Single cell RNA-seq published

Following an earlier work on single cell chromosomal maps (together with Peter Fraser’s group, Yaniv was leading it on our side – paper is here), we have now published Effi’s work on single cell RNA-seq (with Ido Amit’s people, paper is here). The crux of the Hi-C paper is the ability to look at several thousands of contacts from individual cells, and it taught us some new lessons as to the importance of cleaning single cell datasets from artifacts and the need in larger and larger single cell ensembles (that we still don’t have in Hi-C) to go deep into the description of the dynamics within the population. In the new RNA-seq paper we are running along these two themes again – cleaning a great deal of artifacts and obtaining data in which the technical noise is distributed (almost) as predicted from sampling theory (it is therefore at least 10 fold smaller than in previous studies!), and increasing the number of cells in our experiments from dozens to thousands. Now is the time to go back to the computational biology paradigms of inferring gene networks – because unlike any data before, the new samples are true snapshots (partial, but technically sound) of individual network states!

Some of you may render the single cell genomics field as too noisy and hype-driven, which is probably partially true. But in my mind, once technology can move from measuring “a single cell” to characterize real cell populations based on sampling of a large number of cells, then it can really have deep, and non-hypish implications on biology.