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Defining cell populations with single-cell gene expression profiling: correlations and identification of astrocyte subpopulations.

Ståhlberg A, Andersson D, Aurelius J, Faiz M, Pekna M, Kubista M, Pekny M - Nucleic Acids Res. (2010)

Bottom Line: We show how subpopulations of cells can be identified at single-cell level using unsupervised algorithms and that gene correlations can be used to identify differences in activity of important transcriptional pathways.We identified two subpopulations of astrocytes with distinct gene expression profiles.One had an expression profile very similar to that of neurosphere cells, whereas the other showed characteristics of activated astrocytes in vivo.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Repair and Rehabilitation, Department of Clinical Neuroscience and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Medicinaregatan 9A, 413 90 Gothenburg, Sweden. anders.stahlberg@neuro.gu.se

ABSTRACT
Single-cell gene expression levels show substantial variations among cells in seemingly homogenous populations. Astrocytes perform many control and regulatory functions in the central nervous system. In contrast to neurons, we have limited knowledge about functional diversity of astrocytes and its molecular basis. To study astrocyte heterogeneity and stem/progenitor cell properties of astrocytes, we used single-cell gene expression profiling in primary mouse astrocytes and dissociated mouse neurosphere cells. The transcript number variability for astrocytes showed lognormal features and revealed that cells in primary cultures to a large extent co-express markers of astrocytes and neural stem/progenitor cells. We show how subpopulations of cells can be identified at single-cell level using unsupervised algorithms and that gene correlations can be used to identify differences in activity of important transcriptional pathways. We identified two subpopulations of astrocytes with distinct gene expression profiles. One had an expression profile very similar to that of neurosphere cells, whereas the other showed characteristics of activated astrocytes in vivo.

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Gene interactions. (A) Three different types of interaction between two genes can be identified using partial correlations. Case 1 shows a direct interaction between genes A and B. Case 2 represents a direct interaction that can be partly explained by a third gene, while case 3 represents an indirect interaction that can be fully explained by a third gene. We used a decrease of 0.15 in correlation as a cut off for partially explained interactions (Case 2) and a complete loss of significance for indirect correlation (Case 3). (B) A detailed interaction map for Vim. The interaction between Vim and Nes/ETBR is direct (Case 1), while the interactions with GFAP, GS and Sox2 can be partially explained by other genes (GS and Nes, Case 2). The interaction between Vim and GFAPδ was indirect and can be fully explained by interactions through GFAP or ETBR. See Supplementary Figure S3 for detailed interaction maps for other genes. (C) Nine of 20 observed correlations in Table 2 represented direct interactions that could not be explained by the other genes.
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Figure 3: Gene interactions. (A) Three different types of interaction between two genes can be identified using partial correlations. Case 1 shows a direct interaction between genes A and B. Case 2 represents a direct interaction that can be partly explained by a third gene, while case 3 represents an indirect interaction that can be fully explained by a third gene. We used a decrease of 0.15 in correlation as a cut off for partially explained interactions (Case 2) and a complete loss of significance for indirect correlation (Case 3). (B) A detailed interaction map for Vim. The interaction between Vim and Nes/ETBR is direct (Case 1), while the interactions with GFAP, GS and Sox2 can be partially explained by other genes (GS and Nes, Case 2). The interaction between Vim and GFAPδ was indirect and can be fully explained by interactions through GFAP or ETBR. See Supplementary Figure S3 for detailed interaction maps for other genes. (C) Nine of 20 observed correlations in Table 2 represented direct interactions that could not be explained by the other genes.

Mentions: To discriminate between direct and indirect interactions among the observed correlations, we calculated partial correlations between gene expression levels in individual cells. This was done by specifying a control gene that may interact with two other correlated genes and thus account for the observed correlation (50). The resulting partial correlation then becomes a unique correlation between the two initial genes that remains when the correlated variance explained by the control gene has been removed. Using partial correlations, we could determine if a measured correlation between two genes was unique or rather a consequence of the two genes both being dependent on a third gene (Figure 3A). Figure 3B shows the gene interaction map for Vim based on partial correlations. Vim interacts directly with ETBR and Nes. These interactions are independent of the other genes studied. The Vim interactions with GS, GFAP and Sox2 were partially direct, while its interaction with GFAPδ was indirect and could be explained as being a consequence of Vim’s interaction with either GFAP or ETBR. Interaction maps for the other genes are shown in Supplementary Figure S3. From the 20 statistically significant correlations in Table 2, nine interactions were direct while eleven could be fully explained by other genes using partial correlations. All direct correlations were dependent on Vim except for those that involved GFAPδ and the interaction between Sox2 and Wnt3. Figure 3C shows the complete interaction map based on the correlations in Table 2.Figure 3.


Defining cell populations with single-cell gene expression profiling: correlations and identification of astrocyte subpopulations.

Ståhlberg A, Andersson D, Aurelius J, Faiz M, Pekna M, Kubista M, Pekny M - Nucleic Acids Res. (2010)

Gene interactions. (A) Three different types of interaction between two genes can be identified using partial correlations. Case 1 shows a direct interaction between genes A and B. Case 2 represents a direct interaction that can be partly explained by a third gene, while case 3 represents an indirect interaction that can be fully explained by a third gene. We used a decrease of 0.15 in correlation as a cut off for partially explained interactions (Case 2) and a complete loss of significance for indirect correlation (Case 3). (B) A detailed interaction map for Vim. The interaction between Vim and Nes/ETBR is direct (Case 1), while the interactions with GFAP, GS and Sox2 can be partially explained by other genes (GS and Nes, Case 2). The interaction between Vim and GFAPδ was indirect and can be fully explained by interactions through GFAP or ETBR. See Supplementary Figure S3 for detailed interaction maps for other genes. (C) Nine of 20 observed correlations in Table 2 represented direct interactions that could not be explained by the other genes.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3045576&req=5

Figure 3: Gene interactions. (A) Three different types of interaction between two genes can be identified using partial correlations. Case 1 shows a direct interaction between genes A and B. Case 2 represents a direct interaction that can be partly explained by a third gene, while case 3 represents an indirect interaction that can be fully explained by a third gene. We used a decrease of 0.15 in correlation as a cut off for partially explained interactions (Case 2) and a complete loss of significance for indirect correlation (Case 3). (B) A detailed interaction map for Vim. The interaction between Vim and Nes/ETBR is direct (Case 1), while the interactions with GFAP, GS and Sox2 can be partially explained by other genes (GS and Nes, Case 2). The interaction between Vim and GFAPδ was indirect and can be fully explained by interactions through GFAP or ETBR. See Supplementary Figure S3 for detailed interaction maps for other genes. (C) Nine of 20 observed correlations in Table 2 represented direct interactions that could not be explained by the other genes.
Mentions: To discriminate between direct and indirect interactions among the observed correlations, we calculated partial correlations between gene expression levels in individual cells. This was done by specifying a control gene that may interact with two other correlated genes and thus account for the observed correlation (50). The resulting partial correlation then becomes a unique correlation between the two initial genes that remains when the correlated variance explained by the control gene has been removed. Using partial correlations, we could determine if a measured correlation between two genes was unique or rather a consequence of the two genes both being dependent on a third gene (Figure 3A). Figure 3B shows the gene interaction map for Vim based on partial correlations. Vim interacts directly with ETBR and Nes. These interactions are independent of the other genes studied. The Vim interactions with GS, GFAP and Sox2 were partially direct, while its interaction with GFAPδ was indirect and could be explained as being a consequence of Vim’s interaction with either GFAP or ETBR. Interaction maps for the other genes are shown in Supplementary Figure S3. From the 20 statistically significant correlations in Table 2, nine interactions were direct while eleven could be fully explained by other genes using partial correlations. All direct correlations were dependent on Vim except for those that involved GFAPδ and the interaction between Sox2 and Wnt3. Figure 3C shows the complete interaction map based on the correlations in Table 2.Figure 3.

Bottom Line: We show how subpopulations of cells can be identified at single-cell level using unsupervised algorithms and that gene correlations can be used to identify differences in activity of important transcriptional pathways.We identified two subpopulations of astrocytes with distinct gene expression profiles.One had an expression profile very similar to that of neurosphere cells, whereas the other showed characteristics of activated astrocytes in vivo.

View Article: PubMed Central - PubMed

Affiliation: Center for Brain Repair and Rehabilitation, Department of Clinical Neuroscience and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Medicinaregatan 9A, 413 90 Gothenburg, Sweden. anders.stahlberg@neuro.gu.se

ABSTRACT
Single-cell gene expression levels show substantial variations among cells in seemingly homogenous populations. Astrocytes perform many control and regulatory functions in the central nervous system. In contrast to neurons, we have limited knowledge about functional diversity of astrocytes and its molecular basis. To study astrocyte heterogeneity and stem/progenitor cell properties of astrocytes, we used single-cell gene expression profiling in primary mouse astrocytes and dissociated mouse neurosphere cells. The transcript number variability for astrocytes showed lognormal features and revealed that cells in primary cultures to a large extent co-express markers of astrocytes and neural stem/progenitor cells. We show how subpopulations of cells can be identified at single-cell level using unsupervised algorithms and that gene correlations can be used to identify differences in activity of important transcriptional pathways. We identified two subpopulations of astrocytes with distinct gene expression profiles. One had an expression profile very similar to that of neurosphere cells, whereas the other showed characteristics of activated astrocytes in vivo.

Show MeSH