Identifying single-cell molecular programs by stochastic profiling

Abstract

Cells within tissues can be morphologically indistinguishable yet show molecular expression patterns that are remarkably heterogeneous. Here, we describe an approach for comprehensively identifying coregulated, heterogeneously expressed genes among cells that otherwise appear identical. The technique, called “stochastic profiling”, involves the 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 co-expressed. 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 nuclear factor-κB signaling, which were independently confirmed by RNA fluorescence in situ hybridization. Thus, stochastic profiling can reveal single-cell heterogeneities without measuring individual cells explicitly.

Cancer cells in epithelial-to-mesenchymal transition and tumor-propagating–cancer stem cells: distinct, overlapping or same populations

“…Different thyroid cells in vitro begin to respond to an acute thyroid-stimulating hormone stimulus with a delay of 5 min to 3 h for secretion, of 18 h to 3 days for DNA synthesis (Neve and Dumont, 1970;Baptist et al, 1993). Single expression of mRNA in individual cells in a population occurs in bursts, which may be synchronized or not (Janes et al, 2010;Stahlberg and Bengtsson, 2010). Cells in culture take 12 h to shift from the mesenchymal to ameboid types.…”

Single cell transcriptional profiling reveals heterogeneity of human induced pluripotent stem cells

“…Rather, stem cells display an inherent heterogeneity at the molecular level, which underlies the probabilistic element of their fate determination (5)(6)(7)(8)(9)(10)(11)(12)(13). Single cell analysis has aided our ability to scrutinize the dynamic fluctuations in gene expression changes among seemingly homogenous cell populations in other settings (7,12,14), but single cell analysis of hiPSCs has not been previously reported (see Supplemental Discussion; supplemental material available online with this article; doi:10.1172/JCI44635DS1). Here, we use a microfluidic platform to resolve the gene expression profiles of single hiPSCs for an array of 28 genes known to characterize the pluripotent state of hESCs as well as 14 genes associated with differentiated lineages.…”

End Sequence Analysis Toolkit (ESAT) expands the extractable information from single-cell RNA-seq data

“…Many studies of single cells showed critical differences between single cells that are masked in bulk cell data (Apostolou and Thanos 2008;Janes et al 2010;Zhao et al 2012;Bajikar et al 2014). Single-cell RNAseq techniques have enabled single-cell transcriptomics, and we find that the properties of end-sequencing have made DGE the basis for many single-cell sequencing protocols (Hashimshony et al 2012;Jaitin et al 2014;Soumillon et al 2014;Klein et al 2015;Macosko et al 2015).…”