Single cell RNA‐sequencing: A powerful yet still challenging technology to study cellular heterogeneity

Abstract

Almost all biomedical research to date has relied upon mean measurements from cell populations, however it is well established that what it is observed at this macroscopic level can be the result of many interactions of several different single cells. Thus, the observable macroscopic ‘average’ cannot outright be used as representative of the ‘average cell’. Rather, it is the resulting emerging behaviour of the actions and interactions of many different cells. Single‐cell RNA sequencing (scRNA‐Seq) enables the comparison of the transcriptomes of individual cells. This provides high‐resolution maps of the dynamic cellular programmes allowing us to answer fundamental biological questions on their function and evolution. It also allows to address medical questions such as the role of rare cell populations contributing to disease progression and therapeutic resistance. Furthermore, it provides an understanding of context‐specific dependencies, namely the behaviour and function that a cell has in a specific context, which can be crucial to understand some complex diseases, such as diabetes, cardiovascular disease and cancer. Here, we provide an overview of scRNA‐Seq, including a comparative review of emerging technologies and computational pipelines. We discuss the current and emerging applications and focus on tumour heterogeneity a clear example of how scRNA‐Seq can provide new understanding of a complex disease. Additionally, we review the limitations and highlight the need of powerful computational pipelines and reproducible protocols for the broader acceptance of this technique in basic and clinical research.

RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding

“…Research in many areas of biology focuses on single-cell studies, particularly single-cell RNA sequencing (scRNA-seq) mainly using microfluidic-based methods [1][2][3][4][5][6] , with the 10x Genomics Chromium instruments achieving particularly wide usage 7 . Comparative benchmarking has been reported for many of the available methods (including CEL-Seq2,…”

“…A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types.Research in many areas of biology focuses on single-cell studies, particularly single-cell RNA sequencing (scRNA-seq) mainly using microfluidic-based methods [1][2][3][4][5][6] , with the 10x Genomics Chromium instruments achieving particularly wide usage 7 . Comparative benchmarking has been reported for many of the available methods (including CEL-Seq2,…”

Impact of dual‐baculovirus infection on the Sf9 insect cell transcriptome during rAAV production using single‐cell RNA‐seq

“…Nevertheless, these studies are limited by their approach of assessing cell populations instead of single cells, thus ignoring potential heterogeneity within the production platform. Moreover, single‐cell RNA‐sequencing (scRNA‐seq) would have the added benefit of identifying subpopulations of cells with distinct gene expression profiles, which might result in phenotypically beneficial traits (Ke et al, 2022).…”

“…Moreover, single-cell RNA-sequencing (scRNA-seq) would have the added benefit of identifying subpopulations of cells with distinct gene expression profiles, which might result in phenotypically beneficial traits (Ke et al, 2022).…”

RNA-based sensitive fungal pathogen detection

“…In particular the field of single-cell RNA sequencing has demonstrated that a lot of valuable information can be obtained even from minimal amounts of RNA. 16,17 Specifically, the protocols developed in this field advanced reverse transcription efficiency e.g. by utilization of optimized enzymes, adapted buffer conditions, addition of other reagents like crowding reagents, longer incubation times, and thermal cycling to unfold RNA secondary structures.…”