Abstract
Regenerative medicine and tissue engineering aim to promote functional rebuilding of damaged tissue. Comprehensively profiling cell identity, function and interaction in healthy tissues, as well as understanding how these change upon tissue disruption, such as that caused by injury, ageing or infection, is foundational to advancing tissue engineering and regenerative therapeutics. Tissue injury response is a highly dynamic process driven by complex interactions between immune and stromal cell populations, with dysregulation leading to deleterious fibrosis and chronic inflammation. Advances in single-cell RNA sequencing now allow in-depth mapping of the complex cellular response to injury and biomaterial implantation. In this Review, we first describe the fundamentals of sequencing and computational methods for the generation and analysis of high-dimensional single-cell RNA sequencing data sets. We then highlight how these methods can be applied to study tissue injury responses and guide the rational design of biomaterials and regenerative therapeutics.
Key points
-
Single-cell RNA sequencing (scRNA-seq) affords unprecedented resolution in profiling cellular transcriptomics by simultaneously detecting the expression of thousands of genes on an individual cell basis.
-
Tissue engineers can leverage scRNA-seq to comprehensively map healthy and perturbed (such as injured or diseased) tissue environments and explore cellular heterogeneity, gene expression shifts, differentiation trajectories and interaction networks.
-
Insights gained by scRNA-seq profiling of biological systems can be leveraged to guide the rational design of new biomaterials and regenerative therapeutics.
-
scRNA-seq can be used to characterize the host response to implanted engineered constructs or regenerative therapeutics and discern mechanisms of action (regenerative or fibrotic).
-
Sharing of data sets in public repositories, development of large-scale atlases and formation of dedicated consortiums promote low-cost accessibility, increase diversity and maximize exploration of generated scRNA-seq data sets.
-
Interdisciplinary teams of basic scientists, bioinformaticians, tissue engineers and clinicians should work together to connect computational approaches to outstanding biological questions, driving innovation of new regenerative therapeutics.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 digital issues and online access to articles
£99.00 per year
only £8.25 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Slyper, M. et al. A single-cell and single-nucleus RNA-Seq toolbox for fresh and frozen human tumors. Nat. Med. 26, 792–802 (2020).
Wu, H., Kirita, Y., Donnelly, E. L. & Humphreys, B. D. Advantages of single-nucleus over single-cell RNA sequencing of adult kidney: rare cell types and novel cell states revealed in fibrosis. J. Am. Soc. Nephrol. 30, 23–32 (2019).
Grindberg, R. V. et al. RNA-sequencing from single nuclei. Proc. Natl Acad. Sci. USA 110, 19802–19807 (2013).
Autengruber, A., Gereke, M., Hansen, G., Hennig, C. & Bruder, D. Impact of enzymatic tissue disintegration on the level of surface molecule expression and immune cell function. Eur. J. Microbiol. Immunol. 2, 112–120 (2012).
Reichard, A. & Asosingh, K. Best practices for preparing a single cell suspension from solid tissues for flow cytometry. Cytometry A 95, 219–226 (2019).
van den Brink, S. C. et al. Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations. Nat. Methods 14, 935–936 (2017).
Denisenko, E. et al. Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows. Genome Biol. 21, 130 (2020). This study compares gene expression and cellular composition of single-cell and single-nucleus suspensions generated implementing different dissociation protocols and different storage methods to identify potential artefacts and biases.
Sutermaster, B. A. & Darling, E. M. Considerations for high-yield, high-throughput cell enrichment: fluorescence versus magnetic sorting. Sci. Rep. 9, 227 (2019).
Stoeckius, M. et al. Cell Hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).
Gehring, J., Hwee Park, J., Chen, S., Thomson, M. & Pachter, L. Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat. Biotechnol. 38, 35–38 (2020).
Srivatsan, S. R. et al. Massively multiplex chemical transcriptomics at single-cell resolution. Science 367, 45–51 (2020).
Ding, J. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat. Biotechnol. 38, 737–746 (2020).
Mereu, E. et al. Benchmarking single-cell RNA-sequencing protocols for cell atlas projects. Nat. Biotechnol. 38, 747–755 (2020).
Zhao, S. & Zhang, B. A comprehensive evaluation of ensembl, RefSeq, and UCSC annotations in the context of RNA-seq read mapping and gene quantification. BMC Genomics 16, 97 (2015).
Cunningham, F. et al. Ensembl 2022. Nucleic Acids Res. 50, D988–D995 (2022).
O’Leary, N. A. et al. Reference sequence (RefSeq) database at NCBI: current status, taxonomic expansion, and functional annotation. Nucleic Acids Res. 44, D733–D745 (2016).
Nassar, L. R. et al. The UCSC Genome Browser database: 2023 update. Nucleic Acids Res. 51, D1188–D1195 (2023).
Brüning, R. S., Tombor, L., Schulz, M. H., Dimmeler, S. & John, D. Comparative analysis of common alignment tools for single-cell RNA sequencing. Gigascience 11, giac001 (2022).
10x Genomics. Cell Ranger. 10x Genomics https://support.10xgenomics.com/single-cell-vdj/software/pipelines/latest/what-is-cell-ranger (2020).
Kaminow, B., Yunusov, D. & Dobin, A. STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data. Preprint at bioRxiv https://doi.org/10.1101/2021.05.05.442755 (2021).
Slovin, S. et al. Single-cell RNA sequencing analysis: a step-by-step overview. Methods Mol. Biol. 2284, 343–365 (2021). This review covers the main considerations on the laboratory and computational sides of scRNA-seq data generation and analysis with pipelines for data processing.
McGinnis, C. S., Murrow, L. M. & Gartner, Z. J. DoubletFinder: doublet detection in single-cell RNA sequencing data using artificial nearest neighbors. Cell Syst. 8, 329–337.e4 (2019).
Xi, N. M. & Li, J. J. Benchmarking computational doublet-detection methods for single-cell RNA sequencing data. Cell Syst. 12, 176–194.e6 (2021).
Young, M. D. & Behjati, S. SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience 9, giaa151 (2020).
Lytal, N., Ran, D. & An, L. Normalization methods on single-cell RNA-seq data: an empirical survey. Front. Genet. 11, 41 (2020).
Chen, W. et al. A comparison of methods accounting for batch effects in differential expression analysis of UMI count based single cell RNA sequencing. Comput. Struct. Biotechnol. J. 18, 861–873 (2020).
Tran, H. T. N. et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 21, 12 (2020).
Luecken, M. D. et al. Benchmarking atlas-level data integration in single-cell genomics. Nat. Methods 19, 41–50 (2022).
Kobak, D. & Berens, P. The art of using t-SNE for single-cell transcriptomics. Nat. Commun. 10, 5416 (2019).
Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37, 38–44 (2019).
Moon, K. R. et al. Visualizing structure and transitions in high-dimensional biological data. Nat. Biotechnol. 37, 1482–1492 (2019).
Xiang, R. et al. A comparison for dimensionality reduction methods of single-cell RNA-seq data. Front. Genet. 12, 646936 (2021).
Duò, A., Robinson, M. D. & Soneson, C. A systematic performance evaluation of clustering methods for single-cell RNA-seq data. F1000Research 7, 1141 (2018).
Kiselev, V. Y., Andrews, T. S. & Hemberg, M. Challenges in unsupervised clustering of single-cell RNA-seq data. Nat. Rev. Genet. 20, 273–282 (2019).
Pasquini, G., Rojo Arias, J. E., Schäfer, P. & Busskamp, V. Automated methods for cell type annotation on scRNA-seq data. Comput. Struct. Biotechnol. J. 19, 961–969 (2021).
Huang, Q., Liu, Y., Du, Y. & Garmire, L. X. Evaluation of cell type annotation R packages on single-cell RNA-seq data. Genomics Proteomics Bioinformatics 19, 267–281 (2021).
Yi, H., Plotkin, A. & Stanley, N. Benchmarking differential abundance methods for finding condition-specific prototypical cells in multi-sample single-cell datasets. Preprint at bioRxiv https://doi.org/10.1101/2023.02.24.529894 (2023).
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Wang, T., Li, B., Nelson, C. E. & Nabavi, S. Comparative analysis of differential gene expression analysis tools for single-cell RNA sequencing data. BMC Bioinformatics 20, 40 (2019).
Liberzon, A. et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics 27, 1739–1740 (2011).
Culhane, A. C. et al. GeneSigDB: a manually curated database and resource for analysis of gene expression signatures. Nucleic Acids Res. 40, D1060–D1066 (2012).
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).
Deconinck, L., Cannoodt, R., Saelens, W., Deplancke, B. & Saeys, Y. Recent advances in trajectory inference from single-cell omics data. Curr. Opin. Syst. Biol. 27, 100344 (2021).
Bergen, V., Lange, M., Peidli, S., Wolf, F. A. & Theis, F. J. Generalizing RNA velocity to transient cell states through dynamical modeling. Nat. Biotechnol. 38, 1408–1414 (2020).
Gorin, G., Fang, M., Chari, T. & Pachter, L. RNA velocity unraveled. PLoS Comput. Biol. 18, e1010492 (2022).
Weiler, P., Van den Berge, K., Street, K. & Tiberi, S. in Single Cell Transcriptomics Methods and Protocols (eds Calogero, R. A. & Benes, V.) 269–292 (Springer, 2022).
Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & Van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018).
Shlyakhtina, Y., Bloechl, B. & Portal, M. M. BdLT-Seq as a barcode decay-based method to unravel lineage-linked transcriptome plasticity. Nat. Commun. 14, 1085 (2023).
Pratapa, A., Jalihal, A. P., Law, J. N., Bharadwaj, A. & Murali, T. M. Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data. Nat. Methods 17, 147–154 (2020).
Chen, S. & Mar, J. C. Evaluating methods of inferring gene regulatory networks highlights their lack of performance for single cell gene expression data. BMC Bioinformatics 19, 232 (2018).
Efremova, M., Vento-Tormo, M., Teichmann, S. A. & Vento-Tormo, R. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15, 1484–1506 (2020).
Browaeys, R., Saelens, W. & Saeys, Y. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17, 159–162 (2020).
Shao, X., Lu, X., Liao, J., Chen, H. & Fan, X. New avenues for systematically inferring cell-cell communication: through single-cell transcriptomics data. Protein Cell 11, 866–880 (2020).
Almet, A. A., Cang, Z., Jin, S. & Nie, Q. The landscape of cell–cell communication through single-cell transcriptomics. Curr. Opin. Syst. Biol. 26, 12–23 (2021).
Fischer, D. S., Schaar, A. C. & Theis, F. J. Modeling intercellular communication in tissues using spatial graphs of cells. Nat. Biotechnol. 41, 332–336 (2023).
Jerby-Arnon, L. & Regev, A. DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data. Nat. Biotechnol. 40, 1467–1477 (2022).
Clark, I. C. et al. Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science 372, eabf1230 (2021).
Giladi, A. et al. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38, 629–637 (2020).
Stickels, R. R. et al. Highly sensitive spatial transcriptomics at near-cellular resolution with Slide-seqV2. Nat. Biotechnol. 39, 313–319 (2021).
Wei, X. et al. Single-cell Stereo-seq reveals induced progenitor cells involved in axolotl brain regeneration. Science 377, eabp9444 (2022). This article reports the development of a high-resolution, single-cell spatial transcriptomics approach Stereo-seq to profile developmental and post-injury regenerative neurogenesis in axolotl telencephalon.
Moffitt, J. R. et al. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. Proc. Natl Acad. Sci. USA 113, 11046–11051 (2016).
Alon, S. et al. Expansion sequencing: spatially precise in situ transcriptomics in intact biological systems. Science 371, eaax2656 (2021).
Lee, J., Yoo, M. & Choi, J. Recent advances in spatially resolved transcriptomics: challenges and opportunities. BMB Rep. 55, 113–124 (2022).
Williams, C. G., Lee, H. J., Asatsuma, T., Vento-Tormo, R. & Haque, A. An introduction to spatial transcriptomics for biomedical research. Genome Med. 14, 68 (2022).
Thomas, S. M., Ackert-Bicknell, C. L., Zuscik, M. J. & Payne, K. A. Understanding the Transcriptomic Landscape to Drive New Innovations in Musculoskeletal Regenerative Medicine. Curr. Osteoporos. Rep. 20, 141–152 (2022).
Rai, M. F. et al. Single cell omics for musculoskeletal research. Curr. Osteoporos. Rep. 19, 131–140 (2021).
Sarmiento, P. & Little, D. Tendon and multiomics: advantages, advances, and opportunities. NPJ Regen. Med. 6, 61 (2021).
Baldwin, M. J., Cribbs, A. P., Guilak, F. & Snelling, S. J. B. Mapping the musculoskeletal system one cell at a time. Nat. Rev. Rheumatol. 17, 247–248 (2021).
Paik, D. T., Cho, S., Tian, L., Chang, H. Y. & Wu, J. C. Single-cell RNA sequencing in cardiovascular development, disease and medicine. Nat. Rev. Cardiol. 17, 457–473 (2020).
Chaudhry, F. et al. Single-cell RNA sequencing of the cardiovascular system: new looks for old diseases. Front. Cardiovasc. Med. 6, 173 (2019).
Schreibing, F. & Kramann, R. Mapping the human kidney using single-cell genomics. Nat. Rev. Nephrol. 18, 347–360 (2022).
Clark, A. R. & Greka, A. The power of one: advances in single-cell genomics in the kidney. Nat. Rev. Nephrol. 16, 73–74 (2020).
Alexander, M. J., Budinger, G. R. S. & Reyfman, P. A. Breathing fresh air into respiratory research with single-cell RNA sequencing. Eur. Resp. Rev. 29, 200060 (2020).
Theocharidis, G., Tekkela, S., Veves, A., McGrath, J. A. & Onoufriadis, A. Single‐cell transcriptomics in human skin research: available technologies, technical considerations and disease applications. Exp. Dermatol. 31, 655–673 (2022).
Dubois, A., Gopee, N., Olabi, B. & Haniffa, M. Defining the skin cellular community using single-cell genomics to advance precision medicine. J. Invest. Dermatol. 141, 255–264 (2021).
Colonna, M. & Brioschi, S. Neuroinflammation and neurodegeneration in human brain at single-cell resolution. Nat. Rev. Immunol. 20, 81–82 (2020).
Cao, Y., Zhu, S., Yu, B. & Yao, C. Single‐cell RNA sequencing for traumatic spinal cord injury. FASEB J. 36, e22656 (2022).
Guerrero-Juarez, C. F. et al. Single-cell analysis reveals fibroblast heterogeneity and myeloid-derived adipocyte progenitors in murine skin wounds. Nat. Commun. 10, 650 (2019).
Oprescu, S. N., Yue, F., Qiu, J., Brito, L. F. & Kuang, S. Temporal dynamics and heterogeneity of cell populations during skeletal muscle regeneration. iScience 23, 100993 (2020). This study reports the use of scRNA-seq and cell lineage tracing to profile the kinetics and transcriptional dynamics of skeletal muscle regeneration, considering both the stromal and immune cell compartments in various tissue injury phases (uninjured to 21 days post-injury).
Farbehi, N. et al. Single-cell expression profiling reveals dynamic flux of cardiac stromal, vascular and immune cells in health and injury. eLife 8, e43882 (2019).
Dick, S. A. et al. Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction. Nat. Immunol. 20, 29–39 (2019).
Vafadarnejad, E. et al. Dynamics of cardiac neutrophil diversity in murine myocardial infarction. Circ. Res. 127, e232–e249 (2020).
Ruiz-Villalba, A. et al. Single-cell RNA sequencing analysis reveals a crucial role for CTHRC1 (collagen triple helix repeat containing 1) cardiac fibroblasts after myocardial infarction. Circulation 142, 1831–1847 (2020).
Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C. & Humphreys, B. D. Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury. Proc. Natl Acad. Sci. USA 117, 15874–15883 (2020).
Abbasi, S. et al. Distinct regulatory programs control the latent regenerative potential of dermal fibroblasts during wound healing. Cell Stem Cell 27, 396–412.e6 (2020).
Lin, Y. et al. Single-cell RNA-seq of UVB-radiated skin reveals landscape of photoaging-related inflammation and protection by vitamin D. Gene 831, 146563 (2022).
Foster, D. S. et al. Integrated spatial multiomics reveals fibroblast fate during tissue repair. Proc. Natl Acad. Sci. USA 118, e2110025118 (2021). This article reports the use of multi-modal integration (scRNA-seq, scATAC-seq and spatial transcriptomics) to map the kinetics of splinted excisional skin injury to compare cell populations at various wound locations (inner or outer) over the wound-healing time course (uninjured to 14 days post-injury).
Croft, A. P. et al. Distinct fibroblast subsets drive inflammation and damage in arthritis. Nature 570, 246–251 (2019).
Zhang, F. et al. Defining inflammatory cell states in rheumatoid arthritis joint synovial tissues by integrating single-cell transcriptomics and mass cytometry. Nat. Immunol. 20, 928–942 (2019).
Alivernini, S. et al. Distinct synovial tissue macrophage subsets regulate inflammation and remission in rheumatoid arthritis. Nat. Med. 26, 1295–1306 (2020).
Wei, K. et al. Notch signalling drives synovial fibroblast identity and arthritis pathology. Nature 582, 259–264 (2020).
Knights, A. J. et al. Synovial fibroblasts assume distinct functional identities and secrete R-spondin 2 in osteoarthritis. Ann. Rheum. Dis. 82, 272–282 (2023).
Habermann, A. C. et al. Single-cell RNA sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis. Sci. Adv. 6, eaba1972 (2020).
Aran, D. et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat. Immunol. 20, 163–172 (2019).
Zhao, C. Q. et al. Heterogeneity of T cells and macrophages in chlorine-induced acute lung injury in mice using single-cell RNA sequencing. Inhal. Toxicol. 34, 399–411 (2022).
Peyser, R. et al. Defining the activated fibroblast population in lung fibrosis using single-cell sequencing. Am. J. Respir. Cell Mol. Biol. 61, 74–85 (2019).
Milich, L. M. et al. Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord. J. Exp. Med. 218, e20210040 (2021).
Hammond, T. R. et al. Single-cell RNA sequencing of microglia throughout the mouse lifespan and in the injured brain reveals complex cell-state changes. Immunity 50, 253–271.e6 (2019).
Jordão, M. J. C. et al. Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Science 363, eaat7554 (2019).
Dell’Orso, S. et al. Single cell analysis of adult mouse skeletal muscle stem cells in homeostatic and regenerative conditions. Development 146, dev174177 (2019).
Reyes, N. S. et al. Sentinel p16INK4a+ cells in the basement membrane form a reparative niche in the lung. Science 378, 192–201 (2022).
Leigh, N. D. et al. Transcriptomic landscape of the blastema niche in regenerating adult axolotl limbs at single-cell resolution. Nat. Commun. 9, 5153 (2018).
Gerber, T. et al. Single-cell analysis uncovers convergence of cell identities during axolotl limb regeneration. Science 362, eaaq0681 (2018).
Benhar, I. et al. Temporal single-cell atlas of non-neuronal retinal cells reveals dynamic, coordinated multicellular responses to central nervous system injury. Nat. Immunol. 24, 700–713 (2023).
Lust, K. et al. Single-cell analyses of axolotl telencephalon organization, neurogenesis, and regeneration. Science 377, eabp9262 (2022).
Armingol, E., Officer, A., Harismendy, O. & Lewis, N. E. Deciphering cell–cell interactions and communication from gene expression. Nat. Rev. Genet. 22, 71–88 (2021).
De Micheli, A. J. et al. Single-cell analysis of the muscle stem cell hierarchy identifies heterotypic communication signals involved in skeletal muscle regeneration. Cell Rep. 30, 3583–3595.e5 (2020).
Linnerbauer, M. et al. Intranasal delivery of a small-molecule ErbB inhibitor promotes recovery from acute and late-stage CNS inflammation. JCI Insight 7, e154824 (2022).
Theocharidis, G. et al. Single cell transcriptomic landscape of diabetic foot ulcers. Nat. Commun. 13, 181 (2022). This study reports the use of scRNA-seq to profile the cellular landscape of human DFU injuries (local tissue biopsies and peripheral blood) and identify unique populations enriched in patients with effective wound healing.
Mascharak, S. et al. Multi-omic analysis reveals divergent molecular events in scarring and regenerative wound healing. Cell Stem Cell 29, 315–327.e6 (2022).
Phan, Q. M., Sinha, S., Biernaskie, J. & Driskell, R. R. Single‐cell transcriptomic analysis of small and large wounds reveals the distinct spatial organization of regenerative fibroblasts. Exp. Dermatol. 30, 92–101 (2021).
Cui, M. et al. Dynamic transcriptional responses to injury of regenerative and non-regenerative cardiomyocytes revealed by single-nucleus RNA sequencing. Dev. Cell 53, 102–116.e8 (2020).
Wang, Z. et al. Cell-type-specific gene regulatory networks underlying murine neonatal heart regeneration at single-cell resolution. Cell Rep. 33, 108472 (2020).
Aztekin, C. et al. Identification of a regeneration-organizing cell in the Xenopus tail. Science 364, 653–658 (2019).
Londono, R. et al. Single cell sequencing analysis of lizard phagocytic cell populations and their role in tail regeneration. J. Immunol. Regen. Med. 8, 100029 (2020).
Qin, T. et al. A population of stem cells with strong regenerative potential discovered in deer antlers. Science 379, 840–847 (2023).
Chen, T. et al. A road map from single-cell transcriptome to patient classification for the immune response to trauma. JCI Insight 6, e145108 (2021).
Gaudilliere, B. et al. Coordinated surgical immune signatures contain correlates of clinical recovery. Sci. Transl Med. 6, 255ra131 (2014).
Pummerer, C. L. et al. Identification of cardiac myosin peptides capable of inducing autoimmune myocarditis in BALB/c mice. J. Clin. Invest. 97, 2057–2062 (1996).
Rieckmann, M. et al. Myocardial infarction triggers cardioprotective antigen-specific T helper cell responses. J. Clin. Invest. 129, 4922–4936 (2019).
Xia, N. et al. A unique population of regulatory T cells in heart potentiates cardiac protection from myocardial infarction. Circulation 142, 1956–1973 (2020).
Delgobo, M. et al. Myocardial milieu favors local differentiation of regulatory T cells. Circ. Res. 132, 565–582 (2023).
Guo, F. et al. Distinct injury responsive regulatory T cells identified by multi-dimensional phenotyping. Front. Immunol. 13, 833100 (2022).
Hanna, B. S. et al. The gut microbiota promotes distal tissue regeneration via RORγ+ regulatory T cell emissaries. Immunity 56, 829–846.e8 (2023).
Boland, B. S. et al. Heterogeneity and clonal relationships of adaptive immune cells in ulcerative colitis revealed by single-cell analyses. Sci. Immunol. 5, eabb4432 (2020).
Koda, Y. et al. CD8+ tissue-resident memory T cells promote liver fibrosis resolution by inducing apoptosis of hepatic stellate cells. Nat. Commun. 12, 4474 (2021).
Melo Ferreira, R. et al. Integration of spatial and single-cell transcriptomics localizes epithelial cell–immune cross-talk in kidney injury. JCI Insight 6, e147703 (2021).
McKellar, D. W. et al. Large-scale integration of single-cell transcriptomic data captures transitional progenitor states in mouse skeletal muscle regeneration. Commun. Biol. 4, 1280 (2021). This article reports the integration of new and publicly available scRNA-seq and snRNA-seq data sets to create a large-scale atlas of murine skeletal muscle injury for in-depth exploration of rare MuSC differentiation states, and it serves as cell cluster annotation reference for muscle-injury spatial transcriptomics.
Konieczny, P. et al. Interleukin-17 governs hypoxic adaptation of injured epithelium. Science 377, eabg9302 (2022).
Kim, H. K., Ha, T. W. & Lee, M. R. Single-cell transcriptome analysis as a promising tool to study pluripotent stem cell reprogramming. Int. J. Mol. Sci. 22, 5988 (2021).
Camp, J. G., Wollny, D. & Treutlein, B. Single-cell genomics to guide human stem cell and tissue engineering. Nat. Methods 15, 661–667 (2018).
Chen, K. et al. Disrupting mechanotransduction decreases fibrosis and contracture in split-thickness skin grafting. Sci. Transl Med. 14, eabj9152 (2022).
Henn, D. et al. Xenogeneic skin transplantation promotes angiogenesis and tissue regeneration through activated Trem2+ macrophages. Sci. Adv. 7, eabi4528 (2021). This study reports the use of scRNA-seq to investigate the innate immune response to xenogeneic skin transplants, identify unique TREM2+ regenerative macrophages and develop a new cell-laden hydrogel construct to mitigate fibrosis and improve healing of complex skin wounds.
Wang, H. et al. Decoding the annulus fibrosus cell atlas by scRNA-seq to develop an inducible composite hydrogel: a novel strategy for disc reconstruction. Bioact. Mater. 14, 350–363 (2022).
Zhang, X. et al. Msx1+ stem cells recruited by bioactive tissue engineering graft for bone regeneration. Nat. Commun. 13, 5211 (2022).
Xiao, W. et al. Recombinant DTβ4-inspired porous 3D vascular graft enhanced antithrombogenicity and recruited circulating CD93+/CD34+ cells for endothelialization. Sci. Adv. 8, eabn1958 (2022).
Jiang, Y. et al. Wireless, closed-loop, smart bandage with integrated sensors and stimulators for advanced wound care and accelerated healing. Nat. Biotechnol. 41, 652–662 (2022).
Hu, C. et al. Dissecting the microenvironment around biosynthetic scaffolds in murine skin wound healing. Sci. Adv. 7, eabf0787 (2021).
Liang, R. et al. Silk gel recruits specific cell populations for scarless skin regeneration. Appl. Mater. Today 23, 101004 (2021).
Huang, J. et al. Single-cell RNA-seq reveals functionally distinct biomaterial degradation-related macrophage populations. Biomaterials 277, 121116 (2021).
Sadtler, K. et al. Developing a pro-regenerative biomaterial scaffold microenvironment requires T helper 2 cells. Science 352, 366–370 (2016).
Heredia, J. E. et al. Type 2 innate signals stimulate fibro/adipogenic progenitors to facilitate muscle regeneration. Cell 153, 376–388 (2013).
Brown, B. N. et al. Macrophage phenotype as a predictor of constructive remodeling following the implantation of biologically derived surgical mesh materials. Acta Biomater. 8, 978–987 (2012).
Chung, L. et al. Interleukin 17 and senescent cells regulate the foreign body response to synthetic material implants in mice and humans. Sci. Transl Med. 12, eaax3799 (2020).
Sadtler, K. et al. Divergent immune responses to synthetic and biological scaffolds. Biomaterials 192, 405–415 (2019).
Sommerfeld, S. D. et al. Interleukin-36γ–producing macrophages drive IL-17–mediated fibrosis. Sci. Immunol. 4, eaax4783 (2019). This article reports the use of scRNA-seq to profile macrophages from various muscle injury and biomaterial (pro-regenerative ECM scaffold and pro-fibrotic synthetic scaffold) environments to identify unique phenotypes and mechanistic drivers of divergent wound-healing outcomes.
Wang, J. et al. Break monopoly of polarization: CD301b+ macrophages play positive roles in osteoinduction of calcium phosphate ceramics. Appl. Mater. Today 24, 101111 (2021).
Wang, J. et al. CD301b+ macrophages mediate angiogenesis of calcium phosphate bioceramics by CaN/NFATc1/VEGF axis. Bioact. Mater. 15, 446–455 (2022).
Anderson, J. M. Inflammatory response to implants. ASAIO Trans. 34, 101–107 (1988).
Henderson, N. C., Rieder, F. & Wynn, T. A. Fibrosis: from mechanisms to medicines. Nature 587, 555–566 (2020).
Doloff, J. C. et al. The surface topography of silicone breast implants mediates the foreign body response in mice, rabbits and humans. Nat. Biomed. Eng. 5, 1115–1130 (2021).
Padmanabhan, J., Chen, K., Sivaraj, D. et al. Allometrically scaling tissue forces drive pathological foreign-body responses to implants via Rac2-activated myeloid cells. Nat. Biomed. Eng. 7, 1419–1436 (2023).
Sivaraj, D. et al. IQGAP1‐mediated mechanical signaling promotes the foreign body response to biomedical implants. FASEB J. 36, e22007 (2022).
Cherry, C. et al. Transfer learning in a biomaterial fibrosis model identifies in vivo senescence heterogeneity and contributions to vascularization and matrix production across species and diverse pathologies. Geroscience 45, 2559–2587 (2023).
Cherry, C. et al. Computational reconstruction of the signalling networks surrounding implanted biomaterials from single-cell transcriptomics. Nat. Biomed. Eng. 5, 1228–1238 (2021).
Jones, R. C. et al. The Tabula Sapiens: a multiple-organ, single-cell transcriptomic atlas of humans. Science 376, eabl4896 (2022).
Schaum, N. et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).
Almanzar, N. et al. A single-cell transcriptomic atlas characterizes ageing tissues in the mouse. Nature 583, 590–595 (2020).
Buechler, M. B. et al. Cross-tissue organization of the fibroblast lineage. Nature 593, 575–579 (2021).
Cao, J. et al. A human cell atlas of fetal gene expression. Science 370, eaba7721 (2020).
Domcke, S. et al. A human cell atlas of fetal chromatin accessibility. Science 370, eaba7612 (2020).
Sanchez-Vega, F. et al. Oncogenic signaling pathways in the cancer genome atlas. Cell 173, 321–337.e10 (2018).
Prieto, C., Barrios, D. & Villaverde, A. SingleCAnalyzer: interactive analysis of single cell RNA-Seq data on the cloud. Front. Bioinform. 2, 793309 (2022).
Megill, C. et al. Cellxgene: a performant, scalable exploration platform for high dimensional sparse matrices. Preprint at bioRxiv https://doi.org/10.1101/2021.04.05.438318 (2021).
Speir, M. L. et al. UCSC cell browser: visualize your single-cell data. Bioinformatics 37, 4578–4580 (2021).
Reich, M. et al. GenePattern 2.0. Nat. Genet. 38, 500–501 (2006).
Luecken, M. D. & Theis, F. J. Current best practices in single‐cell RNA‐seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019). This review describes best practices and commonly used tools for scRNA-seq analysis and applies them to a publicly available data set as a guide; it also provides recommendations and points out potential pitfalls at each step of the process.
Cirulli, E. T. et al. A missense variant in PTPN22 is a risk factor for drug-induced liver injury. Gastroenterology 156, 1707–1716.e2 (2019).
Delacher, M. et al. Single-cell chromatin accessibility landscape identifies tissue repair program in human regulatory T cells. Immunity 54, 702–720.e17 (2021).
Llorens-Bobadilla, E. et al. A latent lineage potential in resident neural stem cells enables spinal cord repair. Science 370, eabb8795 (2020).
Wang, L. et al. Serum proteomics identifies biomarkers associated with the pathogenesis of idiopathic pulmonary fibrosis. Mol. Cell. Proteom. 22, 100524 (2023).
Ogbeide, S., Giannese, F., Mincarelli, L. & Macaulay, I. C. Into the multiverse: advances in single-cell multiomic profiling. Trends Genet. 38, 831–843 (2022).
Rodriguez-Meira, A. et al. Unravelling intratumoral heterogeneity through high-sensitivity single-cell mutational analysis and parallel RNA sequencing. Mol. Cell 73, 1292–1305.e8 (2019).
Zhu, C. et al. An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome. Nat. Struct. Mol. Biol. 26, 1063–1070 (2019).
Bock, C. et al. High-content CRISPR screening. Nat. Rev. Methods Primers 2, 9 (2022).
Tian, F. et al. Core transcription programs controlling injury-induced neurodegeneration of retinal ganglion cells. Neuron 110, 2607–2624.e8 (2022).
Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).
Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).
Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).
Hashimshony, T. et al. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol. 17, 77 (2016).
Bray, N. L., Pimentel, H., Melsted, P. & Pachter, L. Near-optimal probabilistic RNA-seq quantification. Nat. Biotechnol. 34, 525–527 (2016).
Grandi, F. et al. popsicleR: a R package for pre-processing and quality control analysis of single cell RNA-seq data. J. Mol. Biol. 434, 167560 (2022).
Hong, R. et al. Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data. Nat. Commun. 13, 1688 (2022).
Hippen, A. A. et al. miQC: an adaptive probabilistic framework for quality control of single-cell RNA-sequencing data. PLoS Comput. Biol. 17, e1009290 (2021).
Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).
Bernstein, N. J. et al. Solo: doublet identification in single-cell RNA-seq via semi-supervised deep learning. Cell Syst. 11, 95–101.e5 (2020).
Yang, S. et al. Decontamination of ambient RNA in single-cell RNA-seq with DecontX. Genome Biol. 21, 57 (2020).
Berg, M. et al. FastCAR: fast Correction for Ambient RNA to facilitate differential gene expression analysis in single-cell RNA-sequencing datasets. Preprint at bioRxiv https://doi.org/10.1101/2022.07.19.500594 (2022).
Lun, A. T. L., McCarthy, D. J. & Marioni, J. C. A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor. F1000Research 5, 2122 (2016).
Bacher, R. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14, 584–586 (2017).
Yip, S. H., Wang, P., Kocher, J.-P. A., Sham, P. C. & Wang, J. Linnorm: improved statistical analysis for single cell RNA-seq expression data. Nucleic Acids Res. 45, e179 (2017).
Korsunsky, I. et al. Fast, sensitive and accurate integration of single-cell data with Harmony. Nat. Methods 16, 1289–1296 (2019).
Stuart, T. et al. Comprehensive integration of single-cell data. Cell 177, 1888–1902.e21 (2019).
Hie, B., Bryson, B. & Berger, B. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37, 685–691 (2019).
van Dijk, D. et al. Recovering gene interactions from single-cell data using data diffusion. Cell 174, 716–729.e27 (2018).
Wagner, F., Yan, Y. & Yanai, I. K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq data. Preprint at bioRxiv https://doi.org/10.1101/217737 (2018).
Huang, M. et al. SAVER: gene expression recovery for single-cell RNA sequencing. Nat. Methods 15, 539–542 (2018).
van der Maaten, L. & Hinton, G. Visualizing data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).
McInnes, L., Healy, J., Saul, N. & Großberger, L. UMAP: uniform manifold approximation and projection. J. Open Source Softw. 3, 861 (2018).
Kiselev, V. Y. et al. SC3: consensus clustering of single-cell RNA-seq data. Nat. Methods 14, 483–486 (2017).
Žurauskienė, J. & Yau, C. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinformatics 17, 140 (2016).
Levine, J. H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).
Hou, R., Denisenko, E. & Forrest, A. R. R. scMatch: a single-cell gene expression profile annotation tool using reference datasets. Bioinformatics 35, 4688–4695 (2019).
Fu, R. et al. clustifyr: an R package for automated single-cell RNA sequencing cluster classification. F1000Research 9, 223 (2020).
Tan, Y. & Cahan, P. SingleCellNet: a computational tool to classify single cell RNA-seq data across platforms and across species. Cell Syst. 9, 207–213.e2 (2019).
Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).
Finak, G. et al. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data. Genome Biol. 16, 278 (2015).
Korthauer, K. D. et al. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biol. 17, 222 (2016).
Ji, Z. & Ji, H. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016).
Street, K. et al. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genomics 19, 477 (2018).
La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).
Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat. Methods 14, 1083–1086 (2017).
Jin, S. et al. Inference and analysis of cell-cell communication using CellChat. Nat. Commun. 12, 1088 (2021).
Chou, C.-H. et al. Synovial cell cross-talk with cartilage plays a major role in the pathogenesis of osteoarthritis. Sci. Rep. 10, 10868 (2020).
do Valle Duraes, F. et al. Immune cell landscaping reveals a protective role for regulatory T cells during kidney injury and fibrosis. JCI Insight 5, e130651 (2020).
Rudman-Melnick, V. et al. Single-cell profiling of AKI in a murine model reveals novel transcriptional signatures, profibrotic phenotype, and epithelial-to-stromal crosstalk. J. Am. Soc. Nephrol. 31, 2793–2814 (2020).
Misra, A. et al. Characterizing neonatal heart maturation, regeneration, and scar resolution using spatial transcriptomics. J. Cardiovasc. Dev. Dis. 9, 1 (2022).
Conlon, T. M. et al. Inhibition of LTβR signalling activates WNT-induced regeneration in lung. Nature 588, 151–156 (2020).
Tran, N. M. et al. Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes. Neuron 104, 1039–1055.e12 (2019).
Wheeler, M. A. et al. MAFG-driven astrocytes promote CNS inflammation. Nature 578, 593–599 (2020).
Schirmer, L. et al. Neuronal vulnerability and multilineage diversity in multiple sclerosis. Nature 573, 75–82 (2019).
Henn, D. et al. Cas9-mediated knockout of Ndrg2 enhances the regenerative potential of dendritic cells for wound healing. Nat. Commun. 14, 4729 (2023).
Jin, R. M., Warunek, J. & Wohlfert, E. A. Chronic infection stunts macrophage heterogeneity and disrupts immune-mediated myogenesis. JCI Insight 3, e121459 (2018).
Vu, R. et al. Wound healing in aged skin exhibits systems-level alterations in cellular composition and cell-cell communication. Cell Rep. 40, 111155 (2022).
Han, J. et al. Age-associated senescent - T cell signaling promotes type 3 immunity that inhibits regenerative response. Preprint at bioRxiv https://doi.org/10.1101/2021.08.17.456641 (2022).
Zhang, C. et al. Age‐related decline of interferon‐gamma responses in macrophage impairs satellite cell proliferation and regeneration. J. Cachexia Sarcopenia Muscle 11, 1291–1305 (2020).
Acknowledgements
This work was funded in part by the National Institutes of Health (NIH) Pioneer Award DP1AR076959 (to J.H.E.), Bloomberg~Kimmel Institute and Morton Goldberg Professorship (to J.H.E.). A.R. is funded through NSF GRFP DGE-1746891.
Author information
Authors and Affiliations
Contributions
All authors contributed equally to the preparation of this manuscript.
Corresponding author
Ethics declarations
Competing interests
J.H.E. holds equity in Unity Biotechnology and Aegeria Soft Tissue, and is an advisor for Tessera Therapeutics, HapInScience and Font Bio. The remaining authors declare no competing interests.
Peer review
Peer review information
Nature Reviews Bioengineering thanks Omer Bayraktar and the other, anonymous, reviewers for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ruta, A., Krishnan, K. & Elisseeff, J.H. Single-cell transcriptomics in tissue engineering and regenerative medicine. Nat Rev Bioeng 2, 101–119 (2024). https://doi.org/10.1038/s44222-023-00132-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s44222-023-00132-7
This article is cited by
-
Precision drug delivery to the central nervous system using engineered nanoparticles
Nature Reviews Materials (2024)