Kriston-Vizi Lab
Bioinformatics
Bioimage analysis, an essential and emerging field in biological research, overcomes the limitations of subjective microscopy analysis through the development and implementation of quantitative and reproducible methodologies.
The computational analysis of image data generated via microscopy is a relatively recent advancement in the extensive history of light microscopy for biological discovery. Historically, scientists have depended on the human visual system to interpret biological images, a method ill-suited for reproducible quantification.
Research scientists in this domain primarily engage in image analysis within the context of biomedical research. This interdisciplinary field necessitates expertise in cell biology, image analysis, and computer programming. This generally involves devising pipelines of analysis to extract data from microscopy-acquired images.
Owing to the specialised nature of biomedical research, bioimage analysis often demands a high level of domain-specific and technical knowledge. Researchers in this field must not only be proficient in their own discipline of image analysis, which includes areas such as computer vision, image processing, statistics and computer science, but also possess an understanding of microscopy, sample preparation, and the cell biology associated to their projects. Due to this combination of skills, BIOINIC students will possess all the requisite competencies to excel in this career.
Artificial intelligence driven 3D high-content screening to identify autophagy modulators
Autophagy modulator kinase inhibitor small molecules
Autophagy
3D Spheroid high-content screening
3D Organoid high-content screening
Bioinformatics
AI, Supervised Machine Learning
Bioimage Analysis
High-Content Screening
High-Content Analysis
Selected publications
Krause M, et al (2024). Vaccinia virus subverts xenophagy through phosphorylation and nuclear targeting of p62. Journal of Cell Biology, 223(6): e202104129.
Wilson GA, et al (2023). Active growth signaling promotes senescence and cancer cell sensitivity to CDK7 inhibition. Molecular Cell, 83(22):4078-4092.e6
Ferraro F, et al (2020). Modulation of endothelial organelle size as an antithrombotic strategy. Journal of Thrombosis and Haemostasis, 18(12):3296-3308
Little D, et al (2018). A single cell high content assay detects mitochondrial dysfunction in iPSC-derived neurons with mutations in SNCA. Scientific reports, 8(1):9033