Bacterial Counting: Quick, easy and accurate?

Kunnen, T. H.Moodley, G. K.

Robertson-Andersson, D. V.

University of KwaZulu-Natal, School of Life Science

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

Introduction• The Microbial Loop

• Cyclic interaction

• Trophic linkages

• Bacterial numbers, biomass and productivity

• Nutrient cyclingFigure 1: The microbial loop as conceptualized by Landry and Kirchman (2002)

Introduction contd…

Figure 2: Adapted simplified marine trophic pyramid (www1)

Introduction contd…

Figure 2: Adapted simplified marine trophic pyramid (www1)

Introduction contd...

• Traditional bacterial enumeration

• Photo enlargement

• Nucleic stains, PC’s

• Image analysis software

• Many freeware options

• Recognition errors in counting cells > 0,75 µm

• 53 %

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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”

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• Outdated software and hardware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

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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

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%

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%

IPP Repeated Automatic Counting

Materials and Methods contd...

Binary segmentation with histogram selection

Background noise

Data

1844 objects

123 objects

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

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

Results and Discussion

• Colour blindness – Surprising outcome!

• One volunteer was color blind

• Significant impact on segmentation selection

• 86 %

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Conclusions

Disadvantages Advantages

Manual AutomatedSlow

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 AutomatedYou 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

Conservation Efforts

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Conservation EffortsWastewater

• Principal of the microbial loop

• Recycle our water resources

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Aerobic digestion Dried sludge

Conservation EffortsWastewater

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• Wastewater effluent testing in conjunction with BOD and COD• BOD: up to 20 days to test• COD: less time, requires strong oxidising chemicals

Conservation Efforts

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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

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

CAN COMPUTERS COUNT BACTERIA?

Simpler Better Faster

Thank youAcknowledgementsThank 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.

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