Your Cell Count
Is a Safeguard
How debris changes the answer—and what you can do about it
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The debris
problem
Cell counting is a safeguard step. Every downstream assay—from CAR-T expansion to single-cell genomics chip loading—depends on getting the count right at the start.
Membrane fragments, aggregates, media residue, and lysed cell debris are present in virtually every biological preparation. How does your counting method distinguish a cell from a piece of debris?
The answer depends entirely on the physics of the measurement.
The cost of
Debris visibility
How three
methods
handle debris
The accuracy of your cell count depends on how your instrument distinguishes cells from debris. Each method uses a fundamentally different approach.
Hemocytometers rely on human visual identification. Image-based counters use AI segmentation of 2D images. The Moxi GO II measures electrical impedance of each particle—cells and debris have distinct physical signatures.
Why it matters
If your method depends on algorithms or human judgment, every sample variable introduces potential variability.
Where current
methods
fall short
Image-based counters encounter three key challenges in practice:
1. Baseline error is difficult to eliminate
Even under ideal conditions, 3–4% error per image averages ~10% across a run.
2. Debris adds invisible variability
Total error can reach 10–35% in debris-heavy samples. You can’t tell how far off.
3. Focus dependency adds variance
Optical focus is critical and varies between operators, compounding other errors.
Total counting error
A different approach
Impedance-based counting measures the electrical impedance of each individual particle. A cell’s nucleus, membrane, and volume create a distinct signature. Debris lacks these properties.
The viability gap
Most acellular debris does not stain with viability dyes. When debris is overcounted as cells, the denominator is inflated with unstained particles.
⸻ Key advantages
- No AI segmentation step needed
- No focus adjustment required
- No algorithm retraining when sample composition changes
- Measures debris alongside cells
⚠ The compounding error
You think you have 85% viable cells. You might actually have 75%.
The counting error and viability error compound each other—and the data informing your release decisions may not reflect what’s in the tube.
○ CAR-T manufacturing
Miscounted starting material shifts efficacy curves and can affect patient treatment timelines. Patient cells cannot be re-collected.
⚗ Single-cell genomics
Each 10X chip costs $8–12K. A 20% CV means underloading (wasted depth) or overloading (doublets). Labs report 1–5% of grant budgets lost to failed re-runs.
♦ Drug screening
Counting error changes effective drug-to-cell ratio, potentially generating false positives or false negatives in dose-response assays.
What’s at stake
in your
workflow
Counting errors don’t stay at the bench. They propagate—and the impact grows with each downstream step.
☑ Regulatory advantage
The Moxi GO II is designed for 21 CFR Part 11 compliance with audit trails, secure mode, and stored gate presets.
At every QC checkpoint—from initial cell isolation through expansion to final product release—the instrument delivers reproducible, auditable data.
Turning debris
into an
advantage
Most labs treat debris as noise. The Moxi GO II turns debris into a quantifiable, trackable data point.
◉ Sample quality gating
Preset gates define acceptable debris thresholds.
◉ Go/no-go decisions
Quantify sample quality before expensive steps.
∿ Drift detection
Track debris-to-cell ratio to catch prep drift early.
The results
Key takeaways
Actionable recommendations for improving counting accuracy in your lab.
Quantify your debris
Measure debris concentration alongside cell counts to enable go/no-go gating.
Track prep drift over time
Monitor debris-to-cell ratios as part of your SOP to catch process changes early.
Standardize across operators
Preset gates and stored SOPs deliver the same result regardless of who runs the test.
Choose physics over algorithms
Impedance-based measurement doesn’t need retraining when sample composition changes.
Protect expensive downstream steps
Assess sample quality before committing $8–12K to a chip load or starting a CAR-T expansion.
Build regulatory confidence
21 CFR Part 11 compliance with audit trails and secure mode supports GMP documentation requirements.
See how it
performs on
your samples
See how the Moxi GO II performs on your samples. Run your standard preps, measure cells and debris, store your gates, and decide whether debris quantification changes your workflow.
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