Yuval Kluger

The paper presents a single-cell atlas of noncoding RNAs during mouse organogenesis, revealing their cell-type-specific and lineage-regulated expression patterns.

Locat identifies localized marker genes by testing both enrichment within and depletion outside cell populations in single-cell transcriptomics.

The paper shows how coordinated Wnt and Hedgehog signaling creates sharp cell-type boundaries by synchronizing cell-cycle exit and differentiation.

This study shows that reducing cysteine, not methionine, improves immunometabolic fitness and extends healthy lifespan in aging organisms.

The paper identifies the CXCL9-CXCR3 axis and NAMPT as key drivers of inflammation and kidney injury in acute interstitial nephritis using spatial transcriptomics and imaging.

SO_SCPLOWTACKC_SCPLOW is a foundation model trained on human single-cell data that enables in-context learning for predicting perturbation effects without fine-tuning.

This study maps age-related cellular senescence in human lymph nodes, identifying senescence markers and spatial shifts in germinal center B cells.

MOSAIC integrates single-cell multi-omics data to identify feature connectivity patterns, revealing biological rewiring and improving clinical outcome prediction.

AMULETY generates language model embeddings for adaptive immune receptor sequences using pre-trained models, enabling users to leverage different models for downstream applications.

DiCoLo detects localized differential gene co-expression without clustering or cross-condition alignment, revealing coordinated gene programs in mouse hair follicle development.

This study identifies nerve-associated macrophages (NAMs) as regulators of adipose homeostasis and inflammation during aging, showing their depletion accelerates inflammaging and impairs lipolysis.

LMD identifies localized genes in single-cell RNA-seq data to characterize cellular diversity without batch correction, outperforming existing methods.

The paper shows that coordinated timing between Wnt and Hedgehog signaling generates sharp cell-type boundaries during hair follicle development.

This study shows that elevated taurine reduces age-related inflammation by inhibiting the NLRP3 inflammasome and pyroptosis in mice.

This study improves antigen specificity prediction by fine-tuning pre-trained antibody language models on SARS-CoV-2 and influenza antigens, showing better accuracy than models trained on embeddings alone.

This study uses single-nucleus multiome sequencing to identify HIV-associated transcriptional and epigenetic changes in brain regions of individuals with HIV, revealing altered glial cell states and pro-inflammatory signaling.

BiPCA improves PCA for omics count data by adaptively rescaling noise, enhancing biological interpretation and reducing batch effects across multiple modalities.

This study shows IL-4 promotes B cell activation by upregulating cholesterol synthesis, which is essential for ER expansion, cell cycle progression, and proliferation during influenza infection.

GeneTrajectory infers gene trajectories instead of cell trajectories to uncover progressive gene dynamics in biological processes.

SORBET uses geometric deep learning to predict phenotypes from spatial transcriptomics and proteomics data, showing improved accuracy over existing methods.

This paper introduces scShift, a deep variational model that enables zero-shot characterization of single-cell biological states across datasets by overcoming batch effects.

This study evaluates B cell receptor sequence embeddings for predicting sequence properties and specificity, finding that BCR-specific embeddings and paired-chain sequences improve performance.

SIMVI disentangles intrinsic and spatial-induced variations in spatial omics data, revealing spatial effects and biological dynamics across multiple datasets.

NICHES visualizes cell-cell interactions at the single-cell level using ligand-receptor signaling proxies, enabling analysis of heterogeneous signaling across cell clusters and spatial contexts.

The paper identifies cancer-relevant genes by analyzing their proximity to core cancer genes in protein-protein interaction networks, finding that genes closer to cancer genes show greater functional importance in cancer biology.

Connectome quantifies and visualizes cell-cell signaling networks from single-cell RNA-sequencing data using a software package for R.

The paper introduces a graph-based method for analyzing multiplexed spatial data, applied to imaging mass cytometry to predict treatment sensitivity with 92% accuracy.

DA-seq identifies differentially abundant cell subpopulations between biological states in scRNA-seq data, revealing both known and novel subpopulations with biological insights.

Truncated SVD plus a per-gene threshold at the most negative value fills dropouts while leaving biological zeros alone, and runs on 50,000 cells in about 40 minutes.

The paper introduces DeepCyTOF, a deep learning method for automating cell gating in mass cytometry data, achieving high concordance with manual gating and performing well on benchmark challenges.