Much of current molecular and cell biology research relies on the ability to purify cell types by fluorescence-activated cell sorting (FACS). FACS typically relies on the ability to label cell types of interest with antibodies or fluorescent transgenic constructs. However, antibody availability is often limited, and genetic manipulation is labor intensive or impossible in the case of primary human tissue. To date, no systematic method exists to enrich for cell types without a priori knowledge of cell-type markers. Here, we propose GateID, a computational method that combines single-cell transcriptomics with FACS index sorting to purify cell types of choice using only native cellular properties such as cell size, granularity, and mitochondrial content. We validate GateID by purifying various cell types from zebrafish kidney marrow and the human pancreas to high purity without resorting to specific antibodies or transgenes.
Keywords: FACS gate prediction and normalization; bisulphite sequencing; cell type purification; flow cytometry; human pancreas; machine learning; optimization algorithm; single-cell transcriptomics; zebrafish hematopoiesis.
Copyright © 2019 The Author(s). Published by Elsevier Inc. All rights reserved.