An Overview of the FluidFM® technology for Transcriptomics 

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.

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 of RNA transcripts

  • Proteomics of proteins,

  • Metabolomics of 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 (Morozova et al 2009; Wang 2009 Nature).  

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 quantify RNA molecules in a biological sample. 

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Existing methods in Transcriptomics

Early methods to assess gene expression were either based on quantitative Polymerase Chain Reaction (PCR) analyses or on microarrays (Wolf 2013), 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 (Wang, Nature 2009). 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. 

Schematics of the single-cell biopsy analysis method with FluidFM

Stand-alone - User-friendly and semi-automated FluidFM technology supporting the single-cell biopsy analysis method

This means that the same cell can be analyzed only once, which makes single-cell transcriptomics analysis, a spatially and temporally limited approach. While techniques have been developed to address the limitation of cell lysis, these are either purely computational approaches or are based on tagging cells or proteins, which allows for analysis on short time scales or on a few features only.


More recently, Chen, Guillaume-Gentil et al. 2022 have overcome this limitation with development of the Live-Seq technique and the arousal of a novel novel method for collection of a single-cell biopsy, support by FluidFM technology, for single-cell analysis.


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Single-cell biopsy - A solution designed for Transcriptomics

 
 

Live-Seq, unlike other approaches for single-cell transcriptomics, utilizes the FluidFM technology to collect high-quality 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 (Chen et al 2022). This technology has transformed scRNA-Seq as we know it by addressing both temporal and spatial limitations of current methods.


On the left hand-side, discover how the FluidFM technology can be employed to perform a gentle and accurate extraction of cellular content.

Non-invasive extraction

Gently extract from cytoplasm or nucleus 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.

Empower your transcriptomics with FluidFM

Recommended readings

Genome-wide molecular recording using Live-seq

Chen et al. show the establishment of Live-seq, an approach for single-cell transcriptome profiling that preserves cell viability during RNA extraction using FluidFM. By using a model involving exposure of macrophages with lipopolysaccharide (LPS), they were able to apply a genome-wide ranking of genes based on their ability to impact macrophage LPS response heterogeneity.  Furthermore, they show that Live-seq can be used to sequentially profile the transcriptomes of individual macrophages before and after stimulation with LPS. This enables the direct mapping of a cell’s trajectory and transforms scRNA-seq from an end-point to a temporal analysis approach.


W. Chen, O. Guillaume-Gentil, R. Dainese, P. Yde Rainer, M. Zachara, C. G. Gäbelein, J. A. Vorholt & B. Deplancke. Genome-wide molecular recording using Live-seq. (March 2021) bioRxiv 2021.03.24.436752



Single-Cell Mass Spectrometry

In this publication Guillaume-Gentil et al. show non-destructive and quantitative withdrawal of intracellular fluid with sub-picoliter resolution using FluidFM, followed by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. By this method they detected and identified several metabolites from the cytoplasm of individual HeLa cells. Validated by 13C-Glucose feeding experiments, this showed that metabolite sampling combined with mass spectrometry analysis was possible while preserving the physiological context and the viability of the analyzed cell. Thus, enabling complementary analysis of the cell. 


O. Guillaume-Gentil, T. Rey, P. Kiefer, A.J. Ibáñez, R. Steinhoff, R. Brönnimann, L. Dorwling-Carter, T. Zambelli, R. Zenobi & J.A. Vorholt. Single-Cell Mass Spectrometry of Metabolites Extracted from Live Cells by Fluidic Force Microscopy. (May 2017) Anal Chem., 89(9), 5017-5023. doi:10.1021/acs.analchem.7b00367



Tunable Single-Cell Extraction for Molecular Analyses

Guillaume-Gentil et al. demonstrate the use of FluidFM for quantitative sampling of cytoplasmic and nucleoplasmic fractions from single cells at a sub-picoliter resolution followed by a comprehensive analysis of the soluble molecules withdrawn from the cytoplasm or the nucleus and dispensed adaptable to a broad range of analytical methods, including the detection of enzyme activities and transcript abundances.


O. Guillaume-Gentil, R.V. Grindberg, R. Kooger, L. Dorwling-Carter, V. Martinez, D. Ossola, M. Pilhofer, T. Zambelli & J.A. Vorholt. Tunable Single-Cell Extraction for Molecular Analyses. (Jul 2016) Cell, 166(2), 506-516. doi: 10.1016/j.cell.2016.06.025.

80+ publications with FluidFM

References

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


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


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


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


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