Human cortical organoids recapitulate inter-individual variability in infant brain-growth trajectories

Madison R Glass¹, Nana Matoba¹, Alvaro A Beltran¹, Niyanta K Patel¹, Tala M Farah¹, Karthik Eswar¹, Shivam Bhargava¹, Karen Huang¹, Ian Curtin¹, Sara Ahmed¹, Mary Srivastava¹, Emma Drake¹, Liam T Davis¹, Meghana Yeturi¹, Kexin Sun¹, Michael I Love², Jeremy M Simon¹, Tanya St John³, Natasha Marrus⁴, Juhi Pandey⁵, Annette Estes³, Stephen Dager⁶, Robert T Schultz⁵, Kelly Botteron⁴, Alan Evans⁷, Sun Hyung Kim⁸, Martin Styner⁹, Robert C McKinstry¹⁰, D Louis Collins¹¹, Heather Volk¹², Kelly Benke¹², Lonnie Zwaigenbaum¹³, Heather Hazlett⁸, Adriana S Beltran¹⁴, Jessica B Girault¹⁵, Mark D Shen¹⁶, Joseph Piven¹⁷, Jason L Stein¹⁸; Infant Brain Imaging Study Network

Affiliations

¹ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
² Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
³ Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, USA; University of Washington Autism Center, University of Washington, Seattle, WA, USA.
⁴ Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
⁵ Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA.
⁶ Department of Radiology, University of Washington, Seattle, WA, USA; Department of Bioengineering, University of Washington, Seattle, WA, USA.
⁷ Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
⁸ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Computer Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁰ Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
¹¹ Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
¹² School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
¹³ Department of Developmental Pediatrics, University of Alberta, Edmonton, AB, Canada.
¹⁴ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁵ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: jessica_girault@med.unc.edu.
¹⁶ UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: mark_shen@med.unc.edu.
¹⁷ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: jpiven@med.unc.edu.
¹⁸ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: jason_stein@med.unc.edu.

PMID: 41435817
PMCID: PMC12930428
DOI: 10.1016/j.stem.2025.12.001

Human cortical organoids recapitulate inter-individual variability in infant brain-growth trajectories

Madison R Glass et al. Cell Stem Cell. 2026.

. 2026 Jan 8;33(1):142-156.e7.

doi: 10.1016/j.stem.2025.12.001. Epub 2025 Dec 22.

Authors

Affiliations

¹ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
² Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
³ Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, USA; University of Washington Autism Center, University of Washington, Seattle, WA, USA.
⁴ Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
⁵ Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA; Center for Autism Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania, Philadelphia, PA, USA.
⁶ Department of Radiology, University of Washington, Seattle, WA, USA; Department of Bioengineering, University of Washington, Seattle, WA, USA.
⁷ Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Psychiatry, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
⁸ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
⁹ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Computer Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁰ Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA.
¹¹ Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada; Department of Biomedical Engineering, McGill University, Montreal, QC, Canada.
¹² School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
¹³ Department of Developmental Pediatrics, University of Alberta, Edmonton, AB, Canada.
¹⁴ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
¹⁵ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: jessica_girault@med.unc.edu.
¹⁶ UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: mark_shen@med.unc.edu.
¹⁷ Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: jpiven@med.unc.edu.
¹⁸ Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA. Electronic address: jason_stein@med.unc.edu.

PMID: 41435817
PMCID: PMC12930428
DOI: 10.1016/j.stem.2025.12.001

Abstract

Induced pluripotent stem cell (iPSC)-derived human cortical organoids (hCOs) model neurogenesis on an individual's genetic background. The degree to which hCO phenotypes recapitulate the brain growth of the participants from whom they were derived is not well established. We generated up to 3 iPSC clones from each of 18 participants in the Infant Brain Imaging Study, who underwent longitudinal brain imaging during infancy. We identified consistent hCO morphology and cortical cell types across clones from the same participant. hCO cross-sectional area and production of hem/choroid plexus were associated with in vivo cortical growth rates. Cell-cycle-associated gene expression in early progenitors at the crux of fate-decision trajectories was correlated with cortical growth rates from 6 to 12 months of age and was enriched for microcephaly and neurodevelopmental disorder genes. Our data suggest the hCOs capture inter-individual variation in cortical cell types that influences infant cortical surface area expansion.

Keywords: cell cycle; hem/choroid plexus; infant brain growth; neurogenesis; organoid.

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Conflict of interest statement

Declaration of interests R.C.M. received travel and meals from Siemens Healthcare and Radiation, received meals from Myperfine, and holds stock options in Turing Medical and serves on its advisory board.

Figures

**Fig. 1.. Strategy to Model Inter-Individual Variation in Infant MRI Measures using Cortical Organoids**
a, Experimental Overview. The Infant Brain Imaging Study (IBIS) previously collected longitudinal MRI measurements from three groups of participants: Infants with no family history (low-likelihood, LL) of neuropsychiatric disorders, infants with an older sibling diagnosed with autism spectrum disorder (ASD) who either were not diagnosed at 24 months of age (high likelihood, HL) or received an ASD diagnosis (high likelihood - ASD, HL-ASD). Up to 3 iPSC clones were generated from previous IBIS participants’ PBMCs. iPSC lines were differentiated into cortical organoids and assayed with scRNAseq at day 14 and day 84, 3D imaged at day 14, and imaged throughout the differentiation to measure cross-sectional area and organoid morphology. b, MRI measurements of cortical surface area in IBIS participants in this study. Family history and diagnostic status are indicated by color: HL-ASD (yellow), HL (red), LL (blue). The group mean is shown in bold. c, correlations among MRI measurements of IBIS participants in this study. # indicates FDR < 0.1 d, scRNAseq cell type annotations. Color in the heatmap indicated the significance of Fisher’s Exact test for enrichment in highly variable genes in our hCO dataset and primary human tissue. Our hCO data set is annotated by color bars indicating cell counts, cell type proportion, participant representation and cell cycle states (G1, G2M, S) at day 14 and day 84. e, Representative day 14 organoid showing NCAD+ neuroepithelial buds with PAX6+ radial glia. f, Day 84 organoids showing CTIP2+ lower layer neurons outside of neuroepithelial buds. Abbreviation: oligodendrocytes (OL), oligodendrocyte progenitor cells (OPC), intermediate progenitor cell (IPC), excitatory neuron (EN), inhibitory neuron (IN), radial glia (RG), apical radial glia (aRG), outer radial glia (oRG), truncated radial glia (tRG), ventral radial glia (vRG), cortical projection neuron (CPN), deep layer projection neuron (DLPN), Cycling progenitor (Cyc. prog.), hem/choroid plexus epithelium (hem/CPe), pre-plate (PP), subplate (SP).

**Fig. 2. Consistency of Differentiation across Participants in Morphology and Cell Class Proportions.**
a, Day 14 morphology qualitative rank indicates if visible neuroepithelial buds were present or not (buds outlined in white dashed lines) and if the hCO edges are smooth or not. b, Day 56 morphology rank indicates if clear cysts were present on the organoid or not. c, Quantification of percent rank across day 14 and day 56 for each batch colored with shades indicating diagnostic status and familial history (N= 7–96 hCOs per batch). d, Correlation across and within participants for all percent morphological ranks. e, Cell class proportions across organoid batches from the same participant. Cell class proportions from a single organoid from the same participant are grouped. Participant ID is indicated by the color bar above the cell type proportions. The line represents the average proportion of neuronal populations at day 14 and day 84. f, PCA plot of cell class proportions for samples passing QC. g, Cell type proportions contributing to principal components. The top 5 contributing subclusters are labeled. h, Correlation of major cell classes across and within participants at day 14 (top) and day 84 (bottom). Batches are colored by participant and ordered by mean rank in cross-sectional cortical surface area across 6, 12 and 24 month timepoints so that the rightmost participant has the relative greatest cortical surface area in this cohort. Family history and diagnostic status are indicated by participant ID color: HL-ASD (yellow), HL (red), LL (blue). Batch is the unique combination of differentiation:participant:clone in (c), and the brackets indicate the same organoid in two different library preparations in (e).

**Fig. 3.. Cell Type Proportions Correlate with Infant Cortical Surface area.**
T-statistic from a linear model associating average cell type proportions with cortical surface area measurements at a, day 14 or b, day 84 (N=12–16). Sex is added as a covariate and age at MRI is a covariate for cross-sectional MRI measurements. FDR was calculated for either subclusters or cell classes at each timepoint across MRI measurements. c, Correlation between cortical surface area at 6 months of age and day 14 hem/CPe sc_10. Correlation between cortical surface area growth rate between 6–12 months of age and day 14 d, hem/CPe sc_8 and e, hem/CPe proportions. Family history and diagnostic status are indicated by color for each participant in ce: HL-ASD (yellow), HL (red), LL (blue). Connected dots indicate different batches from the same participant. Day 84 IP association with 12–24 month growth rate was driven by one clone of one participant, so is not shown here as a scatterplot. Cyc .Prog.−2_sc_32 is excluded from the panel due to low cell counts at day 84, but is shown in Table S3.

**Fig. 4. hCO Cross Sectional Area Associations with Infant Cortical Size.**
a, hCO cross-sectional area growth rate for each differentiation over time and example mask of an area measurement across differentiation days (inset). b, Pearson correlation of cross-sectional area measurements at all timepoints across and within participants (N = 18 participants). c, Average cross-sectional area correlations to cortical structure measurements (N= 12–16 participants). Sex is added as a covariate and age at MRI is a covariate for cross-sectional MRI measurements. FDR correction was applied for each area measurement to all MRI measurements. Correlations between day 7 and day 14 hCO growth rate with d, 24 month cortical surface area, e, 12–24 month cortical surface area growth rate and f, 6–12 month cortical surface area growth rate. Family history and diagnostic status are indicated by color for each participant: HL-ASD (yellow), HL (red), LL (blue). Connected dots indicate different batches from the same participant.

**Fig. 5.. Gene Expression Levels Associated with Cortical Surface Area and Growth Rate.**
a, Heatmap showing the distribution of gene counts associated with brain growth rate (left) and surface area (right) across subclusters and time points in our regression model. Color intensity indicates the number of associated genes. In the subsequent panels, we focus on genes associated with CPN_sc_22 at day 14 for growth rate (6–12) and surface area at 12 months (N=8 participants). b, UpsetR plot summarizing gene overlaps, c, Pathway enrichment analysis of associated genes, with key pathways such as “*RHO GTPase effectors”*, “*Cell Cycle”*. d, Scatter plots demonstrating correlations between sex-corrected growth rate and gene expression (three examples from RHO GTPase effectors). Family history and diagnostic status are indicated by color for each participant: HL-ASD (yellow), HL (red), LL (blue). e, PCA of gene expression from subset major cell types (Upper layer neuron (ULN), Inhibitory neuron (IN), cortical projection neuron (CPN), newborn deep layer projection neuron (DPLN), outer radial glia (oRG), apical radial glia (aRG)) colored by cell class (left). Extracted cells from CPN_sc_22 on the same dimension in the left panel. Color intensity indicates cell count density (right). Three lineages (ULN: purple, newborn neuron: blue, RG: red) were annotated using inferred pseudotime. f, CPN_sc_22 genes positively associated with brain growth or surface area were enriched in microcephaly and ASD related genes.

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References

1. Gordon A, Yoon S-J, Tran SS, Makinson CD, Park JY, Andersen J, Valencia AM, Horvath S, Xiao X, Huguenard JR, et al. (2021). Long-term maturation of human cortical organoids matches key early postnatal transitions. Nat. Neurosci 24, 331–342. 10.1038/s41593-021-00802-y. - DOI - PMC - PubMed
1. Kelley KW, and Pașca SP (2022). Human brain organogenesis: Toward a cellular understanding of development and disease. Cell 185, 42–61. 10.1016/j.cell.2021.10.003. - DOI - PubMed
1. Di Lullo E, and Kriegstein AR (2017). The use of brain organoids to investigate neural development and disease. Nat. Rev. Neurosci 18, 573–584. 10.1038/nrn.2017.107. - DOI - PMC - PubMed
1. Glass MR, Waxman EA, Yamashita S, Lafferty M, Beltran AA, Farah T, Patel NK, Singla R, Matoba N, Ahmed S, et al. (2024). Cross-site reproducibility of human cortical organoids reveals consistent cell type composition and architecture. Stem Cell Reports 19, 1351–1367. 10.1016/j.stemcr.2024.07.008. - DOI - PMC - PubMed
1. Yoon S-J, Elahi LS, Pașca AM, Marton RM, Gordon A, Revah O, Miura Y, Walczak EM, Holdgate GM, Fan HC, et al. (2019). Reliability of human cortical organoid generation. Nat. Methods 16, 75–78. 10.1038/s41592-018-0255-0. - DOI - PMC - PubMed

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Human cortical organoids recapitulate inter-individual variability in infant brain-growth trajectories

Affiliations

Human cortical organoids recapitulate inter-individual variability in infant brain-growth trajectories

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

MeSH terms

Grants and funding

LinkOut - more resources

Full Text Sources