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Identifying single-cell molecular programs by stochastic profiling


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-κB 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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Figure 1: Small-sample profiling by stochastic sampling can be used to distinguish transcriptional heterogeneities from normal biological variation.
Figure 2: Quantitative and reproducible small-sample amplification of high-, medium- and low-abundance transcripts from 3–100 cells.
Figure 3: Stochastic profiling of matrix-attached cells at day 10 of MCF10A morphogenesis.
Figure 4: Stochastic profiling identifies clusters of heterogeneously coexpressed transcripts.
Figure 5: Stochastic profiling distinguishes heterogeneous expression patterns that are not exclusively coexpressed.


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We thank T. McDaniel (Illumina) for generously providing the microarrays used in this study, G. Cox (Molecular Probes) for advice during development of the RNA FISH protocol and C. Reinhardt for critically reading the manuscript. This work was supported by the US National Institutes of Health (5-R01-CA105134-07 to J.S.B.), the US National Institutes of Health Director's New Innovator Award Program (1-DP2-OD006464-01 to K.A.J.), the Mary Kay Ash Charitable Foundation (to K.A.J.) and the Pew Scholars Program in the Biomedical Sciences (to K.A.J.).

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Authors and Affiliations



K.A.J. conceived of the study, performed the computational simulations, designed and optimized the experimental protocols, performed the stochastic profiling experiments, analyzed the data and wrote the initial draft of the manuscript. C.C.W. validated the RNA FISH riboprobes, performed the RNA FISH experiments and edited the manuscript. K.J.H. cloned and prepared the RNA FISH riboprobes, segmented the images for quantitation and edited the manuscript. K.C. optimized the microarray hybridization protocols and performed the microarray hybridization experiments. J.S.B. supervised the overall research progress and contributed to the initial draft of the manuscript.

Corresponding authors

Correspondence to Kevin A Janes or Joan S Brugge.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–12, Supplementary Table 1, Supplementary Notes 1–3 (PDF 6885 kb)

Supplementary Software

Matlab files for the Monte Carlo simulations of stochastic sampling. These files correspond to the simulation results shown in Figure 1g-i (ZIP 5 kb)

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Janes, K., Wang, CC., Holmberg, K. et al. Identifying single-cell molecular programs by stochastic profiling. Nat Methods 7, 311–317 (2010).

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