💡 This enables powerful insights into tissue organization, microenvironments & biological processes — while still being compatible with cell-based methods when needed.

GRIDGENE takes a cell-free approach, using transcript density to define biologically relevant tissue regions such as: ✨ Tissue interfaces ✨ Phenotype-enriched compartments ✨ Gene signature–defined zones

🚀 Happy to share our new preprint introducing GRIDGENE — a transcript density–based approach for spatial transcriptomics! biorxiv.org/content/10.1101/… #SpatialTranscriptomics #CancerResearch #Bioinformatics

Instead, PENGUIN preserves critical signal intensity variations, cuts down on noise, and boosts accuracy in tasks like cell segmentation & phenotyping. 🧬💻

📊 What PENGUIN PENGUIN Offers: PENGUIN (Percentile Normalization GUI Image deNoising) stands out with a streamlined, no-fuss approach for preprocessing multiplex imaging data. Unlike other tools, it doesn’t require: Manual annotation Complex machine learning models

🚀 New Tool Alert! 🚀 PENGUIN 🐧is finally published in @CSB_Journal : PENGUIN is a rapid, efficient, and user-friendly tool for denoising and normalizing multiplexed spatial proteomics images! 🔬✨ 🔗sciencedirect.com/science/ar…

By sequentially applying MALDI-MSI and IMC on the same tissue section, we can map the cellular makeup of the tissue microenvironment and simultaneously uncover the metabolic phenotypes of individual cells.

Imaging mass #cytometry (SABR was our previous pipeline but led to some loss of real signal) onlinelibrary.wiley.com/doi/…

🚀 We just developed a new tool for pre-processing #multiplex #imaging data! 🐧 PENGUIN is an easy, straightforward, and highly effective solution to handle noise while preserving real signals. biorxiv.org/content/10.1101/…