4. Slide Staining Project (SSP)
Why is this project important?
Currently, there is no rigid protocol that can be followed to get
the best stained slides (as primary focus is to make parasite visible)
There are a lots of stains available in the market but we don’t
know which one we should prefer
There is a very large variation in staining intensities for the slides
stained here at GMC lab
Stained slides have been observed to lose the stain over time
This project aims at figuring out the best way of staining (thin
blood smears) as well as the best storage conditions in order to
retain the staining for a long time
5. Slide Staining Project
Staining procedure was divided in the following parts and analysed briefly:
Making Smear – Drying 1 – Fixing – Drying 2 – Staining – Washing –
Drying 3 – Storage
1. Making Smear – Experienced Person Required
2. Drying 1 – Air drying! Is time important? Or just dry till all the moisture is lost..?
3. Fixing – Methanol. Time of fixing?
4. Drying 2 – Same issues as Drying 1. what if we keep it for weeks / years?
5. Staining – Method of staining (Horizontal surface Vs. Coplin Jars) / Amount of
Stain / Use of pipette or dropper to drop the stain / Diluting preferred or not?
6. Washing – pH of the solution / Buffer Vs. Distilled Vs. Tap water?
7. Drying 3 – Dry enough to remove moisture
8. Storage – Methods and Conditions of storage
Red – Problems | Black – No Problem
6. SSP: Methodology
PART A : Conducting the Experiments
I tried to optimize each of the above steps in the staining
procedure by conducting small experiments for each step
Parameters that were varied are time, method of washing,
method of staining, etc.
While experimenting for a particular staining step, all the
other staining steps we kept constant
The resulting slides were then compared for staining intensity
7. SSP: Methodology
PART B : Quantification of Images
5 images were taken from each slides as a representation of
the slide
ImageJ software was used to quantify the images in term of
Red channel Mean Grayscale values of cells and glass
Basically:- Select the cells in the image Measure the
intensity; Select the portion excluding cells (glass) Measure
the intensity
Lower the intensity, the darker are the cells i.e. higher is the
effect of staining
Higher the difference between the glass and cells intensities,
easier to distinguish the cells from glass
8. SSP: Methodology
PART C : Storage Conditions
Following are the parameters which were considered for
storage conditions:
Temperature of Storage: Room Temp Vs. 4 °C
No Covering Vs. Coverslips Vs. Oil Immersion Storage
Along with the above conditions, some of the slides were
re-stained to see if helps
Stained slides are stored currently in the respective conditions
and data for their current staining intensities is recorded
These slides should be taken out after a year and quantified
again for the staining intensity using the new images
In this way we can find out the % deterioration in the staining
intensity associated with each storage condition
9. SSP: Summarized Results
Stain Giemsa Hemacolor Giemsa Improved
Drying 1 Time 0-5 minutes 0-5 minutes 0-5 minutes
Fixing Time 1-5 Seconds 5 Seconds 1-5 Seconds
Drying 2 Time 30 - 60 minutes
Staining Method Horizontal Coplin Coplin
Staining Time 20 minutes 3 Sec | 15 Sec 20 minutes
Washing Method Buffer solution/ Distilled water in squeeze bottle
Drying 3 Time Till Drying
10. SSP: Key Points
Quality-wise: Giemsa Improved > Hemacolor > Giemsa
Drying 1 time can be reduced to couple of minutes
No fixing works only for Giemsa stain
Do not dry after fixing near the sink
Drying 2 time is very important in terms of stain absorption
Plastic rack use as a horizontal surface is not recommended
Changing the washing method to either distilled water or buffer
solution in squeeze bottle
Fixing solution often gets contaminated with the marker ink
Slides should not be stacked up until completely dried
Giemsa Improved stain is easily lost if wiped harshly
12. Color Coding Project (CCP)
Why is this project important?
Microsatellite experiments produce a lot of data. It’s very
difficult to make sense out of the whole bunch of data just by
looking at numbers
Better way is to convert the data into a color coded image
which uses different color scales to point out significant
differences in the data
Being automated, It reduces the manual tasks tremendously
and saves time and efforts
13. CCP: Methodology
MATLAB (Matrix Laboratory) is numerical computing
environment and fourth generation programming language
I have designed a program with the functions such as:
- Specifying sheet number from the excel file to color code
- Separate one patient sample from another
- color code the data from specified experimenters only
- Assign different color scales to different locus
- Specify the sensitivity
Excel files were first formatted into a specific form and were
then used as an input to the code
14. CCP: Methodology (cont.)
The program basically:
Imports data from excel file
Formats data in Matlab workspace
Measure the upper and lower limits of the data per locus
Scale the data in divisions proportional to the range of data
Assign different color scales to different locus
Assign one color for each division
Create figure with axes as the sample ID’s and locus names
Display the image
17. Automated Parasite Density Calculation
Why is this project important?
Counting Parasite Density for high parasite densities in quite
tedious and is very tiring when a large number of slides are to
be examined
This method uses multiple images for a slide as an input to the
software and counts the number of parasites and WBC for
each images and saves the information
This data can then be easily used to calculate the parasite
density
18. Methodology
ImageJ is a public domain, Java based image processing
program developed at National Institutes of Health (NIH)
User written plugins (here, programming codes) makes it very
easy to solve image processing related tasks
The code I’ve compiled sets intensity thresholds as well as size
thresholds to count the numbers of parasites as well as WBC’s
in a particular image
19. Methodology
Images from yellow light microscope at light intensity 2 seems
better for the program
Images are taken at 2x digital zoom using the digital camera
(microscope camera would help here)
And then cropped into rectangles
There are multiple ways of selecting the parasites which can
be found out just by playing around with the software
The one I’m using now is the one which splits the images into
RGB (i.e. Red, Green and Blue) channels and works on the G
channel for parasites and R channel for WBC’s
22. Results
I performed a manual counting on first 2 images which was:
1st Image: 121 Parasites
2nd Image: 135 Parasites
While the program counted:
1st Image: 110 Parasites
2nd Image: 145 Parasites
The size limits should be optimised by running the program on
multiple images and slides
This program will work good on high parasite density slides
24. Parasitemia Counting Analysis
What is Percentage Parasitemia?
Percentage Parasitemia is the percentage of infected RBCs in
the total RBCs
It essentially means that if 10 out of 100 RBCs are infected the
parasitemia is 10%
Why are reticles used in counting Parasitemia?
Counting all the RBCs is tedious and takes a lot of time
Reticles allows us to count the RBC in a smaller area and
then scale up the count to get value close to what real count
would have been
25. Parasitemia Counting Analysis
Miller Reticles
Miller reticle provides you with 2 squares in
which area of the smaller square is the
known fraction of the bigger square area
For example, in the right side pictures, the
top one is 1:5 and the bottom one is 1:9
It essentially means that if the area of the
smaller square in the top picture is 10 units,
the area of the bigger square is 50 units
And in the bottom image if area of smaller
square is 10, then that of bigger is 90
26. Parasitemia Counting Analysis
How to use these Reticles?
RBCs occupy area on the slide
Hence, we can estimate the number of RBCs in the bigger
square just by counting the RBCs in the smaller square and
then multiplying with the area factor
Consider this example of 1:9 reticle where
the RBCs are uniformly distributed
RBCs in the smaller square: 4
RBCs in the larger square: 4*9 = 36
27. Parasitemia Counting Analysis
How to obtain a formula? (for ex. Consider 1:5 reticle)
% Parasitemia = x 100
Infected RBCs
Total RBCs
Infected RBCs in bigger reticle
All RBCs in the bigger reticle
= x 100
=
Infected RBCs in bigger reticle x 100
All RBCs in the smaller reticle x 5
= x 20
Infected RBCs in bigger reticle
All RBCs in the smaller reticle
Area
Factor
28. Parasitemia Counting Analysis
But the problem does not end here!
What about the cells on the edges?
If you come across a Scenario like this you need to have a
protocol
Possible methods:-
Count RBCs on -
1. 2 of the four edges of the reticles
2. more than 50% inside
3. All the edges
4. None of the edges
29. Parasitemia Counting Analysis
Errors because of reticle misinformation!
Multiplying with 25 instead of 20 for 1:5 reticle creates:
(25 – 20) x 100 = 25 % Error (overestimation)
20
Multiplying with 10 instead of 11.11 for 1:9 reticle creates:
(11.11 – 10) x 100 = 9.99 % Error (underestimation)
11.11
30. Parasitemia Counting Analysis
Why is a definite protocol necessary worldwide?
We can explain it in terms of error propagation
Lets start with a case where actual % parasitemia is 10
Error in taking blood sample as representation of patient’s
complete blood. (say 10%) : Parasitemia becomes 11%
Error in scanning a particular area of slide as complete slide
representation. (say 10 %) : Parasitemia becomes 12.1%
Error in reticle ratio because of manufacturing errors (we had
25%) : Parasitemia becomes 15.125%
Error in parasitemia counting because of inaccurate methods
(in extreme case say 15%) : Parasitemia becomes 17.39%
31. Parasitemia Counting Analysis
Why is a definite protocol necessary worldwide?
So in the previous example, the parasitemia which was
actually 10% was estimated as 17.39 %
This is over 70% error
This error could go the other way around too and
underestimate the count
Also not having a definite protocol makes the calculation
individual biased creating differences from person to person as
well as from lab to lab
32. Concluding..
Slide staining project can be followed up for making rigid
conclusions
Some of the results are straight forward and should be
incorporated in daily staining procedures right away
Storage conditions should be monitored after a year
MATLAB is installed in the genomics room computer
Parasite Density Automation can be followed up and would be
very useful after obtaining a size limits
Reticle issue should be sorted out
Accurate parasitemia counting procedure should be adopted
as a protocol
