Data-independent Acquisition Approach Research Articles

Small molecule modulation of protein corona for deep plasma proteome profiling

The protein corona formed on nanoparticles (NPs) has potential as a valuable diagnostic tool for improving plasma proteome coverage. Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules allows for the detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depletes the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. Employing an optimized data-independent acquisition approach, the inclusion of phosphatidylcholine leads to the detection of 1436 proteins in a single plasma sample. Our molecular dynamics results reveal that phosphatidylcholine interacts with albumin via hydrophobic interactions, H-bonds, and water bridges. The addition of phosphatidylcholine also enables the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate the widespread adoption of this methodology for the identification and clinical translation of biomarkers.

Optimization of protein identifications through the use of different chromatographic approaches and bioinformatic pipelines.

Selection of proteomic workflows for a given project can be a daunting task. This research provides a guide outlining the impact on protein identification of different steps such as chromatographic separation, data acquisition strategies, and bioinformatic pipelines. The data presented here will help experts and nonexpert proteomic users to increase proteome coverage and peptide identification. HeLa protein digests were analyzed through different C18 chromatographic columns (15 and 50 cm in length), using top 12 data-dependent acquisition (DDA), top 20 DDA, and data-independent acquisition (DIA) with a nanospray source in positive mode in a Thermo Q Exactive instrument. The raw data were analyzed using different search engines, rescoring approaches, and multi-engine searches. The results were analyzed in the context of peptide and protein identifications, precursor properties, and computation requirements to understand the differences between methods. Our results showed that higher column lengths and top N DDA approaches were able to significantly increase protein identifications. The use of multiple search engines yielded limited gains, whereas the use of rescoring methods clearly outperformed other strategies. Finally, DIA approaches, although successful at generating new identifications, had a limited performance influenced by the previous collection of DDA data, which could prohibitively increase instrument time. Nonetheless, the use of library-free methods showed promising results. Our results highlight the impact of different experimental approaches on proteome coverage. Changes in chromatographic columns, data acquisition, or bioinformatic analysis can significantly increase the number of protein identifications (>400%). Thus, this research provides a reference upon which to build a successful proteomic workflow with different considerations at every step.

Deep Plasma Proteome Profiling by Modulating Single Nanoparticle Protein Corona with Small Molecules.

The protein corona, a dynamic biomolecular layer that forms on nanoparticle (NP) surfaces upon exposure to biological fluids is emerging as a valuable diagnostic tool for improving plasma proteome coverage analyzed by liquid chromatography-mass spectrometry (LC-MS/MS). Here, we show that spiking small molecules, including metabolites, lipids, vitamins, and nutrients (namely, glucose, triglyceride, diglycerol, phosphatidylcholine, phosphatidylethanolamine, L-α-phosphatidylinositol, inosine 5'-monophosphate, and B complex), into plasma can induce diverse protein corona patterns on otherwise identical NPs, significantly enhancing the depth of plasma proteome profiling. The protein coronas on polystyrene NPs when exposed to plasma treated with an array of small molecules (n=10) allowed for detection of 1793 proteins marking an 8.25-fold increase in the number of quantified proteins compared to plasma alone (218 proteins) and a 2.63-fold increase relative to the untreated protein corona (681 proteins). Furthermore, we discovered that adding 1000 µg/ml phosphatidylcholine could singularly enable the detection of 897 proteins. At this specific concentration, phosphatidylcholine selectively depleted the four most abundant plasma proteins, including albumin, thus reducing the dynamic range of plasma proteome and enabling the detection of proteins with lower abundance. By employing an optimized data-independent acquisition (DIA) approach, the inclusion of phosphatidylcholine led to the detection of 1436 proteins in a single plasma sample. Our molecular dynamic results revealed that phosphatidylcholine interacts with albumin via hydrophobic interactions, h-bonds, and water-bridges. Addition of phosphatidylcholine also enabled the detection of 337 additional proteoforms compared to untreated protein corona using a top-down proteomics approach. These significant achievements are made utilizing only a single NP type and one small molecule to analyze a single plasma sample, setting a new standard in plasma proteome profiling. Given the critical role of plasma proteomics in biomarker discovery and disease monitoring, we anticipate widespread adoption of this methodology for identification and clinical translation of proteomic biomarkers into FDA approved diagnostics.

Study on diagnostic-sensitive markers of primary immune thrombocytopaenia in children based on plasma proteomics.

To use proteomic techniques to identify sensitive diagnostic biomarkers for paediatric immune thrombocytopenia (ITP). We selected children in ITP and control groups, using a four-dimensional data-independent acquisition approach (4D-DIA) to analyse its protein expression. The significantly differentially expressed proteins were selected for enzyme-linked immunosorbent assay (ELISA) validation in a cohort comprising 50 samples (13 healthy controls, 15 secondary thrombocytopenia controls and 22 children with ITP). Receiver operating characteristics (ROC) were generated to diagnose ITP and to assess the diagnostic effectiveness of this approach. Compared with the control group, 55 differentially expressed proteins (43 increased and 12 decreased) were determined in the ITP group. Matrix metalloproteinases-9 (MMP-9) and thrombospondin-1 (THBS1) were significantly expressed and selected for ELISA. The verification outcomes aligned with the findings from the proteomic examinations. In contrast to the control cohort, the ITP subjects exhibited markedly elevated plasma MMP-9 levels and reduced plasma THBS1 concentrations. Additionally, the ROC curves indicated the diagnostic value of these biomarkers. In conclusion, proteomics facilitates identifying the sensitive biomarkers for ITP diagnosis. We have preliminarily selected two differentially expressed proteins, MMP-9 and THBS1, whose potential role as biomarkers for diagnosing ITP requires further research.

Development of a high-throughput kinase activity platform using nanoLC-MS/MS with DIA approach for studying the anti-cancer mechanism of Taxol in ovarian cancer

BackgroundProtein phosphorylation by protein kinases plays a pivotal role in increasing protein diversity, thereby influencing various cellular functions. However, due to the relatively low abundance of phosphopeptides in a mixture of peptides and the ion-suppression effect of non-phosphorylated peptides, the detection of phosphopeptides is not straightforward. ResultsHerein, a quantitative high-throughput platform was developed for assessing multikinase activity using nano-LC-MS/MS with a data-independent acquisition (DIA) approach. This platform was evaluated by studying the kinase activity in Taxol-treated SKOV3 cells. A library containing 38 peptide substrates was designed and analyzed to determine the activities of major kinases involved in cancer development. Twenty-three synthetic peptide substrates showed significant phosphorylation changes in triplicate biological experiments, as further verified by western blotting. Our findings reveal that Taxol suppressed SKOV3 cell survival by activating AMPK and suppressing the PI3K-Akt-dependent pathway, ultimately leading to mTOR inhibition. Furthermore, in combination with ERK, Akt, SGK, CK1, and ErbB2 inhibitors, Taxol enhanced the inhibitory effect on ovarian cancer. SignificanceThis platform can be an attractive approach for large-scale kinase activity studies to comprehensively uncover the mechanisms of drug-disease treatment and to investigate a more effective therapy strategy.

Comparative salivary proteomics analysis of children with and without early childhood caries using the DIA approach: A pilot study.

To screen differentially expressed proteins (DEPs) in the saliva of Early childhood caries (ECC) with different degrees of severity. The proteomic profiles of salivary of children with ECC of varying severity by data independent acquisition data independent acquisition (DIA) technique. A total of 12 preschool children aged 3-5 years were included in this study. In this study, a total of 15,083 peptides and 1944 proteins were quantified; The results of DEPs screening showed that 34 DEPs were identified between the group H and the group LC, including 18 up-regulated proteins and 16 down-regulated proteins; 34 DEPs were screened between the group H and the group HC, including 17 up-regulated proteins and 17 down-regulated proteins; 42 DEPs were screened between the group LC and the group HC, including 18 up-regulated proteins and 24 down-regulated proteins. Among these DEPs, we screened several key proteins that may play a role in ECC, such as MK, histone H4, TGFβ3, ZG16B, MUC20, and SMR-3B. Salivary proteins, as important host factors of caries, are differentially expressed between the saliva of ECC children and healthy children. Specific DEPs are expected to become potential biomarkers for the diagnosis of ECC.

Comparative proteomics analysis of samples from hepatic cystic echinococcosis patients using data-independent acquisition approach

Cystic echinococcosis is a zoonotic disease resulting from infection caused by the larval stage of Echinococcus granulosus. This study aimed to assess the specific proteins that are potential candidates for the development of a vaccine against E. granulosus. The data-independent acquisition approach was employed to identify differentially expressed proteins (DEPs) in E. granulosus samples. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was employed to identify several noteworthy proteins. Results: The DEPs in E. granulosus samples were identified (245 pericystic wall vs. parasite-free yellowish granuloma (PYG, 1725 PY vs. PYG, 2274 PN vs. PYG). Further examination of these distinct proteins revealed their predominant enrichment in metabolic pathways, amyotrophic lateral sclerosis, and neurodegeneration-associated pathways. Notably, among these DEPs, SH3BGRL, MST1, TAGLN2, FABP5, UBE2V2, and RARRES2 exhibited significantly higher expression levels in the PYG group compared with the PY group (P < 0.05). The findings may contribute to the understanding of the pathological mechanisms underlying echinococcosis, providing valuable insights into the development of more effective diagnostic tools, treatment modalities, and preventive strategies. SignificanceCE is a major public health hazard in the western regions of China, Central Asia, South America, the Mediterranean countries, and eastern Africa. Echinococcus granulosus is responsible for zoonotic disease through infection Our analysis focuses on the proteins in various samples by data-dependent acquisition (DIA) for proteomic analysis. The importance of this research is to develop new strategies and targets to protect against E. granulosus infections in humans.

Effects of maternal and perinatal factors on human milk fat globule membrane proteome: A data independent acquisition approach

Milk fat globule membrane proteins (MFGMP) are essential for supporting infants’ growth and development. The composition of MFGMP may vary depending on maternal and perinatal factors. However, there is a lack of research on the effects of parity, maternal age, and mode of delivery on MFGMP. This study attempts to address this knowledge gap by utilizing a data-independent acquisition (DIA) approach to perform proteomics comparisons of human MFGMP. Mature human milk (MHM) was collected from Chinese women with different parities (multiparous vs. primiparous, MP vs. PP), maternal reproductive ages (age ≥30 vs. age <30, MA1 vs. MA2), and delivery modes (cesarean delivery vs. vaginal delivery, CD vs. VD). We found variations in MFGMP between different cohorts. Differentially expressed proteins (DEP) in MHM of primiparous and multiparous women were mostly related to innate immune response which provides the first line of defense against pathogens. Higher abundances of these DEP were found in the MHM of multiparous women. Meanwhile, DEP in MHM of women with different delivery modes were primarily related to inflammatory response and innate immune response. We postulate that upregulation of the defense response-related DEP in MHM of women who had cesarean delivery was in relation to modulation of the cesarean delivery-induced inflammatory response and to strengthen the immune defense of newborns.

Predicting the efficacy of glucocorticoids in pediatric primary immune thrombocytopenia using plasma proteomics.

Primary immune thrombocytopenia (ITP) is the most common acquired autoimmune bleeding disorder among children. While glucocorticoids are the primary first-line treatment for ITP treatment, they prove ineffective in certain patients. The challenge of identifying biomarkers capable of early prediction regarding the response to glucocorticoid therapy in ITP persists. This study aimed to identify ideal biomarkers for predicting glucocorticoid efficacy in patients with ITP using plasma proteomics. A four-dimensional data-independent acquisition approach was performed to determine the differentially expressed proteins in plasma samples collected from glucocorticoid-sensitive (GCS) (n=18) and glucocorticoid-resistant (GCR) (n=17) children with ITP treated with prednisone. The significantly differentially expressed proteins were selected for enzyme-linked immunosorbent assay validation in a cohort conprising 65 samples(30 healthy controls, 18 GCS and 17 GCR children with ITP). Receiver operating characteristics curves, calibration curves, and clinical decision curve analysis were used to determine the diagnostic efficacy of this method. 47 differentially expressed proteins (36 up-regulated and 11 down-regulated) were identified in the GCR group compared with the GCS group. The significantly differentially expressed proteins myosin heavy chain 9 (MYH9) and fetuin B (FETUB) were selected for enzyme-linked immunosorbent assay validation. The validation results were consistent with the proteomics analyses. Compared with the GCS group, the GCR group exhibited a significantly reduced the plasma concentration of MYH9 and elevated the plasma concentration of FETUB. Furthermore, the receiver operating characteristics curves, calibration curves, and clinical decision curve analysis demonstrated good diagnostic efficacy of these validated biomarkers. This study contributes to the establishment of objective biological indicators for precision therapy in children with ITP. More importantly, the proteins MYH9 and FETUB hold potential as a foundation for making informed decisions regarding alternative treatments for drugresistant patients, thereby preventing treatment delays.

Cannflavins A and B with Anti-Ferroptosis, Anti-Glycation, and Antioxidant Activities Protect Human Keratinocytes in a Cell Death Model with Erastin and Reactive Carbonyl Species.

Precursors of advanced glycation endproducts, namely, reactive carbonyl species (RCSs), are aging biomarkers that contribute to cell death. However, the impact of RCSs on ferroptosis-an iron-dependent form of cell death-in skin cells remains unknown. Herein, we constructed a cellular model (with human keratinocyte; HaCaT cells) to evaluate the cytotoxicity of the combinations of RCSs (including glyoxal; GO and methyglyoxal; MGO) and erastin (a ferroptosis inducer) using bioassays (measuring cellular lipid peroxidation and iron content) and proteomics with sequential window acquisition of all theoretical mass spectra. Additionally, a data-independent acquisition approach was used to characterize RCSs' and erastin's molecular network including genes, canonical pathways, and upstream regulators. Using this model, we evaluated the cytoprotective effects of two dietary flavonoids including cannflavins A and B against RCSs and erastin-induced cytotoxicity in HaCaT cells. Cannflavins A and B (at 0.625 to 20 µM) inhibited ferroptosis by restoring the cell viability (by 56.6-78.6% and 63.8-81.1%) and suppressing cellular lipid peroxidation (by 42.3-70.2% and 28.8-63.6%), respectively. They also alleviated GO + erastin- or MGO + erastin-induced cytotoxicity by 62.2-67.6% and 56.1-69.3%, and 35.6-54.5% and 33.8-62.0%, respectively. Mechanistic studies supported that the cytoprotective effects of cannflavins A and B are associated with their antioxidant activities including free radical scavenging capacity and an inhibitory effect on glycation. This is the first study showing that cannflavins A and B protect human keratinocytes from RCSs + erastin-induced cytotoxicity, which supports their potential applications as dietary interventions for aging-related skin conditions.

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.

DeepFLR facilitates false localization rate control in phosphoproteomics

Protein phosphorylation is a post-translational modification crucial for many cellular processes and protein functions. Accurate identification and quantification of protein phosphosites at the proteome-wide level are challenging, not least because efficient tools for protein phosphosite false localization rate (FLR) control are lacking. Here, we propose DeepFLR, a deep learning-based framework for controlling the FLR in phosphoproteomics. DeepFLR includes a phosphopeptide tandem mass spectrum (MS/MS) prediction module based on deep learning and an FLR assessment module based on a target-decoy approach. DeepFLR improves the accuracy of phosphopeptide MS/MS prediction compared to existing tools. Furthermore, DeepFLR estimates FLR accurately for both synthetic and biological datasets, and localizes more phosphosites than probability-based methods. DeepFLR is compatible with data from different organisms, instruments types, and both data-dependent and data-independent acquisition approaches, thus enabling FLR estimation for a broad range of phosphoproteomics experiments.

Depletion of abundant plasma proteins for extracellular vesicle proteome characterization: benefits and pitfalls

Blood extracellular vesicles (EVs) play essential roles in cell–cell communication and their molecular cargo is a promising source of disease biomarkers. However, proteomic characterization of plasma-derived EVs is challenged by the presence of highly abundant plasma proteins, which limits the detection of less abundant proteins, and by the low number of EVs in biological fluids. The aim of this study was to investigate if the removal of abundant plasma proteins prior to EV isolation could improve plasma-derived EV characterization by LC–MS/MS and expand the proteome coverage. Plasma depletion was performed using a single-use spin column and EVs were isolated from only 100 µL of non-depleted and depleted plasma by size exclusion chromatography. Afterwards, EVs were characterized by nanoparticle tracking analysis and mass spectrometry–based proteomics using a data-independent acquisition approach. Depleted plasma-derived EVs had higher particle concentrations and particle-to-protein ratios. Depletion did increase the protein coverage with a higher number of identifications in EVs from depleted plasma (474 proteins) than from non-depleted (386 proteins). However, EVs derived from non-depleted plasma carried a slightly higher number of common EV markers. Overall, our findings suggest that plasma depletion prior to EV isolation by size exclusion chromatography provides higher yield and protein coverage, but slightly lower identification of EV markers. This study also showed the possibility to characterize the proteome of EVs derived from small plasma volumes, encouraging the clinical feasibility of the discovery of EV biomarkers.

Host cell protein quantification workflow using optimized standards combined with data-independent acquisition mass spectrometry

Monitoring of host cell proteins (HCPs) during the manufacturing of monoclonal antibodies (mAb) has become a critical requirement to provide effective and safe drug products. Enzyme-linked immunosorbent assays are still the gold standard methods for the quantification of protein impurities. However, this technique has several limitations and does, among others, not enable the precise identification of proteins. In this context, mass spectrometry (MS) became an alternative and orthogonal method that delivers qualitative and quantitative information on all identified HCPs. However, in order to be routinely implemented in biopharmaceutical companies, liquid chromatography-MS based methods still need to be standardized to provide highest sensitivity and robust and accurate quantification. Here, we present a promising MS-based analytical workflow coupling the use of an innovative quantification standard, the HCP Profiler solution, with a spectral library-based data-independent acquisition (DIA) method and strict data validation criteria. The performances of the HCP Profiler solution were compared to more conventional standard protein spikes and the DIA approach was benchmarked against a classical data-dependent acquisition on a series of samples produced at various stages of the manufacturing process. While we also explored spectral library-free DIA interpretation, the spectral library-based approach still showed highest accuracy and reproducibility (coefficients of variation < 10%) with a sensitivity down to the sub-ng/mg mAb level. Thus, this workflow is today mature to be used as a robust and straightforward method to support mAb manufacturing process developments and drug products quality control.

Online 2D High-pH and Low-pH Reversed-Phase Nano-LC-MS/MS System for Deep Proteome Analysis.

In bottom-up proteomic profiling, the complexity of proteome composition and wide dynamic range has created challenges on the limited number of protein identification and proteome coverage, especially in sample input-limited nanoflow (nano) LC-MS/MS analysis. Herein, we developed a fully automatic online 2D nano-LC-MS/MS system using both high-pH and low-pH reverse phase (RP) LCs on a single LC instrument toward comprehensive proteomics analysis. Compared to conventional microflow 2D-LC, the high-pH RP trapping column demonstrated a low sample requirement of cellular protein digest at the μg level with good fractionation resolution of >90% peptides in a single fraction. Compared to the offline 2D RP-RP nano-LC-QTOF using C18-HPLC column and C18-Stage Tip, and 1D nano-LC-QTOF system, superior coverage was observed on the higher number of identified protein groups/unique peptides by 1.35-/1.68-, 1.46-/1.75-, and 3.21-/4.35-fold, respectively, using an online 2D RP-RP nano-LC-QTOF mass spectrometer. On the evolution of quantitation performance, the online 2D high-/low-pH RP data-independent acquisition (DIA) showed a higher reproducibility in protein groups intensity (R2 > 0.977) and more quantified proteins than that obtained using the offline 2D high-/low-pH RP DIA approach. Using an advanced Orbitrap Exploris 480 mass spectrometer, ∼1.9-fold higher proteome coverage was also observed in our 2D online RP-RP system (6039 protein groups) compared to the 1D nano-LC system (3133 protein groups). In summary, the online 2D nano-LC-MS/MS platform can be a sensitive and robust approach compatible with conventional nano-LC instruments for deep proteome coverage of trace amounts of samples.

Four-dimensional proteomics analysis of human cerebrospinal fluid with trapped ion mobility spectrometry using PASEF.

A quadrupole time-of-flight mass spectrometer coupled with a trapped ion mobility spectrometry (timsTOF) operated in parallel accumulation-serial fragmentation (PASEF) mode has recently emerged as a platform capable of providing four-dimensional (4D) features comprising of elution time, collision cross section (CCS), mass-to-charge ratio, and intensity of peptides. The PASEF mode provides ∼100% ion sampling efficiency both in data-dependent acquisition (DDA) and data-independent acquisition (DIA) modes without sacrificing sensitivity. In addition, targeted measurements using PASEF integrated parallel reaction monitoring (PRM) mode have also been described. However, only limited number of studies have used timsTOF for analysis of clinical samples. Although Orbitrap mass spectrometers have been used for biomarker discovery from cerebrospinal fluid (CSF) in a variety of neurological diseases, these Orbitrap-derived datasets cannot readily be applied for driving experiments on timsTOF mass spectrometers. We generated a catalog of peptides and proteins in human CSF in DDA mode on a timsTOF mass spectrometer and used these data to build a spectral library. This strategy allowed us to use elution times and ion mobility values from the spectral library to design PRM experiments for quantifying previously discovered biomarkers from CSF samples in Alzheimer's disease. When the same samples were analyzed using a DIA approach combined with a spectral library search, a higher number of proteins were identified than in a library-free approach. Overall, we have established a spectral library of CSF as a resource and demonstrated its utility for PRM and DIA studies, which should facilitate studies of neurological disorders.

Comparative analysis of changes in whey proteins of goat milk throughout the lactation cycle using quantitative proteomics.

The composition and content of goat milk proteins are affected by many factors and have been extensively studied. However, variation in whey protein composition in goat milk throughout the lactation cycle has not been clarified. In the current study, 15 dairy goats were selected, and milk samples were collected at 1, 3, 30, 90, 150, and 240 d after delivery. Whey proteins were separated and digested and then identified using data-independent acquisition (DIA) and data-dependent acquisition proteomics approaches. Protein profiles identified using DIA were consistent with those of the data-dependent acquisition proteomics approach according to clustering and principal component analyses. Significant differences in the abundance of 238 proteins around the lactation cycle were identified using the DIA approach. Developmental changes of the whey proteome corresponding to lactation stage were revealed: plasminogen, α-2-macroglobulin, and fibronectin levels decreased from d 1 to 240, whereas polymeric immunoglobulin receptor, nucleobindin 2, fatty acid-binding protein 3, and lactoperoxidase increased from d 1 to 240. Protein-protein interaction analysis showed that fibronectin with a higher degree of connectivity is a central node. The findings are of great significance to better understanding the potential role of specific proteins and the mechanism of protein biosynthesis or intercellular transport in the mammary glands related to the physiological changes of dairy goats.

Developmental changes in proteins of casein micelles in goat milk using data-independent acquisition-based proteomics methods during the lactation cycle.

Casein micelles (CM) play an important role in milk secretion, stability, and processing. The composition and content of milk proteins are affected by physiological factors, which have been widely investigated. However, the variation in CM proteins in goat milk throughout the lactation cycle has yet to be fully clarified. In the current study, milk samples were collected at d 1, 3, 30, 90, 150, and 240 of lactation from 15 dairy goats. The size of CM was determined using laser light scattering, and CM proteins were separated, digested, and identified using data-independent acquisition (DIA) and data-dependent acquisition (DDA)-based proteomics approaches. According to clustering and principal component analysis, protein profiles identified using DIA were similar to those identified using the DDA approach. Significant differences in the abundance of 115 proteins during the lactation cycle were identified using the DIA approach. Developmental changes in the CM proteome corresponding to lactation stages were revealed: levels of lecithin cholesterol acyltransferase, folate receptor α, and prominin 2 increased from 1 to 240 d, whereas levels of growth/differentiation factor 8, peptidoglycan-recognition protein, and 45 kDa calcium-binding protein decreased in the same period. In addition, lipoprotein lipase, glycoprotein IIIb, and α-lactalbumin levels increased from 1 to 90 d and then decreased to 240 d, which is consistent with the change in CM size. Protein-protein interaction analysis showed that fibronectin, albumin, and apolipoprotein E interacted more with other proteins at the central node. These findings indicate that changes in the CM proteome during lactation could be related to requirements of newborn development, as well as mammary gland development, and may thus contribute to elucidating the physical and chemical properties of CM.

Quantitative Proteomics Identifies Reduced NRF2 Activity and Mitochondrial Dysfunction in Atopic Dermatitis

Atopic dermatitis is the most common inflammatory skin disease and is characterized by a deficient epidermal barrier and cutaneous inflammation. Genetic studies suggest a key role of keratinocytes in atopic dermatitis pathogenesis, but the alterations in the proteome that occur in the full epidermis have not been defined. Using a pressure-cycling technology and data-independent acquisition approach, we performed quantitative proteomics of epidermis from healthy volunteers and lesional and nonlesional patient skin. Results were validated by targeted proteomics using parallel reaction monitoring mass spectrometry and immunofluorescence staining. Proteins that were differentially abundant in the epidermis of patients with atopic dermatitis versus in healthy control reflect the strong inflammation in lesional skin and the defect in keratinocyte differentiation and epidermal stratification that already characterizes nonlesional skin. Most importantly, they reveal impaired activation of the NRF2-antioxidant pathway and reduced abundance of mitochondrial proteins involved in key metabolic pathways in the affected epidermis. Analysis of primary human keratinocytes with small interfering RNA‒mediated NRF2 knockdown revealed that the impaired NRF2 activation and mitochondrial abnormalities are partially interlinked. These results provide insight into the molecular alterations in the epidermis of patients with atopic dermatitis and identify potential targets for pharmaceutical intervention.

Is DIA proteomics data FAIR? Current data sharing practices, available bioinformatics infrastructure and recommendations for the future.

Data independent acquisition (DIA) proteomics techniques have matured enormously in recent years, thanks to multiple technical developments in, for example, instrumentation and data analysis approaches. However, there are many improvements that are still possible for DIA data in the area of the FAIR (Findability, Accessibility, Interoperability and Reusability) data principles. These include more tailored data sharing practices and open data standards since public databases and data standards for proteomics were mostly designed with DDA data in mind. Here we first describe the current state of the art in the context of FAIR data for proteomics in general, and for DIA approaches in particular. For improving the current situation for DIA data, we make the following recommendations for the future: (i) development of an open data standard for spectral libraries; (ii) make mandatory the availability of the spectral libraries used in DIA experiments in ProteomeXchange resources; (iii) improve the support for DIA data in the data standards developed by the Proteomics Standards Initiative; and (iv) improve the support for DIA datasets in ProteomeXchange resources, including more tailored metadata requirements.