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Caporale, N.* ; Castaldi, D.* ; Rigoli, M.T.* ; Cheroni, C.* ; Valenti, A.* ; Stucchi, S.* ; Lessi, M.* ; Bulgheresi, D.* ; Trattaro, S.* ; Pezzali, M.* ; Vitriolo, A.* ; Lopez-Tobon, A.* ; Bonfanti, M.* ; Ricca, D.* ; Schmid, K. ; Heinig, M. ; Theis, F.J. ; Villa, C.E.* ; Testa, G.*

Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single-cell resolution.

Nat. Methods, DOI: 10.1038/s41592-024-02555-5 (2024)
Verlagsversion Postprint DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
Dissecting human neurobiology at high resolution and with mechanistic precision requires a major leap in scalability, given the need for experimental designs that include multiple individuals and, prospectively, population cohorts. To lay the foundation for this, we have developed and benchmarked complementary strategies to multiplex brain organoids by pooling cells from different pluripotent stem cell (PSC) lines either during organoid generation (mosaic models) or before single-cell RNA sequencing (scRNA-seq) library preparation (downstream multiplexing). We have also developed a new computational method, SCanSNP, and a consensus call to deconvolve cell identities, overcoming current criticalities in doublets and low-quality cell identification. We validated both multiplexing methods for charting neurodevelopmental trajectories at high resolution, thus linking specific individuals' trajectories to genetic variation. Finally, we modeled their scalability across different multiplexing combinations and showed that mosaic organoids represent an enabling method for high-throughput settings. Together, this multiplexing suite of experimental and computational methods provides a highly scalable resource for brain disease and neurodiversity modeling.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Rna-seq; R Package; Fate; Neurodiversity; Genetics; Autism
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
ISSN (print) / ISBN 1548-7091
e-ISSN 1548-7105
Zeitschrift Nature Methods
Verlag Nature Publishing Group
Verlagsort New York, NY
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
G-553500-001
Förderungen European Institute of Oncology - European Union
Flow Cytometry and Imaging facilities of Human Technopole
DOI 10.1038/s41592-024-02555-5
WOS ID 001372760000001
Scopus ID 85211962534
PubMed ID 39653820
Erfassungsdatum 2024-12-11
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