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| Description
| - The combination of color-coded microspheres as carriers and flow cytometry as a detection platform provides new opportunities for multiplexed measurement of biomolecules. Here, we developed a software tool capable of automated gating of color-coded microspheres, automatic extraction of statistics from all subsets and validation, normalization, and cross-sample analysis. The approach presented in this article enabled us to harness the power of high-content cellular proteomics. In size exclusion chromatography-resolved microsphere-based affinity proteomics (Size-MAP), antibody-coupled microspheres are used to measure biotinylated proteins that have been separated by size exclusion chromatography. The captured proteins are labeled with streptavidin phycoerythrin and detected by multicolor flow cytometry. When the results from multiple size exclusion chromatography fractions are combined, binding is detected as discrete reactivity peaks (entities). The information obtained might be approximated to a multiplexed western blot. We used a microsphere set with >1,000 subsets, presenting an approach to extract biologically relevant information. The R-project environment was used to sequentially recognize subsets in two-dimensional space and gate them. The aim was to extract the median streptavidin phycoerythrin fluorescence intensity for all 1,000+ microsphere subsets from a series of 96 measured samples. The resulting text files were subjected to algorithms that identified entities across the 24 fractions. Thus, the original 24 data points for each antibody were compressed to 14 integrated values representing the areas of individual antibody reactivity peaks. Finally, we provide experimental data on cellular protein changes induced by treatment of leukemia cells with imatinib mesylate. The approach presented here exemplifies how large-scale flow cytometry data analysis can be efficiently processed to employ flow cytometry as a high-content proteomics method.
- The combination of color-coded microspheres as carriers and flow cytometry as a detection platform provides new opportunities for multiplexed measurement of biomolecules. Here, we developed a software tool capable of automated gating of color-coded microspheres, automatic extraction of statistics from all subsets and validation, normalization, and cross-sample analysis. The approach presented in this article enabled us to harness the power of high-content cellular proteomics. In size exclusion chromatography-resolved microsphere-based affinity proteomics (Size-MAP), antibody-coupled microspheres are used to measure biotinylated proteins that have been separated by size exclusion chromatography. The captured proteins are labeled with streptavidin phycoerythrin and detected by multicolor flow cytometry. When the results from multiple size exclusion chromatography fractions are combined, binding is detected as discrete reactivity peaks (entities). The information obtained might be approximated to a multiplexed western blot. We used a microsphere set with >1,000 subsets, presenting an approach to extract biologically relevant information. The R-project environment was used to sequentially recognize subsets in two-dimensional space and gate them. The aim was to extract the median streptavidin phycoerythrin fluorescence intensity for all 1,000+ microsphere subsets from a series of 96 measured samples. The resulting text files were subjected to algorithms that identified entities across the 24 fractions. Thus, the original 24 data points for each antibody were compressed to 14 integrated values representing the areas of individual antibody reactivity peaks. Finally, we provide experimental data on cellular protein changes induced by treatment of leukemia cells with imatinib mesylate. The approach presented here exemplifies how large-scale flow cytometry data analysis can be efficiently processed to employ flow cytometry as a high-content proteomics method. (en)
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| Title
| - An automated analysis of highly complex flow cytometry-based proteomic data
- An automated analysis of highly complex flow cytometry-based proteomic data (en)
|
| skos:prefLabel
| - An automated analysis of highly complex flow cytometry-based proteomic data
- An automated analysis of highly complex flow cytometry-based proteomic data (en)
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| skos:notation
| - RIV/00064203:_____/12:8071!RIV13-MZ0-00064203
|
| http://linked.open...avai/riv/aktivita
| |
| http://linked.open...avai/riv/aktivity
| - P(2B06064), P(7F09102), P(NS10473), Z(MSM0021620813)
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
| |
| http://linked.open...ai/riv/idVysledku
| - RIV/00064203:_____/12:8071
|
| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - protein array; flow cytometry; data analysis; bioinformatics; acute lymphoblastic-leukemia; chronic lymphoproliferative disorders; chromosome-positive leukemia; chronic myeloid-leukemia; bcr-abl; philadelphia-chromosome; clinical resistance; induced apoptosis; cell lines; kinase (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
| - US - Spojené státy americké
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| http://linked.open...ontrolniKodProRIV
| |
| http://linked.open...i/riv/nazevZdroje
| |
| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
| |
| http://linked.open...vavai/riv/projekt
| |
| http://linked.open...UplatneniVysledku
| |
| http://linked.open...v/svazekPeriodika
| |
| http://linked.open...iv/tvurceVysledku
| - Kalina, Tomáš
- Hrušák, Ondřej
- Holm, A.
- Kanderová, Veronika
- Lund-Johansen, F.
- Stuchlý, Jan
- Wu, WW
- Černá, Daniela
- Fišer, Karel
|
| http://linked.open...ain/vavai/riv/wos
| |
| http://linked.open...n/vavai/riv/zamer
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| issn
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| number of pages
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