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.

Deciphering mechanisms of endocrine cell induction, specification and lineage allocation in vivo will provide valuable insights into how the islets of Langerhans are generated. Currently, it is ill defined how endocrine progenitors segregate into different endocrine subtypes during development. Here, we generated a novel Neurogenin3 (Ngn3)-Venus fusion (NVF) reporter mouse line, that closely mirrors the transient endogenous Ngn3 protein expression. To define an in vivo roadmap of endocrinogenesis, we performed single-cell RNA-sequencing of 36,351 pancreatic epithelial and NVF⁺ cells during secondary transition. This allowed to time-resolve and distinguish Ngn3^low endocrine progenitors, Ngn3^high endocrine precursors, Fev⁺ endocrine lineage and hormone⁺ endocrine subtypes and delineate molecular programs during the stepwise lineage restriction steps. Strikingly, we identified 58 novel signature genes that show the same transient expression dynamics as Ngn3 in the 7,260 profiled Ngn3-expressing cells. The differential expression of these genes in endocrine precursors associated with their cell-fate allocation towards distinct endocrine cell types. Thus, the generation of an accurately regulated NVF reporter allowed us to temporally resolve endocrine lineage development to provide a fine-grained single-cell molecular profile of endocrinogenesis in vivo.

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.

Single-cell genomic technologies enable the multimodal profiling of millions of cells across temporal and spatial dimensions. However, experimental limitations hinder the comprehensive measurement of cells under native temporal dynamics and in their native spatial tissue niche. Optimal transport has emerged as a powerful tool to address these constraints and has facilitated the recovery of the original cellular context ^1–4 . Yet, most optimal transport applications are unable to incorporate multimodal information or scale to single-cell atlases. Here we introduce multi-omics single-cell optimal transport (moscot), a scalable framework for optimal transport in single-cell genomics that supports multimodality across all applications. We demonstrate the capability of moscot to efficiently reconstruct developmental trajectories of 1.7 million cells from mouse embryos across 20 time points. To illustrate the capability of moscot in space, we enrich spatial transcriptomic datasets by mapping multimodal information from single-cell profiles in a mouse liver sample and align multiple coronal sections of the mouse brain. We present moscot.spatiotemporal, an approach that leverages gene-expression data across both spatial and temporal dimensions to uncover the spatiotemporal dynamics of mouse embryogenesis. We also resolve endocrine-lineage relationships of delta and epsilon cells in a previously unpublished mouse, time-resolved pancreas development dataset using paired measurements of gene expression and chromatin accessibility. Our findings are confirmed through experimental validation of NEUROD2 as a regulator of epsilon progenitor cells in a model of human induced pluripotent stem cell islet cell differentiation. Moscot is available as open-source software, accompanied by extensive documentation.

The field of gene therapy is striving more than ever to define a path to the clinic and the market. Twenty gene therapy products have already been approved and over two thousand human gene therapy clinical trials have been reported worldwide. These advances raise great hope to treat devastating rare and inherited diseases as well as incurable illnesses. Understanding of the precise pathomechanisms of diseases as well as the development of efficient and specific gene targeting and delivery tools are revolutionizing the global market. Currently, human cancers and monogenic disorders are indications number one. The elevated prevalence of genetic disorders and cancers, clear gene manipulation guidelines and increasing financial support for gene therapy in clinical trials are major trends. Gene therapy is presently starting to become commercially profitable as a number of gene and cell-based gene therapy products have entered the market and the clinic. This article reviews the history and development of twenty approved human gene and cell-based gene therapy products that have been approved up-to-now in clinic and markets of mainly North America, Europe and Asia.