ArticleMolecular biology of the cell2022
Cell Painting predicts impact of lung cancer variants.
Article in Molecular biology of the cell, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 70 citations in OpenAlex.
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- A host-centric morphological profiling approach to identify repurposed antiviral drugs.iScience · 2026Article
- Progress and new challenges in image-based profiling.Molecular systems biology · 2026Review
- Visualization, Data Extraction, and Multiparametric Analysis of 3D Pancreatic and Colorectal Cancer Cell Lines for High-Throughput Screening.Biomedicines · 2026Article
- PhenoProfiler: advancing phenotypic learning for image-based drug discovery.Nature communications · 2025Article
- Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy.PLoS computational biology · 2025Article
- Joint profiling of cell morphology and gene expression during in vitro neurodevelopment.eLife · 2025Article
- SubCell: Proteome-aware vision foundation models for microscopy capture single-cell biology.bioRxiv : the preprint server for biology · 2025Article
- Evolving stratification and biomarker discovery in cancer research with technological advancement of proteomics: 35 years and counting.Bioscience reports · 2025Review
- Alternate dyes for image-based profiling assays.SLAS discovery : advancing life sciences R & D · 2025Article
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- Synergizing multimodal data and fingerprint space exploration for mechanism of action prediction.Bioinformatics (Oxford, England) · 2025Article
- CellPHIE: Integrating Pathway Discovery With Pooled Profiling of Perturbations Uncovers Pathways of Huntington's Disease, Including Genetic Modifiers of Neuronal Development and Morphology.bioRxiv : the preprint server for biology · 2025Article
- High-content microscopy and machine learning characterize a cell morphology signature ofbioRxiv : the preprint server for biology · 2025Article
- A genome-wide atlas of human cell morphology.Nature methods · 2025Article
- Evaluating feature extraction in ovarian cancer cell line co-cultures using deep neural networks.Communications biology · 2025Article
- Imaging flow cytometry-based cellular screening elucidates pathophysiology in individuals with Variants of Uncertain Significance.Genome medicine · 2025Article
- Cell Painting: a decade of discovery and innovation in cellular imaging.Nature methods · 2025Review
- Article
- SPACe: an open-source, single-cell analysis of Cell Painting data.Nature communications · 2024Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
Abstract
Most variants in most genes across most organisms have an unknown impact on the function of the corresponding gene. This gap in knowledge is especially acute in cancer, where clinical sequencing of tumors now routinely reveals patient-specific variants whose functional impact on the corresponding genes is unknown, impeding clinical utility. Transcriptional profiling was able to systematically distinguish these variants of unknown significance as impactful vs. neutral in an approach called expression-based variant-impact phenotyping. We profiled a set of lung adenocarcinoma-associated somatic variants using Cell Painting, a morphological profiling assay that captures features of cells based on microscopy using six stains of cell and organelle components. Using deep-learning-extracted features from each cell's image, we found that cell morphological profiling (cmVIP) can predict variants' functional impact and, particularly at the single-cell level, reveals biological insights into variants that can be explored at our public online portal. Given its low cost, convenient implementation, and single-cell resolution, cmVIP profiling therefore seems promising as an avenue for using non-gene specific assays to systematically assess the impact of variants, including disease-associated alleles, on gene function.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.