Computational Immunobiology lab
The Talavera-López lab studies the mechanism of homeostasis, inflammation and infection across organs and species using spatially-resolved single-cell technologies and computational biology methods, including machine learning and AI. Our combined approach allows us to understand how cellular processes operate in each organ to keep homeostasis and how cells adapt to different challenges throughout the lifespan of an organism.
Characterising the cellular circuits of stromal-myeloid niches in tissue repair:
We use cutting-edge spatially-resolved single-cell multiomics and computational biology to identify and characterise stromal-myeloid niches in tissue repair across different organs and systems. The overarching goal is to elucidate the circuits of gene regulation, metabolism and cell-cell communication between different cellular effectors, uncovering their collaborative roles in tissue homeostasis, repair, and regeneration. We combine gene expression and chromatin accessibility to profile stromal and myeloid cells at single-cell resolution. We use deep learning methods to link these circuits and map them in situ with spatial biology technologies to construct detailed cellular circuits and identify key regulatory nodes. Following our previous work on the human heart (Talavera-López, 2020 & Kanemaru, 2023), we aim to expand the characterisation of cellular systems across other organs, species and different developmental stages. Furthermore, we leverage our clinical and computational expertise to integrate our work at the cellular level into the clinic. Using deep learning, we integrate cellular circuits with more accessible data modalities such as clinical laboratory tests and electronic health records. This eclectic approach allows us to bridge the gap between cellular behaviour and clinical trajectories to identify biomarkers for early diagnosis and help develop innovative therapeutic interventions.
Elucidating cell-cell communication networks in evolutionary developmental biology:
Our primary objective is to decode the intricate interplay of cell-to-cell communication and metabolism that governs organismal development. We employ spatially-resolved single-cell multiomics to create comprehensive, high-resolution maps of cellular interactions throughout developmental stages. Using spatial biology, our work seeks to capture the dynamic state of individual cells, offering unparalleled insights into their interactions, differentiation, and adaptive cellular behaviour during development. Using explainable AI, we build on prior knowledge to understand the patterns of cell-cell interactions that give rise to individual organs, providing a novel perspective on cellular adaptive behaviours and evolutionary potential across multiple species. This exploration carries substantial clinical implications. As we gain insights into tissue repair mechanisms, we aim to translate our findings into novel therapeutic strategies for regenerative medicine.
Studying the cellular circuits of infection:
Our research is dedicated to decoding the host-pathogen dynamic during infection. We delve into the host’s cellular circuitry and the pathogen’s genomic adaptations to grasp the intricacies of disease progression. Using Natural Language Processing (NLP) methods and genomics, we explore how pathogens manipulate their genome to generate virulence factors and evade the immune system (Talavera-López, 2019, Talavera-López 2021 & Talavera-López 2022). On the host side, we use spatial and single-cell biology to characterise the niches interacting with the pathogen in situ (Sungnak, 2020). This approach enables a comprehensive perspective highlighting dynamic responses during infection. Importantly, we emphasise using explainable AI (xAI), ensuring the results are interpretable and allowing researchers to link the model results with prior knowledge to facilitate the clinical translation of our findings. Moreover, using xAI, we aim to construct reliable patient trajectories during acute infection, potentially highlighting novel tools for early diagnosis. Our work contributes significantly to understanding disease pathogenesis and informs the development of personalised therapies for infectious diseases.