Exosome Analysis Services

There isn't one "best" method—choose based on your priority. SEC is often preferred when downstream functional assays or omics need higher purity and reproducibility. Polymer precipitation can maximize yield but may co-enrich proteins/lipoproteins that interfere with assays. Immunoaffinity/immunomagnetic capture increases specificity for marker-defined vesicles but can bias toward captured subpopulations and reduce total yield. A practical rule: purity-sensitive readouts → SEC; rapid enrichment/yield → polymer; marker-specific questions → immunoaffinity.

Use converging evidence: particle metrics, morphology, and marker data plus at least one negative marker. A common approach is multiple positive EV proteins (CD9/CD63/CD81 plus intraluminal proteins such as TSG101/ALIX/HSP70) and a negative marker (often calnexin, sample-dependent) to flag cellular contamination. Combining these reduces false positives from protein aggregates or debris.

A robust WB set typically includes 2–3 surface markers (commonly CD9, CD63, CD81), 1–2 intraluminal markers (e.g., TSG101, ALIX, HSP70), and at least one negative control (often calnexin, depending on sample). Using more than one marker class improves interpretability and cross-study comparability.

They answer different questions. NTA estimates particle concentration and size distribution for the total particle population but cannot phenotype individual vesicles. ExoView/SP-IRIS is antibody-capture single-particle phenotyping: it quantifies marker-defined captured subsets and co-expression patterns, so results depend on panel design and capture biology. Many studies use NTA for global trending and ExoView for phenotype-specific quantification.

SP-IRIS detects and counts individual nanoparticles captured on an antibody array using interferometric imaging, and can overlay fluorescence to assess marker co-expression. It is strongest for counts and phenotypes within captured marker subsets, not for an unbiased census of all EVs in the sample.

Isolation changes the purity–yield balance and co-isolated background (especially in plasma/serum where abundant proteins and lipoproteins are present). These differences can shift apparent biomarker signals and affect proteomics/metabolomics sensitivity and missingness. Standardizing one workflow, documenting metadata, and keeping controls consistent are key to interpretable cohort comparisons.

Yes, but low-abundance matrices require careful feasibility planning and stronger controls (process blanks, consistent handling metadata, and replicates where possible). Workflows should prioritize background reduction and sensitive readouts aligned to the claim you need (e.g., phenotype shift vs mechanistic discovery).

Yes, but low-abundance matrices require careful feasibility planning and stronger controls (process blanks, consistent handling metadata, and replicates where possible). Workflows should prioritize background reduction and sensitive readouts aligned to the claim you need (e.g., phenotype shift vs mechanistic discovery).

Use total readouts when you need global trending across many samples or conditions. Use subpopulation profiling when biology depends on cell-of-origin markers, co-expression, or cargo association (common in engineered exosomes and disease heterogeneity). Single-particle antibody-capture methods are designed to resolve this heterogeneity.

Usually yes for cohort comparisons and process trending. Switching isolation methods can introduce systematic shifts in purity, yield, and background carryover that mimic biological differences. If a change is unavoidable, run a bridging set (the same samples processed by both methods) to quantify the shift and preserve interpretability.