Cell biology is fundamentally limited in its ability to collect complete data on cellular
phenotypes and the wide range of responses to perturbation. Areas such as computer vision …

Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to
quantify the gene expression profile of thousands of single cells simultaneously. Analysis of …

Despite the emergence of experimental methods for simultaneous measurement of multiple
omics modalities in single cells, most single-cell datasets include only one modality. A major …

Single-cell omics technologies have enabled the creation of comprehensive cell atlases
across tissues and species and delivered key insights into the biological mechanisms …

To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of
computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state …

Chimeric antigen receptor T cells (CAR-T cells) have emerged as a powerful treatment
option for individuals with B cell malignancies but have yet to achieve success in treating …

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine
learning has been leveraged to automate cell annotation predictions for some time, there …

The increasing availability of large-scale single-cell atlases has enabled the detailed
description of cell states. In parallel, advances in deep learning allow rapid analysis of newly …

Human neural organoids, generated from pluripotent stem cells in vitro, are useful tools to
study human brain development, evolution and disease. However, it is unclear which parts …

Although multiple pancreatic islet single-cell RNA-sequencing (scRNA-seq) datasets have
been generated, a consensus on pancreatic cell states in development, homeostasis and …