Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts

TY - JOUR

T1 - Single-cell transcriptomics and data analyses for prokaryotes-Past, present and future concepts

AU - Münch, Julia M

AU - Sobol, Morgan S

AU - Brors, Benedikt

AU - Kaster, Anne-Kristin

N1 - Copyright © 2023. Published by Elsevier Inc.

PY - 2023

Y1 - 2023

N2 - Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.

AB - Transcriptomics, or more specifically mRNA sequencing, is a powerful tool to study gene expression at the single-cell level (scRNA-seq) which enables new insights into a plethora of biological processes. While methods for single-cell RNA-seq in eukaryotes are well established, application to prokaryotes is still challenging. Reasons for that are rigid and diverse cell wall structures hampering lysis, the lack of polyadenylated transcripts impeding mRNA enrichment, and minute amounts of RNA requiring amplification steps before sequencing. Despite those obstacles, several promising scRNA-seq approaches for bacteria have been published recently, albeit difficulties in the experimental workflow and data processing and analysis remain. In particular, bias is often introduced by amplification which makes it difficult to distinguish between technical noise and biological variation. Future optimization of experimental procedures and data analysis algorithms are needed for the improvement of scRNA-seq but also to aid in the emergence of prokaryotic single-cell multi-omics. to help address 21st century challenges in the biotechnology and health sector.

KW - Transcriptome

KW - Sequence Analysis, RNA/methods

KW - Single-Cell Analysis/methods

KW - Data Analysis

KW - RNA, Messenger

U2 - 10.1016/bs.aambs.2023.04.002

DO - 10.1016/bs.aambs.2023.04.002

M3 - SCORING: Journal article

C2 - 37400172

VL - 123

SP - 1

EP - 39

JO - ADV APPL MICROBIOL

JF - ADV APPL MICROBIOL

SN - 0065-2164

ER -