Sriram Pendyala
treependyala.bsky.social
Sriram Pendyala
@treependyala.bsky.social
MD/PhD Student in Doug Fowler's Lab, UW Genome Sciences
🤝 Many people to thank, including dougfowler.bsky.social for his mentorship as well as shawnfayer.bsky.social, dholmes.bsky.social, fritzroth.bsky.social, afrubin.bsky.social, leastarita.bsky.social, wnoble.bsky.social‬ and others, and NHGRI and CZI chanzuckerberg.bsky.social��� for funding.

9/9
July 7, 2025 at 2:44 AM
🔮 Multidimensional variant information may help empower or constrain next generation predictors! Current variant effect predictors perform poorly on molecular and cellular phenotypes, and struggle to parse complex variant-disease relationships.

8/9
July 7, 2025 at 2:44 AM
🌎 LMNA variant ➡️ structure ➡️ abundance and localization ➡️ function! VIS-seq maps LMNA variant effects across scales of cellular organization and discovered a new subset of gain-of-function LMNA variants.

7/9
July 7, 2025 at 2:44 AM
📊 PTEN variants ≠ one axis of “function”. VIS-seq’s multidimensional representations discriminate between PTEN autism- and tumor syndrome-associated variants.

6/9
July 7, 2025 at 2:44 AM
🌐 Generalizability and scale: ~3000 LMNA or PTEN variants • 11.4 million cells • 1000+ image-derived features per cell • Fluorescent proteins, antibodies, and RNA FISH readouts • Cancer cell lines, iPS and derived cells.

5/9
July 7, 2025 at 2:44 AM
🎯 Barcoded circular RNAs are co-expressed in each cell along with a tagged variant ➡️ pooled imaging ➡️ in situ sequencing decodes the barcode ➡️ CellProfiler extracts thousands of molecular & cellular features per cell!

4/9
July 7, 2025 at 2:44 AM
🤿 Dive-in yourself at visseq.gs.washington.edu! A website showing profiles, features, and cell images of LMNA and PTEN variants built by lab member and recent UW Computer Science graduate Nick Bradley.

3/9
VISSEQ Data Exporer
visseq.gs.washington.edu
July 7, 2025 at 2:44 AM
⚡ I developed VIS-seq with the help of dougfowler.bsky.social and others at UW Genome Sciences. Check out my preprint:
www.biorxiv.org/content/10.1...

2/9
Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects
Genetic variants often produce complex phenotypic effects that confound current assays and predictive models. We developed Variant in situ sequencing (VIS-seq), a pooled, image-based method that measures variant effects on molecular and cellular phenotypes in diverse cell types. Applying VIS-seq to ~3,000 LMNA and PTEN variants yielded high-dimensional morphological profiles that captured variant-driven changes in protein abundance, localization, activity and cell architecture. We identified gain-of-function LMNA variants that reshape the nucleus and autism-associated PTEN variants that mislocalize. Morphological profiles predicted variant pathogenicity with near-perfect accuracy and distinguished autism-linked from tumor syndrome-linked PTEN variants. Most variants impacted a multidimensional continuum of phenotypes not recapitulated by any single functional readout. By linking protein variation to cell images at scale, we illuminate how variant effects cascade from molecular to subcellular to cell morphological phenotypes, providing a framework for resolving the complexity of variant function. ### Competing Interest Statement FPR is an advisor and shareholder in Constantiam Biosciences. National Human Genome Research Institute, https://ror.org/00baak391, RM1HG010461, R01HG013025 National Institute of General Medical Sciences, R35GM152106 National Heart Lung and Blood Institute, https://ror.org/012pb6c26, K99HL177347, R01HL171174, R01HL164675 Chan Zuckerberg Initiative (United States), CZIF2024-010284, CP-2-1-Fowler Brotman Baty Institute, https://ror.org/03jxvbk42, CC28 United States Department of Veterans Affairs, I01BX006428, IK2BX004642 Novo Nordisk Foundation, https://ror.org/04txyc737, Alex's Lemonade Stand for Childhood Cancer RUNX1 Foundation
www.biorxiv.org
July 7, 2025 at 2:44 AM