Diverse reprogramming codes for neuronal identity.

The transcriptional programs that establish neuronal identity evolved to produce the rich diversity of neuronal cell types that arise sequentially during development. Remarkably, transient expression of certain transcription factors can also endow non-neural cells with neuronal properties. The relationship between reprogramming factors and the transcriptional networks that produce neuronal identity and diversity remains largely unknown. Here, from a screen of 598 pairs of transcription factors, we identify 76 pairs of transcription factors that induce mouse fibroblasts to differentiate into cells with neuronal features. By comparing the transcriptomes of these induced neuronal cells (iN cells) with those of endogenous neurons, we define a 'core' cell-autonomous neuronal signature. The iN cells also exhibit diversity; each transcription factor pair produces iN cells with unique transcriptional patterns that can predict their pharmacological responses. By linking distinct transcription factor input 'codes' to defined transcriptional outputs, this study delineates cell-autonomous features of neuronal identity and diversity and expands the reprogramming toolbox to facilitate engineering of induced neurons with desired patterns of gene expression and related functional properties.

Exploring applications of crowdsourcing to cryo-EM.

Extraction of particles from cryo-electron microscopy (cryo-EM) micrographs is a crucial step in processing single-particle datasets. Although algorithms have been developed for automatic particle picking, these algorithms generally rely on two-dimensional templates for particle identification, which may exhibit biases that can propagate artifacts through the reconstruction pipeline. Manual picking is viewed as a gold-standard solution for particle selection, but it is too time-consuming to perform on data sets of thousands of images. In recent years, crowdsourcing has proven effective at leveraging the open web to manually curate datasets. In particular, citizen science projects such as Galaxy Zoo have shown the power of appealing to users' scientific interests to process enormous amounts of data. To this end, we explored the possible applications of crowdsourcing in cryo-EM particle picking, presenting a variety of novel experiments including the production of a fully annotated particle set from untrained citizen scientists. We show the possibilities and limitations of crowdsourcing particle selection tasks, and explore further options for crowdsourcing cryo-EM data processing.