Why is my liposome size significantly larger in DLS compared to Cryo-TEM?

This discrepancy is primarily due to the hydration shell measured by DLS (hydrodynamic diameter) versus the electron-dense core measured by Cryo-TEM, and the intensity-weighted distribution bias of DLS towards larger particles.

Should I report Z-average or Number Mean for regulatory submissions?

The Z-average is the ISO standard for QC reporting. However, for detailed characterization, report the Z-average alongside orthogonal data (like Cryo-TEM) and explain weighting differences.

Can Cryo-TEM detect sample aggregation better than DLS?

DLS is more sensitive to detecting the presence of aggregates, but Cryo-TEM is superior for identifying the type and morphology of the aggregation (flocculation, fusion, etc.).

How does PEGylation affect the size difference between these methods?

PEGylation increases the hydrodynamic radius (DLS) due to water association, but PEG chains are often invisible in Cryo-TEM, leading to a larger discrepancy between DLS and TEM results.

What is the PDI threshold where DLS data becomes unreliable?

A PDI above 0.2–0.3 suggests a polydisperse sample where DLS Z-average values may be less representative, necessitating orthogonal methods like Cryo-TEM or AF4.

Cryo-TEM vs. DLS: Interpreting Discrepancies in Liposome Particle Size Data

The Analytical Dilemma in Nanomedicine QC

In the rigorous world of liposome quality control (QC), conflicting data is a common but frustrating hurdle. A typical scenario involves a lipid nanoparticle formulation showing a Z-average size of 120 nm via Dynamic Light Scattering (DLS), while subsequent imaging via Cryogenic Transmission Electron Microscopy (Cryo-TEM) reveals a population of distinct spherical vesicles averaging only 90 nm.

Are the instruments out of calibration? Is the sample aggregating in situ? Or do these techniques fundamentally measure different physical properties? For analytical scientists, resolving these discrepancies is not just an academic exercise—it is a regulatory necessity for establishing Critical Quality Attributes (CQAs). This resource elucidates the physical basis of these measurement divergences and provides a framework for interpreting conflicting datasets.

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Dynamic Light Scattering (DLS): The Hydrodynamic Reality

DLS is the industry workhorse for high-throughput size estimation. However, it does not measure size directly. Instead, it measures the Brownian motion of particles in suspension. Using the Stokes-Einstein equation, this diffusion coefficient is converted into a Hydrodynamic Diameter (D_h).

Key Characteristics of D_h

Solvation Layer Inclusion: DLS detects the particle plus the shell of solvent molecules and ions moving with it. For PEGylated liposomes, this hydration layer can add 5–10 nm to the diameter.
Intensity Bias (r⁶): The scattering intensity is proportional to the diameter to the power of six. A few large aggregates can overshadow a vast population of smaller liposomes, artificially inflating the Z-average.
Spherical Assumption: DLS algorithms assume particles are perfect spheres. When liposomes are ellipsoidal or discoidal, the calculated D_h is the diameter of an equivalent sphere with the same diffusion speed.

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DLS Measurement Profile

Metric Hydrodynamic Diameter (D_h)

State Solvated, dynamic environment

Bias Heavily weighted to larger particles

Throughput High (Minutes per sample)

Cryo-TEM: Visualizing the Structural Truth

Cryo-TEM Measurement Profile

Metric Projected Area Diameter (D_geo)

State Vitrified (Frozen-hydrated)

Bias Number-weighted (Counts individual particles)

Resolution Sub-nanometer (Direct bilayer visualization)

Cryo-TEM is considered the "gold standard" for structural analysis because it visualizes liposomes in a near-native state. By rapidly freezing the sample in vitreous ice (vitrification), water crystallization is prevented, preserving the liposome's morphology without the artifacts associated with staining or drying used in conventional TEM.

What Cryo-TEM Measures

Unlike DLS, Cryo-TEM provides a Number-Weighted Distribution. It measures the physical boundaries of the lipid bilayer (electron-dense region).

Core Size Only: It typically does not visualize the extended hydration shell or PEG chains unless conjugated with heavy metals (e.g., gold nanoparticles).
Morphological Discrimination: It allows the operator to distinguish between unilamellar vesicles, multilamellar vesicles (MLVs), and non-liposomal aggregates—nuances that DLS lumps into a single "size" value.

5 Key Factors Causing DLS vs. Cryo-TEM Size Discrepancies

Why your D_h (DLS) is almost always larger than your D_geo (TEM)

1. The Hydration Shell

DLS sees the "hydrodynamic" size, which includes the solvent layer moving with the particle. Cryo-TEM visualizes the electron-dense lipid core. For PEGylated liposomes, this hydration layer is significant, causing DLS >> TEM.

2. Weighting Algorithms

DLS is Intensity-weighted (r⁶), meaning large contaminants disproportionately skew the mean size up. Cryo-TEM is Number-weighted, treating every particle equally, resulting in a smaller numeric mean.

3. Polydispersity

In highly polydisperse samples (PDI > 0.2), the difference between the intensity mean (Z-average) and the number mean becomes drastic. Cryo-TEM reveals the true breadth of distribution that DLS fitting algorithms often smooth over.

4. Shape Anisotropy

If liposomes are ellipsoidal, DLS calculates the size of a sphere with equivalent diffusion. Cryo-TEM provides 2D projections. Discrepancies arise when converting 2D images to 3D volumes versus hydrodynamic models.

5. Sample Concentration

High concentrations in DLS can lead to multiple scattering events, artificially reducing apparent size. Conversely, particle-particle interactions at high concentrations can slow diffusion, artificially increasing size. Cryo-TEM avoids these optical artifacts.

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Strategic Reconciliation: What to Report?

Regulatory bodies like the FDA and EMA encourage the use of orthogonal methods for characterizing nanomedicines. When creating your Common Technical Document (CTD), do not simply report disparate numbers. Instead, explain the physical basis of the difference.

1

Convert DLS Data for Comparison

While the Z-average (Intensity mean) is robust for QC, utilize the Mie theory algorithms in your DLS software to generate a Volume or Number distribution. These derived values should align more closely with Cryo-TEM data, provided the Refractive Index (RI) and Absorption parameters are correct.

2

Use Cryo-TEM for Morphology, DLS for Statistics

Position Cryo-TEM as the qualitative validator of morphology (confirming unilamellarity and lack of aggregation) and DLS as the quantitative tool for batch-to-batch statistical consistency. Acknowledging that DLS D_h includes the hydration shell demonstrates technical competence to regulators.

3

Validate In Vitro Stability Orthogonally

When assessing stability in serum (in vitro), DLS often fails due to protein corona formation and background scattering. In these complex matrices, Cryo-TEM or Nanoparticle Tracking Analysis (NTA) becomes indispensable for distinguishing liposomes from serum proteins.

Frequently Asked Questions

This discrepancy is expected and primarily due to two factors:

Hydration Shell: DLS measures the hydrodynamic diameter, which includes the water molecules and ions moving with the particle (the electric double layer). Cryo-TEM typically visualizes only the electron-dense lipid bilayer.
Intensity Weighting: DLS results are intensity-weighted (r⁶), meaning larger particles contribute disproportionately to the signal. Cryo-TEM is number-weighted. Even a small fraction of aggregates can skew DLS results significantly higher.

The Z-average is the most robust and stable parameter produced by DLS and is the ISO standard for QC reporting. However, for detailed characterization sections of an IND/NDA filing, it is advisable to provide the Z-average alongside orthogonal data (like Cryo-TEM or NTA) and explain the weighting differences. Do not rely solely on the DLS Number Mean, as it is a derived value prone to error if the optical properties are not perfectly defined.

It depends on the type of aggregation. DLS is extremely sensitive to the presence of large aggregates (trace amounts will spike the PDI and Z-average). However, DLS cannot tell you what the aggregate is. Cryo-TEM allows you to visualize the nature of the aggregation—whether it is flocculation, fusion of membranes, or micellar contamination—providing mechanistic insight that DLS cannot offer.

PEGylation increases the hydrodynamic radius significantly due to the extension of polymer chains and their associated water molecules. Since PEG chains have low electron density, they are often invisible in standard Cryo-TEM. Therefore, highly PEGylated liposomes will show a much larger size in DLS compared to Cryo-TEM images of the core.

Generally, a Polydispersity Index (PDI) below 0.1 indicates a monodisperse sample where DLS algorithms perform excellently. PDI values between 0.1 and 0.2 are acceptable for many lipid nanoparticles. Once PDI exceeds 0.2–0.3, the DLS "cumulants analysis" (Z-average) becomes less representative of the true distribution. In these cases, Cryo-TEM or separation techniques (like AF4) are strongly recommended to resolve the specific populations.