Next Challenges in Single Cell Lineage Tracing
Find out more about the single cell lineage tracing and related uses cases from single-cell omics to cancer research.
Go straight to: Overview | Current challenges | Benefits of the FluidFM technology
Unravelling Cellular Heterogeneity with Single Cell Lineage Tracing
What is Cell Lineage Tracing?
Cell lineage tracing is the identification of all the offspring of a single cell. The term cell lineage refers to the collective history of cell divisions, as well as the birth, division and death times of a cell’s ancestors and clonal relatives.  Cell lineage tracing involves labeling cells with a marker that can be tracked over time, allowing researchers to trace the lineage and fate of those cells as they differentiate into various cell types. This technique can be used to answer questions about cell proliferation, metastasis, and other processes that are critical to understanding the biology of both normal and diseased tissues.
Reconstructing cell lineages that lead to the formation of tissues, organs, and complete organisms is of crucial importance in developmental biology. Elucidating the lineage relationships among the diverse cell types can provide key insights into the fundamental processes underlying normal tissue development as well as valuable information on what goes wrong in developmental diseases. [2-5] Furthermore, lineage tracing has experienced a growing interest in stem cell and cancer research, notably, to model cellular heterogeneity.
How is cell lineage determined?
In lineage tracing, a single cell is identified and marked. A group of labeled clones is produced because of the mark being passed on to the cell's offspring. The number of descendants of the founder cell, their location, and their level of differentiation can all be learned using lineage tracing. Lineages can be depicted as detailed trees of mitotic events or, alternatively, as clonal units derived from a common progenitor cell. Lineage measurements, however, do not provide biological information about the states of the cells they trace – they only allow to track the cell. As such, they are typically combined with other measurements notably to provide for example, cell position, morphology, or gene expression.  Lastly, lineage tracing can be performed prospectively or retrospectively, so by introducing a label for marking cells in a specified state or based on phylogenetic reconstruction of endogenous genetic polymorphisms, respectively.  Having defined how is cell lineage tracing performed and what it could bring to research, we’ll now move on to discuss the current challenges in this field.
Current challenges in single cell lineage tracing
At the heart of cell lineage tracing, is the marker.
From direct microscopic observation to DNA barcoding
Originally, cell lineage tracing was performed via direct microscopic observation to look at elements that influence cell fate, and later, via cell labeling with dyes and radioactive tracers to track the cells of interest directly and physically. For circumstances where direct observation by light or fluorescence microscopy was not feasible, genetically engineered markers were introduced by transfection or viral transduction, or reporter genes expressed in specific cell populations were employed. Indeed, since the early 1990s, genetic recombination has also been employed for lineage tracing and is now the method of choice in most circumstances. In this method, a conditional reporter gene is activated by the cell- or tissue-specific production of a recombinase enzyme, which permanently genetically tagging all offspring of the marked cells. 
Lineage tracing approaches have recently evolved to allow tracing of cell clones via sequencing of inherited DNA sequences, or “barcodes”. Direct injection, transfection, or viral transduction are all methods for introducing genetic markers. Two important aspects of DNA-barcoding lineage are the diversity and the precision. Those aspects highlight one important limitation for DNA-barcoding lineage tracing:
Reproducibility & Temporal Specificity
One critical aspect for DNA-barcoding tracing is the requirement to trace a minimum of two cells per clone to ensure adequate analysis of their resulting offspring’s. As an example, although current methods offer insightful data, they frequently only include a small number of markers and cells, and since they lack associated gene expression data, they are unable to identify the many cellular identities of the monitored cells and how they relate to lineage branching. Furthermore, the time interval in which cells are marked affects strongly clonal-tracing experimental results. Up to now, no methods was reported that offers both tissue and temporal specificity in barcoding.
Those limitations have led scientist to develop sequencing-based lineage tracing by combining lineage tracing analysis and high-throughput single-cell RNA sequencing.
Combining Gene Expression Dynamics & Cell Lineage Tracing
Single-cell RNA sequencing allows researchers to analyze the gene expression profiles of individual cells and trace their lineage based on similarities in gene expression. Scientists can now gain a greater understanding of the cell-fate mapping by gathering two types of tracing, clonal and population. Practically, this method can, for example, help improving the temporal specificity in barcoding by precisely setting the time interval in which the cells are labelled.
Another aspect of temporal cell state analysis can be tackled with the clonal resampling approach. In this technique, as the clone differentiates, a repeated extraction of a portion of clone is performed for further downstream single-cell transcriptomics analysis. This technique makes it easier to create state manifolds on which the trajectories of individual clones can be shown when scaled to huge numbers of cells. This method necessitates that cells divide symmetrically such that all cells within a clone initially have comparable states, as well as those cells be sampled throughout time without significantly altering the behavior of the surviving cells. The latter requirement could greatly benefit from the support of the FluidFM technology.
Recently, a non-destructive single-cell temporal transcriptome profiling analysis, the Live-seq approach, could be performed in two steps. The first step comprised the use of cytoplasmic biopsies performed with the FluidFM, as a sampling method realized on the very same cell, prior to the sequencing stage.  In this approach, gene expression profile comparison between smart-seq2 and live-seq revealed that cytoplasmic mRNA biopsies are suitable representations of full cell transcriptomes. Because of this, the FluidFM may significantly improve lineage tracing outcomes, notably by offering a smooth and accurate collection of picolitre extract from individual cells for temporal single-cell analysis.
All those techniques and tracers present both advantages and inconveniences. But they all demonstrate the same need to ensure a robust labelling from the very first cell. On one hand, this crucial step requires a reliable and non-destructive cell transfection method to directly inject the tracer into the cell while keeping the cell alive. On the other hand, an effective single-cell sampling method on the very same cell is also needed to tackle the lack of temporal resolution of lineage tracing. One instrument - the FluidFM OMNIUM, could help solving those limitations.
How can cell lineage tracing benefit from the FluidFM technology?
The FluidFM technology provides unique single-cell capabilities. Both FluidFM direct intranuclear injection and intracellular extraction, show a strong potential to support lineage tracing. Additionally, such versatile platform can help researchers reducing the variability between the various cell-fate studies by standardizing the sampling process, and thus, facilitating the routine measurement of cell state. Finally, recent studies have combined single-cell RNA-sequencing and CRISPR-Cas9 barcode editing for elucidating developmental lineages at the whole organism level.  Those findings indicate that the FluidFM technology, with its single-cell manipulation and CRISPR gene editing capabilities, could contribute greatly to push further the limits of live lineage tracing.
Biopsies without compromising viability.
Gentle, force-controlled extraction keeps your cell alive after taking a biopsy.
Live monitoring of single cells.
Take several biopsies of the same cell, while monitoring its development over time.
Temporal Gene Expression Profiling
Biopsies are snapshot representations of a cell’s transcriptome that can be analyzed downstream.
 Wagner, D. E., & Klein, A. M. (2020). Lineage tracing meets single-cell omics: opportunities and challenges. Nature Reviews Genetics, 21(7), 410-427.
 Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230 (2017).
 Spanjaard, B. & Junker, J. P. Methods for lineage tracing on the organism-wide level. Curr. Opin. cell Biol. 49, 16–21 (2017).
 Kester, L. & van Oudenaarden, A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell. 23, 166–179 (2018)
 Zafar, H., Lin, C., & Bar-Joseph, Z. (2020). Single-cell lineage tracing by integrating CRISPR-Cas9 mutations with transcriptomic data. Nature communications, 11(1), 3055.
 Kretzschmar, Kai, and Fiona M. Watt. "Lineage tracing." Cell 148.1 (2012): 33-45.
 Naik, S. H. et al. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 496, 229 (2013).
 Barker, N. et al. Identification of stem cells in small intestine and colon by marker gene Lgr5. Nature 449, 1003 (2007).
 Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322 (2014).
 Pei, W. et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456 (2017).
 Ju, Y. S. et al. Somatic mutations reveal asymmetric cellular dynamics in the early human embryo. Nature 543, 714 (2017).
 Zafar, H., Tzen, A., Navin, N., Chen, K. & Nakhleh, L. SiFit: inferring tumor trees from single-cell sequencing data under finite-sites models. Genome Biol. 18, 178 (2017).
 Frumkin, D., Wasserstrom, A., Kaplan, S., Feige, U. & Shapiro, E. Genomic variability within an organism exposes its cell lineage tree. PLoS computational Biol. 1, e50 (2005).
 Mooijman, D., Dey, S. S., Boisset, J.-C., Crosetto, N. & Van Oudenaarden, A. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nat. Biotechnol. 34, 852 (2016)
 Chen, Wanze, et al. "Live-seq enables temporal transcriptomic recording of single cells." Nature 608.7924 (2022): 733-740.