Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood

• Published in: Nano letters 2023-03, Vol.23 (6), p.2065-2073
• Main Authors: 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.
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Bioprinting; Escherichia coli; Gold - chemistry; Letter; Metal Nanoparticles - chemistry; Spectrum Analysis, Raman - methods; Staphylococcus epidermidis
• ISSN: 1530-6984; 1530-6992
• DOI: 10.1021/acs.nanolett.2c03015

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