Thesis Flow Cytometry

Thesis Flow Cytometry-21
It was found that our staining method successfully labeled neuronal nuclei using a Neu N antibody, but did not label enriched markers of medium spiny neurons.Further studies are needed to increase the signal-to-back-ground ratio of these stainings, which would allow FANS to be applied to wild type animals. FANS utilizes flow cytometry to isolate cell nuclei from CNS subtypes and microarray analysis of nuclear m RNA.

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The imaging functionality allows the localisation of cellular antigens to be determined (i.e.

cell surface, cytoplasm, nuclear), a feature that cannot be achieved by standard flow cytometry.

While helpful to explore underlying biology, manual gating– often based on subjective combinations of one- or two-dimensional gates– may not efficiently capture all phenotypes to be sorted.

The challenges deepen if phenotypes are classified by clustering, or identified by machine learning or dimensionality reduction techniques such as t-SNE or UMAP.

We will look at the detection of disrupted PML protein in acute promyelocytic leukaemia and the ability to distinguish between cytoplasmic and nuclear NPM in acute myeloid leukaemia.

The webinar will also present exciting new data demonstrating the ability to detect chromosomal abnormalities by FISH in phenotyped leukaemia cells in chronic lymphocytic leukaemia.

Our study targets pixel intensity-based thresholding techniques in single cell detection, cell area calculation and nucleus isolation.

The studies in this thesis also indicate the advantages of implementing simpler image morphology algorithms for collecting cell morphology features used in imaging flow cytometry applications.

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