The intersectional genetics landscape for humans
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/103936 |
Resumo: | BACKGROUND: The human body is made up of hundreds-perhaps thousands-of cell types and states, most of which are currently inaccessible genetically. Intersectional genetic approaches can increase the number of genetically accessible cells, but the scope and safety of these approaches have not been systematically assessed. A typical intersectional method acts like an "AND" logic gate by converting the input of 2 or more active, yet unspecific, regulatory elements (REs) into a single cell type specific synthetic output. RESULTS: Here, we systematically assessed the intersectional genetics landscape of the human genome using a subset of cells from a large RE usage atlas (Functional ANnoTation Of the Mammalian genome 5 consortium, FANTOM5) obtained by cap analysis of gene expression sequencing (CAGE-seq). We developed the heuristics and algorithms to retrieve and quality-rank "AND" gate intersections. Of the 154 primary cell types surveyed, >90% can be distinguished from each other with as few as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential "versatile entry codes" (VEnCodes). Each of the 158 cancer cell types surveyed could also be distinguished from the healthy primary cell types with small VEnCodes, most of which were robust to intra- and interindividual variation. Methods for the cross-validation of CAGE-seq-derived VEnCodes and for the extraction of VEnCodes from pooled single-cell sequencing data are also presented. CONCLUSIONS: Our work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies. |
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The intersectional genetics landscape for humanscell classifiercell targetingcombinatorial geneticsenhancersgene regulationpromotersComputer Science ApplicationsHealth InformaticsSDG 3 - Good Health and Well-beingBACKGROUND: The human body is made up of hundreds-perhaps thousands-of cell types and states, most of which are currently inaccessible genetically. Intersectional genetic approaches can increase the number of genetically accessible cells, but the scope and safety of these approaches have not been systematically assessed. A typical intersectional method acts like an "AND" logic gate by converting the input of 2 or more active, yet unspecific, regulatory elements (REs) into a single cell type specific synthetic output. RESULTS: Here, we systematically assessed the intersectional genetics landscape of the human genome using a subset of cells from a large RE usage atlas (Functional ANnoTation Of the Mammalian genome 5 consortium, FANTOM5) obtained by cap analysis of gene expression sequencing (CAGE-seq). We developed the heuristics and algorithms to retrieve and quality-rank "AND" gate intersections. Of the 154 primary cell types surveyed, >90% can be distinguished from each other with as few as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential "versatile entry codes" (VEnCodes). Each of the 158 cancer cell types surveyed could also be distinguished from the healthy primary cell types with small VEnCodes, most of which were robust to intra- and interindividual variation. Methods for the cross-validation of CAGE-seq-derived VEnCodes and for the extraction of VEnCodes from pooled single-cell sequencing data are also presented. CONCLUSIONS: Our work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies.Centro de Estudos de Doenças Crónicas (CEDOC)NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)RUNMacedo, AndreGontijo, Alisson M.2020-09-11T22:43:27Z2020-08-012020-08-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/103936eng2047-217XPURE: 19792706https://doi.org/10.1093/gigascience/giaa083info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:49:32Zoai:run.unl.pt:10362/103936Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:40:05.908581Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
The intersectional genetics landscape for humans |
title |
The intersectional genetics landscape for humans |
spellingShingle |
The intersectional genetics landscape for humans Macedo, Andre cell classifier cell targeting combinatorial genetics enhancers gene regulation promoters Computer Science Applications Health Informatics SDG 3 - Good Health and Well-being |
title_short |
The intersectional genetics landscape for humans |
title_full |
The intersectional genetics landscape for humans |
title_fullStr |
The intersectional genetics landscape for humans |
title_full_unstemmed |
The intersectional genetics landscape for humans |
title_sort |
The intersectional genetics landscape for humans |
author |
Macedo, Andre |
author_facet |
Macedo, Andre Gontijo, Alisson M. |
author_role |
author |
author2 |
Gontijo, Alisson M. |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Centro de Estudos de Doenças Crónicas (CEDOC) NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) RUN |
dc.contributor.author.fl_str_mv |
Macedo, Andre Gontijo, Alisson M. |
dc.subject.por.fl_str_mv |
cell classifier cell targeting combinatorial genetics enhancers gene regulation promoters Computer Science Applications Health Informatics SDG 3 - Good Health and Well-being |
topic |
cell classifier cell targeting combinatorial genetics enhancers gene regulation promoters Computer Science Applications Health Informatics SDG 3 - Good Health and Well-being |
description |
BACKGROUND: The human body is made up of hundreds-perhaps thousands-of cell types and states, most of which are currently inaccessible genetically. Intersectional genetic approaches can increase the number of genetically accessible cells, but the scope and safety of these approaches have not been systematically assessed. A typical intersectional method acts like an "AND" logic gate by converting the input of 2 or more active, yet unspecific, regulatory elements (REs) into a single cell type specific synthetic output. RESULTS: Here, we systematically assessed the intersectional genetics landscape of the human genome using a subset of cells from a large RE usage atlas (Functional ANnoTation Of the Mammalian genome 5 consortium, FANTOM5) obtained by cap analysis of gene expression sequencing (CAGE-seq). We developed the heuristics and algorithms to retrieve and quality-rank "AND" gate intersections. Of the 154 primary cell types surveyed, >90% can be distinguished from each other with as few as 3 to 4 active REs, with quantifiable safety and robustness. We call these minimal intersections of active REs with cell-type diagnostic potential "versatile entry codes" (VEnCodes). Each of the 158 cancer cell types surveyed could also be distinguished from the healthy primary cell types with small VEnCodes, most of which were robust to intra- and interindividual variation. Methods for the cross-validation of CAGE-seq-derived VEnCodes and for the extraction of VEnCodes from pooled single-cell sequencing data are also presented. CONCLUSIONS: Our work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-09-11T22:43:27Z 2020-08-01 2020-08-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/103936 |
url |
http://hdl.handle.net/10362/103936 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2047-217X PURE: 19792706 https://doi.org/10.1093/gigascience/giaa083 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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application/pdf |
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reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799138016591085568 |