In preprints: the fast-paced field of single-cell lineage tracing

Rodriguez-Fraticelli, Alejo; Morris, Samantha A.

doi:10.1242/dev.200877

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…We designed gUMI-BEAR to be an easy-to-use and robust method to track changes in population makeup and variant fitness. Previously available methods [ 1 , 3 , 10 ] to track such changes were not scalable and were difficult to deploy on new lines and modalities or more costly. A library can be created using the gUMI-BEAR method in a few days and our results show that it provides an accurate, cost-effective, and versatile means of tracking subtle changes that propagate into major population shifts in a way that can easily be adapted to other biological systems.…”

Section: Discussionmentioning

confidence: 99%

“…However, how individual adaptations translate into success or failure, or how population dynamics are affected by individual variations and their degree, is not yet clear. A number of studies are beginning to shed light on these questions by employing procedures to quantify a population's clonal makeup [6][7][8][9][10][11][12][13]. Using these methods, multiple lineages within a population can be distinguished by the introduction of DNA barcodes, short segments of DNA inserted into cells' genomes and passed on to their progenies.…”

Section: Introductionmentioning

confidence: 99%

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gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution

et al. 2023

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Cellular lineage tracking provides a means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones’ relative fitness. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the system’s application and resolution by applying it to track tens of thousands of Saccharomyces cerevisiae lineages growing together under varying environmental conditions applied across multiple generations, revealing fitness differences and lineage-specific adaptations. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening of a huge number of randomly generated variants of the Hsp82 gene. We further show how our method allows isolation of variants, even if their frequency in the population is low, thus enabling unsupervised identification of modifications that lead to a behaviour of interest.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution

et al. 2023

View full text Add to dashboard Cite

show abstract

“…However, how individual adaptations translate into success or failure, or how population dynamics are affected by individual variations, is not yet clear. A number of experiments are beginning to shed light on these questions by employing procedures to quantify a population's clonal makeup [6][7][8][9][10][11][12][13] . Using these methods, multiple lineages can be distinguished by the introduction of DNA barcodes, short segments of DNA inserted into cells' genomes and passed on to their progenies.…”

Section: Introductionmentioning

confidence: 99%

gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution

Rezenman

Knafo

Tsigalnitski

et al. 2022

Preprint

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Cellular lineage tracking provides the means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones' relative fitness. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the system's application and resolution by applying it to track millions of Saccharomyces cerevisiae lineages growing together across multiple generations, revealing fitness differences, lineage-specific adaptations to environmental changes and subtle dynamic shifts. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening and optimisation of virtually any number of gene variants, thus enabling unsupervised identification of individuals optimised for particular tasks. Comparison between multiple, identical libraries allowed us to reveal the interplay between stochastic and deterministic outcomes in this experiment.

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