The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/858870bc-ce57-4782-8ee6-48cfd171db63

Boot, Ruben; Roscani, Alessio; van Buren, Lennard; Maity, Samadarshi ; Koenderink, G.H. (Gijsje) et. al. (2024): Data underlying the publication: High-throughput mechanophenotyping of multicellular spheroids using a microfluidic micropipette aspiration chip. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/858870bc-ce57-4782-8ee6-48cfd171db63.v1

Dataset

• Other Biological Sciences
• Other Physical Sciences
• Physical Sciences
• Biological Sciences

Microfluidics, Soft Matter, Spheroids, Tissue mechanics

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Ruben Boot , Alessio Roscani , Lennard van Buren , Samadarshi Maity , G.H. (Gijsje) Koenderink , Pouyan Boukany

Research paper published in Lab on a Chip, on the development of a microfluidic chip that allows high-throughput aspiration of multicellular spheroids to characterize tissue mechanics. Data includes Excel files with measured spheroid mechanical parameters such as their elasticity and viscosity. Additionally, the Python codes used for analysis and the AutoCAD files including the microfluidic chip designs are included.

• 2024-02-14 first online, published, posted

4TU.ResearchData

PDF, Excel and Word

High-throughput mechanophenotyping of multicellular spheroids using a microfluidic micropipette aspiration chip

TU Delft, Faculty of Applied Sciences, Department of Chemical Engineering