Intercellular signaling dynamics from a single cell atlas of the biomaterials response
Abstract
Biomaterials serve as the basis of implants, tissue engineering scaffolds, and multiple other biomedical therapeutics. New technologies, such as single cell RNA sequencing (scRNAseq), are enabling characterization of the biomaterial response to an unprecedented level of detail, facilitating new discoveries in the complex cellular environment surrounding materials. We performed scRNAseq and integrated data sets from multiple experiments to create a single cell atlas of the biomaterials response that contains 42,156 cells from biological extracellular matrix (ECM)-derived and synthetic polyester (polycaprolactone, PCL) scaffold biomaterials implanted in murine muscle wounds. We identified 18 clusters of cells, including natural killer (NK) cells, multiple subsets of fibroblasts, and myeloid cells, many of which were previously unknown in the biomaterial response. To determine intra and intercellular signaling occurring between the numerous cell subsets, including immune-stromal interactions in the biomaterial response, we developed Domino (github.com/chris-cherry/domino), a computational tool which allows for identification of condition specific intercellular signaling patterns connected to transcription factor activation from single cell data. The Domino networks self-assembled into signaling modules and cellular subsets involved in signaling independent of clustering, defining interactions between immune, fibroblast, and tissue-specific modules with biomaterials-specific communication patterns. Further compilation and integration of biomaterials single cell data sets will delineate the impact of materials chemical and physical properties and biological factors, such as anatomical placement, age, or systemic disease, that will direct biomaterials design.
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Significant heterogeneity exists in different T cell populations, and not all these cells play equal roles in diseases, as reflected by different gene expression responses to drug treatment.
“Significant heterogeneity exists in different T cell populations; not all these cells play equal roles in the diseases62,63, reflected by different gene expression responses to drug treatment64.”
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A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
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“…provide valuable insights into the impact of ligand– receptor interactions on the cellular state of receiver cells through targeted downstream gene expression, while also enabling the prioritization of candidate interactions between cells or across contexts [177,178,181,186,190,199,212,221].”
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“…CellChat [11], CellPhoneDB [10], ICELLNET [16], iTALK [17], and SingleCellSignalR [18]; network-based methods such as Con-nectome [19], CytoTalk [20], Domino [21], NATMI [22], NicheNet [12], and scMLnet [23]; and ST-based methods such as CellPhoneDB v3 [13], Giotto [24], and stLearn [25] (Table 1).”
MethodASGARD is a single-cell guided pipeline to aid repurposing of drugs
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Shen, S., Sun, Y., Matsumoto, M., Shim, W. J., Sinniah, E., Wilson, S. B., Werner, T., Wu, Z., Bradford, S. T., Hudson, J. E., et al. (2021) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2021.09.006 · Cited by 8 · see citing works
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of DrugsPreprint
He, B., Xiao, Y., Liang, H., Huang, Q., Du, Y., Li, Y., Garmire, D., Sun, D., & Garmire, L. X. (2021) · PubMed · doi:10.48550/arxiv.2109.06377 · Cited by 2 · see citing works
Self-citation
- Authors
- Cherry, C., Maestas, D. R., Han, J., Andorko, J. I., Cahan, P., Fertig, E. J., Garmire, L. X., & Elisseeff, J. H.
- ORCID
- Publisher
- openRxiv
- Published
- 2020-07-25
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- Type
- Preprint
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