AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Coupling imaging mass cytometry with Alcian blue histochemical staining for a single-slide approach

Imaging mass cytometry (IMC) is a metal mass spectrometry-based method allowing highly multiplex immunophenotyping of cells within tissue samples. However, some limitations of IMC are its 1-µm resolution and its time and costs of analysis limiting respectively the detailed histopathological analysis of IMC-produced images and its application to small selected tissue regions of interest (ROI) of one to few square millimeters. Coupling on a single-tissue section, IMC and histopathological analyses could permit a better selection of the ROI for IMC analysis as well as co-analysis of immunophenotyping and histopathological data until the single-cell level. The development of this method is the aim of the present study in which we point to the feasibility of applying the IMC process to tissue sections previously Alcian blue-stained and digitalized before IMC tissue destructive analyses. This method could help to improve the process of IMC in terms of ROI selection, time of analysis, and the confrontation between histopathological and immunophenotypic data of cells.

Development of 42 marker panel for in-depth study of cancer associated fibroblast niches in breast cancer using imaging mass cytometry

Imaging Mass Cytometry (IMC) is a novel, and formidable high multiplexing imaging method emerging as a promising tool for in-depth studying of tissue architecture and intercellular communications. Several studies have reported various IMC antibody panels mainly focused on studying the immunological landscape of the tumor microenvironment (TME). With this paper, we wanted to address cancer associated fibroblasts (CAFs), a component of the TME very often underrepresented and not emphasized enough in present IMC studies. Therefore, we focused on the development of a comprehensive IMC panel that can be used for a thorough description of the CAF composition of breast cancer TME and for an in-depth study of different CAF niches in relation to both immune and breast cancer cell communication. We established and validated a 42 marker panel using a variety of control tissues and rigorous quantification methods. The final panel contained 6 CAF-associated markers (aSMA, FAP, PDGFRa, PDGFRb, YAP1, pSMAD2). Breast cancer tissues (4 cases of luminal, 5 cases of triple negative breast cancer) and a modified CELESTA pipeline were used to demonstrate the utility of our IMC panel for detailed profiling of different CAF, immune and cancer cell phenotypes.

Detection and Quantification of Cryptococcus Uptake by Phagocytic Cells Using Imaging Flow Cytometry

Cryptococcus neoformans, the predominant etiological agent of cryptococcosis, is an encapsulated fungal pathogen found ubiquitously in the environment that causes pneumonia and life-threatening infections of the central nervous system. Following inhalation of yeasts or desiccated basidiospores into the lung alveoli, resident pulmonary phagocytic cells aid in the identification and eradication of Cryptococcus yeast through their arsenal of pattern recognition receptors (PRRs). PRRs recognize conserved pathogen-associated molecular patterns (PAMPs), such as branched mannans, β-glucans, and chitins that are the major components of the fungal cell wall. However, the key receptors/ligand interactions required for cryptococcal recognition and eventual fungal clearance have yet to be elucidated. Here we present an imaging flow cytometer (IFC) method that offers a novel quantitative cellular imaging and population statistics tool to accurately measure phagocytosis of fungal cells. It has the capacity to measure two distinct steps of phagocytosis: association/attachment and internalization in a high-throughput and quantitative manner that is difficult to achieve with other technologies. Results from these IFC studies allow for the potential to identify PRRs required for recognition, uptake, and subsequent activation of cytokine production, as well as other effector cell responses required for fungal clearance.

Imaging Mass Cytometry for In Situ Immune Profiling

The complexities and cellular heterogeneity associated with tissues necessitate the concurrent detection of markers beyond the limitations of conventional imaging approaches in order to spatially resolve the relationships between immune cell populations and their environments. This is a necessary complement to single-cell suspension-based methods to inform a better understanding of the events that may underlie pathological conditions. Imaging mass cytometry is a high-dimensional imaging modality that allows for the concurrent detection of up to 40 protein markers of interest across tissues at subcellular resolution. Here, we present an optimized staining protocol for imaging mass cytometry with modifications that integrate RNAscope. This unique addition enables combined protein and single-molecule RNA detection, thereby expanding the utility of imaging mass cytometry to researchers investigating low abundance or noncoding targets. In general, the procedure described is broadly applicable for comprehensive immune profiling of host-pathogen interactions, tumor microenvironments and inflammatory conditions, all within the tissue contexture.

OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration

Analysis of imaging mass cytometry (IMC) data and other low-resolution multiplexed tissue imaging technologies is often confounded by poor single-cell segmentation and suboptimal approaches for data visualization and exploration. This can lead to inaccurate identification of cell phenotypes, states, or spatial relationships compared to reference data from single-cell suspension technologies. To this end we have developed the "OPTimized Imaging Mass cytometry AnaLysis (OPTIMAL)" framework to benchmark any approaches for cell segmentation, parameter transformation, batch effect correction, data visualization/clustering, and spatial neighborhood analysis. Using a panel of 27 metal-tagged antibodies recognizing well-characterized phenotypic and functional markers to stain the same Formalin-Fixed Paraffin Embedded (FFPE) human tonsil sample tissue microarray over 12 temporally distinct batches we tested several cell segmentation models, a range of different arcsinh cofactor parameter transformation values, 5 different dimensionality reduction algorithms, and 2 clustering methods. Finally, we assessed the optimal approach for performing neighborhood analysis. We found that single-cell segmentation was improved by the use of an Ilastik-derived probability map but that issues with poor segmentation were only really evident after clustering and cell type/state identification and not always evident when using "classical" bivariate data display techniques. The optimal arcsinh cofactor for parameter transformation was 1 as it maximized the statistical separation between negative and positive signal distributions and a simple Z-score normalization step after arcsinh transformation eliminated batch effects. Of the five different dimensionality reduction approaches tested, PacMap gave the best data structure with FLOWSOM clustering out-performing phenograph in terms of cell type identification. We also found that neighborhood analysis was influenced by the method used for finding neighboring cells with a "disc" pixel expansion outperforming a "bounding box" approach combined with the need for filtering objects based on size and image-edge location. Importantly, OPTIMAL can be used to assess and integrate with any existing approach to IMC data analysis and, as it creates .FCS files from the segmentation output and allows for single-cell exploration to be conducted using a wide variety of accessible software and algorithms familiar to conventional flow cytometrists.

A novel process for H&E, immunofluorescence, and imaging mass cytometry on a single slide with a concise analytics pipeline

Imaging mass cytometry (IMC) is a powerful spatial technology that utilizes cytometry time of flight to acquire multiplexed image datasets with up to 40 markers, via metal-tagged antibodies. Recent advances in IMC have led to the inclusion of RNAScope probes and multiple new analysis pipelines have led to faster analyses and better results. However, IMC still suffers from lower resolution (1 μm2 pixels) and relatively small regions of interest (ROIs) (<2 mm2 ) compared to other, light-based microscope technologies. Capturing higher-resolution images on serial sections causes great difficulty when attempting to align cells and structures across serial sections, especially when observing smaller cell types and structures. Therefore, we demonstrate the combination of H&E and multiplex immunofluorescence imaging, for much higher resolution of the structural and cellular compartments found throughout the entire tissue section, with the high-dimensionality of IMC for specific ROIs on a single slide. Additionally, we demonstrate a simple and effective open-source cell segmentation and IMC analysis pipeline with previously published and freely available software.

DNA-ICM as an adjuvant method applied on oral cytological specimens

OBJECTIVE

This study aimed to evaluate cytology diagnosis accuracy using adjuvant methods in clinical routine for oral cancer.

STUDY DESIGN

This prospective study was conducted on 98 patients with clinically potentially malignant or malignant oral cavity lesions. One oral lesion smear was taken from each patient using a cytobrush before biopsy and stored at PreservCyt Thinprep. Samples were cytologically analyzed, and DNA ploidy measurement was performed on the same slide. The diagnostic methods' accuracy was then calculated.

RESULTS

In clinical inspection, 61 patients had suspicious lesions for malignancy, whereas 37 had potentially malignant disorders. Cytology associated with DNA image cytometry presented a sensitivity of 81.2% and specificity of 90.9%. When analyzing lesions located in high-risk sites to oral malignancies individually, cytology associated with DNA image cytometry presented a sensitivity of 88.2%, specificity of 100.0%, accuracy of 90.0%, and Kappa value of 0.77 (CI 95%: 0.48-1.00).

CONCLUSIONS

Association between cytology and DNA image cytometry is an objective and non-invasive diagnostic method that demonstrated high sensitivity and specificity in diagnosing malignant epithelial squamous cell transformation in the oral cavity.

High-throughput method to analyze the cytotoxicity of CAR-T Cells in a 3D tumor spheroid model using image cytometry

Solid tumors account for approximately 90% of all adult human cancers. As such, the development of novel cellular therapies has become of increasing importance to target solid tumor malignancies, such as prostate, lung, breast, bladder, colon, and liver cancers. One such cellular therapy relies on the use of chimeric antigen receptor T cells (CAR-T cells). CAR-T cells are engineered to target specific antigens on tumor cells. To date, there are six FDA-approved CAR-T cell therapies that have been utilized for hematologic B cell malignancies. Immune cell trafficking and immunosuppressive factors within the tumor microenvironment increase the relative difficulty in developing a robust CAR-T cell therapy against solid tumors. Therefore, it is critical to develop novel methodologies for high-throughput phenotypic and functional assays using 3D tumor spheroid models to assess CAR-T cell products against solid tumors. In this manuscript, we discuss the use of CAR-T cells targeted towards PSMA, an antigen that is found on prostate cancer tumor cells, the second most common cause of cancer deaths among men worldwide. We demonstrate the use of high-throughput, plate-based image cytometry to characterize CAR-T cell-mediated cytotoxic potency against 3D prostate tumor spheroids. We were able to kinetically evaluate the efficacy and therapeutic value of PSMA CAR-T cells by analyzing the cytotoxicity against prostate tumor spheroids. In addition, the CAR-T cells were fluorescently labeled to visually identify the location of the T cells as cytotoxicity occurs, which may provide more meaningful information for assessing the functionality of the CAR-T cells. The proposed image cytometry method can overcome limitations placed on traditional methodologies to effectively assess cell-mediated 3D tumor spheroid cytotoxicity and efficiently generate time- and dose-dependent results.

Artificial intelligence-based classification of peripheral blood nucleated cells using label-free imaging flow cytometry

Label-free image identification of circulating rare cells, such as circulating tumor cells within peripheral blood nucleated cells (PBNCs), the vast majority of which are white blood cells (WBCs), remains challenging. We previously described developing label-free image cytometry for classifying live cells using computer vision technology for pattern recognition, based on the subcellular structure of the quantitative phase microscopy images. We applied our image recognition methods to cells flowing in a flow cytometer microfluidic channel, and differentiated WBCs from cancer cell lines (area under receiver operating characteristic curve = 0.957). We then applied this method to healthy volunteers' and advanced cancer patients' blood samples and found that the non-WBC fraction rates (NWBC-FRs), defined as the percentage of cells classified as non-WBCs of the total PBNCs, were significantly higher in cancer patients than in healthy volunteers. Furthermore, we monitored NWBC-FRs over the therapeutic courses in cancer patients, which revealed the potential ability in monitoring the clinical status during therapy. Our image recognition system has the potential to provide a morphological diagnostic tool for circulating rare cells as non-WBC fractions.

Application of High-Throughput Imaging Mass Cytometry Hyperion in Cancer Research

Imaging mass cytometry (IMC) enables the in situ analysis of in-depth-phenotyped cells in their native microenvironment within the preserved architecture of a single tissue section. To date, it permits the simultaneous analysis of up to 50 different protein- markers targeted by metal-conjugated antibodies. The application of IMC in the field of cancer research may notably help 1) to define biomarkers of prognostic and theragnostic significance for current and future treatments against well-established and novel therapeutic targets and 2) to improve our understanding of cancer progression and its resistance mechanisms to immune system and how to overcome them. In the present article, we not only provide a literature review on the use of the IMC in cancer-dedicated studies but we also present the IMC method and discuss its advantages and limitations among methods dedicated to deciphering the complexity of cancer tissue.

Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment

A holistic understanding of tissue and organ structure and function requires the detection of molecular constituents in their original three-dimensional (3D) context. Imaging mass cytometry (IMC) enables simultaneous detection of up to 40 antigens and transcripts using metal-tagged antibodies but has so far been restricted to two-dimensional imaging. Here we report the development of 3D IMC for multiplexed 3D tissue analysis at single-cell resolution and demonstrate the utility of the technology by analysis of human breast cancer samples. The resulting 3D models reveal cellular and microenvironmental heterogeneity and cell-level tissue organization not detectable in two dimensions. 3D IMC will prove powerful in the study of phenomena occurring in 3D space such as tumor cell invasion and is expected to provide invaluable insights into cellular microenvironments and tissue architecture.

Aptamer Probes Labeled with Lanthanide-Doped Carbon Nanodots Permit Dual-Modal Fluorescence and Mass Cytometric Imaging

High-dimensional imaging mass cytometry (IMC) enables simultaneous quantification of over 35 biomarkers on one tissue section. However, its limited resolution and ultralow acquisition speed remain major issues for general clinical application. Meanwhile, conventional immunofluorescence microscopy (IFM) allows sub-micrometer resolution and rapid identification of the region of interest (ROI), but only operates with low multiplicity. Herein, a series of lanthanide-doped blue-, green-, and red-fluorescent carbon nanodots (namely, B-Cdots(Ln1 ), G-Cdots(Ln2 ), and R-Cdots(Ln3 )) as fluorescence and mass dual-modal tags are developed. Coupled with aptamers, B-Cdots(159 Tb)-A10-3.2, G-Cdots(165 Ho)-AS1411, and R-Cdots(169 Tm)-SYL3C dual-functional aptamer probes, which are then multiplexed with commercially available Maxpar metal-tagged antibodies for analyzing clinical formalin-fixed, paraffin-embedded (FFPE) prostatic adenocarcinoma (PaC) tissue, are further synthesized. The rapid identification of ROI with IFM using fluorescence signals and subsequent multiplexed detection of in situ ROI with IMC using the same tissue section is demonstrated. Dual-modal probes save up to 90% IMC blind scanning time for a standard 3.5 mm × 3.5 mm overall image. Meanwhile, the IFM provides refined details and topological spatial distributions for the functional proteins at optical resolution, which compensates for the low resolution of the IMC imaging.

Spatial UMAP and Image Cytometry for Topographic Immuno-oncology Biomarker Discovery

Multiplex immunofluorescence (mIF) can detail spatial relationships and complex cell phenotypes in the tumor microenvironment (TME). However, the analysis and visualization of mIF data can be complex and time-consuming. Here, we used tumor specimens from 93 patients with metastatic melanoma to develop and validate a mIF data analysis pipeline using established flow cytometry workflows (image cytometry). Unlike flow cytometry, spatial information from the TME was conserved at single-cell resolution. A spatial uniform manifold approximation and projection (UMAP) was constructed using the image cytometry output. Spatial UMAP subtraction analysis (survivors vs. nonsurvivors at 5 years) was used to identify topographic and coexpression signatures with positive or negative prognostic impact. Cell densities and proportions identified by image cytometry showed strong correlations when compared with those obtained using gold-standard, digital pathology software (R2 > 0.8). The associated spatial UMAP highlighted "immune neighborhoods" and associated topographic immunoactive protein expression patterns. We found that PD-L1 and PD-1 expression intensity was spatially encoded-the highest PD-L1 expression intensity was observed on CD163+ cells in neighborhoods with high CD8+ cell density, and the highest PD-1 expression intensity was observed on CD8+ cells in neighborhoods with dense arrangements of tumor cells. Spatial UMAP subtraction analysis revealed numerous spatial clusters associated with clinical outcome. The variables represented in the key clusters from the unsupervised UMAP analysis were validated using established, supervised approaches. In conclusion, image cytometry and the spatial UMAPs presented herein are powerful tools for the visualization and interpretation of single-cell, spatially resolved mIF data and associated topographic biomarker development.

Characterisation of tumour microenvironment remodelling following oncogene inhibition in preclinical studies with imaging mass cytometry

Mouse models are critical in pre-clinical studies of cancer therapy, allowing dissection of mechanisms through chemical and genetic manipulations that are not feasible in the clinical setting. In studies of the tumour microenvironment (TME), multiplexed imaging methods can provide a rich source of information. However, the application of such technologies in mouse tissues is still in its infancy. Here we present a workflow for studying the TME using imaging mass cytometry with a panel of 27 antibodies on frozen mouse tissues. We optimise and validate image segmentation strategies and automate the process in a Nextflow-based pipeline (imcyto) that is scalable and portable, allowing for parallelised segmentation of large multi-image datasets. With these methods we interrogate the remodelling of the TME induced by a KRAS G12C inhibitor in an immune competent mouse orthotopic lung cancer model, highlighting the infiltration and activation of antigen presenting cells and effector cells.

Cell Mechanics Based Computational Classification of Red Blood Cells Via Machine Intelligence Applied to Morpho-Rheological Markers

Despite fluorescent cell-labelling being widely employed in biomedical studies, some of its drawbacks are inevitable, with unsuitable fluorescent probes or probes inducing a functional change being the main limitations. Consequently, the demand for and development of label-free methodologies to classify cells is strong and its impact on precision medicine is relevant. Towards this end, high-throughput techniques for cell mechanical phenotyping have been proposed to get a multidimensional biophysical characterization of single cells. With this motivation, our goal here is to investigate the extent to which an unsupervised machine learning methodology, which is applied exclusively on morpho-rheological markers obtained by real-time deformability and fluorescence cytometry (RT-FDC), can address the difficult task of providing label-free discrimination of reticulocytes from mature red blood cells. We focused on this problem, since the characterization of reticulocytes (their percentage and cellular features) in the blood is vital in multiple human disease conditions, especially bone-marrow disorders such as anemia and leukemia. Our approach reports promising label-free results in the classification of reticulocytes from mature red blood cells, and it represents a step forward in the development of high-throughput morpho-rheological-based methodologies for the computational categorization of single cells. Besides, our methodology can be an alternative but also a complementary method to integrate with existing cell-labelling techniques.

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.

Non-invasive image-based cytometry for high throughput NK cell cytolysis analysis

Natural Killer (NK) cells are lymphocytes that are the first line of defense against malignantly transformed cells, virally infected cells and other stressed cell types. To study the cytolytic function of NK cells in vitro, a cytotoxicity assay is normally conducted against a target cancerous cell line. Current assay methods are typically performed in mixed 2D cocultures with destructive endpoints and low throughput, thereby limiting the scale, time-resolution, and relevance of the assay to in vivo conditions. Here, we evaluated a novel, non-invasive, quantitative image-based cytometry (qIBC) assay for detection of NK-mediated killing of target cells in 2D and 3D environments in vitro and compared its performance to two common flow cytometry- and fluorescence-based cytotoxicity assays. Similar to the other methods evaluated, the qIBC assay allowed for reproducible detection of target cell killing across a range of effector-to-target ratios with reduced variability. The qIBC assay also allowed for detection of NK cytolysis in 3D spheroids, which enabled scalable measurements of cell cytotoxicity in 3D models. Our findings suggest that quantitative image-based cytometry would be suitable for rapid, high-throughput screening of NK cytolysis in vitro, including in quasi-3D structures that model tissue environments in vivo.

Biomarker Discovery in Patients with Immunotherapy-Treated Melanoma with Imaging Mass Cytometry

PURPOSE

Imaging mass cytometry (IMC) is among the first tools with the capacity for multiplex analysis of more than 40 targets, which provides a novel approach to biomarker discovery. Here, we used IMC to characterize the tumor microenvironment of patients with metastatic melanoma who received immunotherapy in efforts to find indicative factors of treatment response. In spite of the new power of IMC, the image analysis aspects are still limited by the challenges of cell segmentation.

EXPERIMENTAL DESIGN

Here, rather than segment, we performed image analysis using a newly designed version of the AQUA software to measure marker intensity in molecularly defined compartments: tumor cells, stroma, T cells, B cells, and macrophages. IMC data were compared with quantitative immunofluorescence (QIF) and digital spatial profiling.

RESULTS

Validation of IMC results for immune markers was confirmed by regression with additional multiplexing methods and outcome assessment. Multivariable analyses by each compartment revealed significant associations of 12 markers for progression-free survival and seven markers for overall survival (OS). The most compelling indicative biomarker, beta2-microglobulin (B2M), was confirmed by correlation with OS by QIF in the discovery cohort and validated in an independent published cohort profiled by mRNA expression.

CONCLUSIONS

Using digital image analysis based on pixel colocalization to assess IMC data allowed us to quantitively measure 25 markers simultaneously on formalin-fixed, paraffin-embedded tissue microarray samples. In addition to showing high concordance with other multiplexing technologies, we identified a series of potentially indicative biomarkers for immunotherapy in metastatic melanoma, including B2M.

ImaCytE: Visual Exploration of Cellular Micro-Environments for Imaging Mass Cytometry Data

Tissue functionality is determined by the characteristics of tissue-resident cells and their interactions within their microenvironment. Imaging Mass Cytometry offers the opportunity to distinguish cell types with high precision and link them to their spatial location in intact tissues at sub-cellular resolution. This technology produces large amounts of spatially-resolved high-dimensional data, which constitutes a serious challenge for the data analysis. We present an interactive visual analysis workflow for the end-to-end analysis of Imaging Mass Cytometry data that was developed in close collaboration with domain expert partners. We implemented the presented workflow in an interactive visual analysis tool; ImaCytE. Our workflow is designed to allow the user to discriminate cell types according to their protein expression profiles and analyze their cellular microenvironments, aiding in the formulation or verification of hypotheses on tissue architecture and function. Finally, we show the effectiveness of our workflow and ImaCytE through a case study performed by a collaborating specialist.

Metal-Labeled Aptamers as Novel Nanoprobes for Imaging Mass Cytometry Analysis

Imaging mass cytometry (IMC) is an emerging imaging technology that exploits the multiplexed analysis capabilities of the CyTOF mass cytometer to make spatially resolved measurements for tissue sections. In a comprehensive view of tissue composition and marker distribution, recent developments of IMC require highly sensitive, multiplexed assays. Approaching the sensitivity of the IMC technique, we designed a novel type of biocompatible metal-labeled aptamer nanoprobe (MAP), named ¹⁶⁷Er-A10-3.2. The small molecular probe was synthesized by conjugating ¹⁶⁷Er-polymeric pentetic acid (¹⁶⁷Er-DTPA) with an RNA aptamer A10-3.2. For demonstration, ¹⁶⁷Er-A10-3.2 was applied for observing protein spatial distribution on prostatic epithelium cell of paraffin embedded Prostatic adenocarcinoma (PaC) tissue sections by IMC technology. The ¹⁶⁷Er-A10-3.2 capitalizes on the ability of the aptamer to specifically bind target cancer cells as well as the small size of ¹⁶⁷Er-A10-3.2 can accommodate multiple aptamer binding antigen labeled at high density. The detection signal of ¹⁶⁷Er-A10-3.2 probe was 3-fold higher than that of PSMA antibody probe for a targeted cell under lower temperature epitope retrieval (37 °C) of PaC tissue. Furthermore, we successfully demonstrated the simultaneously staining ability of aptamer probes in IMC analysis. The successful imaging acquisition using aptamers probes in IMC technology may offer opportunity for the diagnosis of malignancies in the future.

A high-throughput chemotaxis detection method for CCR4+ T cell migration inhibition using image cytometry

Chemotaxis is an important aspect of immune cell behavior within the tumor microenvironment (TME). One prominent example of chemotaxis within the TME is the migration of regulatory T cells (Tregs) in response to the chemokine ligands CCL17 and CCL22. Tregs within the TME cause the suppression of anti-tumor immunity and inhibition of the effect of immunotherapeutic treatments. Therefore, the ability to screen for therapeutic antibodies that can inhibit or stimulate the chemotaxis of various immune cell types is crucial. Traditionally, chemotaxis is studied by determining the number of cells in the bottom reservoir of a Transwell microplate using flow cytometry; however, this method is time-consuming and thus not appropriate for high-throughput screening purposes. The Celigo Image Cytometer has been employed to perform high-throughput cell-based assays and was used to develop a new detection method for chemotaxis measurement. The image-based detection method was developed using chemokine ligands CCL17 and CCL22 to induce the migration of CCR4⁺ T cells and directly count them on the bottom of the Transwell plates. Finally, the method was applied to measure the inhibitory effects of commercially available anti-CCL17 and anti-CCL22 antibodies, which caused a dose-dependent decrease in the number of migrated T cells. The proposed image cytometry method allowed screening of multiple antibodies at various concentrations, simultaneously, which can improve the efficiency for discovering potential antibody candidates that can induce or inhibit recruitment of immune cells to the tumor microenvironment.

Mass Cytometry Imaging for the Study of Human Diseases-Applications and Data Analysis Strategies

High parameter imaging is an important tool in the life sciences for both discovery and healthcare applications. Imaging Mass Cytometry (IMC) and Multiplexed Ion Beam Imaging (MIBI) are two relatively recent technologies which enable clinical samples to be simultaneously analyzed for up to 40 parameters at subcellular resolution. Importantly, these "Mass Cytometry Imaging" (MCI) modalities are being rapidly adopted for studies of the immune system in both health and disease. In this review we discuss, first, the various applications of MCI to date. Second, due to the inherent challenge of analyzing high parameter spatial data, we discuss the various approaches that have been employed for the processing and analysis of data from MCI experiments.

H-EM: An algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry

Fluorescent cytometry refers to the quantification of cell physical properties and surface biomarkers using fluorescently-tagged antibodies. The generally preferred techniques to perform such measurements are flow cytometry, which performs rapid single cell analysis by flowing cells one-by-one through a channel, and microscopy, which eliminates the complexity of the flow channel, offering multi-cell analysis at a lesser throughput. Low-magnification image-based cytometers, also called "cell astronomy" systems, hold promise of simultaneously achieving both instrumental simplicity and high throughput. In this magnification regime, a single cell is mapped to a handful of pixels in the image. While very attractive, this idea has, so far, not been proven to yield quantitative results of cell-labeling, mainly due to the poor signal-to-noise ratio present in those images and to partial volume effects. In this work we present a cell astronomy system that, when coupled with custom-developed algorithms, is able to quantify cell intensities and diameters reliably. We showcase the system using calibrated MESF beads and fluorescently stained leukocytes, achieving good population identification in both cases. The main contribution of the proposed system is in the development of a novel algorithm, H-EM, that enables inter-cluster separation at a very low magnification regime (2x). Such algorithm provides more accurate brightness estimates than DAOSTORM when compared to manual analysis, while fitting cell location, brightness, diameter, and background level concurrently. The algorithm first performs Fisher discriminant analysis to detect bright spots. From each spot an expectation-maximization algorithm is initialized over a heterogeneous mixture model (H-EM), this algorithm recovers both the cell fluorescence and diameter with sub-pixel accuracy while discriminating the background noise. Finally, a recursive splitting procedure is applied to discern individual cells in cell clusters.

Methods for analyzing tellurium imaging mass cytometry data

Imaging mass cytometry (IMC) is a technique allowing visualization and quantification of over 40 biological parameters in a single experiment with subcellular spatial resolution, however most IMC experiments are limited to endpoint analysis with antibodies and DNA stains. Small molecules containing tellurium are promising probes for IMC due to their cell permeability, synthetic versatility, and most importantly their application to sequential labelling with isotopologous probes (SLIP) experiments. SLIP experiments with tellurium-containing probes allow quantification of intracellular biology at multiple timepoints with IMC. Despite the promise of tellurium in IMC, there are unique challenges in image processing associated with tellurium IMC data. Here, we address some of these issues by demonstrating the removal of xenon background signal, combining channels to improve signal-to-noise ratio, and calculating isotope transmission efficiency biases. These developments add accuracy to the unique temporal resolution afforded by tellurium IMC probes.

Evaluation of Deep Learning Strategies for Nucleus Segmentation in Fluorescence Images

Identifying nuclei is often a critical first step in analyzing microscopy images of cells and classical image processing algorithms are most commonly used for this task. Recent developments in deep learning can yield superior accuracy, but typical evaluation metrics for nucleus segmentation do not satisfactorily capture error modes that are relevant in cellular images. We present an evaluation framework to measure accuracy, types of errors, and computational efficiency; and use it to compare deep learning strategies and classical approaches. We publicly release a set of 23,165 manually annotated nuclei and source code to reproduce experiments and run the proposed evaluation methodology. Our evaluation framework shows that deep learning improves accuracy and can reduce the number of biologically relevant errors by half. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Giant cells and osteoclasts present in bone grafted with nacre differ by nuclear cytometry evaluated by texture analysis

Nacre (mother of pearl) is a natural biomaterial used to prepare orthopedic devices. We have implanted screws and plates made with nacre in five sheeps. Bone were harvested after two months and embedded in poly(methyl methacrylate). Blocks were saws and the thick slabs were grinded, polished and surface stained. Sections were photographed at an ×1000 magnification. Giant cells were found in contact with nacre in eroded areas and true osteoclasts were found at distance in the neighboring bone in Howship lacunae. A texture analysis of the nuclei of giant cells and osteoclasts was done using the run-length method of the MaZda freeware. The size of the nuclei was reduced in osteoclast and their mean gray level appeared reduced. Texture analysis revealed that chromatin had a completely different pattern in giant cells when compared to osteoclasts. Giant cells had a fine repartition of the chromatin with large clear areas around prominent nucleoli. On the contrary, osteoclast nuclei had chromatin blocks evenly dispersed in the nuclei. This reflects the different origin of these cells expressing different functions.

The role of DNA image cytometry in screening oral potentially malignant lesions using brushings: A systematic review

It is believed that the majority of oral cancers develop from oral potentially malignant lesions (OPML). Though they can be easily detected during screening, risk stratification is difficult. During screening clinicians often find it difficult to distinguish OPMLs from benign lesions, and predicting OPML at risk of malignant transformation is particularly challenging. DNA aneuploidy has been known to be a marker of malignancy in a number of sites including the oral cavity. We performed a systematic review to evaluate the effectiveness of DNA-ICM using brushings in differentiating OPMLs from benign/inflammatory lesions during screening and in predicting malignant transformation. MEDLINE, Pubmed, EMBASE electronic databases were systematically searched using a combination of keywords and subject headings. A total of 11 articles satisfied our inclusion criteria. These studies reported a wide range of sensitivity (16-96.4%) and specificity (90-100%) due to the differences in study design, definitions of high risk or low risk lesions and DNA-ICM protocol used. No long-term longitudinal studies were identified to assess the role of DNA-ICM using brushings in predicting malignant transformation. No studies evaluated the role of DNA-ICM in community screening settings. A number of studies combined DNA-ICM with other techniques like cytology or argyrophilic nucleolar organizer region counts leading to improved test results. In spite of DNA aneuploidy being accepted as a marker of malignancy, there is limited evidence of DNA-ICM using brushings being successful as an adjunct oral cancer screening tool. Longitudinal studies and large community screening studies need to be undertaken to draw stronger conclusion.

TePhe, a tellurium-containing phenylalanine mimic, allows monitoring of protein synthesis in vivo with mass cytometry

Protein synthesis is central to maintaining cellular homeostasis and its study is critical to understanding the function and dysfunction of eukaryotic systems. Here we report L-2-tellurienylalanine (TePhe) as a noncanonical amino acid for direct measurement of protein synthesis. TePhe is synthetically accessible, nontoxic, stable under biological conditions, and the tellurium atom allows its direct detection with mass cytometry, without postexperiment labeling. TePhe labeling is competitive with phenylalanine but not other large and aromatic amino acids, demonstrating its molecular specificity as a phenylalanine mimic; labeling is also abrogated in vitro and in vivo by the protein synthesis inhibitor cycloheximide, validating TePhe as a translation reporter. In vivo, imaging mass cytometry with TePhe visualizes translation dynamics in the mouse gut, brain, and tumor. The strong performance of TePhe as a probe for protein synthesis, coupled with the operational simplicity of its use, suggests TePhe could become a broadly applied molecule for measuring translation in vitro and in vivo.

Deep Learning in Image Cytometry: A Review

Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. © 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry

Type 1 diabetes (T1D) results from the autoimmune destruction of insulin-producing β cells. A comprehensive picture of the changes during T1D development is lacking due to limited sample availability, inability to sample longitudinally, and the paucity of technologies enabling comprehensive tissue profiling. Here, we analyzed 1,581 islets from 12 human donors, including eight with T1D, using imaging mass cytometry (IMC). IMC enabled simultaneous measurement of 35 biomarkers with single-cell and spatial resolution. We performed pseudotime analysis of islets through T1D progression from snapshot data to reconstruct the evolution of β cell loss and insulitis. Our analyses revealed that β cell destruction is preceded by a β cell marker loss and by recruitment of cytotoxic and helper T cells. The approaches described herein demonstrate the value of IMC for improving our understanding of T1D pathogenesis, and our data lay the foundation for hypothesis generation and follow-on experiments.

Multiplexed In Situ Imaging Mass Cytometry Analysis of the Human Endocrine Pancreas and Immune System in Type 1 Diabetes

The interaction between the immune system and endocrine cells in the pancreas is crucial for the initiation and progression of type 1 diabetes (T1D). Imaging mass cytometry (IMC) enables multiplexed assessment of the abundance and localization of more than 30 proteins on the same tissue section at 1-μm resolution. Herein, we have developed a panel of 33 antibodies that allows for the quantification of key cell types including pancreatic exocrine cells, islet cells, immune cells, and stromal components. We employed this panel to analyze 12 pancreata obtained from donors with clinically diagnosed T1D and 6 pancreata from non-diabetic controls. In the pancreata from donors with T1D, we simultaneously visualized significant alterations in islet architecture, endocrine cell composition, and immune cell presentation. Indeed, we demonstrate the utility of IMC to investigate complex events on the cellular level that will provide new insights on the pathophysiology of T1D.

A high-throughput inhibition assay to study MERS-CoV antibody interactions using image cytometry

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Development of protein binding inhibition assay.

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Enable rapid measurement of antibody binding to cells expressing target protein.

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Combine high-throughput nature of ELISA with protein conformation advantage of FACS.

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Can be utilized to characterize potential prophylactic or therapeutic antibodies.

The emergence of new pathogens, such as Middle East respiratory syndrome coronavirus (MERS-CoV), poses serious challenges to global public health and highlights the urgent need for methods to rapidly identify and characterize potential therapeutic or prevention options, such as neutralizing antibodies. Spike (S) proteins are present on the surface of MERS-CoV virions and mediate viral entry. S is the primary target for MERS-CoV vaccine and antibody development, and it has become increasingly important to understand MERS-CoV antibody binding specificity and function. Commonly used serological methods like ELISA, biolayer interferometry, and flow cytometry are informative, but limited. Here, we demonstrate a high-throughput protein binding inhibition assay using image cytometry. The image cytometry-based high-throughput screening method was developed by selecting a cell type with high DPP4 expression and defining optimal seeding density and protein binding conditions. The ability of monoclonal antibodies to inhibit MERS-CoV S binding was then tested. Binding inhibition results were comparable with those described in previous literature for MERS-CoV spike monomer and showed similar patterns as neutralization results. The coefficient of variation (CV) of our cell-based assay was <10%. The proposed image cytometry method provides an efficient approach for characterizing potential therapeutic antibodies for combating MERS-CoV that compares favorably with current methods. The ability to rapidly determine direct antibody binding to host cells in a high-throughput manner can be applied to study other pathogen-antibody interactions and thus can impact future research on viral pathogens.

Automated detection of cancer cells in effusion specimens by DNA karyometry

Background

The average sensitivity of conventional cytology for the identification of cancer cells in effusion specimens is only approximately 58%. DNA image cytometry (DNA‐ICM), which exploits the DNA content of morphologically suspicious nuclei measured on digital images, has a sensitivity of up to 91% for the detection of cancer cells. However, when performed manually, to our knowledge to date, an expert needs approximately 60 minutes for the analysis of a single slide.

Methods

In the current study, the authors present a novel method of supervised machine learning for the automated identification of morphologically suspicious mesothelial and epithelial nuclei in Feulgen‐stained effusion specimens. The authors compared this with manual DNA‐ICM and a gold standard cytological diagnosis for 121 cases. Furthermore, the authors retrospectively analyzed whether the amount of morphometrically abnormal mesothelial or epithelial nuclei detected by the digital classifier could be used as an additional diagnostic marker.

Results

The presented semiautomated DNA karyometric solution identified more diagnostically relevant abnormal nuclei compared with manual DNA‐ICM, which led to a higher sensitivity (76.4% vs 68.5%) at a specificity of 100%. The ratio between digitally abnormal and all mesothelial nuclei was found to identify cancer cell–positive slides at 100% sensitivity and 70% specificity. The time effort for an expert therefore is reduced to the verification of a few nuclei with exceeding DNA content, which to our knowledge can be accomplished within 5 minutes.

Conclusions

The authors have created and validated a computer‐assisted bimodal karyometric approach for which both nuclear morphology and DNA are quantified from a Feulgen‐stained slide. DNA karyometry thus increases the diagnostic accuracy and reduces the workload of an expert when compared with manual DNA‐ICM.

An automated procedure for the detection of cancer cells in effusion specimens is presented. It provides a sensitivity of 76.4% and a specificity of 100% at a time effort for clinicians of approximately 5 minutes per specimen.

Evaluation of Viral Genome Copies Within Viral Factories on Different DNA Viruses

This article describes a simple method of measuring the number of viral genomes within viral factories. For this purpose, we use three DNA viruses replicating in the cytoplasm of the infected cells: wild-type African swine fever virus (ASFV)-Georgia 2007, culture-adapted type ASFV-BA71V, and Vaccinia virus (VV). The measurements are conducted in three steps. In the first step, after DNA staining, we evaluate Integrated Optical Density (IOD) of total DNA for each viral factory. The second step involves the calculations of the mass of DNA in the viral factories in picograms (pg). And, in the third step, by dividing the mass of DNA within viral factory by the weight of a single viral genome, we obtain the number of viral genomes within the factory.

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.

High-throughput assessment of mechanical properties of stem cell derived red blood cells, toward cellular downstream processing

Stem cell products, including manufactured red blood cells, require efficient sorting and purification methods to remove components potentially harmful for clinical application. However, standard approaches for cellular downstream processing rely on the use of specific and expensive labels (e.g. FACS or MACS). Techniques relying on inherent mechanical and physical properties of cells offer high-throughput scalable alternatives but knowledge of the mechanical phenotype is required. Here, we characterized for the first time deformability and size changes in CD34+ cells, and expelled nuclei, during their differentiation process into red blood cells at days 11, 14, 18 and 21, using Real-Time Deformability Cytometry (RT-DC) and Atomic Force Microscopy (AFM). We found significant differences (p < 0.0001; standardised mixed model) between the deformability of nucleated and enucleated cells, while they remain within the same size range. Expelled nuclei are smaller thus could be removed by size-based separation. An average Young's elastic modulus was measured for nucleated cells, enucleated cells and nuclei (day 14) of 1.04 ± 0.47 kPa, 0.53 ± 0.12 kPa and 7.06 ± 4.07 kPa respectively. Our identification and quantification of significant differences (p < 0.0001; ANOVA) in CD34+ cells mechanical properties throughout the differentiation process could enable development of new routes for purification of manufactured red blood cells.

Quantitative Assay for Quality Assurance of Human Cells for Clinical Transplantation

Transplantation of human cells after isolation and culture has become an important alternative for treatment of acute or chronic skin wounds. To increase the efficacy and reduce cost for transplantation of skin cells, more efficient and accurate techniques for evaluation of cell proliferation are needed. Hemocytometer counts provide a valid assessment of cell proliferation and viability, but they are very labor intensive and require removal of the cells from their substrate. In this study, hemocytometer counts were compared with a fluorometric assay (n = 21 per condition) that uses the commercially available reagent alamarBlue™, which is reduced to a fluorescent substrate by cellular dehydrogenases. Human epidermal keratinocytes were inoculated at 200, 600, 2000, and 6000 cells/cm2 incubated for 6 days in modified MCDB 153 medium. Alamar Blue™ was incubated with cells for 2 h at 37°C, and fluorescence was measured with a microplate reader at 590 nm. Hemocytometer counts (×10-4) from the respective cell inoculation densities were 0.30 ± 0.04, 1.07 ± 0.10, 6.37 ± 0.62, and 16.99 ± 0.96. Fluorescence values (×10–3) for the respective inoculation densities were 0.14 ± 0.01, 0.34 ± 0.02, 1.20 ± 0.09, and 1.79 ± 0.12. Regression analysis showed a statistical significant (p < 0.0001) correlation (r2 = 0.87) between cell counts and optical density from the alamarBlue™ assay. These data demonstrate that alamarBlue™ provides a valid substitute for cell counts to assess cell proliferation before clinical transplantation of engineered skin. AlamarBlue™ also allows repeated, nondamaging assessment of living cells over time. These advantages are expected to increase the validity and reliability of quality assurance standards for transplanted skin cells, and to increase the efficacy of healing of cutaneous wounds.

Quantitative image cytometry for analyzing intracellular trafficking of G protein-coupled receptors on a chemical-trapping single cell array

G protein-coupled receptors (GPCRs) are important targets in medical and pharmaceutical research fields, because they play key roles in a variety of biological processes. Recently, intracellular trafficking of GPCRs involving endosomal internalization and recycling to the plasma membrane has been studied as a regulation mechanism for GPCR activities. However, the absence of a quantitative single-cell analysis method has hampered conditional GPCR trafficking studies and the possibility of gaining significant insights into the mechanism of regulation of GPCR signaling. Here, we report a facile image cytometry method to analyze the trafficking of GPCRs. In this method, GPCR-expressing cells were arrayed with a photo-responsive cell-immobilizing reagent in a single-cell manner, and the tagged GPCR was visualized by pulse-labeling with a fluorescent dye through sortase-mediated peptide-tag ligation. We quantified the intracellular distribution changes of a pH-dependent GPCR, G2A, by time-course observation under mildly acidic and slightly basic pH conditions. The difference in pH-dependent G2A trafficking between individual cells was automatically detected by an image analysis custom software program, and simultaneously, the average distribution ratios were also determined for understanding the properties of G2A. The present method should be applicable for investigating the dynamic intracellular trafficking of a wide variety of GPCRs under various conditions in a high-throughput manner.

High-Throughput Particle Uptake Analysis by Imaging Flow Cytometry

Quantifying the efficiency of particle uptake by host cells is important in the fields of infectious diseases, autoimmunity, cancer, developmental biology, and drug delivery. Here we present a protocol for high-throughput analysis of particle uptake by imaging flow cytometry, using the bacterium Neisseria gonorrhoeae attached to and internalized by neutrophils as an example. Cells are exposed to fluorescently labeled bacteria, fixed, and stained with a bacteria-specific antibody of a different fluorophore. Thus, in the absence of a permeabilizing agent, extracellular bacteria are double-labeled with two fluorophores while intracellular bacteria remain single-labeled. A spot count algorithm is used to determine the number of single- and double-labeled bacteria in individual cells, to calculate the percent of cells associated with bacteria, percent of cells with internalized bacteria, and percent of cell-associated bacteria that are internalized. These analyses quantify bacterial association and internalization across thousands of cells and can be applied to diverse experimental systems. © 2017 by John Wiley & Sons, Inc.

High-Throughput Spectral and Lifetime-Based FRET Screening in Living Cells to Identify Small-Molecule Effectors of SERCA

A robust high-throughput screening (HTS) strategy has been developed to discover small-molecule effectors targeting the sarco/endoplasmic reticulum calcium ATPase (SERCA), based on a fluorescence microplate reader that records both the nanosecond decay waveform (lifetime mode) and the complete emission spectrum (spectral mode), with high precision and speed. This spectral unmixing plate reader (SUPR) was used to screen libraries of small molecules with a fluorescence resonance energy transfer (FRET) biosensor expressed in living cells. Ligand binding was detected by FRET associated with structural rearrangements of green fluorescent protein (GFP, donor) and red fluorescent protein (RFP, acceptor) fused to the cardiac-specific SERCA2a isoform. The results demonstrate accurate quantitation of FRET along with high precision of hit identification. Fluorescence lifetime analysis resolved SERCA's distinct structural states, providing a method to classify small-molecule chemotypes on the basis of their structural effect on the target. The spectral analysis was also applied to flag interference by fluorescent compounds. FRET hits were further evaluated for functional effects on SERCA's ATPase activity via both a coupled-enzyme assay and a FRET-based calcium sensor. Concentration-response curves indicated excellent correlation between FRET and function. These complementary spectral and lifetime FRET detection methods offer an attractive combination of precision, speed, and resolution for HTS.

Spectral Unmixing Plate Reader: High-Throughput, High-Precision FRET Assays in Living Cells

We have developed a microplate reader that records a complete high-quality fluorescence emission spectrum on a well-by-well basis under true high-throughput screening (HTS) conditions. The read time for an entire 384-well plate is less than 3 min. This instrument is particularly well suited for assays based on fluorescence resonance energy transfer (FRET). Intramolecular protein biosensors with genetically encoded green fluorescent protein (GFP) donor and red fluorescent protein (RFP) acceptor tags at positions sensitive to structural changes were stably expressed and studied in living HEK cells. Accurate quantitation of FRET was achieved by decomposing each observed spectrum into a linear combination of four component (basis) spectra (GFP emission, RFP emission, water Raman, and cell autofluorescence). Excitation and detection are both conducted from the top, allowing for thermoelectric control of the sample temperature from below. This spectral unmixing plate reader (SUPR) delivers an unprecedented combination of speed, precision, and accuracy for studying ensemble-averaged FRET in living cells. It complements our previously reported fluorescence lifetime plate reader, which offers the feature of resolving multiple FRET populations within the ensemble. The combination of these two direct waveform-recording technologies greatly enhances the precision and information content for HTS in drug discovery.

Quantifying autophagy: Measuring LC3 puncta and autolysosome formation in cells using multispectral imaging flow cytometry

The use of multispectral imaging flow cytometry has been gaining popularity due to its quantitative power, high throughput capabilities, multiplexing potential and its ability to acquire images of every cell. Autophagy is a process in which dysfunctional organelles and cellular components that accumulate during growth and differentiation are degraded via the lysosome and recycled. During autophagy, cytoplasmic LC3 is processed and recruited to the autophagosomal membranes; the autophagosome then fuses with the lysosome to form the autolysosome. Therefore, cells undergoing autophagy can be identified by visualizing fluorescently labeled LC3 puncta and/or the co-localization of fluorescently labeled LC3 and lysosomal markers. Multispectral imaging flow cytometry is able to collect imagery of large numbers of cells and assess autophagy in an objective, quantitative, and statistically robust manner. This review will examine the four predominant methods that have been used to measure autophagy via multispectral imaging flow cytometry.

Assessment of Cell Viability with Single-, Dual-, and Multi-Staining Methods Using Image Cytometry

The ability to accurately measure cell viability is important for any cell-based assay. Traditionally, viability measurements have been performed using the trypan blue exclusion method on a hemacytometer, which allows researchers to visually distinguish viable from nonviable cells. While the trypan blue method can work for cell lines or primary cells that have been rigorously purified, in more complex samples such as PBMCs, bone marrow, whole blood, or any sample with low viability, this method can lead to errors. In recent years, advances in optics and fluorescent dyes have led to the development of automated benchtop image-based cell counters for rapid cell concentration and viability measurement. In this work, we demonstrate the use of image-based cytometry for cell viability detection using single-, dual-, or multi-stain techniques. Single-staining methods using nucleic acid stains such as EB, PI, 7-AAD, DAPI, SYTOX Green, and SYTOX Red, and enzymatic stains such as CFDA and Calcein AM, were performed. Dual-staining methods using AO/PI, CFDA/PI, Calcein AM/PI, Hoechst/PI, Hoechst/DRAQ7, and DRAQ5/DAPI that enumerate viable and nonviable cells were also performed. Finally, Hoechst/Calcein AM/PI was used for a multi-staining method. Fluorescent viability staining allows exclusion of cellular debris and nonnucleated cells from analysis, which can eliminate the need to perform purification steps during sample preparation and improve efficiency. Image cytometers increase speed and throughput, capture images for visual confirmation of results, and can greatly simplify cell count and viability measurements.

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.

Trypan blue as an affordable marker for automated live-dead cell analysis in image cytometry

The aim of the present study was to combine image cytometry and trypan blue (TB) exclusion staining for a reproducible high-throughput detection of dead cells, enabling TB as an inexpensive marker, to be affordable for many studies and creating the possibility to combine fluorochromes without or with less spectral overlap. Capillary blood was drawn from a healthy volunteer, red blood cells were lysed and leukocyte cell death was induced. Samples were stained with CD45-FITC, CD14-PE, TB and DAPI, and then analyzed using image cytometry (iCys). TB quenching control tests were performed using DAPI and CD45-FITC. Images were generated in .TIF and .JPEG format using iCys image cytometer. The images were analyzed using CellProfiler (CP) modules to optimize the analysis based on the aims of each phase of this study. CellProfiler Analyst (CPA) was used to classify cells throughout machine learning and to calculate sensibility of the classification. A sensitivity of 0.94 for dead cells and 0.99 for live cells was calculated using CPA. We did not see any quenching effects of the FITC staining. DAPI signal was reduced in the presence of TB. The results of the present study revealed that TB serves as a dead cell marker in an image cytometric analysis, being able to be combined with other fluorescence markers without loss of fluorescence intensity signal or overlapping emission spectrum. SCANNING 38:857-863, 2016. © 2016 Wiley Periodicals, Inc.

Total cellular protein presence of the transcription factor IRF8 does not necessarily correlate with its nuclear presence

The transcription factor interferon regulatory factor-8 (IRF8) plays an essential role in myeloid differentiation and lineage commitment, based largely on molecular and genetic studies. The detection of IRF8 in specific cell populations by flow cytometry (FCM) has the potential to provide new insights into normal and pathologic myelopoiesis, but critical validation of this protein-based approach, particularly in human samples, is lacking. In this study, the assessment of total cellular IRF8 presence was compared to its specific nuclear presence as assessed by imaging flow cytometry (IFC) analysis. Peptide neutralization of the IRF8-specific antibody that has been predominantly used to date in the literature served as a negative control for the immunofluorescent labeling. Expression of total IRF8 was analyzed by total cellular fluorescence analogous to the mean fluorescence intensity readout of conventional FCM. Additionally, specific nuclear fluorescence and the similarity score between the nuclear image (DAPI) and the corresponding IRF8 image for each cell were analyzed as parameters for nuclear localization of IRF8. IFC showed that peptide blocking eliminated binding of the IRF8 antibody in the nucleus. It also reduced cytoplasmic binding of the antibody but not to the extent observed in the nucleus. In agreement with the similarity score data, the total cellular IRF8 as well as nuclear IRF8 intensities decreased with peptide blocking. In healthy donor peripheral blood subpopulations and a positive control cell line (THP-1), the assessment of IRF8 by total cellular presence correlated well with its specific nuclear presence and correlated with the known distribution of IRF8 in these cells. In clinical samples of myeloid-derived suppressors cells derived from patients with renal carcinoma, however, total cellular IRF8 did not necessarily correlate with its nuclear presence. Discordance was primarily associated with peptide blocking having a proportionally greater effect on the IRF8 nuclear localization versus total fluorescence assessment. The data thus indicate that IRF8 can have cytoplasmic presence and that during disease its nuclear-cytoplasmic distribution may be altered, which may provide a basis for potential myeloid defects during certain pathologies.

Atypical Adenomatous Hyperplasia (Adenosis) of the Prostate: DNA Ploidy Analysis and Immunophenotype

Atypical adenomatous hyperplasia (AAH) of the prostate is a microscopic proliferation of small acini that may be mistaken for adenocarcinoma. Although some data suggest that AAH is associated with adenocarcinoma arising in the transition zone, the clinical significance of this lesion is uncertain. Therefore we studied the DNA ploidy pattern and immunophenotype of AAH as compared with nodular hyperplasia and well-differentiated adenocarcinoma in 23 formalin-fixed, paraffin-embedded, whole-mounted retropubic prostatectomies. Representative sections were immunostained for keratin 34β-E12, chromogranin, bcl-2, c-erbB-2, ki67-MIB1, and factor VIII (microvessel density). DNA ploidy was determined by image analysis and Feul gen-stained sections. There were rare scattered immunoreactive cells for chromogranin, bcl-2, and c-erbB-2 in nodular hyperplasia and AAH (mainly in the basal cell compartment) and in carcinoma. The ki67-MIB1 labeling index was different between nodular hyperplasia and AAH (p<0.001) and carcinoma (p=0.003) but not between AAH and carcinoma (p=0.203). Microvessel density was different between AAH and carcinoma (p=0.001) but not between nodular hyperplasia and AAH (p=0.105) or carcinoma (p=0.0820). All foci of nodular hyperplasia, AAH, and carcinoma were diploid. Ploidy status and our selected panel of antibodies did not discriminate among these 3 entities reliably.

Comparison of a Visual to a Computer Assisted Technique for Detecting Apoptosis

In many studies, fluorescent dyes (ethidium bromide [EB] and acridine orange [AO]) are used to stain DNA to determine if nuclei are apoptotic. However, there are numerous visual methods for counting these stained DNA that may lead to inaccuracies Measuring apoptosis by the visual counting method may be imprecise because of the variability of individuals’ perception of color. Therefore, the authors compared a visual method of counting chromatin for apoptosis with a method relying on a computer program. They began counting chromatin using the visual method, in which individuals identify the stained DNA using their own visual perception. For comparison, they used a software-based counting method (analySIS software) to determine the color (hue) of the stained DNA. Using the numeric hue values from the software eliminates the variations in human color perception. Intra and interrater reliability of the visual and computerassisted counting methods were evaluated with Spearman’s. The authors found statistical significance in the intrarater reliability (r = 1.0,P = 0.0001 for all chromatin categories) and interrater reliability (r = 0.975,P = 0.005 for both readings) when using the software program. No statistical significance was found for the visual counting method, indicating inaccuracy between and within raters. Thus, the computerassisted counting method of identifying the damaged DNA is more accurate and precise than the individual’s visual perception of color. Based on these data, apoptosis measurements using color staining with EB and AO should be determined using hue values generated by a computer program and not by a researcher’s visual assessment.