Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Regulation of Angiogenesis and Blood Supply

Regulation of Angiogenesis and Blood Supply

Rapidly dividing tumors, embryos, and wounded tissues require more oxygen than usual, lowering the oxygen concentration in the blood. At low oxygen or hypoxic conditions, an oxygen-sensitive transcription factor called the hypoxia-inducible factor 1 or HIF1 is activated. HIF1 is a dimeric protein of alpha (ɑ) and beta (β) subunits. Under optimal oxygen conditions, HIF1β is present in the nucleus while HIF1ɑ remains in the cytosol. HIF1ɑ is hydroxylated by prolyl...

Aquaporins

Aquaporins

Aquaporins or AQPs are a family of integral membrane proteins whose primary function is to transport water, while some called aquaglyceroporins also transport glycerol. In addition, aquaporins have also been suspected to be involved in transporting volatile substances, such as carbon dioxide and ammonia, across membranes. Such AQPs that act as gas channels are often highly expressed in cells involved in the gaseous exchange, such as red blood cells, epithelial cells, and pulmonary capillaries.

Adaptive Mechanisms in Cancer Cells

Adaptive Mechanisms in Cancer Cells

Cancer cells accumulate genetic changes at an abnormally rapid rate due to the defects in the DNA repair mechanisms. From an evolutionary perspective, such genetic instability is advantageous for cancer development. Mutant cell lines accumulate a series of beneficial mutations that contribute to their progression into cancer.
Some of the advantages that cancer cells have on normal cells include - enhanced ability to divide without terminally differentiating, induce new blood vessel formation,...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Prostate cancer tissue mapping and stratification using DRAQ5 and Eosin fluorescent labels integrated with AI classification and segmentation algorithms.

PloS one·2026

Same author

Association of DDR pathway proteins and breast cancer risk in a Pakistani population.

Future oncology (London, England)·2025

Same author

Stroma-derived extracellular vesicle mRNA signatures inform histological nature of prostate cancer.

Journal of extracellular vesicles·2021

Same author

Dog breeds and body conformations with predisposition to osteosarcoma in the UK: a case-control study.

Canine medicine and genetics·2021

Same author

Design of experiment (DoE)-driven <i>in vitro</i> and <i>in vivo</i> uptake studies of exosomes for pancreatic cancer delivery enabled by copper-free click chemistry-based labelling.

Journal of extracellular vesicles·2020

Same author

Correction: Development and characterisation of acoustofluidic devices using detachable electrodes made from PCB.

Lab on a chip·2020

Search research articles

Related Experiment Video

Updated: Nov 7, 2025

Co-immunoprecipitation Assay Using Endogenous Nuclear Proteins from Cells Cultured Under Hypoxic Conditions

Co-immunoprecipitation Assay Using Endogenous Nuclear Proteins from Cells Cultured Under Hypoxic Conditions

Published on: August 2, 2018

Aquaglyceroporin-3's Expression and Cellular Localization Is Differentially Modulated by Hypoxia in Prostate Cancer

Andreia de Almeida¹, Dimitris Parthimos¹, Holly Dew¹

¹Tissue MicroEnvironment Group, Division of Cancer and Genetics, School of Medicine, Cardiff University, Cardiff CF14 4XN, UK.

|April 30, 2021

Summary

Aquaporin 3 (AQP3) expression varies in prostate cancer cells, impacting their adaptation to low oxygen (hypoxia). Machine learning identified cellular texture as a key indicator of hypoxia, more so than AQP3 localization.

Keywords:

aquaglyceroporin-3 aquaporins hypoxia prostate cancer translocation

More Related Videos

Tracking Hypoxic Signaling within Encapsulated Cell Aggregates

Tracking Hypoxic Signaling within Encapsulated Cell Aggregates

Published on: December 16, 2011

Induction of Hypoxia in Living Frog and Zebrafish Embryos

Induction of Hypoxia in Living Frog and Zebrafish Embryos

Published on: June 26, 2017

Related Experiment Videos

Last Updated: Nov 7, 2025

Co-immunoprecipitation Assay Using Endogenous Nuclear Proteins from Cells Cultured Under Hypoxic Conditions

Co-immunoprecipitation Assay Using Endogenous Nuclear Proteins from Cells Cultured Under Hypoxic Conditions

Published on: August 2, 2018

Tracking Hypoxic Signaling within Encapsulated Cell Aggregates

Tracking Hypoxic Signaling within Encapsulated Cell Aggregates

Published on: December 16, 2011

Induction of Hypoxia in Living Frog and Zebrafish Embryos

Induction of Hypoxia in Living Frog and Zebrafish Embryos

Published on: June 26, 2017

Area of Science:

Cell Biology
Cancer Research
Biophysics

Background:

Aquaporins facilitate rapid cell adaptation to osmotic and metabolic changes.
Aquaglyceroporins are vital for supplying cancer cells with glycerol for metabolic requirements.

Purpose of the Study:

To investigate the differential expression and localization of Aquaporin 3 (AQP3) in prostate cancer cell lines.
To determine the role of AQP3 in cellular adaptation to hypoxic conditions.
To validate findings using machine learning classification of cellular morphology and texture.

Main Methods:

Differential expression analysis of AQP3 in LNCaP, Du145, and PC3 prostate cancer cell lines under normoxic and hypoxic conditions.
Immunofluorescence microscopy to assess AQP3 cellular localization.
Machine learning classification using cytoskeletal, nuclear, and cellular texture features.

Main Results:

AQP3 exhibited distinct localization patterns across cell lines: cell membrane and cytoplasm in LNCaP, exclusively cytoplasm in Du145 and PC3.
LNCaP cells demonstrated enhanced growth under hypoxia, while Du145 and PC3 cells showed stress factors, suggesting AQP3's role in plasma membrane adaptation to hypoxia.
Machine learning models accurately classified cell lines and hypoxia exposure, with cellular texture features (73.9% accuracy) being stronger predictors of hypoxic load than AQP3 distribution (60.3%).

Conclusions:

AQP3 plays a role in prostate cancer cell adaptation to hypoxia, with its plasma membrane localization being critical for LNCaP cells.
Cellular morphology and texture features, particularly texture, are robust indicators for identifying cell lines and their response to hypoxic stress.
Machine learning provides a powerful tool for dissecting complex cellular responses and identifying predictive biomarkers in cancer research.