Name: Standardized SDS-PAGE Workflow for Personalized Protein Corona Profiling in Early Cancer Detection (Video) | JoVE Journal
Uploaded: 2025-12-19T12:50:00.000+00:00
Duration: 10 min 2 s
Description: Learn standardized SDS-PAGE workflow for personalized protein corona profiling using graphene oxide nanoparticles. This reproducible method enables early detection of pancreatic ductal adenocarcinoma with high diagnostic accuracy.

ai_agent_IP

ai_agent

maintenance

instantTrialDurationMinutes

isBiopharma

enableNewMyJoveSectionDesign

educationFeatures

seoMetaApi

ai_copilot_institution_blacklist

ai_chat_institution_blacklist

freeAccessWindow

ssoRoleCapture

scheduledMaintenanceBanner

joveLabsFeatures

disable_autoplay_for_bots

extended_trial_duration

5 minutes

The research was partly funded by MUR, National Plan for Complementary Investments to the NRRP, Funded by the European Union-NextGenerationEU, Project &#34;D3 4 HEALTH-Digital Driven Diagnostics, prognostics and therapeutics for sustainable Health care&#34;. PNC0000001, Spoke 3 Linea tematica 2, missione 4, componente 2 CUP: B53C22006120001. This work was also supported by the AIRC Foundation under the IG 2020-ID. 24521 project, P.I. Pozzi Daniela.

This protocol provides a detailed and standardized workflow for the preparation, incubation with human plasma (HP), isolation, and analysis of the PC formed on various types of NPs. Figure 2 is a graphical representation of the entire protocol, outlining the sequential experimental steps from NP synthesis to multivariate SDS-PAGE analysis of protein profiles. Importantly, the methodology has been standardized by our group, as demonstrated in previous works18,26, ensuring reproducibility and reliability of the results. The reagents and the equipment used are listed in the Table of Materials.
1. Preparation and characterization of NPs
<ol>
	<li>GO nanosheets
	<ol>
		<li>Dilute a 4 mg/mL stock solution in ultrapure water to the desired concentration. Sonicate for 2 min at 28% amplitude (pulse: 0.8/0.6 s) to ensure proper dispersion.</li>
		<li>Quantify concentration using UV-Vis spectroscopy</li>
		<li>Measure hydrodynamic size and zeta potential by dynamic light scattering (DLS) using a 633 nm He-Ne laser. Prepare samples by dilution in ultrapure water based on cuvette detection limits (e.g., 1:100 dilution for 1 mL cuvettes). Record mean ± standard deviation (SD) from three replicates.</li>
	</ol>
	</li>
	<li>Gold NPs
	<ol>
		<li>Use citrate-stabilized 100 nm gold NPs (concentration from stock: 3.8E+9 particles/mL) without further modification.</li>
		<li>Measure hydrodynamic size and zeta potential by dynamic light scattering (DLS) using a 633 nm He-Ne laser. Prepare samples by dilution in ultrapure water based on cuvette detection limits (e.g., 1:100 dilution for 1 mL cuvettes). Record mean ± SD from three replicates.</li>
	</ol>
	</li>
	<li>DOTAP liposomes
	<ol>
		<li>Dissolve 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP) powder in chloroform inside a certified chemical fume hood while wearing appropriate PPE (lab coat, gloves, goggles).Evaporate under vacuum for 2 h, resuspend the lipid film with ultrapure water to reach a final concentration of 1 mg/mL, and hydrate overnight at 4 °C. Finally, extrude the liposome solution 20 times through a 0.1 µm polycarbonate filter.</li>
		<li>Measure hydrodynamic size and zeta potential by dynamic light scattering (DLS) using a 633 nm He-Ne laser. Prepare samples by dilution in ultrapure water based on cuvette detection limits (e.g., 1:100 dilution for 1 mL cuvettes). Record mean ± SD from three replicates. 
		CAUTION: Handle chloroform in a fume hood with full PPE. Dispose of organic solvent waste in accordance with institutional chemical safety procedures. 
		NOTE: For GO with size distributions centered approximately at 300 nm, sonicate for 10 min at 125 W, centrifuge at 7,700 x g for 30 min at 4 °C, collect supernatant, centrifuge again at 18,620 x g for 30 min, and resuspend the pellet. For GO with size distributions centered approximately at 100 nm, sonicate 3 × 180 min at 4 °C, centrifuging at 10,000 x g for 30 min, at 4 °C after each round, and collecting the final supernatant.</li>
	</ol>
	</li>
</ol>
2. Preparation of human plasma
<ol>
	<li>Commercial human plasma (HP)
	<ol>
		<li>Dilute commercial lyophilized plasma with ultrapure water as needed.</li>
		<li>Centrifuge the reconstituted plasma at 18620 x g for 15 min at 4 °C, collect the supernatant and store at -80 °C until use.</li>
	</ol>
	</li>
	<li>Patient-derived HP
	<ol>
		<li>Centrifuge blood samples at 1216 x g for 10 min at 4 °C and collect the supernatant for plasma separation.</li>
		<li>Aliquot 450 µL plasma samples in 0.5 mL protein low-bind tubes and store at -80 °C until use. 
		NOTE: All human plasma samples are collected in accordance with institutional ethical guidelines, and informed consent was obtained from all participants. 
		CAUTION: Human plasma is biohazardous. Handle inside a biosafety cabinet (BSC) using gloves, a lab coat, and protective eyewear. Dispose of all plasma-containing materials according to institutional biosafety and waste-disposal regulations.</li>
	</ol>
	</li>
</ol>
3. Formation of PC
<ol>
	<li>Manual liquid handling
	<ol>
		<li>Determine optimal NP: plasma ratio by quantifying protein concentration via Bicinchoninic Acid (BCA) assay to adjust plasma dilution.</li>
		<li>Add 100 µL of NP suspension to 100 µL of plasma to reach desired plasma concentrations.</li>
		<li>Incubate at 37 °C for 1 h. 
		​NOTE: For BCA assay, mix Reagents A and B (50:1), prepare BSA standard dilutions, add 10-25 µL sample/standard + 200 µL reagent, incubate 30 min at 37 °C, and measure absorbance at 562 nm. Plasma concentrations are typically adjusted between 0.5% and 50% v/v relative to NP volume, depending on NP type. For GO, use lower concentrations (from 0.5% to 5% recommended); for DOTAP/gold NPs, use higher concentrations (up to 50%). Use protein 1.5 mL low-bind tubes.</li>
	</ol>
	</li>
	<li>Automated incubation protocol
	<ol>
		<li>Load the liquid handling software protocol onto the automated pipetting platform (20 µL + 200 µL heads)</li>
		<li>Dispense 100 µL of each NP type into low-bind 1.5 mL tubes.</li>
		<li>Add reconstituted plasma prepared as in 2.1 and 2.2, according to the following formula: 
		where VNP is the NP suspension volume. For 100 µL of NP: 
		<img alt="Hydroponics nutrient calculation formula, HP(volume) equation for solution concentration." src="/files/ftp_upload/69090/69090eq1.jpg" data-altupdatedat="1783396841000" data-alt="Equation 1"> 
		5%: 5.3 µL HP 
		20%: 25 µL HP 
		50%: 100 µL HP</li>
		<li>Add ultrapure water to reach 200 µL: 
		5%: 94.7 µL 
		20%: 75 µL 
		50%: none</li>
		<li>Mix samples: 
		5%: 15 µL cycles 
		20%: 50 µL cycles 
		50%: 100 µL cycles3.2.6. Incubate at 37 °C for 1 h 
		NOTE: Supplementary Figure 1 illustrates the graphical interface of the automated setup used to prepare NP-HP mixtures at three final plasma concentrations (5%, 20%, and 50% v/v), each in triplicate. The deck layout includes tip racks, a waste reservoir, and a 3 × 4 low-bind tube rack containing NP suspensions (GO, gold nanospheres, and DOTAP liposome) (NP), HP, and ultrapure water (H2O). Use protein low-bind tubes.</li>
	</ol>
	</li>
</ol>
4. PC analysis by SDS-PAGE
<ol>
	<li>PC isolation
	<ol>
		<li>Centrifuge the NP-HP samples at 18,620 x g, 4 °C, for 15 min. A visible pellet should be present at the bottom of the tube. Carefully discard the supernatant, which should appear clear before proceeding (see Supplementary Figure 2).</li>
		<li>Wash pellet with 200 µL ultrapure water, resuspend it, and centrifuge it again. Repeat this step twice. After the final wash, a compact pellet should still be visible, confirming successful isolation of NP-protein complexes (see Supplementary Figure 2).</li>
		<li>Prepare loading buffer (LB):</li>
		<li>Calculate total LB volume based on plasma condition and mix reducing agent (1:10), SDS sample buffer denature proteins (1:2), and water.</li>
		<li>Resuspend pellet in appropriate volume of 1x LB and boil at 100 °C for 10 min.</li>
		<li>Centrifuge at 18,620 x g, 4 °C, for 15 min. Collect the supernatant.</li>
	</ol>
	</li>
	<li>SDS-PAGE sample loading
	<ol>
		<li>Prepare the ladder by diluting the molar weight (MW) standard with the loading buffer.</li>
		<li>Dilute 10x Tris/Glycine/SDS running buffer to 1x.</li>
		<li>Assemble the electrophoresis chamber with 4%-20% stain-free gradient gel and add running buffer until it reaches the recommended level indicated by the manufacturer.</li>
		<li>Load 10 µL samples and 7 µL ladder in each well. Run gel at 150 V for ~90 min at room temperature. 
		NOTE: The volume of LB should be calculated based on the plasma concentration selected in 3.1.1 to ensure optimal protein visualization in the gel image (typically 10 µg proteins in 20 µL LB). 
		CAUTION: For optimal reproducibility, perform SDS-PAGE analysis using freshly prepared NP-plasma samples. Storage of washed pellets at -80 °C for 24 h resulted in detectable alterations of both physicochemical properties (after resuspension and DLS/zeta potential measurements) and protein corona fingerprints (when pellets were processed starting from Step 4.3.1 of the protocol; see Supplementary Figure 3).</li>
	</ol>
	</li>
</ol>
5. Protein patterns analysis
<ol>
	<li>Image processing
	<ol>
		<li>Image gels using a chemiluminescence imager.</li>
		<li>Process the gel image as follows:
		<ol>
			<li>Import the gel image and save it as a TIFF file, correct it for undesired effects, e.g., oblique lane distortions that particularly affect peripheral lanes. Detect all gel lanes. Subtract the background signal from the image.</li>
			<li>Calibrate the vertical displacements by converting pixel units into molecular weight units, based on a double-term exponential fit of the marker lane. Generate graphical outputs to visualize intensity profiles and lane-specific signal distributions.</li>
		</ol>
		</li>
		<li>Export the processed data, i.e., the computed one-dimensional profiles.</li>
		<li>Compute the molecular weight distributions by normalizing the profiles, and calculate integral areas across the desired MW ranges for each lane.</li>
		<li>Export the output data, i.e., the integral areas within the specified molecular weight ranges.</li>
	</ol>
	</li>
	<li>Multivariate analysis for diagnostic
	<ol>
		<li>Apply linear discriminant analysis (LDA) or other multivariate methods for classification analysis.</li>
		<li>Calculate accuracy, sensitivity, specificity, and perform ROC analysis. 
		NOTE: The employed scripts used for image processing, profile normalization, fitting, and classification are available from27, where the corresponding user manual is also provided. 
		CAUTION: Follow biosafety protocols when handling human plasma.</li>
	</ol>
	</li>
</ol>

<ol><li>Cai, R., Chen, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+crown+and+the+scepter%3A+Roles+of+the+protein+corona+in+nanomedicine%5BTitle%5D)+AND+%22Adv+Mater%22%5BJournal%5D)">The crown and the scepter: Roles of the protein corona in nanomedicine.</a> Adv Mater. 31 (45), 1805740(2019).</li>
<li>Caputo, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nanotechnology+meets+oncology%3A+A+perspective+on+the+role+of+the+personalized+nanoparticle-protein+corona+in+the+development+of+technologies+for+pancreatic+cancer+detection%5BTitle%5D)+AND+%22Int+J+Mol+Sci%22%5BJournal%5D)">Nanotechnology meets oncology: A perspective on the role of the personalized nanoparticle-protein corona in the development of technologies for pancreatic cancer detection.</a> Int J Mol Sci. 23 (18), 10591(2022).</li>
<li>Lundqvist, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+evolution+of+the+protein+corona+around+nanoparticles%3A+A+test+study%5BTitle%5D)+AND+%22ACS+Nano%22%5BJournal%5D)">The evolution of the protein corona around nanoparticles: A test study.</a> ACS Nano. 5 (9), 7503-7509 (2011).</li>
<li>Hajipour, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Personalized+protein+coronas%3A+A+%26%2334%3Bkey%26%2334%3B+factor+at+the+nanobiointerface%5BTitle%5D)+AND+%22Biomater+Sci%22%5BJournal%5D)">Personalized protein coronas: A &#34;key&#34; factor at the nanobiointerface.</a> Biomater Sci. 2 (9), 1210-1221 (2014).</li>
<li>Quagliarini, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein+corona-enabled+serological+tests+for+early-stage+cancer+detection%5BTitle%5D)+AND+%22Sens+Int%22%5BJournal%5D)">Protein corona-enabled serological tests for early-stage cancer detection.</a> Sens Int. 1, 100025(2020).</li>
<li>Caracciolo, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Size+and+charge+of+nanoparticles+following+incubation+with+human+plasma+of+healthy+and+pancreatic+cancer+patients%5BTitle%5D)+AND+%22Colloids+Surf+B+Biointerfaces%22%5BJournal%5D)">Size and charge of nanoparticles following incubation with human plasma of healthy and pancreatic cancer patients.</a> Colloids Surf B Biointerfaces. 123, 673-678 (2014).</li>
<li>Barrán-Berdón, A. L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Time+evolution+of+nanoparticle-protein+corona+in+human+plasma%3A+Relevance+for+targeted+drug+delivery%5BTitle%5D)+AND+%22Langmuir%22%5BJournal%5D)">Time evolution of nanoparticle-protein corona in human plasma: Relevance for targeted drug delivery.</a> Langmuir. 29 (21), 6485-6494 (2013).</li>
<li>Yu, Y., Luan, Y., Dai, W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Dynamic+process%2C+mechanisms%2C+influencing+factors%2C+and+study+methods+of+protein+corona+formation%5BTitle%5D)+AND+%22Int+J+Biol+Macromol%22%5BJournal%5D)">Dynamic process, mechanisms, influencing factors, and study methods of protein corona formation.</a> Int J Biol Macromol. 205, 731-739 (2022).</li>
<li>Hadjidemetriou, M., Kostarelos, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Evolution+of+the+nanoparticle+corona%5BTitle%5D)+AND+%22Nat+Nanotechnol%22%5BJournal%5D)">Evolution of the nanoparticle corona.</a> Nat Nanotechnol. 12 (4), 288-290 (2017).</li>
<li>Caputo, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+protein+corona-enabled+blood+test+for+early+cancer+detection%5BTitle%5D)+AND+%22Nanoscale%22%5BJournal%5D)">A protein corona-enabled blood test for early cancer detection.</a> Nanoscale. 9 (1), 349-354 (2017).</li>
<li>Pozzi, D., Caracciolo, G. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aPozzi%2C+D+%22Exploiting+differences+in+personal+nanoparticle+corona+profiles+for+cancer+diagnostics%22">Exploiting differences in personal nanoparticle corona profiles for cancer diagnostics</a>. , Taylor & Francis. 431-433 (2025).</li>
<li>Vence, M. G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Potential+clinical+applications+of+the+personalized%2C+disease-specific+protein+corona+on+nanoparticles%5BTitle%5D)+AND+%22Clin+Chim+Acta%22%5BJournal%5D)">Potential clinical applications of the personalized, disease-specific protein corona on nanoparticles.</a> Clin Chim Acta. 501, 102-111 (2020).</li>
<li>Di Domenico, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nanoparticle-biomolecular+corona%3A+A+new+approach+for+the+early+detection+of+non-small-cell+lung+cancer%5BTitle%5D)+AND+%22J+Cell+Physiol%22%5BJournal%5D)">Nanoparticle-biomolecular corona: A new approach for the early detection of non-small-cell lung cancer.</a> J Cell Physiol. 234 (6), 9378-9386 (2019).</li>
<li>Papi, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Exploitation+of+nanoparticle-protein+interactions+for+early+disease+detection%5BTitle%5D)+AND+%22Appl+Phys+Lett%22%5BJournal%5D)">Exploitation of nanoparticle-protein interactions for early disease detection.</a> Appl Phys Lett. 114 (16), 163702(2019).</li>
<li>Di Santo, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein+corona+profile+of+graphene+oxide+allows+detection+of+glioblastoma+multiforme+using+a+simple+one-dimensional+gel+electrophoresis+technique%3A+A+proof-of-concept+study%5BTitle%5D)+AND+%22Biomater+Sci%22%5BJournal%5D)">Protein corona profile of graphene oxide allows detection of glioblastoma multiforme using a simple one-dimensional gel electrophoresis technique: A proof-of-concept study.</a> Biomater Sci. 9 (13), 4671-4678 (2021).</li>
<li>Caputo, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Synergistic+analysis+of+protein+corona+and+haemoglobin+levels+detects+pancreatic+cancer%5BTitle%5D)+AND+%22Cancers%22%5BJournal%5D)">Synergistic analysis of protein corona and haemoglobin levels detects pancreatic cancer.</a> Cancers. 13 (1), 93(2020).</li>
<li>Colapicchioni, V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Personalized+liposome-protein+corona+in+the+blood+of+breast%2C+gastric+and+pancreatic+cancer+patients%5BTitle%5D)+AND+%22Int+J+Biochem+Cell+Biol%22%5BJournal%5D)">Personalized liposome-protein corona in the blood of breast, gastric and pancreatic cancer patients.</a> Int J Biochem Cell Biol. 75, 180-187 (2016).</li>
<li>Di Santo, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Personalized+graphene+oxide-protein+corona+in+the+human+plasma+of+pancreatic+cancer+patients%5BTitle%5D)+AND+%22Front+Bioeng+Biotechnol%22%5BJournal%5D)">Personalized graphene oxide-protein corona in the human plasma of pancreatic cancer patients.</a> Front Bioeng Biotechnol. 8, 491(2020).</li>
<li>Digiacomo, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Efficient+pancreatic+cancer+detection+through+personalized+protein+corona+of+gold+nanoparticles%5BTitle%5D)+AND+%22Biointerphases%22%5BJournal%5D)">Efficient pancreatic cancer detection through personalized protein corona of gold nanoparticles.</a> Biointerphases. 16 (1), 011010(2021).</li>
<li>Caputo, D., Caracciolo, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nanoparticle-enabled+blood+tests+for+early+detection+of+pancreatic+ductal+adenocarcinoma%5BTitle%5D)+AND+%22Cancer+Lett%22%5BJournal%5D)">Nanoparticle-enabled blood tests for early detection of pancreatic ductal adenocarcinoma.</a> Cancer Lett. 470, 191-196 (2020).</li>
<li>Caracciolo, G., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Challenges+in+molecular+diagnostic+research+in+cancer+nanotechnology%5BTitle%5D)+AND+%22Nano+Today%22%5BJournal%5D)">Challenges in molecular diagnostic research in cancer nanotechnology.</a> Nano Today. 27, 6-10 (2019).</li>
<li>Ashkarran, A. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Measurements+of+heterogeneity+in+proteomics+analysis+of+the+nanoparticle+protein+corona+across+core+facilities%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Measurements of heterogeneity in proteomics analysis of the nanoparticle protein corona across core facilities.</a> Nat Commun. 13 (1), 6610(2022).</li>
<li>Pederzoli, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Protein+corona+and+nanoparticles%3A+How+can+we+investigate+on%5BTitle%5D)+AND+%22Wiley+Interdiscip+Rev+Nanomed+Nanobiotechnol%22%5BJournal%5D)">Protein corona and nanoparticles: How can we investigate on.</a> Wiley Interdiscip Rev Nanomed Nanobiotechnol. 9 (6), e1467(2017).</li>
<li>Otoo, J. A., Schlappi, T. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((REASSURED+multiplex+diagnostics%3A+A+critical+review+and+forecast%5BTitle%5D)+AND+%22Biosensors%22%5BJournal%5D)">REASSURED multiplex diagnostics: A critical review and forecast.</a> Biosensors. 12 (2), 124(2022).</li>
<li>Papi, M., Caracciolo, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Principal+component+analysis+of+personalized+biomolecular+corona+data+for+early+disease+detection%5BTitle%5D)+AND+%22Nano+Today%22%5BJournal%5D)">Principal component analysis of personalized biomolecular corona data for early disease detection.</a> Nano Today. 21, 14-17 (2018).</li>
<li>Giulimondi, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Reproducibility+of+biomolecular+corona+experiments%3A+A+primer+for+reliable+results%5BTitle%5D)+AND+%22Part+Part+Syst+Charact%22%5BJournal%5D)">Reproducibility of biomolecular corona experiments: A primer for reliable results.</a> Part Part Syst Charact. 40 (2), 2200169(2023).</li>
<li>Renzi, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Structuring+lipid+nanoparticles%2C+DNA%2C+and+protein+corona+into+stealth+bionanoarchitectures+for+in+vivo+gene+delivery%5BTitle%5D)+AND+%22Nat+Commun%22%5BJournal%5D)">Structuring lipid nanoparticles, DNA, and protein corona into stealth bionanoarchitectures for in vivo gene delivery.</a> Nat Commun. 15 (1), 9119(2024).</li>
<li>Van Dorpe, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Integrating+automated+liquid+handling+in+the+separation+workflow+of+extracellular+vesicles+enhances+specificity+and+reproducibility%5BTitle%5D)+AND+%22J+Nanobiotechnology%22%5BJournal%5D)">Integrating automated liquid handling in the separation workflow of extracellular vesicles enhances specificity and reproducibility.</a> J Nanobiotechnology. 21 (1), 157(2023).</li>
<li>Quagliarini, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+influence+of+protein+corona+on+graphene+oxide%3A+Implications+for+biomedical+theranostics%5BTitle%5D)+AND+%22J+Nanobiotechnology%22%5BJournal%5D)">The influence of protein corona on graphene oxide: Implications for biomedical theranostics.</a> J Nanobiotechnology. 21 (1), 267(2023).</li>
<li>Jiříčková, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Synthesis+and+applications+of+graphene+oxide%5BTitle%5D)+AND+%22Materials%22%5BJournal%5D)">Synthesis and applications of graphene oxide.</a> Materials. 15 (3), 920(2022).</li>
<li>Saorin, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+redefined+protocol+for+protein+corona+analysis+on+graphene+oxide%5BTitle%5D)+AND+%22ACS+Nanosci+Au%22%5BJournal%5D)">A redefined protocol for protein corona analysis on graphene oxide.</a> ACS Nanosci Au. , (2025).</li>
<li>Digiacomo, L., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Nanoparticle-protein+corona+enhances+accuracy+of+Ca-19.9-based+pancreatic+cancer+classification%5BTitle%5D)+AND+%22Nanoscale%22%5BJournal%5D)">Nanoparticle-protein corona enhances accuracy of Ca-19.9-based pancreatic cancer classification.</a> Nanoscale. 17 (12), 7066-7075 (2025).</li>
<li>Caputo, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Improving+the+accuracy+of+pancreatic+cancer+clinical+staging+by+exploitation+of+nanoparticle-blood+interactions%3A+A+pilot+study%5BTitle%5D)+AND+%22Pancreatology%22%5BJournal%5D)">Improving the accuracy of pancreatic cancer clinical staging by exploitation of nanoparticle-blood interactions: A pilot study.</a> Pancreatology. 18 (6), 661-665 (2018).</li>
<li>Quagliarini, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+decade+of+the+liposome-protein+corona%3A+Lessons+learned+and+future+breakthroughs+in+theranostics%5BTitle%5D)+AND+%22Nano+Today%22%5BJournal%5D)">A decade of the liposome-protein corona: Lessons learned and future breakthroughs in theranostics.</a> Nano Today. 47, 101657(2022).</li>
<li>Wu, S. Y., An, S. S. A., Hulme, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Current+applications+of+graphene+oxide+in+nanomedicine%5BTitle%5D)+AND+%22Int+J+Nanomedicine%22%5BJournal%5D)">Current applications of graphene oxide in nanomedicine.</a> Int J Nanomedicine. 10 Suppl 1, 9-24 (2015).</li>
<li>Caputo, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multiplexed+detection+of+pancreatic+cancer+by+combining+a+nanoparticle-enabled+blood+test+and+plasma+levels+of+acute-phase+proteins%5BTitle%5D)+AND+%22Cancers%22%5BJournal%5D)">Multiplexed detection of pancreatic cancer by combining a nanoparticle-enabled blood test and plasma levels of acute-phase proteins.</a> Cancers. 14 (19), 4658(2022).</li>
</ol>

The authors have no conflicts of interest to disclose.

Method versatility and reproducibility across NP types
The protocol presented here provides a standardized and reproducible workflow for isolating and characterizing the PC formed on NP upon incubation with HP. As demonstrated in previous and current studies33, the method enables the generation of SDS-PAGE-based molecular fingerprints that capture biologically and diagnostically relevant features of the personalized PC. The observed consistency across m...

Nanoparticle protein corona (NP-PC)
The characterization of the PC that forms on the surface of NPs following their exposure to biological fluids has emerged as a powerful strategy for disease diagnostics and personalized medicine1. This layer of adsorbed proteins, dynamic and disease-specific in its composition, transforms the synthetic identity of NPs and drives their biological interactions2. The composition of the PC is not random but highly dependent on both the physicochemical properties of the NPs and the biological environment in which they are introduced3. Factors such as NP size, shape, surface charge, and composition can significantly influence the affinity, kinetics, and structural arrangement of adsorbed proteins. Likewise, the biological fluid, its source, complexity, and disease state, play a key role in determining the corona&#39;s final makeup4,5,6.
The dynamic nature of the corona further complicates its analysis, as its structure evolves over time from a soft, loosely bound outer layer to a more stable and tightly adsorbed inner core7,8. This evolving architecture, resulting from competitive protein adsorption events, requires time-resolved and standardized approaches to be meaningfully characterized9. Despite this complexity, it is now widely recognized that the PC is not a mere artifact, but a biologically informative layer capable of capturing systemic disease alterations in a non-invasive manner. The goal of the method described in this paper is to standardize the use of SDS-PAGE for the robust, reproducible, and accessible profiling of the personalized PC, particularly for early cancer detection through liquid biopsy approaches10.
The personalized PC and its relevance in cancer detection
The scientific basis for this method lies in the concept of the personalized PC. Upon incubation with patient plasma, NPs adsorb a unique fingerprint of proteins that reflects the individual&#39;s physiological or pathological status11,12. This concept has been substantiated by studies showing significant alterations in PC composition in the presence of disease, including pancreatic ductal adenocarcinoma (PDAC), lung cancer13, meningioma14, and glioblastoma multiforme15. Specifically, in PDAC, PCs derived from patient plasma differ substantially from those of healthy donors. These differences have been exploited in classifier models that achieve up to 90% diagnostic accuracy. The PC thus serves as a nanoscale concentrator of disease-specific signals16.
Recent work has demonstrated that PCs from cancer patients are enriched in proteins involved in inflammation, coagulation, complement activation, and extracellular vesicle transport, biological pathways known to be dysregulated in malignancy17. These protein patterns are highly reproducible within individual patient classes, yet clearly distinct across different disease states, suggesting that the corona can function not only as a general disease sensor but also as a disease-specific classifier. In the context of PDAC, one of the deadliest and most insidious malignancies, our group has pioneered NP-enabled blood (NEB) tests based on PC characterization18,19,20. This minimally invasive approach leverages the capacity of NPs to selectively enrich low-abundance and disease-altered plasma proteins, making them detectable via simple, benchtop electrophoretic separation.
Advantages over conventional proteomic techniques
The rationale for developing this approach stems from the limitations of conventional proteomics techniques such as mass spectrometry (MS), which, while offering deep proteome coverage, are expensive, time-consuming, and ill-suited for high-throughput screening or point-of-care diagnostics21. Another key challenge lies in the intrinsic complexity of MS, where variations in sample preparation, instrument calibration, acquisition parameters, and data analysis workflows contribute to significant inter-laboratory differences in the reported PC composition22. In contrast, SDS-PAGE analysis of the PC offers a rapid and cost-effective means to capture disease-related proteomic patterns. Crucially, the SDS-PAGE readout is not reliant on the absolute identification of individual biomarkers but on the detection of systematic changes in the global protein profile, which can be analyzed through densitometry and multivariate statistics.
Unlike antibody-based assays, which are limited to known targets and prone to cross-reactivity, SDS-PAGE offers an unbiased view of the protein landscape, preserving the integrity of the original sample and allowing downstream integration with complementary platforms such as LC-MS/MS for protein identification. Moreover, since SDS-PAGE does not require sample fractionation or depletion steps, it is especially suitable for analyzing low-volume biological samples, an essential feature for applications involving early diagnosis or screening of fragile populations23. For all these reasons, this protocol is consistent with several of the WHO&#39;s REASSURED criteria (Real-time connectivity, Ease of specimen collection, Affordable, Sensitive, Specific, User-friendly, Robust, Equipment-free, and Deliverable), making it a practical tool for scalable diagnostics24. Based on this evidence, it was hypothesized that NPs incubated with patient plasma adsorb a reproducible, disease-specific PC that can be systematically profiled using SDS-PAGE. We further propose that standardized electrophoretic analysis of these personalized coronas can distinguish cancer patients from healthy individuals, thereby providing a robust and clinically translatable diagnostic tool.
Workflow and clinical applicability
The SDS-PAGE-based corona profiling protocol systematizes each step: from NPs preparation and incubation with diluted plasma, to corona isolation, gel electrophoresis, and quantitative densitometric analysis (Figure 1). Key parameters such as incubation time, plasma dilution, and NP type25 are finely tuned to ensure optimal protein capture and gel image resolution. The protocol also addresses practical concerns such as sample throughput, storage stability, and batch-to-batch consistency26. Moreover, since it is a non-destructive technique, SDS-PAGE can be seamlessly combined with complementary proteomic approaches such as MS to identify the molecular composition of individual protein bands15. The workflow has been optimized for high inter-operator reproducibility, including compatibility with manual and automated pipetting platforms, and it is robust across different NP chemistries and plasma concentrations. These features ensure that the method can be applied across diverse laboratory settings, making it amenable to both centralized diagnostic hubs and decentralized research laboratories. Thus, this technique is particularly well-suited for researchers and clinicians involved in early diagnostics, biomarker discovery, and translational nanomedicine. It is applicable to a wide range of pathologies and adaptable to diverse biological fluids beyond plasma, including saliva or urine. The flexibility and robustness of this protocol render it a strong candidate for integration into clinical validation pipelines, large-scale screening studies, and point-of-care platforms.

<table><tbody><tr><td>1.5ML Protein LoBind TUBE</td><td>eppendorf</td><td>22431081</td><td></td></tr><tr><td>10x Tris/Glycine/SDS</td><td>Biorad</td><td>1610732</td><td></td></tr><tr><td>Centrifuge</td><td>Labortechnik</td><td>Z 216 MK Hermle</td><td>Used at 18,620 RCF for 20 min at 4 &amp;deg;C</td></tr><tr><td>ChemiDoc imaging system</td><td>Bio-Rad</td><td>12003153</td><td>Used for gel image acquisition</td></tr><tr><td>Chloroform</td><td>Sigma Aldrich</td><td>C2432-1L</td><td>Solvent for DOTAP</td></tr><tr><td>Corning 1-200 &amp;micro;L Universal Fit Bulk Packed Pipet Tips</td><td>corning</td><td>4845</td><td></td></tr><tr><td>Criterion TGX Stain-Free Precast Gels</td><td>Biorad</td><td>5678095</td><td></td></tr><tr><td>Distilled Water 6 x 1L</td><td>thermo fisher</td><td>15230001</td><td></td></tr><tr><td>DOTAP (1,2-dioleoyl-3-trimethylammonium-propane)</td><td>Avanti Polar Lipids</td><td>890890P-200mg</td><td>Used to prepare cationic liposomes; dissolved in chloroform, evaporated, hydrated, and extruded</td></tr><tr><td>Enduro 300V Power Supply&amp;nbsp;</td><td>Labnet&amp;nbsp;</td><td>E0303-230V</td><td></td></tr><tr><td>GloMax Discover Microplate Reader</td><td>Promega</td><td>GM3000</td><td></td></tr><tr><td>Gold nanoparticles (citrate-stabilized)</td><td>Sigma-Aldrich</td><td>742031-25ML</td><td>Used as nanomaterial; used without further purification</td></tr><tr><td>Gradient polyacrylamide gel (4&amp;ndash;20% TGX)</td><td>Bio-Rad</td><td>5678095</td><td>Used for SDS&amp;ndash;PAGE</td></tr><tr><td>Grant Instrument&amp;nbsp;Thermoshaker HC24N (24 x 1.5mL microtubes)</td><td>Fisher scientific</td><td>PHMT-PSC24N</td><td></td></tr><tr><td>Graphene Oxide (GO) solution</td><td>Graphenea</td><td>GO-4-1000</td><td>Used as nanomaterial; diluted to 0.25 mg/mL and sonicated</td></tr><tr><td>Human Plasma (HP)</td><td>Sigma-Aldrich</td><td>P9523-5ML</td><td>Used for nanoparticle incubation</td></tr><tr><td>ImageLab Software</td><td>Bio-Rad. Version 6.1.0.07</td><td>/</td><td>Used for exporting gel image as tiff files</td></tr><tr><td>Laemmli loading buffer 2x</td><td>Biorad</td><td>1610737</td><td>Used for resuspending pellets</td></tr><tr><td>MATLAB</td><td>MathWorks. Version R2022a</td><td>/</td><td>Used for image processing, data analysis and profile computation</td></tr><tr><td>Mini-Extruder</td><td>Avanti Polar Lipids</td><td>610020</td><td>Used for liposome preparation</td></tr><tr><td>NuPAGE Sample Reducing Agent (10X)</td><td>Thermo Scientific</td><td>NP004</td><td></td></tr><tr><td>Phosphate Saline Buffer</td><td>Corning&amp;nbsp;</td><td>21-031-cm</td><td>Used for DOTAP-HP samples</td></tr><tr><td>Pierce BCA Protein Assay Kit, 1 Kit (1 L)</td><td>ThermoFisher</td><td>23225</td><td></td></tr><tr><td>Pipet tips 0.5-10ul clear</td><td>Axygen</td><td>T-300-STK</td><td></td></tr><tr><td>PIPETMAN L P10L, 0.5-10 &amp;micro;L, Metal Ejector</td><td>Gilson</td><td>FA10002M</td><td></td></tr><tr><td>PIPETMAN P20, 2-20 &amp;micro;L, Metal Ejector</td><td>Gilson</td><td>F144056M</td><td></td></tr><tr><td>PIPETMAN P200, 20-200 &amp;micro;L, Metal Ejector</td><td>Gilson</td><td>F144058M</td><td></td></tr><tr><td>PIPETMAX automated pipetting system</td><td>Gilson</td><td>GSYS0021</td><td>Used for automated sample handling</td></tr><tr><td>Polycarbonate filter (0.1 &amp;micro;m)</td><td>Avanti Polar Lipids</td><td>610005-1EA</td><td>Used for liposome extrusion</td></tr><tr><td>Precision Plus Protein All Blue Prestained Protein Standards</td><td>Biorad</td><td>1610373</td><td></td></tr><tr><td>Precision Plus Protein Unstained Standards&amp;nbsp;</td><td>Biorad</td><td>1610363EDU</td><td></td></tr><tr><td>Vibra cell sonicator VC505</td><td>Sonics and Materials inc.</td><td>VC505</td><td></td></tr><tr><td>ZetaSizer</td><td>Malvern</td><td>ZS90</td><td>Used for dynamic light scattering (DLS) and microelectrophoresis (ME) measurements</td></tr></tbody></table>

standardized sds-page workflow for personalized protein corona profiling in early cancer detection

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

The formation of a personalized protein corona (PC) on nanoparticles (NPs) upon exposure to biological fluids has emerged as a promising, minimally invasive strategy for early cancer detection. This study introduces a standardized protocol for profiling the protein corona using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE), optimized to ensure robustness, reproducibility, and automation. The method was rigorously validated across a variety of nanoparticle (NP) platforms, plasma sources, and experimental conditions, demonstrating consistently reproducible profiles with minimal operator-dependent variability. In comparison to conventional proteomics techniques, this approach offers a faster, more cost-effective solution that meets the World Health Organization's REASSURED criteria for diagnostics. When applied to pancreatic ductal adenocarcinoma (PDAC), a highly aggressive malignancy with limited early biomarkers, this workflow achieved classification accuracies of up to 90%, even in early-stage patients. This standardized platform marks a significant advancement towards scalable, clinically translatable, and affordable diagnostic tools for cancer screening.

One of the major challenges in standardizing PC experiments lies in minimizing technical and operator-dependent variability, as previously highlighted by this study26. Open questions still persist regarding the influence of experimental variables such as operator bias and the impact of automated tools on reproducibility18,28.
Firstly, this study p...

This protocol standardizes SDS-PAGE analysis for personalized protein corona profiling on nanoparticles, enabling reproducible, scalable, and low-cost detection of cancer-specific signatures. Designed for early diagnosis of pancreatic ductal adenocarcinoma, it offers a practical, REASSURED-aligned alternative to complex proteomic methods in both research and clinical settings.

The formation of a personalized protein corona (PC) on nanoparticles (NPs) upon exposure to biological fluids has emerged as a promising, minimally ...

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.

standardized-sds-page-workflow-for-personalized-protein-corona

Research

JoVE Journal

Cancer Research

We described a procedure for the disaggregation of colorectal cancer (CRC) to produce viable single cells, which are then captured on customized antibody microarrays recognizing surface antigens (DotScan CRC microarray).  Sub-populations of cells bound to the microarray can be profiled by fluorescence multiplexing using monoclonal antibodies tagged with fluorescent dyes.

Colorectal Cancer Cell Surface Protein Profiling Using an Antibody Microarray and Fluorescence Multiplexing

RPPA enables the protein expression of hundreds of samples, printed on nitrocellulose slides to be interrogated simultaneously, using fluorescently labelled antibodies. This technique has been applied to study the effect of drug treatment heterogeneity within clear cell renal carcinoma.

The Use of Reverse Phase Protein Arrays (RPPA) to Explore Protein Expression Variation within Individual Renal Cell Cancers

In-depth analyses of cancer cell proteomes facilitate identification of novel drug targets and diagnostic biomarkers. We describe an experimental workflow for quantitative analysis of (phospho-)proteomes in cancer cell subpopulations derived from liquid and solid tumors. This is achieved by combining cellular enrichment strategies with quantitative Super-SILAC-based mass spectrometry.

Quantitative Mass Spectrometric Profiling of Cancer-cell Proteomes Derived From Liquid and Solid Tumors

The goal of this video is to demonstrate how to perform automated DNA extraction from formalin-fixed paraffin-embedded (FFPE) reference standard cell lines and digital droplet PCR (ddPCR) analysis to detect rare mutations in a clinical setting. Detecting mutations in FFPE samples demonstrates the clinical utility of ddPCR in FFPE samples.

Employing Digital Droplet PCR to Detect BRAF V600E Mutations in Formalin-fixed Paraffin-embedded Reference Standard Cell Lines

The use of an RNA-based approach to determine quantitative immune profiles of solid tumor tissues and leverage clinical cohorts for immune-oncology biomarker discovery is described through a molecular and informatics protocols.

Predictive Immune Modeling of Solid Tumors

Here, we describe a protocol for fine-tuning regions of interest (ROIs) for Spatial Omics technologies to better characterize the tumor microenvironment and identify specific cell populations. For proteomics assays, automated customized protocols can guide ROI selection, while transcriptomics assays can be fine-tuned utilizing ROIs as small as 50 µm.

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Proteomic dysregulation plays an important role in the spread of diffusely infiltrating gliomas, but several relevant proteins remain unidentified. Digital spatial processing (DSP) offers an efficient, high-throughput approach for characterizing the differential expression of candidate proteins that may contribute to the invasion and migration of infiltrative gliomas.

Digital Spatial Profiling for Characterization of the Microenvironment in Adult-Type Diffusely Infiltrating Glioma

Here, we establish a mass spectrometry-based proteomic method using isolated regions of interest in formalin-fixed, paraffin-embedded tissue sections. This protocol is used to analyze proteome from specific tissue areas in archived formalin-fixed, paraffin-embedded tissue sections.

A Streamlined Approach for Mass Spectrometry-Based Proteomics Using Selected Tissue Regions

Here, we present a protocol to enable the capture and identification of disease-specific protein-protein interactions from native cells and tissues using chemical probes and mass spectrometry. The resulting interaction datasets are analyzed through a dedicated web-based platform to reveal dynamic network dysfunctions and pathway alterations linked to disease.

Mapping Dysfunctional Protein-Protein Interactions in Disease

This protocol was designed to train a machine learning algorithm to use a combination of imaging parameters derived from magnetic resonance imaging (MRI) and positron emission tomography/computed tomography (PET/CT) in a rat model of breast cancer bone metastases to detect early metastatic disease and predict subsequent progression to macrometastases.

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Learn standardized SDS-PAGE workflow for personalized protein corona profiling using graphene oxide nanoparticles. This reproducible method enables early detection of pancreatic ductal adenocarcinoma with high diagnostic accuracy.

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

Explore More Videos