Enhancing protein biomarker quantification in cervicovaginal fluid (CVF) through data independent acquisition proteomics approach

Cervicovaginal Fluid (CVF) is a promising source of protein biomarkers, given its role in the homeostasis and immunity of the female lower genital tract. Comprising vaginal fluids, cervical mucus, and endometrial and oviductal fluids, CVF can be collected with minimal invasiveness, making it ideal for searching protein biomarkers of female reproductive diseases. However, its complexity presents a technical challenge for profiling the entire protein composition for biomarker discovery. The objective of this work is to generate a robust and reproducible nLC-MS/MS method via dia-PASEF for the large-scale study of patient cohorts in cervicovaginal fluid. This is reflected in a comparative analysis using deep proteomics methodologies by offline fractionation, where a large number of missing values were observed, generating uncertainty at the level of relative quantification by LFQ.

Six female patients were analyzed over seven days of their cycle, with ovulation determined by a surge in luteinizing hormone (LH) from day zero (LH0) to six (LH6). Each protein sample was digested. DP samples were fractionated and analyzed by TimsTOF in DDA mode, isolating the ten most abundant ions. Protein identification used MSFragger against a Uniprot Homo sapiens database. DIA samples were analyzed using a spectral library built from DP data with DIA-NN.

Panel D of the poster's Figure 2 reports 4218 quantifiable proteins for dia-PASEF versus 2817 for dda-FracOnline.

DIA's superior sensitivity and reproducibility make it suitable for biomarker quantification in CVF, enabling accurate analysis of complex samples. This method holds significant potential for early detection of female reproductive complications, particularly in longitudinal retrospective studies. Such validated biomarkers can be used in downstream clinical applications, including routine laboratory and immunoassay-based techniques. Selecting an appropriate proteomic strategy like DIA can enhance our ability to identify biomarkers for female reproductive pathologies, facilitating earlier interventions and treatments that are more effective. This study highlights the importance of advanced proteomic methods in maximizing CVF information for better clinical outcomes in female reproductive disorders.