Please use this identifier to cite or link to this item: doi:10.22028/D291-43664
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Title: Signal-amplifying Biohybrid Material Circuits for CRISPR/Cas-based single-stranded RNA Detection
Author(s): Mohsenin, Hasti
Schmachtenberg, Rosanne
Kemmer, Svenja
Wagner, Hanna J.
Johnston, Midori
Madlener, Sibylle
Dincer, Can
Timmer, Jens
Weber, Wilfried
Language: English
Publisher/Platform: medRxiv
Year of Publication: 2024
Free key words: biosensing
CRISPR/Cas-powered diagnostics
information processing materials
mathematical modelling
miRNA
ssRNA
synthetic biology
DDC notations: 570 Life sciences, biology
610 Medicine and health
Publikation type: Other
Abstract: The functional integration of biological switches with synthetic buildingblocks enables the design of modular, stimulus-responsive biohybridmaterials. By connecting the individual modules via diffusible signals,information-processing circuits can be designed. Such systems are, however,mostly limited to respond to either small molecules, proteins, or optical inputthus limiting the sensing and application scope of the material circuits. Here,a highly modular biohybrid material is design based on CRISPR/Cas13a totranslate arbitrary single-stranded RNAs into a biomolecular material response.This system exemplified by the development of a cascade of communicatingmaterials that can detect the tumor biomarker microRNA miR19b in patientsamples or sequences specific for SARS-CoV. Specificity of the system is furtherdemonstrated by discriminating between input miRNA sequences with single-nucleotide differences. To quantitatively understand information processing inthe materials cascade, a mathematical model is developed. The model is usedto guide systems design for enhancing signal amplification functionality of theoverall materials system. The newly designed modular materials can be usedto interface desired RNA input with stimulus-responsive and information-processing materials for building point-of-care suitable sensors as well as multi-input diagnostic systems with integrated data processing and interpretation.
DOI of the first publication: 10.1101/2024.06.12.24308852
URL of the first publication: https://www.medrxiv.org/content/10.1101/2024.06.12.24308852v1
Link to this record: urn:nbn:de:bsz:291--ds-436647
hdl:20.500.11880/39132
http://dx.doi.org/10.22028/D291-43664
Date of registration: 6-Dec-2024
Notes: Preprint
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Biowissenschaften
Professorship: NT - Prof. Dr. Wilfried Weber
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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