Proteomic landscape of primary and metastatic brain tumors for heterogeneity discovery.

PURPOSE: Despite recent advancements in our understanding of driver gene mutations and heterogeneity within brain tumors, whether primary or metastatic (also known as secondary), our comprehension of proteomic changes remains inadequate. The aim of this study is to provide an informative source for brain tumor researches, and distinguish primary brain tumors and secondary brain tumors from extracranial origins based on proteomic analysis. EXPERIMENTAL DESIGN: We assembled the most frequent brain tumors as follows: gliomas from WHO grade 2 to 4, with IDH1 mutations and wildtypes; brain metastases (BrMs) originating from lung cancer (LC), breast cancer (BC), ovarian cancer (OC), and colorectal cancer (CC). A total of 29 tissue samples were analyzed by label free quantitative mass spectrometry-based proteomics. RESULTS: In total, 8165 protein groups were quantified, of which 4383 proteins were filtered at 50% valid intensity values for downstream analysis. Proteomic analysis of BrMs reveals conserved features shared among multiple origins. While proteomic heterogeneities were found for discriminating different grades of gliomas, as well as IDH1 mutant and wildtype gliomas. In addition, notable distinctions were observed at the pathway level between BrMs and gliomas. Specifically, BrMs exhibited characteristic pathways focused on proliferation and immunomodulation after colonizing the brain, whereas gliomas primarily engaged in invasion processes. CONCLUSIONS AND CLINICAL RELEVANCE: We characterized an extensive proteomic landscape of BrMs and gliomas. These findings have promising implications for the development of targeted therapies for BrMs and gliomas.

Circulating proteomic panels for risk stratification of intracranial aneurysm and its rupture.

The prevalence of intracranial aneurysm (IA) is increasing, and the consequences of its rupture are severe. This study aimed to reveal specific, sensitive, and non-invasive biomarkers for diagnosis and classification of ruptured and unruptured IA, to benefit the development of novel treatment strategies and therapeutics altering the course of the disease. We first assembled an extensive candidate biomarker bank of IA, comprising up to 717 proteins, based on altered proteins discovered in the current tissue and serum proteomic analysis, as well as from previous studies. Mass spectrometry assays for hundreds of biomarkers were efficiently designed using our proposed deep learning-based method, termed DeepPRM. A total of 113 potential markers were further quantitated in serum cohort I (n = 212) & II (n = 32). Combined with a machine-learning-based pipeline, we built two sets of biomarker combinations (P6 & P8) to accurately distinguish IA from healthy controls (accuracy: 87.50%) or classify IA rupture patients (accuracy: 91.67%) upon evaluation in the external validation set (n = 32). This extensive circulating biomarker development study provides valuable knowledge about IA biomarkers.

Comprehensive mass spectrometry for development of proteomic biomarkers of intracranial aneurysms.

Protein biomarkers of intracranial aneurysm (IA) are essential for early detection and prediction of its rupture to facilitate the diagnosis and clinical management of the disease, monitor treatment response and detect recurrence. Here, we developed a comprehensive strategy for IA biomarker discovery by analyzing tissues from an animal model (n = 4) and serum from human patients (n = 60) using isobaric tandem mass tags-based quantitative proteomics. A total of 4811 and 562 proteins were identified from aneurysm tissue and serum samples, respectively. The 223 candidate protein biomarkers were further validated in an independent serum cohort (n = 30) by multiple reaction monitoring analysis. Combined with a logistic regression model, we built a diagnostic classifier P2 (FCN2 & RARRES2) to differentiate IA from healthy controls with accuracy of 93.3%, as well as a diagnostic classifier P7 (ADAM12, APOL3, F9, C3, CEACAM1, ICAM3, KLHDC7A) to classify ruptured IA from unruptured IA with accuracy of 95.0%. Taken together, our results suggest a valuable strategy for biomarker discovery and patient stratification in IA.

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer.

Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

Ultrasensitive Trace Sample Proteomics Unraveled the Protein Remodeling during Mesenchymal-Amoeboid Transition.

Deep mining the proteome of trace biological samples is critical for biomedical applications. However, it remains a challenge due to the loss of analytes caused by current sample preparation procedures. To address this, we recently developed a single-pot and miniaturized in-solution digestion (SMID) method for minute sample handling with three streamlined steps and completed within 3 h. The SMID approach outperformed the traditional workflow in substantially saving time, reducing sample loss, and exhibiting extensive applicability for 10-100 000 cell analysis. This user-friendly and high-sensitivity strategy enables ∼5300 proteins and 53 000 peptides to be confidently identified within 1 h of mass spectrometry (MS) time from a small amount of 1000 HeLa cells. In addition, we accurately and robustly detected proteomes in 10 mouse oocytes with excellent reproducibility. We further adopted SMID for the proteome analysis in cell migration under confinement, which induced cells to undergo a mesenchymal-amoeboid transition (MAT). During the MAT, a systematic quantitative proteome map of 1000 HeLa cells was constructed with seven expression profile clusters, which illustrated the application of SMID and provided a fundamental resource to investigate the mechanism of MAT.

Rapid Sample Preparation Workflow for Serum Sample Analysis with Different Mass Spectrometry Acquisition Strategies.

Fast, robust, and high-throughput mass spectrometry-based serum proteomic pipelines have great potential to yield information for biomarker discovery and daily clinical practice. Here, we developed a simple and rapid sample preparation (RSP) workflow by reducing the classical pretreatment time from overnight to less than 1.5 h in an ordinary system. In HeLa cell lysates and serum samples, the number of proteins and tryptic peptides generated using the RSP was comparable to that generated using conventional methods. For fast scanning of the serum proteome, the RSP-supported pipeline could complete a test in less than 2 h with 30 min of LC-MS/MS analysis. Nearly 390 proteins spanning 8 magnitudes of abundance range were identified with high reproducibility, containing over 90 cancer-associated proteins and over 50 FDA-approved biomarkers. For fast assay development, eight candidate biomarker peptides for cardiovascular disease (CVD) were quantified by MRM with high accuracy (CV% <10). After a simple highly abundant protein removal, a deep serum proteome of over 1400 proteins was reached. By analyzing the depleted serum in DIA acquisition mode, over 700 proteins were quantified. The differentially expressed proteins could help us unambiguously distinguish the serum samples from healthy people and patients with pancreatic cancer (PC). Potential biomarkers for PC were also found. The new RSP method, which is rapid and simple, meets the demands of both deep mining and fast analysis of serum proteins. We believe that it will be widely used in serum protein studies and accelerate the transformation from biomarker discovery to clinical application.

LC-MS/MS and SWATH based serum metabolomics enables biomarker discovery in pancreatic cancer.

BACKGROUND: Pancreatic cancer (PC) is the fourth leading cause of cancer death because of its subtle clinical symptoms in the early stage. To discover particular serum metabolites as potential biomarkers to differentiate pancreatic carcinoma from benign disease (BD) is on urgent demand. METHOD: To comprehensively analyze serum metabolites obtained from 14 patients with PC, 10 patients with BD and 10 healthy individuals (normal control, NC), we separated the metabolites using both reversed-phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC). The data were acquired on a high-resolution quadrupole time-of-flight mass spectrometer operated in negative (ESI-) and positive (ESI+) ionization modes, respectively. Differential metabolites were selected by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Sequential window acquisition of all theoretical spectra (SWATH) analysis was further utilized to validate the metabolites found in discovery stage. The receiver operator characteristics (ROC) curve analysis was performed to evaluate predictive clinical usefulness of 8 metabolites. RESULTS: A total of 8 metabolites including taurocholic acid, glycochenodexycholic acid, glycocholic acid, L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine were identified and relatively quantified as differential metabolites for discriminating PC, BD and NC. The 8 metabolites and their combination discriminated PC from BD and NC with well-performed area under the curve (AUC) values, sensitivity and specificity. CONCLUSION: Bile acids (especially taurocholic acid) performed to be potential biomarkers in PC diagnosis. Other amino acids (such as L-glutamine, glutamic acid, L-phenylalanine, L-tryptophan, and L-arginine) in serum samples from PC patients might provide a sensitive, blood-borne diagnostic signature for the presence of PC or its precursor lesions.

A Comprehensive Proteome Analysis of Peripheral Blood Mononuclear Cells (PBMCs) to Identify Candidate Biomarkers of Pancreatic Cancer.

BACKGROUND/AIM: Pancreatic cancer (PC) is currently the fourth leading cause of cancer-related mortality worldwide. Peripheral blood mononuclear cells (PBMCs) is a subpopulation of accessible and functional immune cells. Comparative analysis of the proteome of PBMCs can help us elucidate the mechanism of disease and find potential biomarkers for diagnosis. MATERIALS AND METHODS: PBMCs were collected from healthy individuals, patients with benign diseases, and pancreatic cancer. iTRAQ-2DLC-MS/MS and SWATH methodologies were applied to make a comparative proteomics analysis of PBMCs. RESULTS: A total of 3,357 proteins with a false discovery rate (FDR) <1% were identified, of which 114 proteins were found dysregulated in the PC group. An extensive SWATH library was constructed which showed a potential application for large scale clinical sample analysis. CONCLUSION: A PBMCs proteome with extensive protein representation was achieved, which will potentially allow the identification of novel biomarkers for PC.

Identification of Genes Relevant to Pesticides and Biology from Global Transcriptome Data of Monochamus alternatus Hope (Coleoptera: Cerambycidae) Larvae.

Monochamus alternatus Hope is the main vector in China of the Pine Wilt Disease caused by the pine wood nematode Bursaphelenchus xylophilus. Although chemical control is traditionally used to prevent pine wilt disease, new strategies based in biological control are promising ways for the management of the disease. However, there is no deep sequence analysis of Monochamus alternatus Hope that describes the transcriptome and no information is available about gene function of this insect vector. We used next generation sequencing technology to sequence the whole fourth instar larva transcriptome of Monochamus alternatus Hope and successfully built a Monochamus alternatus Hope transcriptome database. In total, 105,612 unigenes were assigned for Gene Ontology (GO) terms, information for 16,730 classified unigenes was obtained in the Clusters of Orthologous Groups (COGs) database, and 13,024 unigenes matched with 224 predicted pathways in the Kyoto Encyclopedia of Genes and Genome (KEGG). In addition, genes related to putative insecticide resistance-related genes, RNAi, the Bt receptor, intestinal digestive enzymes, possible future insect control targets and immune-related molecules are described. This study provides valuable basic information that can be used as a gateway to develop new molecular tools for Monochamus alternatus Hope control strategies.