High-throughput longitudinal proteomics of cervicovaginal fluid during consecutive days of women’s menstrual cycles: a new robust, sensitive, and efficient method

High-throughput dia-PASEF workflow improves proteome coverage and quantitative performance in repeated-measures proteomics of cervicovaginal fluid within a preconception cohort | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search High-throughput dia-PASEF workflow improves proteome coverage and quantitative performance in repeated-measures proteomics of cervicovaginal fluid within a preconception cohort View ORCID Profile Mauricio Hernandez , View ORCID Profile Pablo Saldivia , View ORCID Profile Veronica Latapiat , View ORCID Profile Barbara Antilef , View ORCID Profile Guillermo Nourdin , View ORCID Profile Felipe Castro , View ORCID Profile Cristian Vargas , View ORCID Profile Elard S. Koch doi: https://doi.org/10.1101/2025.05.19.25327800 Mauricio Hernandez 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile 3 PhD Program in Applied Clinical Sciences, Faculties of Dentistry and Pharmacy, University of Concepción Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mauricio Hernandez Pablo Saldivia 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile 2 PhD Program in Molecular Biotechnology, Faculty of Biological Sciences, University of Concepción , Concepción, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Pablo Saldivia Veronica Latapiat 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Veronica Latapiat Barbara Antilef 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Barbara Antilef Guillermo Nourdin 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Guillermo Nourdin Felipe Castro 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Felipe Castro Cristian Vargas 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Cristian Vargas Elard S. Koch 1 MELISA Institute , Dalcahue 1120, San Pedro de la Paz, 4133515, Concepción, Chile Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Elard S. Koch For correspondence: ekoch{at}melisainstitute.org Abstract Full Text Info/History Metrics Data/Code Preview PDF Abstract Proteomic studies of the female reproductive system have the potential to advance women’s reproductive health and improve understanding of fertility-related biological processes. Cervicovaginal fluid (CVF), a complex mixture of uterine, cervical, and vaginal secretions, represents a valuable source of molecular information reflecting time-dependent molecular changes across the menstrual cycle. However, large-scale biomarker discovery in CVF remains challenging due to limitations in sensitivity, reproducibility, and data completeness in mass spectrometry–based workflows, particularly within repeated-measures longitudinal study designs. Here, we developed and evaluated a standardized high-throughput dia-PASEF–based proteomics workflow for longitudinal repeated-measures analysis of CVF samples. These samples were collected day-by-day across an ovulation-anchored 7-day window (day 0) and the subsequent six post-ovulatory days within a prospective preconception cohort (EARLY-PREG). This data-independent acquisition approach was directly compared with a conventional DDA-FracOffline strategy. Across a 7-day ovulation anchored window, DDA-FracOffline detected 2,817 quantifiable proteins, whereas dia-PASEF identified 4,229 quantifiable proteins (approximately 50% increase), with mean coefficients of variation 9.37 ± 1.75% and 5.27 ± 0.68%, respectively. Using day 0 (ovulation day) as the time reference for day-to-day comparisons, dia-PASEF identified higher numbers of differentially expressed proteins (DEPs) than did DDA-FracOffline across pairwise day comparisons (952–1,116 DEPs for dia-PASEF versus 161–460 DEPs for DDA-FracOffline). Pathway enrichment analyses demonstrated broader biological coverage using dia-PASEF. Furthermore, when evaluated against a literature-reported panel of clinical biomarker candidates relevant to female reproductive tract pathophysiology, the dia-PASEF workflow showed improved quantitative consistency and enhanced dynamic range sensitivity. These findings indicate that the implemented dia-PASEF approach provides a sensitive, reproducible, and scalable workflow for longitudinal repeated-measures proteomics of CVF. The method is adaptable to other biofluids and supports robust investigation of time-dependent molecular signatures associated with key biological pathways under physiologically relevant conditions in female reproductive system research. Introduction Proteomic studies of the female reproductive system are increasingly recognised as relevant for advancing women’s reproductive health and fertility research. At the molecular level, insights gained from proteomic studies of tissues and biofluids 1 may contribute to our understanding of the pathophysiology of different biological systems. Despite its importance, the proteome and its time-dependent molecular changes in female reproductive fluids and tissues under physiological and pathological conditions remain relatively underexplored 2 – 4 . Furthermore, despite the accelerated development of proteomics research, the discovery of reliable biomarkers remains challenging, mainly due to methodological and analytical limitations 5 . Cervicovaginal fluid (CVF) is recognised as a relevant biofluid source for biomarker discovery in proteomics because of its continuity with the female reproductive tract and pivotal role in fertility, pregnancy outcomes, and the local immune responses 6 – 6 , 12 , 13 . As a noninvasive source of biological information, CVF reflects physiological and pathological changes, including the menstrual cycle, pregnancy, and disease progression. Recent studies have linked specific proteins in CVF to endometriosis 14 , 15 , pregnancy-related complications 16 , 17 and several cancer types, such as ovarian, endometrial, and cervical malignancies 6 – 11 . High-throughput proteomics has substantially advanced protein identification and quantification. However, most studies rely on the data-dependent acquisition (DDA) of proteins, a time-consuming workflow with limitations in quantitative capabilities, reproducibility, and accuracy, particularly in large-scale longitudinal analyses with repeated measures over time 18 . In the search for new strategies to obtain a more complete proteomic analysis, data-independent acquisition data-independent acquisition (DIA)-based methodologies have recently been developed 19 . For example, Njoku and collaborators 7 recently applied a DIA method using sequential window acquisition of all theoretical fragment ion mass spectra (SWATH-MS) to analyse CVF samples, which enabled the quantification of a greater number of proteins compared with a standard DDA approach. Addressing DDA limitations is important for improving clinical applications of CVF proteomics, particularly through longitudinal cohort studies 20 , 21 that enable the identification of biomarkers and the characterisation of time-dependent pathophysiological changes 22 , 23 . In fact, following biomarker variation over time may reveal molecular signatures linked to specific biological processes, offering additional insight into the course of female reproductive health and disease. Despite its potential, studies applying spectral library-based DIA to CVF are relatively rare compared with studies applying the traditional DDA, which has long been the dominant approach 24 , 25 . Furthermore, to date, no proteomic studies have systematically evaluated longitudinal changes in the CVF proteome across consecutive days of the menstrual cycle. Although SWATH-MS has been explored as an alternative DIA approach to assess the CVF proteome, its reliance on fixed isolation windows may introduce spectral interferences that potentially limit resolution. With respect to new technologies that address this limitation, 4D proteomics, enabled by timsTOF mass spectrometry, integrates trapped ion mobility spectrometry (TIMS) with MS parallel accumulation-serial fragmentation (PASEF), increasing proteome resolution and coverage 26 – 28 . In this context, DIA strategies such as parallel accumulation–serial fragmentation combined with data-independent acquisition (dia-PASEF) have been applied as quantitative research tools 29 for complex biofluids such as plasma, where ionic mobility enables improved separation of ions based on their shape, size, and charge 26 . In this study, we aimed to implement and validate an optimised and reproducible dia-PASEF method for high-throughput time-dependent proteomic analyses of CVF samples collected from menstrual cycles of women participating in the EARLY-PREG longitudinal preconception open cohort 30 . The new dia-PASEF approach was compared with the traditional DDA coupled with the offline peptide fractionation (DDA-FracOffline) method. We hypothesised that the dia-PASEF approach would provide greater proteome coverage and reproducibility compared with traditional DDA, supporting its application for biomarker discovery in longitudinal studies of women’s reproductive health, fertility, and early pregnancy. Methods 1. EARLY-PREG Cohort EARLY-PREG ( ClinicalTrials.gov identifier NCT07358026 ) is a preconception longitudinal open cohort designed to investigate maternal–embryonic communication at the ultra-early stages of conception through the serial and standardised collection of multiple biofluids, including CVF. Within this cohort, synchronised and well-documented samples were used to compare dia-PASEF with the traditional DDA approach coupled with offline peptide fractionation (DDA-FracOffline). 2. CVF longitudinal samples The CVF samples for this proteomics MS implementation and validation study were obtained from the biorepository of the EARLY-PREG preconception cohort. This biorepository includes different biofluids from 223 female volunteers collected daily during their menstrual cycles from the end of the menstrual period, which represents the beginning of a new cycle. Ovulation was determined by quantifying the luteinising hormone (LH) surge in urine. The samples were collected from the day after the end of menstruation until the 14th day post ovulation, as determined by the LH surge in each cycle, using a previously described method 31 which detects ovulation within ±1 day (95.7% confidence). LH concentrations were determined through radioimmunoassay (IRMA) (Barnafi-Krause Laboratory, Santiago, Chile). This approach allowed LH to be defined as a day 0 ( d0 ), which served as a cycle-anchored time reference for synchronising repeated measures across individuals. Participants were grouped into three cycle categories: conception, non-conception, and abstinence cycles. Due to the methodological focus of this validation study, homogeneity was prioritised over sample size. A subgroup of CVF samples from six women (n=6) belonging to non-conception cycles was selected according to criteria that controlled for factors such as body mass index, age, menstrual cycle length, and reproductive history. To enable a within-individual repeated-measures longitudinal analysis, we defined an ovulation-anchored luteal phase window comprising seven consecutive days ( d0–d6 ). Women were selected if at least seven consecutive post-ovulatory CVF samples were available in the biorepository. To minimise intra- and inter-individual variability and reduce heteroscedasticity in repeated-measures analyses, we further prioritised homogeneity within selected individuals by considering menstrual cycle length, menstruation duration, age, sampling completeness across days, and other relevant variables within comparable ranges. Detailed characteristics of the selected participants are provided in Table 1 . View this table: View inline View popup Download powerpoint Table 1: Information on the patients whose samples were used in the proteomic experiments. 3. Protein extraction and processing for MS analysis CVF aliquots (100–500 µL) were thawed on ice and combined with equal volumes of 100 mM NH4HCO3 and 100 mM DTT. The mixture was homogenised by vortexing, incubated at 95 °C for 15 minutes, and cooled on ice for 5 minutes. The samples were then subjected to cold ultrasound disruption at 4 °C (10-second on/off pulses at 50% amplitude) and subsequently centrifuged at 19,000 × g for 20 minutes at 4 °C. The supernatant (soluble phase) was carefully separated, while the pellet (insoluble phase) was retained for further processing. The soluble phase was concentrated in Vivaspin tubes (10,000 MWCO filter, Sartorius), washed with 400 µL of ultrapure water, and centrifuged at 12,000 × g for 10 minutes at 4 °C. The filtrate (10,000 Da) were recovered by washing with ∼600 µL of water, combined, and dried. The insoluble pellet and soluble protein concentrate were resuspended in 8M urea with 25 mM NH₄HCO₃. The mixture was sonicated on ice, and centrifuged at 19,000 × g for 15 minutes at 4 °C. The protein content was quantified using a Qubit protein assay (Invitrogen, #Q33212). For MS preparation, 300 µg of total protein from the soluble and insoluble phases was reduced with 20 mM DTT in 25 mM NH₄HCO₃ at 25 °C for 1 hour. Alkylation was performed with 20 mM iodoacetamide in 25 mM NH₄HCO₃ in the dark for 1 hour. The samples were diluted eightfold with 25 mM NH₄HCO₃ and digested with sequencing-grade trypsin (#V5071, Promega) at a 1:50 protease/protein ratio for 16 hours at 37 °C. Digestion was stopped by adding 10% formic acid. Peptides were cleaned using Sep-Pak C18 Spin columns (Waters), dried in a rotary concentrator at 40 °C, and stored for subsequent MS analysis. 4. DDA-FracOffline 4.1. Peptide Fractionation Offline high-pH reversed-phase fractionation of CVF peptides was performed using a protocol adapted from our previously published tear proteomics workflow 32 . A total of 200 µg of tryptic peptides from soluble and insoluble CVF phases were separated using an ÄKTA avant 25 system with refrigerated fraction collection at 6° C. Peptides were loaded onto a BHE reversed-phase column (2.1 cm × 5 cm, Waters) at pH 10 and a flow rate of 0.2 mL/min. The binary gradient increased linearly from 3% to 40% buffer B (90% ACN in 5 mM NH₄HCO₂) over 30 minutes, followed by increases to 60% over 15 minutes, and to 85% in the final 5 minutes. We collected a total of 24 fractions (400 µL each), which were vacuum-dried and then reconstituted in water containing 2% ACN and 0.1% FA. These fractions were concatenated into 8 final fractions. 4.2. Mass Spectrometry nLC–MS was performed on a NanoElute (Bruker Daltonics) system coupled online with a hybrid TIMS-quadrupole TOF mass spectrometer, timsTOF Pro (Bruker Daltonics, Germany), via a nanoelectrospray ion source (CaptiveSpray, Bruker Daltonics) 33 . For long gradient nLC–MS/MS runs, we followed the standard protocol described by Meier et al., 2021. Approximately 200 ng of peptides were separated onto an Aurora series reversed-phase column (25 cm × 75 µm ID, 1.9 µm, IonOpticks, Australia) at a flow rate of 400 nL/min in an oven compartment heated to 50 °C. For the analysis of CVF off-line fractionated samples, we applied a multi-step gradient beginning with an increase from 2% to 17% B over 60 minutes, progressing to 25% B in 30 minutes, then to 37% B in 10 minutes, and finally to 95% B in 10 min, which was held for 10 min. The column was equilibrated using 4 volumes of solvent A. The mass spectrometer was operated in data-dependent PASEF 34 mode, consisting of 1 TIMS-MS survey and 10 PASEF MS/MS scans per acquisition cycle. Mobility values were acquired across a 1/K0 range of 1.6 to 0.6 Vs cm-2, using equal ion accumulation and ramp time of 100 ms each in the dual-TIMS analyser. 4.3. Data searching Computational processing of DDA data was performed using the FragPipe v19.1 software. The pipeline integrates MSFragger (version 3.7) 35 search engine, for peptide-spectrum matching, Philosopher (version 3.4.13) 36 for FDR control and validation, and EasyPQP ( https://github.com/grosenberger/easypqp ; version 0.1.9) for spectral library generation. Raw (.d) files were used as input for MSFragger searches. Protein identification was conducted against reference proteome of Homo sapiens (UP000005640) from UniProt/SwissProt (reviewed sequences only; downloaded on Feb. 15, 2021), supplemented with database of common contaminant proteins in MS experiments available in FragPipe. Decoy sequences were generated by reversed protein sequence entries to enable target-decoy estimation. For MSFragger analysis, mass tolerance for precursor and (initial) fragments were set to 20 ppm. The enzyme specificity was defined as ‘strict trypsin’, allowing fully tryptic cut and a maximum of two missed cleavages. Carbamidomethylation of cysteine was included as a fixed modification, whereas methionine oxidation and N-terminus acetylation were set as variable modifications. The final spectral library was filtered to 1% protein- and peptide FDR levels. 5. dia-PASEF 5.1 Mass spectrometry The timsTOF Pro was operated in dia-PASEF mode with the following settings: mass range, 100 to 1700 m/z; ion mobility range, 1/k0 start, 0.57 V/cm2, end, 1.47 V/cm2; ramp and accumulation times, 100 ms; capillary voltage, 1600 V; dry gas, 3 L/min; and dry temperature, 160 °C. The dia-PASEF Ion Mobility Parameters were as follows: Each cycle consisted of a one MS1 full scan and 64x MS2 windows covering 400–1200 m/z and 0.57–1.46 1/K0. Each window was 25 Th wide by 0.2 V s/cm2 high. There was an overlap of 0.05 in 1/K0 and no overlap in m/z. The cycle time was 1.80 seconds. The CID collision energy was 20 to 52 eV as a function of the inverse mobility of the precursor 24 . 5.2 Data search For the construction of the DDA spectral library, a pooled of all samples of tryptic-digested peptides was used. For peptide fractionation, mass spectrometry and data searching, the method described in the DDA section was used (see methods sections 3.1, 3.2 and 3.3). Proteomic data analyses were performed on a high-performance computing server with dual AMD EPYC 7552 48-core processors (2.20 GHz) and 512 GB of RAM. Raw dia-PASEF data were processed via DIA-NN v1.7.15 software 37 , using the spectral library generated in the previous step. Mass accuracy thresholds were set to a default average of 10 ppm for MS1 and MS2 spectra. Match-between-runs (MBR) was enabled; quantification mode was set to “any LC (high accuracy)”. All remaining settings were left default. Identifications were filtered using a precursor-level q value < 1% and a global protein-level q value < 1%. 6. Bioinformatics and statistical analysis The intensity matrix reports from MSFragger and DIANN were exported and processed in the R statistical environment. The intensity values for each run were normalised by adjusting the medians. Missing values were imputed via the missForest algorithm 38 , applying the criterion that the intensity value should be present in at least 50% of the samples per analysed category 39 . Longitudinal differentially expressed proteins (DEPs) along the menstrual cycles were represented both as absolute and relative numbers (%), and the significant difference was determined by using a using Bayesian t-tests for pairwise day comparisons (e.g., d0 vs. dn, d1 vs. dn), considering the normalised intensity means of biological replicates 40 . AProteins were considered statistically significant at p < 0.05. Exploratory analysis and data visualisation were performed in R (v.3.6.0) using EnhancedVolcano (v.1.2.0), ComplexHeatmap (v.2.0.0), and standard base packages. The coefficients of variation were determined using the statistically significant differential expression values of each methodology for both dia-PASEF and DDA-FracOffline. Quantifiable proteins were defined as those that were consistently detected across each condition and presented measurable relative abundance values. 7. Pathway and tissue enrichment analysis Pathway enrichment analysis was developed using ReactomePA (v.1.42.0), which queries the human database with log2 fold change (logFC) and p value data obtained for each statistical comparison. For the analysis of enrichment by human systems, tissues and cells, we used the TISSUES 2.0 database 41 , considering the same statistical parameters used for the pathway analysis through topGO (v.2.5.0). Boxplots were generated to represent the distribution of protein intensities using ggpubr (v.0.6.0). Statistical comparisons between groups were conducted using t tests and chi-square tests with rstatix (v.0.7.2). Adjusted p values were calculated without additional correction, and significance levels were directly annotated on the graphs. The z score was calculated based on the logFC of each protein belonging to the designated reactome and tissue categories using the GOplot (v.1.0.2) package. Importantly, this z score is not equivalent to the conventional z score; instead, it provides a directional estimate indicating whether a biological process, molecular function, or cellular component tends to be upregulated (positive value) or downregulated (negative value). The z score was determined based on the number of proteins assigned as upregulated (logFC > 0) and those classified as downregulated (log FC < 0), labelled as “up” and “down” in the dataset. 8. Biomarker selection To assess the efficiency of dia-PASEF in biomarker detection, an FDA-proposed biomarker panel derived from a previously published study 42 , was used to directly compare the ability of dia-PASEF and DDA-FracOffline to detect and quantify these clinically relevant proteins. In addition, we conducted an extensive literature-based curation of candidate proteins associated with physiological and pathological conditions of the female reproductive tract. Searches were performed using keywords including “menstrual cycle”, “ovarian carcinoma”, “endometrial carcinoma”, “cervical carcinoma”, and “endometriosis”, integrating evidence from proteomic, transcriptomic, and genomic studies. 9. Data availability Proteomics data can be accessed through the ProteomeXchange Consortium via PRIDE, with the dataset identifier PXD071350 and PXD071584. 10. Ethics approval and consent to participate The EARLY-PREG protocol was approved by the Ethics Committee of the Servicio de Salud de Concepción, Chile (CEC: 17-03-06). All participants provided written informed consent prior to enrolment. All procedures were conducted in accordance with the Declaration of Helsinki and Good Clinical Practice guidelines. RESULTS 1. Patient Characterization and Sampling After screening all non-conception fertile cycles of women with complete records in the EARLY-PREG cohort database, we selected six women with homogeneity in terms of age, weight, height, BMI, waist circumference, age at menarche, menstrual cycle length, and menstruation length. The length of the menstrual phase of the cycle (5.8 ± 0.4), the length of the fertile cycle (29.0 ± 3.0), age (32.3 ± 3.6), and other variables were within comparable ranges. Detailed participant characteristics are provided in Table 1 . Forty-two CVF samples (n=42) stored at -80 °C were processed for protein extraction and tryptic digestion and then analysed with the two mass spectrometry methodologies ( Figure 1A ) as described in the Methods. To minimise batch effects, all 42 samples were analysed on the timsTOF Pro mass spectrometer within the same calibration batch. This included 336 chromatographic runs using the DDA approach and 42 direct runs using dia-PASEF over a two-week period. Thus, with each protocol, we obtained a 7-day time series ( d0 to d6 ) of CVF proteomes for each woman. For each analytical workflow, this design yielded a within-individual repeated-measures time series comprising a 7-day ovulation-anchored window (d0–d6) of CVF proteomes per participant. Download figure Open in new tab Figure 1. Proteomics workflow for the longitudinal repeated-measures analysis of CVF. Schematic representation of the experimental design and strategies used to analyse CVF samples from women in the EARLY-PREG cohort. (A) CVF samples were collected across a 7-day ovulation-anchored window (d0–d6) of the menstrual cycle, enabling within-individual repeated-measures analysis. (B) Two nLC-MS/MS proteomics approaches were compared: DDA-FracOffline and dia-PASEF. Both workflows included protein extraction, digestion, and mass spectrometry analysis using the nanoElute system coupled to a timsTOF Pro instrument. The right panel summarises the comparative evaluation of both methods, including missing values, number of quantified proteins, coefficient of variation, abundance rank distribution, and their ability to identify putative biomarkers and capture time-dependent longitudinal proteomic changes. 2. Quality Control Factors analysed to compare DDA-FracOffline and the dia-PASEF We conducted a comparative analysis focusing on key quality control parameters, including reproducibility, the proportion of missing values, and protein identification and quantification performance. First, reproducibility was assessed using correlation coefficients between biological replicates. The mean correlation coefficient was approximately 0.70 for DDA-FracOffline and 0.90 for dia-PASEF (Figure S1). Second, we evaluated the proportion of missing values across samples. The dia-PASEF workflow showed a substantially lower proportion of missing values compared with DDA-FracOffline, which reached up to 75% at its lowest detection point ( Figure 2B ). Third, correlation analysis across samples demonstrated lower inter-sample consistency for DDA-FracOffline relative to dia-PASEF ( Figure 2A ). Next, we analysed protein identification and quantification performance. DDA-FracOffline identified a total of 9,593 proteins, whereas dia-PASEF identified 5,986 proteins. However, when considering quantifiable proteins (identified and consistently measured across samples), DDA-FracOffline quantified 2,817 proteins, whereas dia-PASEF quantified 4,229 proteins ( Figure 2E , left), corresponding to a 50% higher quantification yield for dia-PASEF. The mean coefficient of variation (CV) for quantifiable proteins—calculated as the average of individual protein CVs across all samples—was 5.27 ± 0.68% for dia-PASEF and 9.37 ± 1.75% for DDA-FracOffline ( Figure 2E , centre) indicating improved quantitative consistency in the dia-PASEF workflow. Finally, across the ovulation-anchored time series (d0–d6), DDA-FracOffline identified between 1,491 and 7,183 proteins across time points ( Figure 2C ), whereas dia-PASEF detected between 3,811 and 5,398 proteins across time points ( Figure 2D ). Although DDA-FracOffline showed broader overall protein identification, dia-PASEF demonstrated greater consistency in protein detection across the ovulation-anchored repeated-measures time series. Download figure Open in new tab Figure 2. Quality control of proteomic data obtained by DDA-FracOffline and dia-PASEF. (A) Pearson correlations between chromatographic runs, positive correlations (red) and no correlations (white). (B) Bar plots of the proportion of missing values (%MVs) for each sample, showing present values (blue) and missing values (red). (C-D) Number of identified proteins per sample: green bars correspond to DDA-FracOffline (C), and orange bars correspond to dia-PASEF (D). (E) Overview of identified and quantifiable proteins: bar plots showing the total numbers of identified proteins (left) and quantifiable proteins (centre) for DDA-FracOffline (green) and dia-PASEF (orange). The violin plot (right) represents the coefficient of variation (%CV) for each method. The Venn diagram illustrates the shared and unique proteins identified by each analytical strategy. (F) Number of differentially expressed proteins (DEPs) identified by each strategy through longitudinal comparisons at day d0. The bar plot displays the total number of DEPs obtained by each strategy, indicating the statistical significance (***, p value < 0.001) above the bars. The statistical significance was determined using chi-square tests for categorical variables. DEPs were identified using DDA-FracOffline (green) and dia-PASEF (orange). 2.1. Differential Protein Expression Following quality control, we assessed the time-dependent dynamics of differentially expressed proteins (DEPs) across the menstrual cycle to characterise variations in protein abundance in CVF within the ovulation-anchored, repeated-measures 7-day window ( d0–d6 ). Comparative analyses revealed significant differences between methodologies in the detection and quantification of DEPs across longitudinal CVF samples (Figure S2). At most time points, dia-PASEF identified higher absolute and relative numbers of DEPs than did DDA-FracOffline, e.g. 985 (23%) vs. 251 (8.9%) between d0 and d1 and 1,057 (24.9%) vs 161 (5.7%) between d0 and d2 . From d0 to d6, dia-PASEF maintained a more stable detection rate ( Figure 2F ). These findings indicate that dia-PASEF enables more consistent detection and quantification of differentially expressed proteins in longitudinal repeated-measures designs, facilitating the capture of time-dependent proteomic changes across the cycle ( Figure 2F and Figure S2). 3. Longitudinal enrichment analysis of pathways, systems, tissues and cells To interpret the functional relevance of the quantified proteins, we performed tissue and pathway enrichment analyses. We evaluated tissue representation based on DEPs identified across longitudinal CVF comparisons within the ovulation-anchored repeated-measures window (Figure S3). The analysis revealed enrichment in tissues associated with the female reproductive system, including the uterus and uterine cervix, as well as immune-related tissues. The dia-PASEF workflow demonstrated broader and more consistent tissue coverage, particularly at later time points ( d5 and d6 ), with elevated enrichment scores (-log10 adjusted p value) and the highest protein densities. We further examined the biological processes associated with the differential protein profiles detected across the 7-day window. Common pathways identified with both strategies between time points d0 to d6 ( Figure 3 ) included ’Formation of the cornified envelope’, ’Metabolism of proteins’, and ’Processing of capped intron-containing pre-mRNA’. Additional pathways, such as ’Signalling by RHO GTPases’ and ’Mitotic Metaphase/Anaphase Transition’ were consistent with cell signalling and cell-cycle–related processes ( Figure 3 ). Compared with DDA-FracOffline, dia-PASEF showed increased pathway coverage and enrichment across the time-dependent series. Download figure Open in new tab Figure 3. Differentially expressed proteins and biological pathways identified via longitudinal analysis using DDA-FracOffline and dia-PASEF. Reactome enrichment analysis, emphasising the most abundant biological pathways. DEPs were derived from longitudinal comparisons within each method. The Y-axis includes Reactome pathways and X-axis represents the activation coefficient (Z score) of each pathway. 4. Identification of Physiological and pathological biomarkers We evaluated the ability of each strategy to identify and quantify known biomarkers of physiological and pathological processes. We began with a set of disease biomarkers proposed by the U.S. Food and Drug Administration (FDA-proposed biomarkers) 42 , focusing on fluids related to various pathologies and examining literature-curated sets of proteins related to female reproductive-tract pathophysiology (Table S1). 4.1 Comparison of FDA-proposed biomarkers via DDA-FracOffline and dia-PASEF The FDA-proposed biomarker panel comprised 121 proteins detectable in biological fluids and associated with female reproductive pathologies 42 . Figure 4 compares the ability of each strategy to detect these biomarkers in CVF. Dia-PASEF and DDA-FracOffline identified 91 and 76 of the 121 proteins, corresponding to 75% and 63%, respectively; 92 proteins (76%) were identified by both strategies ( Figure 4A ). A protein rank-abundance plot demonstrated that dia-PASEF captured proteins across a broader dynamic range compared with DDA-FracOffline ( Figure 4B ). Fifteen FDA biomarkers were unique to dia-PASEF (NT5E, ACE, GLA, C4A, C1QC, ACP5, PROS1, BRAF, VWF, GALT, MSH2, TFRC, TOP2A, and BTD), whereas the protein GFAP was detected only by DDA-FracOffline ( Figure 4B ). Overall, dia-PASEF showed improved quantification consistency and reproducibility relative to DDA-FracOffline for the detection of clinically relevant biomarker candidates in CVF. 4.2 Selected biomarkers from literature review Download figure Open in new tab Figure 4. Biomarker identification and longitudinal protein analysis from d0 to d6 by DDA-FracOffline and dia-PASEF. (A) Venn diagram illustrating the overlap of proteins identified and FDA-approved biomarkers, highlighting 76 shared proteins and unique identifications for each strategy. (B) Abundance rank plot of proteins identified in CVF samples via DDA-FracOffline alone (green), dia-PASEF alone (orange), and both strategies (purple). FDA approved biomarker proteins are highlighted. The right panel displays the density distribution of protein abundances. (C, D) Box plots show longitudinal expression patterns of the pregnancy-pathology-associated gene IGFBP4 and the physiological protein MUC-1. (E, F) Box plots of MUC16 and WFDC2. (***, p value < 0.001; **, p value < 0.01; *, p value < 0.05) (G) Trajectory of the longitudinal expression of IGFBP4, MUC1, MUC16 and WFDC2. A literature-based curated list of biomarkers previously reported in pregnancy, cervical, ovarian, and endometrial carcinomas, as well as endometriosis, is provided in Table 2 . We examined the time-dependent behaviour of four representative biomarkers selected from the curated list to illustrate the longitudinal detection capability of the dia-PASEF workflow ( Figure 4C–G ). To illustrate the longitudinal repeated-measures capability of dia-PASEF in detecting clinically relevant proteins, we tracked insulin-like growth factor-binding protein 4 (IGFBP-4), implicated in early pregnancy and foetal growth restriction; MUC1, a mucin contributing to reproductive tract integrity; and the ovarian cancer biomarkers CA-125 (MUC16) and HE4 (WFDC2). Insulin-like growth factor-binding protein 4 (IGFBP4) has been associated with early pregnancy and foetal growth restriction ( Figure 4C , Figure S4). MUC1, expressed throughout the female reproductive tract—including endocervical, ectocervical, and vaginal epithelia—plays a role in maintaining epithelial barrier function ( Figure 4D , Figure S4). Both proteins are physiologically expressed across the menstrual cycle. In addition, clinically relevant biomarkers CA-125 ( Figure 4E , Figure S4) and HE4 ( Figure 4F , Figure S4), encoded by MUC16 and WFDC2 respectively, were consistently detected. Longitudinal analysis across the ovulation-anchored repeated-measures window ( d0–d6 ) revealed significant day-to-day variation in all four proteins ( Figure 4C–G ; Figure S4), supporting the suitability of dia-PASEF for time-dependent biomarker profiling. View this table: View inline View popup Download powerpoint Table 2: Summary of biomarkers associated with female pathologies. Discussion CVF is well established as a valuable biological source for research on the pathophysiology of the female reproductive tract. This biofluid contributes to multiple biological processes, including barrier function, immune responses, microbiota preservation, sperm transport, and menstrual cycle regulation. The emergence of advanced proteomics workflows and MS instrumentation provides opportunities for large-scale identification of reproductive health biomarkers in CVF 5 . The present study evaluated the performance of dia-PASEF as a high-throughput proteomic workflow for analysing CVF in longitudinal repeated-measures designs with time-series sampling. To our knowledge, this is among the first applications of dia-PASEF to CVF collected across consecutive days of the menstrual cycle, showing improved performance relative to DDA-FracOffline. We observed greater quantification depth, reproducibility, and sensitivity for low-abundance proteins following comprehensive comparative analyses. The reduced variability (CV = 5.27 ± 0.68%) and lower proportion of missing values enabled more consistent quantification across samples, improving the robustness of longitudinal analyses compared with DDA-FracOffline ( Figure 2 ). These findings indicate that dia-PASEF supports reliable tracking of protein abundance over time, facilitating the investigation of time-dependent molecular changes in CVF. Studies of longitudinal effects are essential for assessing the development of physiological phenomena over time, such as ultra-early pregnancy, during which the earliest stages remain poorly understood. These analyses are also relevant for monitoring disease progression and therapeutic responses from a pathophysiological perspective. However, longitudinal analysis of CVF remains technically challenging due to the intrinsic complexity of this biofluid and the limited availability of well-characterised repeated-measures datasets spanning the menstrual cycle. In this context, the application of advanced acquisition strategies such as dia-PASEF represents a methodological advancement, as it provides analytical features suited for complex biofluids and improved detection of low-abundance proteins in time-dependent study designs. We observed a shorter data processing time for dia-PASEF (a few minutes per sample) than for DDA-FracOffline (approximately 1–2 hours per sample), supporting its suitability for high-throughput longitudinal repeated-measures proteomic studies. The higher reproducibility observed with dia-PASEF is consistent with its systematic precursor selection strategy, which enhances proteome depth and sensitivity 77 . In contrast, the sequential precursor selection inherent to DDA-FracOffline is associated with greater variability and reduced efficiency in protein sampling, limiting its applicability in large-scale and time-dependent study designs. The ability of dia-PASEF to capture comprehensive fragmentation data within a single acquisition cycle contributes to improved protein identification, reduced missing values, and broader proteome coverage. The greater quantitative stability observed with dia-PASEF further supports its application in large-scale longitudinal proteomics, in line with previous reports describing the analytical advantages of data-independent acquisition strategies 26 . Although both methodologies allow in-depth protein identification, we observed that dia-PASEF enabled more consistent protein quantification than DDA-FracOffline, primarily because the latter showed lower quantifiable protein coverage and a higher proportion of missing peptides 29 . When comparing dia-PASEF with other DIA approaches applied to CVF, such as SWATH-MS–based methods 7 , dia-PASEF identified and quantified a substantially higher number of proteins. Previous proteomic studies using SWATH-MS in CVF have reported limitations including reduced sensitivity for low-abundance proteins, peptide co-fragmentation effects 26 , and higher proportions of missing values. Enrichment analyses enabled exploration of the functional relevance of identified proteins contributing to CVF pathophysiology ( Figure 3 ). Both strategies highlighted pathways related to cellular signalling, protein metabolism, immune responses, interleukin signalling, mitotic metaphase–anaphase transition, mRNA splicing, and RHO GTPase signalling, which are implicated in reproductive system biology 78 , 79 . dia-PASEF showed higher enrichment scores across the time-dependent window, suggesting improved biological coverage. For example, neutrophil gelatinase-associated lipocalin (NGAL; encoded by LCN2), involved in neutrophil degranulation 15 signalling pathways, was consistently detected ( Figure 3 , Figure 4H ). Tissue enrichment analysis confirmed the biological relevance of the identified proteins, highlighting associations with tissues of the female reproductive system 80 , including the uterus and uterine cervix. These findings are consistent with the ability of dia-PASEF to provide system-specific sensitivity and time-dependent biological profiling, particularly for reproductive and immune-related functions in longitudinal CVF proteomics. We compared proteins identified by dia-PASEF and DDA-FracOffline with the FDA-proposed biomarker panel 42 . Using the dia-PASEF workflow, we identified 15 unique quantifiable FDA-proposed biomarkers, compared with one unique quantifiable biomarker detected using DDA-FracOffline ( Figure 4A ). Dia-PASEF identified biomarkers across a broader dynamic range, including both high- and low-abundance proteins ( Figure 4B ), supporting its suitability for comprehensive biomarker profiling in CVF. These findings indicate improved detection performance of dia-PASEF for FDA-proposed biomarkers within this longitudinal repeated-measures design. Proteins detected at low abundance were linked to biological processes relevant to menstrual cycle regulation and gynaecological diseases. For example, C1QC and VWF are involved in immune responses and vascular regulation. A broader and more uniform dynamic range was observed with dia-PASEF, likely reflecting improved signal-to-noise performance, a feature that is advantageous for longitudinal analyses in complex biofluids 34 such as CVF. As an additional strategy for evaluating clinically relevant biomarkers, dia-PASEF facilitated the detection of low-abundance proteins across the time-dependent repeated-measures window. IGFBP-4 ( Figure 4C ), whose levels have been associated with early pregnancy and foetal growth restriction 48 , 50 and MUC1 ( Figure 4D ), a mucin expressed throughout the female reproductive tract—including endocervical, ectocervical, and vaginal epithelia—were consistently detected. MUC1 plays a role in maintaining epithelial integrity and barrier function 62 , 63 . We also detected CVF proteins associated with pathological processes, including alpha-actinin-4 (ACTN4) and annexin A2 (ANXA2), which have been reported in previous studies 51 – 53 ( Table 2 ). In the context of gynaecological carcinomas, two clinically relevant proteins were identified: cancer antigen 125 (CA-125), encoded by the MUC16 gene ( Figure 4E ), and human epididymis protein 4 (HE4), encoded by the WFDC2 gene ( Figure 4F ). These proteins were detected in CVF samples collected across the ovulation-anchored window ( d0–d6 ). Both biomarkers have been widely investigated in ovarian 58 , 59 and endometrial 64 , 65 carcinoma. Longitudinal analysis revealed significant day-to-day variation across the repeated-measures window (Figure S4). These findings suggest that dia-PASEF facilitates time-dependent monitoring of clinically relevant proteins in CVF, supporting its potential application in studies of pathologies related to the female reproductive tract. Proteins present in CVF may also represent promising candidates for identifying longitudinal pathophysiological markers. Owing to its reduced proportion of missing values and more consistent time-dependent protein profiling, dia-PASEF generated a more complete quantitative dataset ( Figure 2B ). This characteristic supports its application for tracking protein dynamics over time in physiological processes, such as endometrial receptivity, as well as in pathological contexts. A consistent pattern of protein variation was observed across specific days of the cycle ( Figure 4G , Figure S4), providing insight into defined physiological windows within the ovulation-anchored repeated-measures framework. These findings support the feasibility of longitudinal biomarker monitoring in CVF, including potential applications in therapeutic follow-up studies. This study is focused primarily on achieving methodological robustness to enable a reliable comparison between strategies and to highlight the advantages of the dia-PASEF approach. Despite the limited sample size, our parsimonious approach 81 minimises variability by selecting a homogeneous subgroup of volunteers based on exclusion criteria that control for factors such as body mass index, age, menstrual cycle length, and reproductive history. The participants were also synchronised according to the surge in luteinising hormone from d0 to d6 (peri-ovulatory to early luteal phase of menstruation), which minimised interindividual variability. This approach enabled the selection of volunteers with complete repeated-measures data for implementation and validation of the proteomic workflows, thereby minimising technical and biological noise in biomarker identification. By using a within-individual longitudinal framework with a semi-counterfactual structure, this design further reduced the influence of population heterogeneity. Owing to the methodological scope and limited sample size of the study, the findings are not directly generalisable to pathological conditions. However, future expansion of the cohort to include participants with defined pathologies will be necessary to evaluate the potential of dia-PASEF in identifying disease-specific biomarkers. In addition, functional validation of candidate biomarkers will be essential to clarify how these proteins are involved in specific physiological or pathological processes and to assess their potential clinical relevance This study demonstrates the suitability of dia-PASEF as a robust analytical platform for longitudinal repeated-measures CVF proteomics, addressing limitations inherent to DDA-based workflows and supporting biomarker discovery in fertility, early pregnancy, and gynaecological research. Overall, this approach provides a scalable methodological framework for time-dependent proteomic investigations in complex reproductive biofluids. Author’s roles E.S.K. led the study. M.H. and E.S.K. drafted the manuscript and conceptualised the study. E.S.K., M.H., and C.V. contributed to the study design. M.H. designed, supervised, and performed the proteomics methodology. P.S., B.A., and V.L. contributed to study conception. M.H., P.S., and G.N. developed the experimental system, performed the experiments, and analysed the data. F.C. and G.N. supported the experimental work. M.H., P.S., B.A., and V.L. reviewed and commented on manuscript drafts. E.S.K. revised the manuscript and approved the final version. E.S.K. is the guarantor of the study and accepts full responsibility for the integrity of the work. Data Availability All data produced in the present study are available upon reasonable request to the authors Funding This work forms part of the EARLY-PREG preconception open cohort and has been supported by research grants awarded by Fundación de Investigación San Ramón (FISAR), based in Chile. The pilot study and first recruitment wave were supported by grants #MEL109112011 and #MEL109112011R4 awarded to E.S.K., C.V., and J.F.S. The second recruitment wave was supported by grants #MEL109112011R5 and #MEL131032017R1 awarded to E.S.K. The third wave was supported by grant #MEL205062018 awarded to E.S.K. and M.H. Current funding for the fourth recruitment wave and mass spectrometry research on maternal CVF is supported by grant REH042024-01 awarded to M.H., G.N., and E.S.K. Conflict of interest E.S.K. serves in an unpaid honorary capacity as a research advisor and reviewer for Fundación de Investigación San Ramón (FISAR). No other competing interests are declared. Acknowledgements The authors thank Dr. Ute Woehlbier (Center for Integrative Biology, Universidad Mayor) for English language editing. We also acknowledge Estefanía Nova-Lamperti (Molecular and Translational Immunology Laboratory, Department of Clinical Biochemistry and Immunology, Faculty of Pharmacy, Universidad de Concepción, Chile) for assistance with figure design. I.F.-M. received professional service fees from Fundación de Investigación San Ramón (FISAR), Concepción, Chile. 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Share High-throughput dia-PASEF workflow improves proteome coverage and quantitative performance in repeated-measures proteomics of cervicovaginal fluid within a preconception cohort Mauricio Hernandez , Pablo Saldivia , Veronica Latapiat , Barbara Antilef , Guillermo Nourdin , Felipe Castro , Cristian Vargas , Elard S. Koch medRxiv 2025.05.19.25327800; doi: https://doi.org/10.1101/2025.05.19.25327800 Share This Article: Copy Citation Tools High-throughput dia-PASEF workflow improves proteome coverage and quantitative performance in repeated-measures proteomics of cervicovaginal fluid within a preconception cohort Mauricio Hernandez , Pablo Saldivia , Veronica Latapiat , Barbara Antilef , Guillermo Nourdin , Felipe Castro , Cristian Vargas , Elard S. 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