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Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging
Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Korean Society for Molecular and Cellular Biology
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399003/ https://www.ncbi.nlm.nih.gov/pubmed/30764600 http://dx.doi.org/10.14348/molcells.2019.0006 |
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author | Wu, Szu-Hsien (Sam) Lee, Ji-Hyun Koo, Bon-Kyoung |
author_facet | Wu, Szu-Hsien (Sam) Lee, Ji-Hyun Koo, Bon-Kyoung |
author_sort | Wu, Szu-Hsien (Sam) |
collection | PubMed |
description | Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease. |
format | Online Article Text |
id | pubmed-6399003 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Korean Society for Molecular and Cellular Biology |
record_format | MEDLINE/PubMed |
spelling | pubmed-63990032019-03-07 Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging Wu, Szu-Hsien (Sam) Lee, Ji-Hyun Koo, Bon-Kyoung Mol Cells Minireview Tracking the fate of individual cells and their progeny through lineage tracing has been widely used to investigate various biological processes including embryonic development, homeostatic tissue turnover, and stem cell function in regeneration and disease. Conventional lineage tracing involves the marking of cells either with dyes or nucleoside analogues or genetic marking with fluorescent and/or colorimetric protein reporters. Both are imaging-based approaches that have played a crucial role in the field of developmental biology as well as adult stem cell biology. However, imaging-based lineage tracing approaches are limited by their scalability and the lack of molecular information underlying fate transitions. Recently, computational biology approaches have been combined with diverse tracing methods to overcome these limitations and so provide high-order scalability and a wealth of molecular information. In this review, we will introduce such novel computational methods, starting from single-cell RNA sequencing-based lineage analysis to DNA barcoding or genetic scar analysis. These novel approaches are complementary to conventional imaging-based approaches and enable us to study the lineage relationships of numerous cell types during vertebrate, and in particular human, development and disease. Korean Society for Molecular and Cellular Biology 2019-02-28 2019-02-13 /pmc/articles/PMC6399003/ /pubmed/30764600 http://dx.doi.org/10.14348/molcells.2019.0006 Text en © The Korean Society for Molecular and Cellular Biology. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/. |
spellingShingle | Minireview Wu, Szu-Hsien (Sam) Lee, Ji-Hyun Koo, Bon-Kyoung Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging |
title | Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging |
title_full | Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging |
title_fullStr | Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging |
title_full_unstemmed | Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging |
title_short | Lineage Tracing: Computational Reconstruction Goes Beyond the Limit of Imaging |
title_sort | lineage tracing: computational reconstruction goes beyond the limit of imaging |
topic | Minireview |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6399003/ https://www.ncbi.nlm.nih.gov/pubmed/30764600 http://dx.doi.org/10.14348/molcells.2019.0006 |
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