Deep undepleted human serum proteome profiling toward biomarker discovery for Alzheimer’s disease
Open accessAbstract
BACKGROUND: Blood-based protein measurement is a routine practice for detecting biomarkers in human disease. Comprehensive profiling of blood/plasma/serum proteome is a challenge due to an extremely large dynamic range, as exemplified by a small subset of highly abundant proteins. Antibody-based depletion of these abundant proteins alleviates the problem but introduces experimental variations. We aimed to establish a method for direct profiling of undepleted human serum and apply the method toward biomarker discovery for Alzheimer's disease (AD), as AD is the most common form of dementia without available blood-based biomarkers in clinic. METHODS: = 5) sera were analyzed in this pilot study. In addition, we implemented a multiplexed targeted LC-MS3 method (TOMAHAQ) for the validation of selected target proteins. RESULTS: The TMT-LC/LC-MS/MS platform is capable of analyzing 4826 protein components (4368 genes), covering at least 6 orders of magnitude in dynamic range, representing one of the deepest serum proteome analysis. We defined intra- and inter- group variability in the AD and control groups. Statistical analysis revealed differentially expressed proteins in AD (26 decreased and 4 increased). Notably, these altered proteins are enriched in the known pathways of mitochondria, fatty acid beta oxidation, and AGE/RAGE. Finally, we set up a TOMAHAQ method to confirm the decrease of PCK2 and AK2 in our AD samples. CONCLUSIONS: Our results show an ultra-deep serum discovery study by TMT-LC/LC-MS/MS, and a validation experiment by TOMAHAQ targeted LC-MS3. The MS-based discovery and validation methods are of general use for biomarker discovery from complex biofluids (e.g. serum proteome). This pilot study also identified deregulated proteins, in particular proteins associated with mitochondrial function in the AD serum samples. These proteins may serve as novel AD candidate biomarkers.
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
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 this study, consistent and evident mitochondrial protein decreases were identified in Alzheimer's disease CSF and serum samples.
“In this study, we identified consistent and evident mitochondrial protein decreases in AD CSF and serum samples, which have rarely reported until the availability of the deep CSF/serum profiling (our reference datasets) [25, 33].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [25]
The Results and discussion section reports that 12 proteins downregulated in AD serum samples were localized in mitochondria. This paper profiles serum, not CSF, so it does not support the CSF part of the claim. No page number is available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredMitochondrial protein decreases in Alzheimer's disease CSF and serum have rarely been reported until the availability of deep CSF/serum profiling.
“In this study, we identified consistent and evident mitochondrial protein decreases in AD CSF and serum samples, which have rarely reported until the availability of the deep CSF/serum profiling (our reference datasets) [25, 33].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [25]
The Results and discussion section (page unavailable) reports that deep serum profiling found 12 downregulated mitochondrial proteins in AD samples. The paper does not report mitochondrial protein decreases in CSF or establish that such decreases had rarely been reported before deep CSF/serum profiling. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe authors' group previously used TMT-LC/LC-MS/MS to bypass depletion and detect about 5000 proteins in human CSF and serum.
“More recently, our group utilized the superior separation capacity of the latest TMT-LC/LC-MS/MS to bypass the depletion step and detect about 5000 proteins in human CSF and serum toward AD biomarker discovery [18, 25].”
From Integrated analysis of ultra-deep proteomes in cortex, cerebrospinal fluid and serum reveals a mitochondrial signature in Alzheimer’s disease (2020) · cites this paper as [25]
A large-scale proteomic profile of human serum specimens has revealed consistent mitochondrial protein changes between control and AD samples.
“Recently, a large-scale proteomic profile of human serum specimens has revealed consistent mitochondrial protein changes between control and AD samples [152].”
From Role of Oxidative Damage in Alzheimer’s Disease and Neurodegeneration: From Pathogenic Mechanisms to Biomarker Discovery (2021) · cites this paper as [152]
Discovery TMT proteomics was recently applied to plasma samples of two independent cohorts to predict Aβ burden in preclinical disease with high accuracy.
“For example, discovery TMT proteomics was recently applied to plasma samples of two independent cohorts to predict Aβ burden in preclinical disease with high accuracy [181].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [181]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Gentleman R: R: A language for data analysis and graphics
Ihaka, R., & Gentleman, R. (1996) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.1996.10474713 · Cited by 11,747 · see citing works
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436 · see citing works
2016 Alzheimer's disease facts and figures
Alzheimer's Association (2016) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2016.03.001 · Cited by 5,069 · see citing works
Anderson, N. L., & Anderson, N. G. (2002) · Molecular & Cellular Proteomics · doi:10.1074/mcp.r200007-mcp200 · Cited by 4,414 · see citing works
Forecasting the global burden of Alzheimer's disease
Brookmeyer, R., Johnson, E., Ziegler‐Graham, K., & Arrighi, H. M. (2007) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2007.04.381 · Cited by 3,370 · see citing works
Hyman, B. T., Phelps, C. H., Beach, T. G., Bigio, E. H., Cairns, N. J., Carrillo, M. C., Dickson, D. W., Duyckaerts, C., Frosch, M. P., Masliah, E., et al. (2012) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2011.10.007 · Cited by 3,092 · see citing works
Self-citationThompson, A., Schäfer, J., Kuhn, K., Kienle, S., Schwarz, J., Schmidt, G., Neumann, T., & Hamon, C. (2003) · Analytical Chemistry · doi:10.1021/ac0262560 · Cited by 2,386 · see citing works
Mass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334 · see citing works
Ishihama, Y., Oda, Y., Tabata, T., Sato, T., Nagasu, T., Rappsilber, J., & Mann, M. (2005) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m500061-mcp200 · Cited by 2,055 · see citing works
Olsson, B., Lautner, R., Andreasson, U., Öhrfelt, A., Portelius, E., Bjerke, M., Hölttä, M., Rosén, C., Olsson, C., Strobel, G., et al. (2016) · The Lancet Neurology · doi:10.1016/s1474-4422(16)00070-3 · Cited by 2,049 · see citing works
Protein biomarker discovery and validation: the long and uncertain path to clinical utility
Rifai, N., Gillette, M. A., & Carr, S. A. (2006) · Nature Biotechnology · doi:10.1038/nbt1235 · Cited by 1,867 · see citing works
High performance plasma amyloid-β biomarkers for Alzheimer's disease
Nakamura, A., Kaneko, N., Villemagne, V. L., Kato, T., Doecke, J., Doré, V., Fowler, C., Li, Q.-X., Martins, R., Rowe, C., et al. (2018) · Nature · doi:10.1038/nature25456 · Cited by 1,681 · see citing works
Proteogenomics connects somatic mutations to signalling in breast cancer
CPTAC, N., Mertins, P., Mani, D. R., Ruggles, K. V., Gillette, M. A., Clauser, K. R., Wang, P., Wang, X., Qiao, J. W., Cao, S., et al. (2016) · Nature · doi:10.1038/nature18003 · Cited by 1,676 · see citing works
Self-citationPeng, J., Elias, J. E., Thoreen, C. C., Licklider, L., & Gygi, S. P. (2002) · Journal of Proteome Research · doi:10.1021/pr025556v · Cited by 1,652 · see citing works
Self-citationProtein Analysis by Shotgun/Bottom-up Proteomics
Zhang, Y., Fonslow, B. R., Shan, B., Baek, M.-C., & Yates, J. R. (2013) · Chemical Reviews · doi:10.1021/cr3003533 · Cited by 1,491 · see citing works
Architecture of the human interactome defines protein communities and disease networks
Huttlin, E. L., Bruckner, R. J., Paulo, J. A., Cannon, J. R., Ting, L., Baltier, K., Colby, G., Gebreab, F., Gygi, M. P., Parzen, H., et al. (2017) · Nature · doi:10.1038/nature22366 · Cited by 1,437 · see citing works
PGC-1 coactivators: inducible regulators of energy metabolism in health and disease
Finck, B. N. (2006) · Journal of Clinical Investigation · doi:10.1172/jci27794 · Cited by 1,427 · see citing works
Schweiger, D., Baufeld, C., Drescher, P., Oltrogge, B., Höpfner, S., Mess, A., Lüttke, J., Rippke, F., Filbry, A. W., & Max, H. (2013) · Skin Pharmacology and Physiology · doi:10.1159/000348473 · Cited by 1,348 · see citing works
MS3 eliminates ratio distortion in isobaric multiplexed quantitative proteomics
Ting, L., Rad, R., Gygi, S. P., & Haas, W. (2011) · Nature Methods · doi:10.1038/nmeth.1714 · Cited by 1,009 · see citing works
Data‐independent acquisition‐based SWATH‐MS for quantitative proteomics: a tutorial
Ludwig, C., Gillet, L. C. J., Rosenberger, G., Amon, S., Collins, B. C., & Aebersold, R. H. (2018) · Molecular Systems Biology · doi:10.15252/msb.20178126 · Cited by 999 · see citing works
Revisiting biomarker discovery by plasma proteomics
Geyer, P. E., Holdt, L. M., Teupser, D., & Mann, M. (2017) · Molecular Systems Biology · doi:10.15252/msb.20156297 · Cited by 917 · see citing works
Deutsch, E. W., Csordás, A., Sun, Z., Jarnuczak, A. F., Pérez‐Riverol, Y., Ternent, T., Campbell, D. S., Llinares, M. B., Okuda, S., Kawano, S., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw936 · Cited by 864 · see citing works
Arican, O., Aral, M., Sasmaz, S., & Ciragil, P. (2005) · Mediators of Inflammation · doi:10.1155/mi.2005.273 · Cited by 805 · see citing works
The Alzheimer’s disease mitochondrial cascade hypothesis: progress and perspectives
Swerdlow, R. H., Burns, J. M., & Khan, S. M. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2013.09.010 · Cited by 786 · see citing works
Next-generation proteomics: towards an integrative view of proteome dynamics
Altelaar, M. A. F., Muñoz, J., & Heck, A. J. R. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3356 · Cited by 776 · see citing works
Al-Atrache, Z., Cader, A., & Appelt, D. M. (2016) · Alzheimer s & Dementia · doi:10.1016/j.jalz.2016.06.894 · Cited by 765 · see citing works
Mitochondrial dysfunction is a trigger of Alzheimer's disease pathophysiology
Moreira, P. I., Carvalho, C., Zhu, X., Smith, M. A., & Perry, G. (2010) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2009.10.006 · Cited by 734 · see citing works
Plasma Proteome Profiling to Assess Human Health and Disease
Geyer, P. E., Kulak, N. A., Pichler, G., Holdt, L. M., Teupser, D., & Mann, M. (2016) · Cell Systems · doi:10.1016/j.cels.2016.02.015 · Cited by 734 · see citing works
A scored human protein–protein interaction network to catalyze genomic interpretation
Li, T., Wernersson, R., Hansen, R. B., Horn, H., Mercer, J., Slodkowicz, G., Workman, C. T., Rigina, O., Rapacki, K., Stærfeldt, H., et al. (2016) · Nature Methods · doi:10.1038/nmeth.4083 · Cited by 613 · see citing works
Isobaric Labeling-Based Relative Quantification in Shotgun Proteomics
Rauniyar, N., & Yates, J. R. (2014) · Journal of Proteome Research · doi:10.1021/pr500880b · Cited by 597 · see citing works
Quantitative analysis of peptides and proteins in biomedicine by targeted mass spectrometry
Gillette, M. A., & Carr, S. A. (2012) · Nature Methods · doi:10.1038/nmeth.2309 · Cited by 497 · see citing works
Peng, J., Kim, M. J., Cheng, D., Duong, D. M., Gygi, S. P., & Sheng, M. (2004) · Journal of Biological Chemistry · doi:10.1074/jbc.m400103200 · Cited by 473 · see citing works
Self-citationBeach, T. G., Adler, C. H., Sue, L. I., Serrano, G., Shill, H. A., Walker, D. G., Lue, L., Roher, A. E., Dugger, B. N., Maarouf, C., et al. (2015) · Neuropathology · doi:10.1111/neup.12189 · Cited by 460 · see citing works
Self-citationA High-Confidence Human Plasma Proteome Reference Set with Estimated Concentrations in PeptideAtlas
Farrah, T., Deutsch, E. W., Omenn, G. S., Campbell, D. S., Sun, Z., Bletz, J., Mallick, P., Katz, J. E., Malmström, J. A., Ossola, R., et al. (2011) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m110.006353 · Cited by 446 · see citing works
Depletion of Abundant Plasma Proteins and Limitations of Plasma Proteomics
Tu, C., Rudnick, P. A., Martinez, M. Y., Cheek, K. L., Stein, S. E., Slebos, R. J. C., & Liebler, D. C. (2010) · Journal of Proteome Research · doi:10.1021/pr100646w · Cited by 390 · see citing works
PGC-1α Expression Decreases in the Alzheimer Disease Brain as a Function of Dementia
Qin, W., Haroutunian, V. H., Katsel, P., Cardozo, C. P., Ho, L., Buxbaum, J. D., & Pasinetti, G. M. (2009) · Archives of Neurology · doi:10.1001/archneurol.2008.588 · Cited by 381 · see citing works
Tan, H., Yang, K., Li, Y., Shaw, T. I., Wang, Y., Blanco, D. B., Wang, X., Cho, J.-H., Wang, H., Rankin, S., et al. (2017) · Immunity · doi:10.1016/j.immuni.2017.02.010 · Cited by 372 · see citing works
Self-citationPieper, R., Su, Q., Gatlin, C. L., Huang, S., Anderson, N. L., & Steiner, S. (2003) · PROTEOMICS · doi:10.1002/pmic.200390057 · Cited by 356 · see citing works
Quantitative variability of 342 plasma proteins in a human twin population
Liu, Y., Buil, A., Collins, B. C., Gillet, L. C., Blum, L. C., Cheng, L., Vitek, O., Mouritsen, J., Lachance, G., Spector, T. D., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145728 · Cited by 328 · see citing works
Plasma Proteome Database as a resource for proteomics research: 2014 update
Nanjappa, V., Thomas, J. K., Marimuthu, A., Muthusamy, B., Radhakrishnan, A., Sharma, R., Khan, A. A., Balakrishnan, L., Sahasrabuddhe, N. A., Kumar, S., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt1251 · Cited by 315 · see citing works
Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome
Kusebauch, U., Campbell, D. S., Deutsch, E. W., Chu, C. S., Spicer, D. A., Brusniak, M.-Y., Slagel, J., Sun, Z., Stevens, J., Grimes, B., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.041 · Cited by 310 · see citing works
Hippo/Mst signalling couples metabolic state and immune function of CD8α+ dendritic cells
Du, X., Wen, J., Wang, Y., Karmaus, P. W. F., Khatamian, A., Tan, H., Li, Y., Guy, C. S., Nguyen, T.-L. M., Dhungana, Y., et al. (2018) · Nature · doi:10.1038/s41586-018-0177-0 · Cited by 224 · see citing works
Self-citationSchwenk, J. M., Omenn, G. S., Sun, Z., Campbell, D. S., Baker, M. S., Overall, C. M., Aebersold, R. H., Moritz, R. L., & Deutsch, E. W. (2017) · Journal of Proteome Research · doi:10.1021/acs.jproteome.7b00467 · Cited by 223 · see citing works
Qian, W.-J., Kaleta, D. T., Petritis, B. O., Jiang, H., Liu, T., Zhang, X., Mottaz, H. M., Varnum, S. M., Camp, D. G., Huang, L., et al. (2008) · Molecular & Cellular Proteomics · doi:10.1074/mcp.m800008-mcp200 · Cited by 208 · see citing works
Systematical Optimization of Reverse-Phase Chromatography for Shotgun Proteomics
Xu, P., Duong, D. M., & Peng, J. (2009) · Journal of Proteome Research · doi:10.1021/pr900251d · Cited by 202 · see citing works
Self-citationHigh performance plasma amyloid-beta biomarkers for Alzheimer's disease
Nakamura, A., Kaneko, N., Villemagne, V. L., Kato, T., Doecke, J. D., Doré, V., Fowler, C. J., Li, Q., Martins, R. N., Rowe, C. C., et al. (2018) · Nature · Cited by 196 · see citing works
JUMP: a tag-based database search tool for peptide identification with high sensitivity and accuracy
Wang, X., Li, Y., Wu, Z., Wang, H., Tan, H., & Peng, J. (2014) · Molecular & Cellular Proteomics · doi:10.1074/mcp.o114.039586 · Cited by 194 · see citing works
Self-citationFukui, H., Diaz, F., Garcia, S., & Moraes, C. T. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0705738104 · Cited by 185 · see citing works
Suppression of Alzheimer's Disease-Related Phenotypes by Expression of Heat Shock Protein 70 in Mice
Hoshino, T., Murao, N., Namba, T., Takehara, M., Adachi, H., Katsuno, M., Sobue, G., Matsushima, T., Suzuki, T., & Mizushima, T. (2011) · Journal of Neuroscience · doi:10.1523/jneurosci.5478-10.2011 · Cited by 185 · see citing works
Walker, D. G., Link, J., Lue, L., Dalsing-Hernandez, J. E., & Boyes, B. E. (2005) · Journal of Leukocyte Biology · doi:10.1189/jlb.0705377 · Cited by 180 · see citing works
Neuroimaging and Other Biomarkers for Alzheimer's Disease: The Changing Landscape of Early Detection
Risacher, S. L., & Saykin, A. J. (2013) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-050212-185535 · Cited by 155 · see citing works
Stewart, E., McEvoy, J., Wang, H., Chen, X., Honnell, V., Ocarz, M., Gordon, B., Dapper, J., Blankenship, K., Yang, Y., et al. (2018) · Cancer Cell · doi:10.1016/j.ccell.2018.07.012 · Cited by 153 · see citing works
Self-citationErickson, B. K., Rose, C. M., Braun, C. R., Erickson, A. R., Knott, J., McAlister, G. C., Wühr, M., Paulo, J. A., Everley, R. A., & Gygi, S. P. (2017) · Molecular Cell · doi:10.1016/j.molcel.2016.12.005 · Cited by 126 · see citing works
Sathe, G., Na, C. H., Renuse, S., Madugundu, A. K., Albert, M., Moghekar, A., & Pandey, A. (2019) · PROTEOMICS – Clinical Applications · doi:10.1002/prca.201800105 · Cited by 126 · see citing works
Bai, B., Tan, H., Pagala, V., High, A. A., Ichhaporia, V. P., Hendershot, L. M., & Peng, J. (2016) · Methods in enzymology on CD-ROM/Methods in enzymology · doi:10.1016/bs.mie.2016.10.007 · Cited by 119 · see citing works
Self-citationNiu, M., Cho, J.-H., Kodali, K., Pagala, V., High, A. A., Wang, H., Wu, Z., Li, Y., Bi, W., Zhang, H., et al. (2017) · Analytical Chemistry · doi:10.1021/acs.analchem.6b04415 · Cited by 118 · see citing works
Self-citationWang, H., Yang, Y., Li, Y., Bai, B., Wang, X., Tan, H., Liu, T., Beach, T. G., Peng, J., & Wu, Z. (2015) · Journal of Proteome Research · doi:10.1021/pr500882h · Cited by 96 · see citing works
Self-citationQuantitative Protein Analysis by Mass Spectrometry
Pagala, V., High, A. A., Wang, X., Tan, H., Kodali, K., Mishra, A., Kavdia, K., Xu, Y., Wu, Z., & Peng, J. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2425-7_17 · Cited by 66 · see citing works
Self-citationClinical proteomics in neurodegenerative diseases
Zhou, J., Hanfelt, J., & Peng, J. (2007) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.200700378 · Cited by 23 · see citing works
Self-citationBrown, A. M., Gordon, D., Lee, H., Vrièze, F. W., Cellini, E., Bagnoli, S., Nacmias, B., Sorbi, S., Hardy, J., & Blass, J. P. (2007) · Neurochemical Research · doi:10.1007/s11064-006-9235-3 · Cited by 22 · see citing works
Rose, C. M., Erickson, B. K., Schweppe, D. K., Viner, R., Choi, J. H., Rogers, J. C., Bomgarden, R. D., Gygi, S. P., & Kirkpatrick, D. S. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00767 · Cited by 19 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery
Bai, B., Vanderwall, D., Li, Y., Wang, X., Poudel, S., Wang, H., Dey, K. K., Chen, P., Yang, K., & Peng, J. (2021) · Molecular Neurodegeneration · doi:10.1186/s13024-021-00474-z · Cited by 252 · see citing works
Self-citationDewanjee, S., Chakraborty, P., Bhattacharya, H., Chacko, L., Singh, B., Chaudhary, A., Javvaji, K., Pradhan, S. R., Vallamkondu, J., Dey, A., et al. (2022) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2022.09.032 · Cited by 235 · see citing works
Ignjatović, V., Geyer, P. E., Palaniappan, K. K., Chaaban, J. E., Omenn, G. S., Baker, M. S., Deutsch, E. W., & Schwenk, J. M. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00503 · Cited by 226 · see citing works
Wang, H., Dey, K. K., Chen, P.-C., Li, Y., Niu, M., Cho, J.-H., Wang, X., Bai, B., Jiao, Y., Chepyala, S. R., et al. (2020) · Molecular Neurodegeneration · doi:10.1186/s13024-020-00384-6 · Cited by 210 · see citing works
Self-citationBuccellato, F. R., D’Anca, M., Fenoglio, C., Scarpini, E., & Galimberti, D. (2021) · Antioxidants · doi:10.3390/antiox10091353 · Cited by 179 · see citing works
Liu, C.-C., Zhao, J., Fu, Y., Inoue, Y., Ren, Y., Chen, Y., Doss, S. V., Shue, F., Jeevaratnam, S., Bastea, L., et al. (2022) · Nature Neuroscience · doi:10.1038/s41593-022-01127-0 · Cited by 168 · see citing works
Self-citationApplications of Tandem Mass Spectrometry (MS/MS) in Protein Analysis for Biomedical Research
Neagu, A.-N., Jayathirtha, M., Baxter, E., Donnelly, M., Petre, B. A., & Darie, C. C. (2022) · Molecules · doi:10.3390/molecules27082411 · Cited by 144 · see citing works
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 · see citing works
27-Plex Tandem Mass Tag Mass Spectrometry for Profiling Brain Proteome in Alzheimer’s Disease
Wang, Z., Yu, K., Tan, H., Wu, Z., Cho, J.-H., Han, X., Sun, H., Beach, T. G., & Peng, J. (2020) · Analytical Chemistry · doi:10.1021/acs.analchem.0c00655 · Cited by 113 · see citing works
Self-citationWozniak, J. M., Mills, R. H., Olson, J., Caldera, J. R., Sepich‐Poore, G. D., Carrillo-Terrazas, M., Tsai, C., Vargas, F., Knight, R. D., Dorrestein, P. C., et al. (2020) · Cell · doi:10.1016/j.cell.2020.07.040 · Cited by 116 · see citing works
Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau
Rayaprolu, S., Higginbotham, L., Bagchi, P., Watson, C. M., Zhang, T., Levey, A. I., Rangaraju, S., & Seyfried, N. T. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-00840-3 · Cited by 118 · see citing works
Insights Into the Role of CSF1R in the Central Nervous System and Neurological Disorders
Hu, B., Duan, S., Wang, Z., Li, X., Zhou, Y., Zhang, X., Zhang, Y., Xu, H., & Zheng, H. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.789834 · Cited by 105 · see citing works
Mullane, K., & Williams, M. J. (2020) · Biochemical Pharmacology · doi:10.1016/j.bcp.2020.113945 · Cited by 106 · see citing works
Alzheimer’s disease and type 2 diabetes mellitus: A systematic review of proteomic studies
Pereira, J. D., Fraga, V. G., Santos, A. L. M., Ferriani, M. D. G. C., Caramelli, P., & Gomes, K. B. (2020) · Journal of Neurochemistry · doi:10.1111/jnc.15166 · Cited by 99 · see citing works
Dayon, L., Cominetti, O., & Affolter, M. (2022) · Expert Review of Proteomics · doi:10.1080/14789450.2022.2070477 · Cited by 90 · see citing works
Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer’s Disease
Shao, Y., Ouyang, Y., Li, T., Liu, X., Xu, X., Li, S., Xu, G., & Le, W. (2020) · Aging and Disease · doi:10.14336/ad.2020.0217 · Cited by 79 · see citing works
Katzeff, J. S., Bright, F., Lo, K., Kril, J. J., Connolly, A. M., Crossett, B., Ittner, L. M., Kassiou, M., Loy, C. T., Hodges, J. R., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-70687-7 · Cited by 77 · see citing works
Robust Microflow LC-MS/MS for Proteome Analysis: 38 000 Runs and Counting
Bian, Y., Bayer, F. P., Chang, Y.-C., Meng, C., Höfer, S., Deng, N., Zheng, R., Boychenko, O., & Küster, B. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c00257 · Cited by 73 · see citing works
Wang, H., Diaz, A. K., Shaw, T. I., Li, Y., Niu, M., Cho, J.-H., Paugh, B. S., Zhang, Y., Sifford, J. M., Bai, B., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11661-4 · Cited by 67 · see citing works
Self-citationTan, M. S., Cheah, P., Chin, A., Looi, L., & Chang, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104947 · Cited by 64 · see citing works
Acute depletion of CTCF rewires genome-wide chromatin accessibility
Xu, B., Wang, H., Wright, S., Hyle, J. W., Zhang, Y., Shao, Y., Niu, M., Fan, Y., Rosikiewicz, W., Djekidel, M. N., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02466-0 · Cited by 57 · see citing works
Self-citationMitochondrial dysfunction in Alzheimer’s disease - a proteomics perspective
Abyadeh, M., Gupta, V., Chitranshi, N., Gupta, V., Wu, Y., Saks, D., Wall, R. V., Fitzhenry, M. J., Basavarajappa, D. K., You, Y., et al. (2021) · Expert Review of Proteomics · doi:10.1080/14789450.2021.1918550 · Cited by 50 · see citing works
Gaun, A., Hardell, K. N. L., Olsson, N., O’Brien, J. J., Gollapudi, S., Smith, M., McAlister, G. C., Huguet, R., Keyser, R., Buffenstein, R., et al. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00681 · Cited by 46 · see citing works
Nielsen, J. E., Honoré, B., Vestergård, K., Maltesen, R. G., Christiansen, G., Bøge, A. U., Kristensen, S. R., & Pedersen, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-97969-y · Cited by 45 · see citing works
Pla2g12b drives expansion of triglyceride-rich lipoproteins
Thierer, J. H., Foresti, O., Yadav, P. K., Wilson, M. H., Moll, T. O. C., Shen, M., Busch‐Nentwich, E. M., Morash, M., Mohlke, K. L., Rawls, J. F., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46102-4 · Cited by 42 · see citing works
Blaschke, C. R. K., Black, A. P., Mehta, A. S., Angel, P. M., & Drake, R. R. (2020) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.0c00213 · Cited by 39 · see citing works
Molloy, M. P., Hill, C. J., O’Rourke, M. B., Chandra, J., Steffen, P., McKay, M. J., Pascovici, D., & Herbert, B. R. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00971 · Cited by 36 · see citing works
Yarbro, J. M., Shrestha, H. K., Wang, Z., Zhang, X., Zaman, M., Chu, M., Wang, X., Yu, G., & Peng, J. (2025) · Molecular Neurodegeneration · doi:10.1186/s13024-025-00874-5 · Cited by 41 · see citing works
Self-citationA new generation of AD biomarkers: 2019 to 2021
Hawksworth, J. I., Fernández, E., & Gevaert, K. (2022) · Ageing Research Reviews · doi:10.1016/j.arr.2022.101654 · Cited by 34 · see citing works
Deep Profiling of Microgram-Scale Proteome by Tandem Mass Tag Mass Spectrometry
Liu, D., Yang, S., Kavdia, K., Sifford, J. M., Wu, Z., Xie, B., Wang, Z., Pagala, V., Wang, H., Yu, K., et al. (2020) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00426 · Cited by 34 · see citing works
Self-citationAyyadevara, S., Ganne, A., Balasubramaniam, M., & Reis, R. J. S. (2021) · Metabolic Brain Disease · doi:10.1007/s11011-021-00791-8 · Cited by 35 · see citing works
HBFP: a new repository for human body fluid proteome
Shao, D., Huang, L., Wang, Y., Cui, X., Li, Y., Wang, Y., Ma, Q., Du, W., & Cui, J. (2021) · Database · doi:10.1093/database/baab065 · Cited by 34 · see citing works
Self-citationKononikhin, A. S., Zakharova, N. V., Semenov, S. D., Bugrova, A. E., Brzhozovskiy, A. G., Indeykina, M. I., Fedorova, Y. B., Колыхалов, И. В., Strelnikova, P. A., Ikonnikova, A. Y., et al. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23147907 · Cited by 32 · see citing works
The CD8 + T Cell Noncytotoxic Antiviral Responses
Morvan, M. G., Teque, F., Locher, C. P., & Levy, J. A. (2021) · Microbiology and Molecular Biology Reviews · doi:10.1128/mmbr.00155-20 · Cited by 33 · see citing works
On-Chip Comparison of the Performance of First- and Second-Generation Micropillar Array Columns
Vankeerberghen, B., Beeck, J. O. D., & Desmet, G. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.3c01829 · Cited by 29 · see citing works
H3 relaxin protects against calcium oxalate crystal‐induced renal inflammatory pyroptosis
Liu, J., Yang, K., Jin, Y., Liu, Y., Chen, Y., Zhang, X., Yu, S., Song, E., Chen, S., Zhang, J., et al. (2020) · Cell Proliferation · doi:10.1111/cpr.12902 · Cited by 28 · see citing works
Dong, Y., Song, X., Wang, X., Wang, S., & He, Z. (2024) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.70060 · Cited by 32 · see citing works
Self-citationAhsan, N., Fornelli, L., Najar, F. Z., Gamagedara, S., Hossan, M. R., Rao, R. S. P., Punyamurtula, U., Bauer, A. M., Yang, Z., Foster, S. B., et al. (2023) · PROTEOMICS · doi:10.1002/pmic.202300150 · Cited by 27 · see citing works
Genomic, proteomic, and systems biology approaches in biomarker discovery for multiple sclerosis
Huizar, C. C., Raphael, I., & Forsthuber, T. G. (2020) · Cellular Immunology · doi:10.1016/j.cellimm.2020.104219 · Cited by 26 · see citing works
Yu, Q.-S., Feng, W.-Q., Shi, L., Niu, R., & Liu, J. (2022) · Brain Sciences · doi:10.3390/brainsci12081022 · Cited by 23 · see citing works
Dissecting intercellular signaling with mass spectrometry–based proteomics
Swietlik, J. J., Sinha, A., & Meissner, F. (2020) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2019.12.002 · Cited by 23 · see citing works
Integrated transcriptomics reveals the brain and blood biomarkers in Alzheimer's disease
Yu, H., Wang, F., Wu, J., Gong, J., Bi, S., Mao, Y., Jia, D., & Chai, G. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14316 · Cited by 21 · see citing works
Ahmed, T. A., Cordeiro, C. M. M., Elebute, O. O., & Hincke, M. T. (2022) · BioMed Research International · doi:10.1155/2022/7813921 · Cited by 20 · see citing works
Mass Spectrometry Structural Proteomics Enabled by Limited Proteolysis and Cross‐Linking
Lu, H., Zhu, Z., Fields, L. G., Zhang, H., & Li, L. (2024) · Mass Spectrometry Reviews · doi:10.1002/mas.21908 · Cited by 26 · see citing works
Wang, Z., Kavdia, K., Dey, K. K., Pagala, V., Kodali, K., Liu, D., Lee, D. G., Sun, H., Chepyala, S. R., Cho, J.-H., et al. (2020) · Journal of Visualized Experiments · doi:10.3791/61684 · Cited by 22 · see citing works
Self-citationA new ELISA assay demonstrates sex differences in the concentration of serum polysialic acid
Tajik, A., Phillips, K. L., Nitz, M., & Willis, L. M. (2020) · Analytical Biochemistry · doi:10.1016/j.ab.2020.113743 · Cited by 19 · see citing works
Systematic characterization of extracellular glycoproteins using mass spectrometry
Sun, F., Suttapitugsakul, S., & Wu, R. (2021) · Mass Spectrometry Reviews · doi:10.1002/mas.21708 · Cited by 19 · see citing works
Genetic Mapping of Behavioral Traits Using the Collaborative Cross Resource
Wei, X., Zhang, L., Zhang, Y., Sun, X., Wang, J., Li, X., Zhang, L., Wang, X., Morahan, G., & Qin, C. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms24010682 · Cited by 18 · see citing works
Self-citationVeettil, T. C. P., & Wood, B. R. (2022) · Sensors · doi:10.3390/s22124528 · Cited by 18 · see citing works
Proteomics Landscape of Alzheimer’s Disease
Jain, A., & Sathe, G. J. (2021) · Proteomes · doi:10.3390/proteomes9010013 · Cited by 18 · see citing works
Malicek, D., Wittig, I., Luger, S., & Foerch, C. (2021) · Frontiers in Neurology · doi:10.3389/fneur.2021.713124 · Cited by 16 · see citing works
Combining selinexor with alisertib to target the p53 pathway in neuroblastoma
Nguyen, R., Wang, H., Sun, M., Lee, D. G., Peng, J., & Thiele, C. J. (2022) · Neoplasia · doi:10.1016/j.neo.2022.100776 · Cited by 17 · see citing works
Self-citationChambers, A., Bury, J. J., Minett, T., Richardson, C. D., Brayne, C., Ince, P. G., Shaw, P. J., Garwood, C. J., Heath, P. R., Simpson, J. E., et al. (2020) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlaa064 · Cited by 15 · see citing works
Phochantachinda, S., Chantong, B., Reamtong, O., & Chatchaisak, D. (2021) · BMC Veterinary Research · doi:10.1186/s12917-021-02744-w · Cited by 15 · see citing works
Zhang, Y., Ghose, U., Buckley, N. J., Engelborghs, S., Sleegers, K., Frisoni, G. B., Wallin, A., Lleó, A., Popp, J., Martínez‐Lage, P., et al. (2022) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2022.1040001 · Cited by 15 · see citing works
Weiner, S., Blennow, K., Zetterberg, H., & Gobom, J. (2023) · Expert Review of Proteomics · doi:10.1080/14789450.2023.2255752 · Cited by 14 · see citing works
Bridle, T. G., Kumarathasan, P., & Gailer, J. (2021) · Molecules · doi:10.3390/molecules26113408 · Cited by 14 · see citing works
Proteomic Profiling of Cerebrospinal Fluid by 16-Plex TMT-Based Mass Spectrometry
Dey, K. K., Sun, H., Wang, Z., Niu, M., Wang, H., Jiao, Y., Sun, X., Li, Y., & Peng, J. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1936-0_3 · Cited by 14 · see citing works
Self-citationPoudel, S., Vanderwall, D., Yuan, Z., Wu, Z., Peng, J., & Li, Y. (2022) · PROTEOMICS · doi:10.1002/pmic.202100369 · Cited by 16 · see citing works
Self-citationZhang, Y., Liu, W., Feng, W., Wang, X., Lei, T., Chen, Z., & Song, W. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.816249 · Cited by 13 · see citing works
Self-citationTissue Proteomic Approaches to Understand the Pathogenesis of Inflammatory Bowel Disease
Martin, M. B., Chaparro, M., & Gisbert, J. P. (2021) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa352 · Cited by 13 · see citing works
Kumar, A., Doan, V. M., Kunkli, B., & Csősz, É. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.633050 · Cited by 12 · see citing works
Plasma/Serum Proteomics based on Mass Spectrometry
Zhu, Y. (2024) · Protein and Peptide Letters · doi:10.2174/0109298665286952240212053723 · Cited by 12 · see citing works
Moayedi, K., Orandi, S., Ebrahimi, R., Tanhapour, M., Moradi, M., Abbastabar, M., & Golestani, A. (2022) · Journal of Cellular Physiology · doi:10.1002/jcp.30779 · Cited by 12 · see citing works
Meraz‐Ríos, M. A., Rochín-Hernández, L. J., Ramírez-Reyes, L., Figueroa‐Corona, M. D. P., Sánchez-González, V., Orozco-Barajas, M., & Jiménez-Acosta, M. A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241612606 · Cited by 10 · see citing works
Chen, Y., Zeng, X., Olvera‐Rojas, M., Sewell, K. R., Sehrawat, A., Gu, J. M., Triviño‐Ibáñez, E. M., Solis‐Urra, P., Gómez-Río, M. M., Oberlin, L. E., et al. (2026) · Nature Communications · doi:10.1038/s41467-026-68372-w · Cited by 11 · see citing works
Serum Glycoproteomics and Identification of Potential Mechanisms Underlying Alzheimer’s Disease
Kerdsaeng, N., Roytrakul, S., Chanprasertyothin, S., Charernwat, P., Chansirikarnjana, S., Sritara, P., & Sirivarasai, J. (2021) · Behavioural Neurology · doi:10.1155/2021/1434076 · Cited by 10 · see citing works
Balasubramaniam, M., Ganne, A., Mainali, N., Pahal, S., Ayyadevara, S., & Reis, R. J. S. (2023) · iScience · doi:10.1016/j.isci.2023.108745 · Cited by 9 · see citing works
PEG Precipitation Followed by Albumin Depletion for Plasma Proteomics Analysis
Wang, Y., Cai, M., Song, Y., Jiang, K., Liu, T., Zhang, X., Zhu, H., Sha, J., Du, L., Qi, S., et al. (2025) · Analytical Chemistry · doi:10.1021/acs.analchem.5c00593 · Cited by 9 · see citing works
Self-citationSarıhan, M., Gulsen, M., Albayrak, B., Kasap, M., Akpınar, G., & Koçyiğit, E. (2023) · Journal of Biological Methods · doi:10.14440/jbm.2023.394 · Cited by 9 · see citing works
Zhao, Y., Nayak, S., Raidas, S., Guo, L., Gatta, G. D., Koppolu, S., Halász, G., Montasser, M. E., Shuldiner, A. R., Mao, Y., et al. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100595 · Cited by 9 · see citing works
SPRING is a Dedicated Licensing Factor for SREBP-Specific Activation by S1P
Hendrix, S., Tan, J. M. E., Ndoj, K., Kingma, J., Valiloo, M., Zijlstra, L. F., Ottenhoff, R., Seidah, N. G., Loregger, A., Kober, D. L., et al. (2024) · Molecular and Cellular Biology · doi:10.1080/10985549.2024.2348711 · Cited by 9 · see citing works
Argentati, C., Tortorella, I., Bazzucchi, M., Emiliani, C., Morena, F., & Martino, S. (2020) · Journal of Personalized Medicine · doi:10.3390/jpm10030115 · Cited by 9 · see citing works
Zheng, R., Govorukhina, N. I., Arrey, T. N., Pynn, C. J., van der Zee, A. G. J., Marko‐Varga, G., Bischoff, R., & Boychenko, O. (2021) · Analytical Chemistry · doi:10.1021/acs.analchem.1c01291 · Cited by 9 · see citing works
Guo, Q., Xie, M., Wang, X., Han, C., Gao, G., Wang, Q., Li, J., Duan, L., & Bao, X. (2025) · Journal of Neuroinflammation · doi:10.1186/s12974-025-03446-y · Cited by 8 · see citing works
Self-citationProteomic Markers and Early Prediction of Alzheimer’s Disease
Zakharova, N. V., Bugrova, A. E., Indeykina, M. I., Fedorova, Y. B., Колыхалов, И. В., Si, G., Николаев, Е. Н., & Kononikhin, A. S. (2022) · Biochemistry (Moscow) · doi:10.1134/s0006297922080089 · Cited by 7 · see citing works
Chou, C., Chang, C., & Chen, C. (2019) · Rapid Communications in Mass Spectrometry · doi:10.1002/rcm.8580 · Cited by 7 · see citing works
Tiwari, V., & Shukla, S. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1057068 · Cited by 7 · see citing works
Proteomic Analysis on Sequential Samples of Cystic Fluid Obtained from Human Brain Tumors
Magrassi, L., Brambilla, F., Viganò, R., Silvestre, D. D., Benazzi, L., Bellantoni, G., Danesino, G. M., Comincini, S., & Mauri, P. (2023) · Cancers · doi:10.3390/cancers15164070 · Cited by 7 · see citing works
Site-specific immobilization of the endosialidase reveals QSOX2 is a novel polysialylated protein
Hunter, C. D., Derksen, T., Makhsous, S., Doll, M., Perez, S. R., Scott, N. E., & Willis, L. M. (2024) · Glycobiology · doi:10.1093/glycob/cwae026 · Cited by 7 · see citing works
Jo, Y., Kim, Y. J., Kang, R., Lee, S., Nguyen, D. D., Park, S., Lee, D., Han, J. W., Mook‐Jung, I., Lee, L. P., et al. (2025) · Advanced Science · doi:10.1002/advs.202505660 · Cited by 8 · see citing works
Chen, Z. Z., Dufresne, J., Bowden, P., Miao, M., & Marshall, J. G. (2024) · Analytical Biochemistry · doi:10.1016/j.ab.2024.115497 · Cited by 7 · see citing works
A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification
Muazzam, A., Chiasserini, D., Kelsall, J., Geifman, N., Whetton, A. D., & Townsend, P. A. (2021) · Cancers · doi:10.3390/cancers13215580 · Cited by 7 · see citing works
A new SPRING in lipid metabolism
Hendrix, S., & Zelcer, N. (2023) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000000894 · Cited by 6 · see citing works
Comprehensive analysis of blood proteome response to necrotic enteritis in broiler chicken
Tretiak, S., Maia, T. M., Rijsselaere, T., Immerseel, F. V., Ducatelle, R., Impens, F., & Antonissen, G. (2025) · Veterinary Research · doi:10.1186/s13567-025-01519-7 · Cited by 5 · see citing works
Proteomic study of mesothelial and endothelial cross-talk: key lessons
Sacnun, J. M., Herzog, R., & Kratochwill, K. (2022) · Expert Review of Proteomics · doi:10.1080/14789450.2023.2174851 · Cited by 5 · see citing works
Bai, J. P. F., & Yu, L. (2022) · Journal of Pharmaceutical Sciences · doi:10.1016/j.xphs.2022.10.016 · Cited by 5 · see citing works
Zhang, X., Sun, H., Wang, Z., Zhou, S., Fu, Y., High, A. A., & Peng, J. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2978-9_8 · Cited by 5 · see citing works
Self-citationHigh-Throughput and In-Depth Proteomic Profiling of 5 μL Plasma and Serum Using TMTpro 16-Plex
Zhou, Y., Sun, R., Li, S., Liang, X., Qian, L., Yue, L., & Guo, T. (2023) · Methods in molecular biology · doi:10.1007/978-1-0716-2978-9_6 · Cited by 5 · see citing works
Updates on the study of lysosomal protein dynamics: possibilities for the clinic
Arora, D., Hackenberg, Y., Li, J., & Winter, D. (2023) · Expert Review of Proteomics · doi:10.1080/14789450.2023.2190515 · Cited by 5 · see citing works
Precision health in Alzheimer disease: Risk assessment‐based strategies
Azar, J., Salama, M., Chidambaram, S. B., Al‐Balushi, B., Essa, M. M., & Qoronfleh, M. W. (2021) · Precision Medical Sciences · doi:10.1002/prm2.12036 · Cited by 7 · see citing works
Elgamal, S., Cocucci, E., Sass, E. J., Mo, X. M., Blissett, A. R., Calomeni, E. P., Rogers, K. A., Woyach, J. A., Bhat, S. A., Muthusamy, N., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.137937 · Cited by 5 · see citing works
SPRINGing off the lock: the role of SPRING in S1P activity and SREBP-regulated lipid metabolism
Micallo, I., Bullington, A. V., Kober, D. L., & Zelcer, N. (2025) · Current Opinion in Lipidology · doi:10.1097/mol.0000000000001003 · Cited by 4 · see citing works
Insolubilome profiling defines molecular features that influence protein insolubility with aging
Stephan, A., Graça, F. A., Pagala, V., Hunt, L. C., Chuang, C.-L., Grime, J., Alford, D., Wang, X., High, A. A., Peng, J., et al. (2026) · Developmental Cell · doi:10.1016/j.devcel.2026.01.015 · Cited by 4 · see citing works
Self-citationAdvances in ultrasensitive detection technologies for blood-based biomarkers of Alzheimer's disease
Mou, Y., Sun, C., Deng, K., Lin, F., Yang, Z., Wang, Y., Deng, X., Yang, F., Hu, X., & Jiang, Q. (2026) · Journal of Materials Chemistry B · doi:10.1039/d5tb02633d · Cited by 4 · see citing works
Serum proteomic profiling of patients with amphetamine use disorder
Alasmari, F. F., Alsanea, S., Masood, A., Alhazzani, K., Alanazi, I. O., Musambil, M., Alfadda, A. A., Alshammari, M. A., Alasmari, A. F., & Benabdelkamel, H. (2020) · Drug and Alcohol Dependence · doi:10.1016/j.drugalcdep.2020.108157 · Cited by 4 · see citing works
Strawa, A., Skarżyńska, E., Zborowska, H., Jakimiuk, A. J., & Lisowska‐Myjak, B. (2020) · Journal of obstetrics and gynaecology research · doi:10.1111/jog.14365 · Cited by 4 · see citing works
Blood plasma proteomic markers of Alzheimer’s disease, current status and application prospects
Strelnikova, P. A., Zakharova, N. V., Kononikhin, A. S., Bugrova, A. E., Indeykina, M. I., Mitkevich, V. A., Makarov, A. А., & Николаев, Е. Н. (2025) · Expert Review of Proteomics · doi:10.1080/14789450.2025.2450804 · Cited by 5 · see citing works
Tan, H., Blanco, D. B., Xie, B., Li, Y., Wu, Z., Chi, H., & Peng, J. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1311-5_23 · Cited by 5 · see citing works
Self-citationWhitham, D., Bruno, P. S., Haaker, N., Arcaro, K. F., Pentecost, B. T., & Darie, C. C. (2024) · Expert Review of Proteomics · doi:10.1080/14789450.2024.2320158 · Cited by 4 · see citing works
- Authors
- Dey, K. K., Wang, H., Niu, M., Bai, B., Wang, X., Li, Y., Cho, J.-H., Tan, H., Mishra, A., High, A. A., Chen, P.-C., Wu, Z., Beach, T. G., & Peng, J.
- Affiliations
- St. Jude Children's Research Hospital (US)
- Banner Sun Health Research Institute (US)
- Corresponding author
- Peng, Junmin
- ORCID
- Venue
- Clinical Proteomics
- Published
- 2019-04-17
- Volume
- 16
- Issue
- 1
- Pages
- 16-16
- ISSN
- 1542-6416 (ISSN-L)
- 1559-0275
- PMC
- PMC6472024
- OpenAlex
- W2946202802
- Cited by
- 135
- References
- 61
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 10 · 2023: 19 · 2022: 20 · 2021: 31 · 2020: 22 · 2019: 4
- FWCI
- 8.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Clinical Proteomics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institutes of Health (R01AG047928, R01AG053987, U24NS072026, P30AG19610, P30CA021765)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Keywords
- Alzheimer's disease, serum proteome, blood-based biomarkers, antibody depletion, differentially expressed proteins, mitochondrial function, fatty acid beta-oxidation, AGE-RAGE signaling pathway, PCK2, AK2, biomarker discovery
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
