Abstract B052: Utilizing Dimensionality Reduction for Classification of Cell Senescence and Immune Synapse Formation via Imaging Flow Cytometry
Bibliographic record
Abstract
Abstract The advent of imaging flow cytometry has become a key area of growth for the analysis of cellular process by integrating the dimensionality, resolution, and throughput of flow cytometry with spatial information from image data. Flow cytometry is a single cell method for the characterization of cells or particles in a suspension. Unsupervised machine learning applied to high-parameter flow cytometry datasets allows for an unbiased exploration of complex, unresolved cell populations compared to manual gating. However, these methodologies have not been significantly applied to imaging-derived parameters from imaging flow cytometry. This study investigates dimensionality reduction and clustering for classifying two key cell populations from two assays: the SPIDER B-gal fluorescent assay for detecting cell senescence in fibroblasts and a co-culture assay evaluating synapse formation between engineered CAR T-cells and tumor cell lines. Data were obtained using the FACSDiscover S8 imaging flow cytometer. Typically, flow cytometry analysis involves gating—setting thresholds on forward scatter (FSC), side scatter (SSC), and fluorescence intensity plots to isolate cell subsets based on size, granularity, and expression. This study utilized these approaches to identify senescent cells and immune synapse aggregates based on morphological image-derived features. The analytical workflow for both datasets included debris and apoptotic cell clean-up gates, parameter scaling, and t-SNE application on light loss, FSC, and SSC parameters. Senescence entails permanent cell division cessation alongside gene and morphology changes and is crucial in aging and tumorigenesis. We hypothesized that senescence-induced morphological changes are observable using imaging parameters alone. Validation involved overlaying SPIDER B-gal+ cells on t-SNE post-gating non-debris singlet events, showing SPIDER B-gal+ clustering on a t-SNE island. A heatmap corroborated imaging features of senescent morphology. Assessing immune synapse formation is vital for evaluating CAR T-cell therapy efficacy, facilitated by imaging flow cytometry for high-throughput synapse assessment. CAR-T cells were co-cultured with target cells under varied conditions and timepoints, then acquired on the instrument. A manual gating strategy identified T-cell and target cell synapse formation using CD45 and target antigen expressions. Manually gated events were overlayed on a t-SNE plot utilizing light loss, FSC, and SSC parameters. Multiple islands correlated with low-order aggregates, further gated for T-cell and target cell synapse events. This approach underscores the potential of imaging-derived parameters for identifying senescence and synapse formation using dimensionality reduction, progressing towards label-free cell population identification. Techniques like Hyperfinder, which auto-generate gating strategies, could then be used to develop unsupervised gating strategies for sorting purposes. Citation Format: Mohamed M. Moustafa, Nicholas Battaglia, Viji Premkumar, Ozzie Civelekoglu, Nikki Heller, Raffaello Cimbro. Utilizing Dimensionality Reduction for Classification of Cell Senescence and Immune Synapse Formation via Imaging Flow Cytometry [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B052.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".