Understanding Cell Fate Decisions with Temporal Attention
Authors: Florian Bürger, Martim Dias Gomes, Adrián E. Granada, Noémie Moreau, Katarzyna Bozek
Organizations: Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany · Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany · Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany · Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany · Granada Lab, Berlin, Germany
Understanding non-genetic determinants of cell fate is critical for developing and improving cancer therapies, as genetically identical cells can exhibit divergent outcomes under the same treatment conditions. In this work, we present a deep learning approach for cell fate prediction from raw long-term live-cell recordings of cancer cell populations under chemotherapeutic treatment. Our Transformer model is trained to predict cell fate directly from raw image sequences, without relying on predefined morphological or molecular features. Beyond classification, we introduce a comprehensive explainability framework for interpreting the temporal and morphological cues guiding the model's predictions. We demonstrate that prediction of cell outcomes is possible based on the video only, our model achieves balanced accuracy of 0.94 and an F1-score of 0.93. Attention and masking experiments further indicate that the signal predictive of the cell fate is not uniquely located in the final frames of a cell trajectory, as reliable predictions are possible up to 10 h before the event. Our analysis reveals distinct temporal distribution of predictive information in the mitotic and apoptotic sequences, as well as the role of cell morphology and p53 signaling in determining cell outcomes. Together, these findings demonstrate that attention-based temporal models enable accurate cell fate prediction while providing biologically interpretable insights into non-genetic determinants of cellular decision-making. The code is available at https://github.com/bozeklab/Cell-Fate-Prediction.
Figures & tables
Figure 1: Overview of the proposed cell fate prediction framework and the explainability using temporal attention analysis. 1) From time-lapse microscopy recordings, we extract single-cell trajectories. Each trajectory starts at the first appearance of a cell and ends at the last frame prior to the fate event (division or death). 2) For each cell patch, frame-level embeddings are computed using a ResNet-50 backbone. We randomly mask a portion of frames at the end of each trajectory during training. 3) The sequence of frame embeddings is processed by a Transformer encoder, which integrates temporal information to predict the final cell fate. 4) To enable interpretability, we extract the attention weights assigned to individual frames and categorize them into high-attention and low-attention frames. 5) In parallel, we compute handcrafted cell features for each patch (e.g., area, eccentricity, p53 concentration). 6) Finally, we compare the feature distributions of high- and low-attention frames to identify biologically meaningful patterns associated with the model’s decision-making process.
Figure 2: Confusion matrix normalized over the ground-truth labels, with absolute counts shown in parentheses.
Figure 3: Temporal truncation analysis to assess the importance of late parts of the cell trajectory for its fate prediction. a) Balanced accuracy when progressively truncating sequences by removing an increasing number of frames from the end of each trajectory. b) Balanced accuracy when restricting the model input to only the last k frames of each sequence. c) Class-wise recall under progressive truncation from the sequence end, reported separately for apoptosis and mitosis. d) Class-wise recall when predictions are based solely on the last k frames, highlighting the contribution of late temporal information for each cell fate.
Figure 4: Aggregated attention weights across correctly classified sequences, aligned to the final frame and normalized by the global minimum and maximum attention values.
Figure 5: Effect size analysis based on Cliff’s delta for handcrafted cell features. For each feature, the median Cliff’s delta and its 95% confidence interval are shown separately for mitosis and apoptosis. Positive values indicate higher feature values in high-attention frames compared to low-attention frames, whereas negative values indicate the opposite. The background shading denotes commonly used effect size categories (negligible, small, medium, and large effects), facilitating interpretation of the magnitude and direction of the observed differences.
Figure 6: Illustrative mitotic trajectory with corresponding temporal attention weights. The sequence depicts progressive cell growth followed by characteristic mitotic rounding in the frames immediately preceding division. Frames assigned high attention by the Transformer model are highlighted in green, whereas representative low-attention frames are marked in red.
Biological foundation models have shown strong performance in single-cell representation learning by applying transformer architectures directly to gene-expression matrices. However, these approaches predominantly operate in static settings and do not explicitly model the temporal evolution of developmental programs in the cell. Modeling such dynamics is important for understanding how cellular states progressively emerge, differentiate, and reorganize during development or disease progression. In this work-in-progress paper, we investigate an alternative temporal graph-based perspective in which cellular states are represented through pseudotime-resolved gene regulatory networks and modeled as evolving graph structures over persistent gene identities. Starting from single-cell transcriptomic data, we infer pseudotime trajectories, discretize cells into developmental snapshots, reconstruct one gene regulatory network per snapshot, and apply temporal graph neural networks to forecast biological states. We evaluate this framework on two publicly available mouse developmental datasets, erythroid gastrulation and pancreatic endocrinogenesis, considering three complementary tasks: gene-expression forecasting, link prediction, and out-degree centrality prediction. Our results show that graph-based models outperform well-known foundation-model such as scGPT and scFoundation, suggesting that explicitly modeling evolving regulatory structure provides useful information beyond static pretrained representations. For link prediction and centrality forecasting, temporal graph learning captures non-trivial regulatory dynamics and enables the identification of temporally important gene hubs. Overall, our findings support temporal graph learning as a promising direction for modeling dynamic biological systems and as a complementary paradigm to current foundation model approaches in single-cell biology.
Manuel Dileo, Andrea Sottoriva
Center for Computational Biology, Human Technopole Foundation, Milan, Italy
Machine learning models of cellular interaction dynamics hold promise for understanding cell behavior. Natural killer (NK) cell cytotoxicity is a prominent example of such interaction dynamics and is commonly studied using time-resolved multi-channel fluorescence microscopy. Although tumor cell death events can be annotated at single frames, NK cytotoxic outcome emerges over time from cellular interactions and cannot be reliably inferred from frame-wise classification alone. We introduce BLINK, a trajectory-based recurrent state-space model that serves as a cell world model for NK-tumor interactions. BLINK learns latent interaction dynamics from partially observed NK-tumor interaction sequences and predicts apoptosis increments that accumulate into cytotoxic outcomes. Experiments on long-term time-lapse NK-tumor recordings show improved cytotoxic outcome detection and enable forecasting of future outcomes, together with an interpretable latent representation that organizes NK trajectories into coherent behavioral modes and temporally structured interaction phases. BLINK provides a unified framework for quantitative evaluation and structured modeling of NK cytotoxic behavior at the single-cell level.
Iman Nematollahi, Jose Francisco Villena-Ossa, Alina Moter +6
Department of Computer Science, University of Freiburg, Freiburg, Germany · Institute for Transfusion Medicine and Gene Therapy, University Medical Center Freiburg, Freiburg, Germany · Goethe University, Department of Pediatrics, Experimental Immunology and Cell Therapy, Frankfurt am Main, Germany +2
Single-cell trajectory inference maps transcriptomic measurements onto developmental continua, yet configurations that fit the data equally well can assign conflicting cell fates. FateMultiplicity is a label-free framework that constructs a statistically admissible model set, or Rashomon set, without lineage labels, by evaluating model discrepancy on cross-fitted held-out genes under non-inferiority testing calibrated against random-seed variation. Multiplicity is large and depends more on the diversity of the model space than its size: twelve configurations of a second algorithm expose 20.0% of cells where twenty-four of the first expose 3.8%. Whether the per-cell certified fate margin FM yields more reliable assignments than the fitted model already provides is then tested, and it does not. On simulation ground truth, on the same cells, FM discriminates misassignment at AUC 0.682, against 0.965 for the baseline configuration's own decision margin (p = 0.003) and 0.854 for a seed-dispersion baseline. Informativeness is governed by the breadth of the admitted set, not its cardinality: at cardinality four, seed refits give 0.933 and hyperparameter-perturbed sets 0.701. Relaxing the infimum to a q-quantile recovers discrimination but converges toward the single model's own confidence; the supremum reaches 0.973 because theta*'s membership bounds it from below, while the infimum is unanchored. Multiplicity in trajectory inference is worth measuring and reporting, but per-cell certification over a label-free Rashomon set is not a route to more reliable fate calls. Two constructions survive: a margin-erosion ratio separates real from spurious branch points in simulation (AUC 0.890, untested on real data), and against clonally observed fate, uncertified cells disagree with their clone's outcome 16.4 percentage points more often than certified cells (p < 0.001).