Bongaerts, M. | bioRxiv Science

bioRxiv2020

Parallelized manipulation of adherent living cells by magnetic nanoparticles-mediated forces

The remote actuation of cellular processes such as migration or neuronal outgrowth is a challenge for future therapeutic applications in regenerative medicine. Among the different methods that have been proposed, the use of magnetic nanoparticles appears to be promising since magnetic fields can act at a distance without interactions with the surrounding biological system. To control biological processes at a subcellular spatial resolution, magnetic nanoparticles can be used either to induce biochemical reactions locally or to apply forces on different elements of the cell. Here, we show that cell migration and neurite outgrowth can be directed by the forces produced by a switchable parallelized array of micro-magnetic pillars, following passive uptake of nanoparticles. Using live cell imaging, we first demonstrate that adherent cell migration can be biased toward magnetic pillars and that cells can be reversibly trapped onto these pillars. Second, using differentiated neuronal cells we were able to induce events of neurite outgrowth in the direction of the pillars without impending cell viability. Our results show that the range of forces applied needs to be adapted precisely to the cellular process under consideration. We propose that cellular actuation is the result of the force on the plasma membrane caused by magnetically filled endo-compartments, which exert a pulling force on the cell periphery.

bioengineering↗

bioRxiv2020

Screening for inborn errors of metabolism using untargeted metabolomics and out-of-batch controls

MotivationUntargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). In order to judge if metabolite levels are abnormal, analysis of a large number of reference samples is crucial to correct for variations in metabolite concentrations resulting from factors such as diet, age and gender. However, a large number of controls requires the use of out-of-batch controls, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e. technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Methods & resultsBased on six metrics, we compared existing normalization methods on their ability to reduce batch effects from eight independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method which uses 17 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age- and sex as covariates fitted on control samples obtained from all eight batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal as well as in the detection of 178 known biomarkers across 45 IEM patient samples and performed at least similar to an approach using 15 within-batch controls. Furthermore, our regression model indicates that 10-24% of the considered features showed significant age-dependent variations. ConclusionsOur comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch controls to establish clinically-relevant reference values for metabolite concentrations. These findings opens possibilities to use large scale out-of-batch control samples in a clinical setting, increasing throughput and detection accuracy. AvailabilityMetchalizer is available at https://github.com/mbongaerts/Metchalizer/

bioinformatics↗