Publications by authors named "Michela Marini"
Phenotypic profiling by high throughput microscopy, including Cell Painting, has become a leading tool for screening large sets of perturbations in cellular models. To efficiently analyze this big data, available open-source software requires computational resources usually not available to most laboratories. In addition, the cell-to-cell variation of responses within a population, while collected and analyzed, is usually averaged and unused.
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- High throughput microscopy and the Cell Painting assay enable large-scale screening of cellular responses, generating extensive image data vital for research in cell biology.* -
- SPACe is a new, open-source Python platform designed to analyze single-cell image data from Cell Painting experiments, offering significantly faster processing times and maintaining accuracy in mechanism of action recognition.* -
- SPACe improves upon existing software by providing better reproducibility, applicability to multiple cell lines, sensitivity to individual cell variations, and enhanced interpretability of morphological features in experimental data.*
Measuring single cell responses to the universe of chemicals (drugs, natural products, environmental toxicants etc.) is of paramount importance to human health as phenotypic variability in sensing stimuli is a hallmark of biology that is considered during high throughput screening. One of the ways to approach this problem is via high throughput, microscopy-based assays coupled with multi-dimensional single cell analysis methods.
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