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RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells
Retrospective lineage tracing harnesses naturally occurring mutations in cells to elucidate single cell development. Common single-cell phylogenetic fate mapping methods have utilized highly mutable microsatellite loci found within the human genome. Such methods were limited by the introduction of i...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197472/ https://www.ncbi.nlm.nih.gov/pubmed/32127417 http://dx.doi.org/10.1101/gr.255851.119 |
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author | Wei, Christopher Jen-Yue Zhang, Kun |
author_facet | Wei, Christopher Jen-Yue Zhang, Kun |
author_sort | Wei, Christopher Jen-Yue |
collection | PubMed |
description | Retrospective lineage tracing harnesses naturally occurring mutations in cells to elucidate single cell development. Common single-cell phylogenetic fate mapping methods have utilized highly mutable microsatellite loci found within the human genome. Such methods were limited by the introduction of in vitro noise through polymerase slippage inherent in DNA amplification, which we characterized to be approximately 10–100× higher than the in vivo replication mutation rate. Here, we present RETrace, a method for simultaneously capturing both microsatellites and methylation-informative cytosines to characterize both lineage and cell type, respectively, from the same single cell. An important unique feature of RETrace was the introduction of linear amplification of microsatellites in order to reduce in vitro amplification noise. We further coupled microsatellite capture with single-cell reduced representation bisulfite sequencing (scRRBS), to measure the CpG methylation status on the same cell for cell type inference. When compared to existing retrospective lineage tracing methods, RETrace achieved higher accuracy (88% triplet accuracy from an ex vivo HCT116 tree) at a higher cell division resolution (lowering the required number of cell division difference between single cells by approximately 100 divisions). Simultaneously, RETrace demonstrated the ability to capture on average 150,000 unique CpGs per single cell in order to accurately determine cell type. We further formulated additional developments that would allow high-resolution mapping on microsatellite-stable cells or tissues with RETrace. Overall, we present RETrace as a foundation for multi-omics lineage mapping and cell typing of single cells. |
format | Online Article Text |
id | pubmed-7197472 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-71974722020-05-12 RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells Wei, Christopher Jen-Yue Zhang, Kun Genome Res Method Retrospective lineage tracing harnesses naturally occurring mutations in cells to elucidate single cell development. Common single-cell phylogenetic fate mapping methods have utilized highly mutable microsatellite loci found within the human genome. Such methods were limited by the introduction of in vitro noise through polymerase slippage inherent in DNA amplification, which we characterized to be approximately 10–100× higher than the in vivo replication mutation rate. Here, we present RETrace, a method for simultaneously capturing both microsatellites and methylation-informative cytosines to characterize both lineage and cell type, respectively, from the same single cell. An important unique feature of RETrace was the introduction of linear amplification of microsatellites in order to reduce in vitro amplification noise. We further coupled microsatellite capture with single-cell reduced representation bisulfite sequencing (scRRBS), to measure the CpG methylation status on the same cell for cell type inference. When compared to existing retrospective lineage tracing methods, RETrace achieved higher accuracy (88% triplet accuracy from an ex vivo HCT116 tree) at a higher cell division resolution (lowering the required number of cell division difference between single cells by approximately 100 divisions). Simultaneously, RETrace demonstrated the ability to capture on average 150,000 unique CpGs per single cell in order to accurately determine cell type. We further formulated additional developments that would allow high-resolution mapping on microsatellite-stable cells or tissues with RETrace. Overall, we present RETrace as a foundation for multi-omics lineage mapping and cell typing of single cells. Cold Spring Harbor Laboratory Press 2020-04 /pmc/articles/PMC7197472/ /pubmed/32127417 http://dx.doi.org/10.1101/gr.255851.119 Text en © 2020 Wei and Zhang; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by-nc/4.0/ This article, published in Genome Research, is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Method Wei, Christopher Jen-Yue Zhang, Kun RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells |
title | RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells |
title_full | RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells |
title_fullStr | RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells |
title_full_unstemmed | RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells |
title_short | RETrace: simultaneous retrospective lineage tracing and methylation profiling of single cells |
title_sort | retrace: simultaneous retrospective lineage tracing and methylation profiling of single cells |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7197472/ https://www.ncbi.nlm.nih.gov/pubmed/32127417 http://dx.doi.org/10.1101/gr.255851.119 |
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