The objective of this network is to train, through research, a group of 10 young scientists, giving them the practical skills and knowledge necessary to tackleThe function of a neuron is determined by its expression of morphogen receptors and adhesion proteins (to pattern its connectome), of synaptic protein complexes (for intercellular communication), and of ion channels (for neuronal excitability and signal propagation). The protein composition of any neuron is itself dependent on gene expression controlled by transcription factors and by post-transcriptional regulations (splicing, translation, protein stability). Finally, neurons are highly compartmentalized cells and require trafficking of proteins to their correct locations. Alterations of these neuronal features can therefore be observed at multiple levels: molecular, cellular, and behavioral. We will focus on three neuronal features: i) gene expression levels as measured by RNA sequencing and proteomics, ii) assembly of functional multiprotein complexes (by high resolution microscopy and proximity dependent protein labelling), and iii) phenotypic output via a combination of optogenetics, calcium imaging, and behavioral assays. This research program will help define the critical features that differentiate between physiological and pathological states of a given neuron. Such biomarkers would be precious to facilitate drug screens or develop novel therapeutic strategies for human neurological diseases. The interdisciplinary research and training programme will cover a broad spectrum of approaches and topics, from intracellular molecular interactions to cell–cell communication, gene expression profiling, the impact of deleterious mutations, behavioral studies, and translation regulation. Coupled with extensive network-wide transferable skills training, this will prepare the fellows for careers in the medically important field of neurobiology as well as a broad range of carriers including government and industry.

How do cells transition from one identity to another? Are these transitions progressive, or rather sharp? How do transition states compare between natural and induced reprogramming? Addressing these challenging questions requires comprehensive descriptions of cell states, and their mapping to cell fate. Here we propose to examine transition states during neuronal direct reprogramming in the transcriptomic space. We will use 2 in vivo integrated, physiological models, each one with their unique advantages. Since the cellular lineage of all cells is known in C. elegans, it allows us to interrogate natural direct reprogramming at the unique cell level with direct mapping of individual states to cell fate. This unmatched ability to isolate the same defined cell from all animals also bypasses the inherent heterogeneity associated with multiple identical cells purified from a tissue. Such heterogeneity adds noise to the cellular trajectories reconstituted from single cell omics and precludes a clear picture of the transition states between 2 identities. Finally, with its fast development the worm allows to follow the process minute per minute and pre-screen actionable targets to improve reprogramming strategies. In addition, we will use an in vivo TF-based induced neuronal reprogramming system, in an integrated epilepsy mice model. This will allow us not only to assess the reprogramming trajectory but also its ability to generate functional neurons. By comparing reprogramming trajectories in these 2 systems we will highlight shared principles and identify candidates factors to improve direct reprogramming. To this aim, we will develop novel full-length RNA-seq approaches, and use high-end computational pipelines in order to model gene regulation, with a particular focus on the architecture of transcription factors networks underlining the observed cellular transitions. Ultimately, we will assess in vivo which critical network nodes allow us to manipulate cell identity.

It is the ambition of PRESIST-SEQ to provide a new gold standard in single-cell experimental workflows the cancer research community by developing best practices, standard operating procedures (SOPs), and high-quality FAIR data, with the ultimate aim to empower them to unravel therapeutic resistance. Such, that the community can identify urgently needed markers to predict, prevent, and target tumour resistance. Cancer takes 9.6 million lives each year, 90% of which result from untreatable metastatic relapse occurring after initially (seemingly) effective treatment. Therapeutic resistance is hence a primary cause of cancer death that clinically cannot be predicted, prevented, or treated. Addressing the urgent need for smarter therapeutic strategies is however held back by the lack of standardised experimental approaches that enable studying the biology of residual disease and drug tolerant persister cells in full detail. This need encompasses best practices for single-cell sequencing, advanced modelling techniques using patient-derived organoids and xenografts, and data FAIRification for integrated experiments. To address this need, PERSIST-SEQ brings together globally leading groups in single-cell sequencing technologies, cancer modelling and therapeutic resistance. Furthermore, the consortium has a broad range of clinical samples, cell lines, 3D models (PDX and PDOs) and mice models (GEMMs) at its disposal that can be leveraged to answer a broad range of emerging questions. This positions the consortium excellently to (1) design and standardise single-cell experimental approach to study the biology of therapeutic resistance and (2) initiate the largest single-cell profiling initiative on therapeutic resistance. Importantly, PERSIST-SEQ is organised such that it can quickly adapt to emerging insights and techniques during the project, and that ensures the capture of learnings in manners that stimulate replication of workflows elsewhere.