Combining cell sorting with Imaging Flow Cytometry (IFC) to fully realise its tremendous potential, requires real-time image construction and analysis. However, all IFC systems demonstrated to date perform image analysis offline, and the ability to produce, measure, analyse cell images, and to sort cells in a real-time manner, will be the next major milestone for IFC.
Consequently, we developed a solution that extracts cell characteristics in real-time, including the use of Field-Programmable Gate Arrays (FPGA) or Graphics Processing Unit (GPU) to implement various image processing and machine learning algorithms. In other words, we developed an evaluation platform for online monitoring of bacteria and other contaminants in enzyme production, counting bacteria in a given volume eg. 1 mL.
One of the two cameras used in the process is equipped with a sensor for bright-field imaging and the other camera is equipped with two sensors and blue lasers for fluorescence excitation. The bright-field and fluorescence imaging are both controlled from the cameras and the cameras are synchronised to each other with minimum latency between image capture.
The enzymes are pumped out from the main process pipe to a sample tube, that is passed through the system, where particle sizes and types are determined and counted from the combination of bright-field and fluorescence images.