High-Throughput Raman Flow Cytometry and Beyond

High-Resolution Raman Imaging of >300 Patient-Derived Cells from Nine Different Leukemia Subtypes: A Global Clustering Approach

Leukemia comprises a diverse group of bone marrow tumors marked by cell proliferation. Current diagnosis involves identifying leukemia subtypes through visual assessment of blood and bone marrow smears, a subjective and time-consuming method. Our study introduces the characterization of different leukemia subtypes using a global clustering approach of Raman hyperspectral maps of cells. We analyzed bone marrow samples from 19 patients, each presenting one of nine distinct leukemia subtypes, by conducting high spatial resolution Raman imaging on 319 cells, generating over 1.3 million spectra in total. An automated preprocessing pipeline followed by a single-step global clustering approach performed over the entire data set identified relevant cellular components (cytoplasm, nucleus, carotenoids, myeloperoxidase (MPO), and hemoglobin (HB)) enabling the unsupervised creation of high-quality pseudostained images at the single-cell level. Furthermore, this approach provided a semiquantitative analysis of cellular component distribution, and multivariate analysis of clustering results revealed the potential of Raman imaging in leukemia research, highlighting both advantages and challenges associated with global clustering.

Statistical approaches to Raman imaging: principal component score mapping

In this research, Raman imaging was employed to map various samples, and the resulting data were analyzed using a suite of automated tools to extract critical information, including intensity and signal-to-noise ratio. The acquired spectra were further processed to identify similarities and investigate patterns using principal component analysis. The objective of this study was to establish guidelines for investigating Raman imaging results, particularly when dealing with large datasets comprising thousands of relatively low-intensity spectra. The overall quality of the results was assessed, and representative locations were determined based on the main Raman bands. While automated software solutions are insufficient for removing baselines and fitting the data, statistical analysis proved to be a powerful tool for extracting valuable information directly from the raw spectral data. This approach enables the extraction of as much information as possible from large arrays of spectral data, even in complex cases where automated software may fall short. The findings of this study contribute to enhancing the analysis and interpretation of Raman imaging results, providing researchers with a robust methodology for extracting meaningful insights from complex datasets, reducing the amount of effort required during data interpretation and analysis.

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.

Identifying phenotype-associated subpopulations through LP_SGL

Single-cell RNA sequencing (scRNA-seq) enables the resolution of cellular heterogeneity in diseases and facilitates the identification of novel cell types and subtypes. However, the grouping effects caused by cell-cell interactions are often overlooked in the development of tools for identifying subpopulations. We proposed LP_SGL which incorporates cell group structure to identify phenotype-associated subpopulations by integrating scRNA-seq, bulk expression and bulk phenotype data. Cell groups from scRNA-seq data were obtained by the Leiden algorithm, which facilitates the identification of subpopulations and improves model robustness. LP_SGL identified a higher percentage of cancer cells, T cells and tumor-associated cells than Scissor and scAB on lung adenocarcinoma diagnosis, melanoma drug response and liver cancer survival datasets, respectively. Biological analysis on three original datasets and four independent external validation sets demonstrated that the signaling genes of this cell subset can predict cancer, immunotherapy and survival.

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

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

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.

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.

A review on intelligent impedance cytometry systems: Development, applications and advances

Impedance cytometry is a well-established technique for counting and analyzing single cells, with several advantages, such as convenience, high throughput, and no labeling required. A typical experiment consists of the following steps: single-cell measurement, signal processing, data calibration, and particle subtype identification. At the beginning of this article, we compared commercial and self-developed options extensively and provided references for developing reliable detection systems, which are necessary for cell measurement. Then, a number of typical impedance metrics and their relationships to biophysical properties of cells were analyzed with respect to the impedance signal analysis. Given the rapid advances of intelligent impedance cytometry in the past decade, this article also discussed the development of representative machine learning-based approaches and systems, and their applications in data calibration and particle identification. Finally, the remaining challenges facing the field were summarized, and potential future directions for each step of impedance detection were discussed.

Continuous microfluidic flow-through protocol for selective and image-activated electroporation of single cells

Electroporation of cells is a widely-used tool to transport molecules such as proteins or nucleic acids into cells or to extract cellular material. However, bulk methods for electroporation do not offer the possibility to selectively porate subpopulations or single cells in heterogeneous cell samples. To achieve this, either presorting or complex single-cell technologies are required currently. In this work, we present a microfluidic flow protocol for selective electroporation of predefined target cells identified in real-time by high-quality microscopic image analysis of fluorescence and transmitted light. While traveling through the microchannel, the cells are focused by dielectrophoretic forces into the microscopic detection area, where they are classified based on image analysis techniques. Finally, the cells are forwarded to a poration electrode and only the target cells are pulsed. By processing a heterogenically stained cell sample, we were able to selectively porate only target cells (green-fluorescent) while non-target cells (blue-fluorescent) remained unaffected. We achieved highly selective poration with >90% specificity at average poration rates of >50% and throughputs of up to 7200 cells per hour.

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.

Rapid Phenotypic Antimicrobial Susceptibility Testing Using a Coulter Counter and Proliferation Rate Discrepancy

The rapid determination of antimicrobial susceptibility and evidence-based antimicrobial prescription is necessary to combat widespread antimicrobial resistance and promote effectively treatment for bacterial infections. This study developed a rapid phenotypic antimicrobial susceptibility determination method competent for seamless clinical implementation. A laboratory-friendly Coulter counter-based antimicrobial susceptibility testing (CAST) was developed and integrated with bacterial incubation, population growth monitoring, and result analysis to quantitatively detect differences in bacterial growth between resistant and susceptible strains following a 2 h exposure to antimicrobial agents. The distinct proliferation rates of the different strains enabled the rapid determination of their antimicrobial susceptibility phenotypes. We evaluated the performance efficacy of CAST for 74 clinically isolated Enterobacteriaceae subjected to 15 antimicrobials. The results were consistent with those obtained via the 24 h broth microdilution method, showing 90.18% absolute categorical agreement.

Isolation and Cultivation of Human Gut Microorganisms: A Review

Microbial resources from the human gut may find use in various applications, such as empirical research on the microbiome, the development of probiotic products, and bacteriotherapy. Due to the development of "culturomics", the number of pure bacterial cultures obtained from the human gut has significantly increased since 2012. However, there is still a considerable number of human gut microbes to be isolated and cultured. Thus, to improve the efficiency of obtaining microbial resources from the human gut, some constraints of the current methods, such as labor burden, culture condition, and microbial targetability, still need to be optimized. Here, we overview the general knowledge and recent development of culturomics for human gut microorganisms. Furthermore, we discuss the optimization of several parts of culturomics including sample collection, sample processing, isolation, and cultivation, which may improve the current strategies.

Phase flow cytometry with coherent modulation imaging

Label-free imaging and identification of fast-moving cells is a very challenging task. A kind of phase flow cytometry using coherent modulation imaging was proposed to realize label-free imaging and identification on fast-moving cells with compact optical alignment and high accuracy. Phase image of cells under inspection could be computed qualitatively from their diffraction patterns at the accuracy of about 0.01 wavelength and the resolution of about 1.23 μm and the view field of 0.126 mm² . Since the imaging system was mainly composed by a piece of random phase plate a detector without using commonly adopted reference beam and corresponding complex optical alignment, this method has much compacter optical structure and much higher tolerance capability to environmental instability in comparison with other kinds of phase flow cytometry. Current experimental results prove it could be an efficient optical tool for label-free tumor cell detection.

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

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

Constructing an In Vitro and In Vivo Flow Cytometry by Fast Line Scanning of Confocal Microscopy

Composed of a fluidic and an optical system, flow cytometry has been widely used for biosensing. The fluidic flow enables its automatic high-throughput sample loading and sorting while the optical system works for molecular detection by fluorescence for micron-level cells and particles. This technology is quite powerful and highly developed; however, it requires a sample in the form of a suspension and thus only works in vitro. In this study, we report a simple scheme to construct a flow cytometry based on a confocal microscope without any modifications. We demonstrate that line scanning of microscopy can effectively excite fluorescence of flowing microbeads or cells in a capillary tube in vitro and in blood vessels of live mice in vivo. This method can resolve microbeads at several microns and the results are comparable to a classic flow cytometer. The absolute diameter of flowing samples can be indicated directly. The sampling limitations and variations of this method is carefully analyzed. This scheme can be easily accomplished by any commercial confocal microscope systems, expands the function of them, and is of promising potential for simultaneous confocal microscopy and in vivo detection of cells in blood vessels of live animals by a single system.

Trajectory Inference for Unraveling Dynamic Biological Processes from Raman Spectral Data

Cell heterogeneity is a crucial parameter for understanding the complexity of numerous biomedical issues. Trajectory inference-based approaches are recent tools developed for single-cell transcriptomics (scRNA-seq) data analysis. They aim to reconstruct evolving pathways from the variety of cell states that coexist simultaneously in a cell population. We propose to expand this concept to Raman spectroscopy, a label-free modality that probes the global molecular nature of a sample, by investigating the dynamics of adipocyte differentiation.

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Label-free microfluidic cell sorting and detection for rapid blood analysis

Blood tests are considered as standard clinical procedures to screen for markers of diseases and health conditions. However, the complex cellular background (>99.9% RBCs) and biomolecular composition often pose significant technical challenges for accurate blood analysis. An emerging approach for point-of-care blood diagnostics is utilizing "label-free" microfluidic technologies that rely on intrinsic cell properties for blood fractionation and disease detection without any antibody binding. A growing body of clinical evidence has also reported that cellular dysfunction and their biophysical phenotypes are complementary to standard hematoanalyzer analysis (complete blood count) and can provide a more comprehensive health profiling. In this review, we will summarize recent advances in microfluidic label-free separation of different blood cell components including circulating tumor cells, leukocytes, platelets and nanoscale extracellular vesicles. Label-free single cell analysis of intrinsic cell morphology, spectrochemical properties, dielectric parameters and biophysical characteristics as novel blood-based biomarkers will also be presented. Next, we will highlight research efforts that combine label-free microfluidics with machine learning approaches to enhance detection sensitivity and specificity in clinical studies, as well as innovative microfluidic solutions which are capable of fully integrated and label-free blood cell sorting and analysis. Lastly, we will envisage the current challenges and future outlook of label-free microfluidics platforms for high throughput multi-dimensional blood cell analysis to identify non-traditional circulating biomarkers for clinical diagnostics.

Label-free multiphoton imaging flow cytometry

Label-free imaging flow cytometry is a powerful tool for biological and medical research as it overcomes technical challenges in conventional fluorescence-based imaging flow cytometry that predominantly relies on fluorescent labeling. To date, two distinct types of label-free imaging flow cytometry have been developed, namely optofluidic time-stretch quantitative phase imaging flow cytometry and stimulated Raman scattering (SRS) imaging flow cytometry. Unfortunately, these two methods are incapable of probing some important molecules such as starch and collagen. Here, we present another type of label-free imaging flow cytometry, namely multiphoton imaging flow cytometry, for visualizing starch and collagen in live cells with high throughput. Our multiphoton imaging flow cytometer is based on nonlinear optical imaging whose image contrast is provided by two optical nonlinear effects: four-wave mixing (FWM) and second-harmonic generation (SHG). It is composed of a microfluidic chip with an acoustic focuser, a lab-made laser scanning SHG-FWM microscope, and a high-speed image acquisition circuit to simultaneously acquire FWM and SHG images of flowing cells. As a result, it acquires FWM and SHG images (100 × 100 pixels) with a spatial resolution of 500 nm and a field of view of 50 μm × 50 μm at a high event rate of four to five events per second, corresponding to a high throughput of 560-700 kb/s, where the event is defined by the passage of a cell or a cell-like particle. To show the utility of our multiphoton imaging flow cytometer, we used it to characterize Chromochloris zofingiensis (NIES-2175), a unicellular green alga that has recently attracted attention from the industrial sector for its ability to efficiently produce valuable materials for bioplastics, food, and biofuel. Our statistical image analysis found that starch was distributed at the center of the cells at the early cell cycle stage and became delocalized at the later stage. Multiphoton imaging flow cytometry is expected to be an effective tool for statistical high-content studies of biological functions and optimizing the evolution of highly productive cell strains.

Droplet microfluidics for CTC-based liquid biopsy: a review

Circulating tumor cells (CTCs) are important biomarkers of liquid biopsy. The number and heterogeneity of CTCs play an important role in cancer diagnosis and personalized medicine. However, owing to the low-abundance biomarkers of CTCs, conventional assays are only able to detect CTCs at the population level. Therefore, there is a pressing need for a highly sensitive method to analyze CTCs at the single-cell level. As an important branch of microfluidics, droplet microfluidics is a high-throughput and sensitive single-cell analysis platform for the quantitative detection and heterogeneity analysis of CTCs. In this review, we focus on the quantitative detection and heterogeneity analysis of CTCs using droplet microfluidics. Technologies that enable droplet microfluidics, particularly high-throughput droplet generation and high-efficiency droplet manipulation, are first discussed. Then, recent advances in detecting and analyzing CTCs using droplet microfluidics from the different aspects of nucleic acids, proteins, and metabolites are introduced. The purpose of this review is to provide guidance for the continued study of droplet microfluidics for CTC-based liquid biopsy.

Advances in the application of Raman spectroscopy in haematological tumours

Hematologic malignancies are a diverse collection of cancers that affect the blood, bone marrow, and organs. They have a very unpredictable prognosis and recur after treatment. Leukemia, lymphoma, and myeloma are the most prevalent symptoms. Despite advancements in chemotherapy and supportive care, the incidence rate and mortality of patients with hematological malignancies remain high. Additionally, there are issues with the clinical diagnosis because several hematological malignancies lack defined, systematic diagnostic criteria. This work provided an overview of the fundamentals, benefits, and limitations of Raman spectroscopy and its use in hematological cancers. The alterations of trace substances can be recognized using Raman spectroscopy. High sensitivity, non-destructive, quick, real-time, and other attributes define it. Clinicians must promptly identify disorders and keep track of analytes in biological fluids. For instance, surface-enhanced Raman spectroscopy is employed in diagnosing gene mutations in myelodysplastic syndromes due to its high sensitivity and multiple detection benefits. Serum indicators for multiple myeloma have been routinely used for detection. The simultaneous observation of DNA strand modifications and the production of new molecular bonds by tip-enhanced Raman spectroscopy is of tremendous significance for diagnosing lymphoma and multiple myeloma with unidentified diagnostic criteria.

Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon-concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high-throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild-type cells, rendering conventional non-image-based high-event-rate methods unsuitable. Here, we report our demonstration of intelligent image-activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image-activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image-activated sorting for the mutant cell type at 180 events per second, which is 1-2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image-activated cell sorting for connecting genotype-phenotype relations for rare-cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells.

Label-free enzymatic reaction monitoring in water-in-oil microdroplets using ultra-broadband multiplex coherent anti-Stokes Raman scattering spectroscopy

We propose a system for monitoring an enzymatic reaction, i.e., dehydrogenation of ethanol catalyzed by alcohol dehydrogenase, in microdroplets using ultra-broadband multiplex coherent anti-Stokes Raman scattering (CARS) spectroscopy. The reaction solution was encapsulated in water-in-oil microdroplets with diameters of 50 µm. The reaction was monitored by measuring the concentration of coenzymes from the CARS spectrum obtained in one-second exposure time. The results obtained using our system was consistent with those of the conventional fluorescence measurement system and indicate the potential of CARS spectroscopy for droplet-based high-throughput screening of enzymes.

Dual-frequency impedance assays for intracellular components in microalgal cells

Intracellular components (including organelles and biomolecules) at the submicron level are typically analyzed in situ by special preparation or expensive setups. Here, a label-free and cost-effective approach of screening microalgal single-cells at a subcellular resolution is available based on impedance cytometry. To the best of our knowledge, it is the first time that the relationships between impedance signals and submicron intracellular organelles and biomolecules are shown. Experiments were performed on Euglena gracilis (E. gracilis) cells incubated under different incubation conditions (i.e., aerobic and anaerobic) and 15 μm polystyrene beads (reference) at two distinct stimulation frequencies (i.e., 500 kHz and 6 MHz). Based on the impedance detection of tens of thousands of samples at a throughput of about 900 cells per second, three metrics were used to track the changes in biophysical properties of samples. As a result, the electrical diameters of cells showed a clear shrinkage in cell volume and intracellular components, as observed under a microscope. The morphology metric of impedance pulses (i.e., tilt index) successfully characterized the changes in cell shape and intracellular composition distribution. Besides, the electrical opacity showed a stable ratio of the intracellular components to cell volume under the cellular self-regulation. Additionally, simulations were used to support these findings and to elucidate how submicron intracellular components and cell morphology affect impedance signals, providing a basis for future improvements. This work opens up a label-free and high-throughput way to analyze single-cell intracellular components by impedance cytometry.

Revealing the Dynamics of Mitochondrial Microenvironment during Apoptosis under Two-photon Fluorescence Lifetime Microscopy by a Cyclic Iridium (III) Complex

Mitochondria-mediated apoptosis is a major mode of cell death and is inextricably linked to various pathological processes such as tumorigenesis. However, there is still a paucity of non-toxic tools that...

Assessment of the electrical penetration of cell membranes using four-frequency impedance cytometry

The electrical penetration of the cell membrane is vital for determining the cell interior via impedance cytometry. Herein, we propose a method for determining the conductivity of the cell membrane through the tilting levels of impedance pulses. When electrical penetration occurs, a high-frequency current freely passes through the cell membrane; thus, the intracellular distribution can directly act on the high-frequency impedance pulses. Numerical simulation shows that an uneven intracellular component distribution can affect the tilting levels of impedance pulses, and the tilting levels start increasing when the cell membrane is electrically penetrated. Experimental evidence shows that higher detection frequencies (>7 MHz) lead to a wider distribution of the tilting levels of impedance pulses when measuring cell populations with four-frequency impedance cytometry. This finding allows us to determine that a detection frequency of 7 MHz is able to pass through the membrane of Euglena gracilis (E. gracilis) cells. Additionally, we provide a possible application of four-frequency impedance cytometry in the biomass monitoring of single E. gracilis cells. High-frequency impedance (≥7 MHz) can be applied to monitor these biomass changes, and low-frequency impedance (<7 MHz) can be applied to track the corresponding biovolume changes. Overall, this work demonstrates an easy determination method for the electrical penetration of the cell membrane, and the proposed platform is applicable for the multiparameter assessment of the cell state during cultivation.

Recent progress in cytometric technologies and their applications in ecotoxicology and environmental risk assessment

Environmental toxicology focuses on identifying and predicting impact of potentially toxic anthropogenic chemicals on biosphere at various levels of biological organization. Presently there is a significant drive to gain deeper understanding of cellular and sub-cellular mechanisms of ecotoxicity. Most notable is increased focus on elucidation of cellular-response networks, interactomes, and greater implementation of cell-based biotests using high-throughput procedures, while at the same time decreasing the reliance on standard animal models used in ecotoxicity testing. This is aimed at discovery and interpretation of molecular pathways of ecotoxicity at large scale. In this regard, the applications of cytometry are perhaps one of the most fundamental prospective analytical tools for the next generation and high-throughput ecotoxicology research. The diversity of this modern technology spans flow, laser-scanning, imaging, and more recently, Raman as well as mass cytometry. The cornerstone advantages of cytometry include the possibility of multi-parameter measurements, gating and rapid analysis. Cytometry overcomes, thus, limitations of traditional bulk techniques such as spectrophotometry or gel-based techniques that average the results from pooled cell populations or small model organisms. Novel technologies such as cell imaging in flow, laser scanning cytometry, as well as mass cytometry provide innovative and tremendously powerful capabilities to analyze cells, tissues as well as to perform in situ analysis of small model organisms. In this review, we outline cytometry as a tremendously diverse field that is still vastly underutilized and often largely unknown in environmental sciences. The main motivation of this work is to highlight the potential and wide-reaching applications of cytometry in ecotoxicology, guide environmental scientists in the technological aspects as well as popularize its broader adoption in environmental risk assessment.

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

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

Cell Coding Arrays Based on Fluorescent Glycan Nanoparticles for Cell Line Identification and Cell Contamination Evaluation

Cell lines are applied on a large scale in the field of biomedicine, but they are susceptible to issues such as misidentification and cross-contamination. This situation is becoming worse over time due to the rapid growth of the biomedical field, and thus there is an urgent need for a more effective strategy to address the problem. As described herein, a cell coding method is established based on two types of uniform and stable glycan nanoparticles that are synthesized using the graft-copolymerization-induced self-assembly (GISA) method, which further exhibit distinct fluorescent properties due to elaborate modification with fluorescent labeling molecules. The different affinity between each nanoparticle and various cell lines results in clearly distinguishable differences in their endocytosis degrees, thus resulting in distinct characteristic fluorescence intensities. Through flow cytometry measurements, the specific signals of each cell sample can be recorded and turned into a map divided into different regions by statistical processing. Using this sensing array strategy, we have successfully identified six human cell lines, including one normal type and five tumor types. Moreover, cell contamination evaluation of different cell lines with HeLa cells as the contaminant in a semiquantitative analysis has also been successfully achieved. Notably, the whole process of nanoparticle fabrication and fluorescent testing is facile and the results are highly reliable.