Biology subjects

Olman, T.

Publications and source records attributed to Olman, T..

3 recordsLinked to original sources

bioRxiv2026

DropletFactory CORE - a droplet cytometry and sorting platform for fast and accessible screening in biotechnology

Droplet sorting technology has the potential to revolutionize the biotechnology sector as it provides massive high-throughput screening capacity, but the technology remains not accessible for a wider audience yet. There is a need for more affordable droplet sorting platforms to design cell factories and screen cell libraries. In here we demonstrate our droplet cytometry/sorter platform for single-cell screening of yeast cells based on their fluorescence signal.

microbiology↗

bioRxiv2025

Label-free droplet image analysis with Cellprofiler

Droplet microfluidic methods used for microbiological experiments are fast, cost-effective, and provide high-throughput data. However, analysis of such image data can be difficult, and detection of molecular labels is limited by microscope parameters. Currently, there is lack of user-friendly methods to analyse a large volume of label-free droplet images without the need for trained personnel, or expensive, proprietary software. Such methods would make droplet microfluidic technology more widely accessible for a larger range of biological applications. In this paper we demonstrate an image analysis pipeline designed using Cellprofiler, a free, open-source software. This pipeline identifies water-in-oil microfluidic droplets, microplastic particles, and bacterial growth without using fluorescent or other labels.

bioinformatics↗

bioRxiv2024

High-throughput bacterial aggregation analysis in droplets

Microfluidic droplet platforms provide a rapid tool to study and capture bacterial aggregation in a well-controlled micro-environment, while image analysis presents an easily available technique to investigate droplet contents. However, the lack of standardised, well-documented methods and reliance on custom image analysis workflows limits wider adoption of the method and produces inconsistent, incomparable data on aggregation. We present a robust, cost-effective method using both mono- and polydisperse droplets and texture-based image analysis via an open-source software CellProfiler to assess bacterial aggregation. Compared to a manual droplet evaluation carried out by a human expert panel, textural characterisation achieves accuracy over 90% and more than 80% precision. Applying the pipeline, we found that suboptimal antibiotic concentrations can increase aggregation, whereas exposure to microplastic beads and metals reduces it. Overall, the developed pipeline offers high accuracy, easy setup, and broad applicability for bacterial aggregation.

bioinformatics↗