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J Proteomics. 2015 Oct 14;128:132-40. doi: 10.1016/j.jprot.2015.07.024. Epub 2015 Jul 29.

mIMT-visHTS: A novel method for multiplexing isobaric mass tagged datasets with an accompanying visualization high throughput screening tool for protein profiling.

Author information

1
Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
2
Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
3
Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
4
Center for Interdisciplinary Cardiovascular Sciences, Division of Cardiovascular Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA. Electronic address: sasingh@partners.org.

Abstract

Isobaric mass tagging (IMT) methods enable the analysis of thousands of proteins simultaneously. We used tandem mass tagging reagents (TMT™) to monitor the relative changes in the proteome of the mouse macrophage cell line RAW264.7 at the same six time points after no stimulation (baseline phenotype), stimulation with interferon gamma (pro-inflammatory phenotype) or stimulation with interleukin-4 (anti-inflammatory phenotype). The combined TMT datasets yielded nearly 12,000 protein profiles for comparison. To facilitate this large analysis, we developed a novel method that combines or multiplexes the separate IMT (mIMT) datasets into a single super dataset for subsequent model-based clustering and co-regulation analysis. Specially designed visual High Throughput Screening (visHTS) software screened co-regulated proteins. visHTS generates an interactive and visually intuitive color-coded bullseye plot that enables users to browse the cluster outputs and identify co-regulated proteins.

KEYWORDS:

Bioinformatics; Macrophage differentiation; Model-based clustering; Proteomics

PMID:
26232111
DOI:
10.1016/j.jprot.2015.07.024
[Indexed for MEDLINE]

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