Biophysical Characteristics Reveal Neural Stem Cell Differentiation Potential
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{"title"=>"Biophysical characteristics reveal neural stem cell differentiation potential", "type"=>"journal", "authors"=>[{"first_name"=>"Fatima H.", "last_name"=>"Labeed", "scopus_author_id"=>"8610903600"}, {"first_name"=>"Jente", "last_name"=>"Lu", "scopus_author_id"=>"24171794000"}, {"first_name"=>"Hayley J.", "last_name"=>"Mulhall", "scopus_author_id"=>"36630742200"}, {"first_name"=>"Steve A.", "last_name"=>"Marchenko", "scopus_author_id"=>"22941412300"}, {"first_name"=>"Kai F.", "last_name"=>"Hoettges", "scopus_author_id"=>"6507608487"}, {"first_name"=>"Laura C.", "last_name"=>"Estrada", "scopus_author_id"=>"25824788000"}, {"first_name"=>"Abraham P.", "last_name"=>"Lee", "scopus_author_id"=>"7405631407"}, {"first_name"=>"Michael P.", "last_name"=>"Hughes", "scopus_author_id"=>"35616308800"}, {"first_name"=>"Lisa A.", "last_name"=>"Flanagan", "scopus_author_id"=>"7006598952"}], "year"=>2011, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-80053596591", "sgr"=>"80053596591", "pui"=>"362706622", "pmid"=>"21980464", "doi"=>"10.1371/journal.pone.0025458"}, "id"=>"30acdabb-9123-36ff-8146-dba850f7212f", "abstract"=>"BACKGROUND: Distinguishing human neural stem/progenitor cell (huNSPC) populations that will predominantly generate neurons from those that produce glia is currently hampered by a lack of sufficient cell type-specific surface markers predictive of fate potential. This limits investigation of lineage-biased progenitors and their potential use as therapeutic agents. A live-cell biophysical and label-free measure of fate potential would solve this problem by obviating the need for specific cell surface markers.\\n\\nMETHODOLOGY/PRINCIPAL FINDINGS: We used dielectrophoresis (DEP) to analyze the biophysical, specifically electrophysiological, properties of cortical human and mouse NSPCs that vary in differentiation potential. Our data demonstrate that the electrophysiological property membrane capacitance inversely correlates with the neurogenic potential of NSPCs. Furthermore, as huNSPCs are continually passaged they decrease neuron generation and increase membrane capacitance, confirming that this parameter dynamically predicts and negatively correlates with neurogenic potential. In contrast, differences in membrane conductance between NSPCs do not consistently correlate with the ability of the cells to generate neurons. DEP crossover frequency, which is a quantitative measure of cell behavior in DEP, directly correlates with neuron generation of NSPCs, indicating a potential mechanism to separate stem cells biased to particular differentiated cell fates.\\n\\nCONCLUSIONS/SIGNIFICANCE: We show here that whole cell membrane capacitance, but not membrane conductance, reflects and predicts the neurogenic potential of human and mouse NSPCs. Stem cell biophysical characteristics therefore provide a completely novel and quantitative measure of stem cell fate potential and a label-free means to identify neuron- or glial-biased progenitors.", "link"=>"http://www.mendeley.com/research/biophysical-characteristics-reveal-neural-stem-cell-differentiation-potential-5", "reader_count"=>37, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>6, "Student > Doctoral Student"=>1, "Researcher"=>4, "Student > Ph. D. Student"=>17, "Other"=>2, "Student > Master"=>3, "Student > Bachelor"=>3, "Lecturer"=>1}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>6, "Student > Doctoral Student"=>1, "Researcher"=>4, "Student > Ph. D. Student"=>17, "Other"=>2, "Student > Master"=>3, "Student > Bachelor"=>3, "Lecturer"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>12, "Biochemistry, Genetics and Molecular Biology"=>1, "Materials Science"=>1, "Agricultural and Biological Sciences"=>15, "Medicine and Dentistry"=>2, "Neuroscience"=>1, "Physics and Astronomy"=>4, "Chemistry"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>12}, "Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>2}, "Neuroscience"=>{"Neuroscience"=>1}, "Chemistry"=>{"Chemistry"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>4}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>15}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}}, "reader_count_by_country"=>{"United Kingdom"=>1, "Portugal"=>1, "Peru"=>1}, "group_count"=>0}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/729506"], "description"=>"<p>(a) Undifferentiated SC27 and SC23 huNSPCs are grown as adherent cultures on fibronectin (top panels, phase contrast images) and similarly express the stem cell markers nestin and sox2 (bottom panels, fluorescent images). Phase contrast and fluorescent images are not of the same field. (b) Representative DEP spectra of SC27 (black) and SC23 (gray) cells are shown over a range of frequencies. The circles on the graph represent measured data, whilst the line represents the DEP spectrum derived from the best-fit model (n = 5 measurements from the same set of cells in a single experiment, error bars represent s.d.). Light intensity reflects cells' positions in the well after application of the DEP force and is mathematically related to the dielectric properties of the cells (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025458#s4\" target=\"_blank\">Materials and Methods</a>) <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0025458#pone.0025458-Hoettges1\" target=\"_blank\">[41]</a>. SC27 and SC23 huNSPCs do not differ in average cell diameter (inset, p = 0.90, n = 170 or more cells from 3 or more independent experiments, values are means +/− s.e.m.).</p>", "links"=>[], "tags"=>["sets", "undifferentiated", "hunspcs", "morphology", "dielectric"], "article_id"=>399856, "categories"=>["Neuroscience", "Cell Biology", "Developmental Biology"], "users"=>["Fatima H. Labeed", "Jente Lu", "Hayley J. Mulhall", "Steve A. Marchenko", "Kai F. Hoettges", "Laura C. Estrada", "Abraham P. Lee", "Michael P. Hughes", "Lisa A. Flanagan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025458.g002", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unique_sets_of_undifferentiated_huNSPCs_that_are_similar_in_morphology_and_marker_analysis_differ_in_dielectric_properties_/399856", "title"=>"Unique sets of undifferentiated huNSPCs that are similar in morphology and marker analysis differ in dielectric properties.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-30 02:44:16"}
  • {"files"=>["https://ndownloader.figshare.com/files/729665"], "description"=>"<p>(a) Graphs show that the specific membrane capacitance (Cspec, mF = milliFarad) of undifferentiated NSPCs is inversely correlated with the ability of the cells to generate neurons (quantified as the percentage of MAP2-positive cells with extended processes after differentiation). Left panels are data from SC27 and SC23 huNSPCs and right panels are E12 and E16 mouse NSPCs (mNSPCs) (**p<0.01, n = 3 or more separate experiments with different sets of cells). (b) Membrane capacitance (Cspec) values of undifferentiated SC27 (left panel) and SC23 (right panel) cells increase over passage number (dashed blue line), concomitant with a decrease in the generation of neurons from these cells (solid green line). Significance values are reported in the text. (c) Specific membrane conductance (Gspec, S = Siemens) of undifferentiated human and mouse NSPCs does not consistently correlate with their neurogenic potential as shown by the generation of MAP2-positive cells with extended processes after differentiation (n = 3 or more separate experiments with different sets of cells). High passage SC23 huNSPCs are passage 25–28. Error bars in all graphs represent s.e.m.</p>", "links"=>[], "tags"=>["membrane", "capacitance", "correlates", "neurogenic", "conductance"], "article_id"=>400008, "categories"=>["Neuroscience", "Cell Biology", "Developmental Biology"], "users"=>["Fatima H. Labeed", "Jente Lu", "Hayley J. Mulhall", "Steve A. Marchenko", "Kai F. Hoettges", "Laura C. Estrada", "Abraham P. Lee", "Michael P. Hughes", "Lisa A. Flanagan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025458.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_NSPC_membrane_capacitance_correlates_with_neurogenic_potential_but_membrane_conductance_does_not_/400008", "title"=>"NSPC membrane capacitance correlates with neurogenic potential but membrane conductance does not.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-30 00:00:08"}
  • {"files"=>["https://ndownloader.figshare.com/files/729334"], "description"=>"<p>(a) Differentiated SC27 (left panels) and SC23 (right panels) huNSPCs are shown by phase contrast microscopy (top panels) and immunostaining for the neuronal marker MAP2 (middle panels, all cell nuclei stained blue) or the astrocytic marker GFAP (bottom panels, all cell nuclei stained blue). Images are not from the same field. (b) Percentages of differentiated neurons and astrocytes generated from SC27 and SC23 huNSPCs were quantified. Neuron counts were obtained from cells differentiated for 14 days and labelled for the neuronal markers MAP2 and doublecortin (Dcx). Cells with processes positive for MAP2 or doubly-positive for MAP2 and Dcx are expressed as a percentage of the total cells (**p<0.01, n = 5000 or more cells from 3 or more independent experiments). Astrocytes were quantified after differentiation for 7 days. Cells positive for GFAP or GFAP-positive and sox2-negative are expressed as a percentage of the total cells (**p<0.01, n = 1000 or more cells from 3 or more independent experiments). Error bars represent s.e.m.</p>", "links"=>[], "tags"=>["neurogenic", "gliogenic"], "article_id"=>399681, "categories"=>["Neuroscience", "Cell Biology", "Developmental Biology"], "users"=>["Fatima H. Labeed", "Jente Lu", "Hayley J. Mulhall", "Steve A. Marchenko", "Kai F. Hoettges", "Laura C. Estrada", "Abraham P. Lee", "Michael P. Hughes", "Lisa A. Flanagan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025458.g001", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_HuNSPCs_differ_in_neurogenic_and_gliogenic_potential_/399681", "title"=>"HuNSPCs differ in neurogenic and gliogenic potential.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-30 02:41:21"}
  • {"files"=>["https://ndownloader.figshare.com/files/729777"], "description"=>"<p>(a) The crossover frequency of undifferentiated SC23 huNSPCs decreases with passage number (dashed red line) and directly correlates with neurogenic potential of the cells as shown by their ability to generate differentiated process-bearing cells expressing MAP2 (solid green line). Significance values are reported in the text. (b) The crossover frequency of undifferentiated mouse E12 and E16 NSPCs directly correlates with the ability of the cells to generate neurons (shown as the percentage of MAP2-positive cells with extended processes after differentiation). Error bars in all graphs represent s.e.m., **p<0.01, n = 3 or more separate experiments with different sets of cells.</p>", "links"=>[], "tags"=>["crossover", "reflects", "neurogenic"], "article_id"=>400125, "categories"=>["Neuroscience", "Cell Biology", "Developmental Biology"], "users"=>["Fatima H. Labeed", "Jente Lu", "Hayley J. Mulhall", "Steve A. Marchenko", "Kai F. Hoettges", "Laura C. Estrada", "Abraham P. Lee", "Michael P. Hughes", "Lisa A. Flanagan"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0025458.g004", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_NSPC_crossover_frequency_reflects_neurogenic_potential_/400125", "title"=>"NSPC crossover frequency reflects neurogenic potential.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2011-09-30 00:02:05"}
  • {"files"=>["https://ndownloader.figshare.com/files/369006", "https://ndownloader.figshare.com/files/369025", "https://ndownloader.figshare.com/files/369049"], "description"=>"<div><h3>Background</h3><p>Distinguishing human neural stem/progenitor cell (huNSPC) populations that will predominantly generate neurons from those that produce glia is currently hampered by a lack of sufficient cell type-specific surface markers predictive of fate potential. This limits investigation of lineage-biased progenitors and their potential use as therapeutic agents. A live-cell biophysical and label-free measure of fate potential would solve this problem by obviating the need for specific cell surface markers.</p> <h3>Methodology/Principal Findings</h3><p>We used dielectrophoresis (DEP) to analyze the biophysical, specifically electrophysiological, properties of cortical human and mouse NSPCs that vary in differentiation potential. Our data demonstrate that the electrophysiological property membrane capacitance inversely correlates with the neurogenic potential of NSPCs. Furthermore, as huNSPCs are continually passaged they decrease neuron generation and increase membrane capacitance, confirming that this parameter dynamically predicts and negatively correlates with neurogenic potential. In contrast, differences in membrane conductance between NSPCs do not consistently correlate with the ability of the cells to generate neurons. DEP crossover frequency, which is a quantitative measure of cell behavior in DEP, directly correlates with neuron generation of NSPCs, indicating a potential mechanism to separate stem cells biased to particular differentiated cell fates.</p> <h3>Conclusions/Significance</h3><p>We show here that whole cell membrane capacitance, but not membrane conductance, reflects and predicts the neurogenic potential of human and mouse NSPCs. Stem cell biophysical characteristics therefore provide a completely novel and quantitative measure of stem cell fate potential and a label-free means to identify neuron- or glial-biased progenitors.</p> </div>", "links"=>[], "tags"=>["biophysical", "characteristics", "neural", "differentiation"], "article_id"=>132919, "categories"=>["Neuroscience", "Cell Biology", "Developmental Biology"], "users"=>["Fatima H. Labeed", "Jente Lu", "Hayley J. Mulhall", "Steve A. Marchenko", "Kai F. Hoettges", "Laura C. Estrada", "Abraham P. Lee", "Michael P. Hughes", "Lisa A. Flanagan"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0025458.s001", "https://dx.doi.org/10.1371/journal.pone.0025458.s002", "https://dx.doi.org/10.1371/journal.pone.0025458.s003"], "stats"=>{"downloads"=>0, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Biophysical_Characteristics_Reveal_Neural_Stem_Cell_Differentiation_Potential/132919", "title"=>"Biophysical Characteristics Reveal Neural Stem Cell Differentiation Potential", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2011-09-30 00:48:39"}

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Relative Metric

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