Proteome-Wide Evaluation of Two Common Protein Quantification Methods

Unraveling the effects of uric acid on endothelial cells: A global proteomic study

This work aims to understand how normouricemic levels of uric acid can induce endothelial dysfunction seeking global proteomic alterations in Human Umbilical Vein cells (HUVEC). It reveals significant alterations in redox-sensitive and antioxidant proteins, chaperones, and proteins associated with cell migration and adhesion in response to uric acid exposure. Monitoring cellular oxidation with the roGFP2-Grx1 probe proved increased oxidation levels induced by uric acid, which can be attenuated by peroxidasin (PXDN) inhibition, suggesting a regulatory role for PXDN in mitigating oxidative stress induced by uric acid. As a consequence of uric acid oxidation and the formation of reactive intermediate, we identified adducts in proteins (+140 kDa) in a novel post-translation modification named uratylation. Increased misfolded protein levels and p62 aggregation were also found, indicating disturbances in cellular proteostasis. Furthermore, uric acid promoted monocyte adhesion and upregulated ICAM and VCAM protein levels, implicating a pro-inflammatory response in endothelial cells. These findings provide critical insights into the molecular mechanisms underlying vascular damage associated with uric acid.

Systematic evaluation of normalization approaches in tandem mass tag and label-free protein quantification data using PRONE

Despite the significant progress in accuracy and reliability in mass spectrometry technology, as well as the development of strategies based on isotopic labeling or internal standards in recent decades, systematic biases originating from non-biological factors remain a significant challenge in data analysis. In addition, the wide range of available normalization methods renders the choice of a suitable normalization method challenging. We systematically evaluated 17 normalization and 2 batch effect correction methods, originally developed for preprocessing DNA microarray data but widely applied in proteomics, on 6 publicly available spike-in and 3 label-free and tandem mass tag datasets. Opposed to state-of-the-art normalization practice, we found that a reduction in intragroup variation is not directly related to the effectiveness of the normalization methods. Furthermore, our results demonstrated that the methods RobNorm and Normics, specifically developed for proteomics data, in line with LoessF performed consistently well across the spike-in datasets, while EigenMS exhibited a high false-positive rate. Finally, based on experimental data, we show that normalization substantially impacts downstream analyses, and the impact is highly dataset-specific, emphasizing the importance of use-case-specific evaluations for novel proteomics datasets. For this, we developed the PROteomics Normalization Evaluator (PRONE), a unifying R package enabling comparative evaluation of normalization methods, including their impact on downstream analyses, while offering considerable flexibility, acknowledging the lack of universally accepted standards. PRONE is available on Bioconductor with a web application accessible at https://exbio.wzw.tum.de/prone/.

ARF6 as a Novel Activator of HIF-2α in Pulmonary Arterial Hypertension

ARF6 (ADP-ribosylation factor 6), a GTPase associated with cancer metastasis, is activated in the lung endothelium in pulmonary arterial hypertension (PAH). To identify ARF6-regulated pathways relevant to PAH, we performed a state-of-the-art proteomic analysis of human pulmonary artery endothelial cells (HPAECs) overexpressing the wild-type, constitutively active, fast-cycling, and dominant-negative mutants of ARF6. The analysis revealed a novel link of ARF6 with HIF (hypoxia-inducible factor), in addition to endocytotic vesicle trafficking, cell proliferation, angiogenesis, oxidative stress, and lipid metabolism. Active ARF6 markedly increased expression and activity of HIF-2, critical in PAH, with HIF-1 relatively unaffected. Hypoxic ARF6 activation was a prerequisite for HIF-2 activation and HIF-dependent gene expression in HPAECs, PAH blood-derived late-outgrowth endothelial colony-forming cells, and hypoxic mouse lungs in vivo. A novel ARF6 inhibitor, chlortetracycline (CTC), reduced hypoxia-induced HIF-2 activation, proliferation, and angiogenesis in HPAECs and reduced HIF-2 expression in lung and heart tissues of hypoxic mice. PAH endothelial colony-forming cells showed elevated expression and activity of ARF6 and HIF2, which was attenuated by CTC, and oral CTC attenuated development of pulmonary hypertension in chronically hypoxic mice. We identify EGFR (epidermal growth factor receptor) as a direct interactor of ARF6 and EGFR signaling as a crucial mechanism linking ARF6 and HIF activation. In conclusion, we are the first to demonstrate a key role of ARF6 in the regulation of HIF-2α activation in vitro and in vivo and show that HIF-2α, a master regulator of vascular remodeling in PAH, can be targeted by a clinically approved antibiotic CTC.

Long-term CSF responses in adult patients with spinal muscular atrophy type 2 or 3 on treatment with nusinersen

BACKGROUND

5q-associated spinal muscular atrophy (SMA) is a monogenic disease causing progressive alpha motor neuron degeneration, muscle atrophy, and weakness. Intrathecal therapy with the antisense oligonucleotide nusinersen modifies the disease course. However, biomarkers for understanding underlying molecular pathomechanisms and monitoring therapy are not yet known.

METHODS

A total of 130 cerebrospinal fluid (CSF) samples from 24 adult patients with SMA type 2 or 3 were collected over 3.5 years, and CSF proteome was analyzed using mass spectrometry (MS). By applying two complementary MS protein quantification methods, label-free quantification (LFQ) and tandem mass tag (TMT) isotopic labeling, specific protein patterns reflecting changes in the CSF in response to nusinersen therapy were identified. These results were combined with cellular and metabolic profiles.

RESULTS

Nusinersen therapy led to a median motor function improvement of 2.2 Hammersmith Functional Motor Scale-Expanded points after 10 months and 2.6 points after 34 months. CSF macrophages increased in number and showed an altered morphology. Albumin quotient (qAlb), glucose, and lactate concentrations were inversely correlated with clinical improvement. MS analysis of CSF identified 1,674 (TMT) and 441 (LFQ) proteins. Protein profiles reflected reduced inhibition of "nervous system development" and "axogenesis" pathways under therapy. In addition, clinical improvement was associated with upregulation of the interacting proteins α-dystroglycan and beta-1,4-glucuronyltransferase 1, reduction of complement factors, negative correlation in immunoglobulin- and B cell-related pathways, and reduction of cellular mediators such as lymphocytes.

CONCLUSION

The present multi-proteomic analysis contributes to the understanding of the molecular mechanisms underlying nusinersen's therapeutic effects and offers potential biomarkers for monitoring treatment response in SMA.

A Statistical Approach for Identifying the Best Combination of Normalization and Imputation Methods for Label-Free Proteomics Expression Data

Label-free proteomics expression data sets often exhibit data heterogeneity and missing values, necessitating the development of effective normalization and imputation methods. The selection of appropriate normalization and imputation methods is inherently data-specific, and choosing the optimal approach from the available options is critical for ensuring robust downstream analysis. This study aimed to identify the most suitable combination of these methods for quality control and accurate identification of differentially expressed proteins. In this study, we developed nine combinations by integrating three normalization methods, locally weighted linear regression (LOESS), variance stabilization normalization (VSN), and robust linear regression (RLR) with three imputation methods: k-nearest neighbors (k-NN), local least-squares (LLS), and singular value decomposition (SVD). We utilized statistical measures, including the pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD), to assess intragroup and intergroup variation. The combinations yielding the lowest values corresponding to each statistical measure were chosen as the data set's suitable normalization and imputation methods. The performance of this approach was tested using two spiked-in standard label-free proteomics benchmark data sets. The identified combinations returned a low NRMSE and showed better performance in identifying spiked-in proteins. The developed approach can be accessed through the R package named 'lfproQC' and a user-friendly Shiny web application (https://dabiniasri.shinyapps.io/lfproQC and http://omics.icar.gov.in/lfproQC), making it a valuable resource for researchers looking to apply this method to their data sets.

Robust proteome profiling of cysteine-reactive fragments using label-free chemoproteomics

Identifying pharmacological probes for human proteins represents a key opportunity to accelerate the discovery of new therapeutics. High-content screening approaches to expand the ligandable proteome offer the potential to expedite the discovery of novel chemical probes to study protein function. Screening libraries of reactive fragments by chemoproteomics offers a compelling approach to ligand discovery, however, optimising sample throughput, proteomic depth, and data reproducibility remains a key challenge. We report a versatile, label-free quantification proteomics platform for competitive profiling of cysteine-reactive fragments against the native proteome. This high-throughput platform combines SP4 plate-based sample preparation with rapid chromatographic gradients. Data-independent acquisition performed on a Bruker timsTOF Pro 2 consistently identified ~23,000 cysteine sites per run, with a total of ~32,000 cysteine sites profiled in HEK293T and Jurkat lysate. Crucially, this depth in cysteinome coverage is met with high data completeness, enabling robust identification of liganded proteins. In this study, 80 reactive fragments were screened in two cell lines identifying >400 ligand-protein interactions. Hits were validated through concentration-response experiments and the platform was utilised for hit expansion and live cell experiments. This label-free platform represents a significant step forward in high-throughput proteomics to evaluate ligandability of cysteines across the human proteome.

Tandem Mass Tag-Labeling Proteomics Reveals TLN1 as a Potential Factor in Cardiogenic Pulmonary Edema

Heart failure (HF) is a complex disease. The reduced blood flow associated with HF triggers the activation of neurohormonal systems, leading to symptoms such as pulmonary congestion and peripheral edema. The precise mechanisms responsible for edema in HF are poorly understood. In this study, we aimed to analyze the differentially expressed proteins in HF-induced pulmonary edema (HF-PE) and explore the potential underlying mechanisms. Proteins in the serum of patients with HF-PE (n = 10) and patients with HF without PE or those who have not developed PE yet (n = 10) were screened using tandem mass tag-labeling proteomics. The identified proteins were screened for differentially expressed proteins, which were then analyzed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses as well as protein-protein interaction (PPI) visualization. Rats with HF were induced by myocardial infarction, whereas PE was induced by LPS. Finally, the rats were injected with lentiviruses to manipulate the expression of differentially expressed proteins. A total of 1796 proteins were identified. In the serum of patients with HF-PE, there were 143 significantly upregulated proteins and 147 significantly downregulated proteins. TLN1 interacted with multiple proteins and was upregulated in patients with HF-PE. TLN1 was significantly upregulated in the lung tissues of rats with HF-PE, and the progression of HF-PE was inhibited by TLN1 knockdown. This tandem mass tag-labeling proteomics profiling study revealed that TLN1 upregulation contributes to the progression of PE in patients with HF.

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.

Pleiotropic tumor suppressive functions of PTEN missense mutations during gliomagenesis

PTEN plays a crucial role in preventing the development of glioblastoma (GBM), a severe and untreatable brain cancer. In GBM, most PTEN deficiencies are missense mutations that have not been thoroughly examined. Here, we leveraged genetically modified mice and isogenic astrocyte cell cultures to investigate the role of clinically relevant mutations (G36E, L42R, C105F, and R173H) in the development of EGFR-driven GBM. We report that the loss of tumor suppression from these mutants is unrelated to their lipid phosphatase activity and rather relate to elevated localization at the cell membrane. Moreover, expression of these PTEN mutations heightened EGFR activity by sequestering EGFR within endomembranes longer and affected its signaling behavior. Through comprehensive studies on global protein phosphorylation and kinase library analyses in cells with the G36E and L42R PTEN mutations, we identified distinct cancer-promoting pathways activated by EGFR, offering targets for treating GBM with these PTEN alterations.

Stem Cell-Associated Proteins and Extracellular Matrix Composition of the Human Atrioventricular Junction

The human heart regenerates slowly through life, but how new cells are generated is mostly unknown. The atrioventricular junction (AVj) has been indicated as a potential stem cell niche region. Little is known about the protein composition of the human AVj. To map the extracellular matrix (ECM) and expression of stem cell-related biomarkers, this study compares protein and gene expression patterns in AVj and Left Ventricular (LV) tissues. Biopsies were collected from 15 human hearts. Global quantitative proteomics and mRNA sequencing were used to identify differentially expressed proteins and altered genes. Of the total 4904 identified proteins, 1138 were differently expressed between the AVj and LV. While the top proteins in LV were involved in cardiac motor function and energy regulation, the AVj displayed proteins associated with early cardiomyocyte development, differentiation, proliferation, migration, and hypoxia. Furthermore, several developmental signalling pathways, including TGF-β, TNF, WNT, Notch, and FGF, were represented. RNA-seq data verified that the expressed genes were involved with differentiation, cell growth, proliferation, or ECM organization. Immunohistochemistry confirmed the expression of the stem cell-related biomarkers NPPA and POSTN in the AVj, further strengthening the hypothesis of the AVj as a specialized microenvironment conducive to stem cell niche activity.

Proteogenomics offers a novel avenue in neoantigen identification for cancer immunotherapy

Cancer neoantigens are tumor-specific non-synonymous mutant peptides that activate the immune system to produce an anti-tumor response. Personalized cancer vaccines based on neoantigens are currently one of the most promising therapeutic approaches for cancer treatment. By utilizing the unique mutations within each patient's tumor, these vaccines aim to elicit a strong and specific immune response against cancer cells. However, the identification of neoantigens remains challenging due to the low accuracy of current prediction tools and the high false-positive rate of candidate neoantigens. Since the concept of "proteogenomics" emerged in 2004, it has evolved rapidly with the increased sequencing depth of next-generation sequencing technologies and the maturation of mass spectrometry-based proteomics technologies to become a more comprehensive approach to neoantigen identification, allowing the discovery of high-confidence candidate neoantigens. In this review, we summarize the reason why cancer neoantigens have become attractive targets for immunotherapy, the mechanism of cancer vaccines and the advances in cancer immunotherapy. Considerations relevant to the application emerging of proteogenomics technologies for neoantigen identification and challenges in this field are described.

Advancing Nanomaterial Toxicology Screening Through Efficient and Cost-Effective Quantitative Proteomics

Proteomic investigations yield high-dimensional datasets, yet their application to large-scale toxicological assessments is hindered by reproducibility challenges due to fluctuating measurement conditions. To address these limitations, this study introduces an advanced tandem mass tag (TMT) labeling protocol. Although labeling approaches shorten data acquisition time by multiplexing samples compared to traditional label-free quantification (LFQ) methods in general, the associated costs may surge significantly with large sample sets, for example, in toxicological screenings. However, the introduced advanced protocol offers an efficient, cost-effective alternative, reducing TMT reagent usage (by a factor of ten) and requiring minimal biological material (1 µg), while demonstrating increased reproducibility compared to LFQ. To demonstrate its effectiveness, the advanced protocol is employed to assess the toxicity of nine benchmark nanomaterials (NMs) on A549 lung epithelial cells. While LFQ measurements identify 3300 proteins, they proved inadequate to reveal NM toxicity. Conversely, despite detecting 2600 proteins, the TMT protocol demonstrates superior sensitivity by uncovering alterations induced by NM treatment. In contrast to previous studies, the introduced advanced protocol allows simultaneous and straightforward assessment of multiple test substances, enabling prioritization, ranking, and grouping for hazard evaluation. Additionally, it fosters the development of New Approach Methodologies (NAMs), contributing to innovative methodologies in toxicological research.

Proteomic analysis of the pyrenoid-traversing membranes of Chlamydomonas reinhardtii reveals novel components

Pyrenoids are algal CO2-fixing organelles that mediate approximately one-third of global carbon fixation and hold the potential to enhance crop growth if engineered into land plants. Most pyrenoids are traversed by membranes that are thought to supply them with concentrated CO2. Despite the critical nature of these membranes for pyrenoid function, they are poorly understood, with few protein components known in any species.• Here we identify protein components of the pyrenoid-traversing membranes from the leading model alga Chlamydomonas reinhardtii by affinity purification and mass spectrometry of membrane fragments. Our proteome includes previously-known proteins as well as novel candidates.• We further characterize two of the novel pyrenoid-traversing membrane-resident proteins, Cre10.g452250, which we name Pyrenoid Membrane Enriched 1 (PME1), and LCI16. We confirm their localization, observe that they physically interact, and find that neither protein is required for normal membrane morphology.• Taken together, our study identifies the proteome of pyrenoid-traversing membranes and initiates the characterization of a novel pyrenoid-traversing membrane complex, building toward a mechanistic understanding of the pyrenoid.

Mass Spectrometry-Based Proteomics for Assessing Epitranscriptomic Regulations

Epitranscriptomics is a rapidly evolving field that explores chemical modifications in RNA and how they contribute to dynamic and reversible regulations of gene expression. These modifications, for example, N6-methyladenosine (m6A), are crucial in various RNA metabolic processes, including splicing, stability, subcellular localization, and translation efficiency of mRNAs. Mass spectrometry-based proteomics has become an indispensable tool in unraveling the complexities of epitranscriptomics, offering high-throughput, precise protein identification, and accurate quantification of differential protein expression. Over the past two decades, advances in mass spectrometry, including the improvement of high-resolution mass spectrometers and innovative sample preparation methods, have allowed researchers to perform in-depth analyses of epitranscriptomic regulations. This review focuses on the applications of bottom-up proteomics in the field of epitranscriptomics, particularly in identifying and quantifying epitranscriptomic reader, writer, and eraser (RWE) proteins and in characterizing their functions, posttranslational modifications, and interactions with other proteins. Together, by leveraging modern proteomics, researchers can gain deep insights into the intricate regulatory networks of RNA modifications, advancing fundamental biology, and fostering potential therapeutic applications.

Enhanced Sample Multiplexing-Based Targeted Proteomics with Intelligent Data Acquisition

Targeted proteomics has been playing an increasingly important role in hypothesis-driven protein research and clinical biomarker discovery. We previously created a workflow, Tomahto, to enable real-time targeted pathway proteomics assays using two-dimensional multiplexing technology. Coupled with the TMT 11-plex reagent, hundreds of proteins of interest from up to 11 samples can be targeted and accurately quantified in a single-shot experiment with remarkable sensitivity. However, room remains to further improve the sensitivity, accuracy, and throughput, especially for targeted studies demanding a high peptide-level success rate. Here, bearing in mind the goal to improve peptide-level targeting, we introduce several new functionalities in Tomahto, featuring the integration of gas-phase fractionation using the FAIMS device, an accompanying software program (TomahtoPrimer) to customize fragmentation for each peptide target, and support for higher multiplexing capacity with the latest TMTpro reagent. We demonstrate that adding these features to the Tomahto platform significantly improves overall success rate from 89% to 98% in a single 60 min targeted assay of 290 peptides across human cell lines, while boosting quantitative accuracy via reducing TMT reporter ion interference.

Multifaceted Proteome Analysis at Solubility, Redox, and Expression Dimensions for Target Identification

Multifaceted interrogation of the proteome deepens the system-wide understanding of biological systems; however, mapping the redox changes in the proteome has so far been significantly more challenging than expression and solubility/stability analyses. Here, the first high-throughput redox proteomics approach integrated with expression analysis (REX) is devised and combined with the Proteome Integral Solubility Alteration (PISA) assay. The whole PISA-REX experiment with up to four biological replicates can be multiplexed into a single tandem mass tag TMTpro set. For benchmarking this compact tool, HCT116 cells treated with auranofin are analyzed, showing great improvement compared with previous studies. PISA-REX is then applied to study proteome remodeling upon stimulation of human monocytes by interferon α (IFN-α). Applying this tool to study the proteome changes in plasmacytoid dendritic cells (pDCs) isolated from wild-type versus Ncf1-mutant mice treated with interferon α, shows that NCF1 deficiency enhances the STAT1 pathway and modulates the expression, solubility, and redox state of interferon-induced proteins. Providing comprehensive multifaceted information on the proteome, the compact PISA-REX has the potential to become an industry standard in proteomics and to open new windows into the biology of health and disease.

Quality Control in the Mass Spectrometry Proteomics Core: A Practical Primer

The past decade has seen widespread advances in quality control (QC) materials and software tools focused specifically on mass spectrometry-based proteomics, yet the rate of adoption is inconsistent. Despite the fundamental importance of QC, it typically falls behind learning new techniques, instruments, or software. Considering how important QC is in a core setting where data is generated for non-mass spectrometry experts and confidence in delivered results is paramount, we have created this quick-start guide focusing on off-the-shelf QC materials and relatively easy-to-use QC software. We hope that by providing a background on the different levels of QC, different materials and their uses, describing QC design options, and highlighting some current QC software, implementing QC in a core setting will be easier than ever. There continues to be development in each of these areas (such as new materials and software), and the current generation of QC for mass spectrometry-based proteomics is more than capable of conveying confidence in results as well as minimizing laboratory downtime by guiding experimental, technical, and analytical troubleshooting from sample to results.

Fibre-specific mitochondrial protein abundance is linked to resting and post-training mitochondrial content in the muscle of men

Analyses of mitochondrial adaptations in human skeletal muscle have mostly used whole-muscle samples, where results may be confounded by the presence of a mixture of type I and II muscle fibres. Using our adapted mass spectrometry-based proteomics workflow, we provide insights into fibre-specific mitochondrial differences in the human skeletal muscle of men before and after training. Our findings challenge previous conclusions regarding the extent of fibre-type-specific remodelling of the mitochondrial proteome and suggest that most baseline differences in mitochondrial protein abundances between fibre types reported by us, and others, might be due to differences in total mitochondrial content or a consequence of adaptations to habitual physical activity (or inactivity). Most training-induced changes in different mitochondrial functional groups, in both fibre types, were no longer significant in our study when normalised to changes in markers of mitochondrial content.

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

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

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

The recent technological and computational advances in mass spectrometry-based single-cell proteomics have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell remains challenging. To address some of those limitations, we present a dedicated sample preparation chip, the proteoCHIP EVO 96 that directly interfaces with the Evosep One. This, in combination with the Bruker timsTOF demonstrates double the identifications without manual sample handling and the newest generation timsTOF Ultra identifies up to 4000 with an average of 3500 protein groups per single HEK-293T without a carrier or match-between runs. Our workflow spans 4 orders of magnitude, identifies over 50 E3 ubiquitin-protein ligases, and profiles key regulatory proteins upon small molecule stimulation. This study demonstrates that the proteoCHIP EVO 96-based sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

Multilevel Proteomic Profiling of Colorectal Adenocarcinoma Caco-2 Cell Differentiation to Characterize an Intestinal Epithelial Model

Emergent advancements on the role of the intestinal microbiome for human health and disease necessitate well-defined intestinal cellular models to study and rapidly assess host, microbiome, and drug interactions. Differentiated Caco-2 cell line is commonly utilized as an epithelial model for drug permeability studies and has more recently been utilized for investigating host-microbiome interactions. However, its suitability to study such interactions remains to be characterized. Here, we employed multilevel proteomics to demonstrate that both spontaneous and butyrate-induced Caco-2 differentiations displayed similar protein and pathway changes, including the downregulation of proteins related to translation and proliferation and upregulation of functions implicated in host-microbiome interactions, such as cell adhesion, tight junction, extracellular vesicles, and responses to stimuli. Lysine acetylomics revealed that histone protein acetylation levels were decreased along with cell differentiation, while the acetylation in proteins associated with mitochondrial functions was increased. This study also demonstrates that, compared to spontaneous differentiation methods, butyrate-containing medium accelerates Caco-2 differentiation, with earlier upregulation of proteins related to host-microbiome interactions, suggesting its superiority for assay development using this intestinal model. Altogether, this multiomics study emphasizes the controlled progression of Caco-2 differentiation toward a specialized intestinal epithelial-like cell and establishes its suitability for investigating the host-microbiome interactions.

Evaluation of Protein Identification and Quantification by the diaPASEF Method on timsTOF SCP

Accurate and precise quantification is crucial in modern proteomics, particularly in the context of exploring low-amount samples. While the innovative 4D-data-independent acquisition (DIA) quantitative proteomics facilitated by timsTOF mass spectrometers gives enhanced sensitivity and selectivity for protein identification, the diaPASEF (parallel accumulation-serial fragmentation combined with data-independent acquisition) parameters have not been systematically optimized, and a comprehensive evaluation of the quantification is currently lacking. In this study, we conducted a thorough optimization of key parameters on a timsTOF SCP instrument, including sample loading amount (50 ng), ramp/accumulation time (140 ms), isolation window width (20 m/z), and gradient time (60 min). To further improve the identification of proteins in low-amount samples, we utilized different column settings and introduced 0.02% n-dodecyl-β-d-maltoside (DDM) in the sample reconstitution solution, resulting in a remarkable 19-fold increase in protein identification at the single-cell-equivalent level. Moreover, a comprehensive comparison of protein quantification using a tandem mass tag reporter (TMT-reporter), complement TMT ions (TMTc), and diaPASEF revealed a strong correlation between these methods. Both diaPASEF and TMTc have effectively addressed the issue of ratio compression, highlighting the diaPASEF method's effectiveness in achieving accurate quantification data compared to TMT reporter quantification. Additionally, an in-depth analysis of in-group variation positioned diaPASEF between the TMT-reporter and TMTc methods. Therefore, diaPASEF quantification on the timsTOF SCP instrument emerges as a precise and accurate methodology for quantitative proteomics, especially for samples with small amounts.

Multiplexing the Identification of Microorganisms via Tandem Mass Tag Labeling Augmented by Interference Removal through a Novel Modification of the Expectation Maximization Algorithm

Having fast, accurate, and broad spectrum methods for the identification of microorganisms is of paramount importance to public health, research, and safety. Bottom-up mass spectrometer-based proteomics has emerged as an effective tool for the accurate identification of microorganisms from microbial isolates. However, one major hurdle that limits the deployment of this tool for routine clinical diagnosis, and other areas of research such as culturomics, is the instrument time required for the mass spectrometer to analyze a single sample, which can take ∼1 h per sample, when using mass spectrometers that are presently used in most institutes. To address this issue, in this study, we employed, for the first time, tandem mass tags (TMTs) in multiplex identifications of microorganisms from multiple TMT-labeled samples in one MS/MS experiment. A difficulty encountered when using TMT labeling is the presence of interference in the measured intensities of TMT reporter ions. To correct for interference, we employed in the proposed method a modified version of the expectation maximization (EM) algorithm that redistributes the signal from ion interference back to the correct TMT-labeled samples. We have evaluated the sensitivity and specificity of the proposed method using 94 MS/MS experiments (covering a broad range of protein concentration ratios across TMT-labeled channels and experimental parameters), containing a total of 1931 true positive TMT-labeled channels and 317 true negative TMT-labeled channels. The results of the evaluation show that the proposed method has an identification sensitivity of 93-97% and a specificity of 100% at the species level. Furthermore, as a proof of concept, using an in-house-generated data set composed of some of the most common urinary tract pathogens, we demonstrated that by using the proposed method the mass spectrometer time required per sample, using a 1 h LC-MS/MS run, can be reduced to 10 and 6 min when samples are labeled with TMT-6 and TMT-10, respectively. The proposed method can also be used along with Orbitrap mass spectrometers that have faster MS/MS acquisition rates, like the recently released Orbitrap Astral mass spectrometer, to further reduce the mass spectrometer time required per sample.

Optimizing differential expression analysis for proteomics data via high-performing rules and ensemble inference

Identification of differentially expressed proteins in a proteomics workflow typically encompasses five key steps: raw data quantification, expression matrix construction, matrix normalization, missing value imputation (MVI), and differential expression analysis. The plethora of options in each step makes it challenging to identify optimal workflows that maximize the identification of differentially expressed proteins. To identify optimal workflows and their common properties, we conduct an extensive study involving 34,576 combinatoric experiments on 24 gold standard spike-in datasets. Applying frequent pattern mining techniques to top-ranked workflows, we uncover high-performing rules that demonstrate optimality has conserved properties. Via machine learning, we confirm optimal workflows are indeed predictable, with average cross-validation F1 scores and Matthew's correlation coefficients surpassing 0.84. We introduce an ensemble inference to integrate results from individual top-performing workflows for expanding differential proteome coverage and resolve inconsistencies. Ensemble inference provides gains in pAUC (up to 4.61%) and G-mean (up to 11.14%) and facilitates effective aggregation of information across varied quantification approaches such as topN, directLFQ, MaxLFQ intensities, and spectral counts. However, further development and evaluation are needed to establish acceptable frameworks for conducting ensemble inference on multiple proteomics workflows.

High-Throughput Nanoflow Proteomics Using a Dual-Column Electrospray Source

With current trends in proteomics, especially regarding clinical and low input (to single cell) samples, it is increasingly important to both maximize the throughput of the analysis and maintain as much sensitivity as possible. The new generation of mass spectrometers (MS) are taking a huge leap in sensitivity, allowing analysis of samples with shorter liquid chromatography (LC) methods while digging as deep in the proteome. However, the throughput can be doubled by implementing a dual column nano-LC-MS configuration. For this purpose, we used a dual-column setup with a two-outlet electrospray source and compared it to a classic dual-column setup with a single-outlet source.

Effect of Semaglutide and Empagliflozin on Pulmonary Structure and Proteomics in Obese Mice

Objective

This study utilized proteomics to investigate changes in protein expression associated with lung health in obese mice exposed to semaglutide and empagliflozin through a high-fat diet.

Methods

Twenty-eight male C57BL/6JC mice were randomly assigned to two groups: a control diet group (n = 7) and a high-fat diet group (n = 21). The HFD group was further divided into three groups: HFD group (n = 7), Sema group (n = 7), and Empa group (n = 7). Post-treatment, mice underwent assessments including glucose tolerance, lipids, oxidative stress markers, body weight, lung weight, and structure. Proteomics identified differentially expressed proteins (DEPs) in lung tissue, and bioinformatics analyzed the biological processes and functions of these proteins.

Results

Semaglutide and empagliflozin significantly attenuated obesity-induced hyperglycemia, abnormal lipid metabolism, oxidative stress response, and can decrease alveolar wall thickness, enlarge alveolar lumen, and reduce collagen content in lung tissue. Both medications also attenuated lung elastic fibre cracking and disintegration. In the HFD/NCD group, there were 66 DEPs, comprising 30 proteins that were increased and 36 that were decreased. Twenty-three DEPs overlapped between Sema/HFD and Empa/HFD, with 11 up-regulated and 12 down-regulated simultaneously. After analysing DEPs in different groups, four proteins - LYVE1, BRAF, RGCC, and CHMP5 - were all downregulated in the HFD group and upregulated by semaglutide and empagliflozin treatment.

Conclusion

This study demonstrates that obesity induced by a high-fat diet causes a reduction in the expression of LYVE1, BRAF, RGCC, and CHMP5 proteins, potentially affecting lung function and structure in mice. Significantly, the administration of semaglutide and empagliflozin elevates the levels of these proteins, potentially offering therapeutic benefits against lung injury caused by obesity. Merging semaglutide with empagliflozin may exert a more pronounced impact.

Cerebrospinal fluid proteomic signatures are associated with symptom severity of first-episode psychosis

Apart from their diagnostic, monitoring, or prognostic utility in clinical settings, molecular biomarkers may be instrumental in understanding the pathophysiology of psychiatric disorders, including schizophrenia. Using untargeted metabolomics, we recently identified eight cerebrospinal fluid (CSF) metabolites unique to first-episode psychosis (FEP) subjects compared to healthy controls (HC). In this study, we sought to investigate the CSF proteomic signatures associated with FEP. We employed 16-plex tandem mass tag (TMT) mass spectrometry (MS) to examine the relative protein abundance in CSF samples of 15 individuals diagnosed with FEP and 15 age-and-sex-matched healthy controls (HC). Multiple linear regression model (MLRM) identified 16 differentially abundant CSF proteins between FEP and HC at p < 0.01. Among them, the two most significant CSF proteins were collagen alpha-2 (IV) chain (COL4A2: standard mean difference [SMD] = -1.12, p = 1.64 × 10-4) and neuron-derived neurotrophic factor (NDNF: SMD = -1.03, p = 4.52 × 10-4) both of which were down-regulated in FEP subjects compared to HC. We also identified several potential CSF proteins associated with the pathophysiology and the symptom profile and severity in FEP subjects, including COL4A2, NDNF, hornerin (HRNR), contactin-6 (CNTN6), voltage-dependent calcium channel subunit alpha-2/delta-3 (CACNA2D3), tropomyosin alpha-3 chain (TPM3 and TPM4). Moreover, several protein signatures were associated with cognitive performance. Although the results need replication, our exploratory study suggests that CSF protein signatures can be used to increase the understanding of the pathophysiology of psychosis.

A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments

We present a framework for the analysis of multiplexed mass spectrometry proteomics data that reduces estimation error when combining multiple isobaric batches. Variations in the number and quality of observations have long complicated the analysis of isobaric proteomics data. Here we show that the power to detect statistical associations is substantially improved by utilizing models that directly account for known sources of variation in the number and quality of observations that occur across batches.In a multibatch benchmarking experiment, our open-source software (msTrawler) increases the power to detect changes, especially in the range of less than twofold changes, while simultaneously increasing quantitative proteome coverage by utilizing more low-signal observations. Further analyses of previously published multiplexed datasets of 4 and 23 batches highlight both increased power and the ability to navigate complex missing data patterns without relying on unverifiable imputations or discarding reliable measurements.

Automated single-cell proteomics providing sufficient proteome depth to study complex biology beyond cell type classifications

Mass spectrometry (MS)-based single-cell proteomics (SCP) has gained massive attention as a viable complement to other single cell approaches. The rapid technological and computational advances in the field have pushed the boundaries of sensitivity and throughput. However, reproducible quantification of thousands of proteins within a single cell at reasonable proteome depth to characterize biological phenomena remains a challenge. To address some of those limitations we present a combination of fully automated single cell sample preparation utilizing a dedicated chip within the picolitre dispensing robot, the cellenONE. The proteoCHIP EVO 96 can be directly interfaced with the Evosep One chromatographic system for in-line desalting and highly reproducible separation with a throughput of 80 samples per day. This, in combination with the Bruker timsTOF MS instruments, demonstrates double the identifications without manual sample handling. Moreover, relative to standard high-performance liquid chromatography, the Evosep One separation provides further 2-fold improvement in protein identifications. The implementation of the newest generation timsTOF Ultra with our proteoCHIP EVO 96-based sample preparation workflow reproducibly identifies up to 4,000 proteins per single HEK-293T without a carrier or match-between runs. Our current SCP depth spans over 4 orders of magnitude and identifies over 50 biologically relevant ubiquitin ligases. We complement our highly reproducible single-cell proteomics workflow to profile hundreds of lipopolysaccharide (LPS)-perturbed THP-1 cells and identified key regulatory proteins involved in interleukin and interferon signaling. This study demonstrates that the proteoCHIP EVO 96-based SCP sample preparation with the timsTOF Ultra provides sufficient proteome depth to study complex biology beyond cell-type classifications.

On the excessive use of coefficient of variation as a metric of quantitation quality in proteomics

The coefficient of variation (CV) is often used in proteomics as a proxy to characterize the performance of a quantitation method and/or the related software. In this note, we question the excessive reliance on this metric in quantitative proteomics that may result in erroneous conclusions. We support this note using a ground-truth Human-Yeast-E. coli dataset demonstrating in a number of cases that erroneous data processing methods may lead to a low CV which has nothing to do with these methods' performances in quantitation.

A Causal Model of Ion Interference Enables Assessment and Correction of Ratio Compression in Multiplex Proteomics

Multiplex proteomics using isobaric labeling tags has emerged as a powerful tool for the simultaneous relative quantification of peptides and proteins across multiple experimental conditions. However, the quantitative accuracy of the approach is largely compromised by ion interference, a phenomenon that causes fold changes to appear compressed. The degree of compression is generally unknown, and the contributing factors are poorly understood. In this study, we thoroughly characterized ion interference at the MS2 level using a defined two-proteome experimental system with known ground-truth. We discovered remarkably poor agreement between the apparent precursor purity in the isolation window and the actual level of observed reporter ion interference in MS2 scans-a discrepancy that we found resolved by considering cofragmentation of peptide ions hidden within the spectral "noise" of the MS1 isolation window. To address this issue, we developed a regression modeling strategy to accurately predict reporter ion interference in any dataset. Finally, we demonstrate the utility of our procedure for improved fold change estimation and unbiased PTM site-to-protein normalization. All computational tools and code required to apply this method to any MS2 TMT dataset are documented and freely available.

Proteomic analysis reveals activation of platelet- and fibrosis-related pathways in hearts of ApoE-/- mice exposed to diesel exhaust particles

Air pollution is an environmental risk factor linked to multiple human diseases including cardiovascular diseases (CVDs). While particulate matter (PM) emitted by diesel exhaust damages multiple organ systems, heart disease is one of the most severe pathologies affected by PM. However, the in vivo effects of diesel exhaust particles (DEP) on the heart and the molecular mechanisms of DEP-induced heart dysfunction have not been investigated. In the current study, we attempted to identify the proteomic signatures of heart fibrosis caused by diesel exhaust particles (DEP) in CVDs-prone apolipoprotein E knockout (ApoE-/-) mice model using tandem mass tag (TMT)-based quantitative proteomic analysis. DEP exposure induced mild heart fibrosis in ApoE-/- mice compared with severe heart fibrosis in ApoE-/- mice that were treated with CVDs-inducing peptide, angiotensin II. TMT-based quantitative proteomic analysis of heart tissues between PBS- and DEP-treated ApoE-/- mice revealed significant upregulation of proteins associated with platelet activation and TGFβ-dependent pathways. Our data suggest that DEP exposure could induce heart fibrosis, potentially via platelet-related pathways and TGFβ induction, causing cardiac fibrosis and dysfunction.

Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends

Label-free proteomics is a fast-growing methodology to infer abundances in mass spectrometry proteomics. Extensive research has focused on spectral quantification and peptide identification. However, research toward modeling and understanding quantitative proteomics data is scarce. Here we propose a Bayesian hierarchical decision model (Baldur) to test for differences in means between conditions for proteins, peptides, and post-translational modifications. We developed a Bayesian regression model to characterize local mean-variance trends in data, to estimate measurement uncertainty and hyperparameters for the decision model. A key contribution is the development of a new gamma regression model that describes the mean-variance dependency as a mixture of a common and a latent trend-allowing for localized trend estimates. We then evaluate the performance of Baldur, limma-trend, and t test on six benchmark datasets: five total proteomics and one post-translational modification dataset. We find that Baldur drastically improves the decision in noisier post-translational modification data over limma-trend and t test. In addition, we see significant improvements using Baldur over the other methods in the total proteomics datasets. Finally, we analyzed Baldur's performance when increasing the number of replicates and found that the method always increases precision with sample size, while showing robust control of the false positive rate. We conclude that our model vastly improves over popular data analysis methods (limma-trend and t test) in several spike-in datasets by achieving a high true positive detection rate, while greatly reducing the false-positive rate.

Sage: An Open-Source Tool for Fast Proteomics Searching and Quantification at Scale

The growing complexity and volume of proteomics data necessitate the development of efficient software tools for peptide identification and quantification from mass spectra. Given their central role in proteomics, it is imperative that these tools are auditable and extensible─requirements that are best fulfilled by open-source and permissively licensed software. This work presents Sage, a high-performance, open-source, and freely available proteomics pipeline. Scalable and cloud-ready, Sage matches the performance of state-of-the-art software tools while running an order of magnitude faster.

Evaluating Proteomics Imputation Methods with Improved Criteria

Quantitative measurements produced by tandem mass spectrometry proteomics experiments typically contain a large proportion of missing values. Missing values hinder reproducibility, reduce statistical power, and make it difficult to compare across samples or experiments. Although many methods exist for imputing missing values, in practice, the most commonly used methods are among the worst performing. Furthermore, previous benchmarking studies have focused on relatively simple measurements of error such as the mean-squared error between imputed and held-out values. Here we evaluate the performance of commonly used imputation methods using three practical, "downstream-centric" criteria. These criteria measure the ability to identify differentially expressed peptides, generate new quantitative peptides, and improve the peptide lower limit of quantification. Our evaluation comprises several experiment types and acquisition strategies, including data-dependent and data-independent acquisition. We find that imputation does not necessarily improve the ability to identify differentially expressed peptides but that it can identify new quantitative peptides and improve the peptide lower limit of quantification. We find that MissForest is generally the best performing method per our downstream-centric criteria. We also argue that existing imputation methods do not properly account for the variance of peptide quantifications and highlight the need for methods that do.

Label-Free Quantitative Thermal Proteome Profiling Reveals Target Transcription Factors with Activities Modulated by MC3R Signaling

Thermal proteome profiling with label-free quantitation using ion-mobility-enhanced LC-MS offers versatile data sets, providing information on protein differential expression, thermal stability, and the activities of transcription factors. We developed a multidimensional data analysis workflow for label-free quantitative thermal proteome profiling (TPP) experiments that incorporates the aspects of gene set enrichment analysis, differential protein expression analysis, and inference of transcription factor activities from LC-MS data. We applied it to study the signaling processes downstream of melanocortin 3 receptor (MC3R) activation by endogenous agonists derived from the proopiomelanocortin prohormone: ACTH, α-MSH, and γ-MSH. The obtained information was used to map signaling pathways downstream of MC3R and to deduce transcription factors responsible for cellular response to ligand treatment. Using our workflow, we identified differentially expressed proteins and investigated their thermal stability. We found in total 298 proteins with altered thermal stability, resulting from MC3R activation. Out of these, several proteins were transcription factors, indicating them as being downstream target regulators that take part in the MC3R signaling cascade. We found transcription factors CCAR2, DDX21, HMGB2, SRSF7, and TET2 to have altered thermal stability. These apparent target transcription factors within the MC3R signaling cascade play important roles in immune responses. Additionally, we inferred the activities of the transcription factors identified in our data set. This was done with Bayesian statistics using the differential expression data we obtained with label-free quantitative LC-MS. The inferred transcription factor activities were validated in our bioinformatic pipeline by the phosphorylated peptide abundances that we observed, highlighting the importance of post-translational modifications in transcription factor regulation. Our multidimensional data analysis workflow allows for a comprehensive characterization of the signaling processes downstream of MC3R activation. It provides insights into protein differential expression, thermal stability, and activities of key transcription factors. All proteomic data generated in this study are publicly available at DOI: 10.6019/PXD039945.

Tissue-based absolute quantification using large-scale TMT and LFQ experiments

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.

Mouse Model of Fragile X Syndrome Analyzed by Quantitative Proteomics: A Comparison of Methods

Multiple methods for quantitative proteomics are available for proteome profiling. It is unclear which methods are most useful in situations involving deep proteome profiling and the detection of subtle distortions in the proteome. Here, we compared the performance of seven different strategies in the analysis of a mouse model of Fragile X Syndrome, involving the knockout of the fmr1 gene that is the leading cause of autism spectrum disorder. Focusing on the cerebellum, we show that data-independent acquisition (DIA) and the tandem mass tag (TMT)-based real-time search method (RTS) generated the most informative profiles, generating 334 and 329 significantly altered proteins, respectively, although the latter still suffered from ratio compression. Label-free methods such as BoxCar and a conventional data-dependent acquisition were too noisy to generate a reliable profile, while TMT methods that do not invoke RTS showed a suppressed dynamic range. The TMT method using the TMTpro reagents together with complementary ion quantification (ProC) overcomes ratio compression, but current limitations in ion detection reduce sensitivity. Overall, both DIA and RTS uncovered known regulators of the syndrome and detected alterations in calcium signaling pathways that are consistent with calcium deregulation recently observed in imaging studies. Data are available via ProteomeXchange with the identifier PXD039885.

Comparison of Quantitative Mass Spectrometric Methods for Drug Target Identification by Thermal Proteome Profiling

Thermal proteome profiling (TPP) provides a powerful approach to studying proteome-wide interactions of small therapeutic molecules and their target and off-target proteins, complementing phenotypic-based drug screens. Detecting differences in thermal stability due to target engagement requires high quantitative accuracy and consistent detection. Isobaric tandem mass tags (TMTs) are used to multiplex samples and increase quantification precision in TPP analysis by data-dependent acquisition (DDA). However, advances in data-independent acquisition (DIA) can provide higher sensitivity and protein coverage with reduced costs and sample preparation steps. Herein, we explored the performance of different DIA-based label-free quantification approaches compared to TMT-DDA for thermal shift quantitation. Acute myeloid leukemia cells were treated with losmapimod, a known inhibitor of MAPK14 (p38α). Label-free DIA approaches, and particularly the library-free mode in DIA-NN, were comparable of TMT-DDA in their ability to detect target engagement of losmapimod with MAPK14 and one of its downstream targets, MAPKAPK3. Using DIA for thermal shift quantitation is a cost-effective alternative to labeled quantitation in the TPP pipeline.

Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling

Repeated measures experimental designs, which quantify proteins in biological subjects repeatedly over multiple experimental conditions or times, are commonly used in mass spectrometry-based proteomics. Such designs distinguish the biological variation within and between the subjects and increase the statistical power of detecting within-subject changes in protein abundance. Meanwhile, proteomics experiments increasingly incorporate tandem mass tag (TMT) labeling, a multiplexing strategy that gains both relative protein quantification accuracy and sample throughput. However, combining repeated measures and TMT multiplexing in a large-scale investigation presents statistical challenges due to unique interplays of between-mixture, within-mixture, between-subject, and within-subject variation. This manuscript proposes a family of linear mixed-effects models for differential analysis of proteomics experiments with repeated measures and TMT multiplexing. These models decompose the variation in the data into the contributions from its sources as appropriate for the specifics of each experiment, enable statistical inference of differential protein abundance, and recognize a difference in the uncertainty of between-subject versus within-subject comparisons. The proposed family of models is implemented in the R/Bioconductor package MSstatsTMT v2.2.0. Evaluations of four simulated datasets and four investigations answering diverse biological questions demonstrated the value of this approach as compared to the existing general-purpose approaches and implementations.

Differential proteomics argues against a general role for CD9, CD81 or CD63 in the sorting of proteins into extracellular vesicles

The tetraspanins CD9, CD81 and CD63 are major components of extracellular vesicles (EVs). Yet, their impact on EV composition remains under-investigated. In the MCF7 breast cancer cell line CD63 was as expected predominantly intracellular. In contrast CD9 and CD81 strongly colocalized at the plasma membrane, albeit with different ratios at different sites, which may explain a higher enrichment of CD81 in EVs. Absence of these tetraspanins had little impact on the EV protein composition as analysed by quantitative mass spectrometry. We also analysed the effect of concomitant knock-out of CD9 and CD81 because these two tetraspanins play similar roles in several cellular processes and associate directly with two Ig domain proteins, CD9P-1/EWI-F/PTGFRN and EWI-2/IGSF8. These were the sole proteins significantly decreased in the EVs of double CD9- and CD81-deficient cells. In the case of EWI-2, this is primarily a consequence of a decreased cell expression level. In conclusion, this study shows that CD9, CD81 and CD63, commonly used as EV protein markers, play a marginal role in determining the protein composition of EVs released by MCF7 cells and highlights a regulation of the expression level and/or trafficking of CD9P-1 and EWI-2 by CD9 and CD81.

Evolutionary Quantitative Proteomics of Reproductive Protein Divergence in Drosophila

Reproductive traits often evolve rapidly between species. Understanding the causes and consequences of this rapid divergence requires characterization of female and male reproductive proteins and their effect on fertilization success. Species in the Drosophila virilis clade exhibit rampant interspecific reproductive incompatibilities, making them ideal for studies on diversification of reproductive proteins and their role in speciation. Importantly, the role of intraejaculate protein abundance and allocation in interspecific divergence is poorly understood. Here, we identify and quantify the transferred male ejaculate proteome using multiplexed isobaric labeling of the lower female reproductive tract before and immediately after mating using three species of the virilis group. We identified over 200 putative male ejaculate proteins, many of which show differential abundance between species, suggesting that males transfer a species-specific allocation of seminal fluid proteins during copulation. We also identified over 2000 female reproductive proteins, which contain female-specific serine-type endopeptidases that showed differential abundance between species and elevated rates of molecular evolution, similar to that of some male seminal fluid proteins. Our findings suggest that reproductive protein divergence can also manifest in terms of species-specific protein abundance patterns.

Recent progress in quantitative phosphoproteomics

INTRODUCTION

Protein phosphorylation is a critical post-translational modification involved in the regulation of numerous cellular processes from signal transduction to modulation of enzyme activities. Knowledge of dynamic changes of phosphorylation levels during biological processes, under various treatments or between healthy and disease models is fundamental for understanding the role of each phosphorylation event. Thereby, LC-MS/MS based technologies in combination with quantitative proteomics strategies evolved as a powerful strategy to investigate the function of individual protein phosphorylation events.

AREAS COVERED

State-of-the-art labeling techniques including stable isotope and isobaric labeling provide precise and accurate quantification of phosphorylation events. Here, we review the strengths and limitations of recent quantification methods and provide examples based on current studies, how quantitative phosphoproteomics can be further optimized for enhanced analytic depth, dynamic range, site localization, and data integrity. Specifically, reducing the input material demands is key to a broader implementation of quantitative phosphoproteomics, not least for clinical samples.

EXPERT OPINION

Despite quantitative phosphoproteomics is one of the most thriving fields in the proteomics world, many challenges still have to be overcome to facilitate even deeper and more comprehensive analyses as required in the current research, especially at single cell levels and in clinical diagnostics.

Disruption of proteome by an oncogenic fusion kinase alters metabolism in fibrolamellar hepatocellular carcinoma

Fibrolamellar hepatocellular carcinoma (FLC) is a usually lethal primary liver cancer driven by a somatic dysregulation of protein kinase A. We show that the proteome of FLC tumors is distinct from that of adjacent nontransformed tissue. These changes can account for some of the cell biological and pathological alterations in FLC cells, including their drug sensitivity and glycolysis. Hyperammonemic encephalopathy is a recurrent problem in these patients, and established treatments based on the assumption of liver failure are unsuccessful. We show that many of the enzymes that produce ammonia are increased and those that consume ammonia are decreased. We also demonstrate that the metabolites of these enzymes change as expected. Thus, hyperammonemic encephalopathy in FLC may require alternative therapeutics.

Enhanced Multiplexing Technology for Proteomics

The identification of thousands of proteins and their relative levels of expression has furthered understanding of biological processes and disease and stimulated new systems biology hypotheses. Quantitative proteomics workflows that rely on analytical assays such as mass spectrometry have facilitated high-throughput measurements of proteins partially due to multiplexing. Multiplexing allows proteome differences across multiple samples to be measured simultaneously, resulting in more accurate quantitation, increased statistical robustness, reduced analysis times, and lower experimental costs. The number of samples that can be multiplexed has evolved from as few as two to more than 50, with studies involving more than 10 samples being denoted as enhanced multiplexing or hyperplexing. In this review, we give an update on emerging multiplexing proteomics techniques and highlight advantages and limitations for enhanced multiplexing strategies.

A Comparative Proteomic Analysis to Explore the Influencing Factors on Endometritis Using LC-MS/MS

The inflammatory system activated by uterine infection is associated with decreased fertility. Diseases can be detected in advance by identifying biomarkers of several uterine diseases. Escherichia coli is one of the most frequent bacteria that is involved in pathogenic processes in dairy goats. The purpose of this study was to investigate the effect of endotoxin on protein expression in goat endometrial epithelial cells. In this study, the LC-MS/MS approach was employed to investigate the proteome profile of goat endometrial epithelial cells. A total of 1180 proteins were identified in the goat Endometrial Epithelial Cells and LPS-treated goat Endometrial Epithelial Cell groups, of which, 313 differentially expressed proteins were accurately screened. The proteomic results were independently verified by WB, TEM and IF techniques, and the same conclusion was obtained. To conclude, this model is suitable for the further study of infertility caused by endometrial damage caused by endotoxin. These findings may provide useful information for the prevention and treatment of endometritis.

Proteomic analysis of uterine lavage fluid of dairy cows at different time after delivery by mass spectrometry

Endometritis is a common disease in the reproductive system, which is the infection and inflammation of the endometrium. In severe cases, it can affect the myometrium and adversely affect the subsequent fertility of dairy cows. We used a mass spectrometry-based technique to compare proteomics of uterine lavage fluid between healthy cows and cows with cytological endometritis classified according to 100-day postpartum pregnancy results and diagnosis result. The uterine lavage fluid of dairy cows collected at 15 and 30 days after delivery was analyzed. 15 days postpartum, we identified a total of 1129 proteins in the control and cytological endometritis (CEM) groups. Among them, 160 proteins were accurately screened out. 30 days postpartum, we identified a total of 846 proteins in the control and cytological endometritis (CEM) groups. Among them, 186 proteins were accurately cytological endometritis (CEM). Endometritis is a costly reproductive disease in lactating cows, which needs to be diagnosed in time. Using proteomics method based on gel mass spectrometry, we compared the proteome of uterine lavage fluid of dairy cows with and without cytological endometritis to characterize the changes of proteomic characteristics associated with postpartum uterine disease. To provide reference for clinical application and basic research.

Chemical Biology Approaches to Identify and Profile Interactors of Chromatin Modifications

In eukaryotes, DNA is packaged with histone proteins in a complex known as chromatin. Both the DNA and histone components of chromatin can be chemically modified in a wide variety of ways, resulting in a complex landscape often referred to as the "epigenetic code". These modifications are recognized by effector proteins that remodel chromatin and modulate transcription, translation, and repair of the underlying DNA. In this Review, we examine the development of methods for characterizing proteins that interact with these histone and DNA modifications. "Mark first" approaches utilize chemical, peptide, nucleosome, or oligonucleotide probes to discover interactors of a specific modification. "Reader first" approaches employ arrays of peptides, nucleosomes, or oligonucleotides to profile the binding preferences of interactors. These complementary strategies have greatly enhanced our understanding of how chromatin modifications effect changes in genomic regulation, bringing us ever closer to deciphering this complex language.

prolfqua: A Comprehensive R-Package for Proteomics Differential Expression Analysis

Mass spectrometry is widely used for quantitative proteomics studies, relative protein quantification, and differential expression analysis of proteins. There is a large variety of quantification software and analysis tools. Nevertheless, there is a need for a modular, easy-to-use application programming interface in R that transparently supports a variety of well principled statistical procedures to make applying them to proteomics data, comparing and understanding their differences easy. The prolfqua package integrates essential steps of the mass spectrometry-based differential expression analysis workflow: quality control, data normalization, protein aggregation, statistical modeling, hypothesis testing, and sample size estimation. The package makes integrating new data formats easy. It can be used to model simple experimental designs with a single explanatory variable and complex experiments with multiple factors and hypothesis testing. The implemented methods allow sensitive and specific differential expression analysis. Furthermore, the package implements benchmark functionality that can help to compare data acquisition, data preprocessing, or data modeling methods using a gold standard data set. The application programmer interface of prolfqua strives to be clear, predictable, discoverable, and consistent to make proteomics data analysis application development easy and exciting. Finally, the prolfqua R-package is available on GitHub https://github.com/fgcz/prolfqua, distributed under the MIT license. It runs on all platforms supported by the R free software environment for statistical computing and graphics.

Advances in Mass Spectrometry-Based Single Cell Analysis

Technological developments and improvements in single-cell isolation and analytical platforms allow for advanced molecular profiling at the single-cell level, which reveals cell-to-cell variation within the admixture cells in complex biological or clinical systems. This helps to understand the cellular heterogeneity of normal or diseased tissues and organs. However, most studies focused on the analysis of nucleic acids (e.g., DNA and RNA) and mass spectrometry (MS)-based analysis for proteins and metabolites of a single cell lagged until recently. Undoubtedly, MS-based single-cell analysis will provide a deeper insight into cellular mechanisms related to health and disease. This review summarizes recent advances in MS-based single-cell analysis methods and their applications in biology and medicine.