Clonal hematopoiesis driven by chromosome 1q/MDM4 trisomy defines a canonical route toward leukemia in Fanconi anemia.

Fanconi anemia (FA) patients experience chromosome instability, yielding hematopoietic stem/progenitor cell (HSPC) exhaustion and predisposition to poor-prognosis myeloid leukemia. Based on a longitudinal cohort of 335 patients, we performed clinical, genomic, and functional studies in 62 patients with clonal evolution. We found a unique pattern of somatic structural variants and mutations that shares features of BRCA-related cancers, the FA-hallmark being unbalanced, microhomology-mediated translocations driving copy-number alterations. Half the patients developed chromosome 1q gain, driving clonal hematopoiesis through MDM4 trisomy downmodulating p53 signaling later followed by secondary acute myeloid lukemia genomic alterations. Functionally, MDM4 triplication conferred greater fitness to murine and human primary FA HSPCs, rescued inflammation-mediated bone marrow failure, and drove clonal dominance in FA mouse models, while targeting MDM4 impaired leukemia cells in vitro and in vivo. Our results identify a linear route toward secondary leukemogenesis whereby early MDM4-driven downregulation of basal p53 activation plays a pivotal role, opening monitoring and therapeutic prospects.

Cellular senescence in malignant cells promotes tumor progression in mouse and patient Glioblastoma.

Glioblastoma (GBM) is the most common primary malignant brain tumor in adults, yet it remains refractory to systemic therapy. Elimination of senescent cells has emerged as a promising new treatment approach against cancer. Here, we investigated the contribution of senescent cells to GBM progression. Senescent cells are identified in patient and mouse GBMs. Partial removal of p16Ink4a-expressing malignant senescent cells, which make up less than 7 % of the tumor, modifies the tumor ecosystem and improves the survival of GBM-bearing female mice. By combining single cell and bulk RNA sequencing, immunohistochemistry and genetic knockdowns, we identify the NRF2 transcription factor as a determinant of the senescent phenotype. Remarkably, our mouse senescent transcriptional signature and underlying mechanisms of senescence are conserved in patient GBMs, in whom higher senescence scores correlate with shorter survival times. These findings suggest that senolytic drug therapy may be a beneficial adjuvant therapy for patients with GBM.

Glioblastoma cell motility depends on enhanced oxidative stress coupled with mobilization of a sulfurtransferase.

Cell motility is critical for tumor malignancy. Metabolism being an obligatory step in shaping cell behavior, we looked for metabolic weaknesses shared by motile cells across the diverse genetic contexts of patients' glioblastoma. Computational analyses of single-cell transcriptomes from thirty patients' tumors isolated cells with high motile potential and highlighted their metabolic specificities. These cells were characterized by enhanced mitochondrial load and oxidative stress coupled with mobilization of the cysteine metabolism enzyme 3-Mercaptopyruvate sulfurtransferase (MPST). Functional assays with patients' tumor-derived cells and -tissue organoids, and genetic and pharmacological manipulations confirmed that the cells depend on enhanced ROS production and MPST activity for their motility. MPST action involved protection of protein cysteine residues from damaging hyperoxidation. Its knockdown translated in reduced tumor burden, and a robust increase in mice survival. Starting from cell-by-cell analyses of the patients' tumors, our work unravels metabolic dependencies of cell malignancy maintained across heterogeneous genomic landscapes.

Unexpected contribution of fibroblasts to muscle lineage as a mechanism for limb muscle patterning.

Positional information driving limb muscle patterning is contained in connective tissue fibroblasts but not in myogenic cells. Limb muscles originate from somites, while connective tissues originate from lateral plate mesoderm. With cell and genetic lineage tracing we challenge this model and identify an unexpected contribution of lateral plate-derived fibroblasts to the myogenic lineage, preferentially at the myotendinous junction. Analysis of single-cell RNA-sequencing data from whole limbs at successive developmental stages identifies a population displaying a dual muscle and connective tissue signature. BMP signalling is active in this dual population and at the tendon/muscle interface. In vivo and in vitro gain- and loss-of-function experiments show that BMP signalling regulates a fibroblast-to-myoblast conversion. These results suggest a scenario in which BMP signalling converts a subset of lateral plate mesoderm-derived cells to a myogenic fate in order to create a boundary of fibroblast-derived myonuclei at the myotendinous junction that controls limb muscle patterning.

A single-cell RNA-sequencing training and analysis suite using the Galaxy framework.

BACKGROUND: The vast ecosystem of single-cell RNA-sequencing tools has until recently been plagued by an excess of diverging analysis strategies, inconsistent file formats, and compatibility issues between different software suites. The uptake of 10x Genomics datasets has begun to calm this diversity, and the bioinformatics community leans once more towards the large computing requirements and the statistically driven methods needed to process and understand these ever-growing datasets. RESULTS: Here we outline several Galaxy workflows and learning resources for single-cell RNA-sequencing, with the aim of providing a comprehensive analysis environment paired with a thorough user learning experience that bridges the knowledge gap between the computational methods and the underlying cell biology. The Galaxy reproducible bioinformatics framework provides tools, workflows, and trainings that not only enable users to perform 1-click 10x preprocessing but also empower them to demultiplex raw sequencing from custom tagged and full-length sequencing protocols. The downstream analysis supports a range of high-quality interoperable suites separated into common stages of analysis: inspection, filtering, normalization, confounder removal, and clustering. The teaching resources cover concepts from computer science to cell biology. Access to all resources is provided at the singlecell.usegalaxy.eu portal. CONCLUSIONS: The reproducible and training-oriented Galaxy framework provides a sustainable high-performance computing environment for users to run flexible analyses on both 10x and alternative platforms. The tutorials from the Galaxy Training Network along with the frequent training workshops hosted by the Galaxy community provide a means for users to learn, publish, and teach single-cell RNA-sequencing analysis.

Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells.

Glioblastoma cell ability to adapt their functioning to microenvironment changes is a source of the extensive intra-tumor heterogeneity characteristic of this devastating malignant brain tumor. A systemic view of the metabolic pathways underlying glioblastoma cell functioning states is lacking. We analyzed public single cell RNA-sequencing data from glioblastoma surgical resections, which offer the closest available view of tumor cell heterogeneity as encountered at the time of patients' diagnosis. Unsupervised analyses revealed that information dispersed throughout the cell transcript repertoires encoded the identity of each tumor and masked information related to cell functioning states. Data reduction based on an experimentally-defined signature of transcription factors overcame this hurdle. It allowed cell grouping according to their tumorigenic potential, regardless of their tumor of origin. The approach relevance was validated using independent datasets of glioblastoma cell and tissue transcriptomes, patient-derived cell lines and orthotopic xenografts. Overexpression of genes coding for amino acid and lipid metabolism enzymes involved in anti-oxidative, energetic and cell membrane processes characterized cells with high tumorigenic potential. Modeling of their expression network highlighted the very long chain polyunsaturated fatty acid synthesis pathway at the core of the network. Expression of its most downstream enzymatic component, ELOVL2, was associated with worsened patient survival, and required for cell tumorigenic properties in vivo. Our results demonstrate the power of signature-driven analyses of single cell transcriptomes to obtain an integrated view of metabolic pathways at play within the heterogeneous cell landscape of patient tumors.