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Quantitative whole-cell MALDI-TOF MS fingerprints distinguishes human monocyte sub-populations activated by distinct microbial ligands

Overview of attention for article published in BMC Biotechnology, April 2015
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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 (83rd percentile)

Mentioned by

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1 news outlet
twitter
1 tweeter

Citations

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20 Dimensions

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57 Mendeley
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Title
Quantitative whole-cell MALDI-TOF MS fingerprints distinguishes human monocyte sub-populations activated by distinct microbial ligands
Published in
BMC Biotechnology, April 2015
DOI 10.1186/s12896-015-0140-1
Pubmed ID
Authors

Damien Portevin, Valentin Pflüger, Patricia Otieno, René Brunisholz, Guido Vogel, Claudia Daubenberger

Abstract

Conventionally, human monocyte sub-populations are classified according to surface marker expression into classical (CD14(++)CD16(-)), intermediate (CD14(++)CD16(+)) and non-classical (CD14(+)CD16(++)) lineages. The involvement of non-classical monocytes, also referred to as proinflammatory monocytes, in the pathophysiology of diseases including diabetes mellitus, atherosclerosis or Alzheimer's disease is well recognized. The development of novel high-throughput methods to capture functional states within the different monocyte lineages at the whole cell proteomic level will enable real time monitoring of disease states. We isolated and characterized (pan-) monocytes, mostly composed of classical CD16(-) monocytes, versus autologous CD16(+) subpopulations from the blood of healthy human donors (n = 8) and compared their inflammatory properties in response to lipopolysaccharides and M.tuberculosis antigens by multiplex cytokine profiling. Following resting and in vitro antigenic stimulation, cells were recovered and subjected to whole-cell mass spectrometry analysis. This approach identified the specific presence/absence of m/z peaks and therefore potential biomarkers that can discriminate pan-monocytes from their CD16 counterparts. Furthermore, we found that semi-quantitative data analysis could capture the subtle proteome changes occurring upon microbial stimulation that differentiate resting, from lipopolysaccharides or M. tuberculosis stimulated monocytic samples. Whole-cell mass spectrometry fingerprinting could efficiently distinguish monocytic sub-populations that arose from a same hematopoietic lineage. We also demonstrate for the first time that mass spectrometry signatures can monitor semi-quantitatively specific activation status in response to exogenous stimulation. As such, this approach stands as a fast and efficient method for the applied immunology field to assess the reactivity of potentially any immune cell types that may sustain health or promote related inflammatory diseases.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 57 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 12 21%
Student > Bachelor 10 18%
Student > Ph. D. Student 8 14%
Student > Master 7 12%
Lecturer > Senior Lecturer 3 5%
Other 11 19%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 23%
Biochemistry, Genetics and Molecular Biology 10 18%
Chemistry 6 11%
Medicine and Dentistry 6 11%
Immunology and Microbiology 5 9%
Other 10 18%
Unknown 7 12%

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 25 February 2016.
All research outputs
#2,633,754
of 19,211,930 outputs
Outputs from BMC Biotechnology
#133
of 884 outputs
Outputs of similar age
#39,276
of 239,671 outputs
Outputs of similar age from BMC Biotechnology
#1
of 1 outputs
Altmetric has tracked 19,211,930 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 884 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.6. This one has done well, scoring higher than 83% 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 239,671 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% 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