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Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells

Nature Protocols volume 8, pages 282–301 (2013)Cite this article

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Abstract

Single-cell variations in gene and protein expression are important during development and disease. Such cell-to-cell heterogeneities can be directly inspected one cell at a time, but global methods are usually not sensitive enough to work with the starting material of a single cell. Here we provide a detailed protocol for stochastic profiling, a method that infers single-cell regulatory heterogeneities by repeatedly sampling small collections of cells selected at random. Repeated stochastic sampling is performed by laser-capture microdissection or limiting dilution, followed by careful exponential cDNA amplification, hybridization to microarrays and statistical analysis. Stochastic profiling surveys the transcriptome for programs that are heterogeneously regulated among cellular subpopulations in their native tissue context. The protocol is readily optimized for specific biological applications and takes about 1 week to complete.

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Figure 1: Theoretical simulations of stochastic profiling for various expression dichotomies defined by different parameter sets.
Figure 2: Rapid nuclear fast red staining of HT-29 colon adenocarcinoma cells for laser-capture microdissection.
Figure 3: Workflow for small-sample cDNA amplification.
Figure 4: Representative optimization of AL1 primer amount and poly(A) PCR cycle number for small-sample cDNA amplification implemented with microdissected melanoma cells.
Figure 5: Reamplification and labeling of ten-cell samples.
Figure 6: Statistical and bioinformatic analysis of stochastic-profiling data.
Figure 7: Optimized small-sample cDNA amplifications in three distinct biological contexts.

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Acknowledgements

We thank S. Bajikar for critically reading this manuscript, C. Slingluff for providing the primary melanoma sample, B. Kang for help with screening low-abundance genes and C. Borgman for help with frozen sectioning. This work was supported by the US National Institutes of Health Director's New Innovator Award Program (1-DP2-OD006464), the American Cancer Society (120668-RSG-11-047-01-DMC), the Pew Scholars Program in the Biomedical Sciences and the David and Lucile Packard Foundation.

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Authors and Affiliations

  1. Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA

    Lixin Wang & Kevin A Janes

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Contributions

L.W. designed the current implementation of stochastic profiling and the optimization protocol for different cellular contexts. K.A.J. conceived of the method, supervised the development of the current implementation, coded all computer simulations and wrote the manuscript with contributions from L.W.

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Correspondence to Kevin A Janes.

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Supplementary information

Supplementary Figure 1

Optimized small-sample cDNA amplifications of SKW 6.4 suspension cells is retained upon frozen storage. 100-cell samples were serially diluted and amplified by small-sample poly(A) PCR either fresh (a) or after having been stored frozen at –80 °C (b). Amplification of the indicated genes was quantified by qPCR as described previously56, 81 (PDF 260 kb)

Supplementary Figure 2

Quantitative small-sample amplification of high-abundance to low-abundance transcripts from 3–100 microdissected cells in a primary melanoma sample. (a–p) 100-cell samples were serially diluted, amplified by small-sample poly(A) PCR, and quantified by qPCR as described previously56, 81. The qPCR cycle threshold for each gene is plotted as a function of starting cellular material and is shown as the median ± range of three replicate small-sample amplifications. Red lines show the log-linear fit of the 3–100-cell dilutions. Note that the one-cell amplifications (gray) often deviate from the log-linear fit. (PDF 299 kb)

Supplementary Figure 3

Quantitative small-sample amplification of high-abundance to low-abundance transcripts from 3–100 microdissected HT-29 cells grown in culture. (a–p) 100-cell samples were serially diluted, amplified by small-sample poly(A) PCR, and quantified by qPCR as described previously56, 81 The qPCR cycle threshold for each gene is plotted as a function of starting cellular material and is shown as the median ± range of three replicate small-sample amplifications. Red lines show the log-linear fit of the 3–100-cell dilutions. Note that the one-cell amplifications (gray) often deviate from the log-linear fit or are frequently not detectable (ND, shown in yellow). (PDF 300 kb)

Supplementary Figure 4

Quantitative small-sample amplification of high-abundance to low-abundance transcripts from 1–100 SKW 6.4 cells grown in suspension. (a–p) 100-cell samples were serially diluted, amplified by small-sample poly(A) PCR, and quantified by qPCR as described previously56, 81. The qPCR cycle threshold for each gene is plotted as a function of starting cellular material and is shown as the median ± range of three replicate small-sample amplifications. Red lines show the log-linear fit of the 3–100-cell dilutions. Note that the one-cell amplifications (gray) are reasonably accurate, consistent with previous work involving live single-cell suspensions32, 83. (PDF 301 kb)

Supplementary Method 1

Text file for the MATLAB program StochProfParameters.m (TXT 89 kb)

Supplementary Method 2

Text file for the MATLAB program StochProfMicroarrayFilt.Am (TXT 5 kb)

Supplementary Method 3

Text file for the MATLAB program StochProfAnalysis.m (TXT 5 kb)

Supplementary Data 1

Tab-delimited ASCII text file (Sampling_example.txt) of representative stochastic samplings. (TXT 5622 kb)

Supplementary Data 2

Tab-delimited ASCII text file (Controls_example.txt) of matched amplification controls. (TXT 5682 kb)

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Wang, L., Janes, K. Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors and cultured cells. Nat Protoc 8, 282–301 (2013). https://doi.org/10.1038/nprot.2012.158

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