A pilot study to evaluate the application of a generic protein standard panel for quality control of biomarker detection technologies

<p>Abstract</p> <p>Background</p> <p>Protein biomarker studies are currently hampered by a lack of measurement standards to demonstrate quality, reliability and comparability across multiple assay platforms. This is especially pertinent for immunoassays where multiple formats for detecting target analytes are commonly used.</p> <p>Findings</p> <p>In this pilot study a generic panel of six non-human protein standards (50 - 10^7 pg/mL) of varying abundance was prepared as a quality control (QC) material. Simulated "normal" and "diseased" panels of proteins were prepared in pooled human plasma and incorporated into immunoassays using the Meso Scale Discovery^®(MSD^®) platform to illustrate reliable detection of the component proteins. The protein panel was also evaluated as a spike-in material for a model immunoassay involving detection of ovarian cancer biomarkers within individual human plasma samples. Our selected platform could discriminate between two panels of the proteins exhibiting small differences in abundance. Across distinct experiments, all component proteins exhibited reproducible signal outputs in pooled human plasma. When individual donor samples were used, half the proteins produced signals independent of matrix effects. These proteins may serve as a generic indicator of platform reliability.</p> <p>Each of the remaining proteins exhibit differential signals across the distinct samples, indicative of sample matrix effects, with the three proteins following the same trend. This subset of proteins may be useful for characterising the degree of matrix effects associated with the sample which may impact on the reliability of quantifying target diagnostic biomarkers.</p> <p>Conclusions</p> <p>We have demonstrated the potential utility of this panel of standards to act as a generic QC tool for evaluating the reproducibility of the platform for protein biomarker detection independent of serum matrix effects.</p

Catalytic gas-phase glycerol processing over SiO2-, Cu-, Ni-and Fe-supported Au nanoparticles

In this study, we investigated different metal pairings of Au nanoparticles (NPs) as potential catalysts for glycerol dehydration for the first time. All of the systems preferred the formation of hydroxyacetone (HYNE). Although the bimetallics that were tested, i.e., Au NPs supported on Ni, Fe and Cu appeared to be more active than the Au/SiO2 system, only Cu supported Au NPs gave high conversion (ca. 63%) and selectivity (ca. 70%) to HYNE

Single-cell multi-omics analysis of the immune response in COVID-19

Analysis of human blood immune cells provides insights into the coordinated response to viral infections such as severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19). We performed single-cell transcriptome, surface proteome and T and B lymphocyte antigen receptor analyses of over 780,000 peripheral blood mononuclear cells from a cross-sectional cohort of 130 patients with varying severities of COVID-19. We identified expansion of nonclassical monocytes expressing complement transcripts (CD16+C1QA/B/C+) that sequester platelets and were predicted to replenish the alveolar macrophage pool in COVID-19. Early, uncommitted CD34+ hematopoietic stem/progenitor cells were primed toward megakaryopoiesis, accompanied by expanded megakaryocyte-committed progenitors and increased platelet activation. Clonally expanded CD8+ T cells and an increased ratio of CD8+ effector T cells to effector memory T cells characterized severe disease, while circulating follicular helper T cells accompanied mild disease. We observed a relative loss of IgA2 in symptomatic disease despite an overall expansion of plasmablasts and plasma cells. Our study highlights the coordinated immune response that contributes to COVID-19 pathogenesis and reveals discrete cellular components that can be targeted for therapy

GJ 3090 b: one of the most favourable mini-Neptune for atmospheric characterisation

We report the detection of GJ 3090 b (TOI-177.01), a mini-Neptune on a 2.9-day orbit transiting a bright (K = 7.3 mag) M2 dwarf located at 22 pc. The planet was identified by the Transiting Exoplanet Survey Satellite and was confirmed with the High Accuracy Radial velocity Planet Searcher radial velocities. Seeing-limited photometry and speckle imaging rule out nearby eclipsing binaries. Additional transits were observed with the LCOGT, Spitzer, and ExTrA telescopes. We characterise the star to have a mass of 0.519 ± 0.013 M⊙ and a radius of 0.516 ± 0.016 R⊙. We modelled the transit light curves and radial velocity measurements and obtained a planetary mass of 3.34 ± 0.72 ME, a radius of 2.13 ± 0.11 RE, and a mean density of 1.89−0.45+0.52 g cm−3. The low density of the planet implies the presence of volatiles, and its radius and insolation place it immediately above the radius valley at the lower end of the mini-Neptune cluster. A coupled atmospheric and dynamical evolution analysis of the planet is inconsistent with a pure H–He atmosphere and favours a heavy mean molecular weight atmosphere. The transmission spectroscopy metric of 221−46+66 means that GJ 3090 b is the second or third most favorable mini-Neptune after GJ 1214 b whose atmosphere may be characterised. At almost half the mass of GJ 1214 b, GJ 3090 b is an excellent probe of the edge of the transition between super-Earths and mini-Neptunes. We identify an additional signal in the radial velocity data that we attribute to a planet candidate with an orbital period of 13 days and a mass of 17.1−3.2+8.9 ME, whose transits are not detected

Wolf 503 b: Characterization of a sub-neptune orbiting a metal-poor k dwarf

Using radial velocity measurements from four instruments, we report the mass and density of a $2.043\pm0.069 ~\rm{R}_{\oplus}$ sub-Neptune orbiting the quiet K-dwarf Wolf 503 (HIP 67285). In addition, we present improved orbital and transit parameters by analyzing previously unused short-cadence $K2$ campaign 17 photometry and conduct a joint radial velocity-transit fit to constrain the eccentricity at $0.41\pm0.05$. The addition of a transit observation by $Spitzer$ also allows us to refine the orbital ephemeris in anticipation of further follow-up. Our mass determination, $6.26^{+0.69}_{-0.70}~\rm{M}_{\odot}$, in combination with the updated radius measurements, gives Wolf 503 b a bulk density of $\rho = 2.92\pm ^{+0.50}_{-0.44}$ $\rm{g}~\rm{cm}^{-3}$. Using interior composition models, we find this density is consistent with an Earth-like core with either a substantial $\rm{H}_2\rm{O}$ mass fraction ($45^{+19.12}_{-16.15}\%$) or a modest H/He envelope ($0.5\pm0.28\%$). The low H/He mass fraction, along with the old age of Wolf 503 ($11\pm2$ Gyrs), makes this sub-Neptune an opportune subject for testing theories of XUV-driven mass loss while the brightness of its host ($J=8.3$ mag) makes it an attractive target for transmission spectroscopy

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers