Plasmonic and Electrostatic Interactions Enable Uniformly Enhanced Liquid Bacterial Surface-Enhanced Raman Scattering (SERS).

Surface-enhanced Raman spectroscopy (SERS) is a promising cellular identification and drug susceptibility testing platform, provided it can be performed in a controlled liquid environment that maintains cell viability. We investigate bacterial liquid-SERS, studying plasmonic and electrostatic interactions between gold nanorods and bacteria that enable uniformly enhanced SERS. We synthesize five nanorod sizes with longitudinal plasmon resonances ranging from 670 to 860 nm and characterize SERS signatures of Gram-negative Escherichia coli and Serratia marcescens and Gram-positive Staphylococcus aureus and Staphylococcus epidermidis bacteria in water. Varying the concentration of bacteria and nanorods, we achieve large-area SERS enhancement that is independent of nanorod resonance and bacteria type; however, bacteria with higher surface charge density exhibit significantly higher SERS signal. Using cryo-electron microscopy and zeta potential measurements, we show that the higher signal results from attraction between positively charged nanorods and negatively charged bacteria. Our robust liquid-SERS measurements provide a foundation for bacterial identification and drug testing in biological fluids.

Toward rapid infectious disease diagnosis with advances in surface-enhanced Raman spectroscopy.

In a pandemic era, rapid infectious disease diagnosis is essential. Surface-enhanced Raman spectroscopy (SERS) promises sensitive and specific diagnosis including rapid point-of-care detection and drug susceptibility testing. SERS utilizes inelastic light scattering arising from the interaction of incident photons with molecular vibrations, enhanced by orders of magnitude with resonant metallic or dielectric nanostructures. While SERS provides a spectral fingerprint of the sample, clinical translation is lagged due to challenges in consistency of spectral enhancement, complexity in spectral interpretation, insufficient specificity and sensitivity, and inefficient workflow from patient sample collection to spectral acquisition. Here, we highlight the recent, complementary advances that address these shortcomings, including (1) design of label-free SERS substrates and data processing algorithms that improve spectral signal and interpretability, essential for broad pathogen screening assays; (2) development of new capture and affinity agents, such as aptamers and polymers, critical for determining the presence or absence of particular pathogens; and (3) microfluidic and bioprinting platforms for efficient clinical sample processing. We also describe the development of low-cost, point-of-care, optical SERS hardware. Our paper focuses on SERS for viral and bacterial detection, in hopes of accelerating infectious disease diagnosis, monitoring, and vaccine development. With advances in SERS substrates, machine learning, and microfluidics and bioprinting, the specificity, sensitivity, and speed of SERS can be readily translated from laboratory bench to patient bedside, accelerating point-of-care diagnosis, personalized medicine, and precision health.

Interpretable Classification of Bacterial Raman Spectra With Knockoff Wavelets.

Deep neural networks and other machine learning models are widely applied to biomedical signal data because they can detect complex patterns and compute accurate predictions. However, the difficulty of interpreting such models is a limitation, especially for applications involving high-stakes decision, including the identification of bacterial infections. This paper considers fast Raman spectroscopy data and demonstrates that a logistic regression model with carefully selected features achieves accuracy comparable to that of neural networks, while being much simpler and more transparent. Our analysis leverages wavelet features with intuitive chemical interpretations, and performs controlled variable selection with knockoffs to ensure the predictors are relevant and non-redundant. Although we focus on a particular data set, the proposed approach is broadly applicable to other types of signal data for which interpretability may be important.

Rapid intraoperative diagnosis of pediatric brain tumors using Raman spectroscopy: A machine learning approach.

Background: Surgical resection is a mainstay in the treatment of pediatric brain tumors to achieve tissue diagnosis and tumor debulking. While maximal safe resection of tumors is desired, it can be challenging to differentiate normal brain from neoplastic tissue using only microscopic visualization, intraoperative navigation, and tactile feedback. Here, we investigate the potential for Raman spectroscopy (RS) to accurately diagnose pediatric brain tumors intraoperatively. Methods: Using a rapid acquisition RS device, we intraoperatively imaged fresh ex vivo brain tissue samples from 29 pediatric patients at the Lucile Packard Children's Hospital between October 2018 and March 2020 in a prospective fashion. Small tissue samples measuring 2-4 mm per dimension were obtained with each individual tissue sample undergoing multiple unique Raman spectra acquisitions. All tissue samples from which Raman spectra were acquired underwent individual histopathology review. A labeled dataset of 678 unique Raman spectra gathered from 160 samples was then used to develop a machine learning model capable of (1) differentiating normal brain from tumor tissue and (2) normal brain from low-grade glioma (LGG) tissue. Results: Trained logistic regression model classifiers were developed using our labeled dataset. Model performance was evaluated using leave-one-patient-out cross-validation. The area under the curve (AUC) of the receiver-operating characteristic (ROC) curve for our tumor vs normal brain model was 0.94. The AUC of the ROC curve for LGG vs normal brain was 0.91. Conclusions: Our work suggests that RS can be used to develop a machine learning-based classifier to differentiate tumor vs non-tumor tissue during resection of pediatric brain tumors.

A pan-cancer organoid platform for precision medicine.

Patient-derived tumor organoids (TOs) are emerging as high-fidelity models to study cancer biology and develop novel precision medicine therapeutics. However, utilizing TOs for systems-biology-based approaches has been limited by a lack of scalable and reproducible methods to develop and profile these models. We describe a robust pan-cancer TO platform with chemically defined media optimized on cultures acquired from over 1,000 patients. Crucially, we demonstrate tumor genetic and transcriptomic concordance utilizing this approach and further optimize defined minimal media for organoid initiation and propagation. Additionally, we demonstrate a neural-network-based high-throughput approach for label-free, light-microscopy-based drug assays capable of predicting patient-specific heterogeneity in drug responses with applicability across solid cancers. The pan-cancer platform, molecular data, and neural-network-based drug assay serve as resources to accelerate the broad implementation of organoid models in precision medicine research and personalized therapeutic profiling programs.

Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning.

Raman optical spectroscopy promises label-free bacterial detection, identification, and antibiotic susceptibility testing in a single step. However, achieving clinically relevant speeds and accuracies remains challenging due to weak Raman signal from bacterial cells and numerous bacterial species and phenotypes. Here we generate an extensive dataset of bacterial Raman spectra and apply deep learning approaches to accurately identify 30 common bacterial pathogens. Even on low signal-to-noise spectra, we achieve average isolate-level accuracies exceeding 82% and antibiotic treatment identification accuracies of 97.0±0.3%. We also show that this approach distinguishes between methicillin-resistant and -susceptible isolates of Staphylococcus aureus (MRSA and MSSA) with 89±0.1% accuracy. We validate our results on clinical isolates from 50 patients. Using just 10 bacterial spectra from each patient isolate, we achieve treatment identification accuracies of 99.7%. Our approach has potential for culture-free pathogen identification and antibiotic susceptibility testing, and could be readily extended for diagnostics on blood, urine, and sputum.