Optimization of SELDI-TOF protein profiling for analysis of cervical mucous

Proteome Mapping of Cervical Mucus and Its Potential as a Source of Biomarkers in Female Tract Disorders

Cervical mucus (CM) is a viscous fluid that is produced by the cervical glands and functions as a uterine cervix plug. Its viscosity decreases during ovulation, providing a window for non-invasive sampling. This study focuses on proteomic characterization of CM to evaluate its potential as a non-invasively acquired source of biomarkers and in understanding of molecular (patho)physiology of the female genital tract. The first objective of this work was to optimize experimental workflow for CM processing and the second was to assess differences in the proteomic composition of CM during natural ovulatory cycles obtained from intrauterine insemination (IUI) cycles and in vitro fertilization (IVF) cycles with controlled ovarian hyperstimulation. Proteomic analysis of CM samples revealed 4370 proteins involved in processes including neutrophil degranulation, cellular stress responses, and hemostasis. Differential expression analysis revealed 199 proteins enriched in IUI samples and 422 enriched in IVF. The proteins enriched in IUI were involved in phosphatidic acid synthesis, responses to external stimulus, and neutrophil degranulation, while those enriched in IVF samples were linked to neutrophil degranulation, formation of a cornified envelope and hemostasis. Subsequent analyses clarified the protein composition of the CM and how it is altered by hormonal stimulation of the uterus.

Light Microscopy and Proteomic Patterns of Ovulation in Cervical Mucus

There is an increasing number of couples interested in identifying the fertile window for the purpose of conceiving. From what has been published so far, it can be concluded that there are no reliable methods to predict ovulation, and, therefore, to predict the fertile window. Proteins of the cervical mucus (CM) could behave as biomarkers to allow the early and precise identification of ovulation. CM samples were collected from the lumen of the cervical canal from women of reproductive age, on three different days of the same menstrual cycle. Samples were first analyzed and classified by light microscopy. High-resolution mass spectrometry and bioinformatic analysis were performed afterwards to determine the in vivo changes of CM protein composition. CM underwent cyclical changes in its biophysical composition, which were evidenced by changes in the crystallographic patterns observed under the light microscope. The proteomic analysis revealed changes in the protein composition of CM along the cycle. Twenty-five out of the forty-eight total proteins identified could become potential biomarkers of ovulation. The coordinated changes in the composition of the CM around the time of ovulation could be happening to specifically grant access to a foreign body, such as the sperm might be.

Insights on Proteomics-Driven Body Fluid-Based Biomarkers of Cervical Cancer

Cervical cancer is one of the top malignancies in women around the globe, which still holds its place despite being preventable at early stages. Gynecological conditions, even maladies like cervical cancer, still experience scrutiny from society owing to prevalent taboo and invasive screening methods, especially in developing economies. Additionally, current diagnoses lack specificity and sensitivity, which prolong diagnosis until it is too late. Advances in omics-based technologies aid in discovering differential multi-omics profiles between healthy individuals and cancer patients, which could be utilized for the discovery of body fluid-based biomarkers. Body fluids are a promising potential alternative for early disease detection and counteracting the problems of invasiveness while also serving as a pool of potential biomarkers. In this review, we will provide details of the body fluids-based biomarkers that have been reported in cervical cancer. Here, we have presented our perspective on proteomics for global biomarker discovery by addressing several pertinent problems, including the challenges that are confronted in cervical cancer. Further, we also used bioinformatic methods to undertake a meta-analysis of significantly up-regulated biomolecular profiles in CVF from cervical cancer patients. Our analysis deciphered alterations in the biological pathways in CVF such as immune response, glycolytic processes, regulation of cell death, regulation of structural size, protein polymerization disease, and other pathways that can cumulatively contribute to cervical cancer malignancy. We believe, more extensive research on such biomarkers, will speed up the road to early identification and prevention of cervical cancer in the near future.

Candidate biomarkers in the cervical vaginal fluid for the (self-)diagnosis of cervical precancer

Purpose

Despite improvement in vaccines against human papilloma virus (HPV), the causative agent of cervical cancer, screening women for cervical precancer will remain indispensable in the coming 30–40 years. A simple test that could be performed at home or at a doctor’s practice and that informs the woman whether she is at risk would significantly help make a broader group of patients who aware that they need medical treatment. Cervical vaginal fluid (CVF) is a body fluid that is very well suited for such a test.

Methods

Narrative review of cervical (pre)cancer candidate biomarkers from cervicovaginal fluid, is based on a detailed review of the literature. We will also discuss the possibilities that these biomarkers create for the development of a self-test or point-of-care test for cervical (pre)cancer.

Results

Several DNA, DNA methylation, miRNA, and protein biomarkers were identified in the cervical vaginal fluid; however, not all of these biomarkers are suited for development of a simple diagnostic assay.

Conclusions

Proteins, especially alpha-actinin-4, are most suited for development of a simple assay for cervical (pre)cancer. Accuracy of the test could further be improved by combination of several proteins or by combination with a new type of biomarker, e.g., originating from the cervicovaginal microbiome or metabolome.

Characterization of the cervical mucus plug in mares

The cervical mucus plug (CMP) is believed to play an integral role in the maintenance of pregnancy in the mare, primarily by inhibiting microbial entry. Unfortunately, very little is known about its composition or origin. To determine the proteomic composition of the CMP, we collected CMPs from mares (n = 4) at 9 months of gestation, and proteins were subsequently analyzed by nano-LC–MS/MS. Results were searched against EquCab2.0, and proteomic pathways were predicted by Ingenuity Pathway Analysis. Histologic sections of the CMP were stained with H&E and PAS. To identify the origin of highly abundant proteins in the CMP, we performed qPCR on endometrial and cervical mucosal mRNA from mares in estrus, diestrus as well as mares at 4 and 10 m gestation on transcripts for lactotransferrin, uterine serpin 14, uteroglobin, uteroferrin, deleted in malignant brain tumors 1 and mucins 4, 5b and 6. Overall, we demonstrated that the CMP is composed of a complex milieu of proteins during late gestation, many of which play an important role in immune function. Proteins traditionally considered to be endometrial proteins were found to be produced by the cervical mucosa suggesting that the primary source of the CMP is the cervical mucosa itself. In summary, composition of the equine CMP is specifically regulated not only during pregnancy but also throughout the estrous cycle. The structural and compositional changes serve to provide both a structural barrier as well as a physiological barrier during pregnancy to prevent infection of the fetus and fetal membranes.

Proteomic characterization of the qualitative and quantitative differences in cervical mucus composition during the menstrual cycle

The chemical composition of the cervical mucus (CM), its physical characteristics and the volume of secretion change cyclically throughout the menstrual cycle.

Sensitive and specific peak detection for SELDI-TOF mass spectrometry using a wavelet/neural-network based approach

SELDI-TOF mass spectrometer's compact size and automated, high throughput design have been attractive to clinical researchers, and the platform has seen steady-use in biomarker studies. Despite new algorithms and preprocessing pipelines that have been developed to address reproducibility issues, visual inspection of the results of SELDI spectra preprocessing by the best algorithms still shows miscalled peaks and systematic sources of error. This suggests that there continues to be problems with SELDI preprocessing. In this work, we study the preprocessing of SELDI in detail and introduce improvements. While many algorithms, including the vendor supplied software, can identify peak clusters of specific mass (or m/z) in groups of spectra with high specificity and low false discover rate (FDR), the algorithms tend to underperform estimating the exact prevalence and intensity of peaks in those clusters. Thus group differences that at first appear very strong are shown, after careful and laborious hand inspection of the spectra, to be less than significant. Here we introduce a wavelet/neural network based algorithm which mimics what a team of expert, human users would call for peaks in each of several hundred spectra in a typical SELDI clinical study. The wavelet denoising part of the algorithm optimally smoothes the signal in each spectrum according to an improved suite of signal processing algorithms previously reported (the LibSELDI toolbox under development). The neural network part of the algorithm combines those results with the raw signal and a training dataset of expertly called peaks, to call peaks in a test set of spectra with approximately 95% accuracy. The new method was applied to data collected from a study of cervical mucus for the early detection of cervical cancer in HPV infected women. The method shows promise in addressing the ongoing SELDI reproducibility issues.

Protein profiling underscores immunological functions of uterine cervical mucus plug in human pregnancy

The cervical mucus plug (CMP) differs from the cervical secretions of non-pregnant women, and is the ultimate sealant of the uterine cavity during pregnancy. Although several studies have analyzed biochemical properties of large glycoproteins in the CMP, comprehensive information about its protein composition is yet unavailable. We hypothesized that protein profiling of the CMP could provide key clues to its physiological functions in pregnancy. For this purpose, five CMPs obtained from women in labor at term were analyzed by LC-MS/MS. Out of 291 total proteins identified, 137 were detected in two or more samples, which included S100A8, S100A9, and complement proteins (C3, C4a, C4b, C6, and C8g). Several proteins, which have not been described in the cervical mucus of non-pregnant women or in cervicovaginal fluids, such as CD81 antigen and pregnancy zone protein, were also identified. Gene ontology analysis of identified proteins showed significant enrichment of 28 biological processes such as 'activation of plasma proteins involved in acute inflammatory response' and 'positive regulation of cholesterol esterification'. We report the proteome of CMPs from pregnant women at term for the first time, and the overall findings strongly suggest an important role for the CMP in the maintenance of pregnancy and parturition.

A protein profile study to discriminate CIN lesions from normal cervical epithelium

Background

Cervical intraepithelial neoplasia (CIN), a frequently encountered disease caused by Human Papilloma Virus (HPV) is often diagnosed in formaldehyde-fixed paraffin embedded (FFPE) punch biopsies. Since it is known that this procedure strongly affects the water-soluble proteins contained in the cervical tissue we decided to investigate whether a water-soluble protein-saving biopsy processing method can be used to support the diagnosis of normal and CIN.

Methods

Cervical punch biopsies from 55 women were incubated for 24 h at 4°C in RPMI1640 medium for protein analysis prior to usual FFPE processing and p16 and Ki67-supported histologic consensus diagnosis was assessed. The biopsy supernatants were subjected to surface-enhanced laser desorption-ionization time of flight mass spectrometry (SELDI-TOF MS) for identifying differentially expressed proteins. Binary logistic regression and classification and regression trees (CART) were used to develop a classification model.

Results

The age of the patients ranged from 26 to 40 years (median 29.7). The consensus diagnoses were normal cervical tissue (n = 10) and CIN2-3 (n = 45). The mean protein concentration was 1.00 and 1.09 mg/ml in the normal and CIN2-3 group, respectively. The peak detection and clustering process resulted in 40 protein peaks. Many of these peaks differed between the two groups, but only three had independent discriminating power. The overall classification results were 88%.

Conclusions

Water-soluble proteins sampled from punch biopsies are promising to assist the diagnosis of normal and CIN2-3.

A magnetic bead-based serum proteomic fingerprinting method for parallel analytical analysis and micropreparative purification

ProteinChip surface-enhanced laser desorption/ionization technology and magnetic beads-based ClinProt system are commonly used for semi-quantitative profiling of plasma proteome in biomarker discovery. Unfortunately, the proteins/peptides detected by MS are non-recoverable. To obtain the protein identity of a MS peak, additional time-consuming and material-consuming purification steps have to be done. In this study, we developed a magnetic beads-based proteomic fingerprinting method that allowed semi-quantitative proteomic profiling and micropreparative purification of the profiled proteins in parallel. The use of different chromatographic magnetic beads allowed us to obtain different proteomic profiles, which were comparable to those obtained by the ProteinChip surface-enhanced laser desorption/ionization technology. Our assays were semi-quantitative. The normalized peak intensity was proportional to concentration measured by immunoassay. Both intra-assay and inter-assay coefficients of variation of the normalized peak intensities were in the range of 4-30%. Our method only required 2 microL of serum or plasma for generating enough proteins for semi-quantitative profiling by MALDI-TOF-MS as well as for gel electrophoresis and subsequent protein identification. The protein peaks and corresponding gel spots could be easily matched by comparing their intensities and masses. Because of its high efficiency and reproducibility, our method has great potentials in clinical research, especially in biomarker discovery.

Characterization of the Human Cervical Mucous Proteome

Introduction

Cervical cancer is among the most common cancers in women worldwide. Discovery of biomarkers for the early detection of cervical cancer would improve current screening practices and reduce the burden of disease.

Objective

In this study, we report characterization of the human cervical mucous proteome as the first step towards protein biomarker discovery.

Methods

The protein composition was characterized using one- and two-dimensional gel electrophoresis, and liquid chromatography coupled with mass spectrometry. We chose to use this combination of traditional biochemical techniques and proteomics to allow a more comprehensive analysis.

Results and Conclusion

A total of 107 unique proteins were identified, with plasma proteins being most abundant. These proteins represented the major functional categories of metabolism, immune response, and cellular transport. Removal of high molecular weight abundant proteins by immunoaffinity purification did not significantly increase the number of protein spots resolved. We also analyzed phosphorylated and glycosylated proteins by fluorescent post-staining procedures. The profiling of cervical mucous proteins and their post-translational modifications can be used to further our understanding of the cervical mucous proteome.