Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection

We study how nanoparticles interact with blood to reveal early cancer signs, aiming for a simple reproducible and primarily non-invasive diagnostic test. To begin, dilute the four milligrams per milliliter stock solution of graphene oxide nano sheets in ultrapure water to the desired concentration. Sonicate the diluted solution for two minutes at 28%amplitude with a pulse setting of eight seconds on and six seconds off to ensure uniform dispersion.

Quantify the concentration of the dispersed solution using ultraviolet visible spectroscopy. Then measure the hydrodynamic size and zeta potential of the solution using dynamic light scattering with a 633 nanometer helium neon laser. Dilute the sample in ultrapure water according to the QVet detection limits.

Next, dilute the commercial lyophilized human plasma with ultrapure water as required. Centrifuge the reconstituted plasma at 18, 620 G for 15 minutes at four degrees Celsius. Collect the supernatant and store it at minus 80 degrees Celsius until further use.

Now, quantify the protein concentration using the bicinchoninic acid assay to adjust plasma dilution and determine the optimal nanoparticle to plasma ratio. Mix the selected volume of nanoparticles with plasma to achieve the target ratio between the two. Incubate the mixture at 37 degrees Celsius for one hour.

To isolate the protein corona, centrifuge the nanoparticle plasma samples at 18, 620 G at four degrees Celsius for 15 minutes. Wash the resulting pellet with 200 microliters of ultrapure water. Then resuspend it and centrifuge again.

After the final wash, confirm the presence of a compact pellet as evidence of nanoparticle protein complex isolation. Next, mix the reducing agent, loading buffer, and water to reach the calculated loading buffer volume. Resuspend the pellet in the prepared loading buffer.

Then boil the suspension at 100 degrees Celsius for 10 minutes. Centrifuge the boiled sample at 18, 620 G at four degrees Celsius for 15 minutes. Collect the supernatant into a new tube.

Now, prepare the protein ladder by diluting the molecular weight standard in unstained buffer with the stained buffer. Dilute 10 x Tris-Glycine-SDS running buffer to one x concentration. Assemble the electrophoresis chamber using a four to 20%stain free gradient gel.

Then pour in the running buffer until it reaches the recommended level indicated by the manufacturer. Load 10 microliters of sample into each well. Then pipette seven microliters of ladder into each well.

Run the gel at 150 volts for approximately 90 minutes at room temperature. Store the gel image as a tiff file in the same folder as the scripts. Then open gel processing.

m in MATLAB. Enter the name of the gel image as image name. Then enter the index of one ladder lane as index marker.

Run the script. The completion of the steps will be indicated in the command window and four windows will pop up on the screen. Now, open figure one with the original gel image on top and its correction for the oblique lane effect at the bottom.

Modify the values of left correction and right correction to tune the correction parameters. Open Figure Two. It contains the image corrected for the oblique lane effect on top and the background removed image at the bottom.

Adjust the value of BKG par X in the script to tune the background removal. Observe that all identified lanes are indicated as vertical white lines in Figure Two. Now, open Figure Three containing the intensity profile of the marker lane with the detected peaks on the left and a nonlinear fit with R square value on the right.

Double check that the number of expected bands in the ladder lane matches the number of detected peaks used for the fitting procedure. Then open Figure Four containing the normalized intensity profiles for all detected lanes in the gel. Set the export parameter to one to export the intensity profiles as functions of molecular weights in an Excel file.

Run the script. After completion, an Excel sheet will be generated in the working folder containing the absolute and normalized profiles as functions of molecular weight in the first column. Then open gel profiles pro m file and run the script.

Four windows will pop up on the screen. Open Figure Five containing the absolute intensity profiles at the top and the corresponding total intensities at the bottom for each lane. And Figure Six containing the normalized intensity profiles for each lane.

With the molecular weight axis subdivided according to the ranges specified as reg in the script. Set the export data to one to export the absolute and normalized integral areas. Run the script.

After completion, a spreadsheet will be generated in the working folder containing the exported absolute and normalized areas within the specified molecular weight ranges. Lastly, open Figure Seven and Eight. They contain the histogram of absolute and normalized areas for each lane, grouped by lane and by area respectively.

The protocol presented here establishes a standardized and reproducible workflow for isolating and characterizing the protein corona formed on nanoparticles after incubation with plasma. This method yields highly consistent SDS page profiles capable of capturing biologically meaningful and diagnostically relevant features of each personalized corona. The SDS page profiles obtained across all conditions were highly reproducible.

Increased plasma concentrations from 5%to 50%resulted in stronger signal intensities in the 30 to 80 kilodalton region of the SDS page profiles. The automated pipetting workflow yielded nearly identical electrophoretic profiles. Graphene oxide consistently produced well-resolved stable corona profiles attributed to its high surface area and charged properties.

Size distributions of small, medium, and large Graphene oxide samples were narrow, non-overlapping, and centered around 100, 300, and 750 nanometers respectively. SDS page profiles of protein Coronas formed on graphene oxide were similar across sizes. While distinct differences were observed between different dilutions, lower plasma concentrations produced more intense bands in specific molecular weight ranges indicating selective protein enrichment.

At intermediate plasma dilutions, the density metric profiles revealed disease specific differences, especially in the 20 to 30 kilodalton region where PDAC samples showed reduced signal intensity. The SDS page-based profiling model alone correctly identified both PDAC and control samples with 83%accuracy and balanced sensitivity and specificity. The combined diagnostic model incorporating SDS page corona profiles and CA 19 to nine levels achieve the highest accuracy, sensitivity, and specificity in classifying PDAC patients.

We use automated workflow, non-technology based techniques, and AI driven tools to advance cancer diagnostic research. Our protocol is rapid, user-friendly, WHO compliant, ensuring the standardized reproducible protein corona fingerprints with strong diagnostic relevance.