Investigating the effects of liquid handling robot pipetting speed on yeast growth and gene expression using growth assays and RNA-seq

Shodai Taguchi; Ryosuke Matsuzawa; Yasuyuki Suda; Kenji Irie; Haruka Ozaki

doi:10.17912/micropub.biology.001566

Investigating the effects of liquid handling robot pipetting speed on yeast growth and gene expression using growth assays and RNA-seq

MicroPubl Biol. 2025 May 13:2025:10.17912/micropub.biology.001566. doi: 10.17912/micropub.biology.001566. eCollection 2025.

Authors

Shodai Taguchi^#^{1

2

3}, Ryosuke Matsuzawa^#^{4

3}, Yasuyuki Suda², Kenji Irie², Haruka Ozaki^{3

5

6}

Affiliations

¹ Ph.D. Program in Humanics, School of Integrative and Global Majors, University of Tsukuba, Tsukuba, Ibaraki, Japan.
² Department of Molecular Cell Biology, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
³ Bioinformatics Laboratory, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
⁴ Doctoral Program in Medical Sciences, Degree Programs in Comprehensive Human Sciences, Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan.
⁵ Center for Artificial Intelligence Research, University of Tsukuba, Tsukuba, Ibaraki, Japan.
⁶ Laboratory for AI Biology, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyōgo, Japan.

^# Contributed equally.

Abstract

Assessing the impact of experimental parameters like pipetting speed is essential in life science research but challenging in manual experiments. Recent advancements in laboratory automation enable precise control and systematic evaluation of these parameters. Here, we employed the Opentrons OT-2, an affordable, open-source liquid handling robot, to systematically investigate the effect of pipetting speed on growth and gene expression in the budding yeast Saccharomyces cerevisiae . Growth assays revealed no significant differences across the tested pipetting speeds (ANOVA, p > 0.05). RNA-seq analysis corroborated these findings, demonstrating highly similar gene expression profiles across all 24 samples (minimum Pearson correlation coefficient = 0.9528), with no differentially expressed genes identified by generalized linear model analysis (false discovery rate > 0.01). Our results highlight the utility of robotic platforms in optimizing experimental parameters, improving reproducibility, and enhancing accuracy in biological research, thus providing valuable insights for future applications.