Single‐cell RNA‐seq has enabled gene expression to be studied at an unprecedented
resolution. The promise of this technology is attracting a growing user base for single‐cell …

New technologies have enabled the investigation of biology and human health at an
unprecedented scale and in multiple dimensions. These dimensions include a myriad of …

Single-cell RNA-seq (scRNA-seq) data exhibits significant cell-to-cell variation due to
technical factors, including the number of molecules detected in each cell, which can …

The mammalian brain is complex, with multiple cell types performing a variety of diverse
functions, but exactly how each cell type is affected in aging remains largely unknown. Here …

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep
biological understanding requires more than a taxonomic listing of clusters. As new methods …

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels
for thousands of genes from up to millions of cells. Common data analysis pipelines include …

Microglia are increasingly recognized for their major contributions during brain development
and neurodegenerative disease. It is currently unknown whether these functions are carried …

Trajectory inference approaches analyze genome-wide omics data from thousands of single
cells and computationally infer the order of these cells along developmental trajectories …

Cell-cell communication via ligand-receptor signaling is a fundamental feature of complex
organs. Despite this, the global landscape of intercellular signaling in mammalian liver has …

Sample multiplexing facilitates scRNA-seq by reducing costs and identifying artifacts such
as cell doublets. However, universal and scalable sample barcoding strategies have not …