Alejandra Bruna

Serval is a modular framework for decoding spatial transcriptomics data that introduces Cosine, a method improving transcript recovery and correlation with expression references.

This study shows chemotherapy induces a Wnt-active diapause-DTP state in breast cancer cells, which can be targeted by combined Wnt and chemotherapy inhibition.

The paper identifies transcriptional noise as a driver of cancer treatment adaptation through AP-1 and KDM5-mediated chromatin rewiring in neuroblastoma and hepatoblastoma.

This study uses single-cell multiomics to trace clonal and phenotypic dynamics in hepatoblastoma after cisplatin treatment, identifying a plasticity-proliferation axis driven by BIRC5.

The paper uses single-cell proteomics and perturbation experiments to improve drug sensitivity predictions in breast cancer and identify genomic features associated with treatment response.

ExSeq enables multiplexed RNA mapping at nanoscale resolution by expanding biological specimens for in situ sequencing.

The paper uses machine learning to predict drug response in tumour models by reducing molecular profile dimensionality, finding multi-gene predictors outperform single-gene markers in several cancer types.