Evaluation of the Sensitivity and Reproducibility of Targeted Proteomic Analysis Using Data-Independent Acquisition for Serum and Cerebrospinal Fluid Proteins

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.

Serum Proteomics Identified TAFI as a Potential Molecule Facilitating the Migration of Peripheral Monocytes to Damaged White Matter During Chronic Cerebral Hypoperfusion

Neuroinflammation is assumed as the critical pathophysiologic mechanism of white matter lesions (WMLs), and infiltrated peripheral monocyte-derived macrophages are implicated in the development of neuroinflammation. This study sought to explore the blood molecules that promote the migration of peripheral monocytes to the sites of WMLs. The serum protein expression profiles of patients and Sprague-Dawley rat models with WMLs were detected by data-independent acquisition (DIA) proteomics technique. Compared with corresponding control groups, we acquired 62 and 41 differentially expressed proteins (DEPs) in the serum of patients and model rats with WMLs respectively. Bioinformatics investigations demonstrated that these DEPs were linked to various Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and Gene Ontology (GO) terms involved in neuroinflammation. Afterward, we identified thrombin-activatable fibrinolysis inhibitor (TAFI) as a shared and overexpressed protein in clinical and animal serum samples, which was further verified by enzyme-linked immunosorbent assay. Additionally, an upregulation of TAFI was also observed in the white matter of rat models, and the inhibition of TAFI impeded the migration of peripheral monocytes to the area of WMLs. In vitro experiments suggested that TAFI could enhance the migration ability of RAW264.7 cells and increase the expression of Ccr2. Our study demonstrates that neuroinflammatory signals can be detected in the peripheral blood of WMLs patients and model rats. TAFI may serve as a potential protein that promotes the migration of peripheral monocytes to WMLs regions, thereby providing a novel molecular target for further investigation into the interaction between the central and peripheral immune systems.

Data-independent acquisition proteomics of cerebrospinal fluid implicates endoplasmic reticulum and inflammatory mechanisms in amyotrophic lateral sclerosis

While unbiased proteomics of human cerebrospinal fluid (CSF) has been used successfully to identify biomarkers of amyotrophic lateral sclerosis (ALS), high-abundance proteins mask the presence of lower abundance proteins that may have diagnostic and prognostic value. However, developments in mass spectrometry (MS) proteomic data acquisition methods offer improved protein depth. In this study, MS with library-free data-independent acquisition (DIA) was used to compare the CSF proteome of people with ALS (n = 40), healthy (n = 15) and disease (n = 8) controls. Quantified protein groups were subsequently correlated with clinical variables. Univariate analysis identified 7 proteins, all significantly upregulated in ALS versus healthy controls, and 9 with altered abundance in ALS versus disease controls (FDR < 0.1). Elevated chitotriosidase-1 (CHIT1) was common to both comparisons and was proportional to ALS disability progression rate (Pearson r = 0.41, FDR-adjusted p = 0.035) but not overall survival. Ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCHL1; upregulated in ALS versus healthy controls) was proportional to disability progression rate (Pearson r = 0.53, FDR-adjusted p = 0.003) and survival (Kaplan Meier log-rank p = 0.013) but not independently in multivariate proportional hazards models. Weighted correlation network analysis was used to identify functionally relevant modules of proteins. One module, enriched for inflammatory functions, was associated with age at symptom onset (Pearson r = 0.58, FDR-adjusted p = 0.005) and survival (Hazard Ratio = 1.78, FDR = 0.065), and a second module, enriched for endoplasmic reticulum proteins, was negatively correlated with disability progression rate (r = -0.42, FDR-adjusted p = 0.109). DIA acquisition methodology therefore strengthened the biomarker candidacy of CHIT1 and UCHL1 in ALS, while additionally highlighted inflammatory and endoplasmic reticulum proteins as novel sources of prognostic biomarkers.

Cerebrospinal Fluid Protein Markers Indicate Neuro-Damage in SARS-CoV-2-Infected Nonhuman Primates

Neurologic manifestations are among the most frequently reported complications of COVID-19. However, given the paucity of tissue samples and the highly infectious nature of the etiologic agent of COVID-19, we have limited information to understand the neuropathogenesis of COVID-19. Therefore, to better understand the impact of COVID-19 on the brain, we used mass-spectrometry-based proteomics with a data-independent acquisition mode to investigate cerebrospinal fluid (CSF) proteins collected from two different nonhuman primates, Rhesus Macaque and African Green Monkeys, for the neurologic effects of the infection. These monkeys exhibited minimal to mild pulmonary pathology but moderate to severe central nervous system (CNS) pathology. Our results indicated that CSF proteome changes after infection resolution corresponded with bronchial virus abundance during early infection and revealed substantial differences between the infected nonhuman primates and their age-matched uninfected controls, suggesting these differences could reflect altered secretion of CNS factors in response to SARS-CoV-2-induced neuropathology. We also observed the infected animals exhibited highly scattered data distributions compared to their corresponding controls indicating the heterogeneity of the CSF proteome change and the host response to the viral infection. Dysregulated CSF proteins were preferentially enriched in functional pathways associated with progressive neurodegenerative disorders, hemostasis, and innate immune responses that could influence neuroinflammatory responses following COVID-19. Mapping these dysregulated proteins to the Human Brain Protein Atlas found that they tended to be enriched in brain regions that exhibit more frequent injury following COVID-19. It, therefore, appears reasonable to speculate that such CSF protein changes could serve as signatures for neurologic injury, identify important regulatory pathways in this process, and potentially reveal therapeutic targets to prevent or attenuate the development of neurologic injuries following COVID-19.

Improved profiling of low molecular weight serum proteome for gastric carcinoma by data-independent acquisition

Low molecular weight proteins (LMWPs) in the bloodstream participate in various biological processes and are closely associated with disease status, whereas identification of serous LMWPs remains a great technical challenge due to the wide dynamic range of protein components. In this study, we constructed an integrated LMWP library by combining the LMWPs obtained by three enrichment methods (50% ACN, 20% ACN + 20 mM ABC, and 30 kDa) and their fractions identified by the data-dependent acquisition method. With this newly constructed library, we comprehensively profiled LMWPs in serum using data-independent acquisition and reliably achieved quantitative results for 75% serous LMWPs. When applying this strategy to quantify LMWPs in human serum samples, we could identify 405 proteins on average per sample, of which 136 proteins were with a MW less than 30 kDa and 293 proteins were with a MW less than 65 kDa. Of note, pre- and post-operative gastric carcinoma (GC) patients showed differentially expressed serous LWMPs, which was also different from the pattern of LWMP expression in healthy controls. In conclusion, our results showed that LMWPs could efficiently distinguish GC patients from healthy controls as well as between pre- and post-operative statuses, and more importantly, our newly developed LMWP profiling platform could be used to discover candidate LMWP biomarkers for disease diagnosis and status monitoring.

Separation of Serum and Plasma Proteins for In-Depth Proteomic Analysis

There are probably no biological samples that did more to spur interest in proteomics than serum and plasma. The belief was that comparing the proteomes of these samples obtained from healthy and disease-affected individuals would lead to biomarkers that could be used to diagnose conditions such as cancer. While the continuing development of mass spectrometers with greater sensitivity and resolution has been invaluable, the invention of strategic strategies to separate circulatory proteins has been just as critical. Novel and creative separation techniques were required because serum and plasma probably have the greatest dynamic range of protein concentration of any biological sample. The concentrations of circulating proteins can range over twelve orders of magnitude, making it a challenge to identify low-abundance proteins where the bulk of the useful biomarkers are believed to exist. The major goals of this article are to (i) provide an historical perspective on the rapid development of serum and plasma proteomics; (ii) describe various separation techniques that have made obtaining an in-depth view of the proteome of these biological samples possible; and (iii) describe applications where serum and plasma proteomics have been employed to discover potential biomarkers for pathological conditions.