Consensus prediction of cell type labels in single-cell data with popV.

Cell-type classification is a crucial step in single-cell sequencing analysis. Various methods have been proposed for transferring a cell-type label from an annotated reference atlas to unannotated query datasets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness.

BICC1 Interacts with PKD1 and PKD2 to Drive Cystogenesis in ADPKD.

Background: Autosomal dominant polycystic kidney disease (ADPKD) is primarily of adult-onset and caused by pathogenic variants in or . Yet, disease expression is highly variable and includes very early-onset PKD presentations or infancy. In animal models, the RNA-binding molecule Bicc1 has been shown to play a crucial role in the pathogenesis of PKD.

Mapping protein-DNA interactions with DiMeLo-seq.

We recently developed directed methylation with long-read sequencing (DiMeLo-seq) to map protein-DNA interactions genome wide. DiMeLo-seq is capable of mapping multiple interaction sites on single DNA molecules, profiling protein binding in the context of endogenous DNA methylation, identifying haplotype-specific protein-DNA interactions and mapping protein-DNA interactions in repetitive regions of the genome that are difficult to study with short-read methods. With DiMeLo-seq, adenines in the vicinity of a protein of interest are methylated in situ by tethering the Hia5 methyltransferase to an antibody using protein A.

Deep generative modeling of transcriptional dynamics for RNA velocity analysis in single cells.

RNA velocity has been rapidly adopted to guide interpretation of transcriptional dynamics in snapshot single-cell data; however, current approaches for estimating RNA velocity lack effective strategies for quantifying uncertainty and determining the overall applicability to the system of interest. Here, we present veloVI (velocity variational inference), a deep generative modeling framework for estimating RNA velocity. veloVI learns a gene-specific dynamical model of RNA metabolism and provides a transcriptome-wide quantification of velocity uncertainty.

Single-cell multiomic analysis of thymocyte development reveals drivers of CD4 T cell and CD8 T cell lineage commitment.

The development of CD4 T cells and CD8 T cells in the thymus is critical to adaptive immunity and is widely studied as a model of lineage commitment. Recognition of self-peptide major histocompatibility complex (MHC) class I or II by the T cell antigen receptor (TCR) determines the CD8 or CD4 T cell lineage choice, respectively, but how distinct TCR signals drive transcriptional programs of lineage commitment remains largely unknown. Here we applied CITE-seq to measure RNA and surface proteins in thymocytes from wild-type and T cell lineage-restricted mice to generate a comprehensive timeline of cell states for each T cell lineage.