Standardization Cannot Be an Afterthought
For nascent fields like nanoparticle development, data standardization must be a foundational practice.
Without globally accepted reference materials, the industry is effectively working with a broken ruler, building complex nanoparticle therapeutics on a foundation of unreliable data.
The High Cost of Uncertainty
Advanced therapeutics, from mRNA vaccines to targeted cancer treatments, rely heavily on nanoparticle characterization.
However, the analytical tools we rely on are full of trade-offs, often yielding conflicting results that drive up development costs.
Flawed Conclusions
Incorrect interpretations of nanoparticle data lead to invalid scientific results and wasted research efforts.
Delayed Approvals
Data uncertainty creates bottlenecks, delaying regulatory approval and market entry of novel nanoparticle-enabled therapeutics.
Lost Opportunities
Resources are misdirected from promising innovations to re-evaluate unreliable data and resolve inconsistencies.
Why is Nanoparticle Analysis So Complex?
Because our two main types of analysis techniques tell two completely different stories:
Ensemble Analysis
Measures multiple particles all at once and reports a single result for the entire population.
The "Average" Trap
Because it averages everything, it is blind to complexity and tends to mask critical sub-populations.
"...like looking at a crowd and only guessing the average height."
Single-Particle Analysis
Measures individual particles one-by-one to build high-resolution results.
The "Individual" Trap
High in resolution, but low-level signals are often indistinguishable from background (instrument) noise.
"...like trying to hear a specific whisper in a roaring stadium."
The Takeaway?
Your ensemble analysis might show a single "perfect" 100 nm peak, while completely masking a 40 nm sub-population.
While single-particle analysis can detect this 40 nm population and(!) provide further insights into this sub-population, the signal is typically close to the instrument's background noise.
Without proper controls, you risk measuring artefacts, not your sample!
Beyond the Instruments: The Standardization Gap
Even with the best tools, inconsistency persists due to two fundamental gaps in the industry:
Gap 1
REFERENCE MATERIALS
A Lack of Representative Standards
Most analyzers are calibrated using polystyrene "beads", which are hard, solid, perfect spheres. Calibrations done in this way create a fundamental disconnect when measuring soft, irregular therapeutic nanoparticles with complex surfaces.
This creates a classic apples-to-oranges problem.
Gap 2
DATA CONSISTENCY
A Lack of Analytical Consistency
User parameters like gain settings, detector thresholds, and gating, distort results by masking sub-populations. Compounding this, each instrument is built on a different physical principle, making meaningful data comparison nearly impossible.
Who is correct? It's difficult to know.
A New Standard for Nanoparticle Analysis
To solve the apples-to-oranges problem, one must first move beyond traditional beads. But what are the alternatives? Commercial standards derived from liposomes or EVs exist, but they are often unstable, inconsistent between batches, and are heterogeneous in both size and morphology.
We specifically engineered Syncles™ to address these trade-offs, delivering the first vesicle standard that is morphologically representative and exceptionally reliable.
Mimetic Morphology
01
Syncles™ are soft, synthetic vesicles with better morphological resemblance to your real-world samples, unlike hard beads.
Rock-Solid Reliability
02
Unlike lipid standards, Syncles™ are homogeneous, highly stable without stringent storage conditions, and are exceptionally consistent from lot to lot.
Versatile Vesicle Palette
03
We offer a large range of Syncles™ with different nanoparticle properties to ensure you have the right standard for your specific application.
A Universal Benchmark for Your Workflow
Syncles™ serve as a robust common reference material to standardize your process and validate your results, irrespective of whether you're comparing data from day-to-day runs, between different labs, or across different instruments.
