Background & Aims

Inflammatory pain associated with tissue injury and infection results from the heightened sensitivity of the peripheral terminals of nociceptor sensory neurons because of exposure to inflammatory mediators (1). Targeting immune-derived ligands, such as prostaglandin E2, reduces inflammatory pain, but more effective and safer therapies are needed (2). However, due to the large variety and complex temporal dynamics of immune cells in inflamed tissues, our understanding of the immune mechanisms governing peripheral sensitization remains limited. The quality of the immune response is also dictated by the type of insult (3). Additionally, sensory neurons in the dorsal root ganglia (DRG) are a heterogeneous population that could react differently to different immune ligands (4). Here, we aim to comprehensively map receptor-ligand interactions between specific immune cells types and DRG neuron types in different injuries to identify cell type- and injury-specific immune regulators of pain.

Methods

To comprehensively map the neuroimmune landscape of inflammatory pain, we decided to characterize how the immune population changes within painful inflamed tissues in diverse inflammatory pain conditions. We performed a single-cell transcriptomic analysis (scRNA-seq) of skin immune cells at various time points following three inflammatory insults: zymosan injection, skin incision, and UV burn which represent distinct clinical conditions: pathogen invasion, trauma, and burn injury. We complemented the immune transcriptomics with a single-nucleus sequencing (snRNA-seq) dataset of DRG neurons (4). When then used a receptor-ligand database compiled by an automated knowledge assembly tool to annotate pain-related receptor-ligand interactions between nociceptors and immune cells (5, 6). Finally, we rely on calcium imaging and electrophysiology to validate the previously unknown interactions predicted by the scRNA seq-based cell-cell interactome.

Results

We found that mice subjected to either the zymosan or skin incision developed a significant thermal hypersensitivity within 4 hours, which then resolved by 24 and 48 hours, respectively while hypersensitivity in the UV burn model developed significantly later and showed no recovery at 48 hours. scRNA-seq data showed the kinetics of infiltration of macrophages and neutrophils into the injured tissues are mirror pain hypersensitivity. Differential gene expression analysis revealed changes within each immune cell type common to all pain models at the peak of hypersensitivity. This included upregulation of inflammation-related genes such as Thbs1, Ptgs2, Hif1a, Spp1 and S100a9 and downregulation of transcription factor genes such as Fosb, Jund, Atf3 and phagocytic genes such as C1qa, Seleop and Cd36. The cell-cell interactome analysis (6) showed that macrophages are the strongest interactors of DRG neurons and predicted interactions common or specific to the injury type. Finally, the neuroimmune interactome predicted an interaction between myeloid cell-derived thrombospondin-1 (Thbs1) and neuronal CD47 shared between all three pain models. We found that thrombospondin-1 acts on DRG neurons and suppresses Trpv1 sensitization of nociceptors via inhibiting PKA activity.

Conclusions

The temporal dynamics of hypersensitivity onset and recovery show that distinct immune responses might be involved in the three inflammatory pain models. Our data identified various immune correlates of pain including infiltration of macrophages and neutrophils, magnitude of transcriptional changes in Cx3cr1hi and MHCII+ macrophages, upregulation of inflammatory genes, and inhibition of CREB signaling pathways. We uncovered novel pain-modulating nociceptor-immune interactions including myeloid cell-derived Thrombospondin-1 interacting with neuronal CD47. Since the macrophages that produce TSP-1 also secrete inflammatory mediators such as PGE2, IL-1, and TNF, TSP-1 is likely to play a role in fine-tuning pain thresholds. The comprehensive dataset of immune changes in the skin at a single-cell level as pain hypersensitivity develops and resolves can serve as a resource to decipher cell types and immune mediators governing different types of acute inflammatory pain.

References

1. Basbaum, A. I., Bautista, D. M., Scherrer, G. & Julius, D. Cellular and Molecular Mechanisms of Pain. Cell vol. 139 Preprint at https://doi.org/10.1016/j.cell.2009.09.028 (2009).
2. Ricciotti, E. & Fitzgerald, G. A. Prostaglandins and inflammation. Arterioscler Thromb Vasc Biol 31, (2011).
3. Jain, A. & Pasare, C. Innate control of adaptive immunity: Beyond the three-signal paradigm. Journal of Immunology 198, (2017).
4. Renthal, W. et al. Transcriptional Reprogramming of Distinct Peripheral Sensory Neuron Subtypes after Axonal Injury. Neuron 108, (2020).
5. Bachman, J. A., Gyori, B. M. & Sorger, P. K. Automated assembly of molecular mechanisms at scale from text mining and curated databases. Mol Syst Biol 19, (2023).
6. Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat Commun 12, (2021).

Presenting Author

Aakanksha Jain

Poster Authors

Aakanksha Jain

Ph.D.

Boston Children’s Hospital

Lead Author

E-mail

Benjamin Gyori

Ph.D.

Harvard Medical School and Northeastern University

Lead Author

E-mail

Sara Hakim

Ph.D.

Harvard Medical School

Lead Author

E-mail

Ashish Jain

Ph.D.

Boston Childrens Hospital

Lead Author

E-mail

Liang Sun

Boston Childrens Hospital

Lead Author

E-mail

Veselina Petrova

Ph.D.

Boston Childrens Hospital

Lead Author

E-mail

Rasheen Powell

Ph.D.

Boston Childrens Hospital

Lead Author

E-mail

Peter Sorger

Ph.D.

Harvard Medical School

Lead Author

E-mail

Clifford Woolf

MB

Boston Childrens Hospital

Lead Author

E-mail

Topics

  • Models: Acute Pain