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.

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.

The G protein-coupled receptor class C, group 6, subtype A (GPRC6A) is suggested to have a physiological function in glucose and bone metabolism, although the precise role lacks consensus due to varying findings in different knockout (KO) mouse models and inconsistent findings on the role of osteocalcin, a proposed GPRC6A agonist. We have further characterized a full locus GPRC6A KO model with respect to energy metabolism, including a long-term high-dose glucocorticoid metabolic challenge. Additionally, we analyzed the microarchitecture of tibiae from young, middle-aged and aged GPRC6A KO mice and wildtype (WT) littermates. Compared to WT, vehicle-treated KO mice presented with normal body composition, unaltered insulin sensitivity and basal serum insulin and glucose levels. Corticosterone (CS) treatment resulted in insulin resistance, abnormal fat accrual, loss of lean mass and suppression of serum osteocalcin levels in both genotypes. Interestingly, serum osteocalcin and skeletal osteocalcin mRNA levels were significantly lower in vehicle-treated GPRC6A KO mice compared to WT animals. However, WT and KO age groups did not differ in long bone mass and structure assessed by micro-computed tomography. We conclude that GPRC6A is not involved in glucose metabolism under normal physiological conditions, nor does it mediate glucocorticoid-induced dysmetabolism in mice. Moreover, GPRC6A does not appear to possess a direct, non-compensable role in long bone microarchitecture under standard conditions.