Advanced molecular probing techniques such as single molecule fluorescence in situ hybridization (smFISH) or RNAscope can be used to assess the quantity and spatial location of mRNA transcripts within cells. Quantifying mRNA expression in large image sets usually involves automated counting of fluorescent spots. Though conventional spot counting algorithms may suffice, they often lack high-throughput capacity and accuracy in cases of crowded signal or excessive noise. Automatic identification of cells and processing of many images is still a challenge. We have developed a method to perform automatic cell boundary identification while providing quantitative data about mRNA transcript levels across many images. Comparisons of mRNA transcript levels identified by the method highly correlate to qPCR measurements of mRNA expression in Drosophila genotypes with different levels of Rhodopsin 1 transcript. We also introduce a graphical user interface to facilitate analysis of large data sets. We expect these methods to translate to model systems where automated image processing can be harnessed to obtain single-cell data.

The described method:

• Provides relative intensity measurements that scale directly with the number of labeled transcript probes within individual cells.
• Allows quantitative assessment of single molecule data from images with crowded signal and moderate signal to noise ratios.


This is the publishers version of Pharris, Matthew & Wu, Tzu-Ching & Chen, Xinping & Wang, Xu & Umulis, David & Weake, Vikki & Kinzer-Ursem, Tamara. (2017). An automated workflow for quantifying RNA transcripts in individual cells in large data-sets. MethodsX. 4. 10.1016/j.mex.2017.08.002.


smFISH, RNAscope, mRNA transcription, machine learning

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