Peter V. Kharchenko

The paper uses in utero barcoding and single-cell transcriptomics to map clonal relationships in mouse mesoderm-derived tissues, revealing lineage relationships in liver and heart mesenchyme.

The paper shows that the relative abundance of transcription factors SP9 and DLX determines neuronal fate allocation in the developing basal ganglia.

The paper introduces clone2vec, a machine learning method for analyzing lineage-resolved single-cell data, revealing clonal patterns and continuous variation in development and disease.

This study identifies 58 genomic regions associated with meiotic recombination, 24 of which are sex-specific, and finds a moderate genetic correlation between male and female recombination rates.

RETrace2 enables high-resolution retrospective lineage tracing and cell-type identification using mutable homopolymers, achieving resolution below five cell divisions in microsatellite-unstable organisms.

The paper uses multiomic single-cell and spatial data to identify gene regulatory networks and transcription factors driving cortical regionality in the human brain.

This paper shows segmentation errors in spatial transcriptomics data confound downstream analysis and proposes a method to mitigate their impact.

The paper introduces scJLIM, a cluster-free method for single-cell colocalization testing, improving power to link GWAS loci with gene expression changes in specific cell types.

The paper presents a multi-modal atlas of human kidney disease, identifying cellular and spatial mechanisms of injury resolution or failure and linking them to clinical outcomes.

This study identifies Sema4A as an extrinsic factor that protects myeloid-biased HSCs from inflammatory stress, preserving functional diversity in the hematopoietic stem cell pool.

The paper profiles clonal landscapes in mouse embryos to reveal spatial modulators of clonal fate biases using single-cell lineage tracing and clone2vec.

This paper introduces scITD, a method to identify multicellular gene expression patterns that distinguish disease individuals, showing its application in SLE and COVID-19 datasets.

The paper introduces DART-FISH, a multiplexed RNA in situ hybridization technique for high-resolution profiling of human tissues, demonstrating its application on brain and kidney samples.

This paper introduces gpsFISH, a method for selecting gene panels in spatial transcriptomics that outperforms existing methods by accounting for platform effects and incorporating cell type hierarchies.

The paper creates a high-resolution cellular atlas of healthy and injured kidney cell states and niches using single-cell and spatial technologies, identifying molecular signatures and pathways related to kidney injury.

scFates is a Python package for analyzing single-cell data trajectories, focusing on pseudotime, bifurcation, and cell fate splits, with integration into the scanpy ecosystem.

The paper identifies selective vulnerability of supragranular layer neurons in schizophrenia through single nucleus RNA sequencing and histological analysis.

This paper introduces Numbat, a method that uses haplotype information to improve detection of copy number variations from scRNA-seq data, revealing subclonal complexity in cancer.

Cacoa packages statistical tests, exploration and plots for comparing groups of scRNA-seq samples, aimed at inter-individual variability, small cohorts and high-dimensional data.

The paper compares cellular diversity in the primary motor cortex across human, marmoset, and mouse using single-nucleus transcriptomics and epigenomics, finding conserved cell types and species-specific adaptations.

The paper creates a comprehensive atlas of mouse primary motor cortex cell types using integrated transcriptomic and epigenomic data from over 600,000 samples.

This study uses single-cell RNA sequencing to analyze developing and mature glia in peripheral sensory ganglia, revealing transcriptional differences and shared molecular features across glial subtypes.

The paper identifies primate sensory neuron types associated with human chronic pain using transcriptomics and maps human pain heritability to these neuronal types.

This paper develops SCoPE2, a single-cell proteomic method, to analyze macrophage heterogeneity and finds a continuous proteome gradient in macrophages without polarizing cytokines.

This paper introduces Baysor, a Bayesian segmentation tool for spatial transcriptomics data that improves cell identification and reduces contamination across multiple protocols.

The paper decomposes mutation rate and spectra variation in human germline using blind source separation, identifying nine processes including those related to DNA damage and replication timing.

The paper introduces Conos, a method for joint clustering of large single-cell RNA-seq datasets to identify recurrent cell subpopulations across multiple samples.

Unspliced versus spliced mRNA in standard scRNA-seq gives RNA velocity, a per-cell vector predicting future state over hours, validated in neural crest and used to trace mouse hippocampal lineages.

The paper introduces a pipeline for processing droplet-based single-cell RNA-seq data to improve molecular count estimation by correcting barcode biases and sequencing errors.

The paper develops two single-cell methods to analyze human brain cells, identifies neuronal subtypes and non-neuronal cell types, and links genetic risk factors to specific cell types.