Going beyond looking at images and images in time-series, the Reshape platform allows complex post-processing to be applied across jobs to generate more complex or high-level data automatically.
A combination of a series of image analysis steps and a series of post processings are referred to as an "Analysis pipeline", and takes in a given set of plate types while outputting a certain set of quantifications.
As an example, we have 9 petri dishes with broccoli seeds germinating on them. Each column represents 3 replicate samples. Across each row is a gradient of sodium chloride concentration applied to the agar.
For each seed, the seed and shoot is detected separately and the size is quantified, as shown in the image below by orange mask indicating shoots, blue mask indicating seeds:
A post-processing is applied which automatically calculates the cumulative germination rate over time (lightly coloured errors indicate the error bars across the replicates). The dashed lines indicate the mean germination time for each treatment.
Additionally, the total root area is calculated over time on each plate, normalised against the number of seeds:
This is just one example, but represents the power of robust, automatic quantification of complex phenotypes, combined with a framework for building analysis pipelines. Using this infrastructure, it is possible to automate a wide range of assays, including
- Seed germination, leaf disk and plant health/growth assays based on phenotypes such as time to germinate, cumulative germination over time, root/shoot length, discoloration/disease phenotype presence and more
- Insect growth and mortality assays based on phenotypes such as growth rate, time to hatch, time to death and activity
- Halo assays such as quantifying the inhibition patterns induced by various treatments or in co-culture experiments as well as enzymatic breakdown assays
Comments
0 comments
Article is closed for comments.