Cellular image analysis and imaging by flow cytometry

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.

Comparative analysis of feature-based ML and CNN for binucleated erythroblast quantification in myelodysplastic syndrome patients using imaging flow cytometry data

Myelodysplastic syndrome is primarily characterized by dysplasia in the bone marrow (BM), presenting a challenge in consistent morphology interpretation. Accurate diagnosis through traditional slide-based analysis is difficult, necessitating a standardized objective technique. Over the past two decades, imaging flow cytometry (IFC) has proven effective in combining image-based morphometric analyses with high-parameter phenotyping. We have previously demonstrated the effectiveness of combining IFC with a feature-based machine learning algorithm to accurately identify and quantify rare binucleated erythroblasts (BNEs) in dyserythropoietic BM cells. However, a feature-based workflow poses challenges requiring software-specific expertise. Here we employ a Convolutional Neural Network (CNN) algorithm for BNE identification and differentiation from doublets and cells with irregular nuclear morphology in IFC data. We demonstrate that this simplified AI workflow, coupled with a powerful CNN algorithm, achieves comparable BNE quantification accuracy to manual and feature-based analysis with substantial time savings, eliminating workflow complexity. This streamlined approach holds significant clinical value, enhancing IFC accessibility for routine diagnostic purposes.

Imaging flow cytometry of tumoroids: A new method for studying GPCR expression

Fluorescence confocal microscopy is commonly used to analyze the regulation membrane proteins expression such as G protein-coupled receptors (GPCRs). With this approach, the internal movement of GPCRs within the cell can be observed with a high degree of resolution. However, these microscopy techniques led to complex and time-consuming analysis and did not allow a large population of events to be sampled. A recent approach termed imaging flow cytometry (IFC), which combines flow cytometry and fluorescence microscopy, had two main advantages to study the regulation of GPCRs expression such as orexins receptors (OXRs): the ability (1) to analyze large numbers of cells and; (2) to visualize cell integrity and fluorescent markers localization. Here, we compare these two technologies using the orexin A (OxA) ligand coupled to rhodamine (OxA-rho) to investigate anti-tumoral OX1R expression in human digestive cancers. IFC has been adapted for cancer epithelial adherent cells and also to 3D cell culture tumoroids which partially mimic tumoral structures. In the absence of specific antibody, expression of OX1R is examined in the presence of OxA-rho. 2D-culture of colon cancer cells HT-29 exhibits a maximum level of OX1R internalization induced by OxA with 19% ± 3% colocalizing to early endosomes. In 3D-culture of HT-29 cells, internalization of OX1R/OxA-rho reached its maximum at 60 min, with 30.7% ± 6.4% of OX1R colocalizing with early endosomes. This is the first application of IFC to the analysis of the expression of a native GPCR, OX1R, in both 2D and 3D cultures of adherent cancer cells.

Siamese deep learning video flow cytometry for automatic and label-free clinical cervical cancer cell analysis

Automatic and label-free screening methods may help to reduce cervical cancer mortality rates, especially in developing regions. The latest advances of deep learning in the biomedical optics field provide a more automatic approach to solving clinical dilemmas. However, existing deep learning methods face challenges, such as the requirement of manually annotated training sets for clinical sample analysis. Here, we develop Siamese deep learning video flow cytometry for the analysis of clinical cervical cancer cell samples in a smear-free manner. High-content light scattering images of label-free single cells are obtained via the video flow cytometer. Siamese deep learning, a self-supervised method, is built to introduce cell lineage cells into an analysis of clinical cells, which utilizes generated similarity metrics as label annotations for clinical cells. Compared with other deep learning methods, Siamese deep learning achieves a higher accuracy of up to 87.11%, with about 5.62% improvement for label-free clinical cervical cancer cell classification. The Siamese deep learning video flow cytometry demonstrated here is promising for automatic, label-free analysis of many types of cells from clinical samples without cell smears.

[WITHDRAWN] MIML: Multiplex Image Machine Learning for High Precision Cell Classification via Mechanical Traits within Microfluidic Systems

This paper has been withdrawn by Khayrul Islam.

In situ monitoring of toxic effects of algal toxin on cells by a novel microfluidic flow cytometry instrument

Algal toxins produced by microalgae, such as domoic acid (DA)1, have toxic effects on humans. However, toxicity tests using mice only yield lethal doses of algal toxins without providing insights into the mechanism of action on cells. In this study, a fast segmentation of microfluidic flow cytometry cell images based on the bidirectional background subtraction (BBS)2 method was developed to get the visual evidence of apoptosis in both bright-field and fluorescence images. This approach enables mapping of changes in cell morphology and activity under algal toxins, allowing for fast (within 60 s) and automated biological detection. By combining microfluidics with flow cytometry, the intricate cellular-level reaction process can be observed in micro samples of 293 T cells and mouse spleen cells, offering potential for future in vitro experiments.

Smartphone Imaging Flow Cytometry for High-Throughput Single-Cell Analysis

We present a portable imaging flow cytometer comprising a smartphone, a small-footprint optical framework, and a PDMS-based microfluidic device. Flow cytometric analysis is performed in a sheathless manner via elasto-inertial focusing with a custom-written Android program, integrating a graphical user interface (GUI) that provides a high degree of user control over image acquisition. The proposed system offers two different operational modes. First, "post-processing" mode enables particle/cell sizing at throughputs of up to 67 000 particles/s. Alternatively, "real-time" mode allows for integrated cell/particle classification with machine learning at throughputs of 100 particles/s. To showcase the efficacy of our platform, polystyrene particles are accurately enumerated within heterogeneous populations using the post-processing mode. In real-time mode, an open-source machine learning algorithm is deployed within a custom-developed Android application to classify samples containing cells of similar size but with different morphologies. The flow cytometer can extract high-resolution bright-field images with a spatial resolution <700 nm using the developed machine learning-based algorithm, achieving classification accuracies of 97% and 93% for Jurkat and EL4 cells, respectively. Our results confirm that the smartphone imaging flow cytometer (sIFC) is capable of both enumerating single particles in flow and identifying morphological features with high resolution and minimal hardware.

Development of high-throughput systems for biodosimetry

Biomarkers for ionising radiation exposure have great utility in scenarios where there has been a potential exposure and physical dosimetry is missing or in dispute, such as for occupational and accidental exposures. Biomarkers that respond as a function of dose are particularly useful as biodosemeters to determine the dose of radiation to which an individual has been exposed. These dose measurements can also be used in medical scenarios to track doses from medical exposures and even have the potential to identify an individual's response to radiation exposure that could help tailor treatments. The measurement of biomarkers of exposure in medicine and for accidents, where a larger number of samples would be required, is limited by the throughput of analysis (i.e. the number of samples that could be processed and analysed), particularly for microscope-based methods, which tend to be labour-intensive. Rapid analysis in an emergency scenario, such as a large-scale accident, would provide dose estimates to medical practitioners, allowing timely administration of the appropriate medical countermeasures to help mitigate the effects of radiation exposure. In order to improve sample throughput for biomarker analysis, much effort has been devoted to automating the process from sample preparation through automated image analysis. This paper will focus mainly on biological endpoints traditionally analysed by microscopy, specifically dicentric chromosomes, micronuclei and gamma-H2AX. These endpoints provide examples where sample throughput has been improved through automated image acquisition, analysis of images acquired by microscopy, as well as methods that have been developed for analysis using imaging flow cytometry.

High-Throughput Single-Cell Analysis of Nanoparticle-Cell Interactions

Understanding nanoparticle-cell interactions at single-nanoparticle and single-cell resolutions is crucial to improving the design of next-generation nanoparticles for safer, more effective, and more efficient applications in nanomedicine. This review focuses on recent advances in the continuous high-throughput analysis of nanoparticle-cell interactions at the single-cell level. We highlight and discuss the current trends in continual flow high-throughput methods for analyzing single cells, such as advanced flow cytometry techniques and inductively coupled plasma mass spectrometry methods, as well as their intersection in the form of mass cytometry. This review further discusses the challenges and opportunities with current single-cell analysis approaches and provides proposed directions for innovation in the high-throughput analysis of nanoparticle-cell interactions.

On-chip label-free cell classification based directly on off-axis holograms and spatial-frequency-invariant deep learning

We present a rapid label-free imaging flow cytometry and cell classification approach based directly on raw digital holograms. Off-axis holography enables real-time acquisition of cells during rapid flow. However, classification of the cells typically requires reconstruction of their quantitative phase profiles, which is time-consuming. Here, we present a new approach for label-free classification of individual cells based directly on the raw off-axis holographic images, each of which contains the complete complex wavefront (amplitude and quantitative phase profiles) of the cell. To obtain this, we built a convolutional neural network, which is invariant to the spatial frequencies and directions of the interference fringes of the off-axis holograms. We demonstrate the effectiveness of this approach using four types of cancer cells. This approach has the potential to significantly improve both speed and robustness of imaging flow cytometry, enabling real-time label-free classification of individual cells.

Analysis of Salmonella lineage-specific traits upon cell sorting

Microbial cell individuality is receiving increasing interest in the scientific community. Individual cells within clonal populations exhibit noticeable phenotypic heterogeneity. The advent of fluorescent protein technology and advances in single-cell analysis has revealed phenotypic cell variant in bacterial populations. This heterogeneity is evident in a wide range of phenotypes, for example, individual cells display variable degrees of gene expression and survival under selective conditions and stresses, and can exhibit differing propensities to host interactions. Last few years, numerous cell sorting approaches have been employed for resolving the properties of bacterial subpopulations. This review provides an overview of applications of cell sorting to analyze Salmonella lineage-specific traits, including bacterial evolution studies, gene expression analysis, response to diverse cellular stresses and characterization of diverse bacterial phenotypic variants.

Recent advances in microfluidics for single-cell functional proteomics

Single-cell proteomics (SCP) reveals phenotypic heterogeneity by profiling individual cells, their biological states and functional outcomes upon signaling activation that can hardly be probed via other omics characterizations. This has become appealing to researchers as it enables an overall more holistic view of biological details underlying cellular processes, disease onset and progression, as well as facilitates unique biomarker identification from individual cells. Microfluidic-based strategies have become methods of choice for single-cell analysis because they allow facile assay integrations, such as cell sorting, manipulation, and content analysis. Notably, they have been serving as an enabling technology to improve the sensitivity, robustness, and reproducibility of recently developed SCP methods. Critical roles of microfluidics technologies are expected to further expand rapidly in advancing the next phase of SCP analysis to reveal more biological and clinical insights. In this review, we will capture the excitement of the recent achievements of microfluidics methods for both targeted and global SCP, including efforts to enhance the proteomic coverage, minimize sample loss, and increase multiplexity and throughput. Furthermore, we will discuss the advantages, challenges, applications, and future prospects of SCP.

The steep road to nonviral nanomedicines: Frequent challenges and culprits in designing nanoparticles for gene therapy

The potential of therapeutically loaded nanoparticles (NPs) has been successfully demonstrated during the last decade, with NP-mediated nonviral gene delivery gathering significant attention as highlighted by the broad clinical acceptance of mRNA-based COVID-19 vaccines. A significant barrier to progress in this emerging area is the wild variability of approaches reported in published literature regarding nanoparticle characterizations. Here, we provide a brief overview of the current status and outline important concerns regarding the need for standardized protocols to evaluate NP uptake, NP transfection efficacy, drug dose determination, and variability of nonviral gene delivery systems. Based on these concerns, we propose wide adherence to multimodal, multiparameter, and multistudy analysis of NP systems. Adoption of these proposed approaches will ensure improved transparency, provide a better basis for interlaboratory comparisons, and will simplify judging the significance of new findings in a broader context, all critical requirements for advancing the field of nonviral gene delivery.

Seminal quality comparison of first and second cryopreserved ejaculates of Alpine kid goats by flow cytometry

Discarding the first ejaculate is recommended as an alternative for improving seminal quality after long sexual resting, especially when semen should be used for cryopreservation. However, when the males are not in sexual resting the necessity to discarding the first ejaculate is still unknown. Therefore, this study aimed to compare by flow cytometry the quality of the first and second ejaculates. Ten kids and uniform goats between 5 and 6 months of age were used in a completely randomized design. Semen collection was carried out every 4 days, until a total of five ejaculates per animal in each treatment was completed. The fresh and frozen semen collected were processed and analyzed using macroscopic and microscopic parameters, resistance test, hypo-osmotic medium test, and flow cytometry (FC). The FC parameters were production of reactive oxygen species, plasma and acrosomal membrane integrity, and lipid peroxidation of the plasma membrane. The ejaculates did not differ for the resistance test, the reactivity in the hypo-osmotic medium and for the macroscopic and microscopic seminal parameters, except for sperm volume and concentration. The first ejaculate had a higher percentage of minor and total defects. None of the FC parameters analyzed differed between the first and second ejaculates. The first and second ejaculates demonstrated similar seminal qualities, so for Alpine kid goats without a sexual resting period, discarding the first ejaculate it is not recommended.

The stallion sperm acrosome: Considerations from a research and clinical perspective

During the fertilization process, the interaction between the sperm and the oocyte is mediated by a process known as acrosomal exocytosis (AE). Although the role of the sperm acrosome on fertilization has been studied extensively over the last 70 years, little is known about the molecular mechanisms that govern acrosomal function, particularly in species other than mice or humans. Even though subfertility due to acrosomal dysfunction is less common in large animals than in humans, the evaluation of sperm acrosomal function should be considered not only as a complementary but a routine test when individuals are selected for breeding potential. This certainly holds true for stallions, which might display lower levels of fertility in the face of "acceptable" sperm quality parameters determined by conventional sperm assays. Nowadays, the use of high throughput technologies such as flow cytometry or mass spectrometry-based proteomic analysis is commonplace in the research arena. Such techniques can also be implemented in clinical scenarios of males with "idiopathic" subfertility. The current review focuses on the sperm acrosome, with particular emphasis on the stallion. We aim to describe the physiological events that lead to the acrosome formation within the testis, the role of very specific acrosomal proteins during AE, the methods to study the occurrence of AE under in vitro conditions, and the potential use of molecular biology techniques to discover new markers of acrosomal function and subfertility associated with acrosomal dysfunction in stallions.

Quantifying Golgi Apparatus Fragmentation Using Imaging Flow Cytometry

Unlike the common conception of the Golgi apparatus as a static organelle, it is, in fact, a dynamic structure, as well as a sensitive sensor for the cellular status. In response to various stimuli, the intact Golgi structure undergoes fragmentation. This fragmentation can yield either partial fragmentation, resulting in several separated chunks, or complete vesiculation of the organelle. These distinct morphologies form the basis of several methods for the quantification of the Golgi status. In this chapter, we describe our imaging flow cytometry-based method for quantifying changes in the Golgi architecture. This method has all the benefits of imaging flow cytometry-namely, it is rapid, high-throughput, and robust-while affording easy implementation and analysis capabilities.

Intelligent sort-timing prediction for image-activated cell sorting

Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort-timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.

High-Throughput γ-H2AX Assay Using Imaging Flow Cytometry

The γ-H2AX assay is a sensitive and reliable method to evaluate radiation-induced DNA double-strand breaks. The conventional γ-H2AX assay detects individual nuclear foci manually, but is labor-intensive and time-consuming, and hence unsuitable for high-throughput screening in cases of large-scale radiation accidents. We have developed a high-throughput γ-H2AX assay using imaging flow cytometry. This method comprises (1) sample preparation from small volumes of blood in the Matrix™ 96-tube format, (2) automated image acquisition of cells stained with immunofluorescence-labeled γ-H2AX using ImageStream®X, and (3) quantification of γ-H2AX levels and batch processing using the Image Data Exploration and Analysis Software (IDEAS®). This enables the rapid analysis of γ-H2AX levels in several thousand of cells from a small volume of blood with accurate and reliable quantitative measurements for γ-H2AX foci and mean fluorescence levels. This high-throughput γ-H2AX assay could be a useful tool not only for radiation biodosimetry in mass casualty events, but also for large-scale molecular epidemiological studies and individualized radiotherapy.

Investigating the resistance mechanism of 5-fluorouracil in colorectal cancer based on surface markers of cancer stemness and cytokine level: A pre-clinical study

BACKGROUND

Colorectal cancer (CRC) is the deadliest malignancy in the world. The first-line chemotherapy used for CRC is 5-fluorouracil (5-FU). 5-FU completely eradicates rapidly proliferating and terminally differentiated tumor cells but fails to target cancer stem cells (CSCs). As a result, the tumor may shrink temporarily, but remnant CSC multiplies and forms a tumor again more aggressively. The recurrence and resistance lead to metastasis.

METHODOLOGY

CRC was induced in 12 Sprague-Dawley (RPCP/IAEC/2019-20/R2) rats by 1,2 dimethyl hydrazine. Later, animals were treated with 5-FU for 7 weeks at a 10 mg/kg dose by the subcutaneous route. At the end of treatment, half population was sacrificed (6), whereas the remaining half (6) was left without treatment of 5-FU for 5 weeks and then sacrificed. Parameters such as body weight, complete blood count (CBC), immune cell subset (CD4, CD8, and NK cells), colon length to weight index, interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α) level, occult blood in stool, tumor multiplicity, and liver metastasis were estimated. In addition, the dissected colon was fixed in formalin and sent to the histology lab for hematoxylin-eosin staining and immunohistochemistry at both intervals.

RESULTS

All blood and tissue-based markers have shown significant differences (p < 0.05) between the animals sacrificed at the end of the 27th week and the end of the 32nd week for 5-FU treatment.

CONCLUSION

It can be concluded that 5-FU up-regulates inflammatory cytokines and cell surface markers of CSC that promote CRC stemness via the Wnt/β-catenin pathway. Also, involvement of Nf-κB, fibronectin, MMP-9, and RANKL leads to tumorigenesis, disease aggressiveness, metastasis, and resistance.

Demands and technical developments of clinical flow cytometry with emphasis in quantitative, spectral, and imaging capabilities

As the gold-standard method for single-cell analysis, flow cytometry enables high-throughput and multiple-parameter characterization of individual biological cells. This review highlights the demands for clinical flow cytometry in laboratory hematology (e.g., diagnoses of minimal residual disease and various types of leukemia), summarizes state-of-the-art clinical flow cytometers (e.g., FACSLyricTM by Becton Dickinson, DxFLEX by Beckman Coulter), then considers innovative technical improvements in flow cytometry (including quantitative, spectral, and imaging approaches) to address the limitations of clinical flow cytometry in hematology diagnosis. Finally, driven by these clinical demands, future developments in clinical flow cytometry are suggested.

Kruppel-like factor 1-GATA1 fusion protein improves the sickle cell disease phenotype in mice both in vitro and in vivo

Sickle cell disease (SCD) and β-thalassemia are among the most common genetic disorders worldwide, affecting global health and mortality. Hemoglobin A2 (HbA2, α2δ2) is expressed at a low level in adult blood due to the lack of the Kruppel-like factor 1 (KLF1) binding motif in the δ-globin promoter region. However, HbA2 is fully functional as an oxygen transporter, and could be a valid antisickling agent in SCD, as well as a substitute for hemoglobin A in β-thalassemia. We have previously demonstrated that KLF1-GATA1 fusion protein could interact with the δ-globin promoter and increase δ-globin expression in human primary CD34+ cells. We report the effects of 2 KLF1-GATA1 fusion proteins on hemoglobin expression, as well as SCD phenotypic correction in vitro and in vivo. Forced expression of KLF1-GATA1 fusion protein enhanced δ-globin gene and HbA2 expression, as well as reduced hypoxia-related sickling, in erythroid cells cultured from both human sickle CD34+ cells and SCD mouse hematopoietic stem cells (HSCs). The fusion proteins had no impact on erythroid cell differentiation, proliferation, and enucleation. Transplantation of highly purified SCD mouse HSCs expressing KLF1-GATA1 fusion protein into SCD mice lessened the severity of the anemia, reduced the sickling of red blood cells, improved SCD-related pathological alterations in spleen, kidney, and liver, and restored urine-concentrating ability in recipient mice. Taken together, these results indicate that the use of KLF1-GATA1 fusion constructs may represent a new gene therapy approach for hemoglobinopathies.

Glioma Stem Cells: Novel Data Obtained by Single-Cell Sequencing

Glioma is the most common type of primary CNS tumor, composed of cells that resemble normal glial cells. Recent genetic studies have provided insight into the inter-tumoral heterogeneity of gliomas, resulting in the updated 2021 WHO classification of gliomas. Thorough understanding of inter-tumoral heterogeneity has already improved the prognosis and treatment outcomes of some types of gliomas. Currently, the challenge for researchers is to study the intratumoral cell heterogeneity of newly defined glioma subtypes. Cancer stem cells (CSCs) present in gliomas and many other tumors are an example of intratumoral heterogeneity of great importance. In this review, we discuss the modern concept of glioma stem cells and recent single-cell sequencing-driven progress in the research of intratumoral glioma cell heterogeneity. The particular emphasis was placed on the recently revealed variations of the cell composition of the subtypes of the adult-type diffuse gliomas, including astrocytoma, oligodendroglioma and glioblastoma. The novel data explain the inconsistencies in earlier glioma stem cell research and also provide insight into the development of more effective targeted therapy and the cell-based immunotherapy of gliomas. Separate sections are devoted to the description of single-cell sequencing approach and its role in the development of cell-based immunotherapies for glioma.

Imaging flow cytometry: a primer

Imaging flow cytometry combines the high throughput nature of flow cytometry with the advantages of single cell image acquisition associated with microscopy. The measurement of large numbers of features from the resulting images provides rich datasets which have resulted in a wide range of novel biomedical applications. In this primer we discuss the typical imaging flow instrumentation, the form of data acquired and the typical analysis tools that can be applied to this data. Using examples from the literature we discuss the progression of the analysis methods that have been applied to imaging flow cytometry data. These methods start from the use of simple single image features and multiple channel gating strategies, followed by the design and use of custom features for phenotype classification, through to powerful machine and deep learning methods. For each of these methods, we outline the processes involved in analyzing typical datasets and provide details of example applications. Finally we discuss the current limitations of imaging flow cytometry and the innovations which are addressing these challenges.

Calibration-Aimed Comparison of Image-Cytometry- and Flow-Cytometry-Based Approaches of Ploidy Analysis

Ploidy analysis is the fundamental method of measuring DNA content. For decades, the principal way of conducting ploidy analysis was through flow cytometry. A flow cytometer is a specialized tool for analyzing cells in a solution. This is convenient in laboratory environments, but prohibits measurement reproducibility and the complete detachment of sample preparation from data acquisition and analysis, which seems to have become paramount with the constant decrease in the number of pathologists per capita all over the globe. As more open computer-aided systems emerge in medicine, the demand for overcoming these shortcomings, and opening access to even more (and more flexible) options, has also emerged. Image-based analysis systems can provide an alternative to these types of workloads, placing the abovementioned problems in a different light. Flow cytometry data can be used as a reference for calibrating an image-based system. This article aims to show an approach to constructing an image-based solution for ploidy analysis, take measurements for a basic comparison of the data produced by the two methods, and produce a workflow with the ultimate goal of calibrating the image-based system.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.

High-speed 3D imaging flow cytometry with optofluidic spatial transformation

Three-dimensional (3D) fluorescence imaging is important to accurately capture and understand biological structures and phenomena. However, because of its slow acquisition speed, it was difficult to implement 3D fluorescence imaging for imaging flow cytometry. Especially, modern flow cytometers operate at a flow velocity of 1-10 m/s, and no 3D fluorescence imaging technique was able to capture cells at such high velocity. Here, we present a high-speed 3D fluorescence imaging technique in which a set of optical cross sections of a cell is captured within a single frame of a camera by combining strobe light-sheet excitation and optofluidic spatial transformation. Using this technique, we demonstrated 3D fluorescence imaging of cells flowing at a velocity of over 10 m/s, which is the fastest to our knowledge. Such technology can allow integration of 3D imaging with flow systems of common flow cytometers and cell sorters.

Immunophenotyping: Analytical approaches and role in preclinical development of nanomedicines

Pharmaceutical products can activate immune cells, suppress their function, or change the immune responses to traditional immunologically active agonists such as those present in microbes. Therefore, the assessment of immunostimulation, immunosuppression, and immunomodulation comprises the backbone of immunotoxicity studies of new drug entities. Depending on physicochemical properties (e.g., size, charge, surface functionalities, hydrophobicity), nanoparticles can be immunostimulatory, immunosuppressive, and immunomodulatory. Various methods and experimental frameworks have been established to support preclinical translational studies of nanotechnology-based drug products. Immunophenotyping after the exposure of cells or preclinical animal models to nanoparticles can provide critical information about the changes in both the numbers of immune cells and their activation status. However, this methodology is underutilized in preclinical studies of engineered nanomaterials. Herein, we review current literature about varieties of instrumentation and methods utilized for immunophenotyping, discuss their advantages and limitations, and propose a roadmap for applying immunophenotyping to support preclinical immunological characterization of nanotechnology-based formulations.

Bat Red Blood Cells Express Nucleic Acid-Sensing Receptors and Bind RNA and DNA

RBCs demonstrate immunomodulatory capabilities through the expression of nucleic acid sensors. However, little is known about bat RBCs, and no studies have examined the immune function of bat erythrocytes. In this study, we show that bat RBCs express the nucleic acid-sensing TLRs TLR7 and TLR9 and bind the nucleic acid ligands, ssRNA, and CpG DNA. Collectively, these data suggest that, like human RBCs, bat erythrocytes possess immune function and may be reservoirs for nucleic acids. These findings provide unique insight into bat immunity and may uncover potential mechanisms by which virulent pathogens of humans are concealed in bats.

A low-cost flow cell for flow cytometry

Flow cytometry is an essential analytical technique used in biomedical diagnostics to measure properties of cells, micro-organisms, and particles. Laser light is scattered from particles focused in a flow cell and collected by light sensors, where the intensity of the scattered light is a function of the scattering angle, the refractive index of the particle and surrounding medium, the wavelength of light, and the size and the shape of the particle. One of the critical parts of the cytometer is the flow cell where the particle stream is constrained into a tight region within 10-30 μm using hydrodynamic focusing. The conventional flow cells use thick quartz flow cells, which are expensive and therefore not suitable for instruments targeted for resource-constrained settings. We demonstrate a compact, economical, bio-compatible flow cell assembly design that incorporates inexpensive and easily available capillaries attached to sturdy polymer fixtures in a simple manner that performs the focusing of a sample stream of particles. The flow cell has been tested by studying the relation between sample core diameter, and sample and sheath flow rates. Small-angle scattering (forward scatter) and wide-angle scattering (side scatter) have been captured for the enumeration and characterization of particles. We show excellent agreement between the size distribution obtained via direct imaging and that obtained from light scattering. The flow cell was also used to successfully size white blood cells in human blood samples.

Deep imaging flow cytometry

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s^-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

Light sheet based volume flow cytometry (VFC) for rapid volume reconstruction and parameter estimation on the go

Optical imaging is paramount for disease diagnosis and to access its progression over time. The proposed optical flow imaging (VFC/iLIFE) is a powerful technique that adds new capabilities (3D volume visualization, organelle-level resolution, and multi-organelle screening) to the existing system. Unlike state-of-the-art point-illumination-based biomedical imaging techniques, the sheet-based VFC technique is capable of single-shot sectional visualization, high throughput interrogation, real-time parameter estimation, and instant volume reconstruction with organelle-level resolution of live specimens. The specimen flow system was realized on a multichannel (Y-type) microfluidic chip that enables visualization of organelle distribution in several cells in-parallel at a relatively high flow-rate (2000 nl/min). The calibration of VFC system requires the study of point emitters (fluorescent beads) at physiologically relevant flow-rates (500-2000 nl/min) for determining flow-induced optical aberration in the system point spread function (PSF). Subsequently, the recorded raw images and volumes were computationally deconvolved with flow-variant PSF to reconstruct the cell volume. High throughput investigation of the mitochondrial network in HeLa cancer cell was carried out at sub-cellular resolution in real-time and critical parameters (mitochondria count and size distribution, morphology, entropy, and cell strain statistics) were determined on-the-go. These parameters determine the physiological state of cells, and the changes over-time, revealing the metastatic progression of diseases. Overall, the developed VFC system enables real-time monitoring of sub-cellular organelle organization at a high-throughput with high-content capacity.

Neural stem cell-derived exosomes suppress neuronal cell apoptosis by activating autophagy via miR-374-5p/STK-4 axis in spinal cord injury

OBJECTIVES

To evaluate the roles of MicroRNAs (miRNAs) enclosed in the neuron-derived exosomes in the recovery of the spinal cord injury (SCI) and the mechanism.

METHODS

The exosomes were isolated from neural stem cells (NSCs) and characterized by transmission electron microscopy (TEM) and NanoSight system (NTA). For in vivo experiments, Basso Mouse Scale, beam walking, and inclined plane tests were used to determine the behavioral symptoms of the SCI mice. For in vitro experiments, H₂O₂ treated HT22 cells were used to simulate SCI cells and cocultured with exosomes to analyze the cell apoptosis using TUNEL assays and flow cytometry. Apoptosis- and autophagy-related protein expression was detected by western blot and the green fluorescent protein (GFP)-LC3 assay was used to detect the level of autophagy. In addition, luciferase assay was performed to assess the relationship between miR-374-5p and SKT-4.

RESULTS

Exosomes from NSCs alleviated spinal cord injury by triggering autophagy flux and suppressing apoptosis. Besides, miR-374-5p was highly expressed in these exosomes and was responsible for the decent in injured neural cell apoptosis by activating autophagy flux. The SKT-4 was the target gene regulated by miR-374-5p in this exosomal protective function to SCI cells.

CONCLUSION

The elevated level of miR-374-5p in neuronal exosomes could enhance spinal cord injury recovery by activating autophagy.

Flow Cytometry and Cell Cycle Analysis: An Overview

Cell cycle analysis is one of the earliest applications in flow cytometry and continues to be highly used to this day. Since the first reported method of Feulgen-DNA staining, cell cycle analysis has continued to grow and mature. With the recent advances in DNA dyes, understanding of additional cell cycle phase markers, and new technologies, cell cycle analysis continues to be a dynamic field within the flow cytometry community. This chapter will give an overview of the current state of cell cycle analysis by flow cytometry.

Single-Cell Technologies to Study Phenotypic Heterogeneity and Bacterial Persisters

Antibiotic persistence is a phenomenon in which rare cells of a clonal bacterial population can survive antibiotic doses that kill their kin, even though the entire population is genetically susceptible. With antibiotic treatment failure on the rise, there is growing interest in understanding the molecular mechanisms underlying bacterial phenotypic heterogeneity and antibiotic persistence. However, elucidating these rare cell states can be technically challenging. The advent of single-cell techniques has enabled us to observe and quantitatively investigate individual cells in complex, phenotypically heterogeneous populations. In this review, we will discuss current technologies for studying persister phenotypes, including fluorescent tags and biosensors used to elucidate cellular processes; advances in flow cytometry, mass spectrometry, Raman spectroscopy, and microfluidics that contribute high-throughput and high-content information; and next-generation sequencing for powerful insights into genetic and transcriptomic programs. We will further discuss existing knowledge gaps, cutting-edge technologies that can address them, and how advances in single-cell microbiology can potentially improve infectious disease treatment outcomes.

Imaging Flow Cytometry and Confocal Immunofluorescence Microscopy of Virus-Host Cell Interactions

Viruses are diverse pathogens that use host factors to enter cells and cause disease. Imaging the entry and replication phases of viruses and their interactions with host factors is key to fully understanding viral infections. This review will discuss how confocal microscopy and imaging flow cytometry are used to investigate virus entry and replication mechanisms in fixed and live cells. Quantification of viral images and the use of cryo-electron microscopy to gather structural information of viruses is also explored. Using imaging to understand how viruses replicate and interact with host factors, we gain insight into cellular processes and identify novel targets to develop antiviral therapeutics and vaccines.

A weakly supervised deep learning approach for label-free imaging flow-cytometry-based blood diagnostics

The application of machine learning approaches to imaging flow cytometry (IFC) data has the potential to transform the diagnosis of hematological diseases. However, the need for manually labeled single-cell images for machine learning model training has severely limited its clinical application. To address this, we present iCellCnn, a weakly supervised deep learning approach for label-free IFC-based blood diagnostics. We demonstrate the capability of iCellCnn to achieve diagnosis of Sézary syndrome (SS) from patient samples on the basis of bright-field IFC images of T cells obtained after fluorescence-activated cell sorting of human peripheral blood mononuclear cell specimens. With a sample size of four healthy donors and five SS patients, iCellCnn achieved a 100% classification accuracy. As iCellCnn is not restricted to the diagnosis of SS, we expect such weakly supervised approaches to tap the diagnostic potential of IFC by providing automatic data-driven diagnosis of diseases with so-far unknown morphological manifestations.

Review: tomographic imaging flow cytometry

Within the last decades, conventional flow cytometry (FC) has evolved as a powerful measurement method in clinical diagnostics, biology, life sciences and healthcare. Imaging flow cytometry (IFC) extends the power of traditional FC by adding high resolution optical and spectroscopic information. However, the conventional IFC only provides a 2D projection of a 3D object. To overcome this limitation, tomographic imaging flow cytometry (tIFC) was developed to access 3D information about the target particles. The goal of tIFC is to visualize surfaces and internal structures in a holistic way. This review article gives an overview of the past and current developments in tIFC.

Isolation of Persister Cells of Bacillus subtilis and Determination of Their Susceptibility to Antimicrobial Peptides

Persister cells are growth-arrested subpopulations that can survive possible fatal environments and revert to wild types after stress removal. Clinically, persistent pathogens play a key role in antibiotic therapy failure, as well as chronic, recurrent, and antibiotic-resilient infections. In general, molecular and physiological research on persister cells formation and compounds against persister cells are much desired. In this study, we firstly demonstrated that the spore forming Gram-positive model organism Bacillus subtilis can be used to generate persister cells during exposure to antimicrobial compounds. Interestingly, instead of exhibiting a unified antibiotic tolerance profile, different number of persister cells and spores were quantified in various stress conditions. qPCR results also indicated that differential stress responses are related to persister formation in various environmental conditions. We propose, for the first time to the best of our knowledge, an effective method to isolate B. subtilis persister cells from a population using fluorescence-activated cell sorting (FACS), which makes analyzing persister populations feasible. Finally, we show that alpha-helical cationic antimicrobial peptides SAAP-148 and TC-19, derived from human cathelicidin LL-37 and human thrombocidin-1, respectively, have high efficiency against both B. subtilis vegetative cells and persisters, causing membrane permeability and fluidity alteration. In addition, we confirm that in contrast to persister cells, dormant B. subtilis spores are not susceptible to the antimicrobial peptides.

Electrical Detection of Innate Immune Cells

Accurately classifying the innate immune players is essential to comprehensively and quantitatively evaluate the interactions between the innate and the adaptive immune systems. In addition, accurate classification enables the development of models to predict behavior and to improve prospects for therapeutic manipulation of inflammatory diseases and cancer. Rapid development in technologies that provide an accurate definition of the type of cell in action, allows the field of innate immunity to the lead in therapy developments. This article presents a novel immunophenotyping technique using electrical characterization to differentiate between the two most important cell types of the innate immune system: dendritic cells (DCs) and macrophages (MACs). The electrical characterization is based on capacitance measurements, which is a reliable marker for cell surface area and hence cell size. We differentiated THP-1 cells into DCs and MACs in vitro and conducted electrical measurements on the three cell types. The results showed average capacitance readings of 0.83 µF, 0.93 µF, and 1.01 µF for THP-1, DCs, and MACs, respectively. This corresponds to increasing cell size since capacitance is directly proportional to area. The results were verified with image processing. Image processing was used for verification because unlike conventional techniques, especially flow cytometry, it avoids cross referencing and by-passes the limitation of a lack of specificity of markers used to detect the different cell types.

Cellular binding, uptake and biotransformation of silver nanoparticles in human T lymphocytes

Our knowledge of uptake, toxicity and detoxification mechanisms as related to nanoparticles' (NPs') characteristics remains incomplete. Here we combine the analytical power of three advanced techniques to study the cellular binding and uptake and the intracellular transformation of silver nanoparticles (AgNPs): single-particle inductively coupled mass spectrometry, mass cytometry and synchrotron X-ray absorption spectrometry. Our results show that although intracellular and extracellularly bound AgNPs undergo major transformation depending on their primary size and surface coating, intracellular Ag in 24 h AgNP-exposed human lymphocytes exists in nanoparticulate form. Biotransformation of AgNPs is dominated by sulfidation, which can be viewed as one of the cellular detoxification pathways for Ag. These results also show that the toxicity of AgNPs is primarily driven by internalized Ag. In fact, when toxicity thresholds are expressed as the intracellular mass of Ag per cell, differences in toxicity between NPs of different coatings and sizes are minimized. The analytical approach developed here has broad applicability in different systems where the aim is to understand and quantify cell-NP interactions and biotransformation.

Deepometry, a framework for applying supervised and weakly supervised deep learning to imaging cytometry

Deep learning offers the potential to extract more than meets the eye from images captured by imaging flow cytometry. This protocol describes the application of deep learning to single-cell images to perform supervised cell classification and weakly supervised learning, using example data from an experiment exploring red blood cell morphology. We describe how to acquire and transform suitable input data as well as the steps required for deep learning training and inference using an open-source web-based application. All steps of the protocol are provided as open-source Python as well as MATLAB runtime scripts, through both command-line and graphic user interfaces. The protocol enables a flexible and friendly environment for morphological phenotyping using supervised and weakly supervised learning and the subsequent exploration of the deep learning features using multi-dimensional visualization tools. The protocol requires 40 h when training from scratch and 1 h when using a pre-trained model.

On-Chip Multicolor Photoacoustic Imaging Flow Cytometry

On-chip imaging flow cytometry has been widely used in cancer biology, immunology, microbiology, and drug discovery. Pure optical imaging combined with flow cytometry to derive chemical, structural, and morphological features of cells provides systematic insights into biological processes. However, due to the high concentration and strong optical attenuation of red blood cells, preprocessing is necessary for optical flow cytometry while dealing with whole blood. In this study, we develop an on-chip photoacoustic imaging flow cytometry (PAIFC), which combines multicolor high-speed photoacoustic microscopy and microfluidics for cell imaging. The device employs a micro-optical scanner to achieve a miniaturized outer size of 30 × 17 × 24 mm³ and ultrafast cross-sectional imaging at a frame rate of 1758 Hz and provides lateral and axial resolutions of 2.2 and 33 μm, respectively. Using a multicolor strategy, PAIFC is able to differentiate cells labeled by external contrast agents, detect melanoma cells with an endogenous contrast in whole blood, and image melanoma cells in blood samples from tumor-bearing mice. The results suggest that PAIFC has sufficient sensitivity and specificity for future cell-on-chip applications.

Analysis of human chromosomes by imaging flow cytometry

Chromosomal analysis is traditionally performed by karyotyping on metaphase spreads, or by fluorescent in situ hybridization (FISH) on interphase cells or metaphase spreads. Flow cytometry was introduced as a new method to analyze chromosomes number (ploidy) and structure (telomere length) in the 1970s with data interpretation largely based on fluorescence intensity. This technology has had little uptake for human cytogenetic applications primarily due to analytical challenges. The introduction of imaging flow cytometry, with the addition of digital images to standard multi-parametric flow cytometry quantitative tools, has added a new dimension. The ability to visualize the chromosomes and FISH signals overcomes the inherent difficulties when the data is restricted to fluorescence intensity. This field is now moving forward with methods being developed to assess chromosome number and structure in whole cells (normal and malignant) in suspension. A recent advance has been the inclusion of immunophenotyping such that antigen expression can be used to identify specific cells of interest for specific chromosomes and their abnormalities. This capability has been illustrated in blood cancers, such as chronic lymphocytic leukemia and plasma cell myeloma. The high sensitivity and specificity achievable highlights the potential imaging flow cytometry has for cytogenomic applications (i.e., diagnosis and disease monitoring). This review introduces and describes the development, current status, and applications of imaging flow cytometry for chromosomal analysis of human chromosomes.

Tools and Methods for Investigating Synthetic Metal-Catalyzed Reactions in Living Cells

Although abiotic catalysts are capable of promoting numerous new-to-nature reactions, only a small subset has so far been successfully integrated into living systems. Research in intracellular catalysis requires an interdisciplinary approach that takes advantage of both chemical and biological tools as well as state-of-the-art instrumentations. In this perspective, we will focus on the techniques that have made studying metal-catalyzed reactions in cells possible using representative examples from the literature. Although the lack of quantitative data in vitro and in vivo has somewhat limited progress in the catalyst development process, recent advances in characterization methods should help overcome some of these deficiencies. Given its tremendous potential, we believe that intracellular catalysis will play a more prominent role in the development of future biotechnologies and therapeutics.

Nucleic acid analysis on paper substrates (NAAPs): an innovative tool for Point of Care (POC) infectious disease diagnosis

The cost-effective rapid diagnosis of infectious diseases is an essential and important factor for curing such diseases in the global public health care picture. Owing to poor infrastructure and lack of sanitation, these diseases have an extreme impact on remote and rural areas, especially in developing countries, and there are unresolved challenges. Molecular diagnosis, such as nucleic acid analysis, plays a key role in the significant treatment of numerous infectious diseases. Current molecular diagnostic assays require a sophisticated laboratory setup with expensive components. Molecular diagnosis on a microfluidic point-of-care (POC) platform is attractive to researchers for disease detection with proper prevention. Compared to various microfluidic substrate materials, paper-based POC technologies offer significant cost-effective solutions over high-cost clinical instruments to fill the gap between the needs of users and affordability. Low-cost paper-based microfluidic POC technologies provide portable and disposable diagnostic systems for multiple disease detection that may be extremely useful in remote areas. This article presents a critical review of paper-based microfluidic device technology which has become an imminent platform to adjust the current health scenario for the detection of diseases using different stages of nucleic acid analysis, such as extraction, amplification and detection of nucleic acid, with future perspectives for paper substrates.

Osteoclasts protect bone blood vessels against senescence through the angiogenin/plexin-B2 axis

Synthetic glucocorticoids (GCs), one of the most effective treatments for chronic inflammatory and autoimmune conditions in children, have adverse effects on the growing skeleton. GCs inhibit angiogenesis in growing bone, but the underlying mechanisms remain unclear. Here, we show that GC treatment in young mice induces vascular endothelial cell senescence in metaphysis of long bone, and that inhibition of endothelial cell senescence improves GC-impaired bone angiogenesis with coupled osteogenesis. We identify angiogenin (ANG), a ribonuclease with pro-angiogenic activity, secreted by osteoclasts as a key factor for protecting the neighboring vascular cells against senescence. ANG maintains the proliferative activity of endothelial cells through plexin-B2 (PLXNB2)-mediated transcription of ribosomal RNA (rRNA). GC treatment inhibits ANG production by suppressing osteoclast formation in metaphysis, resulting in impaired endothelial cell rRNA transcription and subsequent cellular senescence. These findings reveal the role of metaphyseal blood vessel senescence in mediating the action of GCs on growing skeleton and establish the ANG/PLXNB2 axis as a molecular basis for the osteoclast-vascular interplay in skeletal angiogenesis.

2.5D microscopy: Fast, high-throughput imaging via volumetric projection for quantitative subcellular analysis

Imaging-based single-cell analysis is essential to study the expression level and functions of biomolecules at subcellular resolution. However, its low throughput has prevented the measurement of numerous cellular features from multiples cells in a rapid and efficient manner. Here we report 2.5D microscopy that significantly improves the throughput of fluorescence imaging systems while maintaining high-resolution and single-molecule sensitivity. Instead of sequential z-scanning, volumetric information is projected onto a 2D image plane in a single shot by engineering the emitted fluorescence light. Our approach provides an improved imaging speed and uniform focal response within a specific imaging depth, which enabled us to perform quantitative single-molecule RNA measurements over a 2×2 mm2 region within an imaging depth of ~5 μm for mammalian cells in <10 min and immunofluorescence imaging at a >30 Hz volumetric frame rate with reduced photobleaching. Our microscope also offers the ability of multi-color imaging, depth control and super-resolution imaging.