PXD011354-1

PXD011354 is an original dataset announced via ProteomeXchange.

Title	Supervised learning predicts the in vivo fate of engineered nanomaterials
Description	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.
HostingRepository	PRIDE
AnnounceDate	2024-09-14
AnnouncementXML	Submission_2024-09-14_12:53:14.515.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	Jonathan Krieger
SpeciesList	scientific name: Mus musculus (Mouse); NCBI TaxID: 10090;
ModificationList	iodoacetamide derivatized residue
Instrument	LTQ Orbitrap Elite

Revision	Datetime	Status	ChangeLog Entry
0	2018-10-15 02:02:15	ID requested
⏵ 1	2024-09-14 12:53:15	announced
2	2024-10-22 06:58:23	announced	2024-10-22: Updated project metadata.

10.1021/acsnano.9b02774;

Lazarovits J, Sindhwani S, Tavares AJ, Zhang Y, Song F, Audet J, Krieger JR, Syed AM, Stordy B, Chan WCW, Fate of Nanomaterials. ACS Nano, 13(7):8023-8034(2019) [pubmed]

curator keyword: Technical

submitter keyword: Nanoparticles, protein corona, mass spectrometry, neural networks, predictive biology

Warren Chan
contact affiliation	Institute of Biomaterials and Biomedical Engineering, Department of Chemical Engineering, Department of Chemistry, Department of Materials Science and Engineering, University of Toronto, 164 College Street, Toronto, Ontario M5S 3G9, Canada. Terrence Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, 160 College Street, Toronto, Ontario, M5S 3E1, Canada.
contact email	chanwarr@me.com
lab head
Jonathan Krieger
contact affiliation	Bruker
contact email	JRKrieger@gmail.com
dataset submitter

Dataset FTP location NOTE: Most web browsers have now discontinued native support for FTP access within the browser window. But you can usually install another FTP app (we recommend FileZilla) and configure your browser to launch the external application when you click on this FTP link. Or otherwise, launch an app that supports FTP (like FileZilla) and use this address: ftp://ftp.pride.ebi.ac.uk/pride/data/archive/2024/09/PXD011354

PRIDE project URI

• PRIDE
  1. PXD011354
     1. Label: PRIDE project
     2. Name: Supervised learning predicts the in vivo fate of engineered nanomaterials