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Identifying single-cell molecular programs by stochastic profiling.

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.

SUBMITTER: Janes KA

PROVIDER: S-EPMC2849806 | biostudies-literature | 2010 Apr

REPOSITORIES: biostudies-literature

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Identifying single-cell molecular programs by stochastic profiling.

Janes Kevin A KA Wang Chun-Chao CC Holmberg Karin J KJ Cabral Kristin K Brugge Joan S JS

Nature methods 20100314 4

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 transcript ...[more]

PMID: 20228812

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Project description:Multiple biological processes are driven by oscillatory gene expression at different time scales. Pulsatile dynamics are thought to be widespread, and single-cell live imaging of gene expression has lead to a surge of dynamic, possibly oscillatory, data for different gene networks. However, the regulation of gene expression at the level of an individual cell involves reactions between finite numbers of molecules, and this can result in inherent randomness in expression dynamics, which blurs the boundaries between aperiodic fluctuations and noisy oscillators. This underlies a new challenge to the experimentalist because neither intuition nor pre-existing methods work well for identifying oscillatory activity in noisy biological time series. Thus, there is an acute need for an objective statistical method for classifying whether an experimentally derived noisy time series is periodic. Here, we present a new data analysis method that combines mechanistic stochastic modelling with the powerful methods of non-parametric regression with Gaussian processes. Our method can distinguish oscillatory gene expression from random fluctuations of non-oscillatory expression in single-cell time series, despite peak-to-peak variability in period and amplitude of single-cell oscillations. We show that our method outperforms the Lomb-Scargle periodogram in successfully classifying cells as oscillatory or non-oscillatory in data simulated from a simple genetic oscillator model and in experimental data. Analysis of bioluminescent live-cell imaging shows a significantly greater number of oscillatory cells when luciferase is driven by a Hes1 promoter (10/19), which has previously been reported to oscillate, than the constitutive MoMuLV 5' LTR (MMLV) promoter (0/25). The method can be applied to data from any gene network to both quantify the proportion of oscillating cells within a population and to measure the period and quality of oscillations. It is publicly available as a MATLAB package.

Project description:BackgroundAdvanced stage/recurrent clear cell ovarian cancers (CCOCs) are characterized by a low response to chemotherapy and a poor prognosis. There is growing interest in investigating novel/molecular targeted therapies in patients with CCOC in histotype-specific trials. However, CCOCs are not a uniform entity and comprise a number of molecular subtypes and it is unlikely that a single approach to treatment will be appropriate for all patients. The aim of this study was to analyze the results of a multiplatform profiling panel in CCOCs to identify potential therapeutic targets.Patients and methodsTumor profiling was performed on 521 CCOCs. They were grouped into pure (n = 422) and mixed (n = 99) CCOC for analysis. Testing included a combination of DNA sequencing (including next-generation sequencing) using a 46-gene panel, immunohistochemistry, fluorescent or chromogenic in situ hybridization, and RNA fragment analysis.ResultsThe most common findings were in the PIK3CA/Akt/mTOR pathway, with 61% of all CCOCs showing a molecular alteration in one of these pathway components. Next-generation sequencing revealed PIK3CA mutations in 50% of pure CCOCs. Significant differences were observed between pure and mixed CCOCs with respect to hormone receptor expression (9% vs 34.7% for ER, 13.45 vs 26.4% for PR), cMET (24.1% vs 11.6%), PD-1 tumor infiltrating lymphocytes (48.1% vs 100%), expression of PD-L1 (7.4% vs 25%), and TOPO1 (41% vs 27.1%) on immunohistochemistry, whereas next-generation sequencing revealed significant differences in mutation frequency in PIK3CA (50% vs 18.5%), TP53 (18.1% vs 57.7%), KRAS (12.4% vs 3.7%), and cMET (1.9% vs 11.1%).ConclusionsThis large study confirms that the PIK3CA/Akt/mTOR pathway is commonly altered in CCOCs, and highlights the significant differences between pure and mixed CCOCs. Clear cell ovarian cancers are molecularly heterogeneous and there are a number of potential therapeutic targets which could be tested in clinical trials.

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Identifying single-cell molecular programs by stochastic profiling.

Publications

Identifying single-cell molecular programs by stochastic profiling.

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