Inference of high-dimensional dynamics/Single-cell lineage tracing

Caleb Weinreb
Harvard Systems Biology
Lineage tracing on transcriptional landscapes links state to fate during differentiation

Abstract: A challenge in stem cell biology is to associate molecular differences among progenitor cells with their capacity to generate mature cell types. Dynamic inference from static snapshots provides some insight, but there are fundamental limits on how well dynamics can be inferred from single-cell transcriptomes alone. Here, we use expressed DNA barcodes to clonally trace single cells during differentiation and apply this approach to the study of hematopoiesis. Our analysis identifies functional boundaries of cell potential early in the hematopoietic hierarchy and locates them on a continuous transcriptional landscape. We use our approach to benchmark methods of dynamic inference from single-cell snapshots, and provide evidence of strong early fate biases dependent on cellular properties hidden from single-cell RNA sequencing.

Allon Klein
Harvard Systems Biology
Primer: Dynamic inference from single-cell snapshots

Abstract: Snapshots of single-cell gene expression at a single moment in time encode information about cell state dynamics. But there are challenges to inference: multiple dynamic processes could give rise to the same static snapshot, and the sparsity and high dimensionality of single-cell data make calculations difficult. Using the principle of population balance, we explore the different sources of ambiguity that limit inference from static snapshots, describe the conditions under which dynamics can be determined uniquely, and present an inference algorithm that can calculate these dynamics for sparse high-dimensional data based on spectral graph theory. A key lesson from this approach is that there exists a correspondence between graph-based inference algorithms and models of cell dynamics, which emerges from the correspondence between graph Laplacians and differential operators.