However, a long-standing issue that has plagued researchers is:

Have we really “seen” ROS?

Traditional methods, such as fluorescent probes like DCFH-DA and MitoSOX, as well as ESR/EPR techniques, can detect changes in ROS levels; however, they struggle to determine the earliest time of ROS emergence, its precise subcellular origin, or why different cell types exhibit distinct redox responses.

They give us the “final outcome” but leave behind the “dynamic mechanism.”

Paradigm Shift: Introducing “Nanoscale Nuclear Magnetic Resonance” Within Cells

In 2023, the prestigious international journal in chemistry and interdisciplinary sciences, ACS Central Science, published a groundbreaking study. Professor Romana Schirhagl of the University of Groningen in the Netherlands… (Inventor of the Quantum Nuova Nano-Quantum Sensing Platform) A joint reproductive medicine research team has proposed a free-radical detection method based on fluorescence‑nanodiamond (FND) NV‑center quantum sensing.

From the perspective of underlying physical mechanisms, nitrogen-vacancy (NV) defects—negative charge centers within diamond—are highly sensitive to magnetic noise from surrounding paramagnetic species, such as free radicals with unpaired electrons. By monitoring changes in the longitudinal spin relaxation time (T1) of NV‑center quantum spins, researchers can实时 detect local fluctuations in magnetic noise arising from free-radical generation at the nanoscale, thereby enabling noninvasive monitoring of cellular redox states.

Real-World Evidence: The “Mitochondrial Frontline” at the Nanoscale

The research team applied this quantum sensing technology to highly sensitive human primary ovarian granulosa cells and constructed both cytoplasmic‑targeted and mitochondrial‑targeted quantum sensing probes.

The experimental results were extremely shocking:

Under physiological conditions, there is no significant difference in free radical levels between the cytoplasm and mitochondria; however, following Menadione‑induced oxidative stress, the T1 relaxation time measured by the mitochondrial‑targeted probe decreases to a markedly greater extent than that of the cytoplasmic probe, indicating that free radicals accumulate rapidly, preferentially within the mitochondrial compartment.

This discovery provides direct evidence at the nanoscale that:

Mitochondria are not only the central hub of cellular energy metabolism but also the primary source of free radicals during oxidative stress.

Cellular Heterogeneity: The Same Endpoint, Different Fate Codes

More notably, the study further elucidated redox heterogeneity among different cell types. Experiments demonstrated that, under oxidative stress, the magnitude of free radical elevation (as reflected by the slope of T1 shortening) was significantly greater in mural granulosa cells (mGCs) than in cumulus granulosa cells (cGCs).

Cumulus granulosa cells (cGCs) exhibit enhanced resistance to oxidative stress.

This suggests that the cumulus granulosa cells, which closely surround the oocyte, possess enhanced resistance to oxidative stress and can provide critical protection to the oocyte during follicular development.

Meanwhile, quantum sensing technology can detect changes in free radicals within minutes of oxidative stress onset, whereas conventional ROS probes typically require considerably longer to produce a discernible signal.

Researchers will have the opportunity to observe the “initiating event” of ROS production, rather than merely its final outcome.

Why It Matters: The High-Dimensional Data Engine of the AI for Science Era

For ferroptosis, mitochondrial dysfunction, tumor metabolic reprogramming, and the development of innovative therapeutics, this capability for “process dissection” holds milestone significance.

Leveraging the high-fidelity, lossless kinetic data generated by the Quantum Nuova platform, future researchers and AI‑driven drug discovery models (AIDD) are expected to establish a framework that:

The complete dynamic mechanistic cascade: [Early mitochondrial stress] → [Transient ROS burst (ROS spike)] → [Redox imbalance] → [Activation of signaling pathways] → [Cell fate determination].

Traditional methods excel at endpoint analysis, whereas quantum sensing excels at process‑level characterization.

✓ Onset Time

✓ Peak Time

✓ Recovery Rate

✓ Overall stress load (AUC integral)

Conclusion:

The value of quantum sensing technology has already surpassed that of traditional ROS detection tools.

It can not only answer “How many ROS are there?”, but also answer:

“Where ROS originates, when it arises, how it spreads, and how it determines cell fate.”

From “measuring ROS concentrations” to “deciphering redox dynamics,” and from endpoint assays to mechanistic insights, quantum sensing is opening new avenues of research in redox biology, innovative drug discovery, and precision medicine.

When researchers can truly “see” ROS, a new era of research centered on Redox Dynamics has quietly begun.

Image and Text Guide

Figure 1. Schematic illustration of the use of fluorescent nanodiamonds (FND) to detect free radicals in human granulosa cells via relaxation measurements. (A) For women planning to undergo in vitro fertilization (IVF) due to fertility issues, oocytes are retrieved 36 hours after luteinizing hormone (LH) administration, and granulosa cells are collected concurrently. (B) Cumulus granulosa cells (cGCs) and mural granulosa cells (mGCs) were isolated separately from pre-ovulatory follicles, then cultured and co-incubated with FNDs. Two FND subtypes were used: naked FNDs (targeting the cytoplasm) and aVDAC2-FNDs (targeting mitochondria). Relaxometry was performed after 24 hours of incubation in each case. (C) The raw data for a representative T1 relaxation curve are shown, plotted as fluorescence intensity values at different dark‑time points; the inset illustrates the pulse sequence used in the relaxation measurement: the green region indicates the laser‑on period, while the red region denotes the time during which the FND photoluminescence (PL) signal is acquired.

Figure 2. Effects of FNDs on cell viability and intracellular reactive oxygen species (ROS). Separately in cGCs   (A) and mGCs   (B)   In the study, cell viability was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay. Experimental conditions included low concentrations of bare-FND and aVDAC2-FND (1 μg/mL), high concentrations of bare-FND and aVDAC2-FND (5 μg/mL), as well as hydrochloric acid (0.1 M). DCFHDA fluorescence measurements indicated that in cGCs   (C) and mGCs   (D) In the experiment, following 24-hour incubation with bare-FNDs (1 μg/mL), aVDAC2-FNDs (1 μg/mL), and menadione (5 μM), intracellular ROS production was increased. A value of 100% corresponds to the control group that received no treatment. The assay was repeated in cell cultures from six patients, with error bars representing the standard deviation. Data were analyzed by one-way ANOVA followed by Tukey’s post hoc test for comparisons with the control group; ****p < 0.0001.

Figure 3. Subcellular localization of FNDs visualized by confocal microscopy. Naked FNDs (1 μg/mL) and aVDAC2-FND (1 μg/mL) were incubated with cGCs, respectively. (A-B) and mGCs (C-D) Co‑incubate for 24 hours. Mitochondria were labeled with a Tom20 antibody (a marker of the mitochondrial outer membrane). Color coding: blue indicates Tom20; green denotes actin filaments stained with phalloidin–FITC (also referred to as F‑actin); red represents either bare FND or aVDAC2‑FND. Scale bar, 10 μm.

Figure 4. Box-and-whisker plots depict the real-time changes in free radicals, as measured by the T1 parameter, in cGCs from four distinct patients following treatment with menadione. For each patient, the T1 values of 4–6 bare FNDs or aVDAC2-FNDs were measured at different time points (0, 1, 5, 10, 20, and 30 minutes) following treatment with menadione. Patient 1 (A-B) , Patient 2 (C-D) , Patient 3 (E-F) And patient 4 (G-H) The data for naked FNDs (left, green boxes) and aVDAC2-FNDs (right, purple boxes) are shown in the figure; the right-hand Y-axis indicates estimated free-radical concentrations derived from previous studies. Each particle is represented by a distinct color, and each curve corresponds to measurements of the same particle at different time points. Data analysis was performed using paired t-tests, with comparisons made against the control group: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Figure 5. Box-and-whisker plots depict the real-time changes in free radicals, as measured by the T1 parameter, in hepatocytes from four distinct patients following treatment with menadione. For each patient, the T1 values of 4–6 bare FNDs or aVDAC2-FNDs were measured at different time points (0, 1, 5, 10, 20, and 30 minutes) following treatment with menadione: Patient 1. (A-B) , Patient 2 (C-D) , Patient 3 (E-F) And patient 4 (G-H) Naked FNDs (left, green boxes) and aVDAC2-FNDs (right, purple boxes). The right-hand y-axis shows the free-radical concentrations estimated from previous studies. Each particle is represented by a distinct color, and each curve corresponds to measurements of the same particle at different time points. Data were analyzed using paired t-tests compared with the control group. * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Figure 6. A half‑violin plot illustrates the differential responses of cytoplasmic and mitochondrial free radical levels to menadione treatment at different time points. By comparing the T1 measurements of all naked FNDs and aVDAC2-FNDs, cGCs were demonstrated. (A)   and mGCs (B)   The differences in absolute free radical levels in the cytoplasm and mitochondria at 0, 1, 5, 10, 20, and 30 minutes following treatment with menadione; concurrently, the percentage changes in T1 were compared for all naked FNDs and aVDAC2-FNDs, revealing cGCs. (C)   and mGCs (D)   Differences in cytoplasmic and mitochondrial free radical levels at 1, 5, 10, 20, and 30 minutes compared with the control group. Significance was assessed using a t-test: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.

Figure 7. A half-violin plot showing the differences in free radical levels between cGCs and mGCs, as well as their time-dependent responses to menadione treatment. (A) Compare the T1 values measured in cGCs and mGCs for all exposed FNDs to demonstrate the differences in absolute cytoplasmic free radical levels between cGCs and mGCs at 0, 1, 5, 10, 20, and 30 minutes following menadione treatment. (B) Compare the percentage changes in T1 measured for all exposed FNDs in cGCs and mGCs, to illustrate the differences in cytoplasmic radical dynamics between cGCs and mGCs relative to the control group at 1, 5, 10, 20, and 30 minutes post‑treatment. (C) Compare the T1 values measured in cGCs and mGCs for all aVDAC2-FNDs to demonstrate the differences in absolute mitochondrial free radical levels between cGCs and mGCs at 0, 1, 5, 10, 20, and 30 minutes following menadione treatment. (D) Compare the percentage changes in T1 measured for all aVDAC2-FNDs in cGCs and mGCs to illustrate the differences in mitochondrial free radical dynamics between cGCs and mGCs relative to the control group at 1, 5, 10, 20, and 30 minutes post‑treatment. Significance was assessed using a t‑test: * p < 0.05, ** p < 0.01, *** p < 0.001, **** p < 0.0001.