Live-seq: Live-cell transcriptome analysis 
with single-cell cytoplasmic biopsies

What if you could perform time-resolved transcriptome analysis 
by consecutively sampling, the same cell?

Across research disciplines, increasingly complex single-cell multi-omics methodologies are used to understand the heterogeneity of cell responses to external stimuli, or gene expression dynamics that drive cell differentiation. Live-cell sequencing (Live-Seq) provides a unique workflow to assess the drivers of one specific cell response or cell transition, over time. This new concept in transcriptomics is enabled by single-cell biopsies: cytoplasmic extracts of a cell that preserve cell viability.

Live-seq: Live-cell transcriptome analysis  with single-cell cytoplasmic biopsies

Figure 1: RAW-cells expressing mChrerry as a proxy for TNF-α immune activation. Full experimental setup explained further down. [2] CC-BY-4.0.

Now, why is this relevant?

Take LPS-stimulation of macrophages as an example a rapid cell transition, a type of reaction that is known to be rather heterogeneous within a cell population. In this experiment, the macrophages express mCherry as a measure for activation level (Figure 1). Both in the ground- and LPS-activated state a significant difference between activation levels is visible on a single-cell level. Live-seq enables sampling of the same cell’s before and after stimulation. Secondly, this method zooms in single cells with different activation levels and researches the subtle differential gene expression patterns that underly these inter-population differences. Here, we provide a summary of the findings of Chen et al. (2022) who pioneered with Live-seq using FluidFM cytoplasmic biopsies. [2]

Biopsies are transcriptional snapshots of a living cell.

In the study by Chen. et al. (2022), the authors showed that biopsies represent reliable transcriptional snapshots of a single cell. [2] In the publication, they demonstrated that a single cell transcriptome can be obtained from a biopsy, and that, by comparing this to conventional scRNA-seq methods, it had minimal effect on the recorded transcriptome. [2] In a next step, they showed that, with sequential sampling on the same cell, Live-seq can provide temporal readouts, of both rapid and slow cellular state transitions, and that this enables detection of minor differential expression events within populations. All this because biopsies keep the cells alive. This crucial advancement in temporal transcriptomics is supported by four key experiments from the work of Chen. et al. (2022) that are outlined below. [2]

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Live-cell sampling with a FluidFM Nanosyringe preserves cell viability.

To establish Live-seq as a tool for time resolved gene expression analysis, as a first step, the authors set out to show that the method for taking a biopsy preserves cell viability without disruption of the physiology of the cell studied. To start off, the viability after taking a biopsy was assessed for all the cell types used: primary mouse adipose stem and progenitor cells (ASPCs) before and after differentiation, IBA (Interscapular Brown Adipose) cells (fibroblasts) and from quiescent and LPS stimulated macrophage-like RAW264.7 cells. They observed consistent results across three cell types, with viability percentages falling between 85% to 89% for extraction volumes ranging between 1.2 pL to 5.0 pL (mean values) (Table 1). The fact that a cell is resilient enough to have a small biopsy taken from its cytoplasm was already shown by Guillaume-Gentil et al. 2016. [1] In this paper, the authors examined the cell’s survival as a function of the cell mean extracted volume. They showed that after taking up to 4.0 pL from the cytoplasm of HeLa cells, 82% remained viable for over 5 days and behaved similarly compared to non-extracted neighborin cells. (Table 1) In this case, the authors hypothesized that the observed lethality of the method likely resulted from the high extraction volume that represented a large proportion of the cells mean volume of 4.4pL. In addition, they showed that a HeLa cell following the extraction of 2.9 pL, divided normally after ~30 hours, similarly to its non-extracted neighboring cells. [1]

Live-seq: Live-cell transcriptome analysis  with single-cell cytoplasmic biopsies

Table 1: Viability and extraction date for APSCs, IBA and RAW cells, and the mean extraction volumes. HeLa data from Figure 3 Post extraction cell viability. [1] Data adapted from extended data, figure 6a and 6b [2]. CC-BY-4.0.

The recovery behavior of cells after extraction is important when measuring (short) cell state transitions. Therefore, the authors further corroborated on the impact that a biopsy had on macrophage-like RAW264.7 cells. Using time-lapse microscopy, they monitored the growth dynamics of RAW cells after sampling. They discovered that the extracted RAW cells regained their initial volume within 100 to 320 minutes from the time that the biopsy was taken. They showed also that the cells continued to exhibit growth patterns similar to their non-biopsied counterparts. These findings confirm that cells can swiftly recover and continue to proliferate after undergoing a cytoplasmic biopsy.

Live-seq enables the stratification of cell type and state.

To understand cellular identities, Live-seq was used to analyze full-length transcripts across various cell types and states. Biopsies and the corresponding transcriptomes were collected from different cell types: primary mouse adipose stem and progenitor cells (ASPCs) before and after differentiation, IBA (Interscapular Brown Adipose) cells (fibroblasts) and from quiescent and LPS stimulated macrophage-like RAW264.7 cells (Figure 2a). t-SNE analysis of the obtained transcriptomes revealed five distinct clusters, reflecting the cells’ identities and activation states. This demonstrated the prowess of Live-seq to distinguish cell types and states. To further investigate these cellular clusters, the researchers conducted a comprehensive gene expression analysis and Gene Ontology enrichment. Their findings supported the notion that each cluster's distinct characteristics aligned with the inherent features of the cells it represented.  

Live-seq: Live-cell transcriptome analysis  with single-cell cytoplasmic biopsies

Figure 2: a: Schematic describing the experimental setup. Single cell transcriptomes from three different cell types (ASPC, IBA and RAW 264.7) and two different cell states for ASPCS and RAW 264.7 (Pre/post adipocyte differentiation and mock/LPS) were collected. b+c: t-SNE plot from the cell types and states were generated from transcriptomes collected using Live-seq (b) of traditional sc-sequencing (c). Separation of the individual cell types and states are clearly visible. c: t-SNE plot to integrate the data from traditional single-cell sequencing and live-seq transcriptome recording, shows no obvious differences between the methods. [2] CC-BY-4.0.

To validate the reliability of Live-seq, gene expression profiles from cytoplasmic biopsies were compared with those obtained from whole-cell Smart-seq2 assays and integrated using t-SNE plots (Figure 2b, 2c). Although conventional scRNA-seq resulted in a higher average detection of genes per cell, the integration of both data sets revealed a strong correlation between cells with similar types and states (Figure 2f). Ultimately, the researchers were able to confirm the accuracy of cell type and state classifications, regardless of the sampling technique. Remarkably, the Live-seq approach offered the added advantage of preserving the cells' viability, eliminating the need for sacrificing them in the process.

Interested in trying Live-seq? Learn more about the cytoplasmic biopsy solution on FluidFM OMNIUM Platform.

Recording of cell state transitions using sequential Live-seq.

After the initial proof-of-concept experiments, the researchers demonstrated the potential of Live-seq by taking sequential samples of specific cells. They recorded the molecular signature of a cell before and after inducing cell state transition. The team focused on two models of cell state transitions. A rapid response to external stimuli (LPS) and a slower process of differentiation (adipogenesis)


In the first scenario, the researchers sampled 24 RAW cells, exposed them to LPS, and then sampled them again.  By overlaying the sequential Live-seq data with the previously acquired scRNA-seq information (Figure 2), they were able to trace the cells’ trajectory from a basal to an LPS-stimulated state, as demonstrated by the two mapped cell state clusters (Figure 3, green cell traces).


For the second model, the team used a longer time frame of 2 days post introduction of the adipogenic cocktail. They sampled the same progenitor cells twice, while employing a unique barcode to pair them. In total, they sequentially sampled 44 cells, and obtained 8 paired gene expression profiles from ASPCs before and after differentiation. (Figure 3, yellow-brown cell traces) Notably, cell viability remained high seven days after the second extraction, with a 95% survival rate among extracted cells compared to 93% for non-extracted cells.  

Live-seq: Live-cell transcriptome analysis  with single-cell cytoplasmic biopsies

Figure 3: t-SNE-based visualization of integrated Live-seq and scRNA-seq data, illustrating the direct trajectory of sequentially sampled cells from one state to another. [2] CC-BY-4.0.

These findings revealed that, for both rapid and slow transition models, Live-seq data can be harnessed to accurately track the true trajectory of cells and capture dynamic, transcriptomic changes of individual cells in real-time. 


The direct correlation of a cell’s transcriptome to its phenotype enables visualization of minor transcriptional events in heterogeneous cell populations.

Circling back to the LPS-activation experiment where we started at, the final experiment intended to connect the molecular states of individual RAW cells with their downstream response to LPS. This is particularly important because macrophages, including RAW cells, display a heterogeneous reaction to LPS that remains unexplained. In response to LPS, the macrophages express TNF-alpha, which was used as a proxy to assess activation level during this experiment. For this, RAW cells were used and expressed mCherry fused to a TNF-alpha promoter. In that way, highly activated macrophages could be identified from their level of mCherry expression. The typical heterogeneity in the macrophage population was already evident from the variation in the mCherry expression in the ground state (Figure 4).

Live-seq: Live-cell transcriptome analysis  with single-cell cytoplasmic biopsies

Figure 4: Timelapse brightfield and fluorescence images of the live-seq (sequential) sampling in RAW cells. [2] CC-BY-4.0.

First, the ground-state transcriptomes of individual RAW cells were recorded. Then, after exposing these same cells to LPS, mCherry expression was measured. With this, they set out to measure both the dynamics and amplitude to which an individual cell responded to LPS activation. One of the striking findings was the anticorrelation of the gene Nfkbia-Tnf, a key negative regulator of the LPS-NF-κB signaling pathway by suppressing NF-κB and thus activation in macrophages (Figure 5a). This finding could have only been possible with Live-seq’s capacity to measure both ground-state gene expression and downstream phenotypic response. In contrast, conventional “endpoint” scRNA-seq data revealed a positive correlation between Nfkbia and Tnf-mCherry, likely because of averaging data in a highly heterogeneous cell population (Figure 5b).

Live-seq: Live-cell transcriptome analysis  with single-cell cytoplasmic biopsies

Figure 5: Expression correlations between Nfkbia and Tnf as analyzed with Live-seq (5a) and conventional scRNA-seq (5b). [2] CC-BY-4.0.

A Novel Approach to Single-Cell Cytoplasmic Biopsy

Live-seq: Live-cell transcriptome analysis  with single-cell cytoplasmic biopsies

The FluidFM OMNIUM Platform.

Live-seq and cytoplasmic biopsies, open up a variety of possibilities to zoom in on transcriptional behavior of individual cells within cell populations. A streamlined workflow for the collection of single-cell biopsies, is available on the FluidFM® OMNIUM platform. The FluidFM Biopsy solution, supported by the biopsy kit, enables semi-automated cytoplasmic biopsy collection from individually selected cells within 2D mammalian cell cultures. The workflow provides a delicate procedure that systematically collects a small portion of a chosen cell’s cytoplasm, with minimal cell perturbation, and enables users to precisely control the biopsy volume during the collection process to ensure cell viability.

Want to learn more?

Further reading

The total RNA content of a cell ranges between 1pg up to 50pg per cell, depending on the cell type. This comes down to an RNA content of a few pg of RNA per biopsy. Live-Seq as published by Chen et al. (Nature, 2022 [1]) is a combination of a cytoplasmic biopsy taken with a FluidFM Nanosyringe, and a low-input RNA sequencing protocol that is an enhanced version of SmartSeq2. Together with Lexogen, Cytosurge is developing a streamlined, commercially available workflow for biopsy collection and analysis using Lexogen’s LUTHOR HD kit. LUTHOR HD is a high-definition single-cell 3’ mRNA-seq library prep kit based on their proprietary THOR technology for direct RNA amplification. 

Related resources:

[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


Our proprietary FluidFM (Fluidic Force Microscopy) technology integrates the best features of force microscopy and micro-channeled probes. The FluidFM probes, of which the FluidFM Nanosyringe is one example, “senses” the surface which enables highly gentle interactions with cells. The FluidFM Nanosyringe has a sharp tip and an aperture of 600nm that allows to simultaneously sense interaction forces with the cell membrane and to aspirate exact femtoliters.

Find out more about:

[1] 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.


References

[1] 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.

[2] 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/s41586-022-050