Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues
Abstract Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advanc...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-023-43120-6 |
| _version_ | 1797452116681293824 |
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| author | Duy Pham Xiao Tan Brad Balderson Jun Xu Laura F. Grice Sohye Yoon Emily F. Willis Minh Tran Pui Yeng Lam Arti Raghubar Priyakshi Kalita-de Croft Sunil Lakhani Jana Vukovic Marc J. Ruitenberg Quan H. Nguyen |
| author_facet | Duy Pham Xiao Tan Brad Balderson Jun Xu Laura F. Grice Sohye Yoon Emily F. Willis Minh Tran Pui Yeng Lam Arti Raghubar Priyakshi Kalita-de Croft Sunil Lakhani Jana Vukovic Marc J. Ruitenberg Quan H. Nguyen |
| author_sort | Duy Pham |
| collection | DOAJ |
| description | Abstract Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues. |
| first_indexed | 2024-03-09T15:04:08Z |
| format | Article |
| id | doaj.art-90386dc9c14b4450ad2ce44635d342ec |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-03-09T15:04:08Z |
| publishDate | 2023-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-90386dc9c14b4450ad2ce44635d342ec2023-11-26T13:45:31ZengNature PortfolioNature Communications2041-17232023-11-0114112510.1038/s41467-023-43120-6Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissuesDuy Pham0Xiao Tan1Brad Balderson2Jun Xu3Laura F. Grice4Sohye Yoon5Emily F. Willis6Minh Tran7Pui Yeng Lam8Arti Raghubar9Priyakshi Kalita-de Croft10Sunil Lakhani11Jana Vukovic12Marc J. Ruitenberg13Quan H. Nguyen14Institute for Molecular Bioscience, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandGenome Innovation Hub, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandGenome Innovation Hub, The University of QueenslandSchool of Biomedical Sciences, Faculty of Medicine, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandUQ Centre for Clinical Research, The University of QueenslandUQ Centre for Clinical Research, The University of QueenslandSchool of Biomedical Sciences, Faculty of Medicine, The University of QueenslandSchool of Biomedical Sciences, Faculty of Medicine, The University of QueenslandInstitute for Molecular Bioscience, The University of QueenslandAbstract Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor pairs with markedly reduced false discovery rates. Finally, we present a spatial graph-based imputation method with neural network (stSME), to correct for technical noise/dropout and increase ST data coverage. Together, the algorithms that we developed, implemented in the comprehensive and fast stLearn software, allow for robust interrogation of biological processes within healthy and diseased tissues.https://doi.org/10.1038/s41467-023-43120-6 |
| spellingShingle | Duy Pham Xiao Tan Brad Balderson Jun Xu Laura F. Grice Sohye Yoon Emily F. Willis Minh Tran Pui Yeng Lam Arti Raghubar Priyakshi Kalita-de Croft Sunil Lakhani Jana Vukovic Marc J. Ruitenberg Quan H. Nguyen Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues Nature Communications |
| title | Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues |
| title_full | Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues |
| title_fullStr | Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues |
| title_full_unstemmed | Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues |
| title_short | Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues |
| title_sort | robust mapping of spatiotemporal trajectories and cell cell interactions in healthy and diseased tissues |
| url | https://doi.org/10.1038/s41467-023-43120-6 |
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