ArticleNature methods2010
Identifying single-cell molecular programs by stochastic profiling.
Article in Nature methods, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers.
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90 citing papers in PubMed.
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- Cahn-Hilliard dynamical models for condensed biomolecular systems.bioRxiv : the preprint server for biology · 2025Article
- Deciphering the history of ERK activity from fixed-cell immunofluorescence measurements.Nature communications · 2025Article
- Laser capture microdissection.Nature reviews. Methods primers · 2025Article
- Patient-derived response estimates from zero-passage organoids of luminal breast cancer.Breast cancer research : BCR · 2024Article
- Patient-derived response estimates from zero-passage organoids of luminal breast cancer.bioRxiv : the preprint server for biology · 2024Article
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- Stochastic scanning events on the GCN4 mRNA 5' untranslated region generate cell-to-cell heterogeneity in the yeast nutritional stress response.Nucleic acids research · 2023Article
- pYtags enable spatiotemporal measurements of receptor tyrosine kinase signaling in living cells.eLife · 2023Article
- Nucleocytoplasmic transport of active HER2 causes fractional escape from the DCIS-like state.Nature communications · 2023Article
- ANKLE1 cleaves mitochondrial DNA and contributes to cancer risk by promoting apoptosis resistance and metabolic dysregulation.Communications biology · 2023Article
- Substratum stiffness regulates Erk signaling dynamics through receptor-level control.Cell reports · 2021Article
- Metabolic Stress Adaptations Underlie Mammary Gland Morphogenesis and Breast Cancer Progression.Cells · 2021Review
- Laser Capture Proteomics: spatial tissue molecular profiling from the bench to personalized medicine.Expert review of proteomics · 2021Review
30 more citing papers are in PubMed but not listed here.
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Abstract
Cells in tissues can be morphologically indistinguishable yet show molecular expression patterns that are remarkably heterogeneous. Here we describe an approach to comprehensively identify co-regulated, heterogeneously expressed genes among cells that otherwise appear identical. The technique, called stochastic profiling, involves repeated, random selection of very small cell populations via laser-capture microdissection followed by a customized single-cell amplification procedure and transcriptional profiling. Fluctuations in the resulting gene-expression measurements are then analyzed statistically to identify transcripts that are heterogeneously coexpressed. We stochastically profiled matrix-attached human epithelial cells in a three-dimensional culture model of mammary-acinar morphogenesis. Of 4,557 transcripts, we identified 547 genes with strong cell-to-cell expression differences. Clustering of this heterogeneous subset revealed several molecular 'programs' implicated in protein biosynthesis, oxidative-stress responses and NF-kappaB signaling, which we independently confirmed by RNA fluorescence in situ hybridization. Thus, stochastic profiling can reveal single-cell heterogeneities without the need to measure expression in individual cells.
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