Deep learning-enabled single-cell morpholomic atlas of nasal swabs distinguishes chronic inflammation from sinonasal malignancy.

Brittany T Rupp¹, Andreja Jovic², Theresa Weaver¹, Kiran Saini², Meghan Burr², W Jared Martin³, Quinn T Easter¹, Adam J Kimple^3,4, Kevin Matthew Byrd^5,6

¹Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA.

²Deepcell Inc., Menlo Park, CA, USA.

³Department of Otolaryngology-Head and Neck Surgery, University of North Carolina School of Medicine, Chapel Hill, NC, USA.

⁴Marsico Lung Institute, University of North Carolina, Chapel Hill, NC, USA.

⁵Department of Oral and Craniofacial Molecular Biology, Philips Institute for Oral Health Research, Virginia Commonwealth University, Richmond, VA, USA. byrdk6@vcu.edu.

⁶VCU Massey Comprehensive Cancer Center, Virginia Commonwealth University, Richmond, VA, USA. byrdk6@vcu.edu.

PubMed DOI

Abstract

Sinonasal malignancies frequently present with symptoms overlapping chronic inflammatory conditions, complicating early detection and delaying treatment. A fast, non-invasive approach capable of resolving cell states across inflammatory and malignant disease from routine nasal swabs could substantially improve clinical screening. Using the REM-I platform, we generated a reference atlas of deep lea…

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