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Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images

Overview of attention for article published in BMC Bioinformatics, October 2016
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Title
Subcellular protein expression models for microsatellite instability in colorectal adenocarcinoma tissue images
Published in
BMC Bioinformatics, October 2016
DOI 10.1186/s12859-016-1243-y
Pubmed ID
Authors

Violeta N. Kovacheva, Nasir M. Rajpoot

Abstract

New bioimaging techniques capable of visualising the co-location of numerous proteins within individual cells have been proposed to study tumour heterogeneity of neighbouring cells within the same tissue specimen. These techniques have highlighted the need to better understand the interplay between proteins in terms of their colocalisation. We recently proposed a cellular-level model of the healthy and cancerous colonic crypt microenvironments. Here, we extend the model to include detailed models of protein expression to generate synthetic multiplex fluorescence data. As a first step, we present models for various cell organelles learned from real immunofluorescence data from the Human Protein Atlas. Comparison between the distribution of various features obtained from the real and synthetic organelles has shown very good agreement. This has included both features that have been used as part of the model input and ones that have not been explicitly considered. We then develop models for six proteins which are important colorectal cancer biomarkers and are associated with microsatellite instability, namely MLH1, PMS2, MSH2, MSH6, P53 and PTEN. The protein models include their complex expression patterns and which cell phenotypes express them. The models have been validated by comparing distributions of real and synthesised parameters and by application of frameworks for analysing multiplex immunofluorescence image data. The six proteins have been chosen as a case study to illustrate how the model can be used to generate synthetic multiplex immunofluorescence data. Further proteins could be included within the model in a similar manner to enable the study of a larger set of proteins of interest and their interactions. To the best of our knowledge, this is the first model for expression of multiple proteins in anatomically intact tissue, rather than within cells in culture.

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Geographical breakdown

Country Count As %
Unknown 8 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 4 50%
Student > Ph. D. Student 1 13%
Student > Doctoral Student 1 13%
Researcher 1 13%
Unknown 1 13%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 25%
Computer Science 2 25%
Engineering 2 25%
Medicine and Dentistry 1 13%
Unknown 1 13%
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 22 October 2016.
All research outputs
#18,478,448
of 22,896,955 outputs
Outputs from BMC Bioinformatics
#6,331
of 7,299 outputs
Outputs of similar age
#238,656
of 315,610 outputs
Outputs of similar age from BMC Bioinformatics
#94
of 118 outputs
Altmetric has tracked 22,896,955 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,299 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 5th percentile – i.e., 5% of its peers scored the same or lower than it.
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