Single-cell connectomic analysis of adult mammalian lungs - PubMed (original) (raw)

. 2019 Dec 4;5(12):eaaw3851.

doi: 10.1126/sciadv.aaw3851. eCollection 2019 Dec.

Micha Sam Brickman Raredon 1 2 3, Yasir Suhail 5, Jonas Christian Schupp 4, Sergio Poli 6, Nir Neumark 4 7, Katherine L Leiby 1 2 3, Allison Marie Greaney 1 2, Yifan Yuan 8, Corey Horien 3 9, George Linderman 3 10, Alexander J Engler 1 2, Daniel J Boffa 11, Yuval Kluger 7 10 12, Ivan O Rosas 6, Andre Levchenko 1 5, Naftali Kaminski 4, Laura E Niklason 1 2 8

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Single-cell connectomic analysis of adult mammalian lungs

Micha Sam Brickman Raredon et al. Sci Adv. 2019.

Abstract

Efforts to decipher chronic lung disease and to reconstitute functional lung tissue through regenerative medicine have been hampered by an incomplete understanding of cell-cell interactions governing tissue homeostasis. Because the structure of mammalian lungs is highly conserved at the histologic level, we hypothesized that there are evolutionarily conserved homeostatic mechanisms that keep the fine architecture of the lung in balance. We have leveraged single-cell RNA sequencing techniques to identify conserved patterns of cell-cell cross-talk in adult mammalian lungs, analyzing mouse, rat, pig, and human pulmonary tissues. Specific stereotyped functional roles for each cell type in the distal lung are observed, with alveolar type I cells having a major role in the regulation of tissue homeostasis. This paper provides a systems-level portrait of signaling between alveolar cell populations. These methods may be applicable to other organs, providing a roadmap for identifying key pathways governing pathophysiology and informing regenerative efforts.

Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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Figures

Fig. 1

Fig. 1. Cross-species pulmonary scRNAseq dataset.

(A) Lungs from mice, rats, pigs, and human donors were dissociated, barcoded using 10× genomics, and sequenced on Illumina sequencers. Cells were clustered following genome alignment and dimensionality reduction. Markers were identified via differential expression and correlated to literature and the Human Protein Atlas (24) and in-house immunostaining. Comparative ligand-receptor analysis reveals stereotyped cellular roles in tissue homeostasis. (B) A comparable, heterogeneous community of cells was profiled in all four species. Species-conserved cell types include type I, type II, ciliated, and secretory epithelium; capillary, vascular, and lymphatic endothelium; B cells, T cells, and natural killer (NK) cells; SMCs; Col13+ and Col14+ fibroblasts; and two distinct subsets of macrophages.

Fig. 2

Fig. 2. Cross-species cell atlas representation.

(A) The species-split DotPlot shows cross-species comparison of selected top markers for all cell populations profiled across all species. Color saturation indicates the strength of expression in positive cells, while dot size reflects the percentage of each cell cluster expressing the gene. Each color represents one species. Certain cell types were only profiled in certain species, such as basal cells marked by KRT5 (pig and human) and Sox9+ epithelium (rat). ITGA8/Itga8 reliably marks the Col13a1+ fibroblast population across species. Histology for highly expressed specific markers of conserved cell types, from the Human Protein Atlas [immunoperoxidase images (D)] and from in-house immunostaining [immunofluorescent images (B and C)], shows positive physical locations of identified cell type–specific markers. Markers shown are those that were present in the human dataset having homologs in the nonhuman species. Scale bars, 50 μm (D) and 62 μm (B and C).

Fig. 3

Fig. 3. Global network comparison reveals conserved signaling patterns underlying adult lung homeostasis.

(A) Schematic of data processing. Gene expression was averaged by cluster and mapped against the FANTOM5 ligand-receptor database. When two nodes expressed a cognate ligand-receptor pair, a weighted edge was created (see Supplementary Text). (B) Promiscuous expression across cell types (ICAM1) causes a low distribution of edge weights. (C) Cell type–specific expression (VEGFA) causes a higher edge weight distribution (black arrows). (D) Global connectomes across the four species, showing the sum of edge weights between conserved nodes. Vertex (colored cell node) size is proportional to the Kleinberg hub score, while the thickness of the edges is proportional to the sum of the weights between two nodes. Similarities in overall signaling structure between the cell nodes are readily observable. (E) Comparison of degree rankings and the effect of thresholding. Stromal cells have the highest degrees, while lymphoid cells have the lowest. The x axis (“percent_exp”) is percent of cluster expressing the marker, and the y axis is the degree of each node plotted on a logarithmic scale. (F) Quantitative cross-species correlations. Plots of Spearman correlation coefficients (“ρ”), with human as a reference, of the rankings of nodal centrality metrics over increasing thresholds of the fraction of cells in a node that expresses the ligand and receptor. Note that the correlation coefficients are relatively high (>0.75) and remain so up to ~40% expression, meaning that 40% of the cells in the node express either the ligand or the receptor. This demonstrates that node-node relationships are robust to thresholding. This suggests a high degree of evolutionary conservation in pulmonary connectomic structure, because all four species are very highly correlated.

Fig. 4

Fig. 4. Niche character visualizations from species-conserved signaling.

(A) Cell classes in close physical proximity in alveolar lung. Downstream analysis was limited to nine cell types spatially registering to the alveolar septal wall. All mapped ligand-receptor pairs were classified into 1 of 38 signaling families (see legend). Hive plot illustrations of niche character are presented here for (B) alveolar macrophages and (C) capillary endothelium (see fig. S7 for all other niche visualizations). Alveolar macrophages display high connectivity and express distinctly high levels of cell-cell adhesion molecules (*). Capillary endothelium, in contrast, expresses numerous receptors for VEGF family signaling (**).

Fig. 5

Fig. 5. ATI cells dominate VEGF and SEMA family niche networks in all species.

(A) Capillary endothelia and ATI cells are in extremely close proximity in the pulmonary alveolus, often separated by less than 100 nm of shared basement membrane. (B) ATI cells, marked by Aqp5 in green, express high levels of Sema3e and Vegfa at the protein level, correlating with scRNAseq findings. (C) Ligand-receptor mapping shows that endothelial populations express high levels of receptors for both Vegfa and Sema3e, allowing receipt of spatial and growth cue information from ATI cells. (D) Network centrality analysis shows that ATI cells have dominant hub scores and top cumulative outgoing edge weight, while capillary endothelia have dominant authority scores and top cumulative incoming edge weight, in both the VEGF and SEMA family signaling networks.

Fig. 6

Fig. 6. Network centrality analysis categorizes each signaling network by dominant producers and receivers.

(A) The species-conserved connectome is first subset to a single signaling family. Outgoing centrality, defined as the cumulative outgoing edge weight and the Kleinberg network hub score, and incoming centrality, defined as the cumulative incoming edge weight and the Kleinberg network authority score, are then calculated for each node. (B) Identification of signaling family networks dominated by epithelial cells (top) and macrophage populations (bottom). The left side of each panel shows outgoing centrality metrics (cumulative outgoing edge weight for each cell, Kleinberg hub scores), while the right side shows incoming centrality metrics (cumulative incoming edge weight for each cell, Kleinberg authority scores). These trends suggest species-conserved cellular roles in pulmonary tissue and allow identification of dominant producers and receivers of each class of signal in homeostatic lung tissue. Additional results detailing endothelia- and mesenchyme-dominated networks are shown in fig. S9. Note here the ATI dominance of cell-cell adhesion networks acting predominantly on alveolar macrophages versus ATII dominance of CSF networks acting predominantly on interstitial macrophages. Macrophages predictably dominate networks based on immunoregulatory cues such as CCL and interleukin family ligand-receptor pairs.

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References

    1. Zhou X., Franklin R. A., Adler M., Jacox J. B., Bailis W., Shyer J. A., Flavell R. A., Mayo A., Alon U., Medzhitov R., Circuit design features of a stable two-cell system. Cell 172, 744–757.e17 (2018). - PMC - PubMed
    1. Bonnans C., Chou J., Werb Z., Remodelling the extracellular matrix in development and disease. Nat. Rev. Mol. Cell Biol. 15, 786–801 (2014). - PMC - PubMed
    1. Humphrey J. D., Dufresne E. R., Schwartz M. A., Mechanotransduction and extracellular matrix homeostasis. Nat. Rev. Mol. Cell Biol. 15, 802–812 (2014). - PMC - PubMed
    1. Miller A. J., Spence J. R., In vitro models to study human lung development, disease and homeostasis. Physiology 32, 246–260 (2017). - PMC - PubMed
    1. Amit I., Winter D. R., Jung S., The role of the local environment and epigenetics in shaping macrophage identity and their effect on tissue homeostasis. Nat. Immunol. 17, 18–25 (2016). - PubMed

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