| Title |
Integration of Lyoplate Based Flow Cytometry and Computational Analysis for Standardized Immunological Biomarker Discovery
|
|---|---|
| Published in |
PLOS ONE, July 2013
|
| DOI | 10.1371/journal.pone.0065485 |
| Pubmed ID | |
| Authors |
Federica Villanova, Paola Di Meglio, Margaret Inokuma, Nima Aghaeepour, Esperanza Perucha, Jennifer Mollon, Laurel Nomura, Maria Hernandez-Fuentes, Andrew Cope, A. Toby Prevost, Susanne Heck, Vernon Maino, Graham Lord, Ryan R. Brinkman, Frank O. Nestle |
| Abstract |
Discovery of novel immune biomarkers for monitoring of disease prognosis and response to therapy in immune-mediated inflammatory diseases is an important unmet clinical need. Here, we establish a novel framework for immunological biomarker discovery, comparing a conventional (liquid) flow cytometry platform (CFP) and a unique lyoplate-based flow cytometry platform (LFP) in combination with advanced computational data analysis. We demonstrate that LFP had higher sensitivity compared to CFP, with increased detection of cytokines (IFN-γ and IL-10) and activation markers (Foxp3 and CD25). Fluorescent intensity of cells stained with lyophilized antibodies was increased compared to cells stained with liquid antibodies. LFP, using a plate loader, allowed medium-throughput processing of samples with comparable intra- and inter-assay variability between platforms. Automated computational analysis identified novel immunophenotypes that were not detected with manual analysis. Our results establish a new flow cytometry platform for standardized and rapid immunological biomarker discovery with wide application to immune-mediated diseases. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Czechia | 1 | 2% |
| Unknown | 45 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 39% |
| Student > Ph. D. Student | 5 | 11% |
| Other | 4 | 9% |
| Student > Bachelor | 3 | 7% |
| Professor > Associate Professor | 3 | 7% |
| Other | 6 | 13% |
| Unknown | 7 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 24% |
| Biochemistry, Genetics and Molecular Biology | 7 | 15% |
| Immunology and Microbiology | 6 | 13% |
| Medicine and Dentistry | 6 | 13% |
| Computer Science | 3 | 7% |
| Other | 4 | 9% |
| Unknown | 9 | 20% |