Simultaneous Imaging of Zn²⁺ and Cu²⁺ in Living Cells Based on DNAzyme Modified Gold Nanoparticle

Trace Zn²⁺ and Cu²⁺ in living cells play important roles in the regulation of biological function. It is significant to simultaneously detect the cellular Zn²⁺ and Cu²⁺. Here, we present a novel two-color fluorescence nanoprobe based on the DNAzymes for simultaneous imaging of Zn²⁺ and Cu²⁺ in living cells. The probe consists of a 13 nm gold nanoparticle, DNAzymes that are specific for Zn²⁺ and Cu²⁺, and the substrate strands labeled with fluorophores at the 5′ end and quenchers at the 3′ end. The fluorescence of the fluoreophores is quenched both by the gold nanoparticle and the quencher. After the nanoprobes are transferred into the cells, the substrate strands would be cleaved in the presence of the Zn²⁺ and Cu²⁺ target, resulting in disassociation of the shorter DNA fragments containing fluorophores, which produce fluorescence signals correlated with the location and concentration of the Zn²⁺ and Cu²⁺. The nanoprobe exhibits high specificity, nuclease stability, and good biocompatibility. Moreover, the nanoprobe can simultaneously monitor the cellular Zn²⁺ and Cu²⁺ with an on-site manner, providing the information on localization and concentration of targets, which is significant to further research the Zn²⁺- and Cu²⁺-relative cellular events and biological process. The proposed method has shown great potential in the detection of multiple metal ions in living cells, which may help us to better understand the function of metal ions in the fields of biochemistry, molecular biology, and cellular toxicology

Hub regions of the brain functional networks corresponding to the explicit and implicit language tasks, respectively.

<p>Note: “–” indicates that the value of the normalized betweenness centrality in the region was within one standard deviation from the mean. The shaded texts were the shared hub regions detected under both the two tasks.</p

Definitions and descriptions of the global and regional parameters of brain functional networks used in the current study.

<p>Definitions and descriptions of the global and regional parameters of brain functional networks used in the current study.</p

Brain regions showing significant difference in the mean (SD) integrated betweenness centrality between the brain functional networks corresponding to the explicit and implicit language tasks.

<p>Brain regions showing significant difference in the mean (SD) integrated betweenness centrality between the brain functional networks corresponding to the explicit and implicit language tasks.</p

Brain regions used in constructing the human brain functional networks in the present study.

<p>These regions are originally described in the Automated Anatomical Labeling (AAL) template by Tzourio-Mazoyer et al. (2002), and the abbreviations are listed according to Salvador et al. (2005) and Achard et al. (2006). The same 45 brain regions were extracted from the right and left hemispheres to provide 90 regions in total for each subject.</p><p>Note: Abb., abbreviations.</p

Large Scale Brain Functional Networks Support Sentence Comprehension: Evidence from Both Explicit and Implicit Language Tasks

<div><p>Previous studies have indicated that sentences are comprehended via widespread brain regions in the fronto-temporo-parietal network in explicit language tasks (e.g., semantic congruency judgment tasks), and through restricted temporal or frontal regions in implicit language tasks (e.g., font size judgment tasks). This discrepancy has raised questions regarding a common network for sentence comprehension that acts regardless of task effect and whether different tasks modulate network properties. To this end, we constructed brain functional networks based on 27 subjects’ fMRI data that was collected while performing explicit and implicit language tasks. We found that network properties and network hubs corresponding to the implicit language task were similar to those associated with the explicit language task. We also found common hubs in occipital, temporal and frontal regions in both tasks. Compared with the implicit language task, the explicit language task resulted in greater global efficiency and increased integrated betweenness centrality of the left inferior frontal gyrus, which is a key region related to sentence comprehension. These results suggest that brain functional networks support both explicit and implicit sentence comprehension; in addition, these two types of language tasks may modulate the properties of brain functional networks.</p></div

Small-world properties changing with the varied sparsity of the functional networks for both the explicit and implicit language tasks.

<p>Here stands for the normalized clustering coefficient, for the normalized characteristic path length, and σ for the ratio of to . The values of and were evaluated on each individual brain network and then averaged over all subjects in the explicit and implicit language tasks, respectively. In a wide range of sparsity (0.10 ≤ sparsity ≤ 0.49), the functional networks for the implicit or explicit language tasks exhibit >1, ≈1, and σ> 1.1, which indicated prominent small-world properties.</p

Example stimulus materials used in the explicit and implicit language tasks in the present study.

<p>Note: Three types of sentences, high cloze (HC) sentences, low cloze (LC) sentences, and violation sentences (SV), were adopted to manipulate the difficulty levels of the sentence-level semantic unification in both the implicit and explicit language tasks.</p

Illustration of the procedures used to construct brain functional networks.

<p>Raw functional MR images are preprocessed to produce normalized data that are further parcellated by a prior brain atlas into 90 brain regions. Then we averaged the time series over all voxels in each subject for each language task to generate the regional representative time course. The Pearson’s correlations between all possible pairs of 90 time courses for each specific task is computed and averaged for the same task for each subject. A connectivity matrix for a subject is shown for the explicit (SEM) and implicit (FONT) language tasks, respectively. The axial three-dimensional image of the template is shown using MRIcroN software (<a href="http://www.sph.sc.edu/comd/rorden/mricron/" target="_blank">http://www.sph.sc.edu/comd/rorden/mricron/</a>).</p

Integrated global parameters mean (SD) of the human brain functional networks and their statistical difference between the explicit and implicit language tasks.

<p>Note: ,,,,, and correspond to the integrated clustering coefficient, integrated characteristic path length, integrated normalized clustering coefficient, integrated normalized shortest path length, integrated global efficiency, and integrated local efficiency, respectively.</p