The joint analysis of the genome, epigenome, transcriptome, proteome and/or metabolome
from single cells is transforming our understanding of cell biology in health and disease. In …

Microglial research has advanced considerably in recent decades yet has been constrained
by a rolling series of dichotomies such as" resting versus activated" and" M1 versus M2." …

Single‐cell RNA sequencing (scRNA‐seq) technology has become the state‐of‐the‐art
approach for unravelling the heterogeneity and complexity of RNA transcripts within …

Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize
cellular heterogeneity. However, scRNA-seq requires lysing cells, which impedes further …

Single-cell sequencing, including genomics, transcriptomics, epigenomics, proteomics and
metabolomics sequencing, is a powerful tool to decipher the cellular and molecular …

T cells express T cell receptors (TCRs) composed of somatically recombined TCRα and
TCRβ chains, which mediate recognition of major histocompatibility complex (MHC)–antigen …

Single-cell RNA sequencing (scRNA-seq) enables the systematic identification of cell
populations in a tissue, but characterizing their spatial organization remains challenging. We …

Single-cell RNA sequencing (scRNA-seq) technologies allow the dissection of gene
expression at single-cell resolution, which greatly revolutionizes transcriptomic studies. A …

The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …

Single-cell RNA sequencing is an increasingly used method to measure gene expression at
the single cell level and build cell-type atlases of tissues. Hundreds of single-cell …