A label-free method to track individuals and lineages of budding cells

Abstract

Much of biochemical regulation ultimately controls growth rate, particularly in microbes. Although time-lapse microscopy visualises cells, determining their growth rates is challenging because cells often overlap in images, particularly for those that divide asymmetrically, like Saccharomyces cerevisiae. Here we present the Birth Annotator for Budding Yeast (BABY), an algorithm to determine single-cell growth rates from label-free images. Using a convolutional neural network, BABY resolves overlaps through separating cells by size and assigns buds to mothers by identifying bud necks. BABY uses machine learning to track cells and determine lineages, estimates growth rates as the rate of change of volumes, and identifies cytokinesis by how growth varies. Using BABY and a microfluidic device, we show that bud growth is first sizer- then timer-controlled, that the nuclear concentration of Sfp1, a regulator of ribosome biogenesis, varies before the growth rate does, and that growth rate can be used for real-time control. Growth rate and fitness are strongly correlated, and BABY should therefore generate much biological insight.