dia-PASEF data analysis using FragPipe and DIA-NN for deep proteomics of low sample amounts

Human skeletal muscle mitochondrial pathways are impacted by a neuropathologic diagnosis of Alzheimer's disease

Alzheimer's disease (AD) is associated with reduced lean mass and impaired skeletal muscle mitochondrial and motor function. Although primary mitochondrial defects in AD may underlie these findings, molecular alterations in AD have not been thoroughly examined in human skeletal muscle. Here, we used two human skeletal muscle types, quadriceps (n = 81) and temporalis (n = 66), to compare the proteome of individuals with a neuropathologic AD diagnosis based on AD Neuropathologic Change (ADNPC+: n = 54 temporalis, 44 quadriceps) to controls (ADNPC-: n = 27 temporalis, 22 quadriceps). We determined the effects of ADNPC status within each muscle and within apolipoprotein E4 (APOE4) carriers and APOE4 non-carriers. Pathways that support mitochondrial metabolism, including oxidative phosphorylation, were downregulated in skeletal muscle of ADNPC+ versus ADNPC- individuals. Similar mitochondrial effects were observed across muscle types and APOE4 carrier groups, but nearly four times as many proteins were altered in temporalis versus quadriceps tissue and mitochondrial effects were most pronounced in APOE4 carriers compared to APOE4 non-carriers. Of all detected oxidative phosphorylation proteins, the expression of ∼29-61 % (dependent on muscle/APOE4 carrier group) significantly correlated with AD progression, ranked by Clinical Dementia Rating and ADNPC scores. Of these, 23 proteins decreased in expression with greater AD progression in all skeletal muscle type and APOE4 carrier groups. This is the first study to assess differences in the human skeletal muscle proteome in the context of AD. Our work shows that systemic mitochondrial alterations in AD extend to skeletal muscle and these effects are amplified by APOE4 and correlate with AD progression.

Spatial Proteomics by Parallel Accumulation-Serial Fragmentation Supported MALDI MS/MS Imaging: A First Glance Into Multiplexed and Spatial Peptide Identification

RATIONALE

In spatial proteomics, matrix-assisted laser desorption/ionization (MALDI) imaging enables rapid and cost-effective peptide measurements. Yet, in situ peptide identification remains challenging. Therefore, this study aims to integrate the trapped ion mobility spectrometry (TIMS)-based parallel accumulation-serial fragmentation (PASEF) into MALDI imaging of tryptic peptides to enable multiplexed MS/MS imaging.

METHODS

An initial MALDI TIMS MS1 survey measurement was performed, followed by a manual generation of a precursor list containing mass over charge values and ion mobility windows. Inside the dual TIMS system, submitted precursors were trapped, separately eluted by their ion mobility and analyzed in a quadrupole time-of-flight device, thereby enabling multiplexed MALDI MS/MS imaging. Finally, precursors were identified by peptide to spectrum matching.

RESULTS

This study presents the first multiplexed MALDI TIMS MS/MS imaging (iprm-PASEF) of tryptic peptides. Its applicability was showcased on two histomorphologically distinct tissue specimens in a four-plex and five-plex setup. Precursors were successfully identified by the search engine MASCOT in one single MALDI imaging experiment for each respective tissue. Peptide identifications were corroborated by liquid-chromatography tandem mass spectrometry experiments and fragment colocalization analyses.

CONCLUSIONS

In this study, we present a novel pipeline, based on iprm-PASEF, that allows the multiplexed and spatial identification of tryptic peptides in MALDI imaging. Hence, it marks a first step towards the integration of MALDI imaging into the emerging field of spatial proteomics.

Human skeletal muscle possesses both reversible proteomic signatures and a retained proteomic memory after repeated resistance training

Investigating repeated resistance training (RT) separated by a training break enables exploration of the potential for a proteomic memory of RT-induced skeletal muscle growth, i.e. retained protein adaptations from the previous RT. Our aim was to examine skeletal muscle proteome response to 10-week RT (RT1) followed by 10-week training cessation (i.e. detraining, DT), and finally, 10-week retraining (RT2). Thirty healthy, untrained participants conducted either periodic RT (RT1-DT-RT2, n = 17) or a 10-week no-training control period (n = 13) followed by 20 weeks of RT (n = 11). RT included twice-weekly supervised whole-body RT sessions, and resting vastus lateralis biopsies were obtained every 10 weeks for proteomics analysis using high-end dia-PASEF's mass spectrometry. The first RT period altered 150 proteins (93% increased) involved in, for example, energy metabolism and protein processing compared to minor changes during the control period. The proteome adaptations were similar after the second RT compared to baseline demonstrating reproducibility in proteome adaptations to RT. Many of the proteins induced by RT1 were reversed towards baseline after detraining and increased again after retraining. These reversible proteins were especially involved in aerobic energy metabolism. Interestingly, several proteins which increased after RT1 remain elevated (i.e. retained) after detraining, including carbonyl reductase 1 (CBR1) and proteins involved in muscle contraction, cytoskeleton and calcium binding. Among the latter, calcium-activated protease calpain-2 (CAPN2) has been recently identified as an epigenetic muscle memory gene. We show that resistance training evokes retained protein levels even after 2.5 months of no training, which demonstrates a potential proteomic memory of resistance training-induced muscle growth in human skeletal muscle. KEY POINTS: Repeated resistance training in humans separated by a training break (i.e. detraining) enables the identification of temporal protein signatures over the training, detraining and retraining periods, as well as studying reproducibility of protein changes to resistance training. Muscle proteome adaptations were similar after a second period of resistance training, demonstrating reproducibility in proteome adaptations to earlier resistance training. Many of the proteins induced by resistance training were reversed towards baseline after detraining and increased again after retraining. These reversible proteins were especially involved in aerobic energy metabolism. Several proteins increased after resistance training remain elevated (i.e. retained) after detraining, including carbonyl reductase 1 (CBR1) and calcium-binding proteins such as calpain-2 (CAPN2), a recently identified epigenetic muscle memory gene. Human skeletal muscle experiences retained protein changes following resistance training persisting over 2 months, demonstrating a potential proteomic memory of resistance training-induced muscle growth.

Benchmarking SILAC Proteomics Workflows and Data Analysis Platforms

Stable isotope labeling by amino acids in cell culture (SILAC) is a powerful metabolic labeling technique with broad applications and various study designs. SILAC proteomics relies on the accurate identification and quantification of all isotopic versions of proteins and peptides during both data acquisition and analysis. However, a comprehensive comparison and evaluation of SILAC data analysis platforms is currently lacking. To address this critical gap and offer practical guidelines for SILAC proteomics data analysis, we designed a comprehensive benchmarking pipeline to evaluate various in vitro SILAC workflows and commonly used data analysis software. Ten different SILAC data analysis workflows using five software packages (MaxQuant, Proteome Discoverer, FragPipe, DIA-NN, and Spectronaut) were evaluated for static and dynamic SILAC labeling with both DDA and DIA methods. For benchmarking, we used both in-house generated and repository SILAC proteomics datasets from HeLa and neuron culture samples. We assessed twelve performance metrics for SILAC proteomics including identification, quantification, accuracy, precision, reproducibility, filtering criteria, missing values, false discovery rate, protein half-life measurement, data completeness, unique software features, and speed of data analysis. Each method/software has its strengths and weaknesses when evaluated for these performance metrics. Most software reaches a dynamic range limit of 100 folds for accurate quantification of light/heavy ratios. We do not recommend using Proteome Discoverer for SILAC DDA analysis despite its wide use in label-free proteomics. To achieve greater confidence in SILAC quantification, researchers could use more than one software packages to analyze the same dataset for cross-validation. In summary, this study offers the first systematic evaluation of various SILAC data analysis platforms, providing practical guidelines to support decision-making in SILAC proteomics study design and data analysis.

SFRP1 upregulation causes hippocampal synaptic dysfunction and memory impairment

Impaired neuronal and synaptic function are hallmarks of early Alzheimer's disease (AD), preceding other neuropathological traits and cognitive decline. We previously showed that SFRP1, a glial-derived protein elevated in AD brains from preclinical stages, contributes to disease progression, implicating glial factors in early pathogenesis. Here, we generate and analyze transgenic mice overexpressing astrocytic SFRP1. SFRP1 accumulation causes early dendritic and synaptic defects in adult mice, followed by impaired synaptic long-term potentiation and cognitive decline, evident only when the animals age, thereby mimicking AD's structural-functional temporal distinction. This phenotype correlates with proteomic changes, including increased structural synaptic proteins like neurexin, which localizes in close proximity with SFRP1 in cultured hippocampal neurons. We conclude that excessive SFRP1 hinders synaptic protein turnover, reducing synaptic plasticity-a mechanism that may underlie the synaptopathy observed in the brains of prodromal AD patients.

Comprehensive PTM profiling with SCASP-PTM uncovers mechanisms of p62 degradation and ALDOA-mediated tumor progression

Multiple post-translational modification (PTM) proteomics typically combines PTM enrichment with multiplex isobaric labeling and peptide fractionation. However, effective methods for sequentially enriching multiple PTMs from a single sample for data-independent acquisition mass spectrometry (DIA-MS) remain lacking. We present SDS-cyclodextrin-assisted sample preparation (SCASP)-PTM, an approach that enables desalting-free enrichment of diverse PTMs, including phosphopeptides, ubiquitinated peptides, acetylated peptides, glycopeptides, and biotinylated peptides. SCASP-PTM uses SDS for protein denaturation, which is sequestered by cyclodextrins before trypsin digestion, facilitating sequential PTM enrichment without additional purification steps. Combined with DIA-MS, SCASP-PTM quantifies the proteome, ubiquitinome, phosphoproteome, and glycoproteome in HeLa-S3 cell samples, identifying serine 28 phosphorylation as a key driver of poly(I:C)-induced p62 degradation. This method also quantifies PTMs in clinical tissue samples, revealing the critical role of ALDOA K330 ubiquitination/acetylation in tumor progression. SCASP-PTM offers a streamlined workflow for comprehensive PTM analysis in both basic research and clinical applications.

Fibroblast hierarchy dynamics during mammary gland morphogenesis and tumorigenesis

Fibroblasts form a major component of the stroma in normal mammary tissue and breast tumors. Here, we have applied longitudinal single-cell transcriptome profiling of >45,000 fibroblasts in the mouse mammary gland across five different developmental stages and during oncogenesis. In the normal gland, diverse stromal populations were resolved, including lobular-like fibroblasts, committed preadipocytes and adipogenesis-regulatory, as well as cycling fibroblasts in puberty and pregnancy. These specialized cell types appear to emerge from CD34high mesenchymal progenitor cells, accompanied by elevated Hedgehog signaling. During late tumorigenesis, heterogeneous cancer-associated fibroblasts (CAFs) were identified in mouse models of breast cancer, including a population of CD34- myofibroblastic CAFs (myCAFs) that were transcriptionally and phenotypically similar to senescent CAFs. Moreover, Wnt9a was demonstrated to be a regulator of senescence in CD34- myCAFs. These findings reflect a diverse and hierarchically organized stromal compartment in the normal mammary gland that provides a framework to better understand fibroblasts in normal and cancerous states.

A modular, Galaxy-based immunopeptidogenomic (iPepGen) analysis pipeline for discovery, verification, and prioritization of candidate cancer neoantigen peptides

Background

Characterizing peptide antigens, processed from tumor-specific proteoforms, and bound to the major histocompatibility complex, is critical for immuno-oncology research. Next-generation sequencing predicts candidate neoantigen peptides derived from DNA mutations and/or RNA transcripts coding proteoform sequences that differ from the reference proteome. Mass spectrometry (MS)-based immunopeptidomics identifies predicted, MHC-bound neoantigen peptides and other tumor antigens. This "immunopeptidogenomic" approach requires multi-omic software integration, challenging researchers with limited bioinformatics expertise and resources. As a solution, we developed the immunopeptidogenomic (iPepGen) pipeline in the Galaxy ecosystem. iPepGen is composed of five core workflow modules, available via publicly accessible, scalable Galaxy instances, accompanied by training resources to empower community adoption.

Findings

Using representative multi-omic data from malignant peripheral nerve sheath tumors, we demonstrate the operation of iPepGen modules with these functions: 1) Predict neoantigen candidates from sequencing data and generate customized protein sequence databases, including reference and non-reference neoantigen candidate sequences; 2) Discover neoantigen peptide candidates by sequence database searching of tandem mass spectrometry (MS/MS) immunopeptidomics data; 3) Verify discovered peptide candidates through a secondary peptide-centric evaluation method against the MS/MS dataset; 4) Visualize and classify the nature of verified neoantigen peptides encoded by the genome and/or transcriptome; 5) Prioritize neoantigens for further exploration and empirical validation.

Conclusions

We demonstrate the effectiveness of the iPepGen pipeline for candidate neoantigen discovery and characterization. With tools, workflows, and training resources available in the open Galaxy ecosystem, iPepGen should provide cancer researchers with a flexible and accessible informatics resource tailored to accelerating immuno-oncology studies.

dia-PASEF Proteomics of Tumor and Stroma LMD Enriched from Archived HNSCC Samples

We employed laser microdissection to selectively harvest histology-resolved tumors and stroma from formalin-fixed, paraffin-embedded head and neck squamous cell carcinoma (HNSCC) tissues. Peptide digests from the LMD-enriched HNSCC tissue were analyzed by quantitative mass-spectrometry-based proteomics using a data independent analysis approach. In paired samples, excellent proteome coverage was achieved, having quantified 6668 proteins with a median quantitative coefficient of variation under 10%. Significant differences in relevant functional pathways between the tumor and the stroma regions were observed. Extracellular matrix (ECM) was identified as a major component enriched in the stroma, including many cancer-associated fibroblast signature proteins in this compartment. We demonstrate the potential for comparative deep proteome analysis from a very low starting input in a scalable format. Correlating such results with clinical features or disease progression will likely enable the identification of novel targets for disease classification and interventions.

Cell Storage Conditions Impact Single-Cell Proteomic Landscapes

Single cell transcriptomics (SCT) has revolutionized our understanding of cellular heterogeneity, yet the emergence of single cell proteomics (SCP) promises a more functional view of cellular dynamics. A challenge is that not all mass spectrometry facilities can perform SCP, and not all laboratories have access to cell sorting equipment required for SCP, which together motivate an interest in sending bulk cell samples through the mail for sorting and SCP analysis. Shipping requires cell storage, which has an unknown effect on SCP results. This study investigates the impact of cell storage conditions on the proteomic landscape at the single cell level, utilizing Data-Independent Acquisition (DIA) coupled with Parallel Accumulation Serial Fragmentation (diaPASEF). Three storage conditions were compared in 293T cells: (1) 37 °C (control), (2) 4 °C overnight, and (3) -196 °C storage followed by liquid nitrogen preservation. Both cold and frozen storage induced significant alterations in the cell diameter, elongation, and proteome composition. By elucidating how cell storage conditions alter cellular morphology and proteome profiles, this study contributes foundational technical information about SCP sample preparation and data quality.

Recent Developments in Single-Cell Metabolomics by Mass Spectrometry─A Perspective

Recent advancements in single-cell (sc) resolution analyses, particularly in sc transcriptomics and sc proteomics, have revolutionized our ability to probe and understand cellular heterogeneity. The study of metabolism through small molecules, metabolomics, provides an additional level of information otherwise unattainable by transcriptomics or proteomics by shedding light on the metabolic pathways that translate gene expression into functional outcomes. Metabolic heterogeneity, critical in health and disease, impacts developmental outcomes, disease progression, and treatment responses. However, dedicated approaches probing the sc metabolome have not reached the maturity of other sc omics technologies. Over the past decade, innovations in sc metabolomics have addressed some of the practical limitations, including cell isolation, signal sensitivity, and throughput. To fully exploit their potential in biological research, however, remaining challenges must be thoroughly addressed. Additionally, integrating sc metabolomics with orthogonal sc techniques will be required to validate relevant results and gain systems-level understanding. This perspective offers a broad-stroke overview of recent mass spectrometry (MS)-based sc metabolomics advancements, focusing on ongoing challenges from a biologist's viewpoint, aimed at addressing pertinent and innovative biological questions. Additionally, we emphasize the use of orthogonal approaches and showcase biological systems that these sophisticated methodologies are apt to explore.

Toward Real-Time Proteomics: Blood to Biomarker Quantitation in under One Hour

Multistep multihour tryptic proteolysis has limited the utility of bottom-up proteomics for cases that require immediate quantitative information. The power of proteomics to quantify biomarkers of health status cannot practically assist in clinical care if the dynamics of disease outpaces the turnaround of analysis. The recently available hyperthermoacidic archaeal (HTA) protease "Krakatoa" digests samples in a single 5 to 30 min step at pH 3 and >80 °C in conditions that disrupt most cells and tissues, denature proteins, and block disulfide reformation thereby dramatically expediting and simplifying sample preparation. The combination of quick single-step proteolysis with high-throughput dual-trapping single analytical column (DTSC) liquid chromatography-mass spectrometry (LC-MS) returns actionable data in less than 1 h from collection of unprocessed biofluid. The systematic evaluation of this methodology finds that over 160 proteins are quantified in less than 1 h from 1 μL of whole blood. Furthermore, labile Angiotensin I and II bioactive peptides along with a panel of protein species can be measured at 8 min intervals with a 20 min initial lag using targeted MS. With these methods, we analyzed serum and plasma from 53 individuals and quantified Angiotensin I and II and over 150 proteins including at least 46 that were not detected with trypsin. We discuss some of the implications of real-time proteomics including the immediate potential to advance several clinical and research applications.

µMap proximity labeling in living cells reveals stress granule disassembly mechanisms

Phase-separated condensates are membrane-less intracellular structures comprising dynamic protein interactions that organize essential biological processes. Understanding the composition and dynamics of these organelles advances our knowledge of cellular behaviors and disease pathologies related to granule dysregulation. In this study, we apply microenvironment mapping with a HaloTag-based platform (HaloMap) to characterize intracellular stress granule dynamics in living cells. After validating the robustness and sensitivity of this approach, we then profile the stress granule proteome throughout the formation and disassembly and under pharmacological perturbation. These experiments reveal several ubiquitin-related modulators, including the HECT (homologous to E6AP C terminus) E3 ligases ITCH and NEDD4L, as well as the ubiquitin receptor toll-interacting protein TOLLIP, as key mediators of granule disassembly. In addition, we identify an autophagy-related pathway that promotes granule clearance. Collectively, this work establishes a general photoproximity labeling approach for unraveling intracellular protein interactomes and uncovers previously unexplored regulatory mechanisms of stress granule dynamics.

Quantitative analysis of the proteome and protein oxidative modifications in primary human coronary artery endothelial cells and associated extracellular matrix

Vascular endothelial cells (ECs) play a key role in physiology by controlling arterial contraction and relaxation, and molecular transport. EC dysfunction is associated with multiple pathologies. Here, we characterize the cellular and extracellular matrix (ECM) proteomes of primary human coronary artery ECs, from multiple donors, and oxidation/nitration products formed on these during cell culture, using liquid chromatography-mass spectrometry. In total ∼9900 proteins were identified in cells from 3 donors, with ∼7000 proteins per donor. Of these ∼5300 were consistently identified, indicating some heterogeneity across the donors, with age a possible cause. Multiple endogenous oxidation products were detected on both ECM and cellular proteins (and particularly endoplasmic reticulum species). In contrast, nitration was mostly detected on cell proteins and particularly cytoskeletal proteins, consistent with intracellular generation of nitrating agents, possibly from endothelial nitric oxide synthase (eNOS) or peroxidase enzymes. The modifications are ascribed to both physiological enzymatic activity (hydroxylation at proline/lysine; predominantly on ECM proteins and especially collagens) and the formation of reactive species (oxidation at tryptophan/tyrosine/histidine; nitration at tryptophan/tyrosine). The identified sites are present on a limited number of peptides (104 oxidized; 23 nitrated) from a modest number of proteins. A small number of proteins were detected with multiple modifications, consistent with these being selective and specific targets. Several nitrated peptides were consistently detected across all donors, and also in human smooth muscle cells suggesting that these are major targets in the vascular proteome. These data provide a 'background' proteome dataset for studies of endothelial dysfunction in disease.

Recent implementations of data-independent acquisition for cancer biomarker discovery in biological fluids

INTRODUCTION

Cancer is the second-leading cause of death worldwide and accurate biomarkers for early detection and disease monitoring are needed to improve outcomes. Biological fluids, such as blood and urine, are ideal samples for biomarker measurements as they can be routinely collected with relatively minimally invasive methods. However, proteomics analysis of fluids has been a challenge due to the high dynamic range of its protein content. Advances in data-independent acquisition (DIA) mass spectrometry-based proteomics can address some of the technical challenges in the analysis of biofluids, thus enabling the ability for mass spectrometry to propel large-scale biomarker discovery.

AREAS COVERED

We reviewed principles of DIA and its recent applications in cancer biomarker discovery using biofluids. We summarized DIA proteomics studies using biological fluids in the context of cancer research over the past decade, and provided a comprehensive overview of the benefits and challenges of DIA-MS.

EXPERT OPINION

Various studies showed the potential of DIA-MS in identifying putative cancer biomarkers in a high-throughput manner. However, the lack of proper study design and standardization of methods across platforms still needs to be addressed to fully utilize the benefits of DIA-MS to accelerate the biomarker discovery and verification processes.

Decoding bull fertility in vitro: a proteomics exploration from sperm to blastocyst

In brief

Bulls are selected for field fertility and semen quality, but traits such as polyspermy are not considered and can increase aneuploidy during in vitro embryo production. This study links bull-specific proteomic signatures to polyspermy and embryo quality, further refining bull selection criteria.

Abstract

Male fertility plays a pivotal role in the success rates of in vitro embryo production. While livestock breeding programs rigorously select bulls according to their predicted field fertility, specific traits such as polyspermy rates are not routinely evaluated. Despite the known negative impact of polyspermy on embryo survival, the paternal factors involved remain unclear. In this study, we aimed to address this gap by evaluating the in vitro outcomes of four bulls, focusing on sperm motility, fertilization rates, polyspermy incidence, embryo development and quality. In addition, we analyzed the proteome profiles of sperm, 2-4 cell stage embryos and blastocysts derived from those bulls to identify potential molecular factors associated with male fertility. Bulls with comparable sperm motility parameters displayed varying in vitro fertilization outcomes. Notably, the bull with the highest polyspermy rate achieved blastocyst rates similar to those of bulls with lower polyspermy rates. The number of apoptotic cells in the blastocysts was bull-dependent. Proteomic analysis revealed bull-specific signatures in sperm and blastocysts, with no differences at the 2-4 cell stage. Differences in the sperm proteome suggested that bull-dependent penetration and polyspermy rates might be associated with the ability of the sperm to undergo capacitation and acrosomal reaction. At the blastocyst level, the bull with the highest polyspermy rates produced lower quality blastocysts due to imbalances in key proteins and pathways for embryo development. In conclusion, bulls with similar blastocyst rates may differ in polyspermy rates and resulting embryo quality underscoring the importance of careful bull selection for in vitro embryo production.

A Single-Step Protein Extraction for Lung Extracellular Matrix Proteomics Enabled by the Photocleavable Surfactant Azo and timsTOF Pro

The extracellular matrix (ECM) is a dynamic, complex network of proteins, collectively known as the 'matrisome', which not only provides essential structural support to cells and tissues but also regulates critical cellular processes. Dysregulation of the ECM is implicated in many diseases, underscoring the need to characterize the matrisome to better understand disease mechanisms. We have previously developed a dual-step protocol enabled by the photocleavable surfactant Azo for the extraction of ECM proteins from tissue using pH-neutral decellularization followed by solubilization by Azo. While effective for characterization of the ECM proteins, such a dual-step protocol requires two extracts per sample, limiting the throughput and complicating the comparison of protein quantitation across different extraction conditions. Here, we develop a single-step Azo-enabled protein extraction for the solubilization of ECM proteins from lung tissue to improve the throughput for studies with large sample sizes. Using this method, we identified 324 ECM proteins, including 137 core ECM and 187 ECM associated proteins. Core ECM proteins including elastin, fibronectin, and fibrillar collagens were reproducibly identified and quantified. We observed a 94.6% overlap in the ECM proteins identified between the single-step and dual-step Azo extracts, indicating the single-step Azo extraction achieves ECM protein coverage comparable to the dual-step extraction. Overall, we have demonstrated that this single-step Azo extraction is not only highly efficient but also comprehensive for ECM protein identification and quantification, making it a powerful method for ECM proteomics, especially for studies with large sample size.

Data-independent acquisition parallel accumulation-serial fragmentation (diaPASEF) analysis of the separated zebrafish lens improves identifications

Ocular lens fiber cells degrade their organelles during differentiation to prevent light scattering. Organelle degradation occurs continuously throughout an individual's lifespan, creating a spatial gradient of young cortical fiber cells in the lens periphery to older nuclear fiber cells in the center of the lens. Therefore, separation of cortical and nuclear regions enables examination of protein aging. Previously, the human lens cortex and nucleus have been studied using data-independent acquisition (DIA) proteomics, allowing for the identification of low-abundance protein groups. In this study, we employed data-independent acquisition parallel accumulation-serial fragmentation (diaPASEF) proteomics on a timsTOF HT instrument to study the zebrafish lens proteome and compared results to a standard DIA method employed on an Orbitrap Exploris 480. Using the additional ion mobility gas phase separation of diaPASEF, peptide and protein group identifications increased by over 200% relative to an orbitrap DIA method in the zebrafish lens. With diaPASEF, we identified 13,721 and 11,996 unique peptides in the zebrafish lens cortex and nucleus, respectively, which correspond to 1,537 and 1,389 protein groups. Thus, separation of the zebrafish lens into cortical and nuclear regions followed by diaPASEF analysis produced the most comprehensive zebrafish lens proteomic dataset to date.

Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics

Mass spectrometry-based proteomics is about 35 years old, and recent progress appears to be speeding up across all subfields. In this review, we focus on advances over the last two years in select areas within bottom-up proteomics, including approaches to high-throughput experiments, data analysis using machine learning, drug discovery, glycoproteomics, extracellular vesicle proteomics, and structural proteomics.

Discovery of the antifungal compound ilicicolin K through genetic activation of the ilicicolin biosynthetic pathway in Trichoderma reesei

BACKGROUND

Given the global rise in antimicrobial resistance, the discovery of novel antimicrobial agents and production processes thereof are of utmost importance. To this end we have activated the gene cluster encoding for the biosynthesis of the potent antifungal compound ilicicolin H in the fungus Trichoderma reesei. While the biosynthetic gene cluster (BGC) is silent under standard cultivation conditions, we achieved BGC activation by genetically overexpressing the transcription factor TriliR.

RESULTS

Successful activation was confirmed by RT-qPCR, proteomic and metabolomic analyses. Metabolomic profiling upon BGC expression revealed high-yield production of ilicicolin H. To elucidate the enzymatically highly diverse functionality of this BGC, we employed a combination of overexpression and deletions of individual genes in the BGC. While we hardly observed any of the previously reported side- or shunt products associated with heterologous ilicicolin H expression, we discovered that Trichoderma reesei produces a novel member of the ilicicolin family using a metabolomic molecular networking approach. This new compound, ilicicolin K, is expressed in substantial amounts in the genetically engineered Trichoderma reesei. Ilicicolin K differs from ilicicolin H in its structure by a second hydroxylation of the tyrosine derived phenol and an additional ring formed by an intramolecular ether bridge of the hydroxyl group at the pyridone towards the tyrosine moiety of the molecule. Bioactivity tests of ilicicolin K revealed a strong antifungal activity against Saccharomyces cerevisiae and a moderate activity against the human pathogen Candida auris, an emerging multi-drug resistant fungus.

CONCLUSIONS

By activating a silent BGC in T. reesei, we obtained a high-yielding strain for the production of the antifungal compounds ilicicolin H and the novel ilicicolin K. These two compounds share some structural properties and are thus highly likely to act on the fungal cytochrome bc1 complex, a component of the mitochondrial repository chain. However, they possess different bioactive properties, which might suggest that ilicicolin K may overcome certain limitations of ilicicolin H.

In-Depth Proteomic Analysis of Tissue Interstitial Fluid Reveals Biomarker Candidates Related to Varying Differentiation Statuses in Gastric Adenocarcinoma

The proteomic heterogeneity of gastric adenocarcinoma (GC) has been extensively investigated at the bulk tissue level, which can only provide an average molecular state. In this study, we collected an in-depth quantitative proteomic dataset of tissues and interstitial fluids (ISFs) from both poorly and non-poorly differentiated GC and presented a comprehensive analysis from several perspectives. Comparison of proteomes between ISFs and tissues revealed that ISF exhibited higher abundances of proteins associated with blood microparticles, protein-lipid complexes, immunoglobulin complexes, and high-density lipoprotein particles. Also, consistent and inconsistent protein abundance changes between them were revealed by a correlation analysis. Interestingly, a more pronounced difference between tumors and normal adjacent tissues was found at the ISF level, which accurately reflected tissue properties compared to those of bulk tissue. Two ISF-derived biomarker candidates, calsyntenin-1 (CLSTN1) and prosaposin (PSAP), were identified by distinguishing patients with different differentiation statuses and were further validated in serum samples. Additionally, the silencing of CLSTN1 and PSAP was demonstrated to suppress cell proliferation, migration, and invasion in poorly differentiated gastric cancer cell lines. In summary, the ISF proteome offers a new perspective on tumor biology. This study provides a valuable resource that significantly enhances the understanding of GC and may ultimately benefit clinical practice.

Discovery of Cysteine Carboxyalkylations by Real-Time Isotopic Signature Targeted Profiling

Data-dependent acquisition (DDA) is widely applied in shotgun proteomics. However, restricted by the scanning speed of mass spectrometry (MS) instruments, it remains challenging for DDA to directly detect peptides with low abundance. Herein, we developed a real-time targeted MS data acquisition method, "isoSTAR", which identifies target peptides by their unique isotopic signatures during the stage of full-MS scanning and subjects them to targeted MS/MS scans immediately. The method showed dramatic improvement in sensitivity in identifying target peptides with low abundance compared to traditional MS acquisition methods. Using this method, we discovered a series of carboxyalkylations on cysteines during fatty acid metabolism and verified their modification structures using synthetic peptide standards. We envision that isoSTAR will become a powerful and versatile tool to enhance shotgun proteomics applications in profiling protein-centric modifications.

Decrypting the molecular basis of cellular drug phenotypes by dose-resolved expression proteomics

Proteomics is making important contributions to drug discovery, from target deconvolution to mechanism of action (MoA) elucidation and the identification of biomarkers of drug response. Here we introduce decryptE, a proteome-wide approach that measures the full dose-response characteristics of drug-induced protein expression changes that informs cellular drug MoA. Assaying 144 clinical drugs and research compounds against 8,000 proteins resulted in more than 1 million dose-response curves that can be interactively explored online in ProteomicsDB and a custom-built Shiny App. Analysis of the collective data provided molecular explanations for known phenotypic drug effects and uncovered new aspects of the MoA of human medicines. We found that histone deacetylase inhibitors potently and strongly down-regulated the T cell receptor complex resulting in impaired human T cell activation in vitro and ex vivo. This offers a rational explanation for the efficacy of histone deacetylase inhibitors in certain lymphomas and autoimmune diseases and explains their poor performance in treating solid tumors.

Deep Learning Enhances Precision of Citrullination Identification in Human and Plant Tissue Proteomes

Citrullination is a critical yet understudied post-translational modification (PTM) implicated in various biological processes. Exploring its role in health and disease requires a comprehensive understanding of the prevalence of this PTM at a proteome-wide scale. Although mass spectrometry has enabled the identification of citrullination sites in complex biological samples, it faces significant challenges, including limited enrichment tools and a high rate of false positives due to the identical mass with deamidation (+0.9840 Da) and errors in monoisotopic ion selection. These issues often necessitate manual spectrum inspection, reducing throughput in large-scale studies. In this work, we present a novel data analysis pipeline that incorporates the deep learning model Prosit-Cit into the MS database search workflow to improve both the sensitivity and the precision of citrullination site identification. Prosit-Cit, an extension of the existing Prosit model, has been trained on ∼53,000 spectra from ∼2500 synthetic citrullinated peptides and provides precise predictions for chromatographic retention time and fragment ion intensities of both citrullinated and deamidated peptides. This enhances the accuracy of identification and reduces false positives. Our pipeline demonstrated high precision on the evaluation dataset, recovering the majority of known citrullination sites in human tissue proteomes and improving sensitivity by identifying up to 14 times more citrullinated sites. Sequence motif analysis revealed consistency with previously reported findings, validating the reliability of our approach. Furthermore, extending the pipeline to a tissue proteome dataset of the model plant Arabidopsis thaliana enabled the identification of ∼200 citrullination sites across 169 proteins from 30 tissues, representing the first large-scale citrullination mapping in plants. This pipeline can be seamlessly applied to existing proteomics datasets, offering a robust tool for advancing biological discoveries and deepening our understanding of protein citrullination across species.

Human skeletal muscle fiber heterogeneity beyond myosin heavy chains

Skeletal muscle is a heterogenous tissue comprised primarily of myofibers, commonly classified into three fiber types in humans: one "slow" (type 1) and two "fast" (type 2A and type 2X). However, heterogeneity between and within traditional fiber types remains underexplored. We applied transcriptomic and proteomic workflows to 1050 and 1038 single myofibers from human vastus lateralis, respectively. Proteomics was conducted in males, while transcriptomics included ten males and two females. We identify metabolic, ribosomal, and cell junction proteins, in addition to myosin heavy chain isoforms, as sources of multi-dimensional variation between myofibers. Furthermore, whilst slow and fast fiber clusters are identified, our data suggests that type 2X fibers are not phenotypically distinct to other fast fibers. Moreover, myosin heavy chain-based classifications do not adequately describe the phenotype of myofibers in nemaline myopathy. Overall, our data indicates that myofiber heterogeneity is multi-dimensional with sources of variation beyond myosin heavy chain isoforms.

4D-DIA Proteomics Uncovers New Insights into Host Salivary Response Following SARS-CoV-2 Omicron Infection

Since late 2021, Omicron variants have dominated the epidemiological scenario as the most successful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sublineages, driving new and breakthrough infections globally over the past two years. In this study, we investigated for the first time the host salivary response of COVID-19 patients infected with Omicron variants (BA.1, BA.2, and BA.4/5) by using an untargeted four-dimensional data-independent acquisition (4D-DIA)-based proteomics approach. We identified 137 proteins whose abundance levels differed between the COVID-19 positive and negative groups. Salivary signatures were mainly enriched in ribosomal proteins, linked to mRNAviral translation, protein synthesis and processing, immune innate, and antiapoptotic signaling. The higher abundance of 14-3-3 proteins (YWHAG, YWHAQ, YWHAE, and SFN) in saliva, first reported here, may be associated with increased infectivity and improved viral replicative fitness. We also identified seven proteins (ACTN1, H2AC2, GSN, NDKA, CD109, GGH, and PCYOX) that yielded comprehension into Omicron infection and performed outstandingly in screening patients with COVID-19 in a hospital setting. This panel also presented an enhanced anti-COVID-19 and anti-inflammatory signature, providing insights into disease severity, supported by comparisons with other proteome data sets. The salivary signature provided valuable insights into the host's response to SARS-CoV-2 Omicron infection, shedding light on the pathophysiology of COVID-19, particularly in cases associated with mild disease. It also underscores the potential clinical applications of saliva for disease screening in hospital settings. Data are available via ProteomeXchange with the identifier PXD054133.

Impact of Local Air Pressure on Ion Mobilities and Data Consistency in diaPASEF-Based High Throughput Proteomics

Data-independent acquisition (DIA) on ion mobility mass spectrometers enables deep proteome coverage and high data completeness in large-scale proteomics studies. For advanced acquisition schemes such as parallel accumulation serial fragmentation-based DIA (diaPASEF) stability of ion mobility (1/K0) over time is crucial for consistent data quality. We found that minor changes in environmental air pressure systematically affect the vacuum pressure in the TIMS analyzer, causing ion mobility shifts. By comparing experimental ion mobilities with historical weather data, we attributed observed drifts to fluctuations in the ground air pressure. Moderate air pressure changes of e.g. fifteen mbar induce ion mobility shifts of 0.025 Vs/cm2. These drifts negatively impact peptide quantification across consecutively acquired samples due to drift-dependent abundance changes and increased missing values for ions located at the boundaries of diaPASEF isolation windows, which cannot be corrected by postprocessing. To address this, we applied an in-batch mobility autocalibration feature on a run-wise basis, leading to full elimination of ion mobility drifts.

Gel-Based Sample Fractionation with SP3-Purification for Top-Down Proteomics

Precise prefractionation of proteome samples is a potent method for realizing in-depth analysis in top-down proteomics. PEPPI-MS (Passively Eluting Proteins from Polyacrylamide gels as Intact species for MS), a gel-based sample fractionation method, enables high-resolution proteome fractionation based on molecular weight by highly efficient extraction of proteins from polyacrylamide gels after SDS-PAGE separation. Thereafter it is essential to effectively remove contaminants such as CBB and SDS from the PEPPI fraction prior to mass spectrometry. In this study, we developed a complete, robust, and simple sample preparation workflow named PEPPI-SP3 for top-down proteomics by combining PEPPI-MS with the magnetic bead-based protein purification approach used in SP3 (single-pot, solid-phase-enhanced sample preparation), now one of the standard sample preparation methods in bottom-up proteomics. In PEPPI-SP3, proteins extracted from the gel are collected on the surface of SP3 beads, washed with organic solvents, and recovered intact with 100 mM ammonium bicarbonate containing 0.05% (w/v) SDS. The recovered proteins are subjected to mass spectrometry after additional purification using an anion-exchange StageTip. Performance validation using human cell lysates showed a significant improvement in low-molecular-weight protein recovery with a lower coefficient of variation compared to conventional PEPPI workflows using organic solvent precipitation or ultrafiltration.

Malaria exposure remodels the plasma proteome of Ghanaian children

BACKGROUND

Malaria, caused by Plasmodium falciparum, remains a major public health burden causing ~ 200 million deaths annually, especially among children. Although the lack of an effective vaccine has hindered malaria elimination, studies have reported on individuals acquiring natural immunity to malaria in the context of high malaria exposure. However, the immune correlates of protection in these people who acquire natural immunity against malaria are poorly understood.

METHODS

Symptomatic children residing in high and low malaria transmission areas of Ghana were enrolled into the study and followed for 3 weeks from the day of malaria confirmation. The plasma proteome of these children was profiled using a mass spectrometry-based approach and putative protein-based biomarkers and predictors of immune tolerance to malaria were identified.

RESULTS

We identified several differentially abundant proteins in children living in high malaria transmission areas relative to children in low transmission areas. Differentially abundant proteins were enriched in immune response processes, including complement cascade activities and elevated platelet activation. We found IGKV3D-20 protein to be strongly associated with high malaria exposure.

CONCLUSIONS

Our findings confirm earlier reports and identify putative signature proteins implicated in immune tolerance to malaria. Further large-scale and more mechanistic studies will be needed to reveal the key components of the identified pathways that could explain naturally acquired immunity to malaria and possibly be exploited to develop novel therapeutics against P. falciparum.

C-terminal amides mark proteins for degradation via SCF-FBXO31

During normal cellular homeostasis, unfolded and mislocalized proteins are recognized and removed, preventing the build-up of toxic byproducts1. When protein homeostasis is perturbed during ageing, neurodegeneration or cellular stress, proteins can accumulate several forms of chemical damage through reactive metabolites2,3. Such modifications have been proposed to trigger the selective removal of chemically marked proteins3-6; however, identifying modifications that are sufficient to induce protein degradation has remained challenging. Here, using a semi-synthetic chemical biology approach coupled to cellular assays, we found that C-terminal amide-bearing proteins (CTAPs) are rapidly cleared from human cells. A CRISPR screen identified FBXO31 as a reader of C-terminal amides. FBXO31 is a substrate receptor for the SKP1-CUL1-F-box protein (SCF) ubiquitin ligase SCF-FBXO31, which ubiquitylates CTAPs for subsequent proteasomal degradation. A conserved binding pocket enables FBXO31 to bind to almost any C-terminal peptide bearing an amide while retaining exquisite selectivity over non-modified clients. This mechanism facilitates binding and turnover of endogenous CTAPs that are formed after oxidative stress. A dominant human mutation found in neurodevelopmental disorders reverses CTAP recognition, such that non-amidated neosubstrates are now degraded and FBXO31 becomes markedly toxic. We propose that CTAPs may represent the vanguard of a largely unexplored class of modified amino acid degrons that could provide a general strategy for selective yet broad surveillance of chemically damaged proteins.

Global cellular proteo-lipidomic profiling of diverse lysosomal storage disease mutants using nMOST

Lysosomal storage diseases (LSDs) comprise ~50 monogenic disorders marked by the buildup of cellular material in lysosomes, yet systematic global molecular phenotyping of proteins and lipids is lacking. We present a nanoflow-based multiomic single-shot technology (nMOST) workflow that quantifies HeLa cell proteomes and lipidomes from over two dozen LSD mutants. Global cross-correlation analysis between lipids and proteins identified autophagy defects, notably the accumulation of ferritinophagy substrates and receptors, especially in NPC1-/- and NPC2-/- mutants, where lysosomes accumulate cholesterol. Autophagic and endocytic cargo delivery failures correlated with elevated lysophosphatidylcholine species and multilamellar structures visualized by cryo-electron tomography. Loss of mitochondrial cristae, MICOS complex components, and OXPHOS components rich in iron-sulfur cluster proteins in NPC2-/- cells was largely alleviated when iron was provided through the transferrin system. This study reveals how lysosomal dysfunction affects mitochondrial homeostasis and underscores nMOST as a valuable discovery tool for identifying molecular phenotypes across LSDs.

Single Cell Proteomics Reveals Specific Cellular Subtypes in Cardiomyocytes Derived from Human iPSCs and Adult Hearts

Single cell proteomics was performed on human induced pluripotent stem cells (iPSCs), iPSC-derived cardiomyocytes, and adult cardiomyocytes. Over 700 proteins could be simultaneously measured in each cell revealing unique subpopulations. A sub-set of iPSCs expressed higher levels of Lin28a and Tra-1-60 towards the outer edge of cell colonies. In the cardiomyocytes, two distinct populations were found that exhibited complementary metabolic profiles. Cardiomyocytes from iPSCs showed a glycolysis profile while adult cardiomyocytes were enriched in proteins involved with fatty acid metabolism. Interestingly, rare single cells also co-expressed markers of both cardiac and neuronal lineages, suggesting there maybe a novel hybrid cell type in the human heart.

iDIA-QC: AI-empowered data-independent acquisition mass spectrometry-based quality control

Quality control (QC) in mass spectrometry (MS)-based proteomics is mainly based on data-dependent acquisition (DDA) analysis of standard samples. Here, we collect 2754 files acquired by data independent acquisition (DIA) and paired 2638 DDA files from mouse liver digests using 21 mass spectrometers across nine laboratories over 31 months. Our data demonstrate that DIA-based LC-MS/MS-related consensus QC metrics exhibit higher sensitivity compared to DDA-based QC metrics in detecting changes in LC-MS status. We then prioritize 15 metrics and invite 21 experts to manually assess the quality of 2754 DIA files based on those metrics. We develop an AI model for DIA-based QC using 2110 training files. It achieves AUCs of 0.91 (LC) and 0.97 (MS) in the first validation dataset (n = 528), and 0.78 (LC) and 0.94 (MS) in an independent validation dataset (n = 116). Finally, we develop an offline software called iDIA-QC for convenient adoption of this methodology.

An accessible workflow for high-sensitivity proteomics using parallel accumulation-serial fragmentation (PASEF)

Deep and accurate proteome analysis is crucial for understanding cellular processes and disease mechanisms; however, it is challenging to implement in routine settings. In this protocol, we combine a robust chromatographic platform with a high-performance mass spectrometric setup to enable routine yet in-depth proteome coverage for a broad community. This entails tip-based sample preparation and pre-formed gradients (Evosep One) combined with a trapped ion mobility time-of-flight mass spectrometer (timsTOF, Bruker). The timsTOF enables parallel accumulation-serial fragmentation (PASEF), in which ions are accumulated and separated by their ion mobility, maximizing ion usage and simplifying spectra. Combined with data-independent acquisition (DIA), it offers high peak sampling rates and near-complete ion coverage. Here, we explain how to balance quantitative accuracy, specificity, proteome coverage and sensitivity by choosing the best PASEF and DIA method parameters. The protocol describes how to set up the liquid chromatography-mass spectrometry system and enables PASEF method generation and evaluation for varied samples by using the py_diAID tool to optimally position isolation windows in the mass-to-charge and ion mobility space. Biological projects (e.g., triplicate proteome analysis in two conditions) can be performed in 3 d with ~3 h of hands-on time and minimal marginal cost. This results in reproducible quantification of 7,000 proteins in a human cancer cell line in quadruplicate 21-min injections and 29,000 phosphosites for phospho-enriched quadruplicates. Synchro-PASEF, a highly efficient, specific and novel scan mode, can be analyzed by Spectronaut or AlphaDIA, resulting in superior quantitative reproducibility because of its high sampling efficiency.

Salivary proteomics and metaproteomics identifies distinct molecular and taxonomic signatures of type-2 diabetes

BACKGROUND

Saliva is a protein-rich body fluid for noninvasive discovery of biomolecules, containing both human and microbial components, associated with various chronic diseases. Type-2 diabetes (T2D) imposes a significant health and socio-economic burden. Prior research on T2D salivary microbiome utilized methods such as metagenomics, metatranscriptomics, 16S rRNA sequencing, and low-throughput proteomics.

RESULTS

We conducted ultrafast, in-depth MS-based proteomic and metaproteomic profiling of saliva from 15 newly diagnosed T2D individuals and 15 age-/BMI-matched healthy controls (HC). Using state-of-the-art proteomics, over 4500 human and bacterial proteins were identified in a single 21-min run. Bioinformatic analysis revealed host signatures of altered immune-, lipid-, and glucose-metabolism regulatory systems, increased oxidative stress, and possible precancerous changes in T2D saliva. Abundance of peptides for bacterial genera such as Neisseria and Corynebacterium were altered showing biomarker potential, offering insights into disease pathophysiology and microbial applications for T2D management.

CONCLUSIONS

This study presents a comprehensive mapping of salivary proteins and microbial communities, serving as a foundational resource for enhancing understanding of T2D pathophysiology. The identified biomarkers hold promise for advancing diagnostics and therapeutic approaches in T2D and its associated long-term complication Video Abstract.

diaTracer enables spectrum-centric analysis of diaPASEF proteomics data

Data-independent acquisition has become a widely used strategy for peptide and protein quantification in liquid chromatography-tandem mass spectrometry-based proteomics studies. The integration of ion mobility separation into data-independent acquisition analysis, such as the diaPASEF technology available on Bruker's timsTOF platform, further improves the quantification accuracy and protein depth achievable using data-independent acquisition. We introduce diaTracer, a spectrum-centric computational tool optimized for diaPASEF data. diaTracer performs three-dimensional (mass to charge ratio, retention time, ion mobility) peak tracing and feature detection to generate precursor-resolved "pseudo-tandem mass 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, cerebrospinal fluid, and plasma samples, data from phosphoproteomics and human leukocyte antigens 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.

Prediction of brain metastasis development with DNA methylation signatures

Brain metastases (BMs) are the most common and among the deadliest brain tumors. Currently, there are no reliable predictors of BM development from primary cancer, which limits early intervention. Lung adenocarcinoma (LUAD) is the most common BM source and here we obtained 402 tumor and plasma samples from a large cohort of patients with LUAD with or without BM (n = 346). LUAD DNA methylation signatures were evaluated to build and validate an accurate model predicting BM development from LUAD, which was integrated with clinical factors to provide comprehensive patient-specific BM risk probabilities in a nomogram. Additionally, immune and cell interaction gene sets were differentially methylated at promoters in BM versus paired primary LUAD and had aligning dysregulation in the proteome. Immune cells were differentially abundant in BM versus LUAD. Finally, liquid biomarkers identified from methylated cell-free DNA sequenced in plasma were used to generate and validate accurate classifiers for early BM detection. Overall, LUAD methylomes can be leveraged to predict and noninvasively identify BM, moving toward improved patient outcomes with personalized treatment.

From LAL-D to MASLD: Insights into the role of LAL and Kupffer cells in liver inflammation and lipid metabolism

Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent liver pathology worldwide, closely associated with obesity and metabolic disorders. Increasing evidence suggests that macrophages play a crucial role in the development of MASLD. Several human studies have shown an inverse correlation between circulating lysosomal acid lipase (LAL) activity and MASLD. LAL is the sole enzyme known to degrade cholesteryl esters (CE) and triacylglycerols in lysosomes. Consequently, these substrates accumulate when their enzymatic degradation is impaired due to LAL deficiency (LALD). This study aimed to investigate the role of hepatic LAL activity and liver-resident macrophages (i.e., Kupffer cells (KC)) in MASLD. To this end, we analyzed lipid metabolism in hepatocyte-specific (hep)Lal-/- mice and depleted KC with clodronate treatment. When fed a high-fat/high-cholesterol diet (HF/HCD), hepLal-/- mice exhibited CE accumulation and an increased number of macrophages in the liver and significant hepatic inflammation. KC were depleted upon clodronate administration, whereas infiltrating/proliferating CD68-expressing macrophages were less affected. This led to exacerbated hepatic CE accumulation and dyslipidemia, as evidenced by increased LDL-cholesterol concentrations. Along with proteomic analysis of liver tissue, these findings indicate that hepatic LAL-D in HF/HCD-fed mice leads to macrophage infiltration into the liver and that KC depletion further exacerbates hepatic CE concentrations and dyslipidemia.

Single-Cell Multi-Omics Analysis of In Vitro Post-Ovulatory-Aged Oocytes Revealed Aging-Dependent Protein Degradation

Once ovulated, the oocyte has to be fertilized in a short time window or it will undergo post-ovulation aging (POA), whose underlying mechanisms are still not elucidated. Here, we optimized single-cell proteomics methods and performed single-cell transcriptomic, proteomic, and phosphoproteomic analysis of fresh, POA, and melatonin-treated POA oocytes. POA oocytes showed downregulation of most differentially expressed proteins, with little correlation with mRNA expression, and the protein changes can be rescued by melatonin treatment. MG132 treatment rescued the decreased fertilization and polyspermy rates and upregulated fragmentation and parthenogenesis rates of POA oocytes. MG132-treated oocytes displayed health status at proteome, phosphoproteome, and fertilization ability similar to fresh oocytes, suggesting that protein stabilization might be the underlying mechanism for melatonin to rescue POA. The important roles of proteasome-mediated protein degradation during oocyte POA revealed by single-cell multi-omics analyses offer new perspectives for increasing oocyte quality during POA and improving assisted reproduction technologies.

Proteomics-Based Analysis of Laser-Capture Micro-dissected, Formalin-Fixed Paraffin-Embedded Tissue Samples

The ability to bring spatial resolution to omics studies enables a deeper understanding of cell populations and interactions in biological tissues. In the case of proteomics, single-cell and spatial approaches have been particularly challenging, due to limitations in sensitivity and throughput relative to other omics fields. Recent developments at the level of sample handling, chromatography, and mass spectrometry have set the stage for proteomics to be established in these new disciplines.

Calculating and Reporting Coefficients of Variation for DIA-Based Proteomics

The coefficient of variation (CV) is a measure that is frequently used to assess data dispersion for mass spectrometry-based proteomics. In the current era of burgeoning technical developments, there has been an increased focus on using CVs to measure the quantitative precision of new methods. Thus, it has also become important to define a set of guidelines on how to calculate and report the CVs. This perspective shows the effects that the CV equation, data normalization as well as software parameters, can have on data dispersion and CVs, highlighting the importance of reporting all these variables within the methods section. It also proposes a set of recommendations to calculate and report CVs for technical studies, where the main objective is to benchmark technical developments with a focus on precision. To assist in this process, a novel R package to calculate CVs (proteomicsCV) is also included.

LRH-1/NR5A2 targets mitochondrial dynamics to reprogram type 1 diabetes macrophages and dendritic cells into an immune tolerance phenotype

BACKGROUND

The complex aetiology of type 1 diabetes (T1D), characterised by a detrimental cross-talk between the immune system and insulin-producing beta cells, has hindered the development of effective disease-modifying therapies. The discovery that the pharmacological activation of LRH-1/NR5A2 can reverse hyperglycaemia in mouse models of T1D by attenuating the autoimmune attack coupled to beta cell survival/regeneration prompted us to investigate whether immune tolerisation could be translated to individuals with T1D by LRH-1/NR5A2 activation and improve islet survival.

METHODS

Peripheral blood mononuclear cells (PBMCs) were isolated from individuals with and without T1D and derived into various immune cells, including macrophages and dendritic cells. Cell subpopulations were then treated or not with BL001, a pharmacological agonist of LRH-1/NR5A2, and processed for: (1) Cell surface marker profiling, (2) cytokine secretome profiling, (3) autologous T-cell proliferation, (4) RNAseq and (5) proteomic analysis. BL001-target gene expression levels were confirmed by quantitative PCR. Mitochondrial function was evaluated through the measurement of oxygen consumption rate using a Seahorse XF analyser. Co-cultures of PBMCs and iPSCs-derived islet organoids were performed to assess the impact of BL001 on beta cell viability.

RESULTS

LRH-1/NR5A2 activation induced a genetic and immunometabolic reprogramming of T1D immune cells, marked by reduced pro-inflammatory markers and cytokine secretion, along with enhanced mitohormesis in pro-inflammatory M1 macrophages and mitochondrial turnover in mature dendritic cells. These changes induced a shift from a pro-inflammatory to an anti-inflammatory/tolerogenic state, resulting in the inhibition of CD4+ and CD8+ T-cell proliferation. BL001 treatment also increased CD4+/CD25+/FoxP3+ regulatory T-cells and Th2 cells within PBMCs while decreasing CD8+ T-cell proliferation. Additionally, BL001 alleviated PBMC-induced apoptosis and maintained insulin expression in human iPSC-derived islet organoids.

CONCLUSION

These findings demonstrate the potential of LRH-1/NR5A2 activation to modulate immune responses and support beta cell viability in T1D, suggesting a new therapeutic approach.

KEY POINTS

LRH-1/NR5A2 activation in inflammatory cells of individuals with type 1 diabetes (T1D) reduces pro-inflammatory cell surface markers and cytokine release. LRH-1/NR5A2 promotes a mitohormesis-induced immuno-resistant phenotype to pro-inflammatory macrophages. Mature dendritic cells acquire a tolerogenic phenotype via LRH-1/NR5A2-stimulated mitochondria turnover. LRH-1/NR5A2 agonistic activation expands a CD4+/CD25+/FoxP3+ T-cell subpopulation. Pharmacological activation of LRH-1/NR5A2 improves the survival iPSC-islets-like organoids co-cultured with PBMCs from individuals with T1D.

The Analysis of Plasma Proteomics for Luminal A Breast Cancer

BACKGROUND

Breast cancer is the prevailing malignancy among women, exhibiting a discernible escalation in incidence within our nation; hormone receptor-positive (HR+) human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common subtype. In this study, we aimed to search for a non-invasive, specific, blood-based biomarker for the early detection of luminal A breast cancer through proteomic studies.

METHODS

To explore new potential plasma biomarkers, we applied data-independent acquisition (DIA), a technique combining liquid chromatography and tandem mass spectrometry, to quantify breast cancer-associated plasma protein abundance from a small number of plasma samples in 10 patients with luminal A breast cancer, 10 patients with benign breast tumors, and 10 healthy controls.

RESULTS

The proteomes of 30 participants in all cohorts were analyzed using the DIA method, and a total of 517 proteins and 3584 peptides were quantified. We found that there were significant differences in plasma protein expression profiles between breast cancer patients and non-breast cancer patients, and breast cancer was mainly related to lipid metabolism pathways. Finally, the optimal protein combinations for the diagnosis of breast cancer were PON3, IGLV3-10, and IGHV3-73 through multi-model analysis, which had a high prediction accuracy for breast cancer (AUC = 0.92), and the model could also distinguish breast cancer from HC (AUC = 0.92) and breast cancer from benign breast tumor (AUC = 0.91).

CONCLUSIONS

The study revealed proteomic signatures of patients with luminal A breast cancer, identified multiple differential proteins, and identified three plasma proteins as potential diagnostic biomarkers for breast cancer. It provides a reference for the screening of biomarkers for breast cancer.

Proteomics: An Essential Tool to Study Plant-Specialized Metabolism

Plants are a valuable source of specialized metabolites that provide a plethora of therapeutic applications. They are natural defenses that plants use to adapt and respond to their changing environment. Decoding their biosynthetic pathways and understanding how specialized plant metabolites (SPMs) respond to biotic or abiotic stress will provide vital knowledge for plant biology research and its application for the future sustainable production of many SPMs of interest. Here, we focus on the proteomic approaches and strategies that help with the study of plant-specialized metabolism, including the: (i) discovery of key enzymes and the clarification of their biosynthetic pathways; (ii) study of the interconnection of both primary (providers of carbon and energy for SPM production) and specialized (secondary) metabolism; (iii) study of plant responses to biotic and abiotic stress; (iv) study of the regulatory mechanisms that direct their biosynthetic pathways. Proteomics, as exemplified in this review by the many studies performed to date, is a powerful tool that forms part of omics-driven research. The proteomes analysis provides an additional unique level of information, which is absent from any other omics studies. Thus, an integrative analysis, considered versus a single omics analysis, moves us more closely toward a closer interpretation of real cellular processes. Finally, this work highlights advanced proteomic technologies with immediate applications in the field.

mTOR activity paces human blastocyst stage developmental progression

Many mammals can temporally uncouple conception from parturition by pacing down their development around the blastocyst stage. In mice, this dormant state is achieved by decreasing the activity of the growth-regulating mTOR signaling pathway. It is unknown whether this ability is conserved in mammals in general and in humans in particular. Here, we show that decreasing the activity of the mTOR signaling pathway induces human pluripotent stem cells (hPSCs) and blastoids to enter a dormant state with limited proliferation, developmental progression, and capacity to attach to endometrial cells. These in vitro assays show that, similar to other species, the ability to enter dormancy is active in human cells around the blastocyst stage and is reversible at both functional and molecular levels. The pacing of human blastocyst development has potential implications for reproductive therapies.

Cellular resiliency and survival of Neuro-2a cells under extreme stress

Stressors such as hypoxia, hypothermia, and acute toxicity often result in widespread cell death. This study investigated the outcomes of Neuro-2a (N2a; mouse neuroblastoma) cells following a cryogenic storage failure that exposed them to a combination of these stressors over a period of approximately 24-30 hours. Remarkably, a small fraction of the cells survived the event, underwent a period of dormancy, and eventually recovered to a healthy state. To understand the underlying resilience mechanisms, we created a model to replicate the dewar failure event and examined changes in phenotype, transcriptomics, proteomics, and mitochondrial activity of the surviving cells during recovery. We found that the surviving cells initially displayed a stressed morphology with irregular membranes and a clustered apperance. They showed an increased expression of proteins related to DNA repair and chromatin modification pathways as well as heightened mitochondrial function shortly after the stress event. As recovery progressed, the stress-responsive pathways, mitochondrial activity, and growth rates normalized toward that of healthy controls, indicating a return to a stable baseline state. These findings suggest that an initial robust energetic state supports key stress-responsive and repair pathways at the early stages of recovery, facilitating cell survival and resiliency after extreme stress. This work provides valuable insights into cellular resilience mechanisms with potential implications for improving cell preservation and recovery in biomedical applications and developing therapeutic strategies for conditions involving cell damage and stress.

Evaluation of cerebrospinal fluid (CSF) and interstitial fluid (ISF) mouse proteomes for the validation and description of Alzheimer's disease biomarkers

BACKGROUND

Mass spectrometry (MS)-based cerebrospinal fluid (CSF) proteomics is an important method for discovering biomarkers of neurodegenerative diseases. CSF serves as a reservoir for interstitial fluid (ISF), and extensive communication between the two fluid compartments helps to remove waste products from the brain.

NEW METHOD

We performed proteomic analyses of both CSF and ISF fluid compartments using intracerebral microdialysis to validate and detect novel biomarkers of Alzheimer's disease (AD) in APPtg and C57Bl/6J control mice.

RESULTS

We identified up to 625 proteins in ISF and 4483 proteins in CSF samples. By comparing the biofluid profiles of APPtg and C57Bl/6J mice, we detected 37 and 108 significantly up- and downregulated candidates, respectively. In ISF, 7 highly regulated proteins, such as Gfap, Aldh1l1, Gstm1, and Txn, have already been implicated in AD progression, whereas in CSF, 9 out of 14 highly regulated proteins, such as Apba2, Syt12, Pgs1 and Vsnl1, have also been validated to be involved in AD pathogenesis. In addition, we also detected new interesting regulated proteins related to the control of synapses and neurotransmission (Kcna2, Cacng3, and Clcn6) whose roles as AD biomarkers should be further investigated.

COMPARISON WITH EXISTING METHODS

This newly established combined protocol provides better insight into the mutual communication between ISF and CSF as an analysis of tissue or CSF compartments alone.

CONCLUSIONS

The use of multiple fluid compartments, ISF and CSF, for the detection of their biological communication enables better detection of new promising AD biomarkers.

Lost in translation? Evidence for a muted proteomic response to thermal stress in a stenothermal Antarctic fish and possible evolutionary mechanisms

Stenothermal Antarctic notothenioid fishes are noteworthy for their history of isolation in extreme cold and their corresponding lack of the canonical heat shock response. Despite extensive transcriptomic studies, the mechanistic basis for stenothermy has not been fully elucidated. Given that the proteome better represents an organism's physiology, the possibility exists that some aspects of stenothermy arise posttranscriptionally. Here, Antarctic emerald rockcod (Trematomus bernacchii) were sampled after exposure to chronic and/or acute high temperatures, followed by a thorough assessment of proteomic responses in the brain, gill, and kidney. Few cellular stress response proteins were induced, and overall responses were modest in terms of the numbers of differentially expressed proteins and their fold changes. Inconsistencies in protein induction across treatments and tissues are suggestive of dysregulation, rather than an adaptive response. Changes in regulation of the translational machinery in Antarctic notothenioids could explain these patterns. Some components of translational regulatory pathways are highly conserved [e.g., Ser-52, eukaryotic translation initiation factor 2α (eIF2α)], but other proteins comprising the cellular "integrated stress response," specifically, the eIF2α kinases general control nonderepressible 2 (GCN2) and PKR-like endoplasmic reticulum kinase (PERK), may have evolved along different trajectories in Antarctic fishes. Taken together, these observations suggest a novel hypothesis for stenothermy and the absence of a coordinated cellular stress response in Antarctic fishes.NEW & NOTEWORTHY Antarctic fishes have some of the lowest known heat tolerances among vertebrates, but the molecular mechanisms underlying this pattern are not fully understood. By combining detailed analyses of protein expression patterns in several tissues under various heat treatments with a broader evolutionary perspective, this study offers a novel hypothesis to explain the narrow range of temperature tolerance in this extraordinary group of fishes.

Spatial proteomics of single cells and organelles on tissue slides using filter-aided expansion proteomics

Hydrogel-based tissue expansion combined with mass spectrometry (MS) offers an emerging spatial proteomics approach. Here, we present a filter-aided expansion proteomics (FAXP) strategy for spatial proteomics analysis of archived formalin-fixed paraffin-embedded (FFPE) specimens. Compared to our previous ProteomEx method, FAXP employed a customized tip device to enhance both the stability and throughput of sample preparation, thus guaranteeing the reproducibility and robustness of the workflow. FAXP achieved a 14.5-fold increase in volumetric resolution. It generated over 8 times higher peptide yield and a 255% rise in protein identifications while reducing sample preparation time by 50%. We also demonstrated the applicability of FAXP using human colorectal FFPE tissue samples. Furthermore, for the first time, we achieved bona fide single-subcellular proteomics under image guidance by integrating FAXP with laser capture microdissection.

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.