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Image analysis of yeasts experiments
Benoît Clavier
1er septembre 2020
Table des matières
1 Experiments and objectives 2
1.1 Theexperiments ......................... 2
1.2 Objective of the macro . . . . . . . . . . . . . . . . . . . . . . 2
2 Presentation of the macro 3
2.1 Technical difficulties . . . . . . . . . . . . . . . . . . . . . . . 3
2.1.1 Trap placement . . . . . . . . . . . . . . . . . . . . . . 3
2.1.2 Shape of the traps . . . . . . . . . . . . . . . . . . . . 3
2.1.3 Small sacrifices . . . . . . . . . . . . . . . . . . . . . . 4
2.1.4 Data management . . . . . . . . . . . . . . . . . . . . 4
2.2 Step1 ............................... 5
2.3 Step2 ............................... 6
2.4 Data analysis - Jupyter Notebook . . . . . . . . . . . . . . . . 7
2.4.1 Normal size determination . . . . . . . . . . . . . . . . 7
2.4.2 Brightness threshold determination . . . . . . . . . . . 8
3 Results 9
3.1 Areadistribution......................... 9
3.2 Brightness distribution . . . . . . . . . . . . . . . . . . . . . . 9
3.3 Circularity distribution . . . . . . . . . . . . . . . . . . . . . . 10
3.4 Summary of every case . . . . . . . . . . . . . . . . . . . . . . 10
4 Results analysis 12
4.1 Dataquality............................ 12
4.2 Numberofcells.......................... 12
4.3 GFPestimation.......................... 12
4.4 Real value of the area . . . . . . . . . . . . . . . . . . . . . . 13
5 Conclusion 14
6 Annex 15
6.1 Step 1 of the macro - Code . . . . . . . . . . . . . . . . . . . 15
6.2 Step 2 of the macro - Code . . . . . . . . . . . . . . . . . . . 19
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Abstract
The purpose of this short paper is to present the new results of experi-
ments led by the Physical Microfluidics and Bioengineering team in Institut
Pasteur. The results are obtained with an original code for the software
ImageJ®. It automatically analyzes photographs to detect as much cells as
possible without being disturbed by the flaws present on the photograph.
This allows the quick analysis of a large number of cells (up to 150 000 per
experiment), which then gives access to really quantitative results in only a
few minutes, which isn’t possible by hand. After having presented the ex-
periments and the objectives of this project, we will go through the ImageJ
code, which we will call the macro, to see how it works and deals with the
difficulties it encounters. This allows other people to use it on similar expe-
riments, or to adjust it depending on what results they are precisely looking
for.
1 Experiments and objectives
1.1 The experiments
The experiments led by the lab in Institut Pasteur are experiments on
DNA reparation. The principle is to introduce a repeated sequence of nucleo-
tides (called micro-satellites) in the DNA of yeast cells, and then see how
good different endonucleases can repair it. Every experiment is done with
thousands of cells. To observe them all, they are placed in a microfluidic
device where they are trapped in small wells (each well is a 100 µm by 100
µm square). Once the cells are in the microfluidic trap, we put the trap in a
microscope where they are regularly photographed. In order to have a better
monitoring of the development of the cells, we introduce the micro-satellites
in the middle of the GFP gene, which is expressed through the fluorescence
of the cell. Therefore, the microscope takes two pictures every time : one pic-
ture in the bright field, and one picture that only detects the fluorescence of
the cells. This allows to check if the GFP gene has been successfully repaired
by the endonuclease.
1.2 Objective of the macro
After multiple experiments, we can observe some anomalies with some
conditions. One of the main concerns is the GAA-SpCas9 case (GAA is
the microsatellite introduced and SpCas9 is the endonuclease used). In this
case, we can observe that some cells are malformed : they are either not
round, significantly bigger than the others or both. The difficulty to evaluate
the proportion of this kind of malformation by hand is what motivated the
creation of a macro that could efficiently analyze all the cells of an experiment
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to give an effective summary of the cell distribution. The objective is to have
a macro that directly analyzes all the data straight out of the microscope,
and that doesn’t need any hand manipulation. The output of the macro
itself is a simple dataframe (in .csv format). It can then be easily visualized
using diverse tools (such as Matlab ®or Excel ®), here I wrote a Jupyter
Notebook to visualize automatically what we want.
2 Presentation of the macro
2.1 Technical difficulties
To get a better understanding of the macro, it is interesting to look at
what difficulties it had to face first, and how those changed the construction
of the macro.
2.1.1 Trap placement
Since there are about 1500 traps in the device and we want to photograph
them all in a relative short period of time (less than 20 minutes), we can’t
take a photo of every single trap. Therefore, every photograph contains a
certain number of traps, at different places.
Figure 1 – two different photographs of the CGG-Cpf1 experiment
Since we don’t want to do any hand manipulation, the macro has to
detect the traps alone. This is the first task the macro has to fulfill.
2.1.2 Shape of the traps
The first task leads to some problems when cells are one the sides of the
trap. To solve those, a good solution is to convexify the shape of the traps.
This is the second task of the macro. It helps recover almost all cells (except
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those right in the corners sometimes). There will be an error estimation after
the presentation of the macro.
Figure 2 – Photograph - detected shape of trap - convexified shape of trap
GAA - SpCas9 experiment
2.1.3 Small sacrifices
Another small issue we encounter are the bright lines that appear on the
sides of the traps. They are as bright as cells and therefore can be mistaken
as cells, because we use a brightness threshold to detect them. To avoid this,
the macro erodes the sides of the square enough to crop those lines out. We
may lose some cells that were in the region but this prevents a big error due
to those lines.
Figure 3 – Trap with bright lines on the side - Eroded shape of the trap
The red highlighted part on the left is detected as cells
GAA - SpCas9 experiment
2.1.4 Data management
To simplify the data management, I divided the macro in two separate
steps : one that "cleans" the data (isolating the traps and highlighting the
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cells), and one that processes the cleaned data. This allows to run different
analyses on the same data without having to clean it every time . This is
practical and time saving since the initial data is heavy (about 2GB per
experiment) and the first step of the macro takes time (up to 10 minutes for
one experiment).
2.2 Step 1
The commented code of the macro can be found in the annex to this
paper. Now that we know how it has been constructed, let’s break down how
it operates.
Once we isolated the inside of the traps, the highlighting of the cells is
done via a simple threshold with always the same values. After converting
the photo to an 8-bit image, we can observe that the background value is
never above 80. The cells are always above 110, therefore we use the value
100 for the threshold.
We obtain the masks of the cells as a binary image (the pixel has value 1
if it is inside a cell and 0 otherwise). If we multiply this image by the photo
of the green emissions we obtain a representation of the fluorescence of the
cells.
Figure 4 – Binary representation of cells - Green light emissions
The two ingredients for the final image
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