PUBLICATION

Cell Type Purification by Single-Cell Transcriptome-Trained Sorting

Authors
Baron, C.S., Barve, A., Muraro, M.J., van der Linden, R., Dharmadhikari, G., Lyubimova, A., de Koning, E.J.P., van Oudenaarden, A.
ID
ZDB-PUB-191005-11
Date
2019
Source
Cell   179: 527-542.e19 (Journal)
Registered Authors
Keywords
FACS gate prediction and normalization, bisulphite sequencing, cell type purification, flow cytometry, human pancreas, machine learning, optimization algorithm, single-cell transcriptomics, zebrafish hematopoiesis
Datasets
GEO:GSE112438
MeSH Terms
  • Animals
  • Cell Separation/methods*
  • Flow Cytometry/methods*
  • Humans
  • Kidney/cytology
  • Pancreas/cytology
  • Single-Cell Analysis
  • Software*
  • Transcriptome*
  • Zebrafish/anatomy & histology
PubMed
31585086 Full text @ Cell
Abstract
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.
Genes / Markers
Figures
Show all Figures
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Antibodies
Orthology
Engineered Foreign Genes
Mapping