1. Bacterial Counting:
Quick, easy and accurate?
Kunnen, T. H.
Moodley, G. K.
Robertson-Andersson, D. V.
University of KwaZulu-Natal, School of Life Science
2. Overview
Introduction
• Microbial loop
• Bacterial numbers and
biomass
• Image analysis
• Freeware
• Macro coding
Conclusions
• Advantages vs. Disadvantages
• Conservation efforts
• Other applications
Materials and Methods
• Macro coding
• Repeated automated counting
and sizing
• Binary Segmentation
• Testing the system
Results and Discussion
• Human data vs. Automatic
data
• Time differences
3. Introduction
• The Microbial Loop
• Cyclic interaction
• Trophic linkages
• Bacterial numbers,
biomass and
productivity
• Nutrient cycling
Figure 1: The microbial loop as conceptualized by Landry
and Kirchman (2002)
7. Introduction contd...
• CellProfiler (Carpenter et al., 2006; + 3340
more journal articles)
• Headed by Anne Carpenter
• Whitehead Institute, USA
• Pipelines
• CellC (Selinummi et al., 2005; + 79 more journal
articles)
• Written by Jyriki Selinummi for ISB
Seattle
• Calibration
• Wählby Lab (Sadanandan et al., 2016)
• Headed by Carolina Wählby
• Uppsala University, Sweden
• 2013 + 2015
• “I'm not sure how well I calibrated
the analysis for size”
8. Introduction contd...
• Limitations of freeware
• On point functionality
• Outdated software and hardware
• Limited or no technical support
• Website closed down / domain inaccessible
• Author (s) / programmer no longer available
www4
www5
www6
9. Introduction contd...
• Image analysis based on Binary Segmentation
• Automated image analysis by binary segmentation (Krambeck et
al., 1981)
• Commercially available image analysis software – Image Pro
Plus (IPP)
• Macro scripting does what it is told and has the potential to
save time and reduce human bias
10. Materials and Methods
• Coding for automated Z-
stacking of unfocused
images using IPP EDF
(Extended Depth of Field)
This is 48 lines of code
of the 2145 = 2%
11. Materials and Methods contd...
• Coding for repeated
counting and sizing of
bacteria within existing
commercially available
image analysis software
IPP
This is 51 lines of code
of the 242 = 21%
15. Materials and Methods contd...
• 8 volunteers given basic training on IPP
• 60 repeated random bacterial images were supplied to each
volunteer
• Volunteers required to time themselves while counting and
sizing (length and width) “objects” they classify as being
bacterial cells
• Mandatory 2/3 day break
• 10 repeated random bacterial images extracted from the 60 and
volunteers required to time themselves while they re-count
and size
• Directly after, volunteers used the IPP macro to automatically
count and size “objects” within threshold limits (27-87) using
increments of 10
Testing the system
16. Results and Discussion
• No difference between human vs. automated
analysis for numbers and biomass overall
• Mean time reduction of total time (1136.83 %)
and time per cell (822.25 %) of for 8
volunteers for automated analysis
• Equates to average total time differences of
5.06 hr manual vs. 26.71 min auto
• Real time of 2 days vs. 2 hr
17. Results and Discussion
• Colour blindness – Surprising outcome!
• One volunteer was color blind
• Significant impact on segmentation selection
• 86 %
www9-11
18. Conclusions
Disadvantages Advantages
Manual Automated
Slow
Become narrow
minded when
counting
Some images
require editing
Become stricter Non-specific
Miss cells
entirely
Accurate counts
vs. accurate
biomass
Am I counting
individual cells?
Are individual
cells being
counted?
Manual Automated
You know what
you counted
Fast and
relatively easy
Judge individual
cells accordingly
Reproducible
It counts and
sizes what you
tell it to
Non-specific
Reduces the
influence of the
halo effect
21. Conservation Efforts
Wastewater
www12 www13
• Wastewater effluent testing in conjunction with BOD and COD
• BOD: up to 20 days to test
• COD: less time, requires strong oxidising chemicals
23. Conservation Efforts
• All macros are currently being applied in the area of
microplastic research
• Mullet
• Sea Urchins
• Mussels
• Biofilm growth
• All macros also being applied to:
• Abalone aquaculture (health and safety)
• Kosi Bay ecosystem
24. Additional Applications
• New macros are being attempted to track and trace ragged
tooth shark fingerprint markings
• Assessment of bacterial loading
• Rivers
• Estuaries
• Oceans
• Landfill leachate assessment of bacterial loading
• General water quality assessments
• Microplastic counting and sizing
25. CAN COMPUTERS COUNT BACTERIA?
Simpler Better Faster
Thank you
Acknowledgements
Thank you to the MACE lab volunteers and to the NRF for funding
this project. Thanks also go to Theo van Zyl, Riaan Rossow,
Bertrand Denoix and Kevin Payne.
26. References
• Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H.,
Lindquist, R. A., Moffat, J., Golland, P. and Sabatini, D. M. 2006. CellProfiler: image analysis software for
identifying and quantifying cell phenotypes. Genome Biol, 7 (10). R100.
• Eduard , W., Blomquist, G., Nelson, B. H., Heldal, K. K. 2001. Recognition errors in the quantification of micro–
organisms by fluorescence microscopy. Annals of Occupational Hygiene 45: 493–498.
• Krambeck, C., Krambeck, H. J. and Overbeck, J. 1981. Microcomputer assisted biomass determination of
plankton bacteria on scanning electron micrographs. Applied and Environmental Microbiology, 42. 142-149.
• Landry, M. R. and Kirchman, D. L. 2002. Microbial community structure and variability in the tropical Pacific.
Deep-Sea Research II, 49. 2669-2693.
• Sadanandan, S. K., Baltekin, Ö, Magnusson, K. E. G., Boucharin, A., Ranefall, P., Jaldén, J., Elf, J. and Wählby, C.
2016. IEEE Journal of Selected Topics in Signal Processing, 10 (1). 174-184,
• Selinummi, J., Seppälä, J., Yli-Harja, O. and Puhakka, J. 2005. Software for quantification of labeled bacteria
from digital microscope images by automated image analysis. BioTechniques, 39. 859-863.
Full list of internet images and GIF’s available upon request
Editor's Notes
The microbial loop is as its name suggests an integrated loop within the microbial fraction of aquatic microorganisms.
It is the interaction between bacteria, ciliates, flagellates, microzooplankton and zooplankton and the linking to the primary producers.
Although all parts have a role to play, the core are the bacteria and their ability to recycle waste organic matter (which includes all essential macro and minor nutrients) into a form that is usable by incorporating it into their biomass.
In this way nutrients and essential growth limiting nutrients are not lost from the system, but are recycled up the food web to higher trophic organisms.
The microbial loop is as its name suggests an integrated loop within the microbial fraction of aquatic microorganisms.
It is the interaction between bacteria, ciliates, flagellates, microzooplankton and zooplankton and the linking to the primary producers.
Although all parts have a role to play, the core are the bacteria and their ability to recycle waste organic matter (which includes all essential macro and minor nutrients) into a form that is usable by incorporating it into their biomass.
In this way nutrients and essential growth limiting nutrients are not lost from the system, but are recycled up the food web to higher trophic organisms.
The microbial loop is as its name suggests an integrated loop within the microbial fraction of aquatic microorganisms.
It is the interaction between bacteria, ciliates, flagellates, microzooplankton and zooplankton and the linking to the primary producers.
Although all parts have a role to play, the core are the bacteria and their ability to recycle waste organic matter (which includes all essential macro and minor nutrients) into a form that is usable by incorporating it into their biomass.
In this way nutrients and essential growth limiting nutrients are not lost from the system, but are recycled up the food web to higher trophic organisms.
Krambeck et al. 1981
I wrote a series of macro scripts for the commercially available image analysis software Image Pro Plus for repeated counting and sizing of Z stacked images
Although these macros do automate the counting and sizing, no computer software, except AI, can gauge what is a bacterial cell and define the limits of what to count.
But will get to that a bit later
I wrote a series of macro scripts for the commercially available image analysis software Image Pro Plus for repeated counting and sizing of Z stacked images
Although these macros do automate the counting and sizing, no computer software, except AI, can gauge what is a bacterial cell and define the limits of what to count.
But will get to that a bit later
As this example shows, the steps involve taking a Z stacked image, converting this image into greyscale and then the user selects a histogram segmentation level telling the system what must be counted.
Now you will notice that a segmentation level of 77 has been selected and all these cells have not been counted
The segmentation level needs to be changed based on environmental situations, different sample preparations or even to much background light
Irrespective of the reason, the level needs to be changed to accommodate counting and sizing of as many identified cells as possible
Previously I said that the image needs to be converted into a greyscale and that is because the system works based on binary segmentation and the user input comes from the histogram selection of that binary segmentation.
The big peak is all the background noise and everything on the right is possible data
Now this histogram changes with the sample, sometimes the peak will be more to the right, sometimes to the left this is where the user comes in.
By changing the histogram selection from 67 to 87, the user has told the system to reduce the amount of background emission to be included in when identifying objects and has therefore become more accurate.
To test whether this automatic system of counting and sizing is comparable to human manual counting, we took 7 volunteers, including myself
To each volunteer we gave the same randomly selected 60 images, which were randomly mixed for each volunteer
They first (after some training) manually counted all objects that they perceived as a bacterial cell
Secondly, after a few days break they re-counted the same randomized 10 images from the original pool.
And finally they used the macro script to automatically count and size, drawing on their manual sizing skills to determine what should be counted.
Previously I said that the image needs to be converted into a greyscale and that is because the system works based on binary segmentation and the user input comes from the histogram selection of that binary segmentation.
The big peak is all the background noise and everything on the right is possible data
Now this histogram changes with the sample, sometimes the peak will be more to the right, sometimes to the left this is where the user comes in.
By changing the histogram selection from 67 to 87, the user has told the system to reduce the amount of background emission to be included in when identifying objects and has therefore become more accurate.
To test whether this automatic system of counting and sizing is comparable to human manual counting, we took 7 volunteers, including myself
To each volunteer we gave the same randomly selected 60 images, which were randomly mixed for each volunteer
They first (after some training) manually counted all objects that they perceived as a bacterial cell
Secondly, after a few days break they re-counted the same randomized 10 images from the original pool.
And finally they used the macro script to automatically count and size, drawing on their manual sizing skills to determine what should be counted.
What we found was that there was a significant comparable result manual vs automated analysis for both counting and sizing cells, statistically speaking there was no difference between the two
Where there was a significant difference was in the time
Averaged amongst all 7 volunteers, using the same 60 images, and using automated analysis, was 1155% faster compared to manual analysis!
To put this in terms of pure counting time is 4,84 hrs vs 26,92 mins
And a real time of 2 days vs 2 hrs
What we found was that there was a significant comparable result manual vs automated analysis for both counting and sizing cells, statistically speaking there was no difference between the two
Where there was a significant difference was in the time
Averaged amongst all 7 volunteers, using the same 60 images, and using automated analysis, was 1155% faster compared to manual analysis!
To put this in terms of pure counting time is 4,84 hrs vs 26,92 mins
And a real time of 2 days vs 2 hrs
What we found was that there was a significant comparable result manual vs automated analysis for both counting and sizing cells, statistically speaking there was no difference between the two
Where there was a significant difference was in the time
Averaged amongst all 7 volunteers, using the same 60 images, and using automated analysis, was 1155% faster compared to manual analysis!
To put this in terms of pure counting time is 4,84 hrs vs 26,92 mins
And a real time of 2 days vs 2 hrs
What we found was that there was a significant comparable result manual vs automated analysis for both counting and sizing cells, statistically speaking there was no difference between the two
Where there was a significant difference was in the time
Averaged amongst all 7 volunteers, using the same 60 images, and using automated analysis, was 1155% faster compared to manual analysis!
To put this in terms of pure counting time is 4,84 hrs vs 26,92 mins
And a real time of 2 days vs 2 hrs
What we found was that there was a significant comparable result manual vs automated analysis for both counting and sizing cells, statistically speaking there was no difference between the two
Where there was a significant difference was in the time
Averaged amongst all 7 volunteers, using the same 60 images, and using automated analysis, was 1155% faster compared to manual analysis!
To put this in terms of pure counting time is 4,84 hrs vs 26,92 mins
And a real time of 2 days vs 2 hrs