An Information Theoretic, Microfluidic-Based Single Cell Analysis Permits Identification of Subpopulations among Putatively Homogeneous Stem Cells

An incomplete understanding of the nature of heterogeneity within stem cell populations remains a major impediment to the development of clinically effective cell-based therapies. Transcriptional events within a single cell are inherently stochastic and can produce tremendous variability, even among genetically identical cells. It remains unclear how mammalian cellular systems overcome this intrinsic noisiness of gene expression to produce consequential variations in function, and what impact this has on the biologic and clinical relevance of highly ‘purified’ cell subgroups. To address these questions, we have developed a novel method combining microfluidic-based single cell analysis and information theory to characterize and predict transcriptional programs across hundreds of individual cells. Using this technique, we demonstrate that multiple subpopulations exist within a well-studied and putatively homogeneous stem cell population, murine long-term hematopoietic stem cells (LT-HSCs). These subgroups are defined by nonrandom patterns that are distinguishable from noise and are consistent with known functional properties of these cells. We anticipate that this analytic framework can also be applied to other cell types to elucidate the relationship between transcriptional and phenotypic variation

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

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 in 597 single primary blood stem and progenitor cells isolated from mouse bone marrow. We demonstrate that different stem/progenitor populations are characterized by distinctive transcription factor expression states, and through comprehensive bioinformatic analysis reveal positively and negatively correlated transcription factor pairings, including previously unrecognized 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

Diabetes irreversibly depletes bone marrow-derived mesenchymal progenitor cell subpopulations

Diabetic vascular pathology is largely attributable to impairments in tissue recovery from hypoxia. Circulating progenitor cells have been postulated to play a role in ischemic recovery, and deficiencies in these cells have been well described in diabeti

Microfluidic single-cell transcriptional analysis rationally identifies novel surface marker profiles to enhance cell-based therapies

Current progenitor cell therapies have only modest efficacy, which has limited their clinical adoption. This may be the result of a cellular heterogeneity that decreases the number of functional progenitors delivered to diseased tissue, and prevents correction of underlying pathologic cell population disruptions. Here, we develop a high-resolution method of identifying phenotypically distinct progenitor cell subpopulations via single-cell transcriptional analysis and advanced bioinformatics. When combined with high-throughput cell surface marker screening, this approach facilitates the rational selection of surface markers for prospective isolation of cell subpopulations with desired transcriptional profiles. We establish the usefulness of this platform in costly and highly morbid diabetic wounds by identifying a subpopulation of progenitor cells that is dysfunctional in the diabetic state, and normalizes diabetic wound healing rates following allogeneic application. We believe this work presents a logical framework for the development of targeted cell therapies that can be customized to any clinical application

Microfluidic single-cell transcriptional analysis rationally identifies novel surface marker profiles to enhance cell-based therapies

Current progenitor cell therapies have only modest efficacy, which has limited their clinical adoption. This may be the result of a cellular heterogeneity that decreases the number of functional progenitors delivered to diseased tissue, and prevents correction of underlying pathologic cell population disruptions. Here, we develop a high-resolution method of identifying phenotypically distinct progenitor cell subpopulations via single-cell transcriptional analysis and advanced bioinformatics. When combined with high-throughput cell surface marker screening, this approach facilitates the rational selection of surface markers for prospective isolation of cell subpopulations with desired transcriptional profiles. We establish the usefulness of this platform in costly and highly morbid diabetic wounds by identifying a subpopulation of progenitor cells that is dysfunctional in the diabetic state, and normalizes diabetic wound healing rates following allogeneic application. We believe this work presents a logical framework for the development of targeted cell therapies that can be customized to any clinical application