Inna Averbukh

This paper introduces PerturbSpace, a method combining CRISPR perturbations with spatially hashed single-cell multiomics to study gene function in tissue architecture.

This study analyzes multi-omic data from gliomas to identify therapeutic escape mechanisms and TME evolution, finding that N-glycosylation classifies tumor grade and immune transcriptome predicts GBM survival.

The paper introduces Nimbus, a deep learning model for predicting marker positivity in multiplexed imaging data using a pre-trained dataset, and shows its integration with clustering for cell subtype identification.

This study identifies IFNγ-expressing myeloid cells in lipoproteinosis regions of sJIA-daPAP patients, suggesting their role in immune pathology.

This paper introduces a supervised dimensionality reduction method using hybrid subset selection with linear discriminant analysis to improve visualization and analysis of single-cell data.

The paper constructs a spatiotemporal atlas of the maternal-fetal interface using multiomic data to show how gestational age influences immune and stromal cell interactions and spiral artery remodeling.

The paper shows that diffusion-based morphogen shuttling and dynamic nuclear readout refine pattern formation in Drosophila embryos.

The paper shows that repressor-decay timers are more robust than activator-relay timers in Drosophila neuroblast lineages, supporting a repressor-decay mechanism for temporal patterning.