Evolution of single cell RNA sequencing: from scRNA-seq to Live-seq 

This article introduces the way single-cell RNA sequencing has revolutionized the field of cell biology research, outlines current state of the art and limitations. Then, the Live-seq method, created by Chen et al. (2022) will be introduced as a novel technology that brings a paradigm shift into the field of scRNA-seq by opening doors to temporal scRNA-seq on living cells. [1] 

Go straight to: From scRNA-seq to Live-seq | Applications and limitations of scRNA-seq | A short introduction to Live-seq

From scRNA-seq - single-cell RNA sequencing to Live-seq

single-cell RNA sequencing scRNA-seq Live-seq FluidFM Cytosurge

Schematic illustration of RNA sequencing by Cytosurge

Over the years, several next-generation sequencing (NGS) techniques have been proposed to comprehend and influence cell behavior using targeted molecular strategies in ensembles of thousands to billions of cells. Yet, difficulties have persisted with the need to perform the simultaneous examination of the complement of the thousands of proteins (known as the ‘proteome’) expressed by the genome from a single cell. With this challenge, scientists turned to protein-encoding, messenger RNA molecules (mRNA, referred also as ‘transcriptome’), whose expression correlates well with cellular traits and changes in cellular state. The genomic approach at the base of the detection and quantitative analysis of messenger RNA molecules, is RNA sequencing (RNA-seq). With RNA-seq, the opportunity to study entire transcriptomes in detail, has fueled many important breakthroughs and is now a routine method in biology research.

However, RNA-seq is typically performed in bulk. Bulk RNA-seq on pooled cells provides a vast amount of biological information but the averaging employed in the pooling process does not give a sufficiently detailed evaluation of the single cell. Information such as cell-to-cell differences and cellular heterogeneity can be completely missed or masked. This variety, often blurred by bulk RNA-seq approaches, can be revealed by using single cell RNA-seq to specifically investigate cell populations.

single-cell RNA sequencing scRNA-seq Live-seq FluidFM Cytosurge

Schematic illustration of Bulk RNA-seq vs scRNA-seq by Cytosurge

To account for this, new technologies were developed to look at transcriptomes from individual cells. In the early years of scRNA-seq, microarray-based methods were used to measure gene expression. But with the emergence of next-generation sequencing (NGS) technologies became available that had higher sensitivity and were able to detect low-abundant transcripts better. [2]

Since the first use of NGS for RNA-seq, many different NGS technologies for scRNA-seq have been developed each with different strengths and limitations. The most used technologies are:

  • The Illumina RNA-seq technology generates short fragments of cDNA from RNA samples which are sequences. But due to the short read lengths, it is sometimes difficult to identify structural variations and alternative splicing events. [3]
  • The Nanopore RNA-seq technology sequences RNA molecules in real-time and can sequence longer reads with more sequencing dept. It therefore can also detect RNA modifications such as methylation. [4]
  • The PacBio RNA-seq approach sequences the full-length transcripts and detects alternative splicing events and isoforms. Yet, this approach remains relatively expensive and shows a lower throughput. [5]
  • The Drop-seq technique employs microfluidics to encase individual cells in barcoded beads. Each cell's mRNA transcripts are collected by the beads, and each transcript's source may be determined thanks to the barcodes. Studying cell heterogeneity and rare cell populations makes good use of drop-seq. In contrast to Illumina sequencing, it is less sensitive, and the coverage may favor transcripts with high levels of expression.[6]
  • The Smart-seq method uses oligo(dT) primers to obtain full-length mRNA transcripts from single cells. With a higher detection threshold but a lower throughput, smart-seq can identify low-abundance transcripts. [7]

Since then, a growing interest was found for such methods and new scRNA-seq approaches were developed such as smart-seq2 and more recently, Live-seq.  

Curious about Live-seq?

Current challenges and limitations of scRNA-seq

scRNA-seq can be employed to compare the transcriptomes of individual cells within a population of cells for instance in embryonic and immune cells, for very specific cell population identification. [8,9] Current scRNA-seq genome profiling methods require cell lysis – the destruction of the cell. The death of the cell implies that the cell state can no longer be monitored over time – disabling any temporal analysis. That is why, traditional scRNA-seq cell profiling approaches are either based on:

  • A computational approach: It uses snapshot measurements to deduce a cell's history. It does this by modeling mRNA splicing processes or by connecting related biological states into a continuous trajectory. [10-13] Despite statistical predictions, those models support the interpretation and analysis of the data generated by NGS scRNA-seq to better understand and foresee the cells state evolution. 

  • Alternative molecular approaches based on the genetic tagging of a cell [14-19] or on the labelling of specific molecules [20-22], are effective but limited to short time scale and to the study of several parameters per cell.     

For those reasons, live-seq offers a good complementarity to the current scRNA-seq approaches with its non-destructive sampling and temporal cell expression profiling capabilities.

A short introduction to Live-seq

The term “live-seq” refers to a single-cell transcriptome profiling method invented by W. Chen et al (2022). [1] This temporal transcriptomics technique is based on a non-destructive RNA collection using fluidic force microscopy and a low-input RNA library preparation protocol. [1] Fluidic force microscopy (FluidFM) is used to collect cytoplasmic biopsies of single cells. Within the scRNA-seq methods, smart-seq2 was reported as one of the most sensitive methods to detect low-amount of RNA. Live-seq was built on the initial smart-seq2 recovery workflow but it pushes further the detection limit to 1 pg of RNA, which is about 10G% of the total RNA typically found in a cell.

Most importantly, gene expression profile comparison between smart-seq2 and live-seq revealed that cytoplasmic mRNA biopsies are suitable representations of full cell transcriptomes. [1]  Overall, Live-seq may bring new knowledge in the study of cellular heterogeneity and gene expression profiling. On a final note, three main outcomes for Live-seq can be highlighted:

Downstream profiling

Downstream molecular and phenotypic profiling.

Transcriptomic recorder

Record the transcriptome of a single cell and follow the cell down the road.

Cell state monitoring

Track cell state transitions with sequential Live-seq molecular profiling on both long-term (2 days) and short-term scales (4h).


Single-cell RNA sequencing (scRNA-seq) technology has become the state-of-the-art approach for unravelling the heterogeneity and complexity of RNA transcripts within individual cells, as well as revealing the composition of different cell types and functions within biological structures. Since its first discovery in 2009, publications based on scRNA-seq have provided large knowledge across different fields making exciting new advances in better understanding the composition and interaction of cells.

Have you heard of temporal single-cell profiling?

Related Content


[1] Chen, Wanze, et al. "Live-seq enables temporal transcriptomic recording of single cells." Nature 608.7924 (2022): 733-740.

[2] Tang, Fuchou, et al. "mRNA-Seq whole-transcriptome analysis of a single cell." Nature methods 6.5 (2009): 377-382.

[3] Kumar, Ravi, et al. "A high-throughput method for Illumina RNA-Seq library preparation." Frontiers in plant science 3 (2012): 202.

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[6] Bageritz, Josephine, and Gianmarco Raddi. "Single-cell RNA sequencing with drop-seq." Single Cell Methods: Sequencing and Proteomics (2019): 73-85.

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