arXiv subjects

Martim Dias Gomes

Publications and source records attributed to Martim Dias Gomes.

3 recordsLinked to original sources

arXiv2026

Characterization of DLBCL cell of origin-phenotypes based on tumor microenvironment features

Diffuse large B-cell lymphoma (DLBCL) is an aggressive form of non-Hodgkin lymphoma with a high recurrence rate. The molecular profiling of DLBCL tumors culminated in several immunohistochemistry algorithms for prognostic stratification. Among those, the Hans classifier is widely used for classifying DLBCL into germinal center B-cell-like (GCB) and non-germinal center/activated B-cell-like (non-GCB/ABC) subtypes. The Hans classifier primarily evaluates protein expression of tumor-associated markers, however the tumor microenvironment (TME) of DLBCL includes a myriad of immune and stromal cells, cytokines, and extracellular matrix components that contribute to tumor growth, immune evasion, and recurrence rate. Although the Hans classifier provides a practical method for subtype identification, incorporation of TME information may improve risk stratification and further refine patient groups. Here, we present an unbiased deep learning-based approach to extract meaningful features from TME of DLBCL tumors for the automated processing and analysis of multiplexed images of a DLBCL patient cohort. Our pipeline quantifies a range of features that describe tumor sample cell composition, morphology, and its spatial organization. We point to alterations in the proportions of several cell populations between GCB and ABC tumors including increased immune cell proportions of the ABC and its preferential interaction with the M2-macrophages. Our analysis offers an in-depth characterization of the DLBCL subtypes and is exemplary of how our pipeline can be used for detailed quantitative analysis of a tumor and its subtypes.

q-bio.QM↗

arXiv2026

Understanding Cell Fate Decisions with Temporal Attention

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.

cs.CV↗

arXiv2025

TransientTrack: Advanced Multi-Object Tracking and Classification of Cancer Cells with Transient Fluorescent Signals

Tracking cells in time-lapse videos is an essential technique for monitoring cell population dynamics at a single-cell level. Current methods for cell tracking are developed on videos with mostly single, constant signals and do not detect pivotal events such as cell death. Here, we present TransientTrack, a deep learning-based framework for cell tracking in multi-channel microscopy video data with transient fluorescent signals that fluctuate over time following processes such as the circadian rhythm of cells. By identifying key cellular events - mitosis (cell division) and apoptosis (cell death) our method allows us to build complete trajectories, including cell lineage information. TransientTrack is lightweight and performs matching on cell detection embeddings directly, without the need for quantification of tracking-specific cell features. Furthermore, our approach integrates Transformer Networks, multi-stage matching using all detection boxes, and the interpolation of missing tracklets with the Kalman Filter. This unified framework achieves strong performance across diverse conditions, effectively tracking cells and capturing cell division and death. We demonstrate the use of TransientTrack in an analysis of the efficacy of a chemotherapeutic drug at a single-cell level. The proposed framework could further advance quantitative studies of cancer cell dynamics, enabling detailed characterization of treatment response and resistance mechanisms. The code is available at https://github.com/bozeklab/TransientTrack.

cs.CV↗