Assessing the precision of a detergent-assisted cartridge precipitation workflow for non-targeted quantitative proteomics

Detergent-based workflows incorporating sodium dodecyl sulfate (SDS) necessitate additional steps for detergent removal ahead of mass spectrometry (MS). These steps may lead to variable protein recovery, inconsistent enzyme digestion efficiency, and unreliable MS signals. To validate a detergent-based workflow for quantitative proteomics, we herein evaluate the precision of a bottom-up sample preparation strategy incorporating cartridge-based protein precipitation with organic solvent to deplete SDS. The variance of data-independent acquisition (SWATH-MS) data was isolated from sample preparation error by modelling the variance as a function of peptide signal intensity. Our SDS-assisted cartridge workflow yield a coefficient of variance (CV) of 13%-14%. By comparison, conventional (detergent-free) in-solution digestion increased the CV to 50%; in-gel digestion provided lower CVs between 14% and 20%. By filtering peptides predicting to display lower precision, we further enhance the validity of data in global comparative proteomics. These results demonstrate the detergent-based precipitation workflow is a reliable approach for in depth, label-free quantitative proteome analysis.

Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics

Imputing missing values is common practice in label-free quantitative proteomics. Imputation aims at replacing a missing value with a user-defined one. However, the imputation itself may not be optimally considered downstream of the imputation process, as imputed datasets are often considered as if they had always been complete. Hence, the uncertainty due to the imputation is not adequately taken into account. We provide a rigorous multiple imputation strategy, leading to a less biased estimation of the parameters’ variability thanks to Rubin’s rules. The imputation-based peptide’s intensities’ variance estimator is then moderated using Bayesian hierarchical models. This estimator is finally included in moderated t-test statistics to provide differential analyses results. This workflow can be used both at peptide and protein-level in quantification datasets. Indeed, an aggregation step is included for protein-level results based on peptide-level quantification data. Our methodology, named mi4p, was compared to the state-of-the-art limma workflow implemented in the DAPARR package, both on simulated and real datasets. We observed a trade-off between sensitivity and specificity, while the overall performance of mi4p outperforms DAPAR in terms of F-Score.

Statistical inference methods commonly used in quantitative proteomics are based on the measurement of peptide intensities. They allow the deduction of protein abundances provided that sufficient peptides per protein are available. However, they do not satisfactorily consider peptides or proteins whose intensities are missing under certain conditions, even though they are particularly interesting from a biological or medical point of view, since they may explain a difference between the groups being compared. Some state-of-the-art statistical proteomics data processing software proposes to impute these missing values, while others simply remove proteins with too many missing peptides. The statistical treatment is not entirely satisfactory when imputation methods are used, notably multiple imputation techniques. Indeed, even if these statistical tools are relevant in this context, the data sets once imputed are considered as having always been complete in the subsequent analyses: the uncertainty caused by the imputation is not taken into account. These analyses generally conclude with a study of the differences in protein abundances between the different conditions, either using Student’s or Welch’s test for the most rudimentary approaches or using the t-tempered testing techniques based on empirical Bayesian approaches. Thus, we propose a new methodology that starts by imputing missing values at the peptide level and estimating the uncertainty associated with this imputation and naturally extends by incorporating this uncertainty into the current moderated variance estimation techniques.

Combining label-free and label-based accurate quantifications with SWATH-MS: Comparison with SRM and PRM for the evaluation of bovine muscle type effects

Mass spectrometry has proven to be a valuable tool for the accurate quantification of proteins. In this study, the performances of three targeted approaches, namely selected reaction monitoring (SRM), parallel reaction monitoring (PRM) and sequential windowed acquisition of all theoretical fragment ion mass spectra (SWATH-MS), to accurately quantify ten potential biomarkers of beef meat tenderness or marbling in a cohort of 64 muscle samples were evaluated. So as to get the most benefit out of the complete MS2 maps that are acquired in SWATH-MS, an original label-free quantification method to estimate protein amounts using an I-spline regression model was developed. Overall, SWATH-MS outperformed SRM in terms of sensitivity and dynamic range, while PRM still performed the best, and all three strategies showed similar quantification accuracies and precisions for the absolute quantification of targets of interest. This targeted picture was extended by 585 additional proteins for which amounts were estimated using the label-free approach on SWATH-MS; thus, offering a more global profiling of muscle proteomes and further insights into muscle type effect on candidate biomarkers of beef meat qualities as well as muscle metabolism.

Effects of Extraction Buffer on the Solubility and Immunoreactivity of the Pacific Oyster Allergens

Despite recent technological advances, novel allergenic protein discovery is limited by their low abundance, often due to specific physical characteristics restricting their recovery during the extraction process from various allergen sources. In this study, eight different extraction buffers were compared for their ability to recover proteins from Pacific oyster (Crassostrea gigas). The protein composition was investigated using high resolution mass spectrometry. The antibody IgE-reactivity of each extract was determined using a pool of serum from five shellfish-allergic patients. Most of the investigated buffers showed good capacity to extract proteins from the Pacific oyster. In general, a higher concentration of proteins was recovered using high salt buffers or high pH buffers, subsequently revealing more IgE-reactive bands on immunoblotting. In contrast, low pH buffers resulted in a poor protein recovery and reduced IgE-reactivity. Discovery of additional IgE-reactive proteins in high salt buffers or high pH buffers was associated with an increase in allergen abundance in the extracts. In conclusion, increasing the ionic strength and pH of the buffer improves the solubility of allergenic proteins during the extraction process for oyster tissue. This strategy could also be applied for other difficult-to-extract allergen sources, thereby yielding an improved allergen panel for increased diagnostic efficiency.

Phenotypic Adaption of Pseudomonas aeruginosa by Hacking Siderophores Produced by Other Microorganisms

Bacteria secrete siderophores to access iron, a key nutrient poorly bioavailable and the source of strong competition between microorganisms in most biotopes. Many bacteria also use siderophores produced by other microorganisms (exosiderophores) in a piracy strategy. Pseudomonas aeruginosa, an opportunistic pathogen, produces two siderophores, pyoverdine and pyochelin, and is also able to use a panel of exosiderophores. We first investigated expression of the various iron-uptake pathways of P. aeruginosa in three different growth media using proteomic and RT-qPCR approaches and observed three different phenotypic patterns, indicating complex phenotypic plasticity in the expression of the various iron-uptake pathways. We then investigated the phenotypic plasticity of iron-uptake pathway expression in the presence of various exosiderophores (present individually or as a mixture) under planktonic growth conditions, as well as in an epithelial cell infection assay. In all growth conditions tested, catechol-type exosiderophores were clearly more efficient in inducing the expression of their corresponding transporters than the others, showing that bacteria opt for the use of catechol siderophores to access iron when they are present in the environment. In parallel, expression of the proteins of the pyochelin pathway was significantly repressed under most conditions tested, as well as that of proteins of the pyoverdine pathway, but to a lesser extent. There was no effect on the expression of the heme and ferrous uptake pathways. Overall, these data provide precise insights on how P. aeruginosa adjusts the expression of its various iron-uptake pathways (phenotypic plasticity and switching) to match varying levels of iron and competition.

Identification of proteins regulated by the proteasome following induction of endoplasmic reticulum stress

The endoplasmic reticulum (ER) is a major site for protein synthesis, folding and transport, lipid and steroid synthesis, regulating redox potential, as well as calcium storage. It therefore relies on delicate homeostasis, and perturbation of the ER function and induction of ER stress can lead to apoptosis. One cause of disruption of the ER homeostasis is the accumulation of misfolded proteins. To prevent this perturbation, the Endoplasmic Reticulum-Associated Degradation (ERAD) quality control machinery is recruited to remove these proteins in a three-step process: (1) extraction from the ER, (2) ubiquitination, and (3) subsequent proteasomal degradation. However, the identity of the proteins regulated by the proteasome following induction of the ER stress has remained obscure. In the present study, we investigated the role of the proteasome in the modulation of the proteome of HeLa cells after treatment with thapsigargin and tunicamycin, two drugs known to induce ER stress through accumulation of misfolded proteins. Using label-free quantitative proteomics we found that out of the proteins identified to decrease in their level following induction of ER stress, more than 64% are targeted by the proteasome. Among these proteins, key players of the Wnt signaling pathway, such as β-catenin and GSK3, as well as α-catenin which is involved in cell-cell adhesion, were identified as being modulated by the proteasome upon ER stress.

Tube-Gel: A Fast and Effective Sample Preparation Method for High-Throughput Quantitative Proteomics

Sample preparation is a key step in proteomics workflows. Tube-gel (TG) is a fast and repeatable sample preparation method that consists in the instantaneous trapping of the sample in a polyacrylamide gel matrix. It takes advantage of in-gel sample preparations by allowing the use of high concentrations of sodium-dodecyl sulfate but avoids the time-consuming step of electrophoresis. Therefore, TG limits the sample handling and is thus particularly suitable for high-throughput quantitative proteomics when large sample numbers have to be processed, as it is often the case in biomarker research and clinical proteomics projects.

Multi-omics dataset to decipher the complexity of drug resistance in diffuse large B-cell lymphoma

The prognosis of patients with relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) remains unsatisfactory and, despite major advances in genomic studies, the biological mechanisms underlying chemoresistance are still poorly understood. We conducted for the first time a large-scale differential multi-omics investigation on DLBCL patient’s samples in order to identify new biomarkers that could early identify patients at risk of R/R disease and to identify new targets that could determine chemorefractoriness. We compared a well-characterized cohort of R/R versus chemosensitive DLBCL patients by combining label-free quantitative proteomics and targeted RNA sequencing performed on the same tissues samples. The cross-section of both data levels allowed extracting a sub-list of 22 transcripts/proteins pairs whose expression levels significantly differed between the two groups of patients. In particular, we identified significant targets related to tumor metabolism (Hexokinase 3), microenvironment (IDO1, CXCL13), cancer cells proliferation, migration and invasion (S100 proteins) or BCR signaling pathway (CD79B). Overall, this study revealed several extremely promising biomarker candidates related to DLBCL chemorefractoriness and highlighted some new potential therapeutic drug targets. The complete datasets have been made publically available and should constitute a valuable resource for the future research.

Assessing Automated Sample Preparation Technologies for High-Throughput Proteomics of Frozen Well Characterized Tissues from Swedish Biobanks

Large cohorts of carefully collected clinical tissue materials play a central role in acquiring sufficient depth and statistical power to discover disease-related mechanisms and biomarkers of clinical significance. Manual preparation of such large sample cohorts requires experienced laboratory personnel. This carries other possible downsides such as low throughput, high risk of errors, and low reproducibility. In this work, three automated technologies for high-throughput proteomics of frozen sectioned tissues were compared. The instruments evaluated included the Bioruptor for tissue disruption and protein extraction; the Barocycler, which is able to disrupt tissues and digest the proteins; and the AssayMAP Bravo, a microchromatography platform for protein digestion, peptide desalting, and fractionation. Wide varieties of tissue samples from rat spleen, malignant melanoma, and pancreatic tumors were used for the assessment. The three instruments displayed reproducible and consistent results, as was proven by high correlations and low coefficients of variation between technical replicates and even more importantly, between replicates that were processed in different batches or at different time points. The results from this study allowed us to integrate these technologies into an automated sample preparation workflow for large-scale proteomic studies that are currently ongoing. Data are available via ProteomeXchange with identifiers PXD010296 and PXD011295.

Extended investigation of tube-gel sample preparation: a versatile and simple choice for high throughput quantitative proteomics

Sample preparation for quantitative proteomics is a crucial step to ensure the repeatability and the accuracy of the results. However, there is no universal method compatible with the wide variety of protein extraction buffers currently used. We have recently demonstrated the compatibility of tube-gel with SDS-based buffers and its efficiency for label-free quantitative proteomics by comparing it to stacking gel and liquid digestion. Here, we investigated the compatibility of tube-gel with alternatives to SDS-based buffers allowing notably the extraction of proteins in various pH conditions. We also explored the use of photopolymerization to extend the number of possibilities, as it is compatible with a wide range of pH and is non-oxidative. To achieve this goal, we compared six extraction buffers in combination with two polymerization conditions to further optimize the tube-gel protocol and evaluate its versatility. Identification and quantitative results demonstrated the compatibility of tube-gel with all tested conditions by overall raising quite comparable results. In conclusion, tube-gel is a versatile and simple sample preparation method for large-scale quantitative proteomics applications. Complete datasets are available via ProteomeXchange with identifier PXD008656.

Evaluation of Optimized Tube-Gel Methods of Sample Preparation for Large-Scale Plant Proteomics

The so-called tube-gel method is a sample preparation protocol allowing for management of SDS for protein solubilization through in-gel protein trapping. Because of its simplicity, we assumed that once miniaturized, this method could become a standard for large scale experiments. We evaluated the performances of two variants of the miniaturized version of the tube-gel method based on different solubilization buffers (Tris-SDS or urea-SDS). To this end, we compared them to two other digestion methods: (i) liquid digestion after protein solubilization in the absence of SDS (liquid method) and (ii) filter-aided sample preparation (FASP). As large-scale experiments may require long term gel storage, we also examined to which extent gel aging affected the results of the proteomics analysis. We showed that both tube-gel and FASP methods extracted membrane proteins better than the liquid method, while the latter allowed the identification and quantification of a greater number of proteins. All methods were equivalent regarding quantitative stability. However, important differences were observed regarding post-translational modifications. In particular, methionine oxidation was higher with the tube-gel method than with the other methods. Based on these results, and considering time, simplicity, and cost aspects, we conclude that the miniaturized tube-gel method is suitable for sample preparation in the context of large-scale experiments.

Identification of Borrelia protein candidates in mouse skin for potential diagnosis of disseminated Lyme borreliosis

In vector-borne diseases, the skin plays an essential role in the transmission of vector-borne pathogens between the vertebrate host and blood-feeding arthropods and in pathogen persistence. Borrelia burgdorferi sensu lato is a tick-borne bacterium that causes Lyme borreliosis (LB) in humans. This pathogen may establish a long-lasting infection in its natural vertebrate host where it can persist in the skin and some other organs. Using a mouse model, we demonstrate that Borrelia targets the skin regardless of the route of inoculation, and can persist there at low densities that are difficult to detect via qPCR, but that were infective for blood-feeding ticks. Application of immunosuppressive dermocorticoids at 40 days post-infection (PI) significantly enhanced the Borrelia population size in the mouse skin. We used non-targeted (Ge-LC-MS/MS) and targeted (SRM-MS) proteomics to detect several Borrelia-specific proteins in the mouse skin at 40 days PI. Detected Borrelia proteins included flagellin, VlsE and GAPDH. An important problem in LB is the lack of diagnosis methods capable of detecting active infection in humans suffering from disseminated LB. The identification of Borrelia proteins in skin biopsies may provide new approaches for assessing active infection in disseminated manifestations.