Mining cell–cell signaling in single-cell transcriptomics atlases

Deng, Mingxi; Wang, Ying; Yan, Yan

doi:10.1016/j.ceb.2022.102101

Cited by 8 publications

(5 citation statements)

References 56 publications

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“…The expression patterns of the aforementioned genes in various cell types were visualized based on the UMAP plot and tSNE plot. Furthermore, a detailed examination of key cellular subgroups was carried out to identify receptor and ligand expression at the individual cell level, providing a clear understanding of intricate interactions in the cellular microenvironment [39,40]. Lastly, the identi ed key cell subgroups were screened and extracted for differential genes using the "FindMarkers" function (Threshold = 0.5) .…”

Section: Scrna-seq Data Analysismentioning

confidence: 99%

Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization

Guo,

Wu,

Chen

et al. 2024

Preprint

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Background The relationship between immune cells and influenza is a battle between the host immune system and viral invaders, however, the causality and underlying mechanisms have not been fully elucidated. Methods This study first analysed disability-adjusted life years (DALYs) and mortality of influenza using descriptive epidemiology based on the Global Burden of Disease (GBD) data from 1990 to 2019. Potential causal associations between 731 immune cells and influenza were then explored using univariate Mendelian randomization (UVMR), followed by validation of the cellular subpopulations to which the immune cells identified by UVMR belonged at the single-cell level, and then enrichment analysis has been performed. Finally, we also performed MR of key genes in cellular subpopulations, reverse MR analysis, colocalization analysis, potential drug prediction and molecular docking for genes satisfying causal associations. Results Joinpoint regression trend analysis showed a general downward trend in the change of influenza DALYs rate and mortality rate, and then UVMR results showed a strong association between the immune cell HLA-DR on CD14+ CD16- monocyte and influenza (PIVW = 5.47E-05, PFDR = 0.03). The single-cell sequencing (scRNA-Seq) results verified that the immune cell HLA-DR on CD14+ CD16- monocyte identified by UVMR belonged to the Classical monocytes (CMs) subpopulation. MR analysis of key genes in the cellular subpopulation identified a total of 7 genes as causally associated with influenza, and no reverse causal association was found. The 3 genes were identified as druggable by drug prediction, namely VIM, CTSA and CSF3R. Finally, molecular docking results demonstrated the strong potential of the CSF3R gene as a drug target. Conclusions Our study provides new insights into future prevention and treatment strategies for influenza from epidemiology to genetics to bioinformatic analyses and genomic.

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Section: Scrna-seq Data Analysismentioning

confidence: 99%

Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization

Guo,

Wu,

Chen

et al. 2024

Preprint

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“…These databases serve as valuable resources for studying cell-to-cell communication and are often accompanied by published methods for inferring such interactions [175,177,181,182,187,[219][220][221]. They encompass a wide range of ligand-receptor interactions, ranging from several hundred to a few thousands [189,222], and are predominantly focused on human and mouse systems (with a couple of exceptions, such as FlyPhoneDB for Drosohpila [223] and PlatnPhoneDB for plants [224].) An interesting observation is that there appears to be a greater degree of similarity among databases in regards to their collection of ligands or receptors than their interactions with one another [189].…”

Section: Cell-cell Communication: Genes' Viewmentioning

confidence: 99%

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication

Cheng,

Chen,

Jin

et al. 2023

Cells

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Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell–cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell–cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

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“…Comparative benchmarking has been reported for many of the available methods (including CEL-Seq2, MARS-Seq, Quartz-Seq2, gmcSCRB-seq, Smart-Seq2, C1HT-small, C1HT-medium, Chromium, ddSEQ, Drop-Seq, ICELL8, and inDrop) 8 – 10 . Major efforts using scRNA-seq have been applied to define atlases, notably for human and mouse cells 11 , 12 , with a potential for assessing cell–cell and virus-host interactions 13 , 14 facilitated by technical optimization of methodologies 8 , 15 . Other investigations have focused on specific organs, often in relation to pathologies 16 , especially cancer, immunotherapy, and cardiovascular diseases 17 – 19 .…”

Section: Introductionmentioning

confidence: 99%

RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding

Komatsu¹,

Cico²,

Poncin³

et al. 2023

Sci Rep

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Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells. Furthermore, characterizations carried out on cardiomyocytes demonstrate that the hydrogel technology readily accommodates very large cells. Standard analyses are in the 10,000-input cell range with the current gelation device, in order to satisfy common requirements for single-cell research. A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types.

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Mining cell–cell signaling in single-cell transcriptomics atlases

Cited by 8 publications

References 56 publications

Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization

Combination of multidisciplinary approaches reveals potential causal associations between influenza and immune cells: Single-cell RNA sequencing and Mendelian randomization

A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication

RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding

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