An Overview of Transcriptomics and scRNA-seq 

This short note aims to give the reader an overview of the field of transcriptomics with its limitations and future challenges, notably with the emergence of scRNA sequencing (scRNA-seq).

Go straight to:  Definition | Existing methods Limitations and future challenges Single-cell biopsy for transcriptomics

What is Transcriptomics? 

Do you know what transcriptomics mean?

Omics refers to the study of biological molecules that end with the suffix -omics:

  • Genomics is the study of the genes.
  • Transcriptomics refers to the analysis of RNA transcripts.
  • Proteomics describes the investigation of proteins.
  • Metabolomics focuses on metabolites.

When the objects of study end with the suffix – ome  (genome, transcriptome, proteome and metabolome) this refers to the totality of the molecules studied. Transcriptome thus refers to the entire set of RNA transcripts.

Transcriptomics scRNA Cytosurge

Schematic representation of transcriptomics by Cytosurge.

Transcriptomics is the study of the transcriptome – the entire set of ribonucleic acid (RNA) molecules or RNA transcripts – that are expressed in a particular cell or tissue. These RNA transcripts are produced by the genome and exist in multiple forms like messenger RNAs (mRNAs) or microRNAs (miRNas), serving a wide range of cellular activities like growth, proliferation, and differentiation. [1,2]

Transcriptomics aims to understand the transcription as well as expression level, function, location, and degradation of RNA transcripts. Because RNAs have distinct temporal and spatial patterns of expression, transcriptomics provides insight into the relationship between the transcriptome and phenotype, helping us understand how genes are expressed and interconnected in physiological and pathological conditions. In the last thirty years, the field has witnessed extensive efforts towards development of techniques to detect and quantify RNA molecules in a biological sample. 

Transcriptomics has found a growing interest in research fields such as cell lineage tracing and developmental biology. With the development of transcriptomics, several methods for transcriptome profiling were developed, including the RNA-seq approach. 

Interested in single-cell omics with Live-seq?

Existing methods in Transcriptomics

Early methods to assess gene expression were either based on quantitative Polymerase Chain Reaction (PCR) analyses or on microarrays [3], which were low-throughput or limited to detecting transcripts from a known genome sequence, respectively. In the qRT-PCR (Reverse transcription quantitative PCR) method, the RNA is reverse transcribed into cDNA and cDNA is then used as a template for quantitative PCR. To quantify PCR products, fluorescent DNA-binding dyes (unspecific) or reporter probes (specific, detects DNA containing the sequence complementary to the probe) are used. In the microarray method, the RNA from samples is reverse transcribed into complementary DNA (cDNA) and labeled with fluorescent probes. Labelled cDNAs are applied to a microarray chip where they bind to complementary sequences of known annotated genes. Relative amount of fluorescence corresponds to relative expression of genes.

Image and Text

Schematic illustration of existing methods in transcriptomics: qRT-PCR,Microarray and RNA sequencing. Design by Cytosurge.

More recently, development of bulk RNA sequencing (RNA-Seq) opened doors for the first ever high-throughput and quantitative method to characterize the transcriptome in a given sample. RNA-Seq is currently the most widely used tool in the field, helping researchers identify and quantify gene expression in cell populations and tissues. In brief, RNA-seq works by converting a population of RNA to a library of cDNA fragments, sequence these fragments, and align these to a reference genome or reference transcripts to produce a genome-scale transcription map with expression levels for each gene. [2]

The fast development of RNA-seq highlighted certain limitations associated with sequencing bulk tissue and pushed researchers to develop new approaches, notably single-cell RNA sequencing.

Transcriptomics and scRNA-seq - Limitations & Future challenges

One of the biggest limitations encountered by researchers with bulk RNA-seq resides in its starting material. In fact, the starting material for bulk RNA-Seq is the tissue or a cell population that comprises thousands or millions of cells, which disregards the immense cell heterogeneity that exists within and among cell populations.


The need to understand biology at the single cell scale has catalyzed the development of single-cell RNA sequencing (scRNA-Seq) approaches that address the transcriptional diversity between cells. scRNA-Seq methods can have a broad range of application for biology: understanding the link between expression patterns and single-cell state i.e. during the cell cycle or during different development stages; identifying differences in expression of biomarkers in diseased vs. healthy state; and exploring individual cell response to external or internal signals. While scRNA-seq has greatly advanced our ability to study cell heterogeneity, a major drawback of this approach is that it requires lysis of targeted cells to make RNA accessible for analysis. This means that the same cell can be analyzed only once, which makes single-cell transcriptomics analysis, a limited approach.

Up to now, scRNA-seq approaches relied on the use of computational approaches or based on the tagging of cells or proteins. Those methods gave access to important biological information on a short time scales or were focusing on very specific features. With the creation of Live-seq by Chen et al. (2022), those approaches can now be complemented greatly, notably with the temporal profiling of the transcriptome. [4] 

Discuss your transcriptomics experiment with our experts 

Live-seq enables Temporal Transcriptomics

More recently, Chen, Guillaume-Gentil et al. 2022 have overcome this limitation with the creation and development of the Live-Seq approach supported by a novel method for the cytoplasmic biopsies on the same cell. [4] Live-Seq, unlike other approaches for single-cell transcriptomics, utilizes the FluidFM technology to collect cytoplasmic biopsies from live single cells for subsequent transcriptome profiling of the investigated cells. The use of FluidFM technology for collection of cytoplasmic content while preserving cell viability means that both the ground-state transcriptome and downstream phenotypic changes can be quantified in single cells. [4]


In this video, discover how the FluidFM technology can be employed to perform a gentle and accurate extraction of cellular content.

Advantages of Live-seq compared to other scRNA-seq approaches.

Non-destructive extraction

Gently extract from cytoplasm while keeping the cell alive and fully viable.

Save the physiological context

During extraction, keep the targeted cell in its context next to its neighboring cells and conserve established cell-cell interactions.

Continuous analysis

Semi-automated repetition of the gentle extraction several times on the same cell, e.g. before and after stimulation by a specific drug.

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[1] Morozova, O., Hirst, M., & Marra, M. A. (2009). Applications of new sequencing technologies for transcriptome analysis. Annu Rev Genomics Hum Genet, 10, 135-151. doi:10.1146/annurev-genom-082908-145957

[2] Wang, Z., Gerstein, M., & Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet, 10(1), 57-63. doi:10.1038/nrg2484

[3] Wolf, J. B. (2013). Principles of transcriptome analysis and gene expression quantification: an RNA-seq tutorial. Mol Ecol Resour, 13(4), 559-572. doi:10.1111/1755-0998.12109

[4] Chen, W., Guillaume-Gentil, O., Rainer, P. Y., Gabelein, C. G., Saelens, W., Gardeux, V., . . . Deplancke, B. (2022). Live-seq enables temporal transcriptomic recording of single cells. Nature, 608(7924), 733-740. doi:10.1038/s41586-022-05046-9

[5] Horvath, R. (2022). Single-cell temporal transcriptomics from tiny cytoplasmic biopsies. Cell Rep Methods, 2(10), 100319. doi:10.1016/j.crmeth.2022.100319