Label-free imaging flow cytometry for analysis and sorting of enzymatically dissociated tissues

Maik Herbig, Karen Tessmer, Martin Nötzel, Ahmad Ahsan Nawaz, Tiago Santos-Ferreira, Oliver Borsch, Sylvia J. Gasparini, Jochen Guck, Marius Ader

Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells’ properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on physical properties. Sorting real-time deformability cytometry (soRT-DC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with challenges including changes in morphology, or presence of aggregates. Here, we introduce methods to improve robustness of analysis and sorting of single cells from nervous tissue and provide DNNs which can distinguish visually similar cells. We employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye.

Machine learning assisted real-time deformability cytometry of CD34+ cells allows to identify patients with myelodysplastic syndromes

Maik Herbig, Angela Jacobi, Manja Wobus, Heike Weidner, Anna Mies, Martin Kräter, Oliver Otto, Christian Thiede, Marie-Theresa Weickert, et al.

Diagnosis of myelodysplastic syndrome (MDS) mainly relies on a manual assessment of the peripheral blood and bone marrow cell morphology. The WHO guidelines suggest a visual screening of 200 to 500 cells which inevitably turns the assessor blind to rare cell populations and leads to low reproducibility. Moreover, the human eye is not suited to detect shifts of cellular properties of entire populations. Hence, quantitative image analysis could improve the accuracy and reproducibility of MDS diagnosis. We used real-time deformability cytometry (RT-DC) to measure bone marrow biopsy samples of MDS patients and age-matched healthy individuals. RT-DC is a high-throughput (1000 cells/s) imaging flow cytometer capable of recording morphological and mechanical properties of single cells. Properties of single cells were quantified using automated image analysis, and machine learning was employed to discover morpho-mechanical patterns in thousands of individual cells that allow to distinguish healthy vs. MDS samples. We found that distribution properties of cell sizes differ between healthy and MDS, with MDS showing a narrower distribution of cell sizes. Furthermore, we found a strong correlation between the mechanical properties of cells and the number of disease-determining mutations, inaccessible with current diagnostic approaches. Hence, machine-learning assisted RT-DC could be a promising tool to automate sample analysis to assist experts during diagnosis or provide a scalable solution for MDS diagnosis to regions lacking sufficient medical experts.

Mechanical spinal cord transection in larval zebrafish and subsequent whole-mount histological processing

Nora John, Julia Kolb, Daniel Wehner

Zebrafish regenerate their spinal cord after injury, both at larval and adult stages. Larval zebrafish have emerged as a powerful model system to study spinal cord injury and regeneration due to their high optical transparency for in vivo imaging, amenability to high-throughput analysis, and rapid regeneration time. Here, we describe a protocol for the mechanical transection of the larval zebrafish spinal cord, followed by whole-mount tissue processing for in situ hybridization and immunohistochemistry to elucidate principles of regeneration.