Showing papers by "Omid C. Farokhzad published in 2022"

Theranostic Nanomedicine in the NIR-II Window: Classification, Fabrication, and Biomedical Applications.

Abstract: ADVERTISEMENT RETURN TO ISSUEHighlightNEXTTheranostic Nanomedicine in the NIR-II Window: Classification, Fabrication, and Biomedical ApplicationsWei Tao*Wei TaoCenter for Nanomedicine and Department of Anesthesiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, United States*Email: [email protected]More by Wei Tao and Omid C. Farokhzad*Omid C. FarokhzadCenter for Nanomedicine and Department of Anesthesiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, United StatesSeer, Inc., Redwood City, California 94065, United States*Email: [email protected]More by Omid C. FarokhzadCite this: Chem. Rev. 2022, 122, 6, 5405–5407Publication Date (Web):March 23, 2022Publication History Received1 February 2022Published online23 March 2022Published inissue 23 March 2022https://doi.org/10.1021/acs.chemrev.2c00089Copyright © Published 2022 by American Chemical SocietyRIGHTS & PERMISSIONSArticle Views2180Altmetric-Citations2LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (2 MB) Get e-AlertsSUBJECTS:Biological imaging,Carbon nanotubes,Fluorescence imaging,Molecular imaging,Optical imaging Get e-Alerts

Nanoparticle protein corona evolution: from biological impact to biomarker discovery.

Abstract: Nanoparticles exposed to biological fluids such as blood, quickly interact with their surrounding milieu resulting in a biological coating that results in large part as a function of the physicochemical properties of the nanomaterial. The large nanoparticle surface area-to-volume ratio further augments binding of biological molecules and the resulting biomolecular or protein corona, once thought of as problematic biofouling, is now viewed as a rich source of biological information that can guide the development of nanomedicines. This review gives an overview of the utility of the protein corona in proteomic profiling and discusses how a better understanding of nano-bio interactions can accelerate the clinical translation of nanomedicines and facilitate the identification of disease-specific biomarkers. With the FDA requirement of the protein corona analysis of nanoparticles in place, it is envisaged that analyzing the protein corona of nanoparticles on a case-by-case basis can provide highly valuable nano-bio interface information that can aid and improve their clinical translation.

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.

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

Abstract: 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.

Peptide-centric analyses of human plasma enable increased resolution of biological insights into non-small cell lung cancer relative to protein-centric analysis

Abstract: Comprehensive assessment of the human proteome remains challenging due to multiple forms of a protein, or proteoforms, arising from alternative splicing, allelic variation, and protein modifications. As proteoforms can serve distinct functions and act as functional links between genotype and phenotype, proteoform-level knowledge is critical in understanding the molecular mechanisms underlying health and disease. However, identification of proteoforms requires unbiased protein coverage at amino acid resolution. Scalable, deep, and unbiased proteomics studies have been impractical due to cumbersome and lengthy workflows required for complex samples, like blood plasma. Here, we demonstrate the power of the Proteograph™ Product Suite in enabling unbiased, deep, and rapid proteomics at scale in a proof-of-concept proteoform analysis to dissect differences between protein isoforms in plasma samples from 80 healthy controls and 61 patients with early-stage non-small-cell lung cancer (NSCLC). Processing the 141 plasma samples with Proteograph yielded 22,993 peptides corresponding to 2,569 protein groups at a confidence of 1% false discovery rate. We extracted four proteins with peptides with significant abundance differences (p < 0.05; Benjamini-Hochberg corrected) in healthy control and cancer plasma samples. For one, the abundance variation can be explained by underlying annotated protein isoforms. For a second, we find evidence for differentially transcribed isoforms in the broader sequence data, but not in the known annotated protein isoforms. The others may be explained by novel isoforms or post-translational modifications. In addition, we sought to identify protein variants arising from allelic variation. To this end, we first performed whole exome sequencing on buffy coat samples from 29 individuals in the NSCLC study. Then, we created personalized mass spectrometry search databases for each individual subject from the exome sequences. From these libraries, we identified 422 protein variants, one of which has previously been shown to relate to lung cancer. In conclusion, our results demonstrate that Proteograph can generate unbiased and deep plasma proteome profiles that enable identification of proteoforms present in plasma at a scale sufficient to enable population-scale proteomic studies powered to reveal novel mechanistic and biomedical insights.

Enhanced competitive protein exchange at the nano-bio interface enables ultra-deep coverage of the human plasma proteome

Abstract: We have developed a scalable system that leverages protein-nano interactions to overcome current limitations of deep plasma proteomics in large cohorts. Introducing proprietary 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. Here we demonstrate the importance of tuning the protein to NP-surface ratio (P/NP), which determines the competition between proteins for binding. We demonstrate how optimized P/NP ratio affects protein corona composition, ultimately enhancing performance of a fully automated NP-based deep proteomic workflow (Proteograph). By limiting the available binding surface of NPs and increasing the binding competition, we identify 1.2 – 1.7x more proteins with only 1% false discovery rate on the surface of each NP, and up to 3x compared to a standard neat plasma proteomics workflow. Moreover, increased competition means proteins are more consistently identified and quantified across replicates, yielding precise quantification and improved coverage of the plasma proteome when using multiple physicochemically distinct NPs. In summary, by optimizing NPs and assay conditions, we capture a larger and more diverse set of proteins, enabling deep proteomic studies at scale.

Abstract 6340: Deep, unbiased and peptide-centric plasma proteomics with differential analysis of proteoforms enabling proteogenomic studies of NSCLC at scale

Abstract: Introduction: Comprehensive assessment of the proteome remains elusive because of proteoforms arising from alternative splicing, allelic variation, and protein modifications. Characterization of the variable protein forms, or proteoforms will expand our understanding of the molecular mechanisms underlying diseases, however requires unbiased protein coverage at sufficient scale. Scalable, deep and unbiased proteomics studies have been impractical due to cumbersome and lengthy workflows required for complex samples, like blood plasma. Here, we demonstrate the power of Proteograph in a proof-of-concept proteogenomic analysis of 80 healthy controls and 61 early-stage non-small-cell lung cancer (NSCLC) samples to dissect differences between protein isoforms arising from alternative gene splicing, as well as the identification of novel peptides arising from allelic variation. Materials, Methods and Results: Processing the 141 plasma samples with Proteograph yielded 21,959 peptides corresponding to 2,499 protein groups. Using peptides with significant abundance differences (p < 0.05; Benjamini-Hochberg corrected), we extracted proteins comprised of peptides where at least one peptide had significantly higher plasma abundance, and another significantly lower plasma abundance in controls vs. cancer, resulting in a set of putative proteoforms. For three of these proteins, the abundance variation is possibly explained by underlying protein isoforms. To identify protein variants, we performed exome sequencing on 29 individuals from the NSCLC study, created personalized mass spectrometry search libraries for each individual, and identified 464 protein variants. Conclusions: Proteograph can generate unbiased and deep plasma proteome profiles that enable identification of protein variants and peptides present in plasma, at a scale sufficient to enable population-scale proteomic studies. Citation Format: Margaret Donovan, Henry Huang, John Blume, Marwin Ko, Ryan Benz, Theodore Platt, Juan Cuevas, Serafim Batzoglou, Asim Siddiqui, Omid Farokhzad. Deep, unbiased and peptide-centric plasma proteomics with differential analysis of proteoforms enabling proteogenomic studies of NSCLC at scale [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 6340.