The intersectional genetics landscape for humans

  1. Andre Macedo
  2. Alisson M Gontijo

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Abstract

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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  1. GigaScience

    Now published in GigaScience doi: 10.1093/gigascience/giaa083

    Andre Macedo Chronic Diseases Research Center (CEDOC), NOVA Medical School | Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Rua do Instituto Bacteriológico 5, 1150-190, Lisbon, PortugalFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Andre MacedoFor correspondence: andre.macedo@nms.unl.pt alisson.gontijo@nms.unl.ptAlisson M. Gontijo Chronic Diseases Research Center (CEDOC), NOVA Medical School | Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Rua do Instituto Bacteriológico 5, 1150-190, Lisbon, PortugalFind this author on Google ScholarFind this author on PubMedSearch for this author on this siteORCID record for Alisson M. GontijoFor correspondence: andre.macedo@nms.unl.pt alisson.gontijo@nms.unl.pt

    A version of this preprint has been published in the Open Access journal GigaScience (see paper https://doi.org/10.1093/gigascience/giaa083 ), where the paper and peer reviews are published openly under a CC-BY 4.0 license.

    These peer reviews were as follows:

    Reviewer 1: http://dx.doi.org/10.5524/REVIEW.102344 Reviewer 2: http://dx.doi.org/10.5524/REVIEW.102345

  2. Version published to 10.1093/gigascience/giaa083
  3. Version published to 10.1101/552984v3 on bioRxiv
  4. Version published to 10.1101/552984v2 on bioRxiv
  5. Version published to 10.1101/552984v1 on bioRxiv