A perspective view on the nanomotion detection of living organisms and its features.
Leonardo VenturelliAnne-Céline KohlerPetar StuparMaria Ines VillalbaAleksandar KalauziKsenija RadoticMassimiliano BertacchiSimone DinarelliMarco GirasoleMilica PešićJasna BankovićMaria E VelaOsvaldo YantornoRonnie WillaertGiovanni DietlerGiovanni LongoSandor KasasPublished in: Journal of molecular recognition : JMR (2020)
The insurgence of newly arising, rapidly developing health threats, such as drug-resistant bacteria and cancers, is one of the most urgent public-health issues of modern times. This menace calls for the development of sensitive and reliable diagnostic tools to monitor the response of single cells to chemical or pharmaceutical stimuli. Recently, it has been demonstrated that all living organisms oscillate at a nanometric scale and that these oscillations stop as soon as the organisms die. These nanometric scale oscillations can be detected by depositing living cells onto a micro-fabricated cantilever and by monitoring its displacements with an atomic force microscope-based electronics. Such devices, named nanomotion sensors, have been employed to determine the resistance profiles of life-threatening bacteria within minutes, to evaluate, among others, the effect of chemicals on yeast, neurons, and cancer cells. The data obtained so far demonstrate the advantages of nanomotion sensing devices in rapidly characterizing microorganism susceptibility to pharmaceutical agents. Here, we review the key aspects of this technique, presenting its major applications. and detailing its working protocols.
Keyphrases
- drug resistant
- public health
- living cells
- gram negative
- multidrug resistant
- single molecule
- fluorescent probe
- acinetobacter baumannii
- induced apoptosis
- working memory
- healthcare
- cell cycle arrest
- mental health
- spinal cord
- big data
- cell death
- endoplasmic reticulum stress
- young adults
- health information
- pseudomonas aeruginosa
- label free
- saccharomyces cerevisiae
- low cost
- risk assessment
- health promotion
- artificial intelligence
- pi k akt
- cystic fibrosis
- deep learning
- sensitive detection