Label-free proteome quantification and evaluation.

The label-free quantification (LFQ) has emerged as an exceptional technique in proteomics owing to its broad proteome coverage, great dynamic ranges and enhanced analytical reproducibility. Due to the extreme difficulty lying in an in-depth quantification, the LFQ chains incorporating a variety of transformation, pretreatment and imputation methods are required and constructed. However, it remains challenging to determine the well-performing chain, owing to its strong dependence on the studied data and the diverse possibility of integrated chains. In this study, an R package EVALFQ was therefore constructed to enable a performance evaluation on >3000 LFQ chains. This package is unique in (a) automatically evaluating the performance using multiple criteria, (b) exploring the quantification accuracy based on spiking proteins and (c) discovering the well-performing chains by comprehensive assessment. All in all, because of its superiority in assessing from multiple perspectives and scanning among over 3000 chains, this package is expected to attract broad interests from the fields of proteomic quantification. The package is available at https://github.com/idrblab/EVALFQ.

pepDESC: A Method for the Detection of Differentially Expressed Proteins for Mass Spectrometry-Based Single-Cell Proteomics Using Peptide-level Information.

Single-cell proteomics as an emerging field has exhibited potential in revealing cellular heterogeneity at the functional level. However, accurate interpretation of single-cell proteomics data suffers from challenges such as measurement noise, internal heterogeneity, and the limited sample size of label-free quantitative mass spectrometry. Herein, the author describes peptide-level differential expression analysis for single-cell proteomic (pepDESC), a method for detecting differentially expressed proteins using peptide-level information designed for label-free quantitative mass spectrometry-based single-cell proteomics. While, in this study, the author focuses on the heterogeneity among the limited number of samples, pepDESC is also applicable to regular-size proteomics data. By balancing proteome coverage and quantification accuracy using peptide quantification, pepDESC is demonstrated to be effective in real-world single-cell and spike-in benchmark datasets. By applying pepDESC to published single-mouse macrophage data, the author found a large fraction of differentially expressed proteins among three types of cells, which remarkably revealed distinct dynamics of different cellular functions responding to lipopolysaccharide stimulation.

MsImpute: Estimation of Missing Peptide Intensity Data in Label-Free Quantitative Mass Spectrometry.

Mass spectrometry (MS) enables high-throughput identification and quantification of proteins in complex biological samples and can provide insights into the global function of biological systems. Label-free quantification is cost-effective and suitable for the analysis of human samples. Despite rapid developments in label-free data acquisition workflows, the number of proteins quantified across samples can be limited by technical and biological variability. This variation can result in missing values which can in turn challenge downstream data analysis tasks. General purpose or gene expression-specific imputation algorithms are widely used to improve data completeness. Here, we propose an imputation algorithm designated for label-free MS data that is aware of the type of missingness affecting data. On published datasets acquired by data-dependent and data-independent acquisition workflows with variable degrees of biological complexity, we demonstrate that the proposed missing value estimation procedure by barycenter computation competes closely with the state-of-the-art imputation algorithms in differential abundance tasks while outperforming them in the accuracy of variance estimates of the peptide abundance measurements, and better controls the false discovery rate in label-free MS experiments. The barycenter estimation procedure is implemented in the msImpute software package and is available from the Bioconductor repository.

Insight Into the Dynamics of the Ixodes ricinus Nymphal Midgut Proteome.

Ticks are ectoparasites that feed on blood and have an impressive ability to consume and process enormous amounts of host blood, allowing extremely long periods of starvation between blood meals. The central role in the parasitic lifestyle of ticks is played by the midgut. This organ efficiently stores and digests ingested blood and serves as the primary interface for the transmission of tick-borne pathogens. In this study, we used a label-free quantitative approach to perform a novel dynamic proteomic analysis of the midgut of Ixodesricinus nymphs, covering their development from unfed to pre-molt stages. We identified 1534 I. ricinus-specific proteins with a relatively low proportion of host proteins. This proteome dataset, which was carefully examined by manual scrutiny, allowed precise annotation of proteins important for blood meal processing and their dynamic changes during nymphal ontogeny. We focused on midgut molecules related to lipid hydrolysis, storage, and transport, opening a yet unexplored avenue for studying lipid metabolism in ticks. Further dynamic profiling of the tick's multi-enzyme digestive network, protease inhibitors, enzymes involved in redox homeostasis and detoxification, antimicrobial peptides, and proteins responsible for midgut colonization by Borrelia spirochetes promises to uncover new targets for targeting tick nymphs, the most critical life stage for transmission the pathogens that cause tick-borne diseases.

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.

The cuticle proteome of a planktonic crustacean.

The cuticles of arthropods provide an interface between the organism and its environment. Thus, the cuticle's structure influences how the organism responds to and interacts with its surroundings. Here, we used label-free quantification proteomics to provide a proteome of the moulted cuticle of the aquatic crustacean Daphnia magna, which has long been a prominent subject of studies on ecology, evolution, and developmental biology. We detected a total of 278 high-confidence proteins. Using protein sequence domain and functional enrichment analyses, we identified chitin-binding structural proteins and chitin-modifying enzymes as the most abundant protein groups in the cuticle proteome. Structural cuticular protein families showed a similar distribution to those found in other arthropods and indicated proteins responsible for the soft and flexible structure of the Daphnia cuticle. Finally, cuticle protein genes were also clustered as tandem gene arrays in the D. magna genome. The cuticle proteome presented here will be a valuable resource to the Daphnia research community, informing genome annotations and investigations on diverse topics such as the genetic basis of interactions with predators and parasites.

LFQRatio: A Normalization Method to Decipher Quantitative Proteome Changes in Microbial Coculture Systems.

The value of synthetic microbial communities in biotechnology is gaining traction due to their ability to undertake more complex metabolic tasks than monocultures. However, a thorough understanding of strain interactions, productivity, and stability is often required to optimize growth and scale up cultivation. Quantitative proteomics can provide valuable insights into how microbial strains adapt to changing conditions in biomanufacturing. However, current workflows and methodologies are not suitable for simple artificial coculture systems where strain ratios are dynamic. Here, we established a workflow for coculture proteomics using an exemplar system containing two members, Azotobacter vinelandii and Synechococcus elongatus. Factors affecting the quantitative accuracy of coculture proteomics were investigated, including peptide physicochemical characteristics such as molecular weight, isoelectric point, hydrophobicity, and dynamic range as well as factors relating to protein identification such as varying proteome size and shared peptides between species. Different quantification methods based on spectral counts and intensity were evaluated at the protein and cell level. We propose a new normalization method, named "LFQRatio", to reflect the relative contributions of two distinct cell types emerging from cell ratio changes during cocultivation. LFQRatio can be applied to real coculture proteomics experiments, providing accurate insights into quantitative proteome changes in each strain.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics.

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Pilot Evaluation of the Long-Term Reproducibility of Capillary Zone Electrophoresis-Tandem Mass Spectrometry for Top-Down Proteomics of a Complex Proteome Sample.

Mass spectrometry (MS)-based top-down proteomics (TDP) has revolutionized biological research by measuring intact proteoforms in cells, tissues, and biofluids. Capillary zone electrophoresis-tandem MS (CZE-MS/MS) is a valuable technique for TDP, offering a high peak capacity and sensitivity for proteoform separation and detection. However, the long-term reproducibility of CZE-MS/MS in TDP remains unstudied, which is a crucial aspect for large-scale studies. This work investigated the long-term qualitative and quantitative reproducibility of CZE-MS/MS for TDP for the first time, focusing on a yeast cell lysate. Over 1000 proteoforms were identified per run across 62 runs using one linear polyacrylamide (LPA)-coated separation capillary, highlighting the robustness of the CZE-MS/MS technique. However, substantial decreases in proteoform intensity and identification were observed after some initial runs due to proteoform adsorption onto the capillary inner wall. To address this issue, we developed an efficient capillary cleanup procedure using diluted ammonium hydroxide, achieving high qualitative and quantitative reproducibility for the yeast sample across at least 23 runs. The data underscore the capability of CZE-MS/MS for large-scale quantitative TDP of complex samples, signaling its readiness for deployment in broad biological applications. The MS RAW files were deposited in ProteomeXchange Consortium with the data set identifier of PXD046651.

The aryl sulfonamide indisulam inhibits gastric cancer cell migration by promoting the ubiquitination and degradation of the transcription factor ZEB1.

Gastric cancer is one of the cancers with high morbidity and mortality worldwide. The aryl sulfonamide indisulam inhibits the proliferation of several types of cancer cells through its function as a molecular glue to promote the ubiquitination and degradation of RNA-binding motif protein 39 (RBM39). However, it is unknown whether and how indisulam regulates the migration of cancer cells. In this work, using label-free quantitative proteomics, we discover that indisulam significantly attenuates N-cadherin, a marker for epithelial to mesenchymal transition and migration of cancer cells. Our bioinformatics analysis and biochemical experiments reveal that indisulam promotes the interaction between the zinc finger E-box-binding homeobox 1 (ZEB1), a transcription factor of N-cadherin, and DCAF15, a substrate receptor of CRL4 E3 ubiquitin ligase, and enhances ZEB1 ubiquitination and proteasomal degradation. In addition, our cell line-based experiments demonstrate that indisulam inhibits the migration of gastric cancer cells in a ZEB1-dependent manner. Analyses of patient samples and datasets in public databases reveal that tumor tissues from patients with gastric cancer express high ZEB1 mRNA and this high expression reduces patient survival rate. Finally, we show that treatment of gastric tumor samples with indisulam significantly reduces ZEB1 protein levels. Therefore, this work discloses a new mechanism by which indisulam inhibits the migration of gastric cancer cells, indicating that indisulam exhibits different biological functions through distinct signaling molecules.

Label-free quantification of host cell protein impurity in recombinant hemoglobin materials.

Quantitative analysis relies on pure-substance primary calibrators with known mass fractions of impurity. Here, label-free quantification (LFQ) is being evaluated as a readily available, reliable method for determining the mass fraction of host cell proteins (HCPs) in bioengineered proteins which are intended for use as protein calibration standards. In this study a purified hemoglobin-A2 (HbA2) protein, obtained through its overexpression in E. coli, was used. Two different materials were produced: natural and U15N-labeled HbA2. For the quantification of impurities, precursor ion (MS1-) intensities were integrated over all E. coli proteins identified and divided by the intensities obtained for HbA2. This ratio was calibrated against the corresponding results for an E. coli cell lysate, which had been spiked at known mass ratios to pure HbA2. To demonstrate the universal applicability of LFQ, further proteomes (yeast and human K562) were then alternatively used for calibration and found to produce comparable results. Valid results were also obtained when the complexity of the calibrator was reduced to a mix of just nine proteins, and a minimum of five proteins was estimated to be sufficient to keep the sampling error below 15%. For the studied materials, HbA2 mass fractions (or purities) of 923 and 928 mg(HbA2)/g(total protein) were found with expanded uncertainties (U) of 2.8 and 1.3%, resp. Value assignment by LFQ thus contributes up to about 3% of the overall uncertainty of HbA2 quantification when these materials are used as calibrators. Further purification of the natural HbA2 yielded a mass fraction of 999.1 mg/g, with a negligible uncertainty (U = 0.02%), though at a significant loss of material. If an overall uncertainty of 5% is acceptable for protein quantification, working with the original materials would therefore definitely be viable, circumventing the need of further purification.

Precision Sequence-Defined Polymers: From Sequencing to Biological Functions.

Precise sequence-defined polymers (SDPs) with uniform chain-to-chain structure including chain length, unit sequence, and end functionalities represent the pinnacle of sophistication in the realm of polymer science. For example, the absolute control over the unit sequence of SDPs allows for the bottom-up design of polymers with hierarchical microstructures and functions. Accompanied with the development of synthetic techniques towards precision SDPs, the decoding of SDP sequences and construction of advanced functions irreplaceable by other synthetic materials is of central importance. In this Minireview, we focus on recent advances in SDP sequencing techniques including tandem mass spectrometry (MS), chemically assisted primary MS, as well as other non-destructive sequencing methods such as nuclear magnetic resonance (NMR) spectroscopy, circular dichroism (CD), and nanopore sequencing. Additionally, we delve into the promising prospects of SDP functions in the area of cutting-edge biological research. Topics of exploration include gene delivery systems, the development of hybrid materials combining SDPs and nucleic acids, protein recognition and regulation, as well as the interplay between chirality and biological functions. A brief outlook towards the future directions of SDPs is also presented.

Proteome profiling highlights mechanisms underlying pigment and tocopherol accumulation in red and black rice seeds.

Rice is a major component of the human diet and feeds more than 50 million people across the globe. We previously developed two pigmented rice cultivars, Super-hongmi (red seeds) and Super-jami (black seeds), that are highly rich in antioxidants and exhibit high levels of radical scavenging activities. However, the molecular mechanism underlying the accumulation of pigments and different antioxidants in these rice cultivars remains largely elusive. Here, we report the proteome profiles of mature Super-hongmi and Super-jami seeds, and compared them with the Hopum (white seeds) using a label-free quantitative proteomics approach. This approach led to the identification of 5127 rice seed proteins of which 1628 showed significant changes in the pigmented rice cultivar(s). The list of significantly modulated proteins included a phytoene desaturase (PDS3) which suggested accumulation of ζ-carotene in red seeds while the black seeds seem to accumulate more of anthocyanins because of the higher abundance of dihydroflavonol 4-reductase. Moreover, proteins associated with lignin and tocopherol biosynthesis were highly increased in both red and black cultivars. Taken together, these data report the seed proteome of three different colored rice seeds and identify novel components associated with pigment accumulation in rice.

Quantitative Acetylomics Reveals Substrates of Lysine Acetyltransferase GCN5 in Adult and Aging Drosophila.

Protein lysine acetylation is a dynamic post-translational modification (PTM) that regulates a wide spectrum of cellular events including aging. General control nonderepressible 5 (GCN5) is a highly conserved lysine acetyltransferase (KAT). However, the acetylation substrates of GCN5 in vivo remain poorly studied, and moreover, how lysine acetylation changes with age and the contribution of KATs to aging remain to be addressed. Here, using Drosophila, we perform label-free quantitative acetylomic analysis, identifying new substrates of GCN5 in the adult and aging process. We further characterize the dynamics of protein acetylation with age, which exhibits a trend of increase. Since the expression of endogenous fly Gcn5 progressively increases during aging, we reason that, by combining the substrate analysis, the increase in acetylation with age is triggered, at least in part, by GCN5. Collectively, our study substantially expands the atlas of GCN5 substrates in vivo, provides a resource of protein acetylation that naturally occurs with age, and demonstrates how individual KAT contributes to the aging acetylome.

Repeat-Enhancing Featured Ion-Guided Stoichiometry for Identification and Quantification of Direct Infusion Proteome.

The direct infusion-shotgun proteome analysis (DI-SPA) alongside data-independent acquisition mass spectrometry achieved fast proteome identification and quantification without chromatographic separation. However, robust peptide identification and quantification (label and label-free) for the DI-SPA data is still insufficient. We find that in the absence of chromatography, the identification of DI-SPA can be boosted by extending acquisition cycles repeatedly and maximizing the utilization of the featured repetition characteristics, combined with the machine learning-based automatic peptide scoring strategy. Here, we present the repeat-enhancing featured ion-guided stoichiometry (RE-FIGS), a complete and compact solution to (repeated) DI-SPA data. Using our strategy, the peptide identification can be improved above 30% with high reproducibility (70.0%). Notably, the label-free quantification of repeated DI-SPA can be successfully obtained with high accuracy (mean median error, 0.108) and high reproducibility (median error, 0.001). We believe our RE-FIGS method could boost the broad application of the (repeated) DI-SPA method and offer a new choice for proteomic analysis.

Meta4P: A User-Friendly Tool to Parse Label-Free Quantitative Metaproteomic Data and Taxonomic/Functional Annotations.

We present Meta4P (MetaProteins-Peptides-PSMs Parser), an easy-to-use bioinformatic application designed to integrate label-free quantitative metaproteomic data with taxonomic and functional annotations. Meta4P can retrieve, filter, and process identification and quantification data from three levels of inputs (proteins, peptides, PSMs) in different file formats. Abundance data can be combined with taxonomic and functional information and aggregated at different and customizable levels, including taxon-specific functions and pathways. Meta4P output tables, available in various formats, are ready to be used as inputs for downstream statistical analyses. This user-friendly tool is expected to provide a useful contribution to the field of metaproteomic data analysis, helping make it more manageable and straightforward.

An Introduction to Mass Spectrometry-Based Proteomics.

Mass spectrometry is unmatched in its versatility for studying practically any aspect of the proteome. Because the foundations of mass spectrometry-based proteomics are complex and span multiple scientific fields, proteomics can be perceived as having a high barrier to entry. This tutorial is intended to be an accessible illustrated guide to the technical details of a relatively simple quantitative proteomic experiment. An attempt is made to explain the relevant concepts to those with limited knowledge of mass spectrometry and a basic understanding of proteins. An experimental overview is provided, from the beginning of sample preparation to the analysis of protein group quantities, with explanations of how the data are acquired, processed, and analyzed. A selection of advanced topics is briefly surveyed and works for further reading are cited. To conclude, a brief discussion of the future of proteomics is given, considering next-generation protein sequencing technologies that may complement mass spectrometry to create a fruitful future for proteomics.

Triqler for Protein Summarization of Data from Data-Independent Acquisition Mass Spectrometry.

A frequent goal, or subgoal, when processing data from a quantitative shotgun proteomics experiment is a list of proteins that are differentially abundant under the examined experimental conditions. Unfortunately, obtaining such a list is a challenging process, as the mass spectrometer analyzes the proteolytic peptides of a protein rather than the proteins themselves. We have previously designed a Bayesian hierarchical probabilistic model, Triqler, for combining peptide identification and quantification errors into probabilities of proteins being differentially abundant. However, the model was developed for data from data-dependent acquisition. Here, we show that Triqler is also compatible with data-independent acquisition data after applying minor alterations for the missing value distribution. Furthermore, we find that it has better performance than a set of compared state-of-the-art protein summarization tools when evaluated on data-independent acquisition data.

TopPICR: A Companion R Package for Top-Down Proteomics Data Analysis.

Top-down proteomics is the analysis of proteins in their intact form without proteolysis, thus preserving valuable information about post-translational modifications, isoforms, and proteolytic processing. However, it is still a developing field due to limitations in the instrumentation, difficulties with the interpretation of complex mass spectra, and a lack of well-established quantification approaches. TopPIC is one of the popular tools for proteoform identification. We extended its capabilities into label-free proteoform quantification by developing a companion R package (TopPICR). Key steps in the TopPICR pipeline include filtering identifications, inferring a minimal set of protein accessions explaining the observed sequences, aligning retention times, recalibrating measured masses, clustering features across data sets, and finally compiling feature intensities using the match-between-runs approach. The output of the pipeline is an MSnSet object which makes downstream data analysis seamlessly compatible with packages from the Bioconductor project. It also provides the capability for visualizing proteoforms within the context of the parent protein sequence. The functionality of TopPICR is demonstrated on top-down LC-MS/MS data sets of 10 human-in-mouse xenografts of luminal and basal breast tumor samples.

Proteome Coverage after Simultaneous Proteo-Metabolome Liquid-Liquid Extraction.

Proteomics and metabolomics are essential in systems biology, and simultaneous proteo-metabolome liquid-liquid extraction (SPM-LLE) allows isolation of the metabolome and proteome from the same sample. Since the proteome is present as a pellet in SPM-LLE, it must be solubilized for quantitative proteomics. Solubilization and proteome extraction are critical factors in the information obtained at the proteome level. In this study, we investigated the performance of two surfactants (sodium deoxycholate (SDC), sodium dodecyl sulfate (SDS)) and urea in terms of proteome coverage and extraction efficiency of an interphase proteome pellet generated by methanol-chloroform based SPM-LLE. We also investigated how the performance differs when the proteome is extracted from the interphase pellet or by direct cell lysis. We quantified 12 lipids covering triglycerides and various phospholipid classes, and 25 polar metabolites covering central energy metabolism in chloroform and methanol extracts. Our study reveals that the proteome coverages between the two surfactants and urea for the SPM-LLE interphase pellet were similar, but the extraction efficiencies differed significantly. While SDS led to enrichment of basic proteins, which were mainly ribosomal and ribonuclear proteins, urea was the most efficient extraction agent for simultaneous proteo-metabolome analysis. The results of our study also show that the performance of surfactants for quantitative proteomics is better when the proteome is extracted through direct cell lysis rather than an interphase pellet. In contrast, the performance of urea for quantitative proteomics was significantly better when the proteome was extracted from an interphase pellet than by direct cell lysis. We demonstrated that urea is superior to surfactants for proteome extraction from SPM-LLE interphase pellets, with a particularly good performance for the extraction of proteins associated with metabolic pathways. Data are available via ProteomeXchange with identifier PXD027338.