Proteomic Analysis of Cardioembolic and Large Artery Atherosclerotic Clots Using Reverse Phase Protein Array Technology Reveals Key Cellular Interactions Within Clot Microenvironments.

Thrombus characteristics are dependent on clot composition, but identification of the etiology based on histological analysis has proved inconclusive. Identification of proteomic signatures may help to differentiate between clots of different etiologies such as cardioembolic, large artery atherosclerotic, and other known etiologies, information that could enhance an individualized medicine approach to secondary stroke prevention. In this study, total protein extracts from cardioembolic (n=25) and large artery atherosclerotic (n=23) thrombus specimens were arrayed in quadruplicate on nitrocellulose slides and immunostained for 31 proteins using a Dako Autostainer (Agilent Technologies, Inc., Santa Clara, USA). We quantified 31 proteins involved in platelet and/or endothelial function, inflammation, oxidative stress, and metabolism. Pathway analysis showed more heterogeneity and protein network interactions in the cardioembolic clots but no specific correlations with clot etiology. Reverse-phase protein arrays are a powerful tool for assessing cellular interactions within the clot microenvironment and may enhance understanding of clot formation and origination. This tool could be further explored to help in identifying stroke etiology in large vessel occlusion patients with embolic stroke of an undetermined source.

Clinical Utility of a Highly Sensitive Lateral Flow Immunoassay as determined by Titer Analysis for the Detection of anti-SARS-CoV-2 Antibodies at the Point-of-Care.

Coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), became a pandemic in early 2020. Lateral flow immunoassays for antibody testing have been viewed as a cheap and rapidly deployable method for determining previous infection with SARS-CoV-2; however, these assays have shown unacceptably low sensitivity. We report on nine lateral flow immunoassays currently available and compare their titer sensitivity in serum to a best-practice enzyme-linked immunosorbent assay (ELISA) and viral neutralization assay. For a small group of PCR-positive, we found two lateral flow immunoassay devices with titer sensitivity roughly equal to the ELISA; these devices were positive for all PCR-positive patients harboring SARS-CoV-2 neutralizing antibodies. One of these devices was deployed in Northern Italy to test its sensitivity and specificity in a real-world clinical setting. Using the device with fingerstick blood on a cohort of 27 hospitalized PCR-positive patients and seven hospitalized controls, ROC curve analysis gave AUC values of 0.7646 for IgG. For comparison, this assay was also tested with saliva from the same patient population and showed reduced discrimination between cases and controls with AUC values of 0.6841 for IgG. Furthermore, during viral neutralization testing, one patient was discovered to harbor autoantibodies to ACE2, with implications for how immune responses are profiled. We show here through a proof-of-concept study that these lateral flow devices can be as analytically sensitive as ELISAs and adopted into hospital protocols; however, additional improvements to these devices remain necessary before their clinical deployment.

Application of Nanotrap technology for high sensitivity measurement of urinary outer surface protein A carboxyl-terminus domain in early stage Lyme borreliosis.

OBJECTIVES: Prompt antibiotic treatment of early stage Lyme borreliosis (LB) prevents progression to severe multisystem disease. There is a clinical need to improve the diagnostic specificity of early stage Lyme assays in the period prior to the mounting of a robust serology response. Using a novel analyte harvesting nanotechnology, Nanotrap particles, we evaluated urinary Borrelia Outer surface protein A (OspA) C-terminus peptide in early stage LB before and after treatment, and in patients suspected of late stage disseminated LB. METHOD: We employed Nanotrap particles to concentrate urinary OspA and used a highly specific anti-OspA monoclonal antibody (mAb) as a detector of the C-terminus peptides. We mapped the mAb epitope to a narrow specific OspA C-terminal domain OspA236-239 conserved across infectious Borrelia species but with no homology to human proteins and no cross-reactivity with relevant viral and non-Borrelia bacterial proteins. 268 urine samples from patients being evaluated for all categories of LB were collected in a LB endemic area. The urinary OspA assay, blinded to outcome, utilized Nanotrap particle pre-processing, western blotting to evaluate the OspA molecular size, and OspA peptide competition for confirmation. RESULTS: OspA test characteristics: sensitivity 1.7 pg/mL (lowest limit of detection), % coefficient of variation (CV) = 8 %, dynamic range 1.7-30 pg/mL. Pre-treatment, 24/24 newly diagnosed patients with an erythema migrans (EM) rash were positive for urinary OspA while false positives for asymptomatic patients were 0/117 (Chi squared p < 10(-6)). For 10 patients who exhibited persistence of the EM rash during the course of antibiotic therapy, 10/10 were positive for urinary OspA. Urinary OspA of 8/8 patients switched from detectable to undetectable following symptom resolution post-treatment. Specificity of the urinary OspA test for the clinical symptoms was 40/40. Specificity of the urinary OspA antigen test for later serology outcome was 87.5 % (21 urinary OspA positive/24 serology positive, Chi squared p = 4.072e(-15)). 41 of 100 patients under surveillance for persistent LB in an endemic area were positive for urinary OspA protein. CONCLUSIONS: OspA urinary shedding was strongly linked to concurrent active symptoms (e.g. EM rash and arthritis), while resolution of these symptoms after therapy correlated with urinary conversion to OspA negative.

Concentration and Preservation of Very Low Abundance Biomarkers in Urine, such as Human Growth Hormone (hGH), by Cibacron Blue F3G-A Loaded Hydrogel Particles.

Urine is a potential source of diagnostic biomarkers for detection of diseases, and is a very attractive means of non-invasive biospecimen collection. Nonetheless, proteomic measurement in urine is very challenging because diagnostic biomarkers exist in very low concentration (usually below the sensitivity of common immunoassays) and may be subject to rapid degradation. Hydrogel nanoparticles functionalized with Cibacron Blue F3G-A (CB) have been applied to address these challenges for urine biomarker measurement. We chose one of the most difficult low abundance, but medically relevant, hormones in the urine: human growth hormone (hGH). The normal range of hGH in serum is 1 to 10 ng/mL but the urine concentration is suspected to be a thousand times less, well below the detection limit (50 pg/mL) of sensitive clinical hGH immunoassays. We demonstrate that CB particles can capture, preserve and concentrate hGH in urine at physiological salt and urea concentrations, so that hGH can be measured in the linear range of a clinical immunometric assay. Recombinant and cadaveric hGH were captured from synthetic and human urine, concentrated and measured with an Immulite chemiluminescent immunoassay. Values of hGH less than 0.05 ng/mL (the Immulite detection limit) were concentrated to 2 ng/mL, with a urine volume of 1 mL. Dose response studies using 10 mL of urine demonstrated that the concentration of hGH in the particle eluate was linearly dependent on the concentration of hGH in the starting solution, and that all hGH was removed from solution. Thus if the starting urine volume is 100 mL, the detection limit will be 0.1 pg/mL. Urine from a healthy donor whose serum hGH concentration was 1.34 ng/mL was studied in order detect endogenous hGH. Starting from a volume of 33 mL, the particle eluate had an hGH concentration of 58 pg/mL, giving an estimated initial concentration of hGH in urine of 0.175 pg/mL. The nanotechnology described here appears to have the desired precision, accuracy and sensitivity to support large scale clinical studies of urine hGH levels.

Proteomic patterns for classification of ovarian cancer and CTCL serum samples utilizing peak pairs indicative of post-translational modifications.

Proteomic patterns as a potential diagnostic technology has been well established for several cancer conditions and other diseases. The use of machine learning techniques such as decision trees, neural networks, genetic algorithms, and other methods has been the basis for pattern determination. Cancer is known to involve signaling pathways that are regulated through PTM of proteins. These modifications are also detectable with high confidence using high-resolution MS. We generated data using a prOTOF mass spectrometer on two sets of patient samples: ovarian cancer and cutaneous t-cell lymphoma (CTCL) with matched normal samples for each disease. Using the knowledge of mass shifts caused by common modifications, we built models using peak pairs and compared this to a conventional technique using individual peaks. The results for each disease showed that a small number of peak pairs gave classification equal to or better than the conventional technique that used multiple individual peaks. This simple peak picking technique could be used to guide identification of important peak pairs involved in the disease process.

Serum proteomic analysis identifies a highly sensitive and specific discriminatory pattern in stage 1 breast cancer.

BACKGROUND: Mass spectrometry (MS)-based profiling was used to determine whether ion fingerprints could distinguish women with stage 1 breast cancer from women without breast cancer. METHODS: The initial study population consisted of 310 subjects: 155 women with yearly negative breast examination and negative mammography findings for at least 4 years, and 155 women undergoing surgery for pathology-proven stage 1 invasive ductal carcinoma. High-resolution SELDI-TOF (surface-enhanced laser desorption ionization-time of flight) analysis was performed on serum obtained from blood samples collected before mammography in controls, and before surgery in patients with breast cancer. Samples were divided into a training (109 controls and 109 cancers) and blinded (46 controls and 46 cancers) testing set; each group had similar age demographics. In addition, an independent study set of 46 serum samples was analyzed 14 months after the initial study to validate the robustness of the classifier. RESULTS: A discriminatory profile consisting of seven ion peaks found in the training set, when applied to the blinded test set, achieved a sensitivity and specificity of 95.6% and 86.5%, respectively. This same seven-peak profile achieved a 96.5% sensitivity and 85.7% specificity, with correct identification of all of 17 T1a tumors when applied to the validation study set. CONCLUSIONS: Mass spectrometry profiling of human serum generated a robust classifier composed of seven low-molecular-weight ions that yielded a highly sensitive and specific diagnostic procedure for the discrimination of women with stage 1 breast cancer compared with women without breast cancer in this research study set.

Accurate diagnosis of acute graft-versus-host disease using serum proteomic pattern analysis.

OBJECTIVE: The rapid diagnosis of acute graft-versus-host disease (GVHD) following allogeneic hematopoietic cell transplantation (HCT) is important for optimizing the management of this life-threatening complication. Current diagnostic techniques are time-consuming and require invasive tissue sampling. We investigated serum protein pattern analysis using surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectrometry as a tool to diagnose GVHD. PATIENTS AND METHODS: Eighty-eight serum samples were obtained from 34 patients undergoing HCT either pretransplant (n = 28 samples) or at various time points posttransplant (n = 60 samples), including 22 samples obtained on the day of onset of acute GVHD symptoms. Serum proteomic spectra generated from a "training set" of known samples were used to identify distinct proteomic patterns that best categorized a sample as either pretransplant, posttransplant non-GVHD, or GVHD; these distinct proteomic signatures were subsequently used to classify samples from a masked "test" sample set into the appropriate diagnostic category. RESULTS: Proteomic pattern analysis accurately distinguished GVHD samples from both posttransplant non-GVHD samples and pretransplant samples (100% specificity and 100% sensitivity in both cases). Furthermore, distinct serum proteomic signatures were identified that distinguished pretransplant from posttransplant non-GVHD samples (100% specificity and 94% sensitivity). CONCLUSION: These preliminary data suggest a potential application of SELDI-TOF-based proteomic analysis as a rapid and accurate method to diagnose acute GVHD.