Summary of Study ST002248
This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001436. The data can be accessed directly via it's Project DOI: 10.21228/M83Q6B This work is supported by NIH grant, U2C- DK119886.
See: https://www.metabolomicsworkbench.org/about/howtocite.php
This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.
| Study ID | ST002248 |
| Study Title | Quantitative multi-Omics analysis of paclitaxel-loaded Poly(lactide-co-glycolide) nanoparticles for identification of potential biomarkers for head and neck cancer |
| Study Summary | The narrow therapeutic index and significant potential for toxicity of chemotherapeutic drugs are two of the factors that restrict their use. Because of the usage of nanoparticles (NPs) as carriers for chemotherapeutic agents, the therapeutic efficacy of these treatments has been significantly boosted. This was accomplished by increasing the bioavailability of the pharmaceuticals and changing the bio-distribution profile of the drugs. Untargeted metabolomics has recently risen to the forefront as a potentially useful method for better comprehending the growth of tumours and the treatment outcomes of many kinds of cancer cells. In the current study, we used LCMS/MS-based untargeted metabolomics to identify differences in the metabolic profile of head and neck squamous cell carcinomas FaDu that were treated with the anticancer drug paclitaxel (PTX) delivered as free drug versus paclitaxel-loaded poly(lactide-co-glycolide) nanoparticles (PXT-PLGA-NPs). The experimental design consisted of four groups: those treated with DMSO (serving as a control), those treated with drug-free PXT, those treated with PXT-PLGA-NPs, and those treated with PLGA-NPs that lacked PTX. MetaboScape (V4, Bruker Daltonics) was used as the platform for the data analysis, and the results were compared to the Bruker Human Metabolome Data Base (HMDB) spectrum library 2.0. We found a total of 162 metabolites with a high level of confidence ascribed to them. The principal component analysis of the metabolites showed that PTX-free drugs grouped along with PXT-PLGA-NPs, but the control and PLGA-NPs without PXT clustered apart from drug-treated cells but together with each other. In further group pairwise comparisons, it was shown that 37 metabolites were substantially dysregulated (p 0.05) between the PTX-free medication and the PXT-PLGA-NPs. Out of these, it is important to call attention to the metabolites that became more abundant as a result of treatment with PXT-PLGA-NPs. These include 5-Thymidyclic acid with a 7.8-fold change (FC) and 3,4,5-Trimethoxycinnamic acid, both of which have been linked in the past to effective anticancer drug treatment (Quinn et al. 2015; Anantharaju et al. 2017). The findings suggest a more successful anti-drug therapy that makes use of NP, and also indicate a number of metabolites that have the potential to serve as indicators for determining how well an antidrug treatment is working. Our previous findings are consistent with these findings. |
| Institute | University of Sharjah |
| Department | Sharjah institute of medical research |
| Laboratory | Drug Delivery |
| Last Name | Jagal |
| First Name | Jayalakshmi |
| Address | Sharjah |
| jjagal@sharjh.ac.ae | |
| Phone | 0552863009 |
| Submit Date | 2022-07-14 |
| Raw Data Available | Yes |
| Raw Data File Type(s) | d |
| Analysis Type Detail | LC-MS |
| Release Date | 2023-07-14 |
| Release Version | 1 |
Select appropriate tab below to view additional metadata details:
Project:
| Project ID: | PR001436 |
| Project DOI: | doi: 10.21228/M83Q6B |
| Project Title: | Quantitative multi-Omics analysis of paclitaxel-loaded Poly(lactide-co-glycolide) nanoparticles for identification of potential biomarkers for head and neck cancer |
| Project Type: | LC-MS/MS |
| Project Summary: | Chemotherapeutic agents are limited by their narrow therapeutic index and high risk for toxicity. The use of nanoparticles (NPs) as carriers for chemotherapeutic agents has considerably increased the therapeutic effect of those drugs by improving their bioavailability and altering their bio-distribution profile. Untargeted metabolomics has emerged as potential approach to understand better tumor progression and treatment outcome of multiple cancer cell types. Herein, we have employed LCMS/MS based untargeted metabolomics to pinpoint differences in metabolic profile of head and neck squamous cell carcinomas FaDu, when treated with anticancer Paclitaxel (PTX) delivered as free drugversus Paclitaxel-loaded poly(lactide-co-glycolide) nanoparticle (PXT-PLGA-NPs). The experimental design include four groups treated with DMSO (control), treated with drug free PXT, PXT-PLGA-NPs and PLGA-NPs without PXT. Data was analyzed using MetaboScape (V4, Bruker Daltonics) platform and matched to Bruker Human Metabolome Data Base (HMDB) spectral library 2.0. We identified and total of 162 high confident assigned metabolites. Principle component analysis of the metabolites revealed that PTX free drug clustered together with PXT-PLGA-NPs, whereas control and PLGA-NPs without PXT clustered away from drug treated cells but apart from each other (see figure below). Further group pairwise comparisons indicated 37 metabolites significantly (p<0.05) dysregulated between PTX free drug and PXT-PLGA-NPs. Of these, it is worthy highlight metabolites that became more abundant with PXT-PLGA-NPs treatment, such as 5-Thymidyclic acid 7. 8 fold change (FC) and 3,4,5-Trimethoxycinnamic acid that have been associated previously associated with effective anticancer drug treatment (Quinn et al. 2015; Anantharaju et al. 2017). The results are in line with our pervious findings supporting a more effective antidrug treatment using NP and we indicate a number of metabolites that are potential markers for monitoring the efficacy antidrug treatment. |
| Institute: | University of Sharjah |
| Department: | Sharjah Institute for Medical Research |
| Laboratory: | Drug Delivery |
| Last Name: | Jagal |
| First Name: | Jayalakshmi |
| Address: | SIMR, Medical Campus, University of Sharjah, 27272, Sharjah |
| Email: | jjagal@sharjah.ac.ae |
| Phone: | +971552863009 |
Subject:
| Subject ID: | SU002334 |
| Subject Type: | Cultured cells |
| Subject Species: | Homo sapiens |
| Taxonomy ID: | 9606 |
| Species Group: | Mammals |
Factors:
Subject type: Cultured cells; Subject species: Homo sapiens (Factor headings shown in green)
| mb_sample_id | local_sample_id | Treatment |
|---|---|---|
| SA216190 | Control 02_2_8_1_938 | Control |
| SA216191 | Control 01_2_7_1_936 | Control |
| SA216192 | Control 03_1_9_1_939 | Control |
| SA216193 | Control 04_1_10_1_941 | Control |
| SA216194 | Control 04_2_10_1_942 | Control |
| SA216195 | Control 01_1_7_1_935 | Control |
| SA216196 | Control 03_2_9_1_940 | Control |
| SA216197 | Control 02_1_8_1_937 | Control |
| SA216198 | DF nP 02_2_16_1_954 | Drug Free PLGA Nanoparticle |
| SA216199 | DF nP 03_1_17_1_955 | Drug Free PLGA Nanoparticle |
| SA216200 | DF nP 02_1_16_1_953 | Drug Free PLGA Nanoparticle |
| SA216201 | DF nP 01_2_15_1_952 | Drug Free PLGA Nanoparticle |
| SA216202 | DF nP 01_1_15_1_951 | Drug Free PLGA Nanoparticle |
| SA216203 | DF nP 03_2_17_1_956 | Drug Free PLGA Nanoparticle |
| SA216204 | DF nP 04_1_18_1_957 | Drug Free PLGA Nanoparticle |
| SA216205 | DF nP 04_2_18_1_958 | Drug Free PLGA Nanoparticle |
| SA216206 | Free Drug 02_2_12_1_946 | Free PTX |
| SA216207 | Free Drug 01_2_11_1_944 | Free PTX |
| SA216208 | Free Drug 01_1_11_1_943 | Free PTX |
| SA216209 | Free Drug 03_1_13_1_947 | Free PTX |
| SA216210 | Free Drug 03_2_13_1_948 | Free PTX |
| SA216211 | Free Drug 02_1_12_1_945 | Free PTX |
| SA216212 | Free Drug 04_2_14_1_950 | Free PTX |
| SA216213 | Free Drug 04_1_14_1_949 | Free PTX |
| SA216214 | nP 04_1_22_1_965 | PTX-PLGA Nanoparticle |
| SA216215 | nP 03_2_21_1_964 | PTX-PLGA Nanoparticle |
| SA216216 | nP 03_1_21_1_963 | PTX-PLGA Nanoparticle |
| SA216217 | nP 04_2_22_1_966 | PTX-PLGA Nanoparticle |
| SA216218 | nP 01_2_19_1_960 | PTX-PLGA Nanoparticle |
| SA216219 | nP 01_1_19_1_959 | PTX-PLGA Nanoparticle |
| SA216220 | nP 02_2_20_1_962 | PTX-PLGA Nanoparticle |
| SA216221 | nP 02_1_20_1_961 | PTX-PLGA Nanoparticle |
| Showing results 1 to 32 of 32 |
Collection:
| Collection ID: | CO002327 |
| Collection Summary: | The process of extracting metabolites from FaDu cells was carried out in a manner that was similar to that which was utilized for the extraction of proteins. Following the addition of PTX (40 μM), PTX-PLGA NPs (40 μM), and drug-free PLGA NPs to the culture medium, the media was changed to PBS, and the cells were scraped off the plates using cell scrapers. After that, the detached cell pellets were collected by centrifuging them at a speed of 15000 rpm for ten minutes at a temperature of 4 degrees Celsius using a Universal 320R Benchtop Centrifuge (Hettich, Beverly, Massachusetts, United States). The collected cell pellets were first treated with 0.1 % FA in methanol, which was followed by sonication using a probe ultrasonicator Q500 (Terra Universal, Inc., Fullerton, California, USA) for 30 seconds at 30 %amplitude 16. This was done in order to release a wide variety of metabolites. After that, the samples were centrifuged at 15,000 rpm for 10 minutes at 4 degrees Celsius, and the upper phase was transferred into LC vials so that it could be dried further using a Genevac evaporator (SP Industries, Warminster, PA, USA). After the reaction was halted with formic acid (FA) at a concentration of one percent, it was dried using a Genevac evaporator. In preparation for subsequent LC-MS/MS examination, the dried samples were kept at a temperature of 80 degrees Celsius. The version 4.0 of MetaboScape was used for both the processing and the statistical analysis (Bruker Daltonics). Analyte bucketing and identification were accomplished by using the software that was made available to us called T-ReX 2D/3D workflow. The settings that were used were as follows: an intensity threshold of larger than 1000 counts and a peak duration of equal to 7 spectra or higher. Quantification of features was carried out by calculating peak area and for statistical analysis, only characteristics that were present in at least three out of twelve samples for each cell type were taken into account. On import, the spectra of the analyte were averaged, and additional consideration was given to just those features that eluted between 0.3 and 25 minutes and had mz values between 50 and 1000. Using a two-tailed independent Student’s t-test, a comparison was made between each drug treatment condition and DMSO-only treated controls. It was shown that there were significantly differently abundant metabolites between the two groups. As a result of this, volcano plots were developed in order to visualize and display the significance (p-value) of dysregulation of cellular metabolites, along with the fold changes associated with each condition. Metabo analyst, which can be found at https://www.metaboanalyst.ca, was used in order to carry out functional enrichment studies. Analyte metabolite identification was carried out by making use of both MS2 spectra and retention time (RT), although the MS/MS spectra were required as a bare minimum for a valid identification to be made. We conducted annotation using Bruker's version of the Human Metabolome Database (HMDB-4.0) for the set of compounds that satisfied this requirement, either by using MS/MS alone or by utilizing MS/MS plus RT. All of the chosen compounds were then compared to this library to find a match. These putatively matching features were filtered by considering for those each feature with the highest annotation quality score (AQ score) among other putative matches for the same metabolite, i.e., those features exhibiting the best fit across the greatest number of factors such as retention time, MS/MS, m/z values, analyte list, and spectral library matching were ranked first for the associated identifier. |
| Sample Type: | Head and neck Cancer cell line |
Treatment:
| Treatment ID: | TR002346 |
| Treatment Summary: | The extraction of metabolites from FaDu cells was carried out following a similar procedure used in protein extraction. After treatment with PTX (40μM), PTX-PLGA NPs (40 μM), drug-free PLGA NPs and culture media, PBS replaced media and the cells were collected from the dishes using cell scrapers. The detached cell pellets were then collected by centrifugation at 15000 rpm for 10 min at 4oC using Universal 320R Benchtop Centrifuge (Hettich, Beverly, MA, USA) to remove excess PBS. To release a broad range of metabolites, the collected cell pellets were first treated with 0.1% FA in methanol followed by sonication using probe ultrasonicator Q500 (Terra Universal, Inc, Fullerton, CA, USA)for 30 sec at 30% amplitude 16. The samples were then centrifuged at 15,000 rpm for 10 minutes at 4oC, and the upper phase was transferred into LC vials for further drying using a Genevac evaporator (SP Industries, Warminster, PA, USA). The reaction was stopped with 1% formic acid (FA) followed by drying using a Genevac evaporator. Dried samples were stored at −80 °C for further LC-MS/MS analysis. |
Sample Preparation:
| Sampleprep ID: | SP002340 |
| Sampleprep Summary: | After treatment with PTX (40μM), PTX-PLGA NPs (40 μM), drug-free PLGA NPs and culture media , PBS replaced media and the cells were collected from the dishes using cell scrapers. The detached cell pellets were then collected by centrifugation at 15000 rpm for 10 min at 4oC using Universal 320R Benchtop Centrifuge (Hettich, Beverly, MA, USA) to remove excess PBS. To release a broad range of metabolites, the collected cell pellets were first treated with 0.1% FA in methanol followed by sonication using probe ultrasonicator Q500 (Terra Universal, Inc, Fullerton, CA, USA)for 30 sec at 30% amplitude 16. The samples were then centrifuged at 15,000 rpm for 10 minutes at 4oC, and the upper phase was transferred into LC vials for further drying using a Genevac evaporator (SP Industries, Warminster, PA, USA). The reaction was stopped with 1% formic acid (FA) followed by drying using a Genevac evaporator. Dried samples were stored at −80 °C for further LC-MS/MS analysis. |
Combined analysis:
| Analysis ID | AN003674 |
|---|---|
| Analysis type | MS |
| Chromatography type | Reversed phase |
| Chromatography system | Bruker Elute |
| Column | Hamilton Intensity Solo 2 C18 |
| MS Type | ESI |
| MS instrument type | QTOF |
| MS instrument name | Bruker timsTOF |
| Ion Mode | POSITIVE |
| Units | AU |
Chromatography:
| Chromatography ID: | CH002724 |
| Instrument Name: | Bruker Elute |
| Column Name: | Hamilton Intensity Solo 2 C18 |
| Column Temperature: | 35 |
| Flow Gradient: | 1%B to 99%B in 15 min |
| Flow Rate: | 250 uL/min |
| Solvent A: | 100% water; 0.1% formic acid |
| Solvent B: | 100% acetonitrile; 0.1% formic acid |
| Chromatography Type: | Reversed phase |
MS:
| MS ID: | MS003425 |
| Analysis ID: | AN003674 |
| Instrument Name: | Bruker timsTOF |
| Instrument Type: | QTOF |
| MS Type: | ESI |
| MS Comments: | Processing and statistical analysis were performed using MetaboScape® 4.0 (Bruker Daltonics). Analyte bucketing and identification were done using the software provided available T-ReX 2D/3D workflow with the following parameters: intensity threshold greater than 1000 counts and peak length equal to 7 spectra or greater. Feature quantitation was, performed using peak area and , for features present in at least 3 (of 12) samples (per cell type) were considered for statistical analysis. Analyte MS2 spectra were averaged on import and only features eluting between 0.3 and 25 min with mz between 50 and 1000 were considered further. |
| Ion Mode: | POSITIVE |
