GateID predicts non-intuitive FACS gates to purify cell types based on scRNA-seq data
Predicted gates can be normalized and used in an unlimited amount of experiments
Zebrafish hematopoietic and human pancreatic cell types can be enriched up to 100%
Live GateID-purified populations can be used for downstream analyses
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.