UNASSIGNED: Amyloidosis is a life threatening disease caused by deposition of various types of blood serum proteins in organs and tissues. Knowing the type of protein involved is the basis of a correct diagnosis and personalized medical treatment. While the classical approach uses immunohistochemistry, in recent years, laser micro-dissection, followed by high resolution LC-MS/MS, has been shown to provide superior diagnostic sensitivity and specificity. This techniques, however, is only available at major reference proteomics centers.
UNASSIGNED: To perform clinical amyloid protein typing using low-resolution mass spectrometry and no laser micro dissection (LMD), we developed a targeted proteomics approach for the determination of both frequently encountered amyloid proteins (i.e., κ and -λ immunoglobulin light chains and transthyretin (TTR)) and specific reference proteins (i.e., actin (A) for cardiac muscle tissue, or fatty acid binding protein 4 (FBP4) for subcutaneous adipose tissue) in histologic specimens.
UNASSIGNED: Small tissue fragments and/or histological sections were digested to yield a protein mixture that was subsequently reduced, alkylated and trypsinized to obtain a peptide mixture. After SPE purification and LC separation, proteotypic peptides were detected by their MRM transitions.
UNASSIGNED: The method showed high specificity and sensitivity for amyloid protein proteotypic peptides. LODs were 1.0, 0.1, 0.2 picomoles in cardiac muscle tissue (CMT) and 0.1, 0.2, 0.5 picomoles in subcutaneous adipose tissue (SAT) for TTR, κ-, and λ-LC proteins, respectively. Amyloid to tissue-specific protein signal ratios correlated with the presence of amyloid deposits in clinical samples.
UNASSIGNED: This targeted proteomics approach enables sensitive and specific discrimination of amyloidosis affected tissues for the purpose of clinical research.

For ease of measurement and accurate identification of proteins by mass spectrometry, protein targets are commonly cleaved into peptides. Protein digestion is a critical step in sample preparation, yielding peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity; however, it can yield highly variable digestion profiles and is dependent on several factors including digestion buffer, denaturant, trypsin quality selected, and composition/complexity of the sample matrix. Historically, trypsin digestion protocols have relied on lengthy digestion times-which are unsuitable for many clinical applications-to ensure effective proteolysis. Here, we performed an iterative and comprehensive evaluation of digestion conditions for five structurally diverse proteins in plasma and serum: apolipoprotein A-1, retinol-binding protein 4, transthyretin, complement component 9 and C-reactive protein. Conditions were monitored for improvements in signal intensity, reproducibility of digestion profile, and rate of release of proteolytic peptides. This approach yielded an optimized digestion protocol for detection of all five proteins in a single workflow requiring a brief 20 min digestion, without the use of chemical denaturants or reduction/alkylation steps, and only 1 μl of plasma. It is our hope that this data can accelerate the development phase of targeted mass spectrometric protein assays by identifying practical approaches to accelerate and simplify digestion protocols for clinical applications and assist with the selection of tryptic peptides for protein quantitation.

UNASSIGNED: Primary central nervous system lymphoma (PCNSL) is a rare type of non-Hodgkin\'s lymphoma that affects brain parenchyma, eyes, cerebrospinal fluid, and spinal cord. Diagnosing PCNSL can be challenging because imaging studies often show similar patterns as other brain tumors, and stereotactic brain lesion biopsy conformation is invasive and not always possible. This study aimed to validate a previous proteomic profiling (PMID: 32610669) of cerebrospinal fluid (CSF) and develop a CSF-based proteomic panel for accurate PCNSL diagnosis and differentiation.
UNASSIGNED: CSF samples were collected from patients of 30 PCNSL, 30 other brain tumors, and 31 tumor-free/benign controls. Liquid chromatography tandem-mass spectrometry targeted proteomics analysis was used to establish CSF-based proteomic panels.
UNASSIGNED: Final proteomic panels were selected and optimized to diagnose PCNSL from tumor-free controls or other brain tumor lesions with an area under the curve (AUC) of 0.873 (95%CI: 0.723-0.948) and 0.937 (95%CI: 0.807- 0.985), respectively. Pathways analysis showed diagnosis panel features were significantly enriched in pathways related to extracellular matrices-receptor interaction, focal adhesion, and PI3K-Akt signaling, while prion disease, mineral absorption and HIF-1 signaling were significantly enriched with differentiation panel features.
UNASSIGNED: This study suggests an accurate clinical test panel for PCNSL diagnosis and differentiation with CSF-based proteomic signatures, which may help overcome the challenges of current diagnostic methods and improve patient outcomes.

Protein homeostasis in bacteria is regulated by proteases such as the tetradecameric caseinolytic protease P (ClpP). Although substrates of ClpP have been successfully deciphered in genetically engineered cells, methods which directly trap processed proteins within native cells remain elusive. Here, we introduce an in situ trapping strategy which utilizes trifunctional probes that bind to the active site serine of ClpP and capture adjacent substrates with an attached photocrosslinking moiety. After enrichment using an alkyne handle, substrate deconvolution by mass spectrometry (MS) is performed. We show that our two traps bind substoichiometrically to ClpP, retain protease activity, exhibit unprecedented selectivity for Staphylococcus aureus ClpP in living cells and capture numerous known and novel substrates. The exemplary validation of trapped hits using a targeted proteomics approach confirmed the fidelity of this technology. In conclusion, we provide a novel chemical platform suited for the discovery of serine protease substrates beyond genetic engineering.

Targeted proteomics enables sensitive and specific quantification of proteins and post-translational modifications. By coupling peptide immunoaffinity enrichment with targeted mass spectrometry, we have developed the methodology for multiplexed quantification of proteins and phosphosites involved in the RAS/MAPK signaling network. The method uses anti-peptide antibodies to enrich analytes and heavy stable isotope-labeled internal standards, spiked in at known concentrations. The enriched peptides are directly measured by multiple-reaction monitoring (MRM), a well-characterized quantitative mass spectrometry-based method. The analyte (light) peptide response is measured relative to the heavy standard. The method described provides quantitative measurements of phospho-signaling and is generally applicable to other phosphopeptides and sample types.

UNASSIGNED: Proteomics offers potential for detecting and monitoring anorexia nervosa (AN) and its variant, atypical AN (atyp-AN). However, research has been limited by small protein panels, a focus on adult AN, and lack of replication.
UNASSIGNED: In this study, we performed Olink multiplex profiling of 92 inflammation-related proteins in females with AN/atyp-AN (n = 64), all of whom were ≤90% of expected body weight, and age-matched healthy control individuals (n = 44).
UNASSIGNED: Five proteins differed significantly between the primary AN/atyp-AN group and the healthy control group (lower levels: HGF, IL-18R1, TRANCE; higher levels: CCL23, LIF-R). The expression levels of 3 proteins (lower IL-18R1, TRANCE; higher LIF-R) were uniquely disrupted in participants with AN in our primary model. No unique expression levels emerged for atyp-AN. In the total sample, 12 proteins (ADA, CD5, CD6, CXCL1, FGF-21, HGF, IL-12B, IL18, IL-18R1, SIRT2, TNFSF14, TRANCE) were positively correlated with body mass index and 5 proteins (CCL11, FGF-19, IL8, LIF-R, OPG) were negatively correlated with body mass index in our primary models.
UNASSIGNED: Our results replicate the results of a previous study that demonstrated a dysregulated inflammatory status in AN and extend those results to atyp-AN. Of the 17 proteins correlated with body mass index, 11 were replicated from a previous study that used similar methods, highlighting the promise of inflammatory protein expression levels as biomarkers of AN disease monitoring. Our findings underscore the complexity of AN and atyp-AN by highlighting the inability of the identified proteins to differentiate between these 2 subtypes, thereby emphasizing the heterogeneous nature of these disorders.
We examined 73 inflammation proteins in adolescent girls with anorexia nervosa (AN) and atypical AN and compared them with age-matched healthy control girls. Significant differences were found, driven by 5 key proteins (lower: HGF, IL-18R1, TRANCE; higher: CCL23, LIF-R). Three proteins (TRANCE, LIF-R, IL-18R1) uniquely distinguished low-weight participants with AN from control participants. Our study reveals distinct inflammation patterns in AN and atypical AN and sheds light on potential state-specific factors that underlie these disorders.

Diabetic retinopathy (DR) is a serious complication of diabetes featuring abnormal lipid metabolism. However, the specific lipid molecules associated with onset and progression remain unclear. We used a broad-targeted lipidomics approach to assess the lipid changes that occur before the proliferative retinopathy stage and to identify novel lipid biomarkers to distinguish between patients without DR (NDR) and with non-proliferative DR (NPDR). Targeted lipomics analysis was carried out on serum samples from patients with type I diabetes, including 20 NDRs and 20 NPDRs. The results showed that compared with the NDR group, 102 lipids in the NPDR group showed specific expressions. Four lipid metabolites including TAG58:2-FA18:1 were obtained using the Least Absolute Shrink And Selection Operator (LASSO) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) methods. The four-lipid combination diagnostic models showed good predictive ability in both the discovery and validation sets, and were able to distinguish between NDR patients and NPDR patients. The identified lipid markers significantly improved diagnostic accuracy within the NPDR group. Our findings help to better understand the complexity and individual differences of DR lipid metabolism.

Nontargeted single-cell proteomics analysis by mass spectrometry with sample multiplexing utilizing isobaric labeling is often performed using a carrier proteome. The presented protocol describes a targeted approach that replaces the carrier proteome with a set of synthetic peptides from selected proteins, which improves the identification and quantification of these proteins in single human cells.

BACKGROUND: COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients\' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.
METHODS: A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.
RESULTS: Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.
CONCLUSIONS: The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.

BACKGROUND: The Multi-System Inflammatory Syndrome in Children (MIS-C) can develop several weeks after SARS-CoV-2 infection and requires a distinct treatment protocol. Distinguishing MIS-C from SARS-CoV-2 negative sepsis (SCNS) patients is important to quickly institute the correct therapies. We performed targeted proteomics and machine learning analysis to identify novel plasma proteins of MIS-C for early disease recognition.
METHODS: A case-control study comparing the expression of 2,870 unique blood proteins in MIS-C versus SCNS patients, measured using proximity extension assays. The 2,870 proteins were reduced in number with either feature selection alone or with a prior COMBAT-Seq batch effect adjustment. The leading proteins were correlated with demographic and clinical variables. Organ system and cell type expression patterns were analyzed with Natural Language Processing (NLP).
RESULTS: The cohorts were well-balanced for age and sex. Of the 2,870 unique blood proteins, 58 proteins were identified with feature selection (FDR-adjusted P < 0.005, P < 0.0001; accuracy = 0.96, AUC = 1.00, F1 = 0.95), and 15 proteins were identified with a COMBAT-Seq batch effect adjusted feature selection (FDR-adjusted P < 0.05, P < 0.0001; accuracy = 0.92, AUC = 1.00, F1 = 0.89). All of the latter 15 proteins were present in the former 58-protein model. Several proteins were correlated with illness severity scores, length of stay, and interventions (LTA4H, PTN, PPBP, and EGF; P < 0.001). NLP analysis highlighted the multi-system nature of MIS-C, with the 58-protein set expressed in all organ systems; the highest levels of expression were found in the digestive system. The cell types most involved included leukocytes not yet determined, lymphocytes, macrophages, and platelets.
CONCLUSIONS: The plasma proteome of MIS-C patients was distinct from that of SCNS. The key proteins demonstrated expression in all organ systems and most cell types. The unique proteomic signature identified in MIS-C patients could aid future diagnostic and therapeutic advancements, as well as predict hospital length of stays, interventions, and mortality risks.