Live-seq enables temporal transcriptomic recording of single cells
SIB Swiss Institute of Bioinformatics · Chinese Academy of Sciences · +4 more institutions
Abstract
Abstract Single-cell transcriptomics (scRNA-seq) has greatly advanced our ability to characterize cellular heterogeneity 1 . However, scRNA-seq requires lysing cells, which impedes further molecular or functional analyses on the same cells. Here, we established Live-seq, a single-cell transcriptome profiling approach that preserves cell viability during RNA extraction using fluidic force microscopy 2,3 , thus allowing to couple a cell’s ground-state transcriptome to its downstream molecular or phenotypic behaviour. To benchmark Live-seq, we used cell growth, functional responses and whole-cell transcriptome read-outs to demonstrate that Live-seq can accurately stratify diverse cell types and states without…
Citation impact
- FWCI
- 19.14
- Percentile
- 100%
- References
- 76
Authors
12- WCWanze ChenCorresponding
SIB Swiss Institute of Bioinformatics, Chinese Academy of Sciences, Shenzhen Institutes of Advanced Technology, École Polytechnique Fédérale de Lausanne
- OGOrane Guillaume‐Gentil
ETH Zurich
- PYPernille Yde Rainer
SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne
- CGChristoph G. Gäbelein
ETH Zurich
- WSWouter Saelens
SIB Swiss Institute of Bioinformatics, École Polytechnique Fédérale de Lausanne
Topics & keywords
- Transcriptome
- Biology
- Computational biology
- Gene expression profiling
- Phenotype
- Cell type
- RNA-Seq
- Single-cell analysis