Mobile Fluorescence Imaging and Protein Crystal Recognition

The crystallization of biological macromolecules like proteins is an important process to study their molecular structures. The quality of crystals is critical to be able to determine their structures using methods such as X-ray crystallography. Therefore, many wet-lab experiments are conducted using numerous screening plates to obtain successful crystal growth. High-throughput microscopy is useful to quickly collect images from the screening plates. Since the automated systems for imaging require high-end instrumentation, they are costly. This study investigates a small scale, mobile fluorescence imaging system, and application. Our system is composed of a mobile imaging system, a mobile app to capture images from plates, and a machine learning model to recognize the presence of crystals presence from images. For fluorescence imaging, we present an assembly of a smartphone or tablet integrated with a macro lens tube and illumination LEDs. The system presented in this study has magnification range from 20x to 250x macro. For the recognition of crystals, a convolutional neural network model was trained on a computer and then deployed on the mobile app. A data set of 1000 trace fluorescently labeled images was used to train and evaluate the model. The accuracy of the hold-out testing images was about 95%. The mobile app for imaging and protein recognition was developed to run on Apple iOS devices. To evaluate the system further, the recombinant inorganic pyrophosphatase protein from Klebsiella pneumoniae, which was expressed from E. coli, was crystallized using the trace fluorescent labeling method. Our system can capture quality images of protein crystals in both white and fluorescence lights. The overall accuracy of recognizing crystal or non-crystal outcomes on the pilot test is about 93%. This mobile imaging system can be useful for small group research labs and students.