Back to Search
Start Over
A combinatorial panel for flow cytometry-based isolation of enteric nervous system cells from human intestine
- Source :
- Windster , J D , Sacchetti , A , Schaaf , G J , Bindels , E M J , Hofstra , R M W , Wijnen , R M H , Sloots , C E J & Alves , M M 2023 , ' A combinatorial panel for flow cytometry-based isolation of enteric nervous system cells from human intestine ' , EMBO Reports , vol. 24 , no. 4 , e55789 .
- Publication Year :
- 2023
-
Abstract
- Efficient isolation of neurons and glia from the human enteric nervous system (ENS) is challenging because of their rare and fragile nature. Here, we describe a staining panel to enrich ENS cells from the human intestine by fluorescence-activated cell sorting (FACS). We find that CD56/CD90/CD24 co-expression labels ENS cells with higher specificity and resolution than previous methods. Surprisingly, neuronal (CD24, TUBB3) and glial (SOX10) selective markers appear co-expressed by all ENS cells. We demonstrate that this contradictory staining pattern is mainly driven by neuronal fragments, either free or attached to glial cells, which are the most abundant cell types. Live neurons can be enriched by the highest CD24 and CD90 levels. By applying our protocol to isolate ENS cells for single-cell RNA sequencing, we show that these cells can be obtained with high quality, enabling interrogation of the human ENS transcriptome. Taken together, we present a selective FACS protocol that allows enrichment and discrimination of human ENS cells, opening up new avenues to study this complex system in health and disease.
Details
- Database :
- OAIster
- Journal :
- Windster , J D , Sacchetti , A , Schaaf , G J , Bindels , E M J , Hofstra , R M W , Wijnen , R M H , Sloots , C E J & Alves , M M 2023 , ' A combinatorial panel for flow cytometry-based isolation of enteric nervous system cells from human intestine ' , EMBO Reports , vol. 24 , no. 4 , e55789 .
- Notes :
- application/pdf, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1376785743
- Document Type :
- Electronic Resource