Plasma-to-tumour tissue integrated proteomics using nano-omics for biomarker discovery in glioblastoma

Abstract

Glioblastoma (GB) is the most lethal brain cancer, with patient survival rates remaining largely unchanged over the past two decades. Here, we introduce the Nano-omics integrative workflow that links systemic (plasma) and localised (tumour tissue) protein changes associated with GB progression. Mass spectrometry analysis of the nanoparticle biomolecule corona in GL261-bearing mice at different stages of GB revealed plasma protein alterations, even at low tumour burden, with over 30% overlap between GB-specific plasma and tumour tissue proteomes. Analysis of matched plasma and surgically resected tumour samples from high-grade glioma patients demonstrates the clinical applicability of the Nano-omics pipeline. Cross-species correlation identified 48 potential GB biomarker candidates involved in actin cytoskeleton organisation, focal adhesion, platelet activation, leukocyte migration, amino acid biosynthesis, carbon metabolism, and phagosome pathways. The Nano-omics approach holds promise for the discovery of early detection and disease monitoring biomarkers of central nervous system conditions, paving the way for subsequent clinical validation.

Fig. 1. Establishment of a longitudinal GL261 GB murine model.

a Overview of the in vivo study design enabling plasma and brain tissue proteomic profiling in GB tumour-bearing mice. A syngeneic GB murine model was established in C57BL/6 J female mice via intracranial injection of GL261 cells. Control mice underwent sham injection with saline. PEGylated liposome nanoparticles (NPs) were intravenously administered at days 7, 14, and 18 post-intracranial GL261 cell injection to allow protein corona formation. The corona-coated NPs were subsequently recovered from the blood circulation and purified to remove any unbound proteins. Brains were collected from control and tumour-bearing mice at all three-time points of investigation. Created in BioRender. Hadjidemetriou, M. (2025) https://BioRender.com/z49a544. b Histological characterisation of GL261 tumours at day 7 (D7), day 14 (D14), and day 18 (D18) by haematoxylin and eosin (H&E) staining. c Quantification of the tumour volume in GB mice at days 7, 14, and 18 (n = 9 biological replicates for D7 and D14, n = 8 biological replicates for D18; error bars indicate mean ± SEM; *p-value = 0.0409 between D7 and D14, ****p-value < 0.0001 between D7 and D18, ****p-value < 0.0001 between D14 and D18 by One-way ANOVA with Tukey’s multiple comparison test). Source data for Fig. 1c are provided as a Source Data file.

Fig. 2. Nanotechnology-enabled plasma proteomics in the GL261 mouse model of GB.

a Schematic overview of the in vivo plasma proteomics workflow. Following the intracranial injection of GL261 glioma cells (for GB-bearing mice) or saline (for control mice), C57BL/6 J female mice were intravenously administrated liposome NPs at days 7, 14, and 18 post-tumour implantations. Blood-circulating NPs were subsequently recovered, and corona-coated NPs were purified prior to LC-MS/MS analysis. Created in BioRender. Hadjidemetriou, M. (2025) https://BioRender.com/g08d103. b The total amount of protein adsorbed onto the surface of liposome NPs was quantified and expressed as protein binding value (μg of protein/μmol lipid). Error bars indicate mean ± SEM of n = 3 biological replicates (plasma pooled from n = 5 mice for each biological replicate; * p-value = 0.0414 between D7 GBM and D14 GBM; ** p-value = 0.009 between D7 GBM and D18 GBM; One-way ANOVA with Tukey’s multiple comparisons test between three-time points within the GB group; ** p-value = 0.0028 by Sidak’s multiple comparisons between the control and GB groups at D18 time point. c The Venn diagram illustrates the number of common and unique differentially abundant proteins (DAPs) identified at D7, D14, and D18 time points (proteins with a p-value < 0.05). Statistical comparisons of relative protein expressions between corona proteomes from tumour-bearing mice and control mice were conducted using Progenesis QI for proteomics software (v. 3.0; Nonlinear Dynamics). d Bar graph reports the number and percentage of upregulated and downregulated DAPs identified through the analysis of the protein coronas formed in GB and healthy control mice (n = 3 biological replicates; pooled plasma from n = 5 mice per biological replicate). e Volcano plots display the relationship between fold change (shown in x-axis) and statistical significance (shown in y-axis) of the DAPs (with one-way ANOVA p-value < 0.05) at D7, D14 and D18 time points. The comprehensive lists of DAPs are provided in Supplementary Data 1−3. Source data for Fig. 2b are provided as a Source Data file.

Fig. 3. Longitudinal monitoring of the GB-specific plasma proteome in GL261-bearing mice.

a, b. The longitudinal fluctuation in the fold change values of the n = 115 and n = 417 proteins identified as DAPs between GB and control mice at D7 and D18, respectively (p-value = 0.0113 and p-value < 0.0001 by one-way ANOVA with Geisser-Greenhouse correction, respectively). c, d Volcano plots display the relationship between fold change and significance for the DAPs (with one-way ANOVA p-value < 0.05) identified between D7 vs D14 and D14 vs D18 time points (n = 3 biological replicates; pooled plasma samples from n = 5 mice per biological replicate). DAPs identified as a result of mice aging between the three time points were excluded from the analysis by analysing the plasma proteome of sham-injected mice. The 116 common proteins between D7 vs D14 and D14 vs D18 GB mice highlighted in filled dots. The complete lists of DAPs identified between D7 vs D14 and D14 vs D18 time points are provided in Supplementary Data 4 and 5. e Longitudinal kinetics in the abundance of the 32 upregulated (‘Up’) and n = 7 downregulated (‘Down’) DAPs that displayed differential abundance between D7 vs D14 and D14 vs D18 time points (Supplementary Data 6).

Fig. 4. Integrative analysis of the plasma and tumour tissue proteomes in GL261-bearing mice.

a Schematic overview of the laser-capture microdissection (LCM)-coupled LC-MS/MS workflow employed for the proteomics analysis of the brain tissue samples. FFPE-preserved brains (n = 6 biological replicates/time point) were histologically sectioned and stained with haematoxylin and eosin (H&E) to reveal pathological characteristics of the tissue sections. Using a laser capture microscope, a total volume of 0.1 mm³ brain tissue was dissected from the tumour-bearing hemisphere and the ‘non-injected’ lateral hemisphere. Equivalent volumes of brain tissue from the sham injection site and the lateral hemisphere of the same brain were microdissected from sham-injected control mice. Microdissected brain tissue samples were lysed and subjected to S-trap protein digestion prior to their analysis by LC:MS\MS. Created in BioRender. Hadjidemetriou, M. (2025) https://BioRender.com/k25w778. b Volcano plots display the relationship between fold change and significance for the DAPs identified through comparison of healthy and tumour tissues. All proteins with a one-way ANOVA p-value < 0.05 are shown, coloured for upregulation shown in blue and downregulation in black. Any DAPs also identified in sham-injected mice (Supplementary Fig. 3c and Supplementary Data 7–9) were excluded from the lists. DAPs that were common between plasma and brain tissue are highlighted in orange. The full list of DAPs is shown in Supplementary Data 10−12. Venn diagrams illustrate the overlap between the plasma and tumour tissue identified DAPs at D7, D14 and D18 (Full list in Supplementary Data 13). c Dot plot presents the common KEGG pathways identified through independent enrichment analyses of plasma and tumour tissue DAPs at D7, D14 and D18, using a significance threshold of adjusted p-value < 0.05 by one-sided Fisher’s exact test. Pathways are ranked according to the adjusted p-value within each time point. The colour of the dots represents the adjusted p-value, and the size of the dots indicates the gene ratio (genes involved/total number of genes). Source data for Fig. 4c are provided as a Source Data file.

Fig. 5. Human clinical validation of the plasma-to-tumour tissue integrative proteomic analysis pipeline.

a Schematic representation outlining the human clinical cohort study design. Plasma samples obtained from pre-operative high-grade glioma patients were matched with healthy controls, both then formed ex vivo coronas with liposome NPs. Snap-frozen tumour tissue from the same GB patients during tumour surgical resection were cryo-sectioned and annotated by a clinical pathologist before laser capture microdissection (Supplementary Fig. S4b). Both corona and tumour tissue samples were subjected to LC-MS/MS analysis. Created in BioRender. Hadjidemetriou, M. (2025) https://BioRender.com/k40i192. b Volcano plot illustrating the relationship between fold change and significance for DAPs identified by comparing GB patient-corona with healthy control-corona (n = 10 biological replicates). A total of n = 321 proteins (with an FDR-adjusted one-way ANOVA p-value < 0.05) were found to be differentially abundant, of which n = 272 proteins exhibited a fold change > 2. Filled dots represent 140 proteins that were also identified by LC:MS/MS analysis in human tumour tissue. Protein names with log2 fold change > 10 or -log(p-value)   6 are displayed. Full lists of proteins are shown in Supplementary Data 14–16. c Dot plot presents common KEGG pathways identified through independent enrichment analyses of human plasma DAPs and tumour tissue proteins, using a significance threshold of adjusted p-value < 0.001 by one-sided Fisher’s exact test. Pathways are classified into three groups according to the functional category in the KEGG pathway database. The colour of the dots represents the adjusted p-value, and the dot size indicates the gene ratio (genes involved/total number of genes). *Indicates the common enriched pathways between humans and mice. d Chord diagram connects the seven enriched pathways shared between humans and mice, with the n = 48 DAPs identified to be shared between the plasma and tumour tissue analyses. Human DAPs found to be shared with mouse plasma DAPs are represented in medium-grey colour. Human DAPs identified as common with mouse plasma and tumour tissue DAPs are depicted in dark-grey colour. The full list of the n = 48 DAPs is shown in Supplementary Fig. 6.