation of spatial transcriptomics data via deep graph co-embedding. Brief Bioinform 2024; 25:bbae130. [PMID: 38557675 PMCID: PMC10982953 DOI: 10.1093/bib/bbae130] [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: 05/18/2023] [Revised: 02/22/2024] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
Spatial transcriptomics (ST) data have emerged as a pivotal approach to comprehending the function and interplay of cells within intricate tissues. Nonetheless, analyses of ST data are restricted by the low spatial resolution and limited number of ribonucleic acid transcripts that can be detected with several popular ST techniques. In this study, we propose that both of the above issues can be significantly improved by introducing a deep graph co-embedding framework. First, we establish a self-supervised, co-graph convolution network-based deep learning model termed SpatialcoGCN, which leverages single-cell data to deconvolve the cell mixtures in spatial data. Evaluations of SpatialcoGCN on a series of simulated ST data and real ST datasets from human ductal carcinoma in situ, developing human heart and mouse brain suggest that SpatialcoGCN could outperform other state-of-the-art cell type deconvolution methods in estimating per-spot cell composition. Moreover, with competitive accuracy, SpatialcoGCN could also recover the spatial distribution of transcripts that are not detected by raw ST data. With a similar co-embedding framework, we further established a spatial information-aware ST data simulation method, SpatialcoGCN-Sim. SpatialcoGCN-Sim could generate simulated ST data with high similarity to real datasets. Together, our approaches provide efficient tools for studying the spatial organization of heterogeneous cells within complex tissues.
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Affiliation(s)
- Wang Yin
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - You Wan
- Department of Neurobiology, School of Basic Medical Sciences, Neuroscience Research Institute, Peking University, 38 Xueyuan Road, Beijing 100191, China
| | - Yuan Zhou
- Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, 38 Xueyuan Road, Beijing 100191, China
- State Key Laboratory of Vascular Homeostasis and Remodeling, Peking University, 38 Xueyuan Road, Beijing 100191, China
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21
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Li K, Li J, Tao Y, Wang F. stDiff: a diffusion model for imputing spatial transcriptomics through single-cell transcriptomics. Brief Bioinform 2024; 25:bbae171. [PMID: 38628114 PMCID: PMC11021815 DOI: 10.1093/bib/bbae171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 03/11/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024] Open
Abstract
Spatial transcriptomics (ST) has become a powerful tool for exploring the spatial organization of gene expression in tissues. Imaging-based methods, though offering superior spatial resolutions at the single-cell level, are limited in either the number of imaged genes or the sensitivity of gene detection. Existing approaches for enhancing ST rely on the similarity between ST cells and reference single-cell RNA sequencing (scRNA-seq) cells. In contrast, we introduce stDiff, which leverages relationships between gene expression abundance in scRNA-seq data to enhance ST. stDiff employs a conditional diffusion model, capturing gene expression abundance relationships in scRNA-seq data through two Markov processes: one introducing noise to transcriptomics data and the other denoising to recover them. The missing portion of ST is predicted by incorporating the original ST data into the denoising process. In our comprehensive performance evaluation across 16 datasets, utilizing multiple clustering and similarity metrics, stDiff stands out for its exceptional ability to preserve topological structures among cells, positioning itself as a robust solution for cell population identification. Moreover, stDiff's enhancement outcomes closely mirror the actual ST data within the batch space. Across diverse spatial expression patterns, our model accurately reconstructs them, delineating distinct spatial boundaries. This highlights stDiff's capability to unify the observed and predicted segments of ST data for subsequent analysis. We anticipate that stDiff, with its innovative approach, will contribute to advancing ST imputation methodologies.
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Affiliation(s)
- Kongming Li
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Jiahao Li
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Yuhao Tao
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
| | - Fei Wang
- Shanghai Key Lab of Intelligent Information Processing, Handan Street, 200433 Shanghai, China
- School of Computer Science and Technology, Fudan UniversityHandan Street, 200433 Shanghai, China
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22
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He S, Jin Y, Nazaret A, Shi L, Chen X, Rampersaud S, Dhillon BS, Valdez I, Friend LE, Fan JL, Park CY, Mintz RL, Lao YH, Carrera D, Fang KW, Mehdi K, Rohde M, McFaline-Figueroa JL, Blei D, Leong KW, Rudensky AY, Plitas G, Azizi E. Starfysh integrates spatial transcriptomic and histologic data to reveal heterogeneous tumor-immune hubs. Nat Biotechnol 2024:10.1038/s41587-024-02173-8. [PMID: 38514799 DOI: 10.1038/s41587-024-02173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/14/2024] [Indexed: 03/23/2024]
Abstract
Spatially resolved gene expression profiling provides insight into tissue organization and cell-cell crosstalk; however, sequencing-based spatial transcriptomics (ST) lacks single-cell resolution. Current ST analysis methods require single-cell RNA sequencing data as a reference for rigorous interpretation of cell states, mostly do not use associated histology images and are not capable of inferring shared neighborhoods across multiple tissues. Here we present Starfysh, a computational toolbox using a deep generative model that incorporates archetypal analysis and any known cell type markers to characterize known or new tissue-specific cell states without a single-cell reference. Starfysh improves the characterization of spatial dynamics in complex tissues using histology images and enables the comparison of niches as spatial hubs across tissues. Integrative analysis of primary estrogen receptor (ER)-positive breast cancer, triple-negative breast cancer (TNBC) and metaplastic breast cancer (MBC) tissues led to the identification of spatial hubs with patient- and disease-specific cell type compositions and revealed metabolic reprogramming shaping immunosuppressive hubs in aggressive MBC.
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Affiliation(s)
- Siyu He
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Yinuo Jin
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Achille Nazaret
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Lingting Shi
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Xueer Chen
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Sham Rampersaud
- Pharmaceutical Sciences and Pharmacogenomics Graduate Program, University of California, San Francisco, San Francisco, CA, USA
| | - Bahawar S Dhillon
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Izabella Valdez
- The Graduate School of Biomedical Sciences at the Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lauren E Friend
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Joy Linyue Fan
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Cameron Y Park
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
| | - Rachel L Mintz
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Yeh-Hsing Lao
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Pharmaceutical Sciences, University at Buffalo, the State University of New York, Buffalo, NY, USA
| | - David Carrera
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Kaylee W Fang
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Kaleem Mehdi
- Department of Computer Science, Fordham University, New York, NY, USA
| | | | - José L McFaline-Figueroa
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA
| | - David Blei
- Department of Computer Science, Columbia University, New York, NY, USA
- Department of Statistics, Columbia University, New York, NY, USA
| | - Kam W Leong
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY, USA
| | - Alexander Y Rudensky
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Ludwig Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - George Plitas
- Immunology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Howard Hughes Medical Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Ludwig Center, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
- Department of Surgery, Breast Service, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Elham Azizi
- Department of Biomedical Engineering, Columbia University, New York, NY, USA.
- Irving Institute for Cancer Dynamics, Columbia University, New York, NY, USA.
- Department of Computer Science, Columbia University, New York, NY, USA.
- Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY, USA.
- Data Science Institute, Columbia University, New York, NY, USA.
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23
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Guo X, Ning J, Chen Y, Liu G, Zhao L, Fan Y, Sun S. Recent advances in differential expression analysis for single-cell RNA-seq and spatially resolved transcriptomic studies. Brief Funct Genomics 2024; 23:95-109. [PMID: 37022699 DOI: 10.1093/bfgp/elad011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/09/2022] [Accepted: 03/10/2023] [Indexed: 04/07/2023] Open
Abstract
Differential expression (DE) analysis is a necessary step in the analysis of single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) data. Unlike traditional bulk RNA-seq, DE analysis for scRNA-seq or SRT data has unique characteristics that may contribute to the difficulty of detecting DE genes. However, the plethora of DE tools that work with various assumptions makes it difficult to choose an appropriate one. Furthermore, a comprehensive review on detecting DE genes for scRNA-seq data or SRT data from multi-condition, multi-sample experimental designs is lacking. To bridge such a gap, here, we first focus on the challenges of DE detection, then highlight potential opportunities that facilitate further progress in scRNA-seq or SRT analysis, and finally provide insights and guidance in selecting appropriate DE tools or developing new computational DE methods.
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Affiliation(s)
- Xiya Guo
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Jin Ning
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yuanze Chen
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Guoliang Liu
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Liyan Zhao
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Yue Fan
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
| | - Shiquan Sun
- School of Public Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
- Key Laboratory of Trace Elements and Endemic Diseases, Center for Single Cell Omics and Health, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, P.R. China
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24
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Danishuddin, Khan S, Kim JJ. Spatial transcriptomics data and analytical methods: An updated perspective. Drug Discov Today 2024; 29:103889. [PMID: 38244672 DOI: 10.1016/j.drudis.2024.103889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 01/01/2024] [Accepted: 01/15/2024] [Indexed: 01/22/2024]
Abstract
Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.
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Affiliation(s)
- Danishuddin
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
| | - Shawez Khan
- National Center for Cancer Immune Therapy (CCIT-DK), Department of Oncology, Copenhagen University Hospital, Herlev, Denmark
| | - Jong Joo Kim
- Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea.
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25
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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26
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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [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: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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27
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Xie YR, Castro DC, Rubakhin SS, Trinklein TJ, Sweedler JV, Lam F. Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry. Nat Methods 2024; 21:521-530. [PMID: 38366241 PMCID: PMC10927565 DOI: 10.1038/s41592-024-02171-3] [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: 06/05/2023] [Accepted: 01/08/2024] [Indexed: 02/18/2024]
Abstract
Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.
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Affiliation(s)
- Yuxuan Richard Xie
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Daniel C Castro
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Stanislav S Rubakhin
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Timothy J Trinklein
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA
| | - Jonathan V Sweedler
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Molecular and Integrative Physiology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Chemistry, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
| | - Fan Lam
- Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carle-Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Carl R. Woese Institute for Genomic Biology, University of Illinois Urbana-Champaign, Urbana, IL, USA.
- Department of Electrical and Computer Engineering, University of Illinois Urbana-Champaign, Urbana, IL, USA.
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28
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Sun ED, Ma R, Navarro Negredo P, Brunet A, Zou J. TISSUE: uncertainty-calibrated prediction of single-cell spatial transcriptomics improves downstream analyses. Nat Methods 2024; 21:444-454. [PMID: 38347138 DOI: 10.1038/s41592-024-02184-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 01/12/2024] [Indexed: 02/27/2024]
Abstract
Whole-transcriptome spatial profiling of genes at single-cell resolution remains a challenge. To address this limitation, spatial gene expression prediction methods have been developed to infer the spatial expression of unmeasured transcripts, but the quality of these predictions can vary greatly. Here we present Transcript Imputation with Spatial Single-cell Uncertainty Estimation (TISSUE) as a general framework for estimating uncertainty for spatial gene expression predictions and providing uncertainty-aware methods for downstream inference. Leveraging conformal inference, TISSUE provides well-calibrated prediction intervals for predicted expression values across 11 benchmark datasets. Moreover, it consistently reduces the false discovery rate for differential gene expression analysis, improves clustering and visualization of predicted spatial transcriptomics and improves the performance of supervised learning models trained on predicted gene expression profiles. Applying TISSUE to a MERFISH spatial transcriptomics dataset of the adult mouse subventricular zone, we identified subtypes within the neural stem cell lineage and developed subtype-specific regional classifiers.
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Affiliation(s)
- Eric D Sun
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Rong Ma
- Department of Statistics, Stanford University, Stanford, CA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | - Anne Brunet
- Department of Genetics, Stanford University, Stanford, CA, USA
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Glenn Center for the Biology of Aging, Stanford University, Stanford, CA, USA
| | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
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29
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Frolov A, Guzman MA, Hayat G, Martin JR. Two Cases of Sporadic Amyotrophic Lateral Sclerosis With Contrasting Clinical Phenotypes: Genetic Insights. Cureus 2024; 16:e56023. [PMID: 38606235 PMCID: PMC11008550 DOI: 10.7759/cureus.56023] [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] [Accepted: 03/12/2024] [Indexed: 04/13/2024] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a fatal neuromuscular disease that affects individuals of diverse racial and ethnic backgrounds. There is currently no cure for ALS, and the number of efficient disease-modifying drugs for ALS is limited to a few, despite the large number of clinical trials conducted in recent years. The latter could be attributed to the significant heterogeneity of ALS clinical phenotypes even in their familial forms. To address this issue, we conducted postmortem genetic screening of two female patients with sporadic ALS (sALS) and contrasting clinical phenotypes. The results demonstrated that despite their contrasting clinical phenotypes, both patients had rare pathologic/deleterious mutations in five genes: ACSM5, BBS12, HLA-DQB1, MUC20, and OBSCN, with mutations in three of those genes being identical: BBS12, HLA-DQB1, and MUC20. Additional groups of mutated genes linked to ALS, other neurologic disorders, and ALS-related pathologies were also identified. These data are consistent with a hypothesis that an individual could be primed for ALS via mutations in a specific set of genes not directly linked to ALS. The disease could be initiated by a concerted action of several mutated genes linked to ALS and the disease's clinical phenotype will evolve further through accessory gene mutations associated with other neurological disorders and ALS-related pathologies.
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Affiliation(s)
- Andrey Frolov
- Center for Anatomical Science and Education, Saint Louis University School of Medicine, Saint Louis, USA
| | - Miguel A Guzman
- Department of Pathology, Saint Louis University School of Medicine, Saint Louis, USA
| | - Ghazala Hayat
- Department of Neurology, Saint Louis University School of Medicine, Saint Louis, USA
- ALS Center of Excellence, Saint Louis University School of Medicine, Saint Louis, USA
| | - John R Martin
- Center for Anatomical Science and Education, Saint Louis University School of Medicine, Saint Louis, USA
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30
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Lin S, Zhao F, Wu Z, Yao J, Zhao Y, Yuan Z. Streamlining spatial omics data analysis with Pysodb. Nat Protoc 2024; 19:831-895. [PMID: 38135744 DOI: 10.1038/s41596-023-00925-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 10/02/2023] [Indexed: 12/24/2023]
Abstract
Advances in spatial omics technologies have improved the understanding of cellular organization in tissues, leading to the generation of complex and heterogeneous data and prompting the development of specialized tools for managing, loading and visualizing spatial omics data. The Spatial Omics Database (SODB) was established to offer a unified format for data storage and interactive visualization modules. Here we detail the use of Pysodb, a Python-based tool designed to enable the efficient exploration and loading of spatial datasets from SODB within a Python environment. We present seven case studies using Pysodb, detailing the interaction with various computational methods, ensuring reproducibility of experimental data and facilitating the integration of new data and alternative applications in SODB. The approach offers a reference for method developers by outlining label and metadata availability in representative spatial data that can be loaded by Pysodb. The tool is supplemented by a website ( https://protocols-pysodb.readthedocs.io/ ) with detailed information for benchmarking analysis, and allows method developers to focus on computational models by facilitating data processing. This protocol is designed for researchers with limited experience in computational biology. Depending on the dataset complexity, the protocol typically requires ~12 h to complete.
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Affiliation(s)
- Senlin Lin
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Fangyuan Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | | | - Yi Zhao
- Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China.
- University of Chinese Academy of Sciences, Beijing, China.
| | - Zhiyuan Yuan
- Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China.
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31
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Shi Y, Huang L, Dong H, Yang M, Ding W, Zhou X, Lu T, Liu Z, Zhou X, Wang M, Zeng B, Sun Y, Zhong S, Wang B, Wang W, Yin C, Wang X, Wu Q. Decoding the spatiotemporal regulation of transcription factors during human spinal cord development. Cell Res 2024; 34:193-213. [PMID: 38177242 PMCID: PMC10907391 DOI: 10.1038/s41422-023-00897-x] [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: 05/26/2023] [Accepted: 11/02/2023] [Indexed: 01/06/2024] Open
Abstract
The spinal cord is a crucial component of the central nervous system that facilitates sensory processing and motor performance. Despite its importance, the spatiotemporal codes underlying human spinal cord development have remained elusive. In this study, we have introduced an image-based single-cell transcription factor (TF) expression decoding spatial transcriptome method (TF-seqFISH) to investigate the spatial expression and regulation of TFs during human spinal cord development. By combining spatial transcriptomic data from TF-seqFISH and single-cell RNA-sequencing data, we uncovered the spatial distribution of neural progenitor cells characterized by combinatorial TFs along the dorsoventral axis, as well as the molecular and spatial features governing neuronal generation, migration, and differentiation along the mediolateral axis. Notably, we observed a sandwich-like organization of excitatory and inhibitory interneurons transiently appearing in the dorsal horns of the developing human spinal cord. In addition, we integrated data from 10× Visium to identify early and late waves of neurogenesis in the dorsal horn, revealing the formation of laminas in the dorsal horns. Our study also illuminated the spatial differences and molecular cues underlying motor neuron (MN) diversification, and the enrichment of Amyotrophic Lateral Sclerosis (ALS) risk genes in MNs and microglia. Interestingly, we detected disease-associated microglia (DAM)-like microglia groups in the developing human spinal cord, which are predicted to be vulnerable to ALS and engaged in the TYROBP causal network and response to unfolded proteins. These findings provide spatiotemporal transcriptomic resources on the developing human spinal cord and potential strategies for spinal cord injury repair and ALS treatment.
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Affiliation(s)
- Yingchao Shi
- Guangdong Institute of Intelligence Science and Technology, Guangdong, China.
| | - Luwei Huang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Hao Dong
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Meng Yang
- Changping Laboratory, Beijing, China
| | - Wenyu Ding
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Xiang Zhou
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Tian Lu
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xin Zhou
- Changping Laboratory, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Mengdi Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Bo Zeng
- Changping Laboratory, Beijing, China
| | - Yinuo Sun
- Changping Laboratory, Beijing, China
| | - Suijuan Zhong
- Changping Laboratory, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Bosong Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China
| | - Wei Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | | | - Xiaoqun Wang
- State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.
- Changping Laboratory, Beijing, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China.
| | - Qian Wu
- Changping Laboratory, Beijing, China.
- State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, New Cornerstone Science Laboratory, Beijing Normal University, Beijing, China.
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32
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Liu T, Li K, Wang Y, Li H, Zhao H. Evaluating the Utilities of Foundation Models in Single-cell Data Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.08.555192. [PMID: 38464157 PMCID: PMC10925156 DOI: 10.1101/2023.09.08.555192] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Foundation Models (FMs) have made significant strides in both industrial and scientific domains. In this paper, we evaluate the performance of FMs in single-cell sequencing data analysis through comprehensive experiments across eight downstream tasks pertinent to single-cell data. By comparing ten different single-cell FMs with task-specific methods, we found that single-cell FMs may not consistently excel in all tasks than task-specific methods. However, the emergent abilities and the successful applications of cross-species/cross-modality transfer learning of FMs are promising. In addition, we present a systematic evaluation of the effects of hyper-parameters, initial settings, and stability for training single-cell FMs based on a proposed scEval framework, and provide guidelines for pre-training and fine-tuning. Our work summarizes the current state of single-cell FMs and points to their constraints and avenues for future development.
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Kojima Y, Mii S, Hayashi S, Hirose H, Ishikawa M, Akiyama M, Enomoto A, Shimamura T. Single-cell colocalization analysis using a deep generative model. Cell Syst 2024; 15:180-192.e7. [PMID: 38387441 DOI: 10.1016/j.cels.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 03/06/2023] [Accepted: 01/23/2024] [Indexed: 02/24/2024]
Abstract
Analyzing colocalization of single cells with heterogeneous molecular phenotypes is essential for understanding cell-cell interactions, and cellular responses to external stimuli and their biological functions in diseases and tissues. However, existing computational methodologies identified the colocalization patterns between predefined cell populations, which can obscure the molecular signatures arising from intercellular communication. Here, we introduce DeepCOLOR, a computational framework based on a deep generative model that recovers intercellular colocalization networks with single-cell resolution by the integration of single-cell and spatial transcriptomes. Along with colocalized population detection accuracy that is superior to existing methods in simulated dataset, DeepCOLOR identified plausible cell-cell interaction candidates between colocalized single cells and segregated cell populations defined by the colocalization relationships in mouse brain tissues, human squamous cell carcinoma samples, and human lung tissues infected with SARS-CoV-2. DeepCOLOR is applicable to studying cell-cell interactions behind various spatial niches. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Yasuhiro Kojima
- Laboratory of Computational Life Science, National Cancer Center Research Institute, Chuo-ku, Tokyo 104-0045, Japan; Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
| | - Shinji Mii
- Department of Pathology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Shuto Hayashi
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Haruka Hirose
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Masato Ishikawa
- Institute for Life and Medical Sciences, Kyoto University, Kyoto, Kyoto 606-8507, Japan; Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba 277-8561, Japan
| | - Masashi Akiyama
- Department of Dermatology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Atsushi Enomoto
- Department of Pathology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan
| | - Teppei Shimamura
- Department of Computational and Systems Biology, Medical Research Insitute, Tokyo Medical and Dental University, Bunkyo-ku, Tokyo 113-0034, Japan; Division of Systems Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.
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34
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Zhang H, Lu X, Lu B, Gullo G, Chen L. Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future. Future Oncol 2024. [PMID: 38362731 DOI: 10.2217/fon-2023-0658] [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] [Indexed: 02/17/2024] Open
Abstract
Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.
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Affiliation(s)
- Han Zhang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
- UPMC Hillman Cancer Center, Pittsburgh, PA 15232, USA
| | - Binfeng Lu
- Center for Discovery & Innovation, Hackensack Meridian Health, Nutley, NJ 07110, USA
| | - Giuseppe Gullo
- Department of Obstetrics & Gynecology, Villa Sofia Cervello Hospital, University of Palermo, 90146, Palermo, Italy
| | - Lujia Chen
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
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35
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Chai T, Shen J, Sheng Y, Huang Y, Liang W, Zhang Z, Zhao R, Shang H, Cheng W, Zhang H, Chen X, Huang X, Zhang Y, Liu J, Yang H, Wang L, Pan S, Chen Y, Han L, Qiu Q, Gao A, Wei H, Fang X. Effects of flora deficiency on the structure and function of the large intestine. iScience 2024; 27:108941. [PMID: 38333708 PMCID: PMC10850757 DOI: 10.1016/j.isci.2024.108941] [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: 06/27/2023] [Revised: 11/03/2023] [Accepted: 01/15/2024] [Indexed: 02/10/2024] Open
Abstract
The significant anatomical changes in large intestine of germ-free (GF) mice provide excellent material for understanding microbe-host crosstalk. We observed significant differences of GF mice in anatomical and physiological involving in enlarged cecum, thinned mucosal layer and enriched water in cecal content. Furthermore, integration analysis of multi-omics data revealed the associations between the structure of large intestinal mesenchymal cells and the thinning of the mucosal layer. Increased Aqp8 expression in GF mice may contribute to enhanced water secretion or altered hydrodynamics in the cecum. In addition, the proportion of epithelial cells, nutrient absorption capacity, immune function and the metabolome of cecum contents of large intestine were also significantly altered. Together, this is the first systematic study of the transcriptome and metabolome of the cecum and colon of GF mice, and these findings contribute to our understanding of the intricate interactions between microbes and the large intestine.
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Affiliation(s)
- Tailiang Chai
- University of the Chinese Academy of Sciences, College of Life Sciences, Beijing, Beijing, China
- BGI, Shenzhen, Guangdong, China
| | | | - Yifei Sheng
- University of the Chinese Academy of Sciences, College of Life Sciences, Beijing, Beijing, China
- BGI, Shenzhen, Guangdong, China
| | | | | | - Zhao Zhang
- University of the Chinese Academy of Sciences, College of Life Sciences, Beijing, Beijing, China
- BGI, Shenzhen, Guangdong, China
| | - Ruizhen Zhao
- University of the Chinese Academy of Sciences, College of Life Sciences, Beijing, Beijing, China
- BGI, Shenzhen, Guangdong, China
| | - Haitao Shang
- Sun Yat-sen University First Affiliated Hospital, Precision Medicine Institute, Guangzhou, Guangdong, China
| | - Wei Cheng
- Huazhong Agricultural University, College of Animal Sciences and Technology, Wuhan, Hubei, China
| | - Hang Zhang
- Huazhong Agricultural University, College of Animal Sciences and Technology, Wuhan, Hubei, China
| | - Xueting Chen
- University of the Chinese Academy of Sciences, College of Life Sciences, Beijing, Beijing, China
- BGI, Shenzhen, Guangdong, China
| | - Xiang Huang
- University of the Chinese Academy of Sciences, College of Life Sciences, Beijing, Beijing, China
| | - Yin Zhang
- University of the Chinese Academy of Sciences, College of Life Sciences, Beijing, Beijing, China
- BGI, Shenzhen, Guangdong, China
| | | | | | | | | | - Yang Chen
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Lijuan Han
- Department of Scientific Research, Kangmeihuada GeneTech Co., Ltd. (KMHD), Shenzhen, China
| | - Qinwei Qiu
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
| | - Aibo Gao
- Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China
| | - Hong Wei
- Sun Yat-sen University First Affiliated Hospital, Precision Medicine Institute, Guangzhou, Guangdong, China
| | - Xiaodong Fang
- BGI, Shenzhen, Guangdong, China
- State Key Laboratory of Dampness Syndrome of Chinese Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
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36
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Chang Y, Liu J, Jiang Y, Ma A, Yeo YY, Guo Q, McNutt M, Krull J, Rodig SJ, Barouch DH, Nolan G, Xu D, Jiang S, Li Z, Liu B, Ma Q. Graph Fourier transform for spatial omics representation and analyses of complex organs. RESEARCH SQUARE 2024:rs.3.rs-3952048. [PMID: 38410424 PMCID: PMC10896409 DOI: 10.21203/rs.3.rs-3952048/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Spatial omics technologies are capable of deciphering detailed components of complex organs or tissue in cellular and subcellular resolution. A robust, interpretable, and unbiased representation method for spatial omics is necessary to illuminate novel investigations into biological functions, whereas a mathematical theory deficiency still exists. We present SpaGFT (Spatial Graph Fourier Transform), which provides a unique analytical feature representation of spatial omics data and elucidates molecular signatures linked to critical biological processes within tissues and cells. It outperformed existing tools in spatially variable gene prediction and gene expression imputation across human/mouse Visium data. Integrating SpaGFT representation into existing machine learning frameworks can enhance up to 40% accuracy of spatial domain identification, cell type annotation, cell-to-spot alignment, and subcellular hallmark inference. SpaGFT identified immunological regions for B cell maturation in human lymph node Visium data, characterized secondary follicle variations from in-house human tonsil CODEX data, and detected extremely rare subcellular organelles such as Cajal body and Set1/COMPASS. This new method lays the groundwork for a new theoretical model in explainable AI, advancing our understanding of tissue organization and function.
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Affiliation(s)
- Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Jixin Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Yi Jiang
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Anjun Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Yao Yu Yeo
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
| | - Qi Guo
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Megan McNutt
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
| | - Jordan Krull
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Scott J. Rodig
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA 02115 USA
- Department of Pathology, Brigham & Women’s Hospital, Boston, MA 02115, USA
| | - Dan H. Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
- William Bosworth Castle Professor of Medicine, Harvard Medical School
- Ragon Institute of MGH, MIT, and Harvard
| | - Garry Nolan
- Department of Microbiology and Immunology, Stanford University School of Medicine, Stanford, CA 94305, USA
- Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, and Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO 65211, USA
| | - Sizun Jiang
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Boston, MA 02115, USA
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA 02115 USA
- Department of Pathology, Brigham & Women’s Hospital, Boston, MA 02115, USA
- Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
- Department of Microbiology, Harvard Medical School, Boston, MA 02115, USA
| | - Zihai Li
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Bingqiang Liu
- School of Mathematics, Shandong University, Jinan 250100, China
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
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37
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Wu H, Dixon EE, Xuanyuan Q, Guo J, Yoshimura Y, Debashish C, Niesnerova A, Xu H, Rouault M, Humphreys BD. High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing. Nat Commun 2024; 15:1396. [PMID: 38360882 PMCID: PMC10869771 DOI: 10.1038/s41467-024-45752-8] [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: 05/08/2023] [Accepted: 02/02/2024] [Indexed: 02/17/2024] Open
Abstract
Emerging spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ sequencing (dRNA HybISS, Cartana part of 10xGenomics) to compare male and female healthy mouse kidneys and the male kidney injury and repair timecourse. A pre-selected panel of 200 genes is used to identify cell state dynamics patterns during injury and repair. We develop a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting dataset allows us to resolve 13 kidney cell types within distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. At late timepoints after injury, C3+ leukocytes are enriched near pro-inflammatory, failed-repair proximal tubule cells. Integration of snRNA-seq dataset from the same injury and repair samples also allows us to impute the spatial localization of genes not directly measured by dRNA HybISS.
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Affiliation(s)
- Haojia Wu
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Eryn E Dixon
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Qiao Xuanyuan
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Juanru Guo
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | - Yasuhiro Yoshimura
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA
| | | | | | - Hao Xu
- 10X Genomics, Pleasanton, CA, USA
- Aplex Bio AB, Solna, Sweden
| | | | - Benjamin D Humphreys
- Division of Nephrology, Department of Medicine, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
- Department of Developmental Biology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
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38
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Zhao W, Kepecs B, Mahadevan NR, Segerstolpe A, Weirather JL, Besson NR, Giotti B, Soong BY, Li C, Vigneau S, Slyper M, Wakiro I, Jane-Valbuena J, Ashenberg O, Rotem A, Bueno R, Rozenblatt-Rosen O, Pfaff K, Rodig S, Hata AN, Regev A, Johnson BE, Tsankov AM. A cellular and spatial atlas of TP53 -associated tissue remodeling in lung adenocarcinoma. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.28.546977. [PMID: 37425718 PMCID: PMC10327017 DOI: 10.1101/2023.06.28.546977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
TP53 is the most frequently mutated gene across many cancers and is associated with shorter survival in lung adenocarcinoma (LUAD). To define how TP53 mutations affect the LUAD tumor microenvironment (TME), we constructed a multi-omic cellular and spatial tumor atlas of 23 treatment-naïve human lung tumors. We found that TP53 -mutant ( TP53 mut ) malignant cells lose alveolar identity and upregulate highly proliferative and entropic gene expression programs consistently across resectable LUAD patient tumors, genetically engineered mouse models, and cell lines harboring a wide spectrum of TP53 mutations. We further identified a multicellular tumor niche composed of SPP1 + macrophages and collagen-expressing fibroblasts that coincides with hypoxic, pro-metastatic expression programs in TP53 mut tumors. Spatially correlated angiostatic and immune checkpoint interactions, including CD274 - PDCD1 and PVR - TIGIT , are also enriched in TP53 mut LUAD tumors, which may influence response to checkpoint blockade therapy. Our methodology can be further applied to investigate mutation-specific TME changes in other cancers.
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39
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Lei Y, Liang X, Sun Y, Yao T, Gong H, Chen Z, Gao Y, Wang H, Wang R, Huang Y, Yang T, Yu M, Liu L, Yi CX, Wu QF, Kong X, Xu X, Liu S, Zhang Z, Liu T. Region-specific transcriptomic responses to obesity and diabetes in macaque hypothalamus. Cell Metab 2024; 36:438-453.e6. [PMID: 38325338 DOI: 10.1016/j.cmet.2024.01.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/27/2023] [Accepted: 01/05/2024] [Indexed: 02/09/2024]
Abstract
The hypothalamus plays a crucial role in the progression of obesity and diabetes; however, its structural complexity and cellular heterogeneity impede targeted treatments. Here, we profiled the single-cell and spatial transcriptome of the hypothalamus in obese and sporadic type 2 diabetic macaques, revealing primate-specific distributions of clusters and genes as well as spatial region, cell-type-, and gene-feature-specific changes. The infundibular (INF) and paraventricular nuclei (PVN) are most susceptible to metabolic disruption, with the PVN being more sensitive to diabetes. In the INF, obesity results in reduced synaptic plasticity and energy sensing capability, whereas diabetes involves molecular reprogramming associated with impaired tanycytic barriers, activated microglia, and neuronal inflammatory response. In the PVN, cellular metabolism and neural activity are suppressed in diabetic macaques. Spatial transcriptomic data reveal microglia's preference for the parenchyma over the third ventricle in diabetes. Our findings provide a comprehensive view of molecular changes associated with obesity and diabetes.
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Affiliation(s)
- Ying Lei
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Xian Liang
- State Key Laboratory of Genetic Engineering, Department of Endocrinology and Metabolism, Human Phenome Institute, Institute of Metabolism and Integrative Biology, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200438, China; School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Yunong Sun
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Ting Yao
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Xi'an Jiaotong University School of Medicine, Xi'an, Shanxi 710063, China
| | - Hongyu Gong
- School of Life Sciences, Institues of Biomedical Sciences, Inner Mongolia University, Hohhot 010000, China
| | - Zhenhua Chen
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Yuanqing Gao
- Jiangsu Provincial Key Laboratory of Cardiovascular and Cerebrovascular Medicine, School of Pharmacy, Nanjing Medical University, Nanjing 211166, China
| | - Hui Wang
- School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Ru Wang
- School of Kinesiology, Shanghai University of Sport, Shanghai 200438, China
| | - Yunqi Huang
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Tao Yang
- China National GeneBank, BGI-Shenzhen, Shenzhen 518120, China
| | - Miao Yu
- School of Life Sciences, Fudan University, Shanghai 200438, China
| | - Longqi Liu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China
| | - Chun-Xia Yi
- Department of Endocrinology and Metabolism, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105AZ Amsterdam, the Netherlands
| | - Qing-Feng Wu
- State Key Laboratory of Molecular Development Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Xingxing Kong
- School of Life Sciences, Fudan University, Shanghai 200438, China.
| | - Xun Xu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China.
| | - Shiping Liu
- BGI-Research, Hangzhou 310012, China; BGI-Research, Shenzhen 518103, China.
| | - Zhi Zhang
- State Key Laboratory of Genetic Engineering, Department of Endocrinology and Metabolism, Human Phenome Institute, Institute of Metabolism and Integrative Biology, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200438, China; School of Life Sciences, Fudan University, Shanghai 200438, China.
| | - Tiemin Liu
- State Key Laboratory of Genetic Engineering, Department of Endocrinology and Metabolism, Human Phenome Institute, Institute of Metabolism and Integrative Biology, and School of Life Sciences, Zhongshan Hospital, Fudan University, Shanghai 200438, China; School of Life Sciences, Fudan University, Shanghai 200438, China; School of Life Sciences, Institues of Biomedical Sciences, Inner Mongolia University, Hohhot 010000, China.
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40
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Wang X, Wu X, Hong N, Jin W. Progress in single-cell multimodal sequencing and multi-omics data integration. Biophys Rev 2024; 16:13-28. [PMID: 38495443 PMCID: PMC10937857 DOI: 10.1007/s12551-023-01092-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/27/2023] [Indexed: 03/19/2024] Open
Abstract
With the rapid advance of single-cell sequencing technology, cell heterogeneity in various biological processes was dissected at different omics levels. However, single-cell mono-omics results in fragmentation of information and could not provide complete cell states. In the past several years, a variety of single-cell multimodal omics technologies have been developed to jointly profile multiple molecular modalities, including genome, transcriptome, epigenome, and proteome, from the same single cell. With the availability of single-cell multimodal omics data, we can simultaneously investigate the effects of genomic mutation or epigenetic modification on transcription and translation, and reveal the potential mechanisms underlying disease pathogenesis. Driven by the massive single-cell omics data, the integration method of single-cell multi-omics data has rapidly developed. Integration of the massive multi-omics single-cell data in public databases in the future will make it possible to construct a cell atlas of multi-omics, enabling us to comprehensively understand cell state and gene regulation at single-cell resolution. In this review, we summarized the experimental methods for single-cell multimodal omics data and computational methods for multi-omics data integration. We also discussed the future development of this field.
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Affiliation(s)
- Xuefei Wang
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Xinchao Wu
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Ni Hong
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
| | - Wenfei Jin
- Shenzhen Key Laboratory of Gene Regulation and Systems Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen, China
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41
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Qiu C, Martin BK, Welsh IC, Daza RM, Le TM, Huang X, Nichols EK, Taylor ML, Fulton O, O'Day DR, Gomes AR, Ilcisin S, Srivatsan S, Deng X, Disteche CM, Noble WS, Hamazaki N, Moens CB, Kimelman D, Cao J, Schier AF, Spielmann M, Murray SA, Trapnell C, Shendure J. A single-cell time-lapse of mouse prenatal development from gastrula to birth. Nature 2024; 626:1084-1093. [PMID: 38355799 PMCID: PMC10901739 DOI: 10.1038/s41586-024-07069-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: 04/05/2023] [Accepted: 01/15/2024] [Indexed: 02/16/2024]
Abstract
The house mouse (Mus musculus) is an exceptional model system, combining genetic tractability with close evolutionary affinity to humans1,2. Mouse gestation lasts only 3 weeks, during which the genome orchestrates the astonishing transformation of a single-cell zygote into a free-living pup composed of more than 500 million cells. Here, to establish a global framework for exploring mammalian development, we applied optimized single-cell combinatorial indexing3 to profile the transcriptional states of 12.4 million nuclei from 83 embryos, precisely staged at 2- to 6-hour intervals spanning late gastrulation (embryonic day 8) to birth (postnatal day 0). From these data, we annotate hundreds of cell types and explore the ontogenesis of the posterior embryo during somitogenesis and of kidney, mesenchyme, retina and early neurons. We leverage the temporal resolution and sampling depth of these whole-embryo snapshots, together with published data4-8 from earlier timepoints, to construct a rooted tree of cell-type relationships that spans the entirety of prenatal development, from zygote to birth. Throughout this tree, we systematically nominate genes encoding transcription factors and other proteins as candidate drivers of the in vivo differentiation of hundreds of cell types. Remarkably, the most marked temporal shifts in cell states are observed within one hour of birth and presumably underlie the massive physiological adaptations that must accompany the successful transition of a mammalian fetus to life outside the womb.
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Affiliation(s)
- Chengxiang Qiu
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
| | - Beth K Martin
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | | | - Riza M Daza
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Truc-Mai Le
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Xingfan Huang
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Eva K Nichols
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Megan L Taylor
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Olivia Fulton
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Diana R O'Day
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | | | - Saskia Ilcisin
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Sanjay Srivatsan
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Medical Scientist Training Program, University of Washington, Seattle, WA, USA
| | - Xinxian Deng
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Christine M Disteche
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
- Department of Medicine, University of Washington, Seattle, WA, USA
| | - William Stafford Noble
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA
| | - Nobuhiko Hamazaki
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Howard Hughes Medical Institute, Seattle, WA, USA
| | - Cecilia B Moens
- Division of Basic Sciences, Fred Hutchinson Cancer Center, Seattle, WA, USA
| | - David Kimelman
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Department of Biochemistry, University of Washington, Seattle, WA, USA
| | - Junyue Cao
- Laboratory of Single-Cell Genomics and Population dynamics, The Rockefeller University, New York, NY, USA
| | - Alexander F Schier
- Biozentrum, University of Basel, Basel, Switzerland
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
| | - Malte Spielmann
- Max Planck Institute for Molecular Genetics, Berlin, Germany
- Institute of Human Genetics, University Hospitals Schleswig-Holstein, University of Lübeck and Kiel University, Lübeck, Kiel, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Hamburg, Lübeck, Kiel, Lübeck, Germany
| | | | - Cole Trapnell
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA
- Seattle Hub for Synthetic Biology, Seattle, WA, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA, USA.
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA.
- Howard Hughes Medical Institute, Seattle, WA, USA.
- Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA.
- Seattle Hub for Synthetic Biology, Seattle, WA, USA.
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42
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He Z, Chen Q, Wang K, Lin J, Peng Y, Zhang J, Yan X, Jie Y. Single-cell transcriptomics analysis of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. Eur J Neurosci 2024; 59:333-357. [PMID: 38221677 DOI: 10.1111/ejn.16242] [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: 05/17/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 01/16/2024]
Abstract
Single-cell transcriptomics analysis is an advanced technology that can describe the intracellular transcriptome in complex tissues. It profiles and analyses datasets by single-cell RNA sequencing. Neurodegenerative diseases are identified by the abnormal apoptosis of neurons in the brain with few or no effective therapy strategies at present, which has been a growing healthcare concern and brought a great burden to society. The transcriptome of individual cells provides deep insights into previously unforeseen cellular heterogeneity and gene expression differences in neurodegenerative disorders. It detects multiple cell subsets and functional changes during pathological progression, which deepens the understanding of the molecular underpinnings and cellular basis of neurodegenerative diseases. Furthermore, the transcriptome analysis of immune cells shows the regulation of immune response. Different subtypes of immune cells and their interaction are found to contribute to disease progression. This finding enables the discovery of novel targets and biomarkers for early diagnosis. In this review, we emphasize the principles of the technology, and its recent progress in the study of cellular heterogeneity and immune mechanisms in neurodegenerative diseases. The application of single-cell transcriptomics analysis in neurodegenerative disorders would help explore the pathogenesis of these diseases and develop novel therapeutic methods.
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Affiliation(s)
- Ziping He
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Qianqian Chen
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Kaiyue Wang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Clinical Medicine Eight-Year Program, Xiangya School of Medicine, Central South University, Changsha, China
| | - Jiang Lin
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Yilin Peng
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
| | - Jinlong Zhang
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
| | - Xisheng Yan
- Department of Cardiovascular Medicine, Wuhan Third Hospital & Tongren Hospital of Wuhan University, Wuhan, China
| | - Yan Jie
- Department of Forensic Science, School of Basic Medical Science, Central South University, Changsha, China
- Department of Forensic Science, School of Basic Medical Science, Xinjiang Medical University, Urumqi, China
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43
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Song D, Wang Q, Yan G, Liu T, Sun T, Li JJ. scDesign3 generates realistic in silico data for multimodal single-cell and spatial omics. Nat Biotechnol 2024; 42:247-252. [PMID: 37169966 DOI: 10.1038/s41587-023-01772-1] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 03/30/2023] [Indexed: 05/13/2023]
Abstract
We present a statistical simulator, scDesign3, to generate realistic single-cell and spatial omics data, including various cell states, experimental designs and feature modalities, by learning interpretable parameters from real data. Using a unified probabilistic model for single-cell and spatial omics data, scDesign3 infers biologically meaningful parameters; assesses the goodness-of-fit of inferred cell clusters, trajectories and spatial locations; and generates in silico negative and positive controls for benchmarking computational tools.
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Affiliation(s)
- Dongyuan Song
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA
| | - Qingyang Wang
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Guanao Yan
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Tianyang Liu
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Tianyi Sun
- Department of Statistics, University of California, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA, USA.
- Department of Statistics, University of California, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, CA, USA.
- Department of Computational Medicine, University of California, Los Angeles, CA, USA.
- Department of Biostatistics, University of California, Los Angeles, CA, USA.
- Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA, USA.
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44
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Ghazanfar S, Guibentif C, Marioni JC. Stabilized mosaic single-cell data integration using unshared features. Nat Biotechnol 2024; 42:284-292. [PMID: 37231260 PMCID: PMC10869270 DOI: 10.1038/s41587-023-01766-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/28/2023] [Indexed: 05/27/2023]
Abstract
Currently available single-cell omics technologies capture many unique features with different biological information content. Data integration aims to place cells, captured with different technologies, onto a common embedding to facilitate downstream analytical tasks. Current horizontal data integration techniques use a set of common features, thereby ignoring non-overlapping features and losing information. Here we introduce StabMap, a mosaic data integration technique that stabilizes mapping of single-cell data by exploiting the non-overlapping features. StabMap first infers a mosaic data topology based on shared features, then projects all cells onto supervised or unsupervised reference coordinates by traversing shortest paths along the topology. We show that StabMap performs well in various simulation contexts, facilitates 'multi-hop' mosaic data integration where some datasets do not share any features and enables the use of spatial gene expression features for mapping dissociated single-cell data onto a spatial transcriptomic reference.
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Affiliation(s)
- Shila Ghazanfar
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
- School of Mathematics and Statistics, The University of Sydney, Camperdown, New South Wales, Australia.
- Charles Perkins Centre, The University of Sydney, Camperdown, New South Wales, Australia.
| | - Carolina Guibentif
- Sahlgrenska Center for Cancer Research, Inst. Biomedicine, Dept. Microbiology and Immunology, University of Gothenburg, Gothenburg, Sweden
| | - John C Marioni
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK.
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Cambridge, UK.
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK.
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45
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Yang S, Zhou X. SRT-Server: powering the analysis of spatial transcriptomic data. Genome Med 2024; 16:18. [PMID: 38279156 PMCID: PMC10811909 DOI: 10.1186/s13073-024-01288-6] [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] [Received: 05/23/2023] [Accepted: 01/15/2024] [Indexed: 01/28/2024] Open
Abstract
BACKGROUND Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds. RESULTS Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise. CONCLUSIONS Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .
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Affiliation(s)
- Sheng Yang
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, 211166, China.
| | - Xiang Zhou
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, 48109, USA.
- Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, 48109, USA.
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46
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Yan C, Zhu Y, Chen M, Yang K, Cui F, Zou Q, Zhang Z. Integration tools for scRNA-seq data and spatial transcriptomics sequencing data. Brief Funct Genomics 2024:elae002. [PMID: 38267084 DOI: 10.1093/bfgp/elae002] [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: 08/02/2023] [Revised: 09/26/2023] [Accepted: 01/03/2024] [Indexed: 01/26/2024] Open
Abstract
Numerous methods have been developed to integrate spatial transcriptomics sequencing data with single-cell RNA sequencing (scRNA-seq) data. Continuous development and improvement of these methods offer multiple options for integrating and analyzing scRNA-seq and spatial transcriptomics data based on diverse research inquiries. However, each method has its own advantages, limitations and scope of application. Researchers need to select the most suitable method for their research purposes based on the actual situation. This review article presents a compilation of 19 integration methods sourced from a wide range of available approaches, serving as a comprehensive reference for researchers to select the suitable integration method for their specific research inquiries. By understanding the principles of these methods, we can identify their similarities and differences, comprehend their applicability and potential complementarity, and lay the foundation for future method development and understanding. This review article presents 19 methods that aim to integrate scRNA-seq data and spatial transcriptomics data. The methods are classified into two main groups and described accordingly. The article also emphasizes the incorporation of High Variance Genes in annotating various technologies, aiming to obtain biologically relevant information aligned with the intended purpose.
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Affiliation(s)
- Chaorui Yan
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Yanxu Zhu
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Miao Chen
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Kainan Yang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou, 570228, China
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47
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Yuan M, Wan H, Wang Z, Guo Q, Deng M. SPANN: annotating single-cell resolution spatial transcriptome data with scRNA-seq data. Brief Bioinform 2024; 25:bbad533. [PMID: 38279647 PMCID: PMC10818138 DOI: 10.1093/bib/bbad533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 11/13/2023] [Accepted: 12/19/2023] [Indexed: 01/28/2024] Open
Abstract
MOTIVATION The rapid development of spatial transcriptome technologies has enabled researchers to acquire single-cell-level spatial data at an affordable price. However, computational analysis tools, such as annotation tools, tailored for these data are still lacking. Recently, many computational frameworks have emerged to integrate single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics datasets. While some frameworks can utilize well-annotated scRNA-seq data to annotate spatial expression patterns, they overlook critical aspects. First, existing tools do not explicitly consider cell type mapping when aligning the two modalities. Second, current frameworks lack the capability to detect novel cells, which remains a key interest for biologists. RESULTS To address these problems, we propose an annotation method for spatial transcriptome data called SPANN. The main tasks of SPANN are to transfer cell-type labels from well-annotated scRNA-seq data to newly generated single-cell resolution spatial transcriptome data and discover novel cells from spatial data. The major innovations of SPANN come from two aspects: SPANN automatically detects novel cells from unseen cell types while maintaining high annotation accuracy over known cell types. SPANN finds a mapping between spatial transcriptome samples and RNA data prototypes and thus conducts cell-type-level alignment. Comprehensive experiments using datasets from various spatial platforms demonstrate SPANN's capabilities in annotating known cell types and discovering novel cell states within complex tissue contexts. AVAILABILITY The source code of SPANN can be accessed at https://github.com/ddb-qiwang/SPANN-torch. CONTACT dengmh@math.pku.edu.cn.
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Affiliation(s)
- Musu Yuan
- Center for Quantitative Biology, Peking University, Yiheyuan Road, 100871, Beijing, China
| | - Hui Wan
- School of Mathematical Sciences, Peking University, Yiheyuan Road, 100871, Beijing, China
| | - Zihao Wang
- Biomedical Interdisciplinary Research Center, Peking University, Yiheyuan Road, 100871, Beijing, China
| | - Qirui Guo
- Center for Quantitative Biology, Peking University, Yiheyuan Road, 100871, Beijing, China
| | - Minghua Deng
- Center for Quantitative Biology, Peking University, Yiheyuan Road, 100871, Beijing, China
- School of Mathematical Sciences, Peking University, Yiheyuan Road, 100871, Beijing, China
- Center for Statistical Science, Peking University, Yiheyuan Road, 100871, Beijing, China
- Biomedical Interdisciplinary Research Center, Peking University, Yiheyuan Road, 100871, Beijing, China
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48
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Zahedi R, Ghamsari R, Argha A, Macphillamy C, Beheshti A, Alizadehsani R, Lovell NH, Lotfollahi M, Alinejad-Rokny H. Deep learning in spatially resolved transcriptfomics: a comprehensive technical view. Brief Bioinform 2024; 25:bbae082. [PMID: 38483255 PMCID: PMC10939360 DOI: 10.1093/bib/bbae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/22/2024] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.
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Affiliation(s)
- Roxana Zahedi
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Reza Ghamsari
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Ahmadreza Argha
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Callum Macphillamy
- School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, 5371, Australia
| | - Amin Beheshti
- School of Computing, Macquarie University, Sydney, 2109, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Melbourne, VIC, 3216, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Mohammad Lotfollahi
- Computational Health Center, Helmholtz Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Hamid Alinejad-Rokny
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
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49
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Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
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Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
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50
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Patel AG, Ashenberg O, Collins NB, Segerstolpe Å, Jiang S, Slyper M, Huang X, Caraccio C, Jin H, Sheppard H, Xu K, Chang TC, Orr BA, Shirinifard A, Chapple RH, Shen A, Clay MR, Tatevossian RG, Reilly C, Patel J, Lupo M, Cline C, Dionne D, Porter CBM, Waldman J, Bai Y, Zhu B, Barrera I, Murray E, Vigneau S, Napolitano S, Wakiro I, Wu J, Grimaldi G, Dellostritto L, Helvie K, Rotem A, Lako A, Cullen N, Pfaff KL, Karlström Å, Jané-Valbuena J, Todres E, Thorner A, Geeleher P, Rodig SJ, Zhou X, Stewart E, Johnson BE, Wu G, Chen F, Yu J, Goltsev Y, Nolan GP, Rozenblatt-Rosen O, Regev A, Dyer MA. A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.07.574538. [PMID: 38260392 PMCID: PMC10802404 DOI: 10.1101/2024.01.07.574538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.
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Affiliation(s)
- Anand G Patel
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- These authors contributed equally
| | - Orr Ashenberg
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- These authors contributed equally
| | - Natalie B Collins
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- These authors contributed equally
| | - Åsa Segerstolpe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sizun Jiang
- Department of Pathology, Stanford University, Stanford, CA, USA
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Michal Slyper
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Huang
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Chiara Caraccio
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Hongjian Jin
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Heather Sheppard
- Comparative Pathology Core, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ke Xu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Ti-Cheng Chang
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Brent A Orr
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Abbas Shirinifard
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Richard H Chapple
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Amber Shen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Michael R Clay
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ruth G Tatevossian
- Cancer Biomarkers Laboratory, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Colleen Reilly
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Jaimin Patel
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Marybeth Lupo
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Cynthia Cline
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Danielle Dionne
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Caroline B M Porter
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Julia Waldman
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Yunhao Bai
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Bokai Zhu
- Department of Pathology, Stanford University, Stanford, CA, USA
| | | | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Sébastien Vigneau
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Sara Napolitano
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Isaac Wakiro
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Jingyi Wu
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Grace Grimaldi
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Laura Dellostritto
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Karla Helvie
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Asaf Rotem
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Ana Lako
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Nicole Cullen
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Kathleen L Pfaff
- Center for Immuno-Oncology (CIO), Dana-Farber Cancer Institute, Boston, MA, USA
| | - Åsa Karlström
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Judit Jané-Valbuena
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Ellen Todres
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron Thorner
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Paul Geeleher
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Scott J Rodig
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - Xin Zhou
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Elizabeth Stewart
- Department of Oncology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Bruce E Johnson
- Center for Cancer Genomics, Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Gang Wu
- Center for Applied Bioinformatics, St. Jude Children's Research Hospital, Memphis, TN, USA
- Department of Pathology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Fei Chen
- Broad Institute of MIT and Harvard, Boston, MA, USA
| | - Jiyang Yu
- Department of Computational Biology, St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Yury Goltsev
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Garry P Nolan
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Howard Hughes Medical Institute, Chevy Chase, MD, USA
- Koch Institute of Integrative Cancer Research, Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
- Current address: Research and Early Development, Genentech Inc., South San Francisco, CA, 94080, USA
- Lead contacts
| | - Michael A Dyer
- Department of Developmental Neurobiology, St. Jude Children's Research Hospital, Memphis, TN, USA
- Lead contacts
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