eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells
Abstract Background Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during...
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| Format: | Article |
| Language: | English |
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BMC
2023-06-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-023-05355-4 |
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| author | Tomoya Mori Toshiro Takase Kuan-Chun Lan Junko Yamane Cantas Alev Azuma Kimura Kenji Osafune Jun K. Yamashita Tatsuya Akutsu Hiroaki Kitano Wataru Fujibuchi |
| author_facet | Tomoya Mori Toshiro Takase Kuan-Chun Lan Junko Yamane Cantas Alev Azuma Kimura Kenji Osafune Jun K. Yamashita Tatsuya Akutsu Hiroaki Kitano Wataru Fujibuchi |
| author_sort | Tomoya Mori |
| collection | DOAJ |
| description | Abstract Background Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Results This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations. Conclusions eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations. |
| first_indexed | 2024-03-13T04:47:50Z |
| format | Article |
| id | doaj.art-c7beb770fa2146338f81cea694edbec4 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-03-13T04:47:50Z |
| publishDate | 2023-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-c7beb770fa2146338f81cea694edbec42023-06-18T11:26:25ZengBMCBMC Bioinformatics1471-21052023-06-0124112710.1186/s12859-023-05355-4eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cellsTomoya Mori0Toshiro Takase1Kuan-Chun Lan2Junko Yamane3Cantas Alev4Azuma Kimura5Kenji Osafune6Jun K. Yamashita7Tatsuya Akutsu8Hiroaki Kitano9Wataru Fujibuchi10Bioinformatics Center, Institute for Chemical Research, Kyoto UniversityLife Sciences, IBM Consulting, IBM Japan Ltd.Center for iPS Cell Research and Application (CiRA), Kyoto UniversityCenter for iPS Cell Research and Application (CiRA), Kyoto UniversityInstitute for the Advanced Study of Human Biology (ASHBi), Kyoto UniversityCenter for iPS Cell Research and Application (CiRA), Kyoto UniversityCenter for iPS Cell Research and Application (CiRA), Kyoto UniversityCenter for iPS Cell Research and Application (CiRA), Kyoto UniversityBioinformatics Center, Institute for Chemical Research, Kyoto UniversityThe Systems Biology InstituteCenter for iPS Cell Research and Application (CiRA), Kyoto UniversityAbstract Background Bioinformatics capability to analyze spatio–temporal dynamics of gene expression is essential in understanding animal development. Animal cells are spatially organized as functional tissues where cellular gene expression data contain information that governs morphogenesis during the developmental process. Although several computational tissue reconstruction methods using transcriptomics data have been proposed, those methods have been ineffective in arranging cells in their correct positions in tissues or organs unless spatial information is explicitly provided. Results This study demonstrates stochastic self-organizing map clustering with Markov chain Monte Carlo calculations for optimizing informative genes effectively reconstruct any spatio–temporal topology of cells from their transcriptome profiles with only a coarse topological guideline. The method, eSPRESSO (enhanced SPatial REconstruction by Stochastic Self-Organizing Map), provides a powerful in silico spatio–temporal tissue reconstruction capability, as confirmed by using human embryonic heart and mouse embryo, brain, embryonic heart, and liver lobule with generally high reproducibility (average max. accuracy = 92.0%), while revealing topologically informative genes, or spatial discriminator genes. Furthermore, eSPRESSO was used for temporal analysis of human pancreatic organoids to infer rational developmental trajectories with several candidate ‘temporal’ discriminator genes responsible for various cell type differentiations. Conclusions eSPRESSO provides a novel strategy for analyzing mechanisms underlying the spatio–temporal formation of cellular organizations.https://doi.org/10.1186/s12859-023-05355-4Spatio–temporal tissue reconstructionCellular organizationSpatial discriminator geneSelf-organizing map clusteringMarkov chain Monte Carlo optimizationDevelopmental trajectory |
| spellingShingle | Tomoya Mori Toshiro Takase Kuan-Chun Lan Junko Yamane Cantas Alev Azuma Kimura Kenji Osafune Jun K. Yamashita Tatsuya Akutsu Hiroaki Kitano Wataru Fujibuchi eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells BMC Bioinformatics Spatio–temporal tissue reconstruction Cellular organization Spatial discriminator gene Self-organizing map clustering Markov chain Monte Carlo optimization Developmental trajectory |
| title | eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells |
| title_full | eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells |
| title_fullStr | eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells |
| title_full_unstemmed | eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells |
| title_short | eSPRESSO: topological clustering of single-cell transcriptomics data to reveal informative genes for spatio–temporal architectures of cells |
| title_sort | espresso topological clustering of single cell transcriptomics data to reveal informative genes for spatio temporal architectures of cells |
| topic | Spatio–temporal tissue reconstruction Cellular organization Spatial discriminator gene Self-organizing map clustering Markov chain Monte Carlo optimization Developmental trajectory |
| url | https://doi.org/10.1186/s12859-023-05355-4 |
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