CellsFromSpace: a fast, accurate, and reference-free tool to deconvolve and annotate spatially distributed omics data.

Motivation: Spatial transcriptomics enables the analysis of cell crosstalk in healthy and diseased organs by capturing the transcriptomic profiles of millions of cells within their spatial contexts. However, spatial transcriptomics approaches also raise new computational challenges for the multidimensional data analysis associated with spatial coordinates.

Results: In this context, we introduce a novel analytical framework called CellsFromSpace based on independent component analysis (ICA), which allows users to analyze various commercially available technologies without relying on a single-cell reference dataset.

From biology to personalized medicine: Recent knowledge in osteosarcoma.

High-grade osteosarcoma is the most common paediatric bone cancer. More than one third of patients relapse and die of osteosarcoma using current chemotherapeutic and surgical strategies. To improve outcomes in osteosarcoma, two crucial challenges need to be tackled: 1-the identification of hard-to-treat disease, ideally from diagnosis; 2- choosing the best combined or novel therapies to eradicate tumor cells which are resistant to current therapies leading to disease dissemination and metastasize as well as their favorable microenvironment.