# Metal Nanozymes: New Horizons in Cellular Homeostasis Regulation
## 6 Metal Nanozymes as Cellular Probes for Biosensing
The optimized Zr-MOF-based sensor was shown to be highly selective to lysozyme in a wide concentration range and a low detection limit of 3.6 pg mL\({}^{-1}\), with good repeatability, stability, and applicability in real samples. The selectivity of the Zr-MOF-based sensor was explored toward a series of interference proteins. However, this study has not yet provided any cellular-based evidence for the possibility of the test to be used in vivo, and there soon emerged new instances, such as presented in the work of [PERSON] and co-workers, who designed a sandwich-type Zr-MOFs biosensor with amino-functionalization for the detection of human breast cancer MCF-7 cells (human breast cancer cell line with estrogen, progesterone, and glucocorticoid receptors) [167] and developed them as platforms to anchor phosphate group-modified HER2-targeted aptamer (PO\({}_{4}\)-Apt) to sensitively detect MCF-7 cancer cells. The UiO-66-2 NH\({}_{2}\)-based aptasensor exhibited highly significant biosensing to detect MCF-7 cells and showed an extremely low detection limit of 31 cell mL\({}^{-1}\). The electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV) techniques were used as the detection method. As HER-2 is overexpressed in many, especially breast, cancer cells, the choice of the aptamer was highly justified. However, it is worth emphasizing that in the conducted research it is extremely important to prove the significance of the principle of operation of such a sensing method, while the issue of selecting appropriate aptamers is an open issue for modification in accordance with the diagnostic requirements. From the medical point of view, the prospect of imaging cancerous lesions is of particular importance. However, still, there is room for extensive research in this field, so that the research could be translated into methods that can be carried out on living tissue. A nonobvious but extremely promising solution to this issue was presented in the paper of [PERSON] and coworkers [168]. This group reported a high-sensitivity strategy to selectively detect a lung cancer biomarker of patients' exhalation, aldehydes that possess small Raman cross-section via using gold particles coated with ZIF-8 GSPs@ZIF-8 as a SERS substrate. They used core-shell 3D MOF-based biosensor called GSPs@ZIF-8, composed of Au NPs as core and ZIF-8 as shell, with the pore size allowing adsorption of small aromatic compounds. After the Raman-active 4-ATP molecules were grafted onto the ZIF-8 surface, 4-ethylbenzaldehyde reacted with the amino group on 4-ATP and allowed sensitive aldehyde detection at the ppb level. The porous structure of MOF shell allowed better surface-substrate interaction, overcoming long-standing limitations that gaseous molecule difficultly absorb on solid substrates. By nucleophilic addition reaction with the pregrafted Raman-active molecule of 4-ATP, the gaseous aldehydes were selectively captured and could be detected at ppb level. The GSPs@ZIF-8 exhibited additional Raman enhancements due to not only the precise arrangement of nanoparticles but also the altering penetration depth of electromagnetic field that enhanced the Raman signal of analytes. A capability to selectively trace gaseous cancer biomarkers at ppb levels in mixed exhalation gases gives promising prospects for noninvasive recognition of lung malignancies. Another biomarker testing the level of homeostasis is the methylation of DNA, which is a well-known way of regulating gene expression [169]. In recent years, nanozymes have been used for the colorimetric detection of DNA methylation as a potential epigenetic biomarker. For instance, [PERSON] et al. [75] developed a unique method to detect DNA methylation using the peroxidase-like activity of mesoporous iron oxides. The test successfully detected up to a 10% difference in global DNA methylation levels in synthetic samples and cell lines with good reproducibility and specificity (% RSD = <5%, for n = 3).