Lorena Rosas

This paper introduces DeepSAS, a deep graph framework to identify senescent cells and their associated genes, revealing cell-type-specific senescence features in aging and disease.

This study uses MALDI-MSI to analyze lipids and proteins in human lung fibroblasts, revealing senescence-related changes at the single-cell level.

scResolve recovers single-cell expression profiles from multi-cellular spatial transcriptomics data, enabling cell type-specific analysis and rare population detection in human tissues.

The paper identifies LEF1 isoforms as key regulators of cellular senescence and aging through transcriptomic analysis and validation in human and mouse models.

The paper identifies early fibroblast transitions to a profibrotic state in IPF using scRNA-seq and validates MOXD1 as an early marker.

The paper uses spatial transcriptomics and the MAPLE algorithm to analyze fibrotic lung tissue, identifying spatial biomarkers and fibrotic programs in IPF.