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Team:Berkeley/Project/Automation
From 2012.igem.org
We can accommodate libraries of over a million members through our MiCodes design scheme. With a data set this large, we needed to develop methods to make MiCodes a high-throughput screening technique. We realized that two aspects of microscopy needed to be automatable: image acquisition and image processing.
If a MiCode library size is very large, it is important to find a time-efficient method of taking many pictures of yeast cells. This summer we dealt with libraries on the order of 10^3 members, and our images were taken with a regular fluorescence microscope. Larger libraries would most likely make use of automated stages to speed up image acquisition.
How the cell segmentation was done in Matlab.
How we created pipelines in Cell Profiler to detect organelles.
We quickly realized that the most essential portion of automation was determining features. In the context of organelle detection, we developed pipelines to collect geometric measurements characteristic of each organelle (actin, cell periphery, vacuolar membrane, and nucleus), and defined specific measurements as our feature set for each organelle.
To acheive this, we imaged a small sample size of about 100 cells with varying phenotypes, and determined feature sets associated with each of the four organelles. We used CellProfiler, an open-source software designed for quantitative analysis of cellular phenotypes. The software is available at their website.
We used this software to build pipelines that demonstrate the feasibility of automated identification of cellular phenotypes.
Pipeline procedure:
1. Separate cell image into its constituent fluorescent channels. Convert cells to grayscale.
2. Identify unspecific "objects", or blobs of high intensity pixels.
3. Determine the features associated with each "object." Measure geometric properties, as well as intensity.
4. Use the measurements taken to classify each "object" as a particular organelle.
5. Output Our software proved capable of successfully identifying MiCoded nuclei 95% of the time. In the future, with a large, already-constructed dataset (a complete MiCode library), we would likely employ machine learning techniques to improve the automated identification.
