1
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Wang N, Hong W, Wu Y, Chen Z, Bai M, Wang W, Zhu J. Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology. MedComm (Beijing) 2024; 5:e765. [PMID: 39376738 PMCID: PMC11456678 DOI: 10.1002/mco2.765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2024] [Revised: 08/30/2024] [Accepted: 09/03/2024] [Indexed: 10/09/2024] Open
Abstract
The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.
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Affiliation(s)
- Nan Wang
- Cosmos Wisdom Biotech Co. LtdHangzhouChina
| | - Weifeng Hong
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
| | - Yixing Wu
- Department of Pulmonary and Critical Care MedicineZhongshan HospitalFudan UniversityShanghaiChina
| | - Zhe‐Sheng Chen
- Department of Pharmaceutical SciencesCollege of Pharmacy and Health SciencesInstitute for BiotechnologySt. John's UniversityQueensNew YorkUSA
| | - Minghua Bai
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
| | | | - Ji Zhu
- Department of Radiation OncologyZhejiang Cancer HospitalHangzhouChina
- Hangzhou Institute of Medicine (HIM)Chinese Academy of SciencesHangzhouChina
- Zhejiang Key Laboratory of Radiation OncologyHangzhouChina
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2
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Sarwar A, Rue M, French L, Cross H, Chen X, Gillis J. Cross-expression analysis reveals patterns of coordinated gene expression in spatial transcriptomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.17.613579. [PMID: 39345494 PMCID: PMC11429685 DOI: 10.1101/2024.09.17.613579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/01/2024]
Abstract
Spatial transcriptomics promises to transform our understanding of tissue biology by molecularly profiling individual cells in situ. A fundamental question they allow us to ask is how nearby cells orchestrate their gene expression. To investigate this, we introduce cross-expression, a novel framework for discovering gene pairs that coordinate their expression across neighboring cells. Just as co-expression quantifies synchronized gene expression within the same cells, cross-expression measures coordinated gene expression between spatially adjacent cells, allowing us to understand tissue gene expression programs with single cell resolution. Using this framework, we recover ligand-receptor partners and discover gene combinations marking anatomical regions. More generally, we create cross-expression networks to find gene modules with orchestrated expression patterns. Finally, we provide an efficient R package to facilitate cross-expression analysis, quantify effect sizes, and generate novel visualizations to better understand spatial gene expression programs.
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Affiliation(s)
- Ameer Sarwar
- Department of Cell and Systems Biology and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Mara Rue
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Leon French
- Department of Physiology and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
| | - Helen Cross
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Xiaoyin Chen
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jesse Gillis
- Department of Physiology and Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, ON, Canada
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3
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Donovan ML, Jhaveri N, Ma N, Cheikh BB, DeRosa J, Mihani R, Berrell N, Suen JY, Monkman J, Fraser JF, Kulasinghe A. Protocol for high-plex, whole-slide imaging of human formalin-fixed paraffin-embedded tissue using PhenoCycler-Fusion. STAR Protoc 2024; 5:103226. [PMID: 39031553 PMCID: PMC11314888 DOI: 10.1016/j.xpro.2024.103226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2024] [Revised: 05/03/2024] [Accepted: 07/05/2024] [Indexed: 07/22/2024] Open
Abstract
Single-cell spatial analysis of proteins is rapidly becoming increasingly important in revealing biological insights. Here, we present a protocol for automated high-plex multi-slide immunofluorescence staining and imaging of human head and neck cancer formalin-fixed paraffin-embedded (FFPE) sections using PhenoCycler-Fusion 2.0 technology. We describe steps for preparing human head and neck cancer FFPE tissues, staining with a panel of immunophenotyping markers, and Flow Cell assembly. We then detail procedures for setting up for a PhenoCycler-Fusion run, post-run Flow Cell removal, and downstream analyses. For complete details on the use and execution of this protocol, please refer to Jhaveri et al.1.
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Affiliation(s)
- Meg L Donovan
- Queensland Spatial Biology Centre, Wesley Research Institute, Level 8 East Wing, The Wesley Hospital, Auchenflower, QLD 4066, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Niyati Jhaveri
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - Ning Ma
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - Bassem Ben Cheikh
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - James DeRosa
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - Ritu Mihani
- Akoya Biosciences, The Spatial Biology Company, Marlborough, MA, USA
| | - Naomi Berrell
- Queensland Spatial Biology Centre, Wesley Research Institute, Level 8 East Wing, The Wesley Hospital, Auchenflower, QLD 4066, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - Jacky Y Suen
- Critical Care Research Group, The Prince Charles Hospital, Brisbane, QLD 4032, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - James Monkman
- Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia
| | - John F Fraser
- Queensland Spatial Biology Centre, Wesley Research Institute, Level 8 East Wing, The Wesley Hospital, Auchenflower, QLD 4066, Australia; Critical Care Research Group, The Prince Charles Hospital, Brisbane, QLD 4032, Australia; Institute for Molecular Bioscience, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Arutha Kulasinghe
- Queensland Spatial Biology Centre, Wesley Research Institute, Level 8 East Wing, The Wesley Hospital, Auchenflower, QLD 4066, Australia; Frazer Institute, Faculty of Medicine, The University of Queensland, Brisbane, QLD 4102, Australia.
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4
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Kamel M, Sarangi A, Senin P, Villordo S, Sunaal M, Barot H, Wang S, Solbas A, Cano L, Classe M, Bar-Joseph Z, Pla Planas A. SpatialOne: end-to-end analysis of visium data at scale. BIOINFORMATICS (OXFORD, ENGLAND) 2024; 40:btae509. [PMID: 39152991 PMCID: PMC11374018 DOI: 10.1093/bioinformatics/btae509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 07/08/2024] [Accepted: 08/15/2024] [Indexed: 08/19/2024]
Abstract
MOTIVATION Spatial transcriptomics allow to quantify mRNA expression within the spatial context. Nonetheless, in-depth analysis of spatial transcriptomics data remains challenging and difficult to scale due to the number of methods and libraries required for that purpose. RESULTS Here we present SpatialOne, an end-to-end pipeline designed to simplify the analysis of 10x Visium data by combining multiple state-of-the-art computational methods to segment, deconvolve, and quantify spatial information; this approach streamlines the analysis of reproducible spatial-data at scale. AVAILABILITY AND IMPLEMENTATION SpatialOne source code and execution examples are available at https://github.com/Sanofi-Public/spatialone-pipeline, experimental data is available at https://zenodo.org/records/12605154. SpatialOne is distributed as a docker container image.
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Affiliation(s)
- Mena Kamel
- Digital R&D, Sanofi, Paris 75017, France
| | | | | | | | | | - Het Barot
- Digital R&D, Sanofi, Paris 75017, France
| | | | - Ana Solbas
- Digital R&D, Sanofi, Paris 75017, France
| | - Luis Cano
- Precision Medicine & Computational Biology, Sanofi, Vitry-sur-Seine 94400, France
| | - Marion Classe
- Precision Medicine & Computational Biology, Sanofi, Vitry-sur-Seine 94400, France
| | - Ziv Bar-Joseph
- Digital R&D, Sanofi, Water Street 450, Cambridge, MA 02141, USA
| | - Albert Pla Planas
- Digital R&D, Sanofi, Carrer de Rosselló i Porcel 21, Barcelona 08016, Spain
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5
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Gulati GS, D'Silva JP, Liu Y, Wang L, Newman AM. Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics. Nat Rev Mol Cell Biol 2024:10.1038/s41580-024-00768-2. [PMID: 39169166 DOI: 10.1038/s41580-024-00768-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/16/2024] [Indexed: 08/23/2024]
Abstract
Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.
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Affiliation(s)
- Gunsagar S Gulati
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | - Yunhe Liu
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Linghua Wang
- Department of Genomic Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
- The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Houston, TX, USA
| | - Aaron M Newman
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA.
- Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, CA, USA.
- Stanford Cancer Institute, Stanford University, Stanford, CA, USA.
- Chan Zuckerberg Biohub - San Francisco, San Francisco, CA, USA.
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6
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Stassen SV, Kobashi M, Lam EY, Huang Y, Ho JWK, Tsia KK. StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases. Genome Biol 2024; 25:224. [PMID: 39152459 PMCID: PMC11328412 DOI: 10.1186/s13059-024-03347-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: 10/24/2023] [Accepted: 07/23/2024] [Indexed: 08/19/2024] Open
Abstract
Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells' past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.
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Affiliation(s)
- Shobana V Stassen
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong.
| | - Minato Kobashi
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
| | - Edmund Y Lam
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong
- AI Chip Center for Emerging Smart Systems, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Yuanhua Huang
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
- Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong
| | - Joshua W K Ho
- School of Biomedical Sciences, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Pokfulam, Hong Kong
- Laboratory of Data Discovery for Health, Hong Kong Science Park, Shatin, New Territories, Hong Kong
| | - Kevin K Tsia
- Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam, Hong Kong, Hong Kong.
- Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin, New Territories, Hong Kong.
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7
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Hu Y, Xie M, Li Y, Rao M, Shen W, Luo C, Qin H, Baek J, Zhou XM. Benchmarking clustering, alignment, and integration methods for spatial transcriptomics. Genome Biol 2024; 25:212. [PMID: 39123269 PMCID: PMC11312151 DOI: 10.1186/s13059-024-03361-0] [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: 03/12/2024] [Accepted: 07/30/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND Spatial transcriptomics (ST) is advancing our understanding of complex tissues and organisms. However, building a robust clustering algorithm to define spatially coherent regions in a single tissue slice and aligning or integrating multiple tissue slices originating from diverse sources for essential downstream analyses remains challenging. Numerous clustering, alignment, and integration methods have been specifically designed for ST data by leveraging its spatial information. The absence of comprehensive benchmark studies complicates the selection of methods and future method development. RESULTS In this study, we systematically benchmark a variety of state-of-the-art algorithms with a wide range of real and simulated datasets of varying sizes, technologies, species, and complexity. We analyze the strengths and weaknesses of each method using diverse quantitative and qualitative metrics and analyses, including eight metrics for spatial clustering accuracy and contiguity, uniform manifold approximation and projection visualization, layer-wise and spot-to-spot alignment accuracy, and 3D reconstruction, which are designed to assess method performance as well as data quality. The code used for evaluation is available on our GitHub. Additionally, we provide online notebook tutorials and documentation to facilitate the reproduction of all benchmarking results and to support the study of new methods and new datasets. CONCLUSIONS Our analyses lead to comprehensive recommendations that cover multiple aspects, helping users to select optimal tools for their specific needs and guide future method development.
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Affiliation(s)
- Yunfei Hu
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Manfei Xie
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA
| | - Yikang Li
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA
| | - Mingxing Rao
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Wenjun Shen
- Department of Bioinformatics, Shantou University Medical College, 515041, Shantou, China
| | - Can Luo
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA
| | - Haoran Qin
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Jihoon Baek
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA
| | - Xin Maizie Zhou
- Department of Computer Science, Vanderbilt University, 37235, Nashville, USA.
- Department of Biomedical Engineering, Vanderbilt University, 37235, Nashville, USA.
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8
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Affiliation(s)
- Vipul Singhal
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore, Singapore, Republic of Singapore.
| | - Nigel Chou
- Spatial and Single Cell Systems Domain, Genome Institute of Singapore, Singapore, Republic of Singapore
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9
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Zhang M, Zhang W, Ma X. ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation. Brief Bioinform 2024; 25:bbae437. [PMID: 39228303 PMCID: PMC11372132 DOI: 10.1093/bib/bbae437] [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: 05/23/2024] [Revised: 07/31/2024] [Accepted: 08/20/2024] [Indexed: 09/05/2024] Open
Abstract
Recent advances in spatial transcriptomics (ST) enable measurements of transcriptome within intact biological tissues by preserving spatial information, offering biologists unprecedented opportunities to comprehensively understand tissue micro-environment, where spatial domains are basic units of tissues. Although great efforts are devoted to this issue, they still have many shortcomings, such as ignoring local information and relations of spatial domains, requiring alternatives to solve these problems. Here, a novel algorithm for spatial domain identification in Spatial Transcriptomics data with Structure Correlation and Self-Representation (ST-SCSR), which integrates local information, global information, and similarity of spatial domains. Specifically, ST-SCSR utilzes matrix tri-factorization to simultaneously decompose expression profiles and spatial network of spots, where expressional and spatial features of spots are fused via the shared factor matrix that interpreted as similarity of spatial domains. Furthermore, ST-SCSR learns affinity graph of spots by manipulating expressional and spatial features, where local preservation and sparse constraints are employed, thereby enhancing the quality of graph. The experimental results demonstrate that ST-SCSR not only outperforms state-of-the-art algorithms in terms of accuracy, but also identifies many potential interesting patterns.
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Affiliation(s)
- Min Zhang
- School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, 710071 Xi'an Shaanxi, China
- Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University, No. 2 South Taibai Road, 710071 Xi'an Shaanxi, China
| | - Wensheng Zhang
- School of Computer Science and Cyber Engineering, GuangZhou University, No. 230 Wai Huan Xi Road,Guangzhou Higher Education Mega Center, 510006 Guangzhou Guangdong, China
| | - Xiaoke Ma
- School of Computer Science and Technology, Xidian University, No. 2 South Taibai Road, 710071 Xi'an Shaanxi, China
- Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province, Xidian University, No. 2 South Taibai Road, 710071 Xi'an Shaanxi, China
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10
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Jackson KC, Booeshaghi AS, Gálvez-Merchán Á, Moses L, Chari T, Kim A, Pachter L. Identification of spatial homogeneous regions in tissues with concordex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.06.28.546949. [PMID: 39071320 PMCID: PMC11275758 DOI: 10.1101/2023.06.28.546949] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Spatial homogeneous regions (SHRs) in tissues are domains that are homogeneous with respect to cell type composition. We present a method for identifying SHRs using spatial transcriptomics data, and demonstrate that it is efficient and effective at finding SHRs for a wide variety of tissue types. The method is implemented in a tool called concordex, which relies on analysis of k-nearest-neighbor (kNN) graphs. The concordex tool is also useful for analysis of non-spatial transcriptomics data, and can elucidate the extent of concordance between partitions of cells derived from clustering algorithms, and transcriptomic similarity as represented in kNN graphs.
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Affiliation(s)
- Kayla C Jackson
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - A Sina Booeshaghi
- Department of Bioengineering, University of California, Berkeley, CA, USA
| | | | - Lambda Moses
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Tara Chari
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | | | - 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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11
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Andersson A, Behanova A, Avenel C, Windhager J, Malmberg F, Wählby C. Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data. Cytometry A 2024. [PMID: 38958502 DOI: 10.1002/cyto.a.24884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 05/30/2024] [Accepted: 06/13/2024] [Indexed: 07/04/2024]
Abstract
Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces "Points2Regions," a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical andk $$ k $$ -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration.
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Affiliation(s)
- Axel Andersson
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Andrea Behanova
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Christophe Avenel
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Jonas Windhager
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Filip Malmberg
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
| | - Carolina Wählby
- Department of IT and SciLifeLab BioImage Informatics Facility, Uppsala University, Uppsala, Sweden
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12
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van Velthoven CTJ, Gao Y, Kunst M, Lee C, McMillen D, Chakka AB, Casper T, Clark M, Chakrabarty R, Daniel S, Dolbeare T, Ferrer R, Gloe J, Goldy J, Guzman J, Halterman C, Ho W, Huang M, James K, Nguy B, Pham T, Ronellenfitch K, Thomas ED, Torkelson A, Pagan CM, Kruse L, Dee N, Ng L, Waters J, Smith KA, Tasic B, Yao Z, Zeng H. The transcriptomic and spatial organization of telencephalic GABAergic neuronal types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.18.599583. [PMID: 38948843 PMCID: PMC11212977 DOI: 10.1101/2024.06.18.599583] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
The telencephalon of the mammalian brain comprises multiple regions and circuit pathways that play adaptive and integrative roles in a variety of brain functions. There is a wide array of GABAergic neurons in the telencephalon; they play a multitude of circuit functions, and dysfunction of these neurons has been implicated in diverse brain disorders. In this study, we conducted a systematic and in-depth analysis of the transcriptomic and spatial organization of GABAergic neuronal types in all regions of the mouse telencephalon and their developmental origins. This was accomplished by utilizing 611,423 single-cell transcriptomes from the comprehensive and high-resolution transcriptomic and spatial cell type atlas for the adult whole mouse brain we have generated, supplemented with an additional single-cell RNA-sequencing dataset containing 99,438 high-quality single-cell transcriptomes collected from the pre- and postnatal developing mouse brain. We present a hierarchically organized adult telencephalic GABAergic neuronal cell type taxonomy of 7 classes, 52 subclasses, 284 supertypes, and 1,051 clusters, as well as a corresponding developmental taxonomy of 450 clusters across different ages. Detailed charting efforts reveal extraordinary complexity where relationships among cell types reflect both spatial locations and developmental origins. Transcriptomically and developmentally related cell types can often be found in distant and diverse brain regions indicating that long-distance migration and dispersion is a common characteristic of nearly all classes of telencephalic GABAergic neurons. Additionally, we find various spatial dimensions of both discrete and continuous variations among related cell types that are correlated with gene expression gradients. Lastly, we find that cortical, striatal and some pallidal GABAergic neurons undergo extensive postnatal diversification, whereas septal and most pallidal GABAergic neuronal types emerge simultaneously during the embryonic stage with limited postnatal diversification. Overall, the telencephalic GABAergic cell type taxonomy can serve as a foundational reference for molecular, structural and functional studies of cell types and circuits by the entire community.
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Affiliation(s)
| | - Yuan Gao
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Changkyu Lee
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Scott Daniel
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Tim Dolbeare
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Jessica Gloe
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jeff Goldy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Windy Ho
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Mike Huang
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | - Beagan Nguy
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | | | | | | | - Lauren Kruse
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Nick Dee
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Lydia Ng
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Jack Waters
- Allen Institute for Brain Science, Seattle, WA, USA
| | | | | | - Zizhen Yao
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Hongkui Zeng
- Allen Institute for Brain Science, Seattle, WA, USA
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Sun Y, Kong L, Huang J, Deng H, Bian X, Li X, Cui F, Dou L, Cao C, Zou Q, Zhang Z. A comprehensive survey of dimensionality reduction and clustering methods for single-cell and spatial transcriptomics data. Brief Funct Genomics 2024:elae023. [PMID: 38860675 DOI: 10.1093/bfgp/elae023] [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: 12/26/2023] [Revised: 02/29/2024] [Accepted: 05/27/2024] [Indexed: 06/12/2024] Open
Abstract
In recent years, the application of single-cell transcriptomics and spatial transcriptomics analysis techniques has become increasingly widespread. Whether dealing with single-cell transcriptomic or spatial transcriptomic data, dimensionality reduction and clustering are indispensable. Both single-cell and spatial transcriptomic data are often high-dimensional, making the analysis and visualization of such data challenging. Through dimensionality reduction, it becomes possible to visualize the data in a lower-dimensional space, allowing for the observation of relationships and differences between cell subpopulations. Clustering enables the grouping of similar cells into the same cluster, aiding in the identification of distinct cell subpopulations and revealing cellular diversity, providing guidance for downstream analyses. In this review, we systematically summarized the most widely recognized algorithms employed for the dimensionality reduction and clustering analysis of single-cell transcriptomic and spatial transcriptomic data. This endeavor provides valuable insights and ideas that can contribute to the development of novel tools in this rapidly evolving field.
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Affiliation(s)
- Yidi Sun
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lingling Kong
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Jiayi Huang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Hongyan Deng
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Xinling Bian
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Xingfeng Li
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Feifei Cui
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
| | - Lijun Dou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland, OH 44106, United States
| | - Chen Cao
- School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 210029, China
| | - Quan Zou
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, China
- Yangtze Delta Region Institute (Quzhou), University of Electronic Science and Technology of China, Quzhou 324000, China
| | - Zilong Zhang
- School of Computer Science and Technology, Hainan University, Haikou 570228, China
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14
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Thuilliez C, Moquin-Beaudry G, Khneisser P, Marques Da Costa ME, Karkar S, Boudhouche H, Drubay D, Audinot B, Geoerger B, Scoazec JY, Gaspar N, Marchais A. CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data. BIOINFORMATICS ADVANCES 2024; 4:vbae081. [PMID: 38915885 PMCID: PMC11194756 DOI: 10.1093/bioadv/vbae081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Revised: 05/02/2024] [Accepted: 05/29/2024] [Indexed: 06/26/2024]
Abstract
Motivation Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates. Results In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset. The ICA approach deployed in CellsFromSpace decomposes spatial transcriptomics data into interpretable components associated with distinct cell types or activities. ICA also enables noise or artifact reduction and subset analysis of cell types of interest through component selection. We demonstrate the flexibility and performance of CellsFromSpace using real-world samples to demonstrate ICA's ability to successfully identify spatially distributed cells as well as rare diffuse cells, and quantitatively deconvolute datasets from the Visium, Slide-seq, MERSCOPE, and CosMX technologies. Comparative analysis with a current alternative reference-free deconvolution tool also highlights CellsFromSpace's speed, scalability and accuracy in processing complex, even multisample datasets. CellsFromSpace also offers a user-friendly graphical interface enabling non-bioinformaticians to annotate and interpret components based on spatial distribution and contributor genes, and perform full downstream analysis. Availability and implementation CellsFromSpace (CFS) is distributed as an R package available from github at https://github.com/gustaveroussy/CFS along with tutorials, examples, and detailed documentation.
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Affiliation(s)
- Corentin Thuilliez
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Gaël Moquin-Beaudry
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Pierre Khneisser
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif 94805, France
| | - Maria Eugenia Marques Da Costa
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Slim Karkar
- University Bordeaux, CNRS, IBGC, UMR, Bordeaux 33077, France
- Bordeaux Bioinformatic Center CBiB, University of Bordeaux, Bordeaux 33000, France
| | - Hanane Boudhouche
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Damien Drubay
- Office of Biostatistics and Epidemiology, Gustave Roussy, Université Paris-Saclay, Villejuif 94805, France
- Inserm, Université Paris-Saclay, CESP U1018, Oncostat, Labeled Ligue Contre le Cancer, Villejuif 94805, France
| | - Baptiste Audinot
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
| | - Birgit Geoerger
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Jean-Yves Scoazec
- Department of Medical Biology and Pathology, Gustave Roussy Cancer Campus, Villejuif 94805, France
| | - Nathalie Gaspar
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
| | - Antonin Marchais
- INSERM U1015, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif F-94805, France
- Department of Pediatric and Adolescent Oncology, Gustave Roussy Cancer Campus, Université Paris-Saclay, Villejuif 94805, France
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15
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Wang W, Zheng S, Shin SC, Yuan GC. Characterizing Spatially Continuous Variations in Tissue Microenvironment through Niche Trajectory Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590827. [PMID: 38712255 PMCID: PMC11071437 DOI: 10.1101/2024.04.23.590827] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Recent technological developments have made it possible to map the spatial organization of a tissue at the single-cell resolution. However, computational methods for analyzing spatially continuous variations in tissue microenvironment are still lacking. Here we present ONTraC as a strategy that constructs niche trajectories using a graph neural network-based modeling framework. Our benchmark analysis shows that ONTraC performs more favorably than existing methods for reconstructing spatial trajectories. Applications of ONTraC to public spatial transcriptomics datasets successfully recapitulated the underlying anatomical structure, and further enabled detection of tissue microenvironment-dependent changes in gene regulatory networks and cell-cell interaction activities during embryonic development. Taken together, ONTraC provides a useful and generally applicable tool for the systematic characterization of the structural and functional organization of tissue microenvironments.
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Affiliation(s)
- Wen Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shiwei Zheng
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sujung Crystal Shin
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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16
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Duan B, Chen S, Cheng X, Liu Q. Multi-slice spatial transcriptome domain analysis with SpaDo. Genome Biol 2024; 25:73. [PMID: 38504325 PMCID: PMC10949687 DOI: 10.1186/s13059-024-03213-x] [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: 08/14/2023] [Accepted: 03/08/2024] [Indexed: 03/21/2024] Open
Abstract
With the rapid advancements in spatial transcriptome sequencing, multiple tissue slices are now available, enabling the integration and interpretation of spatial cellular landscapes. Herein, we introduce SpaDo, a tool for multi-slice spatial domain analysis, including modules for multi-slice spatial domain detection, reference-based annotation, and multiple slice clustering at both single-cell and spot resolutions. We demonstrate SpaDo's effectiveness with over 40 multi-slice spatial transcriptome datasets from 7 sequencing platforms. Our findings highlight SpaDo's potential to reveal novel biological insights in multi-slice spatial transcriptomes.
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Affiliation(s)
- Bin Duan
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
| | - Shaoqi Chen
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Xiaojie Cheng
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China
| | - Qi Liu
- State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Key Laboratory of Spine and Spinal Cord Injury Repair and Regeneration (Tongji University), Ministry of Education, Orthopaedic Department of Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
- Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, 201804, China.
- Research Institute of Intelligent Computing, Zhejiang Lab, Hangzhou, 311121, China.
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17
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Ruitenberg MJ, Nguyen QH. Cellular neighborhood analysis in spatial omics reveals new tissue domains and cell subtypes. Nat Genet 2024; 56:362-364. [PMID: 38413724 DOI: 10.1038/s41588-023-01646-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/29/2024]
Affiliation(s)
- Marc J Ruitenberg
- School of Biomedical Science, Faculty of Medicine, The University of Queensland, Brisbane, Australia
| | - Quan H Nguyen
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Australia.
- QIMR Berghofter Medical Research Institute, Brisbane, Australia.
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