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Molecular landscapes of human hippocampal immature neurons across lifespan

Nature volume 607, pages 527–533 (2022)Cite this article

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Abstract

Immature dentate granule cells (imGCs) arising from adult hippocampal neurogenesis contribute to plasticity and unique brain functions in rodents1,2 and are dysregulated in multiple human neurological disorders3,4,5. Little is known about the molecular characteristics of adult human hippocampal imGCs, and even their existence is under debate1,6,7,8. Here we performed single-nucleus RNA sequencing aided by a validated machine learning-based analytic approach to identify imGCs and quantify their abundance in the human hippocampus at different stages across the lifespan. We identified common molecular hallmarks of human imGCs across the lifespan and observed age-dependent transcriptional dynamics in human imGCs that suggest changes in cellular functionality, niche interactions and disease relevance, that differ from those in mice9. We also found a decreased number of imGCs with altered gene expression in Alzheimer's disease. Finally, we demonstrated the capacity for neurogenesis in the adult human hippocampus with the presence of rare dentate granule cell fate-specific proliferating neural progenitors and with cultured surgical specimens. Together, our findings suggest the presence of a substantial number of imGCs in the adult human hippocampus via low-frequency de novo generation and protracted maturation, and our study reveals their molecular properties across the lifespan and in Alzheimer's disease.

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Fig. 1: snRNA-seq and immunohistological analyses of imGCs in the human infant hippocampus.
Fig. 2: snRNA-seq analysis of human imGCs across ages.
Fig. 3: Common and divergent molecular features of imGCs across the lifespan and between humans and mice.
Fig. 4: Reduced number and altered gene expression of imGCs in patients with Alzheimer's disease.
Fig. 5: Capacity for neurogenesis in the postnatal human hippocampus across age.

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Data availability

The snRNA-seq data are available at the Gene Expression Omnibus database under accession numbers GSE185553, GSE185277 and GSE198323. Specimen information and sequencing statistics are described in Supplementary Tables 1 and 2. Sources of the published scRNA-seq or snRNA-seq datasets used in this study are described in Supplementary Table 3. Source data are provided with this paper.

Code availability

The computational code used in this study is available at GitHub (https://github.com/zhoujoeyyi/humanImmatureNeurons) or upon request.

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Acknowledgements

We thank all brain donors, patients and their families for the tissue specimens used in this study and members of Ming and Song laboratories for discussion; B. Temsamrit, E. LaNoce and A. Garcia for technical support; A. Angelucci and J. Schnoll for lab coordination; F. H. Gage, G. Kempermann, S. Jessberger and K. M. Christian for comments; O. Spicer, J. Glausier, A. LeFevre, J. Cottrell and J. Hussain for assistance with human tissue acquisition; K. McCanon, Z. Yang, H. Zhu and CDI Laboratories for providing antibody samples; and M. Llorens-Martin for suggestions on immunohistology. Some of the schematic illustrations were modified and/or created with images from https://togotv.dbcls.jp/ or BioRender.com. This work was supported by grants from the National Institutes of Health (K01MH125144 to A.M.B., R35NS097370 to G.-l.M. and R35NS116843 to H.S.), The Lieber Institute for Brain Development (to J.E.K., T.M.H. and D.R.W.), and Dr. Miriam and Sheldon G. Adelson Medical Research Foundation (to G.-l.M.).

Author information

Author notes

  1. These authors contributed equally: Yi Zhou, Yijing Su, Shiying Li

  2. These authors jointly supervised this work: Guo-li Ming, Hongjun Song

Authors and Affiliations

  1. Department of Neuroscience and Mahoney Institute for Neurosciences, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yi Zhou, Yijing Su, Shiying Li, Allison M. Bond, Fadi Jacob, Lu Lu, Guo-li Ming & Hongjun Song

  2. Key Laboratory of Neuroregeneration of Jiangsu and Ministry of Education, Co-innovation Center of Neuroregeneration, Nantong University, Nantong, China

    Shiying Li & Xiaosong Gu

  3. Division of Neurosurgery, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Benjamin C. Kennedy

  4. Department of Neurosurgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Benjamin C. Kennedy, Timothy Lucas, Ryan D. Salinas & H. Isaac Chen

  5. Biochemistry and Molecular Biophysics Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Daniel Y. Zhang

  6. Neuroscience Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Yusha Sun

  7. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Peng Hu & Hao Wu

  8. Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Angela N. Viaene

  9. Division of Neurology, Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Ingo Helbig & Sudha K. Kessler

  10. The Epilepsy NeuroGenetics Initiative (ENGIN), Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Ingo Helbig

  11. Department of Biomedical and Health Informatics (DBHi), Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Ingo Helbig

  12. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Ingo Helbig & Sudha K. Kessler

  13. Institute for Regenerative Medicine, University of Pennsylvania, Philadelphia, PA, USA

    H. Isaac Chen, Hao Wu, Guo-li Ming & Hongjun Song

  14. The Epigenetics Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Hao Wu & Hongjun Song

  15. Lieber Institute for Brain Development, The Solomon H. Snyder Department of Neuroscience, Department of Neurology, and Department of Psychiatry, School of Medicine, Johns Hopkins University, Baltimore, MD, USA

    Joel E. Kleinman, Thomas M. Hyde & Daniel R. Weinberger

  16. Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    David W. Nauen

  17. Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Guo-li Ming & Hongjun Song

  18. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Guo-li Ming

Authors
  1. Yi Zhou
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  2. Yijing Su
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Contributions

Y.Z. performed bioinformatics and immunohistological analyses and slice culture experiments. Y. Su performed snRNA-seq experiments. S.L. performed snRNA-seq library generation with the help of L.L. D.Y.Z. performed cell-interaction analysis. A.M.B. contributed to the ex vivo slice culture idea. F.J. contributed to the initial slice culture setup. B.C.K., T.L. and H.I.C. provided surgical specimens for slice culture with the coordination of I.H., S.K.K. and R.D.S. A.N.V. and D.W.N. provided some of the human postmortem specimens for immunohistological validation. B.C.K., J.E.K., T.M.H. and D.R.W. provided some of the human specimens for snRNA-seq. Y. Sun, P.H., H.W. and X.G. contributed to additional data collection. Y.Z., Y. Su, G-l.M. and H.S. conceived the project and wrote the manuscript with inputs from all authors.

Corresponding authors

Correspondence to Guo-li Ming or Hongjun Song.

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The authors declare no competing interests.

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Nature thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Characteristics of the snRNA-seq dataset of the infant human hippocampus.

a, Expression patterns of marker genes used to determine cluster identities. Ex.: excitatory; OPC: oligodendrocyte precursor cells. b, Uniform Manifold Approximation and Projection (UMAP) visualization of all cells from the four infant hippocampi (0-2 years) colored by specimen. HIP: hippocampus; yrs: years. c, UMAP plots of nuclei from four human infant hippocampal specimens by marker gene expression. The dentate granule cell cluster is highlighted with a dashed line circle.

Extended Data Fig. 2 Machine learning model trained with the mouse early postnatal hippocampal scRNA-seq dataset.

a, b, Unsupervised clustering and t-distributed Stochastic Neighbor Embedding (t-SNE) visualization of all cells from the mouse postnatal (P5) hippocampus9 colored by cluster (a) and marker gene expression (b). imGC: immature dentate granule cell; GC: dentate granule cell; IPC: intermediate progenitor cell; OPC: oligodendrocyte precursor cell. RGL: radial glia-like cell; VLMC: vascular and leptomeningeal cell. c, A schematic illustration of the machine learning-aided analysis using the mouse hippocampal scRNA-seq datasets9, mirroring our analysis pipeline in human studies (Fig. 1a). In brief, Dcx+Calb1−Prox1+ imGCs in the P5 mouse dentate gyrus were selected as prototypes to train a scoring model to comprehensively learn their gene features. The trained model containing an aggregate of weighted features (“gene weights”) was then used to quantitatively evaluate the similarity of each cell to the imGC prototype in query (test) datasets of the early postnatal (P5; self-scoring), the juvenile (P12-35) and the adult (P120-132) hippocampus9. To assess the efficacy of our method, we classified cells with high similarity scores to the imGC prototype as imGCs and compared our model classifications to the published annotations based on unsupervised clustering9 (Shown in Extended Data Fig. 3). d, Measuring performance of the machine learning model. Line plot showing the accuracy score of the machine learning classifier varying with decreasing regularization strength as estimated by cross-validation. Red line shows 95% confidence interval on the estimation of the accuracy score. #Sum abs (coeffs): sum of the absolute value of regression coefficients. e, Heatmap showing expression of top-weighted genes in top-scoring cells of each prototype determined by the machine learning model. Genes listed are the top 25 weights defining mouse imGCs. f, Wheel plot visualizing the scores of each cell to each prototype. Dots represent individual cells whose distance to each prototype is proportional to the score of that prototype. Red and lime green dots represent the prototypical imGCs and all other GCs, respectively. Dotted line indicates a similarity score of 0.85 to each prototypical cell type. Note that unlike in the human system (Fig. 1c), no mature oligodendrocyte (mOli) cluster was present in the P5 mouse hippocampus.

Extended Data Fig. 3 Validation of prototype-based scoring of mouse imGCs across ages by the trained machine learning model with published annotations based on unsupervised clustering.

a, b, d, e, g, h, t-SNE visualization of previously published mouse hippocampal datasets9 at postnatal (a), juvenile (d), and adult (g) stages, colored by four broad cell classes and by similarity score to prototypical imGCs (b, e, h). c, f, i, Benchmarking cells with high similarity scores (P ≥ 0.85) with the published annotations9. Percentage of cells in the GC lineage clusters (based on published annotations9) that are selected as imGCs by our trained machine learning model are indicated in red, bold text.

Extended Data Fig. 4 Machine learning model performance and feature extraction of gene weights defining human imGCs.

a, Efficacy of the machine-learning approach. Line plot showing the accuracy score of the machine learning model varying with decreasing regularization strength as estimated by cross-validation. Red line shows 95% confidence interval on the estimation of the accuracy score. b, Heatmap showing expression of top gene weights in top-scoring cells of each prototype determined by the machine learning model. Genes listed are the top 15 weights defining human imGCs. c, Gene ontology (GO) network of biological processes of the positive gene weights defining human imGCs, colored by functionally related ontology group. Only significantly enriched nodes are displayed (one-sided hypergeometric test, false-discovery rate-adjust p value (FDR) < 0.05). The node size represents the term enrichment significance. Examples of the most significant terms per group are shown. See also Supplementary Table 5 for the list of GO terms. d, Functional protein-protein association network68 of the positive gene weights defining human imGCs, highlighting the first-degree neighbors (high-confidence connections) in orange related to DCX. e, Overlap of the positive gene weights defining imGCs in humans and in mice that were generated by separate machine learning models. See Supplementary Table 4 for the lists of genes. f, g, Immunohistological analysis showing Stmn1 enrichment in immature neurons in the adult mouse dentate gyrus. Shown are sample confocal images (f) and quantification (g) of Stmn1 expression in imGCs in the adult mouse hippocampus. Individual dots represent value of quantification for different sections (f). Scale bars, 10 µm. Box plots similar as in Fig. 1g (n = 4 mice) (g).

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Extended Data Fig. 5 Specificity of the machine learning approach for identification of human immature neurons.

The fractions of cells with high similarity scores (P ≥ 0.85) among non-GC excitatory neuron (a), GABA interneuron (b), and non-neuronal cell (c) clusters in various scRNA-seq or snRNA-seq datasets of the human brains. Box plots represent mean ± s.e.m. with whiskers for max and min. See Supplementary Tables 1, 2, 3 for the specimens used in ours and all published datasets.

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Extended Data Fig. 6 Immunohistological analysis of STMN1 enrichment in human imGCs across the lifespan.

a-d, Sample confocal images (a, b) and quantifications (c, d) of imGCs in the human dentate gyrus across the lifespan. Asterisks indicate DCX+ or CALB1- among STMN1+PROX1+ GCs (a, b). Box plots similar as in Fig. 1g (n = 4 subjects each group) (c, d). The immunohistological signal of STMN1 was noticeably more robust than that of DCX in adult specimens. e, f, Sample confocal images showing NEUROD1+, NEUN+ (e), S100B−, or OLIG2− (f) among STMN1+PROX1+ imGCs in infant or adult human dentate gyrus, confirming their neuronal identity. Asterisks indicate STMN1+PROX1+ imGCs (n = 1 specimen for each immunostaining). All scale bars, 10 µm.

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Extended Data Fig. 7 Age-dependent transcriptomic dynamics are specific to human imGCs.

a-c, In contrast to human imGCs (Fig. 3f), pseudo-age cell alignment of human mature (a), mouse immature (b), and mouse mature (c) GCs9 shows very little age-related divergence, visualized as scatter plots. Cells were colored by age group. Distribution of cells within each age group on the pseudo-age trajectory is displayed in the density plots (bottom left). See summary plots in Fig. 3g. d, Summary plot comparing pseudo-age alignment (y-axis) of mouse mGCs to real age groups (x-axis), with each mGC of the different age groups plotted as a data point in the background. Data points are fitted with loess fitting (lines) with 95% confidence interval (grey shades). Pearson’s r was measured for correlation of pseudo-age and real-age groups. Mouse datasets9 at prenatal (E16.5), neonatal (P0), or early postnatal (P5) stages do not contain mGC populations.

Extended Data Fig. 8 Consistent expression of Neurod4 and Nfia in imGCs of the postnatal mouse hippocampus across ages.

a, b, Sample confocal immunostaining images (a) and quantification (b) of two exemplary genes that display age-dependent expression patterns in human imGCs (Fig. 3j), but consistent expression in mouse imGCs across ages. Scale bar, 10 µm (a). Box plots similar as in Fig. 1g (n = 3 mice per age group) (b).

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Extended Data Fig. 9 Characterization of the slice culture system.

a, b, Sample confocal images (a) and quantification (b) of cell death and oxidative stress level measured in our human hippocampal slice culture in comparison to the post-mortem tissue, using immunohistological analysis of cleaved Caspase 3 and ATF4 (a marker of oxidative stress73), respectively. Dots represent value of quantification for individual sections and boxes represent mean ± s.e.m with whiskers for max and min (n = 2 sections) (b). c, Sample confocal immunostaining images showing baseline cellular composition of slice culture and post-mortem tissue. NEUN+ neurons, IBA1+ microglia, S100B+ astrocytes, and OLIG2+ oligodendrocyte lineage cells were observed. d, Sample confocal images showing EdU-incorporated PROX1+ newborn GCs are absent of the astrocyte marker S100B or the more mature neuron marker CALB1 in slice cultures. Asterisks indicate EdU+PROX1+ GCs. For c, d, n = 1 section for each immunostaining. Scale bars: 50 µm for main panels and 10 µm for insets. e, Quantification of EdU-incorporated newborn imGCs expressing different markers in slice culture of the postnatal human dentate gyrus. Box plots similar as in Fig. 1g (n = 4 subjects).

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Extended Data Fig. 10 Protracted neuronal maturation leads to accumulation of immature neurons in the presence of low frequency of de novo new neuron generation.

a, Process of adult hippocampal neurogenesis1,2. Proliferating intermediate progenitor cells (IPCs) and neuroblasts (brown) arising from activated neural stem cells (NSCs, grey) generate new post-mitotic immature dentate granule cells (imGCs, red), which develop over time into mature dentate granule cells (mGCs, lime-green). b, An “imGC protracted maturation” model explaining how low-rate, continuous IPC generation can lead to a large number of imGCs as a reservoir, as opposed to a “fast maturation” model. The size of the imGC reservoir in the adult hippocampus depends on a number of factors at the cellular level, such as the rate of stem cell activation and IPC generation, the number of progeny each IPC generates, the percentage of progeny that survives79, and the duration of imGCs remaining in the immature state, and these parameters may vary tremendously across species and ages80. Here we illustrate side-by-side two schematic models showing how changing one factor, the length of imGC maturation duration, alone while keeping all other parameters the same can lead to significant differences in the outcome on the number of imGCs at a given time. For IPCs in a newly generated cohort at a given time t, they go through stereotypical developmental stages to become imGCs and then mGCs (x-axis). At time t+1, a new IPC cohort is generated (y-axis). With all other parameters the same, if the imGCs mature fast, very few imGCs will be observed at any given time (left model). In contrast, if the average length of imGC maturation duration is substantially longer, imGCs in various maturation stages accumulate over time and are present as a large population in any “snapshot” (right model). Prolonged maturation duration of new neurons in the hippocampus has been demonstrated in non-human primates using nucleotide analog tracing analysis to be at least six months49 and over a year50. Furthermore, human induced pluripotent stem cell-derived transplanted neurons display significantly slower maturation compared to those of three non-human primates81. c, d, An indifference curve qualitatively depicting different combinations of two factors, the average rate of new neuron generation (\(\bar{{\rm{r}}}\)g) and the average duration of imGC maturation (\(\bar{{\rm{t}}}\)d), to achieve an equal size of imGC reservoirs (c). Hypothetical examples shown in d. A significantly longer \(\bar{{\rm{t}}}\)d in the adult human hippocampus spares the system from high demand of \(\bar{{\rm{r}}}\)g to maintain the same size of imGC reservoir, which is a potential model to explain the seemingly counterintuitive discrepancy between the few IPCs and a large number of imGCs in our results.

Supplementary information

Supplementary Figures

Supplementary Figs 1–6 and Supplementary References.

Reporting Summary

Supplementary Table 1

Summary of human specimens used in the current study.

Supplementary Table 2

Summary of sequencing characteristics of the single-nucleus RNA-seq datasets.

Supplementary Table 3

Summary of published single-cell or single-nucleus RNA-seq datasets used in the current study.

Supplementary Table 4

Gene weights defining immature dentate granule cells in humans and in mice.

Supplementary Table 5

Gene Ontology terms related to the positive gene weights defining human immature dentate granule cells.

Supplementary Table 6

List of genes and Gene Ontology terms enriched in human immature dentate granule cells relative to mature granule cells.

Supplementary Table 7

Lists of common and divergent genes enriched in immature dentate granule cells in humans and in mice.

Supplementary Table 8

Lists of Gene Ontology terms for the five patterns of functional programmes of human immature dentate granule cells across ages.

Supplementary Table 9

Lists of differentially expressed genes in imGCs between Alzheimer's disease samples and matched controls and the associated Gene Ontology terms.

Source Data for Supplementary Fig. 5.

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Zhou, Y., Su, Y., Li, S. et al. Molecular landscapes of human hippocampal immature neurons across lifespan. Nature 607, 527–533 (2022). https://doi.org/10.1038/s41586-022-04912-w

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