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

Abstract

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.

Influence of intersignaling crosstalk on the intracellular localization of YAP/TAZ in lung cells

“…Importantly, omics technologies promise to significantly expand and deepen our knowledge of intra- and intercellular interactions. Single-cell RNA sequencing combined with spatial transcriptomics enables the understanding of regulatory networks, signaling pathways, and cell‒cell communications [ 109 ]. In addition, the development and improvement of methods and systems for single-cell data analysis have enabled the construction of classification pathway databases for the study of signaling interactions [ 59 , 110 – 112 ].…”

Developmental Programming of the Fetal Immune System by Maternal Western-Style Diet: Mechanisms and Implications for Disease Pathways in the Offspring

“…However, many challenges remain as the interpretation and translation of these findings are hampered by our relatively limited mechanistic understanding of immune programming. As single-cell omic technologies are maturing and more high-throughput data analyses are conducted, more information is being gathered on different cell populations, especially immune cell types, rare cell types, disease state cells, and the whole spectrum of cells in between, which lead to complex interactions within tissues [ 216 ]. The signals underlying development of HSPCs, specifically in the fetal liver and bone marrow under the influence of maternal WSD, and the timing and distribution of cells are of critical importance.…”

scAnnoX: an R package integrating multiple public tools for single-cell annotation

“…Single-cell sequencing data provide a high-resolution gene expression perspective within individual cells ( Wen et al, 2023 ), revealing functional and phenotypic differences among individual cells ( Balzer et al, 2021 ; Kolodziejczyk et al, 2015 ; Rossin, Sobrin & Kim, 2021 ; Slovin et al, 2021 ) and thereby revealing the diversity and heterogeneity within cell populations ( Bod et al, 2023 ; Chen et al, 2023 ; Fu et al, 2021 ; Hickey et al, 2023 ; Wang et al, 2022 ). However, when analyzing single-cell data, identifying cell identities is essential and particularly critical ( Brendel et al, 2022 ; Cheng et al, 2023 ; Kim et al, 2021 ). Currently, two main strategies are available for single-cell identity annotation: manual annotation and automatic annotation.…”