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Predicting single-cell gene expression with imaging flow cytometry data and machine learning

Aug 10, 2021

Luminex introduced IFC-seq, a machine learning methodology for predicting the expression profile of every cell in an imaging flow cytometry experiment. Since it is to-date unfeasible to observe singlecell gene expression and morphology in flow, we integrate uncoupled imaging data with an independent transcriptomics dataset by leveraging common surface markers. We demonstrate that IFC-seq successfully models gene expression of a moderate number of key gene-markers for two independent imaging flow cytometry datasets: (i) human blood mononuclear cells and (ii) mouse myeloid progenitor cells. In the case of mouse myeloid progenitor cells IFC-seq can predict gene expression directly from brightfield images in a label-free manner, using a convolutional neural network. The proposed method promises to add gene expression information to existing and new imaging flow cytometry datasets, at no additional cost.

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Luminex offer a comprehensive range of innovative flow cytometers, including the Imaging flow cytometers.

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