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Rui Simoes. Picture.

Rui Simoes

Rui Simöes, Guest researcher

Rui Simoes. Picture.

Optimization of experimental and computational processes to study mitochondrial trafficking and metabolic biomarkers


  • Rui Simoes
  • Teresa Cunha-Oliveira
  • Paulo Oliveira
  • Francisco Pereira

Summary, in English

Background: The complex polarity of neurons requires specialized mechanisms to allocate sufficient number of mitochondria to neurites and synapses to control local calcium and ATP levels. Deficits in trafficking are directly linked to neurodegeneration. Our objective is to demonstrate that alterations of mitochondrial spatial location and metabolism are interconnected and can lead to point-of-no-return situations characterized as the first signs of metabolic rupture.

Material and methods: The neuroblastoma cell line SH-SY5Y was used and differentiated using different protocols to develop a neuronal phenotype. To disturb mitochondrial metabolism and traffic, two mitochondrial poisons, rotenone and 6-hydroxydopamine, were used. Cells were labelled with different fluorescent probes and imaged under a INCell Analyzer. Cell mass and metabolic activity were measured using the SRB and resazurin assays, respectively.

Results: We have shown that a seven-day cell differentiation protocol using retinoic acid provided cells with better neuronal-like morphology, which are suited to study mitochondrial metabolic and dynamic parameters. We also demonstrate that rotenone and 6-hydroxydopamine toxicity is time and dose dependent, altering cell and mitochondria morphology, as well as mitochondrial membrane potential.

Conclusions: Images obtained with increasing concentrations of mitochondrial toxicants showed a gradual effect on different mitochondrial parameters that can be quantified using computational tools. These effects will be paired with metabolic data obtained for the same drug concentrations at later timepoints, to assess the consequences of mitochondrial disruption on cell viability. Building on the collected experimental information, we are training machine learning classification algorithms to accurately predict point-of-no-return situations that unbalance the cell to a state of metabolic catastrophe.

Publishing year







European Journal of Clinical Investigation





Document type

Conference paper: abstract



Conference name

52nd Annual Meeting of the European Society for Clinical Investigation

Conference date

2018-05-30 - 2018-06-01

Conference place

Barcelona, Spain




  • ISSN: 0014-2972