Cancer biology through immunohistology

Oxford Textbook of Cancer Biology ◽

10.1093/med/9780198779452.003.0027 ◽

2019 ◽

pp. 394-409

Author(s):

Karen Pulford ◽

Kevin Gatter

Keyword(s):

Image Analysis ◽

Microscopic Study ◽

High Throughput ◽

Cancer Biology ◽

Critical Component ◽

Predictive Information ◽

Specific Antibodies ◽

Essential Tool ◽

Cellular Components

Immunohistology is the microscopic study of cells and tissues using specific antibodies that bind to individual molecules expressed by the cellular and non-cellular components of the tissues. This branch of science is an essential link in the analysis and interpretation of data from high throughput genomic and proteomic technologies. Its use, both in the research and in the clinical arenas, has led to an increased understanding of cancer biology. This knowledge has also resulted in improvements in diagnosis, the provision of prognostic and predictive information, and highlighted the use of appropriate treatments. Furthermore, immunohistochemistry is a critical component in the search for personalized treatments. The ongoing advances in the availability of specific validated antibodies, continued improvements in staining and image analysis, and the integration of different technologies will ensure that immunohistochemistry becomes an even more essential tool in the study of cancer biology.

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SARS-CoV-2 Multi-Antigen Serology Assay

Methods and Protocols ◽

10.3390/mps4040072 ◽

2021 ◽

Vol 4 (4) ◽

pp. 72

Author(s):

Ramin Mazhari ◽

Shazia Ruybal-Pesántez ◽

Fiona Angrisano ◽

Nicholas Kiernan-Walker ◽

Stephanie Hyslop ◽

...

Keyword(s):

High Throughput ◽

Specific Antibody ◽

Serological Assay ◽

Specific Antibodies ◽

Essential Tool

Serology tests are extremely useful for assessing whether a person has been infected with a pathogen. Since the onset of the COVID-19 pandemic, measurement of anti-SARS-CoV-2-specific antibodies has been considered an essential tool in identifying seropositive individuals and thereby understanding the extent of transmission in communities. The Luminex system is a bead-based technology that has the capacity to assess multiple antigens simultaneously using very low sample volumes and is ideal for high-throughput studies. We have adapted this technology to develop a COVID-19 multi-antigen serological assay. This protocol described here carefully outlines recommended steps to optimize and establish this method for COVID-19-specific antibody measurement in plasma and in saliva. However, the protocol can easily be customized and thus the assay is broadly applicable to measure antibodies to other pathogens.

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Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽

10.1186/s13007-021-00747-0 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Shuo Zhou ◽

Xiujuan Chai ◽

Zixuan Yang ◽

Hongwu Wang ◽

Chenxue Yang ◽

...

Keyword(s):

Computer Vision ◽

Image Analysis ◽

Deep Learning ◽

High Throughput ◽

Batch Processing ◽

Plant Phenotyping ◽

Plant Science ◽

Analysis Software ◽

Image Analysis Software ◽

Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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Friend or Foe: Paradoxical Roles of Autophagy in Gliomagenesis

Cells ◽

10.3390/cells10061411 ◽

2021 ◽

Vol 10 (6) ◽

pp. 1411

Author(s):

Don Carlo Ramos Batara ◽

Moon-Chang Choi ◽

Hyeon-Uk Shin ◽

Hyunggee Kim ◽

Sung-Hak Kim

Keyword(s):

Brain Tumor ◽

Glioblastoma Multiforme ◽

Cancer Biology ◽

Molecular Mechanisms ◽

Primary Brain Tumor ◽

Molecular Target ◽

Therapeutic Strategies ◽

Homeostatic Mechanism ◽

Cellular Context ◽

Cellular Components

Glioblastoma multiforme (GBM) is the most common and aggressive type of primary brain tumor in adults, with a poor median survival of approximately 15 months after diagnosis. Despite several decades of intensive research on its cancer biology, treatment for GBM remains a challenge. Autophagy, a fundamental homeostatic mechanism, is responsible for degrading and recycling damaged or defective cellular components. It plays a paradoxical role in GBM by either promoting or suppressing tumor growth depending on the cellular context. A thorough understanding of autophagy’s pleiotropic roles is needed to develop potential therapeutic strategies for GBM. In this paper, we discussed molecular mechanisms and biphasic functions of autophagy in gliomagenesis. We also provided a summary of treatments for GBM, emphasizing the importance of autophagy as a promising molecular target for treating GBM.

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An Optimized High-Throughput Neutralization Assay for Hepatitis E Virus (HEV) Involving Detection of Secreted Porf2

Viruses ◽

10.3390/v11010064 ◽

2019 ◽

Vol 11 (1) ◽

pp. 64 ◽

Cited By ~ 4

Author(s):

Chang Liu ◽

Wei Cai ◽

Xin Yin ◽

Zimin Tang ◽

Guiping Wen ◽

...

Keyword(s):

High Throughput ◽

Hepatitis E Virus ◽

Hepatitis E ◽

Neutralization Assay ◽

The Novel ◽

Specific Antibodies ◽

Neutralizing Activity ◽

Elisa Kit ◽

Neutralizing Capacity ◽

Potential Threshold

Hepatitis E virus (HEV) is a common cause of acute hepatitis worldwide. Current methods for evaluating the neutralizing activity of HEV-specific antibodies include immunofluorescence focus assays (IFAs) and real-time PCR, which are insensitive and operationally complicated. Here, we developed a high-throughput neutralization assay by measuring secreted pORF2 levels using an HEV antigen enzyme-linked immunosorbent assay (ELISA) kit based on the highly replicating HEV genotype (gt) 3 strain Kernow. We evaluated the neutralizing activity of HEV-specific antibodies and the sera of vaccinated individuals (n = 15) by traditional IFA and the novel assay simultaneously. A linear regression analysis shows that there is a high degree of correlation between the two assays. Furthermore, the anti-HEV IgG levels exhibited moderate correlation with the neutralizing titers of the sera of vaccinated individuals, indicating that immunization with gt 1 can protect against gt 3 Kernow infection. We then determined specificity of the novel assay and the potential threshold of neutralizing capacity using anti-HEV IgG positive sera (n = 27) and anti-HEV IgG negative sera (n = 23). The neutralizing capacity of anti-HEV IgG positive sera was significantly stronger than that of anti-HEV IgG negative. In addition, ROC curve analysis shows that the potential threshold of neutralizing capacity of sera was 8.07, and the sensitivity and specificity of the novel assay was 88.6% and 100%, respectively. Our results suggest that the neutralization assay using the antigen ELISA kit could be a useful tool for HEV clinical research.

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High-Throughput Image Analysis of

Methods in Molecular Biology - Mitochondrial Medicine ◽

10.1007/978-1-0716-1266-8_22 ◽

2021 ◽

pp. 285-303

Author(s):

Nathanael Miller ◽

Dane Wolf ◽

Nour Alsabeeh ◽

Kiana Mahdaviani ◽

Mayuko Segawa ◽

...

Keyword(s):

Image Analysis ◽

High Throughput

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Molecular profiling in cancer research and personalized medicine

Oxford Textbook of Cancer Biology ◽

10.1093/med/9780198779452.003.0024 ◽

2019 ◽

pp. 347-362

Author(s):

Pieter-Jan van Dam ◽

Steven Van Laere

Keyword(s):

Personalized Medicine ◽

High Throughput ◽

Cancer Biology ◽

Complex Disease ◽

Human Cancer ◽

Molecular Profiling ◽

Cancer Genome ◽

The Cancer Genome Atlas ◽

Molecular Heterogeneity ◽

Profiling Techniques

Recent efforts by worldwide consortia such as The Cancer Genome Atlas and the International Cancer Genome Consortium have greatly accelerated our knowledge of human cancer biology. Nowadays, complete sets of human tumours that have been characterized at the genomic, epigenomic, transcriptomic, or proteomic level are available to the research community. The generation of these data was made possible thanks to the application of high-throughput molecular profiling techniques such as microarrays and next-generation sequencing. The primary conclusion from current profiling experiments is that human cancer is a complex disease characterized by extreme molecular heterogeneity, both between and within the classical, tissue-defined cancer types. This molecular variety necessitates a paradigm shift in patient management, away from generalized therapy schemes and towards more personalized treatments. This chapter provides an overview of how molecular cancer profiling can assist in facilitating this transition. First, the state-of-the-art of molecular breast cancer profiling is reviewed to provide a general background. Then, the most pertinent high-throughput molecular profiling techniques along with various data mining techniques (i.e. unsupervised clustering, statistical learning) are discussed. Finally, the challenges and perspectives with respect to molecular cancer profiling, also from the perspective of personalized medicine, are summarized.

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High-throughput microfluidic micropipette aspiration device to probe time-scale dependent nuclear mechanics in intact cells

Lab on a Chip ◽

10.1039/c9lc00444k ◽

2019 ◽

Vol 19 (21) ◽

pp. 3652-3663 ◽

Cited By ~ 10

Author(s):

Patricia M. Davidson ◽

Gregory R. Fedorchak ◽

Solenne Mondésert-Deveraux ◽

Emily S. Bell ◽

Philipp Isermann ◽

...

Keyword(s):

Image Analysis ◽

High Throughput ◽

Time Scale ◽

Viscoelastic Properties ◽

Micropipette Aspiration ◽

Automated Image Analysis ◽

Cell Nuclei ◽

Intact Cells ◽

Nuclear Mechanics ◽

Analysis Platform

We report the development, validation, and application of an easy-to-use microfluidic micropipette aspiration device and automated image analysis platform that enables high-throughput measurements of the viscoelastic properties of cell nuclei.

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Organoid Models for Cancer Research

Annual Review of Cancer Biology ◽

10.1146/annurev-cancerbio-030518-055702 ◽

2019 ◽

Vol 3 (1) ◽

pp. 223-234 ◽

Cited By ~ 9

Author(s):

Hans Clevers ◽

David A. Tuveson

Keyword(s):

Cancer Research ◽

Tumor Microenvironment ◽

Personalized Medicine ◽

High Throughput ◽

Stromal Cells ◽

Transcriptome Sequencing ◽

Cancer Biology ◽

Three Dimensional ◽

Model Systems ◽

Liver Cancers

Organoid cultures have emerged as powerful model systems accelerating discoveries in cellular and cancer biology. These three-dimensional cultures are amenable to diverse techniques, including high-throughput genome and transcriptome sequencing, as well as genetic and biochemical perturbation, making these models well suited to answer a variety of questions. Recently, organoids have been generated from diverse human cancers, including breast, colon, pancreas, prostate, bladder, and liver cancers, and studies involving these models are expanding our knowledge of the etiology and characteristics of these malignancies. Co-cultures of cancer organoids with non-neoplastic stromal cells enable investigation of the tumor microenvironment. In addition, recent studies have established that organoids have a place in personalized medicine approaches. Here, we describe the application of organoid technology to cancer discovery and treatment.

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