SERS Nanowire Chip and Machine Learning-Enabled Classification of Wild-Type and Antibiotic-Resistant Bacteria at Species and Strain Levels

Abstract

Antimicrobial resistance (AMR) is a major health threat worldwide and the culture-based bacterial detection methods are slow. Surface-enhanced Raman spectroscopy (SERS) can be used to identify target analytes in real time with sensitivity down to the single-molecule level, providing a promising solution for the culture-free bacterial detection. We report the fabrication of SERS substrates having tightly packed silver (Ag) nanoparticles loaded onto long silicon nanowires (Si NWs) grown by the metal-assisted chemical etching (MACE) method for the detection of bacteria. The optimized SERS chips exhibited sensitivity down to 10–12 M concentration of R6G molecules and detected reproducible Raman spectra of bacteria down to a concentration of 100 colony forming units (CFU)/mL, which is a thousand times lower than the clinical threshold of bacterial infections like UTI (105 CFU/mL). A Siamese neural network model was used to classify SERS spectra from bacteria specimens. The trained model identified 12 different bacterial species, including those which are causative agents for tuberculosis and urinary tract infection (UTI). Next, the SERS chips and another Siamese neural network model were used to differentiate AMR strains from susceptible strains of Escherichia coli (E. coli). The enhancement offered by SERS chip-enabled acquisitions of Raman spectra of bacteria directly in the synthetic urine by spiking the sample with only 103 CFU/mL E. coli. Thus, the present study lays the ground for the identification and quantification of bacteria on SERS chips, thereby offering a potential future use for rapid, reproducible, label-free, and low limit detection of clinical pathogens.

ZnO@Ag-Functionalized Paper-Based Microarray Chip for SERS Detection of Bacteria and Antibacterial and Photocatalytic Inactivation

“…coli and V. parahemolyticus, which was primarily explained by the fact that peptidoglycan thickness in Gram-positive bacteria was higher (10–20 layers) than that in Gram-negative bacteria (1–3 layers). , Compared to the degree of signal at 731 cm –1 , E. coli and V.…”

“…33 Particularly, the signal level of S. aureus at 731 cm −1 was substantially greater than E. coli and V. parahemolyticus, which was primarily explained by the fact that peptidoglycan thickness in Gram-positive bacteria was higher (10−20 layers) than that in Gram-negative bacteria (1−3 layers). 9,34 Compared to the degree of signal at 731 cm −1 , E. coli and V. parahemolyticus have a more significant signal at 662 cm −1 than S. aureus, which is possibly related to the signal of bacterial outer membrane proteins perceived only in Gram-negative bacteria. 35 These differences revealed that the prepared paperbased microarray SERS chips have a promising capability of distinguishing between various types of pathogenic bacteria.…”

“…Their varied cell wall structures may be the cause of the variation in inactivation performance against S. aureus, E. coli, and V. parahemolyticus. 33,34 Gram-negative bacteria (E. coli and V. parahemolyticus) have a protective lipid membrane on the outside but a thin layer of peptidoglycan (7−8 nm) between the inner and outside cell membranes. Gram-positive bacteria (S. aureus) lack an exterior lipid membrane; however, they do have a thicker peptidoglycan layer (20−80 nm).…”

A novel multifunctional SERS microfluidic sensor based on ZnO/Ag nanoflower arrays for label-free ultrasensitive detection of bacteria

“…18–21 Sathi et al reported a SERS substrate with tightly packed silver (Ag) nanoparticles on silicon nanowires (Si NWs) to detect AMR strains and Escherichia coli , with 10 2 CFU mL −1 detection limits. 22 Wang et al reported polyethyleneimine-modified Au-coated magnetic microspheres and Au@Ag and concentrated Au@Ag nanoparticles (NPs). They detected Escherichia coli and Staphylococcus aureus , demonstrating a detection limit as low as 10 3 CFU mL −1 .…”

Surface-Enhanced Raman Scattering-Based Surface Chemotaxonomy: Combining Bacteria Extracellular Matrices and Machine Learning for Rapid and Universal Species Identification

“…Existing SERS platforms primarily probe the bacterial cell wall. This approach has its limitations because it can be overly ideal and can only be derived in controlled, isolated laboratory conditions, thus not accurately representing bacteria in their natural habitats. − In their natural habitats, bacteria are often encapsulated by a complex extracellular matrix (ECM) comprising exopolysaccharides, proteins, and DNA. This matrix forms around and between individual cells in response to its native environment.…”