Cell Stem Cell
Volume 30, Issue 6, 1 June 2023, Pages 885-903.e10
Journal home page for Cell Stem Cell

Resource
Transcriptional space-time mapping identifies concerted immune and stromal cell patterns and gene programs in wound healing and cancer

https://doi.org/10.1016/j.stem.2023.05.001Get rights and content

Highlights

  • Space-time scRNA-seq of healing skin wounds across immune and non-immune cells

  • Gene program analysis reveals coordinated multicellular movements

  • Prediction of macrophage-fibroblast crosstalk involving OSM, TNC, and POSTN signals

  • Translation of gene program pairs to mouse and human tumor settings

Summary

Tissue repair responses in metazoans are highly coordinated by different cell types over space and time. However, comprehensive single-cell-based characterization covering this coordination is lacking. Here, we captured transcriptional states of single cells over space and time during skin wound closure, revealing choreographed gene-expression profiles. We identified shared space-time patterns of cellular and gene program enrichment, which we call multicellular “movements” spanning multiple cell types. We validated some of the discovered space-time movements using large-volume imaging of cleared wounds and demonstrated the value of this analysis to predict “sender” and “receiver” gene programs in macrophages and fibroblasts. Finally, we tested the hypothesis that tumors are like “wounds that never heal” and found conserved wound healing movements in mouse melanoma and colorectal tumor models, as well as human tumor samples, revealing fundamental multicellular units of tissue biology for integrative studies.

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 RESOURCESOURCEIDENTIFIER
Antibodies
anti-mouse CD16/32 (clone 2.4G2)Tonbo Biosciences70-0161-U500
anti-mouse CD45 Alexa Fluor 647 (clone 30-F11)Biolegend103124
anti-mouse GPNMB eFluor™ 660 (clone CTSREVL)eBioscience50-5708-80
anti-h/mPeriostin (clone 345613)R&D SystemsMAB3548
anti-alpha smooth muscle actin antibody (polyclonal)AbcamAB5694
anti-mouse P-selectin (polyclonal)R&D SystemsAF737
anti-CD31 AlexaFluor647 (clone 390)BioLegend102416
anti-CD11b AlexaFluor594 (clone M1/70)BioLegend101254

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

References (107)

  • S. Menezes et al.

    The heterogeneity of Ly6Chi monocytes controls their differentiation into iNOS+ macrophages or monocyte-derived dendritic cells

    Immunity

    (2016)
  • F.C. Bennett et al.

    A combination of ontogeny and CNS environment establishes microglial identity

    Neuron

    (2018)
  • T.R. Hammond et al.

    Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes

    Immunity

    (2019)
  • R. Blecher-Gonen et al.

    Single-cell analysis of diverse pathogen responses defines a molecular roadmap for generating antigen-specific immunity

    Cell Syst.

    (2019)
  • R.A. Dean et al.

    Macrophage-specific metalloelastase (MMP-12) truncates and inactivates ELR + CXC chemokines and generates CCL2, -7, -8, and -13 antagonists: Potential role of the macrophage in terminating polymorphonuclear leukocyte influx

    Blood

    (2008)
  • C. Schneider et al.

    Tissue-resident Group 2 innate lymphoid cells differentiate by layered ontogeny and in situ perinatal priming

    Immunity

    (2019)
  • R. Vu et al.

    Wound healing in aged skin exhibits systems-level alterations in cellular composition and cell-cell communication

    Cell Rep.

    (2022)
  • G.L. Stein-O’Brien et al.

    Enter the matrix: factorization uncovers knowledge from omics

    Trends Genet.

    (2018)
  • K. Pelka et al.

    Spatially organized multicellular immune hubs in human colorectal cancer

    Cell

    (2021)
  • S. Davidson et al.

    Single-cell RNA sequencing reveals a dynamic stromal niche that supports tumor growth

    Cell Rep.

    (2020)
  • X. Liu et al.

    SOCS3 promotes TLR4 response in macrophages by feedback inhibiting TGF-β1/Smad3 signaling

    Mol. Immunol.

    (2008)
  • M. Binnewies et al.

    Targeting TREM2 on tumor-associated macrophages enhances immunotherapy

    Cell Rep.

    (2021)
  • Y. Katzenelenbogen et al.

    Coupled scRNA-seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer

    Cell

    (2020)
  • M. Molgora et al.

    TREM2 modulation remodels the tumor myeloid landscape enhancing anti-PD-1 immunotherapy

    Cell

    (2020)
  • A.J. Combes et al.

    Discovering dominant tumor immune archetypes in a pan-cancer census

    Cell

    (2022)
  • S.A. Eming et al.

    Metabolic orchestration of the wound healing response

    Cell Metab.

    (2021)
  • T.A. Wynn et al.

    Macrophages in tissue repair, regeneration, and fibrosis

    Immunity

    (2016)
  • M. Adler et al.

    Principles of cell circuits for tissue repair and fibrosis

    iScience

    (2020)
  • A. Asrir et al.

    Tumor-associated high endothelial venules mediate lymphocyte entry into tumors and predict response to PD-1 plus CTLA-4 combination immunotherapy

    Cancer Cell

    (2022)
  • J. Kolter et al.

    A subset of skin macrophages contributes to the surveillance and regeneration of local nerves

    Immunity

    (2019)
  • J.H. Levine et al.

    Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis

    Cell

    (2015)
  • T. Stuart et al.

    Comprehensive Integration of Single-Cell Data

    Cell

    (2019)
  • T. Wertheimer et al.

    Production of BMP4 by endothelial cells is crucial for endogenous thymic regeneration

    Sci. Immunol.

    (2018)
  • C.M. Walesky et al.

    Functional compensation precedes recovery of tissue mass following acute liver injury

    Nat. Commun.

    (2020)
  • M. Rodrigues et al.

    Wound healing: A cellular perspective

    Physiol. Rev.

    (2019)
  • P. Li et al.

    Communication codes in developmental signaling pathways

    Development

    (2019)
  • R. Satija et al.

    Spatial reconstruction of single-cell gene expression data

    Nat. Biotechnol.

    (2015)
  • W. Jin et al.

    Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples

    Nature

    (2015)
  • J.D. Buenrostro et al.

    Single-cell chromatin accessibility reveals principles of regulatory variation

    Nature

    (2015)
  • S. Jin et al.

    Inference and analysis of cell-cell communication using CellChat

    Nat. Commun.

    (2021)
  • M. Efremova et al.

    CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes

    Nat. Protoc.

    (2020)
  • R. Browaeys et al.

    NicheNet: modeling intercellular communication by linking ligands to target genes

    Nat. Methods

    (2020)
  • R. Manco et al.

    Clump sequencing exposes the spatial expression programs of intestinal secretory cells

    Nat. Commun.

    (2021)
  • C. Droin et al.

    Space-time logic of liver gene expression at sub-lobular scale

    Nat. Metab.

    (2021)
  • P. Martin

    Wound healing - Aiming for perfect skin regeneration

    Science

    (1997)
  • S.A. Eming et al.

    Inflammation and metabolism in tissue repair and regeneration

    Science

    (2017)
  • J.C. Brazil et al.

    Innate immune cell–epithelial crosstalk during wound repair

    J. Clin. Invest.

    (2019)
  • S.A. Eming et al.

    Wound repair and regeneration: mechanisms, signaling, and translation

    Sci. Transl. Med.

    (2014)
  • H.N. Wilkinson et al.

    Wound healing: cellular mechanisms and pathological outcomes

    Open Biol.

    (2020)
  • T. Lucas et al.

    Differential roles of macrophages in diverse phases of skin repair

    J. Immunol.

    (2010)
  • Cited by (3)

    • Skeletal muscle niche, at the crossroad of cell/cell communications

      2024, Current Topics in Developmental Biology
    6

    These authors contributed equally

    7

    Lead contact

    View full text