Gabriel Werr

Background

Gabriel joined the EMBLA research group in June 2019. His research is focused on acoustic trapping for bead-based cytokine detection assays, which will be used for continuous organ-on-chip monitoring. Further, the heating effects during acoustic trapping will be investigated and integration of TEER sensors in OoC systems.

Short CV

  • 2019 - current, PhD student in Microsystems Technology, Uppsala University
  • 2018, MSc Medical Engineering, Technische Universität München (Germany)
  • 2015, B.Eng Mechanical Engineering, OTH Regensburg (Germany)
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Research Project

Project 1: On chip cytokine detection for personalised medicine

Microfluidics and organs-on-chip (OoC) have developed into powerful tools for detailed and accurate studies of cell communication and drug delivery. Some cell behaviour cannot be replicated in standard in vitro assays, here OoC approaches can mimic the missing stimulants that exist in the body, like shear stress, matrix stiffness or cell-cell interactions.

Cell-cell interactions are mediated by soluble factors, such as the cytokines TNF-a and IL-6 [1, 2] and one way to study cell behaviour is to analyse their cytokine release. Standard methods include ELISA plates or microarrays where the cytokines of interest are identified after binding to specific antibodies [3]. While typical OoCs work with low system volumes in the range of 200µl, even the commercially available more efficient bead-based-ELISA requires 50µl of sample size, a significant amount of the total OoC volume. Thereby either limiting the amount or the accuracy of these tests on OoC devices.

In this project we are therefore investigating a miniaturaised bead-based-ELISA assay to detect TNF-a collected from an Infection-on-Chip model. To manipulate the beads inside the chip we will be using acoustophoresis to guide and hold them in place. Acoustophoresis is a label free manipulation technique relying only on the mechanical differences of the fluid and the particle to generate a force. It has the additional benefit of acoustic mixing that will increase the amount of liquid flowing around the beads, thereby increasing the exposure.

The goal of the project is to reduce sample and reagent volumes, while also speeding up and automating the detection assay.

 

Visual abstract

Project 2: On chip TEER measurements for detection of cell proliferation and differentiation

When building OoC models the initial focus is often on the performance of the fluidics and cell growth only. Therefore, the integration of Sensors into already established OoCs can be challenging if changes to the design are to be avoided due to the need of revalidation.

In this project we are implementing a flexible thin-film electrode on tape that can be integrated in OoCs where conventional approaches like wires or thin-film electrodes on glass either cause leakages or are too fragile.

The developed electrode can be routed around obstacles and over steps that would not be feasible with the conventional method. With the adhesive tape, the electrodes can be precisely positioned and once in place they are not shifted in the rest of the OoC assembly.

The goal of the project is to provide a simple to include electrode for OoC systems that can be manufactured separately and placed as needed. This separation provides cost savings when not every chip needs an electrode and the parts of the OoC themselves do not have to go through the thin-film fabrication, allowing for more flexibility in the design of either of them.

Visual abstract

Research Themes

The Biomedical Engineering Division's research is consolidated under four key research themes; precision medicine, sustainability, antimicrobial resistance and data driven life science. My research is focused on precision medicine.

Key Competences

  • Integrated electronics
  • Integrated sensors
  • Microfabrication
  • Nanofabrication
  • Microfluidics
  • Numerical modelling
  • Acoustofluidics
  • Acoustic trapping

Profiles

ORCiD: 0000-0001-9503-5481

DiVA: Person ID 74428

LinkedIn: Gabriel Werr

Acknowledgements

This project has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie (grant agreement No. 812954) and the ERC grant agreement No.101043985).

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