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

Area of Science:

• Molecular Biology
• Genomics
• Microbiology

Background:

• Single-cell RNA sequencing (scRNA-seq) is a powerful technique for studying gene expression at an individual cell level.
• Established methods for eukaryotes present significant challenges when applied to prokaryotes.
• Prokaryotic scRNA-seq is hindered by rigid cell walls, lack of polyadenylated transcripts, and small RNA quantities necessitating amplification.

Purpose of the Study:

• To review the current state and challenges of applying single-cell RNA sequencing (scRNA-seq) to prokaryotic organisms.
• To highlight the experimental and data analysis hurdles that need to be overcome for accurate prokaryotic gene expression studies.
• To underscore the potential of improved scRNA-seq for advancing prokaryotic research and single-cell multi-omics.

Main Methods:

• Review of recent literature on prokaryotic single-cell RNA sequencing approaches.
• Analysis of common experimental workflow and data processing challenges.
• Discussion of amplification bias and its impact on distinguishing technical noise from biological variation.

Main Results:

• Several promising scRNA-seq methods for bacteria have emerged despite inherent difficulties.
• Amplification bias remains a significant issue, complicating the interpretation of gene expression data.
• Distinguishing technical noise from true biological variation is a persistent challenge in prokaryotic scRNA-seq.

Conclusions:

• Further optimization of experimental protocols and data analysis algorithms is crucial for advancing prokaryotic scRNA-seq.
• Improved scRNA-seq will enhance our understanding of prokaryotic biology and gene expression.
• This advancement is vital for addressing 21st-century challenges in biotechnology and health through prokaryotic single-cell multi-omics.