Enabling oxygen-controlled microfluidic cultures for spatiotemporal microbial single-cell analysis.

Microfluidic cultivation devices that facilitate O control enable unique studies of the complex interplay between environmental O availability and microbial physiology at the single-cell level. Therefore, microbial single-cell analysis based on time-lapse microscopy is typically used to resolve microbial behavior at the single-cell level with spatiotemporal resolution. Time-lapse imaging then provides large image-data stacks that can be efficiently analyzed by deep learning analysis techniques, providing new insights into microbiology.

CellSium: versatile cell simulator for microcolony ground truth generation.

Summary: To train deep learning-based segmentation models, large ground truth datasets are needed. To address this need in microfluidic live-cell imaging, we present , a flexibly configurable cell simulator built to synthesize realistic image sequences of bacterial microcolonies growing in monolayers. We illustrate that the simulated images are suitable for training neural networks.

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation.

In biotechnology, cell growth is one of the most important properties for the characterization and optimization of microbial cultures. Novel live-cell imaging methods are leading to an ever better understanding of cell cultures and their development. The key to analyzing acquired data is accurate and automated cell segmentation at the single-cell level.

Communities of Niche-optimized Strains (CoNoS) - Design and creation of stable, genome-reduced co-cultures.

Current bioprocesses for production of value-added compounds are mainly based on pure cultures that are composed of rationally engineered strains of model organisms with versatile metabolic capacities. However, in the comparably well-defined environment of a bioreactor, metabolic flexibility provided by various highly abundant biosynthetic enzymes is much less required and results in suboptimal use of carbon and energy sources for compound production. In nature, non-model organisms have frequently evolved in communities where genome-reduced, auxotrophic strains cross-feed each other, suggesting that there must be a significant advantage compared to growth without cooperation.

When a single lineage is not enough: Uncertainty-Aware Tracking for spatio-temporal live-cell image analysis.

Motivation: Microfluidic platforms for live-cell analysis are in dire need of automated image analysis pipelines. In this context, producing reliable tracks of single cells in colonies has proven to be notoriously difficult without manual assistance, especially when image sequences experience low frame rates.

Results: With Uncertainty-Aware Tracking (UAT), we propose a novel probabilistic tracking paradigm for simultaneous tracking and estimation of tracking-induced errors in biological quantities derived from live-cell experiments.

Automated Error Detection in Physiotherapy Training.

Background: Manual skills teaching, such as physiotherapy education, requires immediate teacher feedback for the students during the learning process, which to date can only be performed by expert trainers.

Objectives: A machine-learning system trained only on correct performances to classify and score performed movements, to identify sources of errors in the movement and give feedback to the learner.

Methods: We acquire IMU and sEMG sensor data from a commercial-grade wearable device and construct an HMM-based model for gesture classification, scoring and feedback giving.

Vizardous: interactive analysis of microbial populations with single cell resolution.

Motivation: Single cell time-lapse microscopy is a powerful method for investigating heterogeneous cell behavior. Advances in microfluidic lab-on-a-chip technologies and live-cell imaging render the parallel observation of the development of individual cells in hundreds of populations possible. While image analysis tools are available for cell detection and tracking, biologists are still confronted with the challenge of exploring and evaluating this data.