Biology subjects

Doctor, Y.

Publications and source records attributed to Doctor, Y..

4 recordsLinked to original sources

bioRxiv2025

Multiplexed Pan Soluble Ligandome Assaying via OASIS

Screening soluble protein ligands is essential for understanding signaling interactions and enabling drug discovery. Currently, screens require arrayed formats because ligand diffusibility causes non-cell-autonomous effects. To enable multiplexed pooled assaying, we developed Obligate Autocrine Signaling In situ Screening (OASIS). Using lentiviral delivery of genetically barcoded ligands fused to a tethering domain, we anchor proteins to the expressing cells outer membrane, exclusively enforcing autocrine signaling. Validation using IFNA2 and EGF demonstrated uncompromised autocrine signaling with significantly reduced paracrine activity. We leveraged OASIS to perform a pan-ligandome fitness screen of all 770 validated human ligands in KOLF2.1J hiPSCs, identifying potent self-renewal factors, including FGF family ligands, which we experimentally validated in soluble form. Finally, we performed single-cell Perturb-Seq to map the transcriptional remodeling induced by the pan-ligandome library. OASIS provides a generalizable framework to accelerate functional interrogation of the human ligandome and novel peptide binders within live cells and diverse lineages. SUMMARYWe present Obligate Autocrine Signaling In situ Screening (OASIS), a platform enabling pooled assaying of soluble ligands by tethering genetically barcoded proteins to the surface of their expressing cells, enforcing autocrine signaling. We demonstrate OASIS by assaying all verified human ligands in hiPSCs, measuring fitness and transcriptional impacts. Our results recapitulate canonical interferon and FGF mediated signaling, validating the application of OASIS for rapid interrogation of ligands and engineered binders.

synthetic biology↗

bioRxiv2025

Regulatable In Vivo Gene Expression via Adaptamers

Precise, reversible control of transgene expression is essential for safe and durable gene therapy, yet current inducible systems remain difficult to translate in vivo due to large size, limited induction duration, and dependence on immunogenic regulators. Here we present the adaptamer (ADAR modulatable aptamer), a compact (<120 bp) RNA switch that couples an FDA-approved small-molecule-responsive aptamer with endogenous ADAR-mediated RNA editing to regulate expression. Ligand binding stabilizes a double-stranded RNA structure that recruits ADAR to convert a stop codon into a sense codon, restoring downstream protein translation. This cleavage-free, post-transcriptional mechanism enables precise, small-molecule-dependent modulation without exogenous protein machinery. We demonstrate that adaptamers are highly functional across multiple cell lines, including human T-cells. In mice, AAV-delivered adaptamer-controlled FGF21 expression induced metabolic remodeling, significantly increasing energy expenditure and reversing obesity. This minimal, programmable system offers a clinically compatible approach for tunable genetic medicines and safe deployment of pleiotropic and dose-limited proteins.

synthetic biology↗

bioRxiv2024

A PERTURBATION CELL ATLAS OF HUMAN INDUCED PLURIPOTENT STEM CELLS

Towards comprehensively investigating the genotype-phenotype relationships governing the human pluripotent stem cell state, we generated an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes. Using the transcriptional phenotypes, we created a minimum distortion embedding map of the pluripotent state, demonstrating rich recapitulation of protein complexes, such as strong co-clustering of MRPL, BAF, SAGA, and Ragulator family members. Additionally, we uncovered transcriptional regulators that are uncoupled from cell fitness, discovering potential novel pluripotency (JOSD1, RNF7) and metabolic factors (ZBTB41). We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. Finally, we propose a contrastive human-cell engineering framework (CHEF), a machine learning architecture that learns from perturbation cell atlases to predict perturbation recipes that achieve desired transcriptional states. Taken together, our study presents a comprehensive resource for interrogating the regulatory networks governing pluripotency.

bioengineering↗

bioRxiv2024

Cell Maps for Artificial Intelligence: AI-Ready Maps of Human Cell Architecture from Disease-Relevant Cell Lines

This article describes the Cell Maps for Artificial Intelligence (CM4AI) project and its goals, methods, standards, current datasets, software tools, status, and future directions. CM4AI is the Functional Genomics Data Generation Project in the U.S. National Institute of Healths (NIH) Bridge2AI program. Its overarching mission is to produce ethical, AI-ready datasets of cell architecture, inferred from multimodal data collected for human cell lines, to enable transformative biomedical AI research.

systems biology↗