The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Selective enrichment of newly synthesized proteins for quantitative secretome analysis
Abstract
Secreted proteins constitute a large and biologically important subset of proteins that are involved in cellular communication, adhesion and migration. Yet secretomes are understudied because of technical limitations in the detection of low-abundance proteins against a background of serum-containing media. Here we introduce a method that combines click chemistry and pulsed stable isotope labeling with amino acids in cell culture to selectively enrich and quantify secreted proteins. The combination of these two labeling approaches allows cells to be studied irrespective of the complexity of the background proteins. We provide an in-depth and differential secretome analysis of various cell lines and primary cells, quantifying secreted factors, including cytokines, chemokines and growth factors. In addition, we reveal that serum starvation has a marked effect on secretome composition. We also analyze the kinetics of protein secretion by macrophages in response to lipopolysaccharides.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“the increase in the calculated ‘degradation rate’ – likely reflecting depletion by secretion, or “decreased cellular half life” – of proteins from the recently-characterized secretome of LPS stimulated mouse macrophages (33) (P < 10−10, LPS vs. MOCK; Wilcoxon rank-sum Test, fig. S9C).”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [33]
In the citing paper
“Although some of this is due to turnover from increased secretion of some proteins (figs. S9C, S18), excluding the secretome (33) from our analysis did not strongly alter this global trend (fig. S18).”
From Dynamic profiling of the protein life cycle in response to pathogens (2015) · cites this paper as [33]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003) · Genome Research · doi:10.1101/gr.1239303 · Cited by 51,888
Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Cox, J., & Mann, M. (2008) · Nature Biotechnology · doi:10.1038/nbt.1511 · Cited by 14,855
Bioconductor: open software development for computational biology and bioinformatics
Gentleman, R. C., Carey, V. J., Bates, D. M., Bolstad, B., Dettling, M., Dudoit, S., Ellis, B., Gautier, L., Ge, Y., Gentry, J., et al. (2004) · Genome biology · doi:10.1186/gb-2004-5-10-r80 · Cited by 12,082
Smyth, G. K. (2004) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1027 · Cited by 11,829
SignalP 4.0: discriminating signal peptides from transmembrane regions
Petersen, T. N., Brunak, S., von Heijne, G., & Nielsen, H. (2011) · Nature Methods · doi:10.1038/nmeth.1701 · Cited by 8,847
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment
Cox, J., Neuhauser, N., Michalski, A., Scheltema, R. A., Olsen, J. V., & Mann, M. (2011) · Journal of Proteome Research · doi:10.1021/pr101065j · Cited by 5,520
Anderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414
Widespread changes in protein synthesis induced by microRNAs
Selbach, M., Schwanhäusser, B., Thierfelder, N., Fang, Z., Khanin, R., & Rajewsky, N. (2008) · Nature · doi:10.1038/nature07228 · Cited by 3,429
Cited by
Sorted by citations
mRNAs, proteins and the emerging principles of gene expression control
Buccitelli, C., & Selbach, M. (2020) · Nature Reviews Genetics · doi:10.1038/s41576-020-0258-4 · Cited by 1,243
Cellular Incorporation of Unnatural Amino Acids and Bioorthogonal Labeling of Proteins
Lang, K., & Chin, J. W. (2014) · Chemical Reviews · doi:10.1021/cr400355w · Cited by 1,051
Dynamic profiling of the protein life cycle in response to pathogens
Jovanovic, M., Rooney, M. S., Mertins, P., Przybylski, D., Chevrier, N., Satija, R., Rodriguez, E. H., Fields, A. P., Schwartz, S., Raychowdhury, R., et al. (2015) · Science · doi:10.1126/science.1259038 · Cited by 460
Protein Aggregation Capture on Microparticles Enables Multipurpose Proteomics Sample Preparation*
Batth, T. S., Tollenaere, M. A. X., Rüther, P. L., González-Franquesa, A., Prabhakar, B. S., Bekker‐Jensen, S., Deshmukh, A. S., & Olsen, J. V. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir118.001270 · Cited by 432
The Human RNA-Binding Proteome and Its Dynamics during Translational Arrest
Trendel, J., Schwarzl, T., Horos, R., Prakash, A., Bateman, A. G., Hentze, M. W., & Krijgsveld, J. (2018) · Cell · doi:10.1016/j.cell.2018.11.004 · Cited by 408
Self-citationThe stem cell secretome and its role in brain repair
Drago, D., Cossetti, C., Iraci, N., Gaude, E., Musco, G., Bachi, A., & Pluchino, S. (2013) · Biochimie · doi:10.1016/j.biochi.2013.06.020 · Cited by 354
Chemical reporters for biological discovery
Grammel, M., & Hang, H. C. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1296 · Cited by 351
Lichtenthaler, S. F., Lemberg, M. K., & Fluhrer, R. (2018) · The EMBO Journal · doi:10.15252/embj.201899456 · Cited by 323
Bioanalytical Chemistry of Cytokines–a Review
Stenken, J. A., & Poschenrieder, A. J. (2015) · Analytica Chimica Acta · doi:10.1016/j.aca.2014.10.009 · Cited by 302
Kinetic Analysis of Protein Stability Reveals Age-Dependent Degradation
McShane, E., Sin, C., Zauber, H., Wells, J. N., Donnelly, N., Wang, X., Hou, J., Chen, W., Štorchová, Z., Marsh, J. A., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.015 · Cited by 301
Theivanthiran, B., Evans, K. S., DeVito, N. C., Plebanek, M. P., Sturdivant, M., Wachsmuth, L. P., Salama, A. K. S., Kang, Y., Hsu, D. S., Balko, J. M., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci133055 · Cited by 255
Hatzenpichler, R., Scheller, S., Tavormina, P. L., Babin, B. M., Tirrell, D. A., & Orphan, V. J. (2014) · Environmental Microbiology · doi:10.1111/1462-2920.12436 · Cited by 236
Cell-type-specific metabolic labeling of nascent proteomes in vivo
Alvarez-Castelao, B., Schanzenbächer, C. T., Hanus, C., Glock, C., tom Dieck, S., Dörrbaum, A. R., Bartnik, I., Nassim-Assir, B., Ciirdaeva, E., Mueller, A., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.4016 · Cited by 219
QuaNCAT: quantitating proteome dynamics in primary cells
Howden, A. J. M., Geoghegan, V., Katsch, K., Efstathiou, G., Bhushan, B., Boutureira, O., Thomas, B. C., Trudgian, D. C., Kessler, B. M., Dieterich, D. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2401 · Cited by 162
Proteome Turnover in the Spotlight: Approaches, Applications, and Perspectives
Ross, A. B., Langer, J. D., & Jovanović, M. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r120.002190 · Cited by 156
Membrane trafficking in health and disease
Yarwood, R., Hellicar, J., Woodman, P. G., & Lowe, M. (2020) · Disease Models & Mechanisms · doi:10.1242/dmm.043448 · Cited by 161
Cell type-selective secretome profiling in vivo
Wei, W., Riley, N. M., Yang, A. C., Kim, J. T., Terrell, S. M., Li, V. L., García-Contreras, M., Bertozzi, C. R., & Long, J. Z. (2020) · Nature Chemical Biology · doi:10.1038/s41589-020-00698-y · Cited by 141
The regulation of protein translation and its implications for cancer
Song, P., Yang, F., Jin, H., & Wang, X. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-020-00444-9 · Cited by 144
Recent Advances in Glycoproteomic Analysis by Mass Spectrometry
Suttapitugsakul, S., Sun, F., & Wu, R. (2019) · Analytical Chemistry · doi:10.1021/acs.analchem.9b04651 · Cited by 139
Crotti, S., Piccoli, M., Rizzolio, F., Giordano, A., Nitti, D., & Agostini, M. (2016) · Journal of Cellular Physiology · doi:10.1002/jcp.25658 · Cited by 144
A Perspective on Extracellular Vesicles Proteomics
Rosa-Fernandes, L., Bombarda-Rocha, V., Carregari, V. C., Urbani, A., & Palmisano, G. (2017) · Frontiers in Chemistry · doi:10.3389/fchem.2017.00102 · Cited by 132
Cellular Interactions in the Tumor Microenvironment: The Role of Secretome
da Cunha, B. R., Domingos, C., Stefanini, A. C. B., Henrique, T., Polachini, G. M., Castelo‐Branco, P., & Tajara, E. H. (2019) · Journal of Cancer · doi:10.7150/jca.21780 · Cited by 133
Direct Detection of Biotinylated Proteins by Mass Spectrometry
Schiapparelli, L., McClatchy, D. B., Liu, H.-H., Sharma, P., Yates, J. R., & Cline, H. T. (2014) · Journal of Proteome Research · doi:10.1021/pr5002862 · Cited by 127
Stress-primed secretory autophagy promotes extracellular BDNF maturation by enhancing MMP9 secretion
Martinelli, S., Anderzhanova, E. A., Bajaj, T., Wiechmann, S., Dethloff, F., Weckmann, K., Heinz, D. E., Ebert, T., Hartmann, J., Geiger, T. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-24810-5 · Cited by 136
Eichelbaum, K., & Krijgsveld, J. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.030916 · Cited by 112
Self-citationBioinformatics tools for secretome analysis
Caccia, D., Dugo, M., Callari, M., & Bongarzone, I. (2013) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2013.01.039 · Cited by 112
Cell-selective labelling of proteomes in Drosophila melanogaster
Erdmann, I., Marter, K., Kobler, O., Niehues, S., Abele, J., Müller, A., Bussmann, J., Storkebaum, E., Ziv, T., Thomas, U., et al. (2015) · Nature Communications · doi:10.1038/ncomms8521 · Cited by 98
Pauli, C., Liu, Y., Rohde, C., Cui, C., Fijałkowska, D., Gerloff, D., Walter, C., Krijgsveld, J., Dugas, M., Edemir, B., et al. (2020) · Blood · doi:10.1182/blood.2019004121 · Cited by 96
Self-citationBagert, J. D., Xie, Y. J., Sweredoski, M. J., Qi, Y., Hess, S. Y., Schuman, E. M., & Tirrell, D. A. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.031914 · Cited by 89
An optimized quantitative proteomics method establishes the cell type‐resolved mouse brain secretome
Tüshaus, J., Müller, S. A., Kataka, E. S., Zaucha, J., Sebastian Monasor, L., Su, M., Güner, G., Jocher, G., Tahirovic, S., Frishman, D., et al. (2020) · The EMBO Journal · doi:10.15252/embj.2020105693 · Cited by 88
CD4+ T helper 2 cells suppress breast cancer by inducing terminal differentiation
Boieri, M., Malishkevich, A., Guennoun, R., Marchese, E., Kroon, S., Trerice, K. E., Awad, M. E., Park, J. H., Iyer, S., Kreuzer, J., et al. (2022) · The Journal of Experimental Medicine · doi:10.1084/jem.20201963 · Cited by 89
Recent technical advances in proteomics
Yates, J. R. (2019) · F1000Research · doi:10.12688/f1000research.16987.1 · Cited by 85
Kuhn, T. C., Knobel, J., Burkert-Rettenmaier, S., Li, X., Meyer, I. S., Jungmann, A., Sicklinger, F., Backs, J., Lasitschka, F., Müller, O. J., et al. (2020) · Circulation · doi:10.1161/circulationaha.119.044914 · Cited by 84
Self-citationSystems vaccinology and big data in the vaccine development chain
Raeven, R. H. M., van Riet, E., Meiring, H. D., Metz, B., & Kersten, G. F. A. (2018) · Immunology · doi:10.1111/imm.13012 · Cited by 79
Multi-Omics Strategies Uncover Host–Pathogen Interactions
Khan, M. M., Ernst, O., Manes, N. P., Oyler, B. L., Fraser, I. D. C., Goodlett, D. R., & Nita‐Lazar, A. (2019) · ACS Infectious Diseases · doi:10.1021/acsinfecdis.9b00080 · Cited by 83
A Primer on Concepts and Applications of Proteomics in Neuroscience
Hosp, F., & Mann, M. (2017) · Neuron · doi:10.1016/j.neuron.2017.09.025 · Cited by 80
Proteomics beyond large-scale protein expression analysis
Boersema, P. J., Kahraman, A., & Picotti, P. (2015) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2015.01.005 · Cited by 74
Hui, W. W. Y., Hercík, K., Belsare, S., Alugubelly, N., Clapp, B., Rinaldi, C., & Edelmann, M. J. (2017) · Infection and Immunity · doi:10.1128/iai.00386-17 · Cited by 70
A Dynamic rRNA Ribomethylome Drives Stemness in Acute Myeloid Leukemia
Zhou, F., Aroua, N., Liu, Y., Rohde, C., Cheng, J., Wirth, A.-K., Fijałkowska, D., Göllner, S., Lotze, M., Yun, H., et al. (2022) · Cancer Discovery · doi:10.1158/2159-8290.cd-22-0210 · Cited by 81
Self-citationThe human secretome atlas initiative: Implications in health and disease conditions
Brown, K. J., Seol, H., Pillai, D. K., Sankoorikal, B.-J., Formolo, C. A., Mac, J. T., Edwards, N. J., Rose, M. C., & Hathout, Y. (2013) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2013.04.007 · Cited by 73
Shin, J., Rhim, J.-H., Kwon, Y. M., Choi, S. Y., Shin, S., Ha, C., & Lee, C. (2019) · Scientific Reports · doi:10.1038/s41598-019-39650-z · Cited by 67
Schäfer, M., Oeing, C. U., Rohm, M., Baysal-Temel, E., Lehmann, L., Bauer, R., Volz, H. C., Boutros, M., Sohn, D., Sticht, C., et al. (2015) · Molecular Metabolism · doi:10.1016/j.molmet.2015.11.004 · Cited by 66
Self-citationRegulation of gene expression via translational buffering
Kusnadi, E. P., Timpone, C., Topisirović, I., Larsson, O. S., & Furic, L. (2021) · Biochimica et Biophysica Acta (BBA) - Molecular Cell Research · doi:10.1016/j.bbamcr.2021.119140 · Cited by 66
Site-Specific Protein Immobilization Using Unnatural Amino Acids
Raliski, B. K., Howard, C. A., & Young, D. D. (2014) · Bioconjugate Chemistry · doi:10.1021/bc500443h · Cited by 65
Comparative Secretome Analyses of Primary Murine White and Brown Adipocytes Reveal Novel Adipokines
Khan, A. A., Hansson, J., Weber, P., Föehr, S., Krijgsveld, J., Herzig, S., & Scheideler, M. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000704 · Cited by 65
Self-citationMass spectrometry-based plasma proteomics: state of the art and future outlook
Pernemalm, M., & Lehtiö, J. (2014) · Expert Review of Proteomics · doi:10.1586/14789450.2014.901157 · Cited by 65
Grube, L., Dellen, R., Kruse, F., Schwender, H. R., Stühler, K., & Poschmann, G. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00684 · Cited by 61
Proteome and Secretome Characterization of Glioblastoma-Derived Neural Stem Cells
Okawa, S., Gagrica, S., Blin, C., Ender, C., Pollard, S. M., & Krijgsveld, J. (2016) · Stem Cells · doi:10.1002/stem.2542 · Cited by 59
Self-citationWeigert, C., Lehmann, R., Hartwig, S., & Lehr, S. (2013) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201300094 · Cited by 61
Bowling, H. L., Bhattacharya, A., Zhang, G., Lebowitz, J. Z., Alam, D. H., Smith, P. T., Kirshenbaum, K., Neubert, T. A., Vogel, C., Chao, M. V., et al. (2015) · Neuropharmacology · doi:10.1016/j.neuropharm.2015.07.017 · Cited by 54
A Proteomics Approach to Profiling the Temporal Translational Response to Stress and Growth
Rothenberg, D. A., Taliaferro, J. M., Huber, S. M., Begley, T. J., Dedon, P. C., & White, F. M. (2018) · iScience · doi:10.1016/j.isci.2018.11.004 · Cited by 54
Zhang, G., Bowling, H. L., Hom, N., Kirshenbaum, K., Klann, E., Chao, M. V., & Neubert, T. A. (2014) · Journal of Proteome Research · doi:10.1021/pr5006982 · Cited by 51
Quantitative analysis of newly synthesized proteins
Ma, Y., McClatchy, D. B., Barkallah, S., Wood, W. W., & Yates, J. R. (2018) · Nature Protocols · doi:10.1038/s41596-018-0012-y · Cited by 50
Secretome of Adipose Tissue as the Key to Understanding the Endocrine Function of Adipose Tissue
Pogodziński, D., Ostrowska, L., Smarkusz-Zarzecka, J., & Zyśk, B. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23042309 · Cited by 57
Proteomic approaches to decipher cancer cell secretome
Brandi, J., Manfredi, M., Speziali, G., Gosetti, F., Marengo, E., & Cecconi, D. (2017) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2017.06.030 · Cited by 49
Kraushar, M. L., Krupp, F., Harnett, D., Turko, P., Ambrozkiewicz, M. C., Sprink, T., Imami, K., Günnigmann, M., Zinnall, U., Vieira-Vieira, C. H., et al. (2021) · Molecular Cell · doi:10.1016/j.molcel.2020.11.037 · Cited by 49
Argonaute2 Regulates the Pancreatic β-Cell Secretome
Tattikota, S. G., Sury, M. D., Rathjen, T., Wessels, H., Pandey, A. K., You, X. A., Becker, C. F., Chen, W., Selbach, M., & Poy, M. N. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m112.024786 · Cited by 48
Quantitative Comparison of Proteomes Using SILAC
Deng, J., Erdjument‐Bromage, H., & Neubert, T. A. (2018) · Current Protocols in Protein Science · doi:10.1002/cpps.74 · Cited by 50
Knecht, S., Eberl, H. C., Kreisz, N., Ugwu, U. J., Starikova, T., Küster, B., & Wilhelm, S. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100636 · Cited by 49
Secretomics to Discover Regulators in Diseases
Song, P., Kwon, Y., Joo, J., Kim, D.-G., & Yoon, J. H. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20163893 · Cited by 48
Teaching old NCATs new tricks: using non-canonical amino acid tagging to study neuronal plasticity
Hinz, F. I., Dieterich, D. C., & Schuman, E. M. (2013) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2013.07.021 · Cited by 46
Quantitative secretomic analysis of pancreatic cancer cells in serum-containing conditioned medium
Liu, P., Weng, Y., Sui, Z., Wu, Y., Meng, X., Wu, M., Jin, H., Tan, X., Zhang, L., & Zhang, Y. (2016) · Scientific Reports · doi:10.1038/srep37606 · Cited by 44
Wang, J., Zhang, J., Lee, Y. M., Koh, P.-L., Ng, S., Bao, F., Lin, Q., & Shen, H. (2016) · Autophagy · doi:10.1080/15548627.2016.1196317 · Cited by 42
Selkrig, J., Li, N., Hausmann, A., Mangan, M., Zietek, M., Mateus, A., Bobonis, J., Sueki, A., Imamura, H., Debs, B. E., et al. (2020) · Nature Microbiology · doi:10.1038/s41564-020-0736-7 · Cited by 46
Self-citationSecretome Analysis of Human Mesenchymal Stem Cells Undergoing Chondrogenic Differentiation
Rocha, B., Calamia, V., Casas, V., Carrascal, M., Blanco, F. J., & Ruíz‐Romero, C. (2014) · Journal of Proteome Research · doi:10.1021/pr401030n · Cited by 40
In-Depth Proteomic Quantification of Cell Secretome in Serum-Containing Conditioned Medium
Weng, Y., Sui, Z., Shan, Y., Jiang, H., Zhou, Y., Zhu, X., Liang, Z., Zhang, L., & Zhang, Y. (2016) · Analytical Chemistry · doi:10.1021/acs.analchem.6b00910 · Cited by 42
Zheng, J., Ren, X., Wei, C., Yang, J., Hu, Y., Liu, L., Xu, X., Wang, J., & Jin, Q. (2013) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m113.027318 · Cited by 39
Cell-selective proteomics for biological discovery
Stone, S. E., Glenn, W. S., Hamblin, G. D., & Tirrell, D. A. (2017) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2016.12.026 · Cited by 39
LRG1 is an adipokine that promotes insulin sensitivity and suppresses inflammation
Choi, C. H. J., Barr, W., Zaman, S., Model, C., Park, A., Koenen, M., Lin, Z., Szwed, S. K., Marchildon, F., Crane, A., et al. (2022) · eLife · doi:10.7554/elife.81559 · Cited by 39
Activation of the TGFβ pathway impairs endothelial to haematopoietic transition
Bölükbaşı, Ö. V., Zhang, Y., Kosim, K., Ganter, K., Föehr, S., Mardenborough, Y., Shvartsman, M., Enright, A. J., Krijgsveld, J., & Lancrin, C. (2016) · Scientific Reports · doi:10.1038/srep21518 · Cited by 37
Self-citationHerber, J., Njavro, J. R., Feederle, R., Schepers, U., Müller, U., Bräse, S., Müller, S. A., & Lichtenthaler, S. F. (2018) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.000608 · Cited by 40
Identification of the Secreted Proteins Originated from Primary Human Hepatocytes and HepG2 Cells
Frankó, A., Hartwig, S., Kotzka, J., Ruoß, M., Nussler, A. K., Königsrainer, A., Häring, H., Lehr, S., & Peter, A. (2019) · Nutrients · doi:10.3390/nu11081795 · Cited by 38
Fishing for newly synthesized proteins with phosphonate-handles
Kleinpenning, F., Steigenberger, B. A., Wu, W., & Heck, A. J. R. (2020) · Nature Communications · doi:10.1038/s41467-020-17010-0 · Cited by 38
Ma, Y., & Yates, J. R. (2018) · Expert Review of Proteomics · doi:10.1080/14789450.2018.1500902 · Cited by 37
Li, X., Jiang, J. T., Zhao, X., Wang, J., Han, H., Zhao, Y., Peng, B., Zhong, R., Ying, W., & Qian, X. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081921 · Cited by 36
Ge, J., Zhang, C., Ng, X. W., Peng, B., Pan, S., Du, S., Wang, D., Li, L., Lim, K., Wohland, T., et al. (2016) · Angewandte Chemie International Edition · doi:10.1002/anie.201511030 · Cited by 36
HILAQ: A Novel Strategy for Newly Synthesized Protein Quantification
Ma, Y., McClatchy, D. B., Barkallah, S., Wood, W. W., & Yates, J. R. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00005 · Cited by 36
Swietlik, J. J., Bärthel, S., Falcomatà, C., Fink, D., Sinha, A., Cheng, J., Ebner, S., Landgraf, P., Dieterich, D. C., Daub, H., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38171-8 · Cited by 38
Dissection of the Human Multipotent Adult Progenitor Cell Secretome by Proteomic Analysis
Burrows, G. G., Hof, W. V. ’., Newell, L. F., Reddy, A. P., Wilmarth, P. A., David, L. L., Raber, A., Bogaerts, A., Pinxteren, J., Deans, R., et al. (2013) · Stem Cells Translational Medicine · doi:10.5966/sctm.2013-0031 · Cited by 35
Shin, J., Kwon, Y. M., Lee, S., Na, S., Hong, E. Y., Ju, S., Jung, H.-G., Kaushal, P., Shin, S., Back, J. H., et al. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00475 · Cited by 35
Morigny, P., Kaltenecker, D., Zuber, J., Machado, J., Mehr, L., Tsokanos, F., Kuzi, H., Hermann, C. D., Völkl, M., Monogarov, G., et al. (2021) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.12758 · Cited by 35
Self-citationProteomic identification of protease cleavage sites: cell-biological and biomedical applications
Shahinian, J. H., Tholen, S., & Schilling, O. (2013) · Expert Review of Proteomics · doi:10.1586/14789450.2013.841547 · Cited by 35
Incorporation of non-canonical amino acids into proteins in yeast
Wiltschi, B. (2016) · Fungal Genetics and Biology · doi:10.1016/j.fgb.2016.02.002 · Cited by 33
Sieber, J., Hauer, C., Bhuvanagiri, M., Leicht, S., Krijgsveld, J., Neu‐Yilik, G., Hentze, M. W., & Kulozik, A. E. (2016) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m115.054056 · Cited by 36
Self-citationAltered steady state and activity-dependent de novo protein expression in fragile X syndrome
Bowling, H. L., Bhattacharya, A., Zhang, G., Alam, D. H., Lebowitz, J. Z., Bohm-Levine, N., Lin, D., Singha, P., Mamcarz, M., Puckett, R. E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09553-8 · Cited by 33
Sieving through the cancer secretome
Lin, Q., Tan, H. T., Lim, H. S. R., & Chung, M. C. M. (2013) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2013.01.030 · Cited by 32
Kim, J., Afridi, R., Cho, E., Yoon, J. H., Lim, Y., Lee, H., Ryu, H., & Suk, K. (2022) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2022.100424 · Cited by 35
Secretome Analysis: Reading Cellular Sign Language to Understand Intercellular Communication
Wu, W., & Krijgsveld, J. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100692 · Cited by 36
Self-citationA Genetically Encoded AND Gate for Cell-Targeted Metabolic Labeling of Proteins
Mahdavi, A., Segall-Shapiro, T. H., Kou, S., Jindal, G. A., Hoff, K. G., Liu, S., Chitsaz, M., Ismagilov, R. F., Silberg, J. J., & Tirrell, D. A. (2013) · Journal of the American Chemical Society · doi:10.1021/ja400448f · Cited by 31
Chekulaeva, M., & Landthaler, M. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.08.031 · Cited by 30
Extracellular pH and lung infections in cystic fibrosis
Massip-Copiz, M. M., & Santa-Coloma, T. A. (2018) · European Journal of Cell Biology · doi:10.1016/j.ejcb.2018.06.001 · Cited by 30
Liu, P., Kong, L., Jin, H., Wu, Y., Tan, X., & Song, B. (2019) · Cancer Cell International · doi:10.1186/s12935-019-0980-1 · Cited by 30
Elder, M. K., Erdjument‐Bromage, H., Oliveira, M. M., Mamcarz, M., Neubert, T. A., & Klann, E. (2021) · Communications Biology · doi:10.1038/s42003-021-02324-6 · Cited by 33
Advances in stable isotope labeling: dynamic labeling for spatial and temporal proteomic analysis
Beller, N. C., & Hummon, A. B. (2022) · Molecular Omics · doi:10.1039/d2mo00077f · Cited by 29
Combining Pulsed SILAC Labeling and Click-Chemistry for Quantitative Secretome Analysis
Eichelbaum, K., & Krijgsveld, J. (2014) · Methods in molecular biology · doi:10.1007/978-1-4939-0944-5_7 · Cited by 30
Self-citationProteomic Substrate Identification for Membrane Proteases in the Brain
Müller, S. A., Scilabra, S. D., & Lichtenthaler, S. F. (2016) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2016.00096 · Cited by 28
Advances in Higher Order Multiplexing Techniques in Proteomics
Aggarwal, S., Talukdar, N. C., & Yadav, A. K. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00228 · Cited by 29
Tan, S., Long, Y., Han, Q., Guan, H., Liang, Q., & Ding, M. (2020) · ACS Biomaterials Science & Engineering · doi:10.1021/acsbiomaterials.9b01793 · Cited by 29
Selective inhibition of protein secretion by abrogating receptor–coat interactions during ER export
Gómez‐Navarro, N., Maldutyte, J., Poljak, K., Peak‐Chew, S., Orme, J. P., Bisnett, B. J., Lamb, C. H., Boyce, M., Gianni, D., & Miller, E. A. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2202080119 · Cited by 29
Zhang, W., Liu, T., Dong, H., Bai, H., Tian, F., Shi, Z., Chen, M., Wang, J., Qin, W., & Qian, X. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b04960 · Cited by 29
Metadata
- Authors
- Eichelbaum, K., Winter, M., Diaz, M. B., Herzig, S., & Krijgsveld, J.
- Affiliations
- European Molecular Biology Laboratory (DE)
- German Cancer Research Center (DE)
- Corresponding author
- Krijgsveld, Jeroen
- Venue
- Nature Biotechnology
- Published
- 2012-09-23
- Volume
- 30
- Issue
- 10
- Pages
- 984-990
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- OpenAlex
- W2031704362
- Cited by
- 248
- References
- 44
- Citations by year
- 2026: 9 · 2025: 8 · 2024: 13 · 2023: 18 · 2022: 21 · 2021: 20 · 2020: 31 · 2019: 17 · 2018: 21 · 2017: 20 · 2016: 21 · 2015: 13 · 2014: 14 · 2013: 20 · 2012: 2
- FWCI
- 6.91
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Biotechnology (publishedVersion)
- PubMed (publishedVersion)
- Site cant be reached (submittedVersion)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Organic Chemistry › Click Chemistry and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Keywords
- secretome, secreted proteins, protein enrichment, click chemistry, quantitative proteomics, low-abundance proteins, cytokines, chemokines, growth factors, serum deprivation, macrophages, lipopolysaccharide response
- MeSH terms
- Cell Line, Culture Media, Humans, Isotope Labeling, Kinetics, Lipopolysaccharides, Macrophage Activation, Proteins, Protein Biosynthesis, Culture Media, Serum-Free, Hepatocytes, Click Chemistry, Protein Interaction Maps
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review