Understanding hematopoiesis from a single-cell standpoint

DNA barcoded competitive clone-initiating cell analysis reveals novel features of metastatic growth in a cancer xenograft model

A problematic feature of many human cancers is a lack of understanding of mechanisms controlling organ-specific patterns of metastasis, despite recent progress in identifying many mutations and transcriptional programs shown to confer this potential. To address this gap, we developed a methodology that enables different aspects of the metastatic process to be comprehensively characterized at a clonal resolution. Our approach exploits the application of a computational pipeline to analyze and visualize clonal data obtained from transplant experiments in which a cellular DNA barcoding strategy is used to distinguish the separate clonal contributions of two or more competing cell populations. To illustrate the power of this methodology, we demonstrate its ability to discriminate the metastatic behavior in immunodeficient mice of a well-established human metastatic cancer cell line and its co-transplanted LRRC15 knockdown derivative. We also show how the use of machine learning to quantify clone-initiating cell (CIC) numbers and their subsequent metastatic progeny generated in different sites can reveal previously unknown relationships between different cellular genotypes and their initial sites of implantation with their subsequent respective dissemination patterns. These findings underscore the potential of such combined genomic and computational methodologies to identify new clonally-relevant drivers of site-specific patterns of metastasis.

Clonal and Quantitative In Vivo Assessment of Hematopoietic Stem Cell Differentiation Reveals Strong Erythroid Potential of Multipotent Cells

Hematopoiesis is arguably one of the best understood stem cell systems; however, significant challenges remain to reach a consensus understanding of the lineage potential, heterogeneity, and relationships of hematopoietic stem and progenitor cell populations. To gain new insights, we performed quantitative analyses of mature cell production from hematopoietic stem cells (HSCs) and multiple hematopoietic progenitor populations. Assessment of the absolute numbers of mature cell types produced by each progenitor cell revealed a striking erythroid dominance of all myeloid-competent progenitors assessed, accompanied by strong platelet reconstitution. All populations with myeloid potential also produced robust numbers of red blood cells and platelets in vivo. Clonal analysis by single-cell transplantation and by spleen colony assays revealed that a significant fraction of HSCs and multipotent progenitors have multilineage potential at the single-cell level. These new insights prompt an erythroid-focused model of hematopoietic differentiation.

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RBCs are the predominant cell type produced by multipotent hematopoietic progenitors

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All cell types with myeloid potential also produced RBCs and platelets in vivo

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Single HSCs and MPP^F cells are capable of multilineage hematopoietic reconstitution

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Erythroid cell production emerges as a default hematopoietic fate

On the statistical analysis of single cell lineage trees

Stem cells play a central role in the regeneration and repair of multicellular organisms. However, it remains far from trivial to reliably identify them. Despite decades of work, current techniques to isolate hematopoietic stem cells (HSCs) based on cell-surface markers only result in 50% purity, i.e. half of the sorted cells are not stem cells when functionally tested. Modern microscopy techniques allow us to follow single cells and their progeny for up to weeks in vitro, while recording the cell fates and lifetime of each individual cell. This cell tracking generates so-called lineage trees. Here, we propose statistical techniques to determine if the initial cell in a lineage tree was a HSC. We apply these techniques to murine hematopoietic lineage trees, revealing that 18% of the trees in our HSC dataset display a unique signature, and this signature is compatible with these trees having started from a true stem cell. Assuming 50% purity of HSC empirical datasets, this corresponds to a 0.35 power of the test, and the type-1-error is estimated to be 0.047. In summary, this study shows that statistical analysis of lineage trees could improve the classification of cells, which is currently done based on bio-markers only. Our statistical techniques are not limited to mammalian stem cell biology. Any type of single cell lineage trees, be it from bacteria, single cell eukaryotes, or single cells in a multicellular organism can be investigated. We expect this to contribute to a better understanding of the molecules influencing cellular dynamics at the single cell level.