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Microtubules in Signaling

Microtubules in Signaling

The primary cilium, made up of microtubules, acts as antennae on the cell surfaces for relaying external stimuli into the cells. These fine hair-like structures are present, generally one per cell. These are non-motile cilia in a 9+0 microtubules arrangement, where the central pair of microtubules are absent. The primary cilia arise from the basal body embedded in the cell membrane. Intraflagellar transport (IFT) carries requisite proteins from the cytoplasm to the cilium because the primary...

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Related Experiment Video

Updated: Nov 5, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

Artificial Intelligence Approaches to Assessing Primary Cilia.

Ruchi Bansal¹, Staci E Engle¹, Tisianna K Kamba¹

¹Department of Biology, Indiana University-Purdue University Indianapolis.

Journal of Visualized Experiments : Jove

|May 17, 2021

Summary

This summary is machine-generated.

Artificial intelligence (AI) can now accurately measure cilia length, improving the analysis of cellular signaling pathways. This unbiased, high-throughput method enhances reproducibility in biological research.

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Last Updated: Nov 5, 2025

Artificial Intelligence Approaches to Assessing Primary Cilia

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Published on: May 1, 2021

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Published on: May 15, 2020

Volumetric Imaging and Analysis of Primary Cilia in Musculoskeletal Tissue using the ARL13B-CENTRIN-2 Mouse Model

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Published on: March 28, 2025

Area of Science:

Cell Biology
Bioimaging
Artificial Intelligence in Science

Background:

Cilia are vital microtubule-based cellular structures involved in signaling pathways.
Cilia length is regulated and impacts cellular signaling capacity, but precise measurement is challenging.
Current methods for measuring cilia length are labor-intensive, error-prone, and biased.

Purpose of the Study:

To develop and validate an AI-driven method for accurate and high-throughput analysis of cilia length.
To overcome limitations of manual cilia measurement techniques.
To enable unbiased comparison of cilia characteristics across different biological samples.

Main Methods:

Training an AI module to recognize cilia in diverse biological images (in vivo and in vitro).
Developing applications to analyze cilia length, fluorescence intensity, and co-localization using the trained AI.
Applying the AI tool to analyze cilia in neuronal samples and across different brain regions.

Main Results:

The AI module successfully identified cilia in various sample types.
AI-powered analysis enabled rapid, high-throughput measurement of hundreds of cilia per sample.
The unbiased approach improved confidence and rigor in comparing cilia characteristics between samples and animals.

Conclusions:

AI provides a powerful, unbiased, and reproducible solution for analyzing cilia length and dynamics.
This high-throughput technique is applicable to any cell type and tissue, facilitating robust biological research.
AI-based image analysis is poised to become a standard in scientific research for improved reproducibility.