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Antibody-based subcellular localization of the human proteomeSkogs, Marie January 2016 (has links)
This thesis describes the use of antibodies and immunofluorescence for subcellular localization of proteins. The key objective is the creation of an open-source atlas with information on the subcellular location of every human protein. Knowledge of the spatial distribution and the precise location of a protein within a cell is important for its functional characterization, and describing the human proteome in terms of compartment proteomes is important to decipher cellular organization and function. Immunofluorescence and confocal microscopy of cultured cells were used for high-resolution detection of proteins on a high-throughput scale. Critical to immunofluorescence results are sample preparation and specific antibodies. Antibody staining of cells requires fixation and permeabilization, both of which can result in loss or redistribution of proteins and masking of epitopes. A high-throughput approach demands a standardized protocol suitable for the majority of proteins across cellular compartments. Paper I presents an evaluation of sample preparation techniques from which such a single fixation and permeabilization protocol was optimized. Paper II describes the results from applying this protocol to 4000 human proteins in three cell lines of different origin. Paper III presents a strategy for application-specific antibody validation. Antibodies are the key reagents in immunofluorescence, but all antibodies have potential for off-target binding and should be validated thoroughly. Antibody performance varies across sample types and applications due to the competition present and the effect of the sample preparation on antigen accessibility. In this paper application-specific validation for immunofluorescence was conducted using colocalization with fluorescently tagged protein in transgenic cell lines. / <p>QC 20160509</p>
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Spatial proteome profiling of the compartments of the human cell using an antibody-based approachWiking, Mikaela January 2017 (has links)
The human cell is complex, with countless processes ongoing in parallel in specialized compartments, the organelles. Cells can be studied in vitro by using immortalized cell lines that represent cells in vivo to a varying degree. Gene expression varies between cell types and an average cell line expresses around 10,000-12,000 genes, as measured with RNA sequencing. These genes encode the cell’s proteome; the full set of proteins that perform functions in the cell. In paper I we show that RNA sequencing is a necessary tool for studying the proteome of the human cell. By studying the proteome, and proteins’ localization in the cell, information can be assembled on how the cell functions. Image-based methods allow for detailed spatial resolution of protein localization as well as enable the study of temporal events. Visualization of a protein can be accomplished by using either a cell line that is transfected to express the protein with a fluorescent tag, or by targeting the protein with an affinity reagent such as an antibody. In paper II we present subcellular data for a majority of the human proteins, showing that there is a high degree of complexity in regard to where proteins localize in the cell. Cellular energy is generated in the mitochondria, an important organelle that is also active in many other different functions. Today approximately only a third of the estimated mitochondrial proteome has been validated experimentally, indicating that there is much more to understand with regard to the functions of the mitochondria. In paper III we explore the mitochondrial proteome, based on the results of paper II. We also present a method for sublocalizing proteins to subcompartments that can be performed in a high-throughput manner. To conclude, this thesis shows that transcriptomics is a useful tool for proteome-wide subcellular localization, and presents high-resolution spatial distribution data for the human cell with a deeper analysis of the mitochondrial proteome. / <p>QC 20170512</p>
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