A lipid nanoparticle (LNP) screen should help you decide which formulations meet your preliminary requirements and warrant further testing. Particle size and polydispersity index (PDI) provide a starting point, while the readouts you add depend on whether the screen needs to establish cargo encapsulation, material recovery, or functional delivery.

Before generating samples, connect each attribute the screen needs to establish to the readouts that answer it.

Compare candidates under consistent conditions, so differences in their results can be interpreted on the same basis.

Zeta potential, which reports surface charge, is often measured alongside size. Cryo-electron microscopy (cryo-EM) and small-angle X-ray scattering (SAXS) resolve morphology and internal structure, but their cost and turnaround usually reserve them for a shortlist.

Size and PDI give every candidate a common physical baseline. When samples are measured by dynamic light scattering (DLS), the Z-average diameter provides an estimate of average hydrodynamic particle size. PDI adds context to that average by indicating how narrow or broad the size distribution is within the sample.

Two candidates may have nearly identical Z-average diameters while differing in the breadth of their size distributions. Homogeneity can impact downstream formulation performance, so it is important to read size and PDI together when determining whether a candidate should move forward.

Each readout plays a distinct role in that decision.

The size range and PDI criteria for advancing a candidate should reflect the intended application. Smaller particles or a lower PDI do not, on their own, establish that a candidate is better suited to the next stage of testing, and neither value shows whether the cargo is encapsulated.

For cargo-loaded formulations, encapsulation and recovery determine whether a candidate carries enough cargo into biological testing to produce a measurable response.

Concentration and recovered volume, with assay dilution accounted for, establish the amount available for downstream work. Comparing that amount with the defined input establishes recovery, so a sample may show high encapsulation efficiency while retaining only a small fraction of the original cargo load, which is why these readouts are reviewed together against the material needs of the next experiment.

A formulation that meets your particle and cargo requirements is worth investigating further, but one successful preparation does not establish that the result can be repeated. The same formulation can produce a low PDI each time while its average particle size shifts between preparations.

What a replicate establishes depends on which steps are repeated to produce it.

For particle size, percent coefficient of variation (%CV) summarizes how much Z-average diameter or PDI varies relative to the mean across independent preparations. Average the repeated readings within each preparation before calculating %CV across preparations, so additional readings of one sample are not counted as additional preparations.

For cargo-loaded formulations, also compare encapsulation efficiency and recovery across independent preparations, because consistent particle size does not establish consistent cargo encapsulation or recovery.

Setting the number of independent preparations per candidate before the screen starts keeps a single strong result from deciding the selection. Reporting whether each replicate is a separate preparation or a repeat reading shows reviewers what a %CV reflects.

For the sources of variation these replicates capture, see What Causes Variability in LNP Formulation?

Candidate differences can be attributed to the formulation only when every sample is prepared, stored, and measured the same way. When one candidate is measured immediately after collection and another after buffer exchange or storage, differences in their results may come from processing or sample age as well as the formulation conditions being tested.

Keeping these conditions consistent, or reporting them when they differ, separates processing effects from formulation effects.

Hold these conditions constant while varying the formulation factors under investigation. When studying a processing step, measure candidates at matching points before and after it, and report the processing stage and measurement conditions alongside the results.

Particle size, PDI, and encapsulation do not establish whether cargo produces the intended biological response, so screens selecting for functional delivery also need a biological assay.

For mRNA-loaded LNPs, protein expression provides a functional readout of delivery and translation in the tested cell model. Because formulations with similar particle characteristics and encapsulation efficiency can produce different expression levels, compare candidates carrying the same mRNA under matched assay conditions.

Interpreting those differences depends on defining the conditions under which each formulation is evaluated.

Read expression alongside viability because changes in viable cell number can affect the measured signal and candidate ranking. Keep conclusions specific to the tested model, dose, and conditions, because performance in one assay does not establish performance in other models or in vivo.

Set screening criteria around the intended cargo, the requirements of downstream assays, and the reference formulation used for comparison. Defining those criteria before reviewing results keeps candidate selection focused on the application, rather than whichever readout appears most favorable.

When candidates perform well on some readouts but fall short on others, the pattern across the results points to whether the candidate should advance, be repeated, or be investigated further.

Candidates that advance should carry their process and measurement conditions with them, so the next stage of testing starts from a documented comparison.

The LNP Screening Array transforms your liquid handler into an LNP screening platform. It brings microfluidic formulation onto the deck in a standard SLAS microplate format, so reagent delivery, mixing, and sample collection stay within the automated workflow.

Evaluations run in your lab on your liquid handler, with success criteria you set against your current method.