The First Pituitary Proteome Landscape From Matched Anterior and Posterior Lobes for a Better Understanding of the Pituitary Gland.

To date, very few mass spectrometry (MS)-based proteomics studies are available on the anterior and posterior lobes of the pituitary. In the past, MS-based investigations have focused exclusively on the whole pituitary gland or anterior pituitary lobe. In this study, for the first time, we performed a deep MS-based analysis of five anterior and five posterior matched lobes to build the first lobe-specific pituitary proteome map, which documented 4090 proteins with isoforms, mostly mapped into chromosomes 1, 2, and 11. About 1446 differentially expressed significant proteins were identified, which were studied for lobe specificity, biological pathway enrichment, protein-protein interaction, regions specific to comparison of human brain and other neuroendocrine glands from Human Protein Atlas to identify pituitary-enriched proteins. Hormones specific to each lobe were also identified and validated with parallel reaction monitoring-based target verification. The study identified and validated hormones, growth hormone and thyroid-stimulating hormone subunit beta, exclusively to the anterior lobe whereas oxytocin-neurophysin 1 and arginine vasopressin to the posterior lobe. The study also identified proteins POU1F1 (pituitary-specific positive transcription factor 1), POMC (pro-opiomelanocortin), PCOLCE2 (procollagen C-endopeptidase enhancer 2), and NPTX2 (neuronal pentraxin-2) as pituitary-enriched proteins and was validated for their lobe specificity using parallel reaction monitoring. In addition, three uPE1 proteins, namely THEM6 (mesenchymal stem cell protein DSCD75), FSD1L (coiled-coil domain-containing protein 10), and METTL26 (methyltransferase-like 26), were identified using the NeXtProt database, and depicted tumor markers S100 proteins having high expression in the posterior lobe. In summary, the study documents the first matched anterior and posterior pituitary proteome map acting as a reference control for a better understanding of functional and nonfunctional pituitary adenomas and extrapolating the aim of the Human Proteome Project towards the investigation of the proteome of life.

Identification of State-Specific Proteomic and Transcriptomic Signatures of Microglia-Derived Extracellular Vesicles.

Microglia are resident immune cells of the brain that play important roles in mediating inflammatory responses in several neurological diseases via direct and indirect mechanisms. One indirect mechanism may involve extracellular vesicle (EV) release, so that the molecular cargo transported by microglia-derived EVs can have functional effects by facilitating intercellular communication. The molecular composition of microglia-derived EVs, and how microglial activation states impact EV composition and EV-mediated effects in neuroinflammation, remain poorly understood. We hypothesize that microglia-derived EVs have unique molecular profiles that are determined by microglial activation state. Using size-exclusion chromatography to purify EVs from BV2 microglia, combined with proteomic (label-free quantitative mass spectrometry or LFQ-MS) and transcriptomic (mRNA and noncoding RNA seq) methods, we obtained comprehensive molecular profiles of microglia-derived EVs. LFQ-MS identified several classic EV proteins (tetraspanins, ESCRT machinery, and heat shock proteins), in addition to over 200 proteins not previously reported in the literature. Unique mRNA and microRNA signatures of microglia-derived EVs were also identified. After treating BV2 microglia with lipopolysaccharide (LPS), interleukin-10, or transforming growth factor beta, to mimic pro-inflammatory, anti-inflammatory, or homeostatic states, respectively, LFQ-MS and RNA seq revealed novel state-specific proteomic and transcriptomic signatures of microglia-derived EVs. Particularly, LPS treatment had the most profound impact on proteomic and transcriptomic compositions of microglia-derived EVs. Furthermore, we found that EVs derived from LPS-activated microglia were able to induce pro-inflammatory transcriptomic changes in resting responder microglia, confirming the ability of microglia-derived EVs to relay functionally relevant inflammatory signals. These comprehensive microglia-EV molecular datasets represent important resources for the neuroscience and omics communities and provide novel insights into the role of microglia-derived EVs in neuroinflammation.

On the excessive use of coefficient of variation as a metric of quantitation quality in proteomics.

The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.

Proteomic Profiling of Dilated Cardiomyopathy Plasma Samples ─ Searching for Biomarkers with Potential to Predict the Outcome of Therapy.

Determination of the prognosis and treatment outcomes of dilated cardiomyopathy is a serious problem due to the lack of valid specific protein markers. Using in-depth proteome discovery analysis, we compared 49 plasma samples from patients suffering from dilated cardiomyopathy with plasma samples from their healthy counterparts. In total, we identified 97 proteins exhibiting statistically significant dysregulation in diseased plasma samples. The functional enrichment analysis of differentially expressed proteins uncovered dysregulation in biological processes like inflammatory response, wound healing, complement cascade, blood coagulation, and lipid metabolism in dilated cardiomyopathy patients. The same proteome approach was employed in order to find protein markers whose expression differs between the patients well-responding to therapy and nonresponders. In this case, 45 plasma proteins revealed statistically significant different expression between these two groups. Of them, fructose-1,6-bisphosphate aldolase seems to be a promising biomarker candidate because it accumulates in plasma samples obtained from patients with insufficient treatment response and with worse or fatal outcome. Data are available via ProteomeXchange with the identifier PXD046288.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Comprehensive Proteomics Analysis of the Hemolymph Composition of Sugar-Fed Aedes aegypti Female and Male Mosquitoes.

In arthropods, hemolymph carries immune cells and solubilizes and transports nutrients, hormones, and other molecules that are involved in diverse physiological processes including immunity, metabolism, and reproduction. However, despite such physiological importance, little is known about its composition. We applied mass spectrometry-based label-free quantification approaches to study the proteome of hemolymph perfused from sugar-fed female and male Aedes aegypti mosquitoes. A total of 1403 proteins were identified, out of which 447 of them were predicted to be extracellular. In both sexes, almost half of these extracellular proteins were predicted to be involved in defense/immune response, and their relative abundances (based on their intensity-based absolute quantification, iBAQ) were 37.9 and 33.2%, respectively. Interestingly, among them, 102 serine proteases/serine protease-homologues were identified, with almost half of them containing CLIP regulatory domains. Moreover, proteins belonging to families classically described as chemoreceptors, such as odorant-binding proteins (OBPs) and chemosensory proteins (CSPs), were also highly abundant in the hemolymph of both sexes. Our data provide a comprehensive catalogue of A. aegypti hemolymph basal protein content, revealing numerous unexplored targets for future research on mosquito physiology and disease transmission. It also provides a reference for future studies on the effect of blood meal and infection on hemolymph composition.

Quantitative proteomics reveals cellular responses to individual mAb expression and tunicamycin in CHO cells.

Chinese hamster ovary (CHO) cells are popular in the pharmaceutical industry for their ability to produce high concentrations of antibodies and their resemblance to human cells in terms of protein glycosylation patterns. Current data indicate the relevance of CHO cells in the biopharmaceutical industry, with a high number of product commendations and a significant market share for monoclonal antibodies. To enhance the production capabilities of CHO cells, a deep understanding of their cellular and molecular composition is crucial. Genome sequencing and proteomic analysis have provided valuable insights into the impact of the bioprocessing conditions, productivity, and product quality. In our investigation, we conducted a comparative analysis of proteomic profiles in high and low monoclonal antibody-producing cell lines and studied the impact of tunicamycin (TM)-induced endoplasmic reticulum (ER) stress. We examined the expression levels of different proteins including unfolded protein response (UPR) target genes by using label-free quantification techniques for protein abundance. Our results show the upregulation of proteins associated with protein folding mechanisms in low producer vs. high producer cell line suggesting a form of ER stress related to specific protein production. Further, Hspa9 and Dnaja3 are notable candidates activated by the mitochondria UPR and play important roles in protein folding processes in mitochondria. We identified significant upregulation of Nedd8 and Lgmn proteins in similar levels which may contribute to UPR stress. Interestingly, the downregulation of Hspa5/Bip and Pdia4 in response to tunicamycin treatment suggests a low-level UPR activation. KEY POINTS: • Proteome profiling of recombinant CHO cells under mild TM treatment. • Identified protein clusters are associated with the unfolded protein response (UPR). • The compared cell lines revealed noticeable disparities in protein expression levels.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

A comparative proteomics analysis of four contact allergens in THP-1 cells shows distinct alterations in key metabolic pathways.

Allergic contact dermatitis (ACD) is the predominant form of immunotoxicity in humans. The sensitizing potential of chemicals can be assessed in vitro. However, a better mechanistic understanding could improve the current OECD-validated test battery. The aim of this study was to get insights into toxicity mechanisms of four contact allergens, p-benzoquinone (BQ), 2,4-dinitrochlorobenzene (DNCB), p-nitrobenzyl bromide (NBB) and NiSO4, by analyzing differential proteome alterations in THP-1 cells using two common proteomics workflows, stable isotope labeling by amino acids in cell culture (SILAC) and label-free quantification (LFQ). Here, SILAC was found to deliver more robust results. Overall, the four allergens induced similar responses in THP-1 cells, which underwent profound metabolic reprogramming, including a striking upregulation of the TCA cycle accompanied by pronounced induction of the Nrf2 oxidative stress response pathway. The magnitude of induction varied between the allergens with DNCB and NBB being most potent. A considerable overlap between transcriptome-based signatures of the GARD assay and the proteins identified in our study was found. When comparing the results of this study to a previous proteomics study in human primary monocyte-derived dendritic cells, we found a rather low share in regulated proteins. However, on pathway level, the overlap was high, indicating that affected pathways rather than single proteins are more eligible to investigate proteomic changes induced by contact allergens. Overall, this study confirms the potential of proteomics to obtain a profound mechanistic understanding, which may help improving existing in vitro assays for skin sensitization.

Deciphering the Kidney Matrisome: Identification and Quantification of Renal Extracellular Matrix Proteins in Healthy Mice.

Precise characterization of a tissue's extracellular matrix (ECM) protein composition (matrisome) is essential for biomedicine. However, ECM protein extraction that requires organ-specific optimization is still a major limiting factor in matrisome studies. In particular, the matrisome of mouse kidneys is still understudied, despite mouse models being crucial for renal research. Here, we comprehensively characterized the matrisome of kidneys in healthy C57BL/6 mice using two ECM extraction methods in combination with liquid chromatography tandem mass spectrometry (LC-MS/MS), protein identification, and label-free quantification (LFQ) using MaxQuant. We identified 113 matrisome proteins, including 22 proteins that have not been previously listed in the Matrisome Database. Depending on the extraction approach, the core matrisome (structural proteins) comprised 45% or 73% of kidney ECM proteins, and was dominated by glycoproteins, followed by collagens and proteoglycans. Among matrisome-associated proteins, ECM regulators had the highest LFQ intensities, followed by ECM-affiliated proteins and secreted factors. The identified kidney ECM proteins were primarily involved in cellular, developmental and metabolic processes, as well as in molecular binding and regulation of catalytic and structural molecules' activity. We also performed in silico comparative analysis of the kidney matrisome composition in humans and mice based on publicly available data. These results contribute to the first reference database for the mouse renal matrisome.

Data-Dependent Acquisition with Precursor Coisolation Improves Proteome Coverage and Measurement Throughput for Label-Free Single-Cell Proteomics.

We combined efficient sample preparation and ultra-low-flow liquid chromatography with a newly developed data acquisition and analysis scheme termed wide window acquisition (WWA) to quantify >3,000 proteins from single cells in rapid label-free analyses. WWA employs large isolation windows to intentionally co-isolate and co-fragment adjacent precursors along with the selected precursor. Optimized WWA increased the number of MS2-identified proteins by ≈40 % relative to standard data-dependent acquisition. For a 40-min LC gradient operated at ≈15 nL/min, we identified an average of 3,524 proteins per single-cell-sized aliquot of protein digest. Reducing the active gradient to 20 min resulted in a modest 10 % decrease in proteome coverage. Using this platform, we compared protein expression between single HeLa cells having an essential autophagy gene, atg9a, knocked out, with their isogenic WT parental line. Similar proteome coverage was observed, and 268 proteins were significantly up- or downregulated. Protein upregulation primarily related to innate immunity, vesicle trafficking and protein degradation.

ANPELA: analysis and performance assessment of the label-free quantification workflow for metaproteomic studies.

Label-free quantification (LFQ) with a specific and sequentially integrated workflow of acquisition technique, quantification tool and processing method has emerged as the popular technique employed in metaproteomic research to provide a comprehensive landscape of the adaptive response of microbes to external stimuli and their interactions with other organisms or host cells. The performance of a specific LFQ workflow is highly dependent on the studied data. Hence, it is essential to discover the most appropriate one for a specific data set. However, it is challenging to perform such discovery due to the large number of possible workflows and the multifaceted nature of the evaluation criteria. Herein, a web server ANPELA (https://idrblab.org/anpela/) was developed and validated as the first tool enabling performance assessment of whole LFQ workflow (collective assessment by five well-established criteria with distinct underlying theories), and it enabled the identification of the optimal LFQ workflow(s) by a comprehensive performance ranking. ANPELA not only automatically detects the diverse formats of data generated by all quantification tools but also provides the most complete set of processing methods among the available web servers and stand-alone tools. Systematic validation using metaproteomic benchmarks revealed ANPELA's capabilities in 1 discovering well-performing workflow(s), (2) enabling assessment from multiple perspectives and (3) validating LFQ accuracy using spiked proteins. ANPELA has a unique ability to evaluate the performance of whole LFQ workflow and enables the discovery of the optimal LFQs by the comprehensive performance ranking of all 560 workflows. Therefore, it has great potential for applications in metaproteomic and other studies requiring LFQ techniques, as many features are shared among proteomic studies.

Hyperosmolality in CHO cell culture: effects on the proteome.

Chinese hamster ovary (CHO) cells are the most commonly used host cell lines for therapeutic protein production. Exposure of these cells to highly concentrated feed solution during fed-batch cultivation can lead to a non-physiological increase in osmolality (> 300 mOsm/kg) that affects cell physiology, morphology, and proteome. As addressed in previous studies (and indeed, as recently addressed in our research), hyperosmolalities of up to 545 mOsm/kg force cells to abort proliferation and gradually increase their volume-almost tripling it. At the same time, CHO cells also show a significant hyperosmolality-dependent increase in mitochondrial activity. To gain deeper insight into the molecular mechanisms that are involved in these processes, as detailed in this paper, we performed a comparative quantitative label-free proteome study of hyperosmolality-exposed CHO cells compared with control cells. Our analysis revealed differentially expressed key proteins that mediate mitochondrial activation, oxidative stress amelioration, and cell cycle progression. Our studies also demonstrate a previously unknown effect: the strong regulation of proteins can alter both cell membrane stiffness and permeability. For example, we observed that three types of septins (filamentous proteins that form diffusion barriers in the cell) became strongly up-regulated in response to hyperosmolality in the experimental setup. Overall, these new observations correlate well with recent CHO-based fluxome and transcriptome studies, and reveal additional unknown proteins involved in the response to hyperosmotic pressure by over-concentrated feed in mammalian cells.Key points• First-time comparative proteome analysis of CHO cells exposed to over-concentrated feed.• Discovery of membrane barrier-forming proteins up-regulation under hyperosmolality.• Description of mitochondrial and protein chaperones activation in treated cells.

Functional and Taxonomic Traits of the Gut Microbiota in Type 1 Diabetes Children at the Onset: A Metaproteomic Study.

Type 1 diabetes (T1D) is a chronic autoimmune metabolic disorder with onset in pediatric/adolescent age, characterized by insufficient insulin production, due to a progressive destruction of pancreatic ß-cells. Evidence on the correlation between the human gut microbiota (GM) composition and T1D insurgence has been recently reported. In particular, 16S rRNA-based metagenomics has been intensively employed in the last decade in a number of investigations focused on GM representation in relation to a pre-disease state or to a response to clinical treatments. On the other hand, few works have been published using alternative functional omics, which is more suitable to provide a different interpretation of such a relationship. In this work, we pursued a comprehensive metaproteomic investigation on T1D children compared with a group of siblings (SIBL) and a reference control group (CTRL) composed of aged matched healthy subjects, with the aim of finding features in the T1D patients' GM to be related with the onset of the disease. Modulated metaproteins were found either by comparing T1D with CTRL and SIBL or by stratifying T1D by insulin need (IN), as a proxy of ß-cells damage, showing some functional and taxonomic traits of the GM, possibly related to the disease onset at different stages of severity.

DDASSQ: An open-source, multiple peptide sequencing strategy for label free quantification based on an OpenMS pipeline in the KNIME analytics platform.

In this study we investigated the performance of a computational pipeline for protein identification and label free quantification (LFQ) of LC-MS/MS data sets from experimental animal tissue samples, as well as the impact of its specific peptide search combinatorial approach. The full pipeline workflow was composed of peptide search engine adapters based on different identification algorithms, in the frame of the open-source OpenMS software running within the KNIME analytics platform. Two different in silico tryptic digestion, database-search assisted approaches (X!Tandem and MS-GF+), de novo peptide sequencing based on Novor and consensus library search (SpectraST), were tested for the processing of LC-MS/MS raw data files obtained from proteomic LC-MS experiments done on proteolytic extracts from mouse ex vivo liver samples. The results from proteomic LFQ were compared to those based on the application of the two software tools MaxQuant and Proteome Discoverer for protein inference and label-free data analysis in shotgun proteomics. Data are available via ProteomeXchange with identifier PXD025097.

Simultaneous Improvement in the Precision, Accuracy, and Robustness of Label-free Proteome Quantification by Optimizing Data Manipulation Chains.

The label-free proteome quantification (LFQ) is multistep workflow collectively defined by quantification tools and subsequent data manipulation methods that has been extensively applied in current biomedical, agricultural, and environmental studies. Despite recent advances, in-depth and high-quality quantification remains extremely challenging and requires the optimization of LFQs by comparatively evaluating their performance. However, the evaluation results using different criteria (precision, accuracy, and robustness) vary greatly, and the huge number of potential LFQs becomes one of the bottlenecks in comprehensively optimizing proteome quantification. In this study, a novel strategy, enabling the discovery of the LFQs of simultaneously enhanced performance from thousands of workflows (integrating 18 quantification tools with 3,128 manipulation chains), was therefore proposed. First, the feasibility of achieving simultaneous improvement in the precision, accuracy, and robustness of LFQ was systematically assessed by collectively optimizing its multistep manipulation chains. Second, based on a variety of benchmark datasets acquired by various quantification measurements of different modes of acquisition, this novel strategy successfully identified a number of manipulation chains that simultaneously improved the performance across multiple criteria. Finally, to further enhance proteome quantification and discover the LFQs of optimal performance, an online tool (https://idrblab.org/anpela/) enabling collective performance assessment (from multiple perspectives) of the entire LFQ workflow was developed. This study confirmed the feasibility of achieving simultaneous improvement in precision, accuracy, and robustness. The novel strategy proposed and validated in this study together with the online tool might provide useful guidance for the research field requiring the mass-spectrometry-based LFQ technique.

Label-Free Quantitative Phosphoproteomics of the Fission Yeast Schizosaccharomyces pombe Using Strong Anion Exchange- and Porous Graphitic Carbon-Based Fractionation Strategies.

The phosphorylation of proteins modulates various functions of proteins and plays an important role in the regulation of cell signaling. In recent years, label-free quantitative (LFQ) phosphoproteomics has become a powerful tool to analyze the phosphorylation of proteins within complex samples. Despite the great progress, the studies of protein phosphorylation are still limited in throughput, robustness, and reproducibility, hampering analyses that involve multiple perturbations, such as those needed to follow the dynamics of phosphoproteomes. To address these challenges, we introduce here the LFQ phosphoproteomics workflow that is based on Fe-IMAC phosphopeptide enrichment followed by strong anion exchange (SAX) and porous graphitic carbon (PGC) fractionation strategies. We applied this workflow to analyze the whole-cell phosphoproteome of the fission yeast Schizosaccharomyces pombe. Using this strategy, we identified 8353 phosphosites from which 1274 were newly identified. This provides a significant addition to the S. pombe phosphoproteome. The results of our study highlight that combining of PGC and SAX fractionation strategies substantially increases the robustness and specificity of LFQ phosphoproteomics. Overall, the presented LFQ phosphoproteomics workflow opens the door for studies that would get better insight into the complexity of the protein kinase functions of the fission yeast S. pombe.

Comparison of LFQ and IPTL for Protein Identification and Relative Quantification.

(1) Background: Mass spectrometry-based quantitative proteome profiling is most commonly performed by label-free quantification (LFQ), stable isotopic labeling with amino acids in cell culture (SILAC), and reporter ion-based isobaric labeling methods (TMT and iTRAQ). Isobaric peptide termini labeling (IPTL) was described as an alternative to these methods and is based on crosswise labeling of both peptide termini and MS2 quantification. High quantification accuracy was assumed for IPTL because multiple quantification points are obtained per identified MS2 spectrum. A direct comparison of IPTL with other quantification methods has not been performed yet because IPTL commonly requires digestion with endoproteinase Lys-C. (2) Methods: To enable tryptic digestion of IPTL samples, a novel labeling for IPTL was developed that combines metabolic labeling (Arg-0/Lys-0 and Arg-d4/Lys-d4, respectively) with crosswise N-terminal dimethylation (d4 and d0, respectively). (3) Results: The comparison of IPTL with LFQ revealed significantly more protein identifications for LFQ above homology ion scores but not above identity ion scores. (4) Conclusions: The quantification accuracy was superior for LFQ despite the many quantification points obtained with IPTL.

In Depth Quantification of Extracellular Matrix Proteins from Human Pancreas.

Extracellular matrix (ECM) is an important component of the pancreatic microenvironment which regulates ß cell proliferation, differentiation, and insulin secretion. Protocols have recently been developed for the decellularization of the human pancreas to generate functional scaffolds and hydrogels. In this work, we characterized human pancreatic ECM composition before and after decellularization using isobaric dimethylated leucine (DiLeu) labeling for relative quantification of ECM proteins. A novel correction factor was employed in the study to eliminate the bias introduced during sample preparation. In comparison to the commonly employed sample preparation methods (urea and FASP) for proteomic analysis, a recently developed surfactant and chaotropic agent assisted sequential extraction/on pellet digestion (SCAD) protocol has provided an improved strategy for ECM protein extraction of human pancreatic ECM matrix. The quantitative proteomic results revealed the preservation of matrisome proteins while most of the cellular proteins were removed. This method was compared with a well-established label-free quantification (LFQ) approach which rendered similar expressions of different categories of proteins (collagens, ECM glycoproteins, proteoglycans, etc.). The distinct expression of ECM proteins was quantified comparing adult and fetal pancreas ECM, shedding light on the correlation between matrix composition and postnatal ß cell maturation. Despite the distinct profiles of different subcategories in the native pancreas, the distribution of matrisome proteins exhibited similar trends after the decellularization process. Our method generated a large data set of matrisome proteins from a single tissue type. These results provide valuable insight into the possibilities of constructing a bioengineered pancreas. It may also facilitate better understanding of the potential roles that matrisome proteins play in postnatal ß cell maturation.

Surfactant and Chaotropic Agent Assisted Sequential Extraction/On-Pellet Digestion (SCAD) for Enhanced Proteomics.

As a popular sample preparation approach, filter-aided sample preparation (FASP) has been widely used in proteomic analysis. However, several limitations have been noted, including sample loss during filtration, repetitive centrifugation steps, and the possibility of breakage of filtration membrane. Extraction bias among different sample preparation strategies presents another challenge. To overcome these limitations and address remaining challenges, we developed a novel surfactant and chaotropic agent assisted sequential extraction/on-pellet digestion (SCAD) protocol. The new strategy resulted in higher protein yield and improved peptide recovery and protein coverage compared to two conventional sample preparation methods (FASP and urea). In combination of three strategies, more than 10,000 distinct protein groups were identified with 1% FDR from MDA-MB-231 cells without any prefractionation. This in-depth proteome analysis was accomplished by optimization of protein extraction, enzymatic digestion, LC gradient, and peptide sequencing method. Ingenuity Pathways Analysis (IPA) of proteins exclusively identified in SCAD revealed several crucial signaling pathways that regulate breast cancer progression. SCAD also enabled an unbiased extraction of different categories of proteins (membrane, intracellular, nuclear) associated with tumorigenesis, which integrates the advantages of FASP and urea extraction. This novel strategy expedites comprehensive protein identification, which is applicable for biomarker discovery in various types of cancers.