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

Abstract

Significance Deep 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.

The Future of Drug Delivery

“…Some of the critical factors include whether the treatment is for a child, adult, or elderly patient; whether it needs to be a sustained-release or pulsative-release formulation; the patient’s lifestyle; and addressing the changes in biological barriers among different patients. Machine learning and proteograph were recently used to dissect the contribution of engineered nanoparticles to the composition of protein coronas for deep and large-scale plasma proteomics, which helped identify protein variants across patients . Future drug delivery systems need to be thoroughly evaluated to better understand how physical and chemical properties impact the crossing of biological barriers in a specific disease subtype as well as a specific patient population to develop a personalized plan for drug delivery.…”

Phytoconstituents and biological activities of Melaleuca cajuputi Powell: A scoping review

“…Additionally, a high-throughput real-time endosomal escape imaging assay Then, protocol standardization for large-scale PPC analysis will be employed, including the PC characterization methodologies, high throughput NP formulations, and construction of NP library database and computational modeling. Automated high throughput screening will be ideal for biomarker discoveries since it has better efficiency, speed, and reproducibility (Ferdosi et al, 2022;. The third step would be preclinical testing to examine the PC biological mechanisms using in vitro and in vivo modeling.…”

“…Recent advancements in proteomics analysis using the automated Proteograph workflow and its integrated software, for instance, have successfully improved proteomics analysis coverage, scalability, reproducibility, and accuracy. It simplifies O n l i n e F i r s t the LC-MS preparation stage and significantly enhances the data analysis capability by incorporating genomics analysis enabling more comprehensive pathway mapping (Blume et al, 2020;Ferdosi et al, 2022).…”

Nanoparticle protein corona and its role in disease diagnostic

“…The current advances in the in vitro microfluidics setup have the potential to bridge the gap since it closely mimics the realistic biological in vivo condition by precise control of the physiological factors such as shear stress, Then, protocol standardization for large-scale PPC analysis will be employed, including the PC characterization methodologies, high throughput NP formulations, and construction of NP library database and computational modeling. Automated high throughput screening will be ideal for biomarker discoveries since it has better efficiency, speed, and reproducibility (Ferdosi et al, 2022;. The third step would be preclinical testing to examine the PC biological mechanisms using in vitro and in vivo modeling.…”

“…Recent advancements in proteomics analysis using the automated Proteograph workflow and its integrated software, for instance, have successfully improved proteomics analysis coverage, scalability, reproducibility, and accuracy. It simplifies the LC-MS preparation stage and significantly enhances the data analysis capability by incorporating genomics analysis enabling more comprehensive pathway mapping (Blume et al, 2020;Ferdosi et al, 2022).…”