Combining Acoustic Bioprinting with AI-Assisted Raman Spectroscopy for High-Throughput Identification of Bacteria in Blood.
Fareeha SafirNhat VuLoza F TadesseKamyar FirouziNiaz BanaeiStefanie S JeffreyAmr A E SalehButrus Pierre T Khuri-YakubJennifer A DionnePublished in: Nano letters (2023)
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.
Keyphrases
- raman spectroscopy
- machine learning
- high throughput
- gold nanoparticles
- loop mediated isothermal amplification
- sensitive detection
- single cell
- candida albicans
- artificial intelligence
- deep learning
- wastewater treatment
- escherichia coli
- stem cells
- induced apoptosis
- mesenchymal stem cells
- big data
- cell therapy
- cell proliferation
- mass spectrometry
- high speed
- real time pcr
- bioinformatics analysis