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Updated: Jun 21, 2026

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Live Imaging Followed by Single Cell Tracking to Monitor Cell Biology and the Lineage Progression of Multiple Neural Populations

Published on: December 16, 2017

Information management for high content live cell imaging.

Daniel Jameson¹, David A Turner, John Ankers

¹Manchester Centre for Integrative Systems Biology, School of Chemistry, and Manchester Interdisciplinary Biocentre, University of Manchester, 131, Princess St, Manchester, M1 7DN, UK. daniel.jameson@manchester.ac.uk

BMC Bioinformatics

|July 23, 2009

Summary

This summary is machine-generated.

A new data management solution indexes high-throughput live-cell imaging data, including automated analysis of protein translocation events. This system enhances data retrieval and promotes data sharing for functional proteomics and genomics research.

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Area of Science:

Cellular biology
Proteomics
Genomics

Background:

High-content live-cell imaging generates large datasets, often stored inefficiently.
Existing solutions primarily manage image storage, not derived experimental data.
A need exists for managing metadata and results from live-cell imaging experiments.

Purpose of the Study:

To design and implement an information management solution for high-content live-cell imaging data.
To facilitate the indexing of experimental metadata and results.
To address the challenge of ad-hoc data storage in live-cell imaging studies.

Main Methods:

Developed a data model and information management system.
Integrated an algorithm for automatic annotation of experimental results.
Focused on experiments measuring fluorescently labeled protein translocation.

Main Results:

Implemented a functional data repository for high-content live-cell imaging data.
The automated annotation algorithm successfully identifies translocation timings and oscillations.
The algorithm demonstrated robust performance on diverse, previously unseen data.

Conclusions:

The developed repository effectively indexes and manages high-throughput imaging data.
Automated analysis incentivizes data contribution and sharing.
The solution is adaptable for other functional proteomics and genomics experiments.