Biology subjects

Soulier, J.

Publications and source records attributed to Soulier, J..

5 recordsLinked to original sources

bioRxiv2026

In vivo isogenic modelling unveils a TP53-mediated relapse phenotype in T-cell acute lymphoblastic leukemia

Many patients with T-cell acute lymphoblastic leukemia (T-ALL) relapse into a treatment-resistant disease. The mechanisms driving relapse remain largely elusive, in part due to the lack of faithful experimental models. Here, we leveraged patient-derived xenograft (PDX) pairs generated from diagnosis and relapse T-ALLs to functionally address the cellular mechanisms driving TP53-altered relapse. Beyond inter-T-ALL variability, comparative analyses revealed a unique, cell-intrinsic relapse phenotype that includes greater leukemia-initiating capacity and that can be conferred to diagnosis cells by TP53 silencing. Transcriptomic profiling linked the relapse phenotype to deregulated OXPHOS metabolism and MYC signaling. Integration of single-cell profilings uncovered TP53-wildtype cell populations at diagnosis expressing a relapse profile, possibly reflecting a pre-existing modulation of TP53 signaling. These cells sequentially evolved towards biallelic TP53 inactivation at relapse. Collectively, our findings support a model in which T-ALL relapses emerge from a selected pre-existing transcriptional state characterized by deregulated metabolism that favors subsequent TP53 inactivation.

cancer biology↗

bioRxiv2024

Radiotherapy triggers pro-angiogenic signaling in human lung

Radiotherapy is one of the main therapeutic options for the treatment of lung cancer. Although highly efficient, radiation cause severe damages to normal tissue and radio-induced toxicities vary from mild pneumonitis to pulmonary fibrosis. The mechanism leading to these toxicities remain unclear. To investigate the molecular responses of human lung to radiotherapy, we analyzed, by single cell RNAseq, lung tissue resected in the vicinity of the tumor (i.e. treated with radiation) and compared the transcriptional profiles of the distinct lung populations from the same patient removed at distance from the tumor (i.e. non-treated with radiation). Analysis of six lung samples from patients suffering from Pancoast tumor, a rare lung malignancy that requires neo-adjuvant radiotherapy before surgery, revealed a strong induction of VEGF signaling after radiotherapy. Expression of VEGFA, one of the canonical pro-angiogenic ligands, was found upregulated in multiple cell populations in lung exposed to high doses of radiation. Irradiated capillaries, particularly gCap cells, expressing KDR/VEGFR2, present transcriptional profile similar to tip cells, characterized by sprouting and motility capacities. In addition, we identified a sub-population of alveolar macrophages expressing FLT1/VEGFR1, a receptor for VEGFA, in lung tissues treated by radiotherapy. Cell-Cell communication analysis revealed that FLT1/VEGFR1 positive macrophages interact with tip cells after radiotherapy through IL1B-IL1R signaling. Lastly, analysis of mouse single cell dataset confirmed the increase in the proportion of gCap cells presenting a tip-like phenotype after radiation injury. Altogether, this study describes, at the single cell level, the pro-angiogenic responses of human lung after radiotherapy. These results will lead to a better understanding of the physiopathology of lung radiation injury and may pave the way to optimize treatments to improve patients quality of life.

cancer biology↗

bioRxiv2023

A point cloud segmentation framework for image-based spatial transcriptomics

Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It hence presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is thus applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and cell type calling. On experimental data, our method also demonstrates proficiency in estimating RNA profiles that align with established scRNA-seq datasets. Importantly, ComSeg exhibits a particular efficiency in handling complex tissue, positioning it as a valuable tool for the community.

bioinformatics↗

bioRxiv2020

XPC deficiency increases risk of hematologic malignancies through mutator phenotype and characteristic mutational signature

Recent studies demonstrated a dramatically increased risk of leukemia in patients with a rare genetic disorder, Xeroderma Pigmentosum group C (XP-C), characterized by constitutive deficiency of global genome nucleotide excision repair (GG-NER). However, the genetic mechanisms of non-skin cancers in XP-C patients remain unexplored. In this study, we analyzed a unique collection of internal XP-C tumor genomes including 6 leukemias and 2 sarcomas. We observed an average of 25-fold increase of mutation rates in XP-C vs. sporadic leukemia which we presume leads to its elevated incidence and early appearance. In all XP-C tumors predominant mutational process is characterized by a distinct mutational signature, highlighting a specific mutational pattern in the context of GG-NER deficiency. We observed a strong mutational asymmetry with respect to transcription and the direction of replication in XP-C tumors suggesting association of mutagenesis with bulky purine DNA lesions of probably endogenous origin. These findings suggest existence of a balance between formation and repair of bulky DNA lesions by GG-NER in human body cells which is disrupted in XP-C patients leading to internal cancers.

cancer biology↗

bioRxiv2019

A comprehensive dataset of TLX1 positive ALL-SIL lymphoblasts and primary T-cell acute lymphoblastic leukemias

Most currently available transcriptome data of T-cell acute lymphoblastic leukemia (T-ALL) are based on polyA[+] RNA sequencing methods thus lacking non-polyadenylated transcripts. Here, we present the data of polyA[+] and total RNA sequencing in the context of in vitro TLX1 knockdown in ALL-SIL cells and a primary T-ALL cohort. We extended this dataset with ATAC sequencing and H3K4me1 and H3K4me3 ChIP sequencing data to map putative gene regulatory regions. In this data descriptor, we present a detailed report of how the data were generated and which bioinformatics analyses were performed. Through several technical validations, we showed that our sequencing data are of high quality and that our in vitro TLX1 knockdown was successful. We also validated the quality of the ATAC and ChIP sequencing data and showed that ATAC and H3K4me3 ChIP peaks are enriched at transcription start sites. We believe that this comprehensive set of sequencing data can be reused by others to further unravel the complex biology of T-ALL in general and TLX1 in particular.

molecular biology↗