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Automated analysis of single cells using Laser Tweezers Raman Spectroscopy

Overview of attention for article published in Analyst, January 2016
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40 Mendeley
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Title
Automated analysis of single cells using Laser Tweezers Raman Spectroscopy
Published in
Analyst, January 2016
DOI 10.1039/c5an01851j
Pubmed ID
Authors

S. Casabella, P. Scully, N. Goddard, P. Gardner

Abstract

In recent years, significant progress has been made into the label-free detection and discrimination of individual cancer cells using Laser Tweezers Raman Spectroscopy (LTRS). However, the majority of examples reported have involved manual trapping of cells, which is time consuming and may lead to different cell lines being analysed in discrete batches. A simple, low-cost microfluidic flow chamber is introduced which allows single cells to be optically trapped and analysed in an automated fashion, greatly reducing the level of operator input required. Two implementations of the flow chamber are discussed here; a basic single-channel device in which the fluid velocity is controlled manually, and a dual-channel device which permits the automated capture and analysis of multiple cell lines with no operator input. Results are presented for the discrimination of live epithelial prostate cells and lymphocytes, together with a consideration of the consequences of traditional 'batch analysis' typically used for LTRS of live cells.

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 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 40 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 40 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 28%
Student > Ph. D. Student 8 20%
Student > Master 4 10%
Other 3 8%
Professor 3 8%
Other 5 13%
Unknown 6 15%
Readers by discipline Count As %
Engineering 9 23%
Chemistry 6 15%
Physics and Astronomy 5 13%
Biochemistry, Genetics and Molecular Biology 4 10%
Materials Science 3 8%
Other 6 15%
Unknown 7 18%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 17 January 2016.
All research outputs
#17,777,370
of 22,833,393 outputs
Outputs from Analyst
#4,226
of 5,799 outputs
Outputs of similar age
#267,681
of 393,581 outputs
Outputs of similar age from Analyst
#219
of 503 outputs
Altmetric has tracked 22,833,393 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,799 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 24th percentile – i.e., 24% 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 393,581 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 503 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 50% of its contemporaries.