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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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Astrocyte subpopulations show distinct gene expression profiles. (A) Clustering of astrocyte subpopulations using Kohonen SOMs. Expression levels of all genes were autoscaled. Each dot represents one cell. (B) Principal component analysis confirmed the existence of two subpopulations with coloring according to the Kohonen SOMs classification. (C) Histograms of gene expression profiles (log10 scale) of the two astrocyte subpopulations. Descriptive statistics for the two astrocyte populations are shown in Table 3. PC, principal component.
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Figure 4: Astrocyte subpopulations show distinct gene expression profiles. (A) Clustering of astrocyte subpopulations using Kohonen SOMs. Expression levels of all genes were autoscaled. Each dot represents one cell. (B) Principal component analysis confirmed the existence of two subpopulations with coloring according to the Kohonen SOMs classification. (C) Histograms of gene expression profiles (log10 scale) of the two astrocyte subpopulations. Descriptive statistics for the two astrocyte populations are shown in Table 3. PC, principal component.

Mentions: To identify possible subpopulations of cells based on their expression profile, we applied Kohonen SOMs (Figure 4A). SOM is an unsupervised learning algorithm that divides the cells into a given number of groups based on their characteristics. SOM uses random numbers to initiate and perform the classification. As a consequence reiterated SOM analysis may generate different classifications. If the same SOM is repeatedly produced, it evidences robust classification. The classification depends on gene expression levels. Highly expressed genes have greater influence than lowly expressed genes. This effect can be removed by subtracting the average of the expression level of each gene and dividing it by its standard deviation, i.e. performing autoscaling (24).Figure 4.


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)

Astrocyte subpopulations show distinct gene expression profiles. (A) Clustering of astrocyte subpopulations using Kohonen SOMs. Expression levels of all genes were autoscaled. Each dot represents one cell. (B) Principal component analysis confirmed the existence of two subpopulations with coloring according to the Kohonen SOMs classification. (C) Histograms of gene expression profiles (log10 scale) of the two astrocyte subpopulations. Descriptive statistics for the two astrocyte populations are shown in Table 3. PC, principal component.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 4: Astrocyte subpopulations show distinct gene expression profiles. (A) Clustering of astrocyte subpopulations using Kohonen SOMs. Expression levels of all genes were autoscaled. Each dot represents one cell. (B) Principal component analysis confirmed the existence of two subpopulations with coloring according to the Kohonen SOMs classification. (C) Histograms of gene expression profiles (log10 scale) of the two astrocyte subpopulations. Descriptive statistics for the two astrocyte populations are shown in Table 3. PC, principal component.
Mentions: To identify possible subpopulations of cells based on their expression profile, we applied Kohonen SOMs (Figure 4A). SOM is an unsupervised learning algorithm that divides the cells into a given number of groups based on their characteristics. SOM uses random numbers to initiate and perform the classification. As a consequence reiterated SOM analysis may generate different classifications. If the same SOM is repeatedly produced, it evidences robust classification. The classification depends on gene expression levels. Highly expressed genes have greater influence than lowly expressed genes. This effect can be removed by subtracting the average of the expression level of each gene and dividing it by its standard deviation, i.e. performing autoscaling (24).Figure 4.

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