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Endocrine Signaling

Endocrine Signaling

Endocrine cells produce hormones to communicate with remote target cells found in other organs. The hormone reaches these distant areas using the circulatory system. This exposes the whole organism to the hormone but only those cells expressing hormone receptors or target cells are affected. Thus, endocrine signaling induces slow responses from its target cells but these effects also last longer.

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Related Experiment Video

Updated: Jul 18, 2025

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

Published on: September 30, 2018

Machine Learning Methods for Endocrine Disrupting Potential Identification Based on Single-Cell Data.

Zahir Aghayev^1,2, Adam T Szafran³, Anh Tran^4,5

¹Department of Chemical and Biomolecular Engineering, University of Connecticut, Storrs, CT.

Chemical Engineering Science

|August 28, 2023

Summary

This summary is machine-generated.

Environmental disasters increase exposure to toxic chemicals. A new computational framework accurately predicts endocrine disruption from chemical mixtures, aiding rapid risk assessment and mitigation strategies.

Keywords:

Classification analysis Endocrine disrupting chemicals Estrogen receptor activity High throughput microscopy Machine learning Predictive modeling

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Last Updated: Jul 18, 2025

Single-cell Transcriptomic Analyses of Mouse Pancreatic Endocrine Cells

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Methods to Test Endocrine Disruption in Drosophila melanogaster

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Published on: July 3, 2019

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Area of Science:

Environmental Science
Toxicology
Computational Biology

Background:

Human populations face continuous exposure to toxicants, with risks amplified by environmental catastrophes.
Hazardous chemical mixtures from disasters pose significant threats to human and ecological health.
Assessing endocrine disruption, particularly via the estrogen receptor alpha (ERα), is crucial for mitigating adverse health effects.

Purpose of the Study:

To develop a rapid, data-driven classification framework for assessing the endocrine-disrupting potential of environmental compounds.
To create tools for informed decision-making in mitigating chemical exposure risks after environmental disasters.

Main Methods:

Utilized high-content, high-throughput microscopy-based biosensor assays to measure estrogenic transcriptional activity at the single-cell level.
Combined computational modeling with experimental analysis, employing Principal Component Analysis (PCA) for descriptor projection.
Applied nonlinear machine learning algorithms, including Support Vector Machines (SVM) and Random Forest classifiers, after rigorous data preprocessing.

Main Results:

Developed a classification framework achieving over 96% accuracy in predicting ERα agonists and antagonists for unseen chemicals.
Demonstrated the critical role of preprocessing and PCA in noise reduction and pattern identification within complex biological datasets.
Successfully distinguished between estrogen receptor agonists and antagonists using image analysis descriptors and machine learning.

Conclusions:

The developed data-driven framework provides a highly accurate method for assessing endocrine-disrupting potential of environmental chemicals.
Computational modeling and machine learning offer powerful tools for rapid risk assessment of chemical mixtures following environmental disasters.
This approach aids in understanding and mitigating the health impacts of endocrine-disrupting compounds in diverse environmental scenarios.