Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets
Identification of intercellular signaling changes across multiple single-cell RNA-sequencing (scRNA-seq) datasets as well as how intercellular communications affect intracellular transcription factors (TFs) to regulate target genes is crucial in understanding how distinct cell states respond to evol...
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Frontiers Media S.A.
2021
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oai:doaj.org-article:a66574d5ca9d4361b8dd2af3e268d9142021-11-11T10:26:35ZIdentification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets1664-802110.3389/fgene.2021.751158https://doaj.org/article/a66574d5ca9d4361b8dd2af3e268d9142021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fgene.2021.751158/fullhttps://doaj.org/toc/1664-8021Identification of intercellular signaling changes across multiple single-cell RNA-sequencing (scRNA-seq) datasets as well as how intercellular communications affect intracellular transcription factors (TFs) to regulate target genes is crucial in understanding how distinct cell states respond to evolution, perturbations, and diseases. Here, we first generalized our previously developed tool CellChat, enabling flexible comparison analysis of cell–cell communication networks across any number of scRNA-seq datasets from interrelated biological conditions. This greatly facilitates the ready detection of signaling changes of cell–cell communication in response to any biological perturbations. We then investigated how intercellular communications affect intracellular signaling response by inferring a multiscale signaling network which bridges the intercellular communications at the population level and the cell state–specific intracellular signaling network at the molecular level. The latter is constructed by integrating receptor-TF interactions collected from public databases and TF-target gene regulations inferred from a network-regularized regression model. By applying our approaches to three scRNA-seq datasets from skin development, spinal cord injury, and COVID-19, we demonstrated the capability of our approaches in identifying the predominant signaling changes across conditions and the critical signaling mechanisms regulating target gene expression. Together, our work will facilitate the identification of both intercellular and intracellular dysregulated signaling mechanisms responsible for biological perturbations in diverse tissues.Mengqian HaoMengqian HaoXiufen ZouXiufen ZouSuoqin JinSuoqin JinFrontiers Media S.A.articlescRNA-seq dataintercellular communicationintracellular signalingmultiscale signaling networkdysregulated signalingcomparison analysisGeneticsQH426-470ENFrontiers in Genetics, Vol 12 (2021) |
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DOAJ |
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DOAJ |
| language |
EN |
| topic |
scRNA-seq data intercellular communication intracellular signaling multiscale signaling network dysregulated signaling comparison analysis Genetics QH426-470 |
| spellingShingle |
scRNA-seq data intercellular communication intracellular signaling multiscale signaling network dysregulated signaling comparison analysis Genetics QH426-470 Mengqian Hao Mengqian Hao Xiufen Zou Xiufen Zou Suoqin Jin Suoqin Jin Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets |
| description |
Identification of intercellular signaling changes across multiple single-cell RNA-sequencing (scRNA-seq) datasets as well as how intercellular communications affect intracellular transcription factors (TFs) to regulate target genes is crucial in understanding how distinct cell states respond to evolution, perturbations, and diseases. Here, we first generalized our previously developed tool CellChat, enabling flexible comparison analysis of cell–cell communication networks across any number of scRNA-seq datasets from interrelated biological conditions. This greatly facilitates the ready detection of signaling changes of cell–cell communication in response to any biological perturbations. We then investigated how intercellular communications affect intracellular signaling response by inferring a multiscale signaling network which bridges the intercellular communications at the population level and the cell state–specific intracellular signaling network at the molecular level. The latter is constructed by integrating receptor-TF interactions collected from public databases and TF-target gene regulations inferred from a network-regularized regression model. By applying our approaches to three scRNA-seq datasets from skin development, spinal cord injury, and COVID-19, we demonstrated the capability of our approaches in identifying the predominant signaling changes across conditions and the critical signaling mechanisms regulating target gene expression. Together, our work will facilitate the identification of both intercellular and intracellular dysregulated signaling mechanisms responsible for biological perturbations in diverse tissues. |
| format |
article |
| author |
Mengqian Hao Mengqian Hao Xiufen Zou Xiufen Zou Suoqin Jin Suoqin Jin |
| author_facet |
Mengqian Hao Mengqian Hao Xiufen Zou Xiufen Zou Suoqin Jin Suoqin Jin |
| author_sort |
Mengqian Hao |
| title |
Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets |
| title_short |
Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets |
| title_full |
Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets |
| title_fullStr |
Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets |
| title_full_unstemmed |
Identification of Intercellular Signaling Changes Across Conditions and Their Influence on Intracellular Signaling Response From Multiple Single-Cell Datasets |
| title_sort |
identification of intercellular signaling changes across conditions and their influence on intracellular signaling response from multiple single-cell datasets |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/a66574d5ca9d4361b8dd2af3e268d914 |
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