Combining acoustic bioprinting with AI-assisted Raman spectroscopy for high-throughput identification of bacteria in blood

Abstract

The presence of pathogens in complex, multi-cellular samples such as blood, urine, mucus, and wastewater can serve as indicators of active infection, and their identification can impact how human and environmental health are treated [1][2][3][4][5][6][7]. Surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) can distinguish multiple pathogen species and strains [8][9][10][11], but processing complex fluid samples to sensitively and specifically detect pathogens remains an outstanding challenge. Here, we develop an acoustic bioprinting platform to digitize samples into millions of droplets, each containing just a few cells, which are then identified with SERS and ML. As a proof of concept, we focus on bacterial bloodstream infections. We demonstrate ∼2pL droplet generation from solutions containing S. epidermidis, E. coli, and mouse red blood cells (RBCs) mixed with gold nanorods (GNRs) at 1 kHz ejection rates; use of parallel printing heads would enable processing of mL-volume samples in minutes [12]. Droplets printed with GNRs achieve spectral enhancements of up to 1500x compared to samples printed without GNRs. With this improved signal-to-noise, we train an ML model on droplets consisting of either pure cells with GNRs or mixed, multicellular species with GNRs, using scanning electron microscopy images as our ground truth. We achieve ≥99% classification accuracy of droplets printed from cellularly-pure samples, and ≥87% accuracy in droplets printed from mixtures of S. epidermidis, E. coli, and RBCs. We compute the feature importance at each wavenumber and confirm that the most significant spectral bands for classification correspond to biologically relevant Raman peaks within our cells. This combined acoustic droplet ejection, SERS and ML platform could enable clinical and industrial translation of SERS-based cellular identification. MainReliable detection and identification of microorganisms is crucial for medical diagnostics, environmental monitoring, food production and safety, biodefense, biomanufacturing, and pharmaceutical development. Such samples typically contain as few as 1-100 colony-forming units (CFU)/mL[13-15], necessitating the use of in vitro liquid culturing for pathogen detection. It is estimated that less than 2% of all bacteria can be readily cultured using current laboratory protocols, and even amongst that 2%, culturing can take hours to days depending on the species [16][17][18][19]. In the case of medical diagnostics, broad spectrum antibiotics are often administered while waiting for culture results, leading to an alarming rise in antibiotic resistant bacteria. Antimicrobial resistance currently leads to ∼700,000 deaths per year, and is predicted to become the leading cause of death by 2050 [20]. To combat these trends, it is crucial to develop methods to rapidly detect and identify bacteria in diverse, complex samples.Raman spectroscopy is a label-free, vibrational spectroscopic technique that has recently emerged as a promising platform for bacterial species