Nanolive EVE Analytics Technical Note (Analysis & Translation)

Document type: Nanolive official Technical Note
Document ID: EA-TN-001
Original manufacturer: Nanolive SA (Lausanne, Switzerland) — www.nanolive.ch
Structure: 8 pages / 7 figures (5 selected)

1. What is this technical note about? 🔬

Nanolive's label-free imaging enables high time-resolution + long-duration live cell imaging, but also amplifies the complexity of image analysis. Manual analysis is almost impossible; computer-assisted processing is required.

This note addresses 3 core questions:

1. What are the state-of-the-art cell segmentation techniques? What are their limitations?
2. Why is EVE Analytics (EA)'s segmentation fundamentally different?
3. EA vs fluorescence segmentation — how different are the quantitative metrics? How should customers/researchers choose?

2. State-of-the-art cell segmentation 📚

Background — understand EA's novelty requires understanding current methods.

2.1 Primary target image threshold detection

The basic premise of cell segmentation: cells must have signals distinguishable from background.

Conclusion: Threshold + fluorescent dye alone → cannot stably segment long-duration live cells.

2.2 Two-step labeling (nucleus + cytoplasm)

• Layer 1: nuclear dye → quantify cell count + location anchoring
• Layer 2: cytoplasmic dye (Cell Trace, Cholera toxin B) → quantify cell size/area/shape

Two complications:

1. Inhomogeneous staining intensity within cells
2. Variable cell edge morphology (touching cell segmentation complex → bans simple threshold)

Two-step detection + local threshold can achieve fine segmentation, but phototoxicity and cytotoxicity risks remain.

2.3 EA's fundamental difference

EA relies on a novel segmentation technique, designed specifically for Nanolive's label-free imaging:

• Step 1: signal preparation + advanced threshold → detect cell objects as "anchors"
• Step 2: refractive index (RI) signal-based → capture cell object fine boundaries

Key difference: No fluorescent dyes = no phototoxicity + no cytotoxicity.

3. EVE Analytics demonstration — 90-hour long-duration experiment 🧫

3.1 Experimental design

3.2 EA performance in 90-hour long-duration

Fig 1 — Top row: RI images acquired by CX-A (10×10 grid scan). Middle row: close-ups of boxed regions. Bottom row: cell segmentation masks generated by EA. Core conclusions —

✅ EA segments multiple single cells over thousands of images without quality degradation (regardless of crowding/fusion)
✅ High precision visible at zoom: membrane protrusions (filopodia + lamellipodia) details essentially captured
✅ Touching cells precisely outlined and distinguished — proving EA has no trade-off between scale and precision

💡 90-hour artifact-free + morphological detail preserved = EA has no rivals in long-duration live cell imaging analysis.

4. EA vs fluorescence segmentation — direct comparison ⚖️

Direct comparison between EA and fluorescence segmentation can only be done in short-term experiments — because phototoxicity quickly kills cells.

4.1 Experimental conditions

Comparison baseline: EA (RI only) vs CellProfiler 4 (best fluorescence-based cell contour software).

4.2 Low D+C (low laser, 2-hour) comparison results

Fig 2 — Low D+C comparison:

• A: EA segmentation (RI only)
• B: CP4 segmentation (fluorescence only)
• C: B contours overlaid on RI (segmentation performance comparison)

Core conclusion: overlaying fluorescence contours on RI (C) clearly shows how much cell detail is missed by real-time fluorescence imaging alone.

4.3 High D+C (high laser, 1-hour) comparison results

Fig 3 — High D+C comparison (A/B/C as Fig 2) + cell health:

• A: EA segmentation (RI only)
• B: CP4 segmentation (fluorescence only)
• C: B contours overlaid on RI
• D: Rapid cell health deterioration — the cost of boosting fluorescence signal for better segmentation

Fig 3b (D region of same page) — increasing laser intensity boosts fluorescence signal → improves segmentation. But even with stronger signal, EA's segmentation always outperforms fluorescence-based segmentation — and the fluorescence intensity boost comes at huge cost to experiment duration: High D+C cells show stress at 1 hour, all dead by 2 hours.

5. How do cell metrics differ? 📊

Not only segmentation differs; metric computation differs significantly too.

5.1 Five key metrics: EA vs Fluo comparison

Fig 4 — Low D+C conditions, EA (green) vs Fluo (orange) computed: Cell area / Perimeter / Compactness / Extent / Dry mass / Granularity — 6 metrics.

5.2 Three core observations

Observation 1: Cell-to-cell variability (key biological signal)

• EA captures greater cell-to-cell variability (large interquartile spread of single-cell values per timepoint)
• Why important: cell-to-cell variability reflects ongoing biological processes
• Fluo misses this information

Observation 2: Compactness vs Extent (cell health signals)

Conclusion: Fluo-segmentation-derived metrics mislead cell health assessment. EA metrics more accurately reflect cell stress experienced.

Observation 3: Metrics Fluo cannot provide (EA exclusive capability)

💡 Dry mass + Granularity are EA exclusive metrics — based on RI's physical parameters, not color signals.

6. EA advantages summary 🏆

From original P7:

1. ✅ Cell segmentation based on refractive index signal — best method for quantitative live cell imaging in both precision and non-invasiveness

2. ✅ EA excels in long-duration cell observation — when confluency changes significantly, Nanolive image quality + EA analysis capabilities remain stable

3. ⚠️ EA segmentation currently only analyzes RI signals from Nanolive products — uses proprietary techniques for precise segmentation in diverse environments (cannot analyze third-party microscope fluorescence/brightfield data)

4. ✅ EA provides metrics unobtainable by Fluo segmentation (Dry mass) + fully leverages Granularity texture measurement potential

7. Sales perspective customer Q&A 💼

7.1 Most common customer questions

7.2 Differentiation with 9X Bio portfolio

EA's positioning in 9X Bio portfolio: fills the "label-free + long-duration + morphometric quantification" gap. The other two are not strong in morphological metrics.

8. Key resources 🔗

• Nanolive official: http://nanolive.ch
• Nanolive applications: http://nanolive.ch/applications
• Dry mass formula reference: P.A.Sandoz et al., PLOS, 2019
• Nanolive customer registration: https://www.nanolive.ch/register

📥 Original PDF

📄 CX-A EVE Analytics Technical Note PDF (Chinese version, 8 pages, 1.6 MB)

Contact｜Li Dongwei (李东伟)
📞 18045130660
🌐 Website: https://www.biokingdom.top