Technical Considerations Detecting Transparent Materials in Particle Analysis. Michael Horgan

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1 Technical Considerations Detecting Transparent Materials in Particle Analysis Michael Horgan

2 Who We Are Fluid Imaging Technology Manufacturers of the FlowCam series of particle analyzers FlowCam HQ location Scarborough Maine, USA Boston

3 Dynamic Imaging Particle Analysis Fluid Imaging Technology s Dynamic imaging particle analysis measures the data from 2D microscope images to give information on size and shape measurement of particles in a liquid. Size 2µ µ Testing concentration up to 5,000,000 particles per ml* Provides over 30 different particle measurements as well as a digital image of every particle analyzed Software enables user to look at tens of thousands of particles per minute Throughput 50, ,000 particles per minute* *Very dependent on particle size, throughput and diluent

4 Operation Principle of the FlowCam As the fluid flows in the flowcell past the illumination and camera, the flash illumination turns on and off; freezing the particles in a picture taken by the camera. This picture is then transferred to software where it is analyzed and displayed in our proprietary VisualSpreadsheet software

5 Applications In todays particle detection space there are many particles that cause real difficulty for traditional particle counting and characterizing instruments Detection & Measurement of Size & Shape of Particles Protein Aggregate Silicon Oil Droplets Air bubbles Foreign Particulates Contaminants in container closures Crystals Algae

6 Key Factors For Optimal Biologics Detection Key Considerations: 1. Are all the proteins being detected? Requires- Optimal threshold settings for that application, Dark and Bight thresholds needed to detect several types of proteins. 2. Are the large proteins particles being detected as large particles? Requires- Optimal threshold settings for that application, Dark and Bight thresholds, special software algorithms needed to detect overall protein size. 3. Are small particles being detected? Requires- Optics configuration of system is critical which includes number of pixels of the camera used per micron of sample, software capability and lens configuration. 4. Can software distinguish between the different types of particles, (Silicone, Protein)? Requires- Image quality is very important in order for the specialized software algorithms to operate effectively, as is the system optical configuration. This is especially critical in the 2 to 10 micron range.

7 Advantage of Using the FlowCam to Analyze Biologic Products Detects both dark and transparent particles; essential for detecting protein aggregates and silicone oil droplets. Provides fast and accurate size, shape and quantitative measurements for each particle. Statistical pattern recognition and user defined filters can quickly separate protein aggregates, silicone oil droplets and other contaminants that may be present in the sample.

8 Technical aspects of detecting transparent Particles The two main factors in detecting transparent materials 1. Image quality issues 2. Sensitivity of detection capability

9 Image quality - how well can you identify particle morphology Polystyrene beads Pseudo Protein Glass beads Aged mab aggregates Image quality is extremely important when characterizing, identifying, and differentiating particles such as protein aggregates, silicone oil, air bubbles and other contaminants. Image quality is important for accurate sizing measurements.

10 Image Quality One Comparison FlowCam Other Images of polystyrene beads Raw Images Bit Map Images Used for Measurements

11 Image Quality Protein images from 2 different instruments FlowCam Other

12 Image Quality Comparing the grey level variation across polystyrene beads from 2 different instruments FlowCam has sharper edges and so has less variation in the diameter measurement than company B. The diameter of the bead for company B instrument will vary considerably depending on where the measurement is taken on the Y axis ( threshold value). In this example company B instrument could vary from at least 7.8 to microns (table 1) compared to 9.72 to for the FlowCam FlowCam Other Grey Level Threshold value Sharp : Total Size Variance (varying threshold) = 1.67µm Distance Blurry : Total Size Variance (varying threshold) = 12.86µm Table 1

13 Total Camera Saturation Background Light intensity Total Dark Reason for Detection Sensitivity Area where protein is lighter than background Protein aggregate FIG 1 Area where protein is darker than background Light intensity Light intensity of Protein Fig 1 is a good example of a picture of a hard to identify protein aggregate (20 Microns length) as seen by the FlowCam. The image has both lighter and darker areas compared to the diluent background light intensity. If both light and dark thresholding is not used this aggregate will be fragmented into several particles or might not be detected at all. This is the reason that the FlowCam has built in light and dark thresholding capability.

14 1. Raw Image Nothing detected at this point FlowCam Detection Sensitivity Light and dark segmentation thresholds This is an example of how important it is to use the different FlowCam software techniques and algorithms to properly characterize a protein aggregate. The setup that detected the proteins the best was: dark and light threshold set to 15 and using neighbor analysis software; this is the FlowCam standard setting for detecting protein aggregates. The red area is the area that the software has identified as being part of the aggregate, each green box is considered to be a particle. 2. Dark Threshold Only Using 25 Grey intensity levels Large number of small particles detected, few medium particles 3 Medium particles 35 Small particles 3. Dark Threshold Only 15 Grey intensity levels More large particles detected than no 2 setup 6 Medium particles 8 Small particles 5. Dark Threshold and Light Threshold Both 15 Grey intensity levels 1 Large particle 28 Small particles 4. Light Threshold Only 15 Grey intensity levels Several particles detected, more than number 2 or 3 setups 4 Medium particles 42 Small particles 6. Dark Threshold and Light Threshold (Both 15 Grey intensity levels) + Neighbor Analysis software 1 Large particle Best Characterization of this protein

15 Image Quality Image quality is extremely important for Identifying Characterizing Differentiating particles protein aggregates, silicone oil, air bubbles and other contaminants For accurate sizing and shape measurements.

16 Conclusion When identifying image based instruments, image quality and software technology is vital to correctly identify the particulate, in particular 1. Light and Dark thresholding enables software to capture the full particulate shape 2. Blurry images make measurement and analysis less reliable and difficult to determine the type and size of the particle. 3. Good images give better data

17 Contact Information Michael Horgan Fluid Imaging Technologies Fluid Imaging Technologies, Inc. 200 Enterprise Drive, Scarborough, Maine Telephone: Fax: