Nature MethodsNeurosciencePeer-reviewed journal article
Multiplexing cortical brain organoids for the longitudinal dissection of developmental traits at single cell resolution
Nicolò CaporaleiD, Davide CastaldiiD, Marco Tullio RigoliiD, Cristina CheroniiD, Alessia Valenti, Sarah StucchiiD, Manuel LessiiD, Davide BulgheresiiD, Sebastiano TrattaroiD, Martina PezzaliiD, Alessandro VitrioloiD, Alejandro ToboniD, Matteo BonfantiiD, Dario RiccaiD, Katharina SchmidiD, Matthias HeinigiD, Fabian Joachim TheisiD, Carlo Emanuele VillaiD, Giuseppe TestaiD
Abstract
The combination of brain organoid and single cell omic technologies holds transformative potential to dissect human neurobiology at high resolution and with mechanistic precision. Delivering this promise in the context of human neurodiversity, physiological and pathological alike, requires however 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 implemented and benchmarked complementary strategies to multiplex brain organoids. Following an extended longitudinal design with a uniquely informative set of timepoints, we pooled cells from different induced pluripotent stem cell lines either during organoids generation (upstream multiplexing in mosaic models) or before single cell-RNAseq library preparation (downstream multiplexing). We developed a new method, SCanSNP, and an aggregated call to deconvolve organoids cell identities, overcoming current criticalities in doublets prediction and low quality cells identification and improving accuracy over state of the art algorithms. Integrating single cell transcriptomes and analysing cell types across neurodevelopmental stages and multiplexing modalities, we validated the feasibility of both multiplexing methods in charting neurodevelopmental trajectories at high resolution, linking their specificity to genetic variation between individual lines. Together, this multiplexing suite of experimental and computational methods provides an enabling resource for disease modelling at scale and paves the way towards an in vitro epidemiology paradigm.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- approach: statistical model
- Citations
- 35
- FWCI
- 4.75
- Field percentile
- top 3%
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