Dataset Information

Supervised learning predicts the in vivo fate of engineered nanomaterials

ABSTRACT: Despite having exquisite control over nanoparticle design, controlling nanoparticle fate in vivo remains a major barrier for clinical translation. This is because we do not understand how nanoparticles interact with the surrounding environment in vivo and how this lack of control contributes towards organ accumulation. The suggested link between nanoparticle interactions and organ accumulation are the proteins that adsorb to the nanoparticle surface following administration. How this network of proteins changes during nanoparticle transport, and its influence over the fate of where nanoparticles distribute inside of the body is fundamentally not understood. Here we developed a simple workflow to show that the evolution of proteins on the surface of nanoparticles predicts the biological fate of nanoparticles in vivo. This workflow involves extracting nanoparticles at multiple time points from circulation, isolating the proteins off the surface, and training a neural network to predict nanoparticle biological fate using the proteins as inputs and clearance and organ accumulation as outputs. In a double-blind study, we validated the model by predicting nanoparticle clearance and spleen and liver accumulation with 76-97% accuracy. This work demonstrates that a link between surface adsorbed proteins and the biological fate of nanomaterials exists, and that it can be predicted using the workflow we designed. As we acquire more training data, the strength of these relationships will become more powerful. With more training data we will use more sophisticated neural networks to identify proteins and pathways to target, or create more effective nanomaterial designs to improve clinical translation.

INSTRUMENT(S): LTQ Orbitrap Elite

ORGANISM(S): Mus Musculus (mouse)

TISSUE(S): Blood Serum

SUBMITTER: Jonathan Krieger

LAB HEAD: Warren Chan

PROVIDER: PXD011354 | Pride | 2024-09-14

REPOSITORIES: Pride

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Publications

Supervised Learning and Mass Spectrometry Predicts the <i>in Vivo</i> Fate of Nanomaterials.

Lazarovits James J Sindhwani Shrey S Tavares Anthony J AJ Zhang Yuwei Y Song Fayi F Audet Julie J Krieger Jonathan R JR Syed Abdullah Muhammad AM Stordy Benjamin B Chan Warren C W WCW

ACS nano 20190703 7

The surface of nanoparticles changes immediately after intravenous injection because blood proteins adsorb on the surface. How this interface changes during circulation and its impact on nanoparticle distribution within the body is not understood. Here, we developed a workflow to show that the evolution of proteins on nanoparticle surfaces predicts the biological fate of nanoparticles <i>in vivo</i>. This workflow involves extracting nanoparticles at multiple time points from circulation, isolat ...[more]

PMID: 31268684

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Project description:Multi-walled carbon nanotubes (MWCNT) cause lung fibrosis in rodents and exacerbate airway fibrosis in the mouse ovalbumin model of allergic asthma. Interleukin 13 (IL-13) is a key cytokine secreted by T helper type 2 (Th2) cells. IL-13 is up-regulated in human asthma and animal models that activate pro-fibrotic and pro-proliferative cell signaling cascades in human lung fibroblasts (HLF). This study tested the hypothesis that IL-13 alters the gene expression profile of HLF exposed to MWCNT. Carbon black nanoparticles (CBNP) were also compared to MWCNT as they are relatively inert nanoparticles that do not cause fibrosis. Confluent, quiescent cultures of HLF were treated with 10 ng/ml IL-13 or serum-free defined medium (vehicle) for 24 hours prior to treatment with 10 M-BM-5g/cm2 MWCNT or CBNP. At 4, 24, or 48 hours following nanoparticle exposure, total RNA was isolated and gene expression was measured using the Affymetrix Human Genome U133A2.0 Array. The data were analyzed using the JMP Genomics statistical platform. IL-13 and MWCNT each caused changes in the expression of distinct gene subsets over the time-course investigated. The combination of IL-13 and MWCNT resulted in a gene expression profile that was distinct from patterns induced or suppressed by either IL-13 or MWCNT alone. CBNP caused changes in gene expression that were distinct from IL-13 or MWCNT. Interestingly, the combination of IL-13 and MWCNT increased the expression of IL-17A and increased collagen (Col1A1), while MWCNT alone increased interferon-inducible protein-27 (IFI27), suggesting that Th2 microenvironment containing IL-13 shifts MWCNT-induced gene expression from a Th1 to a Th17 gene expression profile. These data provide insight into the mechanisms by which MWCNT alter the biology of fibroblasts during normal and allergic inflammatory conditions. Adult Lung Fibroblasts were treated with 10 M-BM-5g/cm2 MWCNT, CBNP, or vehicle with or without IL-13 for 4 hours, 24 hours, or 48 hours.

Dataset Information

Supervised learning predicts the in vivo fate of engineered nanomaterials

Publications

Supervised Learning and Mass Spectrometry Predicts the <i>in Vivo</i> Fate of Nanomaterials.

Similar Datasets

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