Diseño de un sistema de software para soportar la escalabilidad en la ingeniería de líneas celulares de mamíferos

Cell line engineering (CLE) is the process of gene editing cell lines for a variety of purposes including research and development or bioproduction processes. Traditionally, CLE workflows have been manual and low throughput. Here, we describe the development of several software-based processes, implemented alongside wet lab automation and robotics, built to improve the throughput of our CLE platform to three times its previous capacity. A markup language (GEML) was developed to enable e-commerce capabilities and connections to internal manufacturing systems. A laboratory information management system (LIMS), specifically designed to track CLE projects through all stages, was created to manufacture the cell line specified by the GEML. Cell line engineering required analysis of images in brightfield without fluorescent staining; therefore, a machine learning (ML)-based method for analysing engineered clones imaged captured on an automated imaging platform was created. Our work demonstrates that combining both wet lab automation and software approaches is essential to allow CLE workflows to reach their full potential, allowing the development of high-throughput robust platforms that meet the increasing demands of the field.