Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano-bio interactions.

SignificanceDeep profiling of the plasma proteome at scale has been a challenge for traditional approaches. We achieve superior performance across the dimensions of precision, depth, and throughput using a panel of surface-functionalized superparamagnetic nanoparticles in comparison to conventional workflows for deep proteomics interrogation. Our automated workflow leverages competitive nanoparticle-protein binding equilibria that quantitatively compress the large dynamic range of proteomes to an accessible scale. Using machine learning, we dissect the contribution of individual physicochemical properties of nanoparticles to the composition of protein coronas. Our results suggest that nanoparticle functionalization can be tailored to protein sets. This work demonstrates the feasibility of deep, precise, unbiased plasma proteomics at a scale compatible with large-scale genomics enabling multiomic studies.

A Scaled Proteomic Discovery Study for Prostate Cancer Diagnostic Markers Using ProteographTM and Trapped Ion Mobility Mass Spectrometry.

There is a significant unmet need for clinical reflex tests that increase the specificity of prostate-specific antigen blood testing, the longstanding but imperfect tool for prostate cancer diagnosis. Towards this endpoint, we present the results from a discovery study that identifies new prostate-specific antigen reflex markers in a large-scale patient serum cohort using differentiating technologies for deep proteomic interrogation. We detect known prostate cancer blood markers as well as novel candidates. Through bioinformatic pathway enrichment and network analysis, we reveal associations of differentially abundant proteins with cytoskeletal, metabolic, and ribosomal activities, all of which have been previously associated with prostate cancer progression. Additionally, optimized machine learning classifier analysis reveals proteomic signatures capable of detecting the disease prior to biopsy, performing on par with an accepted clinical risk calculator benchmark.

Delta-S-Cys-Albumin: A Lab Test that Quantifies Cumulative Exposure of Archived Human Blood Plasma and Serum Samples to Thawed Conditions.

Exposure of blood plasma/serum (P/S) to thawed conditions (> -30 °C) can produce biomolecular changes that skew measurements of biomarkers within archived patient samples, potentially rendering them unfit for molecular analysis. Because freeze-thaw histories are often poorly documented, objective methods for assessing molecular fitness before analysis are needed. We report a 10-µl, dilute-and-shoot, intact-protein mass spectrometric assay of albumin proteoforms called "ΔS-Cys-Albumin" that quantifies cumulative exposure of archived P/S samples to thawed conditions. The relative abundance of S-cysteinylated (oxidized) albumin in P/S increases inexorably but to a maximum value under 100% when samples are exposed to temperatures > -30 °C. The difference in the relative abundance of S-cysteinylated albumin (S-Cys-Alb) before and after an intentional incubation period that drives this proteoform to its maximum level is denoted as ΔS-Cys-Albumin. ΔS-Cys-Albumin in fully expired samples is zero. The range (mean ± 95% CI) observed for ΔS-Cys-Albumin in fresh cardiac patient P/S (n = 97) was, for plasma 12-29% (20.9 ± 0.75%) and for serum 10-24% (15.5 ± 0.64%). The multireaction rate law that governs S-Cys-Alb formation in P/S was determined and shown to predict the rate of formation of S-Cys-Alb in plasma and serum samples-a step that enables back-calculation of the time at which unknown P/S specimens have been exposed to room temperature. A blind challenge demonstrated that ΔS-Cys-Albumin can detect exposure of groups (n = 6 each) of P/S samples to 23 °C for 2 h, 4 °C for 16 h, or -20 °C for 24 h-and exposure of individual specimens for modestly increased times. An unplanned case study of nominally pristine serum samples collected under NIH-sponsorship demonstrated that empirical evidence is required to ensure accurate knowledge of archived P/S biospecimen storage history.

Diagnostic and Prognostic Performance of Blood Plasma Glycan Features in the Women Epidemiology Lung Cancer (WELCA) Study.

Lung cancer is the leading cause of cancer death in women living in the United States, which accounts for approximately the same percentage of cancer deaths in women as breast, ovary, and uterine cancers combined. Targeted blood plasma glycomics represents a promising source of noninvasive diagnostic and prognostic biomarkers for lung cancer. Here, 208 samples from lung cancer patients and 207 age-matched controls enrolled in the Women Epidemiology Lung Cancer (WELCA) study were analyzed by a bottom-up glycan "node" analysis approach. Glycan features, quantified as single analytical signals, including 2-linked mannose, α2-6 sialylation, ß1-4 branching, ß1-6 branching, 4-linked GlcNAc, and antennary fucosylation, exhibited abilities to distinguish cases from controls (ROC AUCs: 0.68-0.92) and predict survival in patients (hazard ratios: 1.99-2.75) at all stages. Notable alterations of glycan features were observed in stages I-II. Diagnostic and prognostic glycan features were mostly independent of smoking status, age, gender, and histological subtypes of lung cancer.

Stage Dependence, Cell-Origin Independence, and Prognostic Capacity of Serum Glycan Fucosylation, ß1-4 Branching, ß1-6 Branching, and α2-6 Sialylation in Cancer.

Glycans represent a promising but only marginally accessed source of cancer markers. We previously reported the development of a molecularly bottom-up approach to plasma and serum (P/S) glycomics based on glycan linkage analysis that captures features such as α2-6 sialylation, ß1-6 branching, and core fucosylation as single analytical signals. Based on the behavior of P/S glycans established to date, we hypothesized that the alteration of P/S glycans observed in cancer would be independent of the tissue in which the tumor originated yet exhibit stage dependence that varied little between cancers classified on the basis of tumor origin. Herein, the diagnostic utility of this bottom-up approach as applied to lung cancer patients (n = 127 stage I; n = 20 stage II; n = 81 stage III; and n = 90 stage IV) as well as prostate (n = 40 stage II), serous ovarian (n = 59 stage III), and pancreatic cancer patients (n = 15 rapid autopsy) compared to certifiably healthy individuals (n = 30), nominally healthy individuals (n = 166), and risk-matched controls (n = 300) is reported. Diagnostic performance in lung cancer was stage-dependent, with markers for terminal (total) fucosylation, α2-6 sialylation, ß1-4 branching, ß1-6 branching, and outer-arm fucosylation most able to differentiate cases from controls. These markers behaved in a similar stage-dependent manner in other types of cancer as well. Notable differences between certifiably healthy individuals and case-matched controls were observed. These markers were not significantly elevated in liver fibrosis. Using a Cox proportional hazards regression model, the marker for α2-6 sialylation was found to predict both progression and survival in lung cancer patients after adjusting for age, gender, smoking status, and stage. The potential mechanistic role of aberrant P/S glycans in cancer progression is discussed.

Ex vivo instability of glycated albumin: A role for autoxidative glycation.

Ex vivo protein modifications occur within plasma and serum (P/S) samples due to prolonged exposure to the thawed state-which includes temperatures above -30 °C. Herein, the ex vivo glycation of human serum albumin from healthy and diabetic subjects was monitored in P/S samples stored for hours to months at -80 °C, -20 °C, and room temperature, as well as in samples subjected to multiple freeze-thaw cycles, incubated at different surface area-to-volume ratios or under different atmospheric compositions. A simple dilute-and-shoot method utilizing trap-and-elute LC-ESI-MS was employed to determine the relative abundances of the glycated forms of albumin-including forms of albumin bearing more than one glucose molecule. Significant increases in glycated albumin were found to occur within hours at room temperature, and within days at -20 °C. These increases continued over a period of 1-2 weeks at room temperature and over 200 days at -20 °C, ultimately resulting in a doubling of glycated albumin in both healthy and diabetic patients. It was also shown that samples stored at lower surface area-to-volume ratios or incubated under a nitrogen atmosphere experienced less rapid glucose adduction of albumin-suggesting a role for oxidative glycation in the ex vivo glycation of albumin.

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics.

Alzheimer's disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimer's Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The Proteograph™ Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

Functionally distinct BMP1 isoforms show an opposite pattern of abundance in plasma from non-small cell lung cancer subjects and controls.

Advancements in deep plasma proteomics are enabling high-resolution measurement of plasma proteoforms, which may reveal a rich source of novel biomarkers previously concealed by aggregated protein methods. Here, we analyze 188 plasma proteomes from non-small cell lung cancer subjects (NSCLC) and controls to identify NSCLC-associated protein isoforms by examining differentially abundant peptides as a proxy for isoform-specific exon usage. We find four proteins comprised of peptides with opposite patterns of abundance between cancer and control subjects. One of these proteins, BMP1, has known isoforms that can explain this differential pattern, for which the abundance of the NSCLC-associated isoform increases with stage of NSCLC progression. The presence of cancer and control-associated isoforms suggests differential regulation of BMP1 isoforms. The identified BMP1 isoforms have known functional differences, which may reveal insights into mechanisms impacting NSCLC disease progression.

Protein Coronas on Functionalized Nanoparticles Enable Quantitative and Precise Large-Scale Deep Plasma Proteomics.

Background: The wide dynamic range of circulating proteins coupled with the diversity of proteoforms present in plasma has historically impeded comprehensive and quantitative characterization of the plasma proteome at scale. Automated nanoparticle (NP) protein corona-based proteomics workflows can efficiently compress the dynamic range of protein abundances into a mass spectrometry (MS)-accessible detection range. This enhances the depth and scalability of quantitative MS-based methods, which can elucidate the molecular mechanisms of biological processes, discover new protein biomarkers, and improve comprehensiveness of MS-based diagnostics. Methods: Investigating multi-species spike-in experiments and a cohort, we investigated fold-change accuracy, linearity, precision, and statistical power for the using the Proteograph™ Product Suite, a deep plasma proteomics workflow, in conjunction with multiple MS instruments. Results: We show that NP-based workflows enable accurate identification (false discovery rate of 1%) of more than 6,000 proteins from plasma (Orbitrap Astral) and, compared to a gold standard neat plasma workflow that is limited to the detection of hundreds of plasma proteins, facilitate quantification of more proteins with accurate fold-changes, high linearity, and precision. Furthermore, we demonstrate high statistical power for the discovery of biomarkers in small- and large-scale cohorts. Conclusions: The automated NP workflow enables high-throughput, deep, and quantitative plasma proteomics investigation with sufficient power to discover new biomarker signatures with a peptide level resolution.

Enhanced Competition at the Nano-Bio Interface Enables Comprehensive Characterization of Protein Corona Dynamics and Deep Coverage of Proteomes.

Introducing engineered nanoparticles (NPs) into a biofluid such as blood plasma leads to the formation of a selective and reproducible protein corona at the particle-protein interface, driven by the relationship between protein-NP affinity and protein abundance. This enables scalable systems that leverage protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Here the importance of the protein to NP-surface ratio (P/NP) is demonstrated and protein corona formation dynamics are modeled, which determine the competition between proteins for binding. Tuning the P/NP ratio significantly modulates the protein corona composition, enhancing depth and precision of a fully automated NP-based deep proteomic workflow (Proteograph). By increasing the binding competition on engineered NPs, 1.2-1.7× more proteins with 1% false discovery rate are identified on the surface of each NP, and up to 3× more proteins compared to a standard plasma proteomics workflow. Moreover, the data suggest P/NP plays a significant role in determining the in vivo fate of nanomaterials in biomedical applications. Together, the study showcases the importance of P/NP as a key design element for biomaterials and nanomedicine in vivo and as a powerful tuning strategy for accurate, large-scale NP-based deep proteomic studies.

Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona.

Large-scale, unbiased proteomics studies are constrained by the complexity of the plasma proteome. Here we report a highly parallel protein quantitation platform integrating nanoparticle (NP) protein coronas with liquid chromatography-mass spectrometry for efficient proteomic profiling. A protein corona is a protein layer adsorbed onto NPs upon contact with biofluids. Varying the physicochemical properties of engineered NPs translates to distinct protein corona patterns enabling differential and reproducible interrogation of biological samples, including deep sampling of the plasma proteome. Spike experiments confirm a linear signal response. The median coefficient of variation was 22%. We screened 43 NPs and selected a panel of 5, which detect more than 2,000 proteins from 141 plasma samples using a 96-well automated workflow in a pilot non-small cell lung cancer classification study. Our streamlined workflow combines depth of coverage and throughput with precise quantification based on unique interactions between proteins and NPs engineered for deep and scalable quantitative proteomic studies.

Behavior of blood plasma glycan features in bladder cancer.

Despite systemic therapy and cystectomy, bladder cancer is characterized by a high recurrence rate. Serum glycomics represents a promising source of prognostic markers for monitoring patients. Our approach, which we refer to as "glycan node analysis", constitutes the first example of molecularly "bottom-up" glycomics. It is based on a global glycan methylation analysis procedure that is applied to whole blood plasma/serum. The approach detects and quantifies partially methylated alditol acetates arising from unique glycan features such as α2-6 sialylation, ß1-4 branching, and core fucosylation that have been pooled together from across all intact glycans within a sample into a single GC-MS chromatographic peak. We applied this method to 122 plasma samples from former and current bladder cancer patients (n = 72 former cancer patients with currently no evidence of disease (NED); n = 38 non-muscle invasive bladder cancer (NMIBC) patients; and n = 12 muscle invasive bladder cancer (MIBC) patients) along with plasma from 30 certifiably healthy living kidney donors. Markers for α2-6 sialylation, ß1-4 branching, ß1-6 branching, and outer-arm fucosylation were able to separate current and former (NED) cases from certifiably healthy controls (ROC curve c-statistics ~ 0.80); but NED, NMIBC, and MIBC were not distinguished from one another. Based on the unexpectedly high levels of these glycan nodes in the NED patients, we hypothesized that recurrence of this disease could be predicted by some of the elevated glycan features. Indeed, α2-6 sialylation and ß1-6 branching were able to predict recurrence from the NED state using a Cox proportional hazards regression model adjusted for age, gender, and time from cancer. The levels of these two glycan features were correlated to C-reactive protein concentration, an inflammation marker and known prognostic indicator for bladder cancer, further strengthening the link between inflammation and abnormal plasma protein glycosylation.

Glycan Node Analysis: A Bottom-up Approach to Glycomics.

Synthesized in a non-template-driven process by enzymes called glycosyltransferases, glycans are key players in various significant intra- and extracellular events. Many pathological conditions, notably cancer, affect gene expression, which can in turn deregulate the relative abundance and activity levels of glycoside hydrolase and glycosyltransferase enzymes. Unique aberrant whole glycans resulting from deregulated glycosyltransferase(s) are often present in trace quantities within complex biofluids, making their detection difficult and sometimes stochastic. However, with proper sample preparation, one of the oldest forms of mass spectrometry (gas chromatography-mass spectrometry, GC-MS) can routinely detect the collection of branch-point and linkage-specific monosaccharides ("glycan nodes") present in complex biofluids. Complementary to traditional top-down glycomics techniques, the approach discussed herein involves the collection and condensation of each constituent glycan node in a sample into a single independent analytical signal, which provides detailed structural and quantitative information about changes to the glycome as a whole and reveals potentially deregulated glycosyltransferases. Improvements to the permethylation and subsequent liquid/liquid extraction stages provided herein enhance reproducibility and overall yield by facilitating minimal exposure of permethylated glycans to alkaline aqueous conditions. Modifications to the acetylation stage further increase the extent of reaction and overall yield. Despite their reproducibility, the overall yields of N-acetylhexosamine (HexNAc) partially permethylated alditol acetates (PMAAs) are shown to be inherently lower than their expected theoretical value relative to hexose PMAAs. Calculating the ratio of the area under the extracted ion chromatogram (XIC) for each individual hexose PMAA (or HexNAc PMAA) to the sum of such XIC areas for all hexoses (or HexNAcs) provides a new normalization method that facilitates relative quantification of individual glycan nodes in a sample. Although presently constrained in terms of its absolute limits of detection, this method expedites the analysis of clinical biofluids and shows considerable promise as a complementary approach to traditional top-down glycomics.