Abstract
Protein-crystallization imaging and classification is a labor-intensive process typically performed either by humans or by instruments that currently cost well over $100 000. This cost puts the use of crystallization-trial imaging outside the reach of most academic laboratories, and also start-up biotechnology firms, where resources are scarce. An imaging system has been designed and prototyped which automatically captures images from multi-well protein-crystallization experiments using both standard and fluorescent imaging techniques at a cost 28 times lower than current market rates. The machine uses a Panowin F1 3D printer as a base and controls it using G-code commands sent from a Python script running on a desktop computer. A graphical user interface (GUI) was developed to enable users to control the machine and facilitate image capture, classification and editing. A 488 nm laser diode and a 525 nm filter were incorporated to allow in situ fluorescent imaging of proteins trace-labeled with a fluorophore, Alexa Fluor 488. The instrument was primarily designed using a 3D printer and augmented using commercially available parts, and this publication aims to serve as a guide for comparable in-laboratory robotics projects.