K-Vector
I asked whether tissue-specific AAV regulatory elements could be identified systematically from public single-cell chromatin data, and whether their mouse-to-human translatability could be predicted before any wet-lab wo
Core trick
Ranks single-cell chromatin peaks by cell-type specificity, size, and motif content, then flags candidates whose gene lacks a mouse ortholog.
Description
I asked whether tissue-specific AAV regulatory elements could be identified systematically from public single-cell chromatin data, and whether their mouse-to-human translatability could be predicted before any wet-lab work.
To test this, I developed OrthoGate as the research method. It applies the same specificity, size, motif, and cross-species analysis across different cell types and returns a ranked list of candidate regulatory elements.
Across seven cell types, the method recovered the expected identity gene as a top result in five, including INS in beta cells, GCG in alpha cells, RYR2 in heart cells, and OLFM4 in intestinal cells. It also rejected broadly active housekeeping genes and recovered known regulatory motifs. Most importantly, the analysis flagged candidates whose associated biology may not translate to mice, including PHGR1, an intestinal gene with no mouse ortholog.
These results suggest that tissue-specific AAV regulatory elements can be identified computationally for multiple cell types, and that cross-species screening can meaningfully change which candidates should advance. This matters because it could reduce avoidable lab and animal studies and help prioritize safer, more translatable AAV designs for tissues outside the liver.
README excerpt
# Can we computationally find — and pre-screen for cross-species translatability — tissue-specific AAV "on-switches" for the tissues where AAV actually matters? **Research track submission.** This is a research project answering a scientific question. **OrthoGate** — the pipeline in this repository — is the *method* we built to answer it; the answer is in the results. (The name: it screens regulatory elements at the *ortholog* gate — does the target biology carry across species — before any wet-lab commitment.) ### The research question > **Can tissue-specific candidate AAV regulatory elements for *extrahepatic* cell > types be identified genome-wide from single-cell chromatin accessibility alone — > and can their mouse↔human translatability be predicted computationally, before > any wet-lab work?** Two sub-questions follow from it: (1) does a single, tissue-agnostic method recover the *known* identity biology of each cell type it's pointed at (a validation that it's finding real signal, not noise)? and (2) is cross-species translatability a filter that actually changes which candidates you'd pursue? ### What we found (short version) 1. **Yes to identification — in 5 of the 7 cell types tested.** One specificity → size → motif funnel, run unchanged, recovered the canonical identity locus as a top hit in **five of seven** cell types — INS (beta), GCG (alpha), RYR2 (heart), ALB (liver), OLFM4/PIGR (intestine) — with housekeeping loci correctly rejected and mot