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Das Adhikari S, Yang J, Wang J, Cui Y. Recent advances in spatially variable gene detection in spatial transcriptomics. Comput Struct Biotechnol J 2024; 23:883-891. [PMID: 38370977 PMCID: PMC10869304 DOI: 10.1016/j.csbj.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 02/20/2024] Open
Abstract
With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.
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Affiliation(s)
- Sikta Das Adhikari
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
| | - Jiaxin Yang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
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2
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Liu L, Chen A, Li Y, Mulder J, Heyn H, Xu X. Spatiotemporal omics for biology and medicine. Cell 2024; 187:4488-4519. [PMID: 39178830 DOI: 10.1016/j.cell.2024.07.040] [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: 03/20/2024] [Revised: 07/05/2024] [Accepted: 07/23/2024] [Indexed: 08/26/2024]
Abstract
The completion of the Human Genome Project has provided a foundational blueprint for understanding human life. Nonetheless, understanding the intricate mechanisms through which our genetic blueprint is involved in disease or orchestrates development across temporal and spatial dimensions remains a profound scientific challenge. Recent breakthroughs in cellular omics technologies have paved new pathways for understanding the regulation of genomic elements and the relationship between gene expression, cellular functions, and cell fate determination. The advent of spatial omics technologies, encompassing both imaging and sequencing-based methodologies, has enabled a comprehensive understanding of biological processes from a cellular ecosystem perspective. This review offers an updated overview of how spatial omics has advanced our understanding of the translation of genetic information into cellular heterogeneity and tissue structural organization and their dynamic changes over time. It emphasizes the discovery of various biological phenomena, related to organ functionality, embryogenesis, species evolution, and the pathogenesis of diseases.
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Affiliation(s)
| | - Ao Chen
- BGI Research, Shenzhen 518083, China
| | | | - Jan Mulder
- Department of Neuroscience, Karolinska Institute, Stockholm, Sweden
| | - Holger Heyn
- Centro Nacional de Análisis Genómico (CNAG), Barcelona, Spain
| | - Xun Xu
- BGI Research, Hangzhou 310030, China; BGI Research, Shenzhen 518083, China.
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3
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Eagles NJ, Bach SV, Tippani M, Ravichandran P, Du Y, Miller RA, Hyde TM, Page SC, Martinowich K, Collado-Torres L. Integrating gene expression and imaging data across Visium capture areas with visiumStitched. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.08.607222. [PMID: 39149358 PMCID: PMC11326277 DOI: 10.1101/2024.08.08.607222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Background Visium is a widely-used spatially-resolved transcriptomics assay available from 10x Genomics. Standard Visium capture areas (6.5mm by 6.5mm) limit the survey of larger tissue structures, but combining overlapping images and associated gene expression data allow for more complex study designs. Current software can handle nested or partial image overlaps, but is designed for merging up to two capture areas, and cannot account for some technical scenarios related to capture area alignment. Results We generated Visium data from a postmortem human tissue sample such that two capture areas were partially overlapping and a third one was adjacent. We developed the R/Bioconductor package visiumStitched, which facilitates stitching the images together with Fiji (ImageJ), and constructing SpatialExperiment R objects with the stitched images and gene expression data. visiumStitched constructs an artificial hexagonal array grid which allows seamless downstream analyses such as spatially-aware clustering without discarding data from overlapping spots. Data stitched with visiumStitched can then be interactively visualized with spatialLIBD. Conclusions visiumStitched provides a simple, but flexible framework to handle various multi-capture area study design scenarios. Specifically, it resolves a data processing step without disrupting analysis workflows and without discarding data from overlapping spots. visiumStiched relies on affine transformations by Fiji, which have limitations and are less accurate when aligning against an atlas or other situations. visiumStiched provides an easy-to-use solution which expands possibilities for designing multi-capture area study designs.
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Affiliation(s)
- Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Svitlana V. Bach
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Madhavi Tippani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Prashanthi Ravichandran
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, 21218, Baltimore, USA
| | - Yufeng Du
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Ryan A. Miller
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- Department of Neurology, Johns Hopkins School of Medicine, 21205, Baltimore, USA
| | - Stephanie C. Page
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, USA
| | - Keri Martinowich
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- Department of Neurology, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, 21205, Baltimore, USA
- Johns Hopkins Kavli Neuroscience Discovery Institute, Johns Hopkins University, 21218, Baltimore, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, 21205 Baltimore, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 21205, Baltimore, USA
- Center for Computational Biology, Johns Hopkins University, 21205, Baltimore, USA
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4
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Yan G, Hua SH, Li JJ. Categorization of 31 computational methods to detect spatially variable genes from spatially resolved transcriptomics data. ARXIV 2024:arXiv:2405.18779v2. [PMID: 38855546 PMCID: PMC11160866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 31 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.
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Affiliation(s)
- Guanao Yan
- Department of Statistics, University of California, Los Angeles, CA 90095-1554
| | - Shuo Harper Hua
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA 90095-1554
- Department of Human Genetics, University of California, Los Angeles, CA 90095-7088
- Department of Computational Medicine, University of California, Los Angeles, CA 90095-1766
- Department of Biostatistics, University of California, Los Angeles, CA 90095-1772
- Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA 02138
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5
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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 PMCID: PMC11398705 DOI: 10.1126/science.adh1938] [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: 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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6
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Ospina OE, Soupir AC, Manjarres-Betancur R, Gonzalez-Calderon G, Yu X, Fridley BL. Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models. Sci Rep 2024; 14:10967. [PMID: 38744956 PMCID: PMC11094014 DOI: 10.1038/s41598-024-61758-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Accepted: 05/09/2024] [Indexed: 05/16/2024] Open
Abstract
Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).
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Affiliation(s)
- Oscar E Ospina
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Alex C Soupir
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | | | | | - Xiaoqing Yu
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA
| | - Brooke L Fridley
- Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
- Biostatistics and Epidemiology Core, Division of Health Services & Outcomes Research, Children's Mercy, Kansas City, MO, USA.
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7
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Li J, Wang Y, Raina MA, Xu C, Su L, Guo Q, Ma Q, Wang J, Xu D. scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.06.592851. [PMID: 38765956 PMCID: PMC11100755 DOI: 10.1101/2024.05.06.592851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.
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8
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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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9
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Yu S, Li WV. spVC for the detection and interpretation of spatial gene expression variation. Genome Biol 2024; 25:103. [PMID: 38641849 PMCID: PMC11027374 DOI: 10.1186/s13059-024-03245-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 04/10/2024] [Indexed: 04/21/2024] Open
Abstract
Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.
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Affiliation(s)
- Shan Yu
- Department of Statistics, Unversity of Virginia, Charlottesville, 22903, VA, USA.
| | - Wei Vivian Li
- Department of Statistics, University of California, Riverside, 92521, CA, USA.
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10
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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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11
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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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12
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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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13
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Chen C, Kim HJ, Yang P. Evaluating spatially variable gene detection methods for spatial transcriptomics data. Genome Biol 2024; 25:18. [PMID: 38225676 PMCID: PMC10789051 DOI: 10.1186/s13059-023-03145-y] [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: 12/08/2022] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method. RESULTS Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. CONCLUSIONS Our study evaluates the performance of each method from multiple aspects and highlights the discrepancy among different methods when calling statistically significant SVGs across diverse datasets. Overall, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of related methods.
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Affiliation(s)
- Carissa Chen
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
| | - Pengyi Yang
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
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14
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Li Z, Patel ZM, Song D, Yan G, Li JJ, Pinello L. Benchmarking computational methods to identify spatially variable genes and peaks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.02.569717. [PMID: 38076922 PMCID: PMC10705556 DOI: 10.1101/2023.12.02.569717] [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: 12/17/2023]
Abstract
Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes. Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran's I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.
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Affiliation(s)
- Zhijian Li
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Zain M. Patel
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Dongyuan Song
- Interdepartmental Program of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Guanao Yan
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Luca Pinello
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
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15
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Adhikari SD, Yang J, Wang J, Cui Y. A SELECTIVE REVIEW OF RECENT DEVELOPMENTS IN SPATIALLY VARIABLE GENE DETECTION FOR SPATIAL TRANSCRIPTOMICS. ARXIV 2023:arXiv:2311.13801v1. [PMID: 38045476 PMCID: PMC10690303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.
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Affiliation(s)
- Sikta Das Adhikari
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Jiaxin Yang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
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16
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Wang J, Li J, Kramer ST, Su L, Chang Y, Xu C, Eadon MT, Kiryluk K, Ma Q, Xu D. Dimension-agnostic and granularity-based spatially variable gene identification using BSP. Nat Commun 2023; 14:7367. [PMID: 37963892 PMCID: PMC10645821 DOI: 10.1038/s41467-023-43256-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 11/03/2023] [Indexed: 11/16/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is critical in linking molecular cell functions with tissue phenotypes. Spatially resolved transcriptomics captures cellular-level gene expression with corresponding spatial coordinates in two or three dimensions and can be used to infer SVGs effectively. However, current computational methods may not achieve reliable results and often cannot handle three-dimensional spatial transcriptomic data. Here we introduce BSP (big-small patch), a non-parametric model by comparing gene expression pattens at two spatial granularities to identify SVGs from two or three-dimensional spatial transcriptomics data in a fast and robust manner. This method has been extensively tested in simulations, demonstrating superior accuracy, robustness, and high efficiency. BSP is further validated by substantiated biological discoveries in cancer, neural science, rheumatoid arthritis, and kidney studies with various types of spatial transcriptomics technologies.
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Affiliation(s)
- Juexin Wang
- Department of BioHealth Informatics, Luddy School of Informatics, Computing, and Engineering, Indiana University Indianapolis, Indianapolis, IN, 46202, USA.
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
| | - Jinpu Li
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Skyler T Kramer
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Li Su
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Yuzhou Chang
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA
| | - Chunhui Xu
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA
| | - Michael T Eadon
- Department of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University Irving Medical Center, New York, NY, 10027, USA
| | - Qin Ma
- Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, 43210, USA.
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, 43210, USA.
| | - Dong Xu
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, 65211, USA.
- Institute for Data Science and Informatics, University of Missouri, Columbia, MO, 65211, USA.
- Christopher S. Bond Life Sciences Center, University of Missouri, Columbia, MO, 65211, USA.
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17
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Chitra U, Arnold BJ, Sarkar H, Ma C, Lopez-Darwin S, Sanno K, Raphael BJ. Mapping the topography of spatial gene expression with interpretable deep learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.10.561757. [PMID: 37873258 PMCID: PMC10592770 DOI: 10.1101/2023.10.10.561757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Spatially resolved transcriptomics technologies provide high-throughput measurements of gene expression in a tissue slice, but the sparsity of this data complicates the analysis of spatial gene expression patterns such as gene expression gradients. We address these issues by deriving a topographic map of a tissue slice-analogous to a map of elevation in a landscape-using a novel quantity called the isodepth. Contours of constant isodepth enclose spatial domains with distinct cell type composition, while gradients of the isodepth indicate spatial directions of maximum change in gene expression. We develop GASTON, an unsupervised and interpretable deep learning algorithm that simultaneously learns the isodepth, spatial gene expression gradients, and piecewise linear functions of the isodepth that model both continuous gradients and discontinuous spatial variation in the expression of individual genes. We validate GASTON by showing that it accurately identifies spatial domains and marker genes across several biological systems. In SRT data from the brain, GASTON reveals gradients of neuronal differentiation and firing, and in SRT data from a tumor sample, GASTON infers gradients of metabolic activity and epithelial-mesenchymal transition (EMT)-related gene expression in the tumor microenvironment.
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Affiliation(s)
- Uthsav Chitra
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Brian J. Arnold
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
| | - Hirak Sarkar
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Ludwig Cancer Institute, Princeton Branch, Princeton University, Princeton, NJ, USA
| | - Cong Ma
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | | | - Kohei Sanno
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Center for Statistics and Machine Learning, Princeton University, Princeton, NJ, USA
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18
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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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19
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Charitakis N, Salim A, Piers AT, Watt KI, Porrello ER, Elliott DA, Ramialison M. Disparities in spatially variable gene calling highlight the need for benchmarking spatial transcriptomics methods. Genome Biol 2023; 24:209. [PMID: 37723583 PMCID: PMC10506280 DOI: 10.1186/s13059-023-03045-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 08/21/2023] [Indexed: 09/20/2023] Open
Abstract
Identifying spatially variable genes (SVGs) is a key step in the analysis of spatially resolved transcriptomics data. SVGs provide biological insights by defining transcriptomic differences within tissues, which was previously unachievable using RNA-sequencing technologies. However, the increasing number of published tools designed to define SVG sets currently lack benchmarking methods to accurately assess performance. This study compares results of 6 purpose-built packages for SVG identification across 9 public and 5 simulated datasets and highlights discrepancies between results. Additional tools for generation of simulated data and development of benchmarking methods are required to improve methods for identifying SVGs.
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Affiliation(s)
- Natalie Charitakis
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
| | - Agus Salim
- Melbourne School of Population and Global Health, University of Melbourne, Bouverie St, Carlton, VIC, 3053, Australia
- School of Mathematics and Statistics, University of Melbourne, Swanston Street, Parkville, VIC, 3010, Australia
| | - Adam T Piers
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Melbourne Centre for Cardiovascular Genomics and Regenerative Medicine, The Royal Children's Hospital, Melbourne, VIC, 3052, Australia
| | - Kevin I Watt
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia
- Department of Diabetes, Monash University, Alfred Centre, Commercial Road, Melbourne, VIC, 3004, Australia
| | - Enzo R Porrello
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Anatomy and Physiology, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
| | - David A Elliott
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
| | - Mirana Ramialison
- Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
- Department of Paediatrics, University of Melbourne, Grattan Street, Parkville, VIC, 3010, Australia.
- Novo Nordisk Foundation Center for Stem Cell Medicine, Murdoch Children's Research Institute, Royal Children's Hospital, Flemington Road, Parkville, VIC, 3052, Australia.
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