Biology subjects

Pasnuri, N.

Publications and source records attributed to Pasnuri, N..

3 recordsLinked to original sources

bioRxiv2025

Oncogenic and tumor-suppressive forces converge on a progenitor-orchestrated niche to shape early tumorigenesis

The benign-to-malignant transition is a defining step in cancer progression. To investigate when and how malignancy initiation occurs and tissue reorganization proceeds, we combine single-cell and spatial transcriptomic profiling in mouse models of pancreatic ductal adenocarcinoma (PDAC) that capture spontaneous p53 loss. Among Kras-mutant cells, we find that oncogenic and tumor-suppressive programs, including those controlled by p53, CDKN2A, and SMAD4, are co-activated in a discrete progenitor-like population, engaging senescence-like responses. Using a framework we develop for spatial analysis, we show that a niche centered on these cells undergoes stepwise remodeling during tumor progression, mirroring invasive PDAC. Transient KRAS inhibition depletes progenitor-like cells and dismantles their niche, delaying malignancy initiation. Conversely, p53 suppression enables progenitor cell expansion, epithelial-mesenchymal reprogramming, and immune-privileged niche formation. These findings position the progenitor-like state at the convergence of cancer-driving mutations, plasticity and tissue remodeling, revealing a critical window for intercepting malignancy.

cancer biology↗

bioRxiv2025

Segger: Fast and accurate cell segmentation of imaging-based spatial transcriptomics data

The accurate assignment of transcripts to their cells of origin remains the Achilles heel of imaging-based spatial transcriptomics, despite being critical for nearly all downstream analyses. Current cell segmentation methods are prone to over- and under-segmentation, misassign transcripts to cells, require manual intervention, and suffer from low sensitivity and scalability. We introduce segger, a versatile graph neural network based on a heterogeneous graph representation of individual transcripts and cells, that frames cell segmentation as a transcript-to-cell link prediction task and can leverage single-cell RNA-seq information to improve transcript assignments. On multiple Xenium dataset benchmarks, segger exhibits superior sensitivity and specificity, while requiring orders of magnitude less compute time than existing methods. The user-friendly open-source software implementation has extensive documentation (https://elihei2.github.io/segger_dev/), requires little manual intervention, integrates seamlessly into existing workflows, and enables atlas-scale applications.

bioinformatics↗

bioRxiv2021

Buffered EGFR signaling regulated by spitz to argos expression ratio is critical for patterning the Drosophila eye

The Epidermal Growth Factor Receptor (EGFR) signaling pathway plays a critical role in regulating tissue patterning. Drosophila EGFR (DER) signaling achieves specificity through multiple ligands and feedback loops to finetune signaling spatiotemporally. The principal Drosophila EGF, cleaved Spitz, and the negative feedback molecule, Argos are diffusible and can act both in a cell autonomous and non-autonomous manner. The relative expression dose of Spitz and Argos early in development has been shown to be critical in patterning the Drosophila eye, but the exact identity of the cells expressing these genes in the larval eyedisc has been elusive. Using single molecule RNA Fluorescence in situ Hybridization (smFISH), we reveal an intriguing differential expression of spitz and argos in the Drosophila third instar eye imaginal disc indicative of directional DER signaling. By genetically tuning DER signaling, we show that rather than absolute levels of expression, the ratio of expression to be critical for determining the adult eye phenotype. Proper ommatidial patterning is robust to thresholds around a tightly maintained wildtype ratio, and breaks down beyond. This provides a powerful instance of developmental buffering.

developmental biology↗