Raman-activated Cell Research Articles

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.

Microwell-assembled aluminum substrates for enhanced single-cell analysis: A novel approach for cancer cell profiling by Raman spectroscopy

Single-cell analysis is critical for advancing personalized medicine, as it reveals cell population heterogeneity that influences disease outcomes. We present a microwell-assembled aluminum substrate platform that enhances single-cell Raman spectroscopy in liquid suspension by isolating individual cells and preventing stacking and movement, which significantly improves signal stability and the signal-to-noise ratio (SNR).We applied this novel platform to analyze PC-9 lung cancer cells and BEAS-2B normal bronchial epithelial cells, identifying distinct biochemical differences. Notably, cancer cells showed higher levels of adenine, cytochromes, DNA/RNA, and unsaturated lipids, along with an increased unsaturation ratio and protein content. These findings were further validated using machine learning models. An eXtreme Gradient Boosting (XGBoost) model achieved perfect classification accuracy of 100 %, underscoring the robustness of the spectral features identified by our platform.Our platform not only enhances single-cell Raman signal detection but also holds promise for biomedical applications, including early cancer detection, treatment monitoring, and drug development. The high-throughput capacity of this platform featuring over 120,000 wells, along with its compatibility with techniques such as Raman-activated cell sorting (RACS) further extends its potential for clinical diagnostics and personalized medicine.

Raman-Activated Cell Ejection for Validating the Reliability of the Raman Fingerprint Database of Foodborne Pathogens

Raman spectroscopy for rapid identification of foodborne pathogens based on phenotype has attracted increasing attention, and the reliability of the Raman fingerprint database through genotypic determination is crucial. In the research, the classification model of four foodborne pathogens was established based on t-distributed stochastic neighbor embedding (t-SNE) and support vector machine (SVM); the recognition accuracy was 97.04%. The target bacteria named by the model were ejected through Raman-activated cell ejection (RACE), and then single-cell genomic DNA was amplified for species analysis. The accuracy of correct matches between the predicted phenotype and the actual genotype of the target cells was at least 83.3%. Furthermore, all anticipant sequencing results brought into correspondence with the species were predicted through the model. In sum, the Raman fingerprint database based on Raman spectroscopy combined with machine learning was reliable and promising in the field of rapid detection of foodborne pathogens.

Raman micro-spectroscopy as a tool to study immunometabolism.

In the past two decades, immunometabolism has emerged as a crucial field, unraveling the intricate molecular connections between cellular metabolism and immune function across various cell types, tissues, and diseases. This review explores the insights gained from studies using the emerging technology, Raman micro-spectroscopy, to investigate immunometabolism. Raman micro-spectroscopy provides an exciting opportunity to directly study metabolism at the single cell level where it can be combined with other Raman-based technologies and platforms such as single cell RNA sequencing. The review showcases applications of Raman micro-spectroscopy to study the immune system including cell identification, activation, and autoimmune disease diagnosis, offering a rapid, label-free, and minimally invasive analytical approach. The review spotlights three promising Raman technologies, Raman-activated cell sorting, Raman stable isotope probing, and Raman imaging. The synergy of Raman technologies with machine learning is poised to enhance the understanding of complex Raman phenotypes, enabling biomarker discovery and comprehensive investigations in immunometabolism. The review encourages further exploration of these evolving technologies in the rapidly advancing field of immunometabolism.

In Situ Discrimination and Cultivation of Active Degraders in Soils by Genome-Directed Cultivation Assisted by SIP-Raman-Activated Cell Sorting.

The identification and in situ cultivation of functional yet uncultivable microorganisms are important to confirm inferences regarding their ecological functions. Here, we developed a new method that couples Raman-activated cell sorting (RACS), stable-isotope probing (SIP), and genome-directed cultivation (GDC)─namely, RACS-SIP-GDC─to identify, sort, and cultivate the active toluene degraders from a complex microbial community in petroleum-contaminated soil. Using SIP, we successfully identified the active toluene degrader Pigmentiphaga, the single cells of which were subsequently sorted and isolated by RACS. We further successfully assembled the genome of Pigmentiphaga based on the metagenomic sequencing of 13C-DNA and genomic sequencing of sorted cells, which was confirmed by gyrB gene comparison and average nucleotide identity determination. Additionally, the genotypes and phenotypes of this degrader were directly linked at the single-cell level, and its complete toluene metabolic pathways in petroleum-contaminated soil were reconstructed. Based on its unique metabolic properties uncovered by genome sequencing, we modified the traditional cultivation medium with antibiotics, amino acids, carbon sources, and growth factors (e.g., vitamins and metals), achieving the successful cultivation of RACS-sorted active degrader Pigmentiphaga sp. Our results implied that RACS-SIP-GDC is a state-of-the-art approach for the precise identification, targeted isolation, and cultivation of functional microbes from complex communities in natural habitats. RACS-SIP-GDC can be used to explore specific and targeted organic-pollution-degrading microorganisms at the single-cell level and provide new insights into their biodegradation mechanisms.

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 Sequencingcan 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-timecheaper, vitality-revealing, heterogeneity-resolving, and automation-prone, SCIVVS is a new technological and data framework for quality assessment of live-cell products.

Identifying the Active Phenanthrene Degraders and Characterizing Their Metabolic Activities at the Single-Cell Level by the Combination of Magnetic-Nanoparticle-Mediated Isolation, Stable-Isotope Probing, and Raman-Activated Cell Sorting (MMI–SIP–RACS)

Magnetic-nanoparticle-mediated isolation coupled with stable-isotope probing (MMI-SIP) is a cultivation-independent higher-resolution approach for isolating active degraders in their natural habitats. However, it addresses the community level and cannot directly link the microbial identities, phenotypes, and in situ functions of the active degraders at the single-cell level within complex microbial communities. Here, we used 13C-labeled phenanthrene as the target and developed a new method coupling MMI-SIP and Raman-activated cell sorting (RACS), namely, MMI-SIP-RACS, to identify the active phenanthrene-degrading bacterial cells from polycyclic aromatic hydrocarbon (PAH)-contaminated wastewater. MMI-SIP-RACS significantly enriched the active phenanthrene degraders and successfully isolated the representative single cells. Amplicon sequencing analysis by SIP, 13C shift of the single cell in Raman spectra, and the 16S rRNA gene from single cell sequencing via RACS confirmed that Novosphingobium was the active phenanthrene degrader. Additionally, MMI-SIP-RACS reconstructed the phenanthrene metabolic pathway and genes of Novosphingobium, including two novel genes encoding phenanthrene dioxygenase and naphthalene dioxygenase. Our findings suggested that MMI-SIP-RACS is a powerful method to efficiently and precisely isolate active PAH degraders from complex microbial communities and directly link their identities to functions at the single-cell level.

Classification and Identification of Archaea Using Single-Cell Raman Ejection and Artificial Intelligence: Implications for Investigating Uncultivated Microorganisms.

Archaea can produce special cellular components such as polyhydroxyalkanoates, carotenoids, rhodopsin, and ether lipids, which have valuable applications in medicine and green energy production. Most of the archaeal species are uncultivated, posing challenges to investigating their biomarker components and biochemical properties. In this study, we applied Raman spectroscopy to examine the biological characteristics of nine archaeal isolates, including halophilic archaea (Haloferax larsenii, Haloarcula argentinensis, Haloferax mediterranei, Halomicrobium mukohataei, Halomicrobium salinus, Halorussus sp., Natrinema gari), thermophilic archaea (Sulfolobus acidocaldarius), and marine group I (MGI) archaea (Nitrosopumilus maritimus). Linear discriminant analysis of the Raman spectra allowed visualization of significant separations among the nine archaeal isolates. Machine-learning classification models based on support vector machine achieved accuracies of 88-100% when classifying the nine archaeal species. The predicted results were validated by DNA sequencing analysis of cells isolated from the mixture by Raman-activated cell sorting. Raman spectra of uncultured archaea (MGII) were also obtained based on Raman spectroscopy and fluorescence in situ hybridization. The results combining multiple Raman-based techniques indicated that MGII may have the ability to produce lipids distinct from other archaeal species. Our study provides a valuable approach for investigating and classifying archaea, especially uncultured species, at the single-cell level.

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

ABSTRACTSoil 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.

Development overview of Raman-activated cell sorting devoted to bacterial detection at single-cell level.

Understanding the metabolic interactions between bacteria in natural habitat at the single-cell level and the contribution of individual cell to their functions is essential for exploring the dark matter of uncultured bacteria. The combination of Raman-activated cell sorting (RACS) and single-cell Raman spectra (SCRS) with unique fingerprint characteristics makes it possible for research in the field of microbiology to enter the single cell era. This review presents an overview of current knowledge about the research progress of recognition and assessment of single bacterium cell based on RACS and further research perspectives. We first systematically summarize the label-free and non-destructive RACS strategies based on microfluidics, microdroplets, optical tweezers, and specially made substrates. The importance of RACS platforms in linking target cell genotype and phenotype is highlighted and the approaches mentioned in this paper for distinguishing single-cell phenotype include surface-enhanced Raman scattering (SERS), biomarkers, stable isotope probing (SIP), and machine learning. Finally, the prospects and challenges of RACS in exploring the world of unknown microorganisms are discussed. KEY POINTS: • Analysis of single bacteria is essential for further understanding of the microbiological world. • Raman-activated cell sorting (RACS) systems are significant protocol for characterizing phenotypes and genotypes of individual bacteria.

Optofluidic Raman-activated cell sorting for targeted genome retrieval or cultivation of microbial cells with specific functions.

Stable isotope labeling of microbial taxa of interest and their sorting provide an efficient and direct way to answer the question "who does what?" in complex microbial communities when coupled with fluorescence in situ hybridization or downstream 'omics' analyses. We have developed a platform for automated Raman-based sorting in which optical tweezers and microfluidics are used to sort individual cells of interest from microbial communities on the basis of their Raman spectra. This sorting of cells and their downstream DNA analysis, such as by mini-metagenomics or single-cell genomics, or cultivation permits a direct link to be made between the metabolic roles and the genomes of microbial cells within complex microbial communities, as well as targeted isolation of novel microbes with a specific physiology of interest. We describe a protocol from sample preparation through Raman-activated live cell sorting. Subsequent cultivation of sorted cells is described, whereas downstream DNA analysis involves well-established approaches with abundant methods available in the literature. Compared with manual sorting, this technique provides a substantially higher throughput (up to 500 cells per h). Furthermore, the platform has very high sorting accuracy (98.3 ± 1.7%) and is fully automated, thus avoiding user biases that might accompany manual sorting. We anticipate that this protocol will empower in particular environmental and host-associated microbiome research with a versatile tool to elucidate the metabolic contributions of microbial taxa within their complex communities. After a 1-d preparation of cells, sorting takes on the order of 4 h, depending on the number of cells required.

Rational design of a microbial consortium of mucosal sugar utilizers reduces Clostridiodes difficile colonization

Many intestinal pathogens, including Clostridioides difficile, use mucus-derived sugars as crucial nutrients in the gut. Commensals that compete with pathogens for such nutrients are therefore ecological gatekeepers in healthy guts, and are attractive candidates for therapeutic interventions. Nevertheless, there is a poor understanding of which commensals use mucin-derived sugars in situ as well as their potential to impede pathogen colonization. Here, we identify mouse gut commensals that utilize mucus-derived monosaccharides within complex communities using single-cell stable isotope probing, Raman-activated cell sorting and mini-metagenomics. Sequencing of cell-sorted fractions reveals members of the underexplored family Muribaculaceae as major mucin monosaccharide foragers, followed by members of Lachnospiraceae, Rikenellaceae, and Bacteroidaceae families. Using this information, we assembled a five-member consortium of sialic acid and N-acetylglucosamine utilizers that impedes C. difficile’s access to these mucosal sugars and impairs pathogen colonization in antibiotic-treated mice. Our findings underscore the value of targeted approaches to identify organisms utilizing key nutrients and to rationally design effective probiotic mixtures.

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.

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

SummaryThe 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.

Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition.

SummaryIn situ measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.

An automated Raman-based platform for the sorting of live cells by functional properties.

Stable-isotope probing is widely used to study the function of microbial taxa in their natural environment, but sorting of isotopically labelled microbial cells from complex samples for subsequent genomic analysis or cultivation is still in its early infancy. Here, we introduce an optofluidic platform for automated sorting of stable-isotope-probing-labelled microbial cells, combining microfluidics, optical tweezing and Raman microspectroscopy, which yields live cells suitable for subsequent single-cell genomics, mini-metagenomics or cultivation. We describe the design and optimization of this Raman-activated cell-sorting approach, illustrate its operation with four model bacteria (two intestinal, one soil and one marine) and demonstrate its high sorting accuracy (98.3 ± 1.7%), throughput (200-500 cells h-1; 3.3-8.3 cells min-1) and compatibility with cultivation. Application of this sorting approach for the metagenomic characterization of bacteria involved in mucin degradation in the mouse colon revealed a diverse consortium of bacteria, including several members of the underexplored family Muribaculaceae, highlighting both the complexity of this niche and the potential of Raman-activated cell sorting for identifying key players in targeted processes.

Effect of Laser Irradiation on Cell Function and Its Implications in Raman Spectroscopy.

ABSTRACTLasers are instrumental in advanced bioimaging and Raman spectroscopy. However, they are also well known for their destructive effects on living organisms, leading to concerns about the adverse effects of laser technologies. To implement Raman spectroscopy for cell analysis and manipulation, such as Raman-activated cell sorting, it is crucial to identify nondestructive conditions for living cells. Here, we evaluated quantitatively the effect of 532-nm laser irradiation on bacterial cell fate and growth at the single-cell level. Using a purpose-built microfluidic platform, we were able to quantify the growth characteristics, i.e., specific growth rates and lag times of individual cells, as well as the survival rate of a population in conjunction with Raman spectroscopy. Representative Gram-negative and Gram-positive species show similar trends in response to a laser irradiation dose. Laser irradiation could compromise the physiological function of cells, and the degree of destruction is both dose and strain dependent, ranging from reduced cell growth to a complete loss of cell metabolic activity and finally to physical disintegration. Gram-positive bacterial cells are more susceptible than Gram-negative bacterial strains to irradiation-induced damage. By directly correlating Raman acquisition with single-cell growth characteristics, we provide evidence of nondestructive characteristics of Raman spectroscopy on individual bacterial cells. However, while strong Raman signals can be obtained without causing cell death, the variety of responses from different strains and from individual cells justifies careful evaluation of Raman acquisition conditions if cell viability is critical.IMPORTANCE In Raman spectroscopy, the use of powerful monochromatic light in laser-based systems facilitates the detection of inherently weak signals. This allows environmentally and clinically relevant microorganisms to be measured at the single-cell level. The significance of being able to perform Raman measurement is that, unlike label-based fluorescence techniques, it provides a “fingerprint” that is specific to the identity and state of any (unlabeled) sample. Thus, it has emerged as a powerful method for studying living cells under physiological and environmental conditions. However, the laser's high power also has the potential to kill bacteria, which leads to concerns. The research presented here is a quantitative evaluation that provides a generic platform and methodology to evaluate the effects of laser irradiation on individual bacterial cells. Furthermore, it illustrates this by determining the conditions required to nondestructively measure the spectra of representative bacteria from several different groups.

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.

Raman-activated cell sorting based on dielectrophoretic single-cell trap and release.

Raman-activated cell sorting (RACS) is a promising single-cell technology that holds several significant advantages, as RACS is label-free, information-rich, and potentially in situ. To date, the ability of the technique to identify single cells in a high-speed flow has been limited by inherent weakness of the spontaneous Raman signal. Here we present an alternative pause-and-sort RACS microfluidic system that combines positive dielectrophoresis (pDEP) for single-cell trap and release with a solenoid-valve-suction-based switch for cell separation. This has allowed the integration of trapping, Raman identification, and automatic separation of individual cells in a high-speed flow. By exerting a periodical pDEP field, single cells were trapped, ordered, and positioned individually to the detection point for Raman measurement. As a proof-of-concept demonstration, a mixture of two cell strains containing carotenoid-producing yeast (9%) and non-carotenoid-producing Saccharomyces cerevisiae (91%) was sorted, which enriched the former to 73% on average and showed a fast Raman-activated cell sorting at the subsecond level.