Tal Nawy

This study identifies the basal cell state in pancreatic cancer as critical for maintaining tumors through an immunosuppressive circuit, showing its ablation leads to tumor collapse.

The paper introduces Suco and Compocyte to classify immune cells from 3,965 patients, identifying new macrophage and monocyte states and T cell memory erosion in metastases.

In mouse pancreatic cancer models, p53, CDKN2A and SMAD4 programs converge on a rare Kras-mutant progenitor-like state whose niche KRAS inhibition collapses and p53 loss expands.

The paper analyzes transcriptomic changes in metastatic pancreatic cancer using a patient's autopsy data to identify site-specific gene programs and clonal phylogeny.

Segger improves cell segmentation in spatial transcriptomics by using a graph neural network, achieving high sensitivity and speed with minimal manual intervention.

This study identifies progressive plasticity in colorectal cancer metastases, showing reprogramming into fetal progenitor states and divergent differentiation, linked to poor survival.

The paper identifies artifacts in single-cell mtDNA variant calling that mislead phylogenetic inference and warns against using low-evidence variant workflows.

This paper introduces COVET and ENVI to model spatial niches and improve gene expression imputation and spatial context inference from single-cell data.

This paper introduces Spectra, an algorithm that identifies and refines gene programs from single-cell data, outperforming existing methods in tumor immune contexts by improving interpretability and capturing cell-state changes.

SEACells identifies metacells from single-cell data, improving gene-peak associations and revealing chromatin dynamics in hematopoiesis and COVID-19.

Sparcle reassigns transcripts to cells in multiplexed images using gene covariation and spatial features, improving quantification and cell boundary delineation without requiring accurate segmentation.