Optical tweezers (OT) technology enables high-precision single-cell manipulation but is fundamentally constrained by the trade-off between hydrodynamic drag and trapping stability; overcoming fluidic forces often requires elevated laser power that compromises cell viability. Here, we present an integrated optical tweezers and microwell array (MA-chip) (OTMA) platform that decouples optical trapping from fluidic transport via a Z-axis "lift-and-drift" retrieval strategy. By using microwells as hydrodynamic shelters, the platform enables deterministic single-cell sorting and automated collection into standard 96-well plates for subsequent culture while minimizing shear-induced perturbations. The system exhibits robust performance for targets ≥3 µm, demonstrating broad applicability from microorganisms to large mammalian tumor cells. Furthermore, it achieves deterministic sorting with near-complete recovery at a throughput of 20-30 cells per min, requiring <1 s of laser exposure per cell. Importantly, the platform exhibits excellent biocompatibility, maintaining >90% viability in fragile mammalian cells and achieving a clonal expansion rate of 93.8% in yeast. By shifting the design priority from maximal throughput to functional cell quality, OTMA shows strong potential as a robust, label-free tool for applications requiring high-fidelity cellular states, such as rare-cell cloning and stress-sensitive single-cell omics.

Unraveling the spatial heterogeneity of tissue microenvironments is essential for understanding complex pathologies like sarcopenia. However, existing spatial omics technologies often compromise between single-cell resolution, macromolecular integrity, and metabolic context. Here, we present "Donut" Laser Ejection Microdissection (DLEM), a high-throughput, non-contact spatial sampling platform. By integrating Spatial Light Modulator (SLM)-based vortex beam shaping with a metal-assisted ejection mechanism, DLEM achieves subcellular precision (5µm) and "cold-cutting" isolation, fundamentally eliminating thermal damage. This ensures superior RNA quality, maintaining > 82.5% genome alignment rates even at the single-cell level. We developed a multimodal NADH-DLEM-seq workflow to link in situ metabolic phenotypes with transcriptomic profiles. Applying this to a murine model of chronic kidney disease (CKD)-induced sarcopenia, we dissected the distinct molecular trajectories of oxidative (Type I) and glycolytic (Type IIB) myofibers. We reveal a dichotomous response to stress: Type I fibers undergo profibrotic remodeling via collagen signaling activation, whereas Type IIB fibers exhibit pronounced catabolic atrophy and mitochondrial disassembly. These findings, obscured in bulk analyses, underscore DLEM as a powerful tool for deciphering the metabolic-transcriptional coupling in aging and disease, offering precise targets for therapeutic intervention in sarcopenia.

The interrogation of single cells is revolutionizing biology by revealing heterogeneity that is masked in bulk analyses. Flow cytometry (FCM) enables high-throughput single-cell analysis but typically depends on exogenous fluorescent labels, which are time-intensive to prepare and may perturb native cellular states. In contrast, Raman scattering provides a label-free alternative with intrinsic molecular specificity. Raman flow cytometry (RFC) combines Raman scattering with FCM, merging high-throughput sample processing with detailed molecular characterization. However, the inherently weak intensity of spontaneous Raman scattering necessitates long integration times, and precise cell positioning in the laser focal volume limits linear flow velocity, resulting in lower throughput compared to conventional fluorescence-based flow cytometry (FFC). Overcoming these limitations demands a multidisciplinary approach. Recent progress in nanofabrication have facilitated the development of microfluidic chips that help address this bottleneck through precise multiphysics-based cell focusing techniques, as well as scalability achieved through parallel channel arrays or droplet systems. This review examines three principal strategies for enhancing the throughput of RFC from the perspective of modern microfluidic frameworks: (ⅰ) advanced cell focusing methods, (ⅱ) Raman signal amplification techniques, and (ⅲ) artificial intelligence (AI)-assisted spectral analysis. By synthesizing recent advances in these areas, we highlight the potential of RFC to advance high-throughput, label-free single-cell analysis in biomedical research.

Raman spectroscopic imaging has emerged as a potent tool due to its non-invasive nature and capability for chemical composition analysis. Line-scan Raman spectroscopy accelerates imaging speed by two orders of magnitude compared to point detection Raman methods. However, further enhancements in imaging speed were constrained by the readout speed of typically used charge-coupled device (CCD) spectroscopic detectors. We developed an ultra-fast line-scan Raman imaging technique based on recently available complementary metal-oxide-semiconductor (CMOS) detectors with low cost and read noise, and fast readout during exposure combined with a global shutter. Employing a high-efficiency transmissiongrating imaging spectrometer, we demonstrate imaging speeds up to two orders of magnitude faster than traditional line scan Raman imaging techniques and up to four orders of magnitude faster than point scan Raman methods, achieving Raman imaging up to 80 kHz spectral rate. We demonstrate that this technology is applicable to a variety of samples, including microplastics, biological cells, and tablets, creating images in an extremely short time frame, showcasing exceptional detection capabilities and the ability to reveal detailed information.

The establishment of monoclonal, stably transduced cell lines is a critical step in functional genomics and drug discovery. However, conventional methods are often time-consuming, labor-intensive, and prone to compromising cell viability. Here, we present a microfluidic single-cell sorting system based on laser-induced jetting (LIJet) that significantly improves the efficiency and quality of stable cell line generation. This system integrates a light-responsive substrate with metal coating and a PDMS microfluidic chip featuring an array of microwells, enabling single-cell capture, identification, and non-contact precision release. A 532 nm nanosecond pulsed laser is used to generate localized microjets, which accurately eject target cells from the microwells. In addition to achieving a 100

Pathogenic bacteria infections are a major public health problem in current society. Rapid and reliable identification of these pathogens can help avoid the misuse of antibiotics and enable precision therapy. In this study, we present a large-spot confocal Raman system based on fiber array (LSCR-FA) for the in situ detection of microbial colonies on agar plates. This method can alleviate the problem of spatial heterogeneity of colonies to a certain extent and is fast and high-throughput. Additionally, we also applied machine learning algorithms with 5-fold cross-validation to analyze colony Raman spectral data and classify seven different pathogenic bacteria. Among them, the Support Vector Machine (SVM) achieved a high accuracy of 98.74%. The results of the study demonstrate that the mentioned LSCR-FA system combined with machine learning algorithms provides a new, fast, and effective strategy for the identification of pathogenic bacteria and precise clinical treatment.

Low-cost and accurate measurement of fluid viscosity based on a pressure-driven flow in digital-printed microfluidics.

Certain virulent strains of Escherichia coli (E. coli), notably the enterohemorrhagic serotype O157:H7, are recognized for causing diarrhea, gastroenteritis, and a range of illnesses that pose significant risks to public health and the safety of drinking water supplies. Early detection and management of E. coli, particularly at low concentrations, are critical for identifying potential sources of contamination. This proactive approach can prevent the spread of diseases, ensure the safety of drinking water, and maintain the hygiene of consumable products. However, detecting low concentrations of E. coli in water samples presents challenges, such as reduced sensitivity, prolonged analysis times, complex sample preparation, susceptibility to interferences, cost limitations, and result variability. To overcome these challenges, we developed an enrichment system that rapidly and efficiently concentrates low-concentration E. coli samples. This system consists of two modules: a primary enrichment module and a secondary enrichment module. The primary enrichment module uses Dean flow technology to enhance E. coli recovery through lateral flushing, achieving recovery rates between 82.7 % and 92.7 %. The secondary enrichment module employs double membrane filtration to further concentrate E. coli. This two-stage enrichment process can amplify E. coli concentrations up to 1000-fold, achieving a recovery rate of 61.8 % within just 30 min. This system enables ultra-high multiplicity enrichment of E. coli from low concentrations in water samples, providing small volumes of highly concentrated samples necessary for subsequent precise detection based on droplet microfluidic technology. The development of this system offers significant benefits for the enrichment and rapid detection of pathogenic bacteria in environmental samples.

Monitoring essential yeasts during fermentation remains challenging, hindering process optimization and quality control of fermented foods and beverages. Here, we propose a novel method that combines automated scanning Raman microscopy (ASRS) with an artificial intelligence model. First, fifteen food- and beverage-associated yeast species were selected to establish a yeast Raman spectral database. Utilizing this database, a Convolutional Neural Network model was trained to classify the 15 yeast species with an accuracy of 97.27 %. Yeast metabolic activity was assessed through D2O probing by calculating the C/D ratio. Concurrently, the ASRS system facilitated automated image stitching, cell recognition, and counting, yielding yeast cell counts consistent with those from pure culture (R2 = 0.91). Second, the method's effectiveness was evaluated using synthetic microbial communities. Results showed no significant difference between the predicted and actual relative abundances of yeasts (P > 0.05). Furthermore, applying our method to monitor the species, quantities, and activities of yeast community succession during Daqu starter fermentation. This study offers a non-destructive and automated approach for the identification, enumeration, and metabolic activity of multiple yeast species in diverse fermentation contexts, significantly enhancing process monitoring and quality control capabilities.

Accurate and efficient sorting of single target cells is crucial for downstream single-cell analysis, such as RNA sequencing, to uncover cellular heterogeneity and functional characteristics. However, conventional single-cell sorting techniques, such as manual micromanipulation or fluorescence-activated cell sorting, do not match current demands and are limited by low throughput, low sorting efficiency and precision, or limited cell viability. Here, we report an automated, highly efficient single-cell sorter, integrating laser-induced forward transfer (LIFT) with a high-throughput picoliter micropore array. The micropore array was surface-functionalized to manipulate liquid surface tension, facilitating the formation of single-cell picoliter droplets in the micropores to realize automated and highly efficient (>80%) single-cell isolation. Using an in-house built microscopic system, rare target cells were identified and automatically retrieved by LIFT with precise sorting efficiency (about 100%) for downstream single-cell analysis while maintaining high cell viability (about 80%). As a case demonstration, we demonstrated the accurate sorting of rare transfected PC-9 cells and post-transfection cell culture, minimizing cell loss and the risk of contamination. Furthermore, we performed single-cell RNA sequencing and showed that high-quality single-cell transcriptome information was efficiently and reliably obtained during cell sorting, preventing additional costs due to low sorting accuracy. The single-cell sorter will become invaluable for single-cell analysis, laying the foundation for multiomics analysis and precision medicine research.

Monitoring essential yeasts during fermentation remains challenging, hindering process optimization and quality control of fermented foods and beverages. Here, we propose a novel method that combines automated scanning Raman microscopy (ASRS) with an artificial intelligence model. First, fifteen food- and beverage-associated yeast species were selected to establish a yeast Raman spectral database. Utilizing this database, a Convolutional Neural Network model was trained to classify the 15 yeast species with an accuracy of 97.27%. Yeast metabolic activity was assessed through D2O probing by calculating the C/D ratio. Concurrently, the ASRS system facilitated automated image stitching, cell recognition, and counting, yielding yeast cell counts consistent with those from pure culture (R2 = 0.91). Second, the method’s effectiveness was evaluated using synthetic microbial communities. Results showed no significant difference between the predicted and actual relative abundances of yeasts (P > 0.05). Furthermore, applying our method to monitor the species, quantities, and activities of yeast community succession during Daqu starter fermentation. This study offers a non-destructive and automated approach for the identification, enumeration, and metabolic activity of multiple yeast species in diverse fermentation contexts, significantly enhancing process monitoring and quality control capabilities.

To achieve rapid and accurate identification at the colony level and improve the efficiency of colony selection, we proposed an adaptive colony Raman acquisition method based on signal-to-noise ratio screening (ACRA-SNR). This method enables in situ spectral acquisition of colonies, effectively mitigating the impact of spatial heterogeneity within colonies on classification and identification. By combining this acquisition method with the Raman Swin Transformer (Ra-ST) model, we achieved fast and accurate classification and identification of colonies. In this work, we analyzed the spectral data of fourteen lactic acid bacteria (LAB) strains, and the Ra-ST model achieved a classification accuracy of 98.2 %. To evaluate the predictive performance of the model, we used it to identify two LAB strains from the same species as those in the classification model but from different sources. The results showed identification accuracy above 70 %, demonstrating good generalization. Additionally, we conducted a comparative analysis between the Ra-ST model and other models, which demonstrated that the Ra-ST model outperformed the others in both classification and prediction performance. Therefore, the combination of the ACRA-SNR technique and the Ra-ST model is expected to facilitate the accurate classification and identification of LAB and other functional bacteria in industrial production, thereby significantly enhancing production efficiency and output.

Cell heterogeneity presents significant challenges for the accurate diagnosis and classification of breast cancer at the single-cell level using Raman spectroscopy. Traditional Raman spectroscopy systems are limited by their small laser spot sizes, which restrict them to capturing localized biochemical information within cells. To address this limitation, we propose a wide-field Raman spectroscopy system with an adjustable spot size (WFRS-AS), capable of collecting Raman signals from entire cells. This approach provides a more comprehensive biochemical fingerprint and effectively reduces the impact of cell heterogeneity. Using supervised classification methods, we compared breast cell spectra acquired by conventional Raman systems and the WFRS-AS system. The results indicate that, when combined with the Support Vector Machine (SVM) algorithm, WFRS-AS achieves 98.18 % accuracy in breast cancer cell diagnosis, representing an improvement of approximately 6.95 %, and 99.26 % accuracy in classifying five breast cell lines, representing an improvement of about 2.83 %. This indicates that integrating WFRS-AS technology with machine learning algorithms offers a powerful and efficient strategy for more accurate and effective breast cancer diagnosis at the single-cell level.

The efficient isolation and molecular analysis of circulating tumor cells (CTCs) from whole blood at single-cell level are crucial for understanding tumor metastasis and developing personalized treatments. The viability of isolated cells is the key prerequisite for the downstream molecular analysis, especially for RNA sequencing. This study develops a laser-induced forward transfer -assisted microfiltration system (LIFT-AMFS) for high-viability CTC enrichment and retrieval from whole blood. The LIFT-compatible double-stepped microfilter (DSMF), central to this system, comprises two micropore layers: the lower layer's smaller micropores facilitate size-based cell separation, and the upper layer's larger micropores enable liquid encapsulating captured cells. By optimizing the design of the DSMFs, the system has a capture efficiency of 88% at the processing throughput of up to 15.0 mL min-1 during the microfilter-based size screening stage, with a single-cell yield of over 95% during the retrieval stage. The retrieved single cells, with high viability, are qualified for ex vivo culture and direct RNA sequencing. The cDNA yield from isolated CTCs surpassed 4.5 ng, sufficient for library construction. All single-cell sequencing data exhibited Q30 scores above 95.92%. The LIFT-AMFS shows promise in cellular and biomedical research.

Microbial detection at the single-cell level offers a novel approach for understanding intricacies of biological systems at the most fundamental level, influencing the advances in therapeutics, drug discoveries, and bioenergy. However, achieving high throughput, high accuracy, specificity, and low damage in sorting and detecting microbial cells have been the most significant hurdles. In this study, we introduced a laser-induced forward transfer (LIFT) with functionalized microwell arrays, called functionalized microwell laser sorting (FMLS). The microwell ejection chip (M-chip) cut the liquid surface tension to form femtoliter droplet arrays by hundreds of thousands of microwell arrays, which enabled efficient capture of single microbial cells and enhanced throughput of microbial detection. The FMLS has achieved over 80 % capture efficiency for individual microorganisms such as Escherichia coli, Saccharomyces cerevisiae, Cyanobacteria spp., and Chlamydomonas spp.. Additionally, it integrated bright-field, fluorescence, and Raman identification methods to enhance specificity for microbial detection. FMLS system exhibited nearly 100 % single-cell sorting efficiency without affecting adjacent cells. The sorted single cells were validated through PCR, confirming the accuracy of single-cell capture and sorting. Through simulations, we optimized the microwell thickness to minimize the required sorting energy, enabling over 95 % cell viability and over 88 % genome coverage of single cells. These highlight the flexibility and technical capabilities of the FMLS system, which will become attractive and invaluable sorting and detection tools for single- cell research, driving forward advancements in diagnostics, environmental science, and biotechnology.

To address the current limitations of time-gated Raman spectroscopy, specifically its narrow spectral range and low spectral resolution, and simultaneously acquire Raman and fluorescence life-time images, we have developed a Fourier-transform photon counting spectroscopy platform. A Mach-Zehnder interferometer employing a high accuracy linear motor stage was combined with photon-counting avalanche diodes and time-tagged acquisition, allowing to sort photons into a matrix of stage positions determined using their coarse arrival time with 50 ns steps of the excitation laser repetition period, and a fine arrival time of 80 ps resolution relative to the excitation pulse of 100 ps duration. The instrument achieves a time resolution of 547 ps, a wide spectral range of −1000 to 10,000 cm−1 Raman shift from the excitation at 532 nm wavelength, and a high spectral resolution of 0.05 cm−1. For experimental validation, we used fluorescently coated silicon wafers and fluorescent plastic microspheres. Raman signal was observed during the laser excitation pulse within the time-resolution, while fluorescence signals dominate afterwards. The results confirm that the instrument can effectively separate Raman and fluorescence signals. This work reports on a new concept of time-resolved photon counting Fourier-transform micro-spectroscopy, allowing continuous scanning by converting photon arrival times to interferometer position.

As Raman spectroscopy is applied to the detection of weak signals, such as in the field of microorganism analysis, the optimization of spectrometer systems that can overcome grating polarization dependency and enhance diffraction efficiency becomes increasingly important. To address these limitations, we proposed a method that combines the polarization state transformation technique with a pulse compression grating (PCG) that exhibits high diffraction efficiency for a particular polarization state. This method not only eliminates the polarization dependence but also significantly improves the overall diffraction efficiency, enabling a remarkable spectrometer throughput of over 84 %. Additionally, we introduced a method to improve the spectral resolution based on the grating anamorphic amplification effect, achieving a 3.7 cm-1 average spectral resolution while miniaturizing the spectrometer, and Escherichia coli colonies were tested to verify the performance of the whole system. Our research contributes to advancing the development of Raman spectrometers towards higher throughput, higher resolution, and miniaturization, enabling their application in a broader range of scenarios.

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.

Droplet microfluidics have found increasing applications across many fields. While droplet generation at a T-junction is a common method, its reliance on trial-and-error operation imposes undesirable constraints on its performance and applicability. In this study, we demonstrate a simple method for on-demand droplet formation at a T-junction with precise temporal control over individual droplet formation. Based on experimental observations, we also develop a physical model to describe the relationships among pressures, droplet generation, device geometry, and interfacial properties. Experimental validation demonstrates excellent performance of the model in predicting the pressure thresholds for switching droplet generation on and off. To address parameter uncertainties arising from real-world complexities, we show that monitoring droplet generation frequency provides a rapid, in situ approach for optimising experimental conditions. Our findings offer valuable guidelines for the design and automation of robust droplet-on-demand microfluidic systems, which can be readily implemented in conventional laboratories for a broad range of applications.

Merbecoviruses comprise four viral species with remarkable genetic diversity: MERS-related coronavirus, Tylonycteris bat coronavirus HKU4, Pipistrellus bat coronavirus HKU5, and Hedgehog coronavirus 1. However, the potential human spillover risk of animal merbecoviruses remains to be investigated. Here, we reported the discovery of HKU5-CoV lineage 2 (HKU5-CoV-2) in bats that efficiently utilize human angiotensin-converting enzyme 2 (ACE2) as a functional receptor and exhibits a broad host tropism. Cryo-EM analysis of HKU5-CoV-2 receptor-binding domain (RBD) and human ACE2 complex revealed an entirely distinct binding mode compared with other ACE2-utilizing merbecoviruses with RBD footprint largely shared with ACE2-using sarbecoviruses and NL63. Structural and functional analyses indicate that HKU5-CoV-2 has a better adaptation to human ACE2 than lineage 1 HKU5-CoV. Authentic HKU5-CoV-2 infected human ACE2-expressing cell lines and human respiratory and enteric organoids. This study reveals a distinct lineage of HKU5-CoVs in bats that efficiently use human ACE2 and underscores their potential zoonotic risk.