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Rapid screening and diagnosis of tuberculosis disease (TB) is still challenging and critically needed for global TB control efforts. In this study, we present a rapid and streamlined technology, using precisely engineered silica nanopore thin films, which are optimized for pore size, structure, capillary force, and film thickness, to isolate Mycobacterium tuberculosis (MTB) antigens in laboratory and clinical samples for rapid TB screening. This technology, referred to here as on-chip fractionation, is integrated with high-throughput matrix-assisted laser desorption/ionization time-of flight mass spectrometry to screen and identify fragments of the MTB antigen, CFP-10, from complex biological samples, without use of immunoaffinity agents. With the use of this comprehensive approach, we were able to clearly distinguish a clinical isolate of MTB from a nonTB species of the genus Mycobacterium avium grown in liquid culture media. This assay can reach a detection limit of 10 fmol and an isolation rate of 90% for the antigen CFP-10. Our strategy has significant potential to fill the conceptual and technical gaps in rapid diagnosis of active TB disease.

Biomarker profiling using mass spectrometry plays an essential role in biological studies and is highly dependent on the data analysis for sample classification. In this study, we introduced power nomination of the mass spectra as a method for systematically altering the weights of peaks at different intensity levels. In combination with the use of support vector machine method (SVM), the impact on the sample classification has been characterized using data in four studies previously reported, including the distinctions of anomeric configurations of sugars, types of bacteria, stages of melanoma, and the types of breast cancer. Comprehensive analysis of the data with normalization at different power normalization index (PNI) was developed and analysis tools, including error-PNI plots, reference profiles, and error source profiles, were used to assess the potential of the analytical methods as well as to find the proper approaches to classify the samples.