Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Herbig M

PROVIDER: S-EPMC8770577 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Exposure to respirable fractions of crystalline silica quartz dust particles is associated with silicosis, cancer and the development of autoimmune conditions. Early cellular interactions are not well understood, partly due to a lack of suitable technological methods. Improved techniques are needed to better quantify and study high-level respirable crystalline silica exposure in human populations. Techniques that can be applied to complex biological matrices are pivotal to understanding particle-cell interactions and the impact of particles within real, biologically complex environments. In this study, we investigated whether imaging flow cytometry could be used to assess the interactions between cells and crystalline silica when present within complex biological matrices. Using the respirable-size fine quartz crystalline silica dust Min-u-sil® 5, we first validated previous reports that, whilst associating with cells, crystalline silica particles can be detected solely through their differential light scattering profile using conventional flow cytometry. This same property reliably identified crystalline silica in association with primary monocytic cells in vitro using an imaging flow cytometry assay, where darkfield intensity measurements were able to detect crystalline silica concentrations as low as 2.5 ?g/mL. Finally, we ultilised fresh whole blood as an exemplary complex biological matrix to test the technique. Even after the increased sample processing required to analyse cells within whole blood, imaging flow cytometry was capable of detecting and assessing silica-association to cells. As expected, in fresh whole blood exposed to crystalline silica, neutrophils and cells of the monocyte/macrophage lineage phagocytosed the particles. In addition to the use of this technique in in vitro exposure models, this method has the potential to be applied directly to ex vivo diagnostic studies and research models, where the identification of crystalline silica association with cells in complex biological matrices such as bronchial lavage fluids, alongside additional functional and phenotypic cellular readouts, is required.

Project description:Impedance flow cytometry (IFC) is a versatile lab-on-chip technology which enables fast and label-free analysis of pollen grains in various plant species, promising new research possibilities in agriculture and plant breeding. Hazelnut is a monoecious, anemophilous species, exhibiting sporophytic self-incompatibility. Its pollen is dispersed by wind in midwinter when temperatures are still low and relative humidity is usually high. Previous research found that hazelnut can be characterized by high degrees of pollen sterility following a reciprocal chromosome translocation occurring in some cultivated genotypes. In this study, IFC was used for the first time to characterize hazelnut pollen biology. IFC was validated via dye exclusion in microscopy and employed to (i) follow pollen hydration over time to define the best pre-hydration treatment for pollen viability evaluation; (ii) test hazelnut pollen viability and sterility on 33 cultivars grown in a collection field located in central Italy, and two wild hazelnuts. The accessions were also characterized by their amount and distribution of catkins in the tree canopy. Pollen sterility rate greatly varied among hazelnut accessions, with one main group of highly sterile cultivars and a second group, comprising wild genotypes and the remaining cultivars, producing good quality pollen. The results support the hypothesis of recurring reciprocal translocation events in Corylus avellana cultivars, leading to the observed gametic semi-sterility. The measured hazelnut pollen viability was also strongly influenced by pollen hydration (R adj2 = 0.83, P ≤ 0.0001) and reached its maximum at around 6 h of pre-hydration in humid chambers. Viable and dead pollen were best discriminated at around the same time of pollen pre-hydration, suggesting that high humidity levels are required for hazelnut pollen to maintain its functionality. Altogether, our results detail the value of impedance flow cytometry for high throughput phenotyping of hazelnut pollen. Further research is required to clarify the causes of pollen sterility in hazelnut, to confirm the role of reciprocal chromosome translocations and to investigate its effects on plant productivity.

Dataset Information

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

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets