1,2-Distearoyl-sn-glycerol-3-phosphocholine(DSPC);1,2-stearoyl-sn-glycerol-3-phosphoethanolamine-N-(methoxy[polyethylene glycol]-2000) (DSPE-PEG-2000) were got from Avanti Polar Lipids (Alabaster, AL). 20,70-Dichlorofluorescin diacetate (DCFH-DA),MDA and GSH were acquired from Beyotime Biotechnology (Shanghai, China). ICG was received from Shanghai yuanye Bio-Technology Co, Ltd (Shanghai, China). Annexin V-FITC Apoptosis Detection Kit and Cell counter kit-8 (CCK-8) were obtained from BD Pharmingen. RSL3 was gained from Shanghai MCE Chemicals Technology (Shanghai, China).
2.2 Preparation of RSL3@O2-ICG NBs
As reported in previous studies, we have prepared RSL3@O2-ICG NBs using the thin-film hydration ultrasound method (45). First, mix DSPC and DSPE-PEG-2000 at a mass ratio of 9:1, and then add 10ul of 5 mM RSL3 in methylene chloride and methanol (2:1, vol/vol). After entirely dissolving and mixing, transfer the above solution to a beaker and evaporate to form a lipid film. The dried film was hydrated into a lipid suspension with 5ml of 100ug/ml ICG in PBS. Next, the suspension was extruded 20 times through a 200nm membrane through a micro-extruder (Avanti Polar Lipids, Alabaster, AL). Then, the squeezed suspension was transferred to a sealed vial, and the syringe was evacuated and replaced with pure oxygen. The mixture was then mechanically shaken in a dental mixer (YJT Medical Apparatus and Instruments, Shanghai, China) for 60 seconds and resuspended in 2 ml of PBS solution for storage at 4°C. All processes are carried out in a dark environment. Use the same method to prepare nanobubbles without RSL3.
Observe the distribution and morphology of RSL3@O2-ICG NBs under a scanning electron microscope (SEM. Hitachi, Japan), and observe the element enrichment. Dynamic light scattering (DLS) analyzer (ZEN3600, Malvern Instruments) can analyze its particle size distribution and zeta potential. An ultraviolet-visible spectrophotometer (Thermo Fisher) was used to obtain the absorption spectrum of the particles.
2.4 cell culture
HepG2 and Huh7 derived from the human hepatocellular carcinoma cell line were included in this experiment. All cells were cultured in DMEM medium containing 10% FBS in a humidified incubator at 37°C and 5% CO2.
2.5 Cell viability of HepG2 and Huh7 cells
HepG2 and Huh7 cells were seeded into 96-well plates at a density of 8000 cells per well. After the application of different ICG concentrations (40, 80, 100, 150, 200, 250, 300µg/ml), they were exposed to LFUS for 30 seconds (1.0 W/cm2) and then assessed by CCK8 assay to kill the cells. Then different RSL3 concentrations (4, 6, 8, 10, 12, 15, 20 µM) and specific concentrations of ICG under the same experimental environment.
2.6 ROS detection
In order to study the ability of O2-ICG-NB to enhance SDT, RSL3 was not carried in the nanobubbles in this experiment. HepG2 and Huh7 cells were respectively seeded in 6-well plates, cultured overnight to remove the medium, and the cells were treated with PBS, ICG, ICG-NBs, and O2-ICG-NBs for another 4 hours. Subsequently, DCFH-DA was added to the cells and incubated at 37°C for 30 minutes, protected from light. After removing the medium, the cells were irradiated with ultrasound for 30 seconds (1.0 W/cm2) and washed twice with DMEM, then 1ml of ice PBS was added, and the cells were quickly observed under a fluorescence microscope. Flow cytometry is performed by digesting cells from a 6-well plate, and the operation is processed as before.
2.7 Analysis of GSH
After various treatments (PBS, RSL3, O2-ICG-NBs + LFUS, RSL3@O2-ICG NBs + LFUS, RSL3@O2-ICG NBs + LFUS + Fer-1), the cells were washed 3 times with PBS and trypsinized Collect the cells and centrifuge at 1000 rpm at 4°C for 5 min twice. According to the weight of the cell pellet, resuspend each milligram in 3 µl of protein removal solution M. After vortexing, place it in liquid nitrogen and in a 37 ℃ water bath for two quick freezing and thawing times, and then place it at 4 ℃. Centrifuge for 10 minutes at 10,000 rpm for 5 minutes. The supernatant is used to determine the content of GSH in the sample. Prepare the appropriate amount of detection working solution and supernatant sample according to the instructions, mix them and incubate at room temperature for 5 minutes, and then add NADPH to the system to trigger the reaction. After 60 minutes, measure the TNB absorbance at 412 nm and calculate the GSH level according to the instructions.
2.8 MDA assay
Collect the protein in HepG2 and Huh7 through various treatments (PBS, RSL3, O2-ICG-NBs + LFUS, RSL3@O2-ICGNBs + LFUS, RSL3@O2-ICG NBs + LFUS + Fer-1), and after lysis 40min After centrifugation, the supernatant was mixed with the TBA detection working solution, heated in a boiling water bath for 15 minutes, cooled to room temperature, 200µl was transferred to a 96-well plate, the absorbance was measured at 532nm, compare the standard curve to calculate the MDA content.
2.9 Measurement of Mitochondrial Membrane Potential
Mitochondrial membrane potential was detected by JC-1 (Beyotime, C2005) detection kit. After treatment in the 4 treatment groups (PBS, RSL3, O2-ICG-NBs + LFUS, RSL3@O2-ICG NBs + LFUS), HepG2 and Huh7 cells were incubated with JC-1 staining working solution for 20 minutes, and then JC-1 Wash twice with buffer solution. After adding ice PBS solution, the stained cells were observed under a fluorescence microscope.
2.10 Cell Cycle Analysis
After various treatments (PBS, RSL3, O2-ICG-NBs + LFUS, RSL3@O2-ICG NBs + LFUS), HepG2 and Huh7 cells were digested and collected with trypsin, and then fixed with 70% ice-cold ethanol at 4°C overnight. Subsequently, add 500µl of the prepared staining working solution (keygentec. Nanjing, China) for staining. The DNA content of each group of cells was determined by flow cytometry.
2.11. Whole transcriptome library construction, sequencing, and analysis
The total RNA of HepG2/Huh7 cells before and after RSL3@O2-ICG NBs + LFUS treatment was taken, respectively, named A, B, C, D group, and quality control was performed. The preparation and deep sequencing of the whole transcriptome library were performed by Novogene Bioinformatics (Beijing, China).
2.11.1 Whole transcriptome library construction and sequencing
A total amount of 3 µg RNA per sample was used as input material for the RNA sample preparations. According to the manufacturer's instructions, the clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumia). After cluster generation, the libraries were sequenced on an Illumina HiSeq 2500 platform, and 125 bp paired-end reads were generated. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE178573 (https://www.ncbi.nlm.nih.gov/geo/query /acc.cgi?acc = GSE14 0729).
2.11.2 RNA-Seq data analysis
Quality control and comparative analysis
The generation of sequencing data goes through multiple steps such as RNA extraction, library construction, and sequencing. These steps will produce some low-quality or invalid data. For example, there will be a deviation in the length of the library during the library construction stage, and a sequencing error will occur during the sequencing stage. Therefore, it is necessary to perform quality control on the raw data obtained from the machine to ensure the accuracy of subsequent analysis. The raw data obtained by sequencing contains a small number of reads with sequencing adapters or low sequencing quality. In order to ensure the quality and reliability of data analysis, it is necessary to filter the original data. The filtering content is as follows: (1) Remove the adapter (2) Remove the reads with N (N means that the base information cannot be determined) greater than 0.002; (3) When the number of low-quality bases contained in a single-ended read exceeds read When the length is 50%, you need to remove the paired reading. After raw data filtering, sequencing error rate check, and GC content distribution check, clean reads for subsequent analysis are obtained. Use Hisat2 to compare the valuable sequencing data (clean reads) to the reference genome.
Quantitative expression level of transcripts
Stringtie software can accurately and accurately splice genes and realize gene quantification. Therefore, we use Stringtie software to assemble the reads into genes and quantify gene expression based on the results of the alignment to the genome.
In this article, the gene expression level of RNA-seq uses FPKM (Fragments Per Kilobase of transcript sequence per Millions of base pairs sequenced) calculation. FPKM is the number of fragments from a gene/transcript per kilobase length per million fragments. It also corrects the influence of sequencing depth and gene length on the count of fragments. It is currently the most commonly used method for estimating gene expression levels.
2.11.3 Differential expression analysis
After the quantitative analysis is completed, the gene expression matrix of all samples is obtained, and the expression level of the gene level can be analyzed for the significance of the difference to find the functional genes related to the treatment group. In this work, we use edgeR software to analyze the significance of gene expression differences. Use p-value or corrected p-value (padj) to determine the significance level. Padj is the value obtained by using the BH method to perform multiple test corrections on the p-value when the false positive rate is high. The smaller the corrected P-value value is, the more significant it is. Use padj < 0.05 as the difference significance criterion (if padj is < 0.05 to screen for too few differences, use p-value < 0.05 for differential screening, and finally get a list of genes with significant differences for subsequent analysis.
2.11.4 GO and KEGG enrichment analysis
In organisms, different genes coordinate with each other to perform their biological functions. By exploring the pathways that are significantly enriched in functional genes, we will further explore the important functional genes involved in important biochemical metabolic pathways and signal transduction pathways among differentially expressed genes. This article uses GOseq software for GO enrichment analysis; KOBAS (2.0) for pathway enrichment analysis. GO is a comprehensive database describing gene functions, divided into molecular function, biological process, and cellular component. GO enrichment takes padj < 0.05 (default) or p-value < 0.05 as significant enrichment. In organisms, different genes coordinate with each other to perform their biological functions, and the most important biochemical metabolic pathways and signal transduction pathways involved in candidate target genes can be determined through Pathway significant enrichment. KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive database that integrates genome, chemistry and system function information. Pathway significant enrichment analysis takes KEGG Pathway as the unit and applies a hypergeometric test to find pathways that are significantly enriched in candidate target genes compared with the background of the entire genome. Similarly, KEGG pathway enrichment takes p-value or padj < 0.05 as significant enrichment.
2.11.5 Expression and immune infiltration in TCGA.
In order to further explore whether the genomic changes caused by RSL3@O2-ICG NBs + LFUS treatment will affect the immunity of hepatocellular carcinoma,we obtained the RNA-seq data of hepatocellular carcinoma from the TCGA database (https://portal.gdc.cancer.gov/). Including gene expression information from 373 samples of hepatocellular carcinoma patients༌we find some of the differential genes that overlap with this research༌and use overlapping differentially expressed genes to perform heat map cluster analysis on TCGA hepatocellular carcinoma patients. At the same time, we used Xcell database (https://xcell.ucsf.edu/) to evaluate the proportion of immune cell infiltration of all liver cancer samples from TCGA. Explore the difference in the proportion of immune cell infiltration between different clusters.
2.11.6. Prognostic analysis of TCGA cohort
In order to further confirm that the genomic changes caused by RSL3@O2-ICG NBs + LFUS treatment can regulate the tumor microenvironment and further affect the survival status of hepatocellular carcinoma, we used the tumor microenvironment score obtained from the Xcell database in the previous step. We used the pearson method to screen out the results. Among the differentially expressed genes, the genes whose correlation with the tumor microenvironment score is more significant than 0.4 and the significance P-value is less than 0.05 will be analyzed later. 373 TCGA hepatocellular carcinoma patients were randomly divided into training set and test set according to the ratio of 7:3, and single-factor cox risk regression and multi-factor risk regression were used to screen the prognostic risk genes that were significantly related to the prognosis (p < 0.05). Calculate each sample's ferroptosis risk score by multiplying the risk gene's expression value by the sum of the risk coefficient of the risk regression and using the median value of the risk score to divide hepatocellular carcinoma samples into high-risk and low-risk groups. Use log-rank test to explore the prognostic difference between high and low risk groups.
2.12 Statistical analysis
All experiments were conducted in triplicate. The biological data are calculated as an average of three replicates ± standard deviation and presented in percentages. All sample values were related to the control (untreated cells) value, which was considered 100% and compared using Student's t-test by GraphPad Prism 8.0 software. The differences between control and samples values were considered to be significant if P < 0.05, highly significant if P < 0.01 and extremely significant if P < 0.001.