Abstract
ABSTRACTBackgroundThe 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.ResultsHere, 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.ConclusionsOur work provides a systematic view of the intersectional genetics landscape in humans and demonstrates the potential of these approaches for future gene delivery technologies.
Highlights
The exact number of different cell types that make up the body of a human adult is yet to be defined, but is expected to be in the order of several hundred, perhaps thousands of different cell types (Valentine et al, 1994; Carrol, 2001)
To quantify how cellular specificity scales with the number of intersecting active regulatory element (RE) (k), we developed algorithms and scripts using Python language to analyze genome-wide data on promoter and enhancer usage for hundreds of primary human cell types obtained by the FANTOM5 consortium (Andersson et al, 2014; FANTOM Consortium and the RIKEN PMI and CLST (DGT), 2014; Lizio et al, 2015)
The FANTOM5 data consists of curated subsets of transcriptional start site “peaks” determined by capped analyses of gene expression (CAGE)-sequencing (CAGE-seq)
Summary
The exact number of different cell types that make up the body of a human adult is yet to be defined, but is expected to be in the order of several hundred, perhaps thousands of different cell types (Valentine et al, 1994; Carrol, 2001). While the activity of a single carefully-chosen RE could theoretically provide sufficient specificity to identify a particular cell type and/or state post-DNA delivery in some cases, it is unlikely to provide the required specificity to distinguish most cell types and/or states between themselves (Mallo, 2006; Luan et al, 2006) Aware of this fact, developmental biologists studying model organisms have devised intersectional genetic methods to increase target cell specificity of gene drivers by exploring the anatomical overlap between expression patterns driven by two independent REs (Lakso et al, 1992; Struhl and Basler, 1994; Awatramani et al, 2003; Suster et al, 2004; Stockinger et al, 2005; Luan et al, 2006; Farago et al, 2006). We hypothesized that the majority of cell types and/or cell states in human could be distinguished post-DNA delivery using multiple input AND gates (intersectional methods of active REs, Figure 1), and that the intersecting inputs could be obtained, quality-ranked, and cross-validated using currently publicly available RE usage databases
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