↓ Skip to main content

Defining cell-type specificity at the transcriptional level in human disease

Overview of attention for article published in Genome Research, August 2013
Altmetric Badge

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 (97th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

news
9 news outlets
blogs
2 blogs
twitter
21 X users
patent
1 patent
facebook
1 Facebook page

Citations

dimensions_citation
200 Dimensions

Readers on

mendeley
218 Mendeley
citeulike
7 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Defining cell-type specificity at the transcriptional level in human disease
Published in
Genome Research, August 2013
DOI 10.1101/gr.155697.113
Pubmed ID
Authors

Wenjun Ju, Casey S. Greene, Felix Eichinger, Viji Nair, Jeffrey B. Hodgin, Markus Bitzer, Young-suk Lee, Qian Zhu, Masami Kehata, Min Li, Song Jiang, Maria Pia Rastaldi, Clemens D. Cohen, Olga G. Troyanskaya, Matthias Kretzler

Abstract

Cell-lineage-specific transcripts are essential for differentiated tissue function, implicated in hereditary organ failure, and mediate acquired chronic diseases. However, experimental identification of cell-lineage-specific genes in a genome-scale manner is infeasible for most solid human tissues. We developed the first genome-scale method to identify genes with cell-lineage-specific expression, even in lineages not separable by experimental microdissection. Our machine-learning-based approach leverages high-throughput data from tissue homogenates in a novel iterative statistical framework. We applied this method to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary and most acquired glomerular kidney disease. In a systematic evaluation of our predictions by immunohistochemistry, our in silico approach was significantly more accurate (65% accuracy in human) than predictions based on direct measurement of in vivo fluorescence-tagged murine podocytes (23%). Our method identified genes implicated as causal in hereditary glomerular disease and involved in molecular pathways of acquired and chronic renal diseases. Furthermore, based on expression analysis of human kidney disease biopsies, we demonstrated that expression of the podocyte genes identified by our approach is significantly related to the degree of renal impairment in patients. Our approach is broadly applicable to define lineage specificity in both cell physiology and human disease contexts. We provide a user-friendly website that enables researchers to apply this method to any cell-lineage or tissue of interest. Identified cell-lineage-specific transcripts are expected to play essential tissue-specific roles in organogenesis and disease and can provide starting points for the development of organ-specific diagnostics and therapies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 21 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 218 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 7 3%
United Kingdom 2 <1%
Germany 2 <1%
Denmark 2 <1%
Netherlands 1 <1%
Switzerland 1 <1%
Canada 1 <1%
Japan 1 <1%
Spain 1 <1%
Other 0 0%
Unknown 200 92%

Demographic breakdown

Readers by professional status Count As %
Researcher 50 23%
Student > Ph. D. Student 46 21%
Student > Master 20 9%
Student > Bachelor 19 9%
Professor > Associate Professor 18 8%
Other 37 17%
Unknown 28 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 72 33%
Biochemistry, Genetics and Molecular Biology 46 21%
Medicine and Dentistry 31 14%
Computer Science 11 5%
Engineering 5 2%
Other 20 9%
Unknown 33 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 88. 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 31 October 2023.
All research outputs
#491,332
of 25,837,817 outputs
Outputs from Genome Research
#121
of 4,469 outputs
Outputs of similar age
#3,566
of 210,823 outputs
Outputs of similar age from Genome Research
#1
of 50 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 4,469 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 16.9. This one has done particularly well, scoring higher than 96% 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 210,823 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 97% of its contemporaries.
We're also able to compare this research output to 50 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.