Abstract
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.
Highlights
To identify individual photoreceptor neurons among distinct ommatidia and quantify mRNA expression levels in each neuron, we have developed a set of algorithms that we designate Cell-by-Cell Relative Integrated Transcript (CCRIT) Quantification
Overall the workflow of the cell relative integrated transcript (CCRIT) method can be summarized as follows: 1) Create maximum intensity projection (MIP), 2) Group MIP intensities into 100 groups, 3) Construct nucleus mask and identify photoreceptors, 4) Construct background mask and assign cells, 5) Apply masks to single molecule fluorescence in situ hybridization (smFISH) multi-stack, 6) Gaussian filter the remaining smFISH signal, 7) Report integrated smFISH signal intensity in each cell
Our CCRIT algorithm will intelligently identify the Rhodopsin 1 (Rh1)-negative photoreceptors for use as internal baselines of RNA expression within each ommatidium
Summary
An automated workflow for quantifying RNA transcripts in individual cells in large data-sets. A noisy image of fluorescently-labeled mRNA transcripts can be analyzed by Cell-by-Cell Relative Integrated Transcript (CCRIT) Quantification to automatically identify cells and cell clusters and quantify each cell’s mRNA expression level
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