Determination of double bond positions in unsaturated fatty acids by pre-column derivatization with dimethyl and dipyridyl disulfide followed by LC-SWATH-MS analysis.

Comprehensive in-depth structural characterization of free mono-unsaturated and polyunsaturated fatty acids often requires the determination of carbon-carbon double bond positions due to their impact on physiological properties and relevance in biological samples or during impurity profiling of pharmaceuticals. In this research, we report on the evaluation of disulfides as suitable derivatization reagents for the determination of carbon-carbon double bond positions of unsaturated free fatty acids by UHPLC-ESI-QTOF-MS/MS analysis and SWATH (sequential windowed acquisition of all theoretical mass spectra) acquisition. Iodine-catalyzed derivatization of C = C double bonds with dimethyl disulfide (DMDS) enabled detection of characteristic carboxy-terminal MS2 fragments for various fatty acids in ESI negative mode. The determination of double bond positions of fatty acids with up to three double bonds, the transfer of the method to plasma samples, and its limitations have been shown. To achieve charge-switching for positive ion mode MS-detection, derivatization with 2,2'-dipyridyldisulfide (DPDS) was investigated. It enabled detection of both corresponding characteristic omega-end- and carboxy-end-fragments for fatty acids with up to two double bonds in positive ion mode. It provides a straightforward strategy for designing MRM transitions for targeted LC-MS/MS assays. Both derivatization techniques represent a simple and inexpensive way for the determination of double bond positions in fatty acids with low number of double bonds. No adaptation of MS hardware is required and the specific isotopic pattern of resulting sulfur-containing products provides additional structural confirmation. This reaction scheme opens up the avenue of structural tuning of disulfide reagents beyond DMDS and DPDS using reagents like cystine and analogs to achieve enhanced performance and sensitivity.

Integration WGCNA with LC-MS data for evaluating the processing status and transformation rules of Ligustri Lucidi Fructus: A novel strategy for evaluating the processing technology of traditional Chinese medicines.

Ligustri Lucidi Fructus (LLF) is a traditional Chinese medicine (TCM) to treat hepatopathy and osteopathy. Wine-processed LLF (WLLF) was much more widely used than raw LLF (RLLF) in clinical practice, however, there is no consensus on processing time. To investigate the processing status of WLLF and transformation rules during processing, a UHPLC-Q-Orbitrap-MS method combined with data-independent acquisition (DIA) mode was firstly established and 227 compounds were identified or tentatively identified. Subsequently, a novel strategy using integration weighted gene co-expression network analysis (WGCNA) with LC-MS data was proposed. A total of 73 differential metabolites were screened out between RLLF and WLLF (wine steaming for 18 h). Meanwhile, the concentration of 11 differential compounds for WLLF was quantified. Finally, correlations between compounds were analyzed by WGCNA and the top five compounds negatively correlated with salidroside were validated, revealing that G13, specnuezhenide, oleuropein, acteoside, and neonuzhenide could be transformed into salidroside and its analogues during processing, respectively. The results indicated that our proposed strategy could be effectively employed to evaluate the processing status of TCMs.

Simple and robust high-throughput serum proteomics workflow with low-microflow LC-MS/MS.

Clinical proteomics has substantially advanced in identifying and quantifying proteins from biofluids, such as blood, contributing to the discovery of biomarkers. The throughput and reproducibility of serum proteomics for large-scale clinical sample analyses require improvements. High-throughput analysis typically relies on automated equipment, which can be costly and has limited accessibility. In this study, we present a rapid, high-throughput workflow low-microflow LC-MS/MS method without automation. This workflow was optimized to minimize the preparation time and costs by omitting the depletion and desalting steps. The developed method was applied to data-independent acquisition (DIA) analysis of 235 samples, and it consistently yielded approximately 6000 peptides and 600 protein groups, including 33 FDA-approved biomarkers. Our results demonstrate that an 18-min DIA high-throughput workflow, assessed through intermittently collected quality control samples, ensures reproducibility and stability even with 2 µL of serum. It was successfully used to analyze serum samples from patients with diabetes having chronic kidney disease (CKD), and could identify five dysregulated proteins across various CKD stages.

Interrogating data-independent acquisition LC-MS/MS for affinity proteomics.

Data-Independent Acquisition (DIA) LC-MS/MS is an attractive partner for co-immunoprecipitation (co-IP) and affinity proteomics in general. Reducing the variability of quantitation by DIA could increase the statistical contrast for detecting specific interactors versus what has been achieved in Data-Dependent Acquisition (DDA). By interrogating affinity proteomes featuring both DDA and DIA experiments, we sought to evaluate the spectral libraries, the missingness of protein quantity tables, and the CV of protein quantities in six studies representing three different instrument manufacturers. We examined four contemporary bioinformatics workflows for DIA: FragPipe, DIA-NN, Spectronaut, and MaxQuant. We determined that (1) identifying spectral libraries directly from DIA experiments works well enough that separate DDA experiments do not produce larger spectral libraries when given equivalent instrument time; (2) experiments involving mock pull-downs or IgG controls may feature such indistinct signals that contemporary software will struggle to quantify them; (3) measured CV values were well controlled by Spectronaut and DIA-NN (and FragPipe, which implements DIA-NN for the quantitation step); and (4) when FragPipe builds spectral libraries and quantifies proteins from DIA experiments rather than performing both operations in DDA experiments, the DIA route results in a larger number of proteins quantified without missing values as well as lower CV for measured protein quantities. Supplementary Information: The online version contains supplementary material available at 10.1007/s42485-024-00166-4.

High-Resolution Tandem Mass Spectrometry for Metabolic Profiling of Ocotea diospyrifolia (Meisn.) Mez Leaves.

Ocotea is an important genus of Lauraceae plant family that comprises over 400 species, many of which pose challenges in taxonomic differentiation due to their complex botanical characteristics. Chemosystematics, and more recently, chemophenetics, have emerged as valuable tools to address these challenges based on their natural products (NPs) composition. O. diospyrifolia (Meisn.) Mez is a poorly studied species with known pharmacological potential. Here, we applied ultra-high performance liquid chromatography coupled with high-resolution tandem mass spectrometry (UHPLC-HRMS) allied to a curated in-house database with all previous isolated NPs from the Ocotea genus (OcoteaDB), gas phase fragmentations reactions, and biosynthesis. The strategy resulted in compounds annotated in confidence levels 2 (n=27), 3 (n=231), and 4 (n=21) according to the Metabolomics Standards Initiative (MSI). Additional annotations based on fragmentation proposals (n=16) were also included. The study revealed that O. diospyrifolia is a great alkaloid producer, even though different lignoids, which also comes from the shikimate pathway, were annotated. Additionally, the flavonoid profile predominantly consists of flavonol glycosides, complementing prior reports. This study provides the first comprehensive chemical profile of O. diospyrifolia leaves, which corroborates the chemotaxonomy of the species, and also contributes to further characterization studies, as the UHPLC-HRMS data is publicly available.

Combining Data Independent Acquisition With Spike-In SILAC (DIA-SiS) Improves Proteome Coverage and Quantification.

Data-independent acquisition (DIA) is increasingly preferred over data-dependent acquisition due to its higher throughput and fewer missing values. Whereas data-dependent acquisition often uses stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mass differential tags for relative and absolute quantification and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios and certain high-throughput experiments. Spike-in SILAC (SiS) methods use an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA-SiS, leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed DIA-SiS and rigorously assessed its performance with mixed-species benchmark samples on bulk and single cell-like amount level. We demonstrate that DIA-SiS substantially improves proteome coverage and quantification compared to label-free approaches and reduces incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate, and comprehensive proteome profiling.

Integrating DIA Single-Cell Proteomics Data with the DiagnoMass Proteomic Hub for Biological Insights.

Single-cell proteomics has emerged as a powerful technology for unraveling the complexities of cellular heterogeneity, enabling insights into individual cell functions and pathologies. One of the primary challenges in single-cell proteomics is data generation, where low mass spectral signals often preclude the triggering of MS2 events. This challenge is addressed by Data Independent Acquisition (DIA), a data acquisition strategy that does not depend on peptide ion isotopic signatures to generate an MS2 event. In this study, we present data generated from the integration of DIA single-cell proteomics with a version of the DiagnoMass Proteomic Hub that was adapted to handle DIA data. DiagnoMass employs a hierarchical clustering methodology that enables the identification of tandem mass spectral clusters that are discriminative of biological conditions, thereby reducing the reliance on search engine biases for identifications. Nevertheless, a search engine (in this work, DIA-NN) can be integrated with DiagnoMass for spectral annotation. We used single-cell proteomic data from iPSC-derived neuroprogenitor cell cultures as a test study of this integrated approach. We were able to differentiate between control and Rett Syndrome patient cells to discern the proteomic variances potentially contributing to the disease's pathology. Our research confirms that the DiagnoMass-DIA synergy significantly enhances the identification of discriminative proteomic signatures, highlighting critical biological variations such as the presence of unique spectra that could be related to Rett Syndrome pathology.

Exploring Top-Down Mass Spectrometric Approaches To Probe Forest Cobra (Naja melanoleuca) Venom Proteoforms.

Snake venoms are comprised of bioactive proteins and peptides that facilitate severe snakebite envenomation symptoms. A comprehensive understanding of venom compositions and the subtle heterogeneity therein is important. While bottom-up proteomics has been the well-established approach to catalogue venom compositions, top-down proteomics has emerged as a complementary strategy to characterize venom heterogeneity at the intact protein level. However, top-down proteomics has not been as widely implemented in the snake venom field as bottom-up proteomics, with various emerging top-down methods yet to be developed for venom systems. Here, we have explored three main top-down mass spectrometry methodologies in a proof-of-concept study to characterize selected three-finger toxin and phospholipase A2 proteoforms from the forest cobra (Naja melanoleuca) venom. We demonstrated the utility of a data-independent acquisition mode "MSE" for untargeted fragmentation on a chromatographic time scale and its improvement in protein sequence coverage compared to conventional targeted tandem mass spectrometry analysis. We also showed that protein identification can be further improved using a hybrid fragmentation approach, combining electron-capture dissociation and collision-induced dissociation. Lastly, we reported the promising application of multifunctional cyclic ion mobility separation and post-ion mobility fragmentation on snake venom proteins for the first time.

Proteomic Characterization of Transfusable Blood Components: Fresh Frozen Plasma, Cryoprecipitate, and Derived Extracellular Vesicles via Data-Independent Mass Spectrometry.

Extracellular vesicles (EVs) are a heterogeneous collection of particles that play a crucial role in cell-to-cell communication, primarily due to their ability to transport molecules, such as proteins. Thus, profiling EV-associated proteins offers insight into their biological effects. EVs can be isolated from various biological fluids, including donor blood components such as cryoprecipitate and fresh frozen plasma (FFP). In this study, we conducted a proteomic analysis of five single donor units of cryoprecipitate, FFP, and EVs derived from these blood components using a quantitative mass spectrometry approach. EVs were successfully isolated from both cryoprecipitate and FFP based on community guidelines. We identified and quantified approximately 360 proteins across all sample groups. Principal component analysis and heatmaps revealed that both cryoprecipitate and FFP are similar. Similarly, EVs derived from cryoprecipitate and FFP are comparable. However, they differ between the originating fluids and their derived EVs. Using the R-package MS-DAP, differentially expressed proteins (DEPs) were identified. The DEPs for all comparisons, when submitted for gene enrichment analysis, are involved in the complement and coagulation pathways. The protein profile generated from this study will have important clinical implications in increasing our knowledge of the proteins that are associated with EVs derived from blood components.

Deep Plasma Proteomics with Data-Independent Acquisition: Clinical Study Protocol Optimization with a COVID-19 Cohort.

Plasma proteomics is a precious tool in human disease research but requires extensive sample preparation in order to perform in-depth analysis and biomarker discovery using traditional data-dependent acquisition (DDA). Here, we highlight the efficacy of combining moderate plasma prefractionation and data-independent acquisition (DIA) to significantly improve proteome coverage and depth while remaining cost-efficient. Using human plasma collected from a 20-patient COVID-19 cohort, our method utilizes commonly available solutions for depletion, sample preparation, and fractionation, followed by 3 liquid chromatography-mass spectrometry/MS (LC-MS/MS) injections for a 360 min total DIA run time. We detect 1321 proteins on average per patient and 2031 unique proteins across the cohort. Differential analysis further demonstrates the applicability of this method for plasma proteomic research and clinical biomarker identification, identifying hundreds of differentially abundant proteins at biological concentrations as low as 47 ng/L in human plasma. Data are available via ProteomeXchange with the identifier PXD047901. In summary, this study introduces a streamlined, cost-effective approach to deep plasma proteome analysis, expanding its utility beyond classical research environments and enabling larger-scale multiomics investigations in clinical settings. Our comparative analysis revealed that fractionation, whether the samples were pooled or separate postfractionation, significantly improved the number of proteins quantified. This underscores the value of fractionation in enhancing the depth of plasma proteome analysis, thereby offering a more comprehensive landscape for biomarker discovery in diseases such as COVID-19.

Identification of protein biomarkers in wastewater linked to the incidence of COVID-19.

Wastewater-based epidemiological (WBE) surveillance is a viable disease surveillance technique capable of monitoring the spread of infectious disease agents in sewershed communities. In addition to detecting viral genomes in wastewater, WBE surveillance can identify other endogenous biomarkers that are significantly elevated and excreted in the saliva, urine, and/or stool of infected individuals. Human protein biomarkers allow the realization of real-time WBE surveillance using highly sensitive biosensors. In this study, we analyzed endogenous protein biomarkers present in wastewater influent through liquid chromatography-tandem mass spectrophotometry and scaffold data-independent acquisition to identify candidate target protein biomarkers for WBE surveillance of SARS-CoV-2. We found that out of the 1382 proteins observed in the wastewater samples, 44 were human proteins associated with infectious diseases. These included immune response substances such as immunoglobulins, cytokine-chemokines, and complements, as well as proteins belonging to antimicrobial and antiviral groups. A significant correlation was observed between the intensity of human infectious disease-related protein biomarkers in wastewater and COVID-19 case numbers. Real-time WBE surveillance using biosensors targeting immune response proteins, such as antibodies or immunoglobulins, in wastewater holds promise for expediting the implementation of relevant policies for the effective prevention of infectious diseases in the near future.

A New Evaluation Metric for Quantitative Accuracy of LC-MS/MS-Based Proteomics with Data-Independent Acquisition.

Data-independent acquisition (DIA) has improved the identification and quantitation coverage of peptides and proteins in liquid chromatography-tandem mass spectrometry-based proteomics. However, different DIA data-processing tools can produce very different identification and quantitation results for the same data set. Currently, benchmarking studies of DIA tools are predominantly focused on comparing the identification results, while the quantitative accuracy of DIA measurements is acknowledged to be important but insufficiently investigated, and the absence of suitable metrics for comparing quantitative accuracy is one of the reasons. A new metric is proposed for the evaluation of quantitative accuracy to avoid the influence of differences in false discovery rate control stringency. The part of the quantitation results with high reliability was acquired from each DIA tool first, and the quantitative accuracy was evaluated by comparing quantification error rates at the same number of accurate ratios. From the results of four benchmark data sets, the proposed metric was shown to be more sensitive to discriminating the quantitative performance of DIA tools. Moreover, the DIA tools with advantages in quantitative accuracy were consistently revealed by this metric. The proposed metric can also help researchers in optimizing algorithms of the same DIA tool and sample preprocessing methods to enhance quantitative accuracy.

Data-independent acquisition in metaproteomics.

INTRODUCTION: Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data. AREAS COVERED: This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed. EXPERT OPINION: Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

Identification of COL3A1 as a candidate protein involved in the crosstalk between obesity and diarrhea using quantitative proteomics and machine learning.

BACKGROUND: Increasing epidemiologic studies have shown a positive correlation between obesity and chronic diarrhea. Nevertheless, the precise etiology remains uncertain. METHODS: We performed a comprehensive proteomics analysis utilizing the data-independent acquisition (DIA) technique on jejunal tissues from patients with obesity and chronic diarrhea (OD, n = 33), obese patients (OB, n = 10), and healthy controls (n = 8). Differentially expressed proteins (DEPs) in OD vs. control and OD vs. OB comparisons were subjected to pathway enrichment and protein-protein interaction (PPI) network analysis. Machine learning algorithms were adopted on overlapping DEPs in both comparisons. The candidate protein was further validated using Western blot, immunohistochemistry (IHC), and in vitro experiments. RESULTS: We identified 189 and 228 DEPs in OD vs. control and OD vs. OB comparisons, respectively. DEPs in both comparisons were co-enriched in extracellular matrix (ECM) organization. Downregulated DEPs were associated with tight junction and ECM-receptor interaction in OD vs. control and OD vs. OB comparisons, respectively. Machine learning algorithms selected 3 proteins from 14 overlapping DEPs in both comparisons, among which collagen alpha-1(III) chain (COL3A1) was identified as a core protein in PPI networks. Western blot and IHC verified the expression of COL3A1. Moreover, the tight junction-related proteins decreased after the knockdown of COL3A1 in Caco2 intestinal cells upon PA challenge, consistent with the proteomics results. CONCLUSIONS: We generated in-depth profiling of a proteomic dataset from samples of OD patients and provided unique insights into disease pathogenesis. COL3A1 was involved in the crosstalk between obesity and intestinal homeostasis via the ECM-receptor interaction pathway.

Amniotic fluid proteomic analysis identifies IL1RL1, APOE, and NECTIN4 as new biomarkers for preterm birth.

BACKGROUND: Despite extensive research, the identification of effective biomarkers for early prediction of preterm birth (PTB) continues to be a challenging endeavor. This study aims to identify amniotic fluid (AF) protein biomarkers useful for the early diagnosis of PTB. METHODS: We initially identified the protein expression profiles in the AF of women with PTB (n = 22) and full-term birth (FTB, n = 22), from the First People's Hospital of Yunnan Province who underwent amniocentesis from November 2019 to February 2020, using mass spectrometry employing the data-independent acquisition (DIA) technique, and then analyzed differentially expressed proteins (DEPs). Subsequently, the least absolute shrinkage and selection operator (LASSO) and random forest analysis were employed to further screen the key proteins for PTB biomarker identification. The receiver operating characteristic (ROC) analysis, calibration plots, and decision curve analyses (DCA) were utilized to assess the discrimination and calibration of the key biomarkers. RESULTS: A total of 25 DEPs were identified between the PTB and FTB groups, comprising 13 up-regulated and 12 down-regulated proteins. Three key protein biomarkers for early PTB diagnosis were identified: IL1RL1 (interleukin-1 receptor-like 1), APOE (apolipoprotein E), and NECTIN4 (nectin cell adhesion molecule 4). The results of the ROC analysis showed that the area under the curve (AUC) of the three proteins combined as a biomarker for early diagnosis of PTB was 0.913 (95% CI: 0.823-1.000), with a sensitivity of 0.864 and a specificity of 0.955, both superior to those of the individual biomarkers. Bootstrap internal validation revealed a concordance index (C-index) of 0.878, with a sensitivity of 0.812 and a specificity of 0.773, indicating the robust predictive performance of these biomarkers. CONCLUSIONS: We identified three previously unexplored yet potentially useful protein biomarkers in AF for early PTB diagnosis: IL1RL1, APOE, and NECTIN4.

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.

Capillary zone electrophoresis-tandem mass spectrometry for in-depth proteomics analysis via data-independent acquisition.

A capillary zone electrophoresis (CZE) system was coupled to an Orbitrap mass spectrometer operating in a data-independent acquisition (DIA) mode for in-depth proteomics analysis. The performance of this CZE-DIA-MS system was systemically evaluated and optimized under different operating conditions. The performance of the fully optimized CZE-DIA-MS system was subsequently compared to the one by using the same CZE-MS system operating in a data-dependent acquisition (DDA) mode. The experimental results show that the numbers of identified peptides and proteins acquired in the DIA mode are much higher than the ones acquired in the DDA mode, especially with the small sample loading amount. Specifically, the numbers of identified peptides and proteins acquired in the DIA mode are 1.8-fold and 2-fold higher than the ones acquired in the DDA mode by using 12.5 ng Hela digests. The proteins identified in the DIA mode also cover almost all the proteins identified in the DDA mode. In addition, a potential cancer biomarker protein, carbohydrate antigen 125, undetected in the DDA mode, can be easily identified in the DIA mode even with 12.5 ng Hela digests. The performance of the CZE-DIA-MS system for in-depth proteomics analysis with a limited sample amount has been fully demonstrated for the first time through this study.

Iron starvation results in up-regulation of a probable Haloferax volcanii siderophore transporter.

The response of the haloarchaeal model organism Haloferax volcanii to iron starvation was analyzed at the proteome level by data-independent acquisition mass spectrometry. Cells grown in minimal medium with normal iron levels were compared to those grown under low iron conditions, with samples being separated into membrane and cytoplasmic fractions in order to focus on import/export processes which are frequently associated with metal homeostasis. Iron starvation not only caused a severe retardation of growth but also altered the levels of many proteins. Using a comprehensive annotated spectral library and data-independent acquisition mass spectrometry (DIA-MS), we found that iron starvation resulted in significant changes to both the membrane and the soluble proteomes of Hfx. volcanii. The most affected protein is the RND family permease HVO_A0467, which is 44-fold enriched in cells grown under iron starvation. The gene HVO_A0467 can be deleted suggesting that it is not essential under standard conditions. Compared to wild type cells the deletion strain shows only slight changes in growth and cell morphologies show no differences. Molecular docking predictions indicated that HVO_A0467 may be an exporter of the siderophore schizokinen for which a potential biosynthesis cluster is encoded in the Hfx. volcanii genome. Together, these findings confirm the importance of iron for archaeal cells and suggest HVO_0467 as a siderophore exporter.

Longitudinal analysis of the lung proteome reveals persistent repair months after mild to moderate COVID-19.

In order to assess homeostatic mechanisms in the lung after COVID-19, changes in the protein signature of bronchoalveolar lavage from 45 patients with mild to moderate disease at three phases (acute, recovery, and convalescent) are evaluated over a year. During the acute phase, inflamed and uninflamed phenotypes are characterized by the expression of tissue repair and host defense response molecules. With recovery, inflammatory and fibrogenic mediators decline and clinical symptoms abate. However, at 9 months, quantified radiographic abnormalities resolve in the majority of patients, and yet compared to healthy persons, all showed ongoing activation of cellular repair processes and depression of the renin-kallikrein-kinin, coagulation, and complement systems. This dissociation of prolonged reparative processes from symptom and radiographic resolution suggests that occult ongoing disruption of the lung proteome is underrecognized and may be relevant to recovery from other serious viral pneumonias.

Reusing Peptide Spectral Reference Libraries to Discover Putative Plasma Biomarkers of Response to Cancer Chemotherapy.

Biofluids such as blood plasma are rich reservoirs of potential biomarkers for disease diagnosis, prognosis, and prediction of treatment response. However, mass spectrometry analysis of circulating plasma proteins remains challenging. The introduction of data-independent acquisition mass spectrometry (DIA-MS) is an important step toward addressing detection of less abundant plasma proteins. Numerous plasma peptide MS/MS spectral library datasets produced from extensive plasma fractionation are accessible from public archives, and these can be repurposed as spectral reference libraries to increase the depth of proteomic analysis when DIA-MS is used. Here we describe the workflow that relies on reusing the existing spectral reference libraries by populating them with locally obtained peptide MS/MS data acquired by DIA-MS. This approach was demonstrated effectively to identify putative plasma biomarkers of response to neoadjuvant chemotherapy in the setting of pancreatic ductal adenocarcinoma (PDAC) (O'Rourke et al., J Proteomics 231:103998, 2021).