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Related Experiment Videos

An automated workflow for quantifying RNA transcripts in individual cells in large data-sets.

Matthew C Pharris1, Tzu-Ching Wu1, Xinping Chen2

  • 1Weldon School of Biomedical Engineering, Purdue University, United States.

Methodsx
|September 22, 2017
PubMed
Summary

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We developed a new method for automated cell identification and mRNA quantification from images, improving accuracy for crowded signals. This technique provides reliable single-molecule data for large-scale biological research.

Area of Science:

  • Molecular Biology
  • Cell Biology
  • Bioimaging

Background:

  • Advanced molecular probing techniques like smFISH and RNAscope quantify mRNA but face challenges with automated cell identification and high-throughput analysis in large image datasets.
  • Conventional automated spot counting algorithms often lack accuracy and capacity for handling crowded signals or significant noise in cellular imaging.

Purpose of the Study:

  • To develop a robust method for automatic cell boundary identification and quantitative mRNA transcript analysis across numerous images.
  • To overcome limitations of existing automated methods in handling complex imaging data with crowded signals and noise.

Main Methods:

  • A novel computational method was developed for automatic cell boundary identification and quantitative mRNA transcript level assessment.
Keywords:
Cell-by-cell relative integrated transcript (CCRIT) quantificationMachine learningRNAscopemRNA transcriptionsmFISH

Related Experiment Videos

  • The method was validated by comparing its mRNA transcript level measurements with quantitative PCR (qPCR) data in Drosophila.
  • A user-friendly graphical interface was created to streamline the analysis of large image datasets.

    • Main Results:

    • The developed method demonstrated high correlation with qPCR measurements for Rhodopsin 1 transcript levels in Drosophila.
    • The technique successfully provided quantitative assessment of single-molecule data, even in images with crowded signals and moderate signal-to-noise ratios.
    • Relative intensity measurements generated by the method scale directly with the number of labeled transcript probes within individual cells.

    Conclusions:

    • The new method enables accurate, automated cell identification and mRNA quantification, significantly advancing high-throughput biological imaging.
    • This approach facilitates the reliable acquisition of single-cell data from complex image sets, applicable to various model systems.
    • The developed tools are expected to enhance the efficiency and accuracy of molecular probing studies in cell biology research.