Deep Proteome Profiling Research Articles

Impaired Abcb1a function and red meat in a translational colitis mouse model induces inflammation and alters microbiota composition.

Abcb1a-deficient colitis mice were fed either casein or red meat-supplemented diets to investigate potential colitis-aggravating components in red meat and their effects on host-microbiota interactions. We conducted deep label free quantitative proteomic inflammation profiling of gastrointestinal tissue (colon, ileum) and urine, and determined the overall microbiome in feces using 16S rRNA gene sequencing. Microbiota shifts by diet and protein transporter impairment were addressed by multivariate statistical analysis. Colon and systemic gut inflammation were validated through histology and immune assays, respectively. A quantitative discovery based proteomic analysis of intestinal tissue and urine revealed associations between ileum and urine proteomes in relation to Abcb1a deficiency. The absence of Abcb1a efflux pump function and diet-induced intestinal inflammation impacted multiple systemic immune processes, including extensive neutrophil extracellular trap (NET) components observed in relation to neutrophil degranulation throughout the gastrointestinal tract. The colitis model's microbiome differed significantly from that of wild-type mice, indicating the substantial influence of efflux transporter deficiency on microbiota. The proteomic and microbiota analyzes of a well-established murine model enabled the correlation of gastrointestinal interactions not readily identifiable in human cohorts. Insights into dysregulated biological pathways in this disease model might offer translational biomarkers based on NETs and improved understanding of IBD pathogenesis in human patients. Our findings demonstrate that drug transporter deficiency induces substantial changes in the microbiota, leading to increased levels of IBD-associated strains and resulting in intestinal inflammation. GRAPHICAL ABSTRACT.

Combined proteomics and CRISPR‒Cas9 screens in PDX identify ADAM10 as essential for leukemia in vivo

BackgroundAcute leukemias represent deadly malignancies that require better treatment. As a challenge, treatment is counteracted by a microenvironment protecting dormant leukemia stem cells.MethodsTo identify responsible surface proteins, we performed deep proteome profiling on minute numbers of dormant patient-derived xenograft (PDX) leukemia stem cells isolated from mice. Candidates were functionally screened by establishing a comprehensive CRISPR‒Cas9 pipeline in PDX models in vivo.ResultsA disintegrin and metalloproteinase domain-containing protein 10 (ADAM10) was identified as an essential vulnerability required for the survival and growth of different types of acute leukemias in vivo, and reconstitution assays in PDX models confirmed the relevance of its sheddase activity. Of translational importance, molecular or pharmacological targeting of ADAM10 reduced PDX leukemia burden, cell homing to the murine bone marrow and stem cell frequency, and increased leukemia response to conventional chemotherapy in vivo.ConclusionsThese findings identify ADAM10 as an attractive therapeutic target for the future treatment of acute leukemias.

A decision-making tool for first-line treatment in advanced non–small-cell lung cancer based on plasma proteome biomarkers.

9122 Background: Initial treatment selection for advanced NSCLC patients without driver mutations is mainly based on evaluating PD-L1 expression levels in the tumor tissue. However, PD-L1 assays are only moderately predictive. In addition, the guidelines for the PD-L1≥50% subpopulation are not definitive, enabling the usage of immune checkpoint inhibitors (ICI) either as a monotherapy or combined with chemotherapy. Here we aim to provide a decision-making tool for the first-line treatment of advanced NSCLC patients based on plasma derived biomarkers obtained before treatment initiation. Methods: Pre-treatment plasma samples were collected from 545 NSCLC patients receiving ICI-based therapy. Clinical benefit was evaluated based on progression-free survival (PFS) at 12 months as a threshold. Deep plasma proteomic profiling was performed using aptamer technology. Based on the proteomic profiles, a computational model, termed PROphet, was developed and evaluated in a blinded manner on the validation subset (n = 272). The model output, clinical benefit probability, was used to stratify the patients into two groups, PROphet-positive or -negative. Results: The model displayed good performance with a high correlation between the predicted clinical benefit and the observed clinical benefit rate (R2= 0.97), outperforming a PD-L1-based prediction model (R2= 0.35). Patients classified as PROphet-positive achieved significantly longer overall survival (OS) than PROphet-negative patients, with a median OS of 25.9 versus 10.8 months (hazard ratio, HR = 0.51; 95% confidence interval, CI = 0.37-0.70; p-value < 0.001). Next, we examined the clinical utility of combining the PROphet output with PD-L1 levels by comparing different treatment modalities. Focusing on PD-L1≥50%, patients with PROphet-positive results displayed similar OS and PFS when receiving ICI monotherapy or ICI-chemotherapy (OS HR = 0.77; CI = 0.42-1.43; p-value = 0.4096), suggesting that these patients may consider monotherapy, thus avoiding chemotherapy-related toxicity. Conversely, PD-L1≥50% patients with a PROphet-negative result displayed a significantly longer OS and PFS when receiving ICI-chemotherapy in comparison to ICI monotherapy, with a median OS that was not reached versus 11.10 months in the combination therapy and monotherapy groups, respectively (HR = 0.29; CI = 0.14-0.59; p-value < 0.001). These patients should consider combination therapy, avoiding a treatment that is suboptimal for them. Results for PD-L1 1-49% and < 1% provide additional clinical information for each subgroup. Conclusions: Plasma proteomic profiling can provide biomarkers that accurately predict the benefit of ICI-based treatment. The model output complements tissue PD-L1 expression levels as a tool to assist therapeutic decisions.

Comprehensive Evaluation and Optimization of the Data-Dependent LC-MS/MS Workflow for Deep Proteome Profiling.

Data-dependent liquid chromatography-tandem mass spectrometry (LC-MS/MS) is widely used in proteomic analyses. A well-performed LC-MS/MS workflow, which involves multiple procedures and interdependent metrics, is a prerequisite for deep proteome profiling. Researchers have previously evaluated LC-MS/MS performance mainly based on the number of identified peptides and proteins. However, this is not a comprehensive approach. This motivates us to develop MSRefine, which aims to evaluate and optimize the performance of the LC-MS/MS workflow for data-dependent acquisition (DDA) proteomics. It extracts 47 kinds of metrics, scores the metrics, and reports visual results, assisting users in evaluating the workflow, locating problems, and providing optimizing strategies. In this study, we compared and analyzed multiple pairs of datasets spanning different samples, methods, and instruments and demonstrated that the comprehensive visual metrics and scores in MSRefine enable us to evaluate the performance of the various experiments and provide optimal strategies for the identification of more peptides and proteins.

Blood Proteomics Analysis Reveals Potential Biomarkers and Convergent Dysregulated Pathways in Autism Spectrum Disorder: A Pilot Study.

Autism spectrum disorder (ASD) is an umbrella term that encompasses several disabling neurodevelopmental conditions. These conditions are characterized by impaired manifestation in social and communication skills with repetitive and restrictive behaviors or interests. Thus far, there are no approved biomarkers for ASD screening and diagnosis; also, the current diagnosis depends heavily on a physician's assessment and family's awareness of ASD symptoms. Identifying blood proteomic biomarkers and performing deep blood proteome profiling could highlight common underlying dysfunctions between cases of ASD, given its heterogeneous nature, thus laying the foundation for large-scale blood-based biomarker discovery studies. This study measured the expression of 1196 serum proteins using proximity extension assay (PEA) technology. The screened serum samples included ASD cases (n = 91) and healthy controls (n = 30) between 6 and 15 years of age. Our findings revealed 251 differentially expressed proteins between ASD and healthy controls, of which 237 proteins were significantly upregulated and 14 proteins were significantly downregulated. Machine learning analysis identified 15 proteins that could be biomarkers for ASD with an area under the curve (AUC) = 0.876 using support vector machine (SVM). Gene Ontology (GO) analysis of the top differentially expressed proteins (TopDE) and weighted gene co-expression analysis (WGCNA) revealed dysregulation of SNARE vesicular transport and ErbB pathways in ASD cases. Furthermore, correlation analysis showed that proteins from those pathways correlate with ASD severity. Further validation and verification of the identified biomarkers and pathways are warranted.

The extracellular matrix landscape in salivary gland carcinomas is defined by cellular differentiation via expression of three distinct protein modules.

The extracellular matrix (ECM) is an integral part of the tumor microenvironment of carcinomas. Even though salivary gland carcinomas (SGCs) display a range of tumor cell differentiation and distinct extracellular matrices, their ECM landscape has not been characterized in depth. The ECM composition of 89 SGC primaries, 14 metastases, and 25 normal salivary gland tissues was assessed using deep proteomic profiling. Machine learning algorithms and network analysis were used to detect tumor groups and protein modules that explain specific ECM landscapes. Multimodal in situ studies to validate exploratory findings and to infer a putative cellular origin of ECM components were applied. We revealed two fundamental SGC ECM classes which align with the presence or absence of myoepithelial tumor differentiation. We describe the SGC ECM through three biologically distinct protein modules that are differentially expressed across ECM classes and cell types. The modules have a distinct prognostic impact on different SGC types. Since targeted therapy is rarely available for SGC, we used the proteomic expression profile to identify putative therapeutic targets. In summary, we provide the first extensive inventory of ECM components in SGC, a difficult-to-treat disease that encompasses tumors with distinct cellular differentiation. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract 185: Global mapping of pathway modules and phosphorylation networks in PDX and corresponding organoid (PDXO) models treated with targeted therapies

Abstract Introduction: Multi-omics-based strategies in precision medicine, including genomic, transcriptomic and proteomic data, have contributed to the molecular-level characterization of cancers and the identification of novel driver genes, as well as a better comprehension of resistance mechanisms. Patient-derived tumor models, including patient-derived xenograft (PDX) and in vitro 3D organoid counterparts (PDXO), are widely recognized as predictive preclinical cancer models closely recapitulating both tumor complexity and patient response and enabling the study of tumor identity for personalized medicine. Taking advantage of Crown Bioscience large PDX collection, with genomic and phenotypic annotation and pharmacological validation, a series of PDXO models have been established and characterized to be used as scalable and high throughput compatible drug screening platforms. Methods: Drug response in matched PDX and PDXO models were investigated on the functional and systems level through deep whole- and phospho-proteomic analysis. For this purpose, analysis and comparisons were made across dose and time series, the effect of several targeted therapies on protein expression and phosphorylation in both PDX and PDXO tumor models. More specifically, a KRAS inhibitor (AMG510), a BCR-ABL TKI (ponatinib) and an EGFR TKI (afatinib) were tested respectively in a lung NSCLC model carrying KRAS G12C mutation, a colorectal model with RET fusion and a lung NSCLC model with EGFR exon 19 deletion. Results: Based on previous data, a specific relationship between area under the curve (AUC) value of organoid drug dose response and in vivo tumor growth has been observed, irrespective of the drug treatment. To interrogate the pathways involved, both PDX and PDXO models were subjected to deep proteome and phospho-proteome profiling using state-of-the-art mass spectrometry-based workflows. An extensive dataset was generated where 16,797 proteins and 12,578 proteins were robustly quantified in PDX and PDXO models respectively. From the same samples, 35,403 phospho-sites were quantified across all PDX samples while 40,754 phospho-sites were quantified across all PDXO models. These data enabled the validation of known pathways (such as RAF/MEK/ERK, PI3K/AKT) and pinpoint new ones modulated through three small molecule inhibitors each targeting a defined genetic background. Conclusion: The predictivity of organoid cultures was demonstrated to model in vivo drug responses and also to serve as a powerful platform to investigate target identification, mechanism of action and resistance mechanism via functional proteome and phospho-proteome analysis. Citation Format: Martin Mehnert, Xiaoxi Xu, Tobias Treiber, Limei Shang, Leilei Chen, Jessie Wang, Marco Tognetti, Christopher Below, Roland Bruderer, Jakob Vowinckel, Binchen Mao, Wubin Qian, Sheng Guo, Ludovic Bourré, Yuehan Feng. Global mapping of pathway modules and phosphorylation networks in PDX and corresponding organoid (PDXO) models treated with targeted therapies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 185.

Abstract 6612: Data-driven hallmarks of cancer as a new paradigm for precision medicine: multi-omics and functional profiling in acute myeloid leukemia

Abstract The cancer hallmark concept defines biological properties that play a key role in cancer development and progression. The hallmarks reflect generic properties across all cancer types, and are not directly quantifiable nor applicable in cancer diagnostics. Here, we applied a data-driven approach to identify quantifiable hallmarks to be used as a basis for precision cancer medicine (PCM) in Acute Myeloid Leukemia (AML). We applied deep exome, transcriptome, DNA methylation and proteome profiling as well as ex-vivo functional testing of 525 drugs to 118 AML patient samples. Unsupervised multi-omic dimensionality reduction defined 11 independent axes of biological variability that we interpreted to reflect data-driven hallmarks (DDHM) of AML. Each DDHM integrates and ranks different data types and features, pinpointing those molecular features that were most informative for each hallmark. We calculated values for the 11 DDHM for each patient, constructing a DDHM-based precision medicine approach for AML diagnosis and therapy assignment. Most DDHMs were driven by other data types than genomics. For several DDHMs, different cytogenetic and mutational drivers converge on the same hallmarks and specific drug vulnerabilities. We also see how DDHM predictors of poor prognosis and high-risk AML are distinct from those that dictate specific drug response vulnerabilities. DDHM 2 reflected the cell differentiation path leading towards resistance to BH3 mimetics, ACK-inhibitors, and anthracyclines, and sensitivity to MEK inhibitors and TLR8 agonists. DDHM 2 derived a strong impact from protein expression of GATA2, MLLT11, DNMT3B and from RNA expression of WT1 and SOX4 as well as from multiple markers of different monocytic subtypes. DDHM 5 depicted cell cycle regulation and the Megakaryocytic-erythrocyte progenitor cell state that was enriched in AML patients with antecedent hematological disease. Interestingly, DDHM5 was linked to the sensitivity to purine analogs and vinca alkaloids. Moreover, DDHMs 1 and 8 captured clinical risk groups, prognosis, and responsiveness to hypomethylating agents. These DDHMs also revealed the phenotypic and functional biology that distinguished patients with high versus low FLT3-ITD mutant allele frequency. Validation of the DDHMs is realized through profiling and analysis of prospective AML samples, AML cell lines and previously published datasets. In summary, we present a data-driven approach for defining hallmarks in AML. The application of the DDHMs in AML provides a new paradigm for PCM and an opportunity for combinatorial therapeutic targeting. Each patient is characterized by a combination of independent and potentially druggable hallmarks, as opposed to traditional stratification in PCM, where each patient is assigned to one specific subgroup defined by genetic or other biomarkers. Citation Format: Tom Erkers, Nona Struyf, Tojo James, Francesco Marabita, Mattias Vesterlund, Nghia Vu, Cornelia Arnroth, Albin Österroos, Anna Bohlin, Sofia Bengtzén, Matthias Stahl, Rozbeh Jafari, Lukas Orre, Yudi Pawitan, Brinton Seashore-Ludlow, Janne Lehtiö, Sören Lehmann, Päivi Östling, Olli Kallioniemi. Data-driven hallmarks of cancer as a new paradigm for precision medicine: multi-omics and functional profiling in acute myeloid leukemia. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6612.

Abstract 2159: A pretreatment blood-based proteomic biomarker for enhanced decision-making in non-small cell lung cancer

Abstract Introduction: Treatment modality selection for metastatic NSCLC patients (immunotherapy alone vs. combination of immunotherapy with chemotherapy) relies mainly on determining the programmed cell death 1 (PD-L1) expression levels in the tumor. However, available assays are only moderately predictive. Here we set to develop PROphet®, a plasma proteomics-based predictive model for informing treatment decisions for NSCLC patients receiving immune checkpoint inhibitor (ICI)-based therapy. Methods: Pre-ICI plasma samples were collected in 12 centers in a clinical trial (NCT04056247) from 367 advanced-stage NSCLC patients. Clinical benefit (CB) was assessed at 12 months based on the occurrence of progression-free survival (PFS) until this time point. Deep proteomic profiling of the plasma samples was performed using aptamer-based technology. A novel machine learning model was developed to determine the CB probability for each patient, and the performance was successfully evaluated on an independent validation set, followed by training and prediction over the entire cohort using cross-validation. The resulting PROphet® score (positive or negative) was determined by setting the median CB rate probability as a threshold. The patients were divided into four subgroups based on their PD-L1 expression level combined with their PROphet® score prediction, and the overall survival (OS) was examined for each subgroup. Results: The PROphet® computational model was evaluated in a blinded manner on a subset of 85 patients and displayed strong predictive capability with area under the curve (AUC) of the receiver operating characteristics (ROC) plot of 0.78 (p-value = 5.00e-05), outperforming a PD-L1-based predictive model (AUC = 0.62; p-value 2.76e-01). When combining PROphet® score with PD-L1 expression levels, four different outcome patterns were identified: (i) Patients with PD-L1 ≥50% and PROphet® negative score, who displayed significantly longer OS when treated with ICI-chemotherapy combination therapy compared to ICI monotherapy and may consider combination therapy. (ii) Patients with PD-L1 ≥50% and PROphet® positive score, who benefit similarly from either treatment modalities, and may consider monotherapy to avoid the potential toxicity of the combination therapy. (iii) Patients with PD-L1<50% and PROphet® negative score, who do not benefit from either treatment modalities and may consider chemotherapy alone or treatment beyond standard of care. (iv) Patients with PD-L1<50% and PROphet® positive score who benefit from combination therapy. Conclusions: Altogether, the PROphet® model, when combined with PD-L1 test, stratifies the patients into four subgroups, providing additional resolution to the PD-L1 biomarker currently used to guide treatment selection. Furthermore, the model accurately predicts CB at 12 months based on proteomic analysis of a pre-treatment plasma sample. Citation Format: Michal Harel, Petros Christopoulos, Coren Lahav, Itamar Sela, Nili Dahan, Niels Reinmuth, Ina Koch, Alona Zer, Mor Moskovitz, Adva Levy-Barda, Michal Lotem, Hovav Nechushtan, Rivka Katzenelson, Abed Agbarya, Mahmoud Abu-Amna, Maya Gottfried, Ido Wolf, Ella Tepper, Yanyan Lou, Raya Leibowitz, Adam P. Dicker, David Gandara, David P. Carbone. A pretreatment blood-based proteomic biomarker for enhanced decision-making in non-small cell lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2159.

Abstract 4352: Leveraging deep proteome profiling of plasma- and serum-derived extracellular vesicles for melanoma biomarker discovery and disease dissection

Abstract Extracellular vesicles (EV) play an important role in melanoma progression but their potential as clinical biomarkers has yet to be realized. EVs can be found in most liquid biopsies (e.g., blood, urine, CSF) and exosomes are the most prominent subcategory of EVs. Exosomes are 50-200 nm lipid-bilayer enclosed particles secreted by all body cells, including tumor cells, and serve as mediators of metastasis formation and typically contain several classes of bioactive molecules such as RNA, proteins, lipids, and metabolites. Blood and its liquid components plasma/serum are the most frequently used matrix for biomarker discovery due to the ease of collection. However, most proteomic platforms for plasma/serum profiling are unable to profile EV proteins due to the high dynamic range of protein concentrations in EV preparations. This is due to 1) EV isolation methods that vary in their potential to separate EVs from free proteins, and 2) the presence of a natural corona of high-abundant blood proteins attached to the EV surface. To tackle this challenge, we developed SEC-DIA-MS an integrated workflow combining size-exclusion chromatography, EV concentration, and optimized mass spectrometry to enable deep profiling of the proteome content of the enriched vesicles. From 200 µl of plasma or serum from a test melanoma patient cohort (6 patients and 3 matched controls), we quantified 2,242 exosome-associated proteins, achieving a 2.5-fold increase in depth compared to previous melanoma studies. To gain a better understanding of the exosome enrichment efficiency, we extensively characterized the plasma/serum proteome by analyzing native, depleted, and EV-enriched blood from the same donors. We successfully validated well-known exosome markers such as CD9, CD63, CD81, PDCD6IP, and TSG101, and found that EV samples are significantly enriched in intact membrane proteins and those related to T cell biology, further underlining the uniqueness of the EV proteome composition. We further assessed the differences between plasma and serum EVs and suggest the use of plasma samples for future studies due to better separation of healthy and melanoma samples. Furthermore, known melanoma markers such as MCAM, TNC, and TGFBI were upregulated in melanoma plasma-derived EV but not depleted plasma samples, highlighting the specific information contained in EVs. Taken together, we demonstrated the applicability and potential of an EV-based proteomic workflow for biomarker discovery in plasma and serum. The ease of automating and scaling up such an approach enables a broader application to other indications and biological matrices. Citation Format: Evelyn Lattmann, Luca Räss, Marco Tognetti, Julia Martinez Gomez, Valérie Lapaire, Roland Bruderer, Lukas Reiter, Yuehan Feng, Lars M. Steinmetz, Mitchell P. Levesque. Leveraging deep proteome profiling of plasma- and serum-derived extracellular vesicles for melanoma biomarker discovery and disease dissection. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 4352.

Proteomic characteristics and diagnostic potential of exhaled breath particles in patients with COVID-19

BackgroundSARS-CoV-2 has been shown to predominantly infect the airways and the respiratory tract and too often have an unpredictable and different pathologic pattern compared to other respiratory diseases. Current clinical diagnostical tools in pulmonary medicine expose patients to harmful radiation, are too unspecific or even invasive. Proteomic analysis of exhaled breath particles (EBPs) in contrast, are non-invasive, sample directly from the pathological source and presents as a novel explorative and diagnostical tool.MethodsPatients with PCR-verified COVID-19 infection (COV-POS, n = 20), and patients with respiratory symptoms but with > 2 negative polymerase chain reaction (PCR) tests (COV-NEG, n = 16) and healthy controls (HCO, n = 12) were prospectively recruited. EBPs were collected using a “particles in exhaled air” (PExA 2.0) device. Particle per exhaled volume (PEV) and size distribution profiles were compared. Proteins were analyzed using liquid chromatography-mass spectrometry. A random forest machine learning classification model was then trained and validated on EBP data achieving an accuracy of 0.92.ResultsSignificant increases in PEV and changes in size distribution profiles of EBPs was seen in COV-POS and COV-NEG compared to healthy controls. We achieved a deep proteome profiling of EBP across the three groups with proteins involved in immune activation, acute phase response, cell adhesion, blood coagulation, and known components of the respiratory tract lining fluid, among others. We demonstrated promising results for the use of an integrated EBP biomarker panel together with particle concentration for diagnosis of COVID-19 as well as a robust method for protein identification in EBPs.ConclusionOur results demonstrate the promising potential for the use of EBP fingerprints in biomarker discovery and for diagnosing pulmonary diseases, rapidly and non-invasively with minimal patient discomfort.

Data Independent Acquisition Reveals In-Depth Serum Proteome Changes in Canine Leishmaniosis.

Comprehensive profiling of serum proteome provides valuable clues of health status and pathophysiological processes, making it the main strategy in biomarker discovery. However, the high dynamic range significantly decreases the number of detectable proteins, obstructing the insights into the underlying biological processes. To circumvent various serum enrichment methods, obtain high-quality proteome wide information using the next-generation proteomic, and study host response in canine leishmaniosis, we applied data-independent acquisition mass spectrometry (DIA-MS) for deep proteomic profiling of clinical samples. The non-depleted serum samples of healthy and naturally Leishmania-infected dogs were analyzed using the label-free 60-min gradient sequential window acquisition of all theoretical mass spectra (SWATH-MS) method. As a result, we identified 554 proteins, 140 of which differed significantly in abundance. Those were included in lipid metabolism, hematological abnormalities, immune response, and oxidative stress, providing valuable information about the complex molecular basis of the clinical and pathological landscape in canine leishmaniosis. Our results show that DIA-MS is a method of choice for understanding complex pathophysiological processes in serum and serum biomarker development.

Deep Proteome Profiling of White Adipose Tissue Reveals Marked Conservation and Distinct Features Between Different Anatomical Depots

White adipose tissue is deposited mainly as subcutaneous adipose tissue (SAT), often associated with metabolic protection, and abdominal/visceral adipose tissue, which contributes to metabolic disease. To investigate the molecular underpinnings of these differences, we conducted comprehensive proteomics profiling of whole tissue and isolated adipocytes from these two depots across two diets from C57Bl/6J mice. The adipocyte proteomes from lean mice were highly conserved between depots, with the major depot-specific differences encoded by just 3% of the proteome. Adipocytes from SAT (SAdi) were enriched in pathways related to mitochondrial complex I and beiging, whereas visceral adipocytes (VAdi) were enriched instructural proteins and positive regulators of mTOR presumably to promote nutrient storage and cellular expansion. This indicates that SAdi are geared toward higher catabolic activity, while VAdi are more suited for lipid storage. By comparing adipocytes from mice fed chow or Western diet (WD), we define a core adaptive proteomics signature consisting of increased extracellular matrix proteins and decreased fatty acid metabolism and mitochondrial Coenzyme Qbiosynthesis. Relative to SAdi, VAdi displayed greater changes with WD including a pronounced decrease in mitochondrial proteins concomitant with upregulation of apoptotic signaling and decreased mitophagy, indicating pervasive mitochondrial stress. Furthermore, WD caused a reduction in lipid handling and glucose uptake pathwaysparticularly in VAdi, consistent with adipocyte de-differentiation. By overlaying the proteomics changes with diet in whole adipose tissue and isolated adipocytes, we uncovered concordance between adipocytes and tissue only in the visceral adipose tissue, indicating a unique tissue-specific adaptation to sustained WD in SAT. Finally, an in-depth comparison of isolated adipocytes and 3T3-L1 proteomes revealed a high degree of overlap, supporting the utility of the 3T3-L1 adipocyte model. These deep proteomes provide an invaluable resource highlighting differences between white adipose depots that may fine-tune their unique functions and adaptation to an obesogenic environment.

Frontiers in mass spectrometry-based clinical proteomics for cancer diagnosis and treatment.

Numerous omics studies, primarily genomics analyses, have been conducted to fully understand the molecular biological characteristics of cancer. In recent years, the depth of proteomic analysis, which comprehensively analyzes proteins and molecules that function directly in vivo, has increased dramatically. Proteomics using mass spectrometry (MS) is a promising technology to directly examine proteoforms, including post-translational modifications and variants originating from genomic aberrations. Recent advances in MS-based proteomics have enabled direct, in depth, and quantitative analysis of the expression levels of various cancer-related proteins, as well as their cancer-specific proteoforms, and proteins that fluctuate with cancer initiation and progression in cell lines and tissue samples. Additionally, the integration of proteomic data with genomic, epigenomic, and transcriptomic data has formed the growing field of proteogenomics, which is already yielding new biological and diagnostic knowledge. Deep proteomic profiling provides clinically useful information in various aspects, including understanding the mechanisms of cancer development and progression and discovering targets for diagnosis and drug development. Furthermore, it is expected to make a significant contribution to the promotion of personalized medicine. In this review, recent advances and impacts in MS-based clinical proteomics are highlighted with a focus on oncology.

In-Depth Blood Proteome Profiling by Extensive Fractionation and Multiplexed Quantitative Mass Spectrometry.

Blood in the circulatory system carries information of physiological and pathological status of the human body, so blood proteins are often used as biomarkers for diagnosis, prognosis, and therapy. Human blood proteome can be explored by the latest technologies in mass spectrometry (MS), creating an opportunity of discovering new disease biomarkers. The extreme dynamic range of protein concentrations in blood, however, poses a challenge to detect proteins of low abundance, namely, tissue leakage proteins. Here, we describe a strategy to directly analyze undepleted blood samples by extensive liquid chromatography (LC) fractionation and 18-plex tandem-mass-tag (TMT) mass spectrometry. The proteins in blood specimens (e.g., plasma or serum) are isolated by acetone precipitation and digested into peptides. The resulting peptides are TMT-labeled, separated by basic pH reverse-phase (RP) LC into at least 40 fractions, and analyzed by acidic pH RPLC and high-resolution MS/MS, leading to the quantification of ~3000 unique proteins. Further increase of basic pH RPLC fractions and adjustment of the fraction concatenation strategy can enhance the proteomic coverage (up to ~5000 proteins). Finally, the combination of multiple batches of TMT experiments allows the profiling of hundreds of blood samples. This TMT-MS-based method provides a powerful platform for deep proteome profiling of human blood samples.

Promitotic Action of Oenothera biennis on Senescent Human Dermal Fibroblasts

Accumulation of senescent dermal fibroblasts drives skin aging. The reactivation of proliferation is one strategy to modulate cell senescence. Recently, we reported the exact chemical composition of the hydrophilic extract of Oenothera biennis cell cultures (ObHEx) and we showed its skin anti-aging properties. The aim of this work is to assess its biological effect specifically on cell senescence. ObHEx action has been evaluated on normal human dermal fibroblasts subjected to stress-induced premature senescence (SIPS) through an ultra-deep proteomic analysis, leading to the most global senescence-associated proteome so far. Mass spectrometry data show that the treatment with ObHEx re-establishes levels of crucial mitotic proteins, strongly downregulated in senescent cells. To validate our proteomics findings, we proved that ObHEx can, in part, restore the activity of 'senescence-associated-ß-galactosidase', the most common hallmark of senescent cells. Furthermore, to assess if the upregulation of mitotic protein levels translates into a cell cycle re-entry, FACS experiments have been carried out, demonstrating a small but significative reactivation of senescent cell proliferation by ObHEx. In conclusion, the deep senescence-associated global proteome profiling published here provides a panel of hundreds of proteins deregulated by SIPS that can be used by the community to further understand senescence and the effect of new potential modulators. Moreover, proteomics analysis pointed to a specific promitotic effect of ObHEx on senescent cells. Thus, we suggest ObHEx as a powerful adjuvant against senescence associated with skin aging.

Deep proteome profiling reveals signatures of age and sex differences in paw skin and sciatic nerve of naïve mice.

The age and sex of studied animals profoundly impact experimental outcomes in biomedical research. However, most preclinical studies in mice use a wide-spanning age range from 4 to 20 weeks and do not assess male and female mice in parallel. This raises concerns regarding reproducibility and neglects potentially relevant age and sex differences, which are largely unknown at the molecular level in naïve mice. Here, we employed an optimized quantitative proteomics workflow in order to deeply profile mouse paw skin and sciatic nerves (SCN) - two tissues implicated in nociception and pain as well as diseases linked to inflammation, injury, and demyelination. Remarkably, we uncovered significant differences when comparing male and female mice at adolescent (4 weeks) and adult (14 weeks) age. Our analysis deciphered protein subsets and networks that were correlated with the age and/or sex of mice. Notably, among these were proteins/biological pathways with known (patho)physiological relevance, e.g., homeostasis and epidermal signaling in skin, and, in SCN, multiple myelin proteins and regulators of neuronal development. Extensive comparisons with available databases revealed that various proteins associated with distinct skin diseases and pain exhibited significant abundance changes in dependence on age and/or sex. Taken together, our study uncovers hitherto unknown sex and age differences at the level of proteins and protein networks. Overall, we provide a unique proteome resource that facilitates mechanistic insights into somatosensory and skin biology, and integrates age and sex as biological variables - a prerequisite for successful preclinical studies in mouse disease models.

Deep Proteome Profiling with Reduced Carryover Using Superficially Porous Microfabricated nanoLC Columns.

In the field of liquid chromatography-mass spectrometry (LC-MS)-based proteomics, increases in the sampling depth and proteome coverage have mainly been accomplished by rapid advances in mass spectrometer technology. The comprehensiveness and quality of the data that can be generated do, however, also depend on the performance provided by nano-liquid chromatography (nanoLC) separations. Proper selection of reversed-phase separation columns can be important to provide the MS instrument with peptides at the highest possible concentration and separated at the highest possible resolution. In the current contribution, we evaluate the use of the prototype generation 2 μPAC nanoLC columns, which use C18-functionalized superficially porous micropillars as a stationary phase. When compared to traditionally used fully porous silica stationary phases, more precursors could be characterized when performing single shot data-dependent LC-MS/MS analyses of a human cell line tryptic digest. Up to 30% more protein groups and 60% more unique peptides were identified for short gradients (10 min) and limited sample amounts (10-100 ng of cell lysate digest). With LC-MS gradient times of 10, 60, 120, and 180 min, respectively, we identified 2252, 6513, 7382, and 8174 protein groups with 25, 500, 1000, and 2000 ng of the sample loaded on the column. Reduction of sample carryover to the next run (up to 2 to 3%) and decreased levels of methionine oxidation (up to 3-fold) were identified as additional figures of merit. When analyzing a disuccinimidyl dibutyric urea-crosslinked synthetic library, 29 to 59 more unique crosslinked peptides could be identified at an experimentally validated false discovery rate of 1-2%.

A deep redox proteome profiling workflow and its application to skeletal muscle of a Duchenne Muscular Dystrophy model.

Perturbation to the redox state accompanies many diseases and its effects are viewed through oxidation of biomolecules, including proteins, lipids, and nucleic acids. The thiol groups of protein cysteine residues undergo an array of redox post-translational modifications (PTMs) that are important for regulation of protein and pathway function. To better understand what proteins are redox regulated following a perturbation, it is important to be able to comprehensively profile protein thiol oxidation at the proteome level. Herein, we report a deep redox proteome profiling workflow and demonstrate its application in measuring the changes in thiol oxidation along with global protein expression in skeletal muscle from mdx mice, a model of Duchenne Muscular Dystrophy (DMD). In-depth coverage of the thiol proteome was achieved with >18,000 Cys sites from 5,608 proteins in muscle being quantified. Compared to the control group, mdx mice exhibit markedly increased thiol oxidation, where a ∼2% shift in the median oxidation occupancy was observed. Pathway analysis for the redox data revealed that coagulation system and immune-related pathways were among the most susceptible to increased thiol oxidation in mdx mice, whereas protein abundance changes were more enriched in pathways associated with bioenergetics. This study illustrates the importance of deep redox profiling in gaining greater insight into oxidative stress regulation and pathways/processes that are perturbed in an oxidizing environment.

Deep Single-Shot NanoLC-MS Proteome Profiling with a 1500 Bar UHPLC System, Long Fully Porous Columns, and HRAM MS.

This study demonstrates how the latest ultrahigh-performance liquid chromatography (UHPLC) technology can be combined with high-resolution accurate-mass (HRAM) mass spectrometry (MS) and long columns packed with fully porous particles to improve bottom-up proteomics analysis with nanoflow liquid chromatography–mass spectrometry (nanoLC-MS) methods. The increased back pressures from the UHPLC system enabled the use of 75 μm I.D. × 75 cm columns packed with 2 μm particles at a typical 300 nL/min flow rate as well as elevated and reduced flow rates. The constant pressure pump operation at 1500 bar reduced sample loading and column washing/equilibration stages and overall overhead time, which maximizes MS utilization time. The versatility of flow rate optimization to balance the sensitivity, throughput with sample loading amount, and capability of using longer gradients contributes to a greater number of peptide and protein identifications for single-shot bottom-up proteomics experiments. The routine proteome profiling and precise quantification of >7000 proteins with single-shot nanoLC-MS analysis open possibilities for large-scale discovery studies with a deep dive into the protein level alterations. Data are available via ProteomeXchange with identifier PXD035665.