Biology subjects

Bergner, B.

Publications and source records attributed to Bergner, B..

2 recordsLinked to original sources

bioRxiv2022

Stimulated emission depletion microscopy with a single depletion laser using five fluorochromes and fluorescence lifetime phasor separation

Stimulated emission depletion (STED) microscopy achieves super-resolution by exciting a diffraction-limited volume and then suppressing fluorescence in its outer parts by depletion. Multiple depletion lasers may introduce misalignment and bleaching. Hence, a single depletion wavelength is preferable for multi-color analyses. However, this limits the number of usable spectral channels. Using cultured cells, common staining protocols, and commercially available fluorochromes and microscopes we exploit that the number of fluorochromes in STED or confocal microscopy can be increased by phasor based fluorescence lifetime separation of two dyes with similar emission spectra but different fluorescent lifetimes. In our multi-color FLIM-STED approach two fluorochromes in the near red (exc. 594 nm, em. 600-630) and two in the far red channel (633/641-680), supplemented by a single further redshifted fluorochrome (670/701-750) were depleted with 775 nm. To the best of our knowledge, these are the first published five color STED images. Generally, this approach doubles the number of fully distinguishable colors in laser scanning microscopy. We provide evidence that eight color FLIM-STED with a single depletion laser would be possible if suitable fluorochromes were identified and we confirm that a fluorochrome may have different lifetimes depending on the molecules to which it is coupled.

cell biology↗

bioRxiv2020

HistoFlow: Label-Efficient and Interactive Deep Learning Cell Analysis

Instance segmentation is a common task in quantitative cell analysis. While there are many approaches doing this using machine learning, typically, the training process requires a large amount of manually annotated data. We present HistoFlow, a software for annotation-efficient training of deep learning models for cell segmentation and analysis with an interactive user interface. It provides an assisted annotation tool to quickly draw and correct cell boundaries and use biomarkers as weak annotations. It also enables the user to create artificial training data to lower the labeling effort. We employ a universal U-Net neural network architecture that allows accurate instance segmentation and the classification of phenotypes in only a single pass of the network. Transfer learning is available through the user interface to adapt trained models to new tissue types. We demonstrate HistoFlow for fluorescence breast cancer images. The models trained using only artificial data perform comparably to those trained with time-consuming manual annotations. They outperform traditional cell segmentation algorithms and match state-of-the-art machine learning approaches. A user test shows that cells can be annotated six times faster than without the assistance of our annotation tool. Extending a segmentation model for classification of epithelial cells can be done using only 50 to 1500 annotations. Our results show that, unlike previous assumptions, it is possible to interactively train a deep learning model in a matter of minutes without many manual annotations.

bioinformatics↗