Raman image-activated cell sorting

Gray-Level Guided Image-Activated Droplet Sorter for Label-Free, High-Accuracy Screening of Single-Cell on Demand

Single-cell encapsulation in droplet microfluidics has become a powerful tool in precision medicine, single-cell analysis, and immunotherapy. However, droplet generation with a single-cell encapsulation is a random process, which also results in a large number of empty and multi-cell droplets. Current microfluidics sorting technologies suffer from drawbacks such as fluorescent labeling, inability to remove multi-cell droplets, or low throughput. This paper presents a gray-level guided image-activated droplet sorter (GL-IADS), which enables label-free, high-accuracy screening of single-cell droplets by rejecting empty and multi-cell droplets. The gray-level based recognition method can accurately classify droplet images (empty, single-cell, and multi-cell droplets), especially in differentiating empty and cell-laden droplets (accuracy of 100%). Crucially, this method reduces the image processing time to ≈300 µs, which makes the GL-IADS possible to reach an ultra-high sorting throughput up to hundreds or even KHz. The GL-IADS integrates the novel recognition method with a detachable acoustofluidic system, achieving sorting purity of 97.9%, 97.4%, and >99% for single-cell, multi-cell, and cell-laden droplets, respectively, with a throughput of 43 Hz. The GL-IADS holds promise for numerous biological applications that are previously difficult with fluorescence-based technologies.

Extracellular vesicle-based point-of-care testing for diagnosis and monitoring of Alzheimer's disease

Extracellular vesicles (EVs) show potential for early diagnosis of Alzheimer's disease (AD) and monitoring of its progression. However, EV-based AD diagnosis faces challenges due to the small size and low abundance of biomarkers. Here, we report a fully integrated organic electrochemical transistor (OECT) sensor for ultrafast, accurate, and convenient point-of-care testing (POCT) of serum EVs from AD patients. By utilizing acoustoelectric enrichment, the EVs can be quickly propelled, significantly enriched, and specifically bound to the OECT detection area, achieving a gain of over 280 times response in 30 s. The integrated POCT sensor can detect serum EVs from AD patients with a limit of detection as low as 500 EV particles/mL and a reduced detection time of just two minutes. Furthermore, the integrated POCT sensors were used to monitor AD progression in an AD mouse model by testing the mouse Aβ EVs at different time courses (up to 18 months) and compared with the Aβ accumulation using high-resolution magnetic resonance imaging (MRI). This innovative technology has the potential for accurate and rapid diagnosis of Alzheimer's and other neurodegenerative diseases, and monitoring of disease progression and treatment response.

Raman spectroscopy for cell analysis: Retrospect and prospect

Cell analysis is crucial to contemporary biomedical research, as it plays a pivotal role in elucidating life processes and advancing disease diagnosis and treatment. Raman spectroscopy, harnessing distinctive molecular vibrational data, provides a non-destructive method for cell analysis. This review surveys the progress of Raman spectroscopy in cellular analysis, emphasizing its utility in identifying individual cells, monitoring biomolecules, and assessing intracellular environments. A significant focus is placed on the novel application of triple-bond molecules as Raman tags, which enhance imaging capabilities by creating a distinctive signature with minimal background noise. The summary of Raman spectroscopy studies provides a forward-looking perspective on its applications.

Phylogeny-metabolism dual-directed single-cell genomics for dissecting and mining ecosystem function by FISH-scRACS-seq

Microbiome-wide association studies (MWASs) have uncovered microbial markers linked to ecosystem traits, but the mechanisms underlying their functions can remain elusive. This is largely due to challenges in validating their in situ metabolic activities and tracing such activities to individual genomes. Here, we introduced a phylogeny-metabolism dual-directed single-cell genomics approach called fluorescence-in situ-hybridization-guided single-cell Raman-activated sorting and sequencing (FISH-scRACS-seq). It directly localizes individual cells from target taxon via an FISH probe for marker organism, profiles their in situ metabolic functions via single-cell Raman spectra, sorts cells of target taxonomy and target metabolism, and produces indexed, high-coverage, and precisely-one-cell genomes. From cyclohexane-contaminated seawater, cells representing the MWAS-derived marker taxon of γ-Proteobacteria and that are actively degrading cyclohexane in situ were directly identified via FISH and Raman, respectively, then sorted and sequenced for one-cell full genomes. In such a Pseudoalteromonas fuliginea cell, we discovered a three-component cytochrome P450 system that can convert cyclohexane to cyclohexanol in vitro, representing a previously unknown group of cyclohexane-degrading enzymes and organisms. Therefore, by unveiling enzymes, pathways, genomes, and their in situ cellular functions specifically for those organisms with ecological relevance at one-cell resolution, FISH-scRACS-seq is a rational and generally applicable approach to dissecting and mining microbiota functions.

Recent Developments (After 2020) in Flow Cytometry Worldwide and Within China

This article reviews recent developments in flow cytometry that have a significant impact on both scientific research and clinical applications in the field of single-cell analysis, from the perspective of instrumentation and technical advances. As a starting point, this article investigates the latest state-of-the-art instruments of flow cytometry including different types in spectral, mass, imaging, nano, and label-free flow cytometry. A comparative analysis of the parameters and features of instruments from different companies elucidates the development trends in flow cytometry instrumentation. Following this, this article delves into cutting-edge technical advancements in flow cytometry. It summarizes the current research status of flow cytometry not only globally but also within China, highlighting emerging trends and innovations in the field. Finally, this article outlines future directions for the development of flow cytometry, indicating that each type of flow cytometry will follow its own trajectory toward achieving enhanced performance and broader applications in diverse fields.

The application of vibrational spectroscopy in forensic analysis of biological evidence

Vibrational spectroscopy is a powerful analytical domain, within which Fourier transform infrared spectroscopy (FTIR) and Raman spectroscopy stand as exemplars, offering high chemical specificity and sensitivity. These methodologies have been instrumental in the characterization of chemical compounds for an extensive period. They are particularly adept at the identification and analysis of minute sample quantities. Both FTIR and Raman spectroscopy are proficient in elucidating small liquid samples and detecting nuanced molecular alterations. The application of chemometrics further augments their analytical prowess. Currently, these techniques are in the research phase within forensic medicine and have yet to be broadly implemented in examination and identification processes. Nonetheless, studies have indicated that a combined classification model utilizing FTIR and Raman spectroscopy yields exceptional results for the identification of biological fluid-related information and the determination of causes of death. The objective of this review is to delineate the current research trajectory and potential applications of these two vibrational spectroscopic techniques in the detection of body fluids and the ascertainment of causes of death within the context of forensic medicine.

Microfluidics for morpholomics and spatial omics applications

Creative designs, precise fluidic manipulation, and automation have supported the development of microfluidics for single-cell applications. Together with the advancements in detection technologies and artificial intelligence (AI), microfluidic-assisted platforms have been increasingly used for new modalities of single-cell investigations and in spatial omics applications. This review explores the use of microfluidic technologies for morpholomics and spatial omics with a focus on single-cell and tissue characterization. We emphasize how various fluid dynamic principles and unique design integrations enable highly precise fluid manipulation, enhancing sample handling in morpholomics. Additionally, we examine the use of microfluidics-assisted spatial barcoding with micrometer resolutions for the spatial profiling of tissue specimens. Finally, we discuss how microfluidics can serve as a bridge for integrating multiple unique fields in omics research and outline key challenges that these technologies may face in practical translation.

Towards high-accuracy bacterial taxonomy identification using phenotypic single-cell Raman spectroscopy data

Single-cell Raman Spectroscopy (SCRS) emerges as a promising tool for single-cell phenotyping in environmental ecological studies, offering non-intrusive, high-resolution, and high-throughput capabilities. In this study, we obtained a large and the first comprehensive SCRS dataset that captured phenotypic variations with cell growth status for 36 microbial strains, and we compared and optimized analysis techniques and classifiers for SCRS-based taxonomy identification. First, we benchmarked five dimensionality reduction (DR) methods, 10 classifiers, and the impact of cell growth variances using a SCRS dataset with both taxonomy and cellular growth stage labels. Unsupervised DR methods and non-neural network classifiers are recommended for at a balance between accuracy and time efficiency, achieved up to 96.1% taxonomy classification accuracy. Second, accuracy variances caused by cellular growth variance (<2.9% difference) was found less than the influence from model selection (up to 41.4% difference). Remarkably, simultaneous high accuracy in growth stage classification (93.3%) and taxonomy classification (94%) were achievable using an innovative two-step classifier model. Third, this study is the first to successfully apply models trained on pure culture SCRS data to achieve taxonomic identification of microbes in environmental samples at an accuracy of 79%, and with validation via Raman-FISH (fluorescence in situ hybridization). This study paves the groundwork for standardizing SCRS-based biotechnologies in single-cell phenotyping and taxonomic classification beyond laboratory pure culture to real environmental microorganisms and promises advances in SCRS applications for elucidating organismal functions, ecological adaptability, and environmental interactions.

Deep Learning-Assisted Label-Free Parallel Cell Sorting with Digital Microfluidics

Sorting specific cells from heterogeneous samples is important for research and clinical applications. In this work, a novel label-free cell sorting method is presented that integrates deep learning image recognition with microfluidic manipulation to differentiate cells based on morphology. Using an Active-Matrix Digital Microfluidics (AM-DMF) platform, the YOLOv8 object detection model ensures precise droplet classification, and the Safe Interval Path Planning algorithm manages multi-target, collision-free droplet path planning. Simulations and experiments revealed that detection model precision, concentration ratios, and sorting cycles significantly affect recovery rates and purity. With HeLa cells and polystyrene beads as samples, the method achieved 98.5% sorting precision, 96.49% purity, and an 80% recovery over three cycles. After a series of experimental validations, this method can also be used to sort HeLa cells from red blood cells, cancer cells from white blood cells (represented by HeLa and Jurkat cells), and differentiate white blood cell subtypes (represented by HL-60 cells and Jurkat cells). Cells sorted using this method can be lysed directly on chip within their hosting droplets, ensuring minimal sample loss and suitability for downstream bioanalysis. This innovative AM-DMF cell sorting technique holds significant potential to advance diagnostics, therapeutics, and fundamental research in cell biology.

Unveiling the Molecular Secrets: A Comprehensive Review of Raman Spectroscopy in Biological Research

Raman spectroscopy has been proven to be a fast, convenient, and nondestructive technique for advancing our understanding of biological systems. The Raman effect originates from the inelastic scattering of light which directly probe vibration/rotational states in biological molecules and materials. Despite numerous advantages over infrared spectroscopy and continuous technical as well as operational improvement in Raman spectroscopy, an advanced development of the device and more applications have become possible. In this review, we explore the principles, techniques, and myriad applications of Raman spectroscopy in the realm of biology. We begin by providing an overview of Raman spectroscopy, highlighting its significance in unraveling the complexities of biological research. The focus of this review is on Raman spectroscopy concepts and methods, clarifying the fundamentals of Raman scattering and spectral interpretation. The review also highlights the key experimental considerations for productive biological applications. We explore the broad range of Raman applications including molecular structure, biomolecular composition, disease detection, and medication discovery. The Raman imaging and mapping can also be used to visualize biological samples at the molecular level. Raman spectroscopy is still developing, giving fresh insights and remedies, from biosensing to its use in tissue engineering and regenerative medicine. This review sheds light on the past, present, and future of Raman spectroscopy; it also highlights promising directions of future research developments and serves as a thorough resource for all researchers.

Flow cell for high throughput Raman spectroscopy of non-transparent solutions

This work introduces a high-throughput setup for Raman analysis of various flowing fluids, both transparent and non-transparent. The setup employs a microfluidic cell, used with an external optical setup, to control the sample flow's position and dimensions via 3-dimensional hydrodynamic focusing. This approach, in contrast to the prevalent use of fused silica capillaries, reduces the risk of sample photodegradation and boosts measurement efficiency, enhancing overall system throughput. The microfluidic cell has been further evolved to laminate two distinct flows from different samples in parallel. Using line excitation, both samples can be simultaneously excited without moving parts, further increasing throughput. This setup also enables real-time monitoring of phenomena like mixing or potential reactions between the two fluids. This development could significantly advance the creation of highly sensitive, high-throughput sensors for fluid composition analysis.

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.

Harnessing Raman spectroscopy for cell therapy bioprocessing

Cell therapy manufacturing requires precise monitoring of critical parameters to ensure product quality, consistency and to facilitate the implementation of cost-effective processes. While conventional analytical methods offer limited real-time insights, integration of process analytical technology tools such as Raman spectroscopy in bioprocessing has the potential to drive efficiency and reliability during the manufacture of cell-based therapies while meeting stringent regulatory requirements. The non-destructive nature of Raman spectroscopy, combined with its ability to be integrated on-line with scalable platforms, allows for continuous data acquisition, enabling real-time correlations between process parameters and critical quality attributes. Herein, we review the role of Raman spectroscopy in cell therapy bioprocessing and discuss how simultaneous measurement of distinct parameters and attributes, such as cell density, viability, metabolites and cell identity biomarkers can streamline on-line monitoring and facilitate adaptive process control. This, in turn, enhances productivity and mitigates process-related risks. We focus on recent advances integrating Raman spectroscopy across various manufacturing stages, from optimizing culture media feeds to monitoring bioprocess dynamics, covering downstream applications such as detection of co-isolated contaminating cells, cryopreservation, and quality control of the drug product. Finally, we discuss the potential of Raman spectroscopy to revolutionize current practices and accelerate the development of advanced therapy medicinal products.

Transformation gap from research findings to large-scale commercialized products in microfluidic field

The field of microfluidics has experienced rapid growth in the last several decades, yet it isn't considered to be a large industry comparable to semiconductor and consumer electronics. In this review, we analyzed the entire process of the transformation from research findings to commercialized products in microfluidics, as well as the significant gap during the whole developing process between microchip fabrication in R&D and large-scale production in the industry. We elaborated in detail on various materials in the microfluidics industry, including silicon, glass, PDMS, and thermoplastics, discussing their characteristics, production processes, and existing products. Despite challenges hindering the large-scale commercialization of microfluidic chips, ongoing advancements and applications are expected to integrate microfluidic technology into everyday life, transforming it into a commercially viable field with substantial potential and promising prospects.

Exploring Microbial Dark Matter for the Discovery of Novel Natural Products: Characteristics, Abundance Challenges and Methods

The objective of this review is to investigate microbial dark matter (MDM) with a focus on its potential for discovering novel natural products (NPs). This first part will examine the characteristics and abundance of these previously unexplored microbial communities, as well as the challenges faced in identifying and harnessing their unique biochemical properties and novel methods in this field. MDMs are thought to hold great potential for the discovery of novel NPs, which could have significant applications in medicine, agriculture, and industry. In recent years, there has been a growing interest in exploring MDM to unlock its potential. In fact, developments in genome-sequencing technologies and sophisticated phylogenetic procedures and metagenomic techniques have contributed to drastically make important changes in our sights on the diversity of microbial life, including the very outline of the tree of life. This has led to the development of novel technologies and methodologies for studying these elusive microorganisms, such as single-cell genomics, metagenomics, and culturomics. These approaches enable researchers to isolate and analyze individual microbial cells, as well as entire communities, providing insights into their genetic and metabolic potential. By delving into the MDM, scientists hope to uncover new compounds and biotechnological advancements that could have far-reaching impacts on various fields.

Toward video-rate compressive spontaneous Raman imaging via single-photon avalanche diode arrays

Spontaneous Raman microscopy is well-known for its remarkable chemical contrast yet suffers from slow acquisition speeds. Recently, the compressive Raman microspectroscopy framework has shown that a significant speed advantage is brought by leveraging shot-noise-limited detection using a single-photon avalanche diode (SPAD). However, current imaging speeds of compressive Raman architectures are fundamentally limited by SPAD sensitivity and dead time. Here, we demonstrate an efficient and scalable compressive Raman parallelization scheme based on SPAD arrays. We show that parallelization using line excitation, instead of spatial multiplexing, allows to reach effective pixel dwell times (τ pdt ) of 0.8 µs. Such fast speed represents over one order-of-magnitude speed-up over previous demonstrations. This effective parallelization not only allows for demonstrating unprecedented chemical imaging speeds using the otherwise weak spontaneous Raman effect but also paves the way for true video-rate inexpensive molecular microspectroscopy.

Label-free high-throughput impedance-activated cell sorting

Cell sorting holds broad applications in fields such as early cancer diagnosis, cell differentiation studies, drug screening, and single-cell sequencing. However, achieving high-throughput and high-purity in label-free single-cell sorting is challenging. To overcome this issue, we propose a label-free, high-throughput, and high-accuracy impedance-activated cell sorting system based on impedance detection and dual membrane pumps. Leveraging the low-latency characteristics of FPGA, the system facilitates real-time dual-frequency single-cell impedance detection with high-throughput (5 × 104 cells per s) for HeLa, MDA-MB-231, and Jurkat cells. Furthermore, the system accomplishes low-latency (less than 0.3 ms), label-free, high-throughput (1000 particles per s) and high-accuracy (almost 99%) single-particle sorting using FPGA-based high-precision sort-timing prediction. In experiments with Jurkat and MDA-MB-231 cells, the system achieved a throughput of up to 1000 cells per s, maintaining a pre-sorting purity of 28.57% and increasing post-sorting purity to 97.09%. These findings indicate that our system holds significant potential for applications in label-free, high-throughput cell sorting.

Deciphering the aging process through single-cell cytometric technologies

The advent of single-cell cytometric technologies, in conjunction with advances in single-cell biology, has significantly propelled forward the field of geroscience, enhancing our comprehension of the mechanisms underlying age-related diseases. Given that aging is a primary risk factor for numerous chronic health conditions, investigating the dynamic changes within the physiological landscape at the granularity of single cells is crucial for elucidating the molecular foundations of biological aging. Utilizing hallmarks of aging as a conceptual framework, we review current literature to delineate the progression of single-cell cytometric techniques and their pivotal applications in the exploration of molecular alterations associated with aging. We next discuss recent advancements in single-cell cytometry in terms of the development in instrument, software, and reagents, highlighting its promising and critical role in driving future breakthrough discoveries in aging research.

Accessible high-speed image-activated cell sorting

Over the past six decades, fluorescence-activated cell sorting (FACS) has become an essential technology for basic and clinical research by enabling the isolation of cells of interest in high throughput. Recent technological advancements have started a new era of flow cytometry. By combining the spatial resolution of microscopy with high-speed cell sorting, new instruments allow cell sorting based on simple image-derived parameters or sophisticated image analysis algorithms, thereby greatly expanding the scope of applications. In this review, we discuss the systems that are commercially available or have been described in enough methodological and engineering detail to allow their replication. We summarize their strengths and limitations and highlight applications that have the potential to transform various fields in basic life science research and clinical settings.

In-line Raman imaging of mixing by herringbone grooves in microfluidic channels

The control over fluid flow achievable in microfluidic devices creates opportunities for applications in many fields. In simple microchannels, flow is purely laminar when one solvent is used, and hence, achieving reliable mixing is an important design consideration. Integration of structures, such as grooves, into the channels to act as static mixers is a commonly used approach. The mixing induced by these structures can be validated by determining concentration profiles in microfluidic channels following convergence of solvent streams from separate inlets. Spatially resolved characterisation is therefore necessary and requires in-line analysis methods. Here we report a line-focused illumination approach to provide operando, spatially resolved Raman spectra across the width of channels in the analysis of single- and multi-phase liquid systems and chemical reactions. A scientific complementary metal oxide semiconductor (sCMOS) sensor is used to overcome smearing encountered during spectral readout of images with CCD sensors. Isotopically labelled probes, in otherwise identical flow streams, show that z-confocality limits the spatial resolution and certainty as to the extent of mixing that can be achieved. These limitations are overcome using fast chemical reactions between reagents entering a microchannel in separate solvent streams. We show here that the progression of a chemical reaction, for which only the product is observable, is a powerful approach to determine the extent of mixing in a microchannel. Specifically resonance enhancement of Raman scattering from a product formed allows for determination of the true efficiency of mixing over the length and width of microchannels. Raman spectral images obtained by line-focused illumination show onset of mixing by observing the product of reagents entering from the separate inlets. Mixing is initially off-centre and immediately before the apex of the first groove of the static mixer, and then evolves along the entire width of the channel after a full cycle of grooves.

Computational Hyperspectral Microflow Cytometry

Miniaturized flow cytometry has significant potential for portable applications, such as cell-based diagnostics and the monitoring of therapeutic cell manufacturing, however, the performance of current techniques is often limited by the inability to resolve spectrally-overlapping fluorescence labels. Here, the study presents a computational hyperspectral microflow cytometer (CHC) that enables accurate discrimination of spectrally-overlapping fluorophores labeling single cells. CHC employs a dispersive optical element and an optimization algorithm to detect the full fluorescence emission spectrum from flowing cells, with a high spectral resolution of ≈3 nm in the range from 450 to 650 nm. CHC also includes a dedicated microfluidic device that ensures in-focus imaging through viscoelastic sheathless focusing, thereby enhancing the accuracy and reliability of microflow cytometry analysis. The potential of CHC for analyzing T lymphocyte subpopulations and monitoring changes in cell composition during T cell expansion is demonstrated. Overall, CHC represents a major breakthrough in microflow cytometry and can facilitate its use for immune cell monitoring.

Searching for the optimal microbial factory: high-throughput biosensors and analytical techniques for screening small molecules

High-throughput screening technologies have been lacking in comparison to the plethora of high-throughput genetic diversification techniques developed in biotechnology. This review explores the challenges and advancements in high-throughput screening for high-value natural products, focusing on the critical need to expand ligand targets for biosensors and increase the throughput of analytical techniques in screening microbial cell libraries for optimal strain performance. The engineering techniques to broaden the scope of ligands for biosensors, such as transcription factors, G protein-coupled receptors and riboswitches are discussed. On the other hand, integration of microfluidics with traditional analytical methods is explored, covering fluorescence-activated cell sorting, Raman-activated cell sorting and mass spectrometry, emphasising recent developments in maximising throughput.

Vibrational imaging of metabolites for improved microbial cell strains

Significance

Biomanufacturing utilizes modified microbial systems to sustainably produce commercially important biomolecules for use in agricultural, energy, food, material, and pharmaceutical industries. However, technological challenges related to non-destructive and high-throughput metabolite screening need to be addressed to fully unlock the potential of synthetic biology and sustainable biomanufacturing.

Aim

This perspective outlines current analytical screening tools used in industrial cell strain development programs and introduces label-free vibrational spectro-microscopy as an alternative contrast mechanism.

Approach

We provide an overview of the analytical instrumentation currently used in the "test" portion of the design, build, test, and learn cycle of synthetic biology. We then highlight recent progress in Raman scattering and infrared absorption imaging techniques, which have enabled improved molecular specificity and sensitivity.

Results

Recent developments in high-resolution chemical imaging methods allow for greater throughput without compromising the image contrast. We provide a roadmap of future work needed to support integration with microfluidics for rapid screening at the single-cell level.

Conclusions

Quantifying the net expression of metabolites allows for the identification of cells with metabolic pathways that result in increased biomolecule production, which is essential for improving the yield and reducing the cost of industrial biomanufacturing. Technological advancements in vibrational microscopy instrumentation will greatly benefit biofoundries as a complementary approach for non-destructive cell screening.

Raman cell sorting for single-cell research

Cells constitute the fundamental units of living organisms. Investigating individual differences at the single-cell level facilitates an understanding of cell differentiation, development, gene expression, and cellular characteristics, unveiling the underlying laws governing life activities in depth. In recent years, the integration of single-cell manipulation and recognition technologies into detection and sorting systems has emerged as a powerful tool for advancing single-cell research. Raman cell sorting technology has garnered attention owing to its non-labeling, non-destructive detection features and the capability to analyze samples containing water. In addition, this technology can provide live cells for subsequent genomics analysis and gene sequencing. This paper emphasizes the importance of single-cell research, describes the single-cell research methods that currently exist, including single-cell manipulation and single-cell identification techniques, and highlights the advantages of Raman spectroscopy in the field of single-cell analysis by comparing it with the fluorescence-activated cell sorting (FACS) technique. It describes various existing Raman cell sorting techniques and introduces their respective advantages and disadvantages. The above techniques were compared and analyzed, considering a variety of factors. The current bottlenecks include weak single-cell spontaneous Raman signals and the requirement for a prolonged total cell exposure time, significantly constraining Raman cell sorting technology's detection speed, efficiency, and throughput. This paper provides an overview of current methods for enhancing weak spontaneous Raman signals and their associated advantages and disadvantages. Finally, the paper outlines the detailed information related to the Raman cell sorting technology mentioned in this paper and discusses the development trends and direction of Raman cell sorting.

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.

Microfluidic-SERS Technologies for CTC: A Perspective on Clinical Translation

Enumeration and phenotypic profiling of circulating tumor cells (CTCs) provide critical information for clinical diagnosis and treatment monitoring in cancer. To achieve this goal, an integrated system is needed to efficiently isolate CTCs from patient samples and sensitively evaluate their phenotypes. Such integration would comprise a high-throughput single-cell processing unit for the isolation and manipulation of CTCs and a sensitive and multiplexed quantitation unit to detect clinically relevant signals from these cells. Surface-enhanced Raman scattering (SERS) has been used as an analytical method for molecular profiling and in vitro cancer diagnosis. More recently, its multiplexing capability and power to create distinct molecular signatures against their targets have garnered attention. Here, we share our insights into the combined power of microfluidics and SERS in realizing CTC isolation, enumeration, and detection from a clinical translation perspective. We highlight the key operational factors in CTC microfluidic processing and SERS detection from patient samples. We further discuss microfluidic-SERS integration and its clinical utility as a paradigm shift in clinical CTC-based cancer diagnosis and prognostication. Finally, we summarize the challenges and attempt to look forward to what lies ahead of us in potentially translating the technique into real clinical applications.

Imaging the intracellular refractive index distribution (IRID) for dynamic label-free living colon cancer cells via circularly depolarization decay model (CDDM)

Label-free detection of intracellular substances for living cancer cells remains a significant hurdle in cancer pathogenesis research. Although the sensitivity of light polarization to intracellular substances has been validated, current studies are predominantly focused on tissue lesions, thus label-free detection of substances within individual living cancer cells is still a challenge. The main difficulty is to find specific detection methods along with corresponding characteristic parameters. With refractive index as an endogenous marker of substances, this study proposes a detection method of intracellular refractive index distribution (IRID) for label-free living colon cancer (LoVo) cells. Utilizing the circular depolarization decay model (CDDM) to calculate the degree of circular polarization (DOCP) modulated by the cell allows for the derivation of the IRID on the focal plane. Experiments on LoVo cells demonstrated the refractive index of single cell can be accurately and precisely measured, with precision of 10-3 refractive index units (RIU). Additionally, chromatin content during the interphases (G1, S, G2) of cell cycle was recorded at 56.5%, 64.4%, and 71.5%, respectively. A significantly finer IRID can be obtained compared to the phase measurement method. This method is promising in providing a dynamic label-free intracellular substances detection method in cancer pathogenesis studies.

Raman Flow Cytometry and Its Biomedical Applications

Raman flow cytometry (RFC) uniquely integrates the "label-free" capability of Raman spectroscopy with the "high-throughput" attribute of traditional flow cytometry (FCM), offering exceptional performance in cell characterization and sorting. Unlike conventional FCM, RFC stands out for its elimination of the dependency on fluorescent labels, thereby reducing interference with the natural state of cells. Furthermore, it significantly enhances the detection information, providing a more comprehensive chemical fingerprint of cells. This review thoroughly discusses the fundamental principles and technological advantages of RFC and elaborates on its various applications in the biomedical field, from identifying and characterizing cancer cells for in vivo cancer detection and surveillance to sorting stem cells, paving the way for cell therapy, and identifying metabolic products of microbial cells, enabling the differentiation of microbial subgroups. Moreover, we delve into the current challenges and future directions regarding the improvement in sensitivity and throughput. This holds significant implications for the field of cell analysis, especially for the advancement of metabolomics.

Advances in succinic acid production: the enhancement of CO2 fixation for the carbon sequestration benefits

Succinic acid (SA), one of the 12 top platform chemicals produced from biomass, is a precursor of various high value-added derivatives. Specially, 1 mol CO2 is assimilated in 1 mol SA biosynthetic route under anaerobic conditions, which helps to achieve carbon reduction goals. In this review, methods for enhanced CO2 fixation in SA production and utilization of waste biomass for SA production are reviewed. Bioelectrochemical and bioreactor coupling systems constructed with off-gas reutilization to capture CO2 more efficiently were highlighted. In addition, the techno-economic analysis and carbon sequestration benefits for the synthesis of bio-based SA from CO2 and waste biomass are analyzed. Finally, a droplet microfluidics-based high-throughput screening technique applied to the future bioproduction of SA is proposed as a promising approach.

Analysis of cellular response to drugs with a microfluidic single-cell platform based on hyperspectral imaging

BACKGROUND

Cellular response to pharmacological action of drugs is significant for drug development. Traditional detection method for cellular response to drugs normally rely on cell proliferation assay and metabolomics examination. In principle, these analytical methods often required cell labeling, invasion analysis, and hours of co-culture with drugs, which are relatively complex and time-consuming. Moreover, these methods can only indicate the drug effectiveness on cell colony rather than single cells. Thus, to meet the requirements of personal precision medicine, the development of drug response analysis on the high resolution of single cell is demanded.

RESULTS

To provide precise result for drug response on single-cell level, a microfluidic platform coupled with the label-free hyperspectral imaging was developed. With the help of horizontal single-cell trapping sieves, hundreds of single cells were trapped independently in microfluidic channels for the purposes of real-time drug delivery and single-cell hyperspectral image recording. To significantly identify the cellular hyperspectral change after drug stimulation, the differenced single-cell spectrum was proposed. Compared with the deep learning classification method based on hyperspectral images, an optimal performance can be achieved by the classification strategy based on differenced spectra. And the cellular response to different reagents, for example, K+, Epidermal Growth Factor (EGF), and Gefitinib at different concentrations can be accurately characterized by the differenced single-cell spectra analysis.

SIGNIFICANCE AND NOVELTY

The high-throughput, rapid analysis of cellular response to drugs at the single-cell level can be accurately performed by our platform. After systematically analyzing the materials and the structures of the single-cell microfluidic chip, the optimal single-cell trapping method was proposed to contribute to the further application of hyperspectral imaging on microfluidic single-cell analysis. And the hyperspectral characterization of single-cell with cancer drug stimulation proved the application potential of our method in personal cancer medication.

Mapping cancer biology in space: applications and perspectives on spatial omics for oncology

Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.

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.

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.

Recent advances in deformation-assisted microfluidic cell sorting technologies

Cell sorting is an essential prerequisite for cell research and has great value in life science and clinical studies. Among the many microfluidic cell sorting technologies, label-free methods based on the size of different cell types have been widely studied. However, the heterogeneity in size for cells of the same type and the inevitable size overlap between different types of cells would result in performance degradation in size-based sorting. To tackle such challenges, deformation-assisted technologies are receiving more attention recently. Cell deformability is an inherent biophysical marker of cells that reflects the changes in their internal structures and physiological states. It provides additional dimensional information for cell sorting besides size. Therefore, in this review, we summarize the recent advances in deformation-assisted microfluidic cell sorting technologies. According to how the deformability is characterized and the form in which the force acts, the technologies can be divided into two categories: (1) the indirect category including transit-time-based and image-based methods, and (2) the direct category including microstructure-based and hydrodynamics-based methods. Finally, the separation performance and the application scenarios of each method, the existing challenges and future outlook are discussed. Deformation-assisted microfluidic cell sorting technologies are expected to realize greater potential in the label-free analysis of cells.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.

Development of a Microfluidic Flow Cytometer with a Uniform Optical Field (Uni-μFCM) Enabling Quantitative Analysis of Single-Cell Proteins and Its Applications in Leukemia Gating, Tumor Classification, and Hierarchy of Cancer Stem Cells

Fast and quantitative estimation of single-cell proteins with various distribution patterns remains a technical challenge. Here, a microfluidic flow cytometer with a uniform optical field (Uni-μFCM) was developed, which enabled the translation of multicolor fluorescence signals of bound antibodies into targeted protein numbers with arbitrary distributions of biological cells. As the core of Uni-μFCM, a uniform optical field for optical excitation and fluorescence detection was realized by adopting a microfabricated metal window to shape the optical beam for excitation, which was modeled and validated by both numerical simulation and experimental characterization. After the validation of Uni-μFCM in single-cell protein quantification by measuring single-cell expressions of three transcriptional factors from four cell lines of variable sizes and origins, Uni-μFCM was applied to (1) quantify membrane and cytoplasmic markers of myeloid and lymphocytic leukocytes to classify cell lines and normal and patient blood samples; (2) measure single-cell expressions of key cytokines affiliated with gene stabilities, differentiating paired oral and colon tumor cell lines with varied malignancies, and (3) quantify single-cell stemming markers of liver tumor cell lines, cell subtypes, and liver patient samples to determine a variety of lineage hierarchy. By quantitatively assessing complex cellular phenotypes, Uni-μFCM substantially expanded the phenotypic space accessible to single-cell applications in leukemia gating, tumor classification, and hierarchy determination of cancer stem cells.

Machine learning implementation strategy in imaging and impedance flow cytometry

Imaging and impedance flow cytometry is a label-free technique that has shown promise as a potential replacement for standard flow cytometry. This is due to its ability to provide rich information and archive high-throughput analysis. Recently, significant efforts have been made to leverage machine learning for processing the abundant data generated by those techniques, enabling rapid and accurate analysis. Harnessing the power of machine learning, imaging and impedance flow cytometry has demonstrated its capability to address various complex phenotyping scenarios. Herein, we present a comprehensive overview of the detailed strategies for implementing machine learning in imaging and impedance flow cytometry. We initiate the discussion by outlining the commonly employed setup to acquire the data (i.e., image or signal) from the cell. Subsequently, we delve into the necessary processes for extracting features from the acquired image or signal data. Finally, we discuss how these features can be utilized for cell phenotyping through the application of machine learning algorithms. Furthermore, we discuss the existing challenges and provide insights for future perspectives of intelligent imaging and impedance flow cytometry.

Coherent Raman scattering microscopy of lipid droplets in cells and tissues

Lipid droplets (LDs) play a key role as the hub for lipid metabolism to maintain cellular metabolic homeostasis. Understanding the functions and changes of LDs in different pathological conditions is crucial for identifying new markers for diagnosis and discovering new targets for treatment. In recent years, coherent Raman scattering (CRS) microscopy has been popularized for the imaging and quantification of LDs in live cells. Compared to spontaneous Raman scattering microscopy, CRS microscopy offers a much higher imaging speed while maintaining similar chemical information. Due to the high lipid density, LDs usually have strong CRS signals and therefore are the most widely studied organelle in the CRS field. In this review, we discuss recent achievements using CRS to study the quantity, distribution, composition, and dynamics of LDs in various systems.

Advanced flow cytometry for biomedical applications

Flow cytometry (FC) is a versatile tool with excellent capabilities to detect and measure multiple characteristics of a population of cells or particles. Notable advancements in in vivo photoacoustic FC, coherent Raman FC, microfluidic FC, and so on, have been achieved in the last two decades, which endows FC with new functions and expands its applications in basic research and clinical practice. Advanced FC broadens the tools available to researchers to conduct research involving cancer detection, microbiology (COVID-19, HIV, bacteria, etc.), and nucleic acid analysis. This review presents an overall picture of advanced flow cytometers and provides not only a clear understanding of their mechanisms but also new insights into their practical applications. We identify the latest trends in this area and aim to raise awareness of advanced techniques of FC. We hope this review expands the applications of FC and accelerates its clinical translation.

Practices, Potential, and Perspectives for Detecting Predisease Using Raman Spectroscopy

Raman spectroscopy shows great potential for practical clinical applications. By analyzing the structure and composition of molecules through real-time, non-destructive measurements of the scattered light from living cells and tissues, it offers valuable insights. The Raman spectral data directly link to the molecular composition of the cells and tissues and provides a "molecular fingerprint" for various disease states. This review focuses on the practical and clinical applications of Raman spectroscopy, especially in the early detection of human diseases. Identifying predisease, which marks the transition from a healthy to a disease state, is crucial for effective interventions to prevent disease onset. Raman spectroscopy can reveal biological processes occurring during the transition states and may eventually detect the molecular dynamics in predisease conditions.

High-precision, low-complexity, high-resolution microscopy-based cell sorting

Continuous flow cell sorting based on image analysis is a powerful concept that exploits spatially-resolved features in cells, such as subcellular protein localisation or cell and organelle morphology, to isolate highly specialised cell types that were previously inaccessible to biomedical research, biotechnology, and medicine. Recently, sorting protocols have been proposed that achieve impressive throughput by combining ultra-high flow rates with sophisticated imaging and data processing protocols. However, moderate image quality and high complex experimental setups still prevent the full potential of image-activated cell sorting from being a general-purpose tool. Here, we present a new low-complexity microfluidic approach based on high numerical aperture wide-field microscopy and precise dielectrophoretic cell handling. It provides high-quality images with unprecedented resolution in image-activated cell sorting (i.e., 216 nm). In addition, it also allows long image processing times of several hundred milliseconds for thorough image analysis, while ensuring reliable and low-loss cell processing. Using our approach, we sorted live T cells based on subcellular localisation of fluorescence signals and demonstrated that purities above 80% are possible while targeting maximum yields and sample volume throughputs in the range of μl min^-1. We were able to recover 85% of the target cells analysed. Finally, we ensure and quantify the full vitality of the sorted cells cultivating the cells for a period of time and through colorimetric viability tests.

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.

Dispersive phase microscopy incorporated with droplet-based microfluidics for biofactory-on-a-chip

Biomolecular imaging of intracellular structures of a single cell and subsequent screening of the cells are of high demand in metabolic engineering to develop strains with the desired phenotype. However, the capability of current methods is limited to population-scale identification of cell phenotyping. To address this challenge, we propose to utilize dispersive phase microscopy incorporated with a droplet-based microfluidic system that combines droplet volume-on-demand generation, biomolecular imaging, and droplet-on-demand sorting, to achieve high-throughput screening of cells with an identified phenotype. Particularly, cells are encapsulated in homogeneous environments with microfluidic droplet formation, and the biomolecule-induced dispersive phase can be investigated to indicate the biomass of a specific metabolite in a single cell. The retrieved biomass information consequently guides the on-chip droplet sorting unit to screen cells with the desired phenotype. To demonstrate the proof of concept, we showcase the method by promoting the evolution of the Haematococcus lacustris strain toward a high production of natural antioxidant astaxanthin. The validation of the proposed system with on-chip single-cell imaging and droplet manipulation functionalities reveals the high-throughput single-cell phenotyping and selection potential that applies to many other biofactory scenarios, such as biofuel production, critical quality attribute control in cell therapy, etc.

The marriage of coherent Raman scattering imaging and advanced computational tools

Coherent Raman scattering microscopy can provide high-contrast tissue and single-cell images based on the inherent molecular vibrations of the sample. However, conventional techniques face a three-way trade-off between Raman spectral bandwidth, imaging speed, and image fidelity. Although currently challenging to address via optical design, this trade-off can be overcome via emerging computational tools such as compressive sensing and machine learning.

High-Speed Stimulated Raman Scattering Microscopy Using Inertia-Free AOD Scanning

High-throughput stimulated Raman scattering (SRS) microscopy is highly desired for large tissue imaging with chemical specificity. However, the mapping speed remains as the major short board of conventional SRS, primarily owing to the mechanical inertia existing in galvanometers or other laser scanning alternatives. Here, we developed inertia-free acousto-optic deflector (AOD)-based high-speed large-field stimulated Raman scattering microscopy, in which both the speed and integration time are ensured by immune of the mechanical response time. To avoid laser beam distortion induced by the intrinsic spatial dispersion of AODs, two spectral compression systems are implemented to compress the broad-band femtosecond pulse to picosecond laser. We achieved an SRS imaging of a 12 × 8 mm² mouse brain slice in only 8 min at an image resolution of approximately 1 μm and 32 slices from a whole brain in 12 h. The AOD-based inertia-free SRS mapping can be much faster after further upgrading and allow broad-spectrum applications of chemical imaging in the future.

Stimulated Raman Scattering Imaging Sheds New Light on Lipid Droplet Biology

A lipid droplet (LD) is a dynamic organelle closely associated with cellular functions and energy homeostasis. Dysregulated LD biology underlies an increasing number of human diseases, including metabolic disease, cancer, and neurodegenerative disorder. Commonly used lipid staining and analytical tools have difficulty providing the information regarding LD distribution and composition at the same time. To address this problem, stimulated Raman scattering (SRS) microscopy uses the intrinsic chemical contrast of biomolecules to achieve both direct visualization of LD dynamics and quantitative analysis of LD composition with high molecular selectivity at the subcellular level. Recent developments of Raman tags have further enhanced sensitivity and specificity of SRS imaging without perturbing molecular activity. With these advantages, SRS microscopy has offered great promise for deciphering LD metabolism in single live cells. This article overviews and discusses the latest applications of SRS microscopy as an emerging platform to dissect LD biology in health and disease.