Correlation of gene expressions between nucleus and cytoplasm reflects single-cell physiology

Abdelmoez, Mahmoud N.; Iida, Kei; Oguchi, Yusuke; Nishikii, Hidekazu; Yokokawa, Ryuji; Kotera, Hidetoshi; Uemura, Shunsuke; Santiago, Juan G.; Shintaku, Hirofumi

doi:10.1101/206672

Cited by 3 publications

(3 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nearly all currently available human data have been generated using snRNAseq. The isolated nuclei used in snRNAseq lack the cytoplasmic compartment and proximal dendrites, and there are systematic differences between the types and amounts of mRNA in nucleus versus cell soma 27 . To evaluate the impact of this issue, we analyzed multiple mouse and human datasets.…”

Section: Resultsmentioning

confidence: 99%

Genetic identification of brain cell types underlying schizophrenia

et al. 2018

View full text Add to dashboard Cite

With few exceptions, the marked advances in knowledge about the genetic basis of schizophrenia have not converged on findings that can be confidently used for precise experimental modeling. By applying knowledge of the cellular taxonomy of the brain from single-cell RNA sequencing, we evaluated whether the genomic loci implicated in schizophrenia map onto specific brain cell types. We found that the common-variant genomic results consistently mapped to pyramidal cells, medium spiny neurons (MSNs) and certain interneurons, but far less consistently to embryonic, progenitor or glial cells. These enrichments were due to sets of genes that were specifically expressed in each of these cell types. We also found that many of the diverse gene sets previously associated with schizophrenia (genes involved in synaptic function, those encoding mRNAs that interact with FMRP, antipsychotic targets, etc.) generally implicated the same brain cell types. Our results suggest a parsimonious explanation: the common-variant genetic results for schizophrenia point at a limited set of neurons, and the gene sets point to the same cells. The genetic risk associated with MSNs did not overlap with that of glutamatergic pyramidal cells and interneurons, suggesting that different cell types have biologically distinct roles in schizophrenia.

show abstract

Section: Resultsmentioning

confidence: 99%

Genetic identification of brain cell types underlying schizophrenia

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Other studies focus on different aspects of the transcriptome. Single-cell integrated nuclear RNA and cytoplasmic RNA sequencing (SINC-seq) allows differential capture and quantification of mRNA from the nucleus and cytoplasm from the same single cell by physically separating the nucleus and cytoplasm, allowing insights into posttranscriptional dynamics and regulation (1). Noncoding RNAs are underexplored at the single-cell level, although some long noncoding RNAs can be captured via their poly(A) tail (77).…”

Section: Rnamentioning

confidence: 99%

Single-Cell (Multi)omics Technologies

Chappell

Russell

Voet

2018

Annu. Rev. Genom. Hum. Genet.

172

140

View full text Add to dashboard Cite

Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

show abstract

“…The data from this study have been deposited into the Sequencing Read Archive and are available for download under accession SRP119800 [ 43 ].…”

Section: Acknowledgementsmentioning

confidence: 99%

SINC-seq: correlation of transient gene expressions between nucleus and cytoplasm reflects single-cell physiology

et al. 2018

Self Cite

View full text Add to dashboard Cite

We report a microfluidic system that physically separates nuclear RNA (nucRNA) and cytoplasmic RNA (cytRNA) from a single cell and enables single-cell integrated nucRNA and cytRNA-sequencing (SINC-seq). SINC-seq constructs two individual RNA-seq libraries, nucRNA and cytRNA, per cell, quantifies gene expression in the subcellular compartments, and combines them to create novel single-cell RNA-seq data. Leveraging SINC-seq, we discover distinct natures of correlation among cytRNA and nucRNA that reflect the transient physiological state of single cells. These data provide unique insights into the regulatory network of messenger RNA from the nucleus toward the cytoplasm at the single-cell level.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1446-9) contains supplementary material, which is available to authorized users.

show abstract

Correlation of gene expressions between nucleus and cytoplasm reflects single-cell physiology

Abstract: These authors contributed equally to this work. Background: Eukaryotes transcribe RNAs in nuclei and

Cited by 3 publications

References 36 publications

Genetic identification of brain cell types underlying schizophrenia

Genetic identification of brain cell types underlying schizophrenia

Single-Cell (Multi)omics Technologies

SINC-seq: correlation of transient gene expressions between nucleus and cytoplasm reflects single-cell physiology

Contact Info

Product

Resources

About