A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing
Summary: Lineage reconstruction is central to understanding tissue development and maintenance. To overcome the limitations of current techniques that typically reconstruct clonal trees using genetically encoded reporters, we report scPECLR, a probabilistic algorithm to endogenously infer lineage tr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-08-01
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| Series: | Cell Reports: Methods |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667237521001089 |
| _version_ | 1819114221651099648 |
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| author | Chatarin Wangsanuwat Alex Chialastri Javier F. Aldeguer Nicolas C. Rivron Siddharth S. Dey |
| author_facet | Chatarin Wangsanuwat Alex Chialastri Javier F. Aldeguer Nicolas C. Rivron Siddharth S. Dey |
| author_sort | Chatarin Wangsanuwat |
| collection | DOAJ |
| description | Summary: Lineage reconstruction is central to understanding tissue development and maintenance. To overcome the limitations of current techniques that typically reconstruct clonal trees using genetically encoded reporters, we report scPECLR, a probabilistic algorithm to endogenously infer lineage trees at a single-cell-division resolution by using 5-hydroxymethylcytosine (5hmC). When applied to 8-cell pre-implantation mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy. In addition, we developed scH&G-seq to sequence both 5hmC and genomic DNA from the same cell. Given that genomic DNA sequencing yields information on both copy number variations and single-nucleotide polymorphisms, when combined with scPECLR it enables more accurate lineage reconstruction of larger trees. Finally, we show that scPECLR can also be used to map chromosome strand segregation patterns during cell division, thereby providing a strategy to test the “immortal strand” hypothesis. Thus, scPECLR provides a generalized method to endogenously reconstruct lineage trees at an individual-cell-division resolution. Motivation: Reconstructing lineage trees is fundamental for gaining insights into basic biological and disease processes. Although powerful tools to infer cellular relationships have been developed, these methods typically have a clonal resolution that prevents the reconstruction of lineage trees at an individual-cell-division resolution. Moreover, these methods require a transgene, which poses a significant barrier to the study of human tissues. In this work, we develop a complementary approach that does not require exogenous labeling and can reconstruct each cell division within a lineage tree. |
| first_indexed | 2024-12-22T04:41:51Z |
| format | Article |
| id | doaj.art-dd37b4cc0c9d43d8b966dc193ddb084d |
| institution | Directory Open Access Journal |
| issn | 2667-2375 |
| language | English |
| last_indexed | 2024-12-22T04:41:51Z |
| publishDate | 2021-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Cell Reports: Methods |
| spelling | doaj.art-dd37b4cc0c9d43d8b966dc193ddb084d2022-12-21T18:38:44ZengElsevierCell Reports: Methods2667-23752021-08-0114100060A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencingChatarin Wangsanuwat0Alex Chialastri1Javier F. Aldeguer2Nicolas C. Rivron3Siddharth S. Dey4Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA 93106, USA; Center for Bioengineering, University of California Santa Barbara, Santa Barbara, CA 93106, USADepartment of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA 93106, USA; Center for Bioengineering, University of California Santa Barbara, Santa Barbara, CA 93106, USAHubrecht Institute – KNAW and University Medical Center Utrecht, Utrecht, the NetherlandsInstitute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), Vienna, AustriaDepartment of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA 93106, USA; Center for Bioengineering, University of California Santa Barbara, Santa Barbara, CA 93106, USA; Neuroscience Research Institute, University of California Santa Barbara, Santa Barbara, CA 93106, USA; Corresponding authorSummary: Lineage reconstruction is central to understanding tissue development and maintenance. To overcome the limitations of current techniques that typically reconstruct clonal trees using genetically encoded reporters, we report scPECLR, a probabilistic algorithm to endogenously infer lineage trees at a single-cell-division resolution by using 5-hydroxymethylcytosine (5hmC). When applied to 8-cell pre-implantation mouse embryos, scPECLR predicts the full lineage tree with greater than 95% accuracy. In addition, we developed scH&G-seq to sequence both 5hmC and genomic DNA from the same cell. Given that genomic DNA sequencing yields information on both copy number variations and single-nucleotide polymorphisms, when combined with scPECLR it enables more accurate lineage reconstruction of larger trees. Finally, we show that scPECLR can also be used to map chromosome strand segregation patterns during cell division, thereby providing a strategy to test the “immortal strand” hypothesis. Thus, scPECLR provides a generalized method to endogenously reconstruct lineage trees at an individual-cell-division resolution. Motivation: Reconstructing lineage trees is fundamental for gaining insights into basic biological and disease processes. Although powerful tools to infer cellular relationships have been developed, these methods typically have a clonal resolution that prevents the reconstruction of lineage trees at an individual-cell-division resolution. Moreover, these methods require a transgene, which poses a significant barrier to the study of human tissues. In this work, we develop a complementary approach that does not require exogenous labeling and can reconstruct each cell division within a lineage tree.http://www.sciencedirect.com/science/article/pii/S2667237521001089lineage reconstructionindividual-cell-division resolution5-hydroxymethylcytosineintegrated single-cell genomic DNA and 5-hydroxymethylcytosine sequencingpreimplantation mouse embryogenesisimmortal strand hypothesis |
| spellingShingle | Chatarin Wangsanuwat Alex Chialastri Javier F. Aldeguer Nicolas C. Rivron Siddharth S. Dey A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing Cell Reports: Methods lineage reconstruction individual-cell-division resolution 5-hydroxymethylcytosine integrated single-cell genomic DNA and 5-hydroxymethylcytosine sequencing preimplantation mouse embryogenesis immortal strand hypothesis |
| title | A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing |
| title_full | A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing |
| title_fullStr | A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing |
| title_full_unstemmed | A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing |
| title_short | A probabilistic framework for cellular lineage reconstruction using integrated single-cell 5-hydroxymethylcytosine and genomic DNA sequencing |
| title_sort | probabilistic framework for cellular lineage reconstruction using integrated single cell 5 hydroxymethylcytosine and genomic dna sequencing |
| topic | lineage reconstruction individual-cell-division resolution 5-hydroxymethylcytosine integrated single-cell genomic DNA and 5-hydroxymethylcytosine sequencing preimplantation mouse embryogenesis immortal strand hypothesis |
| url | http://www.sciencedirect.com/science/article/pii/S2667237521001089 |
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