Rapid, antibiotic incubation-free determination of tuberculosis drug resistance using machine learning and Raman spectroscopy.

Tuberculosis (TB) is the world's deadliest infectious disease, with over 1.5 million deaths and 10 million new cases reported anually. The causative organism Mycobacterium tuberculosis (Mtb) can take nearly 40 d to culture, a required step to determine the pathogen's antibiotic susceptibility. Both rapid identification and rapid antibiotic susceptibility testing of Mtb are essential for effective patient treatment and combating antimicrobial resistance. Here, we demonstrate a rapid, culture-free, and antibiotic incubation-free drug susceptibility test for TB using Raman spectroscopy and machine learning. We collect few-to-single-cell Raman spectra from over 25,000 cells of the Mtb complex strain Bacillus Calmette-Guérin (BCG) resistant to one of the four mainstay anti-TB drugs, isoniazid, rifampicin, moxifloxacin, and amikacin, as well as a pan-susceptible wildtype strain. By training a neural network on this data, we classify the antibiotic resistance profile of each strain, both on dried samples and on patient sputum samples. On dried samples, we achieve >98% resistant versus susceptible classification accuracy across all five BCG strains. In patient sputum samples, we achieve ~79% average classification accuracy. We develop a feature recognition algorithm in order to verify that our machine learning model is using biologically relevant spectral features to assess the resistance profiles of our mycobacterial strains. Finally, we demonstrate how this approach can be deployed in resource-limited settings by developing a low-cost, portable Raman microscope that costs <$5,000. We show how this instrument and our machine learning model enable combined microscopy and spectroscopy for accurate few-to-single-cell drug susceptibility testing of BCG.

Spin-orbit exciton-induced phonon chirality in a quantum magnet.

The interplay of charge, spin, lattice, and orbital degrees of freedom in correlated materials often leads to rich and exotic properties. Recent studies have brought new perspectives to bosonic collective excitations in correlated materials. For example, inelastic neutron scattering experiments revealed non-trivial band topology for magnons and spin-orbit excitons (SOEs) in a quantum magnet CoTiO3 (CTO). Here, we report phonon properties resulting from a combination of strong spin-orbit coupling, large crystal field splitting, and trigonal distortion in CTO. Specifically, the interaction between SOEs and phonons endows chirality to two [Formula: see text] phonon modes and leads to large phonon magnetic moments observed in magneto-Raman spectra. The remarkably strong magneto-phononic effect originates from the hybridization of SOEs and phonons due to their close energy proximity. While chiral phonons have been associated with electronic topology in some materials, our work suggests opportunities may arise by exploring chiral phonons coupled to topological bosons.

Multiple retinal isomerizations during the early phase of the bestrhodopsin photoreaction.

Bestrhodopsins constitute a class of light-regulated pentameric ion channels that consist of one or two rhodopsins in tandem fused with bestrophin ion channel domains. Here, we report on the isomerization dynamics in the rhodopsin tandem domains of Phaeocystis antarctica bestrhodopsin, which binds all-trans retinal Schiff-base (RSB) absorbing at 661 nm and, upon illumination, converts to the meta-stable P540 state with an unusual 11-cis RSB. The primary photoproduct P682 corresponds to a mixture of highly distorted 11-cis and 13-cis RSB directly formed from the excited state in 1.4 ps. P673 evolves from P682 in 500 ps and contains highly distorted 13-cis RSB, indicating that the 11-cis fraction in P682 converts to 13-cis. Next, P673 establishes an equilibrium with P595 in 1.2 µs, during which RSB converts to 11-cis and then further proceeds to P560 in 48 µs and P540 in 1.0 ms while remaining 11-cis. Hence, extensive isomeric switching occurs on the early ground state potential energy surface (PES) on the hundreds of ps to µs timescale before finally settling on a metastable 11-cis photoproduct. We propose that P682 and P673 are trapped high up on the ground-state PES after passing through either of two closely located conical intersections that result in 11-cis and 13-cis RSB. Co-rotation of C11=C12 and C13=C14 bonds results in a constricted conformational landscape that allows thermal switching between 11-cis and 13-cis species of highly strained RSB chromophores. Protein relaxation may release RSB strain, allowing it to evolve to a stable 11-cis isomeric configuration in microseconds.

Quantifying the ultrafast and steady-state molecular reduction potential of a plasmonic photocatalyst.

Plasmonic materials are promising photocatalysts as they are well suited to convert light into hot carriers and heat. Hot electron transfer is suggested as the driving force in many plasmon-driven reactions. However, to date, there are no direct molecular measures of the rate and yield of plasmon-to-molecule electron transfer or energy of these electrons on the timescale of plasmon decay. Here, we use ultrafast and spectroelectrochemical surface-enhanced Raman spectroscopy to quantify electron transfer from a plasmonic substrate to adsorbed methyl viologen molecules. We observe a reduction yield of 2.4 to 3.5% on the picosecond timescale, with plasmon-induced potentials ranging from [Formula: see text]3.1 to [Formula: see text]4.5 mV. Excitingly, some of these reduced species are stabilized and persist for tens of minutes. This work provides concrete metrics toward optimizing material-molecule interactions for efficient plasmon-driven photocatalysis.

Water bend-libration as a cellular Raman imaging probe of hydration.

Water is a ubiquitous and vital component of living systems. Hydration, which is the interaction between water and intracellular biomolecules, plays an important role in cellular processes. However, it is technically challenging to study water structure within cells directly. Here, we demonstrate the utility and power of the water bend-libration combination band as a unique Raman spectral imaging probe of cellular hydration. Hydration maps reveal distinct water environments within subcellular compartments (e.g., nucleolus and lipid droplet) due to the spectral sensitivity of this coupled vibrational band. Spectroscopic studies using the water bend-libration are broadly applicable, offering the potential to capture the chemical complexity of hydration in numerous systems.

SERS analysis of cancer cell-secreted purines reveals a unique paracrine crosstalk in MTAP-deficient tumors.

The tumor microenvironment (TME) is a dynamic pseudoorgan that shapes the development and progression of cancers. It is a complex ecosystem shaped by interactions between tumor and stromal cells. Although the traditional focus has been on the paracrine communication mediated by protein messengers, recent attention has turned to the metabolic secretome in tumors. Metabolic enzymes, together with exchanged substrates and products, have emerged as potential biomarkers and therapeutic targets. However, traditional techniques for profiling secreted metabolites in complex cellular contexts are limited. Surface-enhanced Raman scattering (SERS) has emerged as a promising alternative due to its nontargeted nature and simplicity of operation. Although SERS has demonstrated its potential for detecting metabolites in biological settings, its application in deciphering metabolic interactions within multicellular systems like the TME remains underexplored. In this study, we introduce a SERS-based strategy to investigate the secreted purine metabolites of tumor cells lacking methylthioadenosine phosphorylase (MTAP), a common genetic event associated with poor prognosis in various cancers. Our SERS analysis reveals that MTAP-deficient cancer cells selectively produce methylthioadenosine (MTA), which is taken up and metabolized by fibroblasts. Fibroblasts exposed to MTA exhibit: i) molecular reprogramming compatible with cancer aggressiveness, ii) a significant production of purine derivatives that could be readily recycled by cancer cells, and iii) the capacity to secrete purine derivatives that induce macrophage polarization. Our study supports the potential of SERS for cancer metabolism research and reveals an unprecedented paracrine crosstalk that explains TME reprogramming in MTAP-deleted cancers.

Label-free drug interaction screening via Raman microscopy.

Development of a simple, label-free screening technique capable of precisely and directly sensing interaction-in-solution over a size range from small molecules to large proteins such as antibodies could offer an important tool for researchers and pharmaceutical companies in the field of drug development. In this work, we present a thermostable Raman interaction profiling (TRIP) technique that facilitates low-concentration and low-dose screening of binding between protein and ligand in physiologically relevant conditions. TRIP was applied to eight protein-ligand systems, and produced reproducible high-resolution Raman measurements, which were analyzed by principal component analysis. TRIP was able to resolve time-depending binding between 2,4-dinitrophenol and transthyretin, and analyze biologically relevant SARS-CoV-2 spike-antibody interactions. Mixtures of the spike receptor-binding domain with neutralizing, nonbinding, or binding but nonneutralizing antibodies revealed distinct and reproducible Raman signals. TRIP holds promise for the future developments of high-throughput drug screening and real-time binding measurements between protein and drug.

Carbon-cement supercapacitors as a scalable bulk energy storage solution.

The large-scale implementation of renewable energy systems necessitates the development of energy storage solutions to effectively manage imbalances between energy supply and demand. Herein, we investigate such a scalable material solution for energy storage in supercapacitors constructed from readily available material precursors that can be locally sourced from virtually anywhere on the planet, namely cement, water, and carbon black. We characterize our carbon-cement electrodes by combining correlative EDS-Raman spectroscopy with capacitance measurements derived from cyclic voltammetry and galvanostatic charge-discharge experiments using integer and fractional derivatives to correct for rate and current intensity effects. Texture analysis reveals that the hydration reactions of cement in the presence of carbon generate a fractal-like electron-conducting carbon network that permeates the load-bearing cement-based matrix. The energy storage capacity of this space-filling carbon black network of the high specific surface area accessible to charge storage is shown to be an intensive quantity, whereas the high-rate capability of the carbon-cement electrodes exhibits self-similarity due to the hydration porosity available for charge transport. This intensive and self-similar nature of energy storage and rate capability represents an opportunity for mass scaling from electrode to structural scales. The availability, versatility, and scalability of these carbon-cement supercapacitors opens a horizon for the design of multifunctional structures that leverage high energy storage capacity, high-rate charge/discharge capabilities, and structural strength for sustainable residential and industrial applications ranging from energy autarkic shelters and self-charging roads for electric vehicles, to intermittent energy storage for wind turbines and tidal power stations.

BloodNet: An attention-based deep network for accurate, efficient, and costless bloodstain time since deposition inference.

The time since deposition (TSD) of a bloodstain, i.e., the time of a bloodstain formation is an essential piece of biological evidence in crime scene investigation. The practical usage of some existing microscopic methods (e.g., spectroscopy or RNA analysis technology) is limited, as their performance strongly relies on high-end instrumentation and/or rigorous laboratory conditions. This paper presents a practically applicable deep learning-based method (i.e., BloodNet) for efficient, accurate, and costless TSD inference from a macroscopic view, i.e., by using easily accessible bloodstain photos. To this end, we established a benchmark database containing around 50,000 photos of bloodstains with varying TSDs. Capitalizing on such a large-scale database, BloodNet adopted attention mechanisms to learn from relatively high-resolution input images the localized fine-grained feature representations that were highly discriminative between different TSD periods. Also, the visual analysis of the learned deep networks based on the Smooth Grad-CAM tool demonstrated that our BloodNet can stably capture the unique local patterns of bloodstains with specific TSDs, suggesting the efficacy of the utilized attention mechanism in learning fine-grained representations for TSD inference. As a paired study for BloodNet, we further conducted a microscopic analysis using Raman spectroscopic data and a machine learning method based on Bayesian optimization. Although the experimental results show that such a new microscopic-level approach outperformed the state-of-the-art by a large margin, its inference accuracy is significantly lower than BloodNet, which further justifies the efficacy of deep learning techniques in the challenging task of bloodstain TSD inference. Our code is publically accessible via https://github.com/shenxiaochenn/BloodNet. Our datasets and pre-trained models can be freely accessed via https://figshare.com/articles/dataset/21291825.

Raman spectroscopy analysis of human amniotic fluid cells from fetuses with myelomeningocele.

Prenatal surgery for the treatment of spina bifida (myelomeningocele, MMC) significantly enhances the neurological prognosis of the patient. To ensure better protection of the spinal cord by large defects, the application of skin grafts produced with cells gained from the amniotic fluid is presently studied. In order to determine the most appropriate cells for this purpose, we tried to shed light on the extremely complex amniotic fluid cellular composition in healthy and MMC pregnancies. We exploited the potential of micro-Raman spectroscopy to analyse and characterize human amniotic fluid cells in total and putative (cKit/CD117-positive) stem cells of fetuses with MMC in comparison with amniotic fluid cells from healthy individuals, human fetal dermal fibroblasts and adult adipose derived stem cells. We found that (i) the differences between healthy and MMC amniocytes can be attributed to specific spectral regions involving collagen, lipids, sugars, tryptophan, aspartate, glutamate, and carotenoids, (ii) MMC amniotic fluid contains two particular cell populations which are absent or reduced in normal pregnancies, (iii) the cKit-negative healthy amniocyte subpopulation shares molecular features with human fetal fibroblasts. On the one hand we demonstrate a different amniotic fluid cellular composition in healthy and MMC pregnancies, on the other our work confirms micro-Raman spectroscopy to be a valuable tool for discriminating cell populations in unknown mixtures of cells.

Accurate virus identification with interpretable Raman signatures by machine learning.

Rapid identification of newly emerging or circulating viruses is an important first step toward managing the public health response to potential outbreaks. A portable virus capture device, coupled with label-free Raman spectroscopy, holds the promise of fast detection by rapidly obtaining the Raman signature of a virus followed by a machine learning (ML) approach applied to recognize the virus based on its Raman spectrum, which is used as a fingerprint. We present such an ML approach for analyzing Raman spectra of human and avian viruses. A convolutional neural network (CNN) classifier specifically designed for spectral data achieves very high accuracy for a variety of virus type or subtype identification tasks. In particular, it achieves 99% accuracy for classifying influenza virus type A versus type B, 96% accuracy for classifying four subtypes of influenza A, 95% accuracy for differentiating enveloped and nonenveloped viruses, and 99% accuracy for differentiating avian coronavirus (infectious bronchitis virus [IBV]) from other avian viruses. Furthermore, interpretation of neural net responses in the trained CNN model using a full-gradient algorithm highlights Raman spectral ranges that are most important to virus identification. By correlating ML-selected salient Raman ranges with the signature ranges of known biomolecules and chemical functional groups­for example, amide, amino acid, and carboxylic acid­we verify that our ML model effectively recognizes the Raman signatures of proteins, lipids, and other vital functional groups present in different viruses and uses a weighted combination of these signatures to identify viruses.

Combinatorial Effect of Tricomponent Dual-Rim Nanoring Building Blocks: Label-Free SERS Detection of Biomolecules.

Detecting weakly adsorbing molecules via label-free surface-enhanced Raman scattering (SERS) has presented a significant challenge. To address this issue, we propose a novel approach for creating tricomponent SERS substrates using dual-rim nanorings (DRNs) made of Au, Ag, and CuO, each possessing distinct functionalities. Our method involves depositing different metals on Pt nanoring skeletons to obtain each nanoring with varying surface compositions while maintaining a similar size and shape. Next, the mixture of these nanorings is transferred into a monolayer assembly with homogeneous intermixing on a solid substrate. The surface of the CuO DRNs has dangling bonds (Cu2+) that facilitate the strong adsorption of carboxylates through the formation of chelating bonds, while the combination of Au and Ag DRNs significantly enhances the SERS signal intensity through a strong coupling effect. Notably, the tricomponent assemblies enable the successful SERS-based analysis of biomolecules such as amino acids, proteins, nucleobases, and nucleotides.

Tuning CuMgAl-Layered Double Hydroxide Nanostructures to Achieve CH4 and C2+ Product Selectivity in CO2 Electroreduction.

Electrochemical CO2 reduction reaction (eCO2RR) over Cu-based catalysts is a promising approach for efficiently converting CO2 into value-added chemicals and alternative fuels. However, achieving controllable product selectivity from eCO2RR remains challenging because of the difficulty in controlling the oxidation states of Cu against robust structural reconstructions during the eCO2RR. Herein, we report a novel strategy for tuning the oxidation states of Cu species and achieving eCO2RR product selectivity by adjusting the Cu content in CuMgAl-layered double hydroxide (LDH)-based catalysts. In this strategy, the highly stable Cu2+ species in low-Cu-containing LDHs facilitated the strong adsorption of *CO intermediates and further hydrogenation into CH4. Conversely, the mixed Cu0/Cu+ species in high-Cu-containing LDHs derived from the electroreduction during the eCO2RR accelerated C-C coupling reactions. This strategy to regulate Cu oxidation states using LDH nanostructures with low and high Cu molar ratios produced an excellent eCO2RR performance for CH4 and C2+ products, respectively.

Taming the Distribution of Light in Gate-All-Around Semiconductor Devices.

Optical metrology is ubiquitous, but image-based methods cannot resolve features of dimensions much smaller than the wavelength. However, it has recently been demonstrated that light can be nanofocused into subwavelength semiconducting lines by setting the incident polarization along the direction of these lines. This Letter extends the previous studies to systems with two perpendicular gratings, as found e.g. after replacement gate processing of gate-all-around (GAA) field-effect transistors (FETs). We show that besides the nanofocusing effect, the incident polarization also offers control over which array of lines the light couples into. The interaction of the incident light occurs with the semiconducting lines to which the polarization is parallel with remarkably low interference from the existence of another perpendicular grating. We demonstrate the use of this effect with Raman spectroscopy to simultaneously extract the SiGe volume and the strain in the Si forksheet channels and in the SiGe layers of GAA FETs.

Probing Oxidation Mechanisms in Plasmonic Catalysis: Unraveling the Role of Reactive Oxygen Species.

Plasmon-induced oxidation has conventionally been attributed to the transfer of plasmonic hot holes. However, this theoretical framework encounters challenges in elucidating the latest experimental findings, such as enhanced catalytic efficiency under uncoupled irradiation conditions and superior oxidizability of silver nanoparticles. Herein, we employ liquid surface-enhanced Raman spectroscopy (SERS) as a real-time and in situ tool to explore the oxidation mechanisms in plasmonic catalysis, taking the decarboxylation of p-mercaptobenzoic acid (PMBA) as a case study. Our findings suggest that the plasmon-induced oxidation is driven by reactive oxygen species (ROS) rather than hot holes, holding true for both the Au and Ag nanoparticles. Subsequent investigations suggest that plasmon-induced ROS may arise from hot carriers or energy transfer mechanisms, exhibiting selectivity under different experimental conditions. The observations were substantiated by investigating the cleavage of the carbon-boron bonds. Furthermore, the underlying mechanisms were clarified by energy level theories, advancing our understanding of plasmonic catalysis.

Spin-Phonon Coupling and Magnetic Transition in an Organic Molecule Intercalated Cr2Ge2Te6.

The manipulation of spin-phonon coupling in both formations and explorations of magnetism in two-dimensional van der Waals ferromagnetic semiconductors facilitates unprecedented prospects for spintronic devices. The interlayer engineering with spin-phonon coupling promises controllable magnetism via organic cation intercalation. Here, spectroscopic evidence reveals the intercalation effect on the intrinsic magnetic and electronic transitions in quasi-two-dimensional Cr2Ge2Te6 using tetrabutyl ammonium (TBA+) as the intercalant. The temperature evolution of Raman modes, Eg3 and Ag1, along with the magnetization measurements, unambiguously captures the enhancement of the ferromagnetic Curie temperature in the intercalated heterostructure. Moreover, the Eg4 mode highlights the increased effect of spin-phonon interaction in magnetic-order-induced lattice distortion. Combined with the first-principle calculations, we observed a substantial number of electrons transferred from TBA+ to Cr through the interface. The interplay between spin-phonon coupling and magnetic ordering in van der Waals magnets appeals for further understanding of the manipulation of magnetism in layered heterostructures.

Li Dynamics in Mixed Ionic-Electronic Conducting Interlayer of All-Solid-State Li-metal Batteries.

Lithium-metal (Li0) anodes potentially enable all-solid-state batteries with high energy density. However, it shows incompatibility with sulfide solid-state electrolytes (SEs). One strategy is introducing an interlayer, generally made of a mixed ionic-electronic conductor (MIEC). Yet, how Li behaves within MIEC remains unknown. Herein, we investigated the Li dynamics in a graphite interlayer, a typical MIEC, by using operando neutron imaging and Raman spectroscopy. This study revealed that intercalation-extrusion-dominated mechanochemical reactions during cell assembly transform the graphite into a Li-graphite interlayer consisting of SE, Li0, and graphite-intercalation compounds. During charging, Li+ preferentially deposited at the Li-graphite|SE interface. Upon further plating, Li0-dendrites formed, inducing short circuits and the reverse migration of Li0. Modeling indicates the interface has the lowest nucleation barrier, governing lithium transport paths. Our study elucidates intricate mechano-chemo-electrochemical processes in mixed conducting interlayers. The behavior of Li+ and Li0 in the interlayer is governed by multiple competing factors.

Multimodal analysis and comparison of stoichiometric and structural characteristics of parosteal and conventional osteosarcoma with massive sclerosis in human bone.

Osteosarcoma (OS) is the most common malignant primary bone tumor in humans and occurs in various subtypes. Tumor formation happens through malignant osteoblasts producing immature bone. In the present paper we studied two different subtypes of osteosarcoma, from one individual with conventional OS with massive sclerosis and one individual with parosteal OS, based on a multimodal approach including small angle x-ray scattering (SAXS), wide angle x-ray diffraction (WAXS), backscattered electron imaging (BEI) and Raman spectroscopy. It was found that both tumors showed reduced mineral particle sizes and degree of orientation of the collagen-mineral composite in the affected areas, alongside with a decreased crystallinity. Distinct differences between the tumor material from the two individuals were found in the degree of mineralization. Further differences were observed in the carbonate to phosphate ratio, which is related to the degree of carbonate substitution in bone mineral and indicative of the turnover rate. The contraction of the c-axis of the bone mineral crystals proved to be a further, very sensitive parameter, potentially indicative of malignancy.

Detection of Helicobacter pylori Infection in Human Gastric Fluid Through Surface-Enhanced Raman Spectroscopy Coupled With Machine Learning Algorithms.

Diagnostic methods for Helicobacter pylori infection include, but are not limited to, urea breath test, serum antibody test, fecal antigen test, and rapid urease test. However, these methods suffer drawbacks such as low accuracy, high false-positive rate, complex operations, invasiveness, etc. Therefore, there is a need to develop simple, rapid, and noninvasive detection methods for H. pylori diagnosis. In this study, we propose a novel technique for accurately detecting H. pylori infection through machine learning analysis of surface-enhanced Raman scattering (SERS) spectra of gastric fluid samples that were noninvasively collected from human stomachs via the string test. One hundred participants were recruited to collect gastric fluid samples noninvasively. Therefore, 12,000 SERS spectra (n = 120 spectra/participant) were generated for building machine learning models evaluated by standard metrics in model performance assessment. According to the results, the Light Gradient Boosting Machine algorithm exhibited the best prediction capacity and time efficiency (accuracy = 99.54% and time = 2.61 seconds). Moreover, the Light Gradient Boosting Machine model was blindly tested on 2,000 SERS spectra collected from 100 participants with unknown H. pylori infection status, achieving a prediction accuracy of 82.15% compared with qPCR results. This novel technique is simple and rapid in diagnosing H. pylori infection, potentially complementing current H. pylori diagnostic methods.

Charged Amino Acid Substitutions Affect Conformation of Neuroglobin and Cytochrome c Heme Groups.

Neuroglobin (Ngb) is a cytosolic heme protein that plays an important role in protecting cells from apoptosis through interaction with oxidized cytochrome c (Cyt c) released from mitochondria. The interaction of reduced Ngb and oxidized Cyt c is accompanied by electron transfer between them and the reduction in Cyt c. Despite the growing number of studies on Ngb, the mechanism of interaction between Ngb and Cyt c is still unclear. Using Raman spectroscopy, we studied the effect of charged amino acid substitutions in Ngb and Cyt c on the conformation of their hemes. It has been shown that Ngb mutants E60K, K67E, K95E and E60K/E87K demonstrate changed heme conformations with the lower probability of the heme planar conformation compared to wild-type Ngb. Moreover, oxidized Cyt c mutants K25E, K72E and K25E/K72E demonstrate the decrease in the probability of methyl-radicals vibrations, indicating the higher rigidity of the protein microenvironment. It is possible that these changes can affect electron transfer between Ngb and Cyt c.