Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

: Pap test, cervical swab, and tumor tissue LC-MS/MS proteomics from a case of high grade serous ovarian cancer.

ABSTRACT: The aim of this study was to determine whether the residual fixative from a liquid-based Pap test or a swab of the cervix contained proteins that were also found in the primary tumor of a woman with high grade serous ovarian cancer. A total of 4,934 proteins were identified and analyzed. This study is the first step in determining the feasibility of using the liquid-based Pap test or a cervical swab for the detection of ovarian cancer biomarkers

INSTRUMENT(S): LTQ Orbitrap Velos

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Epithelial Ovarian Cancer Cell, Uterine Cervix

SUBMITTER: Kristin Boylan

LAB HEAD: Amy P.N. Skubitz

PROVIDER: PXD023272 | Pride | 2021-01-12

REPOSITORIES: Pride

ACCESS DATA

Json Xml

Publications

Evaluation of the potential of Pap test fluid and cervical swabs to serve as clinical diagnostic biospecimens for the detection of ovarian cancer by mass spectrometry-based proteomics.

Boylan Kristin L M KLM Afiuni-Zadeh Somaieh S Geller Melissa A MA Argenta Peter A PA Griffin Timothy J TJ Skubitz Amy P N APN

Clinical proteomics 20210107 1

<h4>Background</h4>The purpose of this study was to determine whether the residual fixative from a liquid-based Pap test or a swab of the cervix contained proteins that were also found in the primary tumor of a woman with high grade serous ovarian cancer. This study is the first step in determining the feasibility of using the liquid-based Pap test or a cervical swab for the detection of ovarian cancer protein biomarkers.<h4>Methods</h4>Proteins were concentrated by acetone precipitation from th ...[more]

PMID: 33413078

Similar Datasets

Human Pap test LC-MS/MS metaproteomics

Project description:The human cervical-vaginal area contains proteins derived from microorganisms that may prevent or predispose women to gynecological conditions. The liquid Pap test fixative is an unexplored resource for analysis of microbial communities and the microbe-host interaction. Previously, we showed that the residual cell-free fixative from discarded Pap tests of healthy women could be used for mass spectrometry (MS) based proteomic identification of cervical-vaginal proteins. In this study, we reprocessed these MS raw data files for metaproteomic analysis to characterize the microbial community composition and function of microbial proteins in the cervical-vaginal region. This was accomplished by developing a customized protein sequence database encompassing microbes likely present in the vagina. High-mass accuracy data were searched against the protein FASTA database using a two-step search method within the Galaxy for proteomics platform. Data was analyzed by MEGAN6 (MetaGenomeAnalyzer) for phylogenetic and functional characterization. We identified over 300 unique peptides from a variety of bacterial phyla and Candida. Peptides corresponding to proteins involved in carbohydrate metabolism, oxidation-reduction, and transport were identified. By identifying microbial peptides in Pap test supernatants it may be possible to acquire a functional signature of these microbes, as well as detect specific proteins associated with cervical health and disease.

2018-07-23 | PXD009596 | Pride

ABLE-SWATH - A Case Study and Methodology for OpenSWATH Parameter Optimization Using the ProCan90 Data Set and 45,810 Computational Analysis Runs

Project description:Proteomic methods typically involve lengthy, multi-step sample preparation protocols (14-16 hrs), especially for the lysis and digestion of solid tissue samples or cells. We developed a streamlined proteomic sample preparation protocol termed Accelerated Barocycler Lysis and Extraction (ABLE), that substantially reduces the time and cost of tissue sample processing. ABLE is based on pressure cycling technology (PCT) for rapid tissue solubilisation and reliable, controlled proteolytic digestion. Here, the previously reported PCT protocol was optimised using 1-4 mg biopsy punches from rat kidney. The tissue denaturant urea was substituted with a combination of sodium deoxycholate (SDC) and N-propanol. ABLE produced comparable numbers of protein identifications in half the sample preparation time and with reduced cost, being ready for MS injection in 3 hrs (vs the conventional urea PCT method of 6 hrs). To validate the method, it was applied across a diverse range of rat tissues (kidney, lung, muscle, brain, testis), human HEK 293 cells and ovarian tumours by coupling PCT with SWATH-mass spectrometry (SWATH-MS). There were similar numbers of quantified proteins between ABLE-SWATH and PCT-SWATH methods, with greater than 70% overlap for all sample types, except muscle with 58% overlap. The ABLE tissue processing protocol offers a standardised, high-throughput, efficient and reproducible proteomic preparation method, accelerating sample throughput for any type of MS analysis. Coupled with SWATH-MS, ABLE has the potential to accelerate proteomics analysis towards a clinically relevant turn-around-time.

2019-01-21 | PXD010688 | Pride

Strategies to enable large-scale proteomics for reproducible research.

Project description:An experiment was designed to assess the reproducibility of SWATH-MS measurements collected from different mass spectrometers in a single facility over a period spanning approximately four months. Data were acquired with 90-minute gradient lengths at the Australian Cancer Research Foundation International Centre for the Proteome of Human Cancer (ProCan) on six SCIEX TripleTOF 6600 QTOF mass spectrometers. Multiple replicate aliquots were prepared for eight samples. These comprised a dilution series of ovarian cancer tissue (0%, 3.125%, 6.25%, 12.5%, 25% and 50%) offset by yeast and a fixed proportion (50%) of prostate cancer tissue (Samples 1-6), a 1:1 mix of ovarian cancer tissue and yeast cells (Sample 7), and a human cell line (HEK293T; Sample 8). On each mass spectrometer, sets of 20 replicate aliquots (three aliquots of Samples 2-5, and two aliquots of Samples 1, 6-8) were run during each of thirteen 48-hour periods. Experimental data were acquired in 48-hour time periods on each instrument continuously for eight days (with sets of 20 replicates commencing on days 1, 3, 5 and 7), once per week for the remainder of the month (commencing on days 14, 21 and 28), and then once per month for the remainder of the first three months (commencing on days 56 and 84). After each instrument underwent a major clean, the sets of 20 replicates were again run continuously for a further eight days (commencing on days 101, 103, 105 and 107). Data were therefore acquired during a total of thirteen 48-hour periods over approximately four months, during which time the mass spectrometry facility was fully operational. Mass spectrometer maintenance schedules varied according to each individual instrument's performance, and each instrument commenced data acquisition asynchronously within 28 days from the experiment start.

2020-07-20 | PXD015912 | Pride

ProteoMixture: A Cell Deconvolution Tool for Bulk Tissue Proteomics Data

Project description:Numerous multi-omic investigations of cancer tissue have documented varying and poor pairwise transcript:protein quantitative correlations and most deconvolution tools aiming to predict cell type proportions (cell admixture) have been developed and credentialed using transcript-level data alone. To estimate cell admixture using protein abundance data, we analyzed proteome (and transcriptome data) generated from contrived admixtures of tumor, stroma, and immune cell models or those selectively harvested from the tissue microenvironment by laser microdissection from high grade serous ovarian cancer (HGSOC) tumors. Co-quantified transcripts and proteins performed similarly to estimate stroma and immune cell admixture in two commonly used deconvolution algorithms ESTIMATE and ConsensusTME (r ≥ 0.63). Here we have developed and optimized protein-based signatures to estimate cell admixture proportions and benchmarked these using bulk tumor proteomics data from over 150 HGSOC patients. The optimized protein signatures supporting cell type proportion estimates from bulk tissue proteomic data are available at https://lmdomics.org/ProteoMixture/.

2024-01-12 | PXD044157 | Pride

The impact of commonly used alkylating agents on artefactual peptide modification

Project description:Iodoacetamide is by far the most commonly used agent for alkylation of cysteine during sample preparation for proteomics. An alternative, 2-chloroacetamide, has been recently suggested to reduce the alkylation of residues other than cysteine, such as the N-terminus, Asp, Glu, Lys, Ser, Thr and Tyr. Here, we show that although 2-chloroacetamide reduces the level of off-target alkylation, it exhibits a range of adverse effects. The most significant of these was methionine oxidation, which increases to a maximum of 40% of all Met containing peptides compared to 2-5% with iodoacetamide. Increases were also observed for mono and dioxidised tryptophan. No additional differences between the alkylating reagents were observed for a range of other post-translational modifications and digestion parameters. The adverse impact of 2-chloroacetamide on methionine oxidation suggest it is not the ideal alkylating reagent for proteomics.

2017-08-16 | PXD007071 | Pride

Detection of SARS-CoV-2 in nasal swabs using MALDI-MS

Project description:Detection of SARS-CoV-2 using RT–PCR and other advanced methods can achieve high accuracy. However, their application is limited in countries that lack sufficient resources to handle large-scale testing during the COVID-19 pandemic. Here, we describe a method to detect SARS-CoV-2 in nasal swabs using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) and machine learning analysis. This approach uses equipment and expertise commonly found in clinical laboratories in developing countries. We obtained mass spectra from a total of 362 samples (211 SARS-CoV-2-positive and 151 negative by RT–PCR) without prior sample preparation from three different laboratories. We tested two feature selection methods and six machine learning approaches to identify the top performing analysis approaches and determine the accuracy of SARS-CoV-2 detection. The support vector machine model provided the highest accuracy (93.9%), with 7% false positives and 5% false negatives. Our results suggest that MALDI-MS and machine learning analysis can be used to reliably detect SARS-CoV-2 in nasal swab samples.

2020-09-11 | PXD021388 | Pride

Plasma Proteomics Reveal COVID-19 Associated Network and Severity Biomarkers

Project description:Coronavirus disease 2019 (COVID-19) has been threatening public health for the last 3 years globally. So far, the pathophysiology of the disease and therapeutic strategies have not clearly known yet. In this project, performing label-free plasma proteomics analysis, we aimed at identifying severity biomarkers for COVID-19 prognosis and proposing potential drugs against the disease symptoms by building the signaling network of significantly regulated proteins and finding the corresponding virus-host interactions. A total of 38 plasma samples from 13 COVID-19 PCR positive individuals and 5 plasma samples from healthy individuals were collected for the analysis. According to the WHO criteria, the severity of our patients was categorized as moderate (n=4), severe (n=3), and critical (n=6). Also, blood samples were collected in different time points after the symptom onset: (1) 1-5 day (± 2 days); early infection, (2) 5-15 days (± 2 days); inflammatory response, and after 15 days (± 2 days); recovery which shows the first PCR negative result from a nasal swab. In summary, we found significantly regulated proteins between COVID-19 patients and uninfected individuals and proposed some critical patient-specific prognostic biomarkers, which can be used as an early predictor of the disease severity. Also, we created a COVID-19 related plasma protein network modulated by SARS-CoV2 viral proteins and indicated clinically significant targets for the disease symptoms.

2023-03-11 | PXD031813 | Pride

Capturing SARS-cov in low abundant patient samples: a comparative analysis (metagenomics NS).

Project description:In this study, we tested the efficacy of five commercial probes panels at detecting SARS-CoV-2 genome including panels from Illumina, Twist Bioscience and Arbor Bioscience. To do so, we used 19 patient nasal swab samples broken down into 5 series of 4 samples of equivalent SARS-CoV-2 viral load (cycle threshold (CT): low CT means a high viral load – CT26, CT29, CT32, CT35 and CT36+).

2022-06-01 | E-MTAB-11236 | biostudies-arrayexpress

A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples.

Project description:SARS-CoV-2 has created a global disease burden infecting >100 million humans in just over a year. In this study, we adopted a SISCAPA-based enrichment approach using anti-peptide antibodies generated against peptides from the nucleocapsid protein of SARS-CoV-2. We developed a targeted workflow in which nasopharyngeal samples were digested followed by enrichment of viral peptides using the anti-peptide antibodies and targeted parallel reaction monitoring (PRM) analysis using a high-resolution mass spectrometer. This workflow was applied to 41 RT-PCR-confirmed clinical nasopharyngeal swab samples and 30 negative samples. The SISCAPA-based platform described in the current study can serve as one of the alternative methods for SARS-CoV-2 viral detection.

2022-02-16 | PXD028505 | Pride

A SISCAPA-based approach for detection of SARS-CoV-2 viral antigens from clinical samples.

2022-06-03 | PXD026273 | Pride

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data