gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution
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...
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creator | Rezenman, Shahar Knafo, Maor Tsigalnitski, Ivgeni Barad, Shiri Jona, Ghil Levi, Dikla Dym, Orly Reich, Ziv Kapon, Ruti |
description | 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. |
doi_str_mv | 10.1371/journal.pone.0286696 |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0286696</identifier><identifier>PMID: 37285353</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Bears ; Biology and Life Sciences ; Clone Cells ; Cloning ; CRISPR ; Environmental conditions ; Evaluation ; Evolution ; Experiments ; Fitness ; Genetic aspects ; Genetic research ; Genome ; Genomes ; Heterogeneity ; High resolution ; HSP82 gene ; Humans ; Microorganisms ; Neoplasms ; Physical Sciences ; Reproductive fitness ; Research and Analysis Methods ; Tracking</subject><ispartof>PloS one, 2023-06, Vol.18 (6), p.e0286696-e0286696</ispartof><rights>Copyright: © 2023 Rezenman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Rezenman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Rezenman et al 2023 Rezenman et al</rights><rights>2023 Rezenman et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c603t-36264ffdcfdbc1b92340eb42a1b078d2af6d423cc9803f1a5a97a866c46220233</cites><orcidid>0000-0002-3800-4555 ; 0000-0002-4526-6182 ; 0000-0002-0618-6470</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246843/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246843/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37285353$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rezenman, Shahar</creatorcontrib><creatorcontrib>Knafo, Maor</creatorcontrib><creatorcontrib>Tsigalnitski, Ivgeni</creatorcontrib><creatorcontrib>Barad, Shiri</creatorcontrib><creatorcontrib>Jona, Ghil</creatorcontrib><creatorcontrib>Levi, Dikla</creatorcontrib><creatorcontrib>Dym, Orly</creatorcontrib><creatorcontrib>Reich, Ziv</creatorcontrib><creatorcontrib>Kapon, Ruti</creatorcontrib><title>gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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. 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subjects | Bears Biology and Life Sciences Clone Cells Cloning CRISPR Environmental conditions Evaluation Evolution Experiments Fitness Genetic aspects Genetic research Genome Genomes Heterogeneity High resolution HSP82 gene Humans Microorganisms Neoplasms Physical Sciences Reproductive fitness Research and Analysis Methods Tracking |
title | gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution |
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