Author

Christelle Da Silva

Centre National de la Recherche ScientifiqueORCID 0000-0003-2248-0474Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Cell Biology· 1 Developmental Biology· 1

Signature Assays, Organisms & Methods

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Organism:mouse· 3 Research Focus:cell type annotation· 2 Approach:statistical model· 2 Assay:bulk RNA-seq· 1 Research Focus:cell signaling & trafficking· 1 Research Focus:development & morphogenesis· 1 Assay:electronic health records· 1 Approach:experimental assay· 1

Frequent Co-Authors

Elsa LabruneiD· 3 shared Marie‐Hélène VerlhaciD· 3 shared Corinne BlugeoniD· 2 shared Gaëlle LetortiD· 2 shared Marie‐Emilie TerretiD· 2 shared Adel Al JordiD· 1 shared Adrien Eichmuller· 1 shared Alexis CanetteiD· 1 shared

Activity by Year

2022-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 32 mutual co-author links

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3 shared papersCell Biology

Elsa LabruneORCID

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3 papers

Recent·Most cited

Cell BiologyScience AdvancesPeer-reviewedAug 28, 2026

A novel RNP compartment boosts translation in growing mouse oocytes to avoid cytoplasm dilution

Noemi Zollo, Gabriele Zaffagnini, Ali Haidar…·Marie‐Hélène Verlhac

The paper identifies a novel RNP compartment in mouse oocytes that enhances translation to counteract cytoplasmic dilution during growth.

1 citationtop 3%

mouseproteomics

Developmental BiologybioRxivPreprintAug 20, 2026

The post-transcriptional RNA splicing landscape driving oocyte division

Hady Al Shami, Andres Gordo Ortiz, Julia Orio-Tejada…·Marie‐Hélène Verlhac

This study defines the post-transcriptional RNA splicing landscape in fully grown mouse oocytes and identifies splicing events and regulatory programs essential for meiotic progression and oocyte division.

DOI 10.64898/2026.08.19.745726

mousebulk RNA-seq

BioinformaticsJournal of Cell SciencePeer-reviewedJun 6, 2022

An interpretable and versatile machine learning approach for oocyte phenotyping

Gaëlle Letort, Adrien Eichmuller, Christelle Da Silva…·Marie‐Hélène Verlhac

This paper develops a machine learning framework for oocyte phenotyping using transmitted light images, identifying new features to assess maturation potential in mouse and human oocytes.

37 citations