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Sequential glycan profiling at single cell level with the microfluidic lab-in-a-trench platform: a new era in experimental cell biology

Overview of attention for article published in Lab on a Chip - Miniaturisation for Chemistry & Biology, January 2014
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Title
Sequential glycan profiling at single cell level with the microfluidic lab-in-a-trench platform: a new era in experimental cell biology
Published in
Lab on a Chip - Miniaturisation for Chemistry & Biology, January 2014
DOI 10.1039/c4lc00618f
Pubmed ID
Authors

Tríona M. O'Connell, Damien King, Chandra K. Dixit, Brendan O'Connor, Dermot Walls, Jens Ducrée

Abstract

It is now widely recognised that the earliest changes that occur on a cell when it is stressed or becoming diseased are alterations in its surface glycosylation. Current state-of-the-art technologies in glycoanalysis include mass spectrometry, protein microarray formats, techniques in cytometry and more recently, glyco-quantitative polymerase chain reaction (Glyco-qPCR). Techniques for the glycoprofiling of the surfaces of single cells are either limited to the analysis of large cell populations or are unable to handle multiple and/or sequential probing. Here, we report a novel approach of single live cell glycoprofiling enabled by the microfluidic "Lab-in-a-Trench" (LiaT) platform for performing capture and retention of cells, along with shear-free reagent loading and washing. The significant technical improvement on state-of-the-art is the demonstration of consecutive, spatio-temporally profiling of glycans on a single cell by sequential elution of the previous lectin probe using their corresponding free sugar. We have qualitatively analysed glycan density on the surface of individual cells. This has allowed us to qualitatively co-localise the observed glycans. This approach enables exhaustive glycoprofiling and glycan mapping on the surface of individual live cells with multiple lectins. The possibility of sequentially profiling glycans on cells will be a powerful new tool to add to current glycoanalytical techniques. The LiaT platform will enable cell biologists to perform many high sensitivity assays and also will also make a significant impact on biomarker research.

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The data shown below were collected from the profiles of 2 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 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Ireland 2 4%
United Kingdom 1 2%
France 1 2%
Unknown 41 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 31%
Researcher 9 20%
Student > Bachelor 4 9%
Student > Doctoral Student 3 7%
Professor 3 7%
Other 6 13%
Unknown 6 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 13 29%
Engineering 9 20%
Chemistry 5 11%
Biochemistry, Genetics and Molecular Biology 4 9%
Physics and Astronomy 2 4%
Other 4 9%
Unknown 8 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. 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 July 2014.
All research outputs
#17,517,953
of 25,756,911 outputs
Outputs from Lab on a Chip - Miniaturisation for Chemistry & Biology
#4,630
of 5,997 outputs
Outputs of similar age
#204,461
of 321,307 outputs
Outputs of similar age from Lab on a Chip - Miniaturisation for Chemistry & Biology
#246
of 358 outputs
Altmetric has tracked 25,756,911 research outputs across all sources so far. This one is in the 31st percentile – i.e., 31% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,997 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
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 321,307 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 358 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.