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Automated counting of Drosophila imaginal disc cell nuclei

  • 0Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA.
Biology Open +

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February 12, 2024

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February 12, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

We developed automated cell counting workflows for dense tissues like the developing fruit fly wing. These methods enable accurate quantification of total cells and specific cell populations for biological studies.

Area Of Science

  • Developmental Biology
  • Image Analysis
  • Computational Biology

Background

  • Automated image quantification has advanced image analysis and statistical power in biological studies.
  • High cellular density in developing tissues, such as the fruit fly wing, has hindered efficient cell counting.
  • Previous workflows struggled with the dense cellular populations in imaginal discs.

Purpose Of The Study

  • To present efficient automated cell counting workflows for quantifying cells in the developing fruit fly wing.
  • To enable accurate cell counting in high-density tissues.
  • To address the challenge of cell counting in complex biological structures.

Main Methods

  • Development of automated image quantification workflows.
  • Application of machine learning algorithms for cell segmentation and counting.
  • Utilizing fluorescent nuclear markers for cell identification in imaginal discs.
  • Training algorithms to distinguish heterozygous and homozygous cells in twin-spot labeling.

Main Results

  • Successful implementation of automated workflows for cell counting in the developing wing.
  • Quantification of total cells and fluorescently labeled clones within imaginal discs.
  • Development of a machine learning workflow for segmenting and counting twin-spot labeled nuclei, overcoming intensity variations.

Conclusions

  • The developed workflows provide efficient and accurate cell quantification for dense tissues.
  • These methods are structure-agnostic and applicable to various tissues with nuclear labeling.
  • The workflows enhance the ability to perform high-throughput analyses in developmental biology research.

Keywords: