Dissecting cell-to-cell regulatory heterogeneity by stochastic profiling
To address the challenge, we have developed an alternative approach that provides global information about single-cell regulatory states without the need to profile single cells. The technique, called stochastic profiling (2, 3), analyzes the transcriptomic fluctuations of multiple 10-cell pools collected randomly from a tissue or tumor context. The purpose of pooling is to increase the input material, which dramatically improves the reproducibility and sensitivity of the resulting profiles, enabling pools to be collected by laser capture microdissection. The subsequent statistical analysis deconvolves the 10-cell distributions to identify heterogeneous regulatory states and infer their underlying single-cell distribution in the sampled population (4). We have trained labs across the country in this method and anticipate many applications in the coming years. Ongoing work has adapted stochastic profiling to RNA sequencing, fluorescence-guided applications in genetically engineered mouse models, and primary patient material.
- Janes KA. (2016) Single-cell states versus single-cell atlases—two classes of heterogeneity that differ in meaning and method. Curr Opin Biotechnol, 39, 120-5. [Link to Article]
- Bajikar SS*, Fuchs C*, Roller A, Theis FJ†, Janes KA†. (2014) Parameterizing cell-to-cell regulatory heterogeneities via stochastic transcriptional profiles. Proc Natl Acad Sci, 111, E626-35. [Link to Article]
- Wang L, Janes KA. (2013) Stochastic profiling of transcriptional regulatory heterogeneities in tissues, tumors, and cultured cells. Nat Protoc, 8, 282-301. [Link to Article]
- Janes KA, Wang CC, Holmberg KJ, Cabral K, Brugge JS. (2010) Identifying single-cell molecular programs by stochastic profiling. Nat Methods, 7, 311-7. [Link to Article]