Biopsies for Temporal Transcriptome Analysis
Find out more about how single-cell biopsies are used to add a temporal dimension to your single-cell transcriptome analysis.
Go straight to: Significance of Temporal Dimension | Biopsies as a transcriptional snapshot of a living cell
The Temporal Dimension in Single-Cell Transcriptome Analysis
The temporal dimension is essential in single-cell transcriptome profiling as it refers to changes in gene expression over time of a single cell.
Following changes in gene expression over time of the very same cell, can help researchers better understand the complex mechanisms involved in biological processes such as development, differentiation, and response to stimuli. Monitoring the gene expression evolution of an individual cell represents a significant challenge for researchers across diverse research fields.
Despite the critical importance of the temporal dimension in single-cell transcriptome analysis, few technologies enable the full temporal dimension to be captured for the duration of a cell's lifetime.
Indeed, many existing techniques require removing cells from their natural environment and lysing them, leading to post-lysis analyte changes and loss of context. To overcome these limitations, Chen W. et al. (2022) have proposed a novel scRNA-seq method, the Live-seq approach – supported by a minimally invasive sampling method - the single-cell biopsy workflow - to gain spatial and temporal dimensions in single-cell transcriptome analysis. 
Biopsies as a transcriptional snapshot of a living cell.
The single-cell biopsy workflow represents a breakthrough in cellular research, as it enables the extraction of a minute volume of cytoplasm or nucleus from an individual cell without compromising its viability. This innovative process is built upon the cutting-edge FluidFM (Fluidic Force Microscopy) technology, which employs microscopic channels within force-sensitive probes. By carefully and non-invasively accessing cellular components, this pioneering technique opens up new avenues for understanding and analyzing cells in their most natural state.
In a trailblazing study by Chen W. et al. (2022), the researchers successfully demonstrated that Live-seq with the single-cell biopsy workflow, has the potential to revolutionize cellular assays by transforming them from end-point analyses into temporal workflows. They achieved this by showing that the cytoplasmic material collected using Live-seq is an accurate representation of a living cell's unperturbed transcriptional state. 
This crucial advancement was supported by four key findings detailed in the following and tested across various cell types. The researchers worked with IBA cells, primary mouse adipose stem and progenitor cells (ASPCs), as well as two monocyte or macrophage-like RAW264.7 cell lines: one wild-type and one RAW264.7 subline (RAW-G9) containing an mCherry reporter driven by the Tnf promoter for further downstream analyses.
1. Live-seq enables the stratification of cell type and state.
In the pursuit of understanding cellular intricacies, Live-seq was first used to analyze full-length transcripts across various cell types and states. The data gathered in this work, revealed five distinct clusters, primarily characterized by the cells' unique identities and conditions. The data gathered in this work, revealed five distinct clusters, primarily characterized by the cells' unique identities and conditions. This demonstrated the prowess of Live-seq in efficiently distinguishing between primary, cultured cells and their different 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.
Figure 1: a) Schematic of Experimental setup. b) t-SNE projection of Live-seq data coloured by cell type/states. c) t-SNE projection of scRNA-seq data (Smart-seq2) coloured by cell type or state. f) t-SNE projection of the integrated Live-seq and scRNA-seq data according to cell type and state.  Source: 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.
To validate the reliability of Live-seq, they compared its gene expression profiles with those obtained from whole-cell Smart-seq2 assays, using the same cell types and treatment groups. Although single-cell RNA sequencing (scRNA-seq) resulted in a higher average of genes per cell, the integration of both data sets revealed a strong correlation between cells with similar types and states. 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 as demonstrated in the next section.
2. Live-seq preserves cell viability.
To establish Live-seq as a powerful tool for real-time analysis, it is crucial to ensure that it preserves cell viability and avoids causing any unwanted disruptions to the cells being studied. With this in mind, the researchers first assessed cell viability following the sampling process. Encouragingly, they observed consistent results across three cell types, with viability percentages falling between 85 and 89%. This held true regardless of the extracted volume, which varied from 0.2 to 3.5 picoliters. 
To further corroborate the non-invasive nature of Live-seq, the team monitored the growth dynamics of RAW cells after sampling, employing time-lapse microscopy. They discovered that the sampled RAW cells regained their initial volume within 100 to 320 minutes and continued to exhibit growth patterns similar to their non-sampled counterparts.  These findings suggest that cells can swiftly recover and proceed through their lifecycle even after undergoing cytoplasmic biopsies.
3. Live-seq enables sequential single-cell transcriptomic sampling for direct cell trajectory readout.
In this work, researchers showcased the potential of the sequential Live-seq sampling technique, which enables the recording of a cell's molecular signature before and after a transition in its state. The team focused on two models of cell state transition: 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 this Live-seq data with scRNA-seq information, 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.
For the second model, the team used a longer time frame to sample the same adipose stromal progenitor cells (ASPCs) twice, employing a unique barcode to pair them. In total, they sequentially sampled 44 cells, obtaining 8 paired gene expression profiles from ASPCs before and after differentiation—specifically, two days post adipogenic cocktail induction.  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. 
Figure 2: 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.  Source: 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
These findings reveal that, for both transition models, Live-seq data can be harnessed to accurately track the true trajectory of cells typically processed with conventional scRNA-seq. In summary, the sequential sampling technique using Live-seq enables researchers to capture the dynamic changes in a cell's transcriptome, offering a direct insight into both rapid and slower cell state transitions.
4. Understand the drivers of cell-fate heterogeneity and identify transcriptional predictors with Live-seq.
In a final application of Live-seq, researchers sought to connect the molecular state of individual RAW macrophages with their downstream response to LPS. This is particularly important because macrophages, including RAW cells, display a heterogeneous reaction to LPS that remains unexplained.
First, the team recorded the ground-state transcriptomes of single RAW cells. After exposing these cells to LPS, they measured LPS-induced Tnf promoter-driven mCherry expression. To identify the primary molecular factors responsible for this heterogeneity, they employed a linear regression model that correlated the expression of genes detected by Live-seq in individual cells with their corresponding Tnf-mCherry response profiles. This approach allowed them to predict two key Tnf-mCherry profile parameters: the basal expression (intercept) and the rate of fluorescence intensity increase (slope).
Next, they pinpointed the factors that predicted both the dynamics and amplitude of a cell's response to LPS, based on the rate of Tnf-mCherry intensity decrease. Two important implications emerged from this analysis:
Gelsolin (Gsn) was identified as one negative correlating factor, consistent with its role in suppressing LPS-induced Tnf expression.
The most robust transcriptional predictor of the rate of Tnf-mCherry intensity increase was Nfkbia, indicating that basal Nfkbia-BFP intensity is a transcriptional predictor of the rate of Tnf-mCherry intensity increase.
The latter finding was made possible by Live-seq's capacity to measure both ground-state gene expression and downstream phenotypic response. In contrast, conventional "end-point" scRNA-seq data revealed a positive correlation between Nfkbia and Tnf-mCherry.
Collectively, these results highlight the potential of Live-seq to serve as a powerful tool for temporal profiling the transcriptomes of individual cells and predicting their phenotypic behavior.
Main applications in temporal gene expression profiling
The Live-seq approach and the single-cell biopsy workflow, could be applied to a vast range of biological applications that require temporal transcriptome analysis of single cells. Some of the potential applications include:
Overall, the Live-seq approach and the single-cell biopsy workflow, provide a powerful tool for studying the temporal dynamics of gene expression in single cells, enabling researchers to address a range of biological questions with high temporal resolution. More specifically, two high-potential applications can be highlighted and performed with the single-cell biopsy workflow:
Transcriptome before phenotyping: The recording of transcriptional events over time to reveal how molecular components can influence cell behaviour.
Direct lineage tracing: The direct linkage of an individual cell’s history and trajectory to unravel past cell states and understand lineage decisions.
 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