Combining mechanistic and statistical models to enable Nascent Chain Tracking for multiple mRNAs using a single color

mRNA translation is the ubiquitous cellular process of reading messenger-RNA strands into functional proteins. Within the past decade, large strides in fluorescent microscopy techniques have allowed observation of mRNA translation at a single-molecule resolution for self-consistent time-series measurements in live cells. Dubbed Nascent Chain Tracking (NCT), these methods elucidate translation dynamics lost by other investigatory techniques such as ribosomal footprinting or mRNA-seq; However, NCT has been limited to the observation of one or two mRNA species at a time within the same cell partly due to limits in the number of resolvable fluorescent tags. In this work, we present a hybrid computational pipeline where detailed mechanistic simulations produce realistic NCT videos and machine learning is used to assess experimental designs for their potential to resolve multiple mRNA species sharing a single fluorescent color. With careful application, this hybrid design strategy could in principle be used to extend the number of mRNA species that could be watched simultaneously within the same cell. We present a toy example NCT experiment with 7 different mRNAs species within the same simulated cell and use our ML labeling to label these spots with a 90% accuracy. The possibilities offered by this color palette extension will allow experimentalists access to a plethora of new experimental design possibilities -- especially for investigating cell signals that affect multiple mRNAs at a time.