Time lapse microscopy images of Saccharomyces cerevisiae's full life cycle

The life cycles of biomedical and agriculturally relevant eukaryotic microorganisms involve complex transitions between proliferative and non-proliferative states, such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a microfluidic assay to record the complete sexual life cycle of the model eukaryote Saccharomyces cerevisiae for up to three sexual life cycles. This data set contains TIF image files from the time-lapse imaging of the complete S. cerevisiae life cycle. We envision that it will be used to benchmark new single-cell processing algorithms and as a starting point for quantitatively characterizing other single-..., This data set was recorded using time-lapse microscopy and microfluidics devices (Y04C CellASIC plate with OniX controllers). Microfluidics experiments were performed on an automated Zeiss Axio Observer Z1 microscope controlled by ZEN pro software and with temperature control (Zeiss). Images were acquired at a 12 minute sampling rate using an 40X 1.3 NA oil Ph 3 M27 immersion objective. Image focus was controlled using Definite Focus 3.0. Images were recorded using an AxioCam 712 monochrome. An X-CITE XYLIS XT720S lamp (Excelitas Technologies) was used as a light source. 
Fluorescent channel filter sets were tailored using dichroic mirrors and bandpass filters from Semrock. The mKOκ detection channel was designed using the excitation filter FF01-534/20-25, the dichroic FF552 Di02-25x36, and the emission filter FF01 563/9-25; the mRuby3 detection channel was designed using the excitation filter FF01-563/9-25, the dichroic FF573 Di01-25x36, and the emission filter FF01 598/25-25; and..., , # Time lapse microscopy images of *Saccharomyces* cerevisiae's full life cycle

[https://doi.org/10.5061/dryad.3bk3j9kw0](https://doi.org/10.5061/dryad.3bk3j9kw0)

## **Description of the data and file structure**

Defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative and direct approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations.

Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation.

As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regu...,