Tim Prangemeier

Segmenting cells and tracking their motion over time is a common task in biomedical applications. However, predicting accurate instance-wise segmentation and cell motions from microscopy imagery remains a challenging task. Using microstructured environments for analyzing single cells in a constant flow of media adds additional complexity. While large-scale labeled microscopy datasets are available, we are not aware of any large-scale dataset, including both cells and microstructures. In this paper, we introduce the trapped yeast cell (TYC) dataset, a novel dataset for understanding instance-level semantics and motions of cells in microstructures. We release 105 dense annotated high-resolution brightfield microscopy images, including about 19k instance masks. We also release 261 curated video clips composed of 1293 high-resolution microscopy images to facilitate unsupervised understanding of cell motions and morphology. TYC offers ten times more instance annotations than the previously largest dataset, including cells and microstructures. Our effort also exceeds previous attempts in terms of microstructure variability, resolution, complexity, and capturing device (microscopy) variability. We facilitate a unified comparison on our novel dataset by introducing a standardized evaluation strategy. TYC and evaluation code are publicly available under CC BY 4.0 license.

Cell segmentation is a major bottleneck in extracting quantitative single-cell information from microscopy data. The challenge is exasperated in the setting of microstructured environments. While deep learning approaches have proven useful for general cell segmentation tasks, existing segmentation tools for the yeast-microstructure setting rely on traditional machine learning approaches. Here we present convolutional neural networks trained for multiclass segmenting of individual yeast cells and discerning these from cell-similar microstructures. We give an overview of the datasets recorded for training, validating and testing the networks, as well as a typical use-case. We showcase the method’s contribution to segmenting yeast in microstructured environments with a typical synthetic biology application in mind. The models achieve robust segmentation results, outperforming the previous state-of-the-art in both accuracy and speed. The combination of fast and accurate segmentation is not only beneficial for a posteriori data processing, it also makes online monitoring of thousands of trapped cells or closed-loop optimal experimental design feasible from an image processing perspective.

The robust and precise on and off switching of one or more genes of interest, followed by expression or repression is essential for many biological circuits as well as for industrial applications. However, many regulated systems published to date influence the viability of the host cell, show high basal expression or enable only the overexpression of the target gene without the possibility of fine regulation. Herein, we describe an AND gate designed to overcome these limitations by combining the advantages of three well established systems, namely the scaffold RNA CRISPR/dCas9 platform that is controlled by Gal10 as a natural and by LexA-ER-AD as heterologous transcription factor. We hence developed a predictable and modular, versatile expression control system. The selection of a reporter gene set up combining a gene of interest (GOI) with a fluorophore by the ribosomal skipping T2A sequence allows to adapt the system to any gene of interest without losing reporter function. In order to obtain a better understanding of the underlying principles and the functioning of our system, we backed our experimental findings with the development of a mathematical model and single-cell analysis.