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

Shadi Ferdosi; Behzad Tangeysh; Tristan R. Brown; Patrick A. Everley; Michael Figa; Matthew McLean; Eltaher M. Elgierari; Xiaoyan Zhao; Veder J. Garcia; Tianyu Wang; Matthew E.K. Chang; Kateryna Riedesel; Jessica Chu; Max Mahoney; Hongwei Xia; Evan S. O’Brien; Craig Stolarczyk; Damian Harris; Theodore L. Platt; Philip Ma; Martin Goldberg; Robert Langer; Mark R. Flory; Ryan Benz; Wei Tao; Juan Cruz Cuevas; Serafim Batzoglou; John E. Blume; Asim Siddiqui; Daniel Hornburg; Omid C. Farokhzad

doi:10.1073/PNAS.2106053119

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

Shadi Ferdosi, Behzad Tangeysh, Tristan R. Brown, Patrick A. Everley, Michael Figa, Matthew McLean, Eltaher M. Elgierari, Xiaoyan Zhao, Veder J. Garcia, Tianyu Wang, Matthew E.K. Chang, Kateryna Riedesel, Jessica Chu, Max Mahoney, Hongwei Xia, Evan S. O’Brien, Craig Stolarczyk, Damian Harris, Theodore L. Platt, Philip MaMartin Goldberg, Robert Langer, Mark R. Flory, Ryan Benz, Wei Tao, Juan Cruz Cuevas, Serafim Batzoglou, John E. Blume, Asim Siddiqui, Daniel Hornburg, Omid C. Farokhzad

Knight Cancer Institute

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

Deep interrogation of plasma proteins on a large scale is a challenge due to the number and concentration of proteins, which span a dynamic range of over 10 orders of magnitude. Current plasma proteomics workflows employ labor-intensive protocols combining abundant protein depletion and sample fractionation. We previously demonstrated the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma proteome in terms of proteome depth compared to simple workflows. Here we show the superior depth and precision of a multi-NP workflow compared to conventional deep workflows evaluating multiple gradients and search engines as well as data-dependent and data-independent acquisition. We link the physicochemical properties and surface functionalization of NPs to their differential protein selectivity, a key feature in NP panel profiling performance. We find that individual proteins and protein classes are differentially attracted by specific surface properties, opening avenues to design multi-NP panels for deep interrogation of complex biological samples.

Original language	English (US)
Article number	e2106053119
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	119
Issue number	11
DOIs	https://doi.org/10.1073/PNAS.2106053119
State	Published - Mar 15 2022

Keywords

machine learning
mass spectrometry
nanoparticle
nano–bio interaction
proteomics

ASJC Scopus subject areas

General

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10.1073/PNAS.2106053119

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Ferdosi, S., Tangeysh, B., Brown, T. R., Everley, P. A., Figa, M., McLean, M., Elgierari, E. M., Zhao, X., Garcia, V. J., Wang, T., Chang, M. E. K., Riedesel, K., Chu, J., Mahoney, M., Xia, H., O’Brien, E. S., Stolarczyk, C., Harris, D., Platt, T. L., ... Farokhzad, O. C. (2022). Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano–bio interactions. Proceedings of the National Academy of Sciences of the United States of America, 119(11), Article e2106053119. https://doi.org/10.1073/PNAS.2106053119

Ferdosi, S, Tangeysh, B, Brown, TR, Everley, PA, Figa, M, McLean, M, Elgierari, EM, Zhao, X, Garcia, VJ, Wang, T, Chang, MEK, Riedesel, K, Chu, J, Mahoney, M, Xia, H, O’Brien, ES, Stolarczyk, C, Harris, D, Platt, TL, Ma, P, Goldberg, M, Langer, R, Flory, MR, Benz, R, Tao, W, Cuevas, JC, Batzoglou, S, Blume, JE, Siddiqui, A, Hornburg, D & Farokhzad, OC 2022, 'Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano–bio interactions', Proceedings of the National Academy of Sciences of the United States of America, vol. 119, no. 11, e2106053119. https://doi.org/10.1073/PNAS.2106053119

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title = "Engineered nanoparticles enable deep proteomics studies at scale by leveraging tunable nano–bio interactions",

abstract = "Deep interrogation of plasma proteins on a large scale is a challenge due to the number and concentration of proteins, which span a dynamic range of over 10 orders of magnitude. Current plasma proteomics workflows employ labor-intensive protocols combining abundant protein depletion and sample fractionation. We previously demonstrated the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma proteome in terms of proteome depth compared to simple workflows. Here we show the superior depth and precision of a multi-NP workflow compared to conventional deep workflows evaluating multiple gradients and search engines as well as data-dependent and data-independent acquisition. We link the physicochemical properties and surface functionalization of NPs to their differential protein selectivity, a key feature in NP panel profiling performance. We find that individual proteins and protein classes are differentially attracted by specific surface properties, opening avenues to design multi-NP panels for deep interrogation of complex biological samples.",

keywords = "machine learning, mass spectrometry, nanoparticle, nano–bio interaction, proteomics",

author = "Shadi Ferdosi and Behzad Tangeysh and Brown, {Tristan R.} and Everley, {Patrick A.} and Michael Figa and Matthew McLean and Elgierari, {Eltaher M.} and Xiaoyan Zhao and Garcia, {Veder J.} and Tianyu Wang and Chang, {Matthew E.K.} and Kateryna Riedesel and Jessica Chu and Max Mahoney and Hongwei Xia and O{\textquoteright}Brien, {Evan S.} and Craig Stolarczyk and Damian Harris and Platt, {Theodore L.} and Philip Ma and Martin Goldberg and Robert Langer and Flory, {Mark R.} and Ryan Benz and Wei Tao and Cuevas, {Juan Cruz} and Serafim Batzoglou and Blume, {John E.} and Asim Siddiqui and Daniel Hornburg and Farokhzad, {Omid C.}",

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AU - Ferdosi, Shadi

AU - Tangeysh, Behzad

AU - Brown, Tristan R.

AU - Everley, Patrick A.

AU - Figa, Michael

AU - McLean, Matthew

AU - Elgierari, Eltaher M.

AU - Zhao, Xiaoyan

AU - Garcia, Veder J.

AU - Wang, Tianyu

AU - Chang, Matthew E.K.

AU - Riedesel, Kateryna

AU - Chu, Jessica

AU - Mahoney, Max

AU - Xia, Hongwei

AU - O’Brien, Evan S.

AU - Stolarczyk, Craig

AU - Harris, Damian

AU - Platt, Theodore L.

AU - Ma, Philip

AU - Goldberg, Martin

AU - Langer, Robert

AU - Flory, Mark R.

AU - Benz, Ryan

AU - Tao, Wei

AU - Cuevas, Juan Cruz

AU - Batzoglou, Serafim

AU - Blume, John E.

AU - Siddiqui, Asim

AU - Hornburg, Daniel

AU - Farokhzad, Omid C.

PY - 2022/3/15

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N2 - Deep interrogation of plasma proteins on a large scale is a challenge due to the number and concentration of proteins, which span a dynamic range of over 10 orders of magnitude. Current plasma proteomics workflows employ labor-intensive protocols combining abundant protein depletion and sample fractionation. We previously demonstrated the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma proteome in terms of proteome depth compared to simple workflows. Here we show the superior depth and precision of a multi-NP workflow compared to conventional deep workflows evaluating multiple gradients and search engines as well as data-dependent and data-independent acquisition. We link the physicochemical properties and surface functionalization of NPs to their differential protein selectivity, a key feature in NP panel profiling performance. We find that individual proteins and protein classes are differentially attracted by specific surface properties, opening avenues to design multi-NP panels for deep interrogation of complex biological samples.

AB - Deep interrogation of plasma proteins on a large scale is a challenge due to the number and concentration of proteins, which span a dynamic range of over 10 orders of magnitude. Current plasma proteomics workflows employ labor-intensive protocols combining abundant protein depletion and sample fractionation. We previously demonstrated the superiority of multinanoparticle (multi-NP) coronas for interrogating the plasma proteome in terms of proteome depth compared to simple workflows. Here we show the superior depth and precision of a multi-NP workflow compared to conventional deep workflows evaluating multiple gradients and search engines as well as data-dependent and data-independent acquisition. We link the physicochemical properties and surface functionalization of NPs to their differential protein selectivity, a key feature in NP panel profiling performance. We find that individual proteins and protein classes are differentially attracted by specific surface properties, opening avenues to design multi-NP panels for deep interrogation of complex biological samples.

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KW - mass spectrometry

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KW - nano–bio interaction

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ER -

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

Abstract

Keywords

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