Image-based, pooled phenotyping reveals multidimensional, disease-specific variant effects
Bibliographic record
Abstract
Genetic variants produce complex phenotypic effects that confound current assays and predictive models. We developed variant in situ sequencing (VIS-seq), a pooled, image-based method measuring 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 capturing changes in protein abundance, localization, activity, and cell architecture. VIS-seq identified a subset of linker-subdomain LMNA variants that increase nuclear circularity, in contrast to aggregating or low-abundance rod-subdomain variants that decrease circularity. VIS-seq also identified autism-associated PTEN variants that mislocalize and accurately distinguished autism-linked from tumor syndrome-linked and gnomAD control variants. Most variants impacted a multidimensional phenotypic continuum not recapitulated by any single functional readout. By linking variants to cell images at scale, VIS-seq illuminates how variant effects cascade from molecules to subcellular structures to cells, providing a framework for resolving the complexity of variant function.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".