Computational Aging

Very proud of my co-founders Dr. Janine Sengstack and Dr. Hao Li in publishing their work, originally started in 2017: Sengstack et al, Systematic identification of single transcription factor perturbations that drive cellular and tissue rejuvenation, PNAS, 2026

This is the first research to demonstrate repression of a single transcription factor (TF) can reverse cell aging.

This matters because single-target repression enables siRNA as a modality, significantly improving therapeutic translatability for cell reprogramming with once every 6-12 month dosing, tissue-specificity, low manufacturing costs and a demonstrated safety profile.

Imagine in 2040 you can receive a once-per-year at home injection with a cocktail of siRNA medicines to restore gene networks by targeting TFs in each major tissue.

At Junevity, we built the RESET Platform to identify TF targets for tissue-specific repression with siRNA to treat complex and age-related diseases. We look forward to bringing our first therapeutics to human clinical trials as soon as possible.

Cellular reprogramming via transcription factors is one of the most promising avenues towards lifespan extension. If you can reset your cells back in time, then your tissue function and health can improve.

Janine, one of my co-founders at Junevity, and I wrote a perspective in GEN about why transcription factors, once dismissed as “undruggable” and “too risky,” are now within reach.

The article traces how advances in siRNA, omics, and AI/ML are converging to unlock transcription factors as one of the most powerful and overlooked target classes in medicine.

Resetting transcription factors could lead to a wave of FDA approvals, give new hope for treating our biggest diseases, and open the door to extending healthspan and lifespan.

Read here: https://www.genengnews.com/topics/drug-discovery/unlocking-the-potential-of-transcription-factors/

Now published in PNAS, check out my work from UCSF: Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology: https://www.pnas.org/doi/10.1073/pnas.2319804121https://lnkd.in/dEB37EkQ

We combine stability-driven nonnegative matrix factorization with spatial correlation analysis to explore brain regions and ontology in 3D spatial gene expression profiles.

Our findings reveal a gene expression-defined anatomical ontology and interpretable region-specific genetic architecture captured by the marker genes and spatial co-expression networks in the mouse brain.

This shows the power of genomics + machine learning to create biological maps. As the datasets grow and the algorithms improve, I expect this kind of work will lead to a whole generation of medical breakthroughs.

Code available here: https://github.com/abbasilab/osNMFhttps://lnkd.in/dtymd4gC

Great collaboration with Yu Wang, R. Patrick Xian, Alex Lee in collaboration with the Allen Institute for Brain Science's Hongkui Zeng and Bosiljka Tasic and University of California, Berkeley's Bin Yu. Special thanks to Weill Neurohub and and Sandler Program for Breakthrough Biomedical Research for funding our research.