First person – Keiichiro Sakai

ABSTRACT First Person is a series of interviews with the first authors of a selection of papers published in Journal of Cell Science, helping early-career researchers promote themselves alongside their papers. Keiichiro Sakai is first author on ‘ Near-infrared imaging in fission yeast using a genetically encoded phycocyanobilin biosynthesis system’, published in JCS. Keiichiro is a PhD student in the lab of Kazuhiro Aoki at the Quantitative Biology Research Group, Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki, Aichi, Japan, investigating how phycocyanobilin, a linear tetrapyrrole, brightens near-infrared fluorescent proteins, including iRFP, as a chromophore more efficiently than biliverdin in fission yeast.

Deliver on Time or Pay the Fine: Scheduling in Membrane Trafficking

Membrane trafficking is all about time. Automation in such a biological process is crucial to ensure management and delivery of cellular cargoes with spatiotemporal precision. Shared molecular regulators and differential engagement of trafficking components improve robustness of molecular sorting. Sequential recruitment of low affinity protein complexes ensures directionality of the process and, concomitantly, serves as a kinetic proofreading mechanism to discriminate cargoes from the whole endocytosed material. This strategy helps cells to minimize losses and operating errors in membrane trafficking, thereby matching the appealed deadline. Here, we summarize the molecular pathways of molecular sorting, focusing on their timing and efficacy. We also highlight experimental procedures and genetic approaches to robustly probe these pathways, in order to guide mechanistic studies at the interface between biochemistry and quantitative biology.

Artificial Intelligence-Powered Automated Holotomographic Microscopy Enables Label-Free Quantitative Biology

Holotomographic microscopy (HTM) measures the refractive index (RI) tomograms of living cells and tissues in three dimensions. The ability to observe biological processes at high spatial and temporal resolution opens uncharted territories for cell biologists, however, current HTM devices have a limited throughput. We show here the first automated multi-well plate-compatible HTM device, the CX-A. Thanks to state-of-the-art environment control and a new type of autofocus, the CX-A can record multiple conditions in parallel over large fields of view, while its software EVE supports automated single-cell segmentation and quantification. This opens the door to new applications for HTM, from drug screening to systems biology.

Predictive modeling reveals that higher-order cooperativity drives transcriptional repression in a synthetic developmental enhancer

A challenge in quantitative biology is to predict output patterns of gene expression from knowledge of input transcription factor patterns and from the arrangement of binding sites for these transcription factors on regulatory DNA. We tested whether widespread thermodynamic models could be used to infer parameters describing simple regulatory architectures that inform parameter-free predictions of more complex enhancers in the context of transcriptional repression by Runt in the early fruit fly embryo. By modulating the number and placement of Runt binding sites within an enhancer, and quantifying the resulting transcriptional activity using live imaging, we discovered that thermodynamic models call for higher-order cooperativity between multiple molecular players. This higher-order cooperativity capture the combinatorial complexity underlying eukaryotic transcriptional regulation and cannot be determined from simpler regulatory architectures, highlighting the challenges in reaching a predictive understanding of transcriptional regulation in eukaryotes and calling for approaches that quantitatively dissect their molecular nature.

Focus on biosensors: Looking through the lens of quantitative biology

In recent years, plant biologists interested in quantifying molecules and molecular events in vivo have started to complement reporter systems with genetically encoded fluorescent biosensors (GEFBs) that directly sense an analyte. Such biosensors can allow measurements at the level of individual cells and over time. This information is proving valuable to mathematical modellers interested in representing biological phenomena in silico, because improved measurements can guide improved model construction and model parametrisation. Advances in synthetic biology have accelerated the pace of biosensor development, and the simultaneous expression of spectrally compatible biosensors now allows quantification of multiple nodes in signalling networks. For biosensors that directly respond to stimuli, targeting to specific cellular compartments allows the observation of differential accumulation of analytes in distinct organelles, bringing insights to reactive oxygen species/calcium signalling and photosynthesis research. In conjunction with improved image analysis methods, advances in biosensor imaging can help close the loop between experimentation and mathematical modelling.