Temporal single cell profiling: 
Sequence a cell while keeping it alive.

Single-cell temporal transcriptomics with cytoplasmic biopsies performed on living cells.

Profiling to unveil cellular dynamics.

In the intricate evolution of cellular pathways, cells constantly adapt to their intrinsic programs and external triggers. Therefore, it is imperative to unravel the intricacies of these dynamic transformations and the mechanisms that drive them. Such insight is pivotal for gaining a profound comprehension of cellular processes, both in states of physiological and pathological conditions.

Transcriptome single cell profiling while keeping the cells alive: Live-seq

Sequencing to tackle cellular heterogeneity.

In the study of cellular dynamics, the inherent heterogeneity among cells necessitates a meticulous examination of their transitions at the individual cell level. Throughout the years, various methods, from live-cell imaging to molecular recording approaches, were employed to unravel the intricacies of cellular behavior. 

However, these techniques were limited in their ability to track the dynamic changes of a single or a few specific cellular attributes, such as genes. Consequently, those methods relied on pre-existing knowledge or hypotheses to determine which features to monitor. With the development of single-cell RNA sequencing (scRNA-seq) technologies, researchers could profile the genome-wide gene expression and thus obtain a systematic overview of the cellular states, rather than tracking a limited set of cellular features [1,2]. 

Up to recently, most single-cell RNA sequencing methods required the lysis of the cells, hence, only providing a snapshot of the cellular states.

Transcriptome single cell  profiling while keeping the cells alive: Live-seq

Profiling and inferring cellular dynamics.

To complement those snapshots of cellular states, scientists worked on computational methods to infer cellular dynamics. 

Approaches such as Pseudotime [3] and RNA velocity methods [4], operate on the premise that the collective states of cells within a studied population can reflect the states of a single cell as it goes along its biological journey. 

Over the years, computational models have been combined with other analyses methods such as RNA kinetics from RNA metabolic labeling [5] and clonal relationship from lineage tracing [6], to refine the estimations of critical parameters.

Transcriptome single cell profiling while keeping the cells alive: Live-seq

Sequencing while keeping cells alive: Live-seq

Recently, a non-destructive single cell transcriptome profiling method, called Live-seq, was developed by Chen et al. (2022). [7] 

This approach enables time-resolved transcriptomics by extracting cytoplasmic biopsies from individual cells while keeping them alive, and thereby enabling repeated sampling from the same cell. 

The biopsy solution, on the FluidFM OMNIUM Platform, enables selection of specific individual cells based on their phenotype and performs cytoplasmic extractions for downstream transcriptome analysis, without requiring cell lysis. 

Therefore, it is possible to link a cell’s physical appearance with the underlying transcriptome. This technique represents a promising complementary approach to available methods for the establishment of cellular dynamics, notably by coupling molecular states with phenotypic analysis on the same cell.

Transcriptome single cell profiling while keeping the cells alive: Live-seq

Do you want to find out more about 
Live-seq & cytoplasmic biopsies?

Contact us         OR


[1] Tang F, Barbacioru C, Wang Y, Nordman E, Lee C, Xu N, Wang X, Bodeau J, Tuch BB, Siddiqui A, et al.: mRNA-Seq wholetranscriptome analysis of a single cell. Nat Methods 2009, 6:377-382. 

[2] Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA: The technology and biology of single-cell RNA sequencing. Mol Cell 2015, 58:610-620. 

[3] Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S, Morse M, Lennon NJ, Livak KJ, Mikkelsen TS, Rinn JL: The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 2014, 32:381-386. 

[4] La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V, Lidschreiber K, Kastriti ME, Lonnerberg P, Furlan A, et al.: RNA velocity of single cells. Nature 2018, 560:494-498. 

[5] F. Erhard, A.E. Saliba, A. Lusser, C. Toussaint, T. Hennig, B.K. Prusty, D. Kirschenbaum, K. Abadie, E.A. Miska, C.C. Friedel, et al., Time-resolved single-cell RNA-seq using metabolic RNA labelling, Nat Rev Methods Prim, 2, 2022,77. 

[6] Sankaran VG, Weissman JS, Zon LI: Cellular barcoding to decipher clonal dynamics in disease. Science 2022, 378:eabm5874. 

[7] Chen W, Guillaume-Gentil O, Rainer PY, Gabelein CG, Saelens W, Gardeux V, Klaeger A, Dainese R, Zachara M, Zambelli T, et al. Live-seq enables temporal transcriptomic recording of single cells. Nature 2022, 608:733-740.

Related Resources

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.

[1] W. Chen, O. Guillaume-Gentil, P. Yde Rainer, C. G. Gäbelein, W. Saelens, V. Gardeaux, A. Klaeger, R. Dainese, M. Zachara, T. Zambelli, J. A. Vorholt & B. Deplancke. Live-seq enables temporal transcriptomic recording of single cells. (Aug 2022) Nature, doi:10.1038/s41586-022-05046-9

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. 

[2] 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.

[3] 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.

Mitochondria Transplantation with FluidFM - Story featuring Dr. Christoph Gäbelein.

Temporal transcriptomics recording in single cells with FluidFM - Story featuring Dr. Orane Guillaume-Gentil.