From The Scientist By Karen Zusi
The Paper
[box type=”shadow” align=”alignleft” ]Grün et al. (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:251-55. [
abstract][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]
Individuality
The advent of mRNA sequencing for individual cells has given scientists unprecedented insight into the diversity of cell populations once thought to be uniform. But finding an analysis sensitive enough to pick up rare cell types within a tissue is a challenge, says Dominic Grün, a quantitative biologist now at the Max Planck Institute of Immunobiology and Epigenetics in Germany.
An Intestinal Model

ODDBALLS: Intestinal epithelium from a mouse with a single rare enteroendocrine cell positive for the Reg4 marker (bright pink) – ANNA LYUBIMOVA
To develop a technique that could pinpoint uncommon cells, Grün started with sequencing the cells in intestinal organoids and creating clusters of similar transcriptome profiles. Knowing how much gene-expression variability he could expect in a group of homogeneous cells, Grün picked out clusters of cells whose expression profiles exceeded this threshold. “The trick was to quantitate that,” he says. “I wanted to develop an algorithm that would find these rare cells without any information—without relying on previously known marker genes.”
Searching for Cells
Grün’s algorithm detected enteroendocrine cells, rare, hormone-producing intestinal cells marked by high expression of the Reg4 gene. But the team delved deeper, enriching a new set of organoids for these rare cells and mining for previously unknown enteroendocrine subtypes. The tool was sensitive enough to pick up cell types represented by only a single cell.
Development
“Most universities don’t have the computational infrastructure to analyze data sets like this,” says Chris Lengner, a cell biologist at the University of Pennsylvania who did not participate in the study. “If they can make this tool readily available and user-friendly, it would probably have a pretty big impact.”
Source – The Scientist
From The Scientist By Karen Zusi
The Paper
[box type=”shadow” align=”alignleft” ]Grün et al. (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:251-55. [abstract][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]Individuality
The advent of mRNA sequencing for individual cells has given scientists unprecedented insight into the diversity of cell populations once thought to be uniform. But finding an analysis sensitive enough to pick up rare cell types within a tissue is a challenge, says Dominic Grün, a quantitative biologist now at the Max Planck Institute of Immunobiology and Epigenetics in Germany.
An Intestinal Model
ODDBALLS: Intestinal epithelium from a mouse with a single rare enteroendocrine cell positive for the Reg4 marker (bright pink) – ANNA LYUBIMOVA
To develop a technique that could pinpoint uncommon cells, Grün started with sequencing the cells in intestinal organoids and creating clusters of similar transcriptome profiles. Knowing how much gene-expression variability he could expect in a group of homogeneous cells, Grün picked out clusters of cells whose expression profiles exceeded this threshold. “The trick was to quantitate that,” he says. “I wanted to develop an algorithm that would find these rare cells without any information—without relying on previously known marker genes.”
Searching for Cells
Grün’s algorithm detected enteroendocrine cells, rare, hormone-producing intestinal cells marked by high expression of the Reg4 gene. But the team delved deeper, enriching a new set of organoids for these rare cells and mining for previously unknown enteroendocrine subtypes. The tool was sensitive enough to pick up cell types represented by only a single cell.
Development
Source – The Scientist
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From The Scientist By Karen Zusi
The Paper
[box type=”shadow” align=”alignleft” ]Grün et al. (2015) Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:251-55. [abstract][/fusion_text][/fusion_builder_column_inner][/fusion_builder_row_inner][/fusion_builder_column]Individuality
The advent of mRNA sequencing for individual cells has given scientists unprecedented insight into the diversity of cell populations once thought to be uniform. But finding an analysis sensitive enough to pick up rare cell types within a tissue is a challenge, says Dominic Grün, a quantitative biologist now at the Max Planck Institute of Immunobiology and Epigenetics in Germany.
An Intestinal Model
ODDBALLS: Intestinal epithelium from a mouse with a single rare enteroendocrine cell positive for the Reg4 marker (bright pink) – ANNA LYUBIMOVA
To develop a technique that could pinpoint uncommon cells, Grün started with sequencing the cells in intestinal organoids and creating clusters of similar transcriptome profiles. Knowing how much gene-expression variability he could expect in a group of homogeneous cells, Grün picked out clusters of cells whose expression profiles exceeded this threshold. “The trick was to quantitate that,” he says. “I wanted to develop an algorithm that would find these rare cells without any information—without relying on previously known marker genes.”
Searching for Cells
Grün’s algorithm detected enteroendocrine cells, rare, hormone-producing intestinal cells marked by high expression of the Reg4 gene. But the team delved deeper, enriching a new set of organoids for these rare cells and mining for previously unknown enteroendocrine subtypes. The tool was sensitive enough to pick up cell types represented by only a single cell.
Development
Source – The Scientist
Related Posts
Benchmarking RNA sequencing for more accurate alternative splicing analysis
RNA Sequencing identifies new tick-borne virus that causes flu-like illness
Small RNA sequencing reveals regulatory roles for sdRNAs in acute myeloid leukemia
POND-seq enables non-destructive RNA sequencing in living cells
Worm’s radical transformation shows metamorphosis can change the functions of cells
New method allows scientists to follow gene activity over time in the same cells
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
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