Label-free Quantification Research Articles

Evaluation of Exosomal Proteins as Potential Biomarkers from RBC Stages of Plasmodium falciparum 3D7.

Falciparum malaria relies extensively on cell-to-cell communication, and earlier research on the function of exosomal proteins derived from infected red blood cells (iRBCs) has been classified into numerous important roles. In this study, the exosomes were derived from Pf3D7-iRBCs cultured in vitro during synchronized trophozoite stages. The isolated exosomes were assessed using NTA, FE-SEM, and flow cytometry. Our study reported heterogeneous populations of exosomes during the infection. Additionally, label-free quantification based on LC/MS-MS for protein profiling revealed the presence of both parasitic and host (RBC) proteins; out of a total of 124 proteins detected, 20 Pf3D7 proteins and 80 RBC proteins were identified. Exosomal RBC protein expression is different in cRBCs-Exo and iRBCs-Exo, which shows how the parasite and RBCs interact with each other. Functional classification reported that the majority of these Pf3D7 proteins are uncharacterized with unknown functions, few of which are involved in biological processes such as regulation of complement activation, response to external stimuli, immune system-mediated signaling pathway, protein processing, etc. Hence, studying these exosomal proteins and comparing them to previous research has helped us understand how exosomes help cells to communicate in malaria. It may also reveal new potential biomarkers for diagnostic methods or therapies for malaria.

Comprehensive Evaluation of Advanced Imputation Methods for Proteomic Data Acquired via the Label-Free Approach

Mass-spectrometry-based proteomics frequently utilizes label-free quantification strategies due to their cost-effectiveness, methodological simplicity, and capability to identify large numbers of proteins within a single analytical run. Despite these advantages, the prevalence of missing values (MV), which can impact up to 50% of the data matrix, poses a significant challenge by reducing the accuracy, reproducibility, and interpretability of the results. Consequently, effective handling of missing values is crucial for reliable quantitative analysis in proteomic studies. This study systematically evaluated the performance of selected imputation methods for addressing missing values in proteomic dataset. Two protein identification algorithms, FragPipe and MaxQuant, were employed to generate datasets, enabling an assessment of their influence on im-putation efficacy. Ten imputation methods, representing three methodological categories—single-value (LOD, ND, SampMin), local-similarity (kNN, LLS, RF), and global-similarity approaches (LSA, BPCA, PPCA, SVD)—were analyzed. The study also investigated the impact of data logarithmization on imputation performance. The evaluation process was conducted in two stages. First, performance metrics including normalized root mean square error (NRMSE) and the area under the receiver operating characteristic (ROC) curve (AUC) were applied to datasets with artificially introduced missing values. The datasets were designed to mimic varying MV rates (10%, 25%, 50%) and proportions of values missing not at random (MNAR) (0%, 20%, 40%, 80%, 100%). This step enabled the assessment of data characteristics on the relative effectiveness of the imputation methods. Second, the imputation strategies were applied to real proteomic datasets containing natural missing values, focusing on the true-positive (TP) classification of proteins to evaluate their practical utility. The findings highlight that local-similarity-based methods, particularly random forest (RF) and local least-squares (LLS), consistently exhibit robust performance across varying MV scenarios. Furthermore, data logarithmization significantly enhances the effectiveness of global-similarity methods, suggesting it as a beneficial preprocessing step prior to imputation. The study underscores the importance of tailoring imputation strategies to the specific characteristics of the data to maximize the reliability of label-free quantitative proteomics. Interestingly, while the choice of protein identification algorithm (FragPipe vs. MaxQuant) had minimal influence on the overall imputation error, differences in the number of proteins classified as true positives revealed more nuanced effects, emphasizing the interplay between imputation strategies and downstream analysis outcomes. These findings provide a comprehensive framework for improving the accuracy and reproducibility of proteomic analyses through an informed selection of imputation approaches.

Nanoparticles with "K-edge" Metals Bring "Color" in Multiscale Spectral Photon Counting X-ray Imaging.

Preclinical and clinical diagnostics depend greatly on medical imaging, which enables the identification of physiological and pathological processes in living subjects. It is often necessary to use contrast agents to complement anatomical data with functional information or to describe the disease phenotypically. Nanomaterials are used as contrast agents in many advanced bioimaging techniques and applications because of their high payload, physicochemical properties, improved sensitivity, and multimodality. Metals with k-edge energy within the X-ray bandwidth respond to photon counting and spectral X-ray imaging. This Perspective examines the progress made in the emerging area of nanoparticle-based k-edge contrast agents. These nano "k-edge" particles have been explored with spectral photon counting CT (SPCCT) for multiplexed molecular imaging, pushing the boundaries of resolution and capabilities of CT imaging. Design considerations, contrast properties, and biological behavior are discussed in detail. The key applications are highlighted by categorizing these nanomaterials based on their X-ray, k-edge energy, and biological properties, as well as their synthesis, functionalization, and characterization processes. The article delves into the transformative impact of nano "k-edge" particles on early disease detection and other biomedical applications. The review provides further insights into how the "k-edge signatures" of these nanoparticles combined with photon counting technique can be leveraged for quantitative, multicontrast imaging of diseases. We also discuss the status quo of clinically approved nanoparticles for imaging and highlight the challenges such as toxicity and clearance as well as promising clinical perspectives, providing a balanced view of the potential and limitations of these nanomaterials. Furthermore, we discuss the necessary future research efforts required to clinically translate nano "k-edge" particles as SPCCT contrast agents for early disease diagnosis and tracking.

Proteomic profiling identifies classic Hodgkin lymphoma patients at risk of bleomycin pulmonary toxicity

Advances in treating classic Hodgkin lymphoma (cHL) have improved cure rates, with overall survival exceeding 80%, resulting in a growing population of survivors at risk of long-term complications, particularly cardiac and pulmonary toxicity. Bleomycin, a key component of combination chemotherapy, is associated with bleomycin-induced pulmonary toxicity (BPT). Using label-free quantification nano liquid chromatography-tandem mass spectrometry, protein expression in diagnostic lymphoma samples from patients with and without BPT was compared. Results showed differential protein expression and disrupted cellular pathways, suggesting biological differences in BPT risk. Immunohistochemical analysis revealed higher expression of JAK3, BID, and MMP9, and lower expression of CD20, TPD52, and PIK3R4 in patients with BPT. High BID and low CD20 expression were associated with inferior overall survival, while high BID and low JAK3 and CD20 expression were linked to poorer progression-free survival. These findings highlight altered protein profiles in pretreatment cHL biopsies associated with BPT development.

The first proteomics analysis of tonsils in patients with periodic fever, aphthous stomatitis, pharyngitis, and adenitis syndrome (PFAPA).

Periodic fever, aphthous stomatitis, pharyngitis, and adenitis (PFAPA) syndrome is a recurrent fever syndrome. The exact etiopathogenesis of PFAPA syndrome remains unknown. Biological fluids or tissues may provide disease-specific biomarkers that may help clinicians to find new pathogenic pathways. Tonsil tissues of seven patients with PFAPA were collected during the tonsillectomy. Seven patients who underwent tonsillectomy for reasons other than chronic tonsillitis were enrolled as a control group. The nHPLC LC-MS/MS system was used for protein identification and label-free quantification. Bioinformatics analysis was carried out using the UniProt accession numbers of the identified proteins. Proteomics analysis revealed to identity of proteins of which at least 23 were up and 57 were downregulated. Bioinformatics analysis of differentially regulated proteins by STRING indicated that protein folding and clearance machinery were interrupted in PFAPA patients compared to the controls. The affected pathways underlined the importance of the mitochondrial electron transport chain and ATP biosynthesis process. Although it is not clear that changes in tonsil protein expression whether directly related to pathogenesis or simply result of chronic inflammation, the identification of tonsil biomarkers for PFAPA may provide clinicians an opportunity to understand disease pathogenesis or develop new molecular targets for treatments. Proteomics analyses of tonsils revealed the identity of 80 proteins of which at least 23 were up and 57 were downregulated. Bioinformatics analysis underlined the importance of mitochondrial ETC and regulation of ATP biosynthetic process. This is the first study evaluating the proteomics of the tonsils of PFAPA patients. The identification of tonsil biomarkers for PFAPA may provide clinicians an opportunity to understand disease pathogenesis or develop new molecular targets for treatments.

Automated fast label-free quantification of cardiomyocytes dynamics with raw holograms for cardiotoxicity screening

Automated fast label-free quantification of cardiomyocytes dynamics with raw holograms for cardiotoxicity screening

Tear fluid reflects the altered protein expressions of Alzheimer's disease patients in proteins involved in protein repair and clearance system or the regulation of cytoskeleton.

New biomarkers that improve diagnosis of Alzheimer's disease (AD) are warranted. Tear fluid (TF) containing variety of proteins that reflect pathophysiological changes of systemic diseases makes TF proteins potential biomarker candidates for AD. We investigated the expression levels of TF proteins in persons with mild AD and cognitively healthy controls (CO) to find out if altered proteins may link to the AD pathophysiology. We analyzed the data of the 53 study participants (34 COs, mean age 71 and Mini-Mental State Examination (MMSE) 28.9 ± 1.4 and 19 persons with AD, CDR 0.5-1, mean age 71 and MMSE 23.8 ± 2.8). All went through neurological status examination, cognitive tests, and ophthalmological examination. TF was collected using Schirmer strips. The TF protein content was evaluated via mass spectrometry-based proteomics and label-free quantification. Eleven proteins having a role either in protein repair and clearance system, or regulation of cytoskeleton, showed altered expression in AD group compared to CO group. Seven of them were significantly (p ≤ 0.05) upregulated (Sti1, Twf1, Myl6, Otub1, Pls1 and Caza1) or, downregulated (HSP90) in AD group. Altered expression of all these up- or downregulated proteins may be linked to AD pathophysiology. Thus, our results are encouraging for searching new biomarker candidates for AD. TF is potential biomarker candidate, because TF seems to reflect altered protein levels already in mild AD dementia.

Semiquantitative analysis of protein expression in heated rat lens using shotgun proteomics.

Previous studies have reported that a strong correlation between the estimated cumulative thermal exposure in the crystalline lens and the incidence of nuclear cataracts; however, the precise relationship between temperature and cataracts remains to be fully elucidated. In the present study, the shotgun liquid chromatography/mass spectroscopy‑based global proteomic approach was applied to investigate cataract‑inducing factors in lens cultured at normal (35.0˚C) and slightly warmer (37.5˚C) conditions. In the rat lens, 190 proteins (total) were identified. Of these, 48 proteins (25.3%) were found in lenses cultured at both 35.0˚C and 37.5˚C. Moreover, 85 proteins (44.7%) were unique to lenses cultured at 35.0˚C, while 57 proteins (30.0%) were unique to lenses cultured at 37.5˚C. Protein expression changes in rat lenses cultured at 37.5˚C were examined using a label‑free semiquantitative approach that uses spectral counting and Gene Ontology analysis. Filensin and vimentin protein expression, key factors in maintaining lens structure, were decreased. These findings may serve as a valuable indicator for elucidating the relationship between temperature and the onset of nuclear cataracts.

Identification of Estrogen-Responsive Proteins in Mouse Seminal Vesicles Through Mass Spectrometry-Based Proteomics.

Background: Although estrogenic compounds promise therapeutic potential in treating various conditions, concerns regarding their endocrine-disrupting effects have been raised. Current methodologies for screening estrogenicity in rodent models are limited to the female-specific uterotrophic bioassay. Studies have reported enlargement of the seminal vesicles in orchiectomized males treated with estrogens. However, identifying estrogenicity strictly through changes in wet weights is uninformative regarding the molecular mechanisms of these agents. Therefore, protein-based biomarkers can complement and improve the sensitivity of weight-based assessments. To this end, we present a discovery-driven proteomic analysis of 17β-estradiol's effects on the seminal vesicles. Methods: We treated orchidectomized mice with the hormone for five days and used the vehicle-treated group as a control. Seminal vesicles were analyzed by shotgun approach using data-dependent nanoflow liquid chromatography-tandem mass spectrometry and label-free quantification. Proteins found to be differentially expressed between the two groups were processed through a bioinformatics pipeline focusing on pathway analyses and assembly of protein interaction networks. Results: Out of 668 identified proteins that passed rigorous validation criteria, 133 were regulated significantly by 17β-estradiol. Ingenuity Pathway Analysis® linked them to several hormone-affected pathways, including those associated with immune function such as neutrophil degranulation. The altered protein interaction networks were also related to functions including endocrine disruption, abnormal metabolism, and therapeutic effects. Conclusions: We identified several potential biomarkers for estrogenicity in mouse seminal vesicles, many of them not previously linked with exogenous 17β-estradiol exposure.

Results from a multi-laboratory ocean metaproteomic intercomparison: effects of LC-MS acquisition and data analysis procedures

Abstract. Metaproteomics is an increasingly popular methodology that provides information regarding the metabolic functions of specific microbial taxa and has potential for contributing to ocean ecology and biogeochemical studies. A blinded multi-laboratory intercomparison was conducted to assess comparability and reproducibility of taxonomic and functional results and their sensitivity to methodological variables. Euphotic zone samples from the Bermuda Atlantic Time-series Study (BATS) in the North Atlantic Ocean collected by in situ pumps and the autonomous underwater vehicle (AUV) Clio were distributed with a paired metagenome, and one-dimensional (1D) liquid chromatographic data-dependent acquisition mass spectrometry analysis was stipulated. Analysis of mass spectra from seven laboratories through a common bioinformatic pipeline identified a shared set of 1056 proteins from 1395 shared peptide constituents. Quantitative analyses showed good reproducibility: pairwise regressions of spectral counts between laboratories yielded R2 values averaged 0.62±0.11, and a Sørensen similarity analysis of the top 1000 proteins revealed 70 %–80 % similarity between laboratory groups. Taxonomic and functional assignments showed good coherence between technical replicates and different laboratories. A bioinformatic intercomparison study, involving 10 laboratories using eight software packages, successfully identified thousands of peptides within the complex metaproteomic datasets, demonstrating the utility of these software tools for ocean metaproteomic research. Lessons learned and potential improvements in methods were described. Future efforts could examine reproducibility in deeper metaproteomes, examine accuracy in targeted absolute quantitation analyses, and develop standards for data output formats to improve data interoperability. Together, these results demonstrate the reproducibility of metaproteomic analyses and their suitability for microbial oceanography research, including integration into global-scale ocean surveys and ocean biogeochemical models.

In Situ and Label-Free Quantification of Membrane Protein-Ligand Interactions Using Optical Imaging Techniques: A Review.

Membrane proteins are crucial for various cellular processes and are key targets in pharmacological research. Their interactions with ligands are essential for elucidating cellular mechanisms and advancing drug development. To study these interactions without altering their functional properties in native environments, several advanced optical imaging methods have been developed for in situ and label-free quantification. This review focuses on recent optical imaging techniques such as surface plasmon resonance imaging (SPRi), surface plasmon resonance microscopy (SPRM), edge tracking approaches, and surface light scattering microscopy (SLSM). We explore the operational principles, recent advancements, and the scope of application of these methods. Additionally, we address the current challenges and explore the future potential of these innovative optical imaging strategies in deepening our understanding of biomolecular interactions and facilitating the discovery of new therapeutic agents.

Protein aggregation in plant mitochondria lacking Lon1 inhibits translation and induces unfolded protein responses.

Loss of Lon1 led to stunted plant growth and accumulation of nuclear-encoded mitochondrial proteins including Lon1 substrates. However, an in-depth label-free proteomics quantification of mitochondrial proteins in lon1 revealed that the majority of mitochondrial-encoded proteins decreased in abundance. Additionally, we found that lon1 mutants contained protein aggregates in the mitochondrial that were enriched in metabolic enzymes, ribosomal subunits and PPR-containing proteins of the translation apparatus. These mutants exhibited reduced general mitochondrial translation as well as deficiencies in RNA splicing and editing. These findings support the role of Lon1 in maintaining a functional translational apparatus for mitochondrial-encoded gene translation. Transcriptome analysis of lon1 revealed a mitochondrial unfolded protein response reminiscent of the mitochondrial retrograde signalling dependent on the transcription factor ANAC017. Notably, lon1 mutants exhibited transiently elevated ethylene production, and the shortened hypocotyl observed in lon1 mutants during skotomorphogenesis was partially alleviated by ethylene inhibitors. Furthermore, the short root phenotype was partially ameliorated by introducing a mutation in the ethylene receptor ETR1. Interestingly, the upregulation of only a select few target genes was linked to ETR1-mediated ethylene signalling. Together this provides multiple steps in the link between loss of Lon1 and signalling responses to restore mitochondrial protein homoeostasis in plants.

Identification of Plasma Protein Biomarkers for Predicting Lung Cancer.

Lung cancer remains a leading cause of cancer-related mortality worldwide, necessitating the development of effective early diagnostic strategies. Despite advancements in imaging and screening technologies, late-stage diagnoses remain common, limiting treatment options and reducing survival rates. Thus, there is a critical need for reliable, minimally invasive biomarkers to improve early detection and patient outcomes. Plasma protein biomarkers offer promising potential for early lung cancer detection and continuous disease monitoring. This study explored the potential of specific plasma protein markers as early indicators of lung cancer. Plasma samples were collected from normal healthy individuals and lung cancer patients, and protein purification and analysis were conducted using LC-MS/MS. A mixed-effect model was applied to select lung cancer-related protein markers based on label-free relative quantification values. We identified 29 proteins with potential for early lung cancer diagnosis, including complement proteins (CFB, C3, C8G, C1QA, C1R, C6), orosomucoid proteins (ORM1, ORM2), ceruloplasmin (CP), alpha-1-B glycoprotein (A1BG), and others. These proteins play diverse roles in immune response, inflammation, and cell signaling, suggesting their relevance in lung cancer pathophysiology. Our findings suggest the potential of plasma proteins as early diagnostic biomarkers for lung cancer. Further validation in larger cohorts is needed to confirm their clinical utility. Integrating these biomarkers into existing diagnostic modalities could enhance early detection accuracy, leading to improved patient outcomes.

An In vivo Pilot Study to Estimate the Swelling of the Aneurysm Wall Rabbit Model Generated with Pulsed Fluid Against the Aneurysm Wall.

This study addresses the critical issue of evaluating the risk of rupture of unruptured intracranial aneurysms (UIAs) through the assessment of the mechanical properties of the aneurysm wall. To achieve this, an original approach based on the development of an in vivo deformation device prototype (DDP) of the vascular wall is proposed. The DDP operates by pulsing a physiological fluid onto the vascular wall and measuring the resulting deformation using spectral photon counting computed tomography (SPCCT) imaging. In this preliminary study conducted on a rabbit animal model, an aneurysm was induced on the carotid artery, followed by deformation of the aneurysm sac wall using the DDP. The change in luminal volume of the aneurysm sac induced by the deformation of the vascular wall was then quantified. The initial experimental results demonstrated an increase in the luminal volume of the aneurysm sac in relation to the increased flow rate of the fluid pulsed by the DDP onto the arterial wall. Measurement of the pressure generated by the DDP in relation to the different flow rate values imposed by the pulsation system revealed experimental values of the same order of magnitude as dynamic blood pressure. Furthermore, theoretical pressure values on the deformed area, calculated using Euler's theorem, appeared to be correlated with experimental pressure measurements. This equivalence between theory and experiment is a key element in the use of the DDP for estimating the mechanical properties of the vascular wall, particularly for the use of finite element models to characterise the stress state of the deformed vascular wall. This preliminary work thus presents a novel, innovative, and promising approach for the evaluation and management of the risk of rupture of unruptured intracranial aneurysms.

Alterations in the placental proteome in association with the presence of black carbon particles: A discovery study

BackgroundExposure to ambient air pollution is known to cause direct and indirect molecular expression changes in the placenta, on the DNA, mRNA, and protein levels. Ambient black carbon (BC) particles can be found in the human placenta already very early in gestation. However, the effect of in utero BC exposure on the entire placental proteome has never been studied to date. ObjectivesWe explored whether placental proteome differs between mothers exposed to either high or low BC levels throughout the entire pregnancy. MethodsWe used placental tissue samples from the ENVIRONAGE birth cohort, of 20 non-smoking, maternal- and neonate characteristic-matched women exposed to high (n = 10) or low (n = 10) levels of ambient BC throughout pregnancy. We modeled prenatal BC exposure levels based on the mother's home address and measured BC levels in the fetal side of the placenta. The placental proteome was analyzed by nano-liquid chromatography Q-TOF mass spectrometry. PEAKS software was used for protein identification and label-free quantification. Protein-protein interaction and functional pathway enrichment analyses were performed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) software. ResultsThe accumulation of BC particles in placenta was 2.19 times higher in the high versus low exposure group (20943.4 vs 9542.7 particles/mm³; p = 0.007). Thirteen proteins showed a ≥2-fold expression difference between the two exposure groups, all overexpressed in the placentas of women prenatally exposed to high BC levels. Three protein-protein interactions were enriched within this group, namely between TIMP3 and COL4A2, SERPINE2 and COL4A2, and SERPINE2 and GP1BB. Functional pathway enrichment analysis put forward pathways involved in extracellular matrix-receptor interaction, fibrin clot formation, and sodium ion transport regulation. DiscussionPrenatal BC exposure affects the placental proteome. Future research should focus on the potential consequences of these alterations on placental functioning, and health and disease during early childhood development.

FLASHQuant: A Fast Algorithm for Proteoform Quantification in Top-Down Proteomics.

Accurate quantification of individual proteoforms is a crucial step in identifying proteome-wide alterations in different biological conditions. Intact proteoforms have been analyzed predominantly by liquid chromatography-mass spectrometry (LC-MS)-based top-down proteomics (TDP) and quantified primarily by the label-free quantification (LFQ) method, as it requires no additional costly labeling. In TDP, due to frequent coelution and complex signal structures, overlapping signals deriving from multiple proteoforms complicate accurate quantification. Here, we introduce FLASHQuant for MS1-level LFQ analysis in TDP, which is capable of automatically resolving and quantifying coeluting proteoforms. In benchmark tests performed with both spike-in proteins and proteome-level mixture data sets, FLASHQuant was shown to perform highly accurate and reproducible quantification in short runtimes of just a few minutes per LC-MS run. In particular, it was demonstrated that resolving overlapping proteoforms boosts the quantification accuracy. FLASHQuant is publicly available as platform-independent open-source software at https://openms.org/flashquant/, accompanied by the simple alignment algorithm ConsensusFeatureGroupDetector for multiple LC-MS runs.

Dual Fractions Proteomic Analysis of Silica Nanoparticle Interactions with Protein Extracts

Dual-fraction proteomics reveals a novel class of proteins impacted by nanoparticle exposure. Background: Nanoparticles (NPs) interact with cellular proteomes, altering biological processes. Understanding these interactions requires comprehensive analyses beyond solely characterizing the NP corona. Methods: We utilized a dual-fraction mass spectrometry (MS) approach to analyze both NP-bound and unbound proteins in Saccharomyces cerevisiae sp. protein extracts exposed to silica nanoparticles (SiNPs). We identified unique protein signatures for each fraction and quantified protein abundance changes using spectral counts. Results: Strong correlations were observed between protein profiles in each fraction and non-exposed controls, while minimal correlation existed between the fractions themselves. Linear models demonstrated equal contributions from both fractions in predicting control sample abundance. Combining both fractions revealed a larger proteomic response to SiNP exposure compared to single-fraction analysis. We identified 302/56 proteins bound/unbound to SiNPs and an additional 196 “impacted” proteins demonstrably affected by SiNPs. Conclusion: This dual-fraction MS approach provides a more comprehensive understanding of nanoparticle interactions with cellular proteomes. It reveals a novel class of “impacted” proteins, potentially undergoing conformational changes or aggregation due to NP exposure. Further research is needed to elucidate their biological functions and the mechanisms underlying their impact.

Explore the mechanism of yishenjiangya formula in the treatment of senile hypertension based on multi-omics technology

Ethnopharmacological relevanceThe Yishenjiangya formula (YSJ) is a traditional Chinese medicine (TCM) primarily composed of qi-tonifying components. This classic formula is commonly utilized to treat kidney qi deficiency in elderly patients with hypertension. According to TCM, maintaining a balance between qi and blood is crucial for stable blood pressure. Kidney qi deficiency can disrupt this balance, altering fluid shear force and, ultimately, leading to hypertension, particularly in elderly populations. Despite YSJ's efficacy in treating hypertension, its specific anti-hypertensive mechanisms remain unclear. Aim of the studyYSJ is commonly prescribed for elderly patients with hypertension. Earlier metabolomics studies demonstrated that YSJ exerts antihypertensive effects by influencing four key pathways: linoleic acid metabolism, glycerol phospholipid metabolism, arginine and proline metabolism, and steroid hormone biosynthesis. This study aims to combine metabolomic and proteomic analyses to thoroughly understand the molecular biological mechanisms responsible for YSJ's anti-hypertensive properties. MethodsUltra-Performance Liquid Chromatography-Mass Spectrometry (UPLC-MS) metabolomics, combined with Label-Free Quantitation (LFQ) proteomics, was employed to analyze serum samples from elderly individuals with and without hypertension pre- and post-YSJ intervention. Serum levels of candidate proteins were assessed using enzyme-linked immunosorbent assay, and receiver operating characteristic curves were used to evaluate the diagnostic performance of the target proteins. ResultsEight differentially expressed metabolites and three differentially expressed proteins were identified as potential therapeutic targets of YSJ. These substances are primarily involved in unsaturated fatty acid metabolism, fluid shear stress and atherosclerosis pathway, primary bile acid biosynthesis, proline metabolism, apoptosis, and endoplasmic reticulum stress. YSJ exerts its therapeutic effects on hypertension in the elderly by modulating these pathways. ConclusionsYSJ effectively treats senile hypertension. By analyzing the correlation between therapeutic targets and pathways, YSJ's anti-hypertensive effect was achieved by inhibiting lipid peroxidation and matrix degeneration. Combining metabolomics and proteomics provides an effective method for uncovering YSJ's anti-hypertensive mechanisms.

Spectral channels increase of multi-wavelength visible laser enabled by SHG-SFG hybrid processes.

Spectral evolution in nonlinear optical processes, such as second harmonic generation (SHG) and sum frequency generation (SFG), plays a crucial role in multi-wavelength generation through nonlinear frequency conversion. In this study, the enhancement of spectral performance in a multi-wavelength visible laser facilitated by SHG-SFG hybrid processes is proposed and demonstrated, for the first time to our knowledge. An output of up to eleven wavelengths can be achieved using a six-wavelength pump. Theoretical analysis suggests that the increase in spectral channel count is attributed to the SHG-SFG hybrid processes. Additionally, each nonlinear process operates independently without competition under small-signal approximation, validated through temperature variations. This research not only elucidates the mechanism of spectral evolution in hybrid nonlinear processes but also presents a viable method for improving the spectral performance of a multi-wavelength visible light source.

Characterizing age-related changes in intact mitochondrial proteoforms in murine hearts using quantitative top-down proteomics

BackgroundCardiovascular diseases (CVDs) are the leading cause of death worldwide, and the prevalence of CVDs increases markedly with age. Due to the high energetic demand, the heart is highly sensitive to mitochondrial dysfunction. The complexity of the cardiac mitochondrial proteome hinders the development of effective strategies that target mitochondrial dysfunction in CVDs. Mammalian mitochondria are composed of over 1000 proteins, most of which can undergo post-translational modifications (PTMs). Top-down proteomics is a powerful technique for characterizing and quantifying proteoform sequence variations and PTMs. However, there are still knowledge gaps in the study of age-related mitochondrial proteoform changes using this technique. In this study, we used top-down proteomics to identify intact mitochondrial proteoforms in young and old hearts and determined changes in protein abundance and PTMs in cardiac aging.MethodsIntact mitochondria were isolated from the hearts of young (4-month-old) and old (24-25-month-old) mice. The mitochondria were lysed, and mitochondrial lysates were subjected to denaturation, reduction, and alkylation. For quantitative top-down analysis, there were 12 runs in total arising from 3 biological replicates in two conditions, with technical duplicates for each sample. The collected top-down datasets were deconvoluted and quantified, and then the proteoforms were identified.ResultsFrom a total of 12 LC-MS/MS runs, we identified 134 unique mitochondrial proteins in the different sub-mitochondrial compartments (OMM, IMS, IMM, matrix). 823 unique proteoforms in different mass ranges were identified. Compared to cardiac mitochondria of young mice, 7 proteoforms exhibited increased abundance and 13 proteoforms exhibited decreased abundance in cardiac mitochondria of old mice. Our analysis also detected PTMs of mitochondrial proteoforms, including N-terminal acetylation, lysine succinylation, lysine acetylation, oxidation, and phosphorylation. Data are available via ProteomeXchange with the identifier PXD051505.ConclusionBy combining mitochondrial protein enrichment using mitochondrial fractionation with quantitative top-down analysis using ultrahigh-pressure liquid chromatography (UPLC)-MS and label-free quantitation, we successfully identified and quantified intact proteoforms in the complex mitochondrial proteome. Using this approach, we detected age-related changes in abundance and PTMs of mitochondrial proteoforms in the heart.