Skip to main content
Log in

Dynamics of Non-Canonical Amino Acid-Labeled Intra- and Extracellular Proteins in the Developing Mouse

  • Published:
Cellular and Molecular Bioengineering Aims and scope Submit manuscript

Abstract

Introduction

Mapping protein synthesis and turnover during development will provide insight into functional tissue assembly; however, quantitative in vivo characterization has been hindered by a lack of tools. To address this gap, we previously demonstrated murine embryos can be labeled with the non-canonical amino acid azidohomoalanine (Aha), which enables the enrichment and identification of newly synthesized proteins. Using this technique, we now show how protein turnover varies as a function of both time and cellular compartment during murine development.

Methods

Pregnant C57BL/6 mice were injected with Aha or PBS (control) at different embryonic time points. Aha-labeled proteins from homogenized E12.5 and E15.5 embryos were conjugated with diazo biotin-alkyne, bound to NeutrAvidin beads, selectively released, then processed for either SDS-PAGE or LC–MS/MS. For turnover studies, embryos were harvested 0–48 h after Aha injection at E12.5, separated into different cellular fractions based on solubility, and analyzed via western blotting.

Results

We developed an enhanced method for isolating Aha-labeled proteins from embryos that minimizes background signal from unlabeled proteins and avidin contamination. Approximately 50% of all identified proteins were found only in Aha samples. Comparing proteins present in both Aha and PBS samples, 90% were > 2-fold enriched in Aha-treated embryos. Furthermore, this method could resolve differences in the Aha-labeled proteome between developmental time points. Newly synthesized Aha-labeled proteins were observed by 3 h and peak labeling was around 6 h. Notably, extracellular matrix and cytoskeletal turnover appeared lower than the cytosolic fraction.

Conclusions

The methods developed in this work enable the identification and quantification of protein synthesis and turnover in different tissue fractions during development. This will provide insight into functional tissue assembly and ultimately inform the design of regenerative therapies that seek to promote growth and repair.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5

Similar content being viewed by others

Abbreviations

ACN:

Acetonitrile

AG:

Aminoguanidine

Aha:

Azidohomoalanine

C:

Cytosolic

CS:

Cytoskeletal

CuAAC:

Copper(I)-catalyzed azide-alkyne cycloaddition

DBA:

Diazo biotin-alkyne

DTT:

Dithiothreitol

ECM:

Extracellular matrix

FA:

Formic acid

FDR:

False discovery rate

GO:

Gene ontology

HPLC:

High performance liquid chromatography

LC–MS/MS:

Liquid chromatography–tandem mass spectrometry

LFQ:

Label-free quantification

M:

Membrane

Met:

Methionine

MS:

Mass spectrometry

N:

Nuclear

Na2S2O4 :

Sodium dithionite

NaAsc:

Sodium ascorbate

ncAA:

Non-canonical amino acid

QE HF:

Q exactive HF hybrid quadrupole-orbitrap mass spectrometer

SDS:

Sodium dodecyl sulfate

SDS-PAGE:

Sodium dodecyl sulfate-polyacrylamide gel electrophoresis

SILAC:

Stable isotope labeling by amino acids in cell culture

TBS:

Tris-buffered saline

TFA:

Trifluoroacetic acid

THPTA:

Tris(3-hydroxypropyltriazolylmethyl)amine

References

  1. Alvarez-Castelao, B., C. T. Schanzenbacher, C. Hanus, C. Glock, S. Tom Dieck, A. R. Dorrbaum, I. Bartnik, B. Nassim-Assir, E. Ciirdaeva, A. Mueller, D. C. Dieterich, D. A. Tirrell, J. D. Langer, and E. M. Schuman. Cell-type-specific metabolic labeling of nascent proteomes in vivo. Nat. Biotechnol. 35:1196–1201, 2017.

    Article  Google Scholar 

  2. Ashburner, M., C. A. Ball, J. A. Blake, D. Botstein, H. Butler, J. M. Cherry, A. P. Davis, K. Dolinski, S. S. Dwight, J. T. Eppig, M. A. Harris, D. P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J. C. Matese, J. E. Richardson, M. Ringwald, G. M. Rubin, and G. Sherlock. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat. Genet. 25:25–29, 2000.

    Article  Google Scholar 

  3. Bagert, J. D., Y. J. Xie, M. J. Sweredoski, Y. Qi, S. Hess, E. M. Schuman, and D. A. Tirrell. Quantitative, time-resolved proteomic analysis by combining bioorthogonal noncanonical amino acid tagging and pulsed stable isotope labeling by amino acids in cell culture. Mol. Cell. Proteomics 13:1352–1358, 2014.

    Article  Google Scholar 

  4. Birk, D. E., and P. Brückner. The Extracellular Matrix: An Overview. Berlin: Springer, pp. 77–115, 2011.

    Book  Google Scholar 

  5. Borrmann, A., and J. C. M. van Hest. Bioorthogonal chemistry in living organisms. Chem. Sci. 5:2123–2134, 2014.

    Article  Google Scholar 

  6. Brown, N. H. Extracellular matrix in development: insights from mechanisms conserved between invertebrates and vertebrates. Cold Spring Perspect. Biol. 3(12):a005082, 2011.

    Google Scholar 

  7. Buckingham, M., L. Bajard, T. Chang, P. Daubas, J. Hadchouel, S. Meilhac, D. Montarras, D. Rocancourt, and F. Relaix. The formation of skeletal muscle: from somite to limb. J. Anat. 202:59–68, 2003.

    Article  Google Scholar 

  8. Calve, S., A. J. Witten, A. R. Ocken, and T. L. Kinzer-Ursem. Incorporation of non-canonical amino acids into the developing murine proteome. Sci. Rep. 6:32377, 2016.

    Article  Google Scholar 

  9. Casas-Vila, N., A. Bluhm, S. Sayols, N. Dinges, M. Dejung, T. Altenhein, D. Kappei, B. Altenhein, J. Y. Roignant, and F. Butter. The developmental proteome of Drosophila melanogaster. Genome Res. 27:1273–1285, 2017.

    Article  Google Scholar 

  10. Chen, X., S. Wei, Y. Ji, X. Guo, and F. Yang. Quantitative proteomics using SILAC: principles, applications, and developments. Proteomics 15:3175–3192, 2015.

    Article  Google Scholar 

  11. Cox, J. Mann M (2008) MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26:1367–1372, 2008.

    Article  Google Scholar 

  12. Decaris, M. L., M. Gatmaitan, S. FlorCruz, F. Luo, K. Li, W. E. Holmes, M. K. Hellerstein, S. M. Turner, and C. L. Emson. Proteomic analysis of altered extracellular matrix turnover in bleomycin-induced pulmonary fibrosis. Mol. Cell. Proteomics 13:1741–1752, 2014.

    Article  Google Scholar 

  13. Dekker, C., P. C. Stirling, E. A. McCormack, H. Filmore, A. Paul, R. L. Brost, M. Costanzo, C. Boone, M. R. Leroux, and K. R. Willison. The interaction network of the chaperonin CCT. EMBO J. 27:1827–1839, 2008.

    Article  Google Scholar 

  14. Didangelos, A., X. Yin, K. Mandal, M. Baumert, M. Jahangiri, and M. Mayr. Proteomics characterization of extracellular space components in the human aorta. Mol. Cell Proteomics 9:2048–2062, 2010.

    Article  Google Scholar 

  15. Dieterich, D. C., J. J. Hodas, G. Gouzer, I. Y. Shadrin, J. T. Ngo, A. Triller, D. A. Tirrell, and E. M. Schuman. In situ visualization and dynamics of newly synthesized proteins in rat hippocampal neurons. Nat. Neurosci. 13:897–905, 2010.

    Article  Google Scholar 

  16. Dieterich, D. C., J. J. Lee, A. J. Link, J. Graumann, D. A. Tirrell, and E. M. Schuman. Labeling, detection and identification of newly synthesized proteomes with bioorthogonal non-canonical amino-acid tagging. Nat. Protoc. 2:532–540, 2007.

    Article  Google Scholar 

  17. Dieterich, D. C., A. J. Link, J. Graumann, D. A. Tirrell, and E. M. Schuman. 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.

    Article  Google Scholar 

  18. Dunn, J. A., M. U. Ahmed, M. H. Murtiashaw, J. M. Richardson, M. D. Walla, S. R. Thorpe, and J. W. Baynes. Reaction of ascorbate with lysine and protein under autoxidizing conditions: formation of N epsilon-(carboxymethyl)lysine by reaction between lysine and products of autoxidation of ascorbate. Biochemistry 29:10964–10970, 1990.

    Article  Google Scholar 

  19. Fabregat, A., S. Jupe, L. Matthews, K. Sidiropoulos, M. Gillespie, P. Garapati, R. Haw, B. Jassal, F. Korninger, B. May, M. Milacic, C. D. Roca, K. Rothfels, C. Sevilla, V. Shamovsky, S. Shorser, T. Varusai, G. Viteri, J. Weiser, G. Wu, L. Stein, H. Hermjakob, and P. D’Eustachio. The reactome pathway knowledgebase. Nucleic Acids Res. 46:D649–D655, 2018.

    Article  Google Scholar 

  20. Fornasiero, E. F., S. Mandad, H. Wildhagen, M. Alevra, B. Rammner, S. Keihani, F. Opazo, I. Urban, T. Ischebeck, M. S. Sakib, M. K. Fard, K. Kirli, T. P. Centeno, R. O. Vidal, R. U. Rahman, E. Benito, A. Fischer, S. Dennerlein, P. Rehling, I. Feussner, S. Bonn, M. Simons, H. Urlaub, and S. O. Rizzoli. Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions. Nat. Commun. 9:4230, 2018.

    Article  Google Scholar 

  21. Frydman, J. Folding of newly translated proteins in vivo: the role of molecular chaperones. Annu. Rev. Biochem. 70:603–647, 2001.

    Article  Google Scholar 

  22. Geiger, T., A. Velic, B. Macek, E. Lundberg, C. Kampf, N. Nagaraj, M. Uhlen, J. Cox, and M. Mann. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol. Cell. Proteomics 12:1709–1722, 2013.

    Article  Google Scholar 

  23. Hill, R. C., E. A. Calle, M. Dzieciatkowska, L. E. Niklason, and K. C. Hansen. Quantification of extracellular matrix proteins from a rat lung scaffold to provide a molecular readout for tissue engineering. Mol. Cell. Proteomics 14:961–973, 2015.

    Article  Google Scholar 

  24. Hong, V., S. I. Presolski, C. Ma, and M. G. Finn. Analysis and optimization of copper-catalyzed azide-alkyne cycloaddition for bioconjugation. Angew. Chem. Int. Ed. Engl. 48:9879–9883, 2009.

    Article  Google Scholar 

  25. Kessels, M. Y., L. F. Huitema, S. Boeren, S. Kranenbarg, S. Schulte-Merker, J. L. van Leeuwen, and S. C. de Vries. Proteomics analysis of the zebrafish skeletal extracellular matrix. PLoS ONE 9:e90568, 2014.

    Article  Google Scholar 

  26. Kiick, K. L., E. Saxon, D. A. Tirrell, and C. R. Bertozzi. Incorporation of azides into recombinant proteins for chemoselective modification by the Staudinger ligation. Proc. Natl. Acad. Sci. USA 99:19–24, 2002.

    Article  Google Scholar 

  27. Kiick, K. L., R. Weberskirch, and D. A. Tirrell. Identification of an expanded set of translationally active methionine analogues in Escherichia coli. FEBS Lett. 502:25–30, 2001.

    Article  Google Scholar 

  28. Kim, A. R., and K. W. Choi. TRiC/CCT chaperonins are essential for organ growth by interacting with insulin/TOR signaling in Drosophila. Oncogene 38(24):4739, 2019.

    Article  Google Scholar 

  29. Krogager, T. P., R. J. Ernst, T. S. Elliott, L. Calo, V. Beranek, E. Ciabatti, M. G. Spillantini, M. Tripodi, M. H. Hastings, and J. W. Chin. Labeling and identifying cell-specific proteomes in the mouse brain. Nat. Biotechnol. 36:156–159, 2018.

    Article  Google Scholar 

  30. Kuhlbrandt, W. Structure and function of mitochondrial membrane protein complexes. BMC Biol. 13:89, 2015.

    Article  Google Scholar 

  31. Li, X., C. Zhang, T. Gong, X. Ni, J. Li, D. Zhan, M. Liu, L. Song, C. Ding, J. Xu, B. Zhen, Y. Wang, and J. Qin. A time-resolved multi-omic atlas of the developing mouse stomach. Nat. Commun. 9:4910, 2018.

    Article  Google Scholar 

  32. Lucitt, M. B., T. S. Price, A. Pizarro, W. Wu, A. K. Yocum, C. Seiler, M. A. Pack, I. A. Blair, G. A. Fitzgerald, and T. Grosser. Analysis of the zebrafish proteome during embryonic development. Mol. Cell. Proteomics 7:981–994, 2008.

    Article  Google Scholar 

  33. Lyons, G. E., M. Ontell, R. Cox, D. Sassoon, and M. Buckingham. The expression of myosin genes in developing skeletal muscle in the mouse embryo. J. Cell. Biol. 111:1465–1476, 1990.

    Article  Google Scholar 

  34. Martin-Rivera, L., E. Herrera, J. P. Albar, and M. A. Blasco. Expression of mouse telomerase catalytic subunit in embryos and adult tissues. Proc. Natl. Acad. Sci USA 95:10471–10476, 1998.

    Article  Google Scholar 

  35. McClatchy, D. B., M. Q. Dong, C. C. Wu, J. D. Venable, and J. R. Yates, 3rd. 15 N metabolic labeling of mammalian tissue with slow protein turnover. J. Proteome Res. 6:2005–2010, 2007.

    Article  Google Scholar 

  36. McClatchy, D. B., Y. Ma, C. Liu, B. D. Stein, S. Martinez-Bartolome, D. Vasquez, K. Hellberg, R. J. Shaw, and J. R. Yates, 3rd. Pulsed azidohomoalanine labeling in mammals (PALM) detects changes in liver-specific LKB1 knockout mice. J. Proteome Res. 14:4815–4822, 2015.

    Article  Google Scholar 

  37. Munger, S. C., A. Natarajan, L. L. Looger, U. Ohler, and B. Capel. Fine time course expression analysis identifies cascades of activation and repression and maps a putative regulator of mammalian sex determination. PLoS Genet. 9:e1003630, 2013.

    Article  Google Scholar 

  38. Naba, A., K. R. Clauser, S. Hoersch, H. Liu, S. A. Carr, and R. O. Hynes. The matrisome: in silico definition and in vivo characterization by proteomics of normal and tumor extracellular matrices. Mol. Cell. Proteomics 11:M111-014647, 2012.

    Article  Google Scholar 

  39. Naba, A., K. R. Clauser, and R. O. Hynes. Enrichment of extracellular matrix proteins from tissues and digestion into peptides for mass spectrometry analysis. JoVE 101:e53057, 2015.

    Google Scholar 

  40. Paulo, J. A. Sample preparation for proteomic analysis using a GeLC-MS/MS strategy. J. Biol. Methods 3:e45, 2016.

    Article  Google Scholar 

  41. Reihl, O., M. O. Lederer, and W. Schwack. Characterization and detection of lysine-arginine cross-links derived from dehydroascorbic acid. Carbohydr. Res. 339:483–491, 2004.

    Article  Google Scholar 

  42. Reimand, J., T. Arak, P. Adler, L. Kolberg, S. Reisberg, H. Peterson, and J. Vilo. g:Profiler-a web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 44:W83–89, 2016.

    Article  Google Scholar 

  43. Rostovtsev, V. V., L. G. Green, V. V. Fokin, and K. B. Sharpless. A stepwise huisgen cycloaddition process: copper(I)-catalyzed regioselective “ligation” of azides and terminal alkynes. Angew. Chem. Int. Ed. Engl. 41:2596–2599, 2002.

    Article  Google Scholar 

  44. Rozario, T., and D. W. DeSimone. The extracellular matrix in development and morphogenesis: a dynamic view. Dev. Biol. 341:126–140, 2010.

    Article  Google Scholar 

  45. Rupik, W., K. Jasik, J. Bembenek, and W. Widłak. The expression patterns of heat shock genes and proteins and their role during vertebrate’s development. Comp. Biochem. Phys. A 159:349–366, 2011.

    Article  Google Scholar 

  46. Saleh, A. M., K. M. Wilding, S. Calve, B. C. Bundy, and T. L. Kinzer-Ursem. Non-canonical amino acid labeling in proteomics and biotechnology. J. Biol. Eng. 13:43, 2019.

    Article  Google Scholar 

  47. Schiller, H. B., I. E. Fernandez, G. Burgstaller, C. Schaab, R. A. Scheltema, T. Schwarzmayr, T. M. Strom, O. Eickelberg, and M. Mann. Time- and compartment-resolved proteome profiling of the extracellular niche in lung injury and repair. Mol. Syst. Biol. 11:819, 2015.

    Article  Google Scholar 

  48. Siedner, S., M. Kruger, M. Schroeter, D. Metzler, W. Roell, B. K. Fleischmann, J. Hescheler, G. Pfitzer, and R. Stehle. Developmental changes in contractility and sarcomeric proteins from the early embryonic to the adult stage in the mouse heart. J. Physiol. 548:493–505, 2003.

    Article  Google Scholar 

  49. Sun, L., M. M. Bertke, M. M. Champion, G. Zhu, P. W. Huber, and N. J. Dovichi. Quantitative proteomics of Xenopus laevis embryos: expression kinetics of nearly 4000 proteins during early development. Sci. Rep. 4:4365, 2014.

    Article  Google Scholar 

  50. Sweeney, H. L., and D. W. Hammers. Muscle contraction. Cold Spring Perspect. Biol. 10(2):a023200, 2018.

    Article  Google Scholar 

  51. Varela, E., R. P. Schneider, S. Ortega, and M. A. Blasco. Different telomere-length dynamics at the inner cell mass versus established embryonic stem (ES) cells. Proc. Natl. Acad. Sci. USA 108:15207–15212, 2011.

    Article  Google Scholar 

  52. Warren, C. M., D. L. Geenen, D. L. Helseth, Jr, H. Xu, and R. J. Solaro. Sub-proteomic fractionation, iTRAQ, and OFFGEL-LC-MS/MS approaches to cardiac proteomics. J. Proteomics 73:1551–1561, 2010.

    Article  Google Scholar 

  53. Williams, C., K. P. Quinn, I. Georgakoudi, and L. D. Black, 3rd. Young developmental age cardiac extracellular matrix promotes the expansion of neonatal cardiomyocytes in vitro. Acta Biomater. 10:194–204, 2014.

    Article  Google Scholar 

  54. Yang, Y. Y., M. Grammel, A. S. Raghavan, G. Charron, and H. C. Hang. Comparative analysis of cleavable azobenzene-based affinity tags for bioorthogonal chemical proteomics. Chem. Biol. 17:1212–1222, 2010.

    Article  Google Scholar 

  55. Ying, S., A. Shiraishi, C. W. Kao, R. L. Converse, J. L. Funderburgh, J. Swiergiel, M. R. Roth, G. W. Conrad, and W. W. Kao. Characterization and expression of the mouse lumican gene. J. Biol. Chem. 272:30306–30313, 1997.

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Victoria Hedrick and Uma Ayal at the Purdue Proteomics Core. This work was supported by the National Institutes of Health [R21 AR069248, R01 AR071359 and DP2 AT009833 to S.C.] The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH. This work was also supported by the National Science Foundation [CAREER 1752366 to TKU]. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Conflict of interest

Authors Aya Saleh, Kathryn Jacobson, Tamara Kinzer-Ursem, and Sarah Calve declare that they have no conflicts of interest.

Ethical Standards

All experimental protocols involving animals were performed in compliance with established guidelines and all methods were approved by Purdue Animal Care and Use Committee (PACUC, protocols# 1209000723 and 1801001682). PACUC requires that all animal programs, procedures, and facilities at Purdue University to abide by the policies, recommendations, guidelines, and regulations of the USDA and the United States Public Health Service in accordance with the Animal Welfare Act and Purdue’s Animal Welfare Assurance. Additionally, no human subjects research was conducted in this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sarah Calve.

Additional information

Associate Editor Stephanie Michelle Willerth oversaw the review of this article.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Sarah Calve received her B.S. in Materials Science and Engineering from Cornell University, then a M.S. in Molecular, Cellular and Developmental Biology and a Ph.D. in Macromolecular Science and Engineering from the University of Michigan. Her doctoral research focused on the design and mechanical characterization of self-assembling constructs for musculoskeletal repair, under the guidance of Prof. Ellen Arruda. As a postdoctoral fellow at Northwestern University’s Children’s Memorial Hospital, Sarah investigated the role of extracellular matrix remodeling during newt limb regeneration. Sarah joined the Weldon School of Biomedical Engineering at Purdue University as an assistant professor in 2012. Her research group, the Musculoskeletal Extracellular Matrix Laboratory, is actively developing tools to quantify how the composition, turnover, organization and mechanical properties of the musculoskeletal system change during scar-free tissue assembly. The goal is to use these tools to elucidate how different components integrate to form functional tissues during normal development and identify parameters that will guide the design of regenerative therapies. In 2017, she became the first Purdue professor to receive the NIH Director’s New Innovator Award. Additional recognitions include the BMES-CMBE Rising Star Junior Faculty Award (2018), inclusion in the National Academy of Engineering, Japan–America Frontiers of Engineering Symposium (2018) and being named the Leslie A. Geddes Assistant Professor of Biomedical Engineering at Purdue (2019).

This article is part of the CMBE 2019 Young Innovators special issue.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Figure S1.

Enrichment of Aha-labeled proteins using biotin-alkyne. (a) Western blot analysis of E15.5 embryos harvested from dams injected with 0.1 mg/g Aha or PBS. Soluble lysates (lysate) were reacted with biotin-alkyne using CuAAC (clicked) and isolated from unlabeled proteins using NeutrAvidin beads (2 mg protein was combined with 100 µL beads). Removal of unreacted biotin-alkyne via desalting columns (desalted) did not substantially affect total protein concentration. These conditions isolated the majority of Aha-labeled proteins from the lysates as indicated by minimal signal in the unbound lane. Aha-labeled proteins were eluted using 2 or 4% SDS (lanes 5 and 6 for PBS, lanes 11 and 12 for Aha, respectively). (b) LC–MS/MS analysis of the eluted proteins revealed a high percentage of avidin contamination. Average raw intensity of n = 2 embryos. (c) Non-specific binding of unlabeled proteins due to CuAAC. Control E15.5 embryos from PBS-injected dams were reacted using CuAAC (click) with diazo biotin-alkyne (DBA) and incubated with NeutrAvidin beads. Substantially more proteins were eluted off the beads when the CuAAC and DBA were present in the reaction (lane 2). Supplementary material 1 (EPS 3251 kb)

Figure S2. (a)

Chemical species used for CuAAC. (1) Aminoguanidine (AG); (2) tris(3-hydroxypropyltriazolylmethyl)amine (THPTA); (3) Diazo biotin-alkyne (DAB). (b) The diazobenzene group of DAB is cleaved by Na2S2O4 under neutral pH conditions. Supplementary material 2 (EPS 1215 kb)

Figure S3.

Summary of Aha-enriched proteins found at E12.5 (left) and E15.5 (right). (a) Scatter plot data from Fig. 2 separated by time point. (b) Heat map comparisons of Pearson Correlation coefficients indicated a higher degree of correlation between samples within the Aha and PBS groups. (c) Distribution of raw intensity of proteins found only in Aha as a function of cellular compartment, average of n = 3 biological replicates. Note the absence of Avidin (see also Fig. S1b). Supplementary material 3 (EPS 1388 kb)

Figure S4.

Representative westerns for all 5 fractions analyzed for Aha-labeling study in Fig. 3. Supplementary material 4 (EPS 31123 kb)

Supplementary material 5 (XLSX 23 kb)

Supplementary material 6 (XLSX 9 kb)

Supplementary material 7 (XLSX 762 kb)

Supplementary material 8 (XLSX 882 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saleh, A.M., Jacobson, K.R., Kinzer-Ursem, T.L. et al. Dynamics of Non-Canonical Amino Acid-Labeled Intra- and Extracellular Proteins in the Developing Mouse. Cel. Mol. Bioeng. 12, 495–509 (2019). https://doi.org/10.1007/s12195-019-00592-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12195-019-00592-1

Keywords

Navigation