Mapping human adult hippocampal neurogenesis with single-cell transcriptomics: Reconciling controversy or fueling the debate?

Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

Abstract Biological data analysis often deals with lists of genes arising from various studies. The g:Profiler toolset is widely used for finding biological categories enriched in gene lists, conversions between gene identifiers and mappings to their orthologs. The mission of g:Profiler is to provide a reliable service based on up-to-date high quality data in a convenient manner across many evidence types, identifier spaces and organisms. g:Profiler relies on Ensembl as a primary data source and follows their quarterly release cycle while updating the other data sources simultaneously. The current update provides a better user experience due to a modern responsive web interface, standardised API and libraries. The results are delivered through an interactive and configurable web design. Results can be downloaded as publication ready visualisations or delimited text files. In the current update we have extended the support to 467 species and strains, including vertebrates, plants, fungi, insects and parasites. By supporting user uploaded custom GMT files, g:Profiler is now capable of analysing data from any organism. All past releases are maintained for reproducibility and transparency. The 2019 update introduces an extensive technical rewrite making the services faster and more flexible. g:Profiler is freely available at https://biit.cs.ut.ee/gprofiler.