Here, the conjugation of DNA barcodes to specific gene variants creates a one-to-one association between barcode and variant and allows to use barcode incidence as a proxy for the abundance of each variant in a population. Likewise, it can aid in designing disease treatments. Quantifying the effect of gene-variants allows mapping the fitness landscape of a gene towards the understanding of the biophysical and biochemical requirements of particular regions. The study of heterogeneity need not be limited to individuals within a population but could also be considered in the context of a particular gene or pathway. Successive records can be used to trace the prevalence of individual lineages through time, offering insight into adaptation dynamics and fitness changes as they occur in the context of evolving populations rather than in each strain on its own. By sequencing the barcodes, one can capture a snapshot of the clonal composition of a growing population at any given moment. 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. A number of studies are beginning to shed light on these questions by employing procedures to quantify a population’s clonal makeup. 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. With the advent of single-cell techniques, it became clear that significant heterogeneity exists within populations of seemingly identical cells. 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. 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 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. 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. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. 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.
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