Single-cell Raman Spectroscopy Research Articles

Single-cell exploration of active phosphate-solubilizing bacteria across diverse soil matrices for sustainable phosphorus management.

Phosphate-solubilizing bacteria (PSB) are crucial for enhancing phosphorus bioavailability and regulating phosphorus transformation processes. However, the in situ phosphorus-solubilizing activity and the link between phenotypes and genotypes for PSB remain unidentified. Here we employed single-cell Raman spectroscopy combined with heavy water to discern and quantify soil active PSB. Our results reveal that PSB abundance and in situ activity differed significantly between soil types and fertilization treatments. Inorganic fertilizer input was the key driver for active PSB distribution. Targeted single-cell sorting and metagenomic sequencing of active PSB uncovered several low-abundance genera that are easily overlooked within bulk soil microbiota. We elucidate the underlying functional genes and metabolic pathway, and the interplay between phosphorus and carbon cycling involved in high phosphorus solubilization activity. Our study provides a single-cell approach to exploring PSB from native environments, enabling the development of a microbial solution for the efficient agronomic use of phosphorus and mitigating the phosphorus crisis.

Highly sensitive microfluidic sensor using integrated optical fiber and real-time single-cell Raman spectroscopy for diagnosis of pancreatic cancer

Pancreatic cancer is notoriously lethal due to its late diagnosis and poor patient response to treatments, posing a significant clinical challenge. This study introduced a novel approach that combines a single-cell capturing platform, tumor-targeted silver (Ag) nanoprobes, and precisely docking tapered fiber integrated with Raman spectroscopy. This approach focuses on early detection and progression monitoring of pancreatic cancer. Utilizing tumor-targeted Ag nanoparticles and tapered multimode fibers enhances Raman signals, minimizes light loss, and reduces background noise. This advanced Raman system allows for detailed molecular spectroscopic examination of individual cells, offering more practical information and enabling earlier detection and accurate staging of pancreatic cancer compared to conventional multicellular Raman spectroscopy. Transcriptomic analysis using high-throughput gene screening and transcriptomic databases confirmed the ability and accuracy of this method to identify molecular changes in normal, early, and metastatic pancreatic cancer cells. Key findings revealed that cell adhesion, migration, and the extracellular matrix are closely related to single-cell Raman spectroscopy (SCRS) results, highlighting components such as collagen, phospholipids, and carotene. Therefore, the SCRS approach provides a comprehensive view of the molecular composition, biological function, and material changes in cells, offering a novel, accurate, reliable, rapid, and efficient method for diagnosing and monitoring pancreatic cancer.

Molecular evidence of internal carbon-driven partial denitrification in a mainstream pilot A-B system coupled with side-stream EBPR treating municipal wastewater

Achieving mainstream short-cut nitrogen removal via nitrite has become a carbon and energy efficient way, but still remains challenging for low-strength municipal wastewaters. This study integrated sidestream enhanced biological phosphorus removal system in a pilot-scale adsorption/bio-oxidation (A-B) process (named A-B-S2EBPR system) and nitrite accumulation was successfully achieved for treating the municipal wastewater. Nitrite could accumulate to 5.5 ± 0.3 mg N/L in the intermittently aerated tanks of B-stage with the nitrite accumulation ratio (NAR) of 79.1 ± 6.5 %. The final effluent concentration and removal efficiency of total inorganic nitrogen (TIN) were 4.6 ± 1.8 mg N/L and 84.9 ± 5.6 %, respectively. In-situ process performance of nitrogen conversions, routine batch nitrification/denitrification activity tests and functional gene abundance of nitrifiers collectively suggested that the nitrite accumulation was mainly caused by partial denitrification rather than out-selection of nitrite oxidizing bacteria (NOB). Moreover, the single-cell Raman spectroscopy analysis first demonstrated that there was a specific microbial population that could utilize polyhydroxyalkanoates (PHA) as the potential internal carbon source during the partial denitrification process. The integration of S2EBPR brings unique features to the conventional A-B process, such as extended anaerobic retention time, lower oxidation–reduction potential (ORP), much higher and complex volatile fatty acids (VFAs) etc., which can largely reshape the microbial communities. The dominant genera were Acinetobacter and Comamonadaceae, which accounted for (17.8 ± 15.5)% and (6.7 ± 3.4)%, respectively, while the relative abundance of conventional nitrifiers was less than 0.2%. This study provides insights into phylogenetic and phenotypic shifts of microbial communities when incorporating S2EBPR into the sustainable A-B process to achieve mainstream short-cut nitrogen removal.

Temporal metabolic dynamics and heterogeneity of viable but nonculturable Pseudomonas aeruginosa induced by chlorine disinfection at single-cell resolution

Characterizing the metabolic network and dynamics of Viable But Nonculturable (VBNC) bacteria is crucial for their identification and control. In this study, single-cell Raman spectroscopy was used to explore the metabolic intricacies of VBNC P. aeruginosa induced by chlorine disinfection over 24 h. VBNC P. aeruginosa exhibited significant reductions of 75.98 %, 84.26 % and 81.07 % in carbohydrate, protein and nucleic acid Raman peaks, signaling decreased metabolic activity in these crucial components. Intriguingly, lipid metabolism in VBNC P. aeruginosa remained robust, indicating a potential adaptation to environmental stress. Furthermore, this study analyzed Raman characteristic peaks to examine metabolic conversion in these bacteria. VBNC P. aeruginosa displayed distinct phase-specific metabolic conversion patterns compared to culturable ones. These findings shed light on the temporal dynamics of metabolic processes, such as lipid-carbohydrate conversion, which appeared to be ongoing throughout the incubation period in VBNC P. aeruginosa. Compared to the culturable state, VBNC P. aeruginosa exhibited reduced isotopic assimilation rates for carbon, nitrogen and hydrogen by 40.57 %, 44.62 % and 39.45 %, indicating decreased substrate uptake for biomolecular synthesis. Additionally, the metabolic heterogeneity index of the VBNC bacterial population increased by 37.28 %, with this heterogeneity displaying a progressive increase with the duration of the culture. This observation suggests VBNC P. aeruginosa differentiation into distinct subpopulations, believed crucial for survival in challenging environments. These findings reveal the metabolic dynamics of VBNC state bacteria, providing insights into their adaptive strategies and implications for environmental health risk assessment and precise control technologies.

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis.

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Sewage Sludge Promotes the Accumulation of Antibiotic Resistance Genes in Tomato Xylem.

Xylem serves as a conduit linking soil to the aboveground plant parts and facilitating the upward movement of microbes into leaves and fruits. Despite this potential, the composition of the xylem microbiome and its associated risks, including antibiotic resistance, are understudied. Here, we cultivated tomatoes and analyzed their xylem sap to assess the microbiome and antibiotic resistance profiles following treatment with sewage sludge. Our findings show that xylem microbes primarily originate from soil, albeit with reduced diversity in comparison to those of their soil microbiomes. Using single-cell Raman spectroscopy coupled with D2O labeling, we detected significantly higher metabolic activity in xylem microbes than in rhizosphere soil, with 87% of xylem microbes active compared to just 36% in the soil. Additionally, xylem was pinpointed as a reservoir for antibiotic resistance genes (ARGs), with their abundance being 2.4-6.9 times higher than in rhizosphere soil. Sludge addition dramatically increased the abundance of ARGs in xylem and also increased their mobility and host pathogenicity. Xylem represents a distinct ecological niche for microbes and is a significant reservoir for ARGs. These results could be used to manage the resistome in crops and improve food safety.

Rapid profiling of fish cell nitrogen metabolism with single-cell Raman spectroscopy: Unveiling enzyme's role in ammonia detoxification

Ammonia is a prevalent aquatic pollutant that disrupts cellular functions and energy metabolism in fish, posing significant environmental and health threats. This research investigates the critical role of arginase 2 (ARG2) in mitigating ammonia toxicity in fish cells and its implications in adapting to nitrogen metabolism under high ammonia exposure. Through a CRISPR-Cas9 engineered ARG2 knockdown (KD) in the Epithelioma Papulosum Cyprini (EPC) cell line, we first investigated the biochemical responses of ARG2 KD and wild-type (WT) EPC cells to ammonia stress (NH4Cl treatment), showing diminished urea production and decreased cell viability in ARG2 KD cells. Subsequently, single-cell Raman spectroscopy analysis revealed that ARG2 KD cells exhibited profound metabolic shifts, including changes in protein, nucleic acids, lipid and sugar levels, showing the adjusting role of ARG2 in the balance of carbohydrate and nitrogen metabolism. Furthermore, the upregulated responses of various amino acids, such as glutamine, arginine, alanine, glutamic acid, glycine, histidine, phenylalanine and valine, in WT cells after NH4Cl treatment diminished in ARG2 KD cells except for the decrease in aspartic acid, indicating a switching effect of ARG2 in nitrogen metabolism under ammonia stress. This study highlights ARG2's essential role in ammonia detoxification and emphasizes ARG2's protective function and its importance in metabolism, shedding light on the adaptive mechanisms fish cells deploy against high ammonia environments. These insights contribute to deep understanding of aquatic organisms' molecular responses to environmental ammonia pollution, offering potential strategies for their protection.

Micro-flow cell washing technique combined with single-cell Raman spectroscopy for rapid and automatic antimicrobial susceptibility test of pathogen in urine

Facing the rapid spread of antimicrobial resistance, methods based on single-cell Raman spectroscopy have proven their advances in reducing the turn-around time (TAT) of antimicrobial susceptibility tests (AST). However, the Raman-based methods are still hindered by the prolonged centrifugal cell washing procedure, which may require complex labor operation and induce high mechanical stress, resulting in a pretreatment time of over 1 h as well as a high cell-loss probability. In this study, we developed a micro-flow cell washing device and corresponding Raman-compatible washing chips, which were able to automatically remove the impurities in the samples, retain the bacterial cell and perform Raman spectra acquisition in situ. Results of washing the 5- and 10-μm polymethyl methacrylate (PMMA) microspheres showed that the novel technique achieved a successful removal of 99 % impurity and an 80 % particle retention rate after 6 to 10 cycles of washing. The micro-flow cell washing technique could complete the pretreatment for urine samples in a 96-well plate within 10 min, only taking 15 % of the handling time required by centrifugation. The AST profiles of urine sample spiked with E. coli 25922, E. faecalis 29212, and S. aureus 29213 obtained by the proposed Raman-based approach were found to be 100 % consistent with the results from broth micro-dilution while reducing the TAT to 3 h from several days which is required by the latter. Our study has demonstrated the micro-flow cell washing technique is a reliable, fast and compatible approach to replace centrifuge washing for sample pretreatment of Raman-AST and could be readily applied in clinical scenarios.

Label-Free Identification of AML1-ETO Positive Acute Myeloid Leukemia Using Single-Cell Raman Spectroscopy.

Acute myeloid leukemia (AML) is a malignant hematological tumor disease. Chromosomal abnormality is an independent prognostic factor in AML. AML with t(8:21) (q22; q22)/AML1-ETO (AE) is an independent disease group. In this research, a new method based on Raman spectroscopy is reported for label-free single-cell identification and analysis of AE fusion genes in clinical AML patients. Raman spectroscopy reflects the intrinsic vibration information of molecules in a label-free and non-destructive manner, and the fingerprint Raman spectrum of cells characterizes intracellular molecular types and relative concentration information, so as to realize the identification and molecular metabolism analysis of different kinds of cells. We collected the Raman spectra of bone marrow cells from clinically diagnosed AML M2 patients with and without the AE fusion gene. Through comparison of the average spectra and identification analysis based on multivariate statistical methods such as principal component analysis and linear discriminant analysis, the distinction between AE positive and negative sample cells in M2 AML patients was successfully achieved, and the single-cell identification accuracy was more than 90%. At the same time, the Raman spectra of the two types of cells were analyzed by the multivariate curve resolution alternating least squares decomposition method. It was found that the presence of the AE fusion gene may lead to the metabolic changes of lipid and nucleic acid in AML cells, which was consistent with the results of genomic and metabolomic multi-omics studies. The above results indicate that single-cell Raman spectroscopy has the potential for early identification of AE-positive AML.

Rapid identification of lactic acid bacteria at species/subspecies level via ensemble learning of Ramanomes.

Rapid and accurate identification of lactic acid bacteria (LAB) species would greatly improve the screening rate for functional LAB. Although many conventional and molecular methods have proven efficient and reliable, LAB identification using these methods has generally been slow and tedious. Single-cell Raman spectroscopy (SCRS) provides the phenotypic profile of a single cell and can be performed by Raman spectroscopy (which directly detects vibrations of chemical bonds through inelastic scattering by a laser light) using an individual live cell. Recently, owing to its affordability, non-invasiveness, and label-free features, the Ramanome has emerged as a potential technique for fast bacterial detection. Here, we established a reference Ramanome database consisting of SCRS data from 1,650 cells from nine LAB species/subspecies and conducted further analysis using machine learning approaches, which have high efficiency and accuracy. We chose the ensemble meta-classifier (EMC), which is suitable for solving multi-classification problems, to perform in-depth mining and analysis of the Ramanome data. To optimize the accuracy and efficiency of the machine learning algorithm, we compared nine classifiers: LDA, SVM, RF, XGBoost, KNN, PLS-DA, CNN, LSTM, and EMC. EMC achieved the highest average prediction accuracy of 97.3% for recognizing LAB at the species/subspecies level. In summary, Ramanomes, with the integration of EMC, have promising potential for fast LAB species/subspecies identification in laboratories and may thus be further developed and sharpened for the direct identification and prediction of LAB species from fermented food.

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.

Bifidobacterium species viability in dairy-based probiotic foods: challenges and innovative approaches for accurate viability determination and monitoring of probiotic functionality.

Bifidobacterium species are essential members of a healthy human gut microbiota. Their presence in the gut is associated with numerous health outcomes such as protection against gastrointestinal tract infections, inflammation, and metabolic diseases. Regular intake of Bifidobacterium in foods is a sustainable way of maintaining the health benefits associated with its use as a probiotic. Owing to their global acceptance, fermented dairy products (particularly yogurt) are considered the ideal probiotic carrier foods. As envisioned in the definition of probiotics as "live organisms," the therapeutic functionalities of Bifidobacterium spp. depend on maintaining their viability in the foods up to the point of consumption. However, sustaining Bifidobacterium spp. viability during the manufacture and shelf-life of fermented dairy products remains challenging. Hence, this paper discusses the significance of viability as a prerequisite for Bifidobacterium spp. probiotic functionality. The paper focuses on the stress factors that influence Bifidobacterium spp. viability during the manufacture and shelf life of yogurt as an archetypical fermented dairy product that is widely accepted as a delivery vehicle for probiotics. It further expounds the Bifidobacterium spp. physiological and genetic stress response mechanisms as well as the methods for viability retention in yogurt, such as microencapsulation, use of oxygen scavenging lactic acid bacterial strains, and stress-protective agents. The report also explores the topic of viability determination as a critical factor in probiotic quality assurance, wherein, the limitations of culture-based enumeration methods, the challenges of species and strain resolution in the presence of lactic acid bacterial starter and probiotic species are discussed. Finally, new developments and potential applications of next-generation viability determination methods such as flow cytometry, propidium monoazide-quantitative polymerase chain reaction (PMA-qPCR), next-generation sequencing, and single-cell Raman spectroscopy (SCRS) methods are examined.

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.

On-chip Raman spectroscopy of live single cells for the staging of oesophageal adenocarcinoma progression.

The absence of early diagnosis contributes to oesophageal cancer being the sixth most common cause of global cancer-associated deaths, with a 5-year survival rate of < 20%. Barrett's oesophagus is the main pre-cancerous condition to adenocarcinoma development, characterised by the morphological transition of oesophageal squamous epithelium to metaplastic columnar epithelium. Early tracking and treatment of oesophageal adenocarcinoma could dramatically improve with diagnosis and monitoring of patients with Barrett's Oesophagus. Current diagnostic methods involve invasive techniques such as endoscopies and, with only a few identified biomarkers of disease progression, the detection of oesophageal adenocarcinoma is costly and challenging. In this work, single-cell Raman spectroscopy was combined with microfluidic techniques to characterise the development of oesophageal adenocarcinoma through the progression of healthy epithelial, Barrett's oesophagus and oesophageal adenocarcinoma cell lines. Principal component analysis and linear discriminant analysis were used to classify the different stages of cancer progression. with the ability to differentiate between healthy and cancerous cells with an accuracy of 97%. Whilst the approach could also separate the dysplastic stages from healthy or cancer with high accuracy-the intra-class separation was approximately 68%. Overall, these results highlight the potential for rapid and reliable diagnostic/prognostic screening of Barrett's Oesophagus patients.

Revisiting the role of Acinetobacter spp. in side-stream enhanced biological phosphorus removal (S2EBPR) systems

We piloted the incorporation of side-stream enhanced biological phosphorus removal (S2EBPR) with A/B stage short-cut nitrogen removal processes to enable simultaneous carbon-energy-efficient nutrients removal. This unique configuration and system conditions exerted selective force on microbial populations distinct from those in conventional EBPR. Interestingly, effective P removal was achieved with the predominance of Acinetobacter (21.5 ± 0.1 %) with nearly negligible level of known conical PAOs (Ca. Accumulibacter and Tetrasphaera were 0.04 ± 0.10 % and 0.47 ± 0.32 %, respectively). Using a combination of techniques, such as fluorescence in situ hybridization (FISH) coupled with single cell Raman spectroscopy (SCRS), the metabolic tracing of Acinetobacter-like cells exerted PAO-like phenotypic profiling. In addition, comparative metagenomics analysis of the closely related Acinetobacter spp. revealed the EBPR relevant metabolic pathways. Further oligotyping analysis of 16s rRNA V4 region revealed sub-clusters (microdiversity) of the Acinetobacter and revealed that the sub-group (oligo type 1, identical (100 % alignment identity) hits from Acinetobacter_midas_s_49494, and Acinetobacter_midas_s_55652) correlated with EBPR activities parameters, provided strong evidence that the identified Acinetobacter most likely contributed to the overall P removal in our A/B-shortcut N-S2EBPR system. To the best of our knowledge, this is the first study to confirm the in situ EBPR activity of Acinetobacter using combined genomics and SCRS Raman techniques. Further research is needed to identify the specific taxon, and phenotype of the Acinetobacter that are responsible for the P-removal.

Predicting the multispecies solid-state vinegar fermentation process using single-cell Raman spectroscopy combined with machine learning

Microbial community is a key contributing factor for flavor formation in natural food fermentation. However, it is a challenge to maintain batch-to-batch uniformity during the fermentation process due to the diversity and variability of microbial community. A rapid detection of the structure and function of the microbial community in the whole fermentation process is of great importance for quality control of the final fermentation products. Firstly, we employed amplicon sequencing to target the dominant operational taxonomic units in the microbial community of Zhenjiang aromatic vinegar, a traditional cereal vinegar. Secondly, we isolated and created a Raman database for 13 dominant bacterial species from vinegar culture, enabling us to establish a logistic regression model with 96.4% accuracy in species classification. Finally, a Raman-fermentation phase regression model was established, achieving an R2 of 0.952, accurately determining the actual fermentation phase of vinegar. This study offers a new method for dynamics monitoring of microbial community, prediction of fermentation state, and decision of subsequent production operations in multi-species fermentation processes.

Oxytetracycline and heavy metals promote the migration of resistance genes in the intestinal microbiome by plasmid transfer.

Horizontal gene transfer (HGT) has been considered the most important pathway to introduce antibiotic resistance genes (ARGs), which seriously threatens human health and biological security. The presence of ARGs in the aquatic environment and their effect on the intestinal micro-ecosystem of aquatic animals can occur easily. To investigate the HGT potential and rule of exogenous ARGs in the intestinal flora, a visual conjugative model was developed, including the donor of dual-fluorescent bacterium and the recipient of Xenopus tropicalis intestinal microbiome. Some common pollutants of oxytetracycline (OTC) and three heavy metals (Zn, Cu and Pb) were selected as the stressor. The multi-techniques of flow cytometry (FCM), scanning electron microscopy (SEM), atomic force microscopy (AFM), single-cell Raman spectroscopy with sorting (SCRSS) and indicator analysis were used in this study. The results showed that ARG transfer could occur more easily under stressors. Moreover, the conjugation efficiency mainly depended on the viability of the intestinal bacteria. The mechanisms of OTC and heavy metal stressing conjugation included the upregulation of ompC, traJ, traG and the downregulation of korA gene. Moreover, the enzymatic activities of SOD, CAT, GSH-PX increased and the bacterial surface appearance also changed. The predominant recipient was identified as Citrobacter freundi by SCRSS, in which the abundance and quantity of ARG after conjugation were higher than those before. Therefore, since the diversity of potential recipients in the intestine are very high, the migration of invasive ARGs in the microbiome should be given more attention to prevent its potential risks to public health.

Comparative Analysis Using Raman Spectroscopy of the Cellular Constituents of Lacticaseibacillus paracasei Zhang in a Normal and Viable but Nonculturable State.

This study aimed to investigate the molecular composition of a viable but nonculturable (VBNC) state of a probiotic strain, Lacticaseibacillus paracasei Zhang (L. paracasei Zhang), using single-cell Raman spectroscopy (SCRS). Fluorescent microcopy with live/dead cell staining (propidium iodide and SYTO 9), plate counting, and scanning electron microscopy were used in combination to observe bacteria in an induced VBNC state. We induced the VBNC state by incubating the cells in de Man, Rogosa, and Sharpe broth (MRS) at 4 °C. Cells were sampled for subsequent analyses before VBNC induction, during it, and up to 220 days afterwards. We found that, after cold incubation for 220 days, the viable plate count was zero, but active cells could still be observed (as green fluorescent cells) under a fluorescence microscope, indicating that Lacticaseibacillus paracasei Zhang entered the VBNC state under these conditions. Scanning electron microscopy revealed the altered ultra-morphology of the VBNC cells, characterized by a shortened cell length and a wrinkled cell surface. Principal component analysis of the Raman spectra profiles revealed obvious differences in the intracellular biochemical constituents between normal and VBNC cells. Comparative analysis of the Raman spectra identified 12 main differential peaks between normal and VBNC cells, corresponding to carbohydrates, lipids, nucleic acids, and proteins. Our results suggested that there were obvious cellular structural intracellular macromolecular differences between normal and VBNC cells. During the induction of the VBNC state, the relative contents of carbohydrates (such as fructose), saturated fatty acids (such as palmitic acid), nucleic acid constituents, and some amino acids changed obviously, which could constitute a bacterial adaptive mechanism against adverse environmental conditions. Our study provides a theoretical basis for revealing the formation mechanism of a VBNC state in lactic acid bacteria.

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.