IIT Kanpur researchers unveil Renoir, a new computational tool that maps how cell‑to‑cell signalling shapes gene activity in spatial context, accelerating cancer and developmental research.
IIT Kanpur unveils Renoir
Researchers at the Indian Institute of Technology Kanpur (IIT Kanpur), led by Prof. Hamim Zafar, have developed a new computational tool named Renoir that helps decode how cells communicate within complex tissues. The study, published in Nature Communications, introduces a method to map how signals between cells influence gene activity in specific spatial locations, addressing a long‑standing gap in biomedical research.
Renoir builds on spatial transcriptomics – a technology that captures high‑resolution snapshots of gene activity while preserving the precise location of each cell within a tissue section. By combining these spatial maps with advanced computational modelling, the tool reveals not only which cells are “talking” to each other but also how these interactions reshape downstream gene expression in distinct tissue regions.
Understanding tissue‑scale cell communication
Organs and tissues rely on continuous communication between cells to coordinate development, repair, immune responses, and disease progression. Cells constantly exchange biochemical signals that can switch genes on or off in neighbouring cells, guiding processes such as organ formation, wound healing, and tumour growth.
Recent advances in single‑cell and spatial‑omics technologies allow scientists to measure which genes are active in thousands of individual cells and where those cells sit within a tissue. However, it has remained difficult to connect specific signalling molecules from one cell to the gene‑activity changes they trigger in another, especially within the three‑dimensional architecture of living tissues. Renoir tackles this challenge head‑on by linking spatial transcriptomic data to known cell‑signalling pathways.
How Renoir “paints” gene‑interaction maps
Using spatial transcriptomics as its foundation**, Renoir constructs 2D maps of interacting gene networks across the tissue landscape. The tool identifies “communication niches” – distinct micro‑environments where particular signalling pathways dominate and drive characteristic gene‑expression patterns in receiving cells.
By overlaying protein‑ligand‑receptor interaction data onto the spatial‑omics readouts, Renoir reveals which signalling events are spatially active and which downstream genes respond in specific zones of the tissue. This approach enables researchers to move beyond bulk‑tissue averages and study localised communication hubs that may underlie early tumour growth, angiogenesis, or immune‑cell infiltration.
Prof. Zafar explained that Renoir allows investigators to determine how a specific signalling interaction alters gene activity in the receiving cells. “This opens up new possibilities for identifying disease‑driving communication networks and discovering more precise therapeutic targets, especially in complex conditions like cancer,” he said.
From Impressionist art to bio‑mapping
The researchers named the tool Renoir after the French Impressionist painter Pierre‑Auguste Renoir, whose work captures light and colour in nuanced, layered ways. Narein Rao, former MS student at IIT Kanpur and the study’s first author, said the name reflects how the tool “paints” the activity of interacting genes across the 2D tissue landscape.
Just as an Impressionist canvas blends discrete brushstrokes into a cohesive visual scene, Renoir integrates myriad gene‑expression signals into a coherent map of cell‑to‑cell communication. The tool visualises how different signalling pathways “illuminate” particular regions of the tissue, highlighting areas where communication is most intense and biologically relevant.
Testing Renoir on liver development and cancer
The IIT Kanpur team developed and tested Renoir in collaboration with Prof. Ankur Sharma’s laboratory at the Garvan Institute of Medical Research, Australia. The researchers applied the method to spatial transcriptomic datasets from foetal liver and liver‑cancer samples, seeking to validate its ability to reveal biologically meaningful interactions.
In these datasets, Renoir successfully identified cell‑to‑cell signalling patterns associated with both organ‑development processes and tumour‑progression networks. The tool detected spatially restricted interactions that aligned with known developmental and oncogenic pathways, such as those involved in cell‑proliferation, migration, and immune modulation. These findings suggest that Renoir can help distinguish developmentally essential signals from pathogenic ones in the same tissue.
Applications across cancer and precision medicine
Because Renoir works with major spatial‑profiling platforms used by laboratories worldwide, it can integrate into existing workflows without requiring proprietary hardware. Researchers can apply the tool to diverse tissue types – solid tumours, developing organs, and chronically inflamed tissues – to uncover previously hidden communication networks.
In cancer biology, Renoir may help pinpoint tumour‑permissive micro‑environments, where stromal and immune cells send signals that promote cancer‑cell survival and metastasis. It can illuminate how signalling niches guide cell‑fate decisions during organogenesis in developmental biology. In precision‑medicine programmes, the tool could support the design of therapies that selectively disrupt pathological communication networks while sparing healthy tissue communication.
By making it possible to “see” which cell‑to‑cell signals are active in which regions of a tissue, Renoir stands to accelerate discoveries in cancer research, organ‑development studies, and targeted therapy development, reinforcing India’s role in cutting‑edge computational‑biology innovation.
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