Automated Quantification of Immunohistochemical Staining of Large Animal Brain Tissue Using QuPath Software

Morriss, Nicholas; Conley, Grace; Ospina, Sara M.; Meehan, William P.; Qiu, Jianhua; Mannix, Rebekah

doi:10.1016/j.neuroscience.2020.01.006

Cited by 30 publications

(22 citation statements)

References 31 publications

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“…However, if there were any folds or staining irregularities through a manual check, the slide was omitted from the analysis. To quantify the staining, superpixels were created to analyze the hippocampus [25]. Within the annotated hippocampus, QuPath groups similar pixels into a cluster called a superpixel based on the RGB values set for DAB.…”

Section: Imaging and Qupath Analysismentioning

confidence: 99%

“…Within the annotated hippocampus, QuPath groups similar pixels into a cluster called a superpixel based on the RGB values set for DAB. The pixel-based analysis was chosen as the desired method of quantification because this study is looking at multiple stains, and pixel analysis allows us to follow an almost identical protocol between each group [25]. Superpixel size was set to 25 µm 2 in order to balance capturing positively stained sections at a high resolution and processing speed.…”

Section: Imaging and Qupath Analysismentioning

confidence: 99%

“…This, therefore, allowed the capture of staining intensity across all positively stained cells in the annotated region. In order to visualize the DAB staining thresholds, a "heat map" was generated for each image [25]. QuPath allows users to apply a gradient of color according to the quantifications of DAB staining generated previously.…”

Section: Imaging and Qupath Analysismentioning

confidence: 99%

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Resilience to acute sleep deprivation is associated with attenuation of hippocampal mediated learning impairment

Lee

Lei

Zhu

et al. 2020

APT

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Background: Sleep deprivation is a universal issue that affects individuals in different ways. While some individuals experience a deficit in performance, others experience resiliency as they maintain high levels of physical and mental activity. Sleep loss is known to cause cognitive dysfunction in areas such as learning and memory, but little is known about neural mechanisms that contribute to resilience to this adverse effect. Methods: An existing database of a learning paradigm in sleep deprived and non-sleep deprived 16 to 18-month old C57BL/6 mice was used to identify fast learners and slow learners based on an R2 value representing the learning curve of each individual mouse. Results: Results showed that sleep deprived mice had more slow learners compared to fast learners whereas non-sleep-deprived mice showed the opposite. Hippocampal immunohistochemistry and digital imaging analysis showed sleep deprived, fast learners expressed lower levels of monocyte chemoattractant protein-1 and histone deacetylase 2 and higher levels of synaptophysin and brain-derived neurotrophic factor compared to sleep-deprived slow learners. Conclusions: These observations provide evidence to suggest that sleep-deprived mice that performed well in a cognitive assay show less hippocampal mediated learning impairment and provide the rationale for further investigations into neurobiological resilience to sleep deprivation with increasing age. Keywords: Sleep deprivation, resiliency, learning impairment, aging, neuropathology, hippocampus

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Section: Imaging and Qupath Analysismentioning

confidence: 99%

Section: Imaging and Qupath Analysismentioning

confidence: 99%

Section: Imaging and Qupath Analysismentioning

confidence: 99%

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Resilience to acute sleep deprivation is associated with attenuation of hippocampal mediated learning impairment

Lee

Lei

Zhu

et al. 2020

APT

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“…Most of these previous studies using IHC in machine learning however focused on a smaller number of markers, often well-known biomarkers. These markers were either used to train the algorithm recognizing and measuring the presence of certain cell types within the tissues (13), or to quantify the number of cells positive for a certain marker (14). No previous study has addressed the challenge presented here, training an AI model that distinguishes the cell type-specific protein expression pattern in human IHC samples, applicable to stainings from any type of protein (15,16).…”

Section: Introductionmentioning

confidence: 99%

DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning

Ghoshal

Hikmet

Pineau

et al. 2021

Molecular & Cellular Proteomics

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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“…In this study, we provide in-depth, longitudinal, pathological characterization of multisystemic disease manifestation caused by SARS-CoV-2 infection in male and female golden Syrian hamsters. Furthermore, we objectively measured tissue damage and inflammatory responses by digital image analysis using an open-source platform, QuPath 26,27 . Our results show that inoculating hamsters intranasally with SARS-CoV-2 reliably induces acute damage to the respiratory tract with initial viral replication followed by a macrophage-dominant pulmonary immune response.…”

Section: Introductionmentioning

confidence: 99%

Progression and Resolution of SARS-CoV-2 Infection in Golden Syrian Hamsters

Mulka

Beck²,

Solis

et al. 2021

Preprint

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To catalyze SARS-CoV-2 research including development of novel interventive and preventive strategies, we characterized progression of disease in depth in a robust COVID-19 animal model. In this model, male and female golden Syrian hamsters were inoculated intranasally with SARS-CoV-2 USA-WA1/2020. Groups of inoculated and mock-inoculated uninfected control animals were euthanized at day 2, 4, 7, 14, and 28 days post-inoculation to track multiple clinical, pathology, virology, and immunology outcomes. SARS-CoV-2-inoculated animals consistently lost body weight during the first week of infection, had higher lung weights at terminal timepoints, and developed lung consolidation per histopathology and quantitative image analysis measurements. High levels of infectious virus and viral RNA were reliably present in the respiratory tract at days 2 and 4 post-inoculation, corresponding with widespread necrosis and inflammation. At day 7, when infectious virus was rare, interstitial and alveolar macrophage infiltrates and marked reparative epithelial responses (type II hyperplasia) dominated in the lung. These lesions resolved over time, with only residual epithelial repair evident by day 28 post-inoculation. The use of quantitative approaches to measure cellular and morphologic alterations in the lung provides valuable outcome measures for developing therapeutic and preventive interventions for COVID-19 using the hamster COVID-19 model.

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Automated Quantification of Immunohistochemical Staining of Large Animal Brain Tissue Using QuPath Software

Cited by 30 publications

References 31 publications

Resilience to acute sleep deprivation is associated with attenuation of hippocampal mediated learning impairment

Resilience to acute sleep deprivation is associated with attenuation of hippocampal mediated learning impairment

DeepHistoClass: A Novel Strategy for Confident Classification of Immunohistochemistry Images Using Deep Learning

Progression and Resolution of SARS-CoV-2 Infection in Golden Syrian Hamsters

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