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Benoit SAMSON
| When |
Oct 23, 2026
from 11:00 to 12:00 |
|---|---|
| Contact Name | Salle des Thèses - EQ SCAI |
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Pituitary gonadotroph adenomas exhibit cellular and transcriptional heterogeneity despite the absence of well-established genetic drivers. Current conventional spatial transcriptomics cannot readily distinguish regions of low cellular density from those with reduced transcriptional activity. To address this limitation, we developed Nucount, an AI-based framework combining cell segmentation methods to accurately quantify cell density from H&E-stained tissue images. We demonstrate how these estimates of cell abundance can be integrated into downstream spatial transcriptomic analyses to disentangle cellular organization from transcriptional output. Nucount combined with cell-type deconvolution and spatial analysis of gene expression identifies spatial domains within gonadotroph tumours. These domains distinguish densely populated, transcriptionally active tumour regions from physically compacted regions exhibiting reduced transcriptional activity. Our findings establish the importance of accounting for physical cell abundance when characterizing the spatial organization of intratumoural heterogeneity.