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Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.

Pina C, Teles J, Fugazza C, May G, Wang D, Guo Y, Soneji S, Brown J, Edén P, Ohlsson M, Peterson C, Enver T - Cell Rep (2015)

Bottom Line: Ddit3 loss impairs erythroid colony output from multipotent cells, while forcing Ddit3 in granulo-monocytic progenitors (GMPs) enhances self-renewal and impedes differentiation.Network analysis of Ddit3-transduced GMPs reveals uncoupling of myeloid networks and strengthening of erythroid linkages.The enrichment of Gata2 target genes in Ddit3-dependent transcriptional responses suggests that Ddit3 functions in an erythroid transcriptional network nucleated by Gata2.

View Article: PubMed Central - PubMed

Affiliation: Stem Cell Laboratory, UCL Cancer Institute, University College London, London W1CE 6BT, UK.

No MeSH data available.


Combined Single-Cell Transcriptional Network Inference Methods Implicate Ddit3 in Lineage Specification(A) Representative gene expression distributions for Epor, Gata1, and Tal1 in ECPs are shown.(B) Scatterplot of Gata1 and Epor single-cell expression highlights the dual aspect of the data with both binary (on/off) and continuous (expression-level) components.(C) Contingency table summarizing on/off combination patterns of individual cells for Epor and Gata1. OR quantifies the diagonal versus off-diagonal of this matrix to infer significant positive and negative associations in the binary component of the data. Gata1 and Epor show significant positive association (OR = 3.18; lower95CI > 1).(D) Scatterplot of Epor and Tal1 expression ranks in co-expressing cells. Epor and Tal1 show significant positive correlation in the continuous component of the data inferred by Spearman rank correlation (r = 0.56; p = 0.002).(E) Single-cell transcriptional networks in SR, ECP, MCP, Gata1-ERT, and Pu.1-ERT compartments were inferred by combined use of OR and Spearman rank correlation. Solid red lines, positive associations; dashed black lines, negative associations. Node size is proportional to the relative connectivity in each network.(F) Proportion of negative interactions in the networks in (E) is shown.(G) Proportion of negative interactions in Gata1-ERT networks at each time point is shown.
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fig2: Combined Single-Cell Transcriptional Network Inference Methods Implicate Ddit3 in Lineage Specification(A) Representative gene expression distributions for Epor, Gata1, and Tal1 in ECPs are shown.(B) Scatterplot of Gata1 and Epor single-cell expression highlights the dual aspect of the data with both binary (on/off) and continuous (expression-level) components.(C) Contingency table summarizing on/off combination patterns of individual cells for Epor and Gata1. OR quantifies the diagonal versus off-diagonal of this matrix to infer significant positive and negative associations in the binary component of the data. Gata1 and Epor show significant positive association (OR = 3.18; lower95CI > 1).(D) Scatterplot of Epor and Tal1 expression ranks in co-expressing cells. Epor and Tal1 show significant positive correlation in the continuous component of the data inferred by Spearman rank correlation (r = 0.56; p = 0.002).(E) Single-cell transcriptional networks in SR, ECP, MCP, Gata1-ERT, and Pu.1-ERT compartments were inferred by combined use of OR and Spearman rank correlation. Solid red lines, positive associations; dashed black lines, negative associations. Node size is proportional to the relative connectivity in each network.(F) Proportion of negative interactions in the networks in (E) is shown.(G) Proportion of negative interactions in Gata1-ERT networks at each time point is shown.

Mentions: We exploited the heterogeneity of cells at early stages of spontaneous and TF-driven lineage commitment to explore the transcriptional networks controlling lineage specification. Inspection of patterns of expression for individual genes revealed a fraction of cells in which the gene is off, and a fraction of cells expressing the gene to varying levels (on) (Figure 2A). The on/off status can be described as binary while the distribution of on values represents a continuous component of the data. Thus, for any given gene pair, both binary and continuous relationships are possible; this is exemplified for Gata1 and Epor in Figure 2B. We sought to capture both kinds of information to infer putative transcriptional networks. Methodologically, we used odds ratio (OR) to quantify on/off gene-to-gene associations (Figure 2C) and Spearman rank correlation to measure correlations between gene expression levels (Figure 2D). We combined gene associations obtained by both methodologies to infer putative regulatory networks characterizing SR states and the different modes of lineage commitment (Figure 2E).


Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis.

Pina C, Teles J, Fugazza C, May G, Wang D, Guo Y, Soneji S, Brown J, Edén P, Ohlsson M, Peterson C, Enver T - Cell Rep (2015)

Combined Single-Cell Transcriptional Network Inference Methods Implicate Ddit3 in Lineage Specification(A) Representative gene expression distributions for Epor, Gata1, and Tal1 in ECPs are shown.(B) Scatterplot of Gata1 and Epor single-cell expression highlights the dual aspect of the data with both binary (on/off) and continuous (expression-level) components.(C) Contingency table summarizing on/off combination patterns of individual cells for Epor and Gata1. OR quantifies the diagonal versus off-diagonal of this matrix to infer significant positive and negative associations in the binary component of the data. Gata1 and Epor show significant positive association (OR = 3.18; lower95CI > 1).(D) Scatterplot of Epor and Tal1 expression ranks in co-expressing cells. Epor and Tal1 show significant positive correlation in the continuous component of the data inferred by Spearman rank correlation (r = 0.56; p = 0.002).(E) Single-cell transcriptional networks in SR, ECP, MCP, Gata1-ERT, and Pu.1-ERT compartments were inferred by combined use of OR and Spearman rank correlation. Solid red lines, positive associations; dashed black lines, negative associations. Node size is proportional to the relative connectivity in each network.(F) Proportion of negative interactions in the networks in (E) is shown.(G) Proportion of negative interactions in Gata1-ERT networks at each time point is shown.
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fig2: Combined Single-Cell Transcriptional Network Inference Methods Implicate Ddit3 in Lineage Specification(A) Representative gene expression distributions for Epor, Gata1, and Tal1 in ECPs are shown.(B) Scatterplot of Gata1 and Epor single-cell expression highlights the dual aspect of the data with both binary (on/off) and continuous (expression-level) components.(C) Contingency table summarizing on/off combination patterns of individual cells for Epor and Gata1. OR quantifies the diagonal versus off-diagonal of this matrix to infer significant positive and negative associations in the binary component of the data. Gata1 and Epor show significant positive association (OR = 3.18; lower95CI > 1).(D) Scatterplot of Epor and Tal1 expression ranks in co-expressing cells. Epor and Tal1 show significant positive correlation in the continuous component of the data inferred by Spearman rank correlation (r = 0.56; p = 0.002).(E) Single-cell transcriptional networks in SR, ECP, MCP, Gata1-ERT, and Pu.1-ERT compartments were inferred by combined use of OR and Spearman rank correlation. Solid red lines, positive associations; dashed black lines, negative associations. Node size is proportional to the relative connectivity in each network.(F) Proportion of negative interactions in the networks in (E) is shown.(G) Proportion of negative interactions in Gata1-ERT networks at each time point is shown.
Mentions: We exploited the heterogeneity of cells at early stages of spontaneous and TF-driven lineage commitment to explore the transcriptional networks controlling lineage specification. Inspection of patterns of expression for individual genes revealed a fraction of cells in which the gene is off, and a fraction of cells expressing the gene to varying levels (on) (Figure 2A). The on/off status can be described as binary while the distribution of on values represents a continuous component of the data. Thus, for any given gene pair, both binary and continuous relationships are possible; this is exemplified for Gata1 and Epor in Figure 2B. We sought to capture both kinds of information to infer putative transcriptional networks. Methodologically, we used odds ratio (OR) to quantify on/off gene-to-gene associations (Figure 2C) and Spearman rank correlation to measure correlations between gene expression levels (Figure 2D). We combined gene associations obtained by both methodologies to infer putative regulatory networks characterizing SR states and the different modes of lineage commitment (Figure 2E).

Bottom Line: Ddit3 loss impairs erythroid colony output from multipotent cells, while forcing Ddit3 in granulo-monocytic progenitors (GMPs) enhances self-renewal and impedes differentiation.Network analysis of Ddit3-transduced GMPs reveals uncoupling of myeloid networks and strengthening of erythroid linkages.The enrichment of Gata2 target genes in Ddit3-dependent transcriptional responses suggests that Ddit3 functions in an erythroid transcriptional network nucleated by Gata2.

View Article: PubMed Central - PubMed

Affiliation: Stem Cell Laboratory, UCL Cancer Institute, University College London, London W1CE 6BT, UK.

No MeSH data available.