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Classification of Epithelial Tissues: Overview

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...

Classification of Epithelial Tissues: Stratified Epithelium

Classification of Epithelial Tissues: Stratified Epithelium

Stratified epithelium consists of several stacked layers of cells. They provide the durability to withstand constant physical and chemical attacks. Stratified epithelium is named after the shape of the most apical layer of cells. Stratified squamous epithelium is the most common type found in the human body. In this tissue, the apical cells are squamous, whereas the basal layer contains either columnar or cuboidal cells. The basal cells divide to form new daughter cells, which gradually become...

Classification of Epithelial Tissues: Glandular Epithelium

Classification of Epithelial Tissues: Glandular Epithelium

The glandular epithelium is made of one or more epithelial cells modified to synthesize and secrete chemical substances. Glandular epithelia can be classified based on cell number. Unicellular glands have individual secretory cells scattered across the epithelial monolayer. In contrast, multicellular glands consist of a hollow tubular duct attached to the cluster of secretory cells located in the deep pockets.
Multicellular glands are formed during early development when epithelial budding...

Classification of Epithelial Tissues: Simple Epithelium

Classification of Epithelial Tissues: Simple Epithelium

Simple epithelium consists of a single layer of cells that lines body cavities and blood vessels. The shape of the cells in the epithelium reflects the function of the tissue. Cells in simple squamous epithelium appear as thin scales with flat, elliptical nuclei that mirror the form of the cell.
Because of the thinness of the cells, simple squamous epithelium is present where the rapid passage of chemical compounds is observed. For example, the endothelium that lines the capillaries and vessels...

Renal Tubule and Collecting Duct

Renal Tubule and Collecting Duct

The renal tubule is divided into three parts: the proximal convoluted tubule (PCT), the Loop of Henle (LOH), and the distal convoluted tubule (DCT).
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Related Experiment Video

Updated: Jul 9, 2026

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

Published on: January 22, 2013

Profiling and classification tree applied to renal epithelial tumours.

Y Allory¹, C Bazille, A Vieillefond

¹AP-HP, Hôpital Henri Mondor, Département de Pathologie, INSERM, IMRB U841, Créteil, France. yves.allory@hmn.aphp.fr

|November 27, 2007

Summary

A minimal subset of immunohistochemical markers, including alpha-methylacyl-CoA racemase (AMACR), cytokeratin 7 (CK7), and CD10, can accurately classify renal tumors. This approach simplifies diagnosis for clear cell, papillary, and chromophobe renal cell carcinomas, and oncocytomas.

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Last Updated: Jul 9, 2026

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

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Published on: January 22, 2013

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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Renal pathology
Immunohistochemistry
Biomarker discovery

Background:

Classifying renal tumors using immunohistochemistry (IHC) is challenging due to numerous candidate biomarkers.
Establishing a minimal antibody panel is crucial for efficient and accurate renal tumor subtyping.

Purpose of the Study:

To identify the minimal subset of antibodies required for accurate renal tumor classification.
To evaluate the efficacy of a classification tree model using IHC profiles.

Main Methods:

Immunohistochemical staining of 309 renal tumors (79 clear cell, 88 papillary, 50 chromophobe renal cell carcinomas, and 92 oncocytomas) using 12 antibodies.
Unsupervised hierarchical clustering and Classification And Regression Tree (CART) analysis were employed.

Main Results:

Hierarchical clustering revealed distinct IHC profiles for the four tumor types.
A classification tree using alpha-methylacyl-CoA racemase (AMACR), cytokeratin 7 (CK7), and CD10 achieved high classification rates (86% CC, 87% PAP, 79% CHRO, 78% ONCO).
The classifier aided in diagnosing 22/30 equivocal cases.

Conclusions:

The classification tree method effectively utilizes IHC profiles to create a reliable renal tumor classifier.
A minimal panel of AMACR, CK7, and CD10 offers a practical approach for renal tumor subtyping.