Embryo metabolite analysis and implantation potential prediction using chemiluminescent microfluidic chips with dielectric wetting valves

Domain: chemistry
Published: 2026-03-02
Editorial outcome: minor_revision

Abstract
Analysis of embryo metabolites offers a promising non-invasive strategy for assessing preimplantation developmental potential, yet the limited performance of current sensing platforms restricts physiological insights. Here we report a capillary-driven chemiluminescence microfluidic device equipped with three electrowetting valves, enabling simultaneous, offline quantification of glucose, lactate and pyruvate from as little as 3 μL of spent blastocyst culture medium (SBCM). Using a training set (n = 61) and validation set (n = 108) of human embryo transfers, we systematically recorded morphological development and clinical pregnancy outcomes. Metabolic flux analysis revealed that embryos with higher developmental potential consumed more glucose and pyruvate while producing more lactate. Integration of metabolic and morphological data yielded a predictive model of implantation potential with an area under the curve (AUC) of 92.0%, demonstrating robust performance. This platform establishes a powerful tool for embryo selection and may inform clinical decision-making in assisted reproduction.

Introduction
Non-invasive assessment of human embryo developmental potential before implantation predominantly relies on morphological analysis1. It has been widely utilized by IVF clinics worldwide for over 40 years2, 3. Despite its widespread use, morphological grading is inherently subjective and often lacks predictive accuracy regarding the embryo’s developmental potential. To overcome the limitations of conventional approaches, advances in time-lapse imaging4, 5, comprehensive chromosomal screening6, proteomics7, and metabolomics8 have significantly enhanced the assessment of embryo viability. Among these, metabolic profiling offers critical physiological insights that complement morphological evaluation, providing an alternative way to study embryo’s developmental potential.

Techniques such as mass spectrometry9, high-performance liquid chromatography (HPLC) 8, and Raman spectroscopy10 have been widely used to analyze metabolic changes in embryo culture media, including metabolites such as pyruvate, lactate, and alanine. Each method offers distinct advantages. For example, mass spectrometry provides high sensitivity and comprehensive analytical capabilities; HPLC excels in precise quantification and separation; and Raman spectroscopy enables non-destructive, rapid, and real-time analysis. However, despite their advantages, these technologies typically require complex sample preparation and costly instrumentation, and offer limited capability for manipulating and analyzing microliter-scale fluids, which has largely constrained their translation into routine clinical practice.

Microfluidic technology, with its advantages of minimal sample consumption, high integration, rapid analysis, and exceptional sensitivity, offers promising solutions to major challenges in assisted reproduction and holds substantial translational potential11, 12, 13, 14, 15, 16. In embryo culture, microfluidic systems can create microenvironments more conducive to development by finely regulating physicochemical and biochemical conditions, thereby directly or indirectly enhancing developmental competence16, 17, 18, 19. In the context of metabolic biomarker analysis, various microfluidic devices have been designed to quantify embryo metabolites and their key reactants20, 21, 22, 23, 24. For instance, Urbanski et al. developed an automated microfluidic system for offline, non-invasive monitoring of glucose, lactate and pyruvate in the culture medium of individual mouse embryos16. Studies by Heo and Obeidat further integrated embryo culture and metabolic monitoring within single devices, enabling long-term or continuous assessment of embryonic metabolic activity21, 22.

Microfluidic technology holds considerable promise for embryo metabolic analysis, yet its clinical application faces multiple challenges. First, it must enable sequential detection of multiple target metabolites in extremely small sample volumes25, while remaining non-invasive. Second, sensors must combine low detection limits with a wide linear range to accommodate the large concentration differences among metabolites in embryo culture media—glucose and lactate are typically at mM levels, whereas pyruvate is often at μM concentrations26. Moreover, because the total amount of metabolites from a single embryo over the entire culture period is limited, sensors must be capable of detecting small relative changes against high background concentrations, such as glucose27. For microfluidic sensing to achieve broader clinical adoption, it is essential to minimize reliance on complex, bulky peripheral equipment and labor-intensive chip fabrication28. Currently, most microfluidic systems for embryo metabolic sensing struggle to meet all these requirements simultaneously, representing a major bottleneck to their practical implementation in clinical embryo metabolic analysis

Capillary-driven microfluidic chips offer a compelling alternative, leveraging capillary forces for autonomous fluid transport29. This eliminates the need for external pumps, enhances portability, and enables precise fluid control. Moreover, capillary microfluidic platforms seamlessly integrate diverse analytical techniques—including colorimetry30, fluorescence detection31, electrochemical analysis32, and chemiluminescence33—facilitating rapid, high-sensitivity, and low-power metabolite detection. We developed a capillary microchip with integrated electrowetting valves for chemiluminescence-based metabolite detection. These valves enabled precise fluid regulation within the capillary network, triggering enzymatic oxidation and chemiluminescence reactions. The chip demonstrated excellent sensitivity and a broad linear detection range for multiple metabolites, enabling accurate quantification of glucose, lactate, and pyruvate in embryo culture media and linking metabolic activity to clinical pregnancy outcomes.

Results
The mechanism of metabolite detection
A capillary-driven chemiluminescence microchip incorporating dielectric wetting valves was designed to detect embryo metabolites to establish a multiparameter predictive model for embryo developmental potential (Figure 1a). The microfluidic chip consisted of a Ag electrode layer, a Polydimethylsiloxane (PDMS) dielectric layer, an ITO electrode layer, and a double-sided tape channel (see Supplementary Note 1 for details). The silver electrode (thickness: 6.78 ± 0.12 μm, Supplementary Figure S9), was screen-printed on PET to serve as the excitation electrode. PDMS was spin-coated onto the Ag electrode layer to form a hydrophobic dielectric layer (~20 μm). The etched ITO electrode (thickness: 680 ± 50 nm) functioned as the ground electrode. The double-sided tape (thickness: 170 μm) was patterned to fabricate the microfluidic channel (12 mm × 2 mm). All components were subsequently assembled, with the detailed chip design and fabrication procedure described in the Supplementary Note 2. The ITO electrode (contact angle: 78.2° ± 1.52°, Supplementary Figure S8a) and PDMS dielectric layer (contact angle: 118.5° ± 1.04°, Supplementary Figure S8b) together constituted a hydrophobic valve within the capillary channel. Driven by capillary force on the glass surface (contact angle: 29.2° ± 0.55, Supplementary Figure S8c), the sample (21 μL) spontaneously filled the microchannel and halted at the dielectric valve. Upon application of a 250 V potential, the valve surface became wetted, allowing flow to resume (Figure 1b (i and ii)).

The enzyme and probe were preloaded and dried in designated areas of the PDMS dielectric layer. After the analyte was introduced at the chip inlet, the mixed solution immediately flowed to the front of the valve in each of the three branch channels. GOx, LOx, and POx oxidized their respective analytes, generating H₂O₂ in the corresponding channels. Subsequently, the flow control valves for glucose, lactate, and pyruvate were activated, allowing the H₂O₂-containing solution to enter the detection area, where H₂O₂ oxidized luminol to produce chemiluminescence.

Numerical and experimental studies of the dielectric wetting valves
The mechanism of the dielectric wetting valve has been reported extensively. In the numerical simulation model, the initial contact angles were assigned as 120° for the PDMS surface, 80° for the ITO surface, and 30° for the glass surface. The experimentally measured contact angles, presented in Supplementary Figure S8, closely align with these values. After activation of the voltage of 250V, although the bottom ITO surface had the same contact angle of 80°, the contact angle of the PDMS layer immediately dropped from 120° (Figure 2a (i)) to 90.36° (Figure 2a (ii), (iii)). Meanwhile, the direction of capillary force shifted from upstream to downstream within the flow, thereby enabling the halted liquid to move through the valve as depicted by the gas/liquid interface line in Figure 2a. The numerical model was employed to evaluate the impact of PDMS thickness (15-30 µm) and voltage (50-450V) on the contact angle (Figure 2b). For a given PDMS layer, the contact angle was inversely proportional to the voltage. While at the same voltage, the thinner PDMS led to the lower contact angle. To allow the liquid flow through the activated valve, the contact angle should be smaller than 100°34. Therefore, the required applied voltage exceeded 200 V, 250 V, 300 V, and 300 V for the PDMS thickness of 15, 20, 25, and 30 µm respectively.

In the absence of an applied potential, a droplet of conductive fluid resting on an electrode—coated with a both hydrophobic and dielectric layer—assumes a spherical cap shape at mechanical equilibrium. Upon the application of an electric potential, charges accumulate at the droplet-insulator interface, reducing interfacial tension and, consequently, the contact angle (θ), as illustrated in Figure 2c(i and ii). This dielectric wetting phenomenon is governed by the Young-Lippmann equation (Equation 1-1), which quantitatively describes the relationship between applied voltage and contact angle modification35.

cos𝜃 =cos𝜃 + \epsilon_0\epsilon_d V^2 (1-1）
0
2d\gamma_{LG}

where θ and θ represent the contact angles after and before voltage application, ε and ε are the vacuum dielectric constant and the relative dielectric constant of the dielectric layer, respectively, d represents the thickness of the dielectric layer, γ represents the gas-liquid surface tension, and V represents the applied potential.
LG

The contact angle of 5 μL of PBS on a PDMS dielectric layer was measured at various voltages. The screen-printed silver electrode on the PET substrate was 6.78 ± 0.12 μm thick (Supplementary Figure S9), and the PDMS film was 26.93 ± 0.35 μm thick (Supplementary Figure S10), yielding a total dielectric layer with a thickness of approximately 20 μm. When voltages of 100 V, 150 V, 200 V, 250 V, 300 V, and 350 V were applied, the measured gas-liquid-solid three-phase contact angles were 116.2°±1.8°, 109.9°±2.7°, 101.4°±1.9°, 88.4°±1.6°, 76.1°±1.1°, and 57.0°±1.8°, respectively(Supplementary Figure S11). The contact angle was 0.7°-2.1° larger than those obtained from the simulation (Figure 2c(iii)), the error bars represent the standard deviations of three independent measurements unless otherwise stated. This suggested the accuracy of both simulation and experiments. Therefore, the voltage of 250 V and PDMS thickness of 27 μm were employed for all the rest studies unless stated otherwise.

Following PBS dye loading, spontaneous capillary flow initiated the chip inlet, halting in the hydrophobic region of the dielectric valve (Figure 2d(i), and Supplementary Movie 1). After applying a 250 V voltage, the dye resumed flowing into the detection area (Figure 2d(ii–iv), and Supplementary Movie 2). Subsequently, the detection capability of the device was assessed using 1 mM H₂O₂ solution (Figure 2e). The valve controlled the flow as expected and the chemiluminescence (CL) intensity in the detection area reached a maximum within 0.1 seconds before sharply decreasing.

Study of the detection conditions
The concentrations of probes (luminol, PIP and HRP) and enzymes (GOx, LOx and POx) were evaluated to maximize the CL intensity. For luminol optimization, PIP (1 mM) and HRP (140 U/mL) concentrations were fixed. Increasing luminol from 0.5 to 2 mM produced no significant change in signal (Fig. 3a, P > 0.05), whereas further elevation to 8 mM reduced the intensity by 61.9%. This was also reported in previous studies and might be related to self-quenching at higher luminol concentrations36, 37. When optimizing PIP (p-iodophenol), luminol (2 mM) and HRP (140 U/mL) were held constant. As PIP concentration rose from 0.5 to 1.0 mM, the CL intensity dramatically increased to the maximum of 115.01×10³ counts/gate. It rapidly declined with the further increases in PIP concentration (Figure 3a), potentially due to raised nonradiative transitions reducing luminescence38. Finally, the CL intensity was directly proportional to the concentration of horseradish peroxidase (HRP), reaching a maximum once the concentration exceeded 100 U/mL (Figure 3b), with luminol and PIP fixed at 2.0 mM and 1.0 mM, respectively. Therefore, luminol (2 mM), PIP (1 mM), and HRP (100 U/mL) were used as probes in the subsequent experiments. In the enzyme effect study, the CL intensity was proportional to the concentrations of GOx, LOx and POx and reached maximal values at concentrations larger than 70 U/mL, 20 U/mL, and 25 U/mL, respectively (Figure 3b and 3c). Reaction time was standardized at 5 min. Consequently, the above concentrations were employed in subsequent experiments.

The incubation time of the sample with the enzyme has been reported to drastically impact chemiluminescence (CL) intensity. As shown in Figure 3d, as the incubation time increased from 1 to 6 minutes, the chemiluminescence (CL) intensity of the samples gradually increased and stabilized. Compared to the unincubated samples, the CL intensity of incubated glucose (1 mM), lactate (1 mM), and pyruvate (1 mM) increased by 419-fold, 122-fold, and 62-fold, respectively (Figure 3e). This suggested that the enzymatic oxidation of glucose, lactate, and pyruvate to H₂O₂ reached peak efficiency after incubation enabled by the valve.

To reduce device fabrication time and cost, the reusability of the combination of Ag electrode layer and PDMS dielectric layer was evaluated. After each detection process, the chip was thoroughly cleaned using alcohol (75%) and ultrapure water. In cases where enzymes and detection reagents were not pre-embedded in the dielectric layer, no CL signal appeared upon adding the same sample, confirming that the chip was effectively cleaned and ready for reuse (Supplementary Figure S12). Furthermore, nearly identical detection signals (P > 0.05) were observed in the detection area when the chip was reused up to six times to detect 1 mM of glucose, lactate, and pyruvate (Figure 3f).

Detection performance for glucose, lactate and pyruvate
The microfluidic chip was employed to simultaneously detect glucose, lactate, and pyruvate, with optimized detection conditions (Supplementary Table S2). The real-time CL intensity reached peak values (referred to as CL intensity in the following section unless stated otherwise) within seconds after the fluid passed through the dielectric valve (Supplementary Figures S13-S15). As shown in Figure 4a, CL intensity increased from 59 counts/gate to 121.25×10³ counts/gate for glucose (1.0–2000 μM), from 53 counts/gate to 117.45×10³ counts/gate for lactate (0.5–2000 μM), and from 69 counts/gate to 111.49×10³ counts/gate for pyruvate (2.0–2000 μM). The CL intensity (y) exhibited a linear relationship with either concentration (Cg, Cl, Cp) or the logarithmic concentration (logCg, logCl, logCp) of three analytes. For glucose, the linear regression models were y = 0.056Cg + 0.03 (R² = 0.974) for 1.0–100 μM and y = 91.39logCg - 176.95 (R² = 0.993) for 100–2000 μM (Figure 4b, c). For lactate, the models were y = 0.066Cl + 0.028 (R² = 0.983) for 0.5–100 μM and y = 86.24logCl - 165.46 (R² = 0.996) for 100–2000 μM (Figure 4b, c). For pyruvate, the models were y = 0.019Cp + 0.026 (R² = 0.999) for 2.0–100 μM and y = 81.47logCp - 160.94 (R² = 0.991) for 100–2000 μM (Figure 4b, c). The limits of detection (LOD), calculated as 3STD/S (with S as the slope of the linear regression models and STD as the standard deviation of blank samples), were 0.16 μM, 0.15 μM, and 0.55 μM for glucose, pyruvate and lactate, respectively.

Supplementary Table S3 summarizes chemiluminescence-based methods for glucose and lactate detection, with paper- and cloth-based microfluidic chips widely explored due to their capillary-driven transport and low sample requirements. Li et al. reported a paper device with microchannels of varying lengths for sequential glucose and lactate detection using 2 μL of sample, achieving limits of detection (LODs) of 8 μM and 15 μM, respectively33. Zhang et al. developed a cloth-based platform that combined gravity and capillarity for glucose detection with an LOD of 9.74 μM39. Our group further advanced sensitivity with a spin-disc paper device, achieving LODs of 0.34 μM (glucose) and 0.30 μM (lactate), representing 23- and 50-fold improvements40. The chip developed in this study achieved an additional 53% and 50% reduction in LODs and a 100% and 700% expansion in detection ranges for glucose and lactate, respectively. These improvements stem from using glass as the lower substrate in the dielectric wetting valve chip, which enhances photon collection compared with paper. Supplementary Table S4 summarizes optical microfluidic platforms for pyruvate detection, most relying on flow injection. For instance, Urbanski et al. achieved a 10 μM LOD with automated loading and mixing. In contrast, our chip reduced the pyruvate LOD 17-fold20. Overall, this capillary-driven microfluidic chip enables precise flow control through dielectric valves, prolongs multistep reaction times, and significantly enhances detection performance. Its structural flexibility further supports multi-channel integration, offering strong potential for large-scale applications.

As illustrated in the design schematics, hydrogen peroxide (H₂O₂) is generated only within the enzymatic reaction zones. For H₂O₂ to influence another channel, it would need to diffuse over a sufficiently long distance during the incubation period. To address this, we conducted simulations of 1 mM H₂O₂ diffusion within the rectangular channels to monitor concentration changes across the three enzymatic reaction regions. Figure 4d shows the concentration profiles of 1 mM H₂O₂ after 10, 120, 240, and 360 s of diffusion, and the corresponding concentration–position distributions over time are presented in Figure 4e. The results indicate that after 6 minutes, H₂O₂ at the outermost edge of a channel had diffused only 2.5 mm, leaving a substantial distance from adjacent channels, thereby confirming the independence of the three measurements.

The detection specificity of the chip is shown in Figure 4f. As expected, the target analytes—glucose, lactate, and pyruvate—and their mixtures displayed extremely high CL intensities, whereas the blank sample and non-target analytes showed almost no CL signal. These results indicate that the CL biosensor integrated into the dielectric valve chip possesses high selectivity, a property primarily attributed to the specificity of the enzymatic reactions involved.

Furthermore, the storage stability of the chip with preloaded enzymes was evaluated (Figure 4g (i-iii)). After 15-day storage in a 4 ˚C fridge, the device retained 97.4%, 95.3%, and 96.3% of its initial CL intensity for detecting glucose (1 mM), lactate (1 mM), and pyruvate (1 mM), respectively. There was no significant difference in CL intensity of any analyte over the storage period (P > 0.05), making it potentially suitable for mass production and transport for off-site use.

Detection of metabolites in single blastocyst spent culture medium
The initial concentrations of glucose, lactate, and pyruvate in the fresh media were measured at 2.42 mM, 4.21 mM, and 0.25 mM, respectively (Supplementary Table S5). As the concentrations of glucose, lactate, and pyruvate were separately adjusted to 1.92 mM, 5.21 mM, and 0.15 mM, the experimental error ranges detected using the chip were -1.2% to 5.0%, -4.2% to 1.7%, and -4.0% to 0.6%, respectively. Notably, the relative standard deviations (RSDs) for triplicate measurements remained consistently below 3.22%. Both the experimental error ranges and RSDs demonstrate that the chip exhibits high accuracy in quantitative analysis of analytes within complex media. Subsequently, metabolites in SBCM were quantified using a microfluidic chip, with embryos cultured individually. Each assay required only 3 μL of spent culture medium. Figure 5a presents the metabolic profiles of 61 human embryos, with positive and negative values indicating production and consumption, respectively.

Supplementary Table S6 summarizes major advances in embryo metabolic biomarker detection. Early studies used 96-well plates or glass slides with ultrasensitive fluorescence methods27, 41, while LC-MS, NMR, and HPLC enabled multiplex detection but lacked precise quantification42, 43, 44, 45. With the emergence of microfluidics, embryo culture and metabolic analysis have become more precise and efficient. Urbanski et al. reported an automated microfluidic system with laser-induced fluorescence, enabling noninvasive monitoring of glucose, lactate, and pyruvate in 0.5 μL of spent medium from single mouse embryos (0–1000 μM, LOD: 10 μM)20. Heo et al. developed a computer-controlled platform capable of fluid handling and quantitative glucose detection in the 1–100 μM range21. Obeidat et al. further integrated multi-electrochemical sensors to measure dissolved oxygen, glucose, and lactate in microchambers, achieving real-time metabolic evaluation with LODs of 0.5, 0.4, and 1.22 μM, respectively22. Compared to existing systems, the chip developed in this study exhibits significantly enhanced sensitivity, with detection limits of 1.0 μM for glucose, 0.5 μM for lactate, and 2.0 μM for pyruvate. It also features a substantially wider dynamic detection range, with upper-to-lower limit ratios of 2000, 4000, and 1000, respectively. These characteristics align well with the intrinsic concentration differences of metabolites in embryo culture media—millimolar levels for glucose and lactate, and micromolar levels for pyruvate—enabling the platform to overcome the challenge of low metabolic flux in individual embryos. In summary, this chemiluminescence-based microfluidic platform allows for sensitive, accurate, and non-invasive detection of key embryonic metabolites, offering a powerful tool for embryo metabolomic analysis.

The metabolite profile of each embryo was further correlated to its clinical pregnancy outcome post-implantation. The mean glucose consumption for pregnant embryos was 118.92 ± 23.78 pmol/embryo/h, which was 17.67% higher than that of non-pregnant embryos (101.06 ± 26.41 pmol/embryo/h, p=0.007, Figure 5b). The mean lactate production for pregnant embryos was 231.16 ± 51.24 pmol/embryo/h, while non-pregnant embryos produced 34.96% less lactate (171.28 ± 46.61 pmol/embryo/h, p＜0.001, Figure 5c). Additionally, pyruvate consumption rate of pregnant embryos was 38.21 ± 7.24 pmol/embryo/h and 11.99% higher than that of non-pregnant embryos (Figure 5d). Despite the small difference in average values, a significant difference (34.12 ± 6.69 pmol/embryo/h, p=0.027) was still observed. These results demonstrate that pregnant and non-pregnant embryos already display significant differences in glucose and pyruvate consumption as well as lactate production during culture, suggesting that metabolomic analysis of embryo culture media may serve as an effective tool for predicting pregnancy outcomes.

Multi-parameter predictive model for embryo selection
To evaluate the predictive value of key metabolic parameters—glucose consumption, lactate production and pyruvate consumption—we generated receiver operating characteristic (ROC) curves (Supplementary Fig. S16). Based on the Youden index, the optimal thresholds for predicting clinical pregnancy were 101.73 pmol/embryo/h for glucose consumption, 187.39 pmol/embryo/h for lactate production and 31.45 pmol/embryo/h for pyruvate consumption (Fig. 6a). Embryos were then assigned metabolic scores of 3, 2, 1, or 0 based on their overall metabolic status. In parallel, the Gardner scoring system was employed to evaluate embryonic development morphology. Blastocysts were categorized into four grades-Ⅰ, Ⅱ, Ⅲ, and Ⅳ(Figure 6b). Morphological assessments were conducted on 61 embryos, and the distribution of their scores is presented in Supplementary Figure S17.

The relationship between metabolite concentrations and their morphological scores were further analyzed (Figure 6c). The glucose consumption rates for embryos with morphology scores of Ⅰ-Ⅳ were 125.21 ± 25.26, 113.36 ± 29.99, 111.56 ± 31.36, and 103.96 ± 18.50 pmol/embryo/h, respectively. The corresponding lactate production rates were 232.61 ± 36.09, 219.88 ± 63.42, 195.88 ± 68.37, and 186.75 ± 43.72 pmol/embryo/h, while the corresponding pyruvate consumption rates were 34.55 ± 10.98, 37.80 ± 7.22, 38.13 ± 6.75, and 34.41 ± 6.25 pmol/embryo/h. Moreover, a one-way ANOVA revealed no significant differences across morphology scores for each analyte (p = 0.33, p = 0.15, p = 0.32). These findings suggest that metabolic activity is largely independent of morphological grading.

As shown in Fig. 6d, the numbers of clinical pregnant/total embryos and live births/pregnancies were 22/26 and 20/22 for metabolic score 3, 9/14 and 8/9 for score 2, 2/18 and 2/2 for score 1, and 0/6 and 0/0 for score 0. Similarly, Fig. 6e shows that for morphological grades I, the numbers of clinical pregnant/total embryos and live births/pregnancies were 5/6 and 5/5, 11/19 and 9/11 for grade II, 5/14 and 5/5 for grade III, and 12/22 and 11/12 for grade IV. Notably, a cumulative metabolic score of 3-0 correlated with clinical pregnancy rates of 66.67%, 27.27%, 6.06%, and 0% respectively (Fig. 6f). Furthermore, embryos with a cumulative metabolic score above 1 were identified with a clinical pregnancy rate of 93.94% (Supplementary Table S7). Embryos with morphological scores of Ⅰ- Ⅳ achieved clinical pregnancy rates of 15.15%, 33.34%, 15.15%, and 36.36%, respectively (Fig. 6g). Notably, 48.49% of embryos resulting in pregnancy had high scores (Ⅰ and Ⅱ). Importantly, the metabolic score outperformed morphological assessment, enabling a 45.45% higher detection rate for embryos that became pregnant.

The above analysis revealed that both metabolic and morphological scores independently act as positive predictors of pregnancy outcomes. The ROC curves were generated for the morphology model, metabolomics model, and a combined model (M & M model). The study results demonstrated that the AUC of the pregnancy assessment model based on embryonic morphology was 61.8%, while the AUC of the metabolomics model significantly increased to 89.4% (Fig. 6h). The AUC of the combined model reached 92.0%, significantly higher than that of the morphology model (p < 0.001). Metabolite profiling has been applied to predict embryo implantation potential (Supplementary Table S8). Fernandes et al. employed ESI–MS to measure lactate and pyruvate, achieving predictive AUCs of 72.7% and 83.0%, respectively46. Liang et al. used LC–MS to analyze five metabolites, yielding an AUC of 88%9, while Huo et al. identified amino acid markers by HPLC, raising the AUC to 90.4%8. Although informative, these approaches rely on complex preprocessing and costly instrumentation. By contrast, our microfluidic platform offers a simple, rapid, and highly sensitive assay capable of multiplexed metabolite detection, providing a more practical tool for embryo assessment.

Clinical validation of the embryo selection prediction model
Metabolites in the validation set of 108 human embryo culture media were analyzed using our self-developed dielectric valve–based detection chip. As shown in Fig. 7a, the metabolic profiles of individually cultured embryos were obtained, with positive values indicating metabolite production and negative values indicating consumption. In the validation set data, the average consumption rates of glucose and pyruvate by the embryos were 126.87 ± 40.46 pmol/embryo/h and 33.74 ± 12.81 pmol/embryo/h, respectively, while the lactate production rate was 259.79 ± 129.04 pmol/embryo/h (Supplementary Table S9). Gardner et al. revealed that human embryos consume glucose and pyruvate at rates of 131.0 ± 11.4 pmol/embryo/hour41 and 37.7 ± 1.8 pmol/embryo/hour47, respectively—values that align closely with those observed in this study.

In the validation set, pregnant embryos exhibited a mean glucose consumption rate of 143.57 ± 29.61 pmol/embryo/h, which was 47.53% higher than that of non-pregnant embryos (p < 0.001; Fig. 7b), demonstrating strong statistical significance. This trend was consistent with the training set, where a highly significant difference was also observed (p = 0.007). For lactate production, pregnant embryos in the validation set produced 303.17 ± 120.06 pmol/embryo/h, representing a 65.63% increase over non-pregnant embryos (p < 0.001; Fig. 7c). Similarly, in the training set, lactate production was significantly lower in non-pregnant embryos, with a 35.08% reduction relative to pregnant embryos (p < 0.001), indicating that decreased lactate production consistently characterized non-pregnant embryos across both datasets. In addition, pyruvate consumption in the validation set was 35.79 ± 13.22 pmol/embryo/h for pregnant embryos, 19.3% higher than in non-pregnant embryos (p = 0.025; Fig. 7d), a statistically significant difference that was in line with the training set (p = 0.027). Although embryos resulting in clinical pregnancy exhibited differences in certain metabolic fluxes compared with non-pregnancy embryos, the distributions of individual metabolic parameters still showed considerable overlap when analyzed in isolation, which may lead to misclassification of implantation potential. This observation underscores that flux changes of a single metabolite are insufficient to reliably discriminate clinical pregnancy outcomes. Instead, a combined analysis of multiple key metabolic fluxes in the culture medium is required to achieve a more robust prediction of embryo implantation potential.

We next analyzed the relationship between metabolite concentrations and embryo morphology scores (Fig. 7e). The distribution of morphological scores was presented in Supplementary Figure S18. The glucose consumption rates for embryos with morphology scores I–IV were 138.32 ± 31.23, 126.78 ± 43.02, 140.52 ± 31.52, and 103.91 ± 40.86 pmol per embryo per hour, respectively. No significant differences were observed among embryos with scores I–III (p = 0.27). The corresponding lactate production rates were 279.05 ± 113.66, 252.27 ± 131.74, 268.31 ± 153.59, and 251.61 ± 128.74 pmol per embryo per hour, while pyruvate consumption rates were 35.28 ± 12.09, 33.27 ± 13.67, 34.58 ± 12.02, and 32.59 ± 12.47 pmol per embryo per hour, respectively. No significant differences were detected in lactate or pyruvate metabolism among the morphology groups (p = 0.85 and p = 0.89). These findings were consistent with the training set and further support that embryo metabolic activity is largely independent of morphology scores.

We applied the metabolomics-based scoring system to evaluate implantation potential in the validation cohort (Supplementary Fig. S19). As shown in Fig. 7f, the numbers of clinical pregnant/total embryos and live births/pregnancies were 33/40 and 29/33 for metabolic score 3, 30/33 and 24/30 for score 2, 6/27 and 3/6 for score 1, and 0/8 and 0/0 for score 0. Similarly, Fig. 7g shows that for morphological grades I, the numbers of clinical pregnant/total embryos and live births/pregnancies were 18/23 and 13/18, 34/53 and 27/34 for grade II, 10/13 and 9/10 for grade III, and 7/19 and 7/7 for grade IV. Notably, the cumulative clinical pregnancy rates for embryos with metabolic scores of 3, 2, 1, and 0 were 47.83%, 43.48%, 8.69%, and 0%, respectively (Fig. 7h). Embryos with scores >1 achieved a cumulative pregnancy rate of 91.39% (Supplementary Table S10), closely matching the rate observed in the training set (93.94%). By contrast, clinical pregnancy rates for morphology grades I–IV were 26.09%, 49.28%, 14.49%, and 10.14%, respectively (Fig. 7i). Notably, 75.37% of pregnant embryos were derived from high-grade (I or II), a proportion markedly higher than that observed in the training cohort (48.49%). The data in Supplementary Table S11 indicated that, in both the training and validation cohorts, the effects of potential confounding factors—including maternal age, body mass index (BMI), duration of infertility, endometrial thickness, and oocyte number—could be excluded in the analysis of clinical pregnancy outcomes. These findings indicate that the metabolic scoring system provides greater accuracy and reliability than morphology alone in predicting implantation potential.

Receiver operating characteristic (ROC) curve analysis further assessed model performance. As shown in Fig. 7j, in the validation set, the AUC of the morphology-based model was 68.2%, which was significantly improved by the metabolomics model (86.5%). The combined morphology–metabolomics (M&M) model achieved an AUC of 87.3%, markedly higher than the morphology model (DeLong test, p < 0.001), consistent with the training set results.

The aforementioned research demonstrates that a metabolomic evaluation model can be established to predict embryonic implantation potential by leveraging the metabolic differences between pregnant and non-pregnant embryos. This approach is fully compatible with existing embryo morphological assessment and time-lapse imaging techniques, thereby enabling a multidimensional screening strategy for selecting embryos with high developmental competence. As the method only utilizes spent culture medium without altering the standard in vitro fertilization (IVF) protocol, it holds significant promise as an auxiliary decision-making tool for identifying embryos with the best developmental potential.

Limitations of the study
The primary limitations of this study include the limited sample size, and larger cohort studies will be required to improve model robustness. Culture medium collection followed routine clinical workflows and may therefore not fully capture metabolic dynamics at other developmental stages, such as the eight-cell or blastocyst stages. In addition, the experimental design did not distinguish morphologically similar embryos derived from the same patient, which represents a further limitation. Future work could involve large-scale, prospective multicentre cohort studies integrated within standard clinical workflows to enable metabolic analyses of morphologically similar embryos and embryos across developmental stages. Moreover, integrating metabolomic assessment with emerging non-invasive embryo evaluation approaches, including time-lapse imaging, proteomic profiling, and microRNA analysis, may help establish a multidimensional assessment framework, providing a powerful tool for embryo selection.

Methods
Study Participants and Samples
This study was approved by the Research Ethics Committee of Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly known as Shenzhen Zhongshan Urology Hospital); approval number: SZZSECHU-20180021). The participants were infertility patients who underwent in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) followed by vitrified single-blastocyst transfer at our center between January 2021 and July 2023. In total, 169 samples of spent blastocyst culture medium (SBCM) were collected and allocated to a training set (n = 61) and a validation set (n = 108). The training set was constructed retrospectively with the specific aim of building a predictive model. To ensure a balanced distribution for model development, we selectively included 34 samples from embryos resulting in clinical pregnancy and 27 samples from embryos that did not, with collection dates spanning non-consecutive periods across the study timeframe. In contrast, the validation set was designed prospectively to assess the effectiveness of the model in practical applications. It comprised all SBCM samples that met the inclusion criteria from a defined period (June to July 2023).

Embryo Culture and Sample Collection
Oocytes retrieved by ultrasound-guided aspiration underwent fertilization by either IVF or ICSI. Fertilization was assessed 17 ± 1 h after insemination, and the presence of two pronuclei was taken as evidence of normal fertilization. Zygotes were cultured in Quinn’s Advantage Cleavage Medium (SAGE BioPharma, USA) supplemented with 10% (v/v) serum protein substitute until day 3. Embryo culture was maintained in a Labtech C200 incubator under stable conditions (6% CO₂, 5% O₂, 37 °C, 95% humidity), which were applied consistently. Cleaving embryos were transferred to Quinn’s Advantage Blastocyst Medium, supplemented with 10% (v/v) serum protein substitute, and cultured individually in 25 μL droplets until blastocyst formation. Following vitrification, spent blastocyst culture medium (SBCM) was collected and stored at −80 °C for subsequent analysis.

Embryo Transfer and Confirmation of Clinical Pregnancy
All patients underwent vitrified–warmed single blastocyst transfer. Endometrial preparation followed either natural or artificial protocols. Serum hCG was measured 11 days after transfer, with concentrations >5 IU/L indicating biochemical pregnancy. Clinical pregnancy was confirmed by the presence of an intrauterine gestational sac on transvaginal ultrasound 30 days after transfer.

Characterization of the dielectric wetting valves
The dielectric wetting mechanism within the capillary channel was studied numerically. The level set method was employed to accurately capture the dynamic behavior of the gas-liquid interface. Under the influence of an electric field, the contact angle of the gas-liquid interface was corrected using the Young-Lippmann equation, allowing the precise description of the electric field's modulation of interface behavior. By systematically simulating the variation in contact angle under different voltage and dielectric layer thickness conditions, the study further determined the optimal parameter configuration for the recovery of fluid flow. Detailed simulations are provided in the Supplementary Note 3. The mechanism and performance of the valves were investigated experimentally. Contact angle measurements were performed on a silver electrode–PDMS dielectric layer using a goniometer (Drop Analyzer, Dalue Technologies, Zhuhai, China), with 5 μL deionized water under applied voltages of 0 or 150–400 V. Valve-controlled fluid manipulation was validated using a diluted dye solution under 250 V, with the process recorded by camera. Sequential chemiluminescence detection was further assessed using 1 mM H₂O₂ as the analyte. A 5 μL mixture of 2.0 mM luminol, 1.0 mM PIP and 140 U/mL HRP was preloaded into the detection zone, after which sample introduction and sequential valve actuation generated chemiluminescence signals recorded by the instrument.

Study of the detection conditions
The chemiluminescence (CL) intensity of the detection using the microfluidic chip is influenced by the concentrations of reagents and the enzyme incubation time. To optimize luminescence intensity, the CL intensity of detecting 1 mM H₂O₂ using probes with various concentrations (luminol: 0.5–8 mM, PIP: 0–4 mM, and HRP: 20–140 U/mL). Additionally, the impact of varying concentrations of GOx (10–120 U/mL), LOx (5–30 U/mL), or POx (10–35 U/mL) was evaluated by detecting 1.0 mM glucose, lactate, or pyruvate, respectively. The dielectric valve’s ability to stop the fluid flow was then used to explore the effects of incubation time (0–6 minutes) on the detection signal. The CL intensity optimization process is detailed in the Supplementary Note 4.

The reusability of the dielectric valve chip was exploited to save detection costs. After each detection, the PDMS dielectric layer was detached from the double-sided tape and cleaned by alcohol (75%) and ultrapure water, before being reassembled to a chemiluminescence microfluidic chip. After embedding the enzyme and detection substrates, the chip was reused to detect analytes (1 mM glucose, lactate, and pyruvate). The chip's performance during six reusing cycles was assessed by detecting analytes on the film without pre-embedded reagents.

Detection performance for glucose, lactate and pyruvate
The performance of the chip in detecting a mixture of glucose, lactate, and pyruvate diluted in PBS was evaluated. Five microliters of each enzyme solution (70 U/mL GOx, 20 U/mL LOx, 25 U/mL POx) and 5 μL of substrate solution containing luminol (2.0 mM), PIP (1.0 mM), and HRP (100 U/mL) were respectively pre-embedded to in the enzyme reaction area before the valve and the detection area behind the valve on the PDMS dielectric layer. After assembly of the whole device, 21 μL of the analyte mixture was added to the chip inlet. The mixture liquid immediately flowed to the front of the valves in the three branch channels. After incubating the oxidation reaction for 4, 5 and 6 minutes, the valves controlling the flow of glucose, lactate and pyruvate were respectively activated at the voltage of 250 V. Meanwhile, the CL signal was monitored using a photomultiplier tube (PMT, Hamamatsu H7421) with a gate time of 10 ms to record the signal variation over 60 seconds after each valve activation.

The selectivity of the dielectric valve chip for glucose (1.0 mM), lactate (1.0 mM), and pyruvate (1.0 mM) in the presence of common interferents was evaluated. Additionally, the prepared detection chip was stored at 4°C, and samples containing 1.0 mM glucose, lactate, and pyruvate were tested on days 1, 3, 5, 8, and 15 to assess chemiluminescence (CL) response and stability. Furthermore, the generation and diffusion of 1 mM H₂O₂ within a rectangular microchannel were simulated to evaluate potential cross-interference among the three reaction zones. Further details are provided in the Supplementary Note 5 and 6.

Detection of metabolites in single blastocyst spent culture medium
The chip was first employed to measure the baseline concentrations of glucose, lactate, and pyruvate in a fresh embryo culture medium (Quinn’s Advantage Blastocyst Medium). Briefly, 3 μL medium was mixed with 18 μL PBS buffer (pH 7.4) before being introduced into the chip inlet. Then the CL signal for three analytes was recorded in the detection area. Afterward, the medium was either added with 1.0 mM lactate or diluted to reduce the glucose and pyruvate concentrations by 0.5 mM and 0.1 mM, respectively. This step aimed to simulate the embryo-conditioned medium with lactate production and glucose/pyruvate consumption. The samples were subsequently tested to determine the concentration of the three analytes separately. Finally, the chip was applied to analyze spent culture medium from individual blastocysts in the training cohort (n = 61) to validate its performance.

Multi-parameter predictive model for embryo selection
The embryo metabolic scoring system was established using glucose, lactate, and pyruvate as key indicators of metabolic activity. We quantified glucose and pyruvate consumption rates together with lactate production rates during embryo culture, and correlated these with clinical pregnancy outcomes. Based on optimized metabolic thresholds predictive of pregnancy, embryos were classified such that values above the threshold received a score of 1 and those below received 0. The cumulative scores across the three metabolites stratified embryos into four metabolic grades: 3, 2, 1, or 0. Blastocysts were graded according to the Gardner system, evaluating expansion (scale 1–6), inner cell mass quality (A–C), and trophectoderm cohesiveness (A–C). Based on inner cell mass and trophectoderm scores, blastocysts were classified into four categories: grade Ⅰ (AA), grade Ⅱ (AB, BA), grade Ⅲ (BB), and grade Ⅳ (AC, CA, BC, CB). Finally, this study integrated the embryo metabolic score, clinical morphological score, and transfer experiment results to construct a metabolic model, a morphological model, and a combined metabolism-morphology model to evaluate embryo implantation potential. For further details, please refer to the Supplementary Note 7.

Clinical validation of the embryo selection prediction model
Metabolites in the culture medium of the 108 validation cohort embryos were analyzed using our dielectric valve-based chip. We calculated and benchmarked the consumption/production rates of key metabolites (glucose, pyruvate, lactate) against existing literature. Each embryo then received integrated metabolic and morphological scores via our metabolomic model and the Gardner system, respectively, with results compared to the training set. The predictive power for clinical pregnancy was systematically compared between morphological, metabolomic, and combined models using ROC curve analysis on both datasets, providing a comprehensive evaluation of model accuracy and generalizability.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The work titled “Embryo Metabolite Analysis and Implantation Potential Prediction Using Chemiluminescent Microfluidic Chips with Dielectric Wetting Valves” analyzes the concentration of lactate, glucose, and pyruvate in 3 µL of media from the culture of 61 individually cultured embryos from day 3 to day 5. The authors developed a capillary-driven chemiluminescence microfluidic device equipped with three electrowetting valves.\n\nThe authors found that embryos resulting in pregnancy had:\n1. Greater glucose consumption, with a Youden index (sensitivity + specificity - 1) exceeding 103.64 pmol/embryo/h, indicating a higher likelihood of successful pregnancies.\n2. Greater pyruvate consumption than 31.32 pmol/embryo/h.\n3. Increased lactate production greater than 192.7 pmol/embryo/h.\nThe embryo implantation potential assessment model, which integrates metabolomics and morphological data, achieved an AUC of 91.2%. The authors conclude that their technology exhibits superior sensitivity in multi-analyte detection with minimal sample volume and holds promise as a decision-making tool for selecting embryos with the highest developmental potential.\n\nThe overall subject area is of critical importance, as identifying embryos with higher implantation potential is of great clinical value. The paper thoroughly describes the engineering component of the device and presents results from a group of human embryos.\n\n1. However, there are significant concerns regarding the quality of the human data. Currently, the results are not interpretable due to a lack of critical information about the patients, the IVF protocol, laboratory parameters, and the embryo transfer technique. A table should include details such as age, infertility diagnosis, type of IVF stimulation protocol, the number of embryos generated per patient, culture conditions (media, oxygen concentrations), fertilization method (IVF or ICSI), and whether PGT was performed.\n\nAdditional key points to address include:\n2. Were embryos cultured individually from fertilization to day 3? If not, why?\n3. Was single embryo transfer performed? This information is critical.\n4. Ideally, the authors should provide live birth rates.\n5. A validation cohort should be conducted to confirm the original data.\n\nThere are also minor concerns:\n6. Figures 6F and 6G are confusing. A table showing the number of pregnant and non-pregnant patients for each metabolic or morphological score would be more valuable.\n7. In Figure 6G, the authors use scores A to D, whereas the text mentions morphological scores I to VI (with VI likely being an error, presumably meant to be IV).\n8. Line 360 states that a cumulative metabolic score above 1 was associated with a clinical pregnancy rate of 93.9%, while a score below 2 effectively screened out 88.5% of non-pregnant embryos. This seems incorrect, as a higher metabolic score should be associated with a higher pregnancy rate.", "reviewer_2": "The manuscript provides valuable information related to embryo metabolism for Glucose, Lactate and Pyruvate. The sensitivity exhibited by the chemiluminescent microfluidic procedure seems to be impressive. The data presented shows that glucose and lactate levels in particular are related to embryo viability. The morphology scores also indicate that although Grade 1 embryos have high pregnancy rates, lower grades have a lower pregnancy return rate. Although the current technique of embryo metabolism valuation is novel, the previous data has been known for some time.\n\nThe data provided would need to be supplemented further with the following:\n1. Lack of embryo cohort controls within patients to show that selection can be made confidently between embryos of good grade within a patient’s own embryos\n2. The precise timing of measurement is not elucidated. L272 states “Human embryos cultured from day 3 to 5”. Given the sensitivity of the system, it is feasible that a 1-2h difference in measurements on different days may provide the same variation as seen in production of the metabolites by the embryo linked to viability. Did the authors adjust for the exact time in culture?\n3. Retrospective analysis of AUC is of interest however the study would greatly benefit from prospective data. Examples of high AUCs that have subsequently not been substantiated by prospective analysis have been frequent in the field of embryo viablity prediction. For example, several AI models have proved very efficient in retrospective data but not in RCTs See Illingworth et al Nat Med. 2024 Nov;30(11):3114-3120. The ability to measure the samples non-invasively would allow the authors to perform a non-selection study, whereby embryos could be transferred and the media assessed blinded to outcome. (See Tiegs et al Fertil Steril 2021 Mar;115(3):627-637 for a similar approach to assess PGT outcomes)\n4. Although some analysis can be gleaned from the figures about the time the assay takes, the authors provide some description of how this would fit into practice.\n\nMinor comments\nL47 Each\nMorphology scores read as VI. The authors mean “IV”", "reviewer_3": "Thank you for the opportunity to review this manuscript. Overall, the work described here involves a microfluidic device with which to measure a few metabolic substrates using some engineering technology. A secondary, and minor, aspect of this work is described whereby some preliminary measurements of several substrates in human embryo culture media are made and an effort is made to suggest the device could be useful for selecting embryos with the greatest potential to make a baby. While this reviewer comments on the latter aspect of the manuscript, he/she emphasizes that this is really a device design paper that should be reviewed by microfluidics experts as it has little to do with advancing the field of fertility treatments.\n\nAs for the embryo measurements, the media from 61 embryos was assayed for Glu/Lac/Pyr consumption and metabolism as a reflection of oathways thought to be of importance to mammalian embryo metabolism (itself an oversimplification of current knowledge). From preliminary validation studies authors move to aligning their data streams to morphology grading and search for associations between embryos that resulted in pregnancies vs those that did not following embryo transfer. Small increases in pregnancy rates are noted for embryo media scoring “better” in their data sets. On page 309 the authors conclude:” It suggested that metabolomic analysis of embryo-conditioned media could serve as a predictive tool for pregnancy outcomes.”\n\nWhile mostly a paper on “wetting valves,” this paper has major flaws as far as any use as a tool for embryo selection. No information is provided about patients who provided embryos other than what culture medium was used. This is critical since many previous studies of this kind only select embryos that go from day 3-5 and thus do not represent the cohort in an unbiased way. We do not know what they mean by “pregnancy” outcomes other than it was not live birth rate which is now the only accepted outcome measure for studies of this kind (was it hCG???, ultrasound confirmation). We do not even know whether single embryo transfer was used even though it would be assumed. So this is a device looking for a problem to solve and the work fails to indicate any utility in a clinical setting.", "reviewer_4": "The paper describes the fabrication and application of an EWOD (Electrowetting on dielectric) based microfluidic system, used to predict the implantation potential of 3 to 5 days old human embryos. As my competence is in (a.o. microfluidic) microsystems technology development, I will comment mainly on this aspect of the paper. In general the authors have clearly explained the fabrication technology and operation of the microfluidic sensor system (using chemiluminescent sensors). The principle of EWOD for manipulation of microvolumes of fluids is not new, the choice of the materials used in this paper for fabrication (PET and glass substrates, screen printed Ag and (I guess) PVD (physical vapour deposited) ITO, double sided tape) is quite straightforward and logical, and hence not really new. The main focus and novelty of the paper is on the application, therefore in my opinion the description of construction and operation of the sensor tool is a bit brief here and there in order to fully appreciate the technology development work, done by the authors. I’d like to see a number of fabrication details added and technology related questions answered in a revised version:\n• The PDMS brand name and type should be mentioned in the “materials section” (there are many types of PDMS with wide variety of (chemical, mechanical, etc.) properties. Same for the glass: which type? (sodalime, borosilicate,…). What is the ITO layer thickness? A thickness of 170µm is mentioned for the double sided tape. Does that include the two adhesive layers on front and back? If not, what is the total thickness (tape + adhesives, this determines the height of the microfluidic channel)? How was the PDMS cured after spinning (temperature, duration, atmosphere, …)?\n• I guess that during assembly PET and glass substrate are aligned by hand, no? How much approx. is this misalignment and how critical is it? Does it e.g. influence the minimal voltage to operate the valves? Please comment.\n• I’d like to see a detailed (dimensioned) cross section and top sight drawing of the valve (electrodes and channel). From fig.S2f it seems that silver and corresponding ITO electrodes are slightly shifted with respect to each other. Is this necessary for proper valve operation?\n• Figure S3: contact angles of water on ITO, glass or PDMS strongly depend on the pre-treatment of the substrate. Were there any other treatments of glass + ITO after ITO laser etching or of PET + Ag + PDMS after PDMS curing before final assembly? The supplementary clips show a smooth flow of dyed water in the channels, although one surface (the PDMS) is highly hydrophobic. I guess this is possible only if the channel height (now 170µm) is sufficiently small? Is there a minimum channel height above which capillary flow does not take place anymore?\n• Line 122 (and others) : authors talk about “ITO glass surface” and “non conductive area”. This is confusing, I suppose they mean “ITO surface” and “glass surface” respectively. I suggest to change this.\n• Fig.4e: the authors have monitored sensor stability for up to 15 days (stored at 4degC), which is very valuable work. For a commercial product however shelf lifes of typically 6 months or 1 year are usually required. Do the authors have any estimate for the longer term shelf life? Can the devices be stored at room temperature (at ambient atmosphere)? Representing the data of Fig4e as 2D graphs instead of 3D block diagrams, normalizing the numbers with respect to the initial value (=100%) and magnifying the decrease would allow to see the decreasing trend for each sensor better.\n• Lines 156 – 157: “the PDMS film was 27 μm thick (Supplementary Figure S5), yielding a total dielectric 157 layer with a thickness of approximately 20 μm” : the “dielectric” is exactly the “PDMS film”, so I would expect exactly the same thickness for both. Why are they different? Perhaps “PDMS film” is before curing, “dielectric” is after curing? (PDMS shrinks during curing.\n• Line 150: “θ and θ0 represent the contact angles before and after voltage application”, should not this be: “θ and θ0 represent the contact angles after and before voltage application, respectively”?", "reviewer_5": "This article reports a device to predict the developmental potential of human embryos, by analyzing their utilization (production/consumption) in three substrates, glucose, lactate and pyruvate, which are essential markers of the embryo metabolism, and have been suggested as biomarkers for the assessment of the embryo developmental potential. Specifically, the device uses capillary forces and electro-activated valves to manipulate the samples, and an enzymatic process followed by chemoluminescence for the detection and quantification of the three substrates. One strong aspect of this article is the correlation between the on-chip acquired data, the embryo morphological scoring and the clinical pregnancy outcomes, for 61 embryos, and for women patients and not only animal studies.\n\nHowever, the present reviewer does not recommend this paper for publication in Nature communications for several reasons, as detailed in the following.\n\nIntroduction:\nThe paper refers to morphological scoring at the start. Time-lapse imaging with more advanced morphokinetic parameters is widely used.\nThe authors discuss several techniques (HPLC, MS, Raman, etc.); it would be more accurate to specify which parameters/biomarkers have been analyzed using these techniques. And are those techniques employed for intracellular measurements, as suggested “metabolic changes in embryo” or measurements in medium?\nThe authors bring as criticisms the extensive sample preparation and steps; how does this compare to their device, which also involves sample manipulation and dilution.\nReferences: several statements are not supported by references. Most of the references are old, and the paper does not cite seminal papers on embryo metabolic assessment for their scoring and on microfluidics for embryo culture and “scoring”. Of note, several of these articles are using electrowetting as a microfluidic platform for the analysis of biomarkers in the embryo culture, and none of these articles are cited.\nLine 54: Ref 12 and 13 are not related to embryo culture and scoring.\nReviews on this topic are, for instance:\nhttps://pubmed.ncbi.nlm.nih.gov/14640377/\nhttps://pubmed.ncbi.nlm.nih.gov/17576001/\nhttps://academic.oup.com/molehr/article/23/4/257/2959968\nhttps://academic.oup.com/molehr/article-abstract/23/4/213/2605925?login=false\nhttps://www.sciencedirect.com/science/article/abs/pii/S0021929015001293\nLine 55: several references are for oxygen; is this a metabolite?\nFor all examples provided it would be relevant to specify if the measurements are conducted offline or in situ. Oxygen measurements are in situ; the rest is mostly done offline.\nRef 16: which metabolites?\nLine 68: why is the device fabrication an issue for the adoption of microfluidics by clinicians? And to which extent is the proposed device addressing these concerns of labor-intensive procedures – since sample has to be manipulated and pipetted – and fabrication?\nReferences 19, 20 and 21 come across as randomly chosen.\nThe authors present their developments with CL for glucose and lactate detection; what the present reviewer misses is the required performance for a device to measure embryo metabolism, and these elements are never really discussed in the article: how much medium is available/can be used to consider the measurements as non-invasive? And which LOD should be reached? The concentration is not per an issue for glucose, it is more the concentration difference knowing that the embryo metabolism is limited, and that eventually these measurements should be conducted at the single embryo level in a given amount of medium.\nHere, it is not clear if embryos are cultured individually and in which amount of medium.\n\nMicrofluidic device:\nThe dimensions of the structures should be documented as well as the volumes manipulated in the device.\nWhat is the thickness of the double-sided tape? What is the surface tension so that liquid stops at the valve? The two types of yellow (Fig 1b) are difficult to distinguish.\nDo embryos consume pyruvate and glucose simultaneously or successively? (different phases of development)\nComing back to the microfluidic device: the medium is loaded in one central port which is connected to the three analytical channels, which are thus in contact. To which extent can the three measurements be independent knowing that the three enzymatic reactions will produce H2O2 which is used in the second step of the analytical process? This comes across as a significant flaw in this study.\nAnd has the timing of the different steps been optimized?\n\nNumerical simulations\nHave the considered contact angles been measured? Where do these values come from?\nIt is also not clear which solutions are considered: DI water? PBS? Medium? Will the nature of the solution impact the functioning of the device? One could expect so.\nFor the measurements, how many experiments were conducted? And how many devices were considered?\nThicknesses of the PDMS and electrodes are indicated: was this measured on one device? And how reproducible are these values? Why is the total dielectric layer of ca. 20 um?\nLine 160: 0.7-2.1 larger => is a unit missing?\n\nDetection conditions\nWhat is PIP?\nFigure 3: n=? and how is n defined? Number of biological/analytical replicates? Number of devices used? In this characterization step, what is the concentration in substrate?\nIncubation times should be specified.\nIt is not clear how assays are conducted when the enzymes and detection reagents are not pre-loaded in the device.\nIs re-usability an option in clinical settings when measuring material isolated from several embryos from the same patients?\nOr material from different patients?\nIs ultrapure water enough to “clean”? at least detergents should be used... medium contains a lot of various compounds that stick to all surfaces!\n\nDetection performance\nWhat is the entire timeline for the analysis?\nWhat is the sensitivity of the platform? Once more, since the goal is to measure small consumption in glucose, the difference in concentration is most relevant, and as such the sensitivity of the platform.\nFigure 4\nThere are no error bars. Also in panel a, the CL intensity does not vary linearly with the substrate concentration. Why focusing on the uM range in panels b and c? In medium, the concentration in glucose is much higher, in the mM range! This should be discussed.\nThe result section is very much redundant with the introduction with the same examples being discussed and the same argumentation presented.\nLines 248-249: these statements are not supported by the presented data: accuracy of the flow manipulation, low sample consumption, etc.\nLine 262: is there a reason to wish a device that can be transported off-site in the context of the targeted application?\nLine 264: which medium is used (brand, exact composition)? Is serum included?\nEmbryo culture: were embryos cultured individually in medium? In how much medium? And how much medium is used for these measurements? This should be indicated in the Results section.\nLines 274-276: are these values in line with previous reports (e.g., Houghton et al.)?\nLines 287-296: redundant with introduction?\nAt which development phase were these measurements conducted?\nFigure 6: which day of development?\nLines 346-353: redundant with intro\nLine 382 and following: which species?\nRef 8: what are these metabolites?\nIt is not clear why this device is easier to use; it still involves manual handling of medium and addition of the reagents.\nExp section\nLots of information is directly the same as in the Results"}, "author_response": "We appreciate the constructive comments from five reviewers. They are very useful for us to improve the quality of the paper. All comments and the relevant answers are listed below.\n\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nThe work titled “Embryo Metabolite Analysis and Implantation Potential Prediction Using Chemiluminescent Microfluidic Chips with Dielectric Wetting Valves” analyzes the concentration of lactate, glucose, and pyruvate in 3 µL of media from the culture of 61 individually cultured embryos from day 3 to day 5. The authors developed a capillary-driven chemiluminescence microfluidic device equipped with three electrowetting valves.\n\nThe authors found that embryos resulting in pregnancy had:\n1. Greater glucose consumption, with a Youden index (sensitivity + specificity - 1) exceeding 103.64 pmol/embryo/h, indicating a higher likelihood of successful pregnancies.\n2. Greater pyruvate consumption than 31.32 pmol/embryo/h.\n3. Increased lactate production greater than 192.7 pmol/embryo/h.\nThe embryo implantation potential assessment model, which integrates metabolomics and morphological data, achieved an AUC of 91.2%. The authors conclude that their technology exhibits superior sensitivity in multi-analyte detection with minimal sample volume and holds promise as a decision-making tool for selecting embryos with the highest developmental potential.\nThe overall subject area is of critical importance, as identifying embryos with higher implantation potential is of great clinical value. The paper thoroughly describes the engineering component of the device and presents results from a group of human embryos.\n1. However, there are significant concerns regarding the quality of the human data. Currently, the results are not interpretable due to a lack of critical information about the patients, the IVF protocol, laboratory parameters, and the embryo transfer technique. A table should include details such as age, infertility diagnosis, type of IVF stimulation protocol, the number of embryos generated per patient, culture conditions (media, oxygen concentrations), fertilization method (IVF or ICSI), and whether PGT was performed.\nReply:\nWe thank the reviewer for this thoughtful suggestion. In response, we have provided key information related to the assisted reproduction process—including patient age, infertility diagnosis, IVF stimulation protocol, fertilization method, and the number of embryos generated per patient—in the newly added Supplementary Data. In addition, we have added details in the Methods section describing the IVF procedure, laboratory setup (embryo culture parameters), and embryo transfer techniques used in this study. The specific content is provided below:\nStudy Participants and Samples\nThis study was approved by the Research Ethics Committee of Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly known as Shenzhen Zhongshan Urology Hospital); approval number: SZZSECHU-20180021). The participants were infertility patients who underwent in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) followed by vitrified single-blastocyst transfer at our center between January 2021 and July 2023. In total, 169 samples of spent blastocyst culture medium (SBCM) were collected and allocated to a training set (n = 61) and a validation set (n = 108). The training set was constructed retrospectively with the specific aim of building a predictive model. To ensure a balanced distribution for model development, we selectively included 34 samples from embryos resulting in clinical pregnancy and 27 samples from embryos that did not, with collection dates spanning non-consecutive periods across the study timeframe. In contrast, the validation set was designed prospectively to assess the effectiveness of the model in practical applications. It comprised all SBCM samples that met the inclusion criteria from a defined period (June to July 2023).\nEmbryo Culture and Sample Collection\nOocytes retrieved by ultrasound-guided aspiration underwent fertilization by either IVF or ICSI. Fertilization was assessed 17 ± 1 h after insemination, and the presence of two pronuclei was taken as evidence of normal fertilization. Zygotes were cultured in Quinn’s Advantage Cleavage Medium (SAGE BioPharma, USA) supplemented with 10% (v/v) serum protein substitute until day 3. Embryo culture was maintained in a Labtech C200 incubator under stable conditions (6% CO₂, 5% O₂, 37 °C, 95% humidity), which were applied consistently from day 1 to day 5. Cleaving embryos were transferred to Quinn’s Advantage Blastocyst Medium, supplemented with 10% (v/v) serum protein substitute, and cultured individually in 25 μL droplets until blastocyst formation. Following vitrification, spent blastocyst culture medium (SBCM) was collected and stored at −80 °C for subsequent analysis.\nEmbryo Transfer and Confirmation of Clinical Pregnancy\nAll patients underwent vitrified–warmed single blastocyst transfer. Endometrial preparation followed either natural or artificial protocols. Serum hCG was measured 11 days after transfer, with concentrations >5 IU/L indicating biochemical pregnancy. Clinical pregnancy was confirmed by the presence of an intrauterine gestational sac on transvaginal ultrasound 30 days after transfer.\n2. Were embryos cultured individually from fertilization to day 3? If not, why? Was single embryo transfer performed? This information is critical.\nReply:\nEmbryos were cultured individually from fertilization to the blastocyst stage, followed by single-embryo transfer. This information has been added to the Methods section of the manuscript.\n3. Ideally, the authors should provide live birth rates.\nReply:\nIn accordance with the reviewer’s suggestion, we have added to Supplementary Data the characteristics of patients with different pregnancy outcomes in the training and validation cohorts (including live birth rate, body mass index, duration of infertility, endometrial thickness, and others). Patient information is summarized in Tables S6. In addition, Figures 6d and 6e in the main text have been revised to include descriptions of live birth outcomes. The specific content is provided below:\n“As shown in Fig. 6d, the numbers of clinical pregnant/total embryos and live births/pregnancies were 22/26 and 20/22 for metabolic score 3, 9/14 and 8/9 for score 2, 2/18 and 2/2 for score 1, and 0/6 and 0/0 for score 0. Similarly, Fig. 6e shows that for morphological grades I, the numbers of clinical pregnant/total embryos and live births/pregnancies were 5/6 and 5/5, 11/19 and 9/11 for grade II, 5/14 and 5/5 for grade III, and 12/22 and 11/12 for grade IV.”\nFigure 6. Multi-parameter prediction model for embryo selection. (d) The number of embryos resulting in clinical pregnancy and the total number of embryos were recorded for metabolism scores of 3, 2, 1, and 0. (e) The number of embryos resulting in clinical pregnancy and the total number of embryos with morphology scores of Ⅰ-Ⅳ.”\n4. A validation cohort should be conducted to confirm the original data.\nReply:\nIn response to the reviewer’s comments, we have added a clinical validation of the embryo selection prediction model. Using our dielectric-valve-based microfluidic chip, we prospectively collected and analyzed the spent culture medium from 108 human embryos. These data were used to verify the robustness of the metabolomic assessment model in predicting embryo implantation potential. Details are provided in “Clinical validation of the embryo selection prediction model”.\n5. Figures 6F and 6G are confusing. A table showing the number of pregnant and non-pregnant patients for each metabolic or morphological score would be more valuable.\nReply:\nWe thank the reviewer for this valuable suggestion. In response, key parameters—including pregnancy embryos, live birth, miscarriage, pregnancy rates (%), non-pregnancy rates (%), and AUC—are now presented in Table S7 for the training cohorts.\nTable S7: Comparison of prediction models for embryo selection in training set\nModel Metabolomics model Morphology model\nScore 3 2 1 0 Ⅰ Ⅱ Ⅲ Ⅳ\nTotal\n24 14 17 6 6 19 14 22\nembryos\nPregnancy 22 9 2 0 5 11 5 12\nLive birth 20 8 2 0 5 9 5 11\nMiscarriage 2 1 0 0 0 2 0 1\nPregnancy\n66.67 27.27 6.06 0 15.15 33.34 15.15 36.36\nrates (%)\nNon-\npregnancy 7.14 17.86 53.57 21.43 3.5 28.8 32 35.7\nrates (%)\nAUC 0.894 0.618\n6. In Figure 6G, the authors use scores A to D, whereas the text mentions morphological scores I to VI (with VI likely being an error, presumably meant to be IV).\nReply:\nWe thank the reviewer for pointing out this mistake. The morphological scores in Figure 6g have been corrected to I–IV.\nFigure 6. Multi-parameter prediction model for embryo selection. (g) The percentage of distinct morphological scores in clinical pregnant and non-pregnant embryos.\n7. Line 360 states that a cumulative metabolic score above 1 was associated with a clinical pregnancy rate of 93.9%, while a score below 2 effectively screened out 88.5% of non-pregnant embryos. This seems incorrect, as a higher metabolic score should be associated with a higher pregnancy rate.\nReply:\nIn accordance with the reviewer’s comments, we have revised the wording in the main text. The specific changes are as follows:\n“Notably, a cumulative metabolic score of 3-0 correlated with clinical pregnancy rates of 66.67%, 27.27%, 6.06%, and 0% respectively (Fig. 6f). Furthermore, embryos with a cumulative metabolic score above 1 were identified with a clinical pregnancy rate of 93.94% (Table S7). Embryos with morphological scores of Ⅰ- Ⅳ achieved clinical pregnancy rates of 15.15%, 33.34%, 15.15%, and 36.36%, respectively (Fig. 6g). Notably, 48.49% of embryos resulting in pregnancy had high scores (Ⅰ and Ⅱ). Importantly, the metabolic score outperformed morphological assessment, enabling a 45.45% higher detection rate for embryos that became pregnant.”\nReviewer #2 (Remarks to the Author):\nThe manuscript provides valuable information related to embryo metabolism for Glucose, Lactate and Pyruvate. The sensitivity exhibited by the chemiluminescent microfluidic procedure seems to be impressive. The data presented shows that glucose and lactate levels in particular are related to embryo viability. The morphology scores also indicate that although Grade 1 embryos have high pregnancy rates, lower grades have a lower pregnancy return rate. Although the current technique of embryo metabolism valuation is novel, the previous data has been known for some time.\nThe data provided would need to be supplemented further with the following:\n1. Lack of embryo cohort controls within patients to show that selection can be made confidently between embryos of good grade within a patient’s own embryos\nReply:\nWe sincerely thank the reviewer for the critical and insightful comments. We fully agree that the ultimate benchmark for any new embryo selection approach lies in its ability to distinguish between embryos with similar morphological grades within the same patient—a scenario that is both common and challenging in clinical practice. We acknowledge that our initial study design did not specifically include such intra-patient comparisons between embryos based on metabolomic and morphological assessments.\nIt should be noted that in clinical practice, since only one or two embryos are typically selected for transfer per patient, it is difficult to directly compare pregnancy outcomes among embryos of similar morphological grade. To address this limitation, future studies could employ animal models (e.g., mice), in which multiple embryos of comparable morphological grade derived from the same female can be subjected to both metabolomic assessment and transfer. Such an approach would enable a more direct evaluation of the effectiveness and consistency of our method for embryo selection.\n2. The precise timing of measurement is not elucidated. L272 states “Human embryos cultured from day 3 to 5”. Given the sensitivity of the system, it is feasible that a 1-2h difference in measurements on different days may provide the same variation as seen in production of the metabolites by the embryo linked to viability. Did the authors adjust for the exact time in culture?\nReply:\nWe thank the reviewer for this valuable comment. In our initial metabolic analysis, the culture duration of each embryo was not precisely recorded, which indeed could affect the calculation of metabolic rates. To address this issue, we further verified the exact culture time for each embryo by looking up the culture records in hospital. Based on the corrected culture times, we recalculated the metabolic rates and updated Figures 5 and 6 accordingly.\nBefore correcting for culture time, the threshold values for predicting successful pregnancy were 103.64 pmol/embryo/h for glucose consumption, 192.70 pmol/embryo/h for lactate production, and 31.32 pmol/embryo/h for pyruvate consumption. After correction, the corresponding threshold values were 101.73, 187.39, and 31.45 pmol/embryo/h, , representing differences of 1.8%, 2.7%, and 0.4%, respectively.\nIn terms of clinical pregnancy outcomes, before correction, embryos with cumulative metabolomic scores >1 achieved a clinical pregnancy rate of 93.90%. After correction, the corresponding clinical pregnancy rate was 93.94%.\nAfter verifying the actual culture time of each embryo, we found that the primary source of discrepancy in the metabolic rate calculations was that 14 of the 61 embryos in the training set had been cultured for nearly 6 additional hours. Nevertheless, analysis with corrected culture times demonstrated that this variation did not materially affect our study conclusions.\n3. Retrospective analysis of AUC is of interest however the study would greatly benefit from prospective data. Examples of high AUCs that have subsequently not been substantiated by prospective analysis have been frequent in the field of embryo viablity prediction. For example, several AI models have proved very efficient in retrospective data but not in RCTs See Illingworth et al Nat Med. 2024 Nov;30(11):3114-3120. The ability to measure the samples non-invasively would allow the authors to perform a non-selection study, whereby embryos could be transferred and the media assessed blinded to outcome. (See Tiegs et al Fertil Steril 2021 Mar;115(3):627-637 for a similar approach to assess PGT outcomes（Prospective, blinded, multicenter, nonselection study. All usable blastocysts were biopsied, and the single best morphologic blastocyst was transferred before genetic analysis. Preimplantation genetic testing for aneuploidy was performed after clinical outcome was determined. Clinical outcomes were compared to PGT-A results to calculate the predictive value of a PGT-A aneuploid diagnosis.）\nReply:\nWe thank the reviewer for the insightful comments and valuable references. In the previous version, our embryo metabolomics-based prediction model was conducted as a retrospective study. Following the examples of Illingworth et al. and Tiegs et al.(cited in the main text), we have now supplemented our work with a clinical validation of the prediction model. Specifically, the validation cohort comprised 108 prospectively collected the samples of spent blastocyst culture medium (SBCM). Metabolite analysis of the spent culture media was performed using our dielectric valve-based microfluidic chip. The established metabolomic scoring model was then applied to assess these embryos, thereby validating the predictive power of the model for implantation potential. The specific results are presented as follows:\nClinical validation of the embryo selection prediction model\nFigure 7. Clinical validation of the multi-parameter prediction model for embryo selection. (a) Metabolites consumption or production rates obtained from 108 embryos. (b) The glucose consumption rate, (c) the lactate production rate, and (d) the pyruvate consumption rate of embryos with different pregnancy outcomes in the validation set. The box depth represents the interquartile range (50% of the data), whiskers represent the 5 and 95% quartiles. The horizontal line within the box indicates the median glucose consumption. Single (*) and triple asterisks (***) indicate significant differences with p value of < 0.05 and < 0.001, respectively, compared to gestated blastocysts. (e) The metabolite production/consumption rates of embryos with various morphological scores.\n(f) The number of embryos resulting in pregnancy, live births and the total number of embryos were recorded for metabolism scores of 3, 2, 1, and 0. (g) The number of embryos resulting in pregnancy, live births and the total number of embryos with morphology scores of Ⅰ-Ⅳ. (h) The percentage of various metabolic scores in clinical pregnant and non-pregnant embryos. (i) The percentage of distinct morphological scores in clinical pregnant and non-pregnant embryos. (j) The predictive outcomes for different embryo pregnancy potential predictive models in the validation set. These included the morphological model (yellow line), the metabolomics model (blue line), and the combined model of morphology and metabolomics (M&M model, red line).\n4. Although some analysis can be gleaned from the figures about the time the assay takes, the authors provide some description of how this would fit into practice.\nReply:\nIn response to the reviewer’s suggestion, we have expanded the Discussion section on \"Clinical validation of the embryo selection prediction model\", providing a more detailed description of the potential application scenarios of our embryo metabolite analysis technology.\n“The aforementioned research demonstrates that a metabolomic evaluation model can be established to predict embryonic implantation potential by leveraging the metabolic differences between pregnant and non-pregnant embryos. This approach is fully compatible with existing embryo morphological assessment and time-lapse imaging techniques, thereby enabling a multidimensional screening strategy for selecting embryos with high developmental competence. As the method only utilizes spent culture medium without altering the standard in vitro fertilization (IVF) protocol, it holds significant promise as an auxiliary decision-making tool for identifying embryos with the best developmental potential.”\n5. L47 Each Morphology scores read as VI. The authors mean “IV”\nReply:\nWe thank the reviewer for the valuable comment. In response, we have revised the manuscript and Figure 6 to ensure that the morphological scores are consistently presented as I–IV.\nFigure 6. Multi-parameter prediction model for embryo selection. (b) Representative images of embryos across different grades used for pregnancy potential prediction. (c) The metabolite production/consumption rates of embryos with various morphological scores. (e) The number of embryos resulting in pregnancy and the total number of embryos with morphology scores of Ⅰ-Ⅳ. (g) The percentage of distinct morphological scores in clinical pregnant and non-pregnant embryos.\nReviewer #3 (Remarks to the Author):\n1. While this reviewer comments on the latter aspect of the manuscript, he/she emphasizes that this is really a device design paper that should be reviewed by microfluidics experts as it has little to do with advancing the field of fertility treatments.\nReply:\nTo underscore the clinical value of this study, we made substantial revisions to the manuscript structure. Specifically, we expanded the introduction and discussion to provide a more in-depth analysis of the key bottlenecks in current clinical embryo metabolic assessment. In addition, we established a dedicated clinical validation cohort and incorporated the findings into the discussion, thereby strengthening our argument for how the developed metabolite detection technology overcomes existing limitations and its potential application in improving the success rates of fertility treatments.\n1. Limitations of clinical embryo metabolic analysis\nIn the introduction, we further emphasized the challenges that remain in embryo metabolic analysis. On the one hand, sequential detection of multiple target metabolites must be achieved within only a few to tens of microliters of sample under noninvasive conditions. On the other hand, the sensor must simultaneously offer both low detection limits and wide linear ranges to accommodate the large concentration disparities among biomarkers in embryo culture media—glucose and lactate are typically present at the millimolar level, whereas pyruvate is often only in the micromolar range. Moreover, because the total amount of metabolites produced by a single embryo during the entire culture period is limited, the sensor must be able to resolve subtle relative changes in the presence of high background concentrations (e.g., glucose). For clinical translation, it is also essential to minimize reliance on bulky peripheral equipment and labor-intensive chip fabrication processes. However, most existing microfluidic systems for embryo metabolic sensing remain unable to simultaneously meet these requirements, constituting a critical bottleneck for their application in clinical embryo assessment.\n2. To address the challenges of clinical embryo metabolic analysis, we developed a capillary-driven chemiluminescent microfluidic chip equipped with three electrowetting valves.\nMicrofluidics is widely recognized as one of the most promising technologies in bioanalysis, offering distinct advantages: (i) minimal consumption of reagents, solvents, and samples; (ii) reduced risk of contamination from the external environment; (iii) long-term and controllable embryo culture; and (iv) automated reagent delivery and high-throughput detection.\nChemiluminescence (CL) detection, in turn, is valued for its high sensitivity, operational simplicity, and independence from external light sources. By integrating electrowetting valves, we achieved precise fluidic control for oxidation incubation within the capillary microchannels, which markedly enhanced CL sensitivity. Furthermore, the use of glass as the chip substrate, instead of paper, facilitated more efficient collection of optical signals. As a result, the platform enabled quantitative analysis of glucose, lactate, and pyruvate in only 3.0 μL of culture medium, with dynamic ranges and limits of detection (LOD) of 1–2000 μM (LOD: 0.16 μM), 0.5–2000 μM (LOD: 0.15 μM), and 2.0–2000 μM (LOD: 0.55 μM), respectively. Importantly, the device not only lowered the detection limits (glucose: 1.0 μM; lactate: 0.5 μM; pyruvate: 2.0 μM) but also significantly expanded the dynamic ranges, with upper-to-lower ratio values of 2000, 4000, and 1000, respectively (Table S5). These features render the platform particularly suited for detecting metabolites with vastly different concentration levels in embryo culture medium (glucose and lactate at the millimolar level versus pyruvate at the micromolar level), as well as for overcoming the limited metabolic flux of single embryos.\n3. We subsequently developed a multiparametric metabolic scoring system.\nIn this system, each embryo was assigned 1 point for a given metabolite (glucose consumption, lactate production, or pyruvate consumption) if it exceeded its respective threshold; otherwise, it received 0 points. The cumulative metabolic score was then categorized into four levels: 3, 2, 1, and 0. Notably, metabolic scores of 3–0 corresponded to clinical pregnancy rates of 66.67%, 27.27%, 6.06%, and 0%, respectively. Embryos with a metabolic score greater than 1 exhibited a clinical pregnancy rate of 93.94% (Table S7). By comparison, embryos graded I–IV based on morphology showed clinical pregnancy rates of 15.15%, 33.34%, 15.15%, and 36.36%, with 48.49% of successful pregnancies originating from high-morphology embryos (I or II).\nImportantly, the metabolic scoring outperformed morphological assessment, increasing the detection rate of embryos that achieved pregnancy by 45.45%, indicating higher accuracy in predicting implantation potential.\n4. We performed a clinical validation of the developed embryo metabolic assessment model by evaluating the metabolic status of embryos in the validation cohort.\nEmbryos with metabolic scores of 3, 2, 1, and 0 exhibited cumulative clinical pregnancy rates of 47.83%, 43.48%, 8.69%, and 0%, respectively. Embryos with a metabolic score greater than 1 showed a cumulative pregnancy rate of 91.39%, closely matching the 93.94% observed in the training cohort. In comparison, embryos graded I–IV based on morphology achieved clinical pregnancy rates of 26.09%, 49.28%, 14.49%, and 10.14%, with 75.37% of successful pregnancies originating from high-morphology embryos (I or II). These findings highlight the value of our multi-parameter metabolic assessment model in advancing the prediction of embryo implantation potential.\n2. As for the embryo measurements, the media from 61 embryos was assayed for Glu/Lac/Pyr consumption and metabolism as a reflection of oathways thought to be of importance to mammalian embryo metabolism (itself an oversimplification of current knowledge).\nReply:\nResearch linking carbohydrate utilization in human embryos to subsequent developmental potential remains limited, and existing approaches are largely constrained to non-invasive quantification of only a few known metabolites. Gardner and colleagues1 noted that such studies are still scarce, while Smith and co-workers2 summarized that most established biomarkers of embryo health fall into three categories: (i) oxygen, (ii) amino acids, and (iii) glucose consumption and lactate production as indicators of energy metabolism. Consequently, current knowledge of the relationship between metabolic signatures and embryo viability is largely confined to glucose and lactate, underscoring the lack of analytical strategies capable of simultaneously and precisely monitoring multiple metabolites.\nIn addition, to demonstrate the accuracy of our embryo selection prediction model, we further applied our self-developed dielectric valve–based microfluidic chip to analyze metabolites in the validation set of 108 human embryo culture media samples (see section “Clinical validation of the embryo selection prediction model”). The experimental results confirmed that the metabolic assessment model provides superior predictive power for embryo implantation potential.\nFuture work will focus on systematically integrating multiple classes of biomarkers, such as amino acids and proteins, that are closely associated with developmental potential. This approach will enable a more comprehensive assessment of the physiological state of embryos prior to transfer, thereby further improving the accuracy and robustness of the predictive model.\n3. While mostly a paper on “wetting valves,” this paper has major flaws as far as any use as a tool for embryo selection. No information is provided about patients who provided embryos other than what culture medium was used. This is critical since many previous studies of this kind only select embryos that go from day 3-5 and thus do not represent the cohort in an unbiased way.\nReply:\nWe sincerely thank the reviewer for the valuable comments. This study primarily explored the role of the “wetting valve” technology in enhancing the accuracy of embryo metabolite detection. In future work, we plan to improve the level of automation of the platform and further advance the applicability of the detection chip for clinical embryo metabolic analysis.\nIn response to the reviewer’s concerns, we have added Supplementary Data, which provides detailed patient information regarding the assisted reproduction process, including maternal age, infertility diagnosis, IVF stimulation protocol, fertilization method, number of embryos produced per patient, and live birth outcomes.\nFurthermore, Tables S6 have been added to the supplementary file. These data show that, in both the training and validation sets, potential confounding factors such as maternal age, BMI, duration of infertility, endometrial thickness, and oocyte yield could be excluded when analyzing clinical pregnancy outcomes.\nTable S6: Characteristics for patients with different clinical pregnancy outcomes in training and validation set\ntraining set validation set\npositive negative p positive negative p\nn 33 28 69 39\nFemale age\n31.45±4.07 33.07±4.19 0.137 31.99±3.57 32.67±4.32 0.478\n(years)\nBMI (kg/m2) 22.29±3.34 21.67±3.98 0.376 22.03±4.46 21.13±2.75 0.500\nInfertility\nduration 3.87±2.68 3.28±1.87 0.329 3.81±2.42 4.12±3.64 0.764\n(years)\nPrimary\n5(15.1) 6(21.4) 0.526 39(56.5) 16(41.0) 0.122\ninfertility, n(%)\nStimulation\nprotocol, n(%)\nGnRH\n6(18.2) 4(14.3) 0.364 14(20.3) 12(30.8) 0.447\nagonist\nGnRH\n21(63.6) 22(78.5) 52(75.4) 26(66.7)\nantagonist\nOther 6(18.2) 2(7.2) 3(4.3) 1(2.6)\nNo. of\nretrieved 16.54±8.42 14±5.36 0.17 18.55±8.45(6,38) 16.64±8.98 0.184\noocytes\nFertilization\nmethod, n (%)\nIVF 23(69.7) 19(67.8) 0.877 61(88.4) 35(89.7) 0.832\nICSI 10(30.3) 9(32.2) 8(11.6) 4(10.3)\nEndometrial\n9.18±1.84 9±1.82 0.70 9.68±1.52 9.64±1.51 0.868\nthickness (mm)\nData with continuous variables are expressed as the mean ± standard deviation (SD), and categorical variables are expressed as quantities and ratios. p < 0.05 was considered statistically significant.\n4. We do not know what they mean by “pregnancy” outcomes other than it was not live birth rate which is now the only accepted outcome measure for studies of this kind (was it hCG???, ultrasound confirmation). We do not even know whether single embryo transfer was used even though it would be assumed.\nReply:\nIn this study, the indicator used to evaluate clinical pregnancy was the detection of a gestational sac by ultrasound. We did not use live birth as the study endpoint because the developmental progression from clinical pregnancy to live birth can be influenced by many uncontrollable factors, such as parental health conditions, genetic disorders, and environmental influences.\nIn addition, single embryo transfer (SET) was employed throughout the study to ensure the accuracy of outcome attribution, and this has been clarified in the revised Methods section.\n“Study Participants and Samples\nThis study was approved by the Research Ethics Committee of Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly known as Shenzhen Zhongshan Urology Hospital); approval number: SZZSECHU-20180021). The participants were infertility patients who underwent in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) followed by vitrified single-blastocyst transfer at our center between January 2021 and July 2023. In total, 169 samples of spent blastocyst culture medium (SBCM) were collected and allocated to a training set (n = 61) and a validation set (n = 108). The training set was constructed retrospectively with the specific aim of building a predictive model. To ensure a balanced distribution for model development, we selectively included 34 samples from embryos resulting in clinical pregnancy and 27 samples from embryos that did not, with collection dates spanning non-consecutive periods across the study timeframe. In contrast, the validation set was designed prospectively to assess the effectiveness of the model in practical applications. It comprised all SBCM samples that met the inclusion criteria from a defined period (June to July 2023).\nEmbryo Culture and Sample Collection\nOocytes retrieved by ultrasound-guided aspiration underwent fertilization by either IVF or ICSI. Fertilization was assessed 17 ± 1 h after insemination, and the presence of two pronuclei was taken as evidence of normal fertilization. Zygotes were cultured in Quinn’s Advantage Cleavage Medium (SAGE BioPharma, USA) supplemented with 10% (v/v) serum protein substitute until day 3. Embryo culture was maintained in a Labtech C200 incubator under stable conditions (6% CO₂, 5% O₂, 37 °C, 95% humidity), which were applied consistently. Cleaving embryos were transferred to Quinn’s Advantage Blastocyst Medium, supplemented with 10% (v/v) serum protein substitute, and cultured individually in 25 μL droplets until blastocyst formation. Following vitrification, spent blastocyst culture medium (SBCM) was collected and stored at −80 °C for subsequent analysis.\nEmbryo Transfer and Confirmation of Clinical Pregnancy\nAll patients underwent vitrified–warmed single blastocyst transfer. Endometrial preparation followed either natural or artificial protocols. Serum hCG was measured 11 days after transfer, with concentrations >5 IU/L indicating biochemical pregnancy. Clinical pregnancy was confirmed by the presence of an intrauterine gestational sac on transvaginal ultrasound 30 days after transfer.”\nReviewer #4 (Remarks to the Author):\nThe paper describes the fabrication and application of an EWOD (Electrowetting on dielectric) based microfluidic system, used to predict the implantation potential of 3 to 5 days old human embryos. As my competence is in (a.o. microfluidic) microsystems technology development, I will comment mainly on this aspect of the paper. In general the authors have clearly explained the fabrication technology and operation of the microfluidic sensor system (using chemiluminescent sensors). The principle of EWOD for manipulation of microvolumes of fluids is not new, the choice of the materials used in this paper for fabrication (PET and glass substrates, screen printed Ag and (I guess) PVD (physical vapour deposited) ITO, double sided tape) is quite straightforward and logical, and hence not really new. The main focus and novelty of the paper is on the application, therefore in my opinion the description of construction and operation of the sensor tool is a bit brief here and there in order to fully appreciate the technology development work, done by the authors. I’d like to see a number of fabrication details added and technology related questions answered in a revised version:\n1. The PDMS brand name and type should be mentioned in the “materials section” (there are many types of PDMS with wide variety of (chemical, mechanical, etc.) properties. Same for the glass: which type? (sodalime, borosilicate,…). What is the ITO layer thickness? A thickness of 170µm is mentioned for the double sided tape. Does that include the two adhesive layers on front and back? If not, what is the total thickness (tape + adhesives, this determines the height of the microfluidic channel)? How was the PDMS cured after spinning (temperature, duration, atmosphere, …)?\nReply:\nWe thank the reviewer for this helpful comment. Information regarding the brand names and types of PDMS and ITO glass used in this study has been added to the Supplementary Information.\n“Device design and fabrication\nThe design and fabrication of the dielectric-valve chip are illustrated in Figures S1 and S2.\nThe chip was fabricated in three main steps: etching the ITO electrode to serve as the ground electrode, printing the silver electrode as the excitation electrode, and spin-coating the PDMS layer as a hydrophobic channel surface. Initially, the ITO glass slide (ITO layer thickness: 680 ± 50nm) was laser-etched (Femto-IR-50-40) to define the ITO electrodes surrounded by non-conductive areas (Figure S3a). To clean the ITO electrode surface, the substrates were immersed in acetone for 10 min, rinsed sequentially with ethanol and deionized water to remove solvent residues, and then dried in an oven for subsequent use. A double-sided adhesive tape (170 µm in thickness, including adhesives on both sides) was cut (Fig. S3b) to form a trifurcated microchannel comprising a central junction (diameter, 4 mm) and three branch channels (12 mm × 2 mm each). Three silver electrodes were screen-printed onto a PET layer (29.7 mm×21 mm×0.2 mm) using the conductive silver ink. Afterward, PDMS precursor was prepared by mixing the base and curing agent at a 10:1 mass ratio, spin-coated onto the silver electrode layer (on top of the PET layer) at 3500 rpm for 2 minutes, and cured at 60 °C for 5 h to form a hydrophobic dielectric layer (20μm-thick film, Figure S3c). One inlet (6 mm) and three outlet (3 mm) were subsequently punched into the combination of Ag electrode layer and PDMS dielectric layer, followed by the preloading and drying of enzymes and probes in designated regions (Figure S3d-e). Finally, the combination of Ag electrode layer and PDMS dielectric layer was aligned from the upper-left corner with the ITO electrode layer containing the trifurcated channel and bonded, thereby sealing the channels and completing the final chip (Fig. S3f).”\n“Chemicals and materials\nD- (+)-glucose, sodium L-lactate, and sodium pyruvate were purchased from Sigma Aldrich (St. Louis, USA). Luminol, p-iodophenol (PIP), phosphate buffer solution (PBS, 1X, pH7.2-7.4), Tris–HCl buffer solution (TBS) (1.0M, pH9.0), Hydrogen peroxide (H O ), horseradish peroxidase 2 2 (HRP) and glucose oxidase (GO ) were purchased from Shanghai Macklin Biochemical Co., Ltd. X (Shanghai, China). Lactate oxidase (LO ), pyruvate oxidase (PO ), Flavin adenine dinucleotide X X (FAD), thiamine pyrophosphate (TPP) and magnesium chloride (MgCl ) were purchased from 2 Shanghai yuanye Bio-Technology Co., Ltd. (Shanghai, China). Quinn’s Advantage Blastocyst Medium (ART-1029) was purchased from CooperSurgical Inc. (Trumbull, USA). Double-sided tape (300LSE) was obtained from 3M (St. Paul, MN, USA). The PDMS precursor (substrate and curing agent, Sylgard 184) was obtained from Dow Corning (Midland, MI, USA).The ITO-glass plate (soda-lime substrate) was purchased from Xu Yu new retail center (Luoyang, China). All reagents were of analytical grade, and used without further purification. Ultrapure water was used throughout.”\n2. I guess that during assembly PET and glass substrate are aligned by hand, no? How much approx. is this misalignment and how critical is it? Does it e.g. influence the minimal voltage to operate the valves? Please comment. I’d like to see a detailed (dimensioned) cross section and top sight drawing of the valve (electrodes and channel). From fig.S2f it seems that silver and corresponding ITO electrodes are slightly shifted with respect to each other. Is this necessary for proper valve operation?\nReply:\nWe sincerely thank the reviewer for this valuable comment. The issue identified is indeed critical. Upon verification, we corrected the erroneous schematic of chip fabrication (the original figure mistakenly used the valve testing process diagram), which has now been updated in Figure S3.\nRegarding alignment during assembly, the chip design already accounts for the positioning of each component. Although assembly is performed manually, the potential misalignment is minimal. To further mitigate any impact on performance, we incorporated design features to maximize tolerance: the top Ag electrode is deliberately sized larger than the underlying ITO electrode region to ensure complete coverage, with an additional 0.5 mm margin at both edges. The red circles in Figure S2d indicate the reference points used for aligning the chip components. The finalized design of the dielectric valve chip is shown in Figure S2.\nFigure S2. The design of the dielectric valve chip. (a) ITO electrode. (b) The combination of Ag electrode layer and PDMS dielectric layer. (c) Double-sided tape channel. (d) Assembly of the dielectric valve chip.\nFigure S3. Preparation of the dielectric valve chip. (a) ITO electrode. (b) Double-sided tape channel. (c) The combination of Ag electrode layer and PDMS dielectric layer. (d) Pre-embedding of enzymes. (e) Pre-embedding of detection substrate. (f) Assembly of the dielectric valve chip.\n3. Figure S3: contact angles of water on ITO, glass or PDMS strongly depend on the pre-treatment of the substrate. Were there any other treatments of glass + ITO after ITO laser etching or of PET + Ag + PDMS after PDMS curing before final assembly?\nReply:\nThe entire fabrication process of the PET+Ag+PDMS structure was carried out in a cleanroom environment. After curing, the PDMS surface remained uncontaminated, eliminating the need for additional surface treatment. In contrast, the surface of ITO glass after laser etching may retain dust, oil, and other contaminants. Prior to use, the ITO electrodes were immersed in acetone for 10 minutes to remove such impurities. To prevent acetone residues, the electrodes were subsequently rinsed with industrial-grade ethanol, followed by deionized water to remove ethanol, and finally dried in an oven for further use. These details have been provided in the Supplementary Information.\n“Device design and fabrication\nThe design and fabrication of the dielectric-valve chip are illustrated in Figures S1 and S2.\nThe chip was fabricated in three main steps: etching the ITO electrode to serve as the ground electrode, printing the silver electrode as the excitation electrode, and spin-coating the PDMS layer as a hydrophobic channel surface. Initially, the ITO glass slide (ITO layer thickness: 680 ± 50nm) was laser-etched (Femto-IR-50-40) to define the ITO electrodes surrounded by non-conductive areas (Figure S3a). To clean the ITO electrode surface, the substrates were immersed in acetone for 10 min, rinsed sequentially with ethanol and deionized water to remove solvent residues, and then dried in an oven for subsequent use. A double-sided adhesive tape (170 µm in thickness, including adhesives on both sides) was cut (Fig. S3b) to form a trifurcated microchannel comprising a central junction (diameter, 4 mm) and three branch channels (12 mm × 2 mm each). Three silver electrodes were screen-printed onto a PET layer (29.7 mm×21 mm×0.2 mm) using the conductive silver ink. Afterward, PDMS precursor was prepared by mixing the base and curing agent at a 10:1 mass ratio, spin-coated onto the silver electrode layer (on top of the PET layer) at 3500 rpm for 2 minutes, and cured at 60 °C for 5 h to form a hydrophobic dielectric layer (20μm-thick film, Figure S3c). One inlet (6 mm) and three outlet (3 mm) were subsequently punched into the combination of Ag electrode layer and PDMS dielectric layer, followed by the preloading and drying of enzymes and probes in designated regions (Figure S3d-e). Finally, the combination of Ag electrode layer and PDMS dielectric layer was aligned from the upper-left corner with the ITO electrode layer containing the trifurcated channel and bonded, thereby sealing the channels and completing the final chip (Fig. S3f).”\n4. The supplementary clips show a smooth flow of dyed water in the channels, although one surface (the PDMS) is highly hydrophobic. I guess this is possible only if the channel height (now 170µm) is sufficiently small? Is there a minimum channel height above which capillary flow does not take place anymore?\nReply:\nIn rectangular channels, capillary forces are jointly determined by the contact angles at all four walls. Theoretically, when the top and bottom surfaces are hydrophilic but the side walls are hydrophobic, a critical channel height exists beyond which capillary rise can no longer occur. Our team has previously conducted a systematic study of capillary flow behavior in rectangular channels (Zhu et al., Microfluid Nanofluid, 2010, 8:275–282). The capillary pressure (𝑃𝑐) at the interface within a rectangular channel is given by the following equation:\ncos𝜃 +cos𝜃 cos𝜃 +cos𝜃\n𝑃 =σ( 1 2+ 3 4)\nc 𝑤 ℎ\nHere, 𝜎 denotes the interfacial tension between the two phases; 𝜃 and 𝜃 represent the contact 1 2\nangles at the left and right walls, respectively, while 𝜃 and 𝜃 correspond to the contact angles at 3 4\nthe top and bottom walls. The channel cross-sectional dimensions are given by the height ℎ and width 𝑤.\nAccording to this equation, when the channel height ℎ is much smaller than the width 𝑤, the capillary pressure is primarily determined by the contact angles of the top and bottom walls. Conversely, if ℎ is comparable to 𝑤, the contact angles of all four walls significantly influence the capillary pressure.\nIn the present study, the rectangular channels were designed with a height of 170 μm and a width of 2 mm. Since the height is much smaller than the width, the capillary pressure is mainly governed by the top and bottom wall contact angles (approximately 120° and 30°, respectively), which drives the forward flow of the liquid.\nIf the channel height were not sufficiently small, the contact angles of the left and right walls would also affect capillary flow. In our design, the side walls are composed of hydrophobic double-sided tape; if the hydrophobicity were pronounced, it could reduce capillary pressure and potentially impede sustained liquid flow.\n5. Line 122 (and others) : authors talk about “ITO glass surface” and “non conductive area”. This is confusing, I suppose they mean “ITO surface” and “glass surface” respectively. I suggest to change this.\nReply:\nWe thank the reviewer for this careful comment. In response, we have revised the manuscript to replace the terms “ITO glass surface” and “non-conductive area” with “ITO surface” and “glass surface”, respectively.\n“The mechanism of the dielectric wetting valve has been reported extensively. In the numerical simulation model, the initial contact angles were assigned as 120° for the PDMS surface, 80° for the ITO surface, and 30° for the glass surface. The experimentally measured contact angles, presented in Figure S4, closely align with these values. After activation of the voltage of 250V, although the bottom ITO surface had the same contact angle of 80°, the contact angle of the PDMS layer immediately dropped from 120° (Figure 2a (i)) to 90.36° (Figure 2a (ii), (iii)). Meanwhile, the direction of capillary force shifted from upstream to downstream within the flow, thereby enabling the halted liquid to move through the valve as depicted by the gas/liquid interface line in Figure 2a.”\n6. Fig.4e: the authors have monitored sensor stability for up to 15 days (stored at 4degC), which is very valuable work. For a commercial product however shelf lifes of typically 6 months or 1 year are usually required. Do the authors have any estimate for the longer term shelf life? Can the devices be stored at room temperature (at ambient atmosphere)? Representing the data of Fig4e as 2D graphs instead of 3D block diagrams, normalizing the numbers with respect to the initial value (=100%) and magnifying the decrease would allow to see the decreasing trend for each sensor better.\nReply:\nWe thank the reviewer for the positive evaluation of our sensor performance. In the microfluidic chip–based chemiluminescent detection of embryo metabolites, we employed the Luminol–HRP–H₂O₂ reaction system, whose detection stability is highly dependent on enzyme activity. Sensors of this type typically remain stable for 7–15 days when stored at 4 °C (Zhao et al., Biosensors and Bioelectronics, 2021, 171:112729)3. We have previously tested exposing the chips to ambient conditions, but even a single day of exposure markedly reduced the chemiluminescent detection sensitivity.\nSome studies have proposed embedding nanomaterials into chemiluminescent systems to enhance signal stability and duration (Dang et al., Analytical Chemistry, 2020, 92:5517–5523)4; however, the stability of such sensors generally remains limited to approximately one month. Extending the shelf life of chemiluminescent sensors to six months or even one year remains a significant challenge.\n7. Representing the data of Fig4e as 2D graphs instead of 3D block diagrams, normalizing the numbers with respect to the initial value (=100%) and magnifying the decrease would allow to see the decreasing trend for each sensor better.\nReply:\nIn accordance with the reviewer’s suggestion, we have revised Figure 4e to display the data as a 2D graph. Additionally, the day-0 measurements of glucose, lactate, and pyruvate have been set as the initial values (normalized to 100%), and the chemiluminescence (CL) intensities have been standardized accordingly. This adjustment more clearly highlights the temporal changes, particularly the magnitude of signal decay.\nFigure 4. Detection of glucose, lactate, and pyruvate. (h) The CL intensity of detecting three analytes using chips stored for various days at 4 ˚C. The error bars represent the standard deviations of three independent measurements.\n8. Lines 156 – 157: “the PDMS film was 27 μm thick (Supplementary Figure S5), yielding a total dielectric 157 layer with a thickness of approximately 20 μm” : the “dielectric” is exactly the “PDMS film”, so I would expect exactly the same thickness for both. Why are they different? Perhaps “PDMS film” is before curing, “dielectric” is after curing? (PDMS shrinks during curing.\nReply:\nDuring the design and fabrication of the dielectric valve chip, silver electrodes with a thickness of 6.78 ± 0.12 μm were first fabricated on a PET substrate using screen-printing technology (see Figure S5). Subsequently, a PDMS layer of approximately 26.93 ± 0.35 μm thickness was spin-coated onto the PET film containing the silver electrodes (see Figure S6). This PDMS layer fully encapsulates the silver electrode structures, as illustrated in the figure. Therefore, the final effective dielectric layer thickness can be estimated as the PDMS layer thickness minus the silver electrode thickness, approximately 20 μm.\n9. Line 150: “θ and θ0 represent the contact angles before and after voltage application”, should not this be: “θ and θ0 represent the contact angles after and before voltage application, respectively”?\nReply:\nWe thank the reviewer for this careful comment. We acknowledge that the original description of Equation (1-1) was incomplete, and it has now been corrected according to your suggestion.\ncos𝜃=cos𝜃 + 𝜀0𝜀𝑑 𝑉2 (1-1）\n0 2𝑑𝛾𝐿𝐺\nReviewer #5 (Remarks to the Author):\nThis article reports a device to predict the developmental potential of human embryos, by analyzing their utilization (production/consumption) in three substrates, glucose, lactate and pyruvate, which are essential markers of the embryo metabolism, and have been suggested as biomarkers for the assessment of the embryo developmental potential. Specifically, the device uses capillary forces and electro-activated valves to manipulate the samples, and an enzymatic process followed by chemoluminescence for the detection and quantification of the three substrates. One strong aspect of this article is the correlation between the on-chip acquired data, the embryo morphological scoring and the clinical pregnancy outcomes, for 61 embryos, and for women patients and not only animal studies.\nHowever, the present reviewer does not recommend this paper for publication in Nature communications for several reasons, as detailed in the following.\nIntroduction:\n1. The paper refers to morphological scoring at the start. Time-lapse imaging with more advanced morphokinetic parameters is widely used.\nReply:\nThank you for your valuable feedback. We fully acknowledge that time-lapse imaging and morphokinetic parameters are widely utilized in embryo assessment, and we have added relevant discussion in the Introduction.\n“To overcome the limitations of conventional approaches, advances in time-lapse imaging, comprehensive chromosomal screening, proteomics, and metabolomics have significantly enhanced the assessment of embryo viability. Among these, metabolic profiling offers critical physiological insights that complement morphological evaluation, providing an alternative way to study embryo’s developmental potential.”\nIn the present study, conventional morphological grading was employed due to the clinical setting and the availability of data at the time of collection. Nonetheless, we agree that integrating metabolic profiling with advanced morphokinetic data could enhance predictive performance. This point has been briefly addressed in the revised manuscript to highlight the potential of combining these complementary approaches in future studies (last paragraph of the Results section).\n“The aforementioned research demonstrates that a metabolomic evaluation model can be established to predict embryonic implantation potential by leveraging the metabolic differences between pregnant and non-pregnant embryos. This approach is fully compatible with existing embryo morphological assessment and time-lapse imaging techniques, thereby enabling a multidimensional screening strategy for selecting embryos with high developmental competence. As the method only utilizes spent culture medium without altering the standard in vitro fertilization (IVF) protocol, it holds significant promise as an auxiliary decision-making tool for identifying embryos with the best developmental potential.”\n2. The authors discuss several techniques (HPLC, MS, Raman, etc.); it would be more accurate to specify which parameters/biomarkers have been analyzed using these techniques. And are those techniques employed for intracellular measurements, as suggested “metabolic changes in embryo” or measurements in medium?\nThe authors bring as criticisms the extensive sample preparation and steps; how does this compare to their device, which also involves sample manipulation and dilution.\nReply:\nTechniques such as mass spectrometry (MS), high-performance liquid chromatography (HPLC), and Raman spectroscopy have been employed to analyze metabolites in embryo culture media, including pyruvate, lactate, alanine, glutamine, acetate, and formate, etc., as well as to identify and annotate unknown compounds. Among these, MS offers high sensitivity and broad analyte coverage; HPLC excels in precise quantification and separation; and Raman spectroscopy enables non-destructive, rapid, and real-time analysis. Despite their respective advantages, these methods generally rely on complex sample pretreatment and expensive, large-scale instrumentation, while offering limited capability for manipulating and detecting microliter-scale fluids. These constraints collectively hinder their broad adoption in routine clinical settings.The advent of microfluidic technologies present an opportunity to address these challenges. With their advantages of minimal sample requirements, rapid analysis, and high sensitivity, they show significant promise for both embryo culture and metabolite online detection. According to the reviewer's comments, the second paragraph of the introduction was revised as follows:\n“Techniques such as mass spectrometry (MS), high-performance liquid chromatography (HPLC), and Raman spectroscopy have been widely used to analyze metabolic changes in embryo culture media, including metabolites such as pyruvate, lactate, and alanine. Each method offers distinct advantages: MS provides high sensitivity and broad analytical coverage; HPLC enables precise quantification and separation; and Raman spectroscopy allows for non-invasive, rapid, and real-time analysis. However, despite their advantages, these technologies typically require complex sample preparation and costly instrumentation, and offer limited capability for manipulating and analyzing microliter-scale fluids, which has largely constrained their translation into routine clinical practice.”\n3. References: several statements are not supported by references. Most of the references are old, and the paper does not cite seminal papers on embryo metabolic assessment for their scoring and on microfluidics for embryo culture and “scoring”. Of note, several of these articles are using electrowetting as a microfluidic platform for the analysis of biomarkers in the embryo culture, and none of these articles are cited.\nLine 54: Ref 12 and 13 are not related to embryo culture and scoring.\nReply:\nWe thank the reviewer for this valuable comment. We agree that appropriate and up-to-date referencing is essential to place our work within the context of existing research. In response, we have thoroughly revised the reference list and made the following changes:\nWe have carefully reviewed and updated the cited literature, replacing several outdated references with more recent and relevant studies (see the third paragraph of the Introduction). We added several key publications on the application of microfluidic technology in assisted reproductive technologies, including:\nSuh et al., Human Reproduction Update, 2003\nSmith et al., Theriogenology, 2007\nSmith et al., Molecular Human Reproduction, 2017\nGac et al., Molecular Human Reproduction, 2017\nLai et al., Journal of Biomechanics, 2015\nWe have included recent works that utilize microfluidic and electrowetting platforms for embryo culture and biomarker analysis, which were previously omitted. These include, but are not limited to:\nHeo et al., Human Reproduction, 2010\nAlegretti et al., Cells, 2024\nChen., Sensors andl Actouators: B. Chemical, 2021\nHuang et al., Plos one, 2015\nWe have revised specific statements in the Introduction sections to ensure they are supported by appropriate references.\n“Microfluidic technology, with its advantages of minimal sample consumption, high integration, rapid analysis, and exceptional sensitivity, offers promising solutions to major challenges in assisted reproduction and holds substantial translational potential2, 5, 6, 7, 8, 9. In embryo culture, microfluidic systems can create microenvironments more conducive to development by finely regulating physicochemical and biochemical conditions, thereby directly or indirectly enhancing developmental competence9, 10, 11, 12. In the context of metabolic biomarker analysis, various microfluidic devices have been designed to quantify embryo metabolites and their key reactants13, 14, 15, 16, 17. For instance, Urbanski et al. developed an automated microfluidic system for offline, non-invasive monitoring of glucose, lactate and pyruvate in the culture medium of individual mouse embryos16. Studies by Heo and Obeidat further integrated embryo culture and metabolic monitoring within single devices, enabling long-term or continuous assessment of embryonic metabolic activity14, 15.”\nWe appreciate the reviewer’s suggestion, which has helped us strengthen the scholarly foundation and context of our manuscript.\n4. Line 55: several references are for oxygen; is this a metabolite?\nFor all examples provided it would be relevant to specify if the measurements are conducted offline or in situ. Oxygen measurements are in situ; the rest is mostly done offline.\nRef 16: which metabolites?\nReply:\nWe thank the reviewer for this insightful comment. Oxygen is not a metabolite but a key reactant in metabolism. We have revised the main text accordingly. For the examples provided, we have specified whether the measurements were performed offline or in real time. Additionally, we have included the study by Obeidat et al., which monitored the dynamic changes of dissolved oxygen, glucose, and lactate in bovine embryo culture media in real time.\nObeidat and colleagues reported a multi-sensor integrated microfluidic chip that incorporates three electrochemical sensors into a single microchamber, enabling real-time monitoring of dissolved oxygen, glucose, and lactate dynamics in the culture medium during bovine embryo development from morula to blastocyst.\n5. Line 68: why is the device fabrication an issue for the adoption of microfluidics by clinicians? And to which extent is the proposed device addressing these concerns of labor-intensive procedures – since sample has to be manipulated and pipetted – and fabrication?\nReferences 19, 20 and 21 come across as randomly chosen.\nReply:\nMost current microfluidic chips are still produced through multi-step processes, including mask fabrication, mold preparation, PDMS casting, and bonding. These procedures require sequential manual operations and are difficult to scale, resulting in high production costs. In contrast, the chip proposed here employs screen printing, laser etching, PDMS film preparation, and double-sided tape cutting—steps that are amenable to scalable production, facilitating broader clinical implementation.\nCapillary-driven microfluidic chips offer the advantages of pump-free operation, low cost, ease of integration, and low energy consumption. After integrating dielectric valves, this design achieves more precise fluid control compared with porous-substrate capillary-driven chips (e.g., paper- or fabric-based chips). In clinical practice, users simply add the sample and sequentially trigger the dielectric valves with a power switch to complete the assay, significantly simplifying the workflow while enhancing detection stability and reliability.\nReferences [19–21] have been updated accordingly.\n6. The authors present their developments with CL for glucose and lactate detection; what the present reviewer misses is the required performance for a device to measure embryo metabolism, and these elements are never really discussed in the article: how much medium is available/can be used to consider the measurements as non-invasive? And which LOD should be reached? The concentration is not per an issue for glucose, it is more the concentration difference knowing that the embryo metabolism is limited, and that eventually these measurements should be conducted at the single embryo level in a given amount of medium.\nHere, it is not clear if embryos are cultured individually and in which amount of medium.\nReply:\nIn accordance with the reviewer’s suggestion, we have removed the detailed description of chemiluminescent detection of glucose and lactate. The revised text now focuses on the performance requirements of devices needed for measuring embryo metabolic markers. The specific changes in the main text are as follows:\n“Microfluidic technology holds considerable promise for embryo metabolic analysis, yet its clinical application faces multiple challenges. First, it must enable sequential detection of multiple target metabolites in extremely small sample volumes, ranging from a few to tens of microliters, while remaining non-invasive. Second, sensors must combine low detection limits with a wide linear range to accommodate the large concentration differences among metabolites in embryo culture media—glucose and lactate are typically at mM levels, whereas pyruvate is often at μM concentrations. Moreover, because the total amount of metabolites from a single embryo over the entire culture period is limited, sensors must be capable of detecting small relative changes against high background concentrations, such as glucose. For microfluidic sensing to achieve broader clinical adoption, it is essential to minimize reliance on complex, bulky peripheral equipment and labor-intensive chip fabrication. Currently, most microfluidic systems for embryo metabolic sensing struggle to meet all these requirements simultaneously, representing a major bottleneck to their practical implementation in clinical embryo metabolic analysis.”\n“Capillary-driven microfluidic chips offer a compelling alternative, leveraging capillary forces for autonomous fluid transport18. This eliminates the need for external pumps, enhances portability, and enables precise fluid control. Moreover, capillary microfluidic platforms seamlessly integrate diverse analytical techniques—including colorimetry19, fluorescence detection20, electrochemical analysis21, and chemiluminescence22—facilitating rapid, high-sensitivity, and low-power metabolite detection. We developed a capillary microchip with integrated electrowetting valves for chemiluminescence-based metabolite detection. These valves enabled precise fluid regulation within the capillary network, triggering enzymatic oxidation and chemiluminescence reactions. The chip demonstrated excellent sensitivity and a broad linear detection range for multiple metabolites, enabling accurate quantification of glucose, lactate, and pyruvate in embryo culture media and linking metabolic activity to clinical pregnancy outcomes.”\nMicrofluidic device:\n7. The dimensions of the structures should be documented as well as the volumes manipulated in the device. What is the thickness of the double-sided tape? What is the surface tension so that liquid stops at the valve? The two types of yellow (Fig 1b) are difficult to distinguish.\nReply:\nIn rectangular channels, capillary forces are jointly determined by the contact angles at all four walls. Our team has previously conducted a systematic study of capillary flow behavior in rectangular channels (Zhu et al., Microfluid Nanofluid, 2010, 8:275–282)23. The capillary pressure (𝑃𝑐) at the interface within a rectangular channel is given by the following equation:\ncos𝜃 +cos𝜃 cos𝜃 +cos𝜃\n𝑃 =σ( 1 2+ 3 4)\nc 𝑤 ℎ\nHere, 𝜎 denotes the interfacial tension between the two phases; 𝜃 and 𝜃 represent the contact 1 2\nangles at the left and right walls, respectively, while 𝜃 and 𝜃 correspond to the contact angles at 3 4\nthe top and bottom walls. The channel cross-sectional dimensions are given by the height ℎ and width 𝑤.\nAccording to this equation, when the channel height ℎ is much smaller than the width 𝑤, the capillary pressure is primarily determined by the contact angles of the top and bottom walls. In the present study, the rectangular channels were designed with a height of 170 μm and a width of 2 mm. Since the height is much smaller than the width, the capillary pressure is mainly governed by the top and bottom wall contact angles (approximately 120° and 30°, respectively), which drives the forward flow of the liquid. Upon reaching the dielectric valve, however, the contact angles of the top and bottom surfaces shifted to about 120° and 80°, resulting in a capillary force oriented opposite to the flow direction and thereby impeding further liquid advancement.\nIn accordance with the reviewer’s comments, we have revised the wording in the main text. The specific changes are as follows:\n“A capillary-driven chemiluminescence microchip incorporating dielectric wetting valves was designed to detect embryo metabolites to establish a multiparameter predictive model for embryo viability (Figure 1a). The microfluidic chip consisted of a Ag electrode layer, a Polydimethylsiloxane (PDMS) dielectric layer, an ITO electrode layer, and a double-sided tape channel. The silver electrode (thickness: 6.78 ± 0.12 μm, Figure S4), was screen-printed on PET to serve as the excitation electrode. PDMS was spin-coated onto the Ag electrode layer to form a hydrophobic dielectric layer (~20 μm). The etched ITO electrode (thickness: 680 ± 50 nm) functioned as the ground electrode. The double-sided tape (thickness: 170 μm) was patterned to fabricate the microfluidic channel (12 mm × 2 mm). All components were subsequently assembled, with the detailed chip design and fabrication procedure described in the Supplementary Information. The ITO electrode (contact angle: 79.6°, Figure S4a) and PDMS dielectric layer (contact angle: 119.0°, Figure S4b) together constituted a hydrophobic valve within the capillary channel. Driven by capillary force on the glass surface (contact angle: 28.7°, Figure S4c), the sample (21 μL) spontaneously filled the microchannel and halted at the dielectric valve. Upon application of a 250 V potential, the valve surface became wetted, allowing flow to resume (Figure 1b (i and ii)).”\nWe thank the reviewer for the helpful suggestion. In accordance with this comment, we have revised the color scheme of the ITO electrodes in Figure 1b.\nFigure 1. (b) The sample detection process.\n8. Do embryos consume pyruvate and glucose simultaneously or successively? (different phases of development)\nReply:\nThe pre-implantation metabolic program of mammalian embryos follows a well-established pattern that has been extensively documented. During the cleavage stage, early embryos primarily rely on pyruvate as an energy source, while glucose consumption remains relatively low. As embryos progress to the blastocyst stage, glucose consumption increases markedly, exhibiting a characteristic “glycolytic” metabolic profile in vitro. This indicates that, throughout the development of most mammalian embryos—including humans—there is a clear temporal sequence and metabolic shift from pyruvate utilization to glucose consumption.\n9. Coming back to the microfluidic device: the medium is loaded in one central port which is connected to the three analytical channels, which are thus in contact. To which extent can the three measurements be independent knowing that the three enzymatic reactions will produce H2O2 which is used in the second step of the analytical process? This comes across as a significant flaw in this study. And has the timing of the different steps been optimized?\nReply:\nAs illustrated in the design schematics, hydrogen peroxide (H₂O₂) is generated only within the enzymatic reaction zones. For H₂O₂ to influence another channel, it would need to diffuse over a sufficiently long distance during the incubation period. To address this, we conducted simulations of 1 mM H₂O₂ diffusion within the rectangular channels to monitor concentration changes across the three enzymatic reaction regions. Figure 4d shows the concentration profiles of 1 mM H₂O₂ after 10, 120, 240, and 360 s of diffusion, and the corresponding concentration–position distributions over time are presented in Figure 4e. The results indicate that after 6 minutes, H₂O₂ at the outermost edge of a channel had diffused only 2.5 mm, leaving a substantial distance from adjacent channels, thereby confirming the independence of the three measurements.\nFigure 4. (d) Simulation of H₂O₂ diffusion in three rectangular channels. (e) Concentration distribution of H₂O₂ in a rectangular channel at different times.\nNumerical simulations\n10. Have the considered contact angles been measured? Where do these values come from?\nReply:\nIn this study, the contact angles of three randomly selected samples of each material were measured. The results were as follows: ITO electrodes, 78.2° ± 1.52°; PDMS dielectric layer, 118.5° ± 1.04°; glass surface, 29.2° ± 0.55. Detailed measurement data are provided in Figure S4.\nFigure S4. Contact angle measurements. (a) Photo of water droplet on PDMS surface. (b) Photo of water droplet on ITO surface. (c) Photo of water droplet on glass surface .\n11. It is also not clear which solutions are considered: DI water? PBS? Medium? Will the nature of the solution impact the functioning of the device? One could expect so.\nReply:\nThe properties of the solution can indeed influence device performance. In this study, contact angle measurements and fluid flow control experiments within the dielectric valves were conducted using PBS, as noted in the manuscript. The valves worked as expected during testing of medium samples. Considering that actual samples (embryo culture media) were also diluted sevenfold in PBS prior to use, potential effects of other reagents on device performance were not specifically assessed.\n12. For the measurements, how many experiments were conducted? And how many devices were considered?\nReply:\nFor contact angle measurements on each material surface—including ITO electrodes, the PDMS dielectric layer, and glass—three samples of each type were tested to obtain representative data. Similarly, for validation of fluid flow control within the dielectric valves, three independent chips were tested to ensure the repeatability and reliability of the experimental results.\n13. Thicknesses of the PDMS and electrodes are indicated: was this measured on one device? And how reproducible are these values? Why is the total dielectric layer of ca. 20 um?\nReply:\nThree PDMS films were randomly selected to measure their thickness under a microscope, yielding values of 26.53 μm, 27.21 μm, and 27.04 μm, resulting in an average thickness of 26.93 ± 0.35 μm. Similarly, three Ag electrodes were randomly selected and measured by SEM, with thicknesses of 6.65 μm, 6.89 μm, and 6.81 μm, corresponding to an average thickness of 6.78 ± 0.12 μm.\nFigure S5. Cross-sectional view of Ag electrode thickness\nFigure S6. PDMS thickness cross section\nIn the design and fabrication of the dielectric valve chip, silver electrodes (~6.8 μm thick) were patterned on a PET substrate by screen-printing (Fig. S5). A PDMS layer with a thickness of ~27 μm was subsequently spin-coated onto the PET film carrying the Ag electrodes (Fig. S6). This PDMS layer fully encapsulated the electrode structures, as illustrated below. Accordingly, the effective dielectric layer thickness was estimated as the PDMS thickness minus the electrode thickness, yielding a value of ~20 μm.\n14. Line 160: 0.7-2.1 larger => is a unit missing?\nReply:\nThank you for the reviewer’s reminder. The units have now been added accordingly in the revised manuscript.\n“The contact angle was 0.7°-2.1° larger than those obtained from the simulation (Figure 2c(iii)).”\nDetection conditions\n15. What is PIP?\nReply:\nThank you for the valuable comment. We confirm that PIP refers to p-iodophenol, and this has now been clarified in the revised manuscript.\n“As PIP (p-iodophenol) concentration rose from 0.5 to 1.0 mM, the CL intensity dramatically increased to the maximum of 115.01×10³ counts/gate.”\n16. Figure 3: n=? and how is n defined? Number of biological/analytical replicates? Number of devices used? In this characterization step, what is the concentration in substrate?\nIncubation times should be specified.\nReply:\nIn Figure 3, n = 3, and the error bars represent the standard deviation of three independent measurements. In addition, we have supplemented the manuscript with detailed information on the substrate concentrations at each stage of the detection condition studies, as well as the incubation time used during the enzymatic reaction process.\n“The concentrations of probes (luminol, PIP and HRP) and enzymes (GOx, LOx and POx) were evaluated to maximize the CL intensity. For luminol optimization, PIP (1 mM) and HRP (140 U/mL) concentrations were fixed. Increasing luminol from 0.5 to 2 mM produced no significant change in signal (Fig. 3a, P > 0.05), whereas further elevation to 8 mM reduced the intensity by 61.9%. This was also reported in previous studies and might be related to self-quenching at higher luminol concentrations24, 25. When optimizing PIP (p-iodophenol), luminol (2 mM) and HRP (140 U/mL) were held constant. As PIP concentration rose from 0.5 to 1.0 mM, the CL intensity dramatically increased to the maximum of 115.01×10³ counts/gate. It rapidly declined with the further increases in PIP concentration (Figure 3a), potentially due to raised nonradiative transitions reducing luminescence26. Finally, the CL intensity was directly proportional to the concentration of horseradish peroxidase (HRP), reaching a maximum once the concentration exceeded 100 U/mL (Figure 3b), with luminol and PIP fixed at 2.0 mM and 1.0 mM, respectively. Therefore, luminol (2 mM), PIP (1 mM), and HRP (100 U/mL) were used as probes in the subsequent experiments. In the enzyme effect study, the CL intensity was proportional to the concentrations of GOx, LOx and POx and reached maximal values at concentrations larger than 70 U/mL, 20 U/mL, and 25 U/mL, respectively (Figure 3b and 3c). Reaction time was standardized at 5 min. Consequently, the above concentrations were employed in subsequent experiments.”\n17. It is not clear how assays are conducted when the enzymes and detection reagents are not pre-loaded in the device.\nReply:\nIf the samples and the corresponding enzyme reagents were not subjected to the incubation step, the detailed detection procedure has been provided in the Methods section of the Supplementary Information under “Study of the detection conditions.”\n“The effect of sample incubation on the detection signal was evaluated using a capillary electric valve device. In the incubated sample setup, 5 μL of enzyme solution (70 U/mL GO , 20 X U/mL LO , or 25 U/mL PO ) was first placed in the enzyme reaction area ahead of the electrode, X X while 5 μL of the detection substrate solution (containing 2.0 mM luminol, 1.0 mM PIP, and 100 U/mL HRP) was placed in the detection area. Then, 21 μL of a sample solution (1 mM glucose, 1 mM lactate, and 1 mM pyruvate) was added to the injection port, and after incubation for 1-6 minutes, the electric valves were sequentially opened. The sample flowed into the detection area, and the PMT recorded the chemiluminescence (CL) signals.\nFor non-incubated samples, the enzyme solution and detection substrate were directly pre-placed in the detection area, and the sample was immediately dripped into the injection port. The CL reaction signals for glucose, lactate, and pyruvate were then recorded by the PMT for both incubated and non-incubated conditions.”\n18. Is re-usability an option in clinical settings when measuring material isolated from several embryos from the same patients? Or material from different patients?\nIs ultrapure water enough to “clean”? at least detergents should be used... medium contains a lot of various compounds that stick to all surfaces!\nReply:\nIn this study, the reference to the reusability of the chip was mainly intended to highlight its stable performance and potential for repeated use. However, in current clinical practice, reusing the same chip is generally not recommended due to the precious nature of patient-derived embryo samples and the risk of contamination or performance variation. The repeatability tests conducted in this work were performed during the experimental phase, aiming to validate the chip’s durability, reduce experimental costs, and minimize the number of chips required for fabrication. In addition, we have supplemented the manuscript to clarify that alcohol disinfection was used during the chip cleaning process to ensure cleanliness between different tests.\nNo enzymes or detection reagents were pre-embedded in the dielectric layer. Consequently, no chemiluminescence signal was observed upon sample addition (Fig. S8), confirming that the chip was thoroughly cleaned.\nFigure S8. Changes in CL intensity signal when the dielectric film is not pre-embedded with enzyme and detection reagent.\nDetection performance\n19. What is the entire timeline for the analysis?\nReply:\nThe complete analytical process was completed within 7 minutes. Following sample loading, a 4-minute enzyme incubation was required, after which the dielectric valves were sequentially activated at minutes 4, 5, and 6 to detect glucose, lactate, and pyruvate, respectively. Each assay required 60 seconds.\nThe entire analytical workflow of the device has been described in detail in the Methods section of the manuscript. The specific procedures include sample loading, incubation with enzyme reagents, triggering of the dielectric valves for fluid transport, and subsequent chemiluminescent detection.\n“Detection performance for glucose, lactate and pyruvate\nThe performance of the chip in detecting a mixture of glucose, lactate, and pyruvate diluted in PBS was evaluated. Five microliters of each enzyme solution (70 U/mL GO , 20 U/mL LO , 25 X X U/mL PO ) and 5 μL of substrate solution containing luminol (2.0 mM), PIP (1.0 mM), and HRP X (100 U/mL) were respectively pre-embedded to in the enzyme reaction area before the valve and the detection area behind the valve on the PDMS dielectric layer. After assembly of the whole device, 21 μL of the analyte mixture was added to the chip inlet. The mixture liquid immediately flowed to the front of the valves in the three branch channels. After incubating the oxidation reaction for 4, 5 and 6 minutes, the valves controlling the flow of glucose, lactate and pyruvate were respectively activated at the voltage of 250 V. Meanwhile, the CL signal was monitored using a photomultiplier tube (PMT, Hamamatsu H7421) with a gate time of 10 ms to record the signal variation over 60 seconds after each valve activation. ”\n20. What is the sensitivity of the platform? Once more, since the goal is to measure small consumption in glucose, the difference in concentration is most relevant, and as such the sensitivity of the platform. In medium, the concentration in glucose is much higher, in the mM range! This should be discussed.\nReply:\nIn the Discussion, we further elaborated on the detection sensitivity of the developed platform. First, with regard to the microfluidic detection strategy, we highlighted the critical role of the oxidation–incubation step in significantly enhancing chemiluminescence signal intensity. We then systematically compared the performance of our platform with that of existing microfluidic methods for glucose, lactate, and pyruvate detection, as detailed below.\n“Table S2 summarizes chemiluminescence-based methods for glucose and lactate detection, with paper- and cloth-based microfluidic chips widely explored due to their capillary-driven transport and low sample requirements. Li et al. reported a paper device with microchannels of varying lengths for sequential glucose and lactate detection using 2 μL of sample, achieving limits of detection (LODs) of 8 μM and 15 μM, respectively22. Zhang et al. developed a cloth-based platform that combined gravity and capillarity for glucose detection with an LOD of 9.74 μM27. Our group further advanced sensitivity with a spin-disc paper device, achieving LODs of 0.34 μM (glucose) and 0.30 μM (lactate), representing 23- and 50-fold improvements28.The chip developed in this study achieved an additional 53% and 50% reduction in LODs and a 100% and 700% expansion in detection ranges for glucose and lactate, respectively. These improvements stem from using glass as the lower substrate in the dielectric wetting valve chip, which enhances photon collection compared with paper. Table S3 summarizes optical microfluidic platforms for pyruvate detection, most relying on flow injection. For instance, Urbanski et al. achieved a 10 μM LOD with automated loading and mixing. In contrast, our chip reduced the pyruvate LOD 17-fold13. Overall, this capillary-driven microfluidic chip enables precise flow control through dielectric valves, prolongs multistep reaction times, and significantly enhances detection performance. Its structural flexibility further supports multi-channel integration, offering strong potential for large-scale applications.”\nTable S2. Chemiluminescence microfluidic devices for the detection of glucose and lactate\nNo. Metabolite Method Sample Detection Limit of Author\ns volume range detection\n(μL) (μM) (μM)\nTable S3. Optical microfluidic devices for the detection of pyruvate\nNo. Metabolite Method Sample Detection Limit of Author\ns volume range detection\n(μL) (μM) (μM)\nSecond, we benchmarked the chemiluminescent microfluidic platform against previously reported studies on embryo metabolic biomarker detection. The results demonstrated that our system achieved superior sensitivity and reliability, thereby providing a more robust and sensitive tool for embryo metabolomic analysis. A detailed comparison is presented below.\n“Table S5 summarizes major advances in embryo metabolic biomarker detection. Early studies used 96-well plates or glass slides with ultrasensitive fluorescence methods37, 38, while LC-MS, NMR, and HPLC enabled multiplex detection but lacked precise quantification39, 40, 41, 42. With the emergence of microfluidics, embryo culture and metabolic analysis have become more precise and efficient. Urbanski et al. reported an automated microfluidic system with laser-induced fluorescence, enabling noninvasive monitoring of glucose, lactate, and pyruvate in 0.5 μL of spent medium from single mouse embryos (0–1000 μM, LOD: 10 μM)13. Heo et al. developed a computer-controlled platform capable of fluid handling and quantitative glucose detection in the 1–100 μM range14. Obeidat et al. further integrated multi-electrochemical sensors to measure dissolved oxygen, glucose, and lactate in microchambers, achieving real-time metabolic evaluation with LODs of 0.5, 0.4, and 1.22 μM, respectively15. Compared to existing systems, the chip developed in this study exhibits significantly enhanced sensitivity, with detection limits of 1.0 μM for glucose, 0.5 μM for lactate, and 2.0 μM for pyruvate. It also features a substantially wider dynamic detection range, with upper-to-lower limit ratios of 2000, 4000, and 1000, respectively. These characteristics align well with the intrinsic concentration differences of metabolites in embryo culture media—millimolar levels for glucose and lactate, and micromolar levels for pyruvate—enabling the platform to overcome the challenge of low metabolic flux in individual embryos. In summary, this chemiluminescence-based microfluidic platform allows for sensitive, accurate, and non-invasive detection of key embryonic metabolites, offering a powerful tool for embryo metabolomic analysis.”\nTable S5. The studies on embryo metabolic biomarker detection\nDetection Microfluidic\nVolume LOD Upper/lower\nAuthor Metabolites Method range chip(Yes/\n(μL) (μM) limit ratios\n(μM) No)\nglucose,\n43 phenylalanine, RS – – – – No\nleucine\n39 benzoic acid LC-MS 20 – – – No\npyruvate,\nlactate,\nalanine,\nglutamine,\nacetate,\nformate\nmetabolite\nspectrum\n45 glutamine SPR – 1-100 1.187 100 Yes\nglucose, Fluoresc\n46 – 1-100 – 100 No\nlactate ence\npyruvate,\nFluoresc\n47 20 – – – No\nlactate ence\npyruvate,\nthreonine\namino acid,\nspectrum\npyruvate,\nFluoresc\n48 lactate, 6 – – – No\nence\nglutamate\nSER, ASP, HIS,\nALA\nFluoresc\n50 glucose 15 – – – No\nence\nglucose, 0–9000, 0.5,\nElectro\n15 lactate, – 0–6000, 0.4, – Yes\nchemical\noxygen 0–170 1.22\nFluoresc\n51 oxygen – – – – Yes\nence\nElectro\n52 oxygen – 150-200 – 1.33 No\nchemical\nElectro 30.4–\n53 lactate 20 22.6 8.02 Yes\nchemical 243.9\npyruvate, Conduc 0.5–12.5, 0.03, 25,\n54 2 Yes\nlactate tivity 10–250 0.02 25\nFluoresc\n14 glucose – 1–100 – 100 Yes\nence\nElectro\n16 oxygen – 0–250 – – Yes\nchemical\nglucose, 0–1000, 10,\nFluoresc\n13 lactate, 0.5 0–1000, 10, – Yes\nence\npyruvate 0–1000 10\nglucose, 1–2000, 0.16, 2000,\nThis\nlactate, CL 3 0.5–2000, 0.15, 4000, Yes\nwork\npyruvate 2–2000 0.55 1000\nFigure 4\n21. There are no error bars. Also in panel a, the CL intensity does not vary linearly with the substrate concentration. Why focusing on the uM range in panels b and c?\nReply:\nAll detection results were presented with error bars in the previous version. Given the relatively broad detection range, these error bars appeared visually modest. Figure 4a illustrates the chemiluminescence response across the entire substrate concentration range, which is further detailed in Figures 4b and 4c. Taking glucose as an example, chemiluminescence intensity exhibited a linear relationship with substrate concentration between 1.0 and 100 μM, whereas in the 100–2000 μM range, it correlated linearly with the logarithm of analyte concentration. As summarized in Table S2, this performance ranks among the higher end within the field of microfluidic chemiluminescent detection.\nTable S2. Chemiluminescence microfluidic devices for the detection of glucose and lactate\nNo. Metabolite Method Sample Detection Limit of Author\ns volume range detection\n(μL) (μM) (μM)\n22. The result section is very much redundant with the introduction with the same examples being discussed and the same argumentation presented.\nReply:\nIn response to the reviewer’s comments, we either removed or streamlined sections of the Introduction, while retaining representative examples, which are now presented in greater detail in the Results and Discussion section.\n23. Lines 248-249: these statements are not supported by the presented data: accuracy of the flow manipulation, low sample consumption, etc.\nReply:\nAccording to the reviewers’ comments, this section has been revised:\n“Table S2 summarizes chemiluminescence-based methods for glucose and lactate detection, with paper- and cloth-based microfluidic chips widely explored due to their capillary-driven transport and low sample requirements. Li et al. reported a paper device with microchannels of varying lengths for sequential glucose and lactate detection using 2 μL of sample, achieving limits of detection (LODs) of 8 μM and 15 μM, respectively22. Zhang et al. developed a cloth-based platform that combined gravity and capillarity for glucose detection with an LOD of 9.74 μM27. Our group further advanced sensitivity with a spin-disc paper device, achieving LODs of 0.34 μM (glucose) and 0.30 μM (lactate), representing 23- and 50-fold improvements28.The chip developed in this study achieved an additional 53% and 50% reduction in LODs and a 100% and 700% expansion in detection ranges for glucose and lactate, respectively. These improvements stem from using glass as the lower substrate in the dielectric wetting valve chip, which enhances photon collection compared with paper. Table S3 summarizes optical microfluidic platforms for pyruvate detection, most relying on flow injection. For instance, Urbanski et al. achieved a 10 μM LOD with automated loading and mixing. In contrast, our chip reduced the pyruvate LOD 17-fold13. Overall, this capillary-driven microfluidic chip enables precise flow control through dielectric valves, prolongs multistep reaction times, and significantly enhances detection performance. Its structural flexibility further supports multi-channel integration, offering strong potential for large-scale applications.”\n24. Line 262: is there a reason to wish a device that can be transported off-site in the context of the targeted application?\nReply:\nWe thank the reviewer for raising this important application issue. The sensor’s 15-day stability offers potential for use outside the central laboratory. Our primary design goal was to ensure that the device could be manufactured at scale and withstand transportation, enabling reliable application across hospital departments (e.g., from a central laboratory to specific units) or in other laboratories with established standard operating procedures.\n25. Line 264: which medium is used (brand, exact composition)? Is serum included?\nReply:\nDetailed information on the reagents employed in this study can be found in the “Methods” and “Chemicals and Materials”.\nEmbryo Culture and Sample Collection\nOocytes retrieved by ultrasound-guided aspiration underwent fertilization by either IVF or ICSI. Fertilization was assessed 17 ± 1 h after insemination, and the presence of two pronuclei was taken as evidence of normal fertilization. Zygotes were cultured in Quinn’s Advantage Cleavage Medium (SAGE BioPharma, USA) supplemented with 10% (v/v) serum protein substitute until day 3. Embryo culture was maintained in a Labtech C200 incubator under stable conditions (6% CO₂, 5% O₂, 37 °C, 95% humidity), which were applied consistently. Cleaving embryos were transferred to Quinn’s Advantage Blastocyst Medium, supplemented with 10% (v/v) serum protein substitute, and cultured individually in 25 μL droplets until blastocyst formation. Following vitrification, spent blastocyst culture medium (SBCM) was collected and stored at −80 °C for subsequent analysis.\nChemicals and materials\n“D- (+)-glucose, sodium L-lactate, and sodium pyruvate were purchased from Sigma Aldrich (St. Louis, USA). Luminol, p-iodophenol (PIP), phosphate buffer solution (PBS, 1X, pH7.2-7.4), Tris–HCl buffer solution (TBS) (1.0M, pH9.0), Hydrogen peroxide (H O ), horseradish peroxidase 2 2 (HRP) and glucose oxidase (GO ) were purchased from Shanghai Macklin Biochemical Co., Ltd. X (Shanghai, China). Lactate oxidase (LO ), pyruvate oxidase (PO ), Flavin adenine dinucleotide X X (FAD), thiamine pyrophosphate (TPP) and magnesium chloride (MgCl ) were purchased from 2 Shanghai yuanye Bio-Technology Co., Ltd. (Shanghai, China). Quinn’s Advantage Blastocyst Medium (ART-1029) was purchased from CooperSurgical Inc. (Trumbull, USA). Double-sided tape (300LSE) was obtained from 3M (St. Paul, MN, USA). The PDMS precursor (substrate and curing agent, Sylgard 184) was obtained from Dow Corning (Midland, MI, USA). The ITO-glass plate (soda-lime substrate) was purchased from Xu Yu new retail center (Luoyang, China). All reagents were of analytical grade, and used without further purification. Ultrapure water was used throughout.”\n26. Embryo culture: were embryos cultured individually in medium? In how much medium? And how much medium is used for these measurements? This should be indicated in the Results section.\nReply:\nEmbryos were cultured individually, and each assay required only 3 μL of spent blastocyst culture medium (SBCM) to quantify glucose, lactate, and pyruvate. Additional details are provided in the Results section, “Detection of metabolites in single blastocyst conditioned medium.”\n“Each assay required only 3 μL of spent culture medium. Figure 5a presents the metabolic profiles of 61 human embryos cultured from day 3 to 5, with positive and negative values indicating production and consumption, respectively.”\nA total of 25 μL of spent medium was collected from each embryo culture, as described in “Embryo Culture and Sample Collection”.\n“Oocytes retrieved by ultrasound-guided aspiration underwent fertilization by either IVF or ICSI. Fertilization was assessed 17 ± 1 h after insemination, and the presence of two pronuclei was taken as evidence of normal fertilization. Zygotes were cultured in Quinn’s Advantage Cleavage Medium (SAGE BioPharma, USA) supplemented with 10% (v/v) serum protein substitute until day 3. Embryo culture was maintained in a Labtech C200 incubator under stable conditions (6% CO₂, 5% O₂, 37 °C, 95% humidity), which were applied consistently from day 1 to day 5. Cleaving embryos were transferred to Quinn’s Advantage Blastocyst Medium, supplemented with 10% (v/v) serum protein substitute, and cultured individually in 25 μL droplets until blastocyst formation. Following vitrification, spent blastocyst culture medium (SBCM) was collected and stored at −80 °C for subsequent analysis.”\n27. Lines 274-276: are these values in line with previous reports (e.g., Houghton et al.)?\nReply:\nGardner et al. revealed that human embryos consume glucose and pyruvate at rates of 131.0 ± 11.4 pmol/embryo/hour38 and 37.7 ± 1.8 pmol/embryo/hour44, respectively—values that align closely with those observed in this study (126.58 ± 41.27 pmol/embryo/hour and 33.83 ± 12.89 pmol/embryo/hour, Table S9).\nTable S9. Measurement of embryonic metabolites in different studies\nAnalyte Metabolic rate Species Sample Microfluidic Author\n(pmol/embryo/h) stage chip\n(Yes/ No)\nGlucose 4.82±1.40 Mouse Blastocyst Yes 13\nGlucose 3.85±0.84 Mouse Blastocyst Yes 14\nGlucose 6.88±0.89 Mouse Blastocyst No 46\nGlucose 14.31±1.14 Bovine Blastocyst No 38\nGlucose 8.13±0.93 Bovine Blastocyst Yes 15\nGlucose 131.0 ± 11.40 Human Blastocyst No 37\nGlucose 126.58 ± 41.27 Human Blastocyst Yes This work\nLactate 5.70±2.13 Mouse Blastocyst Yes 13\nLactate 8.31±1.05 Mouse Blastocyst No 46\nLactate 23.62±1.40 Bovine Blastocyst No 38\nLactate 9.97±0.97 Bovine Blastocyst Yes 15\nLactate 261.36 ± 131.58 Human Blastocyst Yes This work\nPyruvate 0.92 ± 0.76 Mouse Blastocyst Yes 13\nPyruvate 16.43 ± 1.26 Bovine Blastocyst No 38\nPyruvate 37.70 ± 1.80 Human Blastocyst No 55\nPyruvate 33.83 ± 12.89 Human Blastocyst Yes This work\n28. Lines 287-296: redundant with introduction? At which development phase were these measurements conducted?\nReply:\nThis section has been revised to focus on current advances in embryo metabolic biomarker detection. We emphasize that our device is particularly well suited to address the substantial concentration disparities among metabolites in embryo culture medium (millimolar levels for glucose and lactate versus micromolar levels for pyruvate) and to overcome the inherent limitation of the low metabolic flux of individual embryos.\n“Table S5 summarizes major advances in embryo metabolic biomarker detection. Early studies used 96-well plates or glass slides with ultrasensitive fluorescence methods37, 38, while LC-MS, NMR, and HPLC enabled multiplex detection but lacked precise quantification39, 40, 41, 42. With the emergence of microfluidics, embryo culture and metabolic analysis have become more precise and efficient. Urbanski et al. reported an automated microfluidic system with laser-induced fluorescence, enabling noninvasive monitoring of glucose, lactate, and pyruvate in 0.5 μL of spent medium from single mouse embryos (0–1000 μM, LOD: 10 μM)13. Heo et al. developed a computer-controlled platform capable of fluid handling and quantitative glucose detection in the 1–100 μM range14. Obeidat et al. further integrated multi-electrochemical sensors to measure dissolved oxygen, glucose, and lactate in microchambers, achieving real-time metabolic evaluation with LODs of 0.5, 0.4, and 1.22 μM, respectively15. Compared to existing systems, the chip developed in this study exhibits significantly enhanced sensitivity, with detection limits of 1.0 μM for glucose, 0.5 μM for lactate, and 2.0 μM for pyruvate. It also features a substantially wider dynamic detection range, with upper-to-lower limit ratios of 2000, 4000, and 1000, respectively. These characteristics align well with the intrinsic concentration differences of metabolites in embryo culture media—millimolar levels for glucose and lactate, and micromolar levels for pyruvate—enabling the platform to overcome the challenge of low metabolic flux in individual embryos. In summary, this chemiluminescence-based microfluidic platform allows for sensitive, accurate, and non-invasive detection of key embryonic metabolites, offering a powerful tool for embryo metabolomic analysis.”\n29. Figure 6: which day of development?\nReply:\nAll embryos shown in Figure 6 were developed to day 5.\n30. Lines 346-353: redundant with intro\nReply:\nThis section has been revised based on the reviewers' comments:\nBefore modification:\n“These findings underscore the critical role of metabolic parameters in predicting embryo viability and successful implantation, complementing traditional morphological assessments. Morphological grading, despite its widespread use, is inherently subjective and often limited in accuracy for predicting embryo pregnancy. To address this limitation, we developed a multi-parameter metabolic scoring system. In this system, embryos received a score of 1 for each parameter—glucose consumption, lactate production, or pyruvate consumption—that exceeded its respective critical threshold, and a score of 0 if it did not. Embryos were then assigned metabolic scores of 3, 2, 1, or 0 based on their overall metabolic status.”\nAfter modification:\n“Subsequently, we developed a multi-parameter metabolic scoring system. In this system, embryos received a score of 1 for each parameter—glucose consumption, lactate production, or pyruvate consumption—that exceeded its respective critical threshold, and a score of 0 if it did not. Embryos were then assigned metabolic scores of 3, 2, 1, or 0 based on their overall metabolic status.”\n31. Line 382 and following: which species? Ref 8: what are these metabolites?\nReply:\nLiang et al. employed liquid chromatography–mass spectrometry (LC–MS) to comprehensively analyze five metabolites: M157T4_2, M159T11_1, M279T3_3, M241T3_2, and M137T4_1. These designations represent unidentified metabolites, corresponding to signal peaks detected under specific mass spectrometric conditions. This section of the manuscript has been revised accordingly.\n“Liang et al. used LC–MS to analyze five metabolites, yielding an AUC of 88%44, while Huo et al. identified amino acid markers by HPLC, raising the AUC to 90.4%49.”\n32. It is not clear why this device is easier to use; it still involves manual handling of medium and addition of the reagents.\nReply:\nFirst, the proposed chip can be fabricated using scalable techniques including screen printing, laser ablation, PDMS film casting, and double-sided tape patterning, enabling cost-effective mass production and broad applicability.\nSecond, the detection reagents are pre-embedded within the chip. In clinical practice, the operator only needs to load the sample and sequentially actuate the dielectric valves to perform the assay. This design not only streamlines the operational steps but also ensures stability, while offering clear potential for integration with automated platforms.\n33. Exp section. Lots of information is directly the same as in the Results\nReply:\nIn response to the comments, we have carefully reviewed the article (specifically the results section) and made the necessary revisions.\nResults\nBefore modification1:\n“The metabolite concentrations in embryos that result in pregnancy differ significantly from those in non-pregnant embryos. Accordingly, the predictive potential of glucose consumption rate, lactate production rate, and pyruvate consumption rate for pregnancy outcomes was assessed through the construction of receiver operating characteristic (ROC) curves. In these analyses, the x-axis represents 1-specificity, while the y-axis denotes sensitivity, as shown in Figure S11. The optimal thresholds for each metabolic parameter were identified using the Youden index, calculated as (Youden index = sensitivity + specificity - 1). As illustrated in Figure 6a, the glucose consumption rate associated with the highest Youden index is 103.64 pmol/embryo/h. This indicates that embryos with a glucose consumption rate exceeding this threshold during culture are more likely to result in successful pregnancies. Similarly, the lactate production rate and pyruvate consumption rate thresholds for predicting successful pregnancy are 192.7 pmol/embryo/h and 31.32 pmol/embryo/h, respectively (Figures 6a). In parallel, the Gardner scoring system was employed to evaluate embryonic development morphology. Blastocysts were categorized into four grades-Ⅰ, Ⅱ, Ⅲ, and Ⅵ-based on the scores of the inner cell mass and trophoblast cells (Figure 6b). Morphological assessments were conducted on 61 embryos, and the distribution of their scores is presented in Figure S12.\nThese findings underscore the critical role of metabolic parameters in predicting embryo viability and successful implantation, complementing traditional morphological assessments. Morphological grading, despite its widespread use, is inherently subjective and often limited in accuracy for predicting embryo pregnancy. To address this limitation, we developed a multi-parameter metabolic scoring system. In this system, embryos received a score of 1 for each parameter—glucose consumption, lactate production, or pyruvate consumption—that exceeded its respective critical threshold, and a score of 0 if it did not. Embryos were then assigned metabolic scores of 3, 2, 1, or 0 based on their overall metabolic status.”\nAfter modification1:\n“To evaluate the predictive value of key metabolic parameters—glucose consumption, lactate production and pyruvate consumption—we generated receiver operating characteristic (ROC) curves (Fig. S12). Based on the Youden index, the optimal thresholds for predicting clinical pregnancy were 101.73 pmol/embryo/h for glucose consumption, 187.39 pmol/embryo/h for lactate production and 31.45 pmol/embryo/h for pyruvate consumption (Fig. 6a). Embryos were then assigned metabolic scores of 3, 2, 1, or 0 based on their overall metabolic status. In parallel, the Gardner scoring system was employed to evaluate embryonic development morphology. Blastocysts were categorized into four grades-Ⅰ, Ⅱ, Ⅲ, and Ⅳ(Figure 6b). Morphological assessments were conducted on 61 embryos, and the distribution of their scores is presented in Figure S13.”\nBefore modification2:\nThe above analysis revealed that both metabolic and morphological scores independently act as positive predictors of pregnancy outcomes. To enhance the predictive accuracy, receiver operating characteristic (ROC) curves were used to integrate both parameters, providing a more comprehensive assessment of embryo pregnancy potential (Figure 6h). The ROC curves were generated for the morphology model, metabolomics model, and a combined model (M & M model). The ROC curve is a statistical tool for assessing model prediction accuracy, with a curve approaching the upper left corner indicating superior predictive performance. The area under the curve (AUC) quantifies the indicator's success rate. The gray line represents the baseline, with curves above this line indicating a prediction success rate of above 50%. The study results demonstrated that the AUC of the pregnancy assessment model based on embryonic morphology was 61.8%, while the AUC of the metabolomics model significantly increased to 89.4%. The AUC of the combined model reached 92.0%, significantly higher than that of the morphology model (p = 0.0001).\nAfter modification2:\nThe above analysis revealed that both metabolic and morphological scores independently act as positive predictors of pregnancy outcomes. The ROC curves were generated for the morphology model, metabolomics model, and a combined model (M & M model). The study results demonstrated that the AUC of the pregnancy assessment model based on embryonic morphology was 61.8%, while the AUC of the metabolomics model significantly increased to 89.4% (Figure 6h). The AUC of the combined model reached 92.0%, significantly higher than that of the morphology model (p < 0.001).\nBefore modification3:\nThe metabolites have been studied to predict embryo pregnancy probabilities (Table S6). For example, Fernandes et al. utilized mass spectrometry (ESI-MS) to analyze lactate and pyruvate levels in embryo-conditioned media, the prediction AUC values were 72.73% and 82.95%48, respectively. The AUC values are lower than those observed in this study since they focused solely on single-variable metabolite changes instead of multiple parameters including three metabolites and morphologic scores. In addition, Liang et al. utilized liquid chromatography-mass spectrometry (LC-MS) to comprehensively analyze five metabolites (M157T4_2, M159T11_1. M279T3_3, M241T3_2, M137T4_1), constructing a predictive model for embryo implantation potential that achieved an 88% AUC value44. Huo et al. performed non-invasive amino acid metabolism analysis on embryo culture medium using high-performance liquid chromatography (HPLC). By examining key components (H-SER, S-ASP, S-SER, S-HIS, S-ALA), they reported a prediction AUC value of 90.4% for the pregnancy group49. Although prior studies have highlighted significant differences in metabolic profiles between pregnant and non-pregnant embryos, and underscored the benefits of combining metabolic data with morphological assessments1, these methods often entail complex preprocessing, expensive equipment, and specialized skills, which may limit their practical application13. In contrast, this study leverages microfluidics technology to simplify procedures, allowing for straightforward, convenient, and highly sensitive detection of multiple metabolites, thereby providing a robust analytical tool for the assessment of embryo pregnancy potential.\nAfter modification3:\n“Metabolite profiling has been applied to predict embryo implantation potential (Table S8). Fernandes et al. employed ESI–MS to measure lactate and pyruvate, achieving predictive AUCs of 72.7% and 83.0%, respectively48. Liang et al. used LC–MS to analyze five metabolites, yielding an AUC of 88%44, while Huo et al. identified amino acid markers by HPLC, raising the AUC to 90.4%49. Although informative, these approaches rely on complex preprocessing and costly instrumentation. By contrast, our microfluidic platform offers a simple, rapid, and highly sensitive assay capable of multiplexed metabolite detection, providing a more practical tool for embryo assessment.”\nMethods\nBefore modification1:\n“The embryo's metabolic score utilized glucose, lactate, and pyruvate as indicators of metabolic activity. The consumption rates of glucose and pyruvate, and the production rate of lactate during embryo culture was assessed in terms of the success rate of embryo transfer.”\nAfter modification1:\n“The embryo metabolic scoring system was established using glucose, lactate, and pyruvate as key indicators of metabolic activity. We quantified glucose and pyruvate consumption rates together with lactate production rates during embryo culture, and correlated these with clinical pregnancy outcomes. Based on optimized metabolic thresholds predictive of pregnancy, embryos were classified such that values above the threshold received a score of 1 and those below received 0. The cumulative scores across the three metabolites stratified embryos into four metabolic grades: 3, 2, 1, or 0.”\nReference：\n1. Gardner DK, Meseguer M, Rubio C, Treff NR. Diagnosis of human preimplantation embryo viability. Human Reproduction Update 21, 727-747 (2015).\n2. Smith GD, Takayama S. Application of microfluidic technologies to human assisted reproduction. Molecular Human Reproduction, (2017).\n3. Zhao L-Z, Fu Y-Z, Ren S-W, Cao J-T, Liu Y-M. A novel chemiluminescence imaging immunosensor for prostate specific antigen detection based on a multiple signal amplification strategy. Biosensors and Bioelectronics 171, (2021).\n4. Dang P, Liu X, Ju H, Wu J. Intensive and Persistent Chemiluminescence System Based on Nano-/Bioenzymes with Local Tandem Catalysis and Surface Diffusion. Anal Chem 92, 5517-5523 (2020).\n5. Suh RS. Rethinking gamete/embryo isolation and culture with microfluidics. Human Reproduction Update 9, 451-461 (2003).\n6. Smith GD, Takayama S. 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Determination of pyruvate and lactate as potential biomarkers of embryo viability in assisted reproduction by capillary electrophoresis with contactless conductivity detection. Electrophoresis 36, 1244-1250 (2015).\n55. Gardner DK, Lane M, Stevens J, Schoolcraft WB. Noninvasive assessment of human embryo nutrient consumption as a measure of developmental potential. Fertility and Sterility 76, 1175-1180 (2001)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "the authors have satisfactorily responded to the comments. The addition of a validation cohort was very valuable", "reviewer_2": "The manuscript by Tong et al provides an extremely novel technology allowing the measurement of glucose, lactate and pyruvate in embryo spent media. Although the measurements are not novel their ability to make these measurements more attainable to an IVF lab compared to more cumbersome previous techniques is noteworthy. They follow the technological novelty with sufficient preliminary biological data to allow the author to believe that this could be a transferable technology in the future which may finally allow IVF labs to adopt a more simplified and clinically meaningful metabolic triaging of embryos.\n\nI feel that the authors have sufficiently answered my previous concerns and have also toned down the reported impact. Although the authors have answered many comments in the reviewer letter some have not been included in the manuscript. I would strongly suggest the inclusion of a Limitations section in the discussion of the results. For example, lack of embryo cohort data, specific timing of sample collection and more importantly that the embryos in this study were largely prefrozen using morphological criteria, indicating that these may have been the only embryos remaining for the patient. Discussion about the large overlap in readings for example in Fig 7b,c and d also need to be commented on.\n\nFinally, I would like to see more clarity in the figure as it is hard to understand the relationship between morphology grade and metabolic scores in some of the figures.", "reviewer_3": "Many serious concerns were raised in the initial review of this manuscript, multiple reviewers from different disciplines, all raising the question why were the authors requested to submit a revision to this journal?\n\nWhile the authors have done a valiant job at revising their paper, to this reviewer it remains unacceptable for a variety of reasons including its technical nature, limited clinical material evaluated, lack of novelty in view of metabolite data, and lack of evidence to support any advancement in the \"arena\" of so-called embryo selection.", "reviewer_6": "Thank you very much for your patience. This was a very difficult manuscript to review. As far as your original question regarding microfluidics portion of the paper, authors have sufficiently addressed questions raised by previous revirewers. It can be published in the revised form"}, "author_response": "RESPONSE TO REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors have satisfactorily responded to the comments. The addition of a validation cohort was very valuable.\nReply:\nWe sincerely thank the reviewer for the positive evaluation of our revised manuscript. We are pleased that the addition of a validation cohort and our detailed responses have satisfactorily addressed the previously raised concerns. We truly appreciate the reviewer’s constructive comments, which have helped us further strengthen the rigor and clarity of this study.\n\nReviewer #2 (Remarks to the Author):\nThe manuscript by Tong et al provides an extremely novel technology allowing the measurement of glucose, lactate and pyruvate in embryo spent media. Although the measurements are not novel their ability to make these measurements more attainable to an IVF lab compared to more cumbersome previous techniques is noteworthy. They follow the technological novelty with sufficient preliminary biological data to allow the author to believe that this could be a transferable technology in the future which may finally allow IVF labs to adopt a more simplified and clinically meaningful metabolic triaging of embryos.\n\n1. I feel that the authors have sufficiently answered my previous concerns and have also toned down the reported impact. Although the authors have answered many comments in the reviewer letter some have not been included in the manuscript. I would strongly suggest the inclusion of a Limitations section in the discussion of the results. For example, lack of embryo cohort data, specific timing of sample collection and more importantly that the embryos in this study were largely prefrozen using morphological criteria, indicating that these may have been the only embryos remaining for the patient. Discussion about the large overlap in readings for example in Fig 7b,c and d also need to be commented on.\nReply:\nWe thank the reviewer for this valuable suggestion. In response, we have added a dedicated “Limitations” subsection to the Discussion to explicitly acknowledge the limitations of the present study, as detailed below.\n“Limitations of the study\nThe primary limitations of this study include the limited sample size, and larger cohort studies will be required to improve model robustness. Culture medium collection followed routine clinical workflows and may therefore not fully capture metabolic dynamics at other developmental stages, such as the eight-cell or blastocyst stages. In addition, the experimental design did not distinguish morphologically similar embryos derived from the same patient, which represents a further limitation. Future work could involve large-scale, prospective multicentre cohort studies integrated within standard clinical workflows to enable metabolic analyses of morphologically similar embryos and embryos across developmental stages. Moreover, integrating metabolomic assessment with emerging non-invasive embryo evaluation approaches, including time-lapse imaging, proteomic profiling, and microRNA analysis, may help establish a multidimensional assessment framework, providing a powerful tool for embryo selection. ”\n2. Discussion about the large overlap in readings for example in Fig 7b,c and d also need to be commented on.\nReply:\nWe thank the reviewer for this valuable comment. In response to the substantial overlap in metabolic flux values among embryos with different pregnancy outcomes shown in Fig. 7b–d, we have added further analysis and discussion in the revised manuscript.\n“Although embryos resulting in clinical pregnancy exhibited differences in certain metabolic fluxes compared with non-pregnancy embryos, the distributions of individual metabolic parameters still showed considerable overlap when analyzed in isolation, which may lead to misclassification of implantation potential. This observation underscores that flux changes of a single metabolite are insufficient to reliably discriminate clinical pregnancy outcomes. Instead, a combined analysis of multiple key metabolic fluxes in the culture medium is required to achieve a more robust prediction of embryo implantation potential.”\n3. Finally, I would like to see more clarity in the figure as it is hard to understand the relationship between morphology grade and metabolic scores in some of the figures.\nReply:\nWe thank the reviewer for the valuable suggestion regarding figure clarity. In response, we have systematically revised the schematic of embryo developmental potential assessment (Fig. 1) to more intuitively illustrate the relationship between embryo morphological grades and metabolic scores. Specifically, the revised Fig. 1 clearly delineates two parallel evaluation pathways—morphology-based assessment and metabolite-based assessment—and presents, in a streamlined workflow, how these approaches are integrated in this study for comprehensive evaluation of embryo developmental potential. The metabolic scoring system is derived from metabolite measurements obtained using our capillary-driven chemiluminescence microchip incorporating dielectric wetting valves and is combined with conventional morphological grading to inform embryo transfer decisions.\nFigure 1. Schematic of the metabolite detection process for embryo developmental potential assessment. (a) The morphology and metabolite comprehensive analysis based on a chemiluminescence dielectric valve chip. (b) The sample detection process. (i) The sample flowed to the enzyme reaction zone and halted before the valve, where it reacted with the preloaded enzyme. (ii) The dielectric valve was activated, allowing fluid to resume flow and triggering the chemiluminescence reaction. (iii) Schematic illustration of the CL mechanism for metabolite detection.\n\nReviewer #3 (Remarks to the Author):\nMany serious concerns were raised in the initial review of this manuscript, multiple reviewers from different disciplines, all raising the question why were the authors requested to submit a revision to this journal? While the authors have done a valiant job at revising their paper, to this reviewer it remains unacceptable for a variety of reasons including its technical nature, limited clinical material evaluated, lack of novelty in view of metabolite data, and lack of evidence to support any advancement in the \"arena\" of so-called embryo selection.\nReply:\nWe sincerely thank you and the other reviewers for the insightful comments provided during the initial review. We have carefully addressed each point through detailed revisions, and these valuable suggestions have substantially improved the completeness and rigor of the manuscript.\nWe also appreciate your positive assessment of our chemiluminescence-based metabolite detection strategy using a dielectric-valve microfluidic chip. This platform enables sensitive discrimination of the intrinsic concentration differences among key metabolites in embryo culture media—glucose and lactate at the millimolar level and pyruvate at the micromolar level—while requiring only 3 μL of culture medium to simultaneously quantify all three analytes (Supplementary Table S5). By integrating these metabolic readouts with conventional morphological assessment, we established an embryo implantation potential prediction model that achieved an area under the curve (AUC) of up to 92.0%, demonstrating strong discriminative performance. Together, these results highlight the methodological innovation and data robustness of the present study.\nIn the revised manuscript, beyond the initial retrospective cohort, we further included a clinical validation cohort comprising 108 prospectively collected blastocyst-conditioned media samples, which were analyzed using the developed dielectric-valve microfluidic chip. The results confirmed the effectiveness of the metabolomics-based model in predicting embryo implantation potential. Moreover, the proposed metabolic scoring strategy is fully compatible with existing morphological grading and time-lapse imaging systems, enabling multidimensional data integration and offering a more comprehensive framework for selecting embryos with high developmental potential.\nWe fully agree with the reviewer’s comment regarding sample size. Although the number of embryos included in this study was limited, larger cohorts will be required to further validate and refine the robustness of the model. Accordingly, we have added a dedicated “Limitations” section in the Discussion to explicitly acknowledge these constraints.\nOnce again, we thank the reviewers for their thorough evaluation and constructive feedback, which have greatly strengthened the quality of this work.\n\nReviewer #6 (Remarks to the Author):\nThank you very much for your patience. This was a very difficult manuscript to review. As far as your original question regarding microfluidics portion of the paper, authors have sufficiently addressed questions raised by previous revirewers. It can be published in the revised form.\nReply:\nWe sincerely thank the reviewers for their time, effort, and careful evaluation of our manuscript. We greatly appreciate the detailed assessment and are pleased to note that the revised manuscript has fully addressed the issues previously raised, particularly those related to the microfluidics aspects of our study."}]

Decision letter