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Conference Poster Year : 2021

Exploring the transcriptional landscape of gonadotroph tumor microenvironment with single cell RNA-seq

Abstract

Pituitary is a small endocrine gland located at the base of brain. The anterior part of the gland produces and secretes several hormones which play important roles in the growth, the development and the functions of numerous organs. Different subtypes of endocrine pituitary tumors exist. While somatic mutations, copy number alterations, cell-cycle dysregulations and epigenetics changes have been identified in most pituitary tumor-subtypes, the transformation and tumor-driving mechanisms in the gonadotroph subtype remain unknown. Here, we want to explore whether the implication of the tumor microenvironment plays a role in gonadotroph adenoma based on its emerging roles as candidate actor and therapeutic target in pituitary tumors. To that extent, our main objective is to characterize the composition and transcriptional landscape of gonadotroph tumors microenvironment through the use of single cell genomics. To address our objective, we performed single cell RNA sequencing (scRNAseq) on surgically resected gonadotroph tumors. Single cells dissociated from tumors were encapsulated using a chromium controller (10X Genomics) prior to library generation and sequencing. During my internship, I develop a single cell RNA-seq (scRNA-seq) pipeline with conda and snakemake to ensure an automatized and reproductible pipeline. The latter comprises three steps: (i) data acquisition (reads quality check, read trimming, demultiplexing and mapping with StarSolo), data cleaning (cells quality check, batch effect correction, and normalization) and cell subpopulation identification both with Seurat. Here, we aim (i) at optimizing a scRNA-seq pipeline to explore the cellular composition of gonadotroph tumors, (ii) at determining if the microenvironment influences the tumorigenesis or if tumor cells have a genetic origin, and (iii) at pointing out new tumorigenesis factors/biomarkers in gonadotroph adenoma.
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lirmm-03544671 , version 1 (26-01-2022)

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  • HAL Id : lirmm-03544671 , version 1

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Benoît Aliaga, Julie Ripoll, Marie Chanal, Hector Hernandez-Vargas, Emmanuel Jouanneau, et al.. Exploring the transcriptional landscape of gonadotroph tumor microenvironment with single cell RNA-seq. SFBI. JOBIM 2021 - 21èmes Journées Ouvertes de Biologie, Informatique et Mathématiques, Jul 2021, Paris, France. , 2021, Actes des Journées Ouvertes en Biologie, Informatique et Mathématiques. ⟨lirmm-03544671⟩
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