Fluorescence Microscopy (High-Content Imaging) SPP for the Evotec Opera automated microscope

From LipidomicsWiki

Input

Automated analysis of lipid droplets requires two microscopy channels. In the first, blue channel, cell nuclei and cytoplasm are detected. In the second, green channel, the lipid droplets are detected. From each well of a multi-well plate, the microscope takes 18 fields times two channels. These 36 images are saved as a stack in a single FLEX file. As an example, the two corresponding images of one field are shown below. Left side: blue channel with nuclei and cytoplasm. Right side: green channel with lipid droplets

Output

The primary images are analyzed to give well-based and cell-based primary parameters.

Well based parameters:

Mean number of lipid droplets per cell (LDs)
Median size of LDs
Median fluorescence intensity of LDs
Total fluorescence intensity

Cell-based parameters:

Number of lipid droplets (LDs)
Size of each single LD
Fluorescence intensity of each single LD
Number of LDs in cluster
Clustered ratio
Fluorescence ratio in cluster
Number of intra-nuclear LDs

These primary parameters are further analyzed to show the

total data distribution (box plot)
assay robustness (coefficient of variance)
statistical significance of differences between wells (ANOVA)

These data are represented by heat maps to allow easy visualization

Processing

Image processing:

All images are processed with the Evotec A Capella software package.

First, in the blue channel the areas of highest signal intensity are detected as cell nuclei. Second, in the blue channel, the areas of medium intensity are detected as cytoplasm (see the images below).

Third, in the green channel, areas of highest signal intensity are detected as droplet clusters. In the clusters, local maxima are detected to define the single lipid droplets (see image below).

To give the desired output parameters (see above), the droplets are assigned to a cluster and a cell, counted, and analyzed for intensity and size. The output is then collected and saved as txt-files for well-based data and csv-files for cell-based data (see examples below).

Data processing:

These raw data are further processed using the MATLAB software statistical toolbox to generate boxplots, heat-maps, and multi-comparison output graphs to show coefficient of variance and statistical significance (see examples below).