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.

Optical-based microbubble for on-demand droplet release from static droplet array (SDA) for dispensing one droplet into one tube.

Static droplet array (SDA) is a pivotal tool for high-capacity screening assays, yet extraction and collection the target droplets that contain unique analytes or cells from the SDA remains one major technical bottleneck that limits its broader application. Here we present an optical-based on-demand droplet release (OODR) system by incorporating a 1064 nm laser-responsive indium tin oxide (ITO) layer into a chamber array-based droplet microfluidic chip. By focusing the 1064 nm laser onto the ITO layer, microbubbles can be created via local heating to selectively push-out the droplets from the chamber. Then the released droplet is readily exported in a one-droplet-one-tube (ODOT) manner by the inherent capillary force into pipette tip. Releasing of the droplets containing fluorescein sodium demonstrated ∼100% successful rate (9 out of 6400 droplets were successfully released) and low residual (only ∼5% of the droplet volume remains in the chamber). White or fluorescence image-based releasing of single-cell-droplets directly after cell loading or multi-cells-droplets derived from on-chip single-cell cultivation for both E. coli and yeast cells further demonstrated the wide applicability of OODR. The present system is user-friendly and has the potential to be applied in various high-throughput screening assays, including single molecule/cell analysis, drug screening, and phenotype-based cell sorting.

Robust Spontaneous Raman Flow Cytometry for Single-Cell Metabolic Phenome Profiling via pDEP-DLD-RFC.

A full-spectrum spontaneous single-cell Raman spectrum (fs-SCRS) captures the metabolic phenome for a given cellular state of the cell in a label-free, landscape-like manner. Herein a positive dielectrophoresis induced deterministic lateral displacement-based Raman flow cytometry (pDEP-DLD-RFC) is established. This robust flow cytometry platform utilizes a periodical positive dielectrophoresis induced deterministic lateral displacement (pDEP-DLD) force that is exerted to focus and trap fast-moving single cells in a wide channel, which enables efficient fs-SCRS acquisition and extended stable running time. It automatically produces deeply sampled, heterogeneity-resolved, and highly reproducible ramanomes for isogenic cell populations of yeast, microalgae, bacteria, and human cancers, which support biosynthetic process dissection, antimicrobial susceptibility profiling, and cell-type classification. Moreover, when coupled with intra-ramanome correlation analysis, it reveals state- and cell-type-specific metabolic heterogeneity and metabolite-conversion networks. The throughput of ≈30-2700 events min-1 for profiling both nonresonance and resonance marker bands in a fs-SCRS, plus the >5 h stable running time, represent the highest performance among reported spontaneous Raman flow cytometry (RFC) systems. Therefore, pDEP-DLD-RFC is a valuable new tool for label-free, noninvasive, and high-throughput profiling of single-cell metabolic phenomes.

Versatile, facile and low-cost single-cell isolation, culture and sequencing by optical tweezer-assisted pool-screening.

Real-time image-based sorting of target cells in a precisely indexed manner is desirable for sequencing or cultivating individual human or microbial cells directly from clinical or environmental samples; however, the versatility of existing methods is limited as they are usually not broadly applicable to all cell sizes. Here, an optical tweezer-assisted pool-screening and single-cell isolation (OPSI) system is established for precise, indexed isolation of individual bacterial, yeast or human-cancer cells. A controllable static flow field that acts as a cell pool is achieved in a microfluidics chip, to enable precise and ready screening of cells of 1 to 40 µm in size by bright-field, fluorescence, or Raman imaging. The target cell is then captured by a 1064 nm optical tweezer and deposited as one-cell-harboring nanoliter microdroplets in a one-cell-one-tube manner. For bacterial, yeast and human cells, OPSI achieves a >99.7% target-cell sorting purity and a 10-fold elevated speed of 10-20 cells per min. Moreover, OPSI-based one-cell RNA-seq of human cancer cells yields high quality and reproducible single-cell transcriptome profiles. The versatility, facileness, flexibility, modularized design, and low cost of OPSI suggest its broad applications for image-based sorting of target cells.

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

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

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.

Single-cell Raman-activated sorting and cultivation (scRACS-Culture) for assessing and mining in situ phosphate-solubilizing microbes from nature.

Due to the challenges in detecting in situ activity and cultivating the not-yet-cultured, functional assessment and mining of living microbes from nature has typically followed a 'culture-first' paradigm. Here, employing phosphate-solubilizing microbes (PSM) as model, we introduce a 'screen-first' strategy that is underpinned by a precisely one-cell-resolution, complete workflow of single-cell Raman-activated Sorting and Cultivation (scRACS-Culture). Directly from domestic sewage, individual cells were screened for in-situ organic-phosphate-solubilizing activity via D2O intake rate, sorted by the function via Raman-activated Gravity-driven Encapsulation (RAGE), and then cultivated from precisely one cell. By scRACS-Culture, pure cultures of strong organic PSM including Comamonas spp., Acinetobacter spp., Enterobacter spp. and Citrobacter spp., were derived, whose phosphate-solubilizing activities in situ are 90-200% higher than in pure culture, underscoring the importance of 'screen-first' strategy. Moreover, employing scRACS-Seq for post-RACS cells that remain uncultured, we discovered a previously unknown, low-abundance, strong organic-PSM of Cutibacterium spp. that employs secretary metallophosphoesterase (MPP), cell-wall-anchored 5'-nucleotidase (encoded by ushA) and periplasmic-membrane located PstSCAB-PhoU transporter system for efficient solubilization and scavenging of extracellular phosphate in sewage. Therefore, scRACS-Culture and scRACS-Seq provide an in situ function-based, 'screen-first' approach for assessing and mining microbes directly from the environment.

Rapid, automated, and reliable antimicrobial susceptibility test from positive blood culture by CAST-R.

Antimicrobial susceptibility tests (ASTs) are pivotal in combating multidrug resistant pathogens, yet they can be time-consuming, labor-intensive, and unstable. Using the AST of tigecycline for sepsis as the main model, here we establish an automated system of Clinical Antimicrobials Susceptibility Test Ramanometry (CAST-R), based on D2O-probed Raman microspectroscopy. Featuring a liquid robot for sample pretreatment and a machine learning-based control scheme for data acquisition and quality control, the 3-h, automated CAST-R process accelerates AST by >10-fold, processes 96 paralleled antibiotic-exposure reactions, and produces high-quality Raman spectra. The Expedited Minimal Inhibitory Concentration via Metabolic Activity is proposed as a quantitative and broadly applicable parameter for metabolism-based AST, which shows 99% essential agreement and 93% categorical agreement with the broth microdilution method (BMD) when tested on 100 Acinetobacter baumannii isolates. Further tests on 26 clinically positive blood samples for eight antimicrobials, including tigecycline, meropenem, ceftazidime, ampicillin/sulbactam, oxacillin, clindamycin, vancomycin, and levofloxacin reveal 93% categorical agreement with BMD-based results. The automation, speed, reliability, and general applicability of CAST-R suggest its potential utility for guiding the clinical administration of antimicrobials.

Artificial intelligence-assisted automatic and index-based microbial single-cell sorting system for One-Cell-One-Tube.

Identification, sorting, and sequencing of individual cells directly from in situ samples have great potential for in-depth analysis of the structure and function of microbiomes. In this work, based on an artificial intelligence (AI)-assisted object detection model for cell phenotype screening and a cross-interface contact method for single-cell exporting, we developed an automatic and index-based system called EasySort AUTO, where individual microbial cells are sorted and then packaged in a microdroplet and automatically exported in a precisely indexed, "One-Cell-One-Tube" manner. The target cell is automatically identified based on an AI-assisted object detection model and then mobilized via an optical tweezer for sorting. Then, a cross-interface contact microfluidic printing method that we developed enables the automated transfer of cells from the chip to the tube, which leads to coupling with subsequent single-cell culture or sequencing. The efficiency of the system for single-cell printing is >93%. The throughput of the system for single-cell printing is ~120 cells/h. Moreover, >80% of single cells of both yeast and Escherichia coli are culturable, suggesting the superior preservation of cell viability during sorting. Finally, AI-assisted object detection supports automated sorting of target cells with high accuracy from mixed yeast samples, which was validated by downstream single-cell proliferation assays. The automation, index maintenance, and vitality preservation of EasySort AUTO suggest its excellent application potential for single-cell sorting.

Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells.

To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications. IMPORTANCE Each isogenic population of cells is characterized by many phenotypes, which change with time and condition. Correlations among such phenotypes are fundamental to system function, yet revelation of such links typically requires multiple samples. Here, we showed that, by exploiting the intrinsic metabolic heterogeneity among individual cells, such interphenotype correlations can be unveiled via just one snapshot of an isogenic cellular population. Specifically, a network of potential metabolite conversions can be reconstructed using intra-ramanome correlation analysis (IRCA), by pairwise correlation of the thousands of Raman peaks or combination of peaks among single-cell Raman spectra sampled from just one instance of the cellular population. The ability to rapidly and noninvasively reveal intermetabolite conversions from just one snapshot of one sample should usher in many new opportunities in functional profiling of cellular systems.

Culture-Free Identification and Metabolic Profiling of Microalgal Single Cells via Ensemble Learning of Ramanomes.

Microalgae are among the most genetically and metabolically diverse organisms on earth, yet their identification and metabolic profiling have generally been slow and tedious. Here, we established a reference ramanome database consisting of single-cell Raman spectra (SCRS) from >9000 cells of 27 phylogenetically diverse microalgal species, each under stationary and exponential states. When combined, prequenching ("pigment spectrum" (PS)) and postquenching ("whole spectrum" (WS)) signals can classify species and states with 97% accuracy via ensemble machine learning. Moreover, the biosynthetic profile of Raman-sensitive metabolites was unveiled at single cells, and their interconversion was detected via intra-ramanome correlation analysis. Furthermore, not-yet-cultured cells from the environment were functionally characterized via PS and WS and then phylogenetically identified by Raman-activated sorting and sequencing. This PS-WS combined approach for rapidly identifying and metabolically profiling single cells, either cultured or uncultured, greatly accelerates the mining of microalgae and their products.

One-Cell Metabolic Phenotyping and Sequencing of Soil Microbiome by Raman-Activated Gravity-Driven Encapsulation (RAGE).

Soil harbors arguably the most metabolically and genetically heterogeneous microbiomes on Earth, yet establishing the link between metabolic functions and genome at the precisely one-cell level has been difficult. Here, for mock microbial communities and then for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) platform, which identifies, sorts, and sequences precisely one bacterial cell via its anabolic (incorporating D from heavy water) and physiological (carotenoid-containing) functions. We showed that (i) metabolically active cells from numerically rare soil taxa, such as Corynebacterium spp., Clostridium spp., Moraxella spp., Pantoea spp., and Pseudomonas spp., can be readily identified and sorted based on D2O uptake, and their one-cell genome coverage can reach ∼93% to allow high-quality genome-wide metabolic reconstruction; (ii) similarly, carotenoid-containing cells such as Pantoea spp., Legionella spp., Massilia spp., Pseudomonas spp., and Pedobacter spp. were identified and one-cell genomes were generated for tracing the carotenoid-synthetic pathways; and (iii) carotenoid-producing cells can be either metabolically active or inert, suggesting culture-based approaches can miss many such cells. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at exactly one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint "who is doing what" in complex ecosystems. IMPORTANCE Soil is home to an enormous and complex microbiome that features arguably the highest genomic diversity and metabolic heterogeneity of cells on Earth. Their in situ metabolic activities drive many natural processes of pivotal ecological significance or underlie industrial production of numerous valuable bioactivities. However, pinpointing "who is doing what" in a soil microbiome, which consists of mainly yet-to-be-cultured species, has remained a major challenge. Here, for soil microbiota, we established a Raman-activated gravity-driven single-cell encapsulation and sequencing (RAGE-Seq) method, which identifies, sorts, and sequences at the resolution of precisely one microbial cell via its catabolic and anabolic functions. As a Raman-activated cell sorter (RACS) family member that can establish a metabolism-genome link at one-cell resolution from soil, RAGE-Seq can help to precisely pinpoint "who is doing what" in complex ecosystems.

D2O-Probed Raman Microspectroscopy Distinguishes the Metabolic Dynamics of Macromolecules in Organellar Anticancer Drug Response.

To profile the metabolic dynamics responding to drugs at the single-cell/organelle resolution, rapid and economical mechanism-revealing methods are required. Here, we introduced D2O-probed Raman microspectroscopy in combination with the multivariate curve resolution-alternating least squares (MCR-ALS or MCR) algorithm. Exploiting MCR to deconvolute each macromolecular component specifically, the method is able to track and distinguish changes in lipid and protein metabolic activities in a human cancer cell line (MCF-7) and in Saccharomyces cerevisiae, in response to the metabolism-inhibitory effect of rapamycin, which inhibits the mammalian/mechanistic target of rapamycin (mTOR) signaling. Under rapamycin, in the lipid bodies of cancer cells, metabolic activities of both protein and lipid are suppressed; in the nucleus, protein synthesis remains active, whereas lipid synthesis is inhibited; in the cytoplasm, syntheses of protein and lipid are both dose- and duration-dependent. Thus, rapamycin differentially influences protein and lipid synthesis in mTOR signaling. Moreover, the strong correlation between macromolecular-specific components of yeast and those in MCF-7 cytoplasm, nucleus, and lipid bodies revealed similarity in rapamycin response. Notably, highly metabolically active cancer cells after high-dosage rapamycin exposure (500 or 5000 × IC50) were revealed, which escape detection by population-level cytotoxicity tests. Thus, by unveiling macromolecule-specific metabolic dynamics at the organelle level, the method is valuable to mechanism-based rapid screening and dissection of drug response.

Positive dielectrophoresis-based Raman-activated droplet sorting for culture-free and label-free screening of enzyme function in vivo.

The potential of Raman-activated cell sorting (RACS) is inherently limited by conflicting demands for signal quality and sorting throughput. Here, we present positive dielectrophoresis-based Raman-activated droplet sorting (pDEP-RADS), where a periodical pDEP force was exerted to trap fast-moving cells, followed by simultaneous microdroplet encapsulation and sorting. Screening of yeasts for triacylglycerol (TAG) content demonstrated near-theoretical-limit accuracy, ~120 cells min-1 throughput and full-vitality preservation, while sorting fatty acid degree of unsaturation (FA-DU) featured ~82% accuracy at ~40 cells min-1. From a yeast library expressing algal diacylglycerol acyltransferases (DGATs), a pDEP-RADS run revealed all reported TAG-synthetic variants and distinguished FA-DUs of enzyme products. Furthermore, two previously unknown DGATs producing low levels of monounsaturated fatty acid-rich TAG were discovered. This first demonstration of RACS for enzyme discovery represents hundred-fold saving in time consumables and labor versus culture-based approaches. The ability to automatically flow-sort resonance Raman-independent phenotypes greatly expands RACS' application.

Rational Optimization of Raman-Activated Cell Ejection and Sequencing for Bacteria.

In Raman-activated cell ejection and sequencing (RACE-Seq), success rate and sequence coverage have generally been low for shotgun sequencing of individual post-RACE cells. Here we quantitatively evaluated the influence of cell lysis condition, nucleic acid amplification condition, and parameters of Raman measurement on RACE-Seq performance. Variations in laser energy input during Raman signal acquisition, but not duration of alkaline lysate lysis, temperature, or measurement under dry or aqueous conditions, are vital to the success of multiple displacement amplification (MDA). In fact, laser irradiation is reversely linked to MDA product quality. However, introduction of oils prior to MDA, by mitigating such negative effects of Raman irradiation, elevates genome coverage of post-RACE Escherichia coli cells from <20% to ∼50%, while greatly improving the success rate of RACE-Seq for soil microbiota. Our findings provide a practical solution for enhancing RACE-Seq performance and pinpoint protection of cells from laser irradiation as a priority in method development.

Knockdown of carbonate anhydrase elevates Nannochloropsis productivity at high CO2 level.

Improving acid tolerance is pivotal to the development of microalgal feedstock for converting flue gas to biomass or oils. In the industrial oleaginous microalga Nannochloropsis oceanica, transcript knockdown of a cytosolic carbonic anhydrase (CA2), which is a key Carbon Concentrating Mechanism (CCM) component induced under 100 ppm CO2 (very low carbon, or VLC), results in ∼45%, ∼30% and ∼40% elevation of photosynthetic oxygen evolution rate, growth rate and biomass accumulation rate respectively under 5% CO2 (high carbon, or HC), as compared to the wild type. Such high-CO2-level activated biomass over-production is reproducible across photobioreactor types and cultivation scales. Transcriptomic, proteomic and physiological changes of the mutant under high CO2 (HC; 5% CO2) suggest a mechanism where the higher pH tolerance is coupled to reduced biophysical CCM, sustained pH hemostasis, stimulated energy intake and enhanced photosynthesis. Thus "inactivation of CCM" can generate hyper-CO2-assimilating and autonomously containable industrial microalgae for flue gas-based oil production.

Seasonal dynamics of the coastal bacterioplankton at intensive fish-farming areas of the Yellow Sea, China revealed by high-throughput sequencing.

Marine aquaculture areas are facing stressed environmental challenges, especially the degradation of coastal ecosystems. Here a coordinated time-series study was used to investigate the coastal bacterioplankton biodiversity dynamics of the Yellow Sea, China. Bacterial 16S rRNA gene sequencing revealed a temporal pattern of decreasing of diversity in summer. Functional prediction indicated that metabolic pathways related to the adenosine triphosphate (ATP)-binding cassette transporters and other membrane transporters were significantly enriched in May, while the genetic information processing category was most abundant in March. The May microbiomes showed most significant positive correlation with phosphate concentration, while the August and November microbiomes correlated with temperature and chemical oxygen demand (COD) most, and the March microbiomes showed significant correlation with Cu2+ level, pH and salinity. The correlations between representative bacteria and environmental parameters revealed in this study may provide insights into the potential influences of human aquaculture activities, on the biodiversity of coastal bacterioplankton.

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 13 C 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 13 C 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 ß-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.

Using Raman spectroscopy and chemometrics to identify the growth phase of Lactobacillus casei Zhang during batch culture at the single-cell level.

BACKGROUND: As microbial cultures are comprised of heterogeneous cells that differ according to their size and intracellular concentrations of DNA, proteins, and other constituents, the detailed identification and discrimination of the growth phases of bacterial populations in batch culture is challenging. Cell analysis is indispensable for quality control and cell enrichment. METHODS: In this paper, we report the results of our investigation on the use of single-cell Raman spectrometry (SCRS) for real-time analysis and prediction of cells in different growth phases during batch culture of Lactobacillus (L.) casei Zhang. A targeted analysis of defined cell growth phases at the level of the single cell, including lag phase, log phase, and stationary phase, was facilitated by SCRS. RESULTS: Spectral shifts were identified in different states of cell growth that reflect biochemical changes specific to each cell growth phase. Raman peaks associated with DNA and RNA displayed a decrease in intensity over time, whereas protein-specific and lipid-specific Raman vibrations increased at different rates. Furthermore, a supervised classification model (Random Forest) was used to specify the lag phase, log phase, and stationary phase of cells based on SCRS, and a mean sensitivity of 90.7% and mean specificity of 90.8% were achieved. In addition, the correct cell type was predicted at an accuracy of approximately 91.2%. CONCLUSIONS: To conclude, Raman spectroscopy allows label-free, continuous monitoring of cell growth, which may facilitate more accurate estimates of the growth states of lactic acid bacterial populations during fermented batch culture in industry.

Raman-Activated Droplet Sorting (RADS) for Label-Free High-Throughput Screening of Microalgal Single-Cells.

Raman-activated cell sorting (RACS) has attracted increasing interest, yet throughput remains one major factor limiting its broader application. Here we present an integrated Raman-activated droplet sorting (RADS) microfluidic system for functional screening of live cells in a label-free and high-throughput manner, by employing AXT-synthetic industrial microalga Haematococcus pluvialis (H. pluvialis) as a model. Raman microspectroscopy analysis of individual cells is carried out prior to their microdroplet encapsulation, which is then directly coupled to DEP-based droplet sorting. To validate the system, H. pluvialis cells containing different levels of AXT were mixed and underwent RADS. Those AXT-hyperproducing cells were sorted with an accuracy of 98.3%, an enrichment ratio of eight folds, and a throughput of ∼260 cells/min. Of the RADS-sorted cells, 92.7% remained alive and able to proliferate, which is equivalent to the unsorted cells. Thus, the RADS achieves a much higher throughput than existing RACS systems, preserves the vitality of cells, and facilitates seamless coupling with downstream manipulations such as single-cell sequencing and cultivation.