Characterization of transcriptional networks in blood stem and progenitor cells using high-throughput single-cell gene expression analysis

Abstract

Cellular decision-making is mediated by a complex interplay of external stimuli with the intracellular environment, in particular transcription factor regulatory networks. Here we have determined the expression of a network of 18 key haematopoietic transcription factors (TFs) in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterised by distinctive TF expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated TF pairings, including previously unrecognised relationships between Gata2, Gfi1 and Gfi1b. Validation using transcriptional and transgenic assays confirmed direct regulatory interactions consistent with a regulatory triad in immature blood stem cells, where Gata2 may function to modulate cross-inhibition between Gfi1 and Gfi1b. Single cell expression profiling therefore identifies network states and allows reconstruction of network hierarchies involved in controlling stem cell fate choices, and provides a blueprint for studying both normal development and human disease.

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

“…Similar results were obtained studying heterogeneity in human ESCs and induced PSCs, in which pluripotency regulators such as Nanog and Oct4 , exhibit similar expression patterns across different subpopulations to the expression patterns of their orthologous counterparts in mouse 2, 11. Gene expression heterogeneity has also been studied in hematopoietic stem cells (HSCs), in which gene expression uni‐ and bimodality have been observed for different genes in different hematopoietic progenitor cells 1, 12. Interestingly, it has also been demonstrated that genes exhibiting bimodal gene expression tend to be co‐expressed, and regulators such as Rex1 , Nanog , and Esrrb display strong correlations 3, 5, which is related to the dynamics in the interplay among TFs for regulating pluripotency genes in ESCs 13.…”

“…More recently, differential network analysis methods, which rely on single‐cell expression data, have also been implemented. These methods have aimed at studying stem/progenitor cell populations during differentiation 12, 76, and at predicting lineage specifiers triggering cell‐fate commitment in different PSCs 74. Nevertheless, despite these attempts to follow a single‐cell based differential network approach to study the differentiation process in heterogeneous PSCs populations, more sophisticated computational models are needed to address relevant questions in the field, such as the identification of signalling pathways and their downstream effects for the activation of master regulator transcription factors and epigenetics mechanisms determining cell fate decisions in different PSC sub‐populations; and which are the perturbations that prime cell subpopulations for differentiation into specific cell fates.…”

“…1) 12, 36, 94. We suggest that different subpopulations employ distinct combinations of positive circuits to stabilize the stem/progenitor state by maintaining the balance between antagonist groups of lineage‐specific regulators.…”

“…Single‐cell measurements have a significant inherent noise 115, 116, as well as experimental drawbacks associated to batch effects and technical variability 117, 118, 119, and the computational methodologies for accounting for these statistical analysis limitations are still under development. Moreover, the reconstruction of GRNs from single‐cell data performed in several studies has yielded rather small networks, involving only a limited group of regulators 12, 99, 120. Despite the above‐mentioned limitations and the need for improvements in the statistical analysis of single‐cell data 118, 119, the generation of more comprehensive and accurate measurements of heterogeneity in the pluripotent state will allow the generation of integrative network approaches, such as the one proposed here.…”

Diffuse large B-cell lymphoma: sub-classification by massive parallel quantitative RT-PCR

“…On the basis of the application efficiency in our experimental system, any qPCR reaction with a DCt value above the cutoff (25) for linear amplification was set to 26. 19 For a small proportion of PCR reactions, there was no evidence of amplification at the maximum 30 cycle set by the manufacturer's default protocol, commonly due to low levels of gene expression (for example GCB genes in ABC-DLBCL or vice versa) or rarely as a result of failed amplification. Any cases with 415% of targets, that is, 4/28 genes, showing a negative result were considered unreliable and excluded from data analysis.…”