Efthymia Papalexi

The paper creates a Human Cytokine Dictionary using single-cell transcriptomes of PBMCs stimulated with cytokines, revealing cell-type-specific responses and communication networks.

The paper uses Perturb-seq to systematically identify signaling regulator targets across multiple cell lines and contexts, introducing Mixscale to improve analysis and infer pathway activation changes.

A 100-million-cell atlas: 50 cancer lines pooled as spheroid villages, treated with 379 drugs at several doses, profiled by combinatorial-barcoding scRNA-seq and embedded with a 10-dimensional scVI model.

The paper introduces CaRPool-seq, a method using Cas13 for combinatorial RNA perturbations in single cells, and applies it to study myeloid differentiation regulators in AML.

This paper introduces an improved combinatorial barcoding method for scRNA-seq that significantly increases sensitivity, detecting more transcripts and genes per cell across various sample types.

Weighted-nearest neighbor (WNN) analysis in Seurat v4 learns per-cell modality weights to define cell states, enabling a 228-antibody human PBMC CITE-seq reference for RNA-only query mapping.

The paper uses multi-modal single-cell screens to identify regulators of PD-L1 expression and develops a computational framework to improve analysis of perturbation data.

Seurat v3 finds scored mutual-nearest-neighbor anchors in canonical correlation space to integrate scRNA-seq batches and transfer labels, protein levels and transcriptome-wide expression across modalities.

The paper introduces ECCITE-seq, a method that simultaneously captures transcriptomes, immune clonotypes, surface markers, sample identity, and CRISPR perturbations from single cells.

Seurat combines canonical correlation analysis and dynamic time warping to co-embed scRNA-seq datasets from different conditions, platforms, or species, outscoring ComBat and limma in alignment score.