ScFates provides an extensive toolset for the analysis of dynamic trajectories comprising tree learning, feature association testing, branch differential expression and with a focus on cell biasing and fate splits at the level of bifurcations. It is meant to be fully integrated into the scanpy ecosystem for seamless analysis of trajectories from single-cell data of various modalities (e.g. RNA and ATAC). scFates is released as open-source software under the BSD 3-Clause ‘New’ License and is available from the Python Package Index at https://pypi.org/project/scFates/. The source code is available on GitHub at https://github.com/LouisFaure/scFates/. Code reproduction and tutorials on published datasets are available on GitHub at https://github.com/LouisFaure/scFates_notebooks. Supplementary data are available at Bioinformatics online.

Schwann cell precursors (SCPs) are nerve‐associated progenitors that can generate myelinating and non‐myelinating Schwann cells but also are multipotent like the neural crest cells from which they originate. SCPs are omnipresent along outgrowing peripheral nerves throughout the body of vertebrate embryos. By using single‐cell transcriptomics to generate a gene expression atlas of the entire neural crest lineage, we show that early SCPs and late migratory crest cells have similar transcriptional profiles characterised by a multipotent “hub” state containing cells biased towards traditional neural crest fates. SCPs keep diverging from the neural crest after being primed towards terminal Schwann cells and other fates, with different subtypes residing in distinct anatomical locations. Functional experiments using CRISPR‐Cas9 loss‐of‐function further show that knockout of the common “hub” gene Sox8 causes defects in neural crest‐derived cells along peripheral nerves by facilitating differentiation of SCPs towards sympathoadrenal fates. Finally, specific tumour populations found in melanoma, neurofibroma and neuroblastoma map to different stages of SCP/Schwann cell development. Overall, SCPs resemble migrating neural crest cells that maintain multipotency and become transcriptionally primed towards distinct lineages.

Different types of spiral ganglion neurons (SGNs) are essential for auditory perception by transmitting complex auditory information from hair cells (HCs) to the brain. Here, we use deep, single cell transcriptomics to study the molecular mechanisms that govern their identity and organization in mice. We identify a core set of temporally patterned genes and gene regulatory networks that may contribute to the diversification of SGNs through sequential binary decisions and demonstrate a role for NEUROD1 in driving specification of a I_c-SGN phenotype. We also find that each trajectory of the decision tree is defined by initial co-expression of alternative subtype molecular controls followed by gradual shifts toward cell fate resolution. Finally, analysis of both developing SGN and HC types reveals cell-cell signaling potentially playing a role in the differentiation of SGNs. Our results indicate that SGN identities are drafted prior to birth and reveal molecular principles that shape their differentiation and will facilitate studies of their development, physiology, and dysfunction.

Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent-like optimization of the graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such as biology, where it can be used for example with single-cell transcriptomic or epigenomic datasets to infer gene expression dynamics and recover differentiation landscapes.

Somatic sensation is defined by the existence of a diversity of primary sensory neurons with unique biological features and response profiles to external and internal stimuli. However, there is no coherent picture about how this diversity of cell states is transcriptionally generated. Here, we use deep single cell analysis to resolve fate splits and molecular biasing processes during sensory neurogenesis in mice. Our results identify a complex series of successive and specific transcriptional changes in post-mitotic neurons that delineate hierarchical regulatory states leading to the generation of the main sensory neuron classes. In addition, our analysis identifies previously undetected early gene modules expressed long before fate determination although being clearly associated with defined sensory subtypes. Overall, the early diversity of sensory neurons is generated through successive bi-potential intermediates in which synchronization of relevant gene modules and concurrent repression of competing fate programs precede cell fate stabilization and final commitment.