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Deborah H Lundgren

Publications and source records attributed to Deborah H Lundgren.

2 recordsLinked to original sources

Systematic characterization of nuclear proteome during apoptosis: a quantitative proteomic study by differential extraction and stable isotope labeling.

Identification and characterization of the nuclear proteome is important for detailed understanding of multiple signaling events in eukaryotic cells. Toward this goal, we extensively characterized the nuclear proteome of human T leukemia cells by sequential extraction of nuclear proteins with different physicochemical properties using three buffer conditions. This large scale proteomic study also tested the feasibility and technical challenges associated with stable isotope labeling by amino acids in cell culture (SILAC) to uncover quantitative changes during apoptosis. Analyzing proteins from three nuclear fractions extracted from naive and apoptotic cells generated 780,530 MS/MS spectra that were used for database searching using the SEQUEST algorithm. This analysis resulted in the identification and quantification of 1,174 putative nuclear proteins. A number of known nuclear proteins involved in apoptosis as well as novel proteins not known to be part of the nuclear apoptotic machinery were identified and quantified. Consistent with SILAC-based quantifications, immunofluorescence staining of nucleus, mitochondria, and some associated proteins from both organelles revealed a dynamic recruitment of mitochondria into nuclear invaginations during apoptosis.

Amino Acid Sequence↗

PROTEOME-3D: an interactive bioinformatics tool for large-scale data exploration and knowledge discovery.

Comprehensive understanding of biological systems requires efficient and systematic assimilation of high-throughput datasets in the context of the existing knowledge base. A major limitation in the field of proteomics is the lack of an appropriate software platform that can synthesize a large number of experimental datasets in the context of the existing knowledge base. Here, we describe a software platform, termed PROTEOME-3D, that utilizes three essential features for systematic analysis of proteomics data: creation of a scalable, queryable, customized database for identified proteins from published literature; graphical tools for displaying proteome landscapes and trends from multiple large-scale experiments; and interactive data analysis that facilitates identification of crucial networks and pathways. Thus, PROTEOME-3D offers a standardized platform to analyze high-throughput experimental datasets for the identification of crucial players in co-regulated pathways and cellular processes.

Computational Biology↗