A Method to Reuse Archived H&amp;E Stained Histology Slides for a Multiplex Protein Biomarker Analysis

Hinton, James P.; Dvorak, Katerina; Roberts, Esteban; French, Wendy J.; Grubbs, Jon C.; Cress, Anne E.; Tiwari, Hina Arif; Nagle, Raymond B.

doi:10.3390/mps2040086

Cited by 32 publications

(15 citation statements)

References 22 publications

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“…Histological analysis was performed by hematoxylin and eosin (H&E) staining to assess the cellular and morphological structures. 36 Additionally, Prussian blue staining was performed in order to detect iron NPs accumulated in tumors or tissues. 37,38 In vivo Antitumor Efficacy…”

Section: In Vivo Mri Fi and Histological Analysismentioning

confidence: 99%

Emodin-Conjugated PEGylation of Fe3O4 Nanoparticles for FI/MRI Dual-Modal Imaging and Therapy in Pancreatic Cancer

Ren¹,

Song²,

Tian³

et al. 2021

IJN

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Section: In Vivo Mri Fi and Histological Analysismentioning

confidence: 99%

Emodin-Conjugated PEGylation of Fe3O4 Nanoparticles for FI/MRI Dual-Modal Imaging and Therapy in Pancreatic Cancer

Ren¹,

Song²,

Tian³

et al. 2021

IJN

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“…Multiplexed or sequential immunolabeling, such as the mIHC approach used in our example, can mitigate this loss of accuracy. If only a few labels are needed to construct the training dataset it would be possible to generate a nearly perfect cell-level registration by digitizing the H&E slide, de-staining, and then re-staining using IHC (Hinton et al, 2019).…”

Section: Pros and Cons Of Ihc-based Weakly Cell-level Annotationmentioning

confidence: 99%

Deep Learning of Histopathology Images at the Single Cell Level

Lee

Lockhart

Xie

et al. 2021

Front. Artif. Intell.

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The tumor immune microenvironment (TIME) encompasses many heterogeneous cell types that engage in extensive crosstalk among the cancer, immune, and stromal components. The spatial organization of these different cell types in TIME could be used as biomarkers for predicting drug responses, prognosis and metastasis. Recently, deep learning approaches have been widely used for digital histopathology images for cancer diagnoses and prognoses. Furthermore, some recent approaches have attempted to integrate spatial and molecular omics data to better characterize the TIME. In this review we focus on machine learning-based digital histopathology image analysis methods for characterizing tumor ecosystem. In this review, we will consider three different scales of histopathological analyses that machine learning can operate within: whole slide image (WSI)-level, region of interest (ROI)-level, and cell-level. We will systematically review the various machine learning methods in these three scales with a focus on cell-level analysis. We will provide a perspective of workflow on generating cell-level training data sets using immunohistochemistry markers to “weakly-label” the cell types. We will describe some common steps in the workflow of preparing the data, as well as some limitations of this approach. Finally, we will discuss future opportunities of integrating molecular omics data with digital histopathology images for characterizing tumor ecosystem.

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“…Immunohistochemical staining was performed on these sections using three different antibodies, (LC3β (H-50): sc-28266, p62/ SQSTM1 (D-3): sc-28359 and Beclin 1 (E-8) sc-48341, all were from Santa Cruz Biotechnology, Inc). Selected tissues were chosen to represent a range of anticipated staining intensities for both hematoxylin and eosin (H&E as imparted using a standard staining protocol [17].…”

Section: Immunohistochemistrymentioning

confidence: 99%

Autophagy Markers p62, LC3II and Beclin1 Correlate with Clark Levels in Melanoma Tumors

Arani¹,

Mohammadpour²,

Moosavi³

et al. 2021

Preprint

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BackgroundThe prognosis of melanoma depends on early diagnosis and timely treatment. Autophagy as a mechanism of degradation/recycling of cellular debris, has potential to be evaluated as prognostic biomarker in current research. MethodsIn this study, ATG5 and Beclin 1 gene expression in different Clark levels of melanoma were evaluated in a retrospective study of 10 years in the cancer institute of Tehran, Iran. The autophagy activity and the correlation with clinicopathological data were also investigated in a tissue microarray series of 52 melanomas after immunohistochemical staining for the autophagy-associated proteins p62, LC3II and Beclin1. The possibility of autophagy biomarkers were assessed by ROC curve analysis.ResultsThe patterns of ATG5 and Beclin1 gene expression are different. While ATG5 was increased in the early stage and then decreased as the stage was progressed in comparison to tumor margin, the Beclin1 expression was decreased and not altered during tumor progression. However, Beclin1 expression at the protein level was increased with tumor progression. The expression of LC3II was also raised while the p62 levels were declined as the tumor progressed, suggesting an increased autophagy activity in melanoma patients. Melanoma ulceration was positively correlated with Beclin 1 and LC3II expression and inversely correlated with p62 (p<0.05). Autophagy markers expression did not significantly correlate with melanoma mitotic rate and thickness. ConclusionsAutophagy is a potential prognostic factor in the early stage of melanoma and could be considered as a therapeutic target.

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A Method to Reuse Archived H&E Stained Histology Slides for a Multiplex Protein Biomarker Analysis

Cited by 32 publications

References 22 publications

Emodin-Conjugated PEGylation of Fe3O4 Nanoparticles for FI/MRI Dual-Modal Imaging and Therapy in Pancreatic Cancer

Emodin-Conjugated PEGylation of Fe3O4 Nanoparticles for FI/MRI Dual-Modal Imaging and Therapy in Pancreatic Cancer

Deep Learning of Histopathology Images at the Single Cell Level

Autophagy Markers p62, LC3II and Beclin1 Correlate with Clark Levels in Melanoma Tumors

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