Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer.

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

Targeted Mass Spectrometry Assays for Specific Quantification of Urinary proPSA Isoforms.

Prostate cancer (PCa) is the second leading cause of male cancer-related deaths in the United States. The pre-mature forms of prostate-specific antigen (PSA), proPSA, were shown to be associated with PCa. However, there is a technical challenge in the development of antibody-based immunoassays for specific recognition of each individual proPSA isoform. Herein, we report the development of highly specific, antibody-free, targeted mass spectrometry assays for simultaneous quantification of [-2], [-4], [-5], and [-7] proPSA isoforms in voided urine. The newly developed proPSA assays capitalize on Lys-C digestion to generate surrogate peptides with appropriate length (9-16 amino acids) along with long-gradient liquid chromatography separation. The assay utility of these isoform markers was evaluated in a cohort of 30 well-established clinical urine samples for distinguishing PCa patients from healthy controls. Under the 95% confidence interval, the combination of [-2] and [-4] proPSA isoforms yields the area under curve (AUC) of 0.86, and the AUC value for the combined all four isoforms was calculated to be 0.85. We have further verified [-2]proPSA, the dominant isoform, in an independent cohort of 34 clinical urine samples. Validation of proPSA isoforms in large-scale cohorts is needed to demonstrate their potential clinical utility.

Activity-Based Protein Profiling Probe for the Detection of Enzymes Catalyzing Polysorbate Degradation.

Polysorbates are nonionic surfactants that have been widely used in biotherapeutic formulations to prevent protein aggregation and denaturation. However, polysorbates are subject to degradation after prolonged storage if certain lipases are present in the biotherapeutic product. Because the degradation of polysorbates compromises the shelf life of biotherapeutics and leads to the formation of undesirable products such as protein aggregates and subvisible particles, it is important to identify the active enzymes that catalyze polysorbate hydrolysis. In this study, we developed a novel fluorophosphonate activity-based protein profiling (ABPP) probe (termed the REGN probe), which mimics the structure of polysorbate and targets lipases catalyzing polysorbate degradation. We demonstrated that the REGN probe could enrich certain lipases from Chinese hamster ovary (CHO) cell lysate by more than 100-fold compared with direct tryptic digestion. Furthermore, we found that the REGN probe had higher lipase enrichment efficiency than commercially available ABPP probes including fluorophosphonate-biotin (FP-biotin) and FP-desthiobiotin. Remarkably, the REGN probe can enrich several lipases that cannot be labeled by commercial probes, such as lysosomal acid lipase and cytosolic phospholipase A2. Additionally, we showed that lipases with abundances as low as 0.08 ppm in drug substances were detected by the REGN probe enrichment and liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Collectively, we have developed a novel ABPP probe with higher enrichment efficiency and broader coverage for lipases compared with commercial probes, and this probe can be used to detect the trace level of lipases in biotherapeutic products and to facilitate their development and manufacturing.

Maximizing hydrophobic peptide recovery in proteomics and antibody development using a mass spectrometry compatible surfactant.

Peptide loss due to surface absorption can happen at any step in a protein analysis workflow and is sometimes especially deleterious for hydrophobic peptides. In this study, we found the LC-MS compatible surfactant, n-Dodecyl-ß-D-maltoside (DDM), can maximize hydrophobic peptide recovery in various samples including single cell digests, mAb clinical PK samples, and mAb peptide mapping samples. In HeLa single cell proteomics analysis, more than half of all unique peptides identified were found only in DDM prepared samples, most of which had significantly higher hydrophobicities compared to peptides in control samples. In clinical PK studies, DDM enhanced hydrophobic complementarity-determining region (CDR) peptide signals significantly. The fold change of CDR peptides' intensity enhancement in DDM added samples compared to controls correlate with peptide retention time and hydrophobicity, providing guidance for surrogate peptide selection and peptide standard handling in PK studies. For peptide mapping analysis of mAbs, DDM can improve hydrophobic peptide signal and solution stability over 48 h in an autosampler at 4 °C, which can aid method qualification and transfer during drug development. Lastly, maximizing hydrophobic peptide recovery from samples dried in vacuo was achieved by DDM reconstitution, which provided higher signal for later eluting peaks and higher proteome coverage overall.

Simple and Sensitive Method for Deep Profiling of Host Cell Proteins in Therapeutic Antibodies by Combining Ultra-Low Trypsin Concentration Digestion, Long Chromatographic Gradients, and BoxCar Mass Spectrometry Acquisition.

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful tool for the analysis of host cell proteins (HCP) during antibody drug process development due to its sensitivity, selectivity, and adaptability. However, the enormous dynamic range between the therapeutic antibody and accompanying HCPs poses a significant challenge for LC-MS based detection of these low abundance impurities. To address this challenge, enrichment of HCPs via immunoaffinity, protein A, 2D-LC, or other strategies is typically performed. However, these enrichments are time-consuming and sometimes require a large quantity of sample. Here, we report a simple and sensitive strategy to analyze HCPs in therapeutic antibody samples without cumbersome enrichment by combining an ultra-low trypsin concentration during digestion under nondenaturing conditions, a long chromatographic gradient, and BoxCar acquisition (ULTLB) on a quadrupole-Orbitrap mass spectrometer. Application of this strategy to the NIST monoclonal antibody standard (NISTmAb) resulted in the identification of 453 mouse HCPs, which is a significant increase in the number of identified HCPs without enrichment compared to previous reports. Known amounts of HCPs were spiked into the purified antibody drug substance, demonstrating that the method sensitivity is as low as 0.5 ppm. Thus, the ULTLB method represents a sensitive and simple platform for deep profiling of HCPs in antibodies.

Simultaneous Proteomic Discovery and Targeted Monitoring using Liquid Chromatography, Ion Mobility Spectrometry, and Mass Spectrometry.

Current proteomic approaches include both broad discovery measurements and quantitative targeted analyses. In many cases, discovery measurements are initially used to identify potentially important proteins (e.g. candidate biomarkers) and then targeted studies are employed to quantify a limited number of selected proteins. Both approaches, however, suffer from limitations. Discovery measurements aim to sample the whole proteome but have lower sensitivity, accuracy, and quantitation precision than targeted approaches, whereas targeted measurements are significantly more sensitive but only sample a limited portion of the proteome. Herein, we describe a new approach that performs both discovery and targeted monitoring (DTM) in a single analysis by combining liquid chromatography, ion mobility spectrometry and mass spectrometry (LC-IMS-MS). In DTM, heavy labeled target peptides are spiked into tryptic digests and both the labeled and unlabeled peptides are detected using LC-IMS-MS instrumentation. Compared with the broad LC-MS discovery measurements, DTM yields greater peptide/protein coverage and detects lower abundance species. DTM also achieved detection limits similar to selected reaction monitoring (SRM) indicating its potential for combined high quality discovery and targeted analyses, which is a significant step toward the convergence of discovery and targeted approaches.

Single Amino Acid Variant Profiles of Subpopulations in the MCF-7 Breast Cancer Cell Line.

Cancers are initiated and developed from a small population of stem-like cells termed cancer stem cells (CSCs). There is heterogeneity among this CSC population that leads to multiple subpopulations with their own distinct biological features and protein expression. The protein expression and function may be impacted by amino acid variants that can occur largely due to single nucleotide changes. We have thus performed proteomic analysis of breast CSC subpopulations by mass spectrometry to study the presence of single amino acid variants (SAAVs) and their relation to breast cancer. We have used CSC markers to isolate pure breast CSC subpopulation fractions (ALDH+ and CD44+/CD24- cell populations) and the mature luminal cells (CD49f-EpCAM+) from the MCF-7 breast cancer cell line. By searching the Swiss-CanSAAVs database, 374 unique SAAVs were identified in total, where 27 are cancer-related SAAVs. 135 unique SAAVs were found in the CSC population compared with the mature luminal cells. The distribution of SAAVs detected in MCF-7 cells was compared with those predicted from the Swiss-CanSAAVs database, where we found distinct differences in the numbers of SAAVs detected relative to that expected from the Swiss-CanSAAVs database for several of the amino acids.

Deep-Dive Targeted Quantification for Ultrasensitive Analysis of Proteins in Nondepleted Human Blood Plasma/Serum and Tissues.

Mass spectrometry-based targeted proteomics (e.g., selected reaction monitoring, SRM) is emerging as an attractive alternative to immunoassays for protein quantification. Recently we have made significant progress in SRM sensitivity for enabling quantification of low nanograms per milliliter to sub-naograms per milliliter level proteins in nondepleted human blood plasma/serum without affinity enrichment. However, precise quantification of extremely low abundance proteins (e.g., ≤ 100 pg/mL in blood plasma/serum) using targeted proteomics approaches still remains challenging, especially for these samples without available antibodies for enrichment. To address this need, we have developed an antibody-independent deep-dive SRM (DD-SRM) approach that capitalizes on multidimensional high-resolution reversed-phase liquid chromatography (LC) separation for target peptide separation and enrichment combined with precise selection of target peptide fractions of interest, significantly improving SRM sensitivity by ∼5 orders of magnitude when compared to conventional LC-SRM. Application of DD-SRM to human serum and tissue provides precise quantification of endogenous proteins at the ∼10 pg/mL level in nondepleted serum and at <10 copies per cell level in tissue. Thus, DD-SRM holds great promise for precisely measuring extremely low abundance proteins or protein modifications, especially when high-quality antibodies are not available.

Advances in targeted proteomics and applications to biomedical research.

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.

A quantitative proteomics analysis of MCF7 breast cancer stem and progenitor cell populations.

Accumulating evidence has demonstrated that breast cancers are initiated and develop from a small population of stem-like cells termed cancer stem cells (CSCs). These cells are hypothesized to mediate tumor metastasis and contribute to therapeutic resistance. However, the molecular regulatory networks responsible for maintaining CSCs in an undifferentiated state have yet to be elucidated. In this study, we used CSC markers to isolate pure breast CSCs fractions (ALDH+ and CD44+CD24- cell populations) and the mature luminal cells (CD49f-EpCAM+) from the MCF7 cell line. Proteomic analysis was performed on these samples and a total of 3304 proteins were identified. A label-free quantitative method was applied to analyze differentially expressed proteins. Using the criteria of greater than twofold changes and p value <0.05, 305, 322 and 98 proteins were identified as significantly different in three pairwise comparisons of ALDH+ versus CD44+CD24-, ALDH+ versus CD49f-EpCAM+ and CD44+CD24- versus CD49f-EpCAM+, respectively. Pathway analysis of differentially expressed proteins by Ingenuity Pathway Analysis (IPA) revealed potential molecular regulatory networks that may regulate CSCs. Selected differential proteins were validated by Western blot assay and immunohistochemical staining. The use of proteomics analysis may increase our understanding of the underlying molecular mechanisms of breast CSCs. This may be of importance in the future development of anti-CSC therapeutics.

Tenascin-C: a novel candidate marker for cancer stem cells in glioblastoma identified by tissue microarrays.

Glioblastoma multiforme (GBM) is a highly aggressive brain tumor, with dismal survival outcomes. Recently, cancer stem cells (CSCs) have been demonstrated to play a role in therapeutic resistance and are considered to be the most likely cause of cancer relapse. The identification of CSCs is an important step toward finding new and effective ways to treat GBM. Tenascin-C (TNC) protein has been identified as a potential marker for CSCs in gliomas based on previous work. Here, we have investigated the expression of TNC in tissue microarrays including 17 GBMs, 18 WHO grade III astrocytomas, 15 WHO grade II astrocytomas, 4 WHO grade I astrocytomas, and 7 normal brain tissue samples by immunohistochemical staining. TNC expression was found to be highly associated with the grade of astrocytoma. It has a high expression level in most of the grade III astrocytomas and GBMs analyzed and a very low expression in most grade II astrocytomas, whereas it is undetectable in grade I astrocytomas and normal brain tissues. Double-immunofluorescence staining for TNC and CD133 in GBM tissues revealed that there was a high overlap between theses two positive populations. The results were further confirmed by flow cytometry analysis of TNC and CD133 in GBM-derived stem-like neurospheres in vitro. A limiting dilution assay demonstrated that the sphere formation ability of CD133(+)/TNC(+) and CD133(-)/TNC(+) cell populations is much higher than that of the CD133(+)/TNC(-) and CD133(-)/TNC(-) populations. These results suggest that TNC is not only a potential prognostic marker for GBM but also a potential marker for glioma CSCs, where the TNC(+) population is identified as a CSC population overlapping with part of the CD133(-) cell population.

Large-scale identification of core-fucosylated glycopeptide sites in pancreatic cancer serum using mass spectrometry.

Glycosylation has significant effects on protein function and cell metastasis, which are important in cancer progression. It is of great interest to identify site-specific glycosylation in search of potential cancer biomarkers. However, the abundance of glycopeptides is low compared to that of nonglycopeptides after trypsin digestion of serum samples, and the mass spectrometric signals of glycopeptides are often masked by coeluting nonglycopeptides due to low ionization efficiency. Selective enrichment of glycopeptides from complex serum samples is essential for mass spectrometry (MS)-based analysis. Herein, a strategy has been optimized using LCA enrichment to improve the identification of core-fucosylation (CF) sites in serum of pancreatic cancer patients. The optimized strategy was then applied to analyze CF glycopeptide sites in 13 sets of serum samples from pancreatic cancer, chronic pancreatitis, healthy controls, and a standard reference. In total, 630 core-fucosylation sites were identified from 322 CF proteins in pancreatic cancer patient serum using an Orbitrap Elite mass spectrometer. Further data analysis revealed that 8 CF peptides exhibited a significant difference between pancreatic cancer and other controls, which may be potential diagnostic biomarkers for pancreatic cancer.

Quantitative analysis of single amino acid variant peptides associated with pancreatic cancer in serum by an isobaric labeling quantitative method.

Single amino acid variations are highly associated with many human diseases. The direct detection of peptides containing single amino acid variants (SAAVs) derived from nonsynonymous single nucleotide polymorphisms (SNPs) in serum can provide unique opportunities for SAAV associated biomarker discovery. In the present study, an isobaric labeling quantitative strategy was applied to identify and quantify variant peptides in serum samples of pancreatic cancer patients and other benign controls. The largest number of SAAV peptides to date in serum including 96 unique variant peptides were quantified in this quantitative analysis, of which five variant peptides showed a statistically significant difference between pancreatic cancer and other controls (p-value < 0.05). Significant differences in the variant peptide SDNCEDTPEAGYFAVAVVK from serotransferrin were detected between pancreatic cancer and controls, which was further validated by selected reaction monitoring (SRM) analysis. The novel biomarker panel obtained by combining α-1-antichymotrypsin (AACT), Thrombospondin-1 (THBS1) and this variant peptide showed an excellent diagnostic performance in discriminating pancreatic cancer from healthy controls (AUC = 0.98) and chronic pancreatitis (AUC = 0.90). These results suggest that large-scale analysis of SAAV peptides in serum may provide a new direction for biomarker discovery research.

Glycoprotein biomarker panel for pancreatic cancer discovered by quantitative proteomics analysis.

Pancreatic cancer is a lethal disease where specific early detection biomarkers would be very valuable to improve outcomes in patients. Many previous studies have compared biosamples from pancreatic cancer patients with healthy controls to find potential biomarkers. However, a range of related disease conditions can influence the performance of these putative biomarkers, including pancreatitis and diabetes. In this study, quantitative proteomics methods were applied to discover potential serum glycoprotein biomarkers that distinguish pancreatic cancer from other pancreas related conditions (diabetes, cyst, chronic pancreatitis, obstructive jaundice) and healthy controls. Aleuria aurantia lectin (AAL) was used to extract fucosylated glycoproteins and then both TMT protein-level labeling and label-free quantitative analysis were performed to analyze glycoprotein differences from 179 serum samples across the six different conditions. A total of 243 and 354 serum proteins were identified and quantified by label-free and TMT protein-level quantitative strategies, respectively. Nineteen and 25 proteins were found to show significant differences in samples between the pancreatic cancer and other conditions using the label-free and TMT strategies, respectively, with 7 proteins considered significant in both methods. Significantly different glycoproteins were further validated by lectin-ELISA and ELISA assays. Four candidates were identified as potential markers with profiles found to be highly complementary with CA 19-9 (p < 0.001). Obstructive jaundice (OJ) was found to have a significant impact on the performance of every marker protein, including CA 19-9. The combination of α-1-antichymotrypsin (AACT), thrombospondin-1 (THBS1), and haptoglobin (HPT) outperformed CA 19-9 in distinguishing pancreatic cancer from normal controls (AUC = 0.95), diabetes (AUC = 0.89), cyst (AUC = 0.82), and chronic pancreatitis (AUC = 0.90). A marker panel of AACT, THBS1, HPT, and CA 19-9 showed a high diagnostic potential in distinguishing pancreatic cancer from other conditions with OJ (AUC = 0.92) or without OJ (AUC = 0.95).

Mass-selected site-specific core-fucosylation of ceruloplasmin in alcohol-related hepatocellular carcinoma.

A mass spectrometry-based methodology has been developed to study changes in core-fucosylation of serum ceruloplasmin that are site-specific between cirrhosis and hepatocellular carcinoma (HCC). The serum samples studied for these changes were from patients affected by cirrhosis or HCC with different etiologies, including alcohol, hepatitis B virus, or hepatitis C virus. The methods involved trypsin digestion of ceruloplasmin into peptides followed by Endo F3 digestion, which removed most of the glycan structure while retaining the innermost N-acetylglucosamine (GlcNAc) and/or core-fucose bound to the peptide. This procedure simplified the structures for further analysis by mass spectrometry, where four core-fucosylated sites (sites 138, 358, 397, and 762) were detected in ceruloplasmin. The core-fucosylation ratio of three of these sites increased significantly in alcohol-related HCC samples (sample size = 24) compared to that in alcohol-related cirrhosis samples (sample size = 18), with the highest AUC value of 0.838 at site 138. When combining the core-fucosylation ratio of site 138 in ceruloplasmin and the alpha-fetoprotein (AFP) value, the AUC value increased to 0.954 (ORsite138 = 12.26, p = 0.017; ORAFP = 3.64, p = 0.022), which was markedly improved compared to that of AFP (AUC = 0.867) (LR test p = 0.0002) alone. However, in HBV- or HCV-related liver diseases, no significant site-specific change in core-fucosylation of ceruloplasmin was observed between HCC and cirrhosis.

Target proteomic profiling of frozen pancreatic CD24+ adenocarcinoma tissues by immuno-laser capture microdissection and nano-LC-MS/MS.

Cellular heterogeneity of solid tumors represents a common problem in mass spectrometry (MS)-based analysis of tissue specimens. Combining immuno-laser capture microdissection (iLCM) and mass spectrometry (MS) provides a means to study proteins that are specific for pure cell subpopulations in complex tissues. CD24, as a cell surface marker for detecting pancreatic cancer stem cells (CSCs), is directly correlated with the development and metastasis of pancreatic cancer. Herein, we describe an in-depth proteomic profiling of frozen pancreatic CD24(+) adenocarcinoma cells from early stage tumors using iLCM and LC-MS/MS and a comparison with CD24(-) cells dissected from patient-matched adjacent normal tissues. Approximately 40 nL of tissue was procured from each specimen and subjected to tandem MS analysis in triplicate. A total of 2665 proteins were identified, with 375 proteins in common that were significantly differentially expressed in CD24(+) versus CD24(-) cells by at least a 2-fold change. The major groups of the differentially overexpressed proteins are involved in promoting tumor cell migration and invasion, immune escape, and tumor progression. Three selected candidates relevant to mediating immune escape, CD59, CD70, and CD74, and a tumor promoter, TGFBI, were further validated by immunohistochemistry analysis on tissue microarrays. These proteins showed significantly increased expression in a large group of clinical pancreatic adenocarcinomas but were negative in all normal pancreas samples. The significant coexpression of these proteins with CD24 suggests that they may play important roles in the progression of pancreatic cancer and could serve as promising prognosis markers and novel therapeutic targets for this deadly disease.

Altered expression of sialylated glycoproteins in ovarian cancer sera using lectin-based ELISA assay and quantitative glycoproteomics analysis.

Herein, we identify and confirm differentially expressed sialoglycoproteins in the serum of patients with ovarian cancer. On the basis of Sambucus nigra (SNA) lectin enrichment and on an isobaric chemical labeling quantitative strategy, clusterin (CLUS), leucine-rich alpha-2-glycoprotein (LRG1), hemopexin (HEMO), vitamin D-binding protein (VDB), and complement factor H (CFH) were found to be differentially expressed in the serum of patients with ovarian cancer compared to benign diseases. The abnormal sialylation levels of CLUS, CFH, and HEMO in serum of ovarian cancer patients were verified by a lectin-based ELISA assay. ELISA assays were further applied to measure total protein level changes of these glycoproteins. Protein levels of CLUS were found to be down-regulated in the serum of ovarian cancer patients, while protein levels of LRG1 were increased. The combination of CLUS and LRG1 (AUC = 0.837) showed improved performance for distinguishing stage III ovarian cancer from benign diseases compared to CA125 alone (AUC = 0.811). In differentiating early stage ovarian cancer from benign diseases or healthy controls, LRG1 showed comparable performance to CA125. An independent sample set was further used to confirm the ability of these candidate markers to detect patients with ovarian cancer. Our study provides a comprehensive strategy for the identification of candidate biomarkers that show the potential for diagnosis of ovarian cancer. Further studies using a large number of samples are necessary to validate the utility of this panel of proteins.

Isobaric protein-level labeling strategy for serum glycoprotein quantification analysis by liquid chromatography-tandem mass spectrometry.

While peptide-level labeling using isobaric tag reagents has been widely applied for quantitative proteomics experiments, there are comparatively few reports of protein-level labeling. Intact protein labeling could be broadly applied to quantification experiments utilizing protein-level separations or enrichment schemes. Here, protein-level isobaric labeling was explored as an alternative strategy to peptide-level labeling for serum glycoprotein quantification. Labeling and digestion conditions were optimized by comparing different organic solvents and enzymes. Digestions with Asp-N and trypsin were found highly complementary; combining the results enabled quantification of 30% more proteins than either enzyme alone. Three commercial reagents were compared for protein-level labeling. Protein identification rates were highest with iTRAQ 4-plex when compared to TMT 6-plex and iTRAQ 8-plex using higher-energy collisional dissociation on an Orbitrap Elite mass spectrometer. The compatibility of isobaric protein-level labeling with lectin-based glycoprotein enrichment was also investigated. More than 74% of lectin-bound labeled proteins were known glycoproteins, which was similar to results from unlabeled and peptide-level labeled serum samples. Finally, protein-level and peptide-level labeling strategies were compared for serum glycoprotein quantification. Isobaric protein-level labeling gave comparable identification levels and quantitative precision to peptide-level labeling.

Immunohistochemical staining, laser capture microdissection, and filter-aided sample preparation-assisted proteomic analysis of target cell populations within tissue samples.

An important problem involves isolating subpopulations of cells defined by protein markers in clinical tissue samples for proteomic studies. We describe a method termed Immunohistochemical staining, laser capture microdissection (LCM) and filter-aided sample preparation (FASP)-Assisted Proteomic analysis of Target cell populations within tissue samples (ILFAPT). The principle of ILFAPT is that a target cell population expressing a protein of interest can be lit up by immunohistochemical staining and isolated from tissue sections using LCM for FASP and proteomic analysis. Using this method, we isolated a small population of CD90(+) stem-like cells from glioblastoma multiforme tissue sections and identified 674 high-confidence (false discovery rate < 0.01) proteins from 32 nL of CD90(+) cells by LC-MS/MS using an Orbitrap Elite mass spectrometer. We further quantified the relative abundance of proteins identified from equal volumes of LCM-captured CD90(+) and CD90(-) cells, where 109 differentially expressed proteins were identified. The major group of these differentially expressed proteins was relevant to cell adhesion and cellular movement. This ILFAPT method has demonstrated the ability to provide in-depth proteome analysis of a very small specific cell population within tissues. It can be broadly applied to the study of target cell populations within clinical specimens.

Design and Numerical Simulation Study of a Novel AICD for Water Control and Gas Production in Gas Wells.

The automatic inflow control device (AICD) used for water control and gas recovery in gas wells as the core component of gas well intelligent layered/segmented production and water control technology is very important for the development of advanced well completion (AWC) technology in water-producing gas reservoirs. Therefore, the design of AICD to ensure that the gas flows smoothly inside it and to keep water under control to a greater extent can maximize the performance of the AICD, and the most important thing is to restrict the water in the formation from entering the wellbore. However, currently, there are very few designs and research on the AICD used for water control and gas production in the gas wells, and the performance of this type of tool and the law of gas and water flow inside it are not perfect, so more in-depth research is needed. In this paper, a new type of AICD is designed to realize the function of water control and gas flow smoothly, and the DoE of the new AICD is carried out, determining the factors that will affect the key technical indicators and the factors that may have interactive effects, using the numerical simulation method of computational fluid dynamics to carry out optimal design, conducting fluid physical property sensitivity analysis, and flow rate applicability analysis. The results show that the tool is not sensitive to the viscosity of water and gas in different gas reservoirs but is very sensitive to the density of water and gas. When the gas/water flow rate ratio is less than 4, it can exert its water control effect. In addition, the results of multiple sets of physical experiments are well consistent with the simulation results; the average deviation of single-phase water is 10.91% and the average deviation of single-phase gas is 11.85%. Computational fluid dynamics and physical experiment results show that, under these conditions, the difference in fluid flow characteristics can be fully exploited; the channel is automatically identified to produce a small gas pressure drop and a large water flow pressure drop. The research in this paper belongs to the key technology of the AWC technology of gas wells in the new water control strategy of the current and has a certain reference value to make up for the defects of drainage gas recovery technology in the water management strategy..