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Eling, N.* ; Dorier, J.* ; Rusakiewicz, S.* ; Liechti, R.* ; Devanand, P.* ; Daniel, M.* ; Windhager, J.* ; Fernandez, B.P.* ; Déglise, S.* ; Despland, L.* ; Benyagoub, A.* ; Możejko, M.* ; Uchal, D.* ; Szczurek, E. ; Loboda, A.* ; Sandkuijl, D.* ; Parsotam, N.* ; Hong, H.S.* ; Morfouace, M.* ; Guex, N.* ; Coukos, G.* ; Bodenmiller, B.* ; Tissot, S.* ; Schulz, D.M.*

Multi-modal image analysis for large-scale cancer tissue studies within IMMUcan.

Cell Rep. Methods 5:101170 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
In cancer research, multiplexed imaging allows detailed characterization of the tumor microenvironment (TME) and its link to patient prognosis. The integrated immunoprofiling of large adaptive cancer patient cohorts (IMMUcan) consortium collects multi-modal imaging data from thousands of patients with cancer to perform broad molecular and cellular spatial profiling. Here, we describe and compare two workflows for multiplexed immunofluorescence (mIF) and imaging mass cytometry (IMC) developed within IMMUcan to enable the generation of standardized data for cancer tissue analysis. The IFQuant software supports web-based, user-friendly, and reproducible analysis of mIF data. High sample throughput for IMC is achieved by optimizing experimental protocols, developing a robotic arm for automated slide loading, and classification-based cell typing. Using our manually labeled single-cell data, we show that tree-based methods outperform other cell-phenotyping tools. These pipelines form the basis for multiplexed image analysis within IMMUcan, and we summarize our learnings from 5 years of development and optimization.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cp: Cancer Biology ; Cp: Immunology ; Immucan Consortium ; Cancer ; Cell Segmentation ; Image Analysis ; Imaging Mass Cytometry ; Multiplexed Imaging ; Single-cell Analysis ; Spatial Proteomics ; Tumor Microenvironment ; Workflow; Signal Amplification; R Package; Cells; Immunotherapy
Sprache englisch
Veröffentlichungsjahr 2025
HGF-Berichtsjahr 2025
ISSN (print) / ISBN 2667-2375
e-ISSN 2667-2375
Zeitschrift Cell Reports Methods
Quellenangaben Band: 5, Heft: 9, Seiten: , Artikelnummer: 101170 Supplement: ,
Verlag Elsevier
Verlagsort 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
Begutachtungsstatus Peer reviewed
Institut(e) Human-Centered AI (HCA)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-540012-001
Förderungen Alliance Healthcare
EFPIA
European Union
Innovative Medicines Initiative 2 Joint Undertaking
DOI 10.1016/j.crmeth.2025.101170
WOS ID 001594989400001
WOS ID 001573817300001
Scopus ID 105015750215
PubMed ID 40930089
Erfassungsdatum 2025-11-04
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