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-cell eukaryotic life cycles that remain incompletely described. The data can be processed using any image analysis software. We also developed the Python package yeastvision, which provides a user interface that allows free access to our image processing and single-cell tracking algorithms for complete life cycle analyses.

Methods
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 the mNG detection channel was designed using the excitation filter FF01-504/12-25, the dichroic  FF518 Di01-25x36, and the emission filter FF01 530/11-25.

The original images were collected as uncompressed uint16 files. Images ending in ‘mKOk_000.tif’, ‘mRuby3_000.tif’, or ‘mNG_000.tif’ were turned into uint8 using MATLAB’s uint8(). For images ending in ‘Ph3_000.tif’, the following scaling in MATLAB was applied: the original uint16 was turned into a double using double(), scaled between 0 - 1 , and then multiplied by 255 to render the image grayscale values between 0 - 255, and then saved as uint8 using uint8(). Rescaling was done by dividing the subtraction of all pixel values minus the minimum value in the original image by the subtraction of the maximum value minus the minimum value in the original image. The resulting 0 - 1 scaled image was multiplied by 255 to achieve an 8-bit grayscale range of 0 - 255.