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Bhatia, H.S. ; Brunner, A.D.* ; Öztürk, F. ; Kapoor, S. ; Rong, Z. ; Mai, H. ; Thielert, M.* ; Ali, M. ; Al-Maskari, R.* ; Paetzold, J.C. ; Kofler, F. ; Todorov, M.I. ; Molbay, M. ; Kolabas, Z.I. ; Negwer, M. ; Höher, L. ; Steinke, H.* ; Dima, A.* ; Gupta, B. ; Kaltenecker, D. ; Caliskan, Ö.S. ; Brandt, D. ; Krahmer, N. ; Müller, S.* ; Lichtenthaler, S.F.* ; Hellal, F. ; Bechmann, I.* ; Menze, B.* ; Theis, F.J. ; Mann, M.* ; Ertürk, A.

Spatial proteomics in three-dimensional intact specimens.

Cell 185, 5040-5058.e19 (2022)
Verlagsversion DOI PMC
Closed
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
Spatial molecular profiling of complex tissues is essential to investigate cellular function in physiological and pathological states. However, methods for molecular analysis of large biological specimens imaged in 3D are lacking. Here, we present DISCO-MS, a technology that combines whole-organ/whole-organism clearing and imaging, deep-learning-based image analysis, robotic tissue extraction, and ultra-high-sensitivity mass spectrometry. DISCO-MS yielded proteome data indistinguishable from uncleared samples in both rodent and human tissues. We used DISCO-MS to investigate microglia activation along axonal tracts after brain injury and characterized early- and late-stage individual amyloid-beta plaques in a mouse model of Alzheimer's disease. DISCO-bot robotic sample extraction enabled us to study the regional heterogeneity of immune cells in intact mouse bodies and aortic plaques in a complete human heart. DISCO-MS enables unbiased proteome analysis of preclinical and clinical tissues after unbiased imaging of entire specimens in 3D, identifying diagnostic and therapeutic opportunities for complex diseases. Video abstract: [Figure presented]
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Ai ; Alzheimer's ; Deep Learning ; Human Heart ; Mass Spectrometry ; Proteomics ; Robotics ; Spatial-omics ; Tbi ; Tissue Clearing; Alzheimers-disease; Mouse Model; Gene-expression; A-beta; Brain; Normalization; Resolution; Platform; Complex; Plaques
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 0092-8674
e-ISSN 1097-4172
Zeitschrift Cell
Quellenangaben Band: 185, Heft: 26, Seiten: 5040-5058.e19 Artikelnummer: , Supplement: ,
Verlag Cell Press
Verlagsort Cambridge, Mass.
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Tissue Engineering and Regenerative Medicine (ITERM)
Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
Institute of Diabetes and Cancer (IDC)
Institute of Diabetes and Obesity (IDO)
Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
90000 - German Center for Diabetes Research
30201 - Metabolic Health
Forschungsfeld(er) Enabling and Novel Technologies
Helmholtz Diabetes Center
PSP-Element(e) G-505800-001
G-530001-001
G-501900-253
G-501900-221
G-502200-001
G-503800-001
Förderungen Max Planck Society for the Advancement of Science
Helmholtz AI program through grant Deeproad
Nomis Heart Atlas Project Grant (Nomis Foundation)
ERC Consolidator Grant (AE)
Bundesministerium fur Bildung und Wissenschaft (BMBF)
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy
Vascular Dementia Research Foundation
International Max Planck Research School for Life Sciences (IMPRS-LS)
DOI 10.1016/j.cell.2022.11.021
WOS ID 000910983000001
WOS ID WOS:000910983000001
Scopus ID 85144246470
PubMed ID 36563667
Erfassungsdatum 2023-01-10
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