Int J Stem Cells.  2024 Nov;17(4):347-362. 10.15283/ijsc23170.

A Roadmap for Selecting and Utilizing Optimal Features in scRNA Sequencing Data Analysis for Stem Cell Research: A Comprehensive Review

• Alani M
• Altarturih H
• Pars S
• Al-mhanawi B
• Wolvetang EJ
• Shaker MR

• KMID: 2562355
• DOI: http://doi.org/10.15283/ijsc23170

Abstract

Stem cells and the cells they produce are unique because they vary from one cell to another. Traditional methods of studying cells often overlook these differences. However, the development of new technologies for studying individual cells has greatly changed biological research in recent years. Among these innovations, single-cell RNA sequencing (scRNA-seq) stands out. This technique allows scientists to examine the activity of genes in each cell, across thousands or even millions of cells. This makes it possible to understand the diversity of cells, identify new types of cells, and see how cells differ across different tissues, individuals, species, times, and conditions. This paper discusses the importance of scRNA-seq and the computational tools and software that are essential for analyzing the vast amounts of data generated by scRNA-seq studies. Our goal is to provide practical advice for bioinformaticians and biologists who are using scRNA-seq to study stem cells. We offer an overview of the scRNA-seq field, including the tools available, how they can be used, and how to present the results of these studies effectively. Our findings include a detailed overview and classification of tools used in scRNA-seq analysis, based on a review of 2,733 scientific publications. This review is complemented by information from the scRNA-tools database, which lists over 1,400 tools for analyzing scRNA-seq data. This database is an invaluable resource for researchers, offering a wide range of options for analyzing their scRNA-seq data.

Keyword

Single-cell gene expression analysis; Stem cells; Computational biology

Figure

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  Fig. 1 Schematic diagram of the overview of the study. (A) Study overview; comprising: (1) A bibliometric assessment of single-cell RNA sequencing (scRNA-seq); (2) Laboratory procedures with an emphasis on stem cell sourcing and sample preparation; (3) A synopsis of prevalent scRNA-seq tools and software; and (4) Inte-grated findings to aid data interpretation. (B) Process diagram; illustrating the four-phases procedure of the study. WoS: Web of Science.

• 

  Fig. 2 Exploring the rapid evolution of single-cell RNA sequencing (scRNA-seq) analysis. (A) The annual growth of publications in the field of scRNA-seq analysis. (B) Shows the top 8 sources’ cumulative growth in the field of scRNA-seq analysis. (C) Top 20 cited tools for analysing scRNA-seq data.

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  Fig. 3 Features of current single-cell RNA sequencing (scRNA-seq) analysis tools and availability of analysis options. (A) Number of features included in the current developed tools. (B) Essential and advanced analysis features in scRNA-Seq availability. UMIs: unique molecular identifiers.

Reference

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