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

An integrated microfluidic device for sorting of tumor organoids using image recognition

Tumor organoids present a challenge in drug screening due to their considerable heterogeneity in morphology and size. To address this issue, we proposed a portable microfluidic device that employs image processing algorithms for specific target organoid recognition and microvalve-controlled deflection for sorting and collection. This morphology-activated organoid sorting system offers numerous advantages, such as automated classification, portability, low cost, label-free sample preparation, and gentle handling of organoids. We conducted classification experiments using polystyrene beads, F9 tumoroids and patient-derived tumor organoids, achieving organoid separation efficiency exceeding 88%, purity surpassing 91%, viability exceeding 97% and classification throughput of 800 per hour, thereby meeting the demands of clinical organoid medicine.

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

Raman-activated cell sorting isolates single cells in a nondestructive and label-free manner, but its throughput is limited by small spontaneous Raman scattering cross section. Coherent Raman scattering integrated with microfluidics enables high-throughput cell analysis, but faces challenges with small cells (<3 μm) and tissue sections. Here, we report stimulated Raman-activated cell ejection (S-RACE) that enables high-throughput single-cell sorting by integrating stimulated Raman imaging, in situ image decomposition, and laser-induced cell ejection. S-RACE allows ejection of live bacteria or fungi guided by their Raman signatures. Furthermore, S-RACE successfully sorted lipid-rich Rhodotorula glutinis cells from a cell mixture with a throughput of ~13 cells per second, and the sorting results were confirmed by downstream quantitative polymerase chain reaction. Beyond single cells, S-RACE shows high compatibility with tissue sections. Incorporating a closed-loop feedback control circuit further enables real-time SRS imaging-identification-ejection. In summary, S-RACE opens exciting opportunities for diverse single-cell sorting applications.

Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome

The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the correlation of phenotype and genotype at the single-cell level, offering a solution to the bottleneck in microbial community functional analysis caused by challenges in cultivating diverse microorganisms. However, current labor-intensive manual procedures fall short in catering to the demands of single-cell functional analysis in microbial communities. To address this issue, we developed an artificial intelligence-assisted Raman-activated cell sorting system (AI-RACS) that integrates precise single-cell positioning, automated data collection, optical tweezers capture, and single-cell printing to elevate microbial single-cell RACS from manual to automated, validating the efficacy of the system by isolating aluminum-tolerant microbes from acidic soil microbiota. Leveraging the AI-RACS framework, we sorted 13 strains from red soil samples under near-in situ conditions, with all demonstrating strong aluminum tolerance. AI-RACS efficiently segregates microbial cells from intricate environmental samples, investigating their functional attributes and presenting a novel tool for microbial research and applications.

Starting with screening strains to construct synthetic microbial communities (SynComs) for traditional food fermentation

With the elucidation of community structures and assembly mechanisms in various fermented foods, core communities that significantly influence or guide fermentation have been pinpointed and used for exogenous restructuring into synthetic microbial communities (SynComs). These SynComs simulate ecological systems or function as adjuncts or substitutes in starters, and their efficacy has been widely verified. However, screening and assembly are still the main limiting factors for implementing theoretic SynComs, as desired strains cannot be effectively obtained and integrated. To expand strain screening methods suitable for SynComs in food fermentation, this review summarizes the recent research trends in using SynComs to study community evolution or interaction and improve the quality of food fermentation, as well as the specific process of constructing synthetic communities. The potential for novel screening modalities based on genes, enzymes and metabolites in food microbial screening is discussed, along with the emphasis on strategies to optimize assembly for facilitating the development of synthetic communities.

Integrated Cross-Scale Manipulation and Modulable Encapsulation of Cell-Laden Hydrogel for Constructing Tissue-Mimicking Microstructures

Engineered microstructures that mimic in vivo tissues have demonstrated great potential for applications in regenerative medicine, drug screening, and cell behavior exploration. However, current methods for engineering microstructures that mimic the multi-extracellular matrix and multicellular features of natural tissues to realize tissue-mimicking microstructures in vitro remain insufficient. Here, we propose a versatile method for constructing tissue-mimicking heterogeneous microstructures by orderly integration of macroscopic hydrogel exchange, microscopic cell manipulation, and encapsulation modulation. First, various cell-laden hydrogel droplets are manipulated at the millimeter scale using electrowetting on dielectric to achieve efficient hydrogel exchange. Second, the cells are manipulated at the micrometer scale using dielectrophoresis to adjust their density and arrangement within the hydrogel droplets. Third, the photopolymerization of these hydrogel droplets is triggered in designated regions by dynamically modulating the shape and position of the excitation ultraviolet beam. Thus, heterogeneous microstructures with different extracellular matrix geometries and components were constructed, including specific cell densities and patterns. The resulting heterogeneous microstructure supported long-term culture of hepatocytes and fibroblasts with high cell viability (over 90%). Moreover, the density and distribution of the 2 cell types had significant effects on the cell proliferation and urea secretion. We propose that our method can lead to the construction of additional biomimetic heterogeneous microstructures with unprecedented potential for use in future tissue engineering applications.

Ultrahigh Throughput Evolution of Tryptophan Synthase in Droplets via an Aptamer Sensor

Tryptophan synthase catalyzes the synthesis of a wide array of noncanonical amino acids and is an attractive target for directed evolution. Droplet microfluidics offers an ultrahigh throughput approach to directed evolution (up to 107 experiments per day), enabling the search for biocatalysts in wider regions of sequence space with reagent consumption minimized to the picoliter volume (per library member). While the majority of screening campaigns in this format on record relied on an optically active reaction product, a new assay is needed for tryptophan synthase. Tryptophan is not fluorogenic in the visible light spectrum and thus falls outside the scope of conventional droplet microfluidic readouts, which are incompatible with UV light detection at high throughput. Here, we engineer a tryptophan DNA aptamer into a sensor to quantitatively report on tryptophan production in droplets. The utility of the sensor was validated by identifying five-fold improved tryptophan synthases from ∼100,000 protein variants. More generally, this work establishes the use of DNA-aptamer sensors with a fluorogenic read-out in widening the scope of droplet microfluidic evolution.

Light-Responsive Materials in Droplet Manipulation for Biochemical Applications

Miniaturized droplets, characterized by well-controlled microenvironments and capability for parallel processing, have significantly advanced the studies on enzymatic evolution, molecular diagnostics, and single-cell analysis. However, manipulation of small-sized droplets, including moving, merging, and trapping of the targeted droplets for complex biochemical assays and subsequent analysis, is not trivial and remains technically demanding. Among various techniques, light-driven methods stand out as a promising candidate for droplet manipulation in a facile and flexible manner, given the features of contactless interaction, high spatiotemporal resolution, and biocompatibility. This review therefore compiles an in-depth discussion of the governing mechanisms underpinning light-driven droplet manipulation. Besides, light-responsive materials, representing the core of light-matter interaction and the key character converting light into different forms of energy, are particularly assessed in this review. Recent advancements in light-responsive materials and the most notable applications are comprehensively archived and evaluated. Continuous innovations and rational engineering of light-responsive materials are expected to propel the development of light-driven droplet manipulation, equip droplets with enhanced functionality, and broaden the applications of droplets for biochemical studies and routine biochemical investigations.

Recent advances in droplet sequential monitoring methods for droplet sorting

Droplet microfluidics is an attractive technology to run parallel experiments with high throughput and scalability while maintaining the heterogeneous features of individual samples or reactions. Droplet sorting is utilized to collect the desired droplets based on droplet characterization and in-droplet content evaluation. A proper monitoring method is critical in this process, which governs the accuracy and maximum frequency of droplet handling. Until now, numerous monitoring methods have been integrated in the microfluidic devices for identifying droplets, such as optical spectroscopy, mass spectroscopy, electrochemical monitoring, and nuclear magnetic resonance spectroscopy. In this review, we summarize the features of various monitoring methods integrated into droplet sorting workflow and discuss their suitable condition and potential obstacles in use. We aim to provide a systematic introduction and an application guide for choosing and building a droplet monitoring platform.

Deformability-Based Microfluidic Microdroplet Screening to Obtain Agarolytic Bacterial Cells

Environmental microorganisms possess enzymes that can digest macromolecules such as agarose into smaller molecules that can be utilized for growth. These enzymes could be valuable for the effective utilization of global resources. However, since most of the microorganisms on Earth remain uncultured, there is significant untapped enzymatic potential in nature. Therefore, it is necessary to develop innovative tools and strategies for exploring these enzymatic resources. To address this, we developed a method for screening microbial cells that secrete hydrogel-degrading enzymes using deformability-based microfluidic microdroplet sorting. In this method, microbial cells are encapsulated as single cells in water-in-oil (W/O) microdroplets with a hydrogel whose shape becomes deformable as the hydrogel is progressively degraded into smaller molecules. Screening is achieved using a microfluidic device that passively sorts the deformed W/O microdroplets. Using this method, we successfully sorted agarose-containing microdroplets, encapsulating single bacterial cells that hydrolyzed agarose. This method can be used to screen various hydrogel-degrading microbial cells.

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.

On-Chip Label-Free Sorting of Living and Dead Cells

With the emergence of various cutting-edge micromachining technologies, lab on a chip is growing rapidly, but it is always a challenge to realize the on-chip separation of living cells from cell samples without affecting cell activity and function. Herein, we report a novel on-chip label-free method for sorting living and dead cells by integrating the hypertonic stimulus and tilted-angle standing surface acoustic wave (T-SSAW) technologies. On a self-designed microfluidic chip, the hypertonic stimulus is used to distinguish cells by producing volume differences between living and dead cells, while T-SSAW is used to separate living and dead cells according to the cell volume difference. Under the optimized operation conditions, for the sample containing 50% of living human umbilical vein endothelial cells (HUVECs) and 50% of dead HUVECs treated with paraformaldehyde, the purity of living cells after the first separation can reach approximately 80%, while after the second separation, it can be as high as 93%; furthermore, the purity of living cells after separation increases with the initial proportion of living cells. In addition, the chip we designed is safe for cells and can robustly handle cell samples with different cell types or different causes of cell death. This work provides a new design of a microfluidic chip for label-free sorting of living and dead cells, greatly promoting the multi-functionality of lab on a chip.

Single-cell rapid identification, in situ viability and vitality profiling, and genome-based source-tracking for probiotics products

Rapid expansion of the probiotics industry demands fast, sensitive, comprehensive, and low-cost strategies for quality assessment. Here, we introduce a culture-free, one-cell-resolution, phenome-genome-combined strategy called Single-Cell Identification, Viability and Vitality tests, and Source-tracking (SCIVVS). For each cell directly extracted from the product, the fingerprint region of D2O-probed single-cell Raman spectrum (SCRS) enables species-level identification with 93% accuracy, based on a reference SCRS database from 21 statutory probiotic species, whereas the C-D band accurately quantifies viability, metabolic vitality plus their intercellular heterogeneity. For source-tracking, single-cell Raman-activated Cell Sorting and Sequencing can proceed, producing indexed, precisely one-cell-based genome assemblies that can reach ~99.40% genome-wide coverage. Finally, we validated an integrated SCIVVS workflow with automated SCRS acquisition where the whole process except sequencing takes just 5 h. As it is >20-fold faster, >10-time cheaper, vitality-revealing, heterogeneity-resolving, and automation-prone, SCIVVS is a new technological and data framework for quality assessment of live-cell products.

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.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

[Application of Droplet-Based Microfluidics in Microbial Research]

Droplet-based microfluidics is a technology that generates and manipulates highly uniform droplets, ranging from picoliter to nanoliter droplets, in microchannels under precise control. In biological research, each droplet can be used to encapsulate a small group of cells or even a single cell, and then serve as an individual container for biochemical reaction, which is well suited for high-throughput and high-resolution biochemical analysis. In the field of microbial research, from cultivation and identification of microbes to the investigation of the spatiotemporal dynamics of microbial communities, from precise quantitation of microbiota to systematic study of microbial interactions, and from the isolation of rare and unculturable microbes to the development of genetically engineered strains, droplet microfluidic technology has played an important promotional role in all these aspects. Droplet microfluidics shows potential for becoming a basic tool for exploring single-cell microbes in microbiological research. In this review, we gave a brief overview of the technical basis of droplet microfluidics. Then, we presented its latest applications in microbial research and had some discussions, aiming to provide a reference for relevant research on microorganisms.

Recent progress in high-throughput droplet screening and sorting for bioanalysis

Owing to its ability to isolate single cells and perform high-throughput sorting, droplet sorting has been widely applied in several research fields. Compared with flow cytometry, droplet allows the encapsulation of single cells for cell secretion or lysate analysis. With the rapid development of this technology in the past decade, various droplet sorting devices with high throughput and accuracy have been developed. A droplet sorter with the highest sorting throughput of 30,000 droplets per second was developed in 2015. Since then, increased attention has been paid to expanding the possibilities of droplet sorting technology and strengthening its advantages over flow cytometry. This review aimed to summarize the recent progress in droplet sorting technology from the perspectives of device design, detection signal, actuating force, and applications. Technical details for improving droplet sorting through various approaches are introduced and discussed. Finally, we discuss the current limitations of droplet sorting for single-cell studies along with the existing gap between the laboratory and industry and provide our insights for future development of droplet sorters.

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.

High precision, high throughput generation of droplets containing single cells

The Poisson limit is a major problem for the isolation of single cells in different single-cell technologies and applications. In droplet-based single-cell assays, a scheme that is increasingly popular, the intrinsic randomness during single-cell encapsulation in droplets requires most of the created droplets to be empty, which has a profound impact on the efficiency and throughput of such techniques, and on the predictability of the combinatory droplet assays. Here we present a simple passive microfluidic system overcoming this limitation with unprecedented efficacy, allowing the generation of single-cell droplets for a wide range of operating conditions, with extremely high throughput (more than 22 000 single-cell loaded droplets per minute) and with an extremely low fault ratio (doublets or empty droplets), applicable to any cells and deformable particles. This versatile technique will shift the paradigm of single-cell encapsulation and will impact single-cell sequencing, rare cell isolation, multicellular/bead studies in immunology or cancer biology, etc.

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.

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.

Medium-chain triglyceride production in Nannochloropsis via a fatty acid chain length discriminating mechanism

Depending on their fatty acid (FA) chain length, triacylglycerols (TAGs) have distinct applications; thus, a feedstock with a genetically designed chain length is desirable to maximize process efficiency and product versatility. Here, ex vivo, in vitro, and in vivo profiling of the large set of type-2 diacylglycerol acyltransferases (NoDGAT2s) in the industrial oleaginous microalga Nannochloropsis oceanica revealed two endoplasmic reticulum-localized enzymes that can assemble medium-chain FAs (MCFAs) with 8-12 carbons into TAGs. Specifically, NoDGAT2D serves as a generalist that assembles C8-C18 FAs into TAG, whereas NoDGAT2H is a specialist that incorporates only MCFAs into TAG. Based on such specialization, stacking of NoDGAT2D with MCFA- or diacylglycerol-supplying enzymes or regulators, including rationally engineering Cuphea palustris acyl carrier protein thioesterase, Cocos nucifera lysophosphatidic acid acyltransferase, and Arabidopsis thaliana WRINKLED1, elevated the medium-chain triacylglycerol (MCT) share in total TAG 66-fold and MCT productivity 64.8-fold at the peak phase of oil production. Such functional specialization of NoDGAT2s in the chain length of substrates and products reveals a dimension of control in the cellular TAG profile, which can be exploited for producing designer oils in microalgae.

Active antibiotic resistome in soils unraveled by single-cell isotope probing and targeted metagenomics

Antimicrobial resistance (AMR) in soils represents a serious risk to human health through the food chain and human-nature contact. However, the active antibiotic-resistant bacteria (ARB) residing in soils that primarily drive AMR dissemination are poorly explored. Here, single-cell Raman-D₂O coupled with targeted metagenomics is developed as a culture-independent approach to phenotypically and genotypically profiling active ARB against clinical antibiotics in a wide range of soils. This method quantifies the prevalence (contamination degree) and activity (spread potential) of soil ARB and reveals a clear elevation with increasing anthropogenic activities such as farming and the creation of pollution, thereby constituting a factor that is critical for the assessment of AMR risks. Further targeted sorting and metagenomic sequencing of the most active soil ARB uncover several uncultured genera and a pathogenic strain. Furthermore, the underlying resistance genes, virulence factor genes, and associated mobile genetic elements (including plasmids, insertion sequences, and prophages) are fully deciphered at the single-cell level. This study advances our understanding of the soil active AMR repertoire by linking the resistant phenome to the genome. It will aid in the risk assessment of environmental AMR and guide the combat under the One Health framework.

Biochemistry and Biotechnology of Lipid Accumulation in the Microalga Nannochloropsis oceanica

Oils are among the most important agricultural commodities and have wide applications in food/nutrition, biofuels, and oleochemicals. The oleaginous microalga Nannochloropsis oceanica can produce large amounts of oils and the high-value ω-3 eicosapentaenoic acid, which represents a promising resource for oil production targeting biodiesel, nutraceutical, and aquaculture industries. In recent years, with the availability of omics databases and the development of genetic tools, N. oceanica has been extensively investigated as a model photosynthetic organism for studying lipid metabolism and as a green cellular factory to produce lipids for industrial applications. This review summarizes the current knowledge on the lipid composition and biosynthetic pathways of N. oceanica and reviews the recent advances in metabolic engineering of lipid production in this microalga.

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.

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

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

Advances in droplet microfluidics for SERS and Raman analysis

Raman spectroscopy can realize qualitative and quantitative characterization, and surface-enhanced Raman spectroscopy (SERS) can further enhance its detection sensitivity. In combination with droplet microfluidics, some significant but insurmountable limitations of SERS and Raman spectroscopy can be overcome to some extent, thus improving their detection capability and extending their application. During the past decade, these systems have constantly developed and demonstrated a great potential in more applications, but there is no new review systematically summarizing the droplet microfluidics-based Raman and SERS analysis system since the first related review was published in 2011. Thus, there is a great need for a new review to summarize the advances. In this review, we focus on droplet microfluidics-based Raman and SERS analysis, and summarize two mainstream research directions on this topic up to now. The one is SERS or Raman detection in the moving droplet microreactors, including analysis of molecules, single cells and chemical reaction processes. The other one is SERS active microparticle fabrication via microfluidic droplet templates covering polymer matrix and photonic crystal microparticles. We also comment on the advantages, disadvantage and correlation resolution of droplet microfluidics for SERS or Raman. Finally, we summarize these systems and illustrate our perspectives for future research directions in this field.

Sorting Gold and Sand (Silica) Using Atomic Force Microscope-Based Dielectrophoresis

Additive manufacturing-also known as 3D printing-has attracted much attention in recent years as a powerful method for the simple and versatile fabrication of complicated three-dimensional structures. However, the current technology still exhibits a limitation in realizing the selective deposition and sorting of various materials contained in the same reservoir, which can contribute significantly to additive printing or manufacturing by enabling simultaneous sorting and deposition of different substances through a single nozzle. Here, we propose a dielectrophoresis (DEP)-based material-selective deposition and sorting technique using a pipette-based quartz tuning fork (QTF)-atomic force microscope (AFM) platform DEPQA and demonstrate multi-material sorting through a single nozzle in ambient conditions. We used Au and silica nanoparticles for sorting and obtained 95% accuracy for spatial separation, which confirmed the surface-enhanced Raman spectroscopy (SERS). To validate the scheme, we also performed a simulation for the system and found qualitative agreement with the experimental results. The method that combines DEP, pipette-based AFM, and SERS may widely expand the unique capabilities of 3D printing and nano-micro patterning for multi-material patterning, materials sorting, and diverse advanced applications.

Droplet microfluidics on analysis of pathogenic microbes for wastewater-based epidemiology

Infectious diseases caused by pathogenic microbes have posed a major health issue for the public, such as the ongoing COVID-19 global pandemic. In recent years, wastewater-based epidemiology (WBE) is emerging as an effective and unbiased method for monitoring public health. Despite its increasing importance, the advancement of WBE requires more competent and streamlined analytical platforms. Herein we discuss the interactions between WBE and droplet microfluidics, focusing on the analysis of pathogens in droplets, which is hard to be tackled by traditional analytical tools. We highlight research works from three aspects, namely, quantitation of pathogen biomarkers in droplets, single-cell analysis in droplets, and living cell biosensors in droplets, as well as providing future perspectives on the synergy between WBE and droplet microfluidics.

UV-Vis Spectra-Activated Droplet Sorting for Label-Free Chemical Identification and Collection of Droplets

We introduce the UV-vis spectra-activated droplet sorter (UVADS) for high-throughput label-free chemical identification and enzyme screening. In contrast to previous absorbance-based droplet sorters that relied on single-wavelength absorbance in the visible range, our platform collects full UV-vis spectra from 200 to 1050 nm at up to 2100 spectra per second. Our custom-built open-source software application, "SpectraSorter," enables real-time data processing, analysis, visualization, and selection of droplets for sorting with any set of UV-vis spectral features. An optimized UV-vis detection region extended the absorbance path length for droplets and allowed for the direct protein quantification down to 10 μM of bovine serum albumin at 280 nm. UV-vis spectral data can distinguish a variety of different chemicals or spurious events (such as air bubbles) that are inaccessible at a single wavelength. The platform is used to measure ergothionase enzyme activity from monoclonal microcolonies isolated in droplets. In a label-free manner, we directly measure the ergothioneine substrate to thiourocanic acid product conversion while tracking the microcolony formation. UVADS represents an important new tool for high-throughput label-free in-droplet chemical analysis.

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.

Recent trends in biocatalysis

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

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.

Droplet flow cytometry for single-cell analysis

The interrogation of single cells has revolutionised biology and medicine by providing crucial unparalleled insights into cell-to-cell heterogeneity. Flow cytometry (including fluorescence-activated cell sorting) is one of the most versatile and high-throughput approaches for single-cell analysis by detecting multiple fluorescence parameters of individual cells in aqueous suspension as they flow past through a focus of excitation lasers. However, this approach relies on the expression of cell surface and intracellular biomarkers, which inevitably lacks spatial and temporal phenotypes and activities of cells, such as secreted proteins, extracellular metabolite production, and proliferation. Droplet microfluidics has recently emerged as a powerful tool for the encapsulation and manipulation of thousands to millions of individual cells within pico-litre microdroplets. Integrating flow cytometry with microdroplet architectures surrounded by aqueous solutions (e.g., water-in-oil-in-water (W/O/W) double emulsion and hydrogel droplets) opens avenues for new cellular assays linking cell phenotypes to genotypes at the single-cell level. In this review, we discuss the capabilities and applications of droplet flow cytometry (DFC). This unique technique uses standard commercially available flow cytometry instruments to characterise or select individual microdroplets containing single cells of interest. We explore current challenges associated with DFC and present our visions for future development.

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.

High-Throughput Raman Flow Cytometry and Beyond

Flow cytometry is a powerful tool with applications in diverse fields such as microbiology, immunology, virology, cancer biology, stem cell biology, and metabolic engineering. It rapidly counts and characterizes large heterogeneous populations of cells in suspension (e.g., blood cells, stem cells, cancer cells, and microorganisms) and dissociated solid tissues (e.g., lymph nodes, spleen, and solid tumors) with typical throughputs of 1,000-100,000 events per second (eps). By measuring cell size, cell granularity, and the expression of cell surface and intracellular molecules, it provides systematic insights into biological processes. Flow cytometers may also include cell sorting capabilities to enable subsequent additional analysis of the sorted sample (e.g., electron microscopy and DNA/RNA sequencing), cloning, and directed evolution. Unfortunately, traditional flow cytometry has several critical limitations as it mainly relies on fluorescent labeling for cellular phenotyping, which is an indirect measure of intracellular molecules and surface antigens. Furthermore, it often requires time-consuming preparation protocols and is incompatible with cell therapy. To overcome these difficulties, a different type of flow cytometry based on direct measurements of intracellular molecules by Raman spectroscopy, or "Raman flow cytometry" for short, has emerged. Raman flow cytometry obtains a chemical fingerprint of the cell in a nondestructive manner, allowing for single-cell metabolic phenotyping. However, its slow signal acquisition due to the weak light-molecule interaction of spontaneous Raman scattering prevents the throughput necessary to interrogate large cell populations in reasonable time frames, resulting in throughputs of about 1 eps. The remedy to this throughput limit lies in coherent Raman scattering methods such as stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS), which offer a significantly enhanced light-sample interaction and hence enable high-throughput Raman flow cytometry, Raman imaging flow cytometry, and even Raman image-activated cell sorting (RIACS). In this Account, we outline recent advances, technical challenges, and emerging opportunities of coherent Raman flow cytometry. First, we review the principles of various types of SRS and CARS and introduce several techniques of coherent Raman flow cytometry such as CARS, multiplex CARS, Fourier-transform CARS, SRS, SRS imaging flow cytometry, and RIACS. Next, we discuss a unique set of applications enabled by coherent Raman flow cytometry, from microbiology and lipid biology to cancer detection and cell therapy. Finally, we describe future opportunities and challenges of coherent Raman flow cytometry including increasing sensitivity and throughput, integration with droplet microfluidics, utilizing machine learning techniques, or achieving in vivo flow cytometry. This Account summarizes the growing field of high-throughput Raman flow cytometry and the bright future it can bring.

Recent Advances on Sorting Methods of High-Throughput Droplet-Based Microfluidics in Enzyme Directed Evolution

Droplet-based microfluidics has been widely applied in enzyme directed evolution (DE), in either cell or cell-free system, due to its low cost and high throughput. As the isolation principles are based on the labeled or label-free characteristics in the droplets, sorting method contributes mostly to the efficiency of the whole system. Fluorescence-activated droplet sorting (FADS) is the mostly applied labeled method but faces challenges of target enzyme scope. Label-free sorting methods show potential to greatly broaden the microfluidic application range. Here, we review the developments of droplet sorting methods through a comprehensive literature survey, including labeled detections [FADS and absorbance-activated droplet sorting (AADS)] and label-free detections [electrochemical-based droplet sorting (ECDS), mass-activated droplet sorting (MADS), Raman-activated droplet sorting (RADS), and nuclear magnetic resonance-based droplet sorting (NMR-DS)]. We highlight recent cases in the last 5 years in which novel enzymes or highly efficient variants are generated by microfluidic DE. In addition, the advantages and challenges of different sorting methods are briefly discussed to provide an outlook for future applications in enzyme DE.

The NanDeSyn database for Nannochloropsis systems and synthetic biology

Nannochloropsis species, unicellular industrial oleaginous microalgae, are model organisms for microalgal systems and synthetic biology. To facilitate community-based annotation and mining of the rapidly accumulating functional genomics resources, we have initiated an international consortium and present a comprehensive multi-omics resource database named Nannochloropsis Design and Synthesis (NanDeSyn; http://nandesyn.single-cell.cn). Via the Tripal toolkit, it features user-friendly interfaces hosting genomic resources with gene annotations and transcriptomic and proteomic data for six Nannochloropsis species, including two updated genomes of Nannochloropsis oceanica IMET1 and Nannochloropsis salina CCMP1776. Toolboxes for search, Blast, synteny view, enrichment analysis, metabolic pathway analysis, a genome browser, etc. are also included. In addition, functional validation of genes is indicated based on phenotypes of mutants and relevant bibliography. Furthermore, epigenomic resources are also incorporated, especially for sequencing of small RNAs including microRNAs and circular RNAs. Such comprehensive and integrated landscapes of Nannochloropsis genomics and epigenomics will promote and accelerate community efforts in systems and synthetic biology of these industrially important microalgae.