Entire Tissue Section Research Articles

Development of a sequential laser microdissection tissue cuts workflow for the spatial and quantitative analysis of drugs in fresh frozen tissue sections

Mass spectrometry imaging (MSI) is a well-established technique that allows to determine the distribution of small molecules, such as lipids, metabolites, and drugs, as well as large molecules in tissue sections. Because of the tissue heterogeneity, resulting in different matrix effects, and to the fact that the measured compounds are not entirely “extracted” from the tissue during the measurement, the absolute quantitative aspect of MSI is limited. To combine compound quantification with spatial information on fresh frozen unstained tissue sections, laser (capture) microdissection has been used to isolate tissue sections for compound extraction and LC-MS/MS quantification. Although this method relying on manual ROIs selection is rather sensitive compared to traditional MSI methods, it lacks the throughput needed to screen entire tissue sections. To apply a higher throughput tissue screening approach, we propose herein a workflow for performing indiscriminate and sequential LMD tissue section cuts that can cover up to 96 cuts collected in a 96 well plate on Leica LMD systems, for further extractions and LC-MS/MS analysis. Our workflow relies on the creation and implementation of 96 squares microgrid templates for the LMD cut of different area sizes (30x30 μm2, 50x50 μm2, 100x100 μm2, 200x200 μm2, 270x270 μm2 and 500x500 μm2) using 5 different magnifications (5x, 10x, 20x, 40x and 63x), on fresh frozen tissue sections. The method was applied on 20μm mouse brain and liver tissue sections. The tissue cut collection yields were evaluated visually and by the detection of the sprayed standards on the tissue sections, and found to be ranging from 78% to 91%, and the throughput of the LMD cuts and collection in a 96 well format, was measured to be from 19 to 37 minutes per tissue section, depending on the 96 squares microgrid template and the corresponding magnification lens used. Further extraction and LC-MS/MS analysis of 3 different compounds previously sprayed on a mouse liver tissue section allowed to determine the LLOQ the workflow allows to achieve when using the different templates.

Automated Whole Slide Imaging for Label-Free Histology Using Photon Absorption Remote Sensing Microscopy.

Pathologists rely on histochemical stains to impart contrast in thin translucent tissue samples, revealing tissue features necessary for identifying pathological conditions. However, the chemical labeling process is destructive and often irreversible or challenging to undo, imposing practical limits on the number of stains that can be applied to the same tissue section. Here we present an automated label-free whole slide scanner using a PARS microscope designed for imaging thin, transmissible samples. Peak SNR and in-focus acquisitions are achieved across entire tissue sections using the scattering signal from the PARS detection beam to measure the optimal focal plane. Whole slide images (WSI) are seamlessly stitched together using a custom contrast leveling algorithm. Identical tissue sections are subsequently H&E stained and brightfield imaged. The one-to-one WSIs from both modalities are visually and quantitatively compared. PARS WSIs are presented at standard 40x magnification in malignant human breast and skin samples. We show correspondence of subcellular diagnostic details in both PARS and H&E WSIs and demonstrate virtual H&E staining of an entire PARS WSI. The one-to-one WSI from both modalities show quantitative similarity in nuclear features and structural information. PARS WSIs are compatible with existing digital pathology tools, and samples remain suitable for histochemical, immunohistochemical, and other staining techniques. This work is a critical advance for integrating label-free optical methods into standard histopathology workflows.

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.

Q-VAT: Quantitative Vascular Analysis Tool.

As our imaging capability increase, so does our need for appropriate image quantification tools. Quantitative Vascular Analysis Tool (Q-VAT) is an open-source software, written for Fiji (ImageJ), that perform automated analysis and quantification on large two-dimensional images of whole tissue sections. Importantly, it allows separation of the vessel measurement based on diameter, allowing the macro- and microvasculature to be quantified separately. To enable analysis of entire tissue sections on regular laboratory computers, the vascular network of large samples is analyzed in a tile-wise manner, significantly reducing labor and bypassing several limitations related to manual quantification. Double or triple-stained slides can be analyzed, with a quantification of the percentage of vessels where the staining's overlap. To demonstrate the versatility, we applied Q-VAT to obtain morphological read-outs of the vasculature network in microscopy images of whole-mount immuno-stained sections of various mouse tissues.

Spatial mapping reveals granuloma diversity and histopathological superstructure in human tuberculosis.

The hallmark of tuberculosis (TB) is the formation of immune cell-enriched aggregates called granulomas. While granulomas are pathologically diverse, their tissue-wide heterogeneity has not been spatially resolved at the single-cell level in human tissues. By spatially mapping individual immune cells in every lesion across entire tissue sections, we report that in addition to necrotizing granulomas, the human TB lung contains abundant non-necrotizing leukocyte aggregates surrounding areas of necrotizing tissue. These cellular lesions were more diverse in composition than necrotizing lesions and could be stratified into four general classes based on cellular composition and spatial distribution of B cells and macrophages. The cellular composition of non-necrotizing structures also correlates with their proximity to necrotizing lesions, indicating these are foci of distinct immune reactions adjacent to necrotizing granulomas. Together, we show that during TB, diseased lung tissue develops a histopathological superstructure comprising at least four different types of non-necrotizing cellular aggregates organized as satellites of necrotizing granulomas.

Alignment, segmentation and neighborhood analysis in cyclic immunohistochemistry data using CASSATT.

Cyclic immunohistochemistry (cycIHC) uses sequential rounds of colorimetric immunostaining and imaging for quantitative mapping of location and number of cells of interest. Additionally, cycIHC benefits from the speed and simplicity of brightfield microscopy, making the collection of entire tissue sections and slides possible at a trivial cost compared to other high dimensional imaging modalities. However, large cycIHC datasets currently require an expert data scientist to concatenate separate open-source tools for each step of image pre-processing, registration, and segmentation, or the use of proprietary software. Here, we present a unified and user-friendly pipeline for processing, aligning, and analyzing cycIHC data - Cyclic Analysis of Single-Cell Subsets and Tissue Territories (CASSATT). CASSATT registers scanned slide images across all rounds of staining, segments individual nuclei, and measures marker expression on each detected cell. Beyond straightforward single cell data analysis outputs, CASSATT explores the spatial relationships between cell populations. By calculating the log odds of interaction frequencies between cell populations within tissues and tissue regions, this pipeline helps users identify populations of cells that interact-or do not interact-at frequencies that are greater than those occurring by chance. It also identifies specific neighborhoods of cells based on the assortment of neighboring cell types that surround each cell in the sample. The presence and location of these neighborhoods can be compared across slides or within distinct regions within a tissue. CASSATT is a fully open source workflow tool developed to process cycIHC data and will allow greater utilization of this powerful staining technique.

Multiplex Spatial Protein Detection by Combining Immunofluorescence with Immunohistochemistry.

Technologies for staining and imaging multiple antigens in single tissue sections are developing rapidly due to their potential to uncover spatial relationships between proteins with cellular resolution. Detections are performed simultaneously or sequentially depending on the approach. However, several technologies can detect limited numbers of antigens or require expensive equipment and reagents. Another serious concern is the lack of flexibility. Most commercialized reagents are validated for defined antibody panels, and introducing any changes is laborious and costly. In this chapter, we describe a method where we combine, for the first time, multiplexed IF followed by sequential immunohistochemistry (IHC) with AEC chromogen on Leica Bond staining processors with paraffin tissue sections. We present data for successful detection of 10 antigens in a single tissue section with preserved tissue integrity. Our method is designed for use with any combination of antibodies of interest, with images collected using whole slide scanners. We include an image viewing and image analysis workflow using nonlinear warping to combine all staining passes in a single full-resolution image of the entire tissue section, aligned at the single cell level.

Abstract 1719: Investigating intra-tumor heterogeneity using multiplexed immunohistochemistry &amp; deep learning: A new approach to spatially map the tumor microenvironment

Abstract The tumor microenvironment (TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells &amp; the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. At present, the usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more stained sections. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC (mIHC) procedure combining multiple labels per round with several sequential rounds, enabling analysis of complex immune cell populations on a single slide through consecutive cycles of staining, destaining &amp; hyperspectral imaging. By integrating serial imaging, sequential labeling &amp; image registration, we are able to spatially map the TME. The process presented is using absorption microscopy, enabling these images to be done in a reasonable time frame. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. Using the counterstain concentration images from our mIHC methodology, we have trained a deep learning segmentation method to accurately segment individual cell nuclei within overlapping clusters of nuclei. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation. We can analyze the cell-cell interactions between immune and tumour cells, enhancing our ability to perform molecularly based single cell analysis of multiple cell types simultaneously within the tissue. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers &amp; enabling an accurate spatial cell level representation of the tissue. Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Investigating intra-tumor heterogeneity using multiplexed immunohistochemistry &amp; deep learning: A new approach to spatially map the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1719.

Customizable Automated High Content Image Acquisition and Analysis for Tissue Biopsies

Abstract:Advancements in high content image analysis have led to an increase in the adoption of these techniques in basic science and clinical research. High-throughput approaches to imaging and image analysis require minimal user interventions, circumventing the often prohibitively time-consuming and unreliable standard manual analysis. In this study, we demonstrate how high content imaging (HCI) techniques, in combination with high content analysis (HCA), can be paired with more traditional manual analysis to quantify both micro- and macro-level features of biopsied tissue sections. High-resolution, full-color images of stained tissue were acquired and stitched together to reconstruct the entire tissue section, which enabled analyses that required accurate identification of a given region's location within the tissue section. A custom region of interest grid was generated that followed the curvature of the tissue. The composite images were used in two separate analyses: tissue layer thickness as a macro-level approach, and nuclei density as a micro-level approach. Ultimately, the flexibility of the HCI and HCA methodologies used in this study allowed for complex analysis of tissue that would not have been otherwise feasible.

Automated Quantification of Multiple Cell Types in Fluorescently Labeled Whole Mouse Brain Sections Using QuPath.

The quantification of labeled cells in tissue sections is crucial to the advancement of biological knowledge. Traditionally, this was a tedious process, requiring hours of careful manual counting in small portions of a larger tissue section. To overcome this, many automated methods for cell analysis have been developed. Recent advances in whole slide scanning technologies have provided the means to image cells in entire tissue sections. However, common automated analysis tools do not have the capacity to deal with the large image files produced. Herein, we present a protocol for the quantification of two fluorescently labeled cell populations, namely pericytes and microglia, in whole brain tissue sections. This protocol uses custom-made scripts within the open source software QuPath to provide a framework for the careful optimization and validation of automated cell detection parameters. Images obtained from a whole-slide scanner are first loaded into a QuPath project. Manual counts are performed on small sample regions to optimize cell detection parameters prior to automated quantification of cells across entire brain regions. Even though we have quantified pericytes and microglia, any fluorescently labeled cell with clear labeling in and around the nucleus can be analyzed using these methods. This protocol provides a user-friendly and cost-effective framework for the automated analysis of whole tissue sections.

Whole-slide imaging and a Fiji-based image analysis workflow of immunohistochemistry staining of pancreatic islets

Quantification of cell populations in tissue sections is frequently examined in studies of human disease. However, traditional manual imaging of sections stained with immunohistochemistry is laborious, time-consuming, and often assesses fields of view rather than the whole tissue section. The analysis is usually manual or utilises expensive proprietary image analysis platforms. Whole-slide imaging allows rapid automated visualisation of entire tissue sections. This approach increases the quantum of data generated per slide, decreases user time compared to manual microscopy, and reduces selection bias. However, such large data sets mean that manual image analysis is no longer practicable, requiring an automated process. In the case of diabetes, the contribution of various pancreatic endocrine cell populations is often investigated in preclinical and clinical samples. We developed a two-part method to measure pancreatic endocrine cell mass, firstly describing imaging using an automated slide-scanner, and secondly, the analysis of the resulting large image data sets using the open-source software, Fiji, which is freely available to all researchers and has cross-platform compatibility. This protocol is highly versatile and may be applied either in full or in part to analysis of IHC images created using other imaging platforms and/or the analysis of other tissues and cell markers.

Abstract 2732: Multiplexed immunohistochemistry and deep learning: a new approach to spatially map the tumor microenvironment

Abstract The usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behavior is visual examination of one or more H&amp;E and/or Immunohistochemistry(IHC) stained sections. The paradigm of digital pathology has changed, moving from single-marker IHC towards multiplexed labeling, increasing the need for more advanced techniques that can be easily integrated in routine clinical pathology. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC procedure combining multiple labels per round, enabling analysis of complex immune cell population's on a single slide through consecutive cycles of staining, destaining, hyperspectral imaging and spectral unmixing of the chromogen biomarkers in each round. Not only does this allow us to completely visualize and spatially map the tumor microenvironment(TME), but through the application of AI we can further recognize common histological features present in the tissue. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. Analyzing the cell-cell interactions between immune and tumor cells and identifying clinically relevant patterns may improve patient outcomes while informing on the likelihood of success of possible treatments. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation, we can improve the accuracy of tissue classification based upon the measured characteristics of these cells and their spatial organization. The two techniques joined can be scaled up to the entire tissue section level, providing an accurate spatial cell level representation of the tissue. Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Multiplexed immunohistochemistry and deep learning: a new approach to spatially map the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2732.

Evolution of the liver biopsy and its future.

Liver biopsies are commonly used to evaluate a wide variety of medical disorders, including neoplasms and post-transplant complications. However, its use is being impacted by improved clinical diagnosis of disorders, and non-invasive methods for evaluating liver tissue and as a result the indications of a liver biopsy have undergone major changes in the last decade. The evolution of highly effective treatments for some of the common indications for liver biopsy in the last decade (e.g., viral hepatitis B and C) has led to a decline in the number of liver biopsies in recent years. At the same time, the emergence of better technologies for histologic evaluation, tissue content analysis and genomics are among the many new and exciting developments in the field that hold great promise for the future and are going to shape the indications for a liver biopsy in the future. Recent advances in slide scanners now allow creation of "digital/virtual" slides that have image of the entire tissue section present in a slide [whole slide imaging (WSI)]. WSI can now be done very rapidly and at very high resolution, allowing its use in routine clinical practice. In addition, a variety of technologies have been developed in recent years that use different light sources and/or microscopes allowing visualization of tissues in a completely different way. One such technique that is applicable to liver specimens combines multiphoton microscopy (MPM) with advanced clearing and fluorescent stains known as Clearing Histology with MultiPhoton Microscopy (CHiMP). Although it has not yet been extensively validated, the technique has the potential to decrease inefficiency, reduce artifacts, and increase data while being readily integrable into clinical workflows. Another technology that can provide rapid and in-depth characterization of thousands of molecules in a tissue sample, including liver tissues, is matrix assisted laser desorption/ionization (MALDI) mass spectrometry. MALDI has already been applied in a clinical research setting with promising diagnostic and prognostic capabilities, as well as being able to elucidate mechanisms of liver diseases that may be targeted for the development of new therapies. The logical next step in huge data sets obtained from such advanced analysis of liver tissues is the application of machine learning (ML) algorithms and application of artificial intelligence (AI), for automated generation of diagnoses and prognoses. This review discusses the evolving role of liver biopsies in clinical practice over the decades, and describes newer technologies that are likely to have a significant impact on how they will be used in the future.

Abstract PO-043: Combining multiplexed immunohistochemistry and deep learning to spatially map the tumor microenvironment

Abstract The tumour microenvironment(TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells and the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. The usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more H&amp;E and/or Immunohistochemistry(IHC) stained sections. The paradigm of digital pathology has changed, moving from single-marker IHC towards multiplexed labeling, increasing the need for more advanced techniques that can be easily integrated in routine clinical pathology. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC procedure combining multiple labels per round with several sequential rounds, where multiple is 12-14 chromogen based antibodies on a single tissue section. Thus, enabling analysis of complex immune cell population’s on a single slide through consecutive cycles of staining, destaining, hyperspectral imaging and spectral unmixing of the chromogen biomarkers in each round. The process presented is using absorption microscopy, enabling these images to be done in a reasonable time frame. Not only does this allow us to completely visualize and spatially map the TME, but through the application of AI we can further recognize common histological features. Stoichiometric DNA following mIHC, improves nuclei identification so we can identify various cell populations present in the tissue. Thus, providing us with an accurate spatial cell level representation of any tissue section using only a single staining process. Although there have been several uprising technologies which characterize the complexities of the TME, our process is capable of doing so in a shorter time frame and at a reduced cost using absorption based methods. Robust, accurate, segmentation of cell nuclei for overlapping nuclei is one of the most significant unsolved issues in digital pathology. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation. We can analyze the cell-cell interactions between immune and tumour cells and identify clinically relevant patterns that may improve patients outcome by informing on the likelihood of success of possible treatments. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers and enabling an accurate spatial cell level representation of the tissue. Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Combining multiplexed immunohistochemistry and deep learning to spatially map the tumor microenvironment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-043.

Quantitative Mass Spectrometry Imaging to Study Drug Distribution in the Intestine Following Oral Dosing.

Local delivery to the lower gut to treat diseases of the colon has become a topic of special attention. Tissue exposure of locally acting agents is not represented by plasma concentrations. Therefore, reliable methods to measure tissue uptake at the primary site of action (e.g., epithelial layer or lamina propria) are vital. This work investigates the suitability of mass spectrometry imaging (MSI) in quantitatively visualizing intestinal transmural drug distribution. Tofacitinib (Tofa), a drug approved for the treatment of several autoimmune diseases, including ulcerative colitis, was selected as a tool compound for feasibility studies. One- and 7-h postdose sections of the ileum, proximal- and distal-colon from rats that received an oral solution of Tofa were subjected to matrix-assisted laser desorption ionization (MALDI)-MSI. A dilution series of individual concentrations sprayed over an entire tissue section allowed for tissue type-specific quantitation. At 1 h (systemic Tmax), the signal was highest in the ileum, whereas at 7 h, the signal was highest in the colon, when the unabsorbed fraction of the compound reached the colon. A combination of three-dimensional (3D) intensity plots and hematoxylin and eosin (H&E) stains showed a visually observable gradual decrease in Tofa concentration from the lumen toward the muscular layer of the proximal colon. The high luminal concentration of Tofa indicated that flushing of the intestines with saline does not result in complete removal of the drug material from the lumen. This could cause an overestimation of drug concentration in gut tissue homogenates by conventional liquid chromatography-mass spectrometry (LC-MS) methods. This study demonstrates the utility of MSI to differentiate between the lumen and intestinal wall layers and enables proper interpretation of tissue distribution data.

Abstract PO-082: Combining multiplex immunohistochemistry and deep learning: A new approach to tracing the tumour microenvironment

Abstract The tumour microenvironment(TME) is a highly complex mixture containing epithelium, stroma and a diverse network of immune cells and the spatial organization of these immune cells within the TME reflects a crucial process in anti-tumor immunity. Not all tumour cells interact similarly; micro-environmental features are often key indicators of treatment response and patient outcome. The usual standard of care for assessing if a patient has cancer, its stage and its likely future biological behaviour is visual examination of one or more H&amp;E and/or Immunohistochemistry(IHC) stained sections. The paradigm of digital pathology has changed, moving from single-¬marker IHC towards multiplexed labeling, increasing the need for more advanced techniques that can be easily integrated in routine clinical pathology. Although recent advances in multiple immunostaining have enabled characterization of several parameters on a single tissue section. For a higher dimensional chromogen based methodology, we have developed a multiplexed IHC procedure combining multiple labels per round with multiple sequential rounds, where multiple is 12-14 chromogen based antibodies on a single tissue section. Thus, enabling analysis of complex immune cell population’s on a single slide through consecutive cycles of staining, destaining, hyperspectral imaging and spectral unmixing of the chromogen biomarkers in each round. That the process presented is chromogen based (absorption microscopy) means that high throughput imaging of 12-14 markers across entire slides is feasible in a reasonable time frame. Robust, accurate, segmentation of cell nuclei for touching/overlapping nuclei is one of the most significant unsolved issues in digital pathology. Analyzing cell-cell interactions between immune and tumour cells and identifying clinically relevant patterns may improve patients outcome by informing on the likelihood of success of possible treatments. By combining a multiplexed IHC technique which enables the detection of multiple markers on a single slide with deep learning segmentation methods to recognize/segment every individual cell nuclei in tissue sections with an accuracy comparable to human annotation, we can improve the accuracy of tissue classification based upon the measured characteristics of these cells and their spatial organization. These two techniques joined can be scaled up to the entire tissue section level, improving our understanding of the biological aggressiveness of specific cancers and enabling an accurate spatial cell level representation of the tissue. Citation Format: Kouther Noureddine, Paul Gallagher, Martial Guillaud, Calum MacAulay. Combining multiplex immunohistochemistry and deep learning: A new approach to tracing the tumour microenvironment [abstract]. In: Proceedings of the AACR Virtual Special Conference on Tumor Heterogeneity: From Single Cells to Clinical Impact; 2020 Sep 17-18. Philadelphia (PA): AACR; Cancer Res 2020;80(21 Suppl):Abstract nr PO-082.

Active Preservation of Osteochondral Graft under Cyclic Hydrostatic Pressure for Maintaining Cartilage Integrity and Subchondral Bone Quality

Objectives:Osteochondral allografts (OCA) are currently stored at 4˚C for an average of 24 days after procurement. However, chondrocyte viability, the major determinant of graft performance in-vivo, decreases and matrix integrity deteriorates with time under such conditions; for instance, chondrocyte viability falls below the acceptable level of 70% by 28 days. Hydrostatic pressure (HP) is a significant component in the mechanical loading environment and its application in vitro (0.5 - 15 MPa) elicited favorable effects on cartilage and bone. The purpose of this study was to investigate the effects of HP during osteochondral storage.Methods:Osteochondral (OC) explants (6x8 mm) were harvested from bovine humeral heads purchased from a local slaughterhouse (Fig. 1). OCs were randomly assigned to one of 4 groups: freshly harvested; stored at 4°C and atmospheric pressure (4˚C AP); stored at 37°C and AP (37˚C AP); and stored at 37°C with cyclic HP at 0-0.5 MPa, 0.5 Hz (37˚C HP). The explants were stored for 7, 14, 21, or 28 days in Dulbecco’s modified eagle medium/Ham’s nutrient mixture F12 (1:1), with antibiotics and with or without fetal bovine serum (FBS). Chondrocyte viability and density were assessed using LIVE/DEAD® staining in the entire tissue section and in the superficial, middle, and deep zones using the Image J program. In addition, we stained OC explants with safranin-O fast green; hematoxylin and eosin, sudan IV and antibodies against proliferating cell nuclear antigen (PCNA), keratan sulfate (KS). Furthermore, the microstructure and material composition of subchondral bone in the explants was quantified using micro-computed tomography (micro-CT, Scanco uCT 35) at a 12 micron nominal resolution.Results:Cartilage thickness was maintained at 37°C HP, significantly increased at 37°C AP and significantly decreased at 4°C AP (P < 0.05) with/without FBS at 28 days. Full-thickness chondrocyte viability was significantly higher at 21 and 28 days in the 37˚C HP group compared to 4°C or 37°C AP regardless of FBS (P<0.05). Specifically, chondrocyte viability in the superficial zone was maintained with 37˚C HP (Fig. 3) versus not in the other culture conditions. Cartilage matrices were similar between the conditions by 14 days (Fig. 4). However, surface fibrillation and degeneration were demonstrated in the 37 °C AP. KS seemed to be maintained in the superficial zone with HP by 28 days but decreased under other conditions (Fig. 5). More proliferating cells with 37°C HP were observed in the superficial zone compared to other conditions at 28 days. Utilization of FBS further increased the number of proliferating cells in each condition. The fatty granules were observed and distributed evenly throughout the subchondral bone and trabecular bone under all conditions at 28 days (Fig. 6). However, in the 4°C and 37°C AP groups, fewer fatty granules were observed in the subchondral bone and more fat in the trabecular bone (Fig. 6). Micro-CT evaluation revealed that the conditions produced similar tissue mineral density (Fig. 7). However, the trabecular number was significantly lower with 37°C AP compared to the other conditions (P<0.05).Conclusion:Osteochondral explants stored with 37°C HP maintained chondrocyte viability, histologically determined cartilage integrity, and subchondral bone quality.Figure 1.

Visualization, Quantification, and Mapping of Immune Cell Populations in the Tumor Microenvironment.

The immune landscape of the tumor microenvironment (TME) is a determining factor in cancer progression and response to therapy. Specifically, the density and the location of immune cells in the TME have important diagnostic and prognostic values. Multiomic profiling of the TME has exponentially increased our understanding of the numerous cellular and molecular networks regulating tumor initiation and progression. However, these techniques do not provide information about the spatial organization of cells or cell-cell interactions. Affordable, accessible, and easy to execute multiplexing techniques that allow spatial resolution of immune cells in tissue sections are needed to complement single cell-based high-throughput technologies. Here, we describe a strategy that integrates serial imaging, sequential labeling, and image alignment to generate virtual multiparameter slides of whole tissue sections. Virtual slides are subsequently analyzed in an automated fashion using user-defined protocols that enable identification, quantification, and mapping of cell populations of interest. The image analysis is done, in this case using the analysis modules Tissuealign, Author, and HISTOmap. We present an example where we applied this strategy successfully to one clinical specimen, maximizing the information that can be obtained from limited tissue samples and providing an unbiased view of the TME in the entire tissue section.

Rigor and Reproducibility in Confocal Fluorescence Microscopy.

Rigor and Reproducibility in Confocal Fluorescence Microscopy.

The Role of MicroRNA-21 in Venous Neointimal Hyperplasia: Implications for Targeting miR-21 for VNH Treatment.

The molecular mechanism of hemodialysis access arteriovenous fistula (AVF) failure due to venous neointimal hyperplasia (VNH) is not known. The role of microRNA-21 (miR-21) in VNH associated with AVF failure was investigated by performing in vivo and in vitro experiments. In situ hybridization results revealed that miR-21 expression increased and was associated with fibroblasts in failed AVFs from patients. In a murine AVF model, qRT-PCR gene expression results showed a significant increase in miR-21 and a decrease in miR-21 target genes in graft veins (GVs) compared to contralateral veins in mouse AVF. miR-21 knockdown in GVs was performed using a lentivirus-mediated small hairpin RNA (shRNA), and this improved AVF patency with a decrease in neointima compared to control GVs. Moreover, loss of miR-21 in GVs significantly decreased the Tgfβ1, Col-Ia, and Col-Iva genes. Immunohistochemistry demonstrated a significant decrease in myofibroblasts and proliferation with an increase in terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) staining in miR-21-knockdown vessels, along with a decrease in hypoxia-inducible factor-1 alpha (HIF-1α) and phospho-SMAD2 (pSMAD-2) and phospho-SMAD3 (pSMAD-3) and an increase in phosphatase and tensin homolog (PTEN) staining. Hypoxic fibroblast knockdown for miR-21 showed a significant decrease in Tgfβ-1 expression and pSMAD-2 and -3 levels and a decrease in myofibroblasts. These results indicate that miR-21 upregulation causes VNH formation by fibroblast-to-myofibroblast differentiation.