There is tremendous enthusiasm for using the power of transcriptomics to unravel the state of single cells in tissues and tumors. If a single-cell profiling method is to be meaningful, however, it must be i) technically reproducible, ii) sensitive to low-abundance molecules, and iii) compatible with cells isolated in situ. There are many exciting techniques under development to profile the transcriptomes of single cells; unfortunately, none of them meet any of these three criteria (1).

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.