Biology subjects

Rosenberg-Hasson, Y.

Publications and source records attributed to Rosenberg-Hasson, Y..

4 recordsLinked to original sources

bioRxiv2023

A rapid ELISA platform with no sample preparation requirement

Since its invention in the 1970s, the enzyme-linked immunosorbent assay (ELISA) has served as the "gold-standard" for blood and plasma protein biomarker quantification. However, ELISAs require significant amounts of sample preparation entailing multiple reagent additions, incubations, and washing steps, limiting their clinical usefulness in the context of diagnosis and prognosis of rapidly evolving medical conditions. In this work, we describe the instant ELISA biosensor platform, a probe that can be exposed directly to blood or other biological samples and quantifies protein biomarkers within 15 minutes. The sensor leverages a novel affinity reagent termed monolithic dual-antibody clamp (MDAC) which preserves the specificity, sensitivity, and generalizability of ELISA while also enabling rapid analysis of unprocessed blood and other complex matrices. Using MDAC in chicken media, we demonstrate picomolar quantification of the inflammatory marker tumor necrosis factor alpha (TNF), as well as monocyte chemotactic protein (MCP)-1, a useful prognostic indicator of cytokine release syndrome (CRS) during chimeric antigen receptor (CAR) T-cell immunotherapy. Finally, we demonstrate MCP-1 quantification in plasma samples from patients who had undergone CAR T-cell treatment.

bioengineering↗

bioRxiv2023

Extending the dynamic range of biomarker quantification through molecular equalization

Precision medicine requires highly scalable methods of multiplexed biomarker quantification that can accurately describe patient physiology. Unfortunately, contemporary molecular detection methods are generally limited to a dynamic range of sensitivity spanning just 3-4 orders of magnitude, whereas the actual physiological dynamic range of the human plasma proteome spans more than 10 orders of magnitude. Current methods rely on sample splitting and differential dilution to compensate for this mismatch, but such measures greatly limit the reproducibility and scalability that can be achieved--in particular, the effects of non-linear dilution can greatly confound the analysis of multiplexed assays. We describe here a two- pronged strategy for equalizing the signal generated by each analyte in a multiplexed panel, thereby enabling simultaneous quantification of targets spanning a wide range of concentrations. We apply our EVROS strategy to a proximity ligation assay and demonstrate simultaneous quantification of four analytes present at concentrations spanning from low femtomolar to mid-nanomolar levels. In this initial demonstration, we achieve a dynamic range spanning seven orders of magnitude in a single 5 {micro}l sample of undiluted human serum, highlighting the opportunity to achieve sensitive, accurate detection of diverse analytes in a highly multiplexed fashion.

bioengineering↗

bioRxiv2019

An Inflammatory Clock Predicts Multi-morbidity, Immunosenescence and Cardiovascular Aging in Humans

While many diseases of aging have been linked to the immunological system, immune metrics with which to identify the most at-risk individuals are lacking. Here, we studied the blood immunome of 1001 individuals age 8-96 and derived an inflammatory clock of aging (iAge), which tracked with multi-morbidity and immunosenescence. In centenarians, iAge was on average, 40 years lower than their corresponding chronological age. The strongest contributor to this metric was the chemokine CXCL9, which was involved in cardiac aging, affected vascular function, and down-regulated Sirtuin-3, a longevity-associated molecule. Thus, our results identify an important link between inflammatory molecules and pathways known to govern lifespan.

immunology↗

bioRxiv2019

SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses

Machine learning holds considerable promise for understanding complex biological processes such as vaccine responses. Capturing interindividual variability is essential to increase the statistical power necessary for building more accurate predictive models. However, available approaches have difficulty coping with incomplete datasets which is often the case when combining studies. Additionally, there are hundreds of algorithms available and no simple way to find the optimal one. Here, we developed Sequential Iterative Modelling "OverNight" or SIMON, an automated machine learning system that compares results from 128 different algorithms and is particularly suitable for datasets containing many missing values. We applied SIMON to data from five clinical studies of seasonal influenza vaccination. The results reveal previously unrecognized CD4+ and CD8+ T cell subsets strongly associated with a robust antibody response to influenza antigens. These results demonstrate that SIMON can greatly speed up the choice of analysis modalities. Hence, it is a highly useful approach for data-driven hypothesis generation from disparate clinical datasets. Our strategy could be used to gain biological insight from ever-expanding heterogeneous datasets that are publicly available.

immunology↗