Martina Uray & Horst Bischof Institute for Computer Graphics and Vision

1. Analysis of Cell Arrays: A Tool for Evaluation of High Density Micro Colony Chips in Fluorescence Assays Martina Uray & Horst Bischof Institute for Computer Graphics and Vision Jochen Gerlach & Ingo Klimant Institute of Analytical Chemistry and Radiochemistry Brigitte Pohn Research Centre Applied Biocatalysis Helmut Schwab Institute for Molecular Biotechnology

2. Overview • Motivation • System Setup • Enzymatic Reaction • Automatic Analysis • Results • Conclusion and Future Work

3. Motivation • Need • Generation of highly active, specific and enantioselective biocatalysts High throughput screening methods, suitable for desired enzyme reaction • New development • Screening of whole cell arrays without decomposition of cells and further cleaning steps • High throughput due to a semi-automatic software tool

4. System Setup I • Two strains with significant difference in esterase activity • Spotting of the array (~5000 colonies) with Microgrid II robot (Biorobotics) • Regular sub-blocks, strains are randomly distributed • Reference spots for activity comparison • Cells grow at 30° over night and form micro colonies • Replication of arrays • Studies on different substrates or reaction conditions • Preparation of the sensor • 100 µm thick • Includes the indicator, substrate and reaction buffer

5. System Setup II • Start of the reaction by putting the array onto the sensor • Substrate diffuses into the micro colonies • Reaction leads to a flux of protons into the sensor • Usage of fluorescent pH indicator which fluorescenceintensity is dependent on proton concentration • Excitation with a blue LED • Signal detection with a CCD camera • Measurement in constant time intervals Image stack which shows the sensory response over time

6. 120 s 220 s 320 s Enzymatic Reaction I

7. Enzymatic Reaction II

8. Automatic Analysis I • Given • Composition of the array (number of blocks, number of spots per block, fix spot-diameter) • Automatically … • …find the position of the spots  Corresponding block Situation inside the block • …find the centre of the colonies Spot-content from centre and diameter • …calculate the kinetic properties of every spot in the array  Intensity development over time (intensity curve)  Gradient (regression line) reflects the turnover rate • …rate colonies  Mapping to high and low activity

9. Automatic Analysis I • Software tool with graphical user interface • Automatically find the centre and the position of the spots (fixed diameter) • Manually select an image and delineate the array with arectangle • Normalize image for comparison

10. Automatic Analysis II 1. Gaussian filter Noise reduction 2. Cross correlation Binary image 3. Summation over stack Grey scale image 4. Radon Transformation Estimation of rotation and starting point of grid Rough grid alignment 5. Fast Mean Shift (adaptive gradient ascent method) points towards the direction of maximum increase in density Exact spot centres

11. Results I

12. Results II Spot positions are … • … clearly identified as long as there is a signal • … approximated when there is no signal • … missed if the real centre is too far from the first grid approximation

13. Results III • Comparison between standard and unknown spots Distinction of high (NJ70) and low (N27) activity esterase mutants spotted randomly over the array

14. Conclusion & Future Work • Developed a semi-automatic system for fast screening of a huge amount of datasets • Demonstrated robust performance • Next Steps: • Calculate grid from reflected light image  Evaluate the exact size of the spots (Fast Mean Shift Algorithm) Calculation of spot intensity in comparison to its size • Handle bright spots on dark background • Evaluate spot intensities in different parts of the array over different time intervals

15. Thank you for your attention!