Fiona Hamey

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

Maintenance of the blood system requires balanced cell fate decisions by hematopoietic stem and progenitor cells (HSPCs). Because cell fate choices are executed at the individual cell level, new single-cell profiling technologies offer exciting possibilities for mapping the dynamic molecular changes underlying HSPC differentiation. Here, we have used single-cell RNA sequencing to profile more than 1600 single HSPCs, and deep sequencing has enabled detection of an average of 6558 protein-coding genes per cell. Index sorting, in combination with broad sorting gates, allowed us to retrospectively assign cells to 12 commonly sorted HSPC phenotypes while also capturing intermediate cells typically excluded by conventional gating. We further show that independently generated single-cell data sets can be projected onto the single-cell resolution expression map to directly compare data from multiple groups and to build and refine new hypotheses. Reconstruction of differentiation trajectories reveals dynamic expression changes associated with early lymphoid, erythroid, and granulocyte-macrophage differentiation. The latter two trajectories were characterized by common upregulation of cell cycle and oxidative phosphorylation transcriptional programs. By using external spike-in controls, we estimate absolute messenger RNA (mRNA) levels per cell, showing for the first time that despite a general reduction in total mRNA, a subset of genes shows higher expression levels in immature stem cells consistent with active maintenance of the stem-cell state. Finally, we report the development of an intuitive Web interface as a new community resource to permit visualization of gene expression in HSPCs at single-cell resolution for any gene of choice.

expression. Potential compensatory processes included upregulation of the integrated stress response pathway and downregulation of proapoptotic gene expression in erythroid progenitors, thus providing a template of how large-scale single-cell transcriptomic studies can bridge between molecular phenotypes and quantitative population changes.

The transcriptional programs that govern hematopoiesis have been investigated primarily by population-level analysis of hematopoietic stem and progenitor cells, which cannot reveal the continuous nature of the differentiation process. Here we applied single-cell RNA-sequencing to a population of hematopoietic cells in zebrafish as they undergo thrombocyte lineage commitment. By reconstructing their developmental chronology computationally, we were able to place each cell along a continuum from stem cell to mature cell, refining the traditional lineage tree. The progression of cells along this continuum is characterized by a highly coordinated transcriptional program, displaying simultaneous suppression of genes involved in cell proliferation and ribosomal biogenesis as the expression of lineage specific genes increases. Within this program, there is substantial heterogeneity in the expression of the key lineage regulators. Overall, the total number of genes expressed, as well as the total mRNA content of the cell, decreases as the cells undergo lineage commitment.