Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis

Advances in omics data for eosinophilic esophagitis: moving towards multi-omics analyses

Eosinophilic esophagitis (EoE) is a chronic, allergic inflammatory disease of the esophagus characterized by eosinophil accumulation and has a growing global prevalence. EoE significantly impairs quality of life and poses a substantial burden on healthcare resources. Currently, only two FDA-approved medications exist for EoE, highlighting the need for broader research into its management and prevention. Recent advancements in omics technologies, such as genomics, epigenetics, transcriptomics, proteomics, and others, offer new insights into the genetic and immunologic mechanisms underlying EoE. Genomic studies have identified genetic loci and mutations associated with EoE, revealing predispositions that vary by ancestry and indicating EoE's complex genetic basis. Epigenetic studies have uncovered changes in DNA methylation and chromatin structure that affect gene expression, influencing EoE pathology. Transcriptomic analyses have revealed a distinct gene expression profile in EoE, dominated by genes involved in activated type 2 immunity and epithelial barrier function. Proteomic approaches have furthered the understanding of EoE mechanisms, identifying potential new biomarkers and therapeutic targets. However, challenges in integrating diverse omics data persist, largely due to their complexity and the need for advanced computational methods. Machine learning is emerging as a valuable tool for analyzing extensive and intricate datasets, potentially revealing new aspects of EoE pathogenesis. The integration of multi-omics data through sophisticated computational approaches promises significant advancements in our understanding of EoE, improving diagnostics, and enhancing treatment effectiveness. This review synthesizes current omics research and explores future directions for comprehensively understanding the disease mechanisms in EoE.

Automated High-Throughput Biological Sex Identification from Archeological Human Dental Enamel Using Targeted Proteomics

Biological sex is key information for archeological and forensic studies, which can be determined by proteomics. However, the lack of a standardized approach for fast and accurate sex identification currently limits the reach of proteomics applications. Here, we introduce a streamlined mass spectrometry (MS)-based workflow for the determination of biological sex using human dental enamel. Our approach builds on a minimally invasive sampling strategy by acid etching, a rapid online liquid chromatography (LC) gradient coupled to a high-resolution parallel reaction monitoring (PRM) assay allowing for a throughput of 200 samples per day (SPD) with high quantitative performance enabling confident identification of both males and females. Additionally, we developed a streamlined data analysis pipeline and integrated it into a Shiny interface for ease of use. The method was first developed and optimized using modern teeth and then validated in an independent set of deciduous teeth of known sex. Finally, the assay was successfully applied to archeological material, enabling the analysis of over 300 individuals. We demonstrate unprecedented performance and scalability, speeding up MS analysis by 10-fold compared to conventional proteomics-based sex identification methods. This work paves the way for large-scale archeological or forensic studies enabling the investigation of entire populations rather than focusing on individual high-profile specimens. Data are available via ProteomeXchange with the identifier PXD049326.

Proteomic analysis of peripheral blood mononuclear cells in first episode psychosis - Protein and peptide-centered approaches to elucidate potential diagnostic biomarkers

Diagnosing patients suffering from psychotic disorders is far from being achieved with molecular support, despite all the efforts to study these disorders from different perspectives. Characterizing the proteome of easily obtainable blood specimens, such as the peripheral blood mononuclear cells (PBMCs), has particular interest in biomarker discovery and generating pathophysiological knowledge. This approach has been explored in psychiatry, and while generating valuable information, it has not translated into meaningful biomarker discovery. In this project, we report the proof-of-concept of a methodology that aims to explore further information obtained with classical proteomics approaches that is easily overlooked. PBMC samples from first-episode psychosis and control subjects were subjected to a SWATH-MS approach, and the classical protein relative quantification was performed, where 389 proteins were found to be important to distinguish the two groups. Individual analysis of the quantified peptides was also performed, highlighting peptides of unchanged proteins that were significantly altered. With the combination of protein- and peptide-centered proteomics approaches, it is possible to highlight that information about proteoforms, namely regulation at the peptide level possibly due to post-translational modifications, is routinely overlooked and that its diagnostic potential should be further explored. SIGNIFICANCE: Our exploratory findings highlight the potential of MS-based proteomics strategies, combining protein- and peptide-centered approaches, to aid clinical decision-making in first-episode psychosis, helping to establish early biomarkers for schizophrenia and other psychotic disorders. Particularly, the less popular peptide-centered approach allows the identification/measurement of overlooked modulated peptides that may have potential biomarker characteristics. The application in parallel of protein- and peptide-centered strategies is transversal to research of other diseases, potentially allowing a more comprehensive characterization of the metabolic/pathophysiological alterations related to a specific disease.

Optimizing SureQuant for Targeted Peptide Quantification: a Technical Comparison with PRM and SWATH-MS Methods

Bacterial infections are a major threat to human health worldwide. A better understanding of the properties and physiology of bacterial pathogens in human tissues is required to develop urgently needed novel control strategies. Mass spectrometry-based proteomics could yield such data, but identifying and quantifying scarce bacterial proteins against a preponderance of human proteins is challenging. Here, we explored the recently introduced SureQuant method for highly sensitive targeted mass spectrometry. Using a major human pathogen, the Gram-positive bacteria Staphylococcus aureus, as an example, we evaluated several parameters, including the number of targets and intensity thresholds, for optimal qualitative and quantitative protein analysis. By comparison, we found that SureQuant achieved the same quantitative performance as standard parallel reaction monitoring while allowing accurate and precise quantification of up to 400 targets. SureQuant also surpassed the sensitivity and quantification capabilities of global data-independent acquisition methods. Finally, to facilitate method development, we provide optimized MS parameters for the sensitive quantification of different peptide panel sizes. This study provides a foundation for the broader application of SureQuant in the analysis of clinical specimens containing trace amounts of bacterial proteins as well as other studies requiring ultrasensitive detection of low-abundant proteins.

Carafe enables high quality in silico spectral library generation for data-independent acquisition proteomics

Data-independent acquisition (DIA)-based mass spectrometry is becoming an increasingly popular mass spectrometry acquisition strategy for carrying out quantitative proteomics experiments. Most of the popular DIA search engines make use of in silico generated spectral libraries. However, the generation of high-quality spectral libraries for DIA data analysis remains a challenge, particularly because most such libraries are generated directly from data-dependent acquisition (DDA) data or are from in silico prediction using models trained on DDA data. In this study, we developed Carafe, a tool that generates high-quality experiment-specific in silico spectral libraries by training deep learning models directly on DIA data. We demonstrate the performance of Carafe on a wide range of DIA datasets, where we observe improved fragment ion intensity prediction and peptide detection relative to existing pretrained DDA models.

Astrocytic GLUT1 reduction paradoxically improves central and peripheral glucose homeostasis

Astrocytes are considered an essential source of blood-borne glucose or its metabolites to neurons. Nonetheless, the necessity of the main astrocyte glucose transporter, i.e., GLUT1, for brain glucose metabolism has not been defined. Unexpectedly, we found that brain glucose metabolism was paradoxically augmented in mice with astrocytic GLUT1 reduction (GLUT1ΔGFAP mice). These mice also exhibited improved peripheral glucose metabolism especially in obesity, rendering them metabolically healthier. Mechanistically, we observed that GLUT1-deficient astrocytes exhibited increased insulin receptor-dependent ATP release, and that both astrocyte insulin signaling and brain purinergic signaling are essential for improved brain function and systemic glucose metabolism. Collectively, we demonstrate that astrocytic GLUT1 is central to the regulation of brain energetics, yet its depletion triggers a reprogramming of brain metabolism sufficient to sustain energy requirements, peripheral glucose homeostasis, and cognitive function.

An Extensive Atlas of Proteome and Phosphoproteome Turnover Across Mouse Tissues and Brain Regions

Understanding how proteins in different mammalian tissues are regulated is central to biology. Protein abundance, turnover, and post-translational modifications like phosphorylation, are key factors that determine tissue-specific proteome properties. However, these properties are challenging to study across tissues and remain poorly understood. Here, we present Turnover-PPT, a comprehensive resource mapping the abundance and lifetime of 11,000 proteins and 40,000 phosphosites across eight mouse tissues and various brain regions, using advanced proteomics and stable isotope labeling. We revealed tissue-specific short- and long-lived proteins, strong correlations between interacting protein lifetimes, and distinct impacts of phosphorylation on protein turnover. Notably, we discovered that peroxisomes are regulated by protein turnover across tissues, and that phosphorylation regulates the stability of neurodegeneration-related proteins, such as Tau and α-synuclein. Thus, Turnover-PPT provides new fundamental insights into protein stability, tissue dynamic proteotypes, and the role of protein phosphorylation, and is accessible via an interactive web-based portal at https://yslproteomics.shinyapps.io/tissuePPT.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition (DIA) has become a widely used strategy for peptide and protein quantification in mass spectrometry-based proteomics studies. The integration of ion mobility separation into DIA analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using DIA. We introduce diaTracer, a new spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (m/z, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-MS/MS" spectra, facilitating direct ("spectral-library free") peptide identification and quantification from diaPASEF data. diaTracer is available as a stand-alone tool and is fully integrated into the widely used FragPipe computational platform. We demonstrate the performance of diaTracer and FragPipe using diaPASEF data from triple-negative breast cancer (TNBC), cerebrospinal fluid (CSF), and plasma samples, data from phosphoproteomics and HLA immunopeptidomics experiments, and low-input data from a spatial proteomics study. We also show that diaTracer enables unrestricted identification of post-translational modifications from diaPASEF data using open/mass-offset searches.

A simplified and efficient extracellular vesicle-based proteomics strategy for early diagnosis of colorectal cancer

Colorectal cancer (CRC) is a major cause of cancer-related death worldwide and an effective screening strategy for diagnosis of early-stage CRC is highly desired. Although extracellular vesicles (EVs) are expected to become some of the most promising tools for liquid biopsy of early disease diagnosis, the existing EV-based proteomics methods for practical application in clinical samples are limited by technical challenges in high-throughput isolation and detection of EVs. In the current study, we have developed a simplified and efficient EV-based proteomics strategy for early diagnosis of CRC. DSPE-functionalized beads were specifically designed that enabled direct capture of EVs from plasma samples in 10 minutes with good reproducibility and comprehensive proteome coverage. The single-pot, solid-phase-enhanced sample-preparation (SP3) technology was then combined with data-independent acquisition mass spectrometry (DIA-MS) for in-depth analysis and quantification of EV proteomes. From a cohort with 30 individuals including 11 healthy controls, 8 patients with adenomatous polyp and 11 patients with early-stage CRC, our streamlined workflow reproducibly quantified over 800 proteins from their plasma-derived EV samples, from which dysregulated protein signatures for molecular diagnosis of CRC were revealed. We selected a panel of 10 protein markers to train a machine learning (ML) model, which resulted in accurate prediction of polyp and early-stage CRC in an independent and single-blind validation cohort with excellent diagnostic ability of 89.3% accuracy. Our simplified and efficient clinical proteomic strategy will serve as a valuable tool for fast, accurate, and cost-effective diagnosis of CRC that can be easily extended to other disease samples for discovery of unique EV-based biomarkers.

Identification of COL3A1 as a candidate protein involved in the crosstalk between obesity and diarrhea using quantitative proteomics and machine learning

BACKGROUND

Increasing epidemiologic studies have shown a positive correlation between obesity and chronic diarrhea. Nevertheless, the precise etiology remains uncertain.

METHODS

We performed a comprehensive proteomics analysis utilizing the data-independent acquisition (DIA) technique on jejunal tissues from patients with obesity and chronic diarrhea (OD, n = 33), obese patients (OB, n = 10), and healthy controls (n = 8). Differentially expressed proteins (DEPs) in OD vs. control and OD vs. OB comparisons were subjected to pathway enrichment and protein-protein interaction (PPI) network analysis. Machine learning algorithms were adopted on overlapping DEPs in both comparisons. The candidate protein was further validated using Western blot, immunohistochemistry (IHC), and in vitro experiments.

RESULTS

We identified 189 and 228 DEPs in OD vs. control and OD vs. OB comparisons, respectively. DEPs in both comparisons were co-enriched in extracellular matrix (ECM) organization. Downregulated DEPs were associated with tight junction and ECM-receptor interaction in OD vs. control and OD vs. OB comparisons, respectively. Machine learning algorithms selected 3 proteins from 14 overlapping DEPs in both comparisons, among which collagen alpha-1(III) chain (COL3A1) was identified as a core protein in PPI networks. Western blot and IHC verified the expression of COL3A1. Moreover, the tight junction-related proteins decreased after the knockdown of COL3A1 in Caco2 intestinal cells upon PA challenge, consistent with the proteomics results.

CONCLUSIONS

We generated in-depth profiling of a proteomic dataset from samples of OD patients and provided unique insights into disease pathogenesis. COL3A1 was involved in the crosstalk between obesity and intestinal homeostasis via the ECM-receptor interaction pathway.

Retinal proteome profiling of inherited retinal degeneration across three different mouse models suggests common drug targets in retinitis pigmentosa

Inherited retinal degenerations (IRDs) are a leading cause of blindness among the population of young people in the developed world. Approximately half of IRDs initially manifest as gradual loss of night vision and visual fields, characteristic of retinitis pigmentosa (RP). Due to challenges in genetic testing, and the large heterogeneity of mutations underlying RP, targeted gene therapies are an impractical largescale solution in the foreseeable future. For this reason, identifying key pathophysiological pathways in IRDs that could be targets for mutation-agnostic and disease-modifying therapies (DMTs) is warranted. In this study, we investigated the retinal proteome of three distinct IRD mouse models, in comparison to sex- and age-matched wild-type mice. Specifically, we used the Pde6βRd10 (rd10) and RhoP23H/WT (P23H) mouse models of autosomal recessive and autosomal dominant RP, respectively, as well as the Rpe65-/- mouse model of Leber´s congenital amaurosis type 2 (LCA2). The mice were housed at two distinct institutions and analyzed using LC-MS in three separate facilities/instruments following data-dependent and data-independent acquisition modes. This cross-institutional and multi-methodological approach signifies the reliability and reproducibility of the results. The largescale profiling of the retinal proteome, coupled with in vivo electroretinography recordings, provided us with a reliable basis for comparing the disease phenotypes and severity. Despite evident inflammation, cellular stress, and downscaled phototransduction observed consistently across all three models, the underlying pathologies of RP and LCA2 displayed many differences, sharing only four general KEGG pathways. The opposite is true for the two RP models in which we identify remarkable convergence in proteomic phenotype even though the mechanism of primary rod death in rd10 and P23H mice is different. Our data highlights the cAMP and cGMP second-messenger signaling pathways as potential targets for therapeutic intervention. The proteomic data is curated and made publicly available, facilitating the discovery of universal therapeutic targets for RP.

Proteomic Characterization of Transfusable Blood Components: Fresh Frozen Plasma, Cryoprecipitate, and Derived Extracellular Vesicles via Data-Independent Mass Spectrometry

Extracellular vesicles (EVs) are a heterogeneous collection of particles that play a crucial role in cell-to-cell communication, primarily due to their ability to transport molecules, such as proteins. Thus, profiling EV-associated proteins offers insight into their biological effects. EVs can be isolated from various biological fluids, including donor blood components such as cryoprecipitate and fresh frozen plasma (FFP). In this study, we conducted a proteomic analysis of five single donor units of cryoprecipitate, FFP, and EVs derived from these blood components using a quantitative mass spectrometry approach. EVs were successfully isolated from both cryoprecipitate and FFP based on community guidelines. We identified and quantified approximately 360 proteins across all sample groups. Principal component analysis and heatmaps revealed that both cryoprecipitate and FFP are similar. Similarly, EVs derived from cryoprecipitate and FFP are comparable. However, they differ between the originating fluids and their derived EVs. Using the R-package MS-DAP, differentially expressed proteins (DEPs) were identified. The DEPs for all comparisons, when submitted for gene enrichment analysis, are involved in the complement and coagulation pathways. The protein profile generated from this study will have important clinical implications in increasing our knowledge of the proteins that are associated with EVs derived from blood components.

Proteomic Analysis Reveals Differential Protein Expression in Placental Tissues of Early-Onset Preeclampsia Patients

Preeclampsia, a significant cause of maternal and perinatal morbidity and mortality, remains poorly understood, in terms of its pathogenesis. This study aims to uncover novel and effective biomarkers for preeclampsia by conducting a comparative analysis of differential proteins in placentas from early onset preeclampsia (EOPE) and normal pregnancies. Utilizing tandem mass tag (TMT)-based quantitative proteomics, we identified differentially expressed proteins in placental tissues from 15 EOPE patients and 15 normal pregnant women. These proteins were subsequently validated by using parallel reaction monitoring (PRM). Our analysis revealed a total of 59 differentially expressed proteins, with 25 up-regulated and 34 down-regulated proteins in EOPE placental tissues compared to those from normal pregnancies. Validation through PRM confirmed the differential expression of 6 proteins. Our findings suggest these 6 proteins could play crucial roles in the pathogenesis of EOPE, highlighting the potential involvement of the estrogen signaling pathway and dilated cardiomyopathy (DCM) pathway in the development of preeclampsia. The data were deposited with the ProteomeXchange Consortium via the iProX partner repository with the identifier PXD055025.

Revisiting Protein Reversed-Phase Chromatography for Bottom-Up Proteomics

We revisited protein reversed-phase chromatography (RP), using state-of-the-art RP columns developed for biopharmaceuticals, such as monoclonal antibodies, in order to evaluate the suitability of this methodology as a prefractionation step for bottom-up proteomics. The protein RP prefractionation (Prot-RP) method was compared with two other widely used prefractionation methods, SDS-PAGE and high-pH peptide RP (Pept-RP) by using cell lysates as samples. The overlap between fractions of Prot-RP was comparable to that of SDS-PAGE, and the protein recovery was approximately 2-fold higher. On the other hand, the overlap between fractions of Prot-RP was slightly larger than that of Pept-RP, but Prot-RP was able to identify more protein termini and more isoform-specific peptides than Pept-RP. Our results indicate that the combination of highly efficient protein prefractionation with modern mass spectrometers is particularly effective for proteoform profiling from cellular samples.

Identification of Potential Growth-Related Proteins in Chick Vitreous during Emmetropization Using SWATH-MS and Targeted-Based Proteomics (MRMHR)

The vitreous humor (VH) is a transparent gelatin-like substance that occupies two-thirds of the eyeball and undergoes the most significant changes during eye elongation. Quantitative proteomics on the normal growth period in the VH could provide new insights into understanding its progression mechanism in the early stages of myopia. In this study, a data-independent acquisition (SWATH-MS) was combined with targeted LC-ESI-MS/MS to identify and quantify the relative protein changes in the vitreous during the normal growth period (4, 7, 14, 21 and 28 days old) in the chick model. Chicks were raised under normal growing conditions (12/12 h Dark/light cycle) for 28 days, where ocular measurements, including refractive and biometric measurements, were performed on days 4 (baseline), 7, 14, 21 and 28 (n = 6 chicks at each time point). Extracted vitreous proteins from individual animals were digested and pooled into a left eye pool and a right pool at each time point for protein analysis. The vitreous proteome for chicks was generated using an information-dependent acquisition (IDA) method by combining injections from individual time points. Using individual pool samples, SWATH-MS was employed to quantify proteins between each time point. DEPs were subsequently confirmed in separate batches of animals individually on random eyes (n = 4) using MRMHR between day 7 and day 14. Refraction and vitreous chamber depth (VCD) were found to be significantly changed (p < 0.05, n = 6 at each time point) during the period. A comprehensive vitreous protein ion library was built with 1576 non-redundant proteins (22987 distinct peptides) identified at a 1% false discovery rate (FDR). A total of 12 up-regulated and 26 down-regulated proteins were found across all time points compared to day 7 using SWATH-MS. Several DEPs, such as alpha-fetoprotein, the cadherin family group, neurocan, and reelin, involved in structural and growth-related pathways, were validated for the first time using MRMHR under this experimental condition. This study provided the first comprehensive spectral library of the vitreous for chicks during normal growth as well as a list of potential growth-related protein biomarker candidates using SWATH-MS and MRMHR during the emmetropization period.

Integrative proteomic and metabolomic elucidation of cardiomyopathy with in vivo and in vitro models and clinical samples

Cardiomyopathy is a prevalent cardiovascular disease that affects individuals of all ages and can lead to life-threatening heart failure. Despite its variety in types, each with distinct characteristics and causes, our understanding of cardiomyopathy at a systematic biology level remains incomplete. Mass spectrometry-based techniques have emerged as powerful tools, providing a comprehensive view of the molecular landscape and aiding in the discovery of biomarkers and elucidation of mechanisms. This review highlights the significant potential of integrating proteomic and metabolomic approaches with specialized databases to identify biomarkers and therapeutic targets across different types of cardiomyopathies. In vivo and in vitro models, such as genetically modified mice, patient-derived or induced pluripotent stem cells, and organ chips, are invaluable in exploring the pathophysiological complexities of this disease. By integrating omics approaches with these sophisticated modeling systems, our comprehension of the molecular underpinnings of cardiomyopathy can be greatly enhanced, facilitating the development of diagnostic markers and therapeutic strategies. Among the promising therapeutic targets are those involved in extracellular matrix remodeling, sarcomere damage, and metabolic remodeling. These targets hold the potential to advance precision therapy in cardiomyopathy, offering hope for more effective treatments tailored to the specific molecular profiles of patients.

Proteomic biomarkers of long-term lung function decline in textile workers: a 35-year longitudinal study

BACKGROUND

Occupational exposures contribute significantly to obstructive lung disease among textile workers. However, biomarkers associated with such declines are not available.

OBJECTIVES

We conducted a large-scale proteomic study to explore protein biomarkers potentially associated with long-term lung function decline.

METHODS

Shanghai Textile Workers Cohort was established in 1981 with 35 years of follow-up, assessing textile workers' lung functions every five years. Quantitative serum proteomics was performed on all 453 workers at 2016 survey. We employed four distinct models to examine the association between forced expiratory volume in one second (FEV1) and proteins, and consolidated the findings using an aggregated Cauchy association test. Furthermore, proteomic data of UK Biobank (UKB) was used to explore the associations of potential protein markers and decline of FEV1, and the interactions of these proteins were examined through STRING database. Associations were also externally validated using two-sample Mendelian randomizations (MR).

RESULTS

15 of 907 analyzed proteins displayed potential associations with long-term FEV1 decline, including two hemoglobin subunits: hemoglobin subunit beta (HBB, FDR-qACAT = 0.040), alpha globin chain (HBA2, FDR-qACAT = 0.045), and four immunoglobulin subunits: immunoglobulin kappa variable 3-7 (IGKV3-7, FDR-qACAT = 0.003), immunoglobulin heavy chain variable region (IgH, FDR-qACAT = 0.011). Five proteins were significantly associated with the rate of decline of FEV1 in UKB, in which RAB6A, LRRN1, and BSG were also found to be associated with proteins identified in Shanghai Textile Workers Cohort using STRING database. MR indicated bidirectional associations between HBB and FEV1 (P < 0.05), while different immunoglobulin subunits exhibited varying associations with FEV1.

IMPACT STATEMENT

We performed a large-scale proteomic study of the longest-follow-up pulmonary function cohort of textile workers to date. We discovered multiple novel proteins associated with long-term decline of FEV1 that have potential for identifying new biomarkers associated with long-term lung function decline among occupational populations, and may identify individuals at risk, as well as potential pharmaceutical targets for early intervention.

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.

An integration of neuroimaging and serum proteomics analysis suggests immune and inflammation are associated with white matter microstructure changes in cerebral small vessel disease with depressive symptoms

INTRODUCTION

Depressive symptoms are a common concomitant of cerebral small vessel disease (CSVD), of which pathogenesis requires more study. White matter microstructural abnormalities and proteomic alternation have been widely reported regarding depression in the elderly with CSVD. Exploring the relationship between cerebral white matter microstructural alterations and serum proteins may complete the explanation of molecular mechanisms for the findings from neuroimaging research of CSVD combined with depressive symptoms.

METHODS

An untargeted proteomics approach based on mass spectrometry was used to obtain serum proteomic profiles, which were clustered into co-expression protein modules. White matter microstructural integrity was measured using the FMRIB Software Library (FSL) and MATLAB to analyze diffusion tensor imaging (DTI) data and calculate the differences in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) for 50 regions of interest (ROI). Integrating the proteome with the DTI results, weighted gene co-expression analysis (WGCNA) was used to identify protein modules related to white matter microstructural alterations, and the proteins of the corresponding modules were analyzed for functional enrichment through bioinformatics techniques.

RESULTS

DTI measurements were analCerebral small vessel disease (CSVD); Depression; Diffusion tensor imaging (DTI); Proteomics; Inflammationyzed between individuals with CSVD and depressive symptoms (CSVD+D) (n = 24) and those without depressive symptoms (CSVD-D) (n = 35). Results showed an overall increase in MD, AD, and RD within the left hemisphere of the CSVD+D group, suggesting widespread loss of white matter integrity and axonal demyelination, including left superior longitudinal fasciculus (SLF), left posterior corona radiata (PCR) and right external capsule (EC). We identified two protein modules associated with DTI diffusivity, and functional enrichment analyses revealed that complement and coagulation cascades and immune responses participate in the alternation of white matter microstructure in the CSVD+D group.

CONCLUSION

The results suggested immune- and inflammation-related mechanism was associated with white matter microstructure changes in CSVD with depressive symptoms.

Combined assembloid modeling and 3D whole-organ mapping captures the microanatomy and function of the human fallopian tube

The fallopian tubes play key roles in processes from pregnancy to ovarian cancer where three-dimensional (3D) cellular and extracellular interactions are important to their pathophysiology. Here, we develop a 3D multicompartment assembloid model of the fallopian tube that molecularly, functionally, and architecturally resembles the organ. Global label-free proteomics, innovative assays capturing physiological functions of the fallopian tube (i.e., oocyte transport), and whole-organ single-cell resolution mapping are used to validate these assembloids through a multifaceted platform with direct comparisons to fallopian tube tissue. These techniques converge at a unique combination of assembloid parameters with the highest similarity to the reference fallopian tube. This work establishes (i) an optimized model of the human fallopian tubes for in vitro studies of their pathophysiology and (ii) an iterative platform for customized 3D in vitro models of human organs that are molecularly, functionally, and microanatomically accurate by combining tunable assembloid and tissue mapping methods.

Proteome-wide copy-number estimation from transcriptomics

Protein copy numbers constrain systems-level properties of regulatory networks, but proportional proteomic data remain scarce compared to RNA-seq. We related mRNA to protein statistically using best-available data from quantitative proteomics and transcriptomics for 4366 genes in 369 cell lines. The approach starts with a protein's median copy number and hierarchically appends mRNA-protein and mRNA-mRNA dependencies to define an optimal gene-specific model linking mRNAs to protein. For dozens of cell lines and primary samples, these protein inferences from mRNA outmatch stringent null models, a count-based protein-abundance repository, empirical mRNA-to-protein ratios, and a proteogenomic DREAM challenge winner. The optimal mRNA-to-protein relationships capture biological processes along with hundreds of known protein-protein complexes, suggesting mechanistic relationships. We use the method to identify a viral-receptor abundance threshold for coxsackievirus B3 susceptibility from 1489 systems-biology infection models parameterized by protein inference. When applied to 796 RNA-seq profiles of breast cancer, inferred copy-number estimates collectively re-classify 26-29% of luminal tumors. By adopting a gene-centered perspective of mRNA-protein covariation across different biological contexts, we achieve accuracies comparable to the technical reproducibility of contemporary proteomics.

Enhanced feature matching in single-cell proteomics characterizes IFN-γ response and co-existence of cell states

Proteome analysis by data-independent acquisition (DIA) has become a powerful approach to obtain deep proteome coverage, and has gained recent traction for label-free analysis of single cells. However, optimal experimental design for DIA-based single-cell proteomics has not been fully explored, and performance metrics of subsequent data analysis tools remain to be evaluated. Therefore, we here formalize and comprehensively evaluate a DIA data analysis strategy that exploits the co-analysis of low-input samples with a so-called matching enhancer (ME) of higher input, to increase sensitivity, proteome coverage, and data completeness. We assess the matching specificity of DIA-ME by a two-proteome model, and demonstrate that false discovery and false transfer are maintained at low levels when using DIA-NN software, while preserving quantification accuracy. We apply DIA-ME to investigate the proteome response of U-2 OS cells to interferon gamma (IFN-γ) in single cells, and recapitulate the time-resolved induction of IFN-γ response proteins as observed in bulk material. Moreover, we uncover co- and anti-correlating patterns of protein expression within the same cell, indicating mutually exclusive protein modules and the co-existence of different cell states. Collectively our data show that DIA-ME is a powerful, scalable, and easy-to-implement strategy for single-cell proteomics.

Peripherally-restricted recurrent infection by engineered E. coli strain modulates hippocampal proteome promoting memory impairments in a rat model

Commensal bacteria that breach endothelial barrier has been reported to induce low grade chronic inflammation producing disease symptoms in major peripheral tissues. In this study, we investigated the role of genetically modified cellular invasive form of commensal E. coli K12 (SK3842) in cognitive impairment. Low-grade systemic infection model was developed using recurring peripheral inoculation of live bacteria in Wistar rats. To examine memory parameters, Novel object recognition test and Radial arm maze test were performed. Differential protein expression profiling of rat hippocampus was carried out using LC-MS/MS and subsequently quantified using SWATH. HBA1/2, NEFH, PFN1 and ATP5d were chosen for validation using quantitative RT-PCR. Results showed drastic decline in Recognition memory of the SK3842 infected rats. Reference and Working Memory of the infected group were also significantly reduced in comparison to control group. Proteome analysis using LC-MS/MS coupled with SWATH revealed differential expression of key proteins that are crucial for the maintenance of various neurological functions. Moreover, expression of NEFH and PFN1transcripts were found to be in line with the proteomics data. Protein interaction network of these validated proteins generated by STRING database converged to RhoA protein. Thus, the present study establishes an association between peripheral infection of a hippocampal protein network dysregulation and overall memory decline.

JA, Meyer A, Vintila S, McIlvin MR, Van Den Bossche T, Searle BC, Griffin TJ, Saito MA, Kleiner M, Jagtap PD. Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome

Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being Data-Dependent Acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-Independent Acquisition Mass Spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

Interrogating data-independent acquisition LC-MS/MS for affinity proteomics

Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42485-024-00166-4.

PTMoreR-enabled cross-species PTM mapping and comparative phosphoproteomics across mammals

To support PTM proteomic analysis and annotation in different species, we developed PTMoreR, a user-friendly tool that considers the surrounding amino acid sequences of PTM sites during BLAST, enabling a motif-centric analysis across species. By controlling sequence window similarity, PTMoreR can map phosphoproteomic results between any two species, perform site-level functional enrichment analysis, and generate kinase-substrate networks. We demonstrate that the majority of real P-sites in mice can be inferred from experimentally derived human P-sites with PTMoreR mapping. Furthermore, the compositions of 129 mammalian phosphoproteomes can also be predicted using PTMoreR. The method also identifies cross-species phosphorylation events that occur on proteins with an increased tendency to respond to the environmental factors. Moreover, the classic kinase motifs can be extracted across mammalian species, offering an evolutionary angle for refining current motifs. PTMoreR supports PTM proteomics in non-human species and facilitates quantitative phosphoproteomic analysis.

Sirt2 Regulates Liver Metabolism in a Sex-Specific Manner

Sirtuin-2 (Sirt2), an NAD+-dependent lysine deacylase enzyme, has previously been implicated as a regulator of glucose metabolism, but the specific mechanisms remain poorly defined. Here, we observed that Sirt2-/- males, but not females, have decreased body fat, moderate hypoglycemia upon fasting, and perturbed glucose handling during exercise compared to wild type controls. Conversion of injected lactate, pyruvate, and glycerol boluses into glucose via gluconeogenesis was impaired, but only in males. Primary Sirt2-/- male hepatocytes exhibited reduced glycolysis and reduced mitochondrial respiration. RNAseq and proteomics were used to interrogate the mechanisms behind this liver phenotype. Loss of Sirt2 did not lead to transcriptional dysregulation, as very few genes were altered in the transcriptome. In keeping with this, there were also negligible changes to protein abundance. Site-specific quantification of the hepatic acetylome, however, showed that 13% of all detected acetylated peptides were significantly increased in Sirt2-/- male liver versus wild type, representing putative Sirt2 target sites. Strikingly, none of these putative target sites were hyperacetylated in Sirt2-/- female liver. The target sites in the male liver were distributed across mitochondria (44%), cytoplasm (32%), nucleus (8%), and other compartments (16%). Despite the high number of putative mitochondrial Sirt2 targets, Sirt2 antigen was not detected in purified wild type liver mitochondria, suggesting that Sirt2's regulation of mitochondrial function occurs from outside the organelle. We conclude that Sirt2 regulates hepatic protein acetylation and metabolism in a sex-specific manner.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Endogenous cell membrane interactome mapping for the GLP-1 receptor in different cell types

The GLP-1 receptor, one of the most successful drug targets for the treatment of type 2 diabetes and obesity, is known to engage multiple intracellular signaling proteins. However, it remains less explored how the receptor interacts with proteins on the cell membrane. Here, we present a ligand-based proximity labeling approach to interrogate the native cell membrane interactome for the GLP-1 receptor after agonist simulation. Our study identified several unreported putative cell membrane interactors for the endogenous receptor in either a pancreatic β cell line or a neuronal cell line. We further uncovered new regulators of GLP-1 receptor-mediated signaling and insulinotropic responses in β cells. Additionally, we obtained a time-resolved cell membrane interactome map for the receptor in β cells. Therefore, our study provides a new approach that is generalizable to map endogenous cell membrane interactomes for G-protein-coupled receptors to decipher the molecular basis of their cell-type-specific functional regulation.

Lactobacillus gasseri and Gardnerella vaginalis produce extracellular vesicles that contribute to the function of the vaginal microbiome and modulate host-Trichomonas vaginalis interactions

Trichomonas vaginalis is an extracellular protozoan parasite of the human urogenital tract, responsible for a prevalent sexually transmitted infection. Trichomoniasis is accompanied by a dysbiotic microbiome that is characterised by the depletion of host-protective commensals such as Lactobacillus gasseri, and the flourishing of a bacterial consortium that is comparable to the one seen for bacterial vaginosis, including the founder species Gardnerella vaginalis. These two vaginal bacteria are known to have opposite effects on T. vaginalis pathogenicity. Studies on extracellular vesicles (EVs) have been focused on the direction of a microbial producer (commensal or pathogen) to a host recipient, and largely in the context of the gut microbiome. Here, taking advantage of the simplicity of the human cervicovaginal microbiome, we determined the molecular cargo of EVs produced by L. gasseri and G. vaginalis and examined how these vesicles modulate the interaction of T. vaginalis and host cells. We show that these EVs carry a specific cargo of proteins, which functions can be attributed to the opposite roles that these bacteria play in the vaginal biome. Furthermore, these bacterial EVs are delivered to host and protozoan cells, modulating host-pathogen interactions in a way that mimics the opposite effects that these bacteria have on T. vaginalis pathogenicity. This is the first study to describe side-by-side the protein composition of EVs produced by two bacteria belonging to the opposite spectrum of a microbiome and to demonstrate that these vesicles modulate the pathogenicity of a protozoan parasite. Such as in trichomoniasis, infections and dysbiosis co-occur frequently resulting in significant co-morbidities. Therefore, studies like this provide the knowledge for the development of antimicrobial therapies that aim to clear the infection while restoring a healthy microbiome.

Quantification of snake venom proteomes by mass spectrometry-considerations and perspectives

The advent of soft ionization mass spectrometry-based proteomics in the 1990s led to the development of a new dimension in biology that conceptually allows for the integral analysis of whole proteomes. This transition from a reductionist to a global-integrative approach is conditioned to the capability of proteomic platforms to generate and analyze complete qualitative and quantitative proteomics data. Paradoxically, the underlying analytical technique, molecular mass spectrometry, is inherently nonquantitative. The turn of the century witnessed the development of analytical strategies to endow proteomics with the ability to quantify proteomes of model organisms in the sense of "an organism for which comprehensive molecular (genomic and/or transcriptomic) resources are available." This essay presents an overview of the strategies and the lights and shadows of the most popular quantification methods highlighting the common misuse of label-free approaches developed for model species' when applied to quantify the individual components of proteomes of nonmodel species (In this essay we use the term "non-model" organisms for species lacking comprehensive molecular (genomic and/or transcriptomic) resources, a circumstance that, as we detail in this review-essay, conditions the quantification of their proteomes.). We also point out the opportunity of combining elemental and molecular mass spectrometry systems into a hybrid instrumental configuration for the parallel identification and absolute quantification of venom proteomes. The successful application of this novel mass spectrometry configuration in snake venomics represents a proof-of-concept for a broader and more routine application of hybrid elemental/molecular mass spectrometry setups in other areas of the proteomics field, such as phosphoproteomics, metallomics, and in general in any biological process where a heteroatom (i.e., any atom other than C, H, O, N) forms integral part of its mechanism.

Proteomic analysis of serum in a population-based cohort did not reveal a biomarker for Modic changes

Introduction

Modic changes (MC) are bone marrow lesions of vertebral bones, which can be detected with magnetic resonance imaging (MRI) adjacent to degenerated intervertebral discs. Defined by their appearance on T1 and T2 weighted images, there are three interconvertible types: MC1, MC2, and MC3. The inter-observer variability of the MRI diagnosis is high, therefore a diagnostic serum biomarker complementing the MRI to facilitate diagnosis and follow-up would be of great value.

Methods

We used a highly sensitive and reproducible proteomics approach: DIA/SWATH-MS to find serum biomarkers in a subset of the Northern Finland Birth Cohort 1966. Separately, we measured a panel of factors involved in inflammation and angiogenesis to confirm some potential biomarkers published before with an ELISA-based method called V-Plex.

Results

We found neither an association between the serum concentrations of the proteins detected with DIA/SWATH-MS with the presence of MC, nor a correlation with the size of the MC lesions. We did not find any association between the factors measured with the V-Plex and the presence of MC or their size.

Conclusion

Altogether, our study suggests that a robust and generally usable biomarker to facilitate the diagnosis of MC cannot readily be found in serum.

SeFilter-DIA: Squeeze-and-Excitation Network for Filtering High-Confidence Peptides of Data-Independent Acquisition Proteomics

Mass spectrometry is crucial in proteomics analysis, particularly using Data Independent Acquisition (DIA) for reliable and reproducible mass spectrometry data acquisition, enabling broad mass-to-charge ratio coverage and high throughput. DIA-NN, a prominent deep learning software in DIA proteome analysis, generates peptide results but may include low-confidence peptides. Conventionally, biologists have to manually screen peptide fragment ion chromatogram peaks (XIC) for identifying high-confidence peptides, a time-consuming and subjective process prone to variability. In this study, we introduce SeFilter-DIA, a deep learning algorithm, aiming at automating the identification of high-confidence peptides. Leveraging compressed excitation neural network and residual network models, SeFilter-DIA extracts XIC features and effectively discerns between high and low-confidence peptides. Evaluation of the benchmark datasets demonstrates SeFilter-DIA achieving 99.6% AUC on the test set and 97% for other performance indicators. Furthermore, SeFilter-DIA is applicable for screening peptides with phosphorylation modifications. These results demonstrate the potential of SeFilter-DIA to replace manual screening, providing an efficient and objective approach for high-confidence peptide identification while mitigating associated limitations.

On the pH-dependence of α-synuclein amyloid polymorphism and the role of secondary nucleation in seed-based amyloid propagation

The aggregation of the protein α-synuclein is closely associated with several neurodegenerative disorders and as such the structures of the amyloid fibril aggregates have high scientific and medical significance. However, there are dozens of unique atomic-resolution structures of these aggregates, and such a highly polymorphic nature of the α-synuclein fibrils hampers efforts in disease-relevant in vitro studies on α-synuclein amyloid aggregation. In order to better understand the factors that affect polymorph selection, we studied the structures of α-synuclein fibrils in vitro as a function of pH and buffer using cryo-EM helical reconstruction. We find that in the physiological range of pH 5.8-7.4, a pH-dependent selection between Type 1, 2, and 3 polymorphs occurs. Our results indicate that even in the presence of seeds, the polymorph selection during aggregation is highly dependent on the buffer conditions, attributed to the non-polymorph-specific nature of secondary nucleation. We also uncovered two new polymorphs that occur at pH 7.0 in phosphate-buffered saline. The first is a monofilament Type 1 fibril that highly resembles the structure of the juvenile-onset synucleinopathy polymorph found in patient-derived material. The second is a new Type 5 polymorph that resembles a polymorph that has been recently reported in a study that used diseased tissues to seed aggregation. Taken together, our results highlight the shallow amyloid energy hypersurface that can be altered by subtle changes in the environment, including the pH which is shown to play a major role in polymorph selection and in many cases appears to be the determining factor in seeded aggregation. The results also suggest the possibility of producing disease-relevant structure in vitro.

Pressure Cycling Technology Combined With MicroLC-SWATH Mass Spectrometry for the Analysis of Sex-Related Differences Between Male and Female Cerebella: A Promising Approach to Investigating Proteomics Differences in Psychiatric and Neurodegenerative Diseases

PURPOSE

Pressure cycling technology (PCT) coupled with data-independent sequential window acquisition of all theoretical mass spectra (SWATH-MS) can be a powerful tool for identifying and quantifying biomarkers (e.g., proteins) in complex biological samples. Mouse models are frequently used in brain studies, including those focusing on different neurodevelopmental and psychiatric disorders. More and more pieces of evidence have suggested that sex-related differences in the brain impact the rates, clinical manifestations, and therapy outcomes of these disorders. However, sex-based differences in the proteomic profiles of mouse cerebella have not been widely investigated.

EXPERIMENTAL DESIGN

In this pilot study, we evaluate the applicability of coupling PCT sample preparation with microLC-SWATH-MS analysis to map and identify differences in the proteomes of two female and two male mice cerebellum samples.

RESULTS

We identified and quantified 174 proteins in mice cerebella. A comparison of the proteomic profiles revealed that the levels of 11 proteins in the female and male mice cerebella varied significantly.

CONCLUSIONS AND CLINICAL RELEVANCE

Although this study utilizes a small sample, our results indicate that the studied male and female mice cerebella possessed differing proteome compositions, mainly with respect to energy metabolism processes.

Comprehensive Overview of Bottom-Up Proteomics Using Mass Spectrometry

Proteomics is the large scale study of protein structure and function from biological systems through protein identification and quantification. "Shotgun proteomics" or "bottom-up proteomics" is the prevailing strategy, in which proteins are hydrolyzed into peptides that are analyzed by mass spectrometry. Proteomics studies can be applied to diverse studies ranging from simple protein identification to studies of proteoforms, protein-protein interactions, protein structural alterations, absolute and relative protein quantification, post-translational modifications, and protein stability. To enable this range of different experiments, there are diverse strategies for proteome analysis. The nuances of how proteomic workflows differ may be challenging to understand for new practitioners. Here, we provide a comprehensive overview of different proteomics methods. We cover from biochemistry basics and protein extraction to biological interpretation and orthogonal validation. We expect this Review will serve as a handbook for researchers who are new to the field of bottom-up proteomics.

Quantitative Comparison of Deparaffinization, Rehydration, and Extraction Methods for FFPE Tissue Proteomics and Phosphoproteomics

Formalin-fixed paraffin-embedded (FFPE) tissues are suitable for proteomic and phosphoproteomic biomarker studies by data-independent acquisition mass spectrometry. The choice of the sample preparation method influences the number, intensity, and reproducibility of identifications. By comparing four deparaffinization and rehydration methods, including heptane, histolene, SubX, and xylene, we found that heptane and methanol produced the lowest coefficients of variation (CVs). Using this, five extraction methods from the literature were modified and evaluated for their performance using kidney, leg muscle, lung, and testicular rat organs. All methods performed well, except for SP3 due to insufficient tissue lysis. Heat n' Beat was the fastest and most reproducible method with the highest digestion efficiency and lowest CVs. S-Trap produced the highest peptide yield, while TFE produced the best phosphopeptide enrichment efficiency. The quantitation of FFPE-derived peptides remains an ongoing challenge with bias in UV and fluorescence assays across methods, most notably in SPEED. Functional enrichment analysis demonstrated that each method favored extracting some gene ontology cellular components over others including chromosome, cytoplasmic, cytoskeleton, endoplasmic reticulum, membrane, mitochondrion, and nucleoplasm protein groups. The outcome is a set of recommendations for choosing the most appropriate method for different settings.

Bridging Targeted (Zeno MRM-HR) and Untargeted (SWATH) LC-HRMS in a Single Run for Sensitive Exposomics

Traditionally, chemical exposure has been assessed by low-resolution mass spectrometry via targeted approaches due to the typically extremely low concentration of such compounds in biological samples. Nevertheless, untargeted approaches are now becoming a promising tool for a broader investigation of the exposome, covering additional compounds, their biotransformation products, and possible metabolic alterations (metabolomics). However, despite broad compound coverage, untargeted metabolomics still underperforms in ultratrace biomonitoring analysis. To overcome these analytical limitations, we present the development of the first combined targeted/untargeted LC-MS method, merging MRM-HR and SWATH experiments in one analytical run, making use of Zeno technology for improved sensitivity. Multiple reaction monitoring transitions were optimized for 135 highly diverse toxicants including mycotoxins, plasticizers, PFAS, personal care products ingredients, and industrial side products as well as potentially beneficial xenobiotics such as phytohormones. As a proof of concept, standard reference materials of human plasma (SRM 1950) and serum (SRM 1958) were analyzed with both Zeno MRM-HR + SWATH and SWATH-only methodologies. Results demonstrated a significant increase in sensitivity represented by the detection of lower concentration levels in spiked SRM materials (mean value: 2.2 and 3 times lower concentrations for SRMs 1950 and 1958, respectively). Overall, the detection frequency was increased by 68% (19 to 32 positive detections) in the MRM-HR + SWATH mode compared to the SWATH-only. This work presents a promising avenue for addressing the outstanding key challenge in the small-molecule omics field: finding a balance between high sensitivity and broad chemical coverage. It was demonstrated for exposomic applications but might be transferred to lipidomics and metabolomics workflows.

Cyanobacterial Proteomics: Diversity and Dynamics

Cyanobacteria (oxygenic photoautrophs) comprise a diverse group holding significance both environmentally and for biotechnological applications. The utilization of proteomic techniques has significantly influenced investigations concerning cyanobacteria. Application of proteomics allows for large-scale analysis of protein expression and function within cyanobacterial systems. The cyanobacterial proteome exhibits tremendous functional, spatial, and temporal diversity regulated by multiple factors that continuously modify protein abundance, post-translational modifications, interactions, localization, and activity to meet the dynamic needs of these tiny blue greens. Modern mass spectrometry-based proteomics techniques enable system-wide examination of proteome complexity through global identification and high-throughput quantification of proteins. These powerful approaches have revolutionized our understanding of proteome dynamics and promise to provide novel insights into integrated cellular behavior at an unprecedented scale. In this Review, we present modern methods and cutting-edge technologies employed for unraveling the spatiotemporal diversity and dynamics of cyanobacterial proteomics with a specific focus on the methods used to analyze post-translational modifications (PTMs) and examples of dynamic changes in the cyanobacterial proteome investigated by proteomic approaches.

Improving Proteomic Identification Using Narrow Isolation Windows with Zeno SWATH Data-Independent Acquisition

Data-independent acquisition (DIA) techniques such as sequential window acquisition of all theoretical mass spectra (SWATH) acquisition have emerged as the preferred strategies for proteomic analyses. Our study optimized the SWATH-DIA method using a narrow isolation window placement approach, improving its proteomic performance. We optimized the acquisition parameter combinations of narrow isolation windows with different widths (1.9 and 2.9 Da) on a ZenoTOF 7600 (Sciex); the acquired data were analyzed using DIA-NN (version 1.8.1). Narrow SWATH (nSWATH) identified 5916 and 7719 protein groups on the digested peptides, corresponding to 400 ng of protein from mouse liver and HEK293T cells, respectively, improving identification by 7.52 and 4.99%, respectively, compared to conventional SWATH. The median coefficient of variation of the quantified values was less than 6%. We further analyzed 200 ng of benchmark samples comprising peptides from known ratios ofEscherichia coli, yeast, and human peptides using nSWATH. Consequently, it achieved accuracy and precision comparable to those of conventional SWATH, identifying an average of 95,456 precursors and 9342 protein groups across three benchmark samples, representing 12.6 and 9.63% improved identification compared to conventional SWATH. The nSWATH method improved identification at various loading amounts of benchmark samples, identifying 40.7% more protein groups at 25 ng. These results demonstrate the improved performance of nSWATH, contributing to the acquisition of deeper proteomic data from complex biological samples.

µPhos: a scalable and sensitive platform for high-dimensional phosphoproteomics

Mass spectrometry has revolutionized cell signaling research by vastly simplifying the analysis of many thousands of phosphorylation sites in the human proteome. Defining the cellular response to perturbations is crucial for further illuminating the functionality of the phosphoproteome. Here we describe µPhos ('microPhos'), an accessible phosphoproteomics platform that permits phosphopeptide enrichment from 96-well cell culture and small tissue amounts in <8 h total processing time. By greatly minimizing transfer steps and liquid volumes, we demonstrate increased sensitivity, >90% selectivity, and excellent quantitative reproducibility. Employing highly sensitive trapped ion mobility mass spectrometry, we quantify ~17,000 Class I phosphosites in a human cancer cell line using 20 µg starting material, and confidently localize ~6200 phosphosites from 1 µg. This depth covers key signaling pathways, rendering sample-limited applications and perturbation experiments with hundreds of samples viable. We employ µPhos to study drug- and time-dependent response signatures in a leukemia cell line, and by quantifying 30,000 Class I phosphosites in the mouse brain we reveal distinct spatial kinase activities in subregions of the hippocampal formation.

Follicular Fluid Proteomic Analysis to Identify Predictive Markers of Normal Embryonic Development

Ageing populations, mass "baby-free" policies and children born to mothers at the age at which they are biologically expected to become grandmothers are growing problems in most developed societies. Therefore, any opportunity to improve the quality of infertility treatments seems important for the survival of societies. The possibility of indirectly studying the quality of developing oocytes by examining their follicular fluids (hFFs) offers new opportunities for progress in our understanding the processes of final oocyte maturation and, consequently, for predicting the quality of the resulting embryos and personalising their culture. Using mass spectrometry, we studied follicular fluids collected individually during in vitro fertilisation and compared their composition with the quality of the resulting embryos. We analysed 110 follicular fluids from 50 oocyte donors, from which we obtained 44 high-quality, 39 medium-quality, and 27 low-quality embryos. We identified 2182 proteins by Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS) using a TripleTOF 5600+ hybrid mass spectrometer, of which 484 were suitable for quantification. We were able to identify several proteins whose concentrations varied between the follicular fluids of different oocytes from the same patient and between patients. Among them, the most important appear to be immunoglobulin heavy constant alpha 1 (IgA1hc) and dickkopf-related protein 3. The first one is found at higher concentrations in hFFs from which oocytes develop into poor-quality embryos, the other one exhibits the opposite pattern. None of these have, so far, had any specific links to fertility disorders. In light of these findings, these proteins should be considered a primary target for research aimed at developing a diagnostic tool for oocyte quality control and pre-fertilisation screening. This is particularly important in cases where the fertilisation of each egg is not an option for ethical or other reasons, or in countries where it is prohibited by law.

High-efficiency effect-directed analysis (EDA) advancing toxicant identification in aquatic environments: Latest progress and application status

Facing the great threats to ecosystems and human health posed by the continuous release of chemicals into aquatic environments, effect-directed analysis (EDA) has emerged as a powerful tool for identifying causative toxicants. However, traditional EDA shows problems of low-coverage, labor-intensive and low-efficiency. Currently, a number of high-efficiency techniques have been integrated into EDA to improve toxicant identification. In this review, the latest progress and current limitations of high-efficiency EDA, comprising high-coverage effect evaluation, high-resolution fractionation, high-coverage chemical analysis, high-automation causative peak extraction and high-efficiency structure elucidation, are summarized. Specifically, high-resolution fractionation, high-automation data processing algorithms and in silico structure elucidation techniques have been well developed to enhance EDA. While high-coverage effect evaluation and chemical analysis should be further emphasized, especially omics tools and data-independent mass acquisition. For the application status in aquatic environments, high-efficiency EDA is widely applied in surface water and wastewater. Estrogenic, androgenic and aryl hydrocarbon receptor-mediated activities are the most concerning, with causative toxicants showing the typical structural features of steroids and benzenoids. A better understanding of the latest progress and application status of EDA would be beneficial to further advance in the field and greatly support aquatic environment monitoring.

Ultrafast Proteomics

Current stage of proteomic research in the field of biology, medicine, development of new drugs, population screening, or personalized approaches to therapy dictates the need to analyze large sets of samples within the reasonable experimental time. Until recently, mass spectrometry measurements in proteomics were characterized as unique in identifying and quantifying cellular protein composition, but low throughput, requiring many hours to analyze a single sample. This was in conflict with the dynamics of changes in biological systems at the whole cellular proteome level upon the influence of external and internal factors. Thus, low speed of the whole proteome analysis has become the main factor limiting developments in functional proteomics, where it is necessary to annotate intracellular processes not only in a wide range of conditions, but also over a long period of time. Enormous level of heterogeneity of tissue cells or tumors, even of the same type, dictates the need to analyze biological systems at the level of individual cells. These studies involve obtaining molecular characteristics for tens, if not hundreds of thousands of individual cells, including their whole proteome profiles. Development of mass spectrometry technologies providing high resolution and mass measurement accuracy, predictive chromatography, new methods for peptide separation by ion mobility and processing of proteomic data based on artificial intelligence algorithms have opened a way for significant, if not radical, increase in the throughput of whole proteome analysis and led to implementation of the novel concept of ultrafast proteomics. Work done just in the last few years has demonstrated the proteome-wide analysis throughput of several hundred samples per day at a depth of several thousand proteins, levels unimaginable three or four years ago. The review examines background of these developments, as well as modern methods and approaches that implement ultrafast analysis of the entire proteome.

A proximity proteomics pipeline with improved reproducibility and throughput

Proximity labeling (PL) via biotinylation coupled with mass spectrometry (MS) captures spatial proteomes in cells. Large-scale processing requires a workflow minimizing hands-on time and enhancing quantitative reproducibility. We introduced a scalable PL pipeline integrating automated enrichment of biotinylated proteins in a 96-well plate format. Combining this with optimized quantitative MS based on data-independent acquisition (DIA), we increased sample throughput and improved protein identification and quantification reproducibility. We applied this pipeline to delineate subcellular proteomes across various compartments. Using the 5HT2A serotonin receptor as a model, we studied temporal changes of proximal interaction networks induced by receptor activation. In addition, we modified the pipeline for reduced sample input to accommodate CRISPR-based gene knockout, assessing dynamics of the 5HT2A network in response to perturbation of selected interactors. This PL approach is universally applicable to PL proteomics using biotinylation-based PL enzymes, enhancing throughput and reproducibility of standard protocols.

PD-1/LAG-3 co-signaling profiling uncovers CBL ubiquitin ligases as key immunotherapy targets

Many cancer patients do not benefit from PD-L1/PD-1 blockade immunotherapies. PD-1 and LAG-3 co-upregulation in T-cells is one of the major mechanisms of resistance by establishing a highly dysfunctional state in T-cells. To identify shared features associated to PD-1/LAG-3 dysfunctionality in human cancers and T-cells, multiomic expression profiles were obtained for all TCGA cancers immune infiltrates. A PD-1/LAG-3 dysfunctional signature was found which regulated immune, metabolic, genetic, and epigenetic pathways, but especially a reinforced negative regulation of the TCR signalosome. These results were validated in T-cell lines with constitutively active PD-1, LAG-3 pathways and their combination. A differential analysis of the proteome of PD-1/LAG-3 T-cells showed a specific enrichment in ubiquitin ligases participating in E3 ubiquitination pathways. PD-1/LAG-3 co-blockade inhibited CBL-B expression, while the use of a bispecific drug in clinical development also repressed C-CBL expression, which reverted T-cell dysfunctionality in lung cancer patients resistant to PD-L1/PD-1 blockade. The combination of CBL-B-specific small molecule inhibitors with anti-PD-1/anti-LAG-3 immunotherapies demonstrated notable therapeutic efficacy in models of lung cancer refractory to immunotherapies, overcoming PD-1/LAG-3 mediated resistance.

Data-Independent Acquisition: A Milestone and Prospect in Clinical Mass Spectrometry-Based Proteomics

Data-independent acquisition (DIA) has revolutionized the field of mass spectrometry (MS)-based proteomics over the past few years. DIA stands out for its ability to systematically sample all peptides in a given m/z range, allowing an unbiased acquisition of proteomics data. This greatly mitigates the issue of missing values and significantly enhances quantitative accuracy, precision, and reproducibility compared to many traditional methods. This review focuses on the critical role of DIA analysis software tools, primarily focusing on their capabilities and the challenges they address in proteomic research. Advances in MS technology, such as trapped ion mobility spectrometry, or high field asymmetric waveform ion mobility spectrometry require sophisticated analysis software capable of handling the increased data complexity and exploiting the full potential of DIA. We identify and critically evaluate leading software tools in the DIA landscape, discussing their unique features, and the reliability of their quantitative and qualitative outputs. We present the biological and clinical relevance of DIA-MS and discuss crucial publications that paved the way for in-depth proteomic characterization in patient-derived specimens. Furthermore, we provide a perspective on emerging trends in clinical applications and present upcoming challenges including standardization and certification of MS-based acquisition strategies in molecular diagnostics. While we emphasize the need for continuous development of software tools to keep pace with evolving technologies, we advise researchers against uncritically accepting the results from DIA software tools. Each tool may have its own biases, and some may not be as sensitive or reliable as others. Our overarching recommendation for both researchers and clinicians is to employ multiple DIA analysis tools, utilizing orthogonal analysis approaches to enhance the robustness and reliability of their findings.

Data acquisition approaches for single cell proteomics

Single-cell proteomics (SCP) aims to characterize the proteome of individual cells, providing insights into complex biological systems. It reveals subtle differences in distinct cellular populations that bulk proteome analysis may overlook, which is essential for understanding disease mechanisms and developing targeted therapies. Mass spectrometry (MS) methods in SCP allow the identification and quantification of thousands of proteins from individual cells. Two major challenges in SCP are the limited material in single-cell samples necessitating highly sensitive analytical techniques and the efficient processing of samples, as each biological sample requires thousands of single cell measurements. This review discusses MS advancements to mitigate these challenges using data-dependent acquisition (DDA) and data-independent acquisition (DIA). Additionally, we examine the use of short liquid chromatography gradients and sample multiplexing methods that increase the sample throughput and scalability of SCP experiments. We believe these methods will pave the way for improving our understanding of cellular heterogeneity and its implications for systems biology.

Neuronal expression of human amyloid-β and Tau drives global phenotypic and multi-omic changes in C. elegans

Alzheimer's disease (AD) and Alzheimer's related diseases (ADRD) are prevalent age-related neurodegenerative disorders characterized by the accumulation of amyloid-β (Aβ) plaques and Tau neurofibrillary tangles. The nematode Caenorhabditis elegan s ( C. elegans ) serves as an invaluable model organism in diseases of old age-due to its rapid aging. Here we performed an unbiased systems analysis of a C. elegans strain expressing both Aβ and Tau proteins within neurons. We set out to determine if there was a phenotypic interaction between Aβ and Tau. In addition, we were interested in determining the temporal order of the phenotypic and multi-omic (geromic) outcomes. At an early stage of adulthood, we observed reproductive impairments and mitochondrial dysfunction consistent with disruptions in mRNA transcript abundance, protein solubility, and metabolite levels. Notably, the expression of these neurotoxic proteins exhibited a synergistic effect, leading to accelerated aging. Our findings shed light on the close relationship between normal aging and ADRD. Specifically, we demonstrate alterations to metabolic functions preceding age-related neurotoxicity, offering a resource for the development of new therapeutic strategies.

A hybrid DDA/DIA-PASEF based assay library for a deep proteotyping of triple-negative breast cancer

Triple-negative breast cancer (TNBC) is the most aggressive subtype of breast cancer, and deeper proteome coverage is needed for its molecular characterization. We present comprehensive library of targeted mass spectrometry assays specific for TNBC and demonstrate its applicability. Proteins were extracted from 105 TNBC tissues and digested. Aliquots were pooled, fractionated using hydrophilic chromatography and analyzed by LC-MS/MS in data-dependent acquisition (DDA) parallel accumulation-serial fragmentation (PASEF) mode on timsTOF Pro LC-MS system. 16 individual lysates were analyzed in data-independent acquisition (DIA)-PASEF mode. Hybrid library was generated in Spectronaut software and covers 244,464 precursors, 168,006 peptides and 11,564 protein groups (FDR = 1%). Application of our library for pilot quantitative analysis of 16 tissues increased identification numbers in Spectronaut 18.5 and DIA-NN 1.8.1 software compared to library-free setting, with Spectronaut achieving the best results represented by 190,310 precursors, 140,566 peptides, and 10,463 protein groups. In conclusion, we introduce assay library that offers the deepest coverage of TNBC proteome to date. The TNBC library is available via PRIDE repository (PXD047793).