Metabolic labeling of secreted matrix to investigate cell–material interactions in tissue engineering and mechanobiology

Claudia Loebel, Aya M. Saleh, Kathryn R. Jacobson, Ryan Daniels, Robert L. Mauck, Sarah Calve, Jason A. Burdick

Published: 2022-02-08 DOI: 10.1038/s41596-021-00652-9

Extended

Extended Data Fig. 1 Viability of cells cultured in different concentrations of AHA.

Chondrocytes were cultured for 7 days within nondegradable hyaluronic acid hydrogels in chondrogenic media supplemented with different concentrations of AHA and cell viability quantified through live-dead staining (n = 4 hydrogels, mean ± SD, **p ≤ 0.01, by one-way ANOVA with Bonferroni post hoc).

Extended Data Fig. 2 Comparison of nascent protein labeling after and before cell fixation.

Representative images of a maximum intensity z-projection of methionine containing nascent proteins labeled with fluorophore-conjugated cyclooctynes (DBCO-488) after (left) and before (right) adding the fixative. Cells were cultured for 3 days within MMP-degradable hyaluronic acid (HA) hydrogels (media supplemented with azidohomoalanine, AHA). Red lines indicate cell boundaries identified with cell membrane staining. Scale bar, 20 µm. Adapted with permission from ref. 30 .

Extended Data Fig. 3 Quantification of nascent matrix volume and area in ImageJ.

a Quantification of nascent matrix volume: Acquire z-stack confocal images of the nascent ECM and cell membrane, transform into 3D object, and split the channels into single 3D images using ImageJ (i). To obtain an image of the nascent matrix only, adjust the threshold for each channel with ‘Otsu thresholding’ and subtract the ‘cell’ image from the ‘nascent matrix’ image (ii). Use the ImageJ ‘3D object counter’ function to measure the nascent matrix volume (iii). Scale bars, 50 µm. b Quantification of nascent matrix area: Acquire z-stack confocal images of the nascent matrix and cell membrane, and split the channels into single z-stack images using ImageJ (i). To obtain an image of the nascent matrix only, adjust the threshold for each channel with ‘Otsu thresholding’ and subtract the ‘cell’ image from the ‘nascent matrix’ image (ii). Use the ImageJ ‘Analyze particles’ function in max projection to measure the nascent matrix area (iii). Scale bars, 50 µm.

Extended Data Fig. 4 Analysis of newly synthesized proteome deposited by chondrocytes within 3D hydrogels.

a Western blot analysis of AHA-labeled and control samples cultured in methionine containing media (Met) showing the influence of iodoacetamide (IAA), sodium ascorbate (SA) and aminoguanidine (AG) at varied concentrations on the efficiency of the click reaction and unspecific labeling. b Western blot analysis of AHA-labeled and Met samples showing the influence of resin to protein ratio on the enrichment and nonspecific binding of labeled proteins by NeutrAvidin affinity purification.

Extended Data Fig. 5 Parameter settings for identification of newly synthesized proteins.

MaxQuant and instrument parameters for identification and quantification.

Extended Data Fig. 6 Diagram illustrating the classification of proteins.

Description of how to classify proteins as cytosolic, nuclear, membrane, cytoskeletal or matrisome-related based on the Gene Ontology Consortium and the Matrisome Project.

Extended Data Fig. 7 Analysis of protein and peptide concentrations during sample preparation.

a Measured protein concentrations using the Pierce Coomassie (Bradford) Protein Assay at step 59 (Start), step 69 (click-reac.) and step 83 (cleaved), n = 6, mean ± SD, color-coded individual points represent one biological replicate. b Measured peptide concentrations using the Pierce Quantitative Fluorometric Peptide Assay at step 88 (Trypsin/LysC), step 89 (SDS removal) and step 94 (C18 clean-up), n = 9, mean ± SD, color-coded individual points represent one biological replicate.

Supplementary information

Supplementary Information

Supplementary Method: LC-MS/MS analysis.

Reporting Summary

Supplementary Table 1

Identification and analysis of newly synthesized proteins. proteinGroups (the Original ‘proteinGroups’ data generated by MaxQuant), Filtered Proteins (Filtered protein IDs and intensities used for downstream data analysis) and Data Analyses (ECM proteins identified with fold change calculations and annotations).

Detect-seq, a chemical labeling and biotin pull-down approach for the unbiased and genome-wide off-target evaluation of programmable cytosine base editors

A systems-level mass spectrometry-based technique for accurate and sensitive quantification of the RNA cap epitranscriptome

References

Re-creating features of the native extracellular matrix (ECM) with engineered biomaterials has become a valuable tool to probe the influence of ECM properties on cellular functions (e.g., differentiation) and toward the engineering of tissues. However, c
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References
ACKNOWLEDGMENTS
ACKNOWLEDGMENTS

Gjorevski, N. & Nelson, C. M. Bidirectional extracellular matrix signaling during tissue morphogenesis. Cytokine Growth Factor Rev. 20, 459–465 (2009).
Herrera, J., Henke, C. A. & Bitterman, P. B. Extracellular matrix as a driver of progressive fibrosis. J. Clin. Invest. 128, 45–53 (2018).
Frantz, C., Stewart, K. M. & Weaver, V. M. The extracellular matrix at a glance. J. Cell Sci. 123, 4195–4200 (2010).
Birk, D. E., Hahn, R. A., Linsenmayer, C. Y. & Zycband, E. I. Characterization of collagen fibril segments from chicken embryo cornea, dermis and tendon. Matrix Biol. 15, 111–118 (1996).
Griffith, L. G. & Swartz, M. A. Capturing complex 3D tissue physiology in vitro. Nat. Rev. Mol. Cell Biol. 7, 211–224 (2006).
Caliari, S. R. & Burdick, J. A. A practical guide to hydrogels for cell culture. Nat. Methods 13, 405–414 (2016).
Loebel, C., Rodell, C. B., Chen, M. H. & Burdick, J. A. Shear-thinning and self-healing hydrogels as injectable therapeutics and for 3D-printing. Nat. Protoc. 12, 1521–1541 (2017).
Prince, E. & Kumacheva, E. Design and applications of man-made biomimetic fibrillar hydrogels. Nat. Rev. Mater. 4, 99–115 (2019).
Tomasek, J. J., Gabbiani, G., Hinz, B., Chaponnier, C. & Brown, R. A. Myofibroblasts and mechano-regulation of connective tissue remodelling. Nat. Rev. Mol. Cell Biol. 3, 349–363 (2002).
Burla, F., Mulla, Y., Vos, B. E., Aufderhorst-Roberts, A. & Koenderink, G. H. From mechanical resilience to active material properties in biopolymer networks. Nat. Rev. Phys. 1, 249–263 (2019).
Storm, C., Pastore, J. J., MacKintosh, F. C., Lubensky, T. C. & Janmey, P. A. Nonlinear elasticity in biological gels. Nature 435, 191–194 (2005).
Matera, D. L. et al. Microengineered 3D pulmonary interstitial mimetics highlight a critical role for matrix degradation in myofibroblast differentiation. Sci. Adv. https://doi.org/10.1126/sciadv.abb5069 (2020).
Davidson, M. D., Burdick, J. A. & Wells, R. G. Engineered biomaterial platforms to study fibrosis. Adv. Healthc. Mater. https://doi.org/10.1002/adhm.201901682 (2020).
Davidson, M. D. et al. Programmable and contractile materials through cell encapsulation in fibrous hydrogel assemblies. Sci. Adv. 7, eabi8157 (2021).
Ma, P. X. Biomimetic materials for tissue engineering. Adv. Drug Deliv. Rev. 60, 184–198 (2008).
Saleh, A. M., Wilding, K. M., Calve, S., Bundy, B. C. & Kinzer-Ursem, T. L. Non-canonical amino acid labeling in proteomics and biotechnology. J. Biol. Eng. 13, 43 (2019).
Dieterich, D. C. et al. In situ visualization and dynamics of newly synthesized proteins in rat hippocampal neurons. Nat. Neurosci. 13, 897–905 (2010).
Dieterich, D. C., Link, A. J., Graumann, J., Tirrell, D. A. & Schuman, E. M. Selective identification of newly synthesized proteins in mammalian cells using bioorthogonal noncanonical amino acid tagging (BONCAT). Proc. Natl Acad. Sci. USA 103, 9482–9487 (2006).
Nessen, M. A. et al. Selective enrichment of azide-containing peptides from complex mixtures. J. Proteome Res. 8, 3702–3711 (2009).
van Hest, J. C. M., Kiick, K. L. & Tirrell, D. A. Efficient incorporation of unsaturated methionine analogues into proteins in vivo. J. Am. Chem. Soc. 122, 1282–1288 (2000).
Beatty, K. E. et al. Fluorescence visualization of newly synthesized proteins in mammalian cells. Angew. Chem., Int. Ed. 45, 7364–7367 (2006).
Zhang, J., Wang, J., Ng, S., Lin, Q. & Shen, H.-M. Development of a novel method for quantification of autophagic protein degradation by AHA labeling. Autophagy 10, 901–912 (2014).
Zhang, M. M., Tsou, L. K., Charron, G., Raghavan, A. S. & Hang, H. C. Tandem fluorescence imaging of dynamic S-acylation and protein turnover. P. Natl Acad. Sci. USA 107, 8627–8632 (2010).
Adelmund, S. M., Ruskowitz, E. R., Farahani, P. E., Wolfe, J. V. & DeForest, C. A. Light-activated proteomic labeling via photocaged bioorthogonal non-canonical amino acids. ACS Chem. Biol. 13, 573–577 (2018).
Alvarez-Castelao, B. et al. Cell-type-specific metabolic labeling of nascent proteomes in vivo. Nat. Biotechnol. 35, 1196–1201 (2017).
Alvarez-Castelao, B., Schanzenbächer, C. T., Langer, J. D. & Schuman, E. M. Cell-type-specific metabolic labeling, detection and identification of nascent proteomes in vivo. Nat. Protoc. 14, 556–575 (2019).
Liu, Y. et al. Application of bio-orthogonal proteome labeling to cell transplantation and heterochronic parabiosis. Nat. Commun. 8, 643 (2017).
Sadlowski, C. et al. Graphene-based biosensor for on-chip detection of bio-orthogonally labeled proteins to identify the circulating biomarkers of aging during heterochronic parabiosis. Lab Chip 18, 3230–3238 (2018).
Bonnevie, E. D. et al. Aberrant mechanosensing in injured intervertebral discs as a result of boundary-constraint disruption and residual-strain loss. Nat. Biomed. Eng. 3, 998–1008 (2019).
Loebel, C., Mauck, R. L. & Burdick, J. A. Local nascent protein deposition and remodelling guide mesenchymal stromal cell mechanosensing and fate in three-dimensional hydrogels. Nat. Mater. 18, 883–891 (2019).
McLeod, C. M. & Mauck, R. L. High fidelity visualization of cell-to-cell variation and temporal dynamics in nascent extracellular matrix formation. Sci. Rep. 6, 38852 (2016).
Saleh, A. M., Jacobson, K. R., Kinzer-Ursem, T. L. & Calve, S. Dynamics of non-canonical amino acid-labeled intra- and extracellular proteins in the developing mouse. Cell Mol. Bioeng. 12, 495–509 (2019).
Calve, S., Witten, A. J., Ocken, A. R. & Kinzer-Ursem, T. L. Incorporation of non-canonical amino acids into the developing murine proteome. Sci. Rep. 6, 32377 (2016).
Loebel, C. et al. Metabolic labeling to probe the spatiotemporal accumulation of matrix at the chondrocyte–hydrogel interface. Adv. Funct. Mater. 30, 1909802 (2020).
Tan, A. R. & Hung, C. T. Concise review: mesenchymal stem cells for functional cartilage tissue engineering: taking cues from chondrocyte-based constructs. Stem Cells Transl. Med. 6, 1295–1303 (2017).
Chen, F. H., Rousche, K. T. & Tuan, R. S. Technology Insight: adult stem cells in cartilage regeneration and tissue engineering. Nat. Clin. Pract. Rheumatol. 2, 373–382 (2006).
Vega, S. L., Kwon, M. Y. & Burdick, J. A. Recent advances in hydrogels for cartilage tissue engineering. Eur. Cell Mater. 33, 59–75 (2017).
Eslahi, N., Abdorahim, M. & Simchi, A. Smart polymeric hydrogels for cartilage tissue engineering: a review on the chemistry and biological functions. Biomacromolecules 17, 3441–3463 (2016).
Huebsch, N. Translational mechanobiology: designing synthetic hydrogel matrices for improved in vitro models and cell-based therapies. Acta Biomater. 94, 97–111 (2019).
Caliari, S. R., Vega, S. L., Kwon, M., Soulas, E. M. & Burdick, J. A. Dimensionality and spreading influence MSC YAP/TAZ signaling in hydrogel environments. Biomaterials 103, 314–323 (2016).
Yang, B. et al. Enhanced mechanosensing of cells in synthetic 3D matrix with controlled biophysical dynamics. Nat. Commun. 12, 3514 (2021).
Uitto, J. Biochemistry of the elastic fibers in normal connective tissues and its alterations in diseases. J. Invest. Dermatol. 72, 1–10 (1979).
Yuet, K. P. et al. Cell-specific proteomic analysis in Caenorhabditis elegans. Proc. Natl Acad. Sci. USA 112, 2705–2710 (2015).
Datta, D., Wang, P., Carrico, I. S., Mayo, S. L. & Tirrell, D. A. A designed phenylalanyl-tRNA synthetase variant allows efficient in vivo incorporation of aryl ketone functionality into proteins. J. Am. Chem. Soc. 124, 5652–5653 (2002).
Dunham, C., Havlioglu, N., Chamberlain, A., Lake, S. & Meyer, G. Adipose stem cells exhibit mechanical memory and reduce fibrotic contracture in a rat elbow injury model. FASEB J. 34, 12976–12990 (2020).
Jowett, G. M. et al. ILC1 drive intestinal epithelial and matrix remodelling. Nat. Mater. 20, 250–259 (2021).
Norman, M. D. A., Ferreira, S. A., Jowett, G. M., Bozec, L. & Gentleman, E. Measuring the elastic modulus of soft culture surfaces and three-dimensional hydrogels using atomic force microscopy. Nat. Protoc. 16, 2418–2449 (2021).
Naba, A. et al. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell Proteom. 11, M111.014647 (2012).
Gramlich, W. M., Kim, I. L. & Burdick, J. A. Synthesis and orthogonal photopatterning of hyaluronic acid hydrogels with thiol-norbornene chemistry. Biomaterials 34, 9803–9811 (2013).
Shih, H. & Lin, C.-C. Cross-linking and degradation of step-growth hydrogels formed by thiol–ene photoclick chemistry. Biomacromolecules 13, 2003–2012 (2012).
Aimetti, A. A., Machen, A. J. & Anseth, K. S. Poly(ethylene glycol) hydrogels formed by thiol-ene photopolymerization for enzyme-responsive protein delivery. Biomaterials 30, 6048–6054 (2009).
Mũnoz, Z., Shih, H. & Lin, C.-C. Gelatin hydrogels formed by orthogonal thiol–norbornene photochemistry for cell encapsulation. Biomater. Sci. 2, 1063–1072 (2014).
Loebel, C. et al. Cross-linking chemistry of tyramine-modified hyaluronan hydrogels alters mesenchymal stem cell early attachment and behavior. Biomacromolecules 18, 855–864 (2017).
Vega, S. L. et al. Combinatorial hydrogels with biochemical gradients for screening 3D cellular microenvironments. Nat. Commun. 9, 614 (2018).
Cosgrove, B. D. et al. N-cadherin adhesive interactions modulate matrix mechanosensing and fate commitment of mesenchymal stem cells. Nat. Mater. 15, 1297–1306 (2016).
Doube, M. et al. BoneJ: free and extensible bone image analysis in ImageJ. Bone 47, 1076–1079 (2010).
Mauck, R. L., Yuan, X. & Tuan, R. S. Chondrogenic differentiation and functional maturation of bovine mesenchymal stem cells in long-term agarose culture. Osteoarthr. Cartil. 14, 179–189 (2006).
Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).
Perkins, D. N., Pappin, D. J., Creasy, D. M. & Cottrell, J. S. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis 20, 3551–3567 (1999).
Eng, J. K., McCormack, A. L. & Yates, J. R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Am. Soc. Mass Spectrom. 5, 976–989 (1994).
Catterall, J. B. et al. Protein modification by deamidation indicates variations in joint extracellular matrix turnover. J. Biol. Chem. 287, 4640–4651 (2012).
Naba, A. et al. Characterization of the extracellular matrix of normal and diseased tissues using proteomics. J. Proteome Res. 16, 3083–3091 (2017).
Ashburner, M. et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25–29 (2000).
Jonkman, J., Brown, C. M., Wright, G. D., Anderson, K. I. & North, A. J. Tutorial: guidance for quantitative confocal microscopy. Nat. Protoc. https://doi.org/10.1038/s41596-020-0313-9 (2020).

Metabolic labeling of secreted matrix to investigate cell–material interactions in tissue engineering and mechanobiology

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