Novel In Situ Hybridization and Multiplex Immunofluorescence Technology Combined With Whole-slide Digital Image Analysis in Kidney Transplantation.
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ABSTRACT:
SUBMITTER: Junger H
PROVIDER: S-EPMC7350082 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Conventional histopathology is the gold standard for allograft monitoring, but its value proposition is increasingly questioned. "-Omics" analysis of tissues, peripheral blood and fluids and targeted serologic studies provide mechanistic insights into allograft injury not currently provided by conventional histology. Microscopic biopsy analysis, however, provides valuable and unique information: (a) spatial-temporal relationships; (b) rare events/cells; (c) complex structural context; and (d) integration into a "systems" model. Nevertheless, except for immunostaining, no transformative advancements have "modernized" routine microscopy in over 100 years. Pathologists now team with hardware and software engineers to exploit remarkable developments in digital imaging, nanoparticle multiplex staining, and computational image analysis software to bridge the traditional histology-global "-omic" analyses gap. Included are side-by-side comparisons, objective biopsy finding quantification, multiplexing, automated image analysis, and electronic data and resource sharing. Current utilization for teaching, quality assurance, conferencing, consultations, research and clinical trials is evolving toward implementation for low-volume, high-complexity clinical services like transplantation pathology. Cost, complexities of implementation, fluid/evolving standards, and unsettled medical/legal and regulatory issues remain as challenges. Regardless, challenges will be overcome and these technologies will enable transplant pathologists to increase information extraction from tissue specimens and contribute to cross-platform biomarker discovery for improved outcomes.
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Project description:Multiplex immunohistochemistry/immunofluorescence (mIHC/mIF) is a developing technology that facilitates the evaluation of multiple, simultaneous protein expressions at single-cell resolution while preserving tissue architecture. These approaches have shown great potential for biomarker discovery, yet many challenges remain. Importantly, streamlined cross-registration of multiplex immunofluorescence images with additional imaging modalities and immunohistochemistry (IHC) can help increase the plex and/or improve the quality of the data generated by potentiating downstream processes such as cell segmentation. To address this problem, a fully automated process was designed to perform a hierarchical, parallelizable, and deformable registration of multiplexed digital whole-slide images (WSIs). We generalized the calculation of mutual information as a registration criterion to an arbitrary number of dimensions, making it well suited for multiplexed imaging. We also used the self-information of a given IF channel as a criterion to select the optimal channels to use for registration. Additionally, as precise labeling of cellular membranes in situ is essential for robust cell segmentation, a pan-membrane immunohistochemical staining method was developed for incorporation into mIF panels or for use as an IHC followed by cross-registration. In this study, we demonstrate this process by registering whole-slide 6-plex/7-color mIF images with whole-slide brightfield mIHC images, including a CD3 and a pan-membrane stain. Our algorithm, WSI, mutual information registration (WSIMIR), performed highly accurate registration allowing the retrospective generation of an 8-plex/9-color, WSI, and outperformed 2 alternative automated methods for cross-registration by Jaccard index and Dice similarity coefficient (WSIMIR vs automated WARPY, P < .01 and P < .01, respectively, vs HALO + transformix, P = .083 and P = .049, respectively). Furthermore, the addition of a pan-membrane IHC stain cross-registered to an mIF panel facilitated improved automated cell segmentation across mIF WSIs, as measured by significantly increased correct detections, Jaccard index (0.78 vs 0.65), and Dice similarity coefficient (0.88 vs 0.79).
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Project description:Betaarterivirus suid 1 and 2 are the causative agents of porcine reproductive and respiratory syndrome (PRRS), which is one of the most significant diseases of the swine industry, causing significant economic losses in the main pig producing countries. Here, we report the development of a novel, RNA-based in situ hybridization technique (RNAscope) to detect PRRS virus (PRRSV) RNA in lung tissues of experimentally infected animals. The technique was applied to lung tissues of 20 piglets, which had been inoculated with a wild-type, highly pathogenic PRRSV-1 strain. To determine the RNAscope's applicability as a semi-quantitative method, we analysed the association between the proportion of the virus-infected cells measured with an image analysis software (QuPath) and the outcome of the real-time quantitative reverse transcription polymerase chain reaction (qRT-PCR) tests performed in parallel. The results of the quantitative approach of these two molecular biological methods show significant association (pseudo R2 = 0.3894, p = 0.004). This is the first time RNAscope assay has been implemented for the detection of PRRSV-1 in experimental animals.
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Project description:Single-molecule fluorescent in situ hybridization (smFISH) has emerged as a powerful technique that allows one to localize and quantify the absolute number of mRNAs in single cells. In combination with immunofluorescence (IF), smFISH can be used to correlate the expression of an mRNA and a protein of interest in single cells. Here, we provide and quantify an smFISH-IF dataset in S. cerevisiae. We measured the expression of the cell cycle-controlled mRNA CLN2 and the cell cycle marker alpha-tubulin. The smFISH-IF protocol describing the dataset generation is published in the accompanying article "Simultaneous detection of mRNA and protein in S. cerevisiae by single-molecule FISH and Immunofluorescence" [1]. Here, we analyze the smFISH data using the freely available software FISH-quant [2]. The provided datasets are intended to assist scientists interested in setting up smFISH-IF protocol in their laboratory. Furthermore, scientists interested in the generation of imaging analysis tools for single-cell approaches may find the provided dataset useful. To this end, we provide the differential interference contrast (DIC) channel, as well as multicolor, raw Z-stacks for smFISH, IF and DAPI.
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Project description:A robust understanding of the tumor immune environment has important implications for cancer diagnosis, prognosis, research, and immunotherapy. Traditionally, immunohistochemistry (IHC) has been regarded as the standard method for detecting proteins in situ, but this technique allows for the evaluation of only one cell marker per tissue sample at a time. However, multiplexed imaging technologies enable the multiparametric analysis of a tissue section at the same time. Also, through the curation of specific antibody panels, these technologies enable researchers to study the cell subpopulations within a single immunological cell group. Thus, multiplexed imaging gives investigators the opportunity to better understand tumor cells, immune cells, and the interactions between them. In the multiplexed imaging technology workflow, once the protocol for a tumor immune micro environment study has been defined, histological slides are digitized to produce high-resolution images in which regions of interest are selected for the interrogation of simultaneously expressed immunomarkers (including those co-expressed by the same cell) by using an image analysis software and algorithm. Most currently available image analysis software packages use similar machine learning approaches in which tissue segmentation first defines the different components that make up the regions of interest and cell segmentation, then defines the different parameters, such as the nucleus and cytoplasm, that the software must utilize to segment single cells. Image analysis tools have driven dramatic evolution in the field of digital pathology over the past several decades and provided the data necessary for translational research and the discovery of new therapeutic targets. The next step in the growth of digital pathology is optimization and standardization of the different tasks in cancer research, including image analysis algorithm creation, to increase the amount of data generated and their accuracy in a short time as described herein. The aim of this review is to describe this process, including an image analysis algorithm creation for multiplex immunofluorescence analysis, as an essential part of the optimization and standardization of the different processes in cancer research, to increase the amount of data generated and their accuracy in a short time.
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Project description:Context Despite the benefits of digital pathology, data storage and management of digital whole slide images introduces new logistical and infrastructure challenges to traditionally analog pathology labs. Aims Our goal was to analyze pathologist slide diagnosis patterns to determine the minimum number of pixels required during the diagnosis. Methods We developed a method of using pathologist viewing patterns to vary digital image resolution across virtual slides, which we call variable resolution images. An additional pathologist reviewed the variable resolution images to determine if diagnoses could still be rendered. Results Across all slides, the pathologists rarely zoomed in to the full resolution level. As a result, the variable resolution images are significantly smaller than the original whole slide images. Despite the reduction in image sizes, the final pathologist reviewer could still proide diagnoses on the variable resolution slide images. Conclusions Future studies will be conducted to understand variability in resolution requirements between and within pathologists. These findings have the potential to dramatically reduce the data storage requirements of high-resolution whole slide images.
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Project description:For a method to be widely adopted in medical research or clinical practice, it needs to be reproducible so that clinicians and regulators can have confidence in its use. Machine learning and deep learning have a particular set of challenges around reproducibility. Small differences in the settings or the data used for training a model can lead to large differences in the outcomes of experiments. In this work, three top-performing algorithms from the Camelyon grand challenges are reproduced using only information presented in the associated papers and the results are then compared to those reported. Seemingly minor details were found to be critical to performance and yet their importance is difficult to appreciate until the actual reproduction is attempted. We observed that authors generally describe the key technical aspects of their models well but fail to maintain the same reporting standards when it comes to data preprocessing which is essential to reproducibility. As an important contribution of the present study and its findings, we introduce a reproducibility checklist that tabulates information that needs to be reported in histopathology ML-based work in order to make it reproducible. Author summary For a method to be used a lot in medical research or clinical practice, it needs to be able to be reproduced so that people can trust it. Machine learning and deep learning have some challenges around this. For example, small changes in how a model is trained can lead to significant changes in the results of experiments. This makes it essential that researchers report how they do things in enough detail for the results of their experiments to be reproducible. In this work, we looked at three different algorithms used for digital pathology image analysis. We tried to reproduce them using only the information reported in their papers. We confirmed that even minor details could be essential. Authors often do not report all the details needed to reproduce their work. We also created a checklist of things that need to be reported to help other researchers make their work reproducible.
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Project description:The paradigm of molecular histopathology is shifting from a single-marker immunohistochemistry towards multiplexed detection of markers to better understand the complex pathological processes. However, there are no systems allowing multiplexed IHC (mIHC) with high-resolution whole-slide tissue imaging and analysis, yet providing feasible throughput for routine use. We present an mIHC platform combining fluorescent and chromogenic staining with automated whole-slide imaging and integrated whole-slide image analysis, enabling simultaneous detection of six protein markers and nuclei, and automatic quantification and classification of hundreds of thousands of cells in situ in formalin-fixed paraffin-embedded tissues. In the first proof-of-concept, we detected immune cells at cell-level resolution (n = 128,894 cells) in human prostate cancer, and analysed T cell subpopulations in different tumour compartments (epithelium vs. stroma). In the second proof-of-concept, we demonstrated an automatic classification of epithelial cell populations (n = 83,558) and glands (benign vs. cancer) in prostate cancer with simultaneous analysis of androgen receptor (AR) and alpha-methylacyl-CoA (AMACR) expression at cell-level resolution. We conclude that the open-source combination of 8-plex mIHC detection, whole-slide image acquisition and analysis provides a robust tool allowing quantitative, spatially resolved whole-slide tissue cytometry directly in formalin-fixed human tumour tissues for improved characterization of histology and the tumour microenvironment.
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Project description:BackgroundIn histopathology, the quantitative assessment of various morphologic features is based on methods originally conceived on specific areas observed through the microscope used. Failure to reproduce the same reference field of view using a different microscope will change the score assessed. Visualization of a digital slide on a screen through a dedicated viewer allows selection of the magnification. However, the field of view is rectangular, unlike the circular field of optical microscopy. In addition, the size of the selected area is not evident, and must be calculated.Materials and methodsA digital slide morphometric system was conceived to reproduce the various methods published for assessing tumor budding in colorectal cancer. Eighteen international experts in colorectal cancer were invited to participate in a web-based study by assessing tumor budding with five different methods in 100 digital slides.ResultsThe specific areas to be tested by each method were marked by colored circles. The areas were grouped in a target-like pattern and then saved as an .xml file. When a digital slide was opened, the .xml file was imported in order to perform the measurements. Since the morphometric tool is composed of layers that can be freely moved on top of the digital slide, the technique was named digital slide dynamic morphometry. Twelve investigators completed the task, the majority of them performing the multiple evaluations of each of the cases in less than 12 minutes.ConclusionsDigital slide dynamic morphometry has various potential applications and might be a useful tool for the assessment of histologic parameters originally conceived for optical microscopy that need to be quantified.
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Project description:To date, there is no commercially available Y chromosome probe that can be used for fluorescence in situ hybridization (FISH) for the male rhesus monkey. We have recently generated a probe for FISH with high specificity to the short arm of the rhesus monkey Y chromosome. In this study, we further describe a method that keeps the integrity of tissue-specific antigenic structures for immunofluorescence staining subsequent to FISH on paraffin-embedded rhesus monkey tissues. We have examined this technique in combination with an epithelial cell-specific marker, cytokeratin 8/18 (CK8/18), on various tissues, including jejunum, liver, kidney, and pancreas. CK8/18 and Y chromosome signals were distinctly seen simultaneously on epithelial cells from the same tissue section from male but not female monkeys. These studies indicate that our FISH immunofluorescence technique can be reliably used to identify and phenotype male cells in paraffin-embedded rhesus monkey tissues.
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