TY - JOUR
T1 - Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics
AU - Cherry, Christopher
AU - Maestas, David R.
AU - Han, Jin
AU - Andorko, James I.
AU - Cahan, Patrick
AU - Fertig, Elana J.
AU - Garmire, Lana X.
AU - Elisseeff, Jennifer H.
N1 - Funding Information:
We acknowledge financial support from the Morton Goldberg Chair, NIH Directors Pioneer Award, the Department of Defense, Bloomberg-Kimmel Institute for Cancer Immunotherapy and R01EB028796 awarded from the National Insitutes for Health (to J.H.E.). J.I.A. was supported by 5T32AG058527 awarded by the National Institute on Aging. L.X.G. is supported by grants K01ES025434 awarded by the National Institute of Environmental Health Sciences through funds provided by the trans-National Insitutes for Health Big Data to Knowledge (BD2K) initiative (https://commonfund.nih.gov/ bd2k), R01 LM012373 and LM012907 awarded by the National Library of Medicine, and R01 HD084633 awarded by the National Institute of Child Health and Human Development. We thank D. Zack, C. Berlinicke and L. D. Huyer for help and expertise.
Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2021/10
Y1 - 2021/10
N2 - The understanding of the foreign-body responses to implanted biomaterials would benefit from the reconstruction of intracellular and intercellular signalling networks in the microenvironment surrounding the implant. Here, by leveraging single-cell RNA-sequencing data from 42,156 cells collected from the site of implantation of either polycaprolactone or an extracellular-matrix-derived scaffold in a mouse model of volumetric muscle loss, we report a computational analysis of intercellular signalling networks reconstructed from predictions of transcription-factor activation. We found that intercellular signalling networks can be clustered into modules associated with specific cell subsets, and that biomaterial-specific responses can be characterized by interactions between signalling modules for immune, fibroblast and tissue-specific cells. In a Il17ra–/– mouse model, we validated that predicted interleukin-17-linked transcriptional targets led to concomitant changes in gene expression. Moreover, we identified cell subsets that had not been implicated in the responses to implanted biomaterials. Single-cell atlases of the cellular responses to implanted biomaterials will facilitate the design of implantable biomaterials and the understanding of the ensuing cellular responses.
AB - The understanding of the foreign-body responses to implanted biomaterials would benefit from the reconstruction of intracellular and intercellular signalling networks in the microenvironment surrounding the implant. Here, by leveraging single-cell RNA-sequencing data from 42,156 cells collected from the site of implantation of either polycaprolactone or an extracellular-matrix-derived scaffold in a mouse model of volumetric muscle loss, we report a computational analysis of intercellular signalling networks reconstructed from predictions of transcription-factor activation. We found that intercellular signalling networks can be clustered into modules associated with specific cell subsets, and that biomaterial-specific responses can be characterized by interactions between signalling modules for immune, fibroblast and tissue-specific cells. In a Il17ra–/– mouse model, we validated that predicted interleukin-17-linked transcriptional targets led to concomitant changes in gene expression. Moreover, we identified cell subsets that had not been implicated in the responses to implanted biomaterials. Single-cell atlases of the cellular responses to implanted biomaterials will facilitate the design of implantable biomaterials and the understanding of the ensuing cellular responses.
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U2 - 10.1038/s41551-021-00770-5
DO - 10.1038/s41551-021-00770-5
M3 - Article
C2 - 34341534
AN - SCOPUS:85111883674
VL - 5
SP - 1228
EP - 1238
JO - Nature Biomedical Engineering
JF - Nature Biomedical Engineering
SN - 2157-846X
IS - 10
ER -