The intersectional genetics landscape for humans

Author(s)

Macedo, Andre ; Gontijo, Alisson M.

• Document type: Journal article
• Source: GIGASCIENCE; v. 9, n. 8, p. 15-pg., 2020-08-01.

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. (AU)

• FAPESP's process: 16/09659-3 - Molecular physiology and evolution of a new developmental stability pathway
• Grantee: Tatiana Teixeira Torres
• Support Opportunities: Regular Research Grants