bioRxiv Science⌕ Search

Biology subjects

Swietlikowska, A.

Publications and source records attributed to Swietlikowska, A..

2 recordsLinked to original sources

Reversible Sandwich-Based Particle Nanoswitch for Continuous Protein Monitoring at Picomolar Concentrations with Automated Calibration

Continuous monitoring of specific proteins is essential for understanding the dynamics of biological systems and for enabling real-time measurement-and-control strategies in bioprocesses. Ideally, sensors for continuous monitoring should be intrinsically reversible and able to perform accurate measurements over long time spans. Here, we present a particle nanoswitch sensor containing two different antibody fragments that bind reversibly to a protein of interest and thus form transient sandwich complexes. The antibody fragments are incorporated into the sensor using site-specific conjugation strategies to achieve optimal antibody orientation. Short-lived sandwich complexes are detected with single-molecule resolution, by tracking the motion of tethered particles. The sensing concept is demonstrated for lactoferrin, an iron-binding and immune-modulating protein. We show continuous measurements of picomolar concentrations with a response time of [~]10 min over periods of 12-15 h. Automated calibration strategies are described that result in a mean absolute relative difference below 10% compared to reference measurements. These results demonstrate how continuous fast protein sensing at picomolar concentrations can be achieved using reversible sandwich-based particle nanoswitches, enabling long-term monitoring of dynamic bioprocesses.

bioengineering↗

"Visualize, describe, compare" - nanoinformatics approaches for material-omics

Bioinformatics and cheminformatics are established disciplines, but nanoinformatics, the development of computational tools for understanding and designing nanomaterials, is still in its infancy. In light of the new data-driven approaches for nanomaterials discovery, there is a growing need for in silico tools tailored to analyze nanomaterials datasets. This is particularly crucial for soft materials, where a crystalline structure cannot be obtained and therefore the characterization datasets are less structured, and there are no standard methods for data mining. Here we present a computational package capable of visualizing, describing, and comparing nanoparticle datasets obtained with super-resolution microscopy at the single-particle and single-molecule level. Our method allows us to: i) visualize multiparametric nanoparticle datasets to grasp material properties and heterogeneity; ii) have a quantitative evaluation of a material through a series of molecular descriptors, and iii) compare different materials quantitatively and globally, going beyond comparison of a single property. We applied this method to a library of targeted nanoparticles revealing particle heterogeneity, similarities, and correlations between the synthesis and the physicochemical properties of the different nanomaterials. Finally, we show the potential of this approach to reveal batch-to-batch variations in time and between users hidden in standard analysis.

bioinformatics↗