Interrogating Bronchoalveolar Lavage Samples via Exclusion-Based Analyte Extraction

• Published in: SLAS technology Vol. 22; no. 3; pp. 348 - 357
• Main Authors: Tokar, Jacob J., Warrick, Jay W., Guckenberger, David J., Sperger, Jamie M., Lang, Joshua M., Ferguson, J. Scott, Beebe, David J.
• Format: Journal Article
• Language: English
• Published: Los Angeles, CA Elsevier Inc 01.06.2017; SAGE Publications
• Subjects: bronchoalveolar; clinical automation; lab on a chip; lavage; lung cancer; microtechnology; solid-phase extraction; lung cancer; lab on a chip; solid-phase extraction; lavage; clinical automation; microtechnology; bronchoalveolar
• ISSN: 2472-6303; 2472-6311
• DOI: 10.1177/2472630317696780

Although average survival rates for lung cancer have improved, earlier and better diagnosis remains a priority. One promising approach to assisting earlier and safer diagnosis of lung lesions is bronchoalveolar lavage (BAL), which provides a sample of lung tissue as well as proteins and immune cells from the vicinity of the lesion, yet diagnostic sensitivity remains a challenge. Reproducible isolation of lung epithelia and multianalyte extraction have the potential to improve diagnostic sensitivity and provide new information for developing personalized therapeutic approaches. We present the use of a recently developed exclusion-based, solid-phase-extraction technique called SLIDE (Sliding Lid for Immobilized Droplet Extraction) to facilitate analysis of BAL samples. We developed a SLIDE protocol for lung epithelial cell extraction and biomarker staining of patient BALs, testing both EpCAM and Trop2 as capture antigens. We characterized captured cells using TTF1 and p40 as immunostaining biomarkers of adenocarcinoma and squamous cell carcinoma, respectively. We achieved up to 90% (EpCAM) and 84% (Trop2) extraction efficiency of representative tumor cell lines. We then used the platform to process two patient BAL samples in parallel within the same sample plate to demonstrate feasibility and observed that Trop2-based extraction potentially extracts more target cells than EpCAM-based extraction.