text: The brain develops from a small number of embryonic progenitors through numerous rounds of divisions that give rise to a diverse set of differentiated neurons and glia. Lineage trees describe the divisions and specializations of cells over time, but the large size and cell type diversity of the brain has hindered the construction of large-scale brain lineage trees. Bushra Raj, James Gagnon and Shristi Pandey in the Schier lab, together with Aaron McKenna in the Shendure lab and Dan Wagner in the Klein lab, established an approach to address this challenge (Nature Biotechnology). They extended the lineage recording method GESTALT, developed by the Schier and Shendure labs (McKenna et al. Science 2016), and combined it with droplet-based single-cell RNA sequencing to provide a snapshot of the heterogeneity of cell types and developmental lineages in a vertebrate brain. They used CRISPR–Cas9 to edit transgenic lineage barcodes during early and late zebrafish development, resulting in the accumulation of >10,000 mutant barcodes over time that uniquely labeled ancestral progenitors. Single-cell RNA sequencing of edited juvenile brains simultaneously identified cellular transcriptomes and mutated lineage barcodes of thousands of cells. This approach defined >100 cell types in the juvenile brain providing a rich catalogue of cell diversity, and comparison of barcodes reconstructed lineage relationships between profiled cells. The resulting lineage trees had hundreds of branches that uncovered restrictions at the level of cell types, brain regions, and gene expression cascades during differentiation. Individual barcodes were mostly regionally enriched, consistent with the limited long-range migration of neurons. Despite regional restriction, progenitors that acquired a specific barcode gave rise to descendants that mapped to multiple different types of neurons, suggesting that most ancestral progenitors were multipotent. But close inspection of the lineage trees also revealed divergent lineage trajectories; e.g., in the hypothalamus, which contained cell types with distinct lineage relationships. These results illustrate that this technology – scGESTALT – can be used to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease, not only in zebrafish but also in any system that allows genome editing and scRNA-seq.
  
  At the end of March, NVIDIA held the GTC 2018 conference (GPU Technology Conference) in American San José. Among others, novelties in the field of computing using GPU accelerators, machine learning, and AI were presented at the conference.
  Indeed, this year’s conference offered a lot of novelties. To those interested, we recommend watching the entire GTC 2018 keynote video prepared by the co-founder and general director of NVIDIA, Mr. Jensen Huang – traditionally dressed in a leather biker jacket:
  At the GTC 2018 conference, NVIDIA presented the successor of their flag graphic accelerator for the field of machine learning – NVIDIA V100 with a 32GB memory (which is twice the capacity of the original card). Another novelty is a complex system for machine learning — the NVIDIA DGX-2 server. It is the successor of the first concept by NVIDIA within this field (DGX-1). DGX-2 concentrates the performance of sixteen newest Tesla V100 32GB accelerators into a single server, which was also presented at the GTC conference.
  Furthermore, NVIDIA presented a support to the Kubernetes technology for the NVIDIA GPU accelerators (for the NVIDIA containers, NVIDIA GPU Cloud — AWS-WC2, GCP, Azure, DGX). The support to this technology significantly simplifies the application of the AI models in the environment of a hybrid cloud.
  Another interesting thing was the extension of the support to applications running in the containers above the NVIDIA GPU cloud or an HPC cloud for HPC visualisation. The combination of NVIDIA DGX-1, Pure Storage, and Arista switch – known as AIRI (i.e. AI ready infrastructure) was also very interesting.