Data-independent Acquisition Mass Spectrometry Research Articles

Improved drug target deconvolution with PISA-DIA using an extended, overlapping temperature gradient.

Thermal proteome profiling (TPP) is a powerful tool for drug target deconvolution. Recently, data-independent acquisition mass spectrometry (DIA-MS) approaches have demonstrated significant improvements to depth and missingness in proteome data, but traditional TPP (a.k.a. CEllular Thermal Shift Assay "CETSA") workflows typically employ multiplexing reagents reliant on data-dependent acquisition (DDA). Herein, we introduce a new experimental design for the Proteome Integral Solubility Alteration via label-free DIA approach (PISA-DIA). We highlight the proteome coverage and sensitivity achieved by using multiple overlapping thermal gradients alongside DIA-MS, which maximizes efficiencies in PISA sample concatenation and safeguards against missing protein targets that exist at high melting temperatures. We demonstrate our extended PISA-DIA design has superior proteome coverage as compared to using tandem-mass tags (TMT) necessitating DDA-MS analysis. Importantly, we demonstrate our PISA-DIA approach has the quantitative and statistical rigor using A-1331852, a specific inhibitor of BCL-xL. Due to the high melt temperature of this protein target, we utilized our extended multiple gradient PISA-DIA workflow to identify BCL-xL. We assert our novel overlapping gradient PISA-DIA-MS approach is ideal for unbiased drug target deconvolution, spanning a large temperature range whilst minimizing target dropout between gradients, increasing the likelihood of resolving the protein targets of novel compounds.

An MSstats workflow for detecting differentially abundant proteins in large-scale data-independent acquisition mass spectrometry experiments with FragPipe processing.

Technological advances in mass spectrometry and proteomics have made it possible to perform larger-scale and more-complex experiments. The volume and complexity of the resulting data create major challenges for downstream analysis. In particular, next-generation data-independent acquisition (DIA) experiments enable wider proteome coverage than more traditional targeted approaches but require computational workflows that can manage much larger datasets and identify peptide sequences from complex and overlapping spectral features. Data-processing tools such as FragPipe, DIA-NN and Spectronaut have undergone substantial improvements to process spectral features in a reasonable time. Statistical analysis tools are needed to draw meaningful comparisons between experimental samples, but these tools were also originally designed with smaller datasets in mind. This protocol describes an updated version of MSstats that has been adapted to be compatible with large-scale DIA experiments. A very large DIA experiment, processed with FragPipe, is used as an example to demonstrate different MSstats workflows. The choice of workflow depends on the user's computational resources. For datasets that are too large to fit into a standard computer's memory, we demonstrate the use of MSstatsBig, a companion R package to MSstats. The protocol also highlights key decisions that have a major effect on both the results and the processing time of the analysis. The MSstats processing can be expected to take 1-3 h depending on the usage of MSstatsBig. The protocol can be run in the point-and-click graphical user interface MSstatsShiny or implemented with minimal coding expertise in R.

P28 Data-independent acquisition mass spectrometry improves spatially resolved analysis of the human skin proteome

Abstract Introduction and aims Proteomic analysis of the extracellular matrix (ECM) presents challenges because of the highly crosslinked and low-solubility nature of ECM proteins. Traditional homogenization and protein digestion approaches result in the loss of crucial information regarding protein localization and spatial relationships. To address this, spatially resolved proteomics emerges as a powerful tool for exploring heterogeneity within bulk tissues. This study aims to determine the minimum tissue volume required for comprehensive proteome coverage using data-independent acquisition mass spectrometry (DIA-MS) on skin tissue. The study focused on optimizing spatially resolved proteomic techniques to enhance depth-of-analysis while preserving spatial specificity. Methods Human abdominal skin biopsies were obtained from a single individual and subsequently cryosectioned. Histological assessment was performed through haematoxylin and eosin staining for visualization purposes. Laser-capture microdissection coupled with mass spectrometry facilitated the precise isolation of target regions. Comparative analyses were performed between data-dependent acquisition mass spectrometry (DDA-MS) and DIA-MS, with a particular emphasis on ECM proteins within the dermis. Results Our findings revealed an improvement in proteome coverage with DIA-MS compared with DDA-MS, in addition to clear scaling relationships between the depth-of-analysis and sample concentration. The Results demonstrated the superiority of DIA-MS in achieving robust and comprehensive proteomic profiles, even with minimal tissue volumes. Preliminary findings suggest the capability of DIA-MS in elucidating the complexities of the skin proteome with spatial resolution. Conclusions In conclusion, our study highlights the efficacy of DIA-MS in spatially resolved proteomics on skin tissue. The optimized approach presented here offers a reliable and efficient method for obtaining in-depth proteome information with minimal tissue requirements. These Results form the foundation for ongoing experiments, utilizing DIA-MS to advance spatially resolved proteomic analyses of human skin.

Mass Spectrometry-Based Proteomics Analysis Unveils PTPRS Inhibits Proliferation and Inflammatory Response of Keratinocytes in Psoriasis.

In this study, we used data-independent acquisition-mass spectrometry (DIA-MS) to analyze the serum proteome in psoriasis vulgaris (PsO). The serum proteomes of seven healthy controls and eight patients with PsO were analyzed using DIA-MS. Weighted gene co-expression network analysis was used to identify differentially expressed proteins (DEPs) that were closely related to PsO. Hub proteins of PsO were also identified. The Proteomics Drug Atlas 2023 was used to predict candidate hub protein drugs. To confirm the expression of the candidate factor, protein tyrosine phosphatase receptor S (PTPRS), in psoriatic lesions and the psoriatic keratinocyte model, immunohistochemical staining, quantitative real-time polymerase chain reaction, and western blotting were performed. A total of 129 DEPs were found to be closely related to PsO. The hub proteins for PsO were PVRL1, FGFR1, PTPRS, CDH2, CDH1, MCAM, and THY1. Five candidate hub protein drugs were identified: encorafenib, leupeptin, fedratinib, UNC 0631, and SCH 530348. PTPRS was identified as a common pharmacological target for these five drugs. PTPRS knockdown in keratinocytes promoted the proliferation and expression of IL-1α, IL-1β, IL-23A, TNF-α, MMP9, CXCL8, and S100A9. PTPRS expression was decreased in PsO, and PTPRS negatively regulated PsO. PTPRS may be involved in PsO pathogenesis through the inhibition of keratinocyte proliferation and inflammatory responses and is a potential treatment target for PsO.

MassDash: A Web-Based Dashboard for Data-Independent Acquisition Mass Spectrometry Visualization.

With the increased usage and diversity of methods and instruments being applied to analyze Data-Independent Acquisition (DIA) data, visualization is becoming increasingly important to validate automated software results. Here we present MassDash, a cross-platform DIA mass spectrometry visualization and validation software for comparing features and results across popular tools. MassDash provides a web-based interface and Python package for interactive feature visualizations and summary report plots across multiple automated DIA feature detection tools, including OpenSwath, DIA-NN, and dreamDIA. Furthermore, MassDash processes peptides on the fly, enabling interactive visualization of peptides across dozens of runs simultaneously on a personal computer. MassDash supports various multidimensional visualizations across retention time, ion mobility, m/z, and intensity, providing additional insights into the data. The modular framework is easily extendable, enabling rapid algorithm development of novel peak-picker techniques, such as deep-learning-based approaches and refinement of existing tools. MassDash is open-source under a BSD 3-Clause license and freely available at https://github.com/Roestlab/massdash, and a demo version can be accessed at https://massdash.streamlit.app.

Use of data-independent acquisition mass spectrometry to identify an objective serum indicator of the need for osteoporotic therapeutic intervention

Osteoporosis is characterized by weakened bone microstructure and loss of bone mass. Current diagnostic criteria for osteoporosis are based on the T-score, which is a measure of bone mineral density. However, osteoporotic fragility fractures can occur regardless of the T-score, underscoring the need for additional criteria for the early detection of patients at fracture risk. To identify indicators of reduced bone strength, we performed serum proteomic analysis using data-independent acquisition mass spectrometry with serum samples from two patient groups, one with osteoporosis but no fractures and the other with osteopenia and fragility fractures. Collective evaluation of the results identified six serum proteins that changed to a similar extent in both patient groups compared with controls. Of these, extracellular matrix protein 1 (ECM1), which contributes to bone formation, showed the most significant increase in serum levels in both patient groups. An ELISA-based assay suggested that ECM1 could serve as a serum indicator of the need for therapeutic intervention; however, further prospective studies with a larger sample size are necessary to confirm these results. The present findings may contribute to the provision of early and appropriate therapeutic strategies for patients at risk of osteoporotic fractures. SignificanceThis study aimed to identify objective serum indicators of the need for therapeutic intervention in individuals at risk of osteoporotic fracture. Comprehensive proteome analyses of serum collected from patients with osteoporosis but no fractures, patients with osteopenia and fragility fractures, and controls were performed by data-independent acquisition mass spectrometry. Collective evaluation of the proteome analysis data and ELISA-based assays identified serum ECM1 as a potential objective marker of the risk of fragility fractures in patients with osteoporosis or osteopenia. The findings are an important step toward the development of appropriate bone health management methods to improve well-being and maintain quality of life.

Proteomic characteristics of the treatment trajectory of patients with COVID-19.

The ongoing COVID-19 pandemic caused by SARS-CoV-2 has prompted global concern due to its profound impact on public health and the economy. Effective treatment of COVID-19 patients in the acute phase or of those with long COVID is a major challenge. Using data-independent acquisition (DIA) technology, we performed proteomic profiling on plasma samples from 22 COVID-19 patients and six healthy controls at Dazhou Central Hospital. Randomforest and least absolute shrinkage and selection operator algorithms were used for analysis at various COVID-19 treatment stages. We identified 79 proteins that were differentially expressed between COVID-19 patients and healthy controls, mainly involving pathways associated with cell processes and binding. Across different treatment stages of COVID-19, five proteins-PI16, GPLD1, IGFBP3, KRT19, and VCAM1-were identified as potential molecular markers for dynamic disease monitoring. Furthermore, the proteins BTD, APOM, IGKV2-28, VWF, C4BPA, and C7 were identified as candidate biomarkers for distinguishing between SARS-CoV-2 positivity and negativity. Analysis of protein change profiles between the follow-up and healthy control groups highlighted cardiovascular changes as a concern for patients recovering from COVID-19. Our study revealed the infection profiles of SARS-CoV-2 at the protein expression level comparing different phases of COVID-19. DIA mass spectrometry analysis of plasma samples from COVID-19 patients undergoing treatment identified key proteins involved in signaling pathways that might be used as markers of the recovery phase. These findings provide insight for the development of therapy options and suggest potential blood biomarkers for COVID-19.

Abstract 5356: Wild type Kirsten rat sarcoma (Kras) activation drives evasion of interferon-mediated immunity in hepatocellular carcinoma

Abstract Background: Immune checkpoint inhibitors (ICIs) exhibit heterogeneous efficacy in patients with advanced hepatocellular carcinoma (HCC). Currently, there are no effective biomarkers to select patients who could benefit the most from ICIs or to predict response outcomes. Increasing evidence indicates that oncogenic pathway activation is associated with the generation of an unfavorable tumor microenvironment, with consequent immunotherapy resistance. Therefore, an understanding of the immune evasive mechanism of HCC is urgently needed. Methods: Utilizing the antigen-expressing c-MycOE/Tp53KO HCC mouse model, a critical oncogenic signaling pathway in immune evasion was identified by data-independent acquisition mass spectrometry (DIA-MS) proteomics. Potential downstream pathways were investigated by scRNA-seq, immune profiling, western blot, and multiplexed immunofluorescence staining. We also examined the therapeutic potential of targeting wild-type Kras using the Kras inhibitor MRTX0902 and the MEK inhibitor trametinib to enhance the efficacy of immunotherapy in HCC. Results: We identified a significant upregulation of wild-type Kras in immune-escaped tumors, with concurrent activation of its ligand-driven EGFR and its downstream MEK/ERK signaling. Likewise, endogenous Kras overexpression in this model resulted in increased tumor burden with reduced survival time in mice, implicating the regulatory role of Kras signaling in immune evasion. Clinically, wild-type Kras showed elevated expression in HCC at both the mRNA and protein levels, and was associated with tumor recurrence and poorer patient survival. Through scRNA-seq analysis, we demonstrated that Kras hindered dendritic cell recruitment, leading to compromised Cd8+ T cell activity via the suppression of interferon-mediated Cxcl9. Wild-type Kras activation further impaired HCC recognition by T cells via the downregulation of MHC-I expression. Notably, the combination therapy of MRTX0902, trametinib, and anti-PD-1 effectively enhanced intratumoral Cd8+ T cell infiltration and demonstrated a favorable response in survival outcomes. Conclusion: This study showed that wild-type Kras, via the activation of its downstream MEK/ERK signaling, acts as a crucial immune evasive mechanism in HCC and contributes to resistance to anti-PD-1 therapy. It also provided new insights into the therapeutic potential of targeting wild-type Kras to enhance the efficacy of anti-PD-1 therapy, providing a foundation for further exploration and potential clinical applications in HCC. Citation Format: Mang Leng Lei, Terence Kin Wah Lee. Wild type Kirsten rat sarcoma (Kras) activation drives evasion of interferon-mediated immunity in hepatocellular carcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5356.

Abstract 2285: Mass spectrometry-based proteomics profiling reveals differential proteome composition in tumor and normal tissues, with implications for normalization

Abstract The composition of the proteome varies significantly across different tissues and disease states creating a challenge of how best to compare expression levels of each protein across these tissue types. Traditionally, a median approach to normalization has been used, but this could obscure meaningful biological differences between tissues. Here, we present a quantitative proteomic study analyzing proteome composition across tumor and normal tissues in order to assess normalization methods. Human proteomes covering 7 cancer indications (244 samples) and 12 normal tissue organs (370 samples) were analyzed by data-independent acquisition mass spectrometry. On average, over 8,000 proteins were quantified across the study cohorts. Comparison of proteomes revealed an average of 11.4% higher protein identification in tumors compared to matched normal tissues (6 tumor types all p<0.003). Tissue origin strongly influenced proteome composition, most notably in bone marrow, while cancer tissues displayed less proteomic composition variation than normal tissues. Compared to global median normalization, riBAQ and tissue-specific quantile normalization reduced technical variances while maintaining biological differences between tissue types. By revealing tissue-specific proteome signatures as well as systemic proteomic alterations in tumors, this study lays the groundwork for appropriate comparative approaches accounting for tissue origins, and establishes a framework for discovery of tissue-specific biomarkers, which may facilitate the development of novel diagnostic and prognostic tests, and guide therapeutic interventions for cancer. Citation Format: Ying Zheng, Lindsay Olita, McIver Nivens, Abby Chiang, Shreya Ahuja, Matthew Glover, John Bullen, Elaine Hurt, Wenyan Zhong. Mass spectrometry-based proteomics profiling reveals differential proteome composition in tumor and normal tissues, with implications for normalization [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2285.

Abstract 7093: Prediction of overall survival in stage II and III colon cancer through machine learning of rapidly-acquired proteomics

Abstract The use of adjuvant therapy in stage II-III colon cancer (CC) has been controversial for decades. No markers or models now is satisfactory enough to be adopted widely by the medical community. Here, we analyzed the proteomics of paraffin-embedded tissue (FFPE) biopsy samples from 230 CC patients (stages II-III) with up to 10 years of survival by using pressure cycling technology (PCT) and data-independent acquisition (DIA) mass spectrometry. Using machine learning, we established a novel and practical protein-based clinical classification system for CC prognosis which was further verified in an independent validation cohort. Using the nine protein-based clinical model, we improved the area under the curve (AUC) value to 0.926 in the training cohort and 0.872 in the validation cohort. Our promising model will be a potential approach to prognostication to aid in rational follow-up schedule-making and risk-adaptive individualized therapies. Citation Format: Kailun Xu, Xiaoyang Yin, Tiannan Guo, Shu Zheng, Yingkuan Shao. Prediction of overall survival in stage II and III colon cancer through machine learning of rapidly-acquired proteomics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7093.

Abstract 5725: Aurkin-A, a TPX2-Aurora A inhibitor disrupts alisertib-induced polyploidy in HG-diffuse large B cell lymphoma

Abstract Chemotherapy induced polyploidy (CIP) is a mechanism of inherited drug resistance resulting in an aggressive disease course in cancer patients. Alisertib, an Aurora Kinase A (AK-A) ATP site inhibitor, induces cell cycle disruption resulting in polyaneuploidy in high-grade (MYC and BCL2 amplified or expressed) Diffuse Large B Cell Lymphoma (HG-DLBCL). Propidium Iodide cycle flow cytometry was utilized to quantify alisertib induced polyploidy in U2932 and VAL cell lines. In U2932 cells, 1µM alisertib generated 8n+ polyploidy in 48% of the total cell population after five days of treatment. Addition of Aurkin A an AK-A/TPX2 site inhibitor, disrupted alisertib induced polyploidy in a dose-dependent manner with associated increased apoptosis in DLBCL cells. Combination of Aurkin A plus alisertib significantly reduced polyploidy and sensitized DLBCL cells to alisertib. The sensitization of alisertib with Aurkin A through complete AK-A inhibition is of high clinical importance as the alisertib dose can be significantly reduced while preventing polyploidy, enhancing efficacy, and significantly reducing toxicity to normal tissue. In clinical trials of alisertib in lymphoma, a maximum tolerated dose of 1.5µM was achieved. In U2932 cells, we found the addition of 100µM Aurkin A reduced the alisertib IC50 from 983nM to 34nM. We generated a stable FUCCI U2932 cell line expressing Geminin-clover (S/G2/M) and cdt1-mKO (G1), to help monitor cell cycle progression in live cells. Using this system, we identified alisertib-induced polyploidy through endomitosis, which was eliminated with Aurkin A treatment. In a VAL cell line mouse xenograft model, we show polyploidy generation in alisertib treated mice versus vehicle control or Aurkin A. Aurkin A plus alisertib significantly reduced polyploidy and tumor growth compared to vehicle control or Aurkin A levels. Finally, we performed Data-Independent Acquisition Mass Spectrometry on alisertib, Aurkin A, and combination alisertib/Aurkin A treated U2932 cells to identify therapy induced proteomic changes. This proteomic analysis has provided insight into the mechanism of alisertib polyploidy induction and the protective mechanism of within the combination therapy. Our in vitro and in vivo studies show that Aurkin A synergizes with alisertib and significantly decreases the alisertib dose needed to disrupt polyploidy while increasing apoptosis in HG-DLBCL cells. Citation Format: Patrick J. Conway, Bárbara De la Peña Avalos, Jonathan Dao, Sebastian Montagnino, Dmytro Kovalskyy, Eloise Dray, Daruka Mahadevan. Aurkin-A, a TPX2-Aurora A inhibitor disrupts alisertib-induced polyploidy in HG-diffuse large B cell lymphoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5725.

Neuropathic Corneal Pain: Tear Proteomic and Neuromediator Profiles, Imaging Features, and Clinical Manifestations

To investigate the tear proteomic and neuromediator profiles, in vivo confocal microscopy (IVCM) imaging features, and clinical manifestations in neuropathic corneal pain (NCP) patients. Cross-sectional study. Twenty NCP patients and twenty age-matched controls were recruited. All subjects were evaluated by corneal sensitivity, Schirmer's test, tear break-up time, corneal and ocular surface staining, Ocular Surface Disease Index and Ocular Pain Assessment Survey questionnaires, as well as IVCM examinations for corneal nerves, microneruomas, epithelial and dendritic cells. Tears were collected for neuromediator and proteomic analysis using enzyme-linked immunosorbent assay and data-independent acquisition mass spectrometry. Burning and sensitivity to light were the two most common symptoms in NCP. A total of 188 significantly dysregulated proteins, such as elevated metallothionein-2, creatine kinases B-type, vesicle-associated membrane protein 2, neurofilament light polypeptide, and myelin basic protein, were identified in the NCP patients. The top 10 dysregulated biological pathways in NCP include neurotoxicity, axonal signaling, wound healing, neutrophil degradation, apoptosis, thrombin signaling mitochondrial dysfunction, RHOGDI and P70S6K signaling pathways. Compared to controls, the NCP cohort presented with significantly decreased corneal sensitivity (P<0.001), decreased corneal nerve fiber length (P=0.003), corneal nerve fiber density (P=0.006), nerve fiber fractal dimension (P=0.033), as well as increased in corneal nerve fiber width (P=0.002), increased length, total area and perimeter of microneuromas (P<0.001, P<0.001, P=0.019), smaller corneal epithelial size (P=0.017), and higher nerve growth factor level in tears (p=0.006). These clinical manifestations, imaging features, and molecular characterizations would contribute to the diagnostics and potential therapeutic targets for NCP.

Profiling Protein-Protein Interactions in the Human Brain by Refined Cofractionation Mass Spectrometry.

Proteins usually execute their biological functions through interactions with other proteins and by forming macromolecular complexes, but global profiling of protein complexes directly from human tissue samples has been limited. In this study, we utilized cofractionation mass spectrometry (CF-MS) to map protein complexes within the postmortem human brain with experimental replicates. First, we used concatenated anion and cation Ion Exchange Chromatography (IEX) to separate native protein complexes in 192 fractions and then proceeded with Data-Independent Acquisition (DIA) mass spectrometry to analyze the proteins in each fraction, quantifying a total of 4,804 proteins with 3,260 overlapping in both replicates. We improved the DIA's quantitative accuracy by implementing a constant amount of bovine serum albumin (BSA) in each fraction as an internal standard. Next, advanced computational pipelines, which integrate both a database-based complex analysis and an unbiased protein-protein interaction (PPI) search, were applied to identify protein complexes and construct protein-protein interaction networks in the human brain. Our study led to the identification of 486 protein complexes and 10054 binary protein-protein interactions, which represents the first global profiling of human brain PPIs using CF-MS. Overall, this study offers a resource and tool for a wide range of human brain research, including the identification of disease-specific protein complexes in the future.

Proteomic analysis of P. gingivalis-Lipopolysaccharide induced neuroinflammation in SH-SY5Y and HMC3 cells.

Chronic periodontitis and its keystone pathogen, Porphyromonas gingivalis, have increasingly been linked with Alzheimer's disease (AD). However, P.gingivalis-lipopolysaccharide (LPS) mediated release of neuroinflammatory proteins contributes to AD remains underexplored. In this study, we utilized data-independent acquisition mass spectrometry to characterize P.gingivalis-LPS induced profile of differentially expressed proteins associated with the neuroinflammatory response in human neuroblastoma (SH-SY5Y) and human microglial (HMC3) cells. We reported a set of 124 proteins in SH-SY5Y cells and 96 proteins in HMC3 cells whose levels were significantly upregulated or downregulated by exposure to P. gingivalis-LPS. Our findings demonstrate that P. gingivalis-LPS contributed to the elevated expressions of dementia biomarkers and pro-inflammatory cytokines that include APP, Aβ1-42, Aβ1-40, T-Tau, p-Tau, VEGF, TGF-β, IL-1β, IL-6 and TNF-α through 2 distinct pathways of extracellular sensing by cell surface receptors and intracellular cytosolic receptors. Interestingly, intracellular signaling proteins activated with P. gingivalis-LPS transfection using Lipofectamine™ 2000 had significantly higher fold change protein expression compared to the extracellular signaling with P. gingivalis-LPS treatment. Additionally, we also explored P. gingivalis-LPS mediated activation of caspase-4 dependent non canonical inflammasome pathway in both SH-SY5Y and HMC3 cells. In summary, P. gingivalis-LPS induced neuroinflammatory protein expression in SH-SY5Y and HMC3 cells, provided insights into the specific inflammatory pathways underlying the potential link between P. gingivalis-LPS infection and the pathogenesis of Alzheimer's disease and related dementias.

Identification of gravity-responsive serum proteins in spaceflight mice using a quantitative proteomic approach with data-independent acquisition mass spectrometry.

Physical inactivity associated with gravity unloading, such as microgravity during spaceflight and hindlimb unloading (HU), can cause various physiological changes. In this study, we attempted to identify serum proteins whose levels fluctuated in response to gravity unloading. First, we quantitatively assessed changes in the serum proteome profiles of spaceflight mice using mass spectrometry with data-independent acquisition. The serum levels of several proteins involved in the responses to estrogen and glucocorticoid, blood vessel maturation, osteoblast differentiation, and ossification were changed by microgravity exposure. Furthermore, a collective evaluation of serum proteomic data from spaceflight and HU mice identified 30 serum proteins, including Mmp2, Igfbp2, Tnc, Cdh5, and Pmel, whose levels varied to a similar extent in both gravity unloading models. These changes in serum levels could be involved in the physiological changes induced by gravity unloading. A collective evaluation of serum, femur, and soleus muscle proteome data of spaceflight mice also showed 24 serum proteins, including Igfbp5, Igfbp3, and Postn, whose levels could be associated with biological changes induced by microgravity. This study examined serum proteome profiles in response to gravity unloading, and may help deepen our understanding of microgravity adaptation mechanisms during prolonged spaceflight missions.

Proteomic changes of botulinum neurotoxin injection on muscle growth in children with spastic cerebral palsy.

The study aims to explore the proteomic profile and specific target proteins associated with muscle growth in response to botulinum neurotoxin A (BoNT-A) treatment, in order to improve spasticity management in children with cerebral palsy (CP). A total of 54 participants provided 60 plasma samples for proteomic analysis. Among them, six children were sampled before and after receiving their first BoNT-A injection. In addition, 48 unrelated children were enrolled, among whom one group had never received BoNT-A injections and another group was sampled after their first BoNT-A injection. Differentially expressed proteins were identified using the data-independent acquisition (DIA) mass spectrometry approach. Gene Ontology (GO), protein-protein interaction network, and Kyoto Encyclopedia of Genes and Genome analysis were conducted to explore the function and relationship among differentially expressed proteins. The expression levels of target proteins were verified by quantitative real-time PCR and western blotting. Analysis identified significant differential expression of 90 proteins across two time points, including 48 upregulated and 42 downregulated proteins. The upregulated thioredoxin, α-actinin-1, and aggrecan, and the downregulated integrin beta-1 may affect the growth of muscles affected by spasticity 3months after BoNT-A injection. This effect is potentially mediated through the activation or inhibition of PI3K-Akt, focal adhesion, and regulation of actin cytoskeleton signaling pathways. BoNT-A injection could lead to a disruption of protein levels and signaling pathways, a condition subsequently associated with muscle growth. This finding might aid clinicians in optimizing the management of spasticity in children with CP.

Characterization of the proteome of stable and unstable carotid atherosclerotic plaques using data-independent acquisition mass spectrometry

BackgroundCurrently, noninvasive imaging techniques and circulating biomarkers are still insufficient to accurately assess carotid plaque stability, and an in-depth understanding of the molecular mechanisms that contribute to plaque instability is still lacking.MethodsWe established a clinical study cohort containing 182 patients with carotid artery stenosis. After screening, 39 stable and 49 unstable plaques were included in the discovery group, and quantitative proteomics analysis based on data independent acquisition was performed for these plaque samples. Additionally, 35 plaques were included in the validation group to validate the proteomics results by immunohistochemistry analysis.ResultsA total of 397 differentially expressed proteins were identified in stable and unstable plaques. These proteins are primarily involved in ferroptosis and lipid metabolism-related functions and pathways. Plaque validation results showed that ferroptosis- and lipid metabolism-related proteins had different expression trends in stable plaques versus unstable fibrous cap regions and lipid core regions. Ferroptosis- and lipid metabolism-related mechanisms in plaque stability were discussed.ConclusionsOur results may provide a valuable strategy for revealing the mechanisms affecting plaque stability and will facilitate the discovery of specific biomarkers to broaden the therapeutic scope.

Regulation of urea cycle by reversible high-stoichiometry lysine succinylation.

The post-translational modification lysine succinylation is implicated in the regulation of various metabolic pathways. However, its biological relevance remains uncertain due to methodological difficulties in determining high-impact succinylation sites. Here, using stable isotope labelling and data-independent acquisition mass spectrometry, we quantified lysine succinylation stoichiometries in mouse livers. Despite the low overall stoichiometry of lysine succinylation, several high-stoichiometry sites were identified, especially upon deletion of the desuccinylase SIRT5. In particular, multiple high-stoichiometry lysine sites identified in argininosuccinate synthase (ASS1), a key enzyme in the urea cycle, are regulated by SIRT5. Mutation of the high-stoichiometry lysine in ASS1 to succinyl-mimetic glutamic acid significantly decreased its enzymatic activity. Metabolomics profiling confirms that SIRT5 deficiency decreases urea cycle activity in liver. Importantly, SIRT5 deficiency compromises ammonia tolerance, which can be reversed by the overexpression of wild-type, but not succinyl-mimetic, ASS1. Therefore, lysine succinylation is functionally important in ammonia metabolism.

Integrating single-cell and spatial transcriptomes reveals COL4A1/2 facilitates the spatial organisation of stromal cells differentiation in breast phyllodes tumours.

Breast phyllodes tumours (PTs) are a unique type of fibroepithelial neoplasms with metastatic potential and recurrence tendency. However, the precise nature of heterogeneity in breast PTs remains poorly understood. This study aimed to elucidate the cell subpopulations composition and spatial structure and investigate diagnostic markers in the pathogenesis of PTs. We applied single-cell RNA sequencing and spatial transcriptomes on tumours and adjacent normal tissues for integration analysis. Immunofluorescence experiments were conducted to verify the tissue distribution of cells. Tumour cells from patients with PTs were cultured to validate the function of genes. To validate the heterogeneity, the epithelial and stromal components of tumour tissues were separated using laser capture microdissection, and microproteomics data were obtained using data-independent acquisition mass spectrometry. The diagnostic value of genes was assessed using immunohistochemistry staining. Tumour stromal cells harboured seven subpopulations. Among them, a population of widely distributed cancer-associated fibroblast-like stroma cells exhibited strong communications with epithelial progenitors which underwent a mesenchymal transition. We identified two stromal subpopulations sharing epithelial progenitors and mesenchymal markers. They were inferred to further differentiate into transcriptionally active stromal subpopulations continuously expressing COL4A1/2. The binding of COL4A1/2 with ITGA1/B1 facilitated a growth pattern from the stroma towards the surrounding glands. Furthermore, we found consistent transcriptional changes between intratumoural heterogeneity and inter-patient heterogeneity by performing microproteomics studies on 30 samples from 11 PTs. The immunohistochemical assessment of 97 independent cohorts identified that COL4A1/2 and CSRP1 could aid in accurate diagnosis and grading. Our study demonstrates that COL4A1/2 shapes the spatial structure of stromal cell differentiation and has important clinical implications for accurate diagnosis of breast PTs.

Proteomic Analysis Identifies GSN as a Noninvasive Circulating Serum Biomarker for Predicting Early Recurrence of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is susceptible to early recurrence, but it lacks effective predictive biomarkers. In this study, we retrospectively selected 179 individuals as a discovery cohort (126 HCC patients and 53 liver cirrhosis (LC) patients) for screening candidate serum biomarkers of early recurrence based on data independent acquisition-mass spectrometry strategy. And then, the candidate biomarkers were validated in an additional independent cohort with 192 individuals (142 HCC patients and 50 LC patients) using parallel reaction monitoring targeted quantitative techniques (PXD047852). Eventually, we validated that gelsolin (GSN) concentrations were significantly lower in HCC than in LC (p < 0.0001), patients with low GSN concentrations had a poor prognosis (p < 0.0001), and GSN concentrations were significantly lower in early recurrence HCC than in late recurrence HCC (p < 0.0001). These trends were also observed in alpha-fetoprotein (AFP)-negative HCC patients. The area under the curve of machine-learning-based predictive model (GSN and microvascular invasion) for predicting early recurrence risk reached 0.803 (95% confidence interval (CI): 0.786-0.820) and maintained the same efficacy in AFP-negative patients. In conclusion, GSN is a novel serum biomarker for early recurrence of HCC. The model could provide timely warning to HCC patients at high risk of recurrence.