Chen Weng

The paper identifies MHC II-expressing bone marrow megakaryocytes as noncanonical antigen-presenting cells capable of activating CD4+ T cells and modulating immune responses.

This study shows that low-heteroplasmy mitochondrial DNA mutations provide reliable clonal information, improving clonal reconstruction in human cells.

The paper uses single-cell multiomic analysis to identify genetic variants and regulatory disruptions linked to childhood B-cell acute lymphoblastic leukemia.

The paper uses CRISPR and single-cell transcriptomics to study how inherited variants in B-cell development contribute to childhood leukemia.

This study maps genetic risk mechanisms for immune-mediated diseases by integrating single-cell multi-omic data with fine-mapped variants from genome-wide association studies.

The paper identifies a genetic variant associated with reduced blood counts and cancer protection, showing it disrupts a lncRNA that regulates HOXA gene expression in hematopoietic stem cells.

MitoDrift models mitochondrial inheritance to enable high-precision lineage tracing in human tissues, revealing clonal dynamics and linking them to transcriptional and epigenetic programs.

The paper identifies an age-related expansion of an inflammatory and quiescent HSC subpopulation using integrated single-cell data.

This study identifies two distinct macrophage differentiation pathways in humans, showing how developmental origin influences macrophage function and identifies a method to expand tissue-resident macrophages.

This paper introduces ReDeeM, a framework for detecting mtDNA mutations in single cells to improve lineage tracing, showing improved accuracy and reliability over existing methods.

Spateo provides a framework for spatiotemporal modeling of single-cell spatial transcriptomics data to study cell-cell interactions and organ development in 3D space.

The paper introduces SCAVENGE, a method mapping causal variants to cellular contexts at single-cell resolution, identifying disease-relevant cell types and mechanisms.

The paper introduces dynamo, a framework for estimating RNA velocities and predicting cell fates using single-cell RNA-seq data, applied to various biological processes.

TWO-SIGMA is a single-cell RNA-seq differential expression method that handles drop-out, overdispersion, and within-sample correlation, outperforming existing methods in simulations and real data.

This study shows that Myt transcription factors prevent over-activation of stress-response genes in pancreatic β cells, maintaining their function and survival.