Cyt‐Geist: Current and Future Challenges in Cytometry: Reports of the <scp>CYTO</scp> 2025 Conference Workshops
Notice bibliographique
Résumé
joint effort begun in 2018 and continued in 2019 by Kamila Czechowska proved to be a valuable reference [1,2].Building on that spirit, we present summaries from CYTO 2025, held in Denver, Colorado, from May 31 to June 4.This manuscript serves as a summary report of 15 workshops held at CYTO 2025.We present, in concise form, the current and future challenges in cytometry identified by workshop organizers and participants.The manuscript is organized into three thematic sections: Building the Cytometry Infrastructure of the Future (Standardization, Sharing, and Sustainability in Cytometric Practice); Applied Innovation Across Modalities (Expanding Possibilities: From Fluorescence to Imaging and Automated Annotation); and the People Behind the Panels (Workflows, Workspaces, and the Human Side of Cytometry).Each section addresses critical aspects of modern cytometry practice, from foundational infrastructure and technical innovation to professional development and operational sustainability.We intend to serve with this joint workshop report the global community involved in single-cell analysis and cytometry. | Section 1: Building the Cytometry Infrastructure of the Future: Sharing and Sustainability in Cytometric PracticeWorkshops 2, 3, 7, 8, and 13 addressed the foundational frameworks needed to ensure success for the cytometry community in the years ahead.The themes ranged from pre-analytical considerations in sample handling to standardization of instruments and file formats, and from data stewardship to the future of public repositories.Together, these discussions underscored that sustainable progress in cytometry depends not only on innovation but also on building robust infrastructure, shared standards, and reliable practices.WS02 (Pre-analytical variables) examined the numerous factors that can affect the quality of peripheral blood mononuclear cells (PBMCs) and the interpretation of downstream assays, emphasizing the importance of defining the context of use (COU) and minimizing variability.WS08 (Spectral Standardization) highlighted the need for community-driven best practices to account for differences in platforms, reagents, and data analysis methods, including nomenclature and unmixing.WS13 (FCS 4.0) provided historical context for FCS file formats and outlined the community's priorities and timeline for modernizing the standard to support spectral data, interoperability, and high-dimensional analysis.WS03 (FlowRepository) focused on the sustainability and governance of this community resource, emphasizing the need for accessibility, clear licensing, and forward-looking technical development.WS07 (Data Management and Sharing) brought the perspective of Shared Resource Laboratories (SRLs), advocating for broad adoption of the FAIR data principles to support reproducibility, accessibility, and long-term scientific value.Taken together, the outcomes of these workshops point to a central conclusion: the future of cytometry depends on shared responsibility for infrastructure.By harmonizing practices, investing in sustainable platforms, and fostering a culture of open data and reproducibility, the community can ensure that cytometry remains a cornerstone of biomedical discovery.The establishment of task forces, working groups, and continued discussions through community platforms demonstrates the commitment to translating workshop insights into actionable progress.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,008 | 0,004 |
| Science ouverte | 0,002 | 0,004 |
| Intégrité de la recherche | 0,003 | 0,005 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,014 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».