Unknown,Transcriptomics,Genomics,Proteomics

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RNA-Seq of single cells from the Arabidopsis root


ABSTRACT: The definition of cell identity is a central problem in biology. Single-cell RNA-seq provides a wealth of information regarding the developmental state of individual cells. However, better methods are needed to map the identity of single cells, especially during identity transitions. We have developed a quantitative classification method that is robust to expression noise and can detect primary and chimeric identities from single-cell RNA sequencing profiles. The method uses existing transcriptome repositories of grouped cell-types to define a set of optimal cell-identity markers, which are then used to define a cell identity metric. This metric accurately classified diverse cell identities in Arabidopsis root tips and human glioblastoma cells. We demonstrate the strength of the approach to resolve a dynamic developmental process by analyzing the identity of single cells captured from regenerating Arabidopsis roots following removal of their stem-cell-niche. We discover that, apart from new niche formation at the vicinity of the cut site, cells that are distant from the injury site also undergo a transient, partial collapse of identity during the regeneration and reorganization of the root, demonstrating the usefulness of a quantitative cell identity metric. Overall design: 4 Technical replicates (pooled-and-split single QC cells), 23 cells from the QC, 3 cells from the Stele, and 4 cells from the stele, 16h following root tip decapitation

INSTRUMENT(S): Illumina HiSeq 2000

ORGANISM(S): Arabidopsis thaliana

SUBMITTER: Idan Efroni 

PROVIDER: E-MTAB-4125 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Quantification of cell identity from single-cell gene expression profiles.

Efroni Idan I   Ip Pui-Leng PL   Nawy Tal T   Mello Alison A   Birnbaum Kenneth D KD  

Genome biology 20150122


The definition of cell identity is a central problem in biology. While single-cell RNA-seq provides a wealth of information regarding cell states, better methods are needed to map their identity, especially during developmental transitions. Here, we use repositories of cell type-specific transcriptomes to quantify identities from single-cell RNA-seq profiles, accurately classifying cells from Arabidopsis root tips and human glioblastoma tumors. We apply our approach to single cells captured from  ...[more]

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