Proteome Fishing for CRISPR/Cas12a-Based Orthogonal Multiplex Aptamer Sensing.

Detection of serum protein biomarkers is extremely challenging owing to the superior complexity of serum. Here, we report a method of proteome fishing from the serum. It uses a magnetic nanoparticle-protein corona and a multiplexed aptamer panel, which we incubated with the nanoparticle-protein corona for biomarker recognition. To transfer protein biomarker detection to aptamer detection, we established a CRISPR/Cas12a-based orthogonal multiplex aptamer sensing (COMPASS) platform by profiling the aptamers of protein corona with clinical nonsmall cell lung cancer (NSCLC) serum samples. Furthermore, we determined the four out of nine (FOON) panel (including HE4, NSE, AFP, and VEGF165) to be the most cost-effective and accurate panel for COMPASS in NSCLC diagnosis. The diagnostic accuracy of NSCLC by the FOON panel with internal and external cohorts was 95.56% (ROC-AUC = 99.40%) and 89.58% (ROC-AUC = 95.41%), respectively. Our developed COMPASS technology circumvents the otherwise challenging multiplexed serum protein amplification problem and avoids aptamer degradation in serum. Therefore, this novel COMPASS could lead to the development of a facile, cost-effective, intelligent, and high-throughput diagnostic platform for large-cohort cancer screening.

Nanoparticle-Protein Corona-Based Tissue Proteomics for the Aging Mouse Proteome Atlas.

Highly abundant proteins present in biological fluids and tissues significantly interfere with low-abundance protein identification by mass spectrometry (MS), limiting proteomic depth and hindering protein biomarker discovery. Herein, to enhance the coverage of tissue proteomics, we developed a nanoparticle-protein corona (NP-PC)-based method for the aging mouse proteome atlas. Based on this method, we investigated the complexity of life process of 5 major organs, including the heart, liver, spleen, lungs, and kidneys, from 4 groups of mice at different ages. Compared with the conventional strategy, NP-PC-based proteomics significantly increased the number of identified protein groups in the heart (from 3007 to 3927; increase of 30.6%), liver (from 2982 to 4610; increase of 54.6%), spleen (from 5047 to 7351; increase of 45.7%), lungs (from 4984 to 6903; increase of 38.5%), and kidneys (from 3550 to 5739; increase of 61.7%), and we identified a total of 10 104 protein groups. The overall data indicated that 3-week-old mice showed more differences compared with the other three age groups. The proteins of amino acid-related metabolism were increased in aged mice compared with those in the 3-week-old mice. Protein-related infections were increased in the spleen of the aged mice. Interestingly, the spliceosome-related pathway significantly changed from youth to elders in the liver, spleen, and lungs, indicating the vital role of the spliceosome during the aging process. Our established aging mouse organ proteome atlas provides comprehensive insights into understanding the aging process, and it may help in prevention and treatment of age-related diseases.

Serum Protein Fishing for Machine Learning-Boosted Diagnostic Classification of Small Nodules of Lung.

Diagnosis of benign and malignant small nodules of the lung remains an unmet clinical problem which is leading to serious false positive diagnosis and overtreatment. Here, we developed a serum protein fishing-based spectral library (ProteoFish) for data independent acquisition analysis and a machine learning-boosted protein panel for diagnosis of early Non-Small Cell Lung Cancer (NSCLC) and classification of benign and malignant small nodules. We established an extensive NSCLC protein bank consisting of 297 clinical subjects. After testing 5 feature extraction algorithms and six machine learning models, the Lasso algorithm for a 15-key protein panel selection and Random Forest was chosen for diagnostic classification. Our random forest classifier achieved 91.38% accuracy in benign and malignant small nodule diagnosis, which is superior to the existing clinical assays. By integrating with machine learning, the 15-key protein panel may provide insights to multiplexed protein biomarker fishing from serum for facile cancer screening and tackling the current clinical challenge in prospective diagnostic classification of small nodules of the lung.