1
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Defard T, Laporte H, Ayan M, Soulier J, Curras-Alonso S, Weber C, Massip F, Londoño-Vallejo JA, Fouillade C, Mueller F, Walter T. A point cloud segmentation framework for image-based spatial transcriptomics. Commun Biol 2024; 7:823. [PMID: 38971915 PMCID: PMC11227573 DOI: 10.1038/s42003-024-06480-3] [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: 12/06/2023] [Accepted: 06/20/2024] [Indexed: 07/08/2024] Open
Abstract
Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https://github.com/fish-quant/ComSeg .
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Affiliation(s)
- Thomas Defard
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, 75006, Paris, France
- Institut Curie, PSL University, 75005, Paris, France
- INSERM, U900, 75005, Paris, France
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015, Paris, France
| | - Hugo Laporte
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
- Institute of Cell Biology (Cancer Research), University Hospital Essen, Essen, Germany
| | - Mallick Ayan
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Juliette Soulier
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Sandra Curras-Alonso
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Christian Weber
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015, Paris, France
- Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015, Paris, France
| | - Florian Massip
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, 75006, Paris, France
- Institut Curie, PSL University, 75005, Paris, France
- INSERM, U900, 75005, Paris, France
| | - José-Arturo Londoño-Vallejo
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Charles Fouillade
- Institut Curie, Inserm U1021-CNRS UMR 3347, University Paris-Saclay, PSL Research University, Centre Universitaire, Orsay, Cedex, France
| | - Florian Mueller
- Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, F-75015, Paris, France.
- Institut Pasteur, Université Paris Cité, Photonic Bio-Imaging, Centre de Ressources et Recherches Technologiques (UTechS-PBI, C2RT), F-75015, Paris, France.
| | - Thomas Walter
- Centre for Computational Biology (CBIO), Mines Paris, PSL University, 75006, Paris, France.
- Institut Curie, PSL University, 75005, Paris, France.
- INSERM, U900, 75005, Paris, France.
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2
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Andersson A, Behanova A, Avenel C, Windhager J, Malmberg F, Wählby C. Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data. Cytometry A 2024. [PMID: 38958502 DOI: 10.1002/cyto.a.24884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/30/2024] [Accepted: 06/13/2024] [Indexed: 07/04/2024]
Abstract
Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces "Points2Regions," a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical andk $$ k $$ -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.
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Affiliation(s)
- Axel Andersson
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Andrea Behanova
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Jonas Windhager
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Carolina Wählby
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
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3
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Jain S, Eadon MT. Spatial transcriptomics in health and disease. Nat Rev Nephrol 2024:10.1038/s41581-024-00841-1. [PMID: 38719971 DOI: 10.1038/s41581-024-00841-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/10/2024] [Indexed: 07/05/2024]
Abstract
The ability to localize hundreds of macromolecules to discrete locations, structures and cell types in a tissue is a powerful approach to understand the cellular and spatial organization of an organ. Spatially resolved transcriptomic technologies enable mapping of transcripts at single-cell or near single-cell resolution in a multiplex manner. The rapid development of spatial transcriptomic technologies has accelerated the pace of discovery in several fields, including nephrology. Its application to preclinical models and human samples has provided spatial information about new cell types discovered by single-cell sequencing and new insights into the cell-cell interactions within neighbourhoods, and has improved our understanding of the changes that occur in response to injury. Integration of spatial transcriptomic technologies with other omics methods, such as proteomics and spatial epigenetics, will further facilitate the generation of comprehensive molecular atlases, and provide insights into the dynamic relationships of molecular components in homeostasis and disease. This Review provides an overview of current and emerging spatial transcriptomic methods, their applications and remaining challenges for the field.
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Affiliation(s)
- Sanjay Jain
- Division of Nephrology, Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA.
| | - Michael T Eadon
- Division of Nephrology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
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4
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Gao Y, Zhao T, Tu T, Tian Y, Zhang Y, Liu Z, Zheng Y, Chen X, Wang H. Spatiotemporal links between meteorological and agricultural droughts impacted by tropical cyclones in China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 912:169119. [PMID: 38070559 DOI: 10.1016/j.scitotenv.2023.169119] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 12/03/2023] [Accepted: 12/03/2023] [Indexed: 12/18/2023]
Abstract
Both droughts and tropical cyclones (TCs) are among the world's most widespread natural disasters. This paper is concentrated on the effects of TCs on the links between meteorological droughts (MDs) and agricultural droughts (ADs). Specifically, changes in characteristics of drought events and variations in propagation features of matched MD and AD event pairs are quantified by using the renowned three-dimensional connected components algorithm; both alleviation and exacerbation effects of TCs are evaluated; and the Spearman's correlation is employed to identify potential contributors to exacerbated droughts after TCs. The results show that TCs exhibit more pronounced and widespread alleviation effects on MD events compared to AD events. >98 % of small-scale drought events are terminated by TCs, leading to 65 % reduction in the total area of MD events smaller than 50,000 km2 and 32 % reduction in AD events of the same scale. In the meantime, TCs can reshape the spatiotemporal links between MDs and ADs by reducing the overall propagation rate from 77 % to 40 % and ameliorating the characteristics of drought event pairs with higher propagation efficiency, by >40 %. After TCs, over 55 % of drought exacerbations in TC-affected regions occur first in the vicinity of the residual large-scale AD events. This occurrence is partially associated with the reduction in moisture exports from these residual droughts downwind to the interior of TC-affected regions, a process potentially facilitated by the TC-induced temperature cooling. The in-depth evaluation of this paper presents useful information for better drought preparation and mitigation under TCs.
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Affiliation(s)
- Yankang Gao
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Tongtiegang Zhao
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China.
| | - Tongbi Tu
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Yu Tian
- Department of Water Resources, China Institute of Water Resource and Hydropower Research, Beijing 100038, China
| | - Yongyong Zhang
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
| | - Zhiyong Liu
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Yanhui Zheng
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Xiaohong Chen
- Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) and School of Civil Engineering, Sun Yat-Sen University, Guangzhou, China
| | - Hao Wang
- Department of Water Resources, China Institute of Water Resource and Hydropower Research, Beijing 100038, China
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5
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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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6
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Chen JG, Chávez-Fuentes JC, O'Brien M, Xu J, Ruiz E, Wang W, Amin I, Sarfraz I, Guckhool P, Sistig A, Yuan GC, Dries R. Giotto Suite: a multi-scale and technology-agnostic spatial multi-omics analysis ecosystem. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.26.568752. [PMID: 38077085 PMCID: PMC10705291 DOI: 10.1101/2023.11.26.568752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/24/2023]
Abstract
Emerging spatial omics technologies continue to advance the molecular mapping of tissue architecture and the investigation of gene regulation and cellular crosstalk, which in turn provide new mechanistic insights into a wide range of biological processes and diseases. Such technologies provide an increasingly large amount of information content at multiple spatial scales. However, representing and harmonizing diverse spatial datasets efficiently, including combining multiple modalities or spatial scales in a scalable and flexible manner, remains a substantial challenge. Here, we present Giotto Suite, a suite of open-source software packages that underlies a fully modular and integrated spatial data analysis toolbox. At its core, Giotto Suite is centered around an innovative and technology-agnostic data framework embedded in the R software environment, which allows the representation and integration of virtually any type of spatial omics data at any spatial resolution. In addition, Giotto Suite provides both scalable and extensible end-to-end solutions for data analysis, integration, and visualization. Giotto Suite integrates molecular, morphology, spatial, and annotated feature information to create a responsive and flexible workflow for multi-scale, multi-omic data analyses, as demonstrated here by applications to several state-of-the-art spatial technologies. Furthermore, Giotto Suite builds upon interoperable interfaces and data structures that bridge the established fields of genomics and spatial data science, thereby enabling independent developers to create custom-engineered pipelines. As such, Giotto Suite creates an immersive ecosystem for spatial multi-omic data analysis.
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Affiliation(s)
- Jiaji George Chen
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | | | - Matthew O'Brien
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Junxiang Xu
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Edward Ruiz
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Wen Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Iqra Amin
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Irzam Sarfraz
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
| | - Pratishtha Guckhool
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ruben Dries
- Section of Hematology and Medical Oncology, Boston University School of Medicine and Boston Medical Center, Boston, MA 02118, USA
- Division of Computational Biomedicine, Boston University School of Medicine, Boston, MA 02118, USA
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7
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Alexandrov T, Saez‐Rodriguez J, Saka SK. Enablers and challenges of spatial omics, a melting pot of technologies. Mol Syst Biol 2023; 19:e10571. [PMID: 37842805 PMCID: PMC10632737 DOI: 10.15252/msb.202110571] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 10/17/2023] Open
Abstract
Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.
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Affiliation(s)
- Theodore Alexandrov
- Structural and Computational Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- BioInnovation InstituteCopenhagenDenmark
| | - Julio Saez‐Rodriguez
- Molecular Medicine Partnership UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational BiomedicineHeidelberg UniversityHeidelbergGermany
| | - Sinem K Saka
- Genome Biology UnitEuropean Molecular Biology LaboratoryHeidelbergGermany
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8
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Si Y, Lee C, Hwang Y, Yun JH, Cheng W, Cho CS, Quiros M, Nusrat A, Zhang W, Jun G, Zöllner S, Lee JH, Kang HM. FICTURE: Scalable segmentation-free analysis of submicron resolution spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.04.565621. [PMID: 37961699 PMCID: PMC10635162 DOI: 10.1101/2023.11.04.565621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Spatial transcriptomics (ST) technologies have advanced to enable transcriptome-wide gene expression analysis at submicron resolution over large areas. Analysis of high-resolution ST data relies heavily on image-based cell segmentation or gridding, which often fails in complex tissues due to diversity and irregularity of cell size and shape. Existing segmentation-free analysis methods scale only to small regions and a small number of genes, limiting their utility in high-throughput studies. Here we present FICTURE, a segmentation-free spatial factorization method that can handle transcriptome-wide data labeled with billions of submicron resolution spatial coordinates. FICTURE is orders of magnitude more efficient than existing methods and it is compatible with both sequencing- and imaging-based ST data. FICTURE reveals the microscopic ST architecture for challenging tissues, such as vascular, fibrotic, muscular, and lipid-laden areas in real data where previous methods failed. FICTURE's cross-platform generality, scalability, and precision make it a powerful tool for exploring high-resolution ST.
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Affiliation(s)
- Yichen Si
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
| | - ChangHee Lee
- Department of Genetics, Harvard Medical School, Boston, MA 02115, USA
| | - Yongha Hwang
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, 48109-2200, USA
- Space Planning and Analysis, University of Michigan Medical School, Ann Arbor, Michigan 48109-2800, USA
| | - Jeong H. Yun
- Channing Division of Network Medicine, and Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Weiqiu Cheng
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
| | - Chun-Seok Cho
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, 48109-2200, USA
| | - Miguel Quiros
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, 48109, USA
| | - Asma Nusrat
- Department of Pathology, University of Michigan Medical School, Ann Arbor, Michigan, 48109, USA
| | - Weizhou Zhang
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, College of Medicine, Gainesville, FL 32610-0275, USA
| | - Goo Jun
- Human Genetics Center, School of Public Health, University of Texas Health Science Center Houston, Houston, TX 77030, USA
| | - Sebastian Zöllner
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
| | - Jun Hee Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, Michigan, 48109-2200, USA
| | - Hyun Min Kang
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, Michigan, 48109-2029, USA
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9
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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10
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Mages S, Moriel N, Avraham-Davidi I, Murray E, Watter J, Chen F, Rozenblatt-Rosen O, Klughammer J, Regev A, Nitzan M. TACCO unifies annotation transfer and decomposition of cell identities for single-cell and spatial omics. Nat Biotechnol 2023; 41:1465-1473. [PMID: 36797494 PMCID: PMC10513360 DOI: 10.1038/s41587-023-01657-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 01/02/2023] [Indexed: 02/18/2023]
Abstract
Transferring annotations of single-cell-, spatial- and multi-omics data is often challenging owing both to technical limitations, such as low spatial resolution or high dropout fraction, and to biological variations, such as continuous spectra of cell states. Based on the concept that these data are often best described as continuous mixtures of cells or molecules, we present a computational framework for the transfer of annotations to cells and their combinations (TACCO), which consists of an optimal transport model extended with different wrappers to annotate a wide variety of data. We apply TACCO to identify cell types and states, decipher spatiomolecular tissue structure at the cell and molecular level and resolve differentiation trajectories using synthetic and biological datasets. While matching or exceeding the accuracy of specialized tools for the individual tasks, TACCO reduces the computational requirements by up to an order of magnitude and scales to larger datasets (for example, considering the runtime of annotation transfer for 1 M simulated dropout observations).
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Affiliation(s)
- Simon Mages
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Gene Center and Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Noa Moriel
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Inbal Avraham-Davidi
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Evan Murray
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jan Watter
- Gene Center and Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Fei Chen
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Johanna Klughammer
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Gene Center and Department of Biochemistry, Ludwig-Maximilians-University Munich, Munich, Germany.
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Massachusetts Institute of Technology, Cambridge, MA, USA.
- Genentech, South San Francisco, CA, USA.
| | - Mor Nitzan
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Racah Institute of Physics, The Hebrew University of Jerusalem, Jerusalem, Israel.
- Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel.
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11
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Tang Z, Li Z, Hou T, Zhang T, Yang B, Su J, Song Q. SiGra: single-cell spatial elucidation through an image-augmented graph transformer. Nat Commun 2023; 14:5618. [PMID: 37699885 PMCID: PMC10497630 DOI: 10.1038/s41467-023-41437-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 09/01/2023] [Indexed: 09/14/2023] Open
Abstract
Recent advances in high-throughput molecular imaging have pushed spatial transcriptomics technologies to subcellular resolution, which surpasses the limitations of both single-cell RNA-seq and array-based spatial profiling. The multichannel immunohistochemistry images in such data provide rich information on the cell types, functions, and morphologies of cellular compartments. In this work, we developed a method, single-cell spatial elucidation through image-augmented Graph transformer (SiGra), to leverage such imaging information for revealing spatial domains and enhancing substantially sparse and noisy transcriptomics data. SiGra applies hybrid graph transformers over a single-cell spatial graph. SiGra outperforms state-of-the-art methods on both single-cell and spot-level spatial transcriptomics data from complex tissues. The inclusion of immunohistochemistry images improves the model performance by 37% (95% CI: 27-50%). SiGra improves the characterization of intratumor heterogeneity and intercellular communication and recovers the known microscopic anatomy. Overall, SiGra effectively integrates different spatial modality data to gain deep insights into spatial cellular ecosystems.
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Affiliation(s)
- Ziyang Tang
- Department of Computer and Information Technology, Purdue University, Indiana, USA
| | - Zuotian Li
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA
- Department of Computer Graphics Technology, Purdue University, Indiana, USA
| | - Tieying Hou
- Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indiana, USA
| | - Tonglin Zhang
- Department of Statistics, Purdue University, Indiana, USA
| | - Baijian Yang
- Department of Computer and Information Technology, Purdue University, Indiana, USA.
| | - Jing Su
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indiana, USA.
| | - Qianqian Song
- Department of Cancer Biology, Wake Forest University School of Medicine, North Carolina, USA.
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Florida, USA.
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12
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Magoulopoulou A, Salas SM, Tiklová K, Samuelsson ER, Hilscher MM, Nilsson M. Padlock Probe-Based Targeted In Situ Sequencing: Overview of Methods and Applications. Annu Rev Genomics Hum Genet 2023; 24:133-150. [PMID: 37018847 DOI: 10.1146/annurev-genom-102722-092013] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/07/2023]
Abstract
Elucidating spatiotemporal changes in gene expression has been an essential goal in studies of health, development, and disease. In the emerging field of spatially resolved transcriptomics, gene expression profiles are acquired with the tissue architecture maintained, sometimes at cellular resolution. This has allowed for the development of spatial cell atlases, studies of cell-cell interactions, and in situ cell typing. In this review, we focus on padlock probe-based in situ sequencing, which is a targeted spatially resolved transcriptomic method. We summarize recent methodological and computational tool developments and discuss key applications. We also discuss compatibility with other methods and integration with multiomic platforms for future applications.
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Affiliation(s)
- Anastasia Magoulopoulou
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Sergio Marco Salas
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Katarína Tiklová
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Erik Reinhold Samuelsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Markus M Hilscher
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
| | - Mats Nilsson
- Department of Biochemistry and Biophysics, Science for Life Laboratory, Stockholm University, Solna, Sweden; , , , , ,
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13
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Heumos L, Schaar AC, Lance C, Litinetskaya A, Drost F, Zappia L, Lücken MD, Strobl DC, Henao J, Curion F, Schiller HB, Theis FJ. Best practices for single-cell analysis across modalities. Nat Rev Genet 2023; 24:550-572. [PMID: 37002403 PMCID: PMC10066026 DOI: 10.1038/s41576-023-00586-w] [Citation(s) in RCA: 137] [Impact Index Per Article: 137.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/14/2023] [Indexed: 04/03/2023]
Abstract
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
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Affiliation(s)
- Lukas Heumos
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany
| | - Christopher Lance
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany
| | - Anastasia Litinetskaya
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Felix Drost
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Luke Zappia
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Malte D Lücken
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany
| | - Daniel C Strobl
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany
| | - Juan Henao
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
| | - Fabiola Curion
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany
| | - Herbert B Schiller
- Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
- Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
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14
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Gurkar AU, Gerencser AA, Mora AL, Nelson AC, Zhang AR, Lagnado AB, Enninful A, Benz C, Furman D, Beaulieu D, Jurk D, Thompson EL, Wu F, Rodriguez F, Barthel G, Chen H, Phatnani H, Heckenbach I, Chuang JH, Horrell J, Petrescu J, Alder JK, Lee JH, Niedernhofer LJ, Kumar M, Königshoff M, Bueno M, Sokka M, Scheibye-Knudsen M, Neretti N, Eickelberg O, Adams PD, Hu Q, Zhu Q, Porritt RA, Dong R, Peters S, Victorelli S, Pengo T, Khaliullin T, Suryadevara V, Fu X, Bar-Joseph Z, Ji Z, Passos JF. Spatial mapping of cellular senescence: emerging challenges and opportunities. NATURE AGING 2023; 3:776-790. [PMID: 37400722 PMCID: PMC10505496 DOI: 10.1038/s43587-023-00446-6] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 05/30/2023] [Indexed: 07/05/2023]
Abstract
Cellular senescence is a well-established driver of aging and age-related diseases. There are many challenges to mapping senescent cells in tissues such as the absence of specific markers and their relatively low abundance and vast heterogeneity. Single-cell technologies have allowed unprecedented characterization of senescence; however, many methodologies fail to provide spatial insights. The spatial component is essential, as senescent cells communicate with neighboring cells, impacting their function and the composition of extracellular space. The Cellular Senescence Network (SenNet), a National Institutes of Health (NIH) Common Fund initiative, aims to map senescent cells across the lifespan of humans and mice. Here, we provide a comprehensive review of the existing and emerging methodologies for spatial imaging and their application toward mapping senescent cells. Moreover, we discuss the limitations and challenges inherent to each technology. We argue that the development of spatially resolved methods is essential toward the goal of attaining an atlas of senescent cells.
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Affiliation(s)
- Aditi U Gurkar
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Ana L Mora
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Andrew C Nelson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Anru R Zhang
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Anthony B Lagnado
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Archibald Enninful
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | | | - David Furman
- Buck Institute for Research on Aging, Novato, CA, USA
- Stanford 1000 Immunomes Project, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Instituto de Investigaciones en Medicina Traslacional (IIMT), Universidad Austral, Pilar, Argentina
| | - Delphine Beaulieu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Diana Jurk
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Elizabeth L Thompson
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Fei Wu
- Buck Institute for Research on Aging, Novato, CA, USA
| | - Fernanda Rodriguez
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Grant Barthel
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Hao Chen
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Hemali Phatnani
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | | | - Jeffrey H Chuang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Jeremy Horrell
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Joana Petrescu
- Columbia University Irving Medical Center and New York Genome Center, Columbia University, New York, NY, USA
| | - Jonathan K Alder
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jun Hee Lee
- Department of Molecular and Integrative Physiology, University of Michigan Medical School, Ann Arbor, MI, USA
| | - Laura J Niedernhofer
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Manoj Kumar
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Melanie Königshoff
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Marta Bueno
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Miiko Sokka
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | | | - Nicola Neretti
- Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, RI, USA
| | - Oliver Eickelberg
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Peter D Adams
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Qianjiang Hu
- Aging Institute, University of Pittsburgh School of Medicine/UPMC and Division of Pulmonary, Allergy and Critical Care Medicine, Allergy, Critical Care and Sleep Medicine, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | - Quan Zhu
- University of California, San Diego, CA, USA
| | - Rebecca A Porritt
- Sanford Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA
| | - Runze Dong
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Samuel Peters
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Stella Victorelli
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA
| | - Thomas Pengo
- Department of Laboratory Medicine and Pathology, Department of Biochemistry, Molecular Biology and Biophysics, Department of Neuroscience and Institute on the Biology of Aging and Metabolism, Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, MN, USA
| | - Timur Khaliullin
- Dorothy M. Davis Heart and Lung Research Institute, Division of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, the Ohio State University, Columbus, OH, USA
| | - Vidyani Suryadevara
- Department of Radiology, Molecular Imaging Program at Stanford, Stanford University, Stanford, CA, USA
| | - Xiaonan Fu
- Department of Biochemistry, Institute for Protein Design and Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Ziv Bar-Joseph
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Zhicheng Ji
- Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Medicine and Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - João F Passos
- Department of Physiology and Biomedical Engineering, Robert and Arlene Kogod Center on Aging, Mayo Clinic, Rochester, MN, USA.
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15
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Park H, Jo SH, Lee RH, Macks CP, Ku T, Park J, Lee CW, Hur JK, Sohn CH. Spatial Transcriptomics: Technical Aspects of Recent Developments and Their Applications in Neuroscience and Cancer Research. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2206939. [PMID: 37026425 PMCID: PMC10238226 DOI: 10.1002/advs.202206939] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Revised: 03/10/2023] [Indexed: 06/04/2023]
Abstract
Spatial transcriptomics is a newly emerging field that enables high-throughput investigation of the spatial localization of transcripts and related analyses in various applications for biological systems. By transitioning from conventional biological studies to "in situ" biology, spatial transcriptomics can provide transcriptome-scale spatial information. Currently, the ability to simultaneously characterize gene expression profiles of cells and relevant cellular environment is a paradigm shift for biological studies. In this review, recent progress in spatial transcriptomics and its applications in neuroscience and cancer studies are highlighted. Technical aspects of existing technologies and future directions of new developments (as of March 2023), computational analysis of spatial transcriptome data, application notes in neuroscience and cancer studies, and discussions regarding future directions of spatial multi-omics and their expanding roles in biomedical applications are emphasized.
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Affiliation(s)
- Han‐Eol Park
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
- School of Biological SciencesSeoul National UniversitySeoul08826Republic of Korea
| | - Song Hyun Jo
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Rosalind H. Lee
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Christian P. Macks
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
| | - Taeyun Ku
- Graduate School of Medical Science and EngineeringKorea Advanced Institute of Science and Technology (KAIST)Daejeon34141Republic of Korea
| | - Jihwan Park
- School of Life SciencesGwangju Institute of Science and Technology (GIST)Gwangju61005Republic of Korea
| | - Chung Whan Lee
- Department of ChemistryGachon UniversitySeongnamGyeonggi‐do13120Republic of Korea
| | - Junho K. Hur
- Department of GeneticsCollege of MedicineHanyang UniversitySeoul04763Republic of Korea
| | - Chang Ho Sohn
- Center for NanomedicineInstitute for Basic ScienceYonsei UniversitySeoul03722Republic of Korea
- Graduate Program in Nanobiomedical EngineeringAdvanced Science InstituteYonsei UniversitySeoul03722Republic of Korea
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16
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Sullivan KE, Kraus L, Kapustina M, Wang L, Stach TR, Lemire AL, Clements J, Cembrowski MS. Sharp cell-type-identity changes differentiate the retrosplenial cortex from the neocortex. Cell Rep 2023; 42:112206. [PMID: 36881508 DOI: 10.1016/j.celrep.2023.112206] [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: 07/26/2022] [Revised: 12/22/2022] [Accepted: 02/15/2023] [Indexed: 03/08/2023] Open
Abstract
The laminae of the neocortex are fundamental processing layers of the mammalian brain. Notably, such laminae are believed to be relatively stereotyped across short spatial scales such that shared laminae between nearby brain regions exhibit similar constituent cells. Here, we consider a potential exception to this rule by studying the retrosplenial cortex (RSC), a brain region known for sharp cytoarchitectonic differences across its granular-dysgranular border. Using a variety of transcriptomics techniques, we identify, spatially map, and interpret the excitatory cell-type landscape of the mouse RSC. In doing so, we uncover that RSC gene expression and cell types change sharply at the granular-dysgranular border. Additionally, supposedly homologous laminae between the RSC and the neocortex are effectively wholly distinct in their cell-type composition. In collection, the RSC exhibits a variety of intrinsic cell-type specializations and embodies an organizational principle wherein cell-type identities can vary sharply within and between brain regions.
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Affiliation(s)
- Kaitlin E Sullivan
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada
| | - Larissa Kraus
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada
| | - Margarita Kapustina
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada
| | - Lihua Wang
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada
| | - Tara R Stach
- School of Biomedical Engineering, Biomedical Research Centre, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, Canada
| | - Andrew L Lemire
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada
| | - Jody Clements
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada
| | - Mark S Cembrowski
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada; Janelia Research Campus, HHMI, 19700 Helix Dr, Ashburn, VA, USA.
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17
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Keller D, Verasztó C, Markram H. Cell-type-specific densities in mouse somatosensory cortex derived from scRNA-seq and in situ RNA hybridization. Front Neuroanat 2023; 17:1118170. [PMID: 37007642 PMCID: PMC10055737 DOI: 10.3389/fnana.2023.1118170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 01/30/2023] [Indexed: 03/17/2023] Open
Abstract
Cells in the mammalian cerebral cortex exhibit layer-dependent patterns in their distribution. Classical methods of determining cell type distributions typically employ a painstaking process of large-scale sampling and characterization of cellular composition. We found that by combining in situ hybridization (ISH) images with cell-type-specific transcriptomes, position-dependent cortical composition in P56 mouse could be estimated in the somatosensory cortex. The method uses ISH images from the Allen Institute for Brain Science. There are two novel aspects of the methodology. First, it is not necessary to select a subset of genes that are particular for a cell type of interest, nor is it necessary to only use ISH images with low variability among samples. Second, the method also compensated for differences in soma size and incompleteness of the transcriptomes. The soma size compensation is particularly important in order to obtain quantitative estimates since relying on bulk expression alone would overestimate the contribution of larger cells. Predicted distributions of broader classes of cell types agreed with literature distributions. The primary result is that there is a high degree of substructure in the distribution of transcriptomic types beyond the resolution of layers. Furthermore, transcriptomic cell types each exhibited characteristic soma size distributions. Results suggest that the method could also be employed to assign transcriptomic cell types to well-aligned image sets in the entire brain.
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18
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Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment. Biosci Rep 2023; 43:232194. [PMID: 36459212 PMCID: PMC9950536 DOI: 10.1042/bsr20221680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 11/30/2022] [Accepted: 12/02/2022] [Indexed: 12/03/2022] Open
Abstract
Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.
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19
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Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH. Nat Biotechnol 2023; 41:222-231. [PMID: 36138169 PMCID: PMC9931581 DOI: 10.1038/s41587-022-01455-3] [Citation(s) in RCA: 22] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 08/01/2022] [Indexed: 12/17/2022]
Abstract
Methods to spatially profile the transcriptome are dominated by a trade-off between resolution and throughput. Here we develop a method named Enhanced ELectric Fluorescence in situ Hybridization (EEL FISH) that can rapidly process large tissue samples without compromising spatial resolution. By electrophoretically transferring RNA from a tissue section onto a capture surface, EEL speeds up data acquisition by reducing the amount of imaging needed, while ensuring that RNA molecules move straight down toward the surface, preserving single-cell resolution. We apply EEL on eight entire sagittal sections of the mouse brain and measure the expression patterns of up to 440 genes to reveal complex tissue organization. Moreover, EEL can be used to study challenging human samples by removing autofluorescent lipofuscin, enabling the spatial transcriptome of the human visual cortex to be visualized. We provide full hardware specifications, all protocols and complete software for instrument control, image processing, data analysis and visualization.
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20
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Glutathione in the Pons Is Associated With Clinical Status Improvements in Subacute Spinal Cord Injury. Invest Radiol 2023; 58:131-138. [PMID: 35926077 DOI: 10.1097/rli.0000000000000905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
OBJECTIVES In spinal cord injury (SCI), the primary mechanical injury is followed by secondary sequelae that develop over the subsequent months and manifests in biochemical, functional, and microstructural alterations, at the site of direct injury but also in the spinal cord tissue above and below the actual lesion site. Noninvasive magnetic resonance spectroscopy (MRS) can be used to assess biochemical modulation occurring in the secondary injury phase, in addition to and supporting conventional MRI, and might help predict and improve patient outcome. In this article, we aimed to examine the metabolic levels in the pons of subacute SCI by means of in vivo proton MRS at 3 T and explore the association to clinical scores. MATERIALS AND METHODS In this prospective study, between November 2015 and February 2018, single-voxel short-echo MRS data were acquired in healthy controls and in SCI subjects in the pons once during rehabilitation. Besides the single-point MRS examination, in addition, in participants with SCI, the clinical status (ie, motor, light touch, and pinprick scores) was assessed twice: (1) around the MRS session (approximately 10 weeks postinjury) and (2) before discharge (at approximately 9 months postinjury). The group differences were assessed with Kruskal-Wallis test, the post hoc comparison was assessed with Wilcoxon rank sum test, and the clinical correlations were conducted with Spearman rank correlation test. Bayes factor calculations completed the statistical part providing relevant evidence values. RESULTS Twenty healthy controls (median age, 50 years; interquartile range, 41-55 years; 18 men) and 18 subjects with traumatic SCI (median age, 50 years; interquartile range, 32-58 years; 16 men) are included. Group comparison showed an increase of total N -acetylaspartate and combined glutamate and glutamine levels in complete SCI and a reduction of total creatine in incomplete paraplegic SCI. The proton MRS-based glutathione levels at baseline correlate to the motor score improvement during rehabilitation in incomplete subacute SCI. CONCLUSIONS This exploratory study showed an association of the metabolite concentration of glutathione in the pons assessed at approximately 10 weeks after injury with the improvements of the motor score during the rehabilitation. Pontine glutathione levels in subjects with traumatic subacute incomplete SCI acquired remote from the injury site correlate to clinical score and might therefore be beneficial in the rehabilitation assessments.
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Yue L, Liu F, Hu J, Yang P, Wang Y, Dong J, Shu W, Huang X, Wang S. A guidebook of spatial transcriptomic technologies, data resources and analysis approaches. Comput Struct Biotechnol J 2023; 21:940-955. [PMID: 38213887 PMCID: PMC10781722 DOI: 10.1016/j.csbj.2023.01.016] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 01/13/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
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Affiliation(s)
- Liangchen Yue
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Feng Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing 400016, China
| | - Jiongsong Hu
- University of South China, Hengyang, Hunan 421001, China
| | - Pin Yang
- Anhui Medical University, Hefei 230022, Anhui, China
| | - Yuxiang Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Junguo Dong
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Wenjie Shu
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
| | - Xingxu Huang
- Zhejiang Provincial Key Laboratory of Pancreatic Disease, the First Affiliated Hospital, and Institute of Translational Medicine, Zhejiang University School of Medicine, Hangzhou 310029, China
- School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China
| | - Shengqi Wang
- Beijing Institute of Microbiology and Epidemiology, Beijing 100850, China
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22
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Warchol S, Krueger R, Nirmal AJ, Gaglia G, Jessup J, Ritch CC, Hoffer J, Muhlich J, Burger ML, Jacks T, Santagata S, Sorger PK, Pfister H. Visinity: Visual Spatial Neighborhood Analysis for Multiplexed Tissue Imaging Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:106-116. [PMID: 36170403 PMCID: PMC10043053 DOI: 10.1109/tvcg.2022.3209378] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
New highly-multiplexed imaging technologies have enabled the study of tissues in unprecedented detail. These methods are increasingly being applied to understand how cancer cells and immune response change during tumor development, progression, and metastasis, as well as following treatment. Yet, existing analysis approaches focus on investigating small tissue samples on a per-cell basis, not taking into account the spatial proximity of cells, which indicates cell-cell interaction and specific biological processes in the larger cancer microenvironment. We present Visinity, a scalable visual analytics system to analyze cell interaction patterns across cohorts of whole-slide multiplexed tissue images. Our approach is based on a fast regional neighborhood computation, leveraging unsupervised learning to quantify, compare, and group cells by their surrounding cellular neighborhood. These neighborhoods can be visually analyzed in an exploratory and confirmatory workflow. Users can explore spatial patterns present across tissues through a scalable image viewer and coordinated views highlighting the neighborhood composition and spatial arrangements of cells. To verify or refine existing hypotheses, users can query for specific patterns to determine their presence and statistical significance. Findings can be interactively annotated, ranked, and compared in the form of small multiples. In two case studies with biomedical experts, we demonstrate that Visinity can identify common biological processes within a human tonsil and uncover novel white-blood cell networks and immune-tumor interactions.
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23
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Seferbekova Z, Lomakin A, Yates LR, Gerstung M. Spatial biology of cancer evolution. Nat Rev Genet 2022; 24:295-313. [PMID: 36494509 DOI: 10.1038/s41576-022-00553-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/04/2022] [Indexed: 12/13/2022]
Abstract
The natural history of cancers can be understood through the lens of evolution given that the driving forces of cancer development are mutation and selection of fitter clones. Cancer growth and progression are spatial processes that involve the breakdown of normal tissue organization, invasion and metastasis. For these reasons, spatial patterns are an integral part of histological tumour grading and staging as they measure the progression from normal to malignant disease. Furthermore, tumour cells are part of an ecosystem of tumour cells and their surrounding tumour microenvironment. A range of new spatial genomic, transcriptomic and proteomic technologies offers new avenues for the study of cancer evolution with great molecular and spatial detail. These methods enable precise characterizations of the tumour microenvironment, cellular interactions therein and micro-anatomical structures. In conjunction with spatial genomics, it emerges that tumours and microenvironments co-evolve, which helps explain observable patterns of heterogeneity and offers new routes for therapeutic interventions.
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24
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Su M, Pan T, Chen QZ, Zhou WW, Gong Y, Xu G, Yan HY, Li S, Shi QZ, Zhang Y, He X, Jiang CJ, Fan SC, Li X, Cairns MJ, Wang X, Li YS. Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications. Mil Med Res 2022; 9:68. [PMID: 36461064 PMCID: PMC9716519 DOI: 10.1186/s40779-022-00434-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 11/18/2022] [Indexed: 12/03/2022] Open
Abstract
The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.
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Affiliation(s)
- Min Su
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Tao Pan
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Qiu-Zhen Chen
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Wei-Wei Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China
| | - Yi Gong
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.,Department of Immunology, Nanjing Medical University, Nanjing, 211166, China
| | - Gang Xu
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Huan-Yu Yan
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Si Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Qiao-Zhen Shi
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China
| | - Ya Zhang
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China
| | - Xiao He
- Department of Laboratory Medicine, Women and Children's Hospital of Chongqing Medical University, Chongqing, 401174, China
| | | | - Shi-Cai Fan
- Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen, 518110, Guangdong, China
| | - Xia Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, Heilongjiang, China.
| | - Murray J Cairns
- School of Biomedical Sciences and Pharmacy, Faculty of Health and Medicine, the University of Newcastle, University Drive, Callaghan, NSW, 2308, Australia. .,Precision Medicine Research Program, Hunter Medical Research Institute, New Lambton Heights, NSW, 2305, Australia.
| | - Xi Wang
- State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing, 211166, China.
| | - Yong-Sheng Li
- College of Biomedical Information and Engineering, the First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, Hainan, China.
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25
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Fu X, Sun L, Dong R, Chen JY, Silakit R, Condon LF, Lin Y, Lin S, Palmiter RD, Gu L. Polony gels enable amplifiable DNA stamping and spatial transcriptomics of chronic pain. Cell 2022; 185:4621-4633.e17. [PMID: 36368323 PMCID: PMC9691594 DOI: 10.1016/j.cell.2022.10.021] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 09/06/2022] [Accepted: 10/18/2022] [Indexed: 11/11/2022]
Abstract
Methods for acquiring spatially resolved omics data from complex tissues use barcoded DNA arrays of low- to sub-micrometer features to achieve single-cell resolution. However, fabricating such arrays (randomly assembled beads, DNA nanoballs, or clusters) requires sequencing barcodes in each array, limiting cost-effectiveness and throughput. Here, we describe a vastly scalable stamping method to fabricate polony gels, arrays of ∼1-micrometer clonal DNA clusters bearing unique barcodes. By enabling repeatable enzymatic replication of barcode-patterned gels, this method, compared with the sequencing-dependent array fabrication, reduced cost by at least 35-fold and time to approximately 7 h. The gel stamping was implemented with a simple robotic arm and off-the-shelf reagents. We leveraged the resolution and RNA capture efficiency of polony gels to develop Pixel-seq, a single-cell spatial transcriptomic assay, and applied it to map the mouse parabrachial nucleus and analyze changes in neuropathic pain-regulated transcriptomes and cell-cell communication after nerve ligation.
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Affiliation(s)
- Xiaonan Fu
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA
| | - Li Sun
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; TopoGene Inc., Seattle, WA 98195, USA
| | - Runze Dong
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA; Graduate Program in Biological Physics, Structure and Design, University of Washington, Seattle, WA, USA
| | - Jane Y Chen
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Runglawan Silakit
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Logan F Condon
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA; Graduate Programs in Medical Scientist Training and Neuroscience, University of Washington, Seattle, WA, USA
| | - Yiing Lin
- Department of Surgery, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Shin Lin
- Division of Cardiology, Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Richard D Palmiter
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, University of Washington, Seattle, WA 98195, USA
| | - Liangcai Gu
- Department of Biochemistry, University of Washington, Seattle, WA 98195, USA; Institute for Protein Design, University of Washington, Seattle, WA 98195, USA.
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26
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Fang S, Chen B, Zhang Y, Sun H, Liu L, Liu S, Li Y, Xu X. Computational Approaches and Challenges in Spatial Transcriptomics. GENOMICS, PROTEOMICS & BIOINFORMATICS 2022:S1672-0229(22)00129-2. [PMID: 36252814 PMCID: PMC10372921 DOI: 10.1016/j.gpb.2022.10.001] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Revised: 09/08/2022] [Accepted: 10/09/2022] [Indexed: 01/19/2023]
Abstract
The development of spatial transcriptomics (ST) technologies has transformed genetic research from a single-cell data level to a two-dimensional spatial coordinate system and facilitated the study of the composition and function of various cell subsets in different environments and organs. The large-scale data generated by these ST technologies, which contain spatial gene expression information, have elicited the need for spatially resolved approaches to meet the requirements of computational and biological data interpretation. These requirements include dealing with the explosive growth of data to determine the cell-level and gene-level expression, correcting the inner batch effect and loss of expression to improve the data quality, conducting efficient interpretation and in-depth knowledge mining both at the single-cell and tissue-wide levels, and conducting multi-omics integration analysis to provide an extensible framework toward the in-depth understanding of biological processes. However, algorithms designed specifically for ST technologies to meet these requirements are still in their infancy. Here, we review computational approaches to these problems in light of corresponding issues and challenges, and present forward-looking insights into algorithm development.
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27
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Gorin G, Fang M, Chari T, Pachter L. RNA velocity unraveled. PLoS Comput Biol 2022; 18:e1010492. [PMID: 36094956 PMCID: PMC9499228 DOI: 10.1371/journal.pcbi.1010492] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Revised: 09/22/2022] [Accepted: 08/14/2022] [Indexed: 11/24/2022] Open
Abstract
We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems. Single-cell sequencing data are snapshots of biological processes, making it challenging to infer dynamic relationships between cell types. RNA velocity attempts to bypass this challenge by treating the unspliced RNA content as a proxy for spliced RNA content in the near future, and using this “extrapolation” to build directional relationships. However, the method, as implemented in several software packages, is not yet reliable enough to be actionable, in part due to the large number of arbitrary, user-set hyperparameters, as well as fundamental incompatibilities between the biophysics of transcription in the living cell and the models used throughout the velocity workflows. In this study, we review these issues, and use existing results from the fields of stochastic modeling and fluorescence transcriptomics to develop an alternative theoretical framework. We show that our framework can facilitate the development and inference of physically consistent models for sequencing data, as well as the unification of single-cell analyses to self-consistently treat variation due to cell type dynamics and identities, the stochasticity inherent to single-molecule processes, and the uncertainty introduced by sequencing experiments.
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Affiliation(s)
- Gennady Gorin
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Meichen Fang
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, California, United States of America
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, California, United States of America
- * E-mail:
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28
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Ran C, Boettcher JC, Kaye JA, Gallori CE, Liberles SD. A brainstem map for visceral sensations. Nature 2022; 609:320-326. [PMID: 36045291 PMCID: PMC9452305 DOI: 10.1038/s41586-022-05139-5] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2021] [Accepted: 07/25/2022] [Indexed: 12/21/2022]
Abstract
The nervous system uses various coding strategies to process sensory inputs. For example, the olfactory system uses large receptor repertoires and is wired to recognize diverse odours, whereas the visual system provides high acuity of object position, form and movement1-5. Compared to external sensory systems, principles that underlie sensory processing by the interoceptive nervous system remain poorly defined. Here we developed a two-photon calcium imaging preparation to understand internal organ representations in the nucleus of the solitary tract (NTS), a sensory gateway in the brainstem that receives vagal and other inputs from the body. Focusing on gut and upper airway stimuli, we observed that individual NTS neurons are tuned to detect signals from particular organs and are topographically organized on the basis of body position. Moreover, some mechanosensory and chemosensory inputs from the same organ converge centrally. Sensory inputs engage specific NTS domains with defined locations, each containing heterogeneous cell types. Spatial representations of different organs are further sharpened in the NTS beyond what is achieved by vagal axon sorting alone, as blockade of brainstem inhibition broadens neural tuning and disorganizes visceral representations. These findings reveal basic organizational features used by the brain to process interoceptive inputs.
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Affiliation(s)
- Chen Ran
- Department of Cell Biology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Jack C Boettcher
- Department of Cell Biology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Judith A Kaye
- Department of Cell Biology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Catherine E Gallori
- Department of Cell Biology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA
| | - Stephen D Liberles
- Department of Cell Biology, Howard Hughes Medical Institute, Harvard Medical School, Boston, MA, USA.
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29
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Cao YH, Ding J, Tang QH, Zhang J, Huang ZY, Tang XM, Liu JB, Ma YS, Fu D. Deciphering cell-cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing. Bioengineered 2022; 13:14974-14986. [PMID: 37105769 DOI: 10.1080/21655979.2023.2185434] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023] Open
Abstract
A tumor's heterogeneity has important implications in terms of its clonal origin, progression, stemness, and drug resistance. Therefore, because of its significance in treatment, it is important to understand the gene expression pattern of a single cell, track gene expression or mutation in heterogeneous cells, evaluate the clonal origin of cancer cells, and determine the selective evolution of different subpopulations of cancer cells. Researchers are able to trace a cell's mutation and identify different types of tumor cells by measuring the whole transcriptome with single-cell sequencing (scRNA-seq). This technology provides a better understanding of the molecular mechanisms driving tumor growth than that offered by traditional RNA sequencing methods. In addition, it has revealed changes in the mutations and functions of somatic cells as a tumor evolves; it has also clarified immune cell infiltration and activation. Research on scRNA-seq technology has recently advanced significantly, suggesting new strategies for the treatment of cancer. In short, cancer researchers have become increasingly dependent on scRNA-seq. This paper reviews the development, detection principles, and processes of scRNA-seq technology and their application in tumor research. It also considers potential clinical applications.
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Affiliation(s)
- Ya-Hong Cao
- Department of Respiratory, Nantong Traditional Chinese Medicine Hospital, Affiliated Nantong Traditional Chinese Medicine Hospital of Nantong University, Nantong, Jiangsu, China
| | - Jie Ding
- Department of Clinical Laboratory, Jingjiang Traditional Chinese Medicine Hospital, Jingjiang, Jiangsu, China
| | - Qing-Hai Tang
- Hunan Key Laboratory for Conservation and Utilization of Biological Resources in the Nanyue Mountainous Region and College of Life Sciences and Environment, Hengyang Normal University, Hengyang, Hunan, China
| | - Jie Zhang
- Department of Immunology, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Zhong-Yan Huang
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
| | - Xiao-Mei Tang
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
| | - Ji-Bin Liu
- Institute of Oncology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China
| | - Yu-Shui Ma
- Cancer Institute, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Xuhui, China
| | - Da Fu
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
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30
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Williams CG, Lee HJ, Asatsuma T, Vento-Tormo R, Haque A. An introduction to spatial transcriptomics for biomedical research. Genome Med 2022; 14:68. [PMID: 35761361 PMCID: PMC9238181 DOI: 10.1186/s13073-022-01075-1] [Citation(s) in RCA: 188] [Impact Index Per Article: 94.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 06/19/2022] [Indexed: 01/04/2023] Open
Abstract
Single-cell transcriptomics (scRNA-seq) has become essential for biomedical research over the past decade, particularly in developmental biology, cancer, immunology, and neuroscience. Most commercially available scRNA-seq protocols require cells to be recovered intact and viable from tissue. This has precluded many cell types from study and largely destroys the spatial context that could otherwise inform analyses of cell identity and function. An increasing number of commercially available platforms now facilitate spatially resolved, high-dimensional assessment of gene transcription, known as 'spatial transcriptomics'. Here, we introduce different classes of method, which either record the locations of hybridized mRNA molecules in tissue, image the positions of cells themselves prior to assessment, or employ spatial arrays of mRNA probes of pre-determined location. We review sizes of tissue area that can be assessed, their spatial resolution, and the number and types of genes that can be profiled. We discuss if tissue preservation influences choice of platform, and provide guidance on whether specific platforms may be better suited to discovery screens or hypothesis testing. Finally, we introduce bioinformatic methods for analysing spatial transcriptomic data, including pre-processing, integration with existing scRNA-seq data, and inference of cell-cell interactions. Spatial -omics methods are already improving our understanding of human tissues in research, diagnostic, and therapeutic settings. To build upon these recent advancements, we provide entry-level guidance for those seeking to employ spatial transcriptomics in their own biomedical research.
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Affiliation(s)
- Cameron G Williams
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Hyun Jae Lee
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Takahiro Asatsuma
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia
| | - Roser Vento-Tormo
- Cellular Genetics Group, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - Ashraful Haque
- Department of Microbiology and Immunology, University of Melbourne, located at the Peter Doherty Institute for Infection and Immunity, Parkville, VIC, 3000, Australia.
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31
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Tiesmeyer S, Sahay S, Müller-Bötticher N, Eils R, Mackowiak SD, Ishaque N. SSAM-lite: A Light-Weight Web App for Rapid Analysis of Spatially Resolved Transcriptomics Data. Front Genet 2022; 13:785877. [PMID: 35295943 PMCID: PMC8918671 DOI: 10.3389/fgene.2022.785877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 01/25/2022] [Indexed: 11/13/2022] Open
Abstract
The combination of a cell’s transcriptional profile and location defines its function in a spatial context. Spatially resolved transcriptomics (SRT) has emerged as the assay of choice for characterizing cells in situ. SRT methods can resolve gene expression up to single-molecule resolution. A particular computational problem with single-molecule SRT methods is the correct aggregation of mRNA molecules into cells. Traditionally, aggregating mRNA molecules into cell-based features begins with the identification of cells via segmentation of the nucleus or the cell membrane. However, recently a number of cell-segmentation-free approaches have emerged. While these methods have been demonstrated to be more performant than segmentation-based approaches, they are still not easily accessible since they require specialized knowledge of programming languages and access to large computational resources. Here we present SSAM-lite, a tool that provides an easy-to-use graphical interface to perform rapid and segmentation-free cell-typing of SRT data in a web browser. SSAM-lite runs locally and does not require computational experts or specialized hardware. Analysis of a tissue slice of the mouse somatosensory cortex took less than a minute on a laptop with modest hardware. Parameters can interactively be optimized on small portions of the data before the entire tissue image is analyzed. A server version of SSAM-lite can be run completely offline using local infrastructure. Overall, SSAM-lite is portable, lightweight, and easy to use, thus enabling a broad audience to investigate and analyze single-molecule SRT data.
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Affiliation(s)
- Sebastian Tiesmeyer
- Digital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Sebastian Tiesmeyer, ; Naveed Ishaque,
| | - Shashwat Sahay
- Digital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Niklas Müller-Bötticher
- Digital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Roland Eils
- Digital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
- Health Data Science Unit, Heidelberg University Hospital, Heidelberg, Germany
| | - Sebastian D. Mackowiak
- Digital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
| | - Naveed Ishaque
- Digital Health Center, Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
- *Correspondence: Sebastian Tiesmeyer, ; Naveed Ishaque,
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32
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Hadadi E, Deschoemaeker S, Vicente Venegas G, Laoui D. Heterogeneity and function of macrophages in the breast during homeostasis and cancer. INTERNATIONAL REVIEW OF CELL AND MOLECULAR BIOLOGY 2022; 367:149-182. [PMID: 35461657 DOI: 10.1016/bs.ircmb.2022.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Macrophages are diverse immune cells populating all tissues and adopting a unique tissue-specific identity. Breast macrophages play an essential role in the development and function of the mammary gland over one's lifetime. In the recent years, with the development of fate-mapping, imaging and scRNA-seq technologies we grew a better understanding of the origin, heterogeneity and function of mammary macrophages in homeostasis but also during breast cancer development. Here, we aim to provide a comprehensive review of the latest improvements in studying the macrophage heterogeneity in healthy mammary tissues and breast cancer.
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Affiliation(s)
- Eva Hadadi
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Sofie Deschoemaeker
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Gerard Vicente Venegas
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Damya Laoui
- Myeloid Cell Immunology Lab, VIB Center for Inflammation Research, Brussels, Belgium; Lab of Cellular and Molecular Immunology, Vrije Universiteit Brussel, Brussels, Belgium.
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33
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Spatial components of molecular tissue biology. Nat Biotechnol 2022; 40:308-318. [PMID: 35132261 DOI: 10.1038/s41587-021-01182-1] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Accepted: 12/03/2021] [Indexed: 02/06/2023]
Abstract
Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly evolving, making it possible to comprehensively characterize cells and tissues in health and disease. To maximize the biological insights obtained using these techniques, it is critical to both clearly articulate the key biological questions in spatial analysis of tissues and develop the requisite computational tools to address them. Developers of analytical tools need to decide on the intrinsic molecular features of each cell that need to be considered, and how cell shape and morphological features are incorporated into the analysis. Also, optimal ways to compare different tissue samples at various length scales are still being sought. Grouping these biological problems and related computational algorithms into classes across length scales, thus characterizing common issues that need to be addressed, will facilitate further progress in spatial transcriptomics and proteomics.
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34
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Abstract
AbstractSpatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
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35
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Behanova A, Klemm A, Wählby C. Spatial Statistics for Understanding Tissue Organization. Front Physiol 2022; 13:832417. [PMID: 35153840 PMCID: PMC8837270 DOI: 10.3389/fphys.2022.832417] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.
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36
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Chen Y, Qian W, Lin L, Cai L, Yin K, Jiang S, Song J, Han RPS, Yang C. Mapping Gene Expression in the Spatial Dimension. SMALL METHODS 2021; 5:e2100722. [PMID: 34927963 DOI: 10.1002/smtd.202100722] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/25/2021] [Indexed: 06/14/2023]
Abstract
The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.
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Affiliation(s)
- Yingwen Chen
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Weizhou Qian
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Li Lin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Linfeng Cai
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Kun Yin
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Shaowei Jiang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
| | - Jia Song
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
| | - Ray P S Han
- Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, 33004, China
| | - Chaoyong Yang
- The MOE Key Laboratory of Spectrochemical Analysis & Instrumentation, the Key Laboratory of Chemical Biology of Fujian Province, State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials, Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, China
- Institute of Molecular Medicine, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200127, China
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37
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Petukhov V, Xu RJ, Soldatov RA, Cadinu P, Khodosevich K, Moffitt JR, Kharchenko PV. Cell segmentation in imaging-based spatial transcriptomics. Nat Biotechnol 2021; 40:345-354. [PMID: 34650268 DOI: 10.1038/s41587-021-01044-w] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/04/2021] [Indexed: 12/14/2022]
Abstract
Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains. We describe a segmentation method, Baysor, that optimizes two-dimensional (2D) or three-dimensional (3D) cell boundaries considering joint likelihood of transcriptional composition and cell morphology. While Baysor can take into account segmentation based on co-stains, it can also perform segmentation based on the detected transcripts alone. To evaluate performance, we extend multiplexed error-robust fluorescence in situ hybridization (MERFISH) to incorporate immunostaining of cell boundaries. Using this and other benchmarks, we show that Baysor segmentation can, in some cases, nearly double the number of cells compared to existing tools while reducing segmentation artifacts. We demonstrate that Baysor performs well on data acquired using five different protocols, making it a useful general tool for analysis of imaging-based spatial transcriptomics.
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Affiliation(s)
- Viktor Petukhov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.,Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Rosalind J Xu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.,Department of Microbiology, Harvard Medical School, Boston, MA, USA.,Department of Chemistry, Harvard University, Boston, MA, USA
| | - Ruslan A Soldatov
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Paolo Cadinu
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.,Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Konstantin Khodosevich
- Biotech Research and Innovation Centre, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Jeffrey R Moffitt
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA.,Department of Microbiology, Harvard Medical School, Boston, MA, USA
| | - Peter V Kharchenko
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. .,Harvard Stem Cell Institute, Cambridge, MA, USA.
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38
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He Y, Tang X, Huang J, Ren J, Zhou H, Chen K, Liu A, Shi H, Lin Z, Li Q, Aditham A, Ounadjela J, Grody EI, Shu J, Liu J, Wang X. ClusterMap for multi-scale clustering analysis of spatial gene expression. Nat Commun 2021; 12:5909. [PMID: 34625546 PMCID: PMC8501103 DOI: 10.1038/s41467-021-26044-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 09/08/2021] [Indexed: 01/15/2023] Open
Abstract
Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.
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Affiliation(s)
- Yichun He
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Xin Tang
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jiahao Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jingyi Ren
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Haowen Zhou
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kevin Chen
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Albert Liu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hailing Shi
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Zuwan Lin
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Qiang Li
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Abhishek Aditham
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Johain Ounadjela
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Emanuelle I Grody
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
| | - Jian Shu
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Whitehead Institute for Biomedical Research, Cambridge, MA, USA
- Cutaneous Biology Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jia Liu
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
| | - Xiao Wang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA, USA.
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39
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Abstract
Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.
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Affiliation(s)
- Ruben Dries
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Jiaji Chen
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Natalie Del Rossi
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Mohammed Muzamil Khan
- Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
- Bioinformatics Graduate Program, Boston University, Boston, Massachusetts 02215, USA
- Section of Computational Biomedicine, Boston University School of Medicine, Boston, Massachusetts 02118, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, New York 10029, USA
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40
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Potential of Multiscale Astrocyte Imaging for Revealing Mechanisms Underlying Neurodevelopmental Disorders. Int J Mol Sci 2021; 22:ijms221910312. [PMID: 34638653 PMCID: PMC8508625 DOI: 10.3390/ijms221910312] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 01/18/2023] Open
Abstract
Astrocytes provide trophic and metabolic support to neurons and modulate circuit formation during development. In addition, astrocytes help maintain neuronal homeostasis through neurovascular coupling, blood-brain barrier maintenance, clearance of metabolites and nonfunctional proteins via the glymphatic system, extracellular potassium buffering, and regulation of synaptic activity. Thus, astrocyte dysfunction may contribute to a myriad of neurological disorders. Indeed, astrocyte dysfunction during development has been implicated in Rett disease, Alexander's disease, epilepsy, and autism, among other disorders. Numerous disease model mice have been established to investigate these diseases, but important preclinical findings on etiology and pathophysiology have not translated into clinical interventions. A multidisciplinary approach is required to elucidate the mechanism of these diseases because astrocyte dysfunction can result in altered neuronal connectivity, morphology, and activity. Recent progress in neuroimaging techniques has enabled noninvasive investigations of brain structure and function at multiple spatiotemporal scales, and these technologies are expected to facilitate the translation of preclinical findings to clinical studies and ultimately to clinical trials. Here, we review recent progress on astrocyte contributions to neurodevelopmental and neuropsychiatric disorders revealed using novel imaging techniques, from microscopy scale to mesoscopic scale.
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41
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Park J, Choi W, Tiesmeyer S, Long B, Borm LE, Garren E, Nguyen TN, Tasic B, Codeluppi S, Graf T, Schlesner M, Stegle O, Eils R, Ishaque N. Cell segmentation-free inference of cell types from in situ transcriptomics data. Nat Commun 2021; 12:3545. [PMID: 34112806 PMCID: PMC8192952 DOI: 10.1038/s41467-021-23807-4] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Accepted: 05/14/2021] [Indexed: 12/24/2022] Open
Abstract
Multiplexed fluorescence in situ hybridization techniques have enabled cell-type identification, linking transcriptional heterogeneity with spatial heterogeneity of cells. However, inaccurate cell segmentation reduces the efficacy of cell-type identification and tissue characterization. Here, we present a method called Spot-based Spatial cell-type Analysis by Multidimensional mRNA density estimation (SSAM), a robust cell segmentation-free computational framework for identifying cell-types and tissue domains in 2D and 3D. SSAM is applicable to a variety of in situ transcriptomics techniques and capable of integrating prior knowledge of cell types. We apply SSAM to three mouse brain tissue images: the somatosensory cortex imaged by osmFISH, the hypothalamic preoptic region by MERFISH, and the visual cortex by multiplexed smFISH. Here, we show that SSAM detects regions occupied by known cell types that were previously missed and discovers new cell types.
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Affiliation(s)
- Jeongbin Park
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
- Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
- Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Wonyl Choi
- Department of Computer Science, Boston University, Boston, MA, USA
| | - Sebastian Tiesmeyer
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
| | - Brian Long
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lars E Borm
- Division of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Emma Garren
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Simone Codeluppi
- Division of molecular neurobiology, Department of medical biochemistry and biophysics, Karolinska Institutet, Stockholm, Sweden
| | - Tobias Graf
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany
| | - Matthias Schlesner
- Bioinformatics and Omics Data Analytics, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Oliver Stegle
- Division of Computational Genomics and System Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
| | - Roland Eils
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany.
- Health Data Science Unit, Heidelberg University Hospital, Heidelberg, Germany.
| | - Naveed Ishaque
- Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Digital Health Center, Kapelle-Ufer 2, 10117, Berlin, Germany.
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