Ly6D+Siglec-H+ precursors contribute to conventional dendritic cells via a Zbtb46+Ly6D+ intermediary stage.

Plasmacytoid and conventional dendritic cells (pDC and cDC) are generated from progenitor cells in the bone marrow and commitment to pDCs or cDC subtypes may occur in earlier and later progenitor stages. Cells within the CD11c+MHCII-/loSiglec-H+CCR9lo DC precursor fraction of the mouse bone marrow generate both pDCs and cDCs. Here we investigate the heterogeneity and commitment of subsets in this compartment by single-cell transcriptomics and high-dimensional flow cytometry combined with cell fate analysis: Within the CD11c+MHCII-/loSiglec-H+CCR9lo DC precursor pool cells expressing high levels of Ly6D and lacking expression of transcription factor Zbtb46 contain CCR9loB220hi immediate pDC precursors and CCR9loB220lo (lo-lo) cells which still generate pDCs and cDCs in vitro and in vivo under steady state conditions. cDC-primed cells within the Ly6DhiZbtb46- lo-lo precursors rapidly upregulate Zbtb46 and pass through a Zbtb46+Ly6D+ intermediate stage before acquiring cDC phenotype after cell division. Type I IFN stimulation limits cDC and promotes pDC output from this precursor fraction by arresting cDC-primed cells in the Zbtb46+Ly6D+ stage preventing their expansion and differentiation into cDCs. Modulation of pDC versus cDC output from precursors by external factors may allow for adaptation of DC subset composition at later differentiation stages.

CellRank for directed single-cell fate mapping.

Computational trajectory inference enables the reconstruction of cell state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank ( https://cellrank.org ) for single-cell fate mapping in diverse scenarios, including regeneration, reprogramming and disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, taking into account the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in velocity vectors. On pancreas development data, CellRank automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. Applied to lineage-traced cellular reprogramming data, predicted fate probabilities correctly recover reprogramming outcomes. CellRank also predicts a new dedifferentiation trajectory during postinjury lung regeneration, including previously unknown intermediate cell states, which we confirm experimentally.

RNA velocity-current challenges and future perspectives.

RNA velocity has enabled the recovery of directed dynamic information from single-cell transcriptomics by connecting measurements to the underlying kinetics of gene expression. This approach has opened up new ways of studying cellular dynamics. Here, we review the current state of RNA velocity modeling approaches, discuss various examples illustrating limitations and potential pitfalls, and provide guidance on how the ensuing challenges may be addressed. We then outline future directions on how to generalize the concept of RNA velocity to a wider variety of biological systems and modalities.

Generalizing RNA velocity to transient cell states through dynamical modeling.

RNA velocity has opened up new ways of studying cellular differentiation in single-cell RNA-sequencing data. It describes the rate of gene expression change for an individual gene at a given time point based on the ratio of its spliced and unspliced messenger RNA (mRNA). However, errors in velocity estimates arise if the central assumptions of a common splicing rate and the observation of the full splicing dynamics with steady-state mRNA levels are violated. Here we present scVelo, a method that overcomes these limitations by solving the full transcriptional dynamics of splicing kinetics using a likelihood-based dynamical model. This generalizes RNA velocity to systems with transient cell states, which are common in development and in response to perturbations. We apply scVelo to disentangling subpopulation kinetics in neurogenesis and pancreatic endocrinogenesis. We infer gene-specific rates of transcription, splicing and degradation, recover each cell's position in the underlying differentiation processes and detect putative driver genes. scVelo will facilitate the study of lineage decisions and gene regulation.

Concepts and limitations for learning developmental trajectories from single cell genomics.

Single cell genomics has become a popular approach to uncover the cellular heterogeneity of progenitor and terminally differentiated cell types with great precision. This approach can also delineate lineage hierarchies and identify molecular programmes of cell-fate acquisition and segregation. Nowadays, tens of thousands of cells are routinely sequenced in single cell-based methods and even more are expected to be analysed in the future. However, interpretation of the resulting data is challenging and requires computational models at multiple levels of abstraction. In contrast to other applications of single cell sequencing, where clustering approaches dominate, developmental systems are generally modelled using continuous structures, trajectories and trees. These trajectory models carry the promise of elucidating mechanisms of development, disease and stimulation response at very high molecular resolution. However, their reliable analysis and biological interpretation requires an understanding of their underlying assumptions and limitations. Here, we review the basic concepts of such computational approaches and discuss the characteristics of developmental processes that can be learnt from trajectory models.