1
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Huuki-Myers LA, Spangler A, Eagles NJ, Montgomery KD, Kwon SH, Guo B, Grant-Peters M, Divecha HR, Tippani M, Sriworarat C, Nguyen AB, Ravichandran P, Tran MN, Seyedian A, Hyde TM, Kleinman JE, Battle A, Page SC, Ryten M, Hicks SC, Martinowich K, Collado-Torres L, Maynard KR. A data-driven single-cell and spatial transcriptomic map of the human prefrontal cortex. Science 2024; 384:eadh1938. [PMID: 38781370 DOI: 10.1126/science.adh1938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Accepted: 12/06/2023] [Indexed: 05/25/2024]
Abstract
The molecular organization of the human neocortex historically has been studied in the context of its histological layers. However, emerging spatial transcriptomic technologies have enabled unbiased identification of transcriptionally defined spatial domains that move beyond classic cytoarchitecture. We used the Visium spatial gene expression platform to generate a data-driven molecular neuroanatomical atlas across the anterior-posterior axis of the human dorsolateral prefrontal cortex. Integration with paired single-nucleus RNA-sequencing data revealed distinct cell type compositions and cell-cell interactions across spatial domains. Using PsychENCODE and publicly available data, we mapped the enrichment of cell types and genes associated with neuropsychiatric disorders to discrete spatial domains.
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Affiliation(s)
- Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Kelsey D Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
| | - Heena R Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Chaichontat Sriworarat
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Annie B Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Prashanthi Ravichandran
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
| | - Matthew N Tran
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Arta Seyedian
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Stephanie C Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
| | - Mina Ryten
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London WC1N 1EH, UK
- Aligning Science Across Parkinson's (ASAP) Collaborative Research Network, Chevy Chase, MD 20815, USA
- NIHR Great Ormond Street Hospital Biomedical Research Centre, University College London, London WC1N 1EH, UK
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21218, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD 21218, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
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2
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Greenwald AC, Darnell NG, Hoefflin R, Simkin D, Mount CW, Gonzalez Castro LN, Harnik Y, Dumont S, Hirsch D, Nomura M, Talpir T, Kedmi M, Goliand I, Medici G, Laffy J, Li B, Mangena V, Keren-Shaul H, Weller M, Addadi Y, Neidert MC, Suvà ML, Tirosh I. Integrative spatial analysis reveals a multi-layered organization of glioblastoma. Cell 2024; 187:2485-2501.e26. [PMID: 38653236 PMCID: PMC11088502 DOI: 10.1016/j.cell.2024.03.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 01/11/2024] [Accepted: 03/21/2024] [Indexed: 04/25/2024]
Abstract
Glioma contains malignant cells in diverse states. Here, we combine spatial transcriptomics, spatial proteomics, and computational approaches to define glioma cellular states and uncover their organization. We find three prominent modes of organization. First, gliomas are composed of small local environments, each typically enriched with one major cellular state. Second, specific pairs of states preferentially reside in proximity across multiple scales. This pairing of states is consistent across tumors. Third, these pairwise interactions collectively define a global architecture composed of five layers. Hypoxia appears to drive the layers, as it is associated with a long-range organization that includes all cancer cell states. Accordingly, tumor regions distant from any hypoxic/necrotic foci and tumors that lack hypoxia such as low-grade IDH-mutant glioma are less organized. In summary, we provide a conceptual framework for the organization of cellular states in glioma, highlighting hypoxia as a long-range tissue organizer.
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Affiliation(s)
- Alissa C Greenwald
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Noam Galili Darnell
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Rouven Hoefflin
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel; Department of Medicine I, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dor Simkin
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Christopher W Mount
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - L Nicolas Gonzalez Castro
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Yotam Harnik
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Sydney Dumont
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Dana Hirsch
- Immunohistochemistry Unit, Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel
| | - Masashi Nomura
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Tom Talpir
- Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel
| | - Merav Kedmi
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Inna Goliand
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Gioele Medici
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Julie Laffy
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel
| | - Baoguo Li
- Department of Systems Immunology, Weizmann Institute of Science, Rehovot, Israel
| | - Vamsi Mangena
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Hadas Keren-Shaul
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Michael Weller
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Yoseph Addadi
- Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel
| | - Marian C Neidert
- Clinical Neuroscience Center, Department of Neurosurgery, University Hospital Zurich, University of Zurich, Zurich, Switzerland; Department of Neurosurgery, Cantonal Hospital St. Gallen, St. Gallen, Switzerland
| | - Mario L Suvà
- Department of Pathology, Center for Cancer Research, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Itay Tirosh
- Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel.
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3
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Ramnauth AD, Tippani M, Divecha HR, Papariello AR, Miller RA, Nelson ED, Pattie EA, Kleinman JE, Maynard KR, Collado-Torres L, Hyde TM, Martinowich K, Hicks SC, Page SC. Spatiotemporal analysis of gene expression in the human dentate gyrus reveals age-associated changes in cellular maturation and neuroinflammation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.20.567883. [PMID: 38045413 PMCID: PMC10690172 DOI: 10.1101/2023.11.20.567883] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
The dentate gyrus of the hippocampus is important for many cognitive functions, including learning, memory, and mood. Here, we investigated age-associated changes in transcriptome-wide spatial gene expression in the human dentate gyrus across the lifespan. Genes associated with neurogenesis and the extracellular matrix were enriched in infants, while gene markers of inhibitory neurons and cell proliferation showed increases and decreases in post-infancy, respectively. While we did not find evidence for neural proliferation post-infancy, we did identify molecular signatures supporting protracted maturation of granule cells. We also identified a wide-spread hippocampal aging signature and an age-associated increase in genes related to neuroinflammation. Our findings suggest major changes to the putative neurogenic niche after infancy and identify molecular foci of brain aging in glial and neuropil enriched tissue.
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4
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Nelson ED, Tippani M, Ramnauth AD, Divecha HR, Miller RA, Eagles NJ, Pattie EA, Kwon SH, Bach SV, Kaipa UM, Yao J, Kleinman JE, Collado-Torres L, Han S, Maynard KR, Hyde TM, Martinowich K, Page SC, Hicks SC. An integrated single-nucleus and spatial transcriptomics atlas reveals the molecular landscape of the human hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590643. [PMID: 38712198 PMCID: PMC11071618 DOI: 10.1101/2024.04.26.590643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
The hippocampus contains many unique cell types, which serve the structure's specialized functions, including learning, memory and cognition. These cells have distinct spatial topography, morphology, physiology, and connectivity, highlighting the need for transcriptome-wide profiling strategies that retain cytoarchitectural organization. Here, we generated spatially-resolved transcriptomics (SRT) and single-nucleus RNA-sequencing (snRNA-seq) data from adjacent tissue sections of the anterior human hippocampus across ten adult neurotypical donors. We defined molecular profiles for hippocampal cell types and spatial domains. Using non-negative matrix factorization and transfer learning, we integrated these data to define gene expression patterns within the snRNA-seq data and infer the expression of these patterns in the SRT data. With this approach, we leveraged existing rodent datasets that feature information on circuit connectivity and neural activity induction to make predictions about axonal projection targets and likelihood of ensemble recruitment in spatially-defined cellular populations of the human hippocampus. Finally, we integrated genome-wide association studies with transcriptomic data to identify enrichment of genetic components for neurodevelopmental, neuropsychiatric, and neurodegenerative disorders across cell types, spatial domains, and gene expression patterns of the human hippocampus. To make this comprehensive molecular atlas accessible to the scientific community, both raw and processed data are freely available, including through interactive web applications.
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5
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Zhuang H, Ji Z. PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.20.585926. [PMID: 38585819 PMCID: PMC10996487 DOI: 10.1101/2024.03.20.585926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Modeling temporal and spatial gene expression patterns in large-scale single-cell and spatial transcriptomics data is a computationally intensive task. We present PreTSA, a method that offers computational efficiency in modeling these patterns and is applicable to single-cell and spatial transcriptomics data comprising millions of cells. PreTSA consistently matches the results of state-of-the-art methods while significantly reducing computational time. PreTSA provides a unique solution for studying gene expression patterns in extremely large datasets.
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Affiliation(s)
- Haotian Zhuang
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Zhicheng Ji
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
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6
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Marconato L, Palla G, Yamauchi KA, Virshup I, Heidari E, Treis T, Vierdag WM, Toth M, Stockhaus S, Shrestha RB, Rombaut B, Pollaris L, Lehner L, Vöhringer H, Kats I, Saeys Y, Saka SK, Huber W, Gerstung M, Moore J, Theis FJ, Stegle O. SpatialData: an open and universal data framework for spatial omics. Nat Methods 2024:10.1038/s41592-024-02212-x. [PMID: 38509327 DOI: 10.1038/s41592-024-02212-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 02/14/2024] [Indexed: 03/22/2024]
Abstract
Spatially resolved omics technologies are transforming our understanding of biological tissues. However, the handling of uni- and multimodal spatial omics datasets remains a challenge owing to large data volumes, heterogeneity of data types and the lack of flexible, spatially aware data structures. Here we introduce SpatialData, a framework that establishes a unified and extensible multiplatform file-format, lazy representation of larger-than-memory data, transformations and alignment to common coordinate systems. SpatialData facilitates spatial annotations and cross-modal aggregation and analysis, the utility of which is illustrated in the context of multiple vignettes, including integrative analysis on a multimodal Xenium and Visium breast cancer study.
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Affiliation(s)
- Luca Marconato
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
- Collaboration for joint PhD degree between EMBL and Heidelberg University, Faculty of Biosciences, Heidelberg, Germany
| | - Giovanni Palla
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany
| | - Kevin A Yamauchi
- Department of Biosystems, Science and Engineering, ETH Zürich, Basel, Switzerland
- Swiss Institute of Bioinformatics, Basel, Switzerland
| | - Isaac Virshup
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | - Elyas Heidari
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
- Division of Artificial Intelligence in Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Tim Treis
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | | | - Marcella Toth
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | - Sonja Stockhaus
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Rahul B Shrestha
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
| | - Benjamin Rombaut
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI and Computational Biology, Ghent, Belgium
| | - Lotte Pollaris
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI and Computational Biology, Ghent, Belgium
| | - Laurens Lehner
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany
- TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany
| | - Harald Vöhringer
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
- Molecular Medicine Partnership Unit, Heidelberg, Germany
- Department of Medicine V, Hematology, Oncology, and Rheumatology, University of Heidelberg, Heidelberg, Germany
| | - Ilia Kats
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany
| | - Yvan Saeys
- Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI and Computational Biology, Ghent, Belgium
| | - Sinem K Saka
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Wolfgang Huber
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany
| | - Moritz Gerstung
- Division of Artificial Intelligence in Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Josh Moore
- German BioImaging - Gesellschaft für Mikroskopie und Bildanalyse e.V, Konstanz, Germany.
- Open Microscopy Environment Consortium, Munich, Germany.
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz, Center Munich, Munich, Germany.
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
- Department of Mathematics, Technical University of Munich, Munich, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Cambridge, UK.
| | - Oliver Stegle
- European Molecular Biology Laboratory, Genome Biology Unit, Heidelberg, Germany.
- Division of Computational Genomics and System Genetics, German Cancer Research Center, Heidelberg, Germany.
- Cellular Genetics Programme, Wellcome Sanger Institute, Cambridge, UK.
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7
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Vierdag WMAM, Saka SK. A perspective on FAIR quality control in multiplexed imaging data processing. FRONTIERS IN BIOINFORMATICS 2024; 4:1336257. [PMID: 38405548 PMCID: PMC10885342 DOI: 10.3389/fbinf.2024.1336257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Accepted: 01/26/2024] [Indexed: 02/27/2024] Open
Abstract
Multiplexed imaging approaches are getting increasingly adopted for imaging of large tissue areas, yielding big imaging datasets both in terms of the number of samples and the size of image data per sample. The processing and analysis of these datasets is complex owing to frequent technical artifacts and heterogeneous profiles from a high number of stained targets To streamline the analysis of multiplexed images, automated pipelines making use of state-of-the-art algorithms have been developed. In these pipelines, the output quality of one processing step is typically dependent on the output of the previous step and errors from each step, even when they appear minor, can propagate and confound the results. Thus, rigorous quality control (QC) at each of these different steps of the image processing pipeline is of paramount importance both for the proper analysis and interpretation of the analysis results and for ensuring the reusability of the data. Ideally, QC should become an integral and easily retrievable part of the imaging datasets and the analysis process. Yet, limitations of the currently available frameworks make integration of interactive QC difficult for large multiplexed imaging data. Given the increasing size and complexity of multiplexed imaging datasets, we present the different challenges for integrating QC in image analysis pipelines as well as suggest possible solutions that build on top of recent advances in bioimage analysis.
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Affiliation(s)
| | - Sinem K. Saka
- Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany
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8
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Cai P, Robinson MD, Tiberi S. DESpace: spatially variable gene detection via differential expression testing of spatial clusters. Bioinformatics 2024; 40:btae027. [PMID: 38243704 PMCID: PMC10868334 DOI: 10.1093/bioinformatics/btae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 12/23/2023] [Accepted: 01/15/2024] [Indexed: 01/21/2024] Open
Abstract
MOTIVATION Spatially resolved transcriptomics (SRT) enables scientists to investigate spatial context of mRNA abundance, including identifying spatially variable genes (SVGs), i.e. genes whose expression varies across the tissue. Although several methods have been proposed for this task, native SVG tools cannot jointly model biological replicates, or identify the key areas of the tissue affected by spatial variability. RESULTS Here, we introduce DESpace, a framework, based on an original application of existing methods, to discover SVGs. In particular, our approach inputs all types of SRT data, summarizes spatial information via spatial clusters, and identifies spatially variable genes by performing differential gene expression testing between clusters. Furthermore, our framework can identify (and test) the main cluster of the tissue affected by spatial variability; this allows scientists to investigate spatial expression changes in specific areas of interest. Additionally, DESpace enables joint modeling of multiple samples (i.e. biological replicates); compared to inference based on individual samples, this approach increases statistical power, and targets SVGs with consistent spatial patterns across replicates. Overall, in our benchmarks, DESpace displays good true positive rates, controls for false positive and false discovery rates, and is computationally efficient. AVAILABILITY AND IMPLEMENTATION DESpace is freely distributed as a Bioconductor R package at https://bioconductor.org/packages/DESpace.
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Affiliation(s)
- Peiying Cai
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich 8057, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich 8057, Switzerland
| | - Simone Tiberi
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich 8057, Switzerland
- Department of Statistical Sciences, University of Bologna, Bologna 40126, Italy
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9
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Weber LM, Divecha HR, Tran MN, Kwon SH, Spangler A, Montgomery KD, Tippani M, Bharadwaj R, Kleinman JE, Page SC, Hyde TM, Collado-Torres L, Maynard KR, Martinowich K, Hicks SC. The gene expression landscape of the human locus coeruleus revealed by single-nucleus and spatially-resolved transcriptomics. eLife 2024; 12:RP84628. [PMID: 38266073 PMCID: PMC10945708 DOI: 10.7554/elife.84628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2024] Open
Abstract
Norepinephrine (NE) neurons in the locus coeruleus (LC) make long-range projections throughout the central nervous system, playing critical roles in arousal and mood, as well as various components of cognition including attention, learning, and memory. The LC-NE system is also implicated in multiple neurological and neuropsychiatric disorders. Importantly, LC-NE neurons are highly sensitive to degeneration in both Alzheimer's and Parkinson's disease. Despite the clinical importance of the brain region and the prominent role of LC-NE neurons in a variety of brain and behavioral functions, a detailed molecular characterization of the LC is lacking. Here, we used a combination of spatially-resolved transcriptomics and single-nucleus RNA-sequencing to characterize the molecular landscape of the LC region and the transcriptomic profile of LC-NE neurons in the human brain. We provide a freely accessible resource of these data in web-accessible and downloadable formats.
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Affiliation(s)
- Lukas M Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public HealthBaltimoreUnited States
| | - Heena R Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
| | - Matthew N Tran
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
- Department of Neuroscience, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
| | - Kelsey D Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
| | - Rahul Bharadwaj
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Stephanie C Page
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of MedicineBaltimoreUnited States
- Department of Neurology, Johns Hopkins School of MedicineBaltimoreUnited States
| | | | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of MedicineBaltimoreUnited States
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical CampusBaltimoreUnited States
- Department of Neuroscience, Johns Hopkins School of MedicineBaltimoreUnited States
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of MedicineBaltimoreUnited States
- The Kavli Neuroscience Discovery Institute, Johns Hopkins UniversityBaltimoreUnited States
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public HealthBaltimoreUnited States
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10
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Kiessling P, Kuppe C. Spatial multi-omics: novel tools to study the complexity of cardiovascular diseases. Genome Med 2024; 16:14. [PMID: 38238823 PMCID: PMC10795303 DOI: 10.1186/s13073-024-01282-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Accepted: 01/02/2024] [Indexed: 01/22/2024] Open
Abstract
Spatial multi-omic studies have emerged as a promising approach to comprehensively analyze cells in tissues, enabling the joint analysis of multiple data modalities like transcriptome, epigenome, proteome, and metabolome in parallel or even the same tissue section. This review focuses on the recent advancements in spatial multi-omics technologies, including novel data modalities and computational approaches. We discuss the advancements in low-resolution and high-resolution spatial multi-omics methods which can resolve up to 10,000 of individual molecules at subcellular level. By applying and integrating these techniques, researchers have recently gained valuable insights into the molecular circuits and mechanisms which govern cell biology along the cardiovascular disease spectrum. We provide an overview of current data analysis approaches, with a focus on data integration of multi-omic datasets, highlighting strengths and weaknesses of various computational pipelines. These tools play a crucial role in analyzing and interpreting spatial multi-omics datasets, facilitating the discovery of new findings, and enhancing translational cardiovascular research. Despite nontrivial challenges, such as the need for standardization of experimental setups, data analysis, and improved computational tools, the application of spatial multi-omics holds tremendous potential in revolutionizing our understanding of human disease processes and the identification of novel biomarkers and therapeutic targets. Exciting opportunities lie ahead for the spatial multi-omics field and will likely contribute to the advancement of personalized medicine for cardiovascular diseases.
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Affiliation(s)
- Paul Kiessling
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany
| | - Christoph Kuppe
- Department of Nephrology, Rheumatology, and Clinical Immunology, University Hospital RWTH Aachen, Aachen, Germany.
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11
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Liu N, Bhuva DD, Mohamed A, Bokelund M, Kulasinghe A, Tan C, Davis M. standR: spatial transcriptomic analysis for GeoMx DSP data. Nucleic Acids Res 2024; 52:e2. [PMID: 37953397 PMCID: PMC10783521 DOI: 10.1093/nar/gkad1026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 09/08/2023] [Accepted: 10/24/2023] [Indexed: 11/14/2023] Open
Abstract
To gain a better understanding of the complexity of gene expression in normal and diseased tissues it is important to account for the spatial context and identity of cells in situ. State-of-the-art spatial profiling technologies, such as the Nanostring GeoMx Digital Spatial Profiler (DSP), now allow quantitative spatially resolved measurement of the transcriptome in tissues. However, the bioinformatics pipelines currently used to analyse GeoMx data often fail to successfully account for the technical variability within the data and the complexity of experimental designs, thus limiting the accuracy and reliability of the subsequent analysis. Carefully designed quality control workflows, that include in-depth experiment-specific investigations into technical variation and appropriate adjustment for such variation can address this issue. Here, we present standR, an R/Bioconductor package that enables an end-to-end analysis of GeoMx DSP data. With four case studies from previously published experiments, we demonstrate how the standR workflow can enhance the statistical power of GeoMx DSP data analysis and how the application of standR enables scientists to develop in-depth insights into the biology of interest.
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Affiliation(s)
- Ning Liu
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Dharmesh D Bhuva
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
| | - Ahmed Mohamed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
| | - Micah Bokelund
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
| | - Arutha Kulasinghe
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Chin Wee Tan
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
| | - Melissa J Davis
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, Melbourne, Victoria 3052, Australia
- Department of Medical Biology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
- South Australian immunoGENomics Cancer Institute (SAiGENCI), Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, SA 5005, Australia
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, Queensland 4102, Australia
- Department of Clinical Pathology, Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Parkville, VIC 3010, Australia
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12
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Meyer L, Eling N, Bodenmiller B. cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data. BMC Bioinformatics 2024; 25:9. [PMID: 38172724 PMCID: PMC10765786 DOI: 10.1186/s12859-023-05546-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/27/2023] [Indexed: 01/05/2024] Open
Abstract
BACKGROUND Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive visualization of multiplexed imaging data is crucial at any step of data analysis to facilitate quality control and the spatial exploration of single cell features. However, tools for interactive visualization of multiplexed imaging data are not available in the statistical programming language R. RESULTS Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. As such, cytoviewer improves image and segmentation quality control, the visualization of cell phenotyping results and qualitative validation of hypothesis at any step of data analysis. The package operates on standard data classes of the Bioconductor project and therefore integrates with an extensive framework for single-cell and image analysis. The graphical user interface allows intuitive navigation and little coding experience is required to use the package. We showcase the functionality and biological application of cytoviewer by analysis of an imaging mass cytometry dataset acquired from cancer samples. CONCLUSIONS The cytoviewer package offers a rich set of features for highly multiplexed imaging data visualization in R that seamlessly integrates with the workflow for image and single-cell data analysis. It can be installed from Bioconductor via https://www.bioconductor.org/packages/release/bioc/html/cytoviewer.html . The development version and further instructions can be found on GitHub at https://github.com/BodenmillerGroup/cytoviewer .
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Affiliation(s)
- Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich/University of Zurich, Zurich, Switzerland
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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13
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [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: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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14
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Masotti M, Osher N, Eliason J, Rao A, Baladandayuthapani V. DIMPLE: An R package to quantify, visualize, and model spatial cellular interactions from multiplex imaging with distance matrices. PATTERNS (NEW YORK, N.Y.) 2023; 4:100879. [PMID: 38106614 PMCID: PMC10724356 DOI: 10.1016/j.patter.2023.100879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 09/11/2023] [Accepted: 10/24/2023] [Indexed: 12/19/2023]
Abstract
A major challenge in the spatial analysis of multiplex imaging (MI) data is choosing how to measure cellular spatial interactions and how to relate them to patient outcomes. Existing methods to quantify cell-cell interactions do not scale to the rapidly evolving technical landscape, where both the number of unique cell types and the number of images in a dataset may be large. We propose a scalable analytical framework and accompanying R package, DIMPLE, to quantify, visualize, and model cell-cell interactions in the TME. By applying DIMPLE to publicly available MI data, we uncover statistically significant associations between image-level measures of cell-cell interactions and patient-level covariates.
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Affiliation(s)
- Maria Masotti
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
| | - Nathaniel Osher
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
| | - Joel Eliason
- University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA
| | - Arvind Rao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
- University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA
| | - Veerabhadran Baladandayuthapani
- University of Michigan, Department of Biostatistics, Ann Arbor, MI 48109, USA
- University of Michigan, Department of Computational Medicine and Bioinformatics, Ann Arbor, MI 48109, USA
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15
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Guo B, Huuki-Myers LA, Grant-Peters M, Collado-Torres L, Hicks SC. escheR: unified multi-dimensional visualizations with Gestalt principles. BIOINFORMATICS ADVANCES 2023; 3:vbad179. [PMID: 38107654 PMCID: PMC10723033 DOI: 10.1093/bioadv/vbad179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 11/14/2023] [Accepted: 12/04/2023] [Indexed: 12/19/2023]
Abstract
Summary The creation of effective visualizations is a fundamental component of data analysis. In biomedical research, new challenges are emerging to visualize multi-dimensional data in a 2D space, but current data visualization tools have limited capabilities. To address this problem, we leverage Gestalt principles to improve the design and interpretability of multi-dimensional data in 2D data visualizations, layering aesthetics to display multiple variables. The proposed visualization can be applied to spatially-resolved transcriptomics data, but also broadly to data visualized in 2D space, such as embedding visualizations. We provide an open source R package escheR, which is built off of the state-of-the-art ggplot2 visualization framework and can be seamlessly integrated into genomics toolboxes and workflows. Availability and implementation The open source R package escheR is freely available on Bioconductor (https://bioconductor.org/packages/escheR).
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Affiliation(s)
- Boyi Guo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, United States
| | - Melissa Grant-Peters
- Genetics and Genomic Medicine, Great Ormond Street Institute of Child Health, University College London, London, WC1N 1EH, United Kingdom
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, 20815, United States
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, United States
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, United States
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD, 21218, United States
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, United States
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, United States
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16
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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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17
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Tippani M, Divecha HR, Catallini JL, Kwon SH, Weber LM, Spangler A, Jaffe AE, Hyde TM, Kleinman JE, Hicks SC, Martinowich K, Collado-Torres L, Page SC, Maynard KR. VistoSeg: Processing utilities for high-resolution images for spatially resolved transcriptomics data. BIOLOGICAL IMAGING 2023; 3:e23. [PMID: 38510173 PMCID: PMC10951916 DOI: 10.1017/s2633903x23000235] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/14/2023] [Accepted: 09/19/2023] [Indexed: 03/22/2024]
Abstract
Spatially resolved transcriptomics (SRT) is a growing field that links gene expression to anatomical context. SRT approaches that use next-generation sequencing (NGS) combine RNA sequencing with histological or fluorescent imaging to generate spatial maps of gene expression in intact tissue sections. These technologies directly couple gene expression measurements with high-resolution histological or immunofluorescent images that contain rich morphological information about the tissue under study. While broad access to NGS-based spatial transcriptomic technology is now commercially available through the Visium platform from the vendor 10× Genomics, computational tools for extracting image-derived metrics for integration with gene expression data remain limited. We developed VistoSeg as a MATLAB pipeline to process, analyze and interactively visualize the high-resolution images generated in the Visium platform. VistoSeg outputs can be easily integrated with accompanying transcriptomic data to facilitate downstream analyses in common programing languages including R and Python. VistoSeg provides user-friendly tools for integrating image-derived metrics from histological and immunofluorescent images with spatially resolved gene expression data. Integration of this data enhances the ability to understand the transcriptional landscape within tissue architecture. VistoSeg is freely available at http://research.libd.org/VistoSeg/.
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Affiliation(s)
- Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Heena R. Divecha
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Joseph L. Catallini
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang H. Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Lukas M. Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- The Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephanie C. Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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18
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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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19
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Windhager J, Zanotelli VRT, Schulz D, Meyer L, Daniel M, Bodenmiller B, Eling N. An end-to-end workflow for multiplexed image processing and analysis. Nat Protoc 2023; 18:3565-3613. [PMID: 37816904 DOI: 10.1038/s41596-023-00881-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 06/23/2023] [Indexed: 10/12/2023]
Abstract
Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .
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Affiliation(s)
- Jonas Windhager
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
- SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Vito Riccardo Tomaso Zanotelli
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Division of Metabolism and Children's Research Center, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Daniel Schulz
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Michelle Daniel
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.
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20
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Larsson L, Franzén L, Ståhl PL, Lundeberg J. Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization. Bioinformatics 2023; 39:btad626. [PMID: 37846051 PMCID: PMC10597621 DOI: 10.1093/bioinformatics/btad626] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 09/20/2023] [Accepted: 10/13/2023] [Indexed: 10/18/2023] Open
Abstract
SUMMARY Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation. AVAILABILITY AND IMPLEMENTATION The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.
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Affiliation(s)
- Ludvig Larsson
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Lovisa Franzén
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
- Respiratory & Immunology, Neuroscience, Vaccines & Immune Therapies Safety, Clinical Pharmacology & Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, 431 83 Mölndal, Gothenburg, Sweden
| | - Patrik L Ståhl
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
| | - Joakim Lundeberg
- Department of Gene Technology, KTH Royal Institute of Technology, Science for Life Laboratory, Tomtebodavägen 23, 171 65 Solna, Stockholm, Sweden
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21
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. PLoS Genet 2023; 19:e1010983. [PMID: 37862362 PMCID: PMC10619839 DOI: 10.1371/journal.pgen.1010983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 11/01/2023] [Accepted: 09/19/2023] [Indexed: 10/22/2023] Open
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
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22
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Peters Couto BZ, Robertson N, Patrick E, Ghazanfar S. MoleculeExperiment enables consistent infrastructure for molecule-resolved spatial omics data in bioconductor. Bioinformatics 2023; 39:btad550. [PMID: 37698995 PMCID: PMC10504467 DOI: 10.1093/bioinformatics/btad550] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 07/18/2023] [Accepted: 09/10/2023] [Indexed: 09/14/2023] Open
Abstract
MOTIVATION Imaging-based spatial transcriptomics (ST) technologies have achieved subcellular resolution, enabling detection of individual molecules in their native tissue context. Data associated with these technologies promise unprecedented opportunity toward understanding cellular and subcellular biology. However, in R/Bioconductor, there is a scarcity of existing computational infrastructure to represent such data, and particularly to summarize and transform it for existing widely adopted computational tools in single-cell transcriptomics analysis, including SingleCellExperiment and SpatialExperiment (SPE) classes. With the emergence of several commercial offerings of imaging-based ST, there is a pressing need to develop consistent data structure standards for these technologies at the individual molecule-level. RESULTS To this end, we have developed MoleculeExperiment, an R/Bioconductor package, which (i) stores molecule and cell segmentation boundary information at the molecule-level, (ii) standardizes this molecule-level information across different imaging-based ST technologies, including 10× Genomics' Xenium, and (iii) streamlines transition from a MoleculeExperiment object to a SpatialExperiment object. Overall, MoleculeExperiment is generally applicable as a data infrastructure class for consistent analysis of molecule-resolved spatial omics data. AVAILABILITY AND IMPLEMENTATION The MoleculeExperiment package is publicly available on Bioconductor at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html. Source code is available on Github at: https://github.com/SydneyBioX/MoleculeExperiment. The vignette for MoleculeExperiment can be found at https://bioconductor.org/packages/release/bioc/html/MoleculeExperiment.html.
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Affiliation(s)
- Bárbara Zita Peters Couto
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
| | - Nicholas Robertson
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
| | - Ellis Patrick
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Centre for Cancer Research, The Westmead Institute for Medical Research, The University of Sydney, Westmead, NSW 2145, Australia
| | - Shila Ghazanfar
- School of Mathematics and Statistics, The University of Sydney, Camperdown, NSW 2006, Australia
- Charles Perkins Centre, The University of Sydney, Camperdown, NSW 2006, Australia
- Sydney Precision Data Science Centre, The University of Sydney, Camperdown, NSW 2006, Australia
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23
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Moses L, Einarsson PH, Jackson K, Luebbert L, Booeshaghi AS, Antonsson S, Bray N, Melsted P, Pachter L. Voyager: exploratory single-cell genomics data analysis with geospatial statistics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.20.549945. [PMID: 37645732 PMCID: PMC10461913 DOI: 10.1101/2023.07.20.549945] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Exploratory spatial data analysis (ESDA) can be a powerful approach to understanding single-cell genomics datasets, but it is not yet part of standard data analysis workflows. In particular, geospatial analyses, which have been developed and refined for decades, have yet to be fully adapted and applied to spatial single-cell analysis. We introduce the Voyager platform, which systematically brings the geospatial ESDA tradition to (spatial) -omics, with local, bivariate, and multivariate spatial methods not yet commonly applied to spatial -omics, united by a uniform user interface. Using Voyager, we showcase biological insights that can be derived with its methods, such as biologically relevant negative spatial autocorrelation. Underlying Voyager is the SpatialFeatureExperiment data structure, which combines Simple Feature with SingleCellExperiment and AnnData to represent and operate on geometries bundled with gene expression data. Voyager has comprehensive tutorials demonstrating ESDA built on GitHub Actions to ensure reproducibility and scalability, using data from popular commercial technologies. Voyager is implemented in both R/Bioconductor and Python/PyPI, and features compatibility tests to ensure that both implementations return consistent results.
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Affiliation(s)
- Lambda Moses
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Pétur Helgi Einarsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Kayla Jackson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Laura Luebbert
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - A. Sina Booeshaghi
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Sindri Antonsson
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | | | - Páll Melsted
- Faculty of Industrial Engineering, Mechanical Engineering and Computer Science, Reykjavík, Iceland
| | - Lior Pachter
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA
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24
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O'Connor LM, O'Connor BA, Lim SB, Zeng J, Lo CH. Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective. J Pharm Anal 2023; 13:836-850. [PMID: 37719197 PMCID: PMC10499660 DOI: 10.1016/j.jpha.2023.06.011] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 06/20/2023] [Accepted: 06/25/2023] [Indexed: 09/19/2023] Open
Abstract
Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.
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Affiliation(s)
- Lance M. O'Connor
- College of Biological Sciences, University of Minnesota, Minneapolis, MN, 55455, USA
| | - Blake A. O'Connor
- School of Pharmacy, University of Wisconsin, Madison, WI, 53705, USA
| | - Su Bin Lim
- Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, 16499, South Korea
| | - Jialiu Zeng
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
| | - Chih Hung Lo
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, 308232, Singapore
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25
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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: 111] [Impact Index Per Article: 111.0] [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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26
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Weber LM, Saha A, Datta A, Hansen KD, Hicks SC. nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes. Nat Commun 2023; 14:4059. [PMID: 37429865 DOI: 10.1038/s41467-023-39748-z] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 06/23/2023] [Indexed: 07/12/2023] Open
Abstract
Feature selection to identify spatially variable genes or other biologically informative genes is a key step during analyses of spatially-resolved transcriptomics data. Here, we propose nnSVG, a scalable approach to identify spatially variable genes based on nearest-neighbor Gaussian processes. Our method (i) identifies genes that vary in expression continuously across the entire tissue or within a priori defined spatial domains, (ii) uses gene-specific estimates of length scale parameters within the Gaussian process models, and (iii) scales linearly with the number of spatial locations. We demonstrate the performance of our method using experimental data from several technological platforms and simulations. A software implementation is available at https://bioconductor.org/packages/nnSVG .
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Affiliation(s)
- Lukas M Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Arkajyoti Saha
- Department of Statistics, University of Washington, Seattle, WA, USA
| | - Abhirup Datta
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kasper D Hansen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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27
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Li JSY, Raghubar AM, Matigian NA, Ng MSY, Rogers NM, Mallett AJ. The Utility of Spatial Transcriptomics for Solid Organ Transplantation. Transplantation 2023; 107:1463-1471. [PMID: 36584371 DOI: 10.1097/tp.0000000000004466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.
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Affiliation(s)
- Jennifer S Y Li
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
| | - Arti M Raghubar
- Kidney Health Service, Royal Brisbane and Women's Hospital, QLD, Australia
- Conjoint Internal Medicine Laboratory, Pathology Queensland, Health Support Queensland, QLD, Australia
- Department of Anatomical Pathology, Pathology Queensland, Health Support Queensland, QLD, Australia
- Faculty of Medicine, University of Queensland, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
| | - Nicholas A Matigian
- QCIF Facility for Advanced Bioinformatics, The University of Queensland, QLD, Australia
| | - Monica S Y Ng
- Kidney Health Service, Royal Brisbane and Women's Hospital, QLD, Australia
- Conjoint Internal Medicine Laboratory, Pathology Queensland, Health Support Queensland, QLD, Australia
- Faculty of Medicine, University of Queensland, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
- Nephrology Department, Princess Alexandra Hospital, QLD, Australia
| | - Natasha M Rogers
- Centre for Transplant and Renal Research, Westmead Institute for Medical Research, Westmead, NSW, Australia
- Sydney Medical School, University of Sydney, Sydney, NSW, Australia
- Department of Renal Medicine, Westmead Hospital, Westmead, NSW, Australia
| | - Andrew J Mallett
- Faculty of Medicine, University of Queensland, QLD, Australia
- Institute for Molecular Bioscience, University of Queensland, QLD, Australia
- College of Medicine and Dentistry, James Cook University, QLD, Australia
- Department of Renal Medicine, Townsville University Hospital, QLD, Australia
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28
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Lütge M, De Martin A, Gil-Cruz C, Perez-Shibayama C, Stanossek Y, Onder L, Cheng HW, Kurz L, Cadosch N, Soneson C, Robinson MD, Stoeckli SJ, Ludewig B, Pikor NB. Conserved stromal-immune cell circuits secure B cell homeostasis and function. Nat Immunol 2023; 24:1149-1160. [PMID: 37202489 PMCID: PMC10307622 DOI: 10.1038/s41590-023-01503-3] [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: 05/02/2022] [Accepted: 04/03/2023] [Indexed: 05/20/2023]
Abstract
B cell zone reticular cells (BRCs) form stable microenvironments that direct efficient humoral immunity with B cell priming and memory maintenance being orchestrated across lymphoid organs. However, a comprehensive understanding of systemic humoral immunity is hampered by the lack of knowledge of global BRC sustenance, function and major pathways controlling BRC-immune cell interactions. Here we dissected the BRC landscape and immune cell interactome in human and murine lymphoid organs. In addition to the major BRC subsets underpinning the follicle, including follicular dendritic cells, PI16+ RCs were present across organs and species. As well as BRC-produced niche factors, immune cell-driven BRC differentiation and activation programs governed the convergence of shared BRC subsets, overwriting tissue-specific gene signatures. Our data reveal that a canonical set of immune cell-provided cues enforce bidirectional signaling programs that sustain functional BRC niches across lymphoid organs and species, thereby securing efficient humoral immunity.
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Affiliation(s)
- Mechthild Lütge
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Angelina De Martin
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Cristina Gil-Cruz
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | | | - Yves Stanossek
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
- Department of Otorhinolaryngology Head and Neck Surgery, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Lucas Onder
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Hung-Wei Cheng
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Lisa Kurz
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Nadine Cadosch
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Charlotte Soneson
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Mark D Robinson
- Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zurich, Zurich, Switzerland
| | - Sandro J Stoeckli
- Department of Otorhinolaryngology Head and Neck Surgery, Kantonsspital St.Gallen, St. Gallen, Switzerland
| | - Burkhard Ludewig
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland.
| | - Natalia B Pikor
- Institute of Immunobiology, Kantonsspital St.Gallen, St. Gallen, Switzerland.
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29
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Fries A, Saidoune F, Kuonen F, Dupanloup I, Fournier N, Guerra de Souza AC, Haniffa M, Ma F, Gudjonsson JE, Roesner L, Li Y, Werfel T, Conrad C, Gottardo R, Modlin RL, Di Domizio J, Gilliet M. Differentiation of IL-26 + T H17 intermediates into IL-17A producers via epithelial crosstalk in psoriasis. Nat Commun 2023; 14:3878. [PMID: 37391412 PMCID: PMC10313793 DOI: 10.1038/s41467-023-39484-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Accepted: 06/15/2023] [Indexed: 07/02/2023] Open
Abstract
Interleukin (IL)-26 is a TH17 cytokine with known antimicrobial and pro-inflammatory functions. However, the precise role of IL-26 in the context of pathogenic TH17 responses is unknown. Here we identify a population of blood TH17 intermediates that produce high levels of IL-26 and differentiate into IL-17A-producing TH17 cells upon TGF-β1 exposure. By combining single cell RNA sequencing, TCR sequencing and spatial transcriptomics we show that this process occurs in psoriatic skin. In fact, IL-26+ TH17 intermediates infiltrating psoriatic skin induce TGF-β1 expression in basal keratinocytes and thereby promote their own differentiation into IL-17A-producing cells. Thus, our study identifies IL-26-producing cells as an early differentiation stage of TH17 cells that infiltrates psoriatic skin and controls its own maturation into IL17A-producing TH17 cells, via epithelial crosstalk involving paracrine production of TGF-β1.
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Affiliation(s)
- Anissa Fries
- Department of Dermatology, CHUV University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Fanny Saidoune
- Department of Dermatology, CHUV University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
| | - François Kuonen
- Department of Dermatology, CHUV University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Isabelle Dupanloup
- Translational Data Science Facility, Agora Cancer Research Center, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Nadine Fournier
- Translational Data Science Facility, Agora Cancer Research Center, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Ana Cristina Guerra de Souza
- Translational Data Science Facility, Agora Cancer Research Center, Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Muzlifah Haniffa
- Department of Dermatology and NIHR Newcastle Biomedical Research Centre, Newcastle Hospitals NHS Foundation Trust, Newcastle upon Tyne, NE2 4LP, UK
| | - Feiyang Ma
- Department of Dermatology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Johann E Gudjonsson
- Department of Dermatology, University of Michigan, Ann Arbor, MI, 48109, USA
| | - Lennart Roesner
- Department of Dermatology, Hannover Medical School, Hannover, Germany
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany
| | - Yang Li
- Department of Computational Biology for Individualised Medicine, Centre for Individualised Infection Medicine (CiiM), Helmholtz Centre for Infection Research (HZI), Hannover Medical School (MHH), Hannover, Germany
| | - Thomas Werfel
- Department of Dermatology, Hannover Medical School, Hannover, Germany
- Cluster of Excellence RESIST (EXC 2155), Hannover Medical School, Hannover, Germany
| | - Curdin Conrad
- Department of Dermatology, CHUV University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Raphael Gottardo
- Biomedical Data Sciences Center, CHUV, UNIL, and SIB, Lausanne, Switzerland
| | - Robert L Modlin
- Division of Dermatology, Department of Medicine, University of California, Los Angeles, CA, 90095, USA
| | - Jeremy Di Domizio
- Department of Dermatology, CHUV University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland.
| | - Michel Gilliet
- Department of Dermatology, CHUV University Hospital and University of Lausanne (UNIL), Lausanne, Switzerland.
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30
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Meyer L, Eling N, Bodenmiller B. cytoviewer: an R/Bioconductor package for interactive visualization and exploration of highly multiplexed imaging data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.24.542115. [PMID: 37292939 PMCID: PMC10245846 DOI: 10.1101/2023.05.24.542115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Highly multiplexed imaging enables single-cell-resolved detection of numerous biological molecules in their spatial tissue context. Interactive data visualization of multiplexed imaging data is necessary for quality control and hypothesis examination. Here, we describe cytoviewer, an R/Bioconductor package for interactive visualization and exploration of multi-channel images and segmentation masks. The cytoviewer package supports flexible generation of image composites, allows side-by-side visualization of single channels, and facilitates the spatial visualization of single-cell data in the form of segmentation masks. The package operates on SingleCellExperiment, SpatialExperiment and CytoImageList objects and therefore integrates with the Bioconductor framework for single-cell and image analysis. Users of cytoviewer need little coding expertise, and the graphical user interface allows user-friendly navigation. We showcase the functionality of cytoviewer by analysis of an imaging mass cytometry dataset of cancer patients.
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Affiliation(s)
- Lasse Meyer
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
- Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Nils Eling
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
| | - Bernd Bodenmiller
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland
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31
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Feng Y, Yang T, Zhu J, Li M, Doyle M, Ozcoban V, Bass GT, Pizzolla A, Cain L, Weng S, Pasam A, Kocovski N, Huang YK, Keam SP, Speed TP, Neeson PJ, Pearson RB, Sandhu S, Goode DL, Trigos AS. Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments. Nat Commun 2023; 14:2697. [PMID: 37188662 DOI: 10.1038/s41467-023-37822-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 03/30/2023] [Indexed: 05/17/2023] Open
Abstract
Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.
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Affiliation(s)
- Yuzhou Feng
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Tianpei Yang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - John Zhu
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Mabel Li
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Maria Doyle
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Volkan Ozcoban
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Greg T Bass
- Research & Development, CSL Innovation, Parkville, VIC, Australia
| | - Angela Pizzolla
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Lachlan Cain
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Sirui Weng
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Anupama Pasam
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | | | - Yu-Kuan Huang
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Simon P Keam
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Terence P Speed
- Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
| | - Paul J Neeson
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Richard B Pearson
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Shahneen Sandhu
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - David L Goode
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia
| | - Anna S Trigos
- Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia.
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32
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Montagne JM, Jaffee EM, Fertig EJ. Multiomics Empowers Predictive Pancreatic Cancer Immunotherapy. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2023; 210:859-868. [PMID: 36947820 PMCID: PMC10236355 DOI: 10.4049/jimmunol.2200660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Accepted: 11/23/2022] [Indexed: 03/24/2023]
Abstract
Advances in cancer immunotherapy, particularly immune checkpoint inhibitors, have dramatically improved the prognosis for patients with metastatic melanoma and other previously incurable cancers. However, patients with pancreatic ductal adenocarcinoma (PDAC) generally do not respond to these therapies. PDAC is exceptionally difficult to treat because of its often late stage at diagnosis, modest mutation burden, and notoriously complex and immunosuppressive tumor microenvironment. Simultaneously interrogating features of cancer, immune, and other cellular components of the PDAC tumor microenvironment is therefore crucial for identifying biomarkers of immunotherapeutic resistance and response. Notably, single-cell and multiomics technologies, along with the analytical tools for interpreting corresponding data, are facilitating discoveries of the systems-level cellular and molecular interactions contributing to the overall resistance of PDAC to immunotherapy. Thus, in this review, we will explore how multiomics and single-cell analyses provide the unprecedented opportunity to identify biomarkers of resistance and response to successfully sensitize PDAC to immunotherapy.
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Affiliation(s)
- Janelle M Montagne
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD
- Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elizabeth M Jaffee
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD
- Bloomberg Kimmel Immunology Institute, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Elana J Fertig
- Department of Oncology, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD
- Convergence Institute, Johns Hopkins University School of Medicine, Baltimore, MD
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33
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.23.533980. [PMID: 36993287 PMCID: PMC10055313 DOI: 10.1101/2023.03.23.533980] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms revealing interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, USA
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver - Anschutz Medical Campus, Aurora, USA
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver - Anschutz Medical Campus, Aurora, USA
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Pirrotta S, Masatti L, Corrà A, Pedrini F, Esposito G, Martini P, Risso D, Romualdi C, Calura E. signifinder enables the identification of tumor cell states and cancer expression signatures in bulk, single-cell and spatial transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.07.530940. [PMID: 36945491 PMCID: PMC10028855 DOI: 10.1101/2023.03.07.530940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
Over the last decade, many studies and some clinical trials have proposed gene expression signatures as a valuable tool for understanding cancer mechanisms, defining subtypes, monitoring patient prognosis, and therapy efficacy. However, technical and biological concerns about reproducibility have been raised. Technical reproducibility is a major concern: we currently lack a computational implementation of the proposed signatures, which would provide detailed signature definition and assure reproducibility, dissemination, and usability of the classifier. Another concern regards intratumor heterogeneity, which has never been addressed when studying these types of biomarkers using bulk transcriptomics. With the aim of providing a tool able to improve the reproducibility and usability of gene expression signatures, we propose signifinder, an R package that provides the infrastructure to collect, implement, and compare expression-based signatures from cancer literature. The included signatures cover a wide range of biological processes from metabolism and programmed cell death, to morphological changes, such as quantification of epithelial or mesenchymal-like status. Collected signatures can score tumor cell characteristics, such as the predicted response to therapy or the survival association, and can quantify microenvironmental information, including hypoxia and immune response activity. signifinder has been used to characterize tumor samples and to investigate intra-tumor heterogeneity, extending its application to single-cell and spatial transcriptomic data. Through these higher-resolution technologies, it has become increasingly apparent that the single-sample score assessment obtained by transcriptional signatures is conditioned by the phenotypic and genetic intratumor heterogeneity of tumor masses. Since the characteristics of the most abundant cell type or clone might not necessarily predict the properties of mixed populations, signature prediction efficacy is lowered, thus impeding effective clinical diagnostics. Through signifinder, we offer general principles for interpreting and comparing transcriptional signatures, as well as suggestions for additional signatures that would allow for more complete and robust data inferences. We consider signifinder a useful tool to pave the way for reproducibility and comparison of transcriptional signatures in oncology.
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Affiliation(s)
| | - Laura Masatti
- Department of Biology, University of Padua, Padua, Italy
| | - Anna Corrà
- Department of Biology, University of Padua, Padua, Italy
| | | | - Giovanni Esposito
- Immunology and Molecular Oncology Diagnostic Unit of The Veneto Institute of Oncology IOV – IRCCS, Padua, Italy
| | - Paolo Martini
- Department of Molecular and Translational Medicine, University of Brescia, Brescia, Italy
| | - Davide Risso
- Department of Statistical Sciences, University of Padua, Italy
| | | | - Enrica Calura
- Department of Biology, University of Padua, Padua, Italy
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35
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Designing spatial transcriptomic experiments. Nat Methods 2023; 20:355-356. [PMID: 36864198 DOI: 10.1038/s41592-023-01801-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
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36
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Nault R, Saha S, Bhattacharya S, Sinha S, Maiti T, Zacharewski T. Single-cell transcriptomics shows dose-dependent disruption of hepatic zonation by TCDD in mice. Toxicol Sci 2023; 191:135-148. [PMID: 36222588 PMCID: PMC9887712 DOI: 10.1093/toxsci/kfac109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023] Open
Abstract
2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) dose-dependently induces the development of hepatic fat accumulation and inflammation with fibrosis in mice initially in the portal region. Conversely, differential gene and protein expression is first detected in the central region. To further investigate cell-specific and spatially resolved dose-dependent changes in gene expression elicited by TCDD, single-nuclei RNA sequencing and spatial transcriptomics were used for livers of male mice gavaged with TCDD every 4 days for 28 days. The proportion of 11 cell (sub)types across 131 613 nuclei dose-dependently changed with 68% of all portal and central hepatocyte nuclei in control mice being overtaken by macrophages following TCDD treatment. We identified 368 (portal fibroblasts) to 1339 (macrophages) differentially expressed genes. Spatial analyses revealed initial loss of portal identity that eventually spanned the entire liver lobule with increasing dose. Induction of R-spondin 3 (Rspo3) and pericentral Apc, suggested dysregulation of the Wnt/β-catenin signaling cascade in zonally resolved steatosis. Collectively, the integrated results suggest disruption of zonation contributes to the pattern of TCDD-elicited NAFLD pathologies.
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Affiliation(s)
- Rance Nault
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, Michigan 48824, USA
| | - Satabdi Saha
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824, USA
| | - Sudin Bhattacharya
- Institute for Integrative Toxicology, Michigan State University, East Lansing, Michigan 48824, USA
- Biomedical Engineering Department, Pharmacology & Toxicology, Institute for Quantitative Health Science and Engineering, Michigan State University, East Lansing, Michigan 48824, USA
| | - Samiran Sinha
- Department of Statistics, Texas A&M University, College Station, Texas 77840, USA
| | - Tapabrata Maiti
- Department of Statistics and Probability, Michigan State University, East Lansing, Michigan 48824, USA
| | - Tim Zacharewski
- Department of Biochemistry & Molecular Biology, Michigan State University, East Lansing, Michigan 48824, USA
- Institute for Integrative Toxicology, Michigan State University, East Lansing, Michigan 48824, USA
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37
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Wrobel J, Harris C, Vandekar S. Statistical Analysis of Multiplex Immunofluorescence and Immunohistochemistry Imaging Data. Methods Mol Biol 2023; 2629:141-168. [PMID: 36929077 DOI: 10.1007/978-1-0716-2986-4_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] [Indexed: 03/18/2023]
Abstract
Advances in multiplexed single-cell immunofluorescence (mIF) and multiplex immunohistochemistry (mIHC) imaging technologies have enabled the analysis of cell-to-cell spatial relationships that promise to revolutionize our understanding of tissue-based diseases and autoimmune disorders. Multiplex images are collected as multichannel TIFF files; then denoised, segmented to identify cells and nuclei, normalized across slides with protein markers to correct for batch effects, and phenotyped; and then tissue composition and spatial context at the cellular level are analyzed. This chapter discusses methods and software infrastructure for image processing and statistical analysis of mIF/mIHC data.
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Affiliation(s)
- Julia Wrobel
- Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA.
| | - Coleman Harris
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Simon Vandekar
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
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38
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Pardo B, Spangler A, Weber LM, Page SC, Hicks SC, Jaffe AE, Martinowich K, Maynard KR, Collado-Torres L. spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data. BMC Genomics 2022; 23:434. [PMID: 35689177 PMCID: PMC9188087 DOI: 10.1186/s12864-022-08601-w] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 05/02/2022] [Indexed: 11/21/2022] Open
Abstract
Background Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user’s computer or through a web application. Conclusions spatialLIBD is available at https://bioconductor.org/packages/spatialLIBD. It is fully compatible with SpatialExperiment and the Bioconductor ecosystem. Its functionality facilitates analyzing and interactively exploring spatially-resolved data from the Visium platform. Supplementary Information The online version contains supplementary material available at (10.1186/s12864-022-08601-w).
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Affiliation(s)
- Brenda Pardo
- Licenciatura de Ciencias Genómicas, Escuela Nacional de Estudios Superiores Unidad Juriquilla, Universidad Nacional Autónoma de México, Querétaro, 76230, Mexico.,Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, Maryland, USA
| | - Abby Spangler
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, Maryland, USA
| | - Lukas M Weber
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, 21205, Maryland, USA
| | - Stephanie C Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, Maryland, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, 21205, Maryland, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, Maryland, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, Maryland, USA
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, Maryland, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, Maryland, USA.
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