Back

High-throughput Mucus Microrheology for Phenotyping and Disease Modeling

Ling, F.; Sahin, A. T.; Miller Naranjo, B.; Aime, S.; Roth, D.; Tepho, N.; Vendrame, A.; Emken, E.; Kiechle, M.; Tesfaigzi, Y.; Lieleg, O.; Nawroth, J. C.

2025-01-14 bioengineering
10.1101/2025.01.09.632077 bioRxiv
Show abstract

One-Sentence SummaryWe here develop and validate an easy to implement, high-throughput microrheology platform to reliably quantify viscoelasticity in as little as 3 {micro}L mucus droplet samples or intact mucosal surfaces based on Differential Dynamic Microscopy (DDM) that we demonstrate in a variety of in vitro and clinical applications. Mucus plays an integral role in the barrier function of many epithelial tissues, including respiratory, gastrointestinal, and reproductive systems. Understanding how mucus flow behavior, i.e., rheology, changes during disease progression and in response to treatments is of great interest for better differential diagnostics, pinpointing patient-specific disease mechanisms and respective personalized treatments. However, both basic and translational research of mucus rheology is greatly hampered by the lack of scalable and user-friendly rheology assays for the small volumes of mucus typically provided by in vitro respiratory models or from clinical samples. We report a streamlined, high-throughput, semi-automated mucus rheology approach, easy to implement for a variety of preclinical and clinical questions. It leverages Differential Dynamic Microscopy (DDM) to reliably measure the frequency-dependent microrheology of minuscule (3-10 {micro}L-sized) mucus samples as well as in situ mucosal surfaces using standard epifluorescence microscopy. Overcoming complex and time-consuming user interventions of previous rheology and particle tracking routines, we achieve microrheology at the time scale of mucus relaxation (1-20 s), greatly reducing assay time. We validated our platform successfully in mucus samples and in situ mucosal surfaces: first in state-of-the-art air-liquid-interface (ALI) human respiratory cultures, where we compare mucus rheology of airway disease models and different culture conditions; second, assessing clinical samples of human patient cervical samples. Overall, our results add to an increasingly appreciated non-invasive, easy to use, body fluid-based personalized toolbox, with unprecedented opportunities for precision medicine and large-scale monitoring to further unveil functional roles of mucus in human health and disease.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.