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Optimization of Immunofluorescent Detection of Bone Marrow Disseminated Tumor Cells

Overview of attention for article published in Biological Procedures Online, July 2018
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Title
Optimization of Immunofluorescent Detection of Bone Marrow Disseminated Tumor Cells
Published in
Biological Procedures Online, July 2018
DOI 10.1186/s12575-018-0078-5
Pubmed ID
Authors

Haley D. Axelrod, Kenneth J. Pienta, Kenneth C. Valkenburg

Abstract

Cancer metastasis is the primary cause of cancer-related deaths and remains incurable. Current clinical methods for predicting metastatic recurrence are not sensitive enough to detect individual cancer cells in the body; therefore, current efforts are directed toward liquid biopsy-based assays to capture circulating and disseminated tumor cells (CTCs and DTCs) in the blood and bone marrow, respectively. The most promising strategy is fluorescence-based immunostaining using cancer cell-specific markers. However, despite recent efforts to develop robust processing and staining platforms, results from these platforms have been discordant among groups, particularly for DTC detection. While the choice of cancer cell-specific markers is a large factor in this discordance, we have found that marker-independent factors causing false signal are just as critical to consider. Bone marrow is particularly challenging to analyze by immunostaining because endogenous immune cell properties and bone marrow matrix components typically generate false staining. For immunostaining of whole tumor tissue containing ample cancer cells, this background staining can be overcome. Application of fluorescent-based staining for rare cells, however, is easily jeopardized by immune cells and autofluorescence that lead to false signal. We have specifically found two types of background staining in bone marrow samples: autofluorescence of the tissue and non-specific binding of secondary antibodies. We systematically optimized a basic immunofluorescence protocol to eliminate this background using cancer cells spiked into human bone marrow. This enhanced the specificity of automated scanning detection software. Our optimized protocol also outperformed a commercial rare cell detection protocol in detecting candidate DTCs from metastatic patient bone marrow. Robust optimization to increase the signal-to-noise ratio of immunofluorescent staining of bone marrow is required in order to achieve the necessary sensitivity and specificity for rare cell detection. Background immunofluorescent staining in bone marrow causes uncertainty and inconsistency among investigators, which can be overcome by systematically addressing each contributing source. Our optimized assay eliminates sources of background signal, and is adaptable to automated staining platforms for high throughput analysis.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 33%
Student > Bachelor 5 19%
Researcher 4 15%
Student > Master 3 11%
Other 2 7%
Other 1 4%
Unknown 3 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 9 33%
Neuroscience 2 7%
Engineering 2 7%
Medicine and Dentistry 2 7%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 4 15%
Unknown 7 26%
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 11 July 2018.
All research outputs
#22,767,715
of 25,382,440 outputs
Outputs from Biological Procedures Online
#171
of 192 outputs
Outputs of similar age
#299,743
of 341,606 outputs
Outputs of similar age from Biological Procedures Online
#2
of 3 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 192 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
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