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.
This is a preview of subscription content, access via your institution
Open Access articles citing this article.
BMC Bioinformatics Open Access 15 March 2021
Scientific Reports Open Access 20 March 2019
Scientific Reports Open Access 28 March 2018
Subscribe to Journal
Get full journal access for 1 year
only $8.25 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Tax calculation will be finalised during checkout.
Get time limited or full article access on ReadCube.
All prices are NET prices.
Wernet, M.F. et al. Stochastic spineless expression creates the retinal mosaic for colour vision. Nature 440, 174–180 (2006).
Chang, H.H., Hemberg, M., Barahona, M., Ingber, D.E. & Huang, S. Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544–547 (2008).
Irish, J.M., Kotecha, N. & Nolan, G.P. Mapping normal and cancer cell signalling networks: towards single-cell proteomics. Nat. Rev. Cancer 6, 146–155 (2006).
Ferrell, J.E. Jr. & Machleder, E.M. The biochemical basis of an all-or-none cell fate switch in Xenopus oocytes. Science 280, 895–898 (1998).
Altan-Bonnet, G. & Germain, R.N. Modeling T cell antigen discrimination based on feedback control of digital ERK responses. PLoS Biol. 3, e356 (2005).
Emmert-Buck, M.R. et al. Laser capture microdissection. Science 274, 998–1001 (1996).
Tietjen, I. et al. Single-cell transcriptional analysis of neuronal progenitors. Neuron 38, 161–175 (2003).
Bahar, R. et al. Increased cell-to-cell variation in gene expression in ageing mouse heart. Nature 441, 1011–1014 (2006).
Fraser, H.B., Hirsh, A.E., Giaever, G., Kumm, J. & Eisen, M.B. Noise minimization in eukaryotic gene expression. PLoS Biol. 2, e137 (2004).
Kurimoto, K. et al. An improved single-cell cDNA amplification method for efficient high-density oligonucleotide microarray analysis. Nucleic Acids Res. 34, e42 (2006).
Debnath, J. & Brugge, J.S. Modelling glandular epithelial cancers in three-dimensional cultures. Nat. Rev. Cancer 5, 675–688 (2005).
Golding, I., Paulsson, J., Zawilski, S.M. & Cox, E.C. Real-time kinetics of gene activity in individual bacteria. Cell 123, 1025–1036 (2005).
Bengtsson, M., Stahlberg, A., Rorsman, P. & Kubista, M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388–1392 (2005).
Newman, J.R. et al. Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise. Nature 441, 840–846 (2006).
Brady, G. & Iscove, N.N. Construction of cDNA libraries from single cells. Methods Enzymol. 225, 611–623 (1993).
Hartmann, C.H. & Klein, C.A. Gene expression profiling of single cells on large-scale oligonucleotide arrays. Nucleic Acids Res. 34, e143 (2006).
Taniguchi, K., Kajiyama, T. & Kambara, H. Quantitative analysis of gene expression in a single cell by qPCR. Nat. Methods 6, 503–506 (2009).
Schmelzle, T. et al. Functional role and oncogene-regulated expression of the BH3-only factor Bmf in mammary epithelial anoikis and morphogenesis. Proc. Natl. Acad. Sci. USA 104, 3787–3792 (2007).
Debnath, J., Walker, S.J. & Brugge, J.S. Akt activation disrupts mammary acinar architecture and enhances proliferation in an mTOR-dependent manner. J. Cell Biol. 163, 315–326 (2003).
Pearson, G.W. & Hunter, T. Real-time imaging reveals that noninvasive mammary epithelial acini can contain motile cells. J. Cell Biol. 179, 1555–1567 (2007).
Pearson, G.W. & Hunter, T. PI-3 kinase activity is necessary for ERK1/2-induced disruption of mammary epithelial architecture. Breast Cancer Res. 11, R29 (2009).
Rakha, E.A., Reis-Filho, J.S. & Ellis, I.O. Basal-like breast cancer: a critical review. J. Clin. Oncol. 26, 2568–2581 (2008).
Ginestier, C. et al. ALDH1 is a marker of normal and malignant human mammary stem cells and a predictor of poor clinical outcome. Cell Stem Cell 1, 555–567 (2007).
Tanaka, M. et al. A novel RNA-binding protein, Ossa/C9orf10, regulates activity of Src kinases to protect cells from oxidative stress-induced apoptosis. Mol. Cell. Biol. 29, 402–413 (2009).
Yamaguchi, A. et al. Stress-associated endoplasmic reticulum protein 1 (SERP1)/Ribosome-associated membrane protein 4 (RAMP4) stabilizes membrane proteins during stress and facilitates subsequent glycosylation. J. Cell Biol. 147, 1195–1204 (1999).
Gross, D.N., van den Heuvel, A.P. & Birnbaum, M.J. The role of FoxO in the regulation of metabolism. Oncogene 27, 2320–2336 (2008).
Karin, M. & Ben-Neriah, Y. Phosphorylation meets ubiquitination: the control of NF-[kappa]B activity. Annu. Rev. Immunol. 18, 621–663 (2000).
Laslo, P. et al. Multilineage transcriptional priming and determination of alternate hematopoietic cell fates. Cell 126, 755–766 (2006).
Yakoby, N. et al. A combinatorial code for pattern formation in Drosophila oogenesis. Dev. Cell 15, 725–737 (2008).
Neve, R.M. et al. A collection of breast cancer cell lines for the study of functionally distinct cancer subtypes. Cancer Cell 10, 515–527 (2006).
Warren, L., Bryder, D., Weissman, I.L. & Quake, S.R. Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR. Proc. Natl. Acad. Sci. USA 103, 17807–17812 (2006).
Sheskin, D.J. Handbook of Parametric and Nonparametric Statistical Procedures 4th edn. (Chapman & Hall, New York, 2007).
Debnath, J., Muthuswamy, S.K. & Brugge, J.S. Morphogenesis and oncogenesis of MCF-10A mammary epithelial acini grown in three-dimensional basement membrane cultures. Methods 30, 256–268 (2003).
Miller-Jensen, K., Janes, K.A., Brugge, J.S. & Lauffenburger, D.A. Common effector processing mediates cell-specific responses to stimuli. Nature 448, 604–608 (2007).
Nagy, Z.B. et al. Real-time polymerase chain reaction-based exponential sample amplification for microarray gene expression profiling. Anal. Biochem. 337, 76–83 (2005).
Schnell, S.A., Staines, W.A. & Wessendorf, M.W. Reduction of lipofuscin-like autofluorescence in fluorescently labeled tissue. J. Histochem. Cytochem. 47, 719–730 (1999).
Giloh, H. & Sedat, J.W. Fluorescence microscopy: reduced photobleaching of rhodamine and fluorescein protein conjugates by n-propyl gallate. Science 217, 1252–1255 (1982).
McKay, A.T. Distribution of the coefficient of variation and the extended 't' distribution. J. R. Stat. Soc. A 95, 695–698 (1932).
Julious, S.A. Using confidence intervals around individual means to assess statistical significance between two means. Pharm. Stat. 3, 217–222 (2004).
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.).
The authors declare no competing financial interests.
About this article
Cite this article
Janes, K., Wang, CC., Holmberg, K. et al. Identifying single-cell molecular programs by stochastic profiling. Nat Methods 7, 311–317 (2010). https://doi.org/10.1038/nmeth.1442
This article is cited by
BMC Bioinformatics (2021)
Scientific Reports (2019)
Scientific Reports (2018)
Differentiation of human gingival mesenchymal stem cells into neuronal lineages in 3D bioconjugated injectable protein hydrogel construct for the management of neuronal disorder
Experimental & Molecular Medicine (2016)
Single-cell transcriptome and epigenomic reprogramming of cardiomyocyte-derived cardiac progenitor cells
Scientific Data (2016)