Sensight enables quantitative multivariate engineering of high-performance chemical imaging tools

Domain: chemistry
Published: 2026-01-27
Editorial outcome: major_revision

Abstract
Chemical imaging probes enable the visualization of dynamic biology; however, engineering high sensitivity in live cells remains challenging. Here we present Sensight, a quantitative multivariate framework that integrates key photophysical and physicochemical descriptors to predict and optimize probe performance. Using a structurally diverse library, we identify five dominant parameters, define their optimal ranges, and unify them into a radar map representation with strong predictive power and intuitive visualization. This framework extends the structure–activity relationship analysis into imaging sensitivity, capturing complex structure–function relationships that shape probe behavior in live cells. Guided by Sensight, we design G3, a superoxide probe with exceptional sensitivity for detecting early oxidative events. The framework’s generality is further demonstrated across distinct systems, including tetrazine–bicyclononyne bioorthogonal chemistry and formaldehyde sensing. Together, these findings establish Sensight as a predictive and generalizable strategy for high-performance probe design, with broad implications for sensing, imaging, and even theranostics.

Introduction
Small-molecule tools such as fluorescent probes and bioorthogonal reagents have revolutionized our ability to interrogate biological systems with chemical precision. These tools are indispensable for visualizing biomolecules1–5, tracking dynamic cellular processes6–8, and controlling molecular events in situ9,10. Among them, activity-based sensing and bioorthogonal chemistry stand out by leveraging selective chemical reactivity within live cells to generate detectable signals or trigger functional responses11–14. This reactivity-driven approach underpins key advances in fluorescence imaging, prodrug activation, and biomolecular labeling15–18, making sensitivity and reaction efficiency critical design priorities.

Achieving high sensitivity is particularly crucial for detecting trace biomolecules or detecting early-stage cellular events. Traditional probe development has focused on maximizing signal gain by developing activatable probes utilizing clever photophysical mechanisms19–21, such as Förster resonance energy transfer (FRET)22, photoinduced electron transfer (PET)23, twisted intramolecular charge transfer (TICT)24. While these strategies have yielded powerful activatable probes25–27, sensitivity in live cells depends on more than fluorogenic responses. Structural factors such as cell permeability, excitation compatibility, and detection efficiency are equally critical. These properties are interdependent, making rational design inherently complex. Despite the growing demand for high-performance probes, no general framework exists to quantitatively integrate these variables into a predictive, design-oriented model.

To address this gap, we introduce Sensight, a sensitivity engineering strategy integrating multivariate methods for guiding high-performance imaging toolkits. Using a structurally diverse, in-house probe library, we systematically analyzed fifteen descriptors for their contribution to live-cell imaging sensitivity and identified five dominant ones that collectively shape probe performance in cells, including fluorogenic contrast, lipophilicity, polar surface area, excitation mismatch, and quantum efficiency. These parameters were unified into a radar map visualization that enables intuitive assessment and prospective design. The model, termed Sensight, was experimentally validated through single-variable-controlled synthesis and imaging studies. It successfully guided the development of G3, a superoxide-responsive probe with desirable sensitivity for detecting early oxidative bursts in live cells. To demonstrate generality, we further applied Sensight to mechanistically distinct systems, including tetrazine-bicyclononyne (BCN) bioorthogonal conjugation and formaldehyde imaging. In each case, model predictions aligned with experimental performance. Together, Sensight provides a generalizable framework for sensitivity-tuned molecular design. It extends the logic of structure–activity relationships to probe engineering and offers a powerful conceptual and practical toolkit for advancing molecular imaging and chemical biology.

Results
Identifying key descriptors governing imaging sensitivity
To establish a quantitative framework for the rational design of high-sensitivity fluorescent imaging probes, we first sought to define which structural and photophysical parameters most critically influence probe performance in live cells. This required a library of probes with: (1) identical target specificity to eliminate confounding biological factors; (2) consistent sensing chemistry and a single well-defined product to ensure response accuracy; and (3) measurable fluorescence enhancement upon activation to enable quantitative evaluation. 1,2,4,5-Tetrazine(Tz)-based activity-based probes were identified as an ideal system to meet these criteria. These probes react selectively with superoxide to form stable oxadiazoles with robust fluorogenic signals28. Leveraging this reactivity, we designed and synthesized a library of Tz-conjugated probes (A–J) covering diverse structural variations(Fig.1a,SupplementaryTable1).

In solution, all probes underwent quantitative conversion to oxadiazoles upon reaction with superoxide (Supplementary Figs. 1–22), and their fluorogenic fold-change values (ΔFs) were determined(Fig.1b,SupplementaryFigs.23–44).These ranged from modest to extraordinary enhancements, consistent with tetrazine’s known quenching efficiency29–31.

To evaluate their imaging sensitivity in live cells, we employed a H2O2-induced superoxide burst model in HepG2 cells32. We first systematically optimized the H2O2 stimulation parameters using probe F-Tz4, a benchmarked Tz-based superoxide probe with well-characterized cellular response behavior28. Titration of H2O2 concentration (0–4 mM) and incubation time (0–4 h) revealed that 2 mM for 2 h produced a maximal and reproducible superoxide burst without compromising cell viability (Supplementary Figs. 45, 46). This optimized condition was applied to all subsequent imaging experiments. To quantify live-cell imaging sensitivity, we defined ΔFc as the fold-increase in cellular fluorescence between H2O2-stimulated and unstimulated (Control) conditions(Fig.1b,SupplementaryFigs.47–56).For each probe, mean fluorescence intensity (MFI) was quantified across three biologically independent replicates. For each replicate, three frames were randomly selected from a single focal plane (non-Z-stack). Fluorescence values were extracted directly from cell-containing regions, and ΔFc was calculated as MFI H2O2/MFI Control. Although all probes showed turn-on behavior, ΔFc values were consistently lower than their ΔFs counterparts, highlighting the limited superoxide availability in cells and underscoring the challenge of achieving high sensitivity in physiological contexts.

We next examined whether a probe’s fluorogenic response in solution (ΔFs) directly correlates with its imaging sensitivity in live cells (ΔFc). A linear regression analysis of ΔFs versus ΔFc revealed little correlation (R²=0.01, Pearson correlation coefficient (PCC)=0.12) (Fig.1c), supporting our central hypothesis that additional structural features beyond fluorogenic contrast contribute critically to in-cell performance. To identify these contributing factors, we compiled a broader set of 15 physicochemical and photophysical descriptors encompassing parameters related to probe solubility, membrane permeability, imaging instrument compatibility, and optical signal quality(SupplementaryTables2,3).Each descriptor was normalized to a 0–1 scale and plotted against the normalized ΔFc. Two complementary metrics were then calculated for each descriptor: entropy, to capture distributional uncertainty and potential nonlinear effects; and PCC, to assess linear relationship with ΔFc (Fig.1d,SupplementaryFig.57,Table4).To rank the descriptors, we summed their normalized entropy and PCC values, providing a transparent and reproducible way to highlight parameters that contribute both linearly and nonlinearly to imaging sensitivity. This analysis highlighted five descriptors with the strongest associations to imaging sensitivity, including topological polar surface area (tPSA), the calculated octanol–water partition coefficient (ClogP), the difference between the probe’s maximum excitation wavelength and the fixed excitation wavelength of the imaging instrument (Δλex), ΔFs, and the Stokes shift. Notably, although tPSA and cLogP are generally considered opposing descriptors, both showed a weak negative correlation with imaging sensitivity (ΔFc) in our dataset(SupplementaryFig.57).This trend likely reflects the fact that their influences on membrane permeability are not strictly linear. These observations further support the use of combined PCC and entropy-based criteria in descriptor selection, which together capture both linear and non-linear contributions to probe performance.

The biological rationale behind the five descriptors is compelling. tPSA and ClogP govern passive membrane permeability33–35. Δλex reflects excitation compatibility with common microscopy setups, while Stokes shift affects detection clarity. Notably, even probes with high ΔFs may fail to perform if they cannot efficiently enter cells or be properly excited under imaging conditions. This multifactorial insight emphasizes that fluorescence contrast alone is insufficient as a predictive measure of probe performance. The significant and previously underappreciated roles of tPSA, ClogP, and Δλex strongly support the need for an integrated, quantitative model that accounts for the interplay of these diverse factors. These findings set the stage for constructing a predictive framework to guide the rational engineering of next-generation imaging probes.

Constructing Sensight for predicting imaging sensitivity
To determine how each descriptor contributes to probe performance, we first classified the probe library into two categories based on their ΔFc: high sensitivity (ΔFc ≥ 1.4) and low sensitivity (ΔFc < 1.4). Recognizing that extreme values of lipophilicity (ClogP) and polarity (tPSA) impair passive membrane diffusion, we empirically selected the median values within the high-sensitivity group as optimal ClogP (~2.0) and tPSA (~70 Å2)(SupplementaryFig.58),providing a balanced approximation of probe performance. For ΔFs and Stokes shift, the optimal values were taken as the maximal values observed in the dataset, reflecting the fact that stronger fluorogenicity and larger spectral separation universally enhance imaging sensitivity. For excitation mismatch (Δλex), because minimal mismatch favors efficient excitation, we incorporated this descriptor through min–max normalization. To enable direct comparison across descriptors of different scales and units, we standardized each parameter using min-max normalization or proximity-to-optimum scoring(SupplementaryTable5).This standardization laid the groundwork for a multivariate regression model capable of quantitatively predicting probe sensitivity in live cells.

We first explored logistic regression to differentiate high- and low-performing probes but found that its binary nature lacked granularity. Instead, we used multivariate linear regression, a strategy commonly employed in medical cohort studies, to derive correlation coefficients reflecting the relative contribution of each descriptor to imaging sensitivity (ΔFc). Using SPSS, we performed regression analysis with the five descriptors as independent variables and ΔFc as the dependent outcome (Fig.2a). The results revealed that tPSA (r=0.699), Δλex (r=0.570), ΔFs (r=0.277), and ClogP (r=0.197) were the most influential contributors to live-cell probe performance, while Stokes shift (r=0.096) was marginal (Fig.2b). These rankings reinforced the importance of integrating physiochemical properties and excitation compatibility, parameters that are often overlooked in probe design.

To convert these multivariate results into an easy-to-understand visualization, we first re-scaled each descriptor value by its regression-derived weight to reflect its relative importance (Supplementary Table 6). This was carried out by multiplying the standardized value by the descriptor’s corresponding weight. The resulting adjusted descriptor values were then used to plot a five-dimensional radar map (workflow in Supplementary Fig. 59), enabling intuitive visualization of each probe’s predicted imaging performance (Fig.2c,SupplementaryFig.60,Table7).The area enclosed by the radar map served as a quantitative summary metric. Remarkably, we observed a strong exponential correlation (R2=0.97) between the radar map area and actual live-cell imaging sensitivity (ΔFc) across the probe library (Fig.2d). This sharply contrasts with the negligible correlation when ΔFc was plotted against ΔFs alone (Fig.1c), confirming the effectiveness of a multivariate approach over traditional single-parameter optimization. Overall, these findings establish a robust and interpretable model, Sensight, that not only captures the multifactorial nature of imaging probe performance but also offers a quantitative and visual tool for guiding probe development.

Validating Sensight through single-variable experiments
While Sensight quantitatively explains the imaging sensitivity of the initial probe library, we sought further validation to confirm its reliability. To this end, we adopted a controlled single-variable approach, where new probes were synthesized to selectively test the four most influential descriptors identified, tPSA, Δλex, ΔFs, and ClogP(Fig.3a).

We first evaluated tPSA, which exhibited the strongest correlation with probe performance. Although probe F2 displayed a robust fluorogenic response in vitro (ΔFs=28.6), it showed low imaging sensitivity in cells (ΔFc=1.14). Sensight attributed this discrepancy to its suboptimal tPSA value (Fig.3b). To test this hypothesis, we designed and synthesized probe W1, which maintained a similar Δλex to F2 but featured a markedly improved tPSA. Both probes showed quantitative conversion to oxadiazoles upon reaction with superoxide, and F2 even had a slightly higher ΔFs (Supplementary Figs. 61–63, Table 8). Nevertheless, W1 achieved significantly greater imaging sensitivity (ΔFc=1.74)(Fig.3e,h),substantiating tPSA as a critical determinant for probe permeability and in-cell performance.

Then, we investigated Δλex, the second-highest ranked descriptor. We compared probe D3 with its analog W2, designed to closely match D3 in both ΔFs and tPSA while significantly reducing Δλex (Fig.3c,SupplementaryFigs.64–66,Table9).As predicted, W2 exhibited higher live-cell imaging sensitivity than D3 (Fig.3f,i), confirming that excitation compatibility with the imaging system is a major factor influencing probe efficacy.

Next, we assessed the contribution of ΔFs by comparing probe E1 with its isomeric counterpart B1, which shared similar tPSA and Δλex but exhibited a higher fluorogenic turn-on response (Fig.3d,SupplementaryFigs.67–69,Table10).As expected, B1 delivered stronger imaging sensitivity (Fig.3g,j), validating ΔFs as a relevant driver of performance.

Lastly, the effect of ClogP was evaluated by synthesizing probe W3 and comparing its imaging performance with C2. W3 demonstrated comparable tPSA (108.08 Å²), ΔFs (77.4), and Δλex (66 nm) to that of probe C2 (tPSA 108.08 Å², ΔFs 78.3, and Δλex 55 nm), but much improved ClogP (2.12 for W3 and 0.72 for C2) (Supplementary Figs. 70–72, Table 11). Under the established imaging conditions, W3 was more sensitive (ΔFc=1.39 for W3 and 1.10 for C2), highlighting the significant impact of ClogP on probe imaging sensitivity.

Together, these experiments provide orthogonal validation of Sensight’s core principles. By isolating individual variables in structurally related probes, we demonstrated that improvements in tPSA, Δλex, ΔFs, and ClogP lead to predictable gains in imaging sensitivity. These results not only reinforce the predictive power of Sensight but also underscore its value as a rational design tool for next-generation activity-based probes.

Validating Sensight through de novo probe design
Having established the predictive power of Sensight, we next explored its utility in de novo probe design. We aimed to develop a superoxide-responsive probe with enhanced imaging sensitivity by prioritizing structural features that maximize performance.

Thirteen probe candidates were designed in silico by conjugating 1,2,4,5-tetrazine to various fluorophores via different linkers. For each candidate, key parameters, including tPSA, Δλex, ClogP, and Stokes shift, were calculated and standardized to their optimal values (Supplementary Tables 12–15). As ΔFs could not be predicted a priori, it was held constant across all designs. We then generated radar plots for each candidate and ranked their predicted imaging sensitivity by radar area (Fig.4a,SupplementaryTable15).

To experimentally validate the predictive capability of Sensight, we selected and synthesized six representative probes (Tz-1, Tz-2, Tz-3, Tz-8(G3), Tz-9, and Tz-13(K2)). The selection was guided by two considerations. First, their predicted imaging sensitivity values spanned the entire distribution of the virtual design library, ensuring evaluation across low-, medium-, and high-performance regions. Second, they could be readily synthesized. All compounds were confirmed to undergo quantitative conversion to the corresponding oxadiazoles upon reaction with superoxide (Supplementary Figs. 73–88; Supplementary Tables 16–21). In HepG2 cells under H2O2-induced superoxide burst, G3 displayed the highest imaging sensitivity with a ΔFc of 2.67, approximately twice that of K2 (ΔFc=1.24) (Fig.4b,c). The experimentally observed trend in sensitivity (ΔFc: G3 (2.67) > Tz-3 (1.56) > Tz-9 (1.45) > Tz-1 (1.25) > K2 (1.24)) closely matched the ranking predicted by Sensight, thereby supporting the robustness of the model.

Although the experimentally measured ΔFc of Tz-2 (1.20) deviates slightly from the predicted rank order, this difference is expected given the very small separation among the predicted radar map areas of Tz-1 (0.4971), Tz-2 (0.4927), and K2 (0.4857) (Supplementary Table 15). Such minor rank inversions among closely clustered probes fall within the experimental variability of live-cell imaging and do not affect the overall predictive trend of Sensight.

Notably, G3 displayed a near-optimal combination of key descriptors, including tPSA, Δλex, ΔFs, and ClogP, and showed the largest radar area among all Tz-based probes in our dataset (Fig.4d,e), consistent with its superior live-cell performance. To evaluate G3’s biological utility, we benchmarked it against dihydroethidium (DHE), a widely used commercial probe for superoxide imaging. While both G3 and DHE responded to superoxide, DHE exhibited broad reactivity to multiple reactive oxygen species (ROS), compromising specificity (Supplementary Figs. 89–91). In contrast, G3 demonstrated higher selectivity and greater storage stability (Supplementary Fig. 92), offering advantages in both precision and practical use.

We then tested G3 in two biological models that involve subtle superoxide bursts. The first is EGF-induced redox signaling. The binding of epidermal growth factor (EGF) to its receptor (EGFR) transiently activates NADPH oxidases (NOXs), generating a localized superoxide burst36. Using A431 cells, which overexpress EGFR37, we observed EGF-dose-dependent fluorescence increases with both G3 and DHE (Supplementary Fig. 93). However, only G3 detected superoxide at lower EGF concentrations (Fig.4f,h,i), highlighting its superior sensitivity. The second is early-stage oxidative stress from paraquat exposure. Paraquat triggers mitochondrial superoxide production, particularly during early exposure38,39. In HepG2 cells treated with increasing doses of paraquat, G3 detected significant fluorescence even at low concentrations where DHE failed (Fig.4g,j,k). Complementary JC-1 staining confirmed reduced mitochondrial membrane potential, even at these early stages (Supplementary Fig. 94)40, validating G3’s ability to detect early oxidative damage.

We also benchmarked G3 against DHE and MitoSOX to evaluate its capability to dynamically track H2O2-induced superoxide bursts. Cells were first loaded with each probe and then challenged with 1 mM H2O2, and the resulting fluorescence changes were recorded in real time using lattice light-sheet microscopy (Supplementary Fig. 95). DHE exhibited minimal fluorescence increase, reflecting its low sensitivity under these conditions. MitoSOX, a mitochondria-targeted analog of DHE, rapidly detected the superoxide burst, consistent with mitochondria being the primary site of superoxide generation in this model, highlighting the importance of organelle targeting for enhancing probe sensitivity. Remarkably, G3, despite lacking mitochondrial targeting, was able to capture the superoxide burst with a ~1.5-fold fluorescence increase within 30 min, demonstrating sufficient sensitivity to monitor cytosolic superoxide dynamics.

Together, these findings demonstrate that G3 is a Sensight-guided, rationally designed probe with unmatched sensitivity and specificity for superoxide detection in live cells. These results affirm the predictive strength and practical utility of Sensight, and establish its potential as a general strategy for engineering high-performance fluorescent probes. In its current form, Sensight deliberately centers on global physicochemical and photophysical descriptors, such as tPSA, ClogP, ΔFs, Δλex, and Stokes shift, that broadly govern intracellular accessibility, excitation efficiency, and fluorogenic turn-on. This choice reflects a rational balance between mechanistic relevance and model generalizability. However, subcellular localization can also modulate observed imaging performance by shaping local analyte availability and modulating microenvironment-dependent fluorescence behaviors. Such localization effects introduce an additional, highly context-dependent layer of complexity that extends beyond the current descriptor space. Future iterations of Sensight could incorporate localization-relevant parameters as more systematic datasets become available.

Sensight generality in the tetrazine-BCN bioorthogonal system
Sensight not only highlighted ΔFs as a central determinant of probe performance but also revealed other underappreciated structural parameters, particularly tPSA and Δλex, as critical contributors to live-cell imaging sensitivity. Recognizing that both activity-based sensing and bioorthogonal chemistry rely on specific chemical transformations within biological systems to produce a signal or effect, we hypothesized that Sensight might have broader applicability beyond redox-based probes.

To test this, we applied Sensight to the well-established Tz-BCN bioorthogonal system (Fig.5a). Tz-BCN cycloadditions enable catalyst-free, rapid, and selective labeling in live cells and have seen wide adoption in chemical biology, from biomolecular tracking to prodrug activation41–45. Before proceeding, we verified that BCN treatment alone did not induce oxidative stress in cells, ensuring that the fluorescence responses reflected bioorthogonal conjugation rather than redox activity (Supplementary Fig. 96).

To assess whether Sensight could predict fluorogenic performance in this context, we first selected two Tz probes, B1 and E1 (Fig.3a), which have similar tPSA and Δλex, but differ markedly in their fluorogenic signal change (ΔFs) upon BCN ligation (Fig.5b; Supplementary Figs. 97–100, Tables 22, 23). Radar map analysis predicted B1 to exhibit superior imaging sensitivity (Supplementary Fig. 101). Live-cell labeling experiments confirmed this. Upon BCN treatment, B1 produced significantly higher ΔFc values than E1, consistent with Sensight predictions (Fig.5e,f,SupplementaryFig.102).This comparison reinforces ΔFs as a primary determinant of probe performance in the bioorthogonal context. We next compared probes F2 and W1 (Fig.3a), which exhibit similar Δλex and ΔFs values but differ in tPSA, with W1 matching more closely to the predicted optimal range (Fig.5c, Supplementary Figs. 103–108, Tables 24, 25). Consistent with W1’s larger radar map area, BCN-triggered fluorescence activation in live cells was significantly higher for W1 than for F2 (Fig.5e,g), supporting tPSA as an independently predictive variable. To further isolate the contribution of Δλex, we evaluated probes D3 and W2 (Fig.3a), which differ primarily in Δλex while maintaining similar values for the other parameters. As predicted, W2, with a smaller Δλex, displayed improved imaging sensitivity relative to D3 (Fig.5d,e,h; Supplementary Figs. 109–114, Tables 26, 27), validating Δλex as a robust predictor across probe scaffolds. We then assessed the influence of lipophilicity and found that ClogP also exerted a strong effect on imaging sensitivity. Probe W3, which possesses a more favorable ClogP, outperformed C2 under identical experimental conditions (Supplementary Figs. 115–120, Tables 28, 29), further demonstrating the generality of Sensight’s predictive framework. Finally, to probe the upper performance limit predicted by Sensight, we tested G3, the highest-ranked structure from the radar map analysis. G3 yielded strong intracellular fluorescence activation even at 25 μM BCN, a level at which the control probe B3 required >100 μM BCN to elicit a similar response (Supplementary Figs. 121–126, Tables 30, 31). Taken together, these results extend the predictive validity of Sensight to the Tz-BCN bioorthogonal platform, confirming its broad utility for rational probe design beyond redox chemistry. The framework could be applied to optimize other reaction-based or triggered probe systems, accelerating the development of next-generation molecular tools for live-cell imaging.

Extending Sensight generality to formaldehyde imaging probes
Building on the successful validation in the Tz–BCN system, we next applied Sensight to a mechanistically distinct platform, formaldehyde (FA) imaging probes. FA plays a critical role in one-carbon metabolism, and its dysregulation has been implicated in diverse pathologies, including neurodegeneration and cancer46–48. While numerous FA-responsive probes have been developed49–53, systematic evaluation and predictive modeling of their imaging sensitivity remain unexplored. This case study thus offers a dual opportunity to test the generality of our framework beyond tetrazine chemistry and to provide improved tools for elucidating FA’s biological functions.

We focused on FA probes leveraging the aza-Cope rearrangement (Fig.6a), a widely used strategy that enables selective FA sensing via reaction with primary amines49–53. We built a structurally and spectrally diverse probe library, deliberately covering several literature probes for benchmarking purposes (Fig.6b). For each probe, we computed tPSA and ClogP values. Stokes shift, and Δλex were determined by the excitation instrument settings and the fluorophore’s spectral properties, both of which are well-defined for standard imaging platforms. While ΔFs remained unpredictable precisely, literature-reported turn-on efficiencies allow reasonable estimation within an appropriate range and were temporarily held constant herein (Supplementary Tables 32–35). Based on these values, radar plots were constructed to generate predicted sensitivity ranking (Fig.6c), enabling pre-synthetic assessment of probe imaging sensitivity.

To validate Sensight’s predictions, we synthesized the probes, confirmed their full conversion to the products under excess FA, and quantified their fluorogenic responses (Supplementary Figs. 127–135, Tables 36–44). For live-cell imaging, we employed an LPS-induced inflammation model in BV2 microglia, where endogenous FA is upregulated as part of the inflammatory cascade54,55. At 2 ng/mL LPS, which sufficed to trigger IL-1β secretion (Fig.6d), only FP9 showed a detectable FA response (Fig.6e,g). At 4 ng/mL, all probes responded (Supplementary Figs. 136–140), and the observed sensitivity ranking (FP9 > FP5 > FP4 > FP8 > FP3 > FP7 > FP6 > FP1 > FP2) closely matched Sensight’s predictions (Fig.6f). Strikingly, a strong linear correlation (R²=0.93) was observed between predicted and experimentally measured sensitivities (Fig.6h), demonstrating Sensight’s quantitative accuracy and generalizability across probe chemistries. Notably, our top-performing probe, FP9, also outperformed FP4 in previous independent studies50, further reinforcing the robustness and translational relevance of our design framework.

Methods
General methods
All chemicals were from commercial supplies and used without further purification except otherwise indicated. 1H NMR spectra were measured on a Bruker 500 Fourier transform spectrometer (500 MHz). 13C NMR spectra were obtained on a Bruker 500 Fourier transform spectrometer (126 MHz) or Asend EVO 600M (151 MHz). Chemical shifts (δ) for 1H and 13C NMR spectra were given in ppm. The residual solvent signals were used as references for 1H and 13C NMR spectra and the chemical shifts were converted to the TMS scale (CDCl3, 7.26 ppm for 1H NMR and 77.16 ppm for 13C NMR; CD3OD, 3.31 ppm for 1H NMR and 49.00 ppm for 13C NMR; (CD3)2SO, 2.50 ppm for 1H NMR and 39.52 ppm for 13C NMR). All chemical shifts were reported in parts per million (ppm) and coupling constants (J) in Hz. The following abbreviations were used to explain the multiplicities: d=doublet, t=triplet, m=multiple, dd=doublet of doublets. High-resolution mass spectra (HRMS) for new compounds were recorded on a Waters TOF MS GCT Premier using ESI ionization. Liquid chromatography equipped with a low-resolution mass detector (LC–MS) was conducted on an Agilent Infinity Lab LC/MSD. High-performance liquid chromatography (HPLC) was measured on an Agilent Technologies 1260 Infinity system. Fluorescence spectra were measured on an Agilent Cary Eclipse Fluorescence Spectrophotometer. Absorption spectra were collected using a Hitachi U-3010 spectrophotometer. The confocal cell imaging experiments were carried out on a Zeiss LSM900 live cell fluorescence microscope, except that K2 was recorded on a Leica STELLARIS 5 confocal microscope. Dynamic imaging was performed with a Zeiss lattice-lightsheet 7 microscope.

Probe synthesis and structure characterization
Tz-based superoxide probes and FP-series formaldehyde probes were synthesized according to procedures outlined in Supplementary Figs. 141–160. Detailed procedures and structure characterization data were described in the supplementary information.

HPLC analysis procedures
To record the reactivity of A–K towards O2•− or BCN, each compound stock solution (5 mM, in DMSO) was diluted in MeCN to make a 50 μM solution. Then KO2 (0–8 eq) or BCN (20 eq) was added. The mixture was then allowed to react either with O2•− for 30 min or with BCN for 2 h at ambient temperature, followed by HPLC analysis. The peak area of the remaining residue and product was used to calculate the conversion rate and yield. The detailed liquid chromatography methods were reported in the supplementary information. The maximum peak area of the remaining residue and the product were normalized and mapped, respectively. The reactivity of FP1–FP9 with formaldehyde (FA) was determined following the procedures described above, except that 20 eq FA was added and incubated at ambient temperature for 2 h. Then, the solution pH was adjusted to 9. After 30 min, the mixture was analyzed by HPLC. Liquid chromatography methods are described in the analytical methods section in the supplementary information.

Optical response analysis
To measure the absorption spectra, the stock solution of A–K was diluted with MeCN containing 1% 18-crown-6. O2•− was added to this solution. After 30 min of incubation, the mixture was diluted with PBS buffer (10 mM, pH=7.4, F2–K2) or MeCN (A–E1) and then measured. The final concentration of the probe was 5 μM. The same tube of solution was used to record the fluorescence spectra. The mixture was then measured for fluorescence. To determine the quantum yields, quinine sulfate (Φstandard=0.577 in 0.1 M H2SO4), fluorescein (Φstandard=0.95 in 0.1 M NaOH), and SiR650 (Φstandard=0.31 in PBS (pH 7.4)) were used as standards according to a published method. For the probe, fluorescein, quinine sulfate, and SiR 650, the absorbance spectra were measured within an absorbance range of 0.01–0.05. The quantum yield was calculated according to Eq. (1):

Φsample = Φstandard × (Fsample / Fstandard) × (Absstandard / Abssample) × (nsample2 / nstandard2) (1)

where Φ is the quantum yield, ΣF is the integrated fluorescence intensity, Abs is the absorbance at the excitation wavelength, and n represents the refractive index of the solvent.

To test the selectivity of the probes, G3 and DHE were diluted with PBS buffer (10 mM, pH 7.4) to make a solution of 5 μM. Aliquots of this solution were then treated with various analytes whose stock solutions were prepared according to the methods described in the supplementary information. The volume change (ca. 1‰) caused by the addition of analytes could be negligible. After incubating at room temperature for 30 min, the fluorescence spectrum was collected. All the fluorescence and absorption spectra data were processed via the software Origin 2022.

Cell culture
The cell lines utilized in this study, including A431, BV2, HeLa, and HepG2, were acquired from the Stem Cell Bank of the Chinese Academy of Sciences. All cultures were maintained at 37 °C in a humidified atmosphere containing 5% CO2. Cells were grown in high-glucose Dulbecco’s modified Eagle medium (DMEM; Gibco), which was supplemented with 10% (v/v) fetal bovine serum (FBS; PAN) and a 1% antibiotic–antimycotic solution (Meilun Bio). To ensure cells remained in an exponential growth phase, cells were passaged when they reached 80–90% confluence.

Fluorescence confocal imaging
All cells were seeded in 15 mm glass-bottomed dishes and cultured under standard conditions for 24 h prior to experimentation. Unless otherwise specified, the following general protocol was applied: following the indicated treatments or incubation periods, cells were washed three times with PBS, then incubated with the respective probe at a final concentration of 5 µM in serum-free medium for 30 min at 37 °C in the dark. Cells were washed with PBS three times before imaging.

For optimizing the oxidative stress condition experiment, HepG2 cells were treated with serum-free medium containing varying concentrations of H2O2 (Sinopharm) for specified durations before staining with the F-Tz4 probe. For evaluating the Tz series probes, a standardized oxidative stress condition (2 mM H2O2, 2 h) was applied, and cells were fixed before imaging.

For the selectivity experiment, HepG2 cells were pre-incubated with Tiron or TEMPO (Aladdin; 300 µM each) in serum-free DMEM for 1 h, followed by co-treatment with the same scavenger and H2O2 (2 mM) for an additional 2 h before staining with probe G3 or DHE (MedChemExpress).

For the experiment on EGF-induced superoxide, A431 cells were co-incubated with serum-free medium containing different concentrations of EGF (MedChemExpress; 0, 8, 16, 50, 100, or 200 nM) together with G3 or DHE for 30 min and imaged.

For the experiment on paraquat-induced superoxide, HepG2 cells were exposed to paraquat (Aladdin; 0, 0.5, or 1 mM) in serum-free medium for 2 h before staining with probe G3, DHE, or JC-1 (MedChemExpress).

For a live-cell kinetic imaging experiment, HeLa cells were first loaded with the probe (G3, DHE, or MitoSOX (MedChemExpress)) for 30 min and washed. Immediately after the addition of H2O2 (1 mM), time-lapse imaging was initiated and continued for 30 min.

For bioorthogonal experiment, HepG2 cells were pretreated with BCN (MedChemExpress; 0, 25, 50, 100, or 200 µM) in serum-free medium for 1 h before B1, B3, C2, D3, E1, G3, W1, W2 or W3 staining.

For the endogenous formaldehyde induction experiment, BV2 cells were stimulated with LPS (MedChemExpress; 0, 1, 2, or 4 ng/mL) in serum-free medium for 24 h before FP1-FP9 staining.

Fluorescence images were primarily acquired using a Zeiss LSM900 confocal microscope equipped with ZEISS ZEN 3.6 (blue edition) software, with specific images from K2 (channel: λex=710 nm, λem=712–834 nm). The imaging parameters were set as follows: A–B3, D2–E3, Tz–1–Tz–3 and W1 channel: λex=405 nm, λem=406–550 nm, C2, C3, F2–H3, W2 and W3 channel: λex=488 nm, λem=489–600 nm, I2–J3, DHE and MitoSOX channel: λex=561 nm, λem=562–650 nm. JC-1 channel: λex=488 nm, λem=489–560 nm and λex=561 nm, λem=562–700 nm. FP1, FP2 and FP5 channel: λex=405 nm, λem=406–504 nm, FP3 channel: λex=405 nm, λem=450–504 nm, FP4 channel: λex=405 nm, λem=450–520 nm, FP6, FP7 and FP8 channel: λex=488 nm, λem=489–550 nm, FP9 channel: λex=561 nm, λem=565–700 nm.

To ensure consistency in background signal, identical microscope parameters were applied across all conditions within a single experiment. Each experiment was performed in three independent biological replicates, with three frames captured from a randomly selected single focal plane (without Z-stack) per replicate. Image analysis was conducted using the ImageJ software. Fluorescence intensity was quantified by measuring the mean gray value, without background subtraction or application of a Gaussian blur filter. The final dataset represented the pooled measurements from all parallel samples across the three independent experiments.

Analysis of IL-1β secretion via ELISA
Following the indicated treatments, cell culture supernatants were harvested and clarified by centrifugation at 21,000 × g for 15 min at 4 °C. The clarified supernatant was carefully transferred to a new microcentrifuge tube. Total protein concentration in the supernatant was determined using a Bicinchoninic acid (BCA) protein quantification assay.

IL-1β levels were quantified using a commercial enzyme-linked immunosorbent assay (ELISA) kit according to the manufacturer’s protocol (ABclona, RK00006). Briefly, samples and serially diluted standards were loaded into the antibody-precoated wells. Subsequently, biotin-conjugated detection antibody and horseradish peroxidase (HRP)-labeled streptavidin were added sequentially, with thorough washing steps between each incubation. The colorimetric reaction was initiated by adding the 3,3′,5,5′-tetramethylbenzidine (TMB) substrate solution. The optical density (OD) of each well was immediately measured at a wavelength of 450 nm using a SpectraMax M5 microplate reader. Raw OD values from sample and standard wells were corrected by subtracting the average OD of the blank (assay diluent only) wells. A standard curve was generated by plotting the corrected OD values of the standards against their known concentrations. The concentration of IL-1β in each sample was then interpolated from this standard curve.

Descriptors extraction and analysis for model development
ClogP and tPSA were obtained using ChemDraw 23.1.1, MR, fchar, nS, and logS were acquired from the ADMETlab 3.0 (https://admetlab3.scbdd.com/). The extraction of the remaining descriptors was performed in a Jupyter Notebook environment within the Anaconda (25.5.1) platform, utilizing the Python (3.13.5) programming language and the RDKit (2025.03.6) open-source cheminformatics toolkit. Normalized, standardized, and adjusted descriptors were obtained by Origin 2022. Standardized descriptors were obtained as described in Supplementary Table 5. Then, standardized descriptors were used multiple linear regression process in IBM SPSS Statistics 27.0 software for model construction. The non-standardized coefficient is used as the correlation coefficient of each descriptor.

Statistics and reproducibility
All the statistical analysis was performed with Graphpad Prism 8.0 software. A two-tailed unpaired Student’s t-test, or two-way ANOVA test, was used for data statistics. All the cell imaging experiments were performed with three biologically independent replicates.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors developed a quantitative multivariate model that systematically integrates chemical structural, physicochemical, and photophysical parameters with live-cell imaging sensitivity. This framework establishes statistical correlations between fluorescent probe structures and their imaging performance. Furthermore, the authors reported the development of an ultra-sensitive superoxide probe, whose performance was in strong agreement with the Sensight predictions. In addition, Sensight successfully predicted probe sensitivity in two conceptually distinct systems: the tetrazine–BCN bioorthogonal reaction and formaldehyde-selective probes. The establishment of a framework capable of predicting fluorescent probe sensitivity in live-cell imaging represents a significant advancement in the field of molecular fluorescence imaging. Nonetheless, further experimental validation is required to substantiate the robustness and generalizability of this approach. A comprehensive analysis is presented below.\n1. In Supplementary Table 2, please clarify whether the descriptors were calculated based on the structures prior to superoxide activation. In addition, please indicate whether quantum yield, Stokes shift, and Δλex were obtained from the activated structures. If so, it would be important to explicitly state that the same approach was applied throughout all subsequent sections where descriptors were calculated.\n2. In Supplementary Table 4, the normalized PCC value appears unusual, as it should range between 0 and 1.\n3. In Fig. 1D, the authors ranked the descriptors by simply summing the normalized entropy and PCC values. However, descriptors such as nHD and NAR exhibited PCC values comparable to the final five selected descriptors, yet were excluded due to their inherently low entropy. It is therefore important to explain why the ranking was performed by simple summation of entropy and PCC, rather than by a weighted sum or product.\n4. In Supplementary Fig. 56, a clearer explanation is needed regarding how the optimal values of ClogP and tPSA were determined. In Supplementary Fig. 55 (panels H and N), both tPSA and ClogP exhibit negative trends, yet the median values were designated as optimal. Additional clarification on this choice is warranted. Moreover, rather than simply using the median, alternative approaches such as fitting a Gaussian curve and selecting the peak should be considered. Furthermore, as tPSA and ClogP are generally considered opposing descriptors, it would be important to provide additional explanation for why they displayed similar trends in this analysis.\n5. In Fig. 2C, the authors present a pentagonal radar map, with each axis representing one descriptor. A more detailed explanation is needed to clarify how the descriptor values were reflected in the radar plot. Given that the correlation coefficients differ among descriptors, it should also be clarified whether these weights were incorporated into the radar map construction and area calculation.\n6. In Figs. 3–6, please include the excitation wavelength information for the cellular imaging data.\n7. In the section “Validating Sensight through single-variable experiments,” validation was conducted for only three descriptors. To further strengthen the robustness of the platform, it would be valuable to extend the validation to all five selected descriptors.\n8. In the section “de novo design of a superoxide probe,” only G3 and K2 were synthesized and compared. To further confirm the predictive capability of Sensight, it would be important to synthesize and test additional compounds with live-cell imaging.\n9. In Supplementary Fig. 73, please add an explanation of the superoxide scavenging mechanisms of Tiron and TEMPO, together with appropriate references.\n10. In Supplementary Fig. 74, please specify, with references, how DHE is transformed over time. In addition, please include the time-dependent changes in DHE fluorescence around 600 nm.\n11. In Supplementary Fig. 76, the figure is labeled as “H₂O₂,” whereas the description refers to “paraquat.” This inconsistency should be corrected.\n12. In Fig. 5, adding the chemical structures of compounds E1, B1, D3, and W2 would improve clarity.\n13. In the section “tetrazine–BCN bioorthogonal system,” presenting the results in parallel with the section “validating Sensight through single-variable experiments” would better demonstrate the platform’s utility. Therefore, including a comparison between F2 and W2 is recommended.\n14. In Fig. 6H, the number of variables, i.e., fluorescent molecules, appears insufficient to robustly calculate the correlation coefficient. As five of the seven molecules cluster in the lower-left quadrant, it would be necessary to include a broader diversity of fluorophores to obtain a more reliable correlation plot.\n15. In the section “formaldehyde imaging probes,” the best-performing probe (FP7) corresponds to an already developed molecule (Bruemmer, K. J. et al. J. Am. Chem. Soc. 139, 5338–5350 (2017)). To strengthen the validation of Sensight, as was done for the superoxide probes, it would be valuable to perform de novo design followed by optimization using Sensight to develop an improved probe.\n16. Minor typographical errors:\n- In the main article, line 18, “desig” should be corrected to “design.”\n- In Fig. 2A, “SPASS” should be corrected to “SPSS.”", "reviewer_2": "This manuscript is an extremely thorough examination of how key probe parameters can be used to predict biological sensing ability. The authors present here a preliminary relationship, and very interesting and novel methodology, for making such predictions. Selection of the tetrazine, which both acts as a superoxide sensor, and a biorthogonal reactive group, enables the authors to demonstrate the utility of their algorithm on a second sensing system. They then further demonstrate its use with an entirely different sensing group. While it will not be straightforward for others to immediately generalise these results to their own compound libraries, nonetheless the work presented here is a big breakthrough in the field and I predict that it will have significant impact. Changes required before publication:\n1. It would help to have a panel in Figure 1 showing the reaction of tetrazine with superoxide\n2. There is very little discussion of how the delta(Fc) parameter is calculated, even though this is the datapoint on which the rest of the manuscript hangs. On what basis do you know that the optimal H2O2 concentration and incubation time were selected? A titration (time and concentration) should be performed for at least a few of the compounds, to confirm that plateau conditions are being studied\n3. It’s not clear why PCC is cited rather than R2 for the data shown in Figure 1C (and yet R2 is used in Figure 2D\n4. Figure 1 caption should explain what the red numbers signify in panel (B)\n5. Figure 2D – should the y-axis be Norm, not Nom?\n6. Supplementary table 2, footnote – stokes shift should be Stokes shift. And there is no footnote 16 despite the superscript in the header row.\n7. DHE is not seen as a gold-standard probe these days – it is well established that it has poor selectivity, so it would is necessary to perform the benchmarking experiments for another, better, superoxide sensor.\n8. What is missing in the discussion is probe localisation: does the analysis presented here only work because the probes all remain cytosolic? Surely any sub-cellular localisation will greatly confound the results. For example, the Stokes shift is heavily dependent on the polarity for many fluorophores, so differing organelle localisation will likely have huge impacts on the Stokes shift – it is not clear that your model would address this.\n9. The statement in the conclusion “beyond activity-based sensing…” is not accurate – all the examples in this paper are examples of activity-based sensing.", "reviewer_3": "Chemical imaging probes have become indispensable tools in biomedical research. The development of high-performance chemical imaging probes is crucial for visualizing low-concentration reactive molecules in cells. Currently, probe design primarily relies on researchers’ empirical knowledge, which not only raises the barrier to probe development but also significantly increases the workload, as optimization of probe performance can only be conducted after experimental data are obtained. To address this, the authors proposed a five-dimensional model (Sensight) to predict probe performance, aiming to guide rational probe design through quantitative calculations. However, Sensight and its ideal parameter values were derived solely from a limited variety of probes and fluorescence modulation mechanisms, casting uncertainty on its general applicability. More importantly, as key parameters of Sensight, ΔFₛ is clearly unpredictable, and Δλₑₓ depends on imaging instrument settings, substantially diminishing the practical utility of the model. Overall, the model offers limited guidance for the design of entirely novel probes. Therefore, I do not believe this manuscript is suitable for publication in Nature Communications.\n(1)The manuscript proposes ideal values for the five parameters of Sensight; however, these ideal values were derived from a limited sample size and narrow variety of probe types.\n(2)The development of Sensight relied on tetrazine-based superoxide fluorescent probes and formaldehyde probes, meaning only two fluorescence modulation mechanisms were considered. However, fluorescence modulation mechanisms are diverse (e.g., TICT), which casts doubt on the general applicability of Sensight.\n(3)ΔFₛ (fluorescence turn-on ratio) is one of the five core parameters in the Sensight model and is inherently dependent on the specific chemical mechanism of fluorescence modulation and the local cellular microenvironment, rendering it unpredictable by computational methods prior to synthesis and experimental characterization. Since Sensight is designed to guide the development of new high-performance probes, this fundamental limitation means that, in practice, only four of the five parameters can be meaningfully calculated. The authors should explicitly state this limitation to help readers better understand the actual capability of Sensight.\n(4)The unpredictability of ΔFₛ will affect the predictive accuracy of Sensight. Sensight functions more as an optimization tool after probe synthesis and testing, rather than an initial design tool.\n(5)The authors designed a series of formaldehyde fluorescent probes and predicted their sensitivity in living cells, with results consistent with the predictions. However, when using Sensight for prediction, ΔFₛ was set as a uniform value. Importantly, would setting ΔFₛ to different uniform values lead to different prediction outcomes (for example, in the manuscript, tPSA was set to 5, what if it were set to 50?)? What value or specific range should tPSA be set to in order to ensure the predictive accuracy of the model?\n(6)Beyond sensitivity, the selectivity of probes is equally important. Sensight cannot predict probe selectivity, indicating that it is only suitable for making rough predictions about the performance of fluorescent probes modified with known recognition groups, and offers limited utility for the development of innovative probes.\n(7)The representation of the substituent denoted as R in Figure 1a is not sufficiently clear. Should \"RTz\" on the probe structure on the left be modified to \"RnTz\" for clarity?"}, "author_response": "Point-by-Point Response to Reviewer Comments\nReviewer #1\nNonetheless, further experimental validation is required to substantiate the robustness and\ngeneralizability of this approach. A comprehensive analysis is presented below.\nResponse:\nWe sincerely thank the reviewer for the positive assessment of our work and for recognizing the\nconceptual advance of Sensight as a quantitative multivariate strategy for predictive probe design. We\nfully agree that additional experimental validation is important to substantiate the robustness and\ngeneralizability of the model. In response, we have substantially expanded the validation experiments in\nthe revised manuscript as follows:\n1) We performed additional analysis of ClogP influence on probe performance. To further examine\nkey physicochemical factors governing probe performance, we systematically analyzed the\neffect of ClogP on imaging sensitivity while keeping tPSA, ΔF, and Δλ consistent between\ns ex\nthe counterparts. For this purpose, we newly synthesized probe W and compared it with C .\nThe 3 2\nresults revealed that probe W approaching the optimal ClogP value, showed better imaging\n3,\nsensitivity, consistent with the model’s weighting of ClogP as a contributing descriptor. This\nadditional analysis strengthens the mechanistic interpretation of the model and supports its\napplicability in guiding probe optimization (Data for W were shown in Supplementary Figs.\n3\n70-72, and those for C were shown in Supplementary Figs. 6, 28, 49).\n2\n2) We extended validation within the superoxide probe family by synthesizing 4 additional\nstructures (Tz-1, Tz-2, Tz-3, Tz-9) from the virtual design set shown in Figure 4 and\nexperimentally evaluated their fluorescence responses and imaging sensitivities toward\nsuperoxide. 6 of the 13 designed structures now have experimental data. The predicted\nsensitivities based on the Sensight radar model correlated well with the measured imaging\nperformances, supporting the reliability of the model’s predictive capability (Data for the 4\nadditional structures were shown in Supplementary Figs. 73-84).\n3) We expanded testing with more formaldehyde-responsive probes. We further extended the\nformaldehyde probe library by synthesizing 2 new derivatives (FP5 and FP8), covering a\nbroader range of predicted sensitivity values. The experimentally determined imaging\nsensitivities showed strong agreement with model predictions, with an overall linear correlation\ncoefficient of R² = 0.93 (Pearson’s r = 0.96) (updated in Figure 6h). These results reinforce the\nquantitative predictive power of Sensight across distinct chemical scaffolds.\n4) We performed additional validation in the tetrazine–BCN bioorthogonal system. To further\nprobe the effect of key descriptors, we introduced two additional compounds with comparable\nΔF and λ but markedly different tPSA values. Their imaging performances clearly\ns ex\ndemonstrated the influence of tPSA predicted by the model (updated in Figs. 5c, 5e, 5g),\nproviding further experimental evidence for descriptor validity. Similarly, the effect of ClogP\nwas also validated (Data were shown in Supplementary Figs. 115-120).\nTogether, these new data demonstrate that Sensight can reliably predict probe sensitivity across\nchemically and mechanistically diverse systems. We believe these comprehensive validations have\nsignificantly strengthened the robustness and generalizability of the framework, fully addressing the\nreviewer’s concern.\n1. In Supplementary Table 2, please clarify whether the descriptors were calculated based on the\n1\nstructures prior to superoxide activation. In addition, please indicate whether quantum yield, Stokes shift,\nand Δλ were obtained from the activated structures. If so, it would be important to explicitly state that\nex\nthe same approach was applied throughout all subsequent sections where descriptors were calculated.\nResponse:\nWe thank the reviewer for this helpful comment and have clarified this information in the revised\nSupplementary Information. Specifically:\nThe photophysical parameters Φ, Stokes shift, and Δλ correspond to the activated (product) forms\nex\nof the probes after sensing superoxide. These parameters were chosen because they determine the\nfluorescence output of the activated state, which directly contributes to the observed imaging signal.\nOther physicochemical descriptors, including nRot, MR, fChar, NSH, tPSA, nS, NAR, NSC, logS,\nnHD, and ClogP, were calculated for the intact probe structures prior to activation. These parameters\nreflect intrinsic molecular properties relevant to solubility, permeability, and distribution.\nThe descriptor ΔFₛ represents the fluorescence enhancement ratio measured in a simple solution\nsystem before and after complete reaction with superoxide. It reflects the maximal achievable signal\nchange of the probe under idealized conditions, without the influence of biological barriers or cellular\nuptake. ΔF represents the fluorescence enhancement observed in live cells, indicating the actual imaging\nc\nsensitivity achieved in cellular environments. These two descriptors were experimentally measured by\ncomparing the probe’s brightness before and after sensing superoxide as just indicated.\nWe have explicitly stated these details in Supplementary Table 2 to ensure transparency. The same\napproach was consistently applied to all subsequent sections of descriptor calculation and model\nconstruction. We also added this note in the caption part of Supplementary Table 2.\n2. In Supplementary Table 4, the normalized PCC value appears unusual, as it should range between 0\nand 1.\nResponse:\nWe thank the reviewer for carefully noting this issue. The reviewer is correct. The normalized\nPearson correlation coefficient (PCC) value was inadvertently mislabeled in the original version due to\na formatting error. We have now corrected the value in the revised Supplementary Table 4. The corrected\nPCC values all fall within the proper range of 0 to 1. This correction does not affect any statistical\nconclusions or interpretations presented in the manuscript.\n3. In Fig. 1D, the authors ranked the descriptors by simply summing the normalized entropy and PCC\nvalues. However, descriptors such as nHD and NAR exhibited PCC values comparable to the final five\nselected descriptors, yet were excluded due to their inherently low entropy. It is therefore important to\nexplain why the ranking was performed by simple summation of entropy and PCC, rather than by a\nweighted sum or product.\nResponse:\nWe appreciate the reviewer’s suggestion that a weighted sum or product might provide an alternative\napproach for ranking descriptors.\nIn our analysis, the normalized entropy and Pearson correlation coefficient (PCC) were summed to\nprovide a transparent and reproducible initial ranking of all candidate descriptors. This simple metric\nallows direct comparison across parameters that capture both linear (PCC) and non-linear (entropy)\ncontributions to imaging sensitivity while avoiding additional weighting choices that could introduce\nunnecessary model complexity.\ndescriptors, their low PCC values indicate limited contribution in capturing linear relationships, which\njustified their exclusion in the initial ranking. Descriptors such as nHD and NSC indeed showed sum\nscores comparable to the selected descriptors; however, their marginal contribution became negligible\nonce the top five descriptors (tPSA, ClogP, ΔF, Δλ , and Stokes shift) were included. Regression\ns ex\nanalyses showed that the correlation coefficients between nHD or NSC with ΔF is weak. For these\nc\nreasons, we focused on the five descriptors that provided the dominant explanatory power, establishing\nthe first version of Sensight.\nImportantly, the five descriptors ultimately selected (tPSA, ClogP, ΔF, Δλ , and Stokes shift), were\ns ex\nnot only identified numerically but also confirmed experimentally to be the most influential in live-cell\nimaging, consistent with known mechanistic considerations such as membrane permeability and\ninstrument excitation compatibility. Therefore, the simple summation serves as a transparent, unbiased\nmethod to highlight the most informative descriptors, while the feasibility of the selection is supported\nby experimental validation. We agree that, as larger probe libraries become available, more\nsophisticated weighting schemes or machine-learning methods could further refine the predictive power\nof the model. Therefore, in the main manuscript (Page 3, paragraph 1), we have made revisions by adding\nthe following words. “To rank the descriptors, we summed their normalized entropy and PCC values,\nproviding a transparent and reproducible way to highlight parameters that contribute both linearly and\nnonlinearly to imaging sensitivity.” In Page 17, paragraph 5, we discussed as follows, “As larger and\nmore diverse probe libraries become available, this framework could be extended to incorporate\nadditional descriptors and leverage machine-learning approaches to refine predictive power further”.\n4. In Supplementary Fig. 56, a clearer explanation is needed regarding how the optimal values of ClogP\nand tPSA were determined. In Supplementary Fig. 55 (panels H and N), both tPSA and ClogP exhibit\nnegative trends, yet the median values were designated as optimal. Additional clarification on this choice\nis warranted. Moreover, rather than simply using the median, alternative approaches such as fitting a\nGaussian curve and selecting the peak should be considered. Furthermore, as tPSA and ClogP are\ngenerally considered opposing descriptors, it would be important to provide additional explanation for\nwhy they displayed similar trends in this analysis.\nResponse:\nWe appreciate the reviewer’s insightful comments.\nIn the revised version, the previous Supplementary Fig. 55 and 56 were updated as Supplementary\nFig. 57, 58.\nWe agree with the Reviewer that tPSA and ClogP are generally considered opposing descriptors\nthat influence membrane permeability in different ways. However, in our dataset, both parameters\ndisplayed weak negative correlations with imaging sensitivity (ΔF). This seemingly similar trend can be\nc\nrationalized by considering that ΔF does not vary linearly with either descriptor but instead follows\nc\na nonlinear, biphasic dependence. Specifically, probes with excessively high tPSA values exhibit poor\nmembrane permeability and thus low intracellular accumulation, whereas probes with extremely low\ntPSA or excessively high ClogP tend to aggregate or become trapped in hydrophobic environments, both\nof which also diminish imaging sensitivity. As a result, optimal probe performance occurs within an\nintermediate window of polarity and lipophilicity, where cellular uptake and signal retention are balanced.\nTo provide a practical estimation of this balance, we adopted the median value as an empirical\nrepresentation of this optimal window, as it reflects the midpoint of the effective distribution of high-\n3\nperforming probes in our dataset. We have clarified this rationale in the revised Supplementary Fig.\n58 caption.\nWe agree with the reviewer that Gaussian fitting could further refine this estimation in future work.\nHowever, given the current dataset size, applying a more complex fitting model would risk\noverparameterization. We will explore this direction as we expand the probe dataset in our ongoing work.\nIn the main manuscript, we have made revisions by adding the following words. Page 3, paragraph\n1, “Notably, although tPSA and cLogP are generally considered opposing descriptors, both showed a\nweak negative correlation with imaging sensitivity (ΔF) in our dataset (Supplementary Fig. 57). This\nc\ntrend likely reflects the fact that their influences on membrane permeability are not strictly linear. These\nobservations further support the use of combined PCC and entropy-based criteria in descriptor selection,\nwhich together capture both linear and non-linear contributions to probe performance.” Page 4,\nparagraph 1, “we empirically selected the median values within the high-sensitivity group as optimal\nClogP (~2.0) and tPSA (~70 Å²) (Supplementary Fig. 58), providing a balanced approximation of probe\nperformance.”\n5. In Fig. 2C, the authors present a pentagonal radar map, with each axis representing one descriptor.\nA more detailed explanation is needed to clarify how the descriptor values were reflected in the radar\nplot. Given that the correlation coefficients differ among descriptors, it should also be clarified whether\nthese weights were incorporated into the radar map construction and area calculation.\nResponse:\nWe thank the reviewer for raising this important point. To plot the pentagonal radar map, descriptor\nvalues of different scales and units were standardized to the fixed scale using min-max normalization or\nproximity-to-optimum scoring. The standardized values were then multiplied by the descriptor’s\ncorresponding weight to get the adjusted descriptor values, which reflect its relative contribution to\nimaging sensitivity. Finally, the resulting weighted values (the adjusted descriptor values) were used to\nplot the radar map, and the overall radar area was calculated based on these weighted axes. This approach\nallows both visual and quantitative representation of the contribution of each descriptor to probe\nperformance. For clarity, we have now included a detailed illustration of the data processing workflow\nin Supplementary Fig. 59.\nIn the main manuscript, we have made revisions by adding the following words. Page 5, paragraph\n1, “To enable direct comparison across descriptors of different scales and units, we standardized each\nparameter using min-max normalization or proximity-to-optimum scoring”; Page 5, paragraph 3, “This\nwas carried out by multiplying the standardized value by the descriptor’s corresponding weight. The\nresulting adjusted descriptor values were then used to plot a five-dimensional radar map (workflow in\nSupplementary Fig. 59)”.\n6. In Figs. 3–6, please include the excitation wavelength information for the cellular imaging data.\nResponse:\nWe thank the reviewer for this suggestion. The excitation wavelengths used in the live-cell imaging\nexperiments have now been added to the captions of Figs. 3–6.\n7. In the section “Validating Sensight through single-variable experiments,” validation was conducted\nfor only three descriptors. To further strengthen the robustness of the platform, it would be valuable to\nextend the validation to all five selected descriptors.\nResponse:\nWe thank the reviewer for this valuable suggestion. We fully agree that validating each descriptor\nwould further strengthen the robustness of Sensight. To address this, we conducted additional\nexperiments to evaluate the impact of ClogP. Specifically, we synthesized probe W which demonstrated\n3\nconsistent tPSA, ΔF, and Δλ with probe C while improved ClogP (Data for W were shown in\ns ex 2 3\nSupplementary Figs. 70-72, and those for C were shown in Supplementary Figs. 6, 28, 49). In agreement\n2\nwith the model prediction, probe W exhibited improved live-cell imaging sensitivity towards superoxide\n3\ncompared to C . Moreover, a similar improving trend was also observed in the tetrazine–BCN\n2\nbioorthogonal system (Data were shown in Supplementary Figs. 115-120). These results highlighted the\nsignificant impact of ClogP on probe imaging sensitivity and further confirmed the reliability of our\nmodel. We have added the related data in the following parts (Fig. C1, C2) for your quick reference.\nIn the main manuscript, we have made revisions by adding the following words. Page 7, paragraph\n4, “Lastly, the effect of ClogP was evaluated by synthesizing probe W and comparing its imaging\n3\nperformance with C . W demonstrated comparable tPSA (108.08 Å²), ΔF (77.4), and Δλ (66 nm) to\n2 3 s ex\nthat of probe C (tPSA 108.08 Å², ΔF 78.3, and Δλ 55 nm), but much improved ClogP (2.12for W and\n2 s ex 3\n0.72 for C ) (Supplementary Figs. 70-72, Table 11). Under the established imaging conditions, W was\n2 3\nmore sensitive (ΔF = 1.39 for W and 1.10 for C ), highlighting the significant impact of ClogP on probe\nc 3 2\nimaging sensitivity.”\nPage 12, paragraph 3, “We then assessed the influence of lipophilicity and found that ClogP also\nexerted a strong effect on imaging sensitivity. Probe W , which possesses a more favorable ClogP,\n3\noutperformed C under identical experimental conditions (Supplementary Figs. 115-120, Tables 28, 29),\n2\nfurther demonstrating the generality of Sensight’s predictive framework.”\nWith this addition, we have experimentally validated four out of the five selected descriptors.\nRegarding Stokes shift, although we attempted to identify a suitable pair of probes that have similar tPSA,\nΔF, Δλ , and ClogP but a significantly different Stokes shift, we found it challenging to obtain such\ns ex\ncompounds. Moreover, for commonly used fluorophores, the Stokes shift is well-characterized and its\nrelative weight in the model is small. For these reasons, we did not perform experimental validation for\nthis parameter.\nWe believe that the additional ClogP validation, together with the previous single-variable\nexperiments, provides strong support for the predictive reliability of Sensight.\nTetrazine (Tz) and superoxide. B) Structures of probe C and W . Their key descriptor values and radar\n2 3\nmaps are shown in C and D, respectively. Representative confocal imaging results are shown in E and\nthe quantitative data shown in F. For confocal imaging, HepG2 cells were treated with 2 mM H O for 2\n2 2\nh to induce oxidative stress, stained with the indicated probe (5 μM, 30 min), and then imaged. C and\n3 ex em\nvalues were analyzed by a two-tailed unpaired t-test, *P<0.05, **P<0.01, ***P<0.001, 95% Confidence\ninterval (n = 3 biologically independent experiments, mean ± s.d.). Scale bar, 20 μm.\nand BCN. B) Structures of probe C and W . Their key descriptor values and radar maps are shown in C\n2 3\nand D, respectively. Representative confocal imaging results are shown in E, and the quantitative data\nare shown in F. For confocal imaging, HepG2 Cells were pre-loaded with various concentrations of BCN,\ntreated with the indicated probe (5 μM, 30 min), and then imaged. C and W channel: λ = 405 nm, λ\n2 3 ex em\n= 406-550 nm. Data were normalized to their respective control group. P values were analyzed by a two-\ntailed unpaired t-test, *P<0.05, **P<0.01, ***P<0.001, 95% Confidence interval (n = 3 biologically\nindependent experiments, mean ± s.d.). Scale bar, 20 μm.\n8. In the section “de novo design of a superoxide probe,” only G and K were synthesized and compared.\n3 2\nTo further confirm the predictive capability of Sensight, it would be important to synthesize and test\nadditional compounds with live-cell imaging.\nResponse:\nWe thank the reviewer for this insightful suggestion. To further validate the predictive capability of\nSensight, we synthesized and tested four additional compounds (Tz-1, Tz-2, Tz-3, Tz-9) from the in silico-\ndesigned library, specifically chosen to span a range of predicted imaging sensitivities. Now 6 of the 13\nprobes were prepared and their superoxide sensing abilities were tested. Live-cell imaging experiments\nconfirmed that the observed sensitivities closely matched the model predictions, providing further\nevidence of the robustness and reliability of Sensight in guiding de novo probe design. The experimental\ndata are provided in Supplementary Figure 73-84. For your quick reference, we also attached the data in\nthe following part (Fig. C3) for your quick reference.\nIn the main manuscript, we have made revisions by adding the following words. Page 9, paragraph 3,\n“To experimentally validate the predictive capability of Sensight, we selected and synthesized six\nrepresentative probes (Tz-1, Tz-2, Tz-3, Tz-8 (G ), Tz-9, and Tz-13 (K )). The selection was guided by\n3 2\ntwo considerations. First, their predicted imaging sensitivity values spanned the entire distribution of the\nvirtual design library, ensuring evaluation across low-, medium-, and high-performance regions. Second,\nthey could be readily synthesized. All compounds were confirmed to undergo quantitative conversion to\nthe corresponding oxadiazoles upon reaction with superoxide (Supplementary Figs. 73-88;\nSupplementary Tables 16-21). In HepG2 cells under H₂O₂-induced superoxide burst, G displayed the\n3\nhighest imaging sensitivity with a ΔF of 2.67, approximately twice that of K (ΔF 1.24) (Figs. 4B, C).\nc 2 c\nThe experimentally observed trend in sensitivity (ΔF: G (2.67) > Tz-3 (1.56) > Tz-9 (1.45) > Tz-1 (1.25) >\nc 3\nK (1.24)) closely matched the ranking predicted by Sensight, thereby supporting the robustness of the\n2\nmodel.”\nprobe structures in Figure 4A of the main manuscript. Their radar maps are shown in B. Representative\nconfocal imaging results are shown in C, and the quantitative data are shown in D. For confocal imaging,\nHepG2 cells were treated with 2 mM H O for 2 h to induce oxidative stress, stained with the indicated\n2 2\nprobe (5 μM, 30 min), and then imaged. Tz-1, Tz-2, Tz-3 channel: λ = 405 nm, λ = 406-550 nm. Tz-\nex em\n9\n9 channel: λ = 561 nm, λ = 565-700 nm. Data were normalized to their respective control group. P\nex em\nvalues were analyzed by a two-tailed unpaired t-test, *P<0.05, **P<0.01, ***P<0.001, 95% Confidence\ninterval (n = 3 biologically independent experiments, mean ± s.d.). Scale bar, 20 μm.\n9. In Supplementary Fig. 73, please add an explanation of the superoxide scavenging mechanisms of\nTiron and TEMPO, together with appropriate references.\nResponse:\nWe thank the reviewer for this suggestion.\nSupplementary Fig. 73 has been updated as Supplementary Fig. 91.\nTiron and TEMPO are commonly used superoxide scavengers, and their mechanisms are as follows.\nTiron (4,5-dihydroxy-1,3-benzenedisulfonic acid) acts as a chelator and electron donor, directly reacting\nwith superoxide anions (O₂•⁻) to form a stable semiquinone radical, thereby effectively reducing\nsuperoxide levels in biological systems. TEMPO (2,2,6,6-Tetramethylpiperidine-1-oxyl) functions as a\nstable nitroxyl radical, which can catalytically dismutate superoxide to molecular oxygen and hydrogen\nperoxide, acting as a redox-active scavenger.\nWe have added this explanation and appropriate references in the figure legend of Supplementary\nFig. 91.\n10. In Supplementary Fig. 74, please specify, with references, how DHE is transformed over time.\nIn addition, please include the time-dependent changes in DHE fluorescence around 600 nm.\nResponse:\nWe thank the reviewer for this insightful suggestion.\nThe original Supplementary Fig. 74 has been revised and updated as Supplementary Fig. 92.\nTo address the reviewer’s request, we have now added (i) the time-dependent fluorescence changes\nof DHE in the 600 nm region and (ii) a schematic summarizing its known transformation pathways,\ntogether with appropriate literature references.\nDihydroethidium (DHE) and its mitochondria-targeted analog MitoSOX are widely used probes for\nsuperoxide detection. DHE exhibits intrinsic blue fluorescence (λ ≈ 400 nm), which has been used as\nem\na readout of probe uptake in cells (doi:10.1016/j.freeradbiomed.2010.01.028). Upon reaction with\nsuperoxide, DHE forms 2-hydroxyethidium (2-OH-E⁺), a diagnostic oxidation product that can be\ndetected by imaging or, more reliably, by LC–MS analysis. However, DHE can also undergo non-\nspecific two-electron oxidation by various reactive species to produce ethidium (E⁺), which emits an\norange fluorescence around 600 nm. Because 2-OH-E⁺ and E⁺ have nearly overlapping emission spectra,\nambiguity in fluorescence-based readouts is a well-recognized limitation of DHE-based imaging.\nFollowing the reviewer’s comment, we re-examined the time-dependent spectral evolution of DHE.\nAs shown in the revised Supplementary Fig. 92, the blue emission (~400 nm) progressively decreased\nwhen DHE was exposed to air, indicating instability and oxidation. Correspondingly, a simultaneous\nincrease in the 600 nm emission band was observed, consistent with the formation of ethidium-type\noxidation products. These spectral changes, together with the annotated reaction pathway and supporting\nreferences, have now been incorporated into the revised figure.\nWe thank the reviewer again for this valuable suggestion, which has significantly strengthened the\ncomparative analysis with G . For your quick reference, we have added the updated data here as Fig. C4.\n3\n3 3\n(10 mM, pH 7.4) to make 5 μM solutions, and were kept at ambient temperature in the dark room. Their\nfluorescence spectra were recorded at 0, 1, 2, 4, 6, 24, 48 h time points, and the intensities at their\nemission maxima were plotted against storage time. A) G , λ = 492 nm; B) Diagram of DHE oxidation\n3 ex\nmechanism. C) DHE, λ = 370 nm; D) DHE, λ = 535/610 nm. According to the reference, DHE might\nex ex\nundergo two-electron transfer reaction with oxygen. This explains the gradual decrease of its native blue\nemission (C) and the gradual intensification of the red emission (D).\n11. In Supplementary Fig. 76, the figure is labeled as “H₂O₂,” whereas the description refers to\n“paraquat.” This inconsistency should be corrected.\nResponse:\nWe thank the reviewer for pointing out this inconsistency. The labeling in Supplementary Fig. 94\n(original Supplementary Fig. 76) has been corrected to accurately indicate “paraquat,” consistent with\nthe figure description and experimental conditions.\n12. In Fig. 5, adding the chemical structures of compounds E1, B1, D3, and W2 would improve clarity.\nResponse:\nWe thank the reviewer for the suggestion. The chemical structures of compounds E , B , D , and\n1 1 3\nW are presented in Fig. 3. To avoid redundancy and due to space constraints, we have not repeated them\n2\nin Fig. 5. We have, however, clarified in the main text that the structures can be found in Fig. 3 for reader\nreference.\n13. In the section “tetrazine–BCN bioorthogonal system,” presenting the results in parallel with the\n11\nsection “validating Sensight through single-variable experiments” would better demonstrate the\nplatform’s utility. Therefore, including a comparison between F and W is recommended.\n2 1\nResponse:\nWe thank the reviewer for the insightful suggestion. Following the recommendation, we conducted\nadditional experiments directly comparing probes F and W , which primarily differed in tPSA. The new\n2 1\ndata have been added to the revised manuscript (Fig. 5c, 5e, 5g; Supplementary Figs. 103–108, Tables\n24–25). Consistent with Sensight predictions, probe W whose tPSA better matches the optimal range,\n1\nexhibited significantly enhanced imaging sensitivity compared to F .\nthe contribution of ClogP within the tetrazine–BCN system. To this end, we compared probes W and\n2\nadditional independent determinant of imaging sensitivity (Supplementary Figs. 115–120). These results\nare now included in the revised manuscript and integrated into the overall discussion of Sensight’s\npredictive capability.\nIn the main manuscript, we have made revisions by adding the following words. Page 12, paragraph\n3, “We next compared probes F and W , which exhibit similar Δλₑₓ and ΔFₛ values but differ in tPSA,\n2 1\nwith W matching more closely to the predicted optimal range (Fig. 5c, Supplementary Figs. 103-108,\n1\nlive cells was significantly higher for W than for F (Figs. 5e, g), supporting tPSA as an independently\n1 2\npredictive variable.” “We then assessed the influence of lipophilicity and found that ClogP also exerted\na strong effect on imaging sensitivity. Probe W , which possesses a more favorable ClogP, outperformed\n2\ndemonstrating the generality of Sensight’s predictive framework.”\nFor your quick reference, we have added the data herein as Figs. C2 (response to comment 7), C5.\nBCN. B) Structures of probes F and W . Their key descriptor values and radar maps are shown in C and\n2 1\nD, respectively. Representative confocal imaging results are shown in E, and the quantitative data\n13\nshown in F. For confocal imaging, HepG2 Cells were pre-loaded with various concentrations of BCN,\ntreated with the indicated probe (5 μM, 30 min), and then imaged. F and W channel: λ = 488 nm, λ\n2 1 ex em\n= 489-600 nm. Data were normalized to their respective control group. P values were analyzed by a two-\ntailed unpaired t-test, *P<0.05, **P<0.01, ***P<0.001, 95% Confidence interval (n = 3 biologically\nindependent experiments, mean ± s.d.). Scale bar, 20 μm.\n14. In Fig. 6H, the number of variables, i.e., fluorescent molecules, appears insufficient to robustly\ncalculate the correlation coefficient. As five of the seven molecules cluster in the lower-left quadrant, it\nwould be necessary to include a broader diversity of fluorophores to obtain a more reliable correlation\nplot.\nResponse:\nWe thank the reviewer for this suggestion. To address the concern regarding the limited number of\nfluorophores in Fig. 6h, we synthesized and systematically characterized 2 additional formaldehyde\nprobe structures (FP5 and FP8) (Supplementary Figs. 131, 134, 138, 139). These new compounds\nexpand the diversity of fluorescent scaffolds and provide a broader coverage of predicted imaging\nsensitivities. With these added data points, the correlation between predicted and experimentally\nmeasured cellular imaging sensitivities is strengthened, with the updated linear correlation coefficient\nreaching R2=0.93. Fig. 6 has been updated accordingly to reflect these results.\nFor your quick reference, we have added the newly obtained data herein as Fig. C6.\nbetween FP probes and formaldehyde. B) Structures of FP5 and FP8. Representative confocal imaging\nresults are shown in C and the quantitative data shown in D. Correlation between predicted ΔF and\nc\nmeasured ΔF values of the probes. The newly added FP5 and FP8 are marked. For confocal imaging,\nc\nBV2 cells first stimulated with LPS (24 h) and then stained with the indicated probe (5 μM, 1 h), and\nthen imaged. FP5 channel: λ = 405 nm, λ = 406-550 nm. FP8channel: λ = 488 nm, λ = 489-600\nex em ex em\nnm. Data were normalized to their respective control group. P values were analyzed by a two-tailed\nunpaired t-test, *P<0.05, **P<0.01, ***P<0.001, 95% Confidence interval (n = 3 biologically\nindependent experiments, mean ± s.d.). Scale bar, 20 μm.\n15. In the section “formaldehyde imaging probes,” the best-performing probe (FP7) corresponds to an\nalready developed molecule (Bruemmer, K. J. et al. J. Am. Chem. Soc. 139, 5338–5350 (2017)). To\n15\nstrengthen the validation of Sensight, as was done for the superoxide probes, it would be valuable to\nperform de novo design followed by optimization using Sensight to develop an improved probe.\nResponse:\nWe thank the reviewer for this insightful suggestion. In the formaldehyde probe study (Fig. 6), we\ndeliberately included a set of previously reported probes, including FP9 (previously named as FP7), to\nserve as reference points and to validate the predictive reliability of Sensight against literature data.\nFollowing this, we attempted de novo design guided by Sensight to optimize probe performance.\nHowever, as summarized in Supplementary Table 44, FP9 already exhibits tPSA, ΔF, Δλ , and ClogP\ns ex\nvalues close to the predicted optimal ranges, resulting in its superior cellular imaging performance. Under\nthese constraints, it was challenging to design a new probe that surpasses this probe in imaging sensitivity.\nThese results, nonetheless, further support the predictive accuracy and practical utility of the Sensight\nframework.\n16. Minor typographical errors:\n- In the main article, line 18, “desig” should be corrected to “design.”\n- In Fig. 2A, “SPASS” should be corrected to “SPSS.”\nResponse:\nWe thank the reviewer for pointing out these typographical errors. Both issues have been corrected\nin the revised manuscript:\n“desig” has been corrected to “design” (line 18, main text).\n“SPASS” has been corrected to “SPSS” (Fig. 2A).\nReviewer #2:\nWhile it will not be straightforward for others to immediately generalise these results to their own\ncompound libraries, nonetheless the work presented here is a big breakthrough in the field and I predict\nthat it will have significant impact.\nResponse:\nWe sincerely thank the reviewer for the thoughtful evaluation and positive assessment of our work.\nWe are pleased that the novelty and potential impact of our multivariate framework, Sensight, and its\nvalidation across superoxide, tetrazine-BCN, and formaldehyde probe systems are recognized. We have\ncarefully considered all suggestions and have provided additional experimental validation, clarifications,\nand typographical corrections throughout the manuscript to improve clarity and rigor.\n1. It would help to have a panel in Figure 1 showing the reaction of tetrazine with superoxide.\nResponse:\nWe thank the reviewer for this helpful suggestion.\nTo clarify the chemical basis of the tetrazine-based superoxide probe, we have added a new panel\nin Figure 1a illustrating the reaction of tetrazine with superoxide, including the formation of the\nfluorescent product. This addition provides a clear visual representation of the chemical transformation\nthat underlies the probe’s activity and helps readers better understand the mechanism of the system.\n2. There is very little discussion of how the delta(Fc) parameter is calculated, even though this is the\ndatapoint on which the rest of the manuscript hangs. On what basis do you know that the optimal H O\n2 2\n16\nconcentration and incubation time were selected? A titration (time and concentration) should be\nperformed for at least a few of the compounds, to confirm that plateau conditions are being studied.\nResponse:\nWe thank the reviewer for highlighting the need for clarification.\nPrior to the main experiments, we performed preliminary titration studies to optimize H₂O₂\nconcentration and incubation time in our HepG2 cell system. Using F-Tz-4 which is a benchmarked\ntetrazine-based superoxide probe with well-characterized cellular response behavior (reference 28), 2\nmM H₂O₂ and a stimulating duration of 2 h was observed producing a maximal and reproducible\nsuperoxide burst without compromising cell viability. This optimized condition was then applied to all\nsubsequent imaging experiments. We have added the data as Supplementary Figs. 45–46.\nFor the calculation of ΔF, we followed the suggestion and have revised the description of our\nc\nfluorescence quantification method for clarity and rigor. Specifically, the fluorescence intensities were\nquantified from three independent biological replicates, with multiple fields of view analyzed within\neach replicate to obtain the mean fluorescence intensity (MFI). We also have updated the Methods\nsection and relevant figure legends to report biological replicates.\nIn the main manuscript, we have made revisions by adding the following words. Page 2, paragraph\n4, “We first systematically optimized the H O stimulation parameters using probe F-Tz4, a benchmarked\n2 2\nTz-based superoxide probe with well-characterized cellular response behavior.28 Titration of H₂O₂\nconcentration (0-4 mM) and incubation time (0-4 h) revealed that 2 mM for 2 h produced a maximal and\nreproducible superoxide burst without compromising cell viability (Supplementary Figs. 45, 46). This\noptimized condition was applied to all subsequent imaging experiments. To quantify live-cell imaging\nsensitivity, we defined ΔF as the fold-increase in cellular probe fluorescence between H O stimulated\nc 2 2\nand unstimulated (Control) conditions (Fig. 1b, Supplementary Figs. 47-56). For each probe, mean\nfluorescence intensity (MFI) was quantified from three biologically independent replicates.\nFluorescence values were extracted directly from cell-containing regions and ΔF was calculated as\nc\nMFI /MFI .”\nH2O2 Control\nFor your quick reference, we have added the data herein as Figs. C7.\n2 2\nmeasured by F-Tz4 imaging. A) Cells were treated with different concentrations of H O for 2 h prior\n2 2\nto the probe treatment (5 µM, 30 min) and them imaged. B) Quantified relative mean fluorescence\nintensity of the cells. C) Cells were treated with 2 mM H O for various durations prior to the probe (5\n2 2\nµM, 30 min) treatment, and then imaged. D) Quantified relative mean fluorescence intensity of the cells.\n18\nλ = 405 nm, λ = 406–550 nm. P values were analyzed by a two-way ANOVA test, ***P<0.001, 95%,\nex em\nConfidence interval, n = 3 biologically independent experiments. Scale bars: 20 μm.\n3. It’s not clear why PCC is cited rather than R2 for the data shown in Figure 1C (and yet R2 is used in\nFigure 2D).\nResponse:\nWe thank the reviewer for this insightful comment. We have updated Figure 1c to include both R2\nand PCC values.\n4. Figure 1 caption should explain what the red numbers signify in panel (B).\nResponse:\nWe thank the reviewer for pointing this out. In the revised figure caption, we have clarified that the\nred numbers indicate compounds with relatively high cellular imaging sensitivity, defined as ΔF ≥ 1.4.\nc\nWe have added the explanation in the Figure caption as “Probes with relatively high cellular imaging\nsensitivity, defined as ΔF ≥ 1.4 were highlighted in red”.\nc\n5. Figure 2D – should the y-axis be Norm, not Nom?\nResponse:\nWe thank the reviewer for noticing this typographical error. The y-axis label has been corrected\nfrom “Nom” to “Norm” in the revised figure.\n6. Supplementary table 2, footnote – stokes shift should be Stokes shift. And there is no footnote 16 despite\nthe superscript in the header row.\nResponse:\nWe thank the reviewer for the careful reading. The typographical error has been corrected to “Stokes\nshift,” and footnote 16 has been added to the table.\n7. DHE is not seen as a gold-standard probe these days – it is well established that it has poor selectivity,\nso it would is necessary to perform the benchmarking experiments for another, better, superoxide sensor.\nResponse:\nWe thank the reviewer for highlighting the limitations of DHE as a benchmark superoxide probe.\nIn response, we performed side-by-side comparisons of our probe G with DHE and MitoSOX under\n3\nidentical experimental conditions. HepG2 cells were first incubated with each probe for 30 min to allow\ncellular uptake, followed by a 1 mM H₂O₂ challenge to induce superoxide generation. Fluorescence\ndynamics were recorded using lattice light-sheet microscopy.\nWe observed that MitoSOX, a mitochondria-targeted analog of DHE, exhibited a robust\nfluorescence increase, reaching approximately 2-fold over the initial intensity after 30 min, consistent\nwith mitochondrial superoxide generation induced by H₂O₂-mediated disruption of the mitochondrial\nmembrane potential. In contrast, DHE showed minimal fluorescence increase under the same conditions,\nhighlighting its poor sensitivity and suboptimal subcellular accumulation. Importantly, our probe G ,\n3\nwhich lacks mitochondrial targeting, was still capable of detecting the H₂O₂-induced superoxide increase,\nwith a 1.5-fold fluorescence enhancement at 30 min, supporting its adequate cellular sensitivity.\nTemporal analysis revealed that MitoSOX fluorescence increased nearly linearly during the first 25\nmin and almost plateaued thereafter, whereas G exhibited rapid signal increase in the first 15 min,\n3\n19\nfollowed by slower growth from 15–30 min. This difference likely reflects the kinetics of mitochondrial\nsuperoxide production versus cytosolic diffusion and probe activation. These results not only confirm\nthe sensitivity of G for live-cell superoxide detection but also emphasize the impact of subcellular\n3\nlocalization on probe performance. Overall, this comparison demonstrates that G achieves sensitivity\n3\ncomparable to MitoSOX in the cytosol without requiring organelle targeting, validating the utility of our\nprobe design strategy. This data has been added as Supplementary Figure 95. We also attached it in the\nfollowing part (Fig. C8) for your quick reference.\nIn the main manuscript, we have made revisions by adding the following words. Page 10, paragraph\n1, “We also benchmarked G against DHE and MitoSOX to evaluate its capability to dynamically track\nH₂O₂, and the resulting fluorescence changes were recorded in real time using lattice light-sheet\nmicroscopy (Supplementary Fig. 95). DHE exhibited minimal fluorescence increase, reflecting its low\nsensitivity under these conditions. MitoSOX, a mitochondria-targeted probe, rapidly detected the\nsuperoxide burst, consistent with mitochondria being the primary site of superoxide generation in this\nmodel, highlighting the importance of organelle targeting for enhancing probe sensitivity. Remarkably,\nG , despite lacking mitochondrial targeting, was able to capture the superoxide burst with a ~1.5-fold\n3\nfluorescence increase within 30 min, demonstrating sufficient sensitivity to monitor cytosolic superoxide\ndynamics.”\n2 2 3\nDHE or MitoSOX. A) Fluorescence imaging images. B) Enlarged views of the white box part in A to\nhighlight organelle distribution. C) Quantified relative mean fluorescence intensities. Cells were first\nloaded with G , DHE or MitoSOX, then treated with H O (1 mM, 30 min), and imaged in a time-lapsed\n3 2 2\nway. The data were normalized to the 0 min group, n = 3, mean ± s.d. G channel: λ = 488 nm, λ =\n3 ex em\n489–600 nm, DHE and MitoSOX channel: λ = 561 nm, λ = 562–650 nm. Scale bar, 150 μm for A\nex em\nand 10 μm for B.\n8. What is missing in the discussion is probe localisation: does the analysis presented here only work\nbecause the probes all remain cytosolic? Surely any sub-cellular localisation will greatly confound the\nresults. For example, the Stokes shift is heavily dependent on the polarity for many fluorophores, so\ndiffering organelle localisation will likely have huge impacts on the Stokes shift – it is not clear that your\nmodel would address this.\nResponse:\nWe fully agree with the reviewer that subcellular localization plays a critical role in determining\nimaging sensitivity. Subcellular localization not only significantly affects probes’ photophysical\nproperties, but also determines the analyte’s local concentration, given the compartmentalized nature of\nbiomolecules. This is particularly relevant for reactive species such as superoxide, which are primarily\ngenerated within specific organelles (e.g., mitochondria). Once diffused away from their generation sites,\nthese species are rapidly decomposed by intracellular enzymes, resulting in markedly decreased\ndetectable concentrations.\nTo address this, we benchmarked G against DHE and MitoSOX in HepG2 cells (as response to\n3\ncomment 7). While MitoSOX, a mitochondrial-targeted derivative of DHE, rapidly reported the H₂O₂-\ninduced superoxide burst consistent with mitochondrial localization, DHE itself showed very limited\nfluorescence change (Supplementary Fig. 95). Notably, our cytosolic probe G , which lacks organelle-\n3\ntargeting motifs, was still able to reliably detect the superoxide burst, although with slightly lower\namplitude than MitoSOX. These results indicate that while proper organelle-targeting can enhance\nsensitivity, the relative contributions of physicochemical descriptors captured by Sensight remain\napplicable to cytosolic probes.\nIn addition to the analyte’s local concentration, probe localization may also affect its Stokes shift\nand Δλ , as the fluorescence intensity of most fluorophores is environment-sensitive (e.g., polarity,\nex\nviscosity, pH). This effect is extremely complex and difficult to rationally analyze. To minimize such\nconfounding effects, our current Sensight analysis focused on cytosolic probes. We appreciate the\nreviewer’s insightful comment and have added a clarifying discussion in the revised manuscript as\nfollows, “In its current form, Sensight deliberately centers on global physicochemical and photophysical\ndescriptors, such as tPSA, ClogP, ΔF, Δλ , and Stokes shift, that broadly govern intracellular\ns ex\naccessibility, excitation efficiency, and fluorogenic turn-on. This choice reflects a rational balance\nbetween mechanistic relevance and model generalizability. However, subcellular localization can also\nmodulate observed imaging performance by shaping local analyte availability and modulating\nmicroenvironment-dependent fluorescence behaviors. However, such localization effects introduce an\nadditional, highly context-dependent layer of complexity that extends beyond the current descriptor\nspace. Future iterations of Sensight could incorporate localization-relevant parameters as more\nsystematic datasets become available.” (Page 10, paragraph 2).\n21\n9. The statement in the conclusion “beyond activity-based sensing…” is not accurate – all the examples\nin this paper are examples of activity-based sensing.\nResponse:\nWe appreciate the reviewer’s careful reading and agree that all examples presented in this work\nfall within the scope of activity-based sensing. We have now revised “Beyond activity-based sensing”\ninto “Beyond activity-based sensing of superoxide” to make the description clear and more accurate.\nReviewer #3:\nHowever, Sensight and its ideal parameter values were derived solely from a limited variety of probes\nand fluorescence modulation mechanisms, casting uncertainty on its general applicability. More\nimportantly, as key parameters of Sensight, ΔFₛ is clearly unpredictable, and Δλₑₓ depends on imaging\ninstrument settings, substantially diminishing the practical utility of the model. Overall, the model offers\nlimited guidance for the design of entirely novel probes. Therefore, I do not believe this manuscript is\nsuitable for publication in Nature Communications.\nResponse:\nWe sincerely thank the reviewer for the thoughtful and critical evaluation of our work.\nWe fully acknowledge that Sensight was initially derived from a relatively limited set of fluorescent\nprobes and fluorescence modulation mechanisms, and that ΔFₛ presents unique challenges for prediction.\nTo address these concerns, we have substantially expanded our experimental validation in the\nrevised manuscript. For superoxide and formaldehyde probes, we synthesized additional derivatives\ncovering a broader chemical space and range of predicted sensitivity values. The experimentally\nmeasured imaging sensitivities consistently agreed with model predictions, supporting the robustness\nand general applicability of Sensight beyond the initial training set. These results are now presented in\nFigs. 4–6 and Supplementary Figs. 67–69, Figs. 73-84, Fig. 95, Figs. 104–109, Figs. 115–120, Fig. 131,\nFig. 134, Fig. 138 and Fig. 139. In the discussion part (Page 17), we have added “While the present\nmodel was developed from a targeted compound set and a defined descriptor pool, its strong predictive\npower lies as the foundation for future refinement. As larger and more diverse probe libraries become\navailable, this framework could be extended to incorporate additional descriptors and leverage machine-\nlearning approaches to refine predictive power” to clarify the limitation.\nRegarding ΔFₛ, we fully agree that this parameter is intrinsically difficult to predict purely by\ncomputation, as it depends on the specific fluorogenic mechanism (e.g., PET, TICT, FRET).\nNevertheless, most contemporary probe designs are based on well-characterized fluorophores, for which\nliterature-reported turn-on efficiencies allow reasonable estimation of ΔFₛ within an appropriate range.\nWhile this does not enable fully precise predictions, it permits practical guidance for ranking and\noptimizing probe sensitivity before synthesis. Similarly, we note that Δλₑₓ is determined by the excitation\ninstrument settings and the fluorophore’s spectral properties, both of which are well-defined for standard\nimaging platforms. Consequently, this descriptor can be reliably incorporated into the model to guide\nprobe design. Therefore, we have added the following words in Page 15, paragraph 2 to clarify the\npredicting process, “For each probe, we computed tPSA and ClogP values. Stokes shift and Δλₑₓ were\ndetermined by the excitation instrument settings and the fluorophore’s spectral properties, both of which\nare well-defined for standard imaging platforms. While ΔFₛ remained unpredictable precisely, literature-\nreported turn-on efficiencies allow reasonable estimation within an appropriate range.”\ncapabilities of Sensight. We believe that, while acknowledging these constraints, Sensight represents a\nmeaningful advance by quantitatively integrating chemical, physicochemical, and photophysical\ndescriptors to guide rational probe design, offering actionable guidance for both initial probe\nprioritization and subsequent optimization. We hope the reviewer will recognize the value of these\ncontributions for the field of chemical imaging.\n(1) The manuscript proposes ideal values for the five parameters of Sensight; however, these ideal values\nwere derived from a limited sample size and narrow variety of probe types.\nResponse:\nWe thank the reviewer for raising this important point. We agree that the number of probes used to\nderive the optimal descriptor values in Sensight is limited, which we now explicitly acknowledge as a\nstudy limitation. However, the probe skeletons cover classical ones, and the descriptor ranges covered\nby these probes are sufficiently broad: tPSA (52.68–141.26), ClogP (0.35–7.32), ΔFₛ (1.7–276), Δλₑₓ (5–\n85), and Stokes shift (22–125). This wide coverage ensures that the model captures diverse chemical and\nphysicochemical characteristics relevant to imaging performance. Furthermore, in the revised manuscript,\nwe synthesized additional probe structures, which fall within these descriptor ranges and experimentally\nvalidated their imaging sensitivities, further supporting the robustness and practical applicability of\nSensight. We have clarified these points in the revised text to acknowledge the limitation while\nhighlighting that the descriptor space already encompasses a meaningful diversity of chemical features.\nIn page 17, paragraph 2, we have clarified these points by adding the following words, “Notably,\nalthough the initial descriptor–performance relationships were derived from a finite set of probes, the\nchemical space they occupy spans a broad and representative range of values, including tPSA (52.68–\n141.26), ClogP (0.35–7.32), ΔFₛ (1.7–276), Δλₑₓ (5–85), and Stokes shift (22–125). This wide coverage\ncaptures diverse photophysical and physicochemical properties relevant to intracellular imaging.\nImportantly, in single-variable validation experiments and de novo probe design cases, the\nexperimentally measured sensitivities for probes newly synthesized align with Sensight predictions,\nsupporting the model’s reliability and highlighting it as a practical and predictive framework for guiding\nprobe optimization beyond empirical trial-and-error.”\n(2) The development of Sensight relied on tetrazine-based superoxide fluorescent probes and\nformaldehyde probes, meaning only two fluorescence modulation mechanisms were considered.\nHowever, fluorescence modulation mechanisms are diverse (e.g., TICT), which casts doubt on the\ngeneral applicability of Sensight.\nResponse:\nWe thank the reviewer for raising this important point. We would like to clarify that Sensight is not\ndesigned to model specific fluorescence “turn-on” or quenching mechanisms (e.g., PET, ICT, or TICT).\nInstead, it focuses on the quantitative relationships between physicochemical and photophysical\ndescriptors, such as tPSA, ClogP, ΔF, and Δλ , and the overall imaging sensitivity observed in live cells.\ns ex\nIn this sense, Sensight functions at a higher abstraction level. It captures how these measurable\nparameters collectively influence a probe’s interaction with the cellular environment, fluorescence\ndetectability, and imaging performance, regardless of the underlying activation mechanism. The\nfluorescence enhancement factor (ΔF) serves as a phenomenological indicator of brightness gain, rather\ns\nthan as a mechanistic parameter.\nbe valuable to test the broader generality of Sensight. We have added this point to the revised Discussion,\nemphasizing that the framework is mechanism-independent in formulation, yet expandable with diverse\nprobe types in future studies. “Moreover, by using ΔFₛ as a mechanism-agnostic descriptor that captures\nthe net fluorescence gain rather than any specific modulation mechanism, Sensight is inherently\nextensible and can, in principle, incorporate probes based on diverse activation chemistries in future\nstudies.”\n(3) ΔFₛ (fluorescence turn-on ratio) is one of the five core parameters in the Sensight model and is\nintrinsically dependent on the specific chemical mechanism of fluorescence modulation and the local\ncellular microenvironment, rendering it unpredictable by computational methods prior to synthesis and\nexperimental characterization. Since Sensight is designed to guide the development of new\nhigh-performance probes, this fundamental limitation means that, in practice, only four of the five\nparameters can be meaningfully calculated. The authors should explicitly state this limitation to help\nreaders better understand the actual capability of Sensight.\nResponse:\nWe thank the reviewer for this insightful and fair comment. Indeed, we fully acknowledge that ΔFₛ\n(fluorescence turn-on ratio) is intrinsically difficult to precisely predict a priori, since it depends on\ncomplex photophysical and environmental factors such as PET, FRET, dark-state transitions, etc.\nAlthough we have made preliminary attempts to estimate ΔFₛ for tetrazine-based probes, these efforts\nconfirmed that reliable computational prediction remains challenging at the current stage.\nNevertheless, we believe that Sensight still provides substantial practical value by, for the first time,\nquantitatively defining how physicochemical parameters, optical properties, and instrument\ncompatibility collectively influence imaging sensitivity. Even when ΔFₛ cannot be pre-determined,\nSensight enables rational prioritization and optimization of other parameters that are computationally\naccessible and strongly correlated with imaging outcomes. We have stated this limitation and its\nimplications in the revised manuscript by adding the following words: “A key limitation is that ΔFₛ\ncannot be computed a priori, as it reflects mechanism-specific excited-state processes and\nmicroenvironment-dependent quenching pathways. Thus, prospective design is chiefly supported by the\nfour fully predictable descriptors, tPSA, ClogP, Δλ , and Stokes shift, which govern membrane\nex\npermeability, excitation efficiency, and spectral separation. In practice, Sensight integrates these\npredictable features with empirically determined ΔFₛ to provide a pragmatic and mechanistically\nagnostic framework for rational probe optimization.” (Page 17)\n(4) The unpredictability of ΔFₛ will affect the predictive accuracy of Sensight. Sensight functions more\nas an optimization tool after probe synthesis and testing, rather than an initial design tool.\nResponse:\nWe appreciate the reviewer’s thoughtful comment. We agree that the unpredictability of ΔFₛ\ninevitably introduces uncertainty into the overall predictive accuracy of Sensight. In its current form,\nSensight indeed performs most effectively as a semi-quantitative optimization tool that bridges\ncomputational prediction and experimental validation, rather than as a fully predictive pre-synthesis\ndesign platform.\nNevertheless, we believe this integrated framework represents an important conceptual advance\nover purely empirical probe development. Sensight enables researchers to quantitatively evaluate how\n24\nphysicochemical and optical parameters affect imaging sensitivity. And many of these parameters can\nbe calculated or experimentally controlled. By incorporating ΔFₛ as an experimentally derived correction\nfactor, Sensight can iteratively refine probe design through feedback between computation and\nexperiment. This hybrid approach transforms traditional trial-and-error optimization into a guided, data-\ndriven process.\nWe have clarified this positioning and practical workflow of Sensight in the revised manuscript to\nbetter reflect its current capabilities and future potential.\nPage 15, paragraph 2, “While ΔFₛ remained unpredictable precisely, literature-reported turn-on\nefficiencies allow reasonable estimation within an appropriate range. Based on these values, radar plots\nwere constructed to generate predicted sensitivity rankings, enabling pre-synthetic assessment of probe\nimaging sensitivity.”\n(5) The authors designed a series of formaldehyde fluorescent probes and predicted their sensitivity in\nliving cells, with results consistent with the predictions. However, when using Sensight for prediction,\nΔFₛ was set as a uniform value. Importantly, would setting ΔFₛ to different uniform values lead to different\nprediction outcomes (for example, in the manuscript, tPSA was set to 5, what if it were set to 50?)? What\nvalue or specific range should ΔFₛ be set to in order to ensure the predictive accuracy of the model?\nResponse:\nWe thank the reviewer for this thoughtful question. In Sensight, the five descriptors are standardized\nby comparing their raw values with the optimal range of each descriptor, and the standardized scores are\nmultiplied by their respective weights to yield the total radar area that represents the predicted imaging\nsensitivity. As illustrated in the workflow in Supplementary Fig. 59, because ΔFₛ was normalized across\ncompounds, setting it to a uniform value serves only as a constant offset and does not influence the\nranking of predicted sensitivities. And this was performed to compare the effects of the other four\ndescriptors. Therefore, using different uniform ΔFₛ values would not alter the relative order of the\npredictions.\nFinally, although ΔFₛ cannot be precisely predicted before synthesis, for probe scaffolds employing\nknown fluorescence modulation mechanisms such as PET or TICT, its approximate range can be\nreasonably estimated from literature precedents. Setting ΔFₛ within a literature-informed range can thus\nfurther reduce prediction deviation without affecting the internal ranking trend of Sensight. Our data in\nFigure 6 verified the feasibility of this estimation, with the actually measured sensitivity ranking aligning\nwell with the predicted one.\n(6) Beyond sensitivity, the selectivity of probes is equally important. Sensight cannot predict probe\nselectivity, indicating that it is only suitable for making rough predictions about the performance of\nfluorescent probes modified with known recognition groups, and offers limited utility for the development\nof innovative probes.\nResponse:\nWe appreciate the reviewer’s insightful comment. We fully agree that molecular selectivity is a\ncritical aspect of probe design. However, Sensight was not developed to predict target recognition\nselectivity, which is primarily governed by the chemical reactivity of the recognition trigger rather than\nthe physicochemical or optical parameters modeled in Sensight.\nIn most practical probe designs, especially for known analytes such as superoxide or formaldehyde,\nthe recognition trigger has well-established selectivity, and structural optimization typically focuses on\n25\nimproving imaging sensitivity and performance rather than target discrimination. In these contexts,\nSensight provides valuable quantitative guidance for tuning fluorophore properties and probe–cell\ninteractions to achieve high imaging performance without compromising established selectivity. For\nnewly designed or unvalidated triggers, we agree that experimental evaluation of selectivity would be\nessential prior to applying Sensight for optimization, but this lies beyond the current study’s intended\nscope.\n(7) The representation of the substituent denoted as R in Figure 1a is not sufficiently clear. Should \"RTz\"\non the probe structure on the left be modified to \"R Tz\" for clarity?\nn\nResponse:\nWe thank the reviewer for this helpful suggestion. We agree that “R Tz” provides a clearer\nn\nrepresentation of the substituent. The figure has been updated accordingly in the revised manuscript.\nReviewer #1:\n1. In the “Constructing Sensight for predicting imaging sensitivity” section, please add the result\nshowing that the correlation coefficients between ΔFc from the regression model and the nHD and NSC\ndescriptors are small. I believe this additional analysis would strengthen the justification for the five\ndescriptors used in Sensight.\nResponse:\nWe thank the reviewer for this constructive suggestion. We would like to clarify that Fig. 1d is\nintended to perform descriptor screening prior to regression modeling. In this screening step, descriptors\nwere ranked by the sum of their normalized Pearson correlation coefficient (PCC) and entropy, which\njointly capture both the strength of association with ΔF and the discriminative power across the chemical\nc\nspace. While nHD and NSC both exhibit measurable PCC and entropy values, their combined scores\n(PCC + entropy: 0.95 for nHD and 0.77 for NSC) are substantially lower than those of the five selected\ndescriptors (1.16–1.74), indicating reduced overall contribution to discriminating imaging sensitivity\nwithin the current chemical space.\nImportantly, incorporating additional descriptors such as nHD or NSC into the regression model of\nSPSS would inevitably alter the regression coefficients of all variables due to collinearity and model\ndependency, which is an inherent feature of multivariate regression models. As a result, the relative\ncontributions of the originally selected descriptors would no longer be directly comparable to those\nobtained from the current model. To maintain methodological consistency, interpretability, and\ntransparency, descriptors were therefore filtered at the screening stage based on their combined PCC +\nentropy scores before regression modeling.\nTo further strengthen this justification, we have now explicitly included the sum values of PCC and\nentropy for all descriptors in Fig. 1d. The individual values are shown in Supplementary Table 4.\n2. On page 9, line 22, Tz-2 appears to be missing from the described trend among the six compounds.\nPlease include Tz-2 in this discussion.\nResponse:\nWe thank the reviewer for this careful observation. We agree that Tz-2 should be explicitly included\nin the discussion. In the revised manuscript, we have now clarified that although Sensight predicted Tz-\n2 to exhibit slightly higher imaging sensitivity than K , experimental measurements showed that Tz-2\n2\ndisplayed a marginally lower ΔF. We note that this discrepancy is small and falls within the expected\nc\nexperimental variability of live-cell imaging assays. We have revised the text accordingly to explicitly\ninclude and discuss Tz-2 as follows (page 5 in main text, last paragraph, highlighted in blue), “Although\nthe experimentally measured ΔF of Tz-2 (1.20) deviates slightly from the predicted rank order, this\nc\ndifference is expected given the very small separation among the predicted radar map areas of Tz-1\n(0.4971), Tz-2 (0.4927), and K (0.4857) (Supplementary Table 15). Such minor rank inversions among\n2\nclosely clustered probes fall within the experimental variability of live-cell imaging and do not affect the\noverall predictive trend of Sensight.”\nReviewer #2 (Remarks to the Author):\nThe authors have very thoroughly addressed all the reviewer comments, and I believe that this work is\nnow suitable for publication.\nResponse:\nWe appreciate the support from the reviewer.\nReviewer #3 (Remarks to the Author):\nAuthors have addressed the raised comments. The manuscript has been greatly improved. Thus, I think\nthis revised version can be published.\nResponse:\nWe appreciate the support from the reviewer."}]

Decision letter