S-nitrosocysteamine-functionalised porous graphene oxide nanosheets as nitric oxide delivery vehicles for cardiovascular applications

Nitric oxide (NO) is a key signalling molecule released by vascular endothelial cells that is essential for vascular health. Low NO bioactivity is associated with cardiovascular diseases, such as hypertension, atherosclerosis, and heart failure and NO donors are a mainstay of drug treatment. However, many NO donors are associated with the development of tolerance and adverse effects, so new formulations for controlled and targeted release of NO would be advantageous. Herein, we describe the design and characterisation of a novel NO delivery system via the reaction of acidified sodium nitrite with thiol groups that had been introduced by cysteamine conjugation to porous graphene oxide nanosheets, thereby generating S-nitrosated nanosheets. An NO electrode, ozone-based chemiluminescence and electron paramagnetic resonance spectroscopy were used to measure NO released from various graphene formulations, which was sustained at >5 × 10-10 mol cm-2 min-1 for at least 3 h, compared with healthy endothelium (cf. 0.5-4 × 10-10 mol cm-2 min-1). Single cell Raman micro-spectroscopy showed that vascular endothelial and smooth muscle cells (SMCs) took up graphene nanostructures, with intracellular NO release detected via a fluorescent NO-specific probe. Functionalised graphene had a dose-dependent effect to promote proliferation in endothelial cells and to inhibit growth in SMCs, which was associated with cGMP release indicating intracellular activation of canonical NO signalling. Chemiluminescence detected negligible production of toxic N-nitrosamines. Our findings demonstrate the utility of porous graphene oxide as a NO delivery vehicle to release physiologically relevant amounts of NO in vitro, thereby highlighting the potential of these formulations as a strategy for the treatment of cardiovascular diseases.

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

BACKGROUND

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.

METHODS

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).

RESULTS

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.

CONCLUSION

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.

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.

Engineering artificial photosynthesis based on rhodopsin for CO2 fixation

Microbial rhodopsin, a significant contributor to sustaining life through light harvesting, holds untapped potential for carbon fixation. Here, we construct an artificial photosynthesis system which combines the proton-pumping ability of rhodopsin with an extracellular electron uptake mechanism, establishing a pathway to drive photoelectrosynthetic CO2 fixation by Ralstonia eutropha (also known as Cupriavidus necator) H16, a facultatively chemolithoautotrophic soil bacterium. R. eutropha is engineered to heterologously express an extracellular electron transfer pathway of Shewanella oneidensis MR-1 and Gloeobacter rhodopsin (GR). Employing GR and the outer-membrane conduit MtrCAB from S. oneidensis, extracellular electrons and GR-driven proton motive force are integrated into R. eutropha's native electron transport chain (ETC). Inspired by natural photosynthesis, the photoelectrochemical system splits water to supply electrons to R. eutropha via the Mtr outer-membrane route. The light-activated proton pump - GR, supported by canthaxanthin as an antenna, powers ATP synthesis and reverses the ETC to regenerate NADH/NADPH, facilitating R. eutropha's biomass synthesis from CO2. Overexpression of a carbonic anhydrase further enhances CO2 fixation. This artificial photosynthesis system has the potential to advance the development of efficient photosynthesis, redefining our understanding of the ecological role of microbial rhodopsins in nature.

Application of magnetic-nanoparticle functionalized whole-cell biosensor array for bioavailability and ecotoxicity estimation at urban contaminated sites

The bioavailability and ecotoxicity of pollutants are important for urban ecological systems and human health, particularly at contaminated urban sites. Therefore, whole-cell bioreporters are used in many studies to assess the risks of priority chemicals; however, their application is restricted by low throughput for specific compounds and complicated operations for field tests. In this study, an assembly technology for manufacturing Acinetobacter-based biosensor arrays using magnetic nanoparticle functionalization was developed to solve this problem. The bioreporter cells maintained high viability, sensitivity, and specificity in sensing 28 priority chemicals, seven heavy metals, and seven inorganic compounds in a high-throughput manner, and their performance remained acceptable for at least 20 d. We also tested the performance by assessing 22 real environmental soil samples from urban areas in China, and our results showed positive correlations between the biosensor estimation and chemical analysis. Our findings prove the feasibility of the magnetic nanoparticle-functionalized biosensor array to recognize the types and toxicities of multiple contaminants for online environmental monitoring at contaminated sites.

High-throughput single-cell sorting by stimulated Raman-activated cell ejection

Single-cell sorting is essential to explore cellular heterogeneity in biology and medicine. Recently developed Raman-activated cell sorting (RACS) circumvents the limitations of fluorescence-activated cell sorting, such as the cytotoxicity of labels. However, the sorting throughputs of all forms of RACS are limited by the intrinsically small cross-section of spontaneous Raman scattering. Here, we report a stimulated Raman-activated cell ejection (S-RACE) platform that enables high-throughput single-cell sorting based on high-resolution multi-channel stimulated Raman chemical imaging, in situ image decomposition, and laser-induced cell ejection. The performance of this platform was illustrated by sorting a mixture of 1 μm polymer beads, where 95% yield, 98% purity, and 14 events per second throughput were achieved. Notably, our platform allows live cell ejection, allowing for the growth of single colonies of bacteria and fungi after sorting. To further illustrate the chemical selectivity, lipid-rich Rhodotorula glutinis cells were successfully sorted from a mixture with Saccharomyces cerevisiae, confirmed by downstream quantitative PCR. Furthermore, by integrating a closed-loop feedback control circuit into the system, we realized real-time single-cell imaging and sorting, and applied this method to precisely eject regions of interest from a rat brain tissue section. The reported S-RACE platform opens exciting opportunities for a wide range of single-cell applications in biology and medicine.

Developing a Blood Cell-Based Diagnostic Test for Myalgic Encephalomyelitis/Chronic Fatigue Syndrome Using Peripheral Blood Mononuclear Cells

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is characterized by debilitating fatigue that profoundly impacts patients' lives. Diagnosis of ME/CFS remains challenging, with most patients relying on self-report, questionnaires, and subjective measures to receive a diagnosis, and many never receiving a clear diagnosis at all. In this study, a single-cell Raman platform and artificial intelligence are utilized to analyze blood cells from 98 human subjects, including 61 ME/CFS patients of varying disease severity and 37 healthy and disease controls. These results demonstrate that Raman profiles of blood cells can distinguish between healthy individuals, disease controls, and ME/CFS patients with high accuracy (91%), and can further differentiate between mild, moderate, and severe ME/CFS patients (84%). Additionally, specific Raman peaks that correlate with ME/CFS phenotypes and have the potential to provide insights into biological changes and support the development of new therapeutics are identified. This study presents a promising approach for aiding in the diagnosis and management of ME/CFS and can be extended to other unexplained chronic diseases such as long COVID and post-treatment Lyme disease syndrome, which share many of the same symptoms as ME/CFS.

Deep insight into oriented propionate production from food waste: Microbiological interpretation and design practice

Using mixed microbial cultures (MMCs) for oriented volatile fatty acids (VFAs) refining in an open environment is a typical challenge due to the microbial diversiform and the process complexity. Especially for carbohydrate-rich waste (such as food waste), butyrate-type fermentation is usually dominant in a single-stage MMCs anaerobic process, while the production of odd-carbon VFAs (such as propionate) is difficult although it plays a significant role in chemicals industries. In this study, firstly, we gave a new perspective on the rationality of the oriented propionate production using MMCs with lactate as feedstock by conducting in-depth microbial informatics and reaction analysis. Secondly, we verified the feasibility of the "food waste-lactate-propionate" route to reverse the original butyrate-type fermentation situation and explore mechanisms for maintaining stability. In the first stage, a defined lactate fermentation microbiome was used to produce lactate-containing broth (80% of total chemical oxygen demand) at pH=4. In the second stage, an undomesticated undefined anaerobic microbiome was used to drive propionate production (45.26% ± 2.23% of total VFAs) under optimized conditions (C/N = 100:1-200:1 and pH=5.0). The low pH environment in the first stage enhanced the lactic acid bacteria to resist the invasion of non-functional flanking bacteria, making the community stable. In the second stage, the system maintained the propionate-type fermentation due to the absence of the ecological niche of the invasive lactic acid bacteria; The selection of propionate-producing specialists was a necessary but not sufficient condition for propionate-type fermentation. At last, this study proposed an enhanced engineering strategy framework for understanding elaborate MMCs fermentation.

Growth-coupled enzyme engineering through manipulation of redox cofactor regeneration

Enzymes need to be efficient, robust, and highly specific for their effective use in commercial bioproduction. These properties can be introduced using various enzyme engineering techniques, with random mutagenesis and directed evolution (DE) often being chosen when there is a lack of structural information -or mechanistic understanding- of the enzyme. The screening or selection step of DE is the limiting part of this process, since it must ideally be (ultra)-high throughput, specifically target the catalytic activity of the enzyme and have an accurately quantifiable metric for said activity. Growth-coupling selection strategies involve coupling a desired enzyme activity to cellular metabolism and therefore growth, where growth (rate) becomes the output metric. Redox cofactors (NAD⁺/NADH and NADP⁺/NADPH) have recently been identified as promising target molecules for growth coupling, owing to their essentiality for cellular metabolism and ubiquitous nature. Redox cofactor oxidation or reduction can be disrupted through metabolic engineering and the use of specific culturing conditions, rendering the cell inviable unless a 'rescue' reaction complements the imposed metabolic deficiency. Using this principle, enzyme variants displaying improved cofactor oxidation or reduction rates can be selected for through an increased growth rate of the cell. In recent years, several E. coli strains have been developed that are deficient in the oxidation or reduction of NAD⁺/NADH and NADP⁺/NADPH pairs, and of non-canonical redox cofactor pairs NMN⁺/NMNH and NCD⁺/NCDH, which provides researchers with a versatile toolbox of enzyme engineering platforms. A range of redox cofactor dependent enzymes have since been engineered using a variety of these strains, demonstrating the power of using this growth-coupling technique for enzyme engineering. This review aims to summarize the metabolic engineering involved in creating strains auxotrophic for the reduced or oxidized state of redox cofactors, and the resulting successes in using them for enzyme engineering. Perspectives on the unique features and potential future applications of this technique are also presented.

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.

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.

Engineering a Rhodopsin-Based Photo-Electrosynthetic System in Bacteria for CO2 Fixation

A key goal of synthetic biology is to engineer organisms that can use solar energy to convert CO₂ to biomass, chemicals, and fuels. We engineered a light-dependent electron transfer chain by integrating rhodopsin and an electron donor to form a closed redox loop, which drives rhodopsin-dependent CO₂ fixation. A light-driven proton pump comprising Gloeobacter rhodopsin (GR) and its cofactor retinal have been assembled in Ralstonia eutropha (Cupriavidus necator) H16. In the presence of light, this strain fixed inorganic carbon (or bicarbonate) leading to 20% growth enhancement, when formate was used as an electron donor. We found that an electrode from a solar panel can replace organic compounds to serve as the electron donor, mediated by the electron shuttle molecule riboflavin. In this new autotrophic and photo-electrosynthetic system, GR is augmented by an external photocell for reductive CO₂ fixation. We demonstrated that this hybrid photo-electrosynthetic pathway can drive the engineered R. eutropha strain to grow using CO₂ as the sole carbon source. In this system, a bioreactor with only two inputs, light and CO₂, enables the R. eutropha strain to perform a rhodopsin-dependent autotrophic growth. Light energy alone, supplied by a solar panel, can drive the conversion of CO₂ into biomass with a maximum electron transfer efficiency of 20%.

Revealing CO2-Fixing SAR11 Bacteria in the Ocean by Raman-Based Single-Cell Metabolic Profiling and Genomics

The majority of marine microbes remain uncultured, which hinders the identification and mining of CO2-fixing genes, pathways, and chassis from the oceans. Here, we investigated CO2-fixing microbes in seawater from the euphotic zone of the Yellow Sea of China by detecting and tracking their 13C-bicarbonate (13C-HCO3-) intake via single-cell Raman spectra (SCRS) analysis. The target cells were then isolated by Raman-activated Gravity-driven Encapsulation (RAGE), and their genomes were amplified and sequenced at one-cell resolution. The single-cell metabolism, phenotype and genome are consistent. We identified a not-yet-cultured Pelagibacter spp., which actively assimilates 13C-HCO3-, and also possesses most of the genes encoding enzymes of the Calvin-Benson cycle for CO2 fixation, a complete gene set for a rhodopsin-based light-harvesting system, and the full genes necessary for carotenoid synthesis. The four proteorhodopsin (PR) genes identified in the Pelagibacter spp. were confirmed by heterologous expression in E. coli. These results suggest that hitherto uncultured Pelagibacter spp. uses light-powered metabolism to contribute to global carbon cycling.

A quantitative RT-qLAMP for the detection of SARS-CoV-2 and human gene in clinical application

Reverse transcription (RT) - loop-mediated isothermal amplification (LAMP) assay is a rapid and one-step method to detect SARS-CoV-2 in the pandemic. Quantitative estimation of the viral load of SARS-CoV-2 in patient samples could help physicians make decisions on clinical treatment and patient management. Here, we propose to use a quantitative LAMP (qLAMP) method to evaluate the viral load of SARS-CoV-2 in samples. We used threshold time (TT) values of qLAMP, the isothermal incubation time required for the fluorescent or colorimetric signal to reach the threshold, to indicate the viral load of clinical samples. Similar to the cycle threshold (C_t ) values in conventional qPCR, TT values of qLAMP show a linear relationship to the copy numbers of SARS-CoV-2. The higher the viral loadings, the lower qLAMP TT values are. The RT-qLAMP assay was demonstrated to quantify the viral loads of synthesized full-length RNA, inactivated viral particles (BBIBP-CorV), and clinical samples within 15 min by fluorescent reading and 25 min by colorimetric reading. The RT-qLAMP has been applied to detect Alpha, Beta, Kappa, Delta, and Omicron variants of SARS-CoV-2, as well as the human beta-actin gene, and their TT values showed the linear patterns. The RT-qLAMP assays were evaluated by 64 clinical samples (25 positives and 39 negatives) for the assessment of viral loads, and it was also used to quantify the human beta-actin gene, which was used as a control and an indicator of sampling quality in clinical swab samples. The result of RT-qLAMP was in good agreement with the result of RT-qPCR. The RT-qLAMP assay detected all clinical samples, including those with C_t  = 35, within 10 min using fluorescent reading.

Diagnosis of Myalgic Encephalomyelitis/Chronic Fatigue Syndrome With Partial Least Squares Discriminant Analysis: Relevance of Blood Extracellular Vesicles

Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a chronic disease characterized by long-lasting persistent debilitating widespread fatigue and post-exertional malaise, remains diagnosed by clinical criteria. Our group and others have identified differentially expressed miRNA profiles in the blood of patients. However, their diagnostic power individually or in combinations seems limited. A Partial Least Squares-Discriminant Analysis (PLS-DA) model initially based on 817 variables: two demographic, 34 blood analytic, 136 PBMC miRNAs, 639 Extracellular Vesicle (EV) miRNAs, and six EV features, selected an optimal number of five components, and a subset of 32 regressors showing statistically significant discriminant power. The presence of four EV-features (size and z-values of EVs prepared with or without proteinase K treatment) among the 32 regressors, suggested that blood vesicles carry relevant disease information. To further explore the features of ME/CFS EVs, we subjected them to Raman micro-spectroscopic analysis, identifying carotenoid peaks as ME/CFS fingerprints, possibly due to erythrocyte deficiencies. Although PLS-DA analysis showed limited capacity of Raman fingerprints for diagnosis (AUC = 0.7067), Raman data served to refine the number of PBMC miRNAs from our previous model still ensuring a perfect classification of subjects (AUC=1). Further investigations to evaluate model performance in extended cohorts of patients, to identify the precise ME/CFS EV components detected by Raman and to reveal their functional significance in the disease are warranted.

Reprogramming Synthetic Cells for Targeted Cancer Therapy

Advances in synthetic biology enable the reprogramming of bacteria as smart agents to specifically target tumors and locally release anticancer drugs in a highly controlled manner. However, the bench-to-bedside translation of engineered bacteria is often impeded by genetic instability and the potential risk of uncontrollable replication of engineered bacteria inside the patient. SimCells (simple cells) are chromosome-free bacteria controlled by designed gene circuits, which can bypass the interference of the native gene network in bacteria and eliminate the risk of bacterial uncontrolled growth. Here, we describe the reprogramming of SimCells and mini-SimCells to serve as “safe and live drugs” for targeted cancer therapy. We engineer SimCells to display nanobodies on the surface for the binding of carcinoembryonic antigen (CEA), which is an important biomarker found commonly in colorectal cancer cells. We show that SimCells and mini-SimCells with surface display of anti-CEA nanobody can specifically bind CEA-expressing Caco2 cancer cells in vitro while leaving the non-CEA-expressing SW80 cancer cells untouched. These cancer-targeting SimCells and mini-SimCells induced cancer cell death in vitro by compromising the plasma membrane of cancer cells. The cancer-killing effect can be further enhanced by an aspirin/salicylate inducible gene circuit that converts salicylate into catechol, a potent anticancer. This work highlights the potential of SimCells and mini-SimCells for targeted cancer therapy and lays the foundation for the application of synthetic biology to medicine.

Clinical Perspective of Antimicrobial Resistance in Bacteria

Antimicrobial resistance (AMR) has become a global clinical problem in recent years. With the discovery of antibiotics, infections were not a deadly problem for clinicians as they used to be. However, worldwide AMR comes with the overuse/misuse of antibiotics and the spread of resistance is deteriorated by a multitude of mobile genetic elements and relevant resistant genes. This review provides an overview of the current situation, mechanism, epidemiology, detection methods and clinical treatment for antimicrobial resistant genes in clinical important bacteria including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), penicillin-resistant Streptococcus pneumoniae (PRSP), extended-spectrum β-lactamase-producing Enterobacteriaceae, acquired AmpC β-lactamase-producing Enterobacteriaceae, carbapenemase-producing Enterobacteriaceae (CPE), multidrug-resistant (MDR) Acinetobacter baumannii and Pseudomonas aeruginosa.

High-Speed Diagnosis of Bacterial Pathogens at the Single Cell Level by Raman Microspectroscopy with Machine Learning Filters and Denoising Autoencoders

Accurate and rapid identification of infectious bacteria is important in medicine. Raman microspectroscopy holds great promise in performing label-free identification at the single-cell level. However, due to the naturally weak Raman signal, it is a challenge to build extensive databases and achieve both accurate and fast identification. Here, we used signal-to-noise ratio (SNR) as a standard indicator for Raman data quality and performed bacterial identification using 11, 141 single-cell Raman spectra from nine bacterial strains. Subsequently, using two machine learning methods, a simple filter, and a neural network-based denoising autoencoder (DAE), we demonstrated 92% (simple filter using 1 s/cell spectra) and 84% (DAE using 0.1 s/cell spectra) identification accuracy. Our machine learning-aided Raman analysis paves the way for high-speed Raman microspectroscopic clinical diagnostics.

Raman spectroscopy and machine learning for the classification of breast cancers

Breast cancer is a major health threat for women. The drug responses associated with different breast cancer subtypes have obvious effects on therapeutic outcomes; therefore, the accurate classification of breast cancer subtypes is critical. Breast cancer subtype classification has recently been examined using various methods, and Raman spectroscopy has emerged as an effective technique that can be used for noninvasive breast cancer analysis. However, the accurate and rapid classification of breast cancer subtypes currently requires a great deal of effort and experience with the processing and analysis of Raman spectra data. Here, we adopted Raman spectroscopy and machine learning techniques to simplify and accelerate the process used to distinguish normal from breast cancer cells and classify breast cancer subtypes. Raman spectra were obtained from cultured breast cancer cell lines, and the data were analyzed by two machine learning algorithms: principal component analysis (PCA)-discriminant function analysis (DFA) and PCA-support vector machine (SVM). The accuracies with which these two algorithms were able to distinguish normal breast cells from breast cancer cells were both greater than 97%, and the accuracies of breast cancer subtype classification for both algorithms were both greater than 92%. Moreover, our results showed evidence to support the use of characteristic Raman spectral features as cancer cell biomarkers, such as the intensity of intrinsic Raman bands, which increased in cancer cells. Raman spectroscopy combined with machine learning techniques provides a rapid method for breast cancer analysis able to reveal differences in intracellular compositions and molecular structures among subtypes.

Redesign of ultrasensitive and robust RecA gene circuit to sense DNA damage

SOS box of the recA promoter, PVRecA from Vibrio natriegens was characterized, cloned and expressed in a probiotic strain E. coli Nissle 1917. This promoter was then rationally engineered according to predicted interactions between LexA repressor and PVRecA . The redesigned PVRecA-AT promoter showed a sensitive and robust response to DNA damage induced by UV and genotoxic compounds. Rational design of PVRecA coupled to an amplification gene circuit increased circuit output amplitude 4.3-fold in response to a DNA damaging compound mitomycin C. A TetR-based negative feedback loop was added to the PVRecA-AT amplifier to achieve a robust SOS system, resistant to environmental fluctuations in parameters including pH, temperature, oxygen and nutrient conditions. We found that E. coli Nissle 1917 with optimized PVRecA-AT adapted to UV exposure and increased SOS response 128-fold over 40 h cultivation in turbidostat mini-reactor. We also showed the potential of this PVRecA-AT system as an optogenetic actuator, which can be controlled spatially through UV radiation. We demonstrated that the optimized SOS responding gene circuits were able to detect carcinogenic biomarker molecules with clinically relevant concentrations. The ultrasensitive SOS gene circuits in probiotic E. coli Nissle 1917 would be potentially useful for bacterial diagnosis.

Rational Design and Characterization of Nitric Oxide Biosensors in E. coli Nissle 1917 and Mini SimCells

Nitric oxide (NO) is an important disease biomarker found in many chronic inflammatory diseases and cancers. A well-characterized nitric sensing system is useful to aid the rapid development of bacteria therapy and synthetic biology. In this work, we engineered a set of NO-responsive biosensors based on the P_norV promoter and its NorR regulator in the norRVW operon; the circuits were characterized and optimized in probiotic Escherichia coli Nissle 1917 and mini SimCells (minicells containing designed gene circuits for specific tasks). Interestingly, the expression level of NorR displayed an inverse correlation to the P_norV promoter activation, as a strong expression of the NorR regulator resulted in a low amplitude of NO-inducible gene expression. This could be explained by a competitive binding mechanism where the activated and inactivated NorR competitively bind to the same site on the P_norV promoter. To overcome such issues, the NO induction performance was further improved by making a positive feedback loop that fine-tuned the level of NorR. In addition, by examining two integration host factor (IHF) binding sites of the P_norV promoter, we demonstrated that the deletion of the second IHF site increased the maximum signal output by 25% (500 μM DETA/NO) with no notable increase in the basal expression level. The optimized NO-sensing gene circuit in anucleate mini SimCells exhibited increased robustness against external fluctuation in medium composition. The NO detection limit of the optimized gene circuit pPnorVβ was also improved from 25.6 to 1.3 nM in mini SimCells. Moreover, lyophilized mini SimCells can maintain function for over 2 months. Hence, SimCell-based NO biosensors could be used as safe sensor chassis for synthetic biology.

Regulation and Characterization of Mutants of fixABCX in Rhizobium leguminosarum

Symbiosis between Rhizobium leguminosarum and Pisum sativum requires tight control of redox balance in order to maintain respiration under the microaerobic conditions required for nitrogenase while still producing the eight electrons and sixteen molecules of ATP needed for nitrogen fixation. FixABCX, a cluster of electron transfer flavoproteins essential for nitrogen fixation, is encoded on the Sym plasmid (pRL10), immediately upstream of nifA, which encodes the general transcriptional regulator of nitrogen fixation. There is a symbiotically regulated NifA-dependent promoter upstream of fixA (P_nifA1), as well as an additional basal constitutive promoter driving background expression of nifA (P_nifA2). These were confirmed by 5'-end mapping of transcription start sites using differential RNA-seq. Complementation of polar fixAB and fixX mutants (Fix^- strains) confirmed expression of nifA from P_nifA1 in symbiosis. Electron microscopy combined with single-cell Raman microspectroscopy characterization of fixAB mutants revealed previously unknown heterogeneity in bacteroid morphology within a single nodule. Two morphotypes of mutant fixAB bacteroids were observed. One was larger than wild-type bacteroids and contained high levels of polyhydroxy-3-butyrate, a complex energy/reductant storage product. A second bacteroid phenotype was morphologically and compositionally different and resembled wild-type infection thread cells. From these two characteristic fixAB mutant bacteroid morphotypes, inferences can be drawn on the metabolism of wild-type nitrogen-fixing bacteroids.[Formula: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Understanding and mathematical modelling of cellular resource allocation in microorganisms: a comparative synthesis

BACKGROUND

The rising consensus that the cell can dynamically allocate its resources provides an interesting angle for discovering the governing principles of cell growth and metabolism. Extensive efforts have been made in the past decade to elucidate the relationship between resource allocation and phenotypic patterns of microorganisms. Despite these exciting developments, there is still a lack of explicit comparison between potentially competing propositions and a lack of synthesis of inter-related proposals and findings.

RESULTS

In this work, we have reviewed resource allocation-derived principles, hypotheses and mathematical models to recapitulate important achievements in this area. In particular, the emergence of resource allocation phenomena is deciphered by the putative tug of war between the cellular objectives, demands and the supply capability. Competing hypotheses for explaining the most-studied phenomenon arising from resource allocation, i.e. the overflow metabolism, have been re-examined towards uncovering the potential physiological root cause. The possible link between proteome fractions and the partition of the ribosomal machinery has been analysed through mathematical derivations. Finally, open questions are highlighted and an outlook on the practical applications is provided. It is the authors' intention that this review contributes to a clearer understanding of the role of resource allocation in resolving bacterial growth strategies, one of the central questions in microbiology.

CONCLUSIONS

We have shown the importance of resource allocation in understanding various aspects of cellular systems. Several important questions such as the physiological root cause of overflow metabolism and the correct interpretation of 'protein costs' are shown to remain open. As the understanding of the mechanisms and utility of resource application in cellular systems further develops, we anticipate that mathematical modelling tools incorporating resource allocation will facilitate the circuit-host design in synthetic biology.

In Vitro Anticancer Drug Sensitivity Sensing through Single-Cell Raman Spectroscopy

Traditional in vitro anticancer drug sensitivity testing at the population level suffers from lengthy procedures and high false positive rates. To overcome these defects, we built a confocal Raman microscopy sensing system and proposed a single-cell approach via Raman-deuterium isotope probing (Raman-DIP) as a rapid and reliable in vitro drug efficacy evaluation method. Raman-DIP detected the incorporation of deuterium into the cell, which correlated with the metabolic activity of the cell. The human non-small cell lung cancer cell line HCC827 and human breast cancer cell line MCF-7 were tested against eight different anticancer drugs. The metabolic activity of cancer cells could be detected as early as 12 h, independent of cell growth. Incubation of cells in 30% heavy water (D₂O) did not show any negative effect on cell viability. Compared with traditional methods, Raman-DIP could accurately determine the drug effect, meanwhile, it could reduce the testing period from 72–144 h to 48 h. Moreover, the heterogeneity of cells responding to anticancer drugs was observed at the single-cell level. This proof-of-concept study demonstrated the potential of Raman-DIP to be a reliable tool for cancer drug discovery and drug susceptibility testing.

Genetic engineering biofilms in situ using ultrasound-mediated DNA delivery

The ability to directly modify native and established biofilms has enormous potential in understanding microbial ecology and application of biofilm in 'real-world' systems. However, efficient genetic transformation of established biofilms at any scale remains challenging. In this study, we applied an ultrasound-mediated DNA delivery (UDD) technique to introduce plasmid to established non-competent biofilms in situ. Two different plasmids containing genes coding for superfolder green fluorescent protein (sfGFP) and the flavin synthesis pathway were introduced into established bacterial biofilms in microfluidic flow (transformation efficiency of 3.9 ± 0.3 × 10-7 cells in biofilm) and microbial fuel cells (MFCs), respectively, both employing UDD. Gene expression and functional effects of genetically modified bacterial biofilms were observed, where some cells in UDD-treated Pseudomonas putida UWC1 biofilms expressed sfGFP in flow cells and UDD-treated Shewanella oneidensis MR-1 biofilms generated significantly (P < 0.05) greater (61%) bioelectricity production (21.9 ± 1.2 µA cm-2 ) in MFC than a wild-type control group (~ 13.6 ± 1.6 µA cm-2 ). The effects of UDD were amplified in subsequent growth under selection pressure due to antibiotic resistance and metabolism enhancement. UDD-induced gene transfer on biofilms grown in both microbial flow cells and MFC systems was successfully demonstrated, with working volumes of 0.16 cm3 and 300 cm3 , respectively, demonstrating a significant scale-up in operating volume. This is the first study to report on a potentially scalable direct genetic engineering method for established non-competent biofilms, which can be exploited in enhancing their capability towards environmental, industrial and medical applications.

Unveiling Cancer Metabolism through Spontaneous and Coherent Raman Spectroscopy and Stable Isotope Probing

Metabolic reprogramming is a common hallmark in cancer. The high complexity and heterogeneity in cancer render it challenging for scientists to study cancer metabolism. Despite the recent advances in single-cell metabolomics based on mass spectrometry, the analysis of metabolites is still a destructive process, thus limiting in vivo investigations. Being label-free and nonperturbative, Raman spectroscopy offers intrinsic information for elucidating active biochemical processes at subcellular level. This review summarizes recent applications of Raman-based techniques, including spontaneous Raman spectroscopy and imaging, coherent Raman imaging, and Raman-stable isotope probing, in contribution to the molecular understanding of the complex biological processes in the disease. In addition, this review discusses possible future directions of Raman-based technologies in cancer research.

Development of a Fast Raman-Assisted Antibiotic Susceptibility Test (FRAST) for the Antibiotic Resistance Analysis of Clinical Urine and Blood Samples

Human health is at great risk due to the spreading of antimicrobial resistance (AMR). The lengthy procedure of conventional antimicrobial susceptibility testing (AST) usually requires a few days. We developed a fast Raman-assisted antibiotic susceptibility test (FRAST), which detects single bacterial metabolic activity in the presence of antibiotics, using Raman single-cell spectroscopy. It was found that single-cell Raman spectra (SCRS) would show a clear and distinguishable Raman band at the "silent zone" (2000-2300 cm^-1), due to the active incorporation of deuterium from heavy water (D₂O) by antibiotic-resistant bacteria. This pilot study has compared the FRAST and the conventional AST for six clinical standard quality controls (four Gram-negative and two Gram-positive bacteria strains) in response to 38 antibiotics. In total, 3200 treatments have been carried out and approximately 64 000 SCRS have been acquired for FRAST analysis. The result showed an overall agreement of 88.0% between the FRAST and the conventional AST assay. The gram-staining classification based on the linear discriminant analysis (LDA) model of SCRS was developed, seamlessly coupling with the FRAST to further reduce the turnaround time. We applied the FRAST to real clinical analysis for nine urinary infectious samples and three sepsis samples. The results were consistent with MALDI-TOF identification and the conventional AST. Under the optimal conditions, the "sample to report" of the FRAST could be reduced to 3 h for urine samples and 21 h for sepsis samples. The FRAST provides fast and reliable susceptibility tests, which could speed up microbiological analysis for clinical practice and facilitate antibiotic stewardship.

From macro to micro: a combined bioluminescence-fluorescence approach to monitor bacterial localization

Bacterial bioluminescence is widely used to study the spatiotemporal dynamics of bacterial populations and gene expression in vivo at a population level but cannot easily be used to study bacterial activity at the level of individual cells. In this study, we describe the development of a new library of mini‐Tn7‐lux and lux::eyfp reporter constructs that provide a wide range of lux expression levels, and which combine the advantages of both bacterial bioluminescence and fluorescent proteins to bridge the gap between macro‐ and micro‐scale imaging techniques. We demonstrate that a dual bioluminescence‐fluorescence approach using the lux operon and eYFP can be used to monitor bacterial movement in plants both macro‐ and microscopically and demonstrate that Pseudomonas syringae pv phaseolicola can colonize the leaf vascular system and systemically infect leaves of common bean (Phaseolus vulgaris). We also show that bacterial bioluminescence can be used to study the impact of plant immune responses on bacterial multiplication, viability and spread within plant tissues. The constructs and approach described in this study can be used to study the spatiotemporal dynamics of bacterial colonization and to link population dynamics and cellular interactions in a wide range of biological contexts.

Elevated intracellular cyclic-di-GMP level in Shewanella oneidensis increases expression of c-type cytochromes

Electrochemically active biofilms are capable of exchanging electrons with solid electron acceptors and have many energy and environmental applications such as bioelectricity generation and environmental remediation. The performance of electrochemically active biofilms is usually dependent on c-type cytochromes, while biofilm development is controlled by a signal cascade mediated by the intracellular secondary messenger bis-(3'-5') cyclic dimeric guanosine monophosphate (c-di-GMP). However, it is unclear whether there are any links between the c-di-GMP regulatory system and the expression of c-type cytochromes. In this study, we constructed a S. oneidensis MR-1 strain with a higher cytoplasmic c-di-GMP level by constitutively expressing a c-di-GMP synthase and it exhibited expected c-di-GMP-influenced traits, such as lowered motility and increased biofilm formation. Compared to MR-1 wild-type strain, the high c-di-GMP strain had a higher Fe(III) reduction rate (21.58 vs 11.88 pM of Fe(III)/h cell) and greater expression of genes that code for the proteins involved in the Mtr pathway, including CymA, MtrA, MtrB, MtrC and OmcA. Furthermore, single-cell Raman microspectroscopy (SCRM) revealed a great increase of c-type cytochromes in the high c-di-GMP strain as compared to MR-1 wild-type strain. Our results reveal for the first time that the c-di-GMP regulation system indirectly or directly positively regulates the expression of cytochromes involved in the extracellular electron transport (EET) in S. oneidensis, which would help to understand the regulatory mechanism of c-di-GMP on electricity production in bacteria.

Fingerprinting Bacterial Metabolic Response to Erythromycin by Raman-Integrated Mid-Infrared Photothermal Microscopy

We report rapid and sensitive phenotyping of bacterial response to antibiotic treatment at single-cell resolution by a Raman-integrated optical mid-infrared photothermal (MIP) microscope. The MIP microscope successfully detected biochemical changes of bacteria in specific to the acting mechanism of erythromycin with 1 h incubation. Compared to Raman spectroscopy, MIP spectroscopy showed a much larger signal-to-noise ratio at the fingerprint region at an acquisition speed as fast as 1 s per spectrum. The high sensitivity of MIP enabled detection of metabolic changes at antibiotic concentrations below minimum inhibitory concentration (MIC). Meanwhile, the single-cell resolution of the technique allowed observation of heteroresistance within one bacterial population, which is of great clinical relevance. This study showcases characterizing antibiotic response as one of the many possibilities of applying MIP microscopy to single-cell biology.

RT-LAMP for rapid diagnosis of coronavirus SARS-CoV-2

The pandemic coronavirus SARS-CoV-2 in the world has caused a large infected population suffering from COVID-19. To curb the spreading of the virus, WHO urgently demanded an extension of screening and testing; thus, a rapid and simple diagnostic method is needed. We applied a reverse transcription-loop-mediated isothermal amplification (RT-LAMP) to achieve the detection of SARS-CoV-2 in 30 min. We designed four sets of LAMP primers (6 primers in each set), targeting the viral RNA of SARS-CoV-2 in the regions of orf1ab, S gene and N gene. A colorimetric change was used to report the results, which enables the outcome of viral RNA amplification to be read by the naked eye without the need of expensive or dedicated instrument. The sensitivity can be 80 copies of viral RNA per ml in a sample. We validated the RT-LAMP method in a hospital in China, employing 16 clinic samples with 8 positives and 8 negatives. The testing results are consistent with the conventional RT-qPCR. In addition, we also show that one-step process without RNA extraction is feasible to achieve RNA amplification directly from a sample. This rapid, simple and sensitive RT-LAMP method paves a way for a large screening at public domain and hospitals, particularly regional hospitals and medical centres in rural areas.

Development of a rapid test kit for SARS-CoV-2: an example of product design

We present an example of applying ‘need-driven’ product design principle to the development of a rapid test kit to detect SARS-COV-2 (COVID-19). The tests are intended for use in the field and, longer term, for home use. They detect whether a subject is currently infected with the virus and is infectious. The urgent need for large numbers of tests in field setting imposes constraints such as short test time and lack of access to specialist equipment, laboratories and skilled technicians to perform the test and interpret results. To meet these needs, an antigen test based on RT-LAMP with colorimetric readout was chosen. Direct use of swab sample with no RNA extraction was explored. After extensive experimental study (reported elsewhere), a rapid test kit has been fabricated to satisfy all design criteria.

Chromosome-free bacterial cells are safe and programmable platforms for synthetic biology

A type of chromosome-free cell called SimCells (simple cells) has been generated from Escherichia coli, Pseudomonas putida, and Ralstonia eutropha. The removal of the native chromosomes of these bacteria was achieved by double-stranded breaks made by heterologous I-CeuI endonuclease and the degradation activity of endogenous nucleases. We have shown that the cellular machinery remained functional in these chromosome-free SimCells and was able to process various genetic circuits. This includes the glycolysis pathway (composed of 10 genes) and inducible genetic circuits. It was found that the glycolysis pathway significantly extended longevity of SimCells due to its ability to regenerate ATP and NADH/NADPH. The SimCells were able to continuously express synthetic genetic circuits for 10 d after chromosome removal. As a proof of principle, we demonstrated that SimCells can be used as a safe agent (as they cannot replicate) for bacterial therapy. SimCells were used to synthesize catechol (a potent anticancer drug) from salicylic acid to inhibit lung, brain, and soft-tissue cancer cells. SimCells represent a simplified synthetic biology chassis that can be programmed to manufacture and deliver products safely without interference from the host genome.

Raman-activated sorting of antibiotic-resistant bacteria in human gut microbiota

The antibiotic‐resistant bacteria (ARB) and antibiotic‐resistant genes (ARGs) in human gut microbiota have significant impact on human health. While high throughput metagenomic sequencing reveals genotypes of microbial communities, the functionality, phenotype and heterogeneity of human gut microbiota are still elusive. In this study, we applied Raman microscopy and deuterium isotope probing (Raman–DIP) to detect metabolic active ARB (MA‐ARB) in situ at the single‐cell level in human gut microbiota from two healthy adults. We analysed the relative abundances of MA‐ARB under different concentrations of amoxicillin, cephalexin, tetracycline, florfenicol and vancomycin. To establish the link between phenotypes and genotypes of the MA‐ARB, Raman‐activated cell sorting (RACS) was used to sort MA‐ARB from human gut microbiota, and mini‐metagenomic DNA of the sorted bacteria was amplified, sequenced and analysed. The sorted MA‐ARB and their associated ARGs were identified. Our results suggest a strong relation between ARB in human gut microbiota and personal medical history. This study demonstrates that the toolkit of Raman–DIP, RACS and DNA sequencing can be useful to unravel both phenotypes and genotypes of ARB in human gut microbiota at the single‐cell level.

River toxicity assessment using molecular biosensors: Heavy metal contamination in the Turag-Balu-Buriganga river systems, Dhaka, Bangladesh

Pollution in rapidly urbanising cities and in delta systems is a serious problem that blights the lives and livelihoods of millions of people, damaging and restricting potable water supply and supplies to industry (Whitehead et al, 2015, 2018). Employing new technology based on luminescent molecular biosensors, the toxicity in the rivers around Dhaka in Bangladesh, namely the Turag, Tongi, Balu and Buriganga, has been assessed. Samples taken at 36 sites during medium and low flow conditions and during the Bishwa Ijtema Festival revealed high levels of cell toxicity, as well as high concentrations of metals, particularly aluminium, cadmium, chromium, iron, zinc, lithium, selenium and nickel. Chemical analysis also revealed low dissolved oxygen levels and anoxic conditions in the rivers at certain sites. The bacterial molecular biosensors were demonstrated to be fast, with results in 30 min, robust and a highly sensitive method for the assessment of water toxicity in the field. Furthermore, the biosensor toxicity analysis correlated with the metals data, and a multivariate regression relationship was developed relating toxicity to key metals, such a selenium, zinc and chromium. The resulting model has been validated against split samples and the Bishwa Ijtema Festival data. The combination of modelling and the molecular biosensor technology provides a new approach to detecting and managing pollution in urban river systems.

Raman-deuterium isotope probing to study metabolic activities of single bacterial cells in human intestinal microbiota

Human intestinal microbiota is important to host health and is associated with various diseases. It is a challenge to identify the functions and metabolic activity of microorganisms at the single-cell level in gut microbial community. In this study, we applied Raman microspectroscopy and deuterium isotope probing (Raman-DIP) to quantitatively measure the metabolic activities of intestinal bacteria from two individuals and analysed lipids and phenylalanine metabolic pathways of functional microorganisms in situ. After anaerobically incubating the human faeces with heavy water (D₂ O), D₂ O with specific substrates (glucose, tyrosine, tryptophan and oleic acid) and deuterated glucose, the C-D band in single-cell Raman spectra appeared in some bacteria in faeces, due to the Raman shift from the C-H band. Such Raman shift was used to indicate the general metabolic activity and the activities in response to the specific substrates. In the two individuals' intestinal microbiota, the structures of the microbial communities were different and the general metabolic activities were 76 ± 1.0% and 30 ± 2.0%. We found that glucose, but not tyrosine, tryptophan and oleic acid, significantly stimulated metabolic activity of the intestinal bacteria. We also demonstrated that the bacteria within microbiota preferably used glucose to synthesize fatty acids in faeces environment, whilst they used glucose to synthesize phenylalanine in laboratory growth environment (e.g. LB medium). Our work provides a useful approach for investigating the metabolic activity in situ and revealing different pathways of human intestinal microbiota at the single-cell level.

Defensive Function of Transposable Elements in Bacteria

It has been widely debated whether transposable elements have a positive or a negative effect on their host cells. This study demonstrated that transposable elements, specifically insertion sequences (ISs), can adopt a defensive role in Escherichia coli. In three different E. coli strains (S17, DH5α, and Nissle 1917), IS1 and IS10 rapidly disrupted the I-CeuI gene (encoding I-CeuI endonuclease) on the plasmid pLO11-ICeuI as early as the first generation, despite the gene-circuit being under control of an arabinose promoter. Proteomics analysis showed that the protein abundance profile of E. coli DH5α with pLO11-ICeuI in the fifth generation was nearly opposite to that of control strain (E. coli with pLO11, no I-CeuI). The DNA damage caused by the leaky expression of I-CeuI was enough to trigger a SOS response and alter lipid synthesis, ribosomal activity, RNA/DNA metabolism, central dogma and cell cycle processes in E. coli DH5α. After the ISs disrupted the expression of I-CeuI, cells fully recovered by the 31st generation had a protein abundance profile similar to that of the control strain. This study showed that ISs readily mutated a harmful gene which subsequently restored host fitness. These observations have implications for the stability of designed gene circuits in synthetic biology.

Correction: A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy

Correction for 'A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy' by Jiabao Xu et al., Analyst, 2019, 144, 913-920.

Single-Cell and Time-Resolved Profiling of Intracellular Salmonella Metabolism in Primary Human Cells

The intracellular pathogen Salmonella enterica has evolved an array of traits for propagation and invasion of the intestinal layers. It remains largely elusive how Salmonella adjusts its metabolic states to survive inside immune host cells. In this study, single-cell Raman biotechnology combined with deuterium isotope probing (Raman-DIP) have been applied to reveal metabolic changes of the typhoidal Salmonella Typhi Ty2, the nontyphoidal Salmonella Typhimurium LT2, and a clinical isolate Typhimurium D23580. By initially labeling the Salmonella strains with deuterium, we employed reverse labeling to track their metabolic changes in the time-course infection of THP-1 cell line, human monocyte-derived dendritic cells (MoDCs) and macrophages (Mf). We found that, in comparison with a noninvasive serovar, the invasive Salmonella strains Ty2 and D23580 have downregulated metabolic activity inside human macrophages and dendritic cells and used lipids as alternative carbon source, perhaps a strategy to escape from the host immune response. Proteomic analysis using high sensitivity mass spectrometry validated the findings of Raman-DIP analysis.

Monitoring Cr toxicity and remediation processes - combining a whole-cell bioreporter and Cr isotope techniques

Bioremediation is a sustainable and cost-effective means of contaminant detoxification. Although Cr(VI) is toxic at high concentrations, various microbes can utilise it as an electron accepter in the bioremediation process, and reduce it to the less toxic form Cr(III). During remediation, it is important to monitor the level of toxicity and effectiveness of Cr(VI) reduction in order to optimize the conditions. This study employed a whole-cell bioreporter Acinetobacter baylyi ADPWH-recA to access the degree of toxicity of different species of Cr over a range of initial concentrations. It also investigated whether Cr isotope fractionation factors were impacted by different levels of Cr toxicity (related to its concentration) and Cr(VI) reduction rates by Cr resistant bacteria Pseudomonas fluorescens LB 300. The results show that, of both Cr₂O₇^2- and CrO₄^2-, the whole-cell bioreporter was efficient in indicating the level of genotoxicity of Cr(VI) at low concentrations and cytotoxicity at high concentrations via variations of bioluminescence. High concentrations (> 100 mg/L) of Cr(III) could also strongly induce the luminescence in the bioreporter, indicating DNA damage at such abundance. Pseudomonas fluorescens LB 300 was found to be effective in reducing Cr(VI) even when the concentration was high (40 mg/L); however, complete Cr(VI) reduction was only observed at low concentrations (< 5 mg/L), since the toxicity of high concentrations of Cr(VI) impacted the effectiveness of reduction by the bacteria. During reduction, the C53r/C52r ratio of remaining Cr(VI) increased from its initial value, and the calculated fractionation factor by bacterial Cr(VI) reduction (ε) was -3.1±0.3‰. The fractionation factor was independent of the initial Cr(VI) concentration. Therefore, a single Cr isotope fractionation factor can be effectively applied in indicating the extent of bioremediation processing of Cr(VI) over a wide range of concentrations. This significantly simplified monitoring of Cr(VI) depletion in bioremediation, since variations of ε normally indicate a change in the reduction mechanism and therefore would complicate the elucidation of processes driving the remediation.

Raman profiling of embryo culture medium to identify aneuploid and euploid embryos

OBJECTIVE

To develop and validate Raman metabolic footprint analysis to determine chromosome euploidy and aneuploidy in embryos fertilized in vitro.

DESIGN

Retrospective study.

SETTING

Academic hospital.

PATIENT(S)

Unselected assisted reproductive technology population.

INTERVENTION(S)

To establish the analysis protocol, spent embryo culture medium samples with known genetic outcomes from 87 human embryos were collected and measured with the use of Raman spectroscopy. Individual Raman spectra were analyzed to find biologic components contributing to either euploidy or aneuploidy. To validate the protocol via machine-learning algorithms, additional 1,107 Raman spectra from 123 embryo culture media (61 euploidy and 62 aneuploidy) were analyzed.

MAIN OUTCOME MEASURE(S)

Raman-based footprint profiling of spent culture media and preimplantation genetic testing for aneuploidy (PGT-A).

RESULT(S)

Mean-centered Raman spectra and principal component analysis showed differences in the footprints of euploid and aneuploid embryos growing in culture medium. Significant differences in Raman bands associated with small RNAs and lipids were also observed. Stacking classification based on k-nearest-neighbor, random forests, and extreme-gradient-boosting algorithms achieved an overall accuracy of 95.9% in correctly assigning either euploidy or aneuploidy based on Raman spectra, which was validated by PGT-A sequencing results.

CONCLUSION(S)

This study suggests that chromosomal abnormalities in embryos should lead to changes of metabolic footprints in embryo growth medium that can be detected by Raman spectroscopy. The ploidy status of embryos was analyzed by means of Raman-based footprint profiling of spent culture media and was consistent with PGT-A testing performed by next-generation sequencing.

A new approach to find biomarkers in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) by single-cell Raman micro-spectroscopy

Single-cell Raman microspectroscopy to detect phenylalanine as a potential biomarker for mitochondrial dysfunction and chronic fatigue syndrome.

Induction of Escherichia coli Into a VBNC State by Continuous-Flow UVC and Subsequent Changes in Metabolic Activity at the Single-Cell Level

A viable but non-culturable (VBNC) state of bacteria induced by disinfection in water treatment poses serious health risks because of possible resuscitation of VBNC cells during transportation. In this study, a setup using continuous-flow ultraviolet (UVC) irradiation ranging from 0 to 172.2 mJ cm^-2 was designed to simulate real-world disinfection in both drinking water (SDW) and reclaimed water (SRW) treatment plants. A systematic investigation of UVC-induced VBNC bacteria, including occurrence, resuscitation, and time-dependent recovery of metabolic activity during post-incubation, was conducted. Different techniques including two new ones of “single cell culture” and D₂O-labeled single-cell Raman spectroscopy were employed to gain comprehensive insights into VBNC cells. Heterotrophic plate counts (HPC) and 5-cyano-2,3-ditoyl tetrazolium chloride flow cytometry (CTC-FCM) assay demonstrated that exposure to continuous-flow UVC can induce E. coli into a VBNC state. Membranes integrity and 16S rRNA transcription level of VBNC bacteria were demonstrated to be unaffected by UVC exposure even at a high dose of 172.2 mJ cm^-2. Resuscitation of VBNC bacteria was identified in a more accurate way based on “single cell culture.” Finally, time-dependent evolution of metabolic activity of UVC-treated cells during post-incubation was examined by D₂O-labeled Raman spectroscopy at a high-resolution of single-cell level. C-D Raman bands resulting from incorporation of D₂O-derived D into bacterial biomass were used as a sensitive and quantitative indicator of bacterial metabolic activity. A lower UVC dose, longer post-incubation time, and higher initial number of bacteria were demonstrated to result in a faster recovery of metabolic activity. Heterogeneous metabolic activity and subpopulation with higher metabolic activity were also revealed by single-cell Raman, even for UVC-treated cells losing cultivability. The comprehensive assessment of VBNC bacteria in UVC-disinfected drinking and reclaimed water points out treatment deficiencies of UVC and the necessity to develop more effective strategies to eliminate VBNC cells.

Raman-activated cell sorting and metagenomic sequencing revealing carbon-fixing bacteria in the ocean

It is of great significance to understand CO₂ fixation in the oceans. Using single cell Raman spectra (SCRS) as biochemical profiles, Raman activated cell ejection (RACE) was able to link phenotypes and genotypes of cells. Here, we show that mini‐metagenomic sequences from RACE can be used as a reference to reconstruct nearly complete genomes of key functional bacteria by binning shotgun metagenomic sequencing data. By applying this approach to ¹³C bicarbonate spiked seawater from euphotic zone of the Yellow Sea of China, the dominant bacteria Synechococcus spp. and Pelagibacter spp. were revealed and both of them contain carotenoid and were able to incorporate ¹³C into the cells at the same time. Genetic analysis of the reconstructed genomes suggests that both Synechococcus spp. and Pelagibacter spp. contained all genes necessary for carotenoid synthesis, light energy harvesting and CO₂ fixation. Interestingly, the reconstructed genome indicates that Pelagibacter spp. harbored intact sets of genes for β‐carotene (precursor of retional), proteorhodopsin synthesis and anaplerotic CO₂ fixation. This novel approach shines light on the role of marine ‘microbial dark matter’ in global carbon cycling, by linking yet‐to‐be‐cultured Synechococcus spp. and Pelagibacter spp. to carbon fixation and flow activities in situ.

Application of a bacterial whole cell biosensor for the rapid detection of cytotoxicity in heavy metal contaminated seawater

A toxicity biosensor Acinetobacter baylyi Tox2 was constructed with the host strain A. baylyi ADP1 harboring a new and medium-copy-number plasmid pWH1274_lux, and was applied to detect the cytotoxicity of heavy metal contaminated seawater. The gene cassette luxCDABE was controlled by constitutively expressed promoter P_tet on pWH1274_lux and the bioluminescence intensity of the biosensor reduces in proportional to the concentrations of toxic compounds. A. baylyi Tox2 exhibits tolerance to salinity, hence it is applicable to seawater samples. A. baylyi Tox2 and Mugilogobius chulae were exposed to different concentrations of heavy metals (Hg²⁺, Zn²⁺, Cu²⁺, and Cd²⁺) in artificial seawater for performance comparison and Pearson correlation analysis showed a significant correlation (p < 0.01) between A. baylyi Tox2 toxicity detection and the fish (M. chulae) exposure test. This suggests that the performance of A. baylyi Tox2 is comparable to the conventional fish toxicity test in terms of cytotoxicity detection of heavy metal contaminated seawater. Furthermore, A. baylyi Tox2 was used to evaluate cytotoxicity of field-collected seawater samples. The results indicate that there was a significant correlation between the luminescence inhibition ratio (IR) of A. baylyi Tox2 and heavy metal concentrations detected by ICP-MS in the samples. Two seawater samples, which contained a high concentration of total heavy metals, exhibited stronger cytotoxicity than the samples containing low concentrations of heavy metals. In conclusion, A. baylyi Tox2 can be used as an alternative tool to aquatic animals for the evaluation of the cytotoxicity of heavy metal contamination in the marine environment.