Rendeiro group

CeMM - Research Center for Molecular Medicine, Austrian Academy of Sciences,

Ludwig Boltzmann Institute for Network Medicine, University of Vienna

Vienna, Austria

group picture

Research highlights

  1. Zheng, et al., Genome Biology, 2025
  2. Abila, Buljan, Zheng, et al., BioRxiv, 2024
  3. Kim, et al., Nature Methods, 2022

Latest news

2025-01-07 - Marsilea is now published at Genome Biology

2024-12-12 - Iva and Tamas attended the inaugural meeting of the European Society for Spatial Biology in Berlin, Germany

2024-12-06 - Exciting opportunity: PhD positions at the LBI-NetMed!

2024-12-03 - Andre gave a talk at the annual conference of the Austrian Platform for Personalized Medicine in Vienna, Austria

2024-11-27 - We had a fun night escaping!

2024-11-18 - The LBI-NetMed inaugural party was a blast!

More news here.

We are a research group at CeMM - the Research Center for Molecular Medicine of the Austrian Academy of Sciences and at the Ludwig Boltzmann Institute for Network Medicine at the University of Vienna working on computational and molecular methods to study human aging and pathology.

A group picture in the CeMM brain lounge, with five lab members looking at the camera.

Spatially resolved,
cross-scale biology
A scientific illustration of a human body with many organs labeled from which a cellular composition and interactions, microanatomy, organ architecture, pathology and disease are derived and studied using graphs represented on the right. In the bottom three icons for histopathology, multiplexed imaging and spatial transcriptomics are shown.
Aging biology,
disease risk and onset
A scientific illustration of the process to derive rich numerical descriptions from histopathological images, which are then used as predictors of biological age and age gaps. In the bottom a concept of how data from blood can be used to predict tissue-specific biological aging insights previously derived from histopathological images.
Principled computational
approaches and software
On the left various examples of deep learning model development for histopathology are shown, on the right icons of various computational tools are shown.

We are interested in uncovering the architectural patterns of the human body across anatomical scales: cellular and micro-anatomical organization, and organ-wide patterns.

We think that these patterns hold the key to understand how cellular changes lead to the progressive loss of physiological ability as individuals age and can be predictive of disease risk and its onset.

To do that, we primarily develop computational methods and software tools for the analysis of spatial data (digital pathology, spatial transcriptomics, highly multiplexed imaging), and its integration with various modalities of molecular and clinical data of individuals along their lifespan.