Detect, Identify, and Characterize Cellular versus Non-cellular Aggregates with the Aura CL System

Significance of the topic

Cellular, protein, and viral aggregates are critical quality attributes for biotherapeutics and cell therapies because they reflect product stability, shelf life, and potential immunogenicity. Accurate quantification and differentiation of subvisible particles in cell-based products (e.g., CAR-T) is especially challenging because intact cells themselves fall into the subvisible size range. Traditional flow imaging and cytometry methods undercount or mis-size cells and aggregates due to low refractive index contrast and fluidics-related artefacts. The Aura CL System, using fluorescence membrane microscopy (FMM) and a fluidics-free membrane imaging workflow, addresses these analytical gaps by enabling reliable detection, identification, and characterization of cellular versus non-cellular aggregates.

Objectives and study overview

The application note demonstrates how the Aura CL System can:

• Differentiate intact cells, cellular multimers (doublets, triplets, larger aggregates), protein aggregates, and non-biological particulates in a single assay.
• Leverage DNA and protein-specific fluorescent stains to provide orthogonal identification of particle type.
• Provide high-throughput, clog-resistant, and quantitative analysis suitable for cell and gene therapy QC and development.

Methodology

Sample preparation and staining: CAR-T cell samples were reconstituted at 1×10^5 cells/mL in PBS and applied to black membrane imaging plates. An on-membrane staining protocol was used: 50 µL of sample was drawn through the membrane, background imaged, then 50 µL of DNA dye (either 1 µg/mL DAPI or 10 µg/mL Hoechst 33342) was added and incubated 10 minutes in the dark. Thioflavin T (ThT) was used as a counterstain for protein aggregates. Notes on dyes: Hoechst 33342 is membrane-permeant (labels live and dead cells); DAPI is membrane-impermeant at low concentrations but can label live cells when used at higher concentrations (>1.0 µg/mL).

Imaging modalities and analysis: The Aura CL System acquires brightfield membrane imaging (BMI) to show morphology, side illumination membrane imaging (SIMI) to assess particle protrusion/rigidity (side-scatter-like information), and fluorescence membrane microscopy (FMM) to detect FL1 (ThT, protein) and FL2 (DNA) signals. Particle Vue software generates scatter plots (e.g., equivalent circular diameter vs. FL2 intensity or FL1 vs. FL2) to classify singlets, multimers, protein aggregates, and non-biological contaminants and to quantify population distributions.

Used instrumentation

• Aura CL System (fluidics-free membrane-based FMM platform)
• Black membrane imaging plates compatible with vacuum filtration
• Fluorescent dyes: DAPI, Hoechst 33342 (DNA stains), Thioflavin T (ThT) for protein aggregates
• Particle Vue analysis software (scatter plotting, population gating, particle sizing)

Main results and discussion

Identification and morphology: Brightfield images show lymphocyte-like morphology (concentric nucleus, scant cytoplasm) while FL2 (DNA) fluorescence confirms nuclear staining of intact CAR-T cells. SIMI intensity helps differentiate rigid, protruding contaminants (high SIMI) from biological material that typically lies flat on the membrane (low SIMI).

Population discrimination: Using two-channel fluorescence (FL1 = ThT, FL2 = DNA) and area/size metrics, the system resolved distinct particle classes:

• Cells (singlets, small/regular/large singlets) and cell multimers (doublets, triplets, larger aggregates) — FL2-positive.
• Protein aggregates — FL1-positive, FL2-negative.
• Non-biological particulates (e.g., plastic fragments) — negative for both dyes but often high SIMI.
• Mixed protein–cell aggregates — dual-positive particles with larger area.

Freeze-thaw stress increased cell clustering and altered DNA stain uptake in large aggregates, demonstrating the platform’s sensitivity to process-induced quality changes.

Throughput and robustness: The fluidics-free membrane workflow prevents clogging and allows measurement efficiency approaching 100% of the imaged sample. The authors claim throughput up to 100× higher than conventional flow imaging while retaining robust sizing and counting rooted in USP <788>-style membrane microscopy principles. Sample volumes were flexible (5 µL to 10 mL), enabling both small-scale research samples and larger process samples.

Benefits and practical applications

• Directly addresses the need for accurate subvisible particle characterization in cell and gene therapies, improving QC during development and manufacturing.
• Enables discrimination between immunologically relevant cellular aggregates and non-cellular particulate contaminants, supporting risk assessment for immunogenicity and safety.
• Fluidics-free operation reduces operational failures from clogging and allows consistent imaging of heterogeneous samples containing cells, proteins, and debris.
• High-throughput and flexible sample-volume operation make the method applicable across R&D, process development, and lot-release testing.

Future trends and potential applications

Expected developments and opportunities include:

• Multiplexed fluorescent panels (additional viability, membrane integrity, or viral-vector markers) to refine identification (live vs. dead cells, transduced vs. non-transduced cells).
• Integration with automated sample handling and LIMS for routine in-process and lot-release QC workflows.
• Application of machine learning to image-based morphological features combined with fluorescence signatures for more robust, automated classification.
• Regulatory acceptance pathways for FMM-based assays as complementary or alternative approaches to flow imaging for cell therapy QC.
• Exploration of label-free optical contrasts (e.g., scattering profiles, phase imaging) to reduce staining requirements where necessary.

Conclusion

The Aura CL System, using fluorescence membrane microscopy and an on-membrane staining workflow, provides a robust, high-throughput approach to distinguish cellular aggregates from protein and non-biological particles in cell therapy matrices. By combining orthogonal signals (DNA and protein stains) with morphological and side-illumination information, the platform enables detailed singlet/multimer analysis and population-level characterization that addresses limitations of conventional flow imaging and cytometry. This capability supports improved quality control, process understanding, and risk mitigation in cell and gene therapy development and manufacturing.

References

1. Wen Y, Jawa V. The Impact of Product and Process Related Critical Quality Attributes on Immunogenicity and Adverse Immunological Effects of Biotherapeutics. Journal of Pharmaceutical Sciences. 2021;110(3):1025–1041. DOI: 10.1016/j.xphs.2020.12.003
2. Clarke D, et al. Managing particulates in cell therapy: Guidance for best practice. Cytotherapy. 2016;18(9):1063–1076. DOI: 10.1016/j.jcyt.2016.05.011
3. Marks P. The FDA’s Regulatory Framework for Chimeric Antigen Receptor-T Cell Therapies. Clinical and Translational Science. 2019;12(5):428–430. DOI: 10.1111/cts.12666
4. Vollrath I, et al. Subvisible Particulate Contamination in Cell Therapy Products—Can We Distinguish? Journal of Pharmaceutical Sciences. 2020;109(1):216–219. DOI: 10.1016/j.xphs.2019.09.002