Cyt‐Geist: Current and Future Challenges in Cytometry: Reports of the <scp>CYTO</scp> 2025 Conference Workshops
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".