From the beginning of the project, we addressed Aim 1 and Aim2, and to some extent Aim3. Firstly, finalized and deeply analyzed the single cell map of the entire neural crest lineage, which became a basis for anew hi fi atlas of cell types relevant to the development and origin of a plethora of diseases, such as neuroblastoma (in the first place), but also melanoma, neurofibromatosis and many more. This atlas was already put to work for the analysis of neuroblastoma cell type heterogeneity during the first reporting period. In the parallel, we generated spatial maps of the neural crest on the tissue slices. Next, we created a principally new method to analyse clonality and cell type heterogeneity in tumors, called NUMBAT. We also created the method for analyzing transitions in cell states called scFates. This is a big achievement, as it helps to build the map of transitions between malignant plastic cell populations with different properties. Furthermore, we generated novel transgenic animal models of neuroblastoma as outlined in aim 2 and observed tumors with 100 % penetrance in these mice. In the models, we deleted KIF1Bβ and NF1 in the embryonic mouse sympatho-adrenal lineage and observed pheochromocytoma, neuroblastoma, and composite tumors arising in aged mice. Deep single-cell RNA sequencing combined with immunohistochemistry and RNA scope revealed neuroblast-chromaffin cell state transitions at embryonic and postnatal stages driving tumor plasticity. We also identified a neuroblastoma-specific transcriptional network involving YAP/TAZ, TEAD, FOSL, and RUNX family proteins that regulates mesenchymal identity and plasticity. Inhibition of YAP/TAZ reduced MES cell proliferation, while their overexpression induced mesenchymal reprogramming. Immune profiling revealed an immunosuppressive tumor microenvironment in neuroblastoma, characterized by TIGIT+ T cells interacting with NECTIN2/3-expressing stromal cells. To understand the clonal evolution in tumors and to compare it to clonality in the neural crest lineage, we adopted the lentiviral barcoding and single-cell transcriptomics to create a clonal atlas of neural crest and neuroblastoma using animal models. This enables revealing spatial and temporal dynamics in cell fate decisions. For this we further developed a new machine learning tool `Clone2vec` to capture clonal fate diversity, linking lineage identity to transcriptional states and uncovering fate biases in neural crest and tumor populations.
