Additional file 1: of Single Nanoparticle Translocation Through Chemically Modified Solid Nanopore

Hydrodynamic diameter of PS microspheres in different pH solutions and the longer duration sticking events. Figure S1. Hydrodynamic diameter. Figure S2. The duration of a long period. (DOCX 407 kb

DataSheet_1_Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.docx

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.</p

Voltage-Driven Translocation of DNA through a High Throughput Conical Solid-State Nanopore

<div><p>Nanopores have become an important tool for molecule detection at single molecular level. With the development of fabrication technology, synthesized solid-state membranes are promising candidate substrates in respect of their exceptional robustness and controllable size and shape. Here, a 30–60 (tip-base) nm conical nanopore fabricated in 100 nm thick silicon nitride (Si₃N₄) membrane by focused ion beam (FIB) has been employed for the analysis of λ-DNA translocations at different voltage biases from 200 to 450 mV. The distributions of translocation time and current blockage, as well as the events frequencies as a function of voltage are investigated. Similar to previously published work, the presence and configurations of λ-DNA molecules are characterized, also, we find that greater applied voltages markedly increase the events rate, and stretch the coiled λ-DNA molecules into linear form. However, compared to 6–30 nm ultrathin solid-state nanopores, a threshold voltage of 181 mV is found to be necessary to drive DNA molecules through the nanopore due to conical shape and length of the pore. The speed is slowed down ∼5 times, while the capture radius is ∼2 fold larger. The results show that the large nanopore in thick membrane with an improved stability and throughput also has the ability to detect the molecules at a single molecular level, as well as slows down the velocity of molecules passing through the pore. This work will provide more motivations for the development of nanopores as a Multi-functional sensor for a wide range of biopolymers and nano materials.</p> </div

DataSheet_2_Machine learning and experimental validation identified autophagy signature in hepatic fibrosis.docx

BackgroundThe molecular mechanisms of hepatic fibrosis (HF), closely related to autophagy, remain unclear. This study aimed to investigate autophagy characteristics in HF.MethodsGene expression profiles (GSE6764, GSE49541 and GSE84044) were downloaded, normalized, and merged. Autophagy-related differentially expressed genes (ARDEGs) were determined using the limma R package and the Wilcoxon rank sum test and then analyzed by GO, KEGG, GSEA and GSVA. The infiltration of immune cells, molecular subtypes and immune types of healthy control (HC) and HF were analyzed. Machine learning was carried out with two methods, by which, core genes were obtained. Models of liver fibrosis in vivo and in vitro were constructed to verify the expression of core genes and corresponding immune cells.ResultsA total of 69 ARDEGs were identified. Series functional cluster analysis showed that ARDEGs were significantly enriched in autophagy and immunity. Activated CD4 T cells, CD56bright natural killer cells, CD56dim natural killer cells, eosinophils, macrophages, mast cells, neutrophils, and type 17 T helper (Th17) cells showed significant differences in infiltration between HC and HF groups. Among ARDEGs, three core genes were identified, that were ATG5, RB1CC1, and PARK2. Considerable changes in the infiltration of immune cells were observed at different expression levels of the three core genes, among which the expression of RB1CC1 was significantly associated with the infiltration of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. In the mouse liver fibrosis experiment, ATG5, RB1CC1, and PARK2 were at higher levels in HF group than those in HC group. Compared with HC group, HF group showed low positive area in F4/80, IL-17 and CD56, indicating decreased expression of macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell. Meanwhile, knocking down RB1CC1 was found to inhibit the activation of hepatic stellate cells and alleviate liver fibrosis.ConclusionATG5, RB1CC1, and PARK2 are promising autophagy-related therapeutic biomarkers for HF. This is the first study to identify RB1CC1 in HF, which may promote the progression of liver fibrosis by regulating macrophage, Th17 cell, natural killer cell and CD56dim natural killer cell.</p

Event scatter plot of current blockage vs translocation time of λ-DNA translocation events as a function of voltage.

<p>A typical normalized histogram of 400 mV is inserted on the bottom-right. The experiments were all performed in 1 M KCl solution with 10 mM TrisHCl and 1 mM EDTA at pH of 8.0.</p

Current blockage histograms as a function of applied voltage.

<p><b>A:</b> For comparison all histograms are normalized as shown in the picture, by fitting all the histograms with Gaussian, a increase of the means of the histograms as a function of voltage can be clearly visualized. <b>B:</b> The plot of the means of the Gaussian fits of the current blockage histograms as a function of voltage, which is fitted by a line, with a slope of ∼0.85 pA/V and a intercept of 181 mV at voltage axis. It indicates that the current blockage increases with the applied voltage, and a threshold voltage of 181 mV.</p

The Schematic illustrations of the microfluidic setup and nanopore detection. A:

<p>Schematic illustration of the microfluidic setup. The ionic solution is separated into two isolated reservoirs by the insulating silicon nitride membrane containing a single nanopore. A couple of Ag/AgCl electrodes which connected to the patch clamp amplifier are placed in each of the two reservoirs. Inset is a SEM image of the nanopore fabricated by FIB, with a scale bar of 100 nm. The red circle of 30 nm is used to represent the pore at tip side, while the blue circle of 60 nm is for the pore at base side. <b>B:</b> Schematic diagram of single DNA molecule translocating through a nanopore, which results a downward spike in current trace (top inset). The sidewall angle (73°) is calculated and shown in red. Two main parameters: time duration of the blockage (t_d) and magnitude of the blockage (Δ<i>I</i>) are shown for a selected single molecule event. <i>I</i>–<i>V</i> curve of the conical nanopore is inserted at the bottom, which is smoothed using Savitzky-Golzy method (solid line), showing a typical non-linearity feature. <b>C:</b> Current trace recorded at 100 mV and 200 mV, after addition of the λ-DNA molecules, the current shows no spikes when 100 mV is applied (top), whereas a series of events is observed at 200 mV (down). It indicates there is a threshold of electric forces to impel the DNA chain through the pore.</p

Simulation of the electric potential and field distributions of the nanopore in two dimensions.

<p>Left: Color coded potential distribution and electric field lines (white) of the 100 nm thick conical nanopore for a applied voltage of 300 mV. Right: A close-up view of the black rectangle area, integrated with the electric field strength () along the center (axis <i>Z</i>) of the pore, where the <i>Z</i> at the tip side of pore is set as 0. A asymmetric electric field is formed due to the conical shape of the pore, where the electric field strength () is higher at the tip side than the base side.</p

Characteristic signals of translocation events.

<p>Four types of typical multiple level translocation events generated by corresponding configurations of DNA molecule, left to right: linear, double local folded, single local folded and fully folded fragments of DNA molecules, respectively.</p

Translocation time histograms and the velocities as a function of applied voltage. A:

<p>Normalized histograms of λ-DNA translocation events as a function of voltage are fitted by Gaussian, and aligned for better comparison. A typical current trace with interpretation of events judgment (red square line) of each voltage is insert on the right respectively, from which the events at each voltage can be well distinguished. The means of the Gaussian fits are plotted as a function of voltage (right axis), with a linear fit. <b>B:</b> The plot of the velocity (mm/s-left axis, bp/<i>µ</i>s-right axis) as a function of voltage.</p