Cell Type Purification by Single-Cell Transcriptome-Trained Sorting

Chloé S. Baron, Aditya Barve, Mauro J. Muraro, Reinier van der Linden, Gitanjali Dharmadhikari, Anna Lyubimova, Eelco J.P. de Koning, Alexander van Oudenaarden*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
19 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)527-542.e19
Issue number2
Publication statusPublished - 3 Oct 2019


  • bisulphite sequencing
  • cell type purification
  • FACS gate prediction and normalization
  • flow cytometry
  • human pancreas
  • machine learning
  • optimization algorithm
  • single-cell transcriptomics
  • zebrafish hematopoiesis


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