Single-cell Raman Spectroscopy Research Articles

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.

An Isotope-Labeled Single-Cell Raman Spectroscopy Approach for Tracking the Physiological Evolution Trajectory of Bacteria toward Antibiotic Resistance.

Understanding evolution of antibiotic resistance is vital for containing its global spread. Yet our ability to in situ track highly heterogeneous and dynamic evolution is very limited. Here, we present a new single-cell approach integrating D2 O-labeled Raman spectroscopy, advanced multivariate analysis, and genotypic profiling to in situ track physiological evolution trajectory toward resistance. Physiological diversification of individual cells from isogenic population with cyclic ampicillin treatment is captured. Advanced multivariate analysis of spectral changes classifies all individual cells into four subsets of sensitive, intrinsic tolerant, evolved tolerant and resistant. Remarkably, their dynamic shifts with evolution are depicted and spectral markers of each state are identified. Genotypic analysis validates the phenotypic shift and provides insights into the underlying genetic basis. The new platform advances rapid phenotyping resistance evolution and guides evolution control.

The effects of lithium on human red blood cells studied using optical spectroscopy and laser trap.

Lithium has been the treatment of choice for patients with bipolar disorder. However, lithium overdose happens more frequently since it has a very narrow therapeutic range in blood, necessitating investigation of its adverse effects on blood cells. The possible changes that lithium exposure may have on functional and morphological characteristics of human red blood cells (RBCs) have been studied ex vivo using single-cell Raman spectroscopy, optical trapping, and membrane fluorescent probe. The Raman spectroscopy was performed with excitation at 532nm light, which also results in simultaneous photoreduction of intracellular hemoglobin (Hb). The level of photoreduction of lithium-exposed RBCs was observed to decline with lithium concentration, indicating irreversible oxygenation of intracellular Hb from lithium exposure. The lithium exposure may also have an effect on RBC membrane, which was investigated via optical stretching in a laser trap and the results suggest lower membrane fluidity for the lithium-exposed RBCs. The membrane fluidity of RBCs was further studied using the Prodan generalized polarization method and the results verify the reduction of membrane fluidity upon lithium exposure.

Metabolic Reprogramming in Colon Cancer Cells Persistently Infected with Newcastle Disease Virus.

Newcastle disease virus (NDV) is an oncolytic agent against various types of mammalian cancers. As with all cancer therapies, the development of cancer resistance, both innate and acquired, is becoming a challenge. In this study, we investigated persistently NDV-infected Caco-2 colon cancer cells, designated as virus-resistant (VR) Caco-2 cells, which were then able to resist NDV-mediated oncolysis. We applied single-cell Raman spectroscopy, combined with deuterium isotope probing (Raman-DIP) techniques, to investigate the metabolic adaptations and dynamics in VR Caco-2 cells. A linear discriminant analysis (LDA) model demonstrated excellent performance in differentiating VR Caco-2 from Caco-2 cells at single-cell level. By comparing the metabolic profiles in a time-resolved manner, the de novo synthesis of proteins and lipids was found upregulated, along with decreased DNA synthesis in VR Caco-2. The results suggest that VR Caco-2 cells might reprogram their metabolism and divert energy from proliferation to protein synthesis and lipidic modulation. The ability to identify and characterise single resistant cells among a population of cancer cells would help develop a deeper understanding of the resistance mechanisms and better tactics for developing effective cancer treatment.

Rapid identification and drug resistance screening of respiratory pathogens based on single-cell Raman spectroscopy.

Respiratory infections rank fourth in the global economic burden of disease. Lower respiratory tract infections are the leading cause of death in low-income countries. The rapid identification of pathogens causing lower respiratory tract infections to help guide the use of antibiotics can reduce the mortality of patients with lower respiratory tract infections. Single-cell Raman spectroscopy is a "whole biological fingerprint" technique that can be used to identify microbial samples. It has the advantages of no marking and fast and non-destructive testing. In this study, single-cell Raman spectroscopy was used to collect spectral data of six respiratory tract pathogen isolates. The T-distributed stochastic neighbor embedding (t-SNE) isolation analysis algorithm was used to compare the differences between the six respiratory tract pathogens. The eXtreme Gradient Boosting (XGBoost) algorithm was used to establish a Raman phenotype database model. The classification accuracy of the isolated samples was 93-100%, and the classification accuracy of the clinical samples was more than 80%. Combined with heavy water labeling technology, the drug resistance of respiratory tract pathogens was determined. The study showed that single-cell Raman spectroscopy-D2O (SCRS-D2O) labeling could rapidly identify the drug resistance of respiratory tract pathogens within 2 h.

Leveraging single-cell Raman spectroscopy and single-cell sorting for the detection and identification of yeast infections

Invasive fungal infection serves as a great threat to human health. Discrimination between fungal and bacterial infections at the earliest stage is vital for effective clinic practice; however, traditional culture-dependent microscopic diagnosis of fungal infection usually requires several days, meanwhile, culture-independent immunological and molecular methods are limited by the detectable type of pathogens and the issues with high false-positive rates. In this study, we proposed a novel culture-independent phenotyping method based on single-cell Raman spectroscopy for the rapid discrimination between fungal and bacterial infections. Three Raman biomarkers, including cytochrome c, peptidoglycan, and nucleic acid, were identified through hierarchical clustering analysis of Raman spectra across 12 types of most common yeast and bacterial pathogens. Compared to those of bacterial pathogens, the single cells of yeast pathogens demonstrated significantly stronger Raman peaks for cytochrome c, but weaker signals for peptidoglycan and nucleic acid. A two-step protocol combining the three biomarkers was established and able to differentiate fungal infections from bacterial infections with an overall accuracy of 94.9%. Our approach was also used to detect ten raw urinary tract infection samples. Successful identification of fungi was achieved within half an hour after sample obtainment. We further demonstrated the accurate fungal species taxonomy achieved with Raman-assisted cell ejection. Our findings demonstrate that Raman-based fungal identification is a novel, facile, reliable, and with a breadth of coverage approach, that has a great potential to be adopted in routine clinical practice to reduce the turn-around time of invasive fungal disease (IFD) diagnostics.

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.

Denitrification and N2O Emission in Estuarine Sediments in Response to Ocean Acidification: From Process to Mechanism.

Global estuarine ecosystems are experiencing severe nitrogen pollution and ocean acidification (OA) simultaneously. Sedimentary denitrification is an important way of reactive nitrogen removal but at the same time leads to the emission of large amounts of nitrous oxide (N2O), a potent greenhouse gas. It is known that OA in estuarine regions could impact denitrification and N2O production; however, the underlying mechanism is still underexplored. Here, sediment incubation and pure culture experiments were conducted to explore the OA impacts on microbial denitrification and the associated N2O emissions in estuarine sediments. Under neutral (in situ) conditions, fungal N2O emission dominated in the sediment, while the bacterial and fungal sources had a similar role under acidification. This indicated that acidification decreased the sedimentary fungal denitrification and likely inhibited the activity of fungal denitrifiers. To explore molecular mechanisms, a denitrifying fungal strain of Penicillium janthinellum was isolated from the sediments. By using deuterium-labeled single-cell Raman spectroscopy and isobaric tags for relative and absolute quantitation proteomics, we found that acidification inhibited electron transfers in P. janthinellum and downregulated expressions of the proteins related to energy production and conservation. Two collaborative pathways of energy generation in the P. janthinellum were further revealed, that is, aerobic oxidative phosphorylation and TCA cycle and anoxic pyruvate fermentation. This indicated a distinct energy supply strategy from bacterial denitrification. Our study provides insights into fungi-mediated nitrogen cycle in acidifying aquatic ecosystems.

Machine Learning-Based Determination of Sampling Depth for Complex Environmental Systems: Case Study with Single-Cell Raman Spectroscopy Data in EBPR Systems.

Rapid progress in various advanced analytical methods, such as single-cell technologies, enable unprecedented and deeper understanding of microbial ecology beyond the resolution of conventional approaches. A major application challenge exists in the determination of sufficient sample size without sufficient prior knowledge of the community complexity and, the need to balance between statistical power and limited time or resources. This hinders the desired standardization and wider application of these technologies. Here, we proposed, tested and validated a computational sampling size assessment protocol taking advantage of a metric, named kernel divergence. This metric has two advantages: First, it directly compares data set-wise distributional differences with no requirements on human intervention or prior knowledge-based preclassification. Second, minimal assumptions in distribution and sample space are made in data processing to enhance its application domain. This enables test-verified appropriate handling of data sets with both linear and nonlinear relationships. The model was then validated in a case study with Single-cell Raman Spectroscopy (SCRS) phenotyping data sets from eight different enhanced biological phosphorus removal (EBPR) activated sludge communities located across North America. The model allows the determination of sufficient sampling size for any targeted or customized information capture capacity or resolution level. Promised by its flexibility and minimal restriction of input data types, the proposed method is expected to be a standardized approach for sampling size optimization, enabling more comparable and reproducible experiments and analysis on complex environmental samples. Finally, these advantages enable the extension of the capability to other single-cell technologies or environmental applications with data sets exhibiting continuous features.

Metabolic characteristics and markers in viable but nonculturable state of Pseudomonas aeruginosa induced by chlorine stress

Many Gram-negative pathogens enter the viable but nonculturable (VBNC) state to resist external environmental stress (such as disinfection). However, little is known about the metabolic properties, especially for the metabolic markers, of VBNC bacteria, which impedes the development of efficient disinfection technologies and causes more potential health risks. In this study, we analyzed the metabolic characteristics of chlorine stress-induced VBNC Pseudomonas aeruginosa at the population and single-cell levels. The overall metabolic activity of VBNC bacteria showed a downward trend, but the glyoxylate cycle, fatty acid and glycerophospholipid metabolism pathways were up-regulated. Based on the metabolic profiles of VBNC bacteria, nine metabolic markers (pyruvate, glyoxylate, guanine, glutamate, sn glycero-3-phos-phocholine, fatty acid, D-alanine, glutathione, N-Butanoyl-D-homoserine lactone) were determined. The results of single-cell Raman spectroscopy showed that the metabolic activity of VBNC bacteria was significantly reduced, but showed more significant metabolic heterogeneity. The redshift of the Raman peaks of 15N and 13C labeled VBNC bacteria was significantly weaker than that of the culturable bacteria, suggesting that the VBNC bacteria have a reduced ability to synthesize proteins, nucleotides, phospholipids, and carbohydrates. The result of this study can help to better understand the metabolic mechanisms and energy management strategy of VBNC bacteria, to achieve precise identification and effective control of VBNC bacteria.