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Harnessing single-cell genomics to improve the physiological fidelity of organoid-derived cell types

Overview of attention for article published in BMC Biology, June 2018
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (94th percentile)

Mentioned by

news
3 news outlets
blogs
1 blog
twitter
41 tweeters

Citations

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24 Dimensions

Readers on

mendeley
130 Mendeley
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Title
Harnessing single-cell genomics to improve the physiological fidelity of organoid-derived cell types
Published in
BMC Biology, June 2018
DOI 10.1186/s12915-018-0527-2
Pubmed ID
Authors

Benjamin E. Mead, Jose Ordovas-Montanes, Alexandra P. Braun, Lauren E. Levy, Prerna Bhargava, Matthew J. Szucs, Dustin A. Ammendolia, Melanie A. MacMullan, Xiaolei Yin, Travis K. Hughes, Marc H. Wadsworth, Rushdy Ahmad, Seth Rakoff-Nahoum, Steven A. Carr, Robert Langer, James J. Collins, Alex K. Shalek, Jeffrey M. Karp

Abstract

Single-cell genomic methods now provide unprecedented resolution for characterizing the component cell types and states of tissues such as the epithelial subsets of the gastrointestinal tract. Nevertheless, functional studies of these subsets at scale require faithful in vitro models of identified in vivo biology. While intestinal organoids have been invaluable in providing mechanistic insights in vitro, the extent to which organoid-derived cell types recapitulate their in vivo counterparts remains formally untested, with no systematic approach for improving model fidelity. Here, we present a generally applicable framework that utilizes massively parallel single-cell RNA-seq to compare cell types and states found in vivo to those of in vitro models such as organoids. Furthermore, we leverage identified discrepancies to improve model fidelity. Using the Paneth cell (PC), which supports the stem cell niche and produces the largest diversity of antimicrobials in the small intestine, as an exemplar, we uncover fundamental gene expression differences in lineage-defining genes between in vivo PCs and those of the current in vitro organoid model. With this information, we nominate a molecular intervention to rationally improve the physiological fidelity of our in vitro PCs. We then perform transcriptomic, cytometric, morphologic and proteomic characterization, and demonstrate functional (antimicrobial activity, niche support) improvements in PC physiology. Our systematic approach provides a simple workflow for identifying the limitations of in vitro models and enhancing their physiological fidelity. Using adult stem cell-derived PCs within intestinal organoids as a model system, we successfully benchmark organoid representation, relative to that in vivo, of a specialized cell type and use this comparison to generate a functionally improved in vitro PC population. We predict that the generation of rationally improved cellular models will facilitate mechanistic exploration of specific disease-associated genes in their respective cell types.

Twitter Demographics

The data shown below were collected from the profiles of 41 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 130 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 130 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 35 27%
Researcher 30 23%
Student > Master 8 6%
Student > Bachelor 8 6%
Other 8 6%
Other 19 15%
Unknown 22 17%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 33 25%
Agricultural and Biological Sciences 32 25%
Medicine and Dentistry 13 10%
Chemical Engineering 5 4%
Engineering 4 3%
Other 16 12%
Unknown 27 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 45. 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 22 August 2021.
All research outputs
#619,598
of 18,763,644 outputs
Outputs from BMC Biology
#159
of 1,607 outputs
Outputs of similar age
#17,353
of 292,002 outputs
Outputs of similar age from BMC Biology
#1
of 1 outputs
Altmetric has tracked 18,763,644 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,607 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.1. This one has done particularly well, scoring higher than 90% 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 292,002 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 94% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them