Identifying Metabolic Subpopulations from Population Level Mass Spectrometry
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Mendeley | Further Information

{"title"=>"Identifying metabolic subpopulations from population level mass spectrometry", "type"=>"journal", "authors"=>[{"first_name"=>"Christine M.", "last_name"=>"DeGennaro", "scopus_author_id"=>"55915246700"}, {"first_name"=>"Yonatan", "last_name"=>"Savir", "scopus_author_id"=>"24465642600"}, {"first_name"=>"Michael", "last_name"=>"Springer", "scopus_author_id"=>"7103099984"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0151659", "sgr"=>"84978063289", "isbn"=>"1932-6203", "pmid"=>"26986964", "issn"=>"19326203", "scopus"=>"2-s2.0-84978063289", "pui"=>"611090273"}, "id"=>"cfb23493-3a7d-3f20-a7fa-b6431db09498", "abstract"=>"Metabolism underlies many important cellular decisions, such as the decisions to proliferate and differentiate, and defects in metabolic signaling can lead to disease and aging. In addition, metabolic heterogeneity can have biological consequences, such as differences in outcomes and drug susceptibilities in cancer and antibiotic treatments. Many approaches exist for characterizing the metabolic state of a population of cells, but technologies for measuring metabolism at the single cell level are in the preliminary stages and are limited. Here, we describe novel analysis methodologies that can be applied to established experimental methods to measure metabolic variability within a population. We use mass spectrometry to analyze amino acid composition in cells grown in a mixture of 12C- and 13C-labeled sugars; these measurements allow us to quantify the variability in sugar usage and thereby infer information about the behavior of cells within the population. The methodologies described here can be applied to a large range of metabolites and macromolecules and therefore have the potential for broad applications.", "link"=>"http://www.mendeley.com/research/identifying-metabolic-subpopulations-population-level-mass-spectrometry", "reader_count"=>19, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Student > Doctoral Student"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>1, "Researcher"=>6, "Student > Ph. D. Student"=>8, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Student > Doctoral Student"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Engineering"=>1, "Biochemistry, Genetics and Molecular Biology"=>5, "Agricultural and Biological Sciences"=>8, "Physics and Astronomy"=>1, "Chemical Engineering"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>5}, "Unspecified"=>{"Unspecified"=>3}, "Chemical Engineering"=>{"Chemical Engineering"=>1}}, "group_count"=>0}

Scopus | Further Information

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  • {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
  • {"unique-ip"=>"4", "full-text"=>"2", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
  • {"unique-ip"=>"3", "full-text"=>"3", "pdf"=>"0", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}

Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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