Fundamental limits on dynamic inference from single-cell snapshots.

Single-cell expression profiling reveals the molecular states of individual cells with unprecedented detail. Because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. However, some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single-cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, population balance analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high-dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.

Setd1a and NURF mediate chromatin dynamics and gene regulation during erythroid lineage commitment and differentiation.

The modulation of chromatin structure is a key step in transcription regulation in mammalian cells and eventually determines lineage commitment and differentiation. USF1/2, Setd1a and NURF complexes interact to regulate chromatin architecture in erythropoiesis, but the mechanistic basis for this regulation is hitherto unknown. Here we showed that Setd1a and NURF complexes bind to promoters to control chromatin structural alterations and gene activation in a cell context dependent manner. In human primary erythroid cells USF1/2, H3K4me3 and the NURF complex were significantly co-enriched at transcription start sites of erythroid genes, and their binding was associated with promoter/enhancer accessibility that resulted from nucleosome repositioning. Mice deficient for Setd1a, an H3K4 trimethylase, in the erythroid compartment exhibited reduced Ter119/CD71 positive erythroblasts, peripheral blood RBCs and hemoglobin levels. Loss of Setd1a led to a reduction of promoter-associated H3K4 methylation, inhibition of gene transcription and blockade of erythroid differentiation. This was associated with alterations in NURF complex occupancy at erythroid gene promoters and reduced chromatin accessibility. Setd1a deficiency caused decreased associations between enhancer and promoter looped interactions as well as reduced expression of erythroid genes such as the adult ß-globin gene. These data indicate that Setd1a and NURF complexes are specifically targeted to and coordinately regulate erythroid promoter chromatin dynamics during erythroid lineage differentiation.