Cell Stem Cell
ResourceTranscriptional space-time mapping identifies concerted immune and stromal cell patterns and gene programs in wound healing and cancer
Graphical abstract
Introduction
Metazoans rely on intricate networks of cell-cell crosstalk (CCC) for the maintenance of tissue homeostasis, repair, and regenerative processes after damage.1,2,3 Given the diversity of cell types within a tissue, all possible ligand-receptor pairings and their signaling dynamics, a formalized method for interrogating CCC over space and time in the tissue remains a daunting task.4 Even a minimal two-actor system can exhibit robustness and return to a stable state following perturbation.5 This same adaptation to perturbation can be seen when increasing the number of cellular actors and, thus, the number of possible “edges” (i.e., CCC axes), such as the combination of stellate cells, hepatocytes, endothelial cells, and Kupffer cells in liver niches.6
The advent of single-cell technologies allows profiling of cells on the transcriptional level at resolutions previously unattainable, generating rich datasets identifying highly resolved cell subsets and subtle variations in gene expression and activation states.7,8,9 Several computational approaches seek to infer CCC via paired ligand-receptor and target gene expression.10,11,12 These inferences are strengthened by applying spatial and temporal context to single-cell transcriptomics that has revealed gene groupings with similar spatiotemporal profiles, shedding light on the spatial segregation of cell functions, the dynamics of cell migration, and tissue zonation.13,14 Similarly, describing gene expression in terms of spatiotemporal patterns revealed signaling pathways and co-regulation of genes in sub-compartments of liver and pancreas.15,16 Concordantly, we were motivated to describe the healing skin wound in terms of spatiotemporal multicellular patterns and gene expression programs as skin wound healing (WH) naturally displays well-defined spatial and temporal dimensions. This process has canonically been segmented into major phases with an initial inflammatory response followed by repair/growth and resolution.17,18 Interspersed are coordinated changes in gene expression patterns in diverse cell types from monocyte/macrophages, neutrophils, fibroblasts, endothelial cells, keratinocytes, and beyond.3,19 Diverse crosstalk mechanisms between these cell types have been identified for regulating the duration of and transition between phases.3,19,20,21,22 Disruption of these mechanisms often results in aberrant healing, demonstrating the interdependent structure of the WH cellular network.23,24 Charting the progression of gene expression in single cells over space and time in the wound would yield information on the coordinated behaviors of myriad cell types in an unbiased manner, and how they drive transitions between healing phases.
With macrophages and fibroblasts representing cell types occupying a continuum of gene expression states,25,26 as opposed to harboring discrete cell subtypes, clustering approaches are insufficient to capture transitions between states. For example, the M1/M2 “binning” of macrophages may represent too reductive a model, as WH macrophages express combinations of canonical M1/M2 genes during all wound phases.26,27,28,29 Therefore, a method for reframing cellular heterogeneity using overlays of gene programs (i.e., collections of co-expressed genes) in the healing wound may better capture the biology underlying the progression of cellular transcriptional heterogeneity.
An additional important rationale for studying space-time progression of multicellular networks relates to chronic disease, where healthy resolution is not achieved. This is exemplified by cancer, where malignant tumor growth co-opts WH programs sans resolution, conceptualized in the idea that tumors are “wounds that never heal.”28,29,30 This idea motivated us to develop a framework based on conserved gene programs to identify if crosstalk elements of the WH cellular network are “borrowed” by tumors. The heterogeneity of a given cell type in different contexts may represent a convolution of conserved differentiation, functional, and tissue-specific expression patterns, as seen in resident immune cells scattered across all tissues.31,32 Describing the common biology between two single-cell datasets may again require going beyond clustering-based approaches that may obscure the identification of overlaid gene programs in a continuum of cell states.33
Using skin WH as a well-defined spatial process in tissue repair, we mapped changes in, both, CD45+ and CD45− cell identity that co-occurred in similar space-time patterns. Layered on the top of cell identity, we identified spatiotemporally expressed gene programs—or factors—that can be grouped based on their unique space-time profile. Because we found these factors based on their shared space-time patterns across multiple cell types, we refer to these co-occurring factors across distinct cell types as multicellular “gene movements.” Informed by spatiotemporal profiles of gene program expression, we predicted stromal-macrophage CCC over the time course of wound closure, which we then verified using orthogonal experimental approaches. Finally, we derived a framework for how to identify movements across tissue contexts and identify the conservation of correlated immune and non-immune gene program pairs in mouse tumor models and human tumors. We then validate our predictions to demonstrate the utility of studying conserved gene program groupings.
Section snippets
Separate waves of immune cell infiltration during wound closure
To establish the compositional changes of immune cells during skin repair, we immunophenotyped cells derived from a 4 mm full-thickness circular wound on the mouse’s back via Cytometry by time of flight (CyTOF) (Figure S1A). This provided an overview of immune cell populations infiltrating the wound engaging in dynamic remodeling (Figures S1B–S1F). In the dimensional space of our CyTOF panel, the wound temporarily reaches the pre-wound composition at around days 7–10 post wounding and
Discussion
Characterizing how diverse cell types are spatially and temporally organized within the tissue will help us understand the underlying dynamic nature of tissues. Here, we established a spatiotemporal framework to study pairing of cell types during the physiologically complex process of wound repair. In this setting, the concept that spatiotemporal correlation may indicate paired biology drove the identification of groups of cell types and gene programs that together form larger cellular
Key resources table
REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies anti-mouse CD16/32 (clone 2.4G2) Tonbo Biosciences 70-0161-U500 anti-mouse CD45 Alexa Fluor 647 (clone 30-F11) Biolegend 103124 anti-mouse GPNMB eFluor™ 660 (clone CTSREVL) eBioscience 50-5708-80 anti-h/mPeriostin (clone 345613) R&D Systems MAB3548 anti-alpha smooth muscle actin antibody (polyclonal) Abcam AB5694 anti-mouse P-selectin (polyclonal) R&D Systems AF737 anti-CD31 AlexaFluor647 (clone 390) BioLegend 102416 anti-CD11b AlexaFluor594 (clone M1/70) BioLegend 101254
Acknowledgments
We would like to thank Drs. Hong-Erh Liang and Richard Locksley for the generous gift of the Arg1-reporter mouse. We also thank Drs. Chris McGinnis and Zev Gartner for the lipid-modified oligonucleotides (LMOs) and comments on the manuscript and Dr. Ian Boothby for advice on the mWH model. Additionally, we thank members of the Krummel lab for comments on the manuscript. This work was supported by funds from NIH R01CA197363. K.H.H. is supported by the American Cancer Society Postdoctoral
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