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Multiplexed In Situ Imaging Mass Cytometry Analysis of the Human Endocrine Pancreas and Immune System in Type 1 Diabetes
Open accessAbstract
The interaction between the immune system and endocrine cells in the pancreas is crucial for the initiation and progression of type 1 diabetes (T1D). Imaging mass cytometry (IMC) enables multiplexed assessment of the abundance and localization of more than 30 proteins on the same tissue section at 1-μm resolution. Herein, we have developed a panel of 33 antibodies that allows for the quantification of key cell types including pancreatic exocrine cells, islet cells, immune cells, and stromal components. We employed this panel to analyze 12 pancreata obtained from donors with clinically diagnosed T1D and 6 pancreata from non-diabetic controls. In the pancreata from donors with T1D, we simultaneously visualized significant alterations in islet architecture, endocrine cell composition, and immune cell presentation. Indeed, we demonstrate the utility of IMC to investigate complex events on the cellular level that will provide new insights on the pathophysiology of T1D.
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
lmerTest Package: Tests in Linear Mixed Effects Models
Kuznetsova, A., Brockhoff, P. B., & Christensen, R. H. B. (2017) · Journal of Statistical Software · doi:10.18637/jss.v082.i13 · Cited by 24,346
Diagnosis and Classification of Diabetes Mellitus
American Diabetes Association (2010) · Diabetes Care · doi:10.2337/dc11-s062 · Cited by 14,094
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
Carpenter, A. E., Jones, T. R., Lamprecht, M. R., Clarke, C., Kang, I. H., Friman, O., Guertin, D. A., Chang, J. H., Lindquist, R. A., Moffat, J., et al. (2006) · Genome biology · doi:10.1186/gb-2006-7-10-r100 · Cited by 5,212
Diagnosis and Classification of diabetes mellitus
Rossi, G. (2014) · Diapedia.org eBooks · doi:10.14496/dia..20 · Cited by 3,212
Atkinson, M. A., Eisenbarth, G. S., & Michels, A. W. (2014) · The Lancet · doi:10.1016/s0140-6736(13)60591-7 · Cited by 2,931
Self-citationmclust 5: Clustering, Classification and Density Estimation Using Gaussian Finite Mixture Models
Scrucca, L., Fop, M., Murphy, T. B., & Adrian, E. R. (2016) · The R Journal · doi:10.32614/rj-2016-021 · Cited by 2,914
Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry
Giesen, C., Wang, H. A. O., Schapiro, D., Zivanovic, N., Jacobs, A., Hattendorf, B., Schüffler, P. J., Grolimund, D., Buhmann, J. M., Brandt, S., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2869 · Cited by 1,951
Eisenbarth (1986) · doi:10.1056/nejm198605223142106 · Cited by 1,659
Gu, G., Dubauskaite, J., & Melton, D. A. (2002) · Development · doi:10.1242/dev.129.10.2447 · Cited by 1,518
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Type 1 diabetes mellitus as a disease of the β-cell (do not blame the immune system?)
Roep, B. O., Thomaidou, S., van Tienhoven, R., & Zaldumbide, A. (2020) · Nature Reviews Endocrinology · doi:10.1038/s41574-020-00443-4 · Cited by 675
Taylor, M. J., Lukowski, J. K., & Anderton, C. (2021) · Journal of the American Society for Mass Spectrometry · doi:10.1021/jasms.0c00439 · Cited by 306
A Map of Human Type 1 Diabetes Progression by Imaging Mass Cytometry
Damond, N., Engler, S., Zanotelli, V. R., Schapiro, D., Wasserfall, C. H., Kusmartseva, I., Nick, H. S., Thorel, F., Herrera, P. L., Atkinson, M. A., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.11.014 · Cited by 301
Self-citationFasolino, M., Schwartz, G. W., Patil, A. R., Mongia, A., Golson, M. L., Wang, Y. J., Morgan, A. R., Liu, C., Schug, J., Liu, J., et al. (2022) · Nature Metabolism · doi:10.1038/s42255-022-00531-x · Cited by 225
Self-citationImmune monitoring using mass cytometry and related high-dimensional imaging approaches
Hartmann, F. J., & Bendall, S. C. (2019) · Nature Reviews Rheumatology · doi:10.1038/s41584-019-0338-z · Cited by 208
Tumor-infiltrating mast cells are associated with resistance to anti-PD-1 therapy
Somasundaram, R., Connelly, T., Choi, R., Choi, H., Samarkina, A., Li, L., Gregorio, E., Chen, Y., Thakur, R., Abdel‐Mohsen, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20600-7 · Cited by 184
Self-citationKaestner, K. H., Powers, A. C., Naji, A., & Atkinson, M. A. (2019) · Diabetes · doi:10.2337/db19-0058 · Cited by 194
Self-citationMass Cytometry Imaging for the Study of Human Diseases—Applications and Data Analysis Strategies
Baharlou, H., Canete, N. P., Cunningham, A. L., Harman, A. N., & Patrick, E. (2019) · Frontiers in Immunology · doi:10.3389/fimmu.2019.02657 · Cited by 180
Autoreactive CD8+ T cell exhaustion distinguishes subjects with slow type 1 diabetes progression
Wiedeman, A. E., Muir, V. S., Rosasco, M. G., DeBerg, H. A., Presnell, S., Haas, B., Dufort, M. J., Speake, C., Greenbaum, C. J., Serti, E., et al. (2019) · Journal of Clinical Investigation · doi:10.1172/jci126595 · Cited by 168
Type 1 diabetes mellitus: much progress, many opportunities
Powers, A. C. (2021) · Journal of Clinical Investigation · doi:10.1172/jci142242 · Cited by 172
Self-citationZakharov, P. N., Hu, H., Wan, X., & Unanue, E. R. (2020) · Journal of Experimental Medicine · doi:10.1084/jem.20192362 · Cited by 149
The physiological role of β-cell heterogeneity in pancreatic islet function
Benninger, R. K. P., & Kravets, V. V. (2021) · Nature Reviews Endocrinology · doi:10.1038/s41574-021-00568-0 · Cited by 141
SEAM is a spatial single nuclear metabolomics method for dissecting tissue microenvironment
Yuan, Z., Zhou, Q., Cai, L., Pan, L., Sun, W., Qumu, S., Yu, S., Feng, J., Zhao, H., Zheng, Y., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01276-3 · Cited by 134
Atkinson, M. A., & Mirmira, R. G. (2023) · Cell Metabolism · doi:10.1016/j.cmet.2023.06.018 · Cited by 139
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Zhang, W., Li, I., Reticker-Flynn, N. E., Good, Z., Chang, S., Samusik, N., Saumyaa, S., Li, Y., Zhou, X., Liang, R., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01498-z · Cited by 108
Weisberg, S. P., Carpenter, D. J., Chait, M., Dogra, P., Gartrell, R. D., Chen, A., Campbell, S., Liu, W., Saraf, P., Snyder, M. E., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.11.056 · Cited by 104
Functional changes in beta cells during ageing and senescence
Aguayo‐Mazzucato, C. (2020) · Diabetologia · doi:10.1007/s00125-020-05185-6 · Cited by 107
Yang, M.-X., Unsihuay, D., Hu, H., Meke, F. N., Qu, Z.-H., Zhang, Z.-Y., & Laskin, J. (2023) · Analytical Chemistry · doi:10.1021/acs.analchem.2c04795 · Cited by 78
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Ramaglia, V., Sheikh‐Mohamed, S., Legg, K., Park, C., Rojas, O. L., Zandee, S., Fu, F., Ornatsky, O., Swanson, E. C., Pitt, D., et al. (2019) · eLife · doi:10.7554/elife.48051 · Cited by 83
Large-scale electron microscopy database for human type 1 diabetes
de Boer, P., Pirozzi, N. M., Wolters, A. H. G., Kuipers, J., Kusmartseva, I., Atkinson, M. A., Campbell‐Thompson, M., & Giepmans, B. N. G. (2020) · Nature Communications · doi:10.1038/s41467-020-16287-5 · Cited by 81
Self-citationThe Human Islet: Mini-Organ With Mega-Impact
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Self-citationShapira, S. N., Naji, A., Atkinson, M. A., Powers, A. C., & Kaestner, K. H. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.09.013 · Cited by 76
Self-citationSu, F., Zhang, W., Meng, L., Zhang, W., Liu, X., Liu, X., Chen, M., Zhang, Y., & Xiao, F. (2022) · Advanced Science · doi:10.1002/advs.202106063 · Cited by 69
Falcone, M., & Fousteri, G. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.00569 · Cited by 72
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The beta cell-immune cell interface in type 1 diabetes (T1D)
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Wu, M., Lee, M. Y. Y., Bahl, V., Traum, D., Schug, J., Kusmartseva, I., Atkinson, M. A., Fan, G., & Kaestner, K. H. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109919 · Cited by 73
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Nigi, L., Maccora, C., Dotta, F., & Sebastiani, G. (2019) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.3264 · Cited by 26
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Bernard, H., Teijeiro, A., Perez, A. C., Perna, C., Satish, B., Novials, A., Wang, J. P., & Djouder, N. (2020) · Cell Reports Medicine · doi:10.1016/j.xcrm.2020.100125 · Cited by 21
Ling, Q., Shen, L., Zhang, W., Qu, D., Wang, H., Wang, B., Liu, Y., Lu, J., Zhu, D., & Bi, Y. (2022) · Molecular Medicine · doi:10.1186/s10020-022-00447-y · Cited by 22
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Zhang, Z., Li, J., Zhang, M., Li, B., Pan, X., Dong, X., Pan, L., & Sun, J. T. (2022) · Molecular Nutrition & Food Research · doi:10.1002/mnfr.202200300 · Cited by 16
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Metadata
- Authors
- Wang, Y. J., Traum, D., Schug, J., Gao, L., Liu, C., Atkinson, M. A., Powers, A. C., Feldman, M. D., Naji, A., Chang, K.-M., & Kaestner, K. H.
- Affiliations
- University of Pennsylvania (US)
- Philadelphia VA Medical Center (US)
- University of Florida Health (US)
- VA Tennessee Valley Healthcare System (US)
- Corresponding author
- Kaestner, Klaus H.
- ORCID
- Venue
- Cell Metabolism
- Publisher
- Elsevier BV
- Published
- 2019-03
- Volume
- 29
- Issue
- 3
- Pages
- 769-783.e4
- ISSN
- 1550-4131 (ISSN-L)
- PMC
- PMC6436557
- OpenAlex
- W2913147009
- Cited by
- 228
- References
- 51
- Citations by year
- 2026: 23 · 2025: 29 · 2024: 28 · 2023: 22 · 2022: 32 · 2021: 47 · 2020: 26 · 2019: 20
- FWCI
- 18.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Metabolism (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- NIDDK (UC4 DK112217, UC4 DK112232, UC4 DK108132, UC4 DK104119)
- University of Pennsylvania Diabetes Research Center (P30 DK19525)
- Vanderbilt Diabetes Research and Training Center (P30 DK20593)
- Department of Veterans Affairs
- JDRF
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Pancreatic function and diabetes
- Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
- Keywords
- type 1 diabetes, imaging mass cytometry, pancreatic islets, endocrine pancreas, immune cells, islet architecture, pancreatic stroma, exocrine pancreas, endocrine-immune interaction
- MeSH terms
- Diabetes Mellitus, Type 1, Humans, Immune System, Islets of Langerhans, Image Cytometry, Endocrine Cells
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