StochasticProfiling

Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles

Even when appearing perfectly homogeneous on a morphological basis, tissues can be substantially heterogeneous in single-cell molecular expression. As such heterogeneities might govern the regulation of cell fate, one is interested in quantifying them in a given tissue. In this project, we infer single-cell regulatory states from expression measurements taken from small groups of cells. This averaging-and-deconvolution approach allows to quantify single-cell regulatory heterogeneities while avoiding the measurement noise of global single-cell techniques. We applied the method to human breast epithelial tissues, which yielded insights about several genes that were strongly associated with breast cancer.

Manuscript

S.S. Bajikar*, C. Fuchs*, A. Roller, F.J. Theis°, K.A. Janes°: Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles. PNAS 2014, 111(5), E626-635

* first authors
° corresponding authors

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