Theme I: Computational Tissue Exposomics
The research line Computational Tissue Exposomics, focuses on understanding how environmental exposures affect tissues and organ systems. Using controlled exposome studies in mouse models and in vitro systems, we investigate the toxicological effects of environmental stressors and how these are reflected in metabolic, lipidomic, transcriptomic and proteomic changes. We also aim to go one step further: to detect and spatially map environmental toxicants as an integral part of the human tissue microenvironment (TME). By integrating tissue exposomics with computational approaches that combine spatial and systems biology, we connect the presence and distribution of environmental agents with tissue morphology, molecular profiles and cellular responses. Our goal is to identify tissue signatures of exposure, understand mechanisms of toxicity across organs, and determine how environmental toxicants become biologically embedded in human tissues, influence immunometabolism, and contribute to dysfunction and disease.
RENAL HEAT MAP | Nierstichting;
Air pollution aggravates renal ischaemia–reperfusion-induced acute kidney injury. Sanches TR, Parra AC, Sun P, Graner MP, Itto LYU, Butter LM, Claessen N, Roelofs JJTH, Florquin S, Veras MM, Andrade MF, Saldiva PHN, Kers J, Andrade L, Tammaro A. The Journal of Pathology (2024).
Global environmental change and the gut-kidney-brain axis: a review and framework of vulnerability and resilience. Adalat S, Giannakou K, Valderrama B, Cárdenas-Aguilera JG, Sakkas GK, Overeem J, Hafez G, Kelly D, Vulevic J, He G, Mihăilă SM, Tammaro A, Ikiz B, Cryan JF, Climate-Kidneys-Cognition Working Group. The Lancet Planetary Health (2026).
Chronic exposure to low-concentration urban PM2.5 accelerates maladaptive repair after ischemic injury via mitochondrial dysfunction and lysosomal stress. Sun P, Parra AC, Sanches TR, Wikuats CFH, Butter L, Claessen N, Baelde HJ, Schimmel IM, van der Wel N, Janssens GE, Houtkooper RH, Vaz FM, Roelofs JJTH, Boor P, Strauch M, Andrade MF, Andrade L, Florquin S, Kers J, Romagnolo A, Tammaro A. bioRxiv (2026).
NNMT inhibition counteracts tubular senescence and fibrosis in early stages of chronic kidney disease. Chanvillard L, Lantermans HC, Wall C, Thevenet J, Butter LM, Tauzin L, Claessen N, Christen S, Holzwarth JA, Karaz S, Lassueur S, Lizzo G, Sanchez-Garcia JL, Métairon S, Ferro V, Moco S, van Bommel EJM, van Baar MJB, Hesp AC, van Raalte DH, Roelofs JJTH, Neelakantan H, Watowich SJ, Sanders MJ, Feige JN, Sorrentino V, Tammaro A. Cell Reports (2026).
Immunometabolic rewiring of tubular epithelial cells in kidney disease. van der Rijt S, Leemans JC, Florquin S, Houtkooper RH, Tammaro A. Nature Reviews Nephrology (2022).
Theme II: Systemic Immunopathology
The development of systemic immune responses relies on a continuous, dynamic dialogue between active defense and tissue tolerance across the multi-organ system. To understand how persistent environmental pressure disrupts this balance, we apply complexity research to systemic immunopathology. We approach tissues as complex adaptive systems where fundamental network motifs dictate the spatial organization of cells within the tissue microenvironment (TME), ultimately self-organizing into emergent, organ-wide states. To decode this architecture, we leverage self-supervised deep learning and graph neural networks to mine spatial topologies directly from human tissue. Because biological injury and immune adaptation are fluid processes, we replace flawed ordinal classifications with deep learning-derived continuous severity scores that mathematically track the topological shift from homeostasis to inflammatory decompensation. Crucially, by mapping macro-level geospatial reasoning directly onto these micro-scale network dynamics within the TME, we pinpoint the exact multi-scalar tipping points where environmental stressors cause resilient tissue to collapse. If systemic disease is an emergent property of destabilized networks, so is immune resolution and tolerance. By uncovering these spatial blueprints, we aim to understand how systemic immune responses can be computationally mapped and ultimately guided toward active multi-organ immune resolution, tissue regeneration, and homeostasis.
BanffNET, a deep learning system for comprehensive histological lesion quantification in kidney transplant biopsies. Buzzanca G, Pala C, He J, Hofstraat-Boersma R, Tammaro A, Van Midden D, Bülow R, Hölscher DL, Mühlfeld AS, Köller M, Kozakowski N, Böhmig GA, Halloran PF, Van der Helm D, Meziyerh S, Venhuizen JH, Haitjema S, Dijkstra J, Hilbrands LB, Steenbergen EJ, Van Zuilen AD, Nurmohamed AS, Bemelman FJ, Bruns IB, Callegaro G, Van de Water B, Pieters TT, Breimer GE, Rossi GM, Fiaccadori E, Maggiore U, Roelofs JJTH, Testa F, Fontana F, Abiola AA, Delsante M, Corthals GL, Peters-Sengers H, Nguyen TQ, LUMC Global Kidney Biopsy Reader Group, Koshy P, Florquin S, Boor P, Teng YKO, Naesens M, De Vries APJ, Kers J. MedRxiv (2026).
Graph neural networks in histopathology: emerging trends and future directions. Brussee S, Buzzanca G, Schrader AMR, Kers J. Medical Image Analysis (2024).
Application of spatial-omics to the classification of kidney biopsy samples in transplantation. Tasca P, Van den Berg BM, Rabelink TJ, Wang G, Heijs B, Van Kooten C, De Vries APJ, Kers J. Nature Reviews Nephrology (2024).
Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Vasey B, Nagendram M, Campbell B, Clifton DA, Collins GS, Denaxas S, Denniston AK, Faes L, Geerts B, Ibrahim M, Liu X, mateen BA, Mathur P, McCradden MD, Morgan L, Ordish J, Rogers C, Saria S, Ting DSW, Watkinson P, Weber W, Wheatstone P, McCulloch P, the DECIDE-AI expert group. Nature Medicine (2022), British Medical Journal (2022).
Deep Learning-based classification of kidney transplant pathology: a retrospective, multicenter, proof of concept study. Kers J, Bülow RD, Klinkhammer BM, Breimer GE, Fontana F, Adefidipe Abiola A, Hofstraat R, Corthals GL, Peters-Sengers H, Djudjaj S, Von Stillfried S, Hölscher DL, Pieters TT, Van Zuilen AD, Bemelman FJ, Nurmohamed AS, Naesens M, Roelofs JJTH, Florquin S, Floege J, Nguyen TQ, Kather JN, Boor P. The Lancet Digital Health (2022).
