Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood
Name
safir-et-al-2023-combining-acoustic-bioprinting-with-ai-assisted-raman-spectroscopy-for-high-throughput-identification.pdf
Description
Published version
Size
6.34 MB
Format
Adobe PDF
Checksum (MD5)
df34cfb9fe6ab1c91e66580710a68ea2
Author(s)
Safir, Fareeha
Vu, Nhat
Tadesse, Loza F.
Firouzi, Kamyar
Banaei, Niaz
Jeffrey, Stefanie S.
Saleh, Amr. A. E.
Khuri-Yakub, Butrus (Pierre) T.
Dionne, Jennifer A.
Date Issued
March 1, 2023
Journal
Nano Letters
Publisher
American Chemical Society
Citation
Safir, Fareeha, Vu, Nhat, Tadesse, Loza F., Firouzi, Kamyar, Banaei, Niaz et al. 2023. "Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood." Nano Letters, 23 (6).
Version
Final published version
Abstract
Identifying pathogens in complex samples such as blood, urine, and wastewater is critical to detect infection and inform optimal treatment. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish among multiple pathogen species, but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinter to digitize samples into millions of droplets, each containing just a few cells, which are identified with SERS and ML. We demonstrate rapid printing of 2 pL droplets from solutions containing S. epidermidis, E. coli, and blood; when they are mixed with gold nanorods (GNRs), SERS enhancements of up to 1500× are achieved.We then train a ML model and achieve ≥99% classification accuracy from cellularly pure samples and ≥87% accuracy from cellularly mixed samples. We also obtain ≥90% accuracy from droplets with pathogen:blood cell ratios <1. Our combined bioprinting and SERS platform could accelerate rapid, sensitive pathogen detection in clinical, environmental, and industrial settings.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1021/acs.nanolett.2c03015
