Vitalii Kleshchevnikov

Human Developmental Cell Atlas integrates ~4.6 million cells/nuclei from 4-22 post-conception weeks into 454 refined cell types and maps 114 spatial niches across 6PCW whole-embryo sections.

The paper presents a spatial transcriptomic atlas of autism-associated genes in the developing human brain, identifying convergence in the thalamus and germinal zones.

This paper creates a human embryonic limb cell atlas using single-cell and spatial transcriptomics, identifying novel cell populations and developmental patterns.

The paper introduces cell2fate, a model-based RNA velocity method that improves cell fate prediction by decomposing velocity solutions into modules and applying it to human brain development data.

This study creates a single-cell and single-nucleus transcriptomic atlas of human skeletal muscle ageing, revealing mechanisms in muscle stem cells, myofibers, and the microenvironment.

The paper identifies human linear motifs by analyzing viral protein interactions, finding 8,400 motif instances with 105 supported by multiple filters.

scvi-tools provides a Python package for probabilistic analysis of single-cell omics data, offering standardized interfaces and extensible tools for various analysis tasks.

cell2location uses a Bayesian model to integrate single-cell and spatial transcriptomics, mapping cell types in situ across mouse brain regions and human lymph node immune compartments.

This study maps the temporal and spatial dynamics of the human endometrium using single-cell and spatial reference maps, identifying key signaling pathways regulating cell fate in vivo and in vitro.

This paper introduces a new spatial transcriptomic technology, WTA, and applies it to map the cellular architecture of the developing human neocortex, showing spatial enrichment of autism-related genes.