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Biomedical Imaging and Instrumentation

MicroBREW - An Automated Pipeline to Quantify Yeast Replicative Lifespan

(E-183) MicroBREW - An Automated Pipeline to Quantify Yeast Replicative Lifespan

Saturday, October 14, 2023

9:30 AM – 10:30 AM PDT

Location: Exhibit Hall - Row E - Poster # 183

Presenting Author(s)

KK

Karolina Kisiel

REU Student
University of Virginia
Norridge, Illinois, United States

Co-Author(s)

BP

Bishal Paudel

Post-doc
University of Virginia, United States
LP

Lindey Power

Graduate Student
University of Virginia, United States
KJ

Kevin Janes

Professor
University of Virginia, United States
JS

Jeffrey Smith

Professor
University of Virginia, United States

Introduction::

Aging represents a complex biological process associated with the onset of diverse pathological conditions. Saccharomyces cerevisiae (budding yeast) has emerged as a valuable model organism for investigating aging due to its evolutionarily conserved regulatory pathways governing the aging process. The replicative lifespan (RLS) of budding yeast, which quantifies the number of cell divisions before a cell dies, is often used to determine molecular and genetic mechanisms of aging in eukaryotes. Currently, RLS is counted manually on a petri dish with a microscope-mounted glass needle, which is tedious, low-throughput, and error-prone. Microfluidic single-cell analysis chips and high-resolution time-lapse microscopy are increasingly used to quantify hundreds of yeast cells in a high-throughput manner. However, counting relies highly on a manual procedure that reduces time efficiency. We address this limitation by developing an automated pipeline that counts RLS and other cellular events. Here, we present Microfluidic-Budding Replicative Events Workflow (Micro-BREW) that automatically tracks yeast events at the single-cell level and facilitates high-throughput quantification of RLS.

Materials and Methods::

Micro-BREW is built within MATLAB. The software automates image processing, tracks individual cellular traps, and extracts pixel intensities for the mother cell along with its budding events. To facilitate data analysis and extract information from the MATLAB output files, we developed an accompanying R package that identifies the mother cell entry, its death, the number of total budding events, and associated budding features. To directly assess the accuracy of Micro-BREW, we manually counted ~200,000 budding events and compared them against outputs from the pipeline.

Results, Conclusions, and Discussions:: We observed a strong correlation between the automated counts and manual counts (Pearson correlation, R = 0.84, p = 1.1e-14) suggesting that Micro-BREW accurately quantifies yeast RLS. Notably, Micro-BREW returns peak features associated with each budding event that are difficult to assess manually. Statistical analysis on the Micro-BREW extracted budding features identified two distinct modes of budding related to size of the buds. With information on mother cell viability status, Micro-BREW allows tracking of the entire lifespan of budding yeast cells at single-cell resolution. Automated counting exhibits superior throughput and reduced user error when compared to manual techniques. By accurately quantifying cellular behavior at the resolution of single-cell, Micro-BREW provides precise RLS measurements, survival rates, and budding modes, accelerating data analysis and increasing statistical rigor of time-lapse data collected from high-throughput assays.

Acknowledgements (Optional): : We thank Dr. Timothy Allen and Kitter Bishop for mentorship and guidance in the NSF REU program at the University of Virginia. Also, we would like to thank Russell Hawes, Jeffrey Hsu, Roza Przanowska, Piotr Przanowski, and Cameron Griffiths for their discussion and help with the project. Additionally, we are grateful for NSF Award #1950374 for funding.

References (Optional): :