Single-cell Raman Spectra Research Articles

Application of single-cell Raman-deuterium isotope probing to reveal the resistance of marine ammonia-oxidizing archaea SCM1 against common antibiotics

Antimicrobial resistance (AMR) in oceans poses a significant threat to human health through the seafood supply chain. Ammonia-oxidizing archaea (AOA) are important marine microorganisms and play a key role in the biogeochemical nitrogen cycle around the world. However, the AMR of marine AOA to aquicultural antibiotics is poorly explored. Here, Raman-deuterium isotope probing (Raman-DIP), a single-cell tool, was developed to reveal the AMR of a typical marine species of AOA, Nitrosopumilus maritimus (designated SCM1), against six antibiotics, including erythromycin, tetracycline, novobiocin, neomycin, bacitracin, and vancomycin. The D2O concentration (30% v/v) and culture period (9 days) were optimized for the precise detection of metabolic activity in SCM1 cells through Raman-DIP. The relative metabolic activity of SCM1 upon exposure to antibiotics was semi-quantitatively calculated based on single-cell Raman spectra. SCM1 exhibited high resistance to erythromycin, tetracycline, novobiocin, neomycin, and vancomycin, with minimum inhibitory concentration (MIC) values between 100 and 400 mg/L, while SCM1 is very sensitive to bacitracin (MIC: 0.8 mg/L). Notably, SCM1 cells were completely inactive under the metabolic activity minimum inhibitory concentration conditions (MA-MIC: 1.6–800 mg/L) for the six antibiotics. Further genomic analysis revealed the antibiotic resistance genes (ARGs) of SCM1, including 14 types categorized into 33 subtypes. This work increases our knowledge of the AMR of marine AOA by linking the resistant phenome to the genome, contributing to the risk assessment of AMR in the underexplored ocean environment. As antibiotic resistance in marine microorganisms is significantly affected by the concentration of antibiotics in coastal environments, we encourage more studies concentrating on both the phenotypic and genotypic antibiotic resistance of marine archaea. This may facilitate a comprehensive evaluation of the capacity of marine microorganisms to spread AMR and the implementation of suitable control measures to protect environmental safety and human health.

In situ single-cell spontaneous Raman spectroscopy differentiates tumor-associated macrophages

The analysis of in situ single cells in tissue is crucial for the study of tumors. However, simultaneous analysis of multiple biomolecules for single cells by fluorescence immunohistochemistry is challenging. Spontaneous Raman spectroscopy provides biochemical fingerprints of individual cells in a label-free and nondestructive manner, yet it is not well applied to single cells in tissue. Here, we develop an in situ single-cell spontaneous Raman spectroscopy (SRS) system that can localize a single cell in tissue with fluorescence imaging, while obtaining the single-cell Raman spectra for molecular analysis. Our system is verified by measuring the standard polystyrene beads of different sizes. Measurements of in situ single macrophages in clinical cancerous and para-cancerous tissues are then performed. By using discriminant analysis, it is shown that CD68+ macrophages in cancerous and para-cancerous tissues are classified with an accuracy of 98.3 %. The molecular content of single macrophages in tissue is analyzed by using multivariate curve resolution-alternating least squares method. We find that the lipid content of tumor-associated macrophages in cancerous tissues is higher while both the contents of protein and nucleic acid are lower than those in para-cancerous tissues, which is challenging to achieve by conventional immunohistochemistry. Our in situ single-cell SRS system has the capability for the obtaining of molecular information of a localized single cell in tissue, which may find potential applications for clinical cancer diagnosis.

A metabolic map and artificial intelligence-aided identification of nasopharyngeal carcinoma via a single-cell Raman platform.

Nasopharyngeal carcinoma (NPC) is a complex cancer influenced by various factors. This study explores the use of single-cell Raman spectroscopy as a potential diagnostic tool for investigating biomolecular changes associated with NPC carcinogenesis. Seven NPC cell lines, one immortalised nasopharyngeal epithelial cell line, six nasopharyngeal mucosa tissues and seven NPC tissue samples were analysed by performing confocal Raman spectroscopic measurements and imaging. The single-cell Raman spectral dataset was used to quantify relevant biomolecules and build machine learning classification models. Metabolomic profiles were investigated using ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS). By generating a metabolic map of seven NPC cell lines, we identified an interplay of altered metabolic processes involving nucleic acids, amino acids, lipids and sugars. The results from spatially resolved Raman maps and UPLC-MS/MS metabolomics were consistent, revealing an increase of unsaturated fatty acids in cancer cells, particularly in highly metastatic 5-8F and poorly differentiated CNE2 cells. The classification model achieved a nearly perfect classification when identifying NPC and non-NPC cells with an ROC-AUC of 0.99 and a value of 0.97 when identifying 13 tissue samples. This study unveils a complex interplay of metabolic network and highlights the potential roles of unsaturated fatty acids in NPC progression and metastasis. This renders further research to provide deeper insights into NPC pathogenesis, identify new metabolic targets and improve the efficacy of targeted therapies in NPC. Artificial intelligence-aided analysis of single-cell Raman spectra has achieved high accuracies in the classification of both cancer cells and patient tissues, paving the way for a simple, less invasive and accurate diagnostic test.

Unveiling the efficacy of a bulk Raman spectra-based model in predicting single cell Raman spectra of microorganisms

In a previous publication, we trained predictive models based on Raman bulk spectra of microorganisms placed on a silicon dioxide protected silver mirror slide to make predictions for new Raman spectra, unknown to the models, of microorganisms placed on a different substrate, namely stainless steel. Now we have combined large sections of this data and trained a convolutional neural network (CNN) to make predictions for single cell Raman spectra. We show that a database based on microbial bulk material is conditionally suited to make predictions for the same species in terms of single cells. Data of 13 different microorganisms (bacteria and yeasts) were used. Two of the 13 species could be identified 90% correctly and five other species 71%–88%. The six remaining species were correctly predicted by only 0%–49%. Especially stronger fluorescence in bulk material compared to single cells but also photodegradation of carotenoids are some effects that can complicate predictions for single cells based on bulk data. The results could be helpful in assessing universal Raman tools or databases.

Rapid detection of six Oceanobacillus species in Daqu starter using single-cell Raman spectroscopy combined with machine learning.

Many traditional fermented foods and beverages industries around the world request the addition of multi-species starter cultures. However, the microbial community in starter cultures is subject to fluctuations due to their exposure to an open environment during fermentation. A rapid detection approach to identify the microbial composition of starter culture is essential to ensure the quality of the final products. Here, we applied single-cell Raman spectroscopy (SCRS) combined with machine learning to monitor Oceanobacillus species in Daqu starter, which plays crucial roles in the process of Chinese baijiu. First, a total of six Oceanobacillus species (O. caeni, O. kimchii, O. iheyensis, O. sojae, O. oncorhynchi subsp. Oncorhynchi and O. profundus) were detected in 44 Daqu samples by amplicon sequencing and isolated by pure culture. Then, we created a reference database of these Oceanobacillus strains which correlated their taxonomic data and single-cell Raman spectra (SCRS). Based on the SCRS dataset, five machine-learning algorithms were used to classify Oceanobacillus strains, among which support vector machine (SVM) showed the highest rate of accuracy. For validation of SVM-based model, we employed a synthetic microbial community composed of varying proportions of Oceanobacillus species and demonstrated a remarkable accuracy, with a mean error was less than 1% between the predicted result and the expected value. The relative abundance of six different Oceanobacillus species during Daqu fermentation was predicted within 60 min using this method, and the reliability of the method was proved by correlating the Raman spectrum with the amplicon sequencing profiles by partial least squares regression. Our study provides a rapid, non-destructive and label-free approach for rapid identification of Oceanobacillus species in Daqu starter culture, contributing to real-time monitoring of fermentation process and ensuring high-quality products.

Rapid and accurate identification of marine bacteria spores at a single-cell resolution by laser tweezers Raman spectroscopy and deep learning.

Marine bacteria have been considered as important participants in revealing various carbon/sulfur/nitrogen cycles of marine ecosystem. Thus, how to accurately identify rare marine bacteria without a culture process is significant and valuable. In this work, we constructed a single-cell Raman spectra dataset from five living bacteria spores and utilized convolutional neural network to rapidly, accurately, nondestructively identify bacteria spores. The optimal CNN architecture can provide a prediction accuracy of five bacteria spore as high as 94.93% ± 1.78%. To evaluate the classification weight of extracted spectra features, we proposed a novel algorithm by occluding fingerprint Raman bands. Based on the relative classification weight arranged from large to small, four Raman bands located at 1518, 1397, 1666, and 1017 cm-1 mostly contribute to producing such high prediction accuracy. It can be foreseen that, LTRS combined with CNN approach have great potential for identifying marine bacteria, which cannot be cultured under normal condition.

Label-free detection of the cytotoxicity effect of cisplatin in human leukemic cells using Raman spectroscopy in conjunction with multivariate analysis.

The single-cell Raman spectra of human leukemic Jurkat cells can be obtained by confocal microscopy Raman spectroscopy, including cell groups treated with different doses of cisplatin (0, 3.5, 7, 10.5 and 14 μmol L-1) for 24 hours and those treated with 10.5 μmol L-1 cisplatin for different times (0, 6, 12, 24 and 36 hours). The structure and amount of protein, nucleic acid and other major molecules from different cell groups show special changes in the percentage of biochemical constituents. Compared with the control group, the two protein Raman bands (1449 and 1659 cm-1) and two DNA bands (1303 and 1338 cm-1) in the treatment groups decrease in intensity with the increase of the drug dose and treatment time of cisplatin. Partial least squares combined with support vector machines was used to develop diagnostic algorithms for distinguishing between control and treatment groups. The support vector machines for classification between the control group (0 μmol L-1) and cell groups treated with 10.5 and 14 μmol L-1 cisplatin for 24 hours have achieved good diagnostic results with a high sensitivity of 100%, specificity of 100% and accuracy of 100%, respectively, indicating that 10.5 μmol L-1 can be used as an appropriate therapeutic dose. Using the same method, the diagnostic sensitivity, specificity and accuracy between the control group (0 hours) and cell groups treated with 10.5 μmol L-1 cisplatin for 24 and 36 hours are all 100%, showing that 24 hours can be used as an appropriate therapeutic time. These results showed that Raman spectroscopy in conjunction with multivariate statistical analysis could be a useful tool for evaluating the cytotoxicity induced by cisplatin in human leukemic cells.

Correlation analysis between Raman spectral signature and transcriptomic features of carbapenem-resistant Klebsiella pneumoniae

The Raman microspectroscopy technology has been successfully applied to evaluate the molecular composition of living cells for identifying cell types and states, but the rationale behind it was not well investigated. In this study, we acquired single-cell Raman spectra (SCRS) of three Klebsiella pneumoniae (K. pneumoniae) strains with different Carbapenem resistant mechanisms and analyzed them with machine learning algorithm. Two carbapenem resistant Klebsiella pneumoniae (CRKP) strains can be successfully distinguished from susceptible strain and CRKP with KPC or IMP carbapenemases can be classified with an overall accuracy achieving 100 %. Furthermore, we performed a correlation analysis between transcriptome and Raman spectra, and found that Raman shifts such as 752 and 1039 cm−1 highly correlated with drug resistance genes expression and could be regarded as Raman biomarkers for CRKP with different mechanisms. The findings of the study provide a theoretical basis for identifying the relationship between Raman spectra and transcriptome of bacteria, as well as a novel method for rapid identification of CRKP and their carbapenemases types.

Laser tweezer Raman spectroscopy combined with deep neural networks for identification of liver cancer cells

Rapid identification of cancer cells is crucial for clinical treatment guidance. Laser tweezer Raman spectroscopy (LTRS) that provides biochemical characteristics of cells can be used to identify cell phenotypes through classification models in a non-invasive and label-free manner. However, traditional classification methods require extensive reference databases and clinical experience, which is challenging when sampling at inaccessible locations. Here, we describe a classification method combing LTRS with deep neural network (DNN) for differential and discriminative analysis of multiple liver cancer (LC) cells. By using LTRS, we obtained high-quality single-cell Raman spectra of normal hepatocytes (HL-7702) and liver cancer cell lines (SMMC-7721, Hep3B, HepG2, SK-Hep1 and Huh7). The tentative assignment of Raman peaks indicated that arginine content was elevated and phenylalanine, glutathione and glutamate content was decreased in liver cancer cells. Subsequently, we randomly selected 300 spectra from each cell line for DNN model analysis, achieving a mean accuracy of 99.2%, a mean sensitivity of 99.2% and a mean specificity of 99.8% for the identification and classification of multiple LC cells and hepatocyte cells. These results demonstrate the combination of LTRS and DNN is a promising method for rapid and accurate cancer cell identification at single cell level.

Robust Spontaneous Raman Flow Cytometry for Single‐Cell Metabolic Phenome Profiling via pDEP‐DLD‐RFC (Adv. Sci. 16/2023)

Raman Flow Cytometry In article number 2207497, Jian Xu, Bo Ma, and co-workers develop a robust Raman flow cytometry capable of acquiring single-cell Raman spectra (SCRS) of variety of phenotypes rapidly. The SCRS-based high-throughput and high-generality biosynthesis dissection, antimicrobial susceptibility profiling and cell-type classification for isogenic cell populations of yeast, microalgae, bacteria, and cancers are demonstrated.

An attempt to simultaneously quantify the polysaccharide, total lipid, protein and pigment in single Cyclotella cryptica cell by Raman spectroscopy

BackgroundAt present, the conventional methods for determining photosynthetic products of microalgae are usually based on a large number of cell mass to reach the measurement baseline, and the result can only reveal the average state at the population level, which is not feasible for large-scale and rapid screening of specific phenotypes from a large number of potential microalgae mutants. In recent years, single-cell Raman spectra (SCRS) has been proved to be able to rapidly and simultaneously quantify the biochemical components of microalgae. However, this method has not been reported to analyze the biochemical components of Cyclotella cryptica (C. cryptica). Thus, SCRS was first attempt to determine these four biochemical components in this diatom.ResultsThe method based on SCRS was established to simultaneously quantify the contents of polysaccharide, total lipids, protein and Chl-a in C. cryptica, with thirteen Raman bands were found to be the main marker bands for the diatom components. Moreover, Partial Least Square Regression (PLSR) models based on full spectrum can reliably predict these four cellular components, with Pearson correlation coefficient for these components reached 0.949, 0.904, 0.801 and 0.917, respectively. Finally, based on SCRS data of one isogenic sample, the pairwise correlation and dynamic transformation process of these components can be analyzed by Intra-ramanome Correlation Analysis (IRCA), and the results showed silicon starvation could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.ConclusionsFirst, method for the simultaneous quantification of the polysaccharide, total lipid, protein and pigment in single C. cryptica cell are established. Second, the instant interconversion of intracellular components was constructed through IRCA, which is based on data set of one isogenic population and more precision and timeliness. Finally, total results indicated that silicon deficiency could promote the carbon in C. cryptica cells to flow from protein and pigment metabolism to polysaccharide and lipid metabolism.

Cultureless enumeration of live bacteria in urinary tract infection by single-cell Raman spectroscopy.

Urinary tract infections (UTIs) are the most common outpatient infections. Obtaining the concentration of live pathogens in the sample is crucial for the treatment. Still, the enumeration depends on urine culture and plate counting, which requires days of turn-around time (TAT). Single-cell Raman spectra combined with deuterium isotope probing (Raman-DIP) has been proven to identify the metabolic-active bacteria with high accuracy but is not able to reveal the number of live pathogens due to bacteria replication during the Raman-DIP process. In this study, we established a new approach of using sodium acetate to inhibit the replication of the pathogen and applying Raman-DIP to identify the active single cells. By combining microscopic image stitching and recognition, we could further improve the efficiency of the new method. Validation of the new method on nine artificial urine samples indicated that the exact number of live pathogens obtained with Raman-DIP is consistent with plate-counting while shortening the TAT from 18 h to within 3 h, and the potential of applying Raman-DIP for pathogen enumeration in clinics is promising.

Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy.

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h.

Accurate identification of living Bacillus spores using laser tweezers Raman spectroscopy and deep learning

Accurately, rapidly, and noninvasively identifying Bacillus spores can greatly contribute to controlling a plenty of infectious diseases. Laser tweezers Raman spectroscopy (LTRS) has confirmed to be a powerful tool for studying Bacillus spores at a single cell level. In this study, we constructed a single-cell Raman spectra dataset of living Bacillus spores and utilized deep learning approach to accurately, nondestructively identify Bacillus spores. The trained convolutional neural network (CNN) could efficiently extract tiny Raman spectra features of five spore species, and provide a prediction accuracy of specie identification as high as 100 %. Moreover, the spectral feature differences in three Raman bands at 660, 826, and 1017 cm−1 were confirmed to mostly contribute to producing such high prediction accuracy. In addition, optimal CNN model was employed to monitor and identify sporulation process at different metabolic phases in one growth cycle. The obtained average prediction accuracy of metabolic phase identification was approximately 88 %. It can be foreseen that, LTRS combined with CNN approach have great potential for accurately identifying spore species and metabolic phases at a single cell level, and can be gradually extended to perform identification for many unculturable bacteria growing in soil, water, and food.

Multi-point scanning confocal Raman spectroscopy for accurate identification of microorganisms at the single-cell level

Raman spectroscopy has been widely used for microbial analysis due to its exceptional qualities as a rapid, simple, non-invasive, reproducible, and real-time monitoring tool. The Raman spectrum of a cell is a superposition of the spectral information of all biochemical components in the laser focus. In the case where the microbial size is larger than the laser spot size, the Raman spectrum measured from a single-point within a cell cannot capture all biochemical information due to the spatial heterogeneity of microorganisms. In this work, we have proposed a method for the accurate identification of microorganisms using multi-point scanning confocal Raman spectroscopy. Through an image recognition algorithm and the control of a high-precision motorized stage, Raman spectra can be integrated at one time to measure the multi-point biochemical information of microorganisms. This solves the problem that the measured single microbial cells are of different sizes, and the laser spot of the confocal Raman system is not easy to change. Here, the single-cell Raman spectra of three Escherichia coli and seven Lactobacillus species were measured separately. The commonly used supervised classification method, support vector machine (SVM), was applied to compare the data based on the single-point spectra and multi-point scanning spectra. Multi-point spectra showed superior performance in terms of their accuracy and recall rates compared with single-point spectra. The results show that multi-point scanning confocal Raman spectra can be used for more accurate species classification at different taxonomic levels, which is of great importance in species identification.

Single-cell Raman spectra reveals the cytochrome c-mediated electron transfer in nanoscale zero-valent iron coupled denitrification process

Nanoscale zero-valent iron (nZVI) coupled denitrification is considered the typical abiotic-biotic synergistic process, but its underlying electron transfer mechanism remains unclear. Herein, we performed a heavy water labeling single-cell Raman spectroscopy (D2O-SCRS) for in-situ and dynamic characterization of the Cytochrome c-mediated electron transfer process with coexisting nZVI and a model denitrifying bacterium. Interestingly, the nZVI resulted in a strong synergistic effect with a high denitrification efficiency of 99.8 % (∼100 mg/L NO3-N), approximately 18 and 12-fold than those of pure nZVI (5.4 %) and model bacterium (8.3 %), respectively. The D2O-SCRS approach revealed the reduced Cytochrome c (Cyt c) facilitated electron transfer to enzymes and is responsible for NO2–→NO, NO → N2O, and N2O → N2 reactions in the denitrification respiratory chain, thus accelerating the occurrence of synergistic effects. Furthermore, the decreased metabolic activity contributed to higher ATP availability for nitrate reduction, thereby favoring denitrification. The metatranscriptomic analysis confirmed that both up-regulated genes encoding Cyt c reductase and the down-regulated Cyt c oxidase promoted the generation of reduced Cyt c, corresponding well with the D2O-SCRS results. Additionally, the elevated complex I, complex III, and excess NAD+ ensured a large number of electrons participated in the respiratory chain. This study deepened the understanding of the biotic-abiotic synergistic mechanism and provided a new method to in-situ characterize the electron transfer process at a single-cell level.

Single-Cell Identification, Drug Susceptibility Test, and Whole-genome Sequencing of Helicobacter pylori Directly from Gastric Biopsy by Clinical Antimicrobial Susceptibility Test Ramanometry.

The battle against Helicobacter pylori (H. pylori) infections demands fast, reliable, and sensitive methods for pathogen identification (ID), antimicrobial susceptibility tests (ASTs) based on metabolic response, and genome-wide mutation profiling that reveals resistance mechanisms. Here we introduce Clinical Antimicrobial Susceptibility Test Ramanometry for H. pylori (CAST-R-HP), and its validation with clinical samples. This method performs rapid ID, metabolism inhibition-based AST, and high-quality whole-genome sequencing for cells of targeted resistance phenotype, all at precisely 1-cell resolution and directly from biopsy samples. In CAST-R-HP, automated acquisition and machine learning of single-cell Raman spectra (SCRS) enable distinguishing individual H. pylori cells directly from a biopsy sample, with 98.5 ± 0.27% accuracy in ID. Moreover, by adding a 48- to72-h D2O feeding and drug exposure step prior to SCRS acquisition, CAST-R-HP reports AST for levofloxacin and clarithromycin with 100% accuracy, based on metabolic inhibition level. Furthermore, CAST-R-HP supports rapid sorting, low-bias DNA amplification, and full genome sequencing of single H. pylori cells with the SCRS defined, targeted drug-susceptibility phenotype, via Raman-activated gravity-driven cell encapsulation and sequencing. The genome-wide mutation map (maximum 99.70% coverage), at precisely 1-cell resolution, not only elucidates the drug-susceptibility phenotypes but also unveils their underlying molecular mechanisms. The culture independency, shorter turnaround time, high resolution, and comprehensive information output suggest that CAST-R-HP is a powerful tool for diagnosing and treating H. pylori infections.

Laser tweezers Raman spectroscopy combined with deep learning to classify marine bacteria

Rapid identification of marine microorganisms is critical in marine ecology, and Raman spectroscopy is a promising means to achieve this. Single cell Raman spectra contain the biochemical profile of a cell, which can be used to identify cell phenotype through classification models. However, traditional classification methods require a substantial reference database, which is highly challenging when sampling at difficult-to-access locations. In this scenario, only a few spectra are available to create a taxonomy model, making qualitative analysis difficult. And the accuracy of classification is reduced when the signal-to-noise ratio of a spectrum is low. Here, we describe a novel method for categorizing microorganisms that combines optical tweezers Raman spectroscopy, Progressive Growing of Generative Adversarial Nets (PGGAN), and Residual network (ResNet) analysis. Using the optical Raman tweezers, we acquired single cell Raman spectra from five deep-sea bacterial strains. We randomly selected 300 spectra from each strain as the database for training a PGGAN model. PGGAN generates a large number of high-resolution spectra similar to the real data for the training of the residual neural network. Experimental validations show that the method enhances machine learning classification accuracy while also reducing the demand for a considerable amount of training data, both of which are advantageous for analyzing Raman spectra of low signal-to-noise ratios. A classification model was built with this method, which reduces the spectra collection time to 1/3 without compromising the classification accuracy.

Rapid and accurate identification of pathogenic bacteria at the single-cell level using laser tweezers Raman spectroscopy and deep learning.

We report a new method for the rapid identification of pathogenic bacterial species at the single-cell level that combines laser tweezers Raman spectroscopy (LTRS) with deep learning (DL). LTRS can accurately measure single-cell Raman spectra (scRS) without destroying and labeling cells. Based on the scRS data, DL rapidly and accurately identifies pathogenic bacteria. We measured scRS of 15 species bacteria using homemade LTRS. For each species, approximately, 160 cells from three different patients were measured, one patient's data were used as test set, and the rest after being augmented was used as training set. A residual network (ResNet) model, trained on the augmented training set, achieved an accuracy of 94.53% on the test set. Moreover, we applied gradient-weighted class activation mapping to visualize the proposed model. Finally, we demonstrated the advantages of ResNet over traditional machine-learning algorithms.

Assessing Efficacy of Clinical Disinfectants for Pathogenic Fungi by Single-Cell Raman Microspectroscopy.

Disinfectants are crucial for root canal therapy (RCT), as metabolism of canal-inhabiting microbes can cause refractory infections. To develop effective yet patient- and environment-friendly disinfectant formulations, we quantitatively assessed the metabolism-inhibiting effects of intracanal disinfectants via D2O-probed Single-Cell Raman Spectra (SCRS), using Candida albicans (C. albicans) as a pathogen model. For chlorhexidine gluconate (CHX), sodium hypochlorite (NaClO), and hydrogen peroxide (H2O2), at their MIC of 4, 168, and 60 μg/ml, respectively, despite the complete growth halt, metabolic activity of individual fungal cells was reduced on average by 0.4%, 93.9%, and 94.1% at 8 h, revealing a “nongrowing but metabolically active” (NGMA) state that may underlie potential refractory infections, particularly for CHX. In contrast, at their Metabolic Activity-based Minimum Inhibitory Concentrations (MIC-MA) of 8, 336, and 120 μg/ml, respectively, metabolic activity of all cells was completely halted throughout 8 h exposure. Moreover, combined use of NaClO+H2O2 (combination at 0.5× MIC-MA each) outperforms solo uses of CHX, NaClO, H2O2, or other binary combinations. Furthermore, dynamics of SCRS revealed distinct fungicidal mechanisms of CHX, NaClO, H2O2, and their pairwise combinations. MIC-MA is advantageous in critically assessing antifungal efficacy, and NaClO+H2O2 can potentially serve as a more efficient disinfectant formula for fungal pathogens.