Biology subjects

Pareja-Lorente, E.

Publications and source records attributed to Pareja-Lorente, E..

3 recordsLinked to original sources

bioRxiv2026

Task-adapted biological foundation models uncover perturbation-centric representations

Foundation models have emerged as powerful tools for learning transferable representations of biological systems, yet their latent spaces are typically optimized to capture cellular state rather than the effects of perturbations. Here, we demonstrate that a biological foundation model can be repurposed to learn a fundamentally different representation by changing its learning objective. We fine-tuned scGPT, a transformer pre-trained on over 30 million single-cell transcriptomes, on more than three million LINCS L1000 perturbation profiles using a supervised objective that predicts perturbation identity. This transformed the latent space into a perturbation-centric representation that aligned transcriptional responses induced by the same chemical or genetic perturbation across heterogeneous experimental conditions. Fine-tuned embeddings substantially outperformed both gene expression profiles and the original pre-trained model, recovering 85-100% of perturbations within the top 100 nearest neighbors and increasing perturbation classification accuracy from 10-19% to 25-49%. Remarkably, although the model was trained exclusively to recognize perturbation identity, the learned representation spontaneously captured orthogonal biological relationships never provided during training, including chemical similarity (AUROC up to 0.81), mechanisms of action (Hit@10 up to 100%), compound-target relationships (AUROC up to 0.74), and functional relationships between genetic perturbations. The resulting embedding space enabled mechanism-of-action annotation of nearly 12,000 previously uncharacterized compounds, prioritization of target-related chemical-genetic associations, and contextualization of unseen perturbations and external transcriptomic datasets. Together, our results establish objective-driven adaptation as a general strategy for repurposing biological foundation models to learn reusable representations of complex biological phenomena.

bioinformatics↗

bioRxiv2025

Systematic assessment of microenvironment-dependent transcriptional patterns and intercellular communication

Understanding cell-cell communication and its dependence on spatial organization is critical for unraveling tissue complexity and organ function. This study integrates single-cell RNA sequencing (scRNA-seq) with spatial transcriptomics (ST) to systematically assess how spatial niches influence gene expression and intercellular communication. Using breast cancer, brain cortex, and heart datasets, our analyses reveal limited global transcriptional changes in cells depending on their spatial microenvironment, with differential gene expression observed in less than half the samples explored. Moreover, cell-cell communication predictions, derived from ligand-receptor pairs, exhibit minimal correlation with spatial colocalization of cell types. Overall, our study underscores the limitations of using scRNA-seq data to capture niche-specific molecular interactions, even when spatial information is leveraged, and it highlights the need for novel strategies to refine our understanding of intercellular communication dynamics at molecular level.

bioinformatics↗

bioRxiv2024

Integration of diverse bioactivity data into the Chemical Checker compound universe

Chemical signatures encode the physicochemical and structural properties of small molecules into numerical descriptors, forming the basis for chemical comparisons and search algorithms. The increasing availability of bioactivity data has improved compound representations to include biological effects, although bioactivity descriptors are often limited to a few well-documented molecules. To address this issue, we implemented a collection of deep neural networks able to leverage the experimentally determined bioactivity data associated to small molecules and infer the missing bioactivity signatures for any compound of interest. However, unlike static chemical descriptors, these bioactivity signatures dynamically evolve with new data and processing strategies. Here, we present a computational protocol to modify or generate novel bioactivity spaces and signatures, describing the main steps needed to leverage diverse bioactivity data with the current knowledge, as catalogued in the Chemical Checker (CC), using the predefined data curation pipeline. We illustrate the functioning of the protocol through four specific examples, including the incorporation of new compounds to an already existing bioactivity space, a change in the data pre-processing without altering the underlying experimental data, and the creation of two novel bioactivity spaces from scratch, which are completed in under 9 hours using GPU computing. Overall, this protocol offers a guideline for installing, testing and running the CC data integration approach on user-provided data, with the aim of extending the annotation presented for a limited number of small molecules to a larger chemical landscape. Key pointsO_LIThe Chemical Checker is a large collection of processed, harmonized and integrated bioactivity signatures for over 1 million small molecules. Data are organized into 5 levels of increasing biological complexity and curation degrees: from chemical properties to clinical outcomes and from raw data representing explicit knowledge to embedded signatures inferred from observed bioactivity patterns. C_LIO_LIThe Chemical Checker package provides a predefined and versatile framework to integrate user-provided data to the Chemical Checker universe of small molecules and generate novel customized bioactivity signatures. C_LI Key referencesDuran-Frigola et al. "Extending the small-molecule similarity principle to all levels of biology with the Chemical Checker." Nature Biotechnology 38.9 (2020): 1087-1096. Bertoni et al. "Bioactivity descriptors for uncharacterized chemical compounds." Nature Communications 12.1 (2021): 3932.

bioinformatics↗