Principles of Amnis Imaging Flow Cytometry

Imaging Flow Cytometry and Convolutional Neural Network-Based Classification Enable Discrimination of Hematopoietic and Leukemic Stem Cells in Acute Myeloid Leukemia

Acute myeloid leukemia (AML) is a heterogenous blood cancer with a dismal prognosis. It emanates from leukemic stem cells (LSCs) arising from the genetic transformation of hematopoietic stem cells (HSCs). LSCs hold prognostic value, but their molecular and immunophenotypic heterogeneity poses challenges: there is no single marker for identifying all LSCs across AML samples. We hypothesized that imaging flow cytometry (IFC) paired with artificial intelligence-driven image analysis could visually distinguish LSCs from HSCs based solely on morphology. Initially, a seven-color IFC panel was employed to immunophenotypically identify LSCs and HSCs in bone marrow samples from five AML patients and ten healthy donors, respectively. Next, we developed convolutional neural network (CNN) models for HSC-LSC discrimination using brightfield (BF), side scatter (SSC), and DNA images. Classification using only BF images achieved 86.96% accuracy, indicating significant morphological differences. Accuracy increased to 93.42% when combining BF with DNA images, highlighting differences in nuclear morphology, although DNA images alone were inadequate for accurate HSC-LSC discrimination. Model development using SSC images revealed minor granularity differences. Performance metrics varied substantially between AML patients, indicating considerable morphologic variations among LSCs. Overall, we demonstrate proof-of-concept results for accurate CNN-based HSC-LSC differentiation, instigating the development of a novel technique within AML monitoring.

Quantitative Analysis of Latex Beads Phagocytosis by Human Macrophages Using Imaging Flow Cytometry with Extended Depth of Field (EDF)

The existing methods of quantitative analysis of phagocytosis are characterized by a number of limitations. The usual method of manually counting phagocytosed objects on photographs obtained by confocal microscopy is very labor-intensive and time-consuming. As well, the resolution of conventional flow cytometry does not allow the fluorescence detection of a large number of phagocytosis objects. Thus, there is a need to combine the rapid analysis by flow cytometry and the visualization capability by confocal microscopy. This is possible due to imaging flow cytometry. However, until now, no protocols have allowed one to quantify phagocytosis at its high intensity. The present paper presents the developed and tested algorithm for assessing the level of phagocytic activity using flow cytometry with visualization and IDEAS software.

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.

Identification of fetal liver stroma in spectral cytometry using the parameter autofluorescence

The fetal liver (FL) is the main hematopoietic organ during embryonic development. The FL is also the unique anatomical site where hematopoietic stem cells expand before colonizing the bone marrow, where they ensure life-long blood cell production and become mostly resting. The identification of the different cell types that comprise the hematopoietic stroma in the FL is essential to understand the signals required for the expansion and differentiation of the hematopoietic stem cells. We used a panel of monoclonal antibodies to identify FL stromal cells in a 5-laser equipped spectral flow cytometry (FCM) analyzer. The "Autofluorescence Finder" of SONY ID7000 software identified two distinct autofluorescence emission spectra. Using autofluorescence as a fluorescence parameter we could assign the two autofluorescent signals to three distinct cell types and identified surface markers that characterize these populations. We found that one autofluorescent population corresponds to hepatoblast-like cells and cholangiocytes whereas the other expresses mesenchymal transcripts and was identified as stellate cells. Importantly, after birth, autofluorescence becomes the unique identifying property of hepatoblast-like cells because mature cholangiocytes are no longer autofluorescent. These results show that autofluorescence used as a parameter in spectral FCM is a useful tool to identify new cell subsets that are difficult to analyze in conventional FCM.

The Role of Dielectrophoresis for Cancer Diagnosis and Prognosis

Liquid biopsy is emerging as a potential diagnostic tool for prostate cancer (PC) prognosis and diagnosis. Unfortunately, most circulating tumor cells (CTC) technologies, such as AdnaTest or Cellsearch®, critically rely on the epithelial cell adhesion molecule (EpCAM) marker, limiting the possibility of detecting cancer stem-like cells (CSCs) and mesenchymal-like cells (EMT-CTCs) that are present during PC progression. In this context, dielectrophoresis (DEP) is an epCAM independent, label-free enrichment system that separates rare cells simply on the basis of their specific electrical properties. As compared to other technologies, DEP may represent a superior technique in terms of running costs, cell yield and specificity. However, because of its higher complexity, it still requires further technical as well as clinical development. DEP can be improved by the use of microfluid, nanostructured materials and fluoro-imaging to increase its potential applications. In the context of cancer, the usefulness of DEP lies in its capacity to detect CTCs in the bloodstream in their epithelial, mesenchymal, or epithelial-mesenchymal phenotype forms, which should be taken into account when choosing CTC enrichment and analysis methods for PC prognosis and diagnosis.

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.

Immunomonitoring of Human Breast Milk Cells During HCMV-Reactivation

Background

Breast milk leukocytes may play a role in protecting the infant from pathogens. The dynamics and the role of lymphocytes in human cytomegalovirus (HCMV)-seropositive mothers shedding HCMV into breast milk during the first months postpartum (p.p.) are mostly unclear.

Methods

Breast milk cells were analyzed by Pappenheim panoptic and alpha-naphthyl acetate esterase staining as well as by imaging and polychromatic flow cytometry to simultaneously establish their morphological and phenotypic properties. The latter were characterized in HCMV-seropositive and seronegative mothers´ breast milk cells at different time points p.p.

Results

Panoptic staining of breast milk cells revealed the presence of monocytes/macrophages, granulocytes and lymphocytes. Imaging flow cytometry data combining phenotypic and morphological analysis identified NKT-like cells, NK cells, epithelial cells, T cells and monocytes/macrophages. HCMV-seropositive but not -seronegative mothers had significantly higher T cell frequencies in mature milk.

Conclusions

The presence of lymphocyte subsets in breast milk may be more influenced by the HCMV-seropositivity of the mother than previously recognized.

Radachlorin-Containing Microparticles for Photodynamic Therapy

Purpose: Reducing the undesirable systemic effect of photodynamic therapy (PDT) can be achieved by incorporating a photosensitizer in microparticles (MPs). This study is devoted to the preparation of biocompatible biodegradable MPs with the inclusion of the natural photosensitizer Radachlorin (RС) and an assessment of the possibility of their use for PDT. Methods: RC-containing MPs (RС MPs) with poly(lactic-co-glycolic acid) copolymer (PLGA) matrix were prepared by a double emulsion solvent evaporation methods. The size and morphology of RC MPs were surveyed using scanning electron microscopy, confocal laser scanning microscopy, and dynamic light scattering. The content of RC, its release from RC MPs, and singlet oxygen generation were evaluated by the optical spectroscopy. Cellular uptake and cytotoxic photodynamic effect of RC MPs were investigated with in vitro assays. Results: The average diameter of the prepared RC MPs was about 2-3 μm. The RC MPs prepared by the water/oil/oil method had a significantly higher inclusion of RC (1.74 μg/mg) then RC MPs prepared by the water/oil/water method (0.089 μg/mg). Exposure of the prepared RC MPs to PDT light radiation was accompanied by the singlet oxygen generation and a cytotoxic effect for tumor cells. The release of the RC from the RC MPs was prolonged and lasted at least two weeks. Conclusion: PLGA RC MPs were found to cause a photoactivated cytotoxic effect for tumor cells and can be used for local application in PDT of tumors.

The Evolution of Single-Cell Analysis and Utility in Drug Development

This review provides a brief history of the advances of cellular analysis tools focusing on instrumentation, detection probes, and data analysis tools. The interplay of technological advancement and a deeper understanding of cellular biology are emphasized. The relevance of this topic to drug development is that the evaluation of cellular biomarkers has become a critical component of the development strategy for novel immune therapies, cell therapies, gene therapies, antiviral therapies, and vaccines. Moreover, recent technological advances in single-cell analysis are providing more robust cellular measurements and thus accelerating the advancement of novel therapies.

Graphical abstract

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.

An Imaging Flow Cytometry Method to Measure Citrullination of H4 Histone as a Read-out for Neutrophil Extracellular Traps Formation

The formation of neutrophil extracellular traps (NETs) is thought to play a critical role in infections and propagating sterile inflammation. Histone citrullination is an essential and early step in NETs formation, detectable prior to the formation of the hallmark extracellular DNA-scaffolded strands. In addition to the classical microscopy method, new technologies are being developed for studies of NETs and their detection, both for research and clinical purposes. Classical microscopy studies of NETs are subjective, low throughput and semi-quantitative, and limited in their ability to capture the early steps. We have developed this novel Imaging Flow Cytometry (IFC) method that specifically identifies and quantifies citrullination of histone H4 as a NETs marker and its relationship with other alterations at nuclear and cellular level. These include nuclear decondensation and super-condensation, multi-lobulated nuclei versus 1-lobe nuclei and cell membrane damage. NETs markers can be quantified following variable periods of treatment with NETs inducers, prior to the formation of the specific extracellular DNA-scaffolded strands. Because these high throughput image-based cell analysis features can be performed with statistical rigor, this protocol is suited for both experimental and clinical applications as well as clinical evaluations of NETosis as a biomarker.

Deep learning-enabled imaging flow cytometry for high-speed Cryptosporidium and Giardia detection

Imaging flow cytometry has become a popular technology for bioparticle image analysis because of its capability of capturing thousands of images per second. Nevertheless, the vast number of images generated by imaging flow cytometry imposes great challenges for data analysis especially when the species have similar morphologies. In this work, we report a deep learning-enabled high-throughput system for predicting Cryptosporidium and Giardia in drinking water. This system combines imaging flow cytometry and an efficient artificial neural network called MCellNet, which achieves a classification accuracy >99.6%. The system can detect Cryptosporidium and Giardia with a sensitivity of 97.37% and a specificity of 99.95%. The high-speed analysis reaches 346 frames per second, outperforming the state-of-the-art deep learning algorithm MobileNetV2 in speed (251 frames per second) with a comparable classification accuracy. The reported system empowers rapid, accurate, and high throughput bioparticle detection in clinical diagnostics, environmental monitoring and other potential biosensing applications.

Full Spectrum Flow Cytometry as a Powerful Technology for Cancer Immunotherapy Research

The Nobel Prize-deserving concept of blocking inhibitory pathways in T cells, to unleash their anti-tumoral capacity, became one of the pillars of cancer treatment in the last decade and has resulted in durable clinical responses for multiple cancer types. Currently, two of the most important goals in cancer immunotherapy are to understand the mechanisms resulting in failure to checkpoint blockade and to identify predictive immunological biomarkers that correlate to treatment response, disease progression or adverse effects. The identification and validation of biomarkers for routine clinical use is not only critical to monitor disease or treatment progression, but also to personalize and develop new therapies. To achieve these goals, powerful research tools are needed. Flow cytometry stands as one of the most successful single-cell analytical tools used to characterize immune cell phenotypes to monitor solid tumors, hematological malignancies, minimal residual disease or metastatic progression. This technology has been fundamental in diagnosis, treatment and translational research in cancer clinical trials. Most recently, the need to evaluate simultaneously more features in each cell has pushed the field to implement more powerful adaptations beyond conventional flow cytometry, including Full Spectrum Flow Cytometry (FSFC). FSFC captures the full emission spectrum of fluorescent molecules using arrays of highly sensitive light detectors, and to date has enabled characterization of 40 parameters in a single sample. We will summarize the contributions of this technology to the advancement of research in immunotherapy studies and discuss best practices to obtain reliable, robust and reproducible FSFC results.

Single-Cell Analysis of Fungal Uptake in Cultured Airway Epithelial Cells Using Differential Fluorescent Staining and Imaging Flow Cytometry

The respiratory epithelium is the initial point of host contact for inhaled particles, leading to orchestrated, but highly heterogeneous, responses. Human airway epithelial cells (AECs) play a crucial role in host defense by promoting uptake and killing of inhaled microorganisms and concomitant cytokine production in order to recruit professional phagocytes to the site of infection. However, inhaled pathogens can also reside and replicate intracellularly to evade host immune defenses or circulating antimicrobial drugs, ultimately causing apoptosis or cell death of the infected AECs. Imaging flow cytometry (IFC) combines flow cytometry, fluorescent microscopy, and advanced data-processing algorithms to dissect the heterogeneity of the interaction of AECs and inhaled microorganisms and its outcomes at the single-cell level. Here, we describe a novel single-cell approach based on differential fluorescent staining and state-of-the-art IFC to identify, quantify, and analyze individual host-pathogen complexes from cultured AECs infected with spores of the major human fungal pathogen Aspergillus fumigatus.

Machine Learning-Based Pipeline for High Accuracy Bioparticle Sizing

High accuracy measurement of size is essential in physical and biomedical sciences. Various sizing techniques have been widely used in sorting colloidal materials, analyzing bioparticles and monitoring the qualities of food and atmosphere. Most imaging-free methods such as light scattering measure the averaged size of particles and have difficulties in determining non-spherical particles. Imaging acquisition using camera is capable of observing individual nanoparticles in real time, but the accuracy is compromised by the image defocusing and instrumental calibration. In this work, a machine learning-based pipeline is developed to facilitate a high accuracy imaging-based particle sizing. The pipeline consists of an image segmentation module for cell identification and a machine learning model for accurate pixel-to-size conversion. The results manifest a significantly improved accuracy, showing great potential for a wide range of applications in environmental sensing, biomedical diagnostical, and material characterization.

Assessment of extracellular vesicles using IFC for application in transfusion medicine

Extracellular vesicles (EVs) have been shown to be involved in various physiological and pathophysiological processes. With respect to Transfusion Medicine, the accumulation of EVs in blood products during hypothermic storage is an indicator of the storage lesion and reportedly correlates with adverse effects after transfusion, including but not limited to immunomodulation, activation of coagulation, endothelial activation, and others. To optimally reduce such an impact on blood product quality degradation and improve post-transfusion outcomes, better methods for detection, enumeration, characterisation by size and phenotype, and functional involvement of EVs in different pathophysiological and physiological processes are required. Currently, Imaging Flow Cytometry (IFC) technology provides the most comprehensive assessment of EV subsets in different body fluids. The unique ability of IFC to detect EVs of 20 nm size by registration of a single pixel of fluorescence signal makes this approach highly promising for comprehensive studies of EVs. In this review, we will focus on the recent breakthrough and advantages of using the ImageStream^X MKII IFC platform for the detection and characterisation of EVs and its future prospects for routine application of IFC in Transfusion Medicine.

Development of the FAST-DOSE assay system for high-throughput biodosimetry and radiation triage

Following a large-scale radiological incident, there is a need for FDA-approved biodosimetry devices and biomarkers with the ability to rapidly determine past radiation exposure with sufficient accuracy for early population triage and medical management. Towards this goal, we have developed FAST-DOSE (Fluorescent Automated Screening Tool for Dosimetry), an immunofluorescent, biomarker-based system designed to reconstruct absorbed radiation dose in peripheral blood samples collected from potentially exposed individuals. The objective of this study was to examine the performance of the FAST-DOSE assay system to quantify intracellular protein changes in blood leukocytes for early biodosimetry triage from humanized NOD-scid-gamma (Hu-NSG) mice and non-human primates (NHPs) exposed to ionizing radiation up to 8 days after radiation exposure. In the Hu-NSG mice studies, the FAST-DOSE biomarker panel was able to generate delivered dose estimates at days 1, 2 and 3 post exposure, whereas in the NHP studies, the biomarker panel was able to successfully classify samples by dose categories below or above 2 Gy up to 8 days after total body exposure. These results suggest that the FAST-DOSE bioassay has large potential as a useful diagnostic tool for rapid and reliable screening of potentially exposed individuals to aid early triage decisions within the first week post-exposure.

3-Step flow focusing enables multidirectional imaging of bioparticles for imaging flow cytometry

Multidirectional imaging flow cytometry (mIFC) extends conventional imaging flow cytometry (IFC) for the image-based measurement of 3D-geometrical features of particles. The innovative core is a flow rotation unit in which a vertical sample lamella is incrementally rotated by 90 degrees into a horizontal lamella. The required multidirectional views are generated by guiding all particles at a controllable shear flow position of the parabolic velocity profile of the capillary slit detection chamber. All particles pass the detection chamber in a two-dimensional sheet under controlled rotation while each particle is imaged multiple times. This generates new options for automated particle analysis. In an experimental application, we used our system for the accurate classification of 15 species of pollen based on 3D-morphological information. We demonstrate how the combination of multi directional imaging with advanced machine learning algorithms can improve the accuracy of automated bio-particle classification. As an additional benefit, we significantly decrease the number of false positives in the classification of foreign particles, i.e. those elements which do not belong to one of the trained classes by the 3D-extension of the classification algorithm.

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches

Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient's life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.

Imaging Flow Cytometry Protocols for Examining Phagocytosis of Microplastics and Bioparticles by Immune Cells of Aquatic Animals

Imaging flow cytometry (IFC) is a powerful tool which combines flow cytometry with digital microscopy to generate quantitative high-throughput imaging data. Despite various advantages of IFC over standard flow cytometry, widespread adoption of this technology for studies in aquatic sciences is limited, probably due to the relatively high equipment cost, complexity of image analysis-based data interpretation and lack of core facilities with trained personnel. Here, we describe the application of IFC to examine phagocytosis of particles including microplastics by cells from aquatic animals. For this purpose, we studied (1) live/dead cell assays and identification of cell types, (2) phagocytosis of degradable and non-degradable particles by Atlantic salmon head kidney cells and (3) the effect of incubation temperature on phagocytosis of degradable particles in three aquatic animals–Atlantic salmon, Nile tilapia, and blue mussel. The usefulness of the developed method was assessed by evaluating the effect of incubation temperature on phagocytosis. Our studies demonstrate that IFC provides significant benefits over standard flow cytometry in phagocytosis measurement by allowing integration of morphometric parameters, especially while identifying cell populations and distinguishing between different types of fluorescent particles and detecting their localization.

Development of a high-throughput γ-H2AX assay based on imaging flow cytometry

Background

Measurement of γ-H2AX foci levels in cells provides a sensitive and reliable method for quantitation of the radiation-induced DNA damage response. The objective of the present study was to develop a rapid, high-throughput γ-H2AX assay based on imaging flow cytometry (IFC) using the ImageStream®^X Mk II (ISX) platform to evaluate DNA double strand break (DSB) repair kinetics in human peripheral blood cells after exposure to ionizing irradiation.

Methods

The γ-H2AX protocol was developed and optimized for small volumes (100 μL) of human blood in Matrix™ 96-tube format. Blood cell lymphocytes were identified and captured by ISX INSPIRE™ software and analyzed by Data Exploration and Analysis Software.

Results

Dose- and time-dependent γ-H2AX levels corresponding to radiation exposure were measured at various time points over 24 h using the IFC system. γ-H2AX fluorescence intensity at 1 h after exposure, increased linearly with increasing radiation dose (R² = 0.98) for the four human donors tested, whereas the dose response for the mean number of γ-H2AX foci/cell was not as robust (R² = 0.81). Radiation-induced γ-H2AX levels rapidly increased within 30 min and reached a maximum by ~ 1 h, after which time there was fast decline by 6 h, followed by a much slower rate of disappearance up to 24 h. A mathematical approach for quantifying DNA repair kinetics using the rate of γ-H2AX decay (decay constant, K_dec), and yield of residual unrepaired breaks (F_res) demonstrated differences in individual repair capacity between the healthy donors.

Conclusions

The results indicate that the IFC-based γ-H2AX protocol may provide a practical and high-throughput platform for measurements of individual global DNA DSB repair capacity which can facilitate precision medicine by predicting individual radiosensitivity and risk of developing adverse effects related to radiotherapy treatment.

Electronic supplementary material

The online version of this article (10.1186/s13014-019-1344-7) contains supplementary material, which is available to authorized users.

Feasibility of photoacoustic imaging for the non-invasive quality management of stored blood bags

BACKGROUND AND OBJECTIVES

During the in vitro storage of red blood cells (RBCs), unfavourable changes (storage lesions) cause a rapid consumption of intracellular diphosphoglycerate. The latter deregulates the oxygen-haemoglobin binding potential, subsequently increasing oxygen saturation (SO₂ ) and membrane degradation, transforming RBCs from biconcave discs to rigid spherical bodies (spheroechinocytes). Current laboratory techniques invasively extract RBC samples to assess the quality of red cell concentrate (RCC) units. Optical technologies could provide a means of assessing quality non-invasively.

MATERIALS AND METHODS

A photoacoustic (PA) imaging technique was developed for acquiring the SO₂ of blood bags non-invasively. Seven RCC units were monitored every 3-5 days until expiry (6 weeks). Measurements were validated against a conventional blood gas analyzer (BGA). Using an image flow cytometry assay, morphological profile trends were compared against the SO₂ trends during blood bag storage.

RESULTS

A strong correlation (r²  ≥ 0·95) was found when comparing temporal data between PA and BGA SO₂ measurements. Inter-sample PA variability was found to be similar to that produced by BGA (±0·8%). A strong correlation was found to exist between the temporal changes in SO₂ and relative spheroechinocyte population (0·79 ≤ r²  ≤ 0·97).

CONCLUSION

This study suggests that PA imaging can non-invasively track the SO₂ of stored RBCs non-invasively. By longitudinally monitoring the change in SO₂ , it is possible to infer the effects of the storage lesion on RBC morphology. This non-invasive monitoring technique allows for the assessment of blood bags, without compromising sterility pre-transfusion.

Automated Identification and Scoring of Micronuclei

Micronucleus (MN) assays are used as a measure of cellular damage, and are often preferred over other chromosomal aberration assays since they possess similar statistical robustness for detection of genotoxins and require less technical expertise, making them easier to perform. However, the traditional visual scoring methods are tedious and prone to scorer subjectivity. A number of techniques to automate the MN assays have been developed using a variety of technologies. This chapter will provide an overview of several current methods used to automate MN assays, including automated slide-scoring and laser scanning cytometry, as well as conventional and imaging flow cytometry techniques.

Automated Triage Radiation Biodosimetry: Integrating Imaging Flow Cytometry with High-Throughput Robotics to Perform the Cytokinesis-Block Micronucleus Assay

The cytokinesis-block micronucleus (CBMN) assay has become a fully-validated and standardized method for radiation biodosimetry. The assay is typically performed using microscopy, which is labor intensive, time consuming and impractical after a large-scale radiological/nuclear event. Imaging flow cytometry (IFC), which combines the statistical power of traditional flow cytometry with the sensitivity and specificity of microscopy, has been recently used to perform the CBMN assay. Since this technology is capable of automated sample acquisition and multi-file analysis, we have integrated IFC into our Rapid Automated Biodosimetry Technology (RABiT-II). Assay development and optimization studies were designed to increase the yield of binucleated cells (BNCs), and improve data acquisition and analysis templates to increase the speed and accuracy of image analysis. Human peripheral blood samples were exposed ex vivo with up to 4 Gy of c rays at a dose rate of 0.73 Gy/min. After irradiation, samples were transferred to microtubes (total volume of 1 ml including blood and media) and organized into a standard 8 × 12 plate format. Sample processing methods were modified by increasing the blood-to-media ratio, adding hypotonic solution prior to cell fixation and optimizing nuclear DRAQ5 staining, leading to an increase of 81% in BNC yield. Modification of the imaging processing algorithms within IFC software also improved BNC and MN identification, and reduced the average time of image analysis by 78%. Finally, 50 ll of irradiated whole blood was cultured with 200 ll of media in 96-well plates. All sample processing steps were performed automatically using the RABiT-II cell: :explorer robotic system adopting the optimized IFC-CBMN assay protocol. The results presented here detail a novel, high-throughput RABiT-IFC CBMN assay that possesses the potential to increase capacity for triage biodosimetry during a large-scale radiological/nuclear event.

Immediate visualization of recombination events and chromosome segregation defects in fission yeast meiosis

Schizosaccharomyces pombe, also known as fission yeast, is an established model for studying chromosome biological processes. Over the years, research employing fission yeast has made important contributions to our knowledge about chromosome segregation during meiosis, as well as meiotic recombination and its regulation. Quantification of meiotic recombination frequency is not a straightforward undertaking, either requiring viable progeny for a genetic plating assay, or relying on laborious Southern blot analysis of recombination intermediates. Neither of these methods lends itself to high-throughput screens to identify novel meiotic factors. Here, we establish visual assays novel to Sz. pombe for characterizing chromosome segregation and meiotic recombination phenotypes. Genes expressing red, yellow, and/or cyan fluorophores from spore-autonomous promoters have been integrated into the fission yeast genomes, either close to the centromere of chromosome 1 to monitor chromosome segregation, or on the arm of chromosome 3 to form a genetic interval at which recombination frequency can be determined. The visual recombination assay allows straightforward and immediate assessment of the genetic outcome of a single meiosis by epi-fluorescence microscopy without requiring tetrad dissection. We also demonstrate that the recombination frequency analysis can be automatized by utilizing imaging flow cytometry to enable high-throughput screens. These assays have several advantages over traditional methods for analyzing meiotic phenotypes.

Automation of the in vitro micronucleus assay using the Imagestream® imaging flow cytometer

The in vitro micronucleus (MN) assay is a well‐established test for evaluating genotoxicity and cytotoxicity. The use of manual microscopy to perform the assay can be laborious and often suffers from user subjectivity and interscorer variability. Automated methods including slide‐scanning microscopy and conventional flow cytometry have been developed to eliminate scorer bias and improve throughput. However, these methods possess several limitations such as lack of cytoplasmic visualization using slide‐scanning microscopy and the inability to visually confirm the legitimacy of MN or storage of image data for re‐evaluation using flow cytometry. The ImageStream^X® MK II (ISX) imaging flow cytometer has been demonstrated to overcome all of these limitations. The ISX combines the speed, statistical robustness, and rare event capture capability of conventional flow cytometry with high resolution fluorescent imagery of microscopy and possesses the ability to store all collected image data. This paper details the methodology developed to perform the in vitro MN assay in human lymphoblastoid TK6 cells on the ISX. High resolution images of micronucleated mono‐ and bi‐nucleated cells as well as polynucleated cells can be acquired at a high rate of capture. All images can then be automatically identified, categorized and enumerated in the data analysis software that accompanies the ImageStream, allowing for the scoring of both genotoxicity and cytotoxicity. The results demonstrate that statistically significant increases in MN frequency when compared with solvent controls can be detected at varying levels of cytotoxicity following exposure to well‐known aneugens and clastogens. This work demonstrates a fully automated method for performing the in vitro micronucleus assay on the ISX imaging flow cytometry platform. © 2018 The Author. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of ISAC.

The potential for complete automated scoring of the cytokinesis block micronucleus cytome assay using imaging flow cytometry

The lymphocyte Cytokinesis-Block Micronucleus (CBMN) assay was originally developed for the measurement of micronuclei (MN) exclusively in binucleated (BN) cells, which represent the population of cells that can express MN because they completed nuclear division. Recently the assay has evolved into a comprehensive cytome method to include biomarkers that measure chromosomal instability and cytotoxicity by quantification of nuclear buds (NBUDs), nucleoplasmic bridges (NPBs) and apoptotic/necrotic cells. Furthermore, enumeration of mono- and polynucleated cells allows for computation of the nuclear division index (NDI) to assess mitotic activity. Typically performed by manual microscopy, the CBMN cytome assay is laborious and subject to scorer bias and fatigue, leading to inter- and intra-scorer variability. Automated microscopy and conventional flow cytometry methods have been developed to automate scoring of the traditional and cytome versions of the assay. However, these methods have several limitations including the requirement to create high-quality microscope slides, lack of staining consistency and sub-optimal nuclear/cytoplasmic visualization. In the case of flow cytometry, stripping of the cytoplasmic membrane makes it impossible to measure MN in BN cells, calculate the NDI or to quantify apoptotic or necrotic cells. Moreover, the absence of cellular visualization using conventional flow cytometry, makes it impossible to quantify NBUDs and NPBs. In this review, we propose that imaging flow cytometry (IFC), which combines high resolution microscopy with flow cytometry, may overcome these limitations. We demonstrate that by using IFC, images from cells in suspension can be captured, removing the need for microscope slides and allowing visualization of intact cytoplasmic membranes and DNA content. Thus, mono-, bi- and polynucleated cells with and without MN can be rapidly and automatically identified and quantified. Finally, we present high-resolution cell images containing NBUDs and NPBs, illustrating that IFC possesses the potential for completely automated scoring of all components of the CBMN cytome assay.

Mechanistic Modeling of Antibody-Drug Conjugate Internalization at the Cellular Level Reveals Inefficient Processing Steps

Antibody-drug conjugates (ADC) offer an avenue for specific drug delivery to target cells. Here, parameters with important roles in the cellular processing of ADCs were quantitatively measured for Ab033, an antibody against EGFR. In EGFR-overexpressing cancer cell lines, Ab033 internalized at rates of 0.047/min and 0.15/min for A431 and H441 cells, respectively. Once internalized, Ab033 either trafficked to the lysosome or was recycled; up to 45% of internalized Ab033 returned to the cell surface. Despite such recycling, intracellular accumulation of Ab033 continually increased over 24 hours. Ab033 was conjugated to form a dual toxin ADC containing both cleavable and non-cleavable linker-drug payloads for release rate comparisons. Intracellular concentrations of freed drug from cleavable linker were greater than from non-cleavable linker and exceeded 5 × 10⁶ drug molecules per A431 cell after 24 hours. Compared with intracellular antibody accumulation, formation of released drug was delayed, likely due to the time needed for endo-lysosomal trafficking and subsequent linker/antibody proteolysis. Informed by the quantitative data, a cellular ADC model was constructed and used to summarize processing inefficiencies. Modeling simulations were conducted to determine parameter sensitivity on intracellular drug concentrations, with rates of EGFR internalization and recycling as well as ADC trafficking found to be the most sensitive toward final intracellular drug concentrations. Overall, this study shows Ab033 ADCs to be a viable strategy for delivery of cytotoxic drugs into tumor cells with subsequent modeling efforts able to highlight key processing steps to be improved for increased drug delivery. Mol Cancer Ther; 17(6); 1341-51. ©2018 AACR.

Of Cytometry, Stem Cells and Fountain of Youth

Outlined are advances of cytometry applications to identify and sort stem cells, of laser scanning cytometry and ImageStream imaging instrumentation to further analyze morphometry of these cells, and of mass cytometry to classify a multitude of cellular markers in large cell populations. Reviewed are different types of stem cells, including potential candidates for cancer stem cells, with respect to their "stemness", and other characteristics. Appraised is further progress in identification and isolation of the "very small embryonic-like stem cells" (VSELs) and their autogenous transplantation for tissue repair and geroprotection. Also assessed is a function of hyaluronic acid, the major stem cells niche component, as a guardian and controller of stem cells. Briefly appraised are recent advances and challenges in the application of stem cells in regenerative medicine and oncology and their future role in different disciplines of medicine, including geriatrics.

Quantitation of IRF3 Nuclear Translocation in Heterogeneous Cellular Populations from Cervical Tissue Using Imaging Flow Cytometry

Imaging flow cytometry (IFC) has become a powerful tool for studying the activation of transcriptional factors in heterogeneous cell populations in high-content imaging mode. With considerable interest to the clinical development of IFC, the question becomes how we can accelerate its application to solid tissues. We developed the first IFC-based procedure to quantify the nuclear translocation of interferon regulatory factor (IRF) 3, an important measure of induction of type I interferon antiviral response, in primary human immune cells including in solid tissues. After tissue digestion and protocol optimization by spectral flow cytometry, cell suspension is stained for intracellular IRF3 and acquired by IFC. Image analysis is performed using an optimized nuclear mask and similarity score parameter to correlate the location of IRF3 staining and a nuclear dye. The technique measures IRF3 activation at a single cell level and can detect small changes in the percent of activated cells providing objective quantitative data for statistical analysis.

Heterogeneity of Metazoan Cells and Beyond: To Integrative Analysis of Cellular Populations at Single-Cell Level

In this paper, we review some of the recent advances in cellular heterogeneity and single-cell analysis methods. In modern research of cellular heterogeneity, there are four major approaches: analysis of pooled samples, single-cell analysis, high-throughput single-cell analysis, and lately integrated analysis of cellular population at a single-cell level. Recently developed high-throughput single-cell genetic analysis methods such as RNA-Seq require purification step and destruction of an analyzed cell often are providing a snapshot of the investigated cell without spatiotemporal context. Correlative analysis of multiparameter morphological, functional, and molecular information is important for differentiation of more uniform groups in the spectrum of different cell types. Simplified distributions (histograms and 2D plots) can underrepresent biologically significant subpopulations. Future directions may include the development of nondestructive methods for dissecting molecular events in intact cells, simultaneous correlative cellular analysis of phenotypic and molecular features by hybrid technologies such as imaging flow cytometry, and further progress in supervised and non-supervised statistical analysis algorithms.

Line-Focused Optical Excitation of Parallel Acoustic Focused Sample Streams for High Volumetric and Analytical Rate Flow Cytometry

Flow cytometry provides highly sensitive multiparameter analysis of cells and particles but has been largely limited to the use of a single focused sample stream. This limits the analytical rate to ∼50K particles/s and the volumetric rate to ∼250 μL/min. Despite the analytical prowess of flow cytometry, there are applications where these rates are insufficient, such as rare cell analysis in high cellular backgrounds (e.g., circulating tumor cells and fetal cells in maternal blood), detection of cells/particles in large dilute samples (e.g., water quality, urine analysis), or high-throughput screening applications. Here we report a highly parallel acoustic flow cytometer that uses an acoustic standing wave to focus particles into 16 parallel analysis points across a 2.3 mm wide optical flow cell. A line-focused laser and wide-field collection optics are used to excite and collect the fluorescence emission of these parallel streams onto a high-speed camera for analysis. With this instrument format and fluorescent microsphere standards, we obtain analysis rates of 100K/s and flow rates of 10 mL/min, while maintaining optical performance comparable to that of a commercial flow cytometer. The results with our initial prototype instrument demonstrate that the integration of key parallelizable components, including the line-focused laser, particle focusing using multinode acoustic standing waves, and a spatially arrayed detector, can increase analytical and volumetric throughputs by orders of magnitude in a compact, simple, and cost-effective platform. Such instruments will be of great value to applications in need of high-throughput yet sensitive flow cytometry analysis.

Imaging flow cytometry analysis of intracellular pathogens

Imaging flow cytometry has been applied to address questions in infection biology, in particular, infections induced by intracellular pathogens. This methodology, which utilizes specialized analytic software makes it possible to analyze hundreds of quantified features for hundreds of thousands of individual cellular or subcellular events in a single experiment. Imaging flow cytometry analysis of host cell-pathogen interaction can thus quantitatively addresses a variety of biological questions related to intracellular infection, including cell counting, internalization score, and subcellular patterns of co-localization. Here, we provide an overview of recent achievements in the use of fluorescently labeled prokaryotic or eukaryotic pathogens in human cellular infections in analysis of host-pathogen interactions. Specifically, we give examples of Imagestream-based analysis of cell lines infected with Toxoplasma gondii or Mycobacterium tuberculosis. Furthermore, we illustrate the capabilities of imaging flow cytometry using a combination of standard IDEAS™ software and the more recently developed Feature Finder algorithm, which is capable of identifying statistically significant differences between researcher-defined image galleries. We argue that the combination of imaging flow cytometry with these software platforms provides a powerful new approach to understanding host control of intracellular pathogens.

An open-source solution for advanced imaging flow cytometry data analysis using machine learning

Imaging flow cytometry (IFC) enables the high throughput collection of morphological and spatial information from hundreds of thousands of single cells. This high content, information rich image data can in theory resolve important biological differences among complex, often heterogeneous biological samples. However, data analysis is often performed in a highly manual and subjective manner using very limited image analysis techniques in combination with conventional flow cytometry gating strategies. This approach is not scalable to the hundreds of available image-based features per cell and thus makes use of only a fraction of the spatial and morphometric information. As a result, the quality, reproducibility and rigour of results are limited by the skill, experience and ingenuity of the data analyst. Here, we describe a pipeline using open-source software that leverages the rich information in digital imagery using machine learning algorithms. Compensated and corrected raw image files (.rif) data files from an imaging flow cytometer (the proprietary .cif file format) are imported into the open-source software CellProfiler, where an image processing pipeline identifies cells and subcellular compartments allowing hundreds of morphological features to be measured. This high-dimensional data can then be analysed using cutting-edge machine learning and clustering approaches using "user-friendly" platforms such as CellProfiler Analyst. Researchers can train an automated cell classifier to recognize different cell types, cell cycle phases, drug treatment/control conditions, etc., using supervised machine learning. This workflow should enable the scientific community to leverage the full analytical power of IFC-derived data sets. It will help to reveal otherwise unappreciated populations of cells based on features that may be hidden to the human eye that include subtle measured differences in label free detection channels such as bright-field and dark-field imagery.