Evaluation of PAC and FASP Performance: DIA-Based Quantitative Proteomic Analysis.

The aim of this study was to compare filter-aided sample preparation (FASP) and protein aggregation capture (PAC) starting from a three-species protein mix (Human, Soybean and Pisum sativum) and two different starting amounts (1 and 10 µg). Peptide mixtures were analyzed by data-independent acquisition (DIA) and raw files were processed by three commonly used software: Spectronaut, MaxDIA and DIA-NN. Overall, the highest number of proteins (mean value of 5491) were identified by PAC (10 µg), while the lowest number (4855) was identified by FASP (1 µg). The latter experiment displayed the worst performance in terms of both specificity (0.73) and precision (0.24). Other tested conditions showed better diagnostic accuracy, with specificity values of 0.95-0.99 and precision values between 0.61 and 0.86. In order to provide guidance on the data analysis pipeline, the accuracy diagnostic of three software was investigated: (i) the highest sensitivity was obtained with Spectronaut (median of 0.67) highlighting the ability of Spectronaut to quantify low-abundance proteins, (ii) the best precision value was obtained by MaxDIA (median of 0.84), but with a reduced number of identifications compared to Spectronaut and DIA-NN data, and (iii) the specificity values were similar (between 0.93 and 0.99). The data are available on ProteomeXchange with the identifier PXD044349.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.

BACKGROUND: Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies. METHODS: In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy. RESULTS: Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69). CONCLUSIONS: To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

Data-Independent Acquisition Mass Spectrometry of EPS-Urine Coupled to Machine Learning: A Predictive Model for Prostate Cancer.

Prostate cancer (PCa) is annually the most frequently diagnosed cancer in the male population. To date, the diagnostic path for PCa detection includes the dosage of serum prostate-specific antigen (PSA) and the digital rectal exam (DRE). However, PSA-based screening has insufficient specificity and sensitivity; besides, it cannot discriminate between the aggressive and indolent types of PCa. For this reason, the improvement of new clinical approaches and the discovery of new biomarkers are necessary. In this work, expressed prostatic secretion (EPS)-urine samples from PCa patients and benign prostatic hyperplasia (BPH) patients were analyzed with the aim of detecting differentially expressed proteins between the two analyzed groups. To map the urinary proteome, EPS-urine samples were analyzed by data-independent acquisition (DIA), a high-sensitivity method particularly suitable for detecting proteins at low abundance. Overall, in our analysis, 2615 proteins were identified in 133 EPS-urine specimens obtaining the highest proteomic coverage for this type of sample; of these 2615 proteins, 1670 were consistently identified across the entire data set. The matrix containing the quantified proteins in each patient was integrated with clinical parameters such as the PSA level and gland size, and the complete matrix was analyzed by machine learning algorithms (by exploiting 90% of samples for training/testing using a 10-fold cross-validation approach, and 10% of samples for validation). The best predictive model was based on the following components: semaphorin-7A (sema7A), secreted protein acidic and rich in cysteine (SPARC), FT ratio, and prostate gland size. The classifier could predict disease conditions (BPH, PCa) correctly in 83% of samples in the validation set. Data are available via ProteomeXchange with the identifier PXD035942.

Comparative proteomic analysis of insulin receptor isoform A and B signaling.

The insulin receptor (IR) gene undergoes differential splicing generating two IR isoforms, IR-A and IR-B. The roles of IR-A in cancer and of IR-B in metabolic regulation are well known but the molecular mechanisms responsible for their different biological effects are poorly understood. We aimed to identify different or similar protein substrates and signaling linked to each IR isoforms. We employed mouse fibroblasts lacking IGF1R gene and expressing exclusively either IR-A or IR-B. By proteomic analysis a total of 2530 proteins were identified and quantified. Proteins and pathways mostly associated with insulin-activated IR-A were involved in cancer, stemness and interferon signaling. Instead, proteins and pathways associated with insulin-stimulated IR-B-expressing cells were mostly involved in metabolic or tumor suppressive functions. These results show that IR-A and IR-B recruit partially different multiprotein complexes in response to insulin, suggesting partially different functions of IR isoforms in physiology and in disease.

Mass Spectrometry-Based Glycoproteomics and Prostate Cancer.

Aberrant glycosylation has long been known to be associated with cancer, since it is involved in key mechanisms such as tumour onset, development and progression. This review will focus on protein glycosylation studies in cells, tissue, urine and serum in the context of prostate cancer. A dedicated section will cover the glycoforms of prostate specific antigen, the molecule that, despite some important limitations, is routinely tested for helping prostate cancer diagnosis. Our aim is to provide readers with an overview of mass spectrometry-based glycoproteomics of prostate cancer. From this perspective, the first part of this review will illustrate the main strategies for glycopeptide enrichment and mass spectrometric analysis. The molecular information obtained by glycoproteomic analysis performed by mass spectrometry has led to new insights into the mechanism linking aberrant glycosylation to cancer cell proliferation, migration and immunoescape.

Proteomic Profile of EPS-Urine through FASP Digestion and Data-Independent Analysis.

Filter-aided sample protocol (FASP) is widely used for proteomics sample preparation because it allows to concentrate diluted samples and it is compatible with a wide variety of detergents. Bottom-up proteomics workflows like FASP increasingly rely on LC-MS/MS methods performed in data-independent analysis (DIA) mode, a scanning method that allows deep proteome coverage and low incidence of missing values. In this report, we will provide the details of a workflow that combines a FASP protocol, a double StageTip purification step and LC-MS/MS in DIA mode for urinary proteome mapping. As a model sample, we analyzed expressed prostatic secretions (EPS)-urine, a sample collected after a digital rectal exam (DRE), which is of interest in prostate cancer biomarker discovery studies.

Proteomic analysis from skin swabs reveals a new set of proteins identifying skin impairment in atopic dermatitis.

Atopic Dermatitis (AD) is a common inflammatory skin disease characterized by skin and systemic inflammation, and barrier dysfunction. Herein, we investigate the proteomic profile of AD skin barrier to identify a unique signature with an easy-performed sampling approach. We enrolled 8 moderate-to-severe AD patients and 8 age- and gender-matched healthy controls. Swabs were obtained from non-lesional skin of retroauricular area and antecubital fold. Peptide mixtures obtained through protein precipitation and in-solution digestion were analysed using NanoLC-MS/MS. Label-free quantification and statistical analysis were conducted in MaxQuant and Perseus. Bioinformatics analysis was performed using Gene Ontology and STRING. We identified 908 proteins and 35 differentially expressed proteins were selected (fold change 2, FDR < 0.05). Particularly, AD skin showed downregulation of skin hydration factors, structural and epidermal proteins, abnormalities in protease-proteasome complex and lipid metabolism profile. Imbalance of antioxidant and inflammatory processes, along with TDRD15 upregulation was also observed. Our result showed partial overlap with skin biopsy/tape-strips studies, showing the reliability of our sampling approach which could be an easier method of detection of hallmark barrier proteins in AD. Furthermore, we displayed a new differentially expressed set of proteins, not yet explored in AD which can have a potential role in AD pathomechanisms.

Human adipose-derived mesenchymal stem cell-conditioned medium ameliorates polyneuropathy and foot ulceration in diabetic BKS db/db mice.

BACKGROUND: Diabetic polyneuropathy (DPN) is the most common and early developing complication of diabetes mellitus, and the key contributor for foot ulcers development, with no specific therapies available. Different studies have shown that mesenchymal stem cell (MSC) administration is able to ameliorate DPN; however, limited cell survival and safety reasons hinder its transfer from bench to bedside. MSCs secrete a broad range of antioxidant, neuroprotective, angiogenic, and immunomodulatory factors (known as conditioned medium), which are all decreased in the peripheral nerves of diabetic patients. Furthermore, the abundance of these factors can be boosted in vitro by incubating MSCs with a preconditioning stimulus, enhancing their therapeutic efficacy. We hypothesize that systemic administration of conditioned medium derived from preconditioned MSCs could reverse DPN and prevent foot ulcer formation in a mouse model of type II diabetes mellitus. METHODS: Diabetic BKS db/db mice were treated with systemic administration of conditioned medium derived from preconditioned human MSCs; conditioned medium derived from non-preconditioned MSCs or vehicle after behavioral signs of DPN was already present. Conditioned medium or vehicle administration was repeated every 2 weeks for a total of four administrations, and several functional and structural parameters characteristic of DPN were evaluated. Finally, a wound was made in the dorsal surface of both feet, and the kinetics of wound closure, re-epithelialization, angiogenesis, and cell proliferation were evaluated. RESULTS: Our molecular, electrophysiological, and histological analysis demonstrated that the administration of conditioned medium derived from non-preconditioned MSCs or from preconditioned MSCs to diabetic BKS db/db mice strongly reverts the established DPN, improving thermal and mechanical sensitivity, restoring intraepidermal nerve fiber density, reducing neuron and Schwann cell apoptosis, improving angiogenesis, and reducing chronic inflammation of peripheral nerves. Furthermore, DPN reversion induced by conditioned medium administration enhances the wound healing process by accelerating wound closure, improving the re-epithelialization of the injured skin and increasing blood vessels in the wound bed in a skin injury model that mimics a foot ulcer. CONCLUSIONS: Studies conducted indicate that MSC-conditioned medium administration could be a novel cell-free therapeutic approach to reverse the initial stages of DPN, avoiding the risk of lower limb amputation triggered by foot ulcer formation and accelerating the wound healing process in case it occurs.

High-throughput detection of low abundance sialylated glycoproteins in human serum by TiO2 enrichment and targeted LC-MS/MS analysis: application to a prostate cancer sample set.

Glycopeptide enrichment can be a strategy to allow the detection of peptides belonging to low abundance proteins in complex matrixes such as blood serum or plasma. Though several glycopeptide enrichment protocols have shown excellent sensitivities in this respect, few reports have demonstrated the applicability of these methods to relatively large sample cohorts. In this work, a fast protocol based on TiO2 enrichment and highly sensitive mass spectrometric analysis by Selected Reaction Monitoring (SRM) has been applied to a cohort of serum samples from prostate cancer and benign prostatic hyperplasia patients in order to detect low abundance proteins in a single LC-MS/MS analysis in nanoscale format, without immunodepletion or peptide fractionation. A peptide library of over 700 formerly N-glycosylated peptides was created by data dependent analysis. Then, 16 medium to low abundance proteins were selected for detection by single injection LC-MS/MS based on selected-reaction monitoring. Results demonstrated the consistent detection of the low-level proteins under investigation. Following label-free quantification, four proteins (Adipocyte plasma membrane-associated protein, Periostin, Cathepsin D and Lysosome-associated membrane glycoprotein 2) were found significantly increased in prostate cancer sera compared to the control group. Graphical abstract ᅟ.

An optimized procedure for on-tissue localized protein digestion and quantification using hydrogel discs and isobaric mass tags: analysis of cardiac myxoma.

An optimized workflow for multiplexed and spatially localized on-tissue quantitative protein analysis is here presented. The method is based on the use of an enzyme delivery platform, a polymeric hydrogel disc, allowing for a localized digestion directly onto the tissue surface coupled with an isobaric mass tag strategy for peptide labeling and relative quantification. The digestion occurs within such hydrogels, followed by peptide solvent extraction and identification by liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-MS/MS). Since this is a histology-directed on-tissue analysis, multiple hydrogels were placed onto morphologically and spatially different regions of interest (ROIs) within the tissue surface, e.g., cardiac myxoma tumor vascularized region and the adjacent hypocellular area. After a microwave digestion step (2 min), enzymatically cleaved peptides were labeled using TMT reagents with isobaric mass tags, enabling analysis of multiple samples per experiment. Thus, N = 8 hydrogel-digested samples from cardiac myxoma serial tissue sections (N = 4 from the vascularized ROIs and N = 4 from the adjacent hypocellular areas) were processed and then combined before a single LC-MS/MS analysis. Regulated proteins from both cardiac myxoma regions were assayed in a single experiment. Graphical abstract The workflow for histology-guided on-tissue localized protein digestion followed by isobaric mass tagging and LC-MS/MS analysis for proteins quantification is here summarized.

Proteome Speciation by Mass Spectrometry: Characterization of Composite Protein Mixtures in Milk Replacers.

The ability of tandem mass spectrometry to determine the primary structure of proteolytic peptides can be exploited to trace back the organisms from which the corresponding proteins were extracted. This information can be important when food products, such as protein powders, can be supplemented with lower-quality starting materials. In order to dissect the origin of proteinaceous material composing a given unknown mixture, a two-step database search strategy for bottom-up nanoscale liquid chromatography-tandem mass spectrometry (nanoLC-MS/MS) data was implemented. A single nanoLC-MS/MS analysis was sufficient not only to determine the qualitative composition of the mixtures under examination, but also to assess the relative percent composition of the various proteomes, if dedicated calibration curves were previously generated. The approach of two-step database search for qualitative analysis and proteome total ion current (pTIC) calculation for quantitative analysis was applied to several binary and ternary mixtures which mimic the composition of milk replacers typically used in calf feeding.

N-glycoprotein analysis discovers new up-regulated glycoproteins in colorectal cancer tissue.

Colorectal cancer is one of the leading causes of death due to cancer worldwide. Therefore, the identification of high-specificity and -sensitivity biomarkers for the early detection of colorectal cancer is urgently needed. Post-translational modifications, such as glycosylation, are known to play an important role in cancer progression. In the present work, we used a quantitative proteomic technique based on (18)O stable isotope labeling to identify differentially expressed N-linked glycoproteins in colorectal cancer tissue samples compared with healthy colorectal tissue from 19 patients undergoing colorectal cancer surgery. We identified 54 up-regulated glycoproteins in colorectal cancer samples, therefore potentially involved in the biological processes of tumorigenesis. In particular, nine of these (PLOD2, DPEP1, SE1L1, CD82, PAR1, PLOD3, S12A2, LAMP3, OLFM4) were found to be up-regulated in the great majority of the cohort, and, interestingly, the association with colorectal cancer of four (PLOD2, S12A2, PLOD3, CD82) has not been hitherto described.