Here, we describe a multiplexed immunohistochemical platform with computational image processing workflows, including image cytometry, enabling simultaneous evaluation of 12 biomarkers in one ...formalin-fixed paraffin-embedded tissue section. To validate this platform, we used tissue microarrays containing 38 archival head and neck squamous cell carcinomas and revealed differential immune profiles based on lymphoid and myeloid cell densities, correlating with human papilloma virus status and prognosis. Based on these results, we investigated 24 pancreatic ductal adenocarcinomas from patients who received neoadjuvant GVAX vaccination and revealed that response to therapy correlated with degree of mono-myelocytic cell density and percentages of CD8+ T cells expressing T cell exhaustion markers. These data highlight the utility of in situ immune monitoring for patient stratification and provide digital image processing pipelines to the community for examining immune complexity in precious tissue sections, where phenotype and tissue architecture are preserved to improve biomarker discovery and assessment.
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•Multiplex IHC and computational image analysis phenotypes tumor-immune complexity•In situ leukocyte density correlates with subclassification and prognosis in HNSCC•Immune complexity stratifies response to vaccination therapy in PDAC•CD8+ T cell and PD-L1 status correlate with outcomes of vaccinated PDAC patients
Tsujikawa et al. develop a multiplex immunohistochemistry and image cytometry platform to reveal immune-based metrics for patient stratification and response monitoring. In HNSCC and PDAC, prognosis correlates with mono-myelocytic cell density. In PDAC, percentages of PD-1, Eomes, Ki67, and granzyme B in CD8+ T cells correlate with response to vaccine therapy.
Immunophenotyping has been the primary assay for characterization of immune cells from patients undergoing therapeutic treatments in clinical research, which is critical for understanding disease ...progression and treatment efficacy. Currently, flow cytometry is the main methodology for characterizing surface marker expression for immunological research and has been proven to be an effective and efficient method for immunophenotyping; however, it requires highly trained users and a large time commitment. As such, there is a need for a higher throughput method for routine surface marker expression analyses that have been identified to determine early indicators of disease development, disease prognosis, or treatment effectiveness.
Recently, a novel image cytometry system (Cellaca® PLX Image Cytometer, Revvity Health Sciences, Inc., Lawrence, MA) was developed as a complementary method to flow cytometry for performing high-throughput immunophenotyping. Effectively combining these two immunophenotyping methodologies will allow for a comprehensive analysis and continuous monitoring of critical surface markers of interest in a high-throughput manner. In this work, we demonstrate a high-throughput image cytometric screening method to characterize immune cell populations, streamlining the analysis of routine surface marker panels. The T cell, B cell, NK cell, and monocyte populations of 46 primary PBMC samples from subjects enrolled in autoimmunity and oncology disease study cohorts were analyzed with two optimized immunophenotyping staining kits: Panel 1 (CD3-Kiravia BlueTM, CD56-PE, CD14-APC) and Panel 2 (CD3-Kiravia BlueTM, CD56-PE, CD19-APC). We validated the proposed image cytometry method by comparing the Cellaca PLX and the Aurora (Cytek®) cytometer, which generated bright field and fluorescent images, as well as scatter plots for the 46 primary PBMC samples and 2 controls. In addition, the image cytometry method can directly determine cell concentrations for downstream assays. The results demonstrated comparable CD3, CD14, CD19, and CD56 cell populations from the primary PBMC samples, which showed an average of 5 – 10% differences using the Bland-Altman statistical analysis. The proposed image cytometry method provides a novel research tool to streamline immunophenotyping workflow for characterizing patient samples in clinical studies.
Reconstructing neuronal circuits at the level of synapses is a central problem in neuroscience, and the focus of the nascent field of connectomics. Previously used to reconstruct the C. elegans ...wiring diagram, serial-section transmission electron microscopy (ssTEM) is a proven technique for the task. However, to reconstruct more complex circuits, ssTEM will require the automation of image processing. We review progress in the processing of electron microscopy images and, in particular, a semi-automated reconstruction pipeline deployed at Janelia Farm. Drosophila circuits underlying identified behaviors are being reconstructed in the pipeline with the goal of generating a complete Drosophila connectome.
Since the work of Golgi and Cajal, light microscopy has remained a key tool for neuroscientists to observe cellular properties. Ongoing advances have enabled new experimental capabilities using light ...to inspect the nervous system across multiple spatial scales, including ultrastructural scales finer than the optical diffraction limit. Other progress permits functional imaging at faster speeds, at greater depths in brain tissue, and over larger tissue volumes than previously possible. Portable, miniaturized fluorescence microscopes now allow brain imaging in freely behaving mice. Complementary progress on animal preparations has enabled imaging in head-restrained behaving animals, as well as time-lapse microscopy studies in the brains of live subjects. Mouse genetic approaches permit mosaic and inducible fluorescence-labeling strategies, whereas intrinsic contrast mechanisms allow in vivo imaging of animals and humans without use of exogenous markers. This review surveys such advances and highlights emerging capabilities of particular interest to neuroscientists.
Background
DNA‐image cytometry (DNA‐ICM) is able to detect gross alterations of cellular DNA‐content representing aneuploidy, a biomarker of malignancy. A Health Canada‐approved DNA‐ICM system, ...ClearCyte® in combination with a cytopathologist's review, has demonstrated high sensitivity (89%) and specificity (97%) in identifying high‐grade oral lesions. The study objective was to create an improved automated algorithm (iClearcyte) and test its robustness in differentiating high grade from benign reactive oral lesions without a cytopathologist's input.
Methods
A set of 214 oral brushing samples of oral cancer (n = 92), severe dysplasia (n = 20), reactive lesions (n = 52), and normal samples (n = 50) were spun down onto slides and stained using Feulgen‐Thionin reaction. Following ClearCyte® scan, nuclear features were calculated, and nuclei categorized into “diploid,” “hyperdiploid,” “tetraploid,” and “aneuploid” DNA ploidy groups by the ClearCyte® software. The samples were randomized into training and test sets (70:30) based on patient's age, sex, tobacco use, and lesion site risk. The training set was used to create a new algorithm which was then validated using the remaining samples in the test set, where sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.
Results
The proposed iClearCyte algorithm (>1 “aneuploid” cell or ≥ 1.7% combined “hyperdiploid” and “tetraploid” nuclei frequency) identified high‐grade samples with sensitivity, specificity, PPV, and NPV of 100.0%, 86.7%, 89.7%, and 100.0%, respectively, in the test set.
Conclusion
The iClearCyte test has potential to serve as a robust non‐invasive automated oral cancer screening tool promoting early oral cancer detection and decreasing the number of unnecessary invasive biopsies.
Quantitative morphology of the CNS has recently undergone major developments. In particular, several new approaches, known as design-based stereologic methods, have become available and have been ...successfully applied to neuromorphological research. However, much confusion and uncertainty remains about the meaning, implications, and advantages of these design-based stereologic methods. The objective of this review is to provide some clarification. It does not comprise a full description of all stereologic methods available. Rather, it is written by users for users, provides the reader with a guided tour through the relevant literature. It has been the experience of the authors that most neuroscientists potentially interested in design-based stereology need to analyze volumes of brain regions, numbers of cells (neurons, glial cells) within these brain regions, mean volumes (nuclear, perikaryal) of these cells, length densities of linear biological structures such as vessels and nerve fibers within brain regions, and the cytoarchitecture of brain regions (i.e. the spatial distribution of cells within a region of interest). Therefore, a comprehensive introduction to design-based stereologic methods for estimating these parameters is provided. It is demonstrated that results obtained with design-based stereology are representative for the entire brain region of interest, and are independent of the size, shape, spatial orientation, and spatial distribution of the cells to be investigated. Also, it is shown that bias (i.e. systematic error) in results obtained with design-based stereology can be limited to a minimum, and that it is possible to assess the variability of these results. These characteristics establish the advantages of design-based stereologic methods in quantitative neuromorphology.
Automatic segmentation of cell nuclei is an essential step in image cytometry and histometry. Despite substantial progress, there is a need to improve accuracy, speed, level of automation, and ...adaptability to new applications. This paper presents a robust and accurate novel method for segmenting cell nuclei using a combination of ideas. The image foreground is extracted automatically using a graph-cuts-based binarization. Next, nuclear seed points are detected by a novel method combining multiscale Laplacian-of-Gaussian filtering constrained by distance-map-based adaptive scale selection. These points are used to perform an initial segmentation that is refined using a second graph-cuts-based algorithm incorporating the method of alpha expansions and graph coloring to reduce computational complexity. Nuclear segmentation results were manually validated over 25 representative images (15 in vitro images and 10 in vivo images, containing more than 7400 nuclei) drawn from diverse cancer histopathology studies, and four types of segmentation errors were investigated. The overall accuracy of the proposed segmentation algorithm exceeded 86%. The accuracy was found to exceed 94% when only over- and undersegmentation errors were considered. The confounding image characteristics that led to most detection/segmentation errors were high cell density, high degree of clustering, poor image contrast and noisy background, damaged/irregular nuclei, and poor edge information. We present an efficient semiautomated approach to editing automated segmentation results that requires two mouse clicks per operation.
•Provided an overview of the recent developments in the use of quantitative phase information for biomedical applications.•Provided a schematic overview of the currently developed ...techniques.•Identified four trends of relevance, i.e. imaging cytometry, setup compactness, 3D phase imaging, AI-assisted methods.•Analysed key aspects of these trends, stressing relevant applications and potentialities.
In the last decades, the understanding of the complex biological mechanisms that regulate the life and growth of single cells and their reciprocal interaction has steadily increased. In this process, Quantitative Phase Imaging (QPI) has emerged as an invaluable tool, thus providing a new way of investigating live cell behaviour in a dynamic, label-free and non-invasive modality. It has proved especially relevant to biomedical and diagnostic applications, as the information-rich quantitative phase data opens new channels to investigate pathophysiology. In this review, we expose the recent developments in the extraction of unique biological information, and we provide a schematic overview of the currently developed techniques. Specifically, we identify four different trends of relevance in the context of biomedical applications, namely QPI-cytometry, development of point-of-care devices, tomographic phase reconstruction techniques and learning-based approaches, providing a current insight in the way they are shifting our approach to the acquisition and treatment of QPI data.
Immune cells in the tumor microenvironment modulate cancer progression and are attractive therapeutic targets. Macrophages and T cells are key components of the microenvironment, yet their phenotypes ...and relationships in this ecosystem and to clinical outcomes are ill defined. We used mass cytometry with extensive antibody panels to perform in-depth immune profiling of samples from 73 clear cell renal cell carcinoma (ccRCC) patients and five healthy controls. In 3.5 million measured cells, we identified 17 tumor-associated macrophage phenotypes, 22 T cell phenotypes, and a distinct immune composition correlated with progression-free survival, thereby presenting an in-depth human atlas of the immune tumor microenvironment in this disease. This study revealed potential biomarkers and targets for immunotherapy development and validated tools that can be used for immune profiling of other tumor types.
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•Mass cytometry reveals the immune cell diversity of the ccRCC tumor ecosystem•PD-1+ cells display heterogeneous combinations of inhibitory receptors•CD38+CD204+CD206− tumor-associated macrophages correlate with immunosuppression•A specific immune signature is linked to shorter progression-free survival
Applying mass cytometry for high-dimensional single-cell analysis depicts an in-depth atlas of the immune microenvironment in clear cell renal cell carcinoma patients, thereby linking immune compositions with clinical features.
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