Quantitative Analysis of Phagocytosis in Whole Blood Using Double Staining and Visualization.

Phagocytosis is an essential innate immunity function in humans and animals. A decrease in the ability to phagocytize is associated with many diseases and aging of the immune system. Assessment of phagocytosis dynamics requires quantification of bacteria inside and outside the phagocyte. Although flow cytometry is the most common method for assessing phagocytosis, it does not include visualization and direct quantification of location of bacteria. Here, we used double-labeled Escherichia coli cells to evaluate phagocytosis by flow cytometry (cell sorting) and confocal microscopy, as well as employed image cytometry to provide high-throughput quantitative and spatial recognition of the double-labeled E. coli associated with the phagocytes. Retention of pathogens on the surface of myeloid and lymphoid cells without their internalization was suggested to be an auxiliary function of innate immunity in the fight against infections. The developed method of bacterial labeling significantly increased the accuracy of spatial and quantitative measurement of phagocytosis in whole blood and can be recommended as a tool for phagocytosis assessment by image cytometry.

Deep cell phenotyping and spatial analysis of multiplexed imaging with TRACERx-PHLEX.

The growing scale and dimensionality of multiplexed imaging require reproducible and comprehensive yet user-friendly computational pipelines. TRACERx-PHLEX performs deep learning-based cell segmentation (deep-imcyto), automated cell-type annotation (TYPEx) and interpretable spatial analysis (Spatial-PHLEX) as three independent but interoperable modules. PHLEX generates single-cell identities, cell densities within tissue compartments, marker positivity calls and spatial metrics such as cellular barrier scores, along with summary graphs and spatial visualisations. PHLEX was developed using imaging mass cytometry (IMC) in the TRACERx study, validated using published Co-detection by indexing (CODEX), IMC and orthogonal data and benchmarked against state-of-the-art approaches. We evaluated its use on different tissue types, tissue fixation conditions, image sizes and antibody panels. As PHLEX is an automated and containerised Nextflow pipeline, manual assessment, programming skills or pathology expertise are not essential. PHLEX offers an end-to-end solution in a growing field of highly multiplexed data and provides clinically relevant insights.

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, ß-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.

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.

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.

Multi-omics and imaging mass cytometry characterization of human kidneys to identify pathways and phenotypes associated with impaired kidney function.

Despite the recent advances in our understanding of the role of lipids, metabolites, and related enzymes in mediating kidney injury, there is limited integrated multi-omics data identifying potential metabolic pathways driving impaired kidney function. The limited availability of kidney biopsies from living donors with acute kidney injury has remained a major constraint. Here, we validated the use of deceased transplant donor kidneys as a good model to study acute kidney injury in humans and characterized these kidneys using imaging and multi-omics approaches. We noted consistent changes in kidney injury and inflammatory markers in donors with reduced kidney function. Neighborhood and correlation analyses of imaging mass cytometry data showed that subsets of kidney cells (proximal tubular cells and fibroblasts) are associated with the expression profile of kidney immune cells, potentially linking these cells to kidney inflammation. Integrated transcriptomic and metabolomic analysis of human kidneys showed that kidney arachidonic acid metabolism and seven other metabolic pathways were upregulated following diminished kidney function. To validate the arachidonic acid pathway in impaired kidney function we demonstrated increased levels of cytosolic phospholipase A2 protein and related lipid mediators (prostaglandin E2) in the injured kidneys. Further, inhibition of cytosolic phospholipase A2 reduced injury and inflammation in human kidney proximal tubular epithelial cells in vitro. Thus, our study identified cell types and metabolic pathways that may be critical for controlling inflammation associated with impaired kidney function in humans.

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.

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.

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.

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.

Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology.

Solid tumors have a dynamic ecosystem in which malignant and non-malignant (endothelial, stromal, and immune) cell types constantly interact. Importantly, the abundance, localization, and functional orientation of each cell component within the tumor microenvironment vary significantly over time and in response to treatment. Such intratumoral heterogeneity influences the tumor course and its sensitivity to treatments. Recently, high-dimensional imaging mass cytometry (IMC) has been developed to explore the tumor ecosystem at the single-cell level. In the last years, several studies demonstrated that IMC is a powerful tool to decipher the tumor complexity. In this review, we summarize the potential of this technology and how it may be useful for cancer research (from preclinical to clinical studies).

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.

Use of DNA image cytometry in conducting oral cancer screening in rural India.

OBJECTIVES: Oral cancer screening can assist in the early detection of oral potentially malignant lesions (OPMLs) and prevention of oral cancers. It can be challenging for clinicians to differentiate OPMLs from benign conditions. Adjunct screening tools such as fluorescence visualisation (FV) and DNA image cytometry (DNA-ICM) have shown success in identifying OPMLs in high-risk clinics. For the first time we aimed to assess these technologies in Indian rural settings and evaluate if these tools helped clinicians identify high-risk lesions during screening. METHODS: Dental students and residents screened participants in five screening camps held in villages outside of Hyderabad, India, using extraoral, intraoral, and FV examinations. Lesion and normal tissue brushings were collected for DNA-ICM analysis and cytology. RESULTS: Of the 1116 participants screened, 184 lesions were observed in 152 participants. Based on white light examination (WLE), 45 lesions were recommended for biopsy. Thirty-five were completed on site; 25 (71%) were diagnosed with low-grade dysplasias (17 mild, 8 moderate) and the remaining 10 showed no signs of dysplasia. FV loss was noted in all but one dysplastic lesion and showed a sensitivity of 96% and specificity of 17%. Cytology combined with DNA-ICM had a sensitivity of 64% and specificity of 86% in detecting dysplasia. CONCLUSION: DNA-ICM combined with cytology identified the majority of dysplastic lesions and identified additional lesions, which were not considered high-risk during WLE and biopsy on site. Efforts to follow-up with these participants are ongoing. FV identified most high-risk lesions but added limited value over WLE.

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.

DNA image cytometry parameters to identify high-grade cervical lesions.

OBJECTIVE: Evaluate the performance of different DNA image cytometry (DNA-ICM) ploidy parameters in the categorisation of DNA-ICM results and identification of high-grade cervical intraepithelial neoplasia or worse (≥ CIN2). METHODS: Cervical samples from 232 women were collected for DNA-ICM analysis and biopsy confirmation. Five DNA parameters were used to define DNA aneuploidy: number of cells with exceeding events (EE) over 2.5cEE, 4cEE, 5cEE and 9cEE, and aneuploid stemlines. DNA-ICM results were categorised as normal, suspicious, and abnormal. RESULTS: For individual DNA ploidy parameters, sensitivity values for 50 cells with 2.5cEE, 45 cells with 4cEE, 1 cell with 9cEE and aneuploid stemline were 72.95%. 54.1%, 69.67% and 54.1%, while specificity values were 80.0%, 90.0%, 89.09% and 95.45%, respectively. For the 5cEE parameter, the sensitivity values for 1, 2, 3, 4 and 5 cells were 93.44%, 85.25%, 81.97%, 77.87% and 75.41%, while specificity values were 46.36%, 63.64%, 74.55%, 76.36% and 80.91%, respectively. For categorised DNA-ICM results, a suspicious result showed superior sensitivity than an abnormal result (87.70% vs 82.79%, P = 0.031), but lower specificity (54.55% vs 75.45%, P < 0.001). Both types of DNA-ICM result were statistically significantly different from a normal result (P < 0.05). CONCLUSION: For prognostic purposes, 1 cell with 9cEE, 45 cells with 4cEE and aneuploid stemline are the best parameters with which to categorise an abnormal DNA-ICM result, followed by 50 cells with 2.5cEE and 4 cells with 5cEE. For screening purposes, 10 cells with 2.5cEE, 10 cells with 4cEE, and 2 cells with 5cEE are suitable parameters with which to categorise a suspicious DNA-ICM result.

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.

Automated Assessment of Cancer Drug Efficacy On Breast Tumor Spheroids in Aggrewell™400 Plates Using Image Cytometry.

Tumor spheroid models have proven useful in the study of cancer cell responses to chemotherapeutic compounds by more closely mimicking the 3-dimensional nature of tumors in situ. Their advantages are often offset, however, by protocols that are long, complicated, and expensive. Efforts continue for the development of high-throughput assays that combine the advantages of 3D models with the convenience and simplicity of traditional 2D monolayer methods. Herein, we describe the development of a breast cancer spheroid image cytometry assay using T47D cells in Aggrewell™400 spheroid plates. Using the Celigo® automated imaging system, we developed a method to image and individually track thousands of spheroids within the Aggrewell™400 microwell plate over time. We demonstrate the use of calcein AM and propidium iodide staining to study the effects of known anti-cancer drugs Doxorubicin, Everolimus, Gemcitabine, Metformin, Paclitaxel and Tamoxifen. We use the image cytometry results to quantify the fluorescence of calcein AM and PI as well as spheroid size in a dose dependent manner for each of the drugs. We observe a dose-dependent reduction in spheroid size and find that it correlates well with the viability obtained from the CellTiter96® endpoint assay. The image cytometry method we demonstrate is a convenient and high-throughput drug-response assay for breast cancer spheroids under 400 µm in diameter, and may lay a foundation for investigating other three-dimensional spheroids, organoids, and tissue samples.