Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications

Abstract

The application of single-cell RNA sequencing (scRNA-seq) in biomedical research has advanced our understanding of the pathogenesis of disease and provided valuable insights into new diagnostic and therapeutic strategies. With the expansion of capacity for high-throughput scRNA-seq, including clinical samples, the analysis of these huge volumes of data has become a daunting prospect for researchers entering this field. Here, we review the workflow for typical scRNA-seq data analysis, covering raw data processing and quality control, basic data analysis applicable for almost all scRNA-seq data sets, and advanced data analysis that should be tailored to specific scientific questions. While summarizing the current methods for each analysis step, we also provide an online repository of software and wrapped-up scripts to support the implementation. Recommendations and caveats are pointed out for some specific analysis tasks and approaches. We hope this resource will be helpful to researchers engaging with scRNA-seq, in particular for emerging clinical applications.

Identification of predictors for neurological outcome after cardiac arrest in peripheral blood mononuclear cells through integrated bioinformatics analysis and machine learning

“…Nowadays, with the advancement of next-generation sequencing, the cost is dropping. New technologies such as singlecell RNA-sequencing help to understand the pathology of diseases and cell-to-cell interactions (Su et al 2022). which can be used to understand CA and find biomarkers and targets.…”

The heterogeneity and drug resistance of malignant cells and intercellular communication of microenvironment in osteosarcoma: Based on single‐cell analysis

“…Su et al. provided a comprehensive review of common scRNA‐seq annotation tools, including automated and manual methods, in 2022 49 . To distinguish cell types with unclear annotations in OS, both primary scRNA‐seq datasets cited in this study employed the Seurat package, a widely used automated tool based on the correlation between each cluster and previously reported cell types.…”

“…Su et al provided a comprehensive review of common scRNA-seq annotation tools, including automated and manual methods, in 2022. 49 To distinguish cell types with unclear annotations in OS, both primary scRNA-seq datasets cited in this study employed the Seurat package, a widely used automated tool based on the correlation between each cluster and previously reported cell types. In Zhou et al's work, cell groups were annotated using DEGs identified by the FindAllMarkers function of the Seurat package and canonical cellular markers from prior literature.…”

The Impact of FBN1-α5β1 Axis in Fibro/Adipogenic Progenitor Cells (FAPCD9–) on Intramuscular Fat Content in Pigs

“…Recently, single-cell RNA sequencing (scRNA-seq) has elucidated the diversity of cell population in tissues, providing insights into the heterogeneity of gene expression across cells. 21,22 Studies based on scRNA-seq have effectively revealed the molecular mechanisms underlying adipogenic formation in distinct regions of adipose tissue. 23−25 Several studies have also documented the single-cell mapping of porcine skeletal muscle, 26,27 revealing the involvement of FAPs in intramuscular fat deposition.…”

“…Previously, intramuscular vascular stem cells (IVSC), composed of satellite cells, FAPs, immune cells, and endothelial cells, − have been isolated from skeletal muscle of newborn piglets and used in adipogenesis studies. ,, However, the precise cellular origin of intramuscular fat cells remains unclear. Recently, single-cell RNA sequencing (scRNA-seq) has elucidated the diversity of cell population in tissues, providing insights into the heterogeneity of gene expression across cells. , Studies based on scRNA-seq have effectively revealed the molecular mechanisms underlying adipogenic formation in distinct regions of adipose tissue. − Several studies have also documented the single-cell mapping of porcine skeletal muscle, , revealing the involvement of FAPs in intramuscular fat deposition. , However, FAPs exhibit heterogeneity, and as of now, the precise origin of IMF remains unclear. Moreover, there are few investigations focusing on the differences in the adipogenic mechanism of FAPs between fat-type and lean-type pigs.…”