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. 2024 Oct 30;25(21):11660.
doi: 10.3390/ijms252111660.

Phenotypic Biomarkers of Aqueous Extracellular Vesicles from Retinoblastoma Eyes

Affiliations

Phenotypic Biomarkers of Aqueous Extracellular Vesicles from Retinoblastoma Eyes

Anne Amacker et al. Int J Mol Sci. .

Abstract

Recent advancements in aqueous humor (AH) cell-free DNA (cfDNA) genomics have opened new avenues for ex vivo molecular profiling of retinoblastoma (RB), the most common pediatric intraocular malignancy, where biopsy is typically prohibited. While these insights offer a genetic blueprint of the tumor, they lack multi-omic molecular phenotyping, which is essential for understanding the functional state. Extracellular vesicles (EVs), naturally present in AH, are promising by offering time-resolved phenotypic information. We employed multiplex bead-based flow cytometry and Single Extracellular Vesicle Nanoscopy (SEVEN) to analyze EV phenotypes in AH from a cohort of five RB, with three uveal melanoma (UM) and two age-matched glaucoma (GLC) samples serving as controls. The studies identified CD133-enriched EVs uniquely in RB AH, absent in both GLC and UM AH. This was corroborated by further analysis of five RB cell lines, including two commercial (Y79, Weri) and three in-house developed lines, confirming CD133 enrichment and supporting its role as an RB-specific EV marker. Single-vesicle analysis demonstrated a strong association of CD133 with CD81 and CD63, with minimal CD9 presence. These results, validated through complementary techniques, position CD133 as a critical marker in RB-derived EVs, paving the way for enhanced multi-omic RB characterization and potential advancements in clinical diagnostics.

Keywords: CD133; aqueous humor; extracellular vesicles; molecular phenotyping; retinoblastoma.

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Conflict of interest statement

Jesse Lee Berry and Liya Xu have filed a patent, “Aqueous Humor Cell-Free DNA for Diagnostic and Prognostic Evaluation of Ophthalmic Disease”. Part of the methodology used in this work is the subject of a provisional patent application. Tijana Jovanovic-Talisman may be entitled to certain compensation through their institutions’ respective intellectual property policies in the event such intellectual property is licensed.

Figures

Figure 1
Figure 1
Surface marker profiles of EV/EPs in aqueous humor from RB, GLC, and UM cases were analyzed using multiplex bead-based flow cytometry. (A) Workflow design of the AH MACSPlex experiment. (B) Mean Fluorescence Intensity bar plots for two GLC aqueous humor samples, scaled to 5 μL. (C) Mean Fluorescence Intensity bar plots for three Uveal Melanoma samples collected prior to treatment. (D) Mean Fluorescence Intensity bar plots for five RB aqueous humor samples collected at the time of primary enucleation.
Figure 2
Figure 2
The ratio of surface marker mean fluorescence intensity (MFI) to mean tetraspanin MFI from RB, GLC, and UM cases using MACSPlex analysis. (A) Heatmap depicting the MFI for each surface marker compared to the mean CD9/63/81 MFI for each GLC AH sample, UM AH sample, RB AH sample, and RB cell line. (B) The ratio of CD133 tetraspanin signal to mean tetraspanin MFI for all AH samples and RB cell lines included in the analysis.
Figure 3
Figure 3
Concordance of MACSPlex Individual Channel Results and SP-IRIS Analysis in Aqueous Humor Sample Case 79. (A) Single and triple-channel MACSPlex experiments were conducted on the AH sample Case 79, where APC-conjugated CD9, CD63, or CD81 antibodies were individually added and processed through flow cytometry. Mean Fluorescence Intensity (MFI) analysis of CD9, CD63, CD81, and CD133 of an RB aqueous humor sample (Case 79) is presented for the antibody mix, CD9-only, CD63-only, and CD81-only experiments. (B) The same Case 79 AH underwent SP-IRIS analysis using the ExoviewR100 system for tetraspanin expression profiling. Representative fluorescent images were captured by fluorescent-conjugated antibodies (red = CD63-AF647, green = CD81-AF555, and blue = CD9-AF488). (C) Subpopulation breakdown of single and double positive EV populations using MACSPlex results compared to Exoview on Case 79.
Figure 4
Figure 4
Multiparametric characterization of single EVs from AH using SEVENi. (A) Representative raw SMLM images of zoomed-in regions; sample dilutions are indicated in the methods section. (B) Number of detected EVs per region of interest normalized for 1 μL of AH; Error bars, SEM. (C) Distribution sizes, molecular contents, and circularity for detected EVs. Boxes denote interquartile ranges, center lines denote medians, crosses denote means, and the dots denote EVs beyond 1.5 times the interquartile range. n = 3 technical replicates (15 ROIs) for CD63/CD81 capture and CD133 capture, n = 4 technical replicates (20 ROIs) for CD9 capture and IgG capture; ** p < 0.01; **** p < 0.0001; ns: not significant. Values and statistics are included in Tables S1 and S2.

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