Data-independent Acquisition Mass Spectrometry Research Articles

Dynamic Data-Independent Acquisition Mass Spectrometry with Real-Time Retrospective Alignment.

Data-independent acquisition (DIA) mass spectrometry has grown in popularity in recent years, because of the reproducibility and quantitative rigor of a systematic tandem mass spectrometry (MS/MS) sampling method. However, traditional DIA methods may spend valuable instrument time acquiring MS/MS spectra with no usable information in them, affecting sensitivity and quantitative performance. We developed a DIA strategy that dynamically adjusts the MS/MS windows during the chromatographic separation. The method focuses MS/MS acquisition on the most relevant mass range at each point in time─increasing the quantitative sensitivity by increasing the time spent on each DIA window. We demonstrate an improved lower limit of quantification, on average, without sacrificing the number of peptides detected.

Bikunin: A Promising Prognostic Biomarker for Acute-on-Chronic Liver Failure in Patients with Viral Hepatitis B.

To screen out potential prognostic biomarkers for HBV-related acute-on-chronic liver failure (HBV-ACLF). Peripheral blood samples of HBV-ACLF patients (n=56) and normal controls (n=15) from the Affiliated Hospital of Southwest Medical University from January 2021 to April 2022 were collected, 5 normal patients and 10 patients with ACLF were randomly selected for Data independent acquisition (DIA) mass spectrometry analysis, and the potential core proteins were screened out via bioinformatics. All samples were validated by Enzyme linked immunosorbent assays (ELISA) technology, and the survival curve was constructed based on the patient's 90-day survival time. A total of 247 differentially expressed proteins (DEPs) were screened, of which 148 were upregulated and 99 were down-regulated. The DEPs were mainly enriched in high-density lipoprotein particle remodeling, coagulation, and hemostasis and participated in signaling pathways such as cholesterol metabolism, coagulation cascades, and PPAR signaling pathway. Finally, bikunin was selected for further study and validated via the ELISA, compared with the normal group, bikunin was poorly expressed in the HBV-ACLF group, the difference was statistically significant (P < 0.0001), the area under the curve (AUC) for Receiver operating characteristic (ROC) analysis was 0.917. Furthermore, compared with the non-survival group, bikunin was highly expressed in the HBV-ACLF survival group, the difference was statistically significant (P=0.0015), and the survival curve showed a positive correlation with patient survival (P=0.0063). The level of plasma bikunin in HBV-ACLF is down-regulated, which is positively correlated with the survival of the patients with HBV-ACLF, and is expected to become a new prognostic biomarker.

Proteomics Identifies Circulating TIMP-1 as a Prognostic Biomarker for Diffuse Large B-Cell Lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease, although disease stratification using in-depth plasma proteomics has not been performed to date. By measuring more than 1000 proteins in the plasma of 147 DLBCL patients using data-independent acquisition mass spectrometry and antibody array, DLBCL patients were classified into four proteomic subtypes (PS-I-IV). Patients with the PS-IV subtype and worst prognosis had increased levels of proteins involved in inflammation, including a high expression of metalloproteinase inhibitor-1 (TIMP-1) that was associated with poor survival across two validation cohorts (n = 180). Notably, the combination of TIMP-1 with the international prognostic index (IPI) identified 64.00% to 88.24% of relapsed and 65.00% to 80.49% of deceased patients in the discovery and two validation cohorts, which represents a 24.00% to 41.67% and 20.00% to 31.70% improvement compared to the IPI score alone, respectively. Taken together, we demonstrate that DLBCL heterogeneity is reflected in the plasma proteome and that TIMP-1, together with the IPI, could improve the prognostic stratification of patients.

Identification of gravity-responsive proteins in the femur of spaceflight mice using a quantitative proteomic approach

Although the microgravity (μ-g) environment that astronauts encounter during spaceflight can cause severe acute bone loss, the molecular mechanism of this bone loss remains unclear. To investigate the gravity-response proteins involved in bone metabolism, it is important to comprehensively determine which proteins exhibit differential abundance associated with mechanical stimuli. However, comprehensive proteomic analysis using small bone samples is difficult because protein extraction in mineralized bone tissue is inefficient. Here, we established a high-sensitivity analysis system for mouse bone proteins using data-independent acquisition mass spectrometry. This system successfully detected 40 proteins in the femoral diaphysis showing differential abundance between mice raised in a μ-g environment, where the bone mass was reduced by gravity unloading, and mice raised in an artificial 1-gravity environment on the International Space Station. Additionally, 22 proteins, including noncollagenous bone matrix proteins, showed similar abundance between the two groups in the mandible, where bone mass was unaltered due to mastication stimuli, suggesting that these proteins are responsive to mechanical stimuli. One of these proteins, SPARCL1, is suggested to promote osteoclastogenesis induced by receptor activator of nuclear factor-κB ligand. We expect these findings to lead to new insights into the mechanisms of bone metabolism induced by mechanical stimuli. SignificanceWe aimed to investigate the gravity-response proteins involved in bone metabolism. To this end, we established a comprehensive analysis system for mouse bone proteins using data-independent acquisition mass spectrometry, which is particularly useful in comprehensively analyzing the bone proteome using small sample volumes. In addition, a comprehensive proteomic analysis of the femoral diaphysis and mandible, which exhibit different degrees of bone loss in mice raised on the International Space Station, identified proteins that respond to mechanical stimuli. SPARCL1, a mechanical stimulus-responsive protein, was consequently suggested to be involved in osteoclast differentiation associated with bone remodeling. Our findings represent an important step toward elucidating the molecular mechanism of bone metabolism induced by mechanical stimuli.

A Comparative Analysis of Data Analysis Tools for Data-Independent Acquisition Mass Spectrometry

Data-independent acquisition (DIA) mass spectrometry-based proteomics generates reproducible proteome data. The complex processing of the DIA data has led to the development of multiple data analysis tools. In this study, we assessed the performance of five tools (OpenSWATH, EncyclopeDIA, Skyline, DIA-NN, and Spectronaut) using six DIA datasets obtained from TripleTOF, Orbitrap, and TimsTOF Pro instruments. By comparing identification and quantification metrics and examining shared and unique cross-tool identifications, we evaluated both library-based and library-free approaches. Our findings indicate that library-free approaches outperformed library-based methods when the spectral library had limited comprehensiveness. However, our results also suggest that constructing a comprehensive library still offers benefits for most DIA analyses. This study provides comprehensive guidance for DIA data analysis tools, benefiting both experienced and novice users of DIA-mass spectrometry technology.

Serum proteome profiles in patients treated with targeted temperature management after out-of-hospital cardiac arrest

BackgroundDefinition of temporal serum proteome profiles after out-of-hospital cardiac arrest may identify biological processes associated with severe hypoxia–ischaemia and reperfusion. It may further explore intervention effects for new mechanistic insights, identify candidate prognostic protein biomarkers and potential therapeutic targets. This pilot study aimed to investigate serum proteome profiles from unconscious patients admitted to hospital after out-of-hospital cardiac arrest according to temperature treatment and neurological outcome.MethodsSerum samples at 24, 48, and 72 h after cardiac arrest at three centres included in the Target Temperature Management after out-of-hospital cardiac arrest trial underwent data-independent acquisition mass spectrometry analysis (DIA-MS) to find changes in serum protein concentrations associated with neurological outcome at 6-month follow-up and targeted temperature management (TTM) at 33 °C as compared to 36 °C. Neurological outcome was defined according to Cerebral Performance Category (CPC) scale as “good” (CPC 1–2, good cerebral performance or moderate disability) or “poor” (CPC 3–5, severe disability, unresponsive wakefulness syndrome, or death).ResultsOf 78 included patients [mean age 66 ± 12 years, 62 (80.0%) male], 37 (47.4%) were randomised to TTM at 36 °C. Six-month outcome was poor in 47 (60.3%) patients. The DIA-MS analysis identified and quantified 403 unique human proteins. Differential protein abundance testing comparing poor to good outcome showed 19 elevated proteins in patients with poor outcome (log2-fold change (FC) range 0.28–1.17) and 16 reduced proteins (log2(FC) between − 0.22 and − 0.68), involved in inflammatory/immune responses and apoptotic signalling pathways for poor outcome and proteolysis for good outcome. Analysis according to level of TTM showed a significant protein abundance difference for six proteins [five elevated proteins in TTM 36 °C (log2(FC) between 0.33 and 0.88), one reduced protein (log2(FC) − 0.6)] mainly involved in inflammatory/immune responses only at 48 h after cardiac arrest.ConclusionsSerum proteome profiling revealed an increase in inflammatory/immune responses and apoptosis in patients with poor outcome. In patients with good outcome, an increase in proteolysis was observed, whereas TTM-level only had a modest effect on the proteome profiles. Further validation of the differentially abundant proteins in response to neurological outcome is necessary to validate novel biomarker candidates that may predict prognosis after cardiac arrest.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

Nf-encyclopedia: A Cloud-Ready Pipeline for Chromatogram Library Data-Independent Acquisition Proteomics Workflows.

Data-independent acquisition (DIA) mass spectrometry methods provide systematic and comprehensive quantification of the proteome; yet, relatively few open-source tools are available to analyze DIA proteomics experiments. Fewer still are tools that can leverage gas phase fractionated (GPF) chromatogram libraries to enhance the detection and quantification of peptides in these experiments. Here, we present nf-encyclopedia, an open-source NextFlow pipeline that connects three open-source tools, MSConvert, EncyclopeDIA, and MSstats, to analyze DIA proteomics experiments with or without chromatogram libraries. We demonstrate that nf-encyclopedia is reproducible when run on either a cloud platform or a local workstation and provides robust peptide and protein quantification. Additionally, we found that MSstats enhances protein-level quantitative performance over EncyclopeDIA alone. Finally, we benchmarked the ability of nf-encyclopedia to scale to large experiments in the cloud by leveraging the parallelization of compute resources. The nf-encyclopedia pipeline is available under a permissive Apache 2.0 license; run it on your desktop, cluster, or in the cloud: https://github.com/TalusBio/nf-encyclopedia.

Overexpression of SERPINA3 suppresses tumor progression by modulating SPOP/NF‑κB in lung cancer.

The pathogenesis mechanism of lung cancer is very complex, with high incidence and mortality. Serpin family A member 3 (SERPINA3) expression levels were reduced in the sera of patients with lung cancer and may be a candidate diagnostic and prognostic survival biomarker in lung cancer, as previously reported. However, the detailed biological functions of SERPINA3 in the pathogenesis of lung cancer remain unknown. In the present study, it was aimed to explore the effects of SERPINA3 on the occurrence of lung cancer. SERPINA3 expression was assessed using bioinformatics database analysis and experimental detection. Then, the biological effects of SERPINA3 were investigated in a cell culture system and a xenograft model of human lung cancer. The potential regulatory mechanism of SERPINA3 in lung cancer was explored by data‑independent acquisition mass spectrometry (DIA‑MS) detection and further validated by western blotting (WB). The results indicated that SERPINA3 expression levels were significantly downregulated in lung cancer tissues and cell lines. At the cellular level, it was revealed that overexpressed SERPINA3 inhibited cell growth, proliferation, migration and invasion and promoted the apoptosis of lung cancer cells. Moreover, overexpressed SERPINA3 enhanced the sensitivity of lung cancer cells to osimertinib. Invivo, a xenograft model of human lung cancer was established with BALB/c nude mice. After the injection of A549 cells, the tumor growth of the tumor‑bearing mice in the SERPINA3‑overexpressing group increased more slowly, and the tumor volume was smaller than that in the empty‑vector group. Mechanistically, a total of 65 differentially expressed proteins were identified. It was found that the speckle‑type POZ protein (SPOP) was significantly upregulated in SERPINA3‑overexpressing H157 cells using DIA‑MS detection and analysis. WB validation showed that SPOP expression increased, and NF‑kappaB (NF‑κB) p65 was inhibited in cell lines and tumor tissues of mice when SERPINA3 was overexpressed. The present findings suggest that SERPINA3 is involved in the development of lung cancer and has an antineoplastic role in lung cancer.

Assessment of a 60-Biomarker Health Surveillance Panel (HSP) on Whole Blood from Remote Sampling Devices by Targeted LC/MRM-MS and Discovery DIA-MS Analysis.

Telehealth, accessing healthcare and wellness remotely, should be a cost-effective and efficient way for individuals to receive care. The convenience of having a reliable remote collection device for blood tests will facilitate access to precision medicine and healthcare. Herein, we tested a 60-biomarker health surveillance panel (HSP), containing 35 FDA/LDT assays and covering at least 14 pathological states, on 8 healthy individuals' ability to collect their own capillary blood from a lancet finger prick and directly compared it to the traditional phlebotomist venous blood and plasma collection methods. All samples were spiked with 114 stable-isotope-labeled (SIL) HSP peptides and quantitatively analyzed by liquid chromatography-multiple reaction monitoring-mass spectrometry (LC/MRM-MS) scheduled method targeting 466 transitions from 114 HSP peptides and by a discovery data-independent acquisition mass spectrometry (DIA-MS) method. The average peak area ratio (PAR) of the HSP quantifier peptide transitions from all 8 volunteers' capillary blood (n = 48), venous blood (n = 48), and matched plasma (n = 24) was <20% coefficients of variation (CV). Heat map analysis of all 8 volunteers demonstrated that each individual had a unique biosignature. Biological replicates from capillary blood and venous blood clustered within each volunteer in k-means clustering analysis. Pearson statistical analysis of the three biofluids indicated that there was >90% similarity. Discovery DIA-MS analysis of the same samples using a plasma spectral library and a pan-human spectral library identified 1121 and 4661 total proteins, respectively. In addition, at least 122 FDA-approved biomarkers were identified. DIA-MS analysis reproducibly quantitated (<30% CV) ∼600-700 proteins in capillary blood, ∼800 proteins in venous blood, and ∼300-400 proteins in plasma, demonstrating that an expansive biomarker panel is possible with current mass spectrometry technology. Both targeted LC/MRM-MS and discovery DIA-MS analysis of whole blood collected on remote sampling devices are viable options for personal proteome biosignature stratification in precision medicine and precision health.

Proteome Landscapes of Human Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma

Liver cancer is among the top leading causes of cancer mortality worldwide. Particularly, hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (CCA) have been extensively investigated from the aspect of tumor biology. However, a comprehensive and systematic understanding of the molecular characteristics of HCC and CCA remains absent. Here, we characterized the proteome landscapes of HCC and CCA using the data-independent acquisition (DIA) mass spectrometry (MS) method. By comparing the quantitative proteomes of HCC and CCA, we found several differences between the two cancer types. In particular, we found an abnormal lipid metabolism in HCC and activated extracellular matrix-related pathways in CCA. We next developed a three-protein classifier to distinguish CCA from HCC, achieving an area under the curve (AUC) of 0.92, and an accuracy of 90% in an independent validation cohort of 51 patients. The distinct molecular characteristics of HCC and CCA presented in this study provide new insights into the tumor biology of these two major important primary liver cancers. Our findings may help develop more efficient diagnostic approaches and new targeted drug treatments.

Identification of membrane proteins regulated by ADAM15 by SUSPECS proteomics.

ADAM15 is a member of the disintegrin-metalloproteinase family of sheddases, which plays a role in several biological processes including cartilage homeostasis. In contrast with well-characterized ADAMs, such as the canonical sheddases ADAM17 and ADAM10, little is known about substrates of ADAM15 or how the enzyme exerts its biological functions. Herein, we used "surface-spanning enrichment with click-sugars (SUSPECS)" proteomics to identify ADAM15 substrates and/or proteins regulated by the proteinase at the cell surface of chondrocyte-like cells. Silencing of ADAM15 by siRNAs significantly altered membrane levels of 13 proteins, all previously not known to be regulated by ADAM15. We used orthogonal techniques to validate ADAM15 effects on 3 of these proteins which have known roles in cartilage homeostasis. This confirmed that ADAM15-silencing increased cell surface levels of the programmed cell death 1 ligand 2 (PDCD1LG2) and reduced cell surface levels of vasorin and the sulfate transporter SLC26A2 through an unknown post-translational mechanism. The increase of PDCD1LG2 by ADAM15 knockdown, a single-pass type I transmembrane protein, suggested it could be a proteinase substrate. However, shed PDCD1LG2 could not be detected even by a data-independent acquisition mass spectrometry, a highly sensitive method for identification and quantification of proteins in complex protein samples, suggesting that ADAM15 regulates PDCD1LG2 membrane levels by a mechanism different from ectodomain shedding.

Exploring molecular dynamic indicators associated with reproductive performance of Bos indicus cattle in blood plasma samples through data-independent acquisition mass spectrometry

Improving reproductive performance of cattle is of paramount importance for sustainable dairy farming. Poor reproduction performance (RP) hinders the genetic improvement of important Bos indicus cattle breeds. It is well known that incorporation of molecular information along with conventional breeding method is far better than use of conventional method alone for the genetic improvement of reproductive performance traits in cattle. Therefore, the present study sought to investigate the plasma proteome of the Deoni cows in cyclical (n = 6) and pregnant (n = 6) reproductive phases with varying reproductive performance (high and low). High-throughput data independent acquisition (DIA) based proteomics was performed to understand corresponding proteome. We identified a total of 430 plasma proteins. Among cyclic cows, twenty proteins were differentially regulated in low RP as compared to high RP. BARD1 and AFP proteins were observed upregulated in cyclical cows whose upregulation reported to affect reproductive performance in cattle. Among the pregnant cows, thirty-five proteins were differentially regulated, including the downregulation of FGL2 and ZNFX1 that modulates the maternal immune response mechanism which is required for successful implantation of the embryo. Also, proteins such as AHSG, CLU and SERPINA6 were upregulated in the pregnant cows whose upregulation reported to reduced reproductive performance. The results of this study will be helpful in establishing a framework for future research on the aspect of improving reproductive performance in Bos indicus cattle breeds. SignificanceThe Indian subcontinent is the center of domestication for Bos indicus cattle breeds and they are known for their disease resistance, heat tolerance, ability to survive in low input regime and harsh climatic conditions. In recent times, population of many important Bos indicus breeds including Deoni cattle is declining due to various factors, especially due to reproductive performance. Traditional breeding methods are not sufficient enough to understand and improve the reproductive performance traits in important Bos indicus cattle breeds. Proteomics approach is a promising technology to understand the complex biological factors which leads to poor reproductive performance in cattle. The present study utilized DIA based LC- MS/MS analysis to identify the plasma proteins associated with reproductive performance in cyclical and pregnant cows. This study if improved further, can be used to develop potential protein markers associated with reproductive performance which is useful for the selection and genetic improvement of important Bos indicus breeds.

Generalized precursor prediction boosts identification rates and accuracy in mass spectrometry based proteomics

Data independent acquisition mass spectrometry (DIA-MS) has recently emerged as an important method for the identification of blood-based biomarkers. However, the large search space required to identify novel biomarkers from the plasma proteome can introduce a high rate of false positives that compromise the accuracy of false discovery rates (FDR) using existing validation methods. We developed a generalized precursor scoring (GPS) method trained on 2.75 million precursors that can confidently control FDR while increasing the number of identified proteins in DIA-MS independent of the search space. We demonstrate how GPS can generalize to new data, increase protein identification rates, and increase the overall quantitative accuracy. Finally, we apply GPS to the identification of blood-based biomarkers and identify a panel of proteins that are highly accurate in discriminating between subphenotypes of septic acute kidney injury from undepleted plasma to showcase the utility of GPS in discovery DIA-MS proteomics.

Data-independent acquisition-based mass spectrometry(DIA-MS) for quantitative analysis of patients with chronic hepatitis B

Chronic hepatitis B is a significant public health problem and complex pathologic process, and unraveling the underlying mechanisms and pathophysiology is of great significance. Data independent acquisition mass spectrometry (DIA-MS) is a label-free quantitative proteomics method that has been successfully applied to the study of a wide range of diseases. The aim of this study was to apply DIA-MS for proteomic analysis of patients with chronic hepatitis B. We performed comprehensive proteomics analysis of protein expression in serum samples from HBV patients and healthy controls by using DIA-MS. Gene Ontology (GO) terms, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein network analysis were performed on differentially expressed proteins and were further combined with literature analysis. We successfully identified a total of 3786 serum proteins with a high quantitative performance from serum samples in this study. We identified 310 differentially expressed proteins (DEPs) (fold change > 1.5 and P value < 0.05 as the criteria for a significant difference) between HBV and healthy samples. A total of 242 upregulated proteins and 68 downregulated proteins were among the DEPs. Some protein expression levels were significantly elevated or decreased in patients with chronic hepatitis B, indicating a relation to chronic liver disease, which should be further investigated.

Proteomics of adjacent-to-tumor samples uncovers clinically relevant biological events in hepatocellular carcinoma.

Normal adjacent tissues (NATs) of hepatocellular carcinoma (HCC) differ from healthy liver tissues and their heterogeneity may contain biological information associated with disease occurrence and clinical outcome that has yet to be fully evaluated at the proteomic level. This study provides a detailed description of the heterogeneity of NATs and the differences between NATs and healthy livers and revealed that molecular features of tumor subgroups in HCC were partially reflected in their respective NATs. Proteomic data classified HCC NATs into two subtypes (Subtypes 1 and 2), and Subtype 2 was associated with poor prognosis and high-risk recurrence. The pathway and immune features of these two subtypes were characterized. Proteomic differences between the two NAT subtypes and healthy liver tissues were further investigated using data-independent acquisition mass spectrometry, revealing the early molecular alterations associated with the progression from healthy livers to NATs. This study provides a high-quality resource for HCC researchers and clinicians and may significantly expand the knowledge of tumor NATs to eventually benefit clinical practice.

Pan-cancer diagnostic proteomic signature of tissue-of-origin (TOO) using data-independent acquisition mass spectrometry (DIA-MS) from 1289 human tissue samples.

3120 Background: Accurate tumour classification based upon tissue of origin (TOO) remains important to guide treatment selection and prognosis but can be challenging in patients with poorly differentiated malignancy, cancer of unknown primary (CUP) or those with prior malignancy. Data-independent acquisition mass spectrometry (DIA-MS)-based proteomics is emerging as a potential clinical diagnostic and prognostic tool. We aimed to develop a protein-based signature to identify histological subtype and adenocarcinoma TOO using DIA-MS data obtained from a pan-cancer study of human tissue samples as an adjunct to histopathological assessment in challenging clinical scenarios. Methods: We performed DIA-MS-based proteomic profiling of 795 fresh frozen tumour and 494 tumour-adjacent normal samples from the Victorian Cancer Biobank in a clinically orientated workflow. We filtered the cohort to include tumour types relevant to CUP. Protein quantification was derived from raw peptide intensity data. Random forest classifiers to identify histological subtype and adenocarcinoma TOO were subsequently trained and tested using 70% and 30% of the data respectively. Evaluation metrics included top-k accuracy (predicting how often the correct class is among the top k predicted classes) and area under the receiver operating curve (AUROC) (one-versus-rest) ± 95% confidence interval. Results: The final tumour cohort consisted of 427 tumour samples representing eight histological subtypes (adenocarcinoma, germ cell tumour, lymphoma, melanoma, renal cell carcinoma, sarcoma, squamous cell carcinoma, thyroid carcinoma) and seven adenocarcinoma TOO (breast, colorectal, liver, lung, ovary, pancreas, prostate). From 9,051 quantified proteins, 83 were identified with potential utility at identifying histological subtype and adenocarcinoma TOO for use in machine learning models. The histological subtype model identified cancer subtype in the test set with top-1 and top-2 accuracy of 0.95 ± 0.03 and 0.98 ± 0.02 respectively. Average test AUROC over all cancer types (n=8) was 0.98 ± 0.02. The adenocarcinoma TOO model identified tumour TOO in the test set with top-1 and top 2- accuracy of 0.88 ± 0.07 and 0.95 ± 0.04 respectively. Average test AUROC over all adenocarcinoma TOO (n=7) was 0.97 ± 0.02. Conclusions: Our clinically orientated DIA-MS-based proteomic workflow and supervised machine learning can identify protein signatures that classify histological subtype and TOO in tumour samples with high accuracy. This technology may assist diagnostic classification of cancer in challenging clinical scenarios, such as CUP.

The acrosome proteins arylsulfatase F and zona pellucida-binding protein 2 are candidate markers associated with Impaired Acrosomal Exocytosis in sperm from subfertile Thoroughbred stallions

Thoroughbred (TB) stallions that carry the linked susceptibility genotype A/A-A/A in exon 5 of the FKBP6 gene (ECA13; EquCab 3.0) are profoundly subfertile due to Impaired Acrosomal Exocytosis (IAE). A clear causation of the FKBP6 genotype and IAE is still unknown. In the current study, the sperm proteome in frozen/thawed semen from three fertile TB stallions (n = 3) and three subfertile TB stallions (FKBP6 genotype A/A-A/A; n = 3) was studied using mass spectrometry. Sperm from both stallion groups were incubated in Modified Whitten's-lactate media (MW-Lac) to induce acrosomal exocytosis in viable sperm (AE/Viable). At 0h, 2h, 4h, and 6h, sperm aliquots were analyzed for AE/Viable using flow cytometry (Fixable Live/Dead Red + FITC-PSA), while the sperm proteome was analyzed via Data-Independent Acquisition mass spectrometry (DIA-MS). Student's t-tests and two-way ANOVA with Benjamini-Hochberg multiple testing correction (FDR q-value 0.05) were used to determine differences in AE/Viable and protein relative abundance between experimental groups and incubation periods. At 4h and 6h of incubation, the mean AE/Viable was higher in the fertile than in the subfertile stallions (41 and 44% vs. 14 and 16%, respectively; P < 0.05). A total of 2220 proteins were identified by DIA-MS. Usingstrict selection criteria (FDR 1.0%, q-value < 0.05, and fold-change < 1.5 or > 1.5), 140 proteins were found to be differentially abundant in sperm from the subfertile stallions when compared to that of the fertile stallions (83 less and 57 more abundant). Using bioinformatic analyses, most of the proteins of lower abundance in sperm from subfertile stallions were found to be overrepresented in the “metabolism” (32 proteins) and “metabolism of lipids” (18 proteins) pathways (Homo sapiens orthologs; Reactome database). Two of these proteins, arylsulfatase F (ARSF; fold-change < 4.02; P < 0.05) and zona pellucida-binding protein 2 (ZPBP2; fold-change < 3.36; P < 0.05), are acrosomal proteins known to play a fundamental role in sperm-oocyte binding. By using immunofluorescence, at 0h of incubation in MW-Lac, ARSF was identified at the acrosome, mid-, and principal piece locations in sperm from fertile TB stallions, while only at the mid- and principal piece regions in sperm from subfertile TB stallions. No evidence of ZPBP2 was observed in sperm from both stallion groups at 0h incubation in Lac-MW. Current studies are focused on determining whether these proteins undergo changes in localization within the sperm during acrosomal exocytosis.

Metaproteomics profiling of the microbial communities in fermentation starters (Daqu) during multi-round production of Chinese liquor.

The special flavor and fragrance of Chinese liquor are closely related to microorganisms in the fermentation starter Daqu. The changes of microbial community can affect the stability of liquor yield and quality. In this study, we used data-independent acquisition mass spectrometry (DIA-MS) for cohort study of the microbial communities of a total of 42 Daqu samples in six production cycles at different times of a year. The DIA MS data were searched against a protein database constructed by metagenomic sequencing. The microbial composition and its changes across production cycles were revealed. Functional analysis of the differential proteins was carried out and the metabolic pathways related to the differential proteins were explored. These metabolic pathways were related to the saccharification process in liquor fermentation and the synthesis of secondary metabolites to form the unique flavor and aroma in the Chinese liquor. We expect that the metaproteome profiling of Daqu from different production cycles will serve as a guide for the control of fermentation process of Chinese liquor in the future.

A 19-protein signature to predict outcomes in a cohort of stage II and III CRC.

3129 Background: Colorectal cancer (CRC) is the second leading cause of cancer-related deaths worldwide and remains an area of unmet clinical need. Despite advances in treating stage II and III disease, 30-50% of patients relapse. Additionally, variable survival outcomes are seen in patients with similar disease stages, making it difficult to predict relapses. Here we outline some of the proteomic pathways of early-stage CRC along with the identification of a proteomic-based prognostic signature that predicts relapse in stage II/III CRC. Methods: Archival formalin fixed paraffin embedded tumour (TUM) specimens and matched normal adjacent tissues (NAT) from a cohort of 495 CRC patients (with upfront resected stage II and III CRC) were prepared for proteomic analyses using data-independent acquisition mass spectrometry (DIA-MS). TUM samples with &lt; 20% tumour were excluded from the analysis. Differential expression analyses were performed to identify the top differentially expressed proteins (DEP) within TUM. DEP were subjected to crosstalk and pathway enrichment analysis (PEA). Survival analysis based on initial univariate and subsequent 100 runs of multivariate Cox regression with Least Absolute Shrinkage and Selection Operator (LASSO) was performed to obtain a reduced list of candidate proteins associated with recurrence-free survival (RFS). The proteins that appeared in greater than 95% of the LASSO runs were used in a multivariate Cox model with recursive feature selection to yield the final list of 19 proteins. A risk score was calculated as the sum of the intensities of each of the 19 proteins multiplied by the corresponding regression coefficients. The median of the risk scores was used as the threshold to dichotomise patients into either a low- or a high-risk group. Results: 399 TUM and 381 NAT were analysed, showing 799 DEP, with 529 upregulated and 270 downregulated in TUM vs NAT, respectively. PEA of the upregulated proteins showed enrichment of pathways related to extracellular matrix organisation, tRNA processing and collagen formation. PEA of the downregulated DEPs revealed pathways related to smooth muscle contraction, focal adhesion and oxidation. The 19-protein signature dichotomised patients into low- and high-risk groups with a C-index of 0.829. The signature was prognostic for RFS (hazard ratio (HR) 2.7, 95% confidence interval (CI): [2.24 – 3.18]) and for overall survival (OS) (HR 1.6, 95%CI [1.40, 1.76]). The prognostic significance of the signature was maintained after adjusting for other clinical variables for both RFS and OS (both p&lt;0.001) using multivariate Cox regression. Conclusions: Our findings show that CRC disrupts the extracellular matrix and upregulates collagen formation, making these potential targets for novel therapies. We have also identified a 19-protein signature that is associated with survival in our cohort independent of other clinical variables.