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In silico lineage tracing through single cell transcriptomics identifies a neural stem cell population in planarians

Overview of attention for article published in Genome Biology, April 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (52nd percentile)

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Title
In silico lineage tracing through single cell transcriptomics identifies a neural stem cell population in planarians
Published in
Genome Biology, April 2016
DOI 10.1186/s13059-016-0937-9
Pubmed ID
Authors

Alyssa M. Molinaro, Bret J. Pearson

Abstract

The planarian Schmidtea mediterranea is a master regenerator with a large adult stem cell compartment. The lack of transgenic labeling techniques in this animal has hindered the study of lineage progression and has made understanding the mechanisms of tissue regeneration a challenge. However, recent advances in single-cell transcriptomics and analysis methods allow for the discovery of novel cell lineages as differentiation progresses from stem cell to terminally differentiated cell. Here we apply pseudotime analysis and single-cell transcriptomics to identify adult stem cells belonging to specific cellular lineages and identify novel candidate genes for future in vivo lineage studies. We purify 168 single stem and progeny cells from the planarian head, which were subjected to single-cell RNA sequencing (scRNAseq). Pseudotime analysis with Waterfall and gene set enrichment analysis predicts a molecularly distinct neoblast sub-population with neural character (νNeoblasts) as well as a novel alternative lineage. Using the predicted νNeoblast markers, we demonstrate that a novel proliferative stem cell population exists adjacent to the brain. scRNAseq coupled with in silico lineage analysis offers a new approach for studying lineage progression in planarians. The lineages identified here are extracted from a highly heterogeneous dataset with minimal prior knowledge of planarian lineages, demonstrating that lineage purification by transgenic labeling is not a prerequisite for this approach. The identification of the νNeoblast lineage demonstrates the usefulness of the planarian system for computationally predicting cellular lineages in an adult context coupled with in vivo verification.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 167 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 1%
Spain 1 <1%
Unknown 164 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 52 31%
Student > Bachelor 27 16%
Researcher 23 14%
Student > Master 13 8%
Student > Doctoral Student 7 4%
Other 20 12%
Unknown 25 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 58 35%
Agricultural and Biological Sciences 53 32%
Neuroscience 11 7%
Computer Science 6 4%
Medicine and Dentistry 5 3%
Other 4 2%
Unknown 30 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 20. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 June 2016.
All research outputs
#1,888,249
of 25,371,288 outputs
Outputs from Genome Biology
#1,575
of 4,467 outputs
Outputs of similar age
#30,406
of 312,583 outputs
Outputs of similar age from Genome Biology
#38
of 80 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,467 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.6. This one has gotten more attention than average, scoring higher than 64% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 312,583 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 52% of its contemporaries.