Aatur Dilip Singhi

SpaReg reconstructs 3D tissue microenvironments from histology, spatial proteomics, and spatial transcriptomics data, preserving morphology and enabling accurate classification and disease modeling.

This study identifies tumor-intrinsic p38 activation as a driver of immune exclusion in epithelial cancers and suggests it as a therapeutic target to overcome immunotherapy resistance.

MitoChontrol improves single-cell RNA sequencing quality control by using a probabilistic framework to filter out compromised cells while preserving biologically relevant mitochondrial content.

This study develops a high-sensitivity spatial proteomics method, SP-Max, to analyze protein expression changes in pancreatic tissues from normal to cancerous states.

The paper identifies a KRT17-positive, low-grade epithelial population in IPMNs with malignant features using spatial transcriptomics and single-cell RNA sequencing.

This paper identifies SLFN11's role in preventing alternative lengthening of telomeres by eliminating ATRX-deficient cells during telomere crisis, contributing to chemoresistance in cancer.

This study identifies PDAC cancer cell subpopulations using multiplexed glycan immunofluorescence and shows consistent aberrant glycan signatures in both tumor and blood samples, improving detection beyond CA19-9 alone.

UNSEG provides an unsupervised method for segmenting cells and their nuclei in complex tissue samples without training data, outperforming deep learning methods in generalization and consistency.

This study contrasts the effects of developmental and acute Sox9 deletion in cHCC-CCA models, showing that Sox9 elimination can reduce tumor burden and highlights therapeutic potential.