Enzymatic colorimetric encoding-based digital medicine for pancreatic cancer diagnosis

Domain: physics
Published: 2026-03-13
Editorial outcome: major_revision

Abstract
Digital medicine leverages digital biomarkers by algebraically integrating multiple biomarkers to reflect disease status. Colorimetric analysis offers an intuitive readout, but colorimetric-based digital medicine remains underexplored. Here we show an Enzymatic Colorimetric Encoding-based Digital Medicine platform (EnCODE). By harnessing enzyme-catalyzed multicolor encoding in tandem with the programmability of DNA technology, EnCODE converts multidimensional miRNA information into recognizable optical signals. We demonstrate that these signals are decodable and can be interpreted by visual inspection or spectral analysis, facilitating dimensionality reduction and visualization of disease states. Additionally, EnCODE integrates a continuous weighting mechanism that enables accurate mapping of digital biomarkers. In a cohort of 163 pancreatic cancer clinical samples, EnCODE achieves 96% detection sensitivity and 90% overall accuracy—comparable to the 96% sensitivity and 91% overall accuracy with conventional molecular diagnostic methods. We increase data density through three-dimensional color encoding and hyperspectral imaging-based analysis, enabling an intuitive color-coded molecular readout.

Introduction
Biomarker detection plays a critical role in diagnosing diseases, guiding medication, and monitoring diseases prognosis1-3. With the development of omics technologies, an increasing number of biomarkers have been discovered and it has been proven that the joint analysis of multiple biomarkers often yields better results4-9. To harness diverse biomarker data, digital medicine has emerged, leveraging digital biomarkers to quantify and mathematically integrate multiple targets through weighted algebraic operations10. However, current multi-target assays require separate reaction tubes, increasing costs, operational complexity, and inter-well variability11,12. Integrating the concept of digital medicine with the programmability of DNA technology enables single-tube multiplex biomarker detection, addressing these limitations while maintaining analytical rigor13-15.

Current solutions predominantly rely on fluorescence signals, using signal type or intensity to determine the positive or negative status of the sample16-19. For instance, Han et al. developed a molecular classifier using fluorescence signals for lung cancer diagnose via serum microRNA (miRNA) 20, while Randolph Lopez et al. designed a molecular classifier to distinguish viral and bacterial respiratory infections21. Although these schemes are all based on DNA technology and have strong designability, they still have limitations, including but not limited to: 1) only non-continuous multiplicative molecular calculations are possible; 2) the output signal is confined to a one-dimensional space, which inevitably imposes high demands on experimental systems to accurately reproduce digital medical models; 3) signal analysis must be performed under specific conditions, making it difficult to directly observe. In contrast, enzymatic colorimetric detection enables direct visual interpretation22,23, with inherent signal amplification and continuous color mixing, thus comprehensively reflecting the different expression states of various biomarkers24-26. Although colorimetric coding remains underexplored in digital medicine, the establishment of a comprehensive theoretical system for colorimetric coding would provide digital medicine with a powerful solution.

To address this, we develop EnCODE, an enzymatic colorimetric encoding-based digital medicine platform that utilizes DNA technology to convert multidimensional biomarker signals into two-dimensional colorimetric outputs for dimensionality reduction and visualization. Unlike fluorescence-based strategies limited to one-dimensional signal outputs, EnCODE harnesses two-dimensional colorimetric encoding to achieve higher information density without specialized optical equipment. We demonstrate that this two-dimensional colorimetric space offers greater combinatorial diversity, detectable via both visual inspection and spectral analysis. In addition, reverse signal decoding validates the system’s mathematical rigor, laying a foundation for enzymatic colorimetric theory. EnCODE integrates continuous weighting, aligning with digital medicine principles. We apply this approach to pancreatic cancer diagnosis, enabling precise multi-miRNA serum profiling for enhanced diagnostic accuracy. Furthermore, integration of three or more colors allows high-dimensional encoding, supporting more precise disease detection or multi-disease classification in a single test. EnCODE integrates hyperspectral imaging to merge rapid visual screening with high-density spectral data capture, enabling real-time in situ detection27,28. This innovation couples enzymatic colorimetric encoding with quantitative decoding, enabling multiplex biomarker integration with instrument-light readout and scalable workflows. These features support a feasible route toward decentralized deployment, particularly when paired with standardized imaging, internal calibration, and automated analysis. The development of related technologies will be beneficial for further promoting the progress of colorimetric coding, multi-omics analysis, digital medicine, POCT, and other fields.

Results
Construction and Diagnostic Application of EnCODE
In the enzymatic colorimetric encoding system, we selected horseradish peroxidase (HRP) and alkaline phosphatase (AP) with their substrates ABTS (green) and pNPP (yellow), respectively, to establish a two-dimensional colorimetric scheme. The final presentation consisted of two colorimetric dimensions: green and yellow (Figure 1a). Since the colorimetric encoding includes two different aspects: color proportion encoding and color concentration encoding, a single colorimetric code can reflect a variety of information (Figure 1b). Additionally, the codes are reversibly decodable through visual inspection or spectral analysis (Figure 1c), releasing the complex information it contains.

To enable high-density information encoding and user-friendly detection of the enzymatic colorimetric encoding system for digital medicine and disease diagnostics, we established EnCODE for multidimensional miRNA computation (Figure 1d). Using rolling circle amplification (RCA)29 and colorimetric enzymes with unique DNA barcodes, we linked color signals to miRNA type and concentration data. By leveraging the specificity of template DNA for target miRNA and the specificity of DNA barcodes for RCA amplification products, we successfully converted complex miRNA information into identifiable color signals in a specific and efficient manner. Additionally, to assign miRNA weights, we designed DNA templates with positive and negative variants, where positive templates proportion determined miRNA weight (Figure 1e-f). By assigning different weights and color signals to different miRNA, we achieved a multidimensional miRNA molecular computing based on colorimetric encoding, compressing multi-dimensional disease biomarker signals into two-dimensional color signals for dimensionality reduction and visualization.

Investigating the Encoding-Decoding Paradigm of EnCODE
Colorimetric signals, rich in information, can be easily encoded and decoded for multi-dimensional biomarkers detection. Colorimetric encoding refers to the changes in visible color and absorption spectra when colors are mixed in varying proportions or concentrations (Figure 2a). To validate this capability, we explored whether the colorimetric encoding adhered to well-defined mathematical relationships to ensure the accuracy of encoding and decoding. Using green (HRP/ABTS) and yellow (AP/pNPP) solutions as initial components, we mixed them in different ratios. The absorption spectrum of the mixed solution shifted predictably (Figure 2b). Further analysis of the characteristic absorption peaks of the green initial solution (415 nm and 730 nm) and the yellow initial solution (400 nm) showed that the absorbance of the mixed solution was linearly related to the proportion of the initial solutions (Figure 2c). The absorption spectrum of the mixed solution equals the sum of the absorption spectra of the initial solutions multiplied by their proportions, within the experimentally validated linear range, confirming a mathematically rigorous encoding system. (Figure 2e, Supplementary Figure 1a). Similarly, RGB values of the mixed solution varied linearly with the proportion of the initial solutions (Figure 2d), and matched the sum of the RGB values of the initial solutions scaled by their proportions, within the unsaturated RGB acquisition regime (Figure 2f, Supplementary Figure 1b). These results emphasized that the colorimetric encoding system had a quantitative and predictable relationship response to its constituent components, ensuring accurate encoding and decoding.

Direct color mixing for encoding poses practical challenges. To enhance the applicability of colorimetric encoding, we controlled colorimetric encoding through the concentration of colorimetric enzymes and tested its enzyme responsiveness (Figure 2g). First, systematic optimization identified robust reaction conditions (2 mM ABTS, 2 mM pNPP, 0.5 mM H₂O₂, and a 30 min reaction), ensuring reliable and reproducible signal generation for subsequent encoding. (Supplementary Figure 2a-e). Additionally, we validated the enzymatic colorimetric performance of AP and HRP, confirming a well-defined mathematical mapping between enzyme concentration and colorimetric output. Both enzymes showed stable catalytic behavior under the selected reaction conditions (Supplementary Figure 2f). Across the validated dynamic range, enzyme concentration exhibited positive linear relationships with both absorbance and RGB features, supporting robust enzyme-driven color modulation (Supplementary Figure 2g–i). For the spectral readout, linear quantification was maintained from 68 pM to 2 nM, whereas for the RGB-based readout, the linear range was approximately 294 pM to 2 nM. For lower-abundance samples, assay sensitivity can be improved by moderately extending the chromogenic development time. Extending the color-development reaction to 1 h improved low-end sensitivity, reducing the spectral LOD to 5.54 pM (HRP–ABTS) and 1.6 pM (AP–pNPP), and the RGB-based LOD to 8.87 pM (HRP–ABTS) and 8.81 pM (AP–pNPP) (Supplementary Figure 3).

Building on the feasibility and predictability of enzymatic colorimetric encoding, we directly mixed AP and HRP at varying concentrations to generate colorimetric encoding. Intuitively, visual color modulation was observed as enzyme types and concentrations varied (Figure 2h). Further analysis of the absorption spectrum showed that the absorption spectra of the mixed solution matched the sum of the spectra from single enzyme solutions (each at the enzyme concentration present in the mixture) minus the blank substrate (Figure 2i, j), with four representative absorption spectra shown in Figure 2i. RGB values exhibited analogous relationships (Figure 2k, l). These quantitative additivity relationships, validated within the experimentally defined linear regime, established the foundation for accurate encoding and decoding of enzymatic colorimetric signals. This additivity was further confirmed for mixed-enzyme reactions at ultralow concentrations (Supplementary Figure 4).

The absorption spectrum of a single enzyme solution scales linearly with enzyme concentration, enabling theoretical prediction of spectra of mixed solutions based on reference spectra of blank substrate, HRP (concentration A), and AP (concentration B). The calculation formula is as follows:
Absorption = (Absorption_HRP - Blank) * (X/A) + (Absorption_AP - Blank) * (Y/B) + Blank (1)
where X and Y represent the concentration of HRP and AP in the mixed solution (Figure 2m). By simplifying spectra to the three critical wavelengths of 400 nm, 415 nm, and 730 nm, we efficiently decoded the concentrations of HRP and AP in the mixed solution (Figure 2n). Additionally, the RGB values can also be used for decoding, with the same resolution capability (Figure 2o).

Therefore, we validated the feasibility of enzymatic colorimetric encoding and confirmed the decoding capability. This enables precise writing and extraction of complex information in colorimetric encoding solutions. Combined with the low cost and simplicity of visible light color sensors, the system holds broad potential for future applications.

Implementation of Multiplex miRNA Detection with EnCODE
We propose a potential application for enzymatic colorimetric encoding. In the field of precision medicine, the collective expression pattern of multiple miRNAs in blood has been considered a more robust and valuable biomarker for tumor detection and prognosis prediction compared to a single miRNA marker30,31. To analyze these complex miRNA combinations efficiently and accurately, we designed EnCODE, a computational platform leveraging enzymatic colorimetric encoding and DNA technology (Figure 3a). EnCODE consists of four steps. First, target miRNAs act as linker primers, circularizing template DNA via SplintR Ligase. The template DNA is designed with functional binding domains, including a target miRNA-specific binding region, a DNA barcode area, and a capture binding region. Subsequently, miRNA-circular template DNA complexes are immobilized on enzyme-linked immunosorbent assay (ELISA) plates via coated capture sequences. Then, the capture strand serves as the amplification primer, and the circular template DNA acts as the template for RCA to amplify signals. Subsequently, colorimetric enzymes with specific DNA barcode bind to the RCA-amplified sequences, converting target miRNA signals into colorimetric enzyme signals. Finally, the enzymatic colorimetric reaction generates a visible encoded color reflecting the collective expression levels of the target miRNA panel. By decoding the color, complex target miRNA contents can be extracted from the color result (Figure 3b). In summary, through this platform, we achieved multiplex detection of miRNAs through triple mapping of target miRNAs, colorimetric enzymes, and color codes.

To validate the EnCODE platform, we employed miRNAs as both linker and amplification primers for RCA signal validation. We found that using miRNA primers enabled efficient template DNA circularization (Supplementary Figure 5a). RCA exhibited high sensitivity to miRNA concentration (5 pM detection limit) and reaction time (5-minute amplification) (Supplementary Figure 5b-d), with high orthogonality between miRNA primers and templates (Supplementary Figure 5e). Also, cross-linking enzymes to DNA barcodes via NHS-PEG4-N3/DBCO click chemistry achieved efficient conjugation. The conjugation yield was approximately 70%, and each enzyme was modified with 1-3 DNA molecules. Enzyme activity remained stable, with 78% of the activity of native HRP and 84% of native AP, confirming that the conjugation process did not significantly affect enzymatic performance. These results validate the chemical integrity and metrological stability of the enzyme-DNA conjugates used in EnCODE (Supplementary Figure 6). To further estimate the enzyme loading per RCA product, we compared colorimetric outputs obtained by directly capturing enzyme–DNA conjugates on the plate-immobilized capture strand (without RCA) versus capturing the same conjugates via RCA products after a 30-min amplification. The RCA-enabled workflow increased signal intensity by ~28-fold for HRP–DNA and ~26-fold for AP–DNA, consistent with average loadings of ~28 HRP and ~26 AP molecules per RCA product under the working conditions. These results support efficient signal amplification while maintaining a quantitatively stable miRNA-to-enzyme signal transfer (Supplementary Figure 7). After solving the technical issues related to platform construction, platform feasibility was further confirmed using fluorescent DNA barcodes. On the ELISA plate, the results showed a proportional relationship between fluorescence intensity and miRNA content (Supplementary Figure 8a). On the magnetic bead platform, the results demonstrated precise dual-target detection (Supplementary Figure 8b-c). These verifications provided critical technical support for miRNA colorimetric encoding detection platform construction.

We optimized the concentration of key components in the EnCODE assay. The capture oligonucleotide was immobilized at the manufacturer-recommended saturating concentration to ensure maximal surface coverage and minimize variability. The template strand concentration was selected at 50 nM, which met two criteria: ensuring efficient circularization while enabling complete capture by the immobilized probes. For the enzyme–DNA conjugates, we titrated 0–100 nM and selected 100 nM to ensure near-saturating signal generation and robust readout. (Supplementary Figure 9).

After constructing the EnCODE platform, we validated its detection performance using two miRNA targets. The system successfully translated distinct miRNA expression patterns into unique colorimetric codes (Figure 3c). EnCODE demonstrated a detection limit in the pM range, with both absorbance and RGB values exhibiting a high linear correlation (R² > 0.98) with target concentrations, confirming precise miRNA-to-enzyme signal mapping (Figure 3d). Additionally, encoded results could be decoded using spectral or colorimetric methods for accurate miRNA quantification (Figure 3e). To assess the platform’s ability to handle sequence variations, we introduced base mismatches between target miRNAs and template DNA. EnCODE successfully distinguished between matched and mismatched sequences, confirming its robustness in detecting miRNAs even with sequence homology within the same family (Figure 3f). These results collectively demonstrate the feasibility, reliability, and clinical diagnostic potential of the EnCODE platform for miRNA detection.

However, in actual clinical practice, the miRNA combinations that can effectively reflect disease conditions often involve multiple markers beyond two. To address this, we expanded the detection throughput of the EnCODE and allow the final color to reflect the overall expression of the miRNA combination. Four simulated miRNA targets (miR-150, miR-636, miR-145, miR-223) were constructed for multiplex validation. Since the detection of multiple targets using a dual-enzyme system requires mapping different targets to the same enzyme, we verified colorimetric consistency across targets (Supplementary Figure 8d, e). Orthogonality between miRNAs and template DNA ensured specific colorimetric reactions only when targets matched their corresponding templates (Figure 3g). Adding non-target miRNAs or templates to the reaction system did not perturb colorimetric signals, confirming the platform’s high specificity and stability (Figure 3h). These results validate EnCODE’s multiplex detection capability.

We implemented molecular computation for multi-miRNA detection. Based on disease prediction models, miRNA markers with similar expression trends in disease states were encoded with identical colorimetric enzymes, while divergent trends were assigned distinct enzymes, enabling arithmetic operations (addition and subtraction) in digital medicine. This strategy compresses multidimensional miRNA data into interpretable two-dimensional color maps, where colors intuitively reflect both miRNA expression dynamics and disease prediction score. Direct classification of samples is achieved through standardized spectral decoding or quantitative color-feature analysis (Figure 3i).

To validate the direct detection of disease prediction scores by EnCODE, we hypothesized a model: Score = miR-150 + miR-636 − (miR-145 + miR-223). By modulating miRNA concentrations, we validated the molecular computational performance of EnCODE (Figure 3j). Consistent color outputs were observed when the prediction scores of the samples are consistent, confirming EnCODE’s capability to accurately reflect the overall miRNA expression profile (Figure 3j, k). Decoding via the logarithmic ratio of the absorbance at 400 nm (the characteristic peak wavelength of the AP colorimetric product) to 730 nm (the characteristic peak wavelength of the HRP colorimetric product) revealed a linear correlation with model scores, enabling effective sample classification (Figure 3l). Visual classification using Hue values further validated diagnostic accuracy, while Hue value is a visual property used to indicate the perception of a color, independent of saturation and brightness (Figure 3m). To quantify the variability of subjective visual interpretation, three blinded raters independently classified representative EnCODE outputs spanning strong/weak positive and strong/weak negative cases, showing substantial inter-rater agreement (accuracy = 94.4%; Supplementary Table 1). This further supports the consistency of visual readout when complemented by standardized imaging and objective decoding. To further assess robustness, we evaluated inter-batch reproducibility and examined the impact of pre-extraction room-temperature holding time. Both intra-plate and inter-batch variations remained low, and the decoded scores showed no appreciable drift within the tested handling-time window (Supplementary Figure 10). A validation set with randomized miRNA concentrations demonstrated that both spectral and color features could accurately classified samples based on model scores (Figure 3n). The above results proved that through standardized spectral decoding or quantitative color-feature analysis, the disease prediction model scores can be directly reflected by EnCODE, enabling sample classification.

Realization of miRNA Weighted Calculation with EnCODE
In the context of multi-disease biomarkers reflecting disease information, different biomarkers often have different weighted contributions, so basic arithmetic operations (addition/subtraction) clearly lack the precision required for digital medicine32,33. To enable precise clinical detection, we introduced weighted calculation via multiplication into EnCODE (Figure 4a). Three weighting schemes were designed (Figure 4b). (i) Signal-to-non-signal template ratio control: Two template DNA strands (signal strand Tem1 with DNA barcode domain vs. non-signal Tem0 without barcode domain) adjust weighting coefficients through their proportional mixing. (ii) DNA barcode copy number modulation: Weighting is dictated by the number of barcode domains on the template DNA. All DNA templates have the same number of bases. (iii) Competitive binding weighting: Varying ratios of enzyme-conjugated DNA barcode and unlabeled DNA barcode control signal output. All schemes achieved effective weighting (Figure 4c). However, scheme (ii) supports only discrete weighting, whereas scheme (iii) showed a weaker target concentration–response, potentially due to the unlabeled DNA barcodes—experiencing less steric hindrance—can more effectively compete with RCA products for binding. Ultimately, scheme (i) was selected for subsequent weighted calculations, as it exhibited the best multi-batch performance with minimal variability (Supplementary Figure 11 and Supplementary Tables 2–3).

To validate the weighted calculation approach, we applied variable weights to miR-150 and miR-223 at concentrations determined in a simulated disease prediction model (Score = W1×miR-150 − W2×miR-223) (Figure 4d). Both spectral features and visual colorimetry accurately reflected model scores and enabled sample classification, confirming the practicality of the weighting scheme (Figure 4e, f).

To validate complex multi-miRNA weighting, we proposed a simulated disease prediction model, Score = 0.5×miR-150 + 0.5×miR-636 − (0.75×miR-143 + 0.25×miR-223), mimicking real-world clinical scenarios (Figure 4g). Randomized miRNA concentrations testing demonstrated that both spectral features and visual colorimetry could effectively reflect the model score, as well as provide a good classification of the samples (Figure 4h). In summary, EnCODE’s integration of weighted computation enables refined digital medical analysis, fulfilling clinical diagnostic demands with enhanced precision.

Clinical Diagnosis of Pancreatic Cancer with EnCODE
To validate EnCODE in actual clinical settings, we tested the platform with clinical blood samples and attempted to construct a pancreatic cancer prediction model. Following the established workflow, total miRNA was extracted from the serum samples, pre-amplified, and analyzed via EnCODE-based colorimetric detection. The results distinguished between cancer and normal samples through spectral analysis or visual readouts (Figure 5a). To address low miRNA concentrations (≤pM) in biological samples, we implemented the linear-after-the-exponential-polymerase chain reaction (LATE-PCR) technique, which employs excess primers and limiting primers during the PCR amplification stage following miRNA reverse transcription34,35. The exponential phase of amplification can be switched to a linear phase shortly after the limiting primers are exhausted. This feature enables near-linear amplification of miRNAs relative to their logarithmic initial concentrations (Supplementary Figure 12a). Consequently, LATE-PCR amplified samples retained critical miRNA quantity information for downstream classification (Supplementary Figures 12b-c). Additionally, the methods generated abundant single-stranded DNA (ssDNA), compatible with EnCODE requirements 35,36. To validate LATE-PCR performance, ssDNA from simulated miRNA amplification showed linear correlation with original miRNA log-concentration (Supplementary Figure 12d). Furthermore, post-amplification miRNA detection achieved 0.01 pM sensitivity—100-fold higher than direct detection—with near-linear absorbance-log concentration relationships in 0.01–10 pM range (Figure 5b, Supplementary Figure 13d-e).

To further assess metrological stability in a workflow-relevant setting, we benchmarked EnCODE using miRNA samples processed through the same pre-amplification step as in the clinical workflow. Across the tested concentration series, EnCODE readouts closely followed the input miRNA levels and showed strong agreement with qRT-PCR quantification, supporting reliable quantitative transfer from miRNA abundance to enzymatic output (Supplementary Figure 13a–c). In parallel, both the HRP–ABTS and AP–pNPP systems exhibited robust linear relationships between miRNA concentration and colorimetric features at the levels of absorbance and RGB values, further supporting a stable miRNA-to-enzyme signal mapping (Supplementary Figure 13d-e).

To evaluate robustness under clinically relevant conditions, we assessed common pre-analytical interferences and therapeutic agents using aliquots from the same-source blood sample. We tested anticoagulants/coagulants, representative pancreatic cancer drugs (5-Fluorouridine, Irinotecan, Oxaliplatin, and Gemcitabine), and simulated matrix interferences (lipemia, high protein, and hemolysis) by adding equal volumes of Intralipid, albumin, or red-blood-cell lysate, respectively. Anticoagulants/coagulants, drugs, and lipemic/high-protein conditions showed no appreciable impact on EnCODE readouts, whereas hemolysis caused a modest upward shift consistent with erythrocyte miRNA release (Supplementary Figure 14a-c). Notably, EnCODE readouts followed the same directional trends as qRT-PCR on the matched samples, supporting that these sample conditions do not materially bias miRNA quantification (Supplementary Figure 14d-f). Based on these findings, we concluded that hemolysis should be avoided in sample collection, as it can impact the miRNA detection accuracy, whereas other common interferences had minimal effects. In addition, we systematically assessed the effects of storage conditions (4°C, -20°C, -80°C) and storage duration (0–72 hours) on the accuracy of miRNA detection. Our results showed that miRNA signals remained stable when samples were stored at -20°C for up to 1 day or at -80°C for up to 3 days, with no significant deviation from fresh controls. These findings provide clear guidance on sample storage conditions for reliable EnCODE analysis (Supplementary Figure 15).

After determining the clinical testing workflow, we applied EnCODE to pancreatic cancer diagnosis and prediction. We attempted to construct a pancreatic cancer diagnosis prediction model. Using the GSE211692 dataset (included serum miRNA expression levels from healthy controls and pancreatic cancer patients) and the GSE163031 dataset (included pancreatic tissue miRNA expression levels from normal and cancerous samples) from the Gene Expression Omnibus (GEO) database, we identified three upregulated and two downregulated miRNAs consistently expressed across both sample types (Figure 5c, Supplementary Figure 16a). Subsequently, a pancreatic cancer diagnosis model was constructed via binary logistic regression: Score = 0.43×miR-154-5p + 0.57×miR-629-5p + 0.41×miR-99a-5p – (0.09×miR-5006-5p + 0.51×miR-575). Notably, initial miRNA concentrations were log10-transformed to make the concentration data compatible with the LATE-PCR amplification mechanism. The model demonstrated strong discrimination between pancreatic cancer and healthy samples in public datasets (Figure 5d) and performed robustly across serum (GSE106817, GSE211692) and tissue (GSE163031) cohorts (Figure 5e).

Prior studies have reported miR-629 upregulation in pancreatic cancer and its contribution to proliferation and invasion, potentially through negative regulation of FOXO337. miR-99a-5p has been implicated in PDAC progression through the NOTCH2/Notch signaling pathway and has been proposed as a candidate prognostic/diagnostic biomarker in multiple cohorts38-40. Although the miR-154 family is frequently reported as tumor-suppressive in several malignancies, dysregulation can be context-dependent across tumor types41, 42. miR-575 has also been reported as a potential biomarker for pancreatic cancer detection43,44. Finally, miR-5006-5p is relatively less studied in cancer, but emerging evidence suggests its involvement in tumor-related processes (e.g., via lncRNA-associated mechanisms)45.

Finally, we evaluated EnCODE using a clinical serum cohort comprising pancreatic cancer (n = 63), benign pancreatic disease controls (pancreatitis, n = 50), and healthy controls (n = 50) (Figure 5f, Supplementary Table 4). EnCODE generated directly interpretable prediction scores and enabled robust classification of pancreatic cancer versus controls, achieving an AUC (area under the ROC curve) of 0.9586 (95% CI: 0.9310–0.9862), with 96.83% sensitivity (95% CI: 89.14–99.44%) and 86.00% specificity (95% CI: 77.86–91.47%). Importantly, EnCODE provides effective discrimination from benign pancreatic disease (pancreatitis) (Figure 5g–j). In parallel, qRT-PCR profiling of the same miRNA panel recapitulated the expression differences in clinical samples and showed comparable discrimination performance, supporting the analytical consistency between EnCODE and conventional PCR-based workflows (Supplementary Figure 16b–d). To further assess generalizability, we tested an independent external validation cohort collected at two additional medical centers (n = 50; pancreatic cancer n = 20, pancreatitis n = 15, healthy n = 15), which yielded consistent classification accuracy (Supplementary Table 5, Figure 5k, l).

Construction of Multidimensional EnCODE Platform
We achieved multidimensional biomarker detection through two-dimensional colorimetric encoding. To enhance information density without increasing the analytical complexity, we developed a three-dimensional colorimetric encoding system by integrating a third color into the existing scheme, achieving precise diagnosis of disease subtypes and differentiating among multiple diseases (Figure 6a). We introduced red, a color with high discriminatory power generated by HRP-catalysis oxidation of Amplex Red with H2O2, exhibiting a characteristic absorption peak at 570 nm (Figure 6c). Analysis of the three original solutions (red, yellow, and green) revealed that their concentrations primarily modulated distinct RGB channels. Specifically, the yellow solution predominantly affected the B value, the red solution primarily influenced the G value, while the green solution affected all RGB values in a balanced way. Additionally, RGB values exhibited linearly correlations with the solution concentrations of each color (Supplementary Figure 17a).

We directly mixed the three original solutions (red, yellow, and green) at varying concentrations to validate the feasibility of three-dimensional colorimetric encoding. Encoded colors exhibited unique visual hues based on the original solution concentrations and compositions, with RGB trends mirroring the original solutions’ contributions (Figure 6b, Supplementary Figure 17b). Furthermore, three-dimensional chromaticity encoding maintained the mathematical characteristics of two-dimensional system. The RGB values of the mixed solution equal the sum of the RGB values from three individual enzyme solutions (each at the enzyme concentration present in the mixture) minus twice the blank substrate (Figure 6d). This relationship is also satisfied by the absorption spectra, enabling constituent quantification via their unique spectral fingerprints (Figure 6c, e, Supplementary Figure 17c). Therefore, decoding of encoded solutions — identifying original solution types and concentrations — is achieved through spectral or colorimetric methods (Figure 6f). Collectively, these characteristics validate the feasibility of three-dimensional colorimetric encoding, demonstrating that encoded colors preserve compositional information through spectral/visual signatures, thereby unlocking rich biochemical data.

To further enhance clinical practicality, we implemented hyperspectral imaging as a key component for rapid and intuitive diagnosis27. Hyperspectral imaging is an advanced imaging technique that combines imaging and spectral analysis, capturing reflectance data across hundreds of wavelengths (visible to mid-infrared), enabling detailed analysis of an object's composition, structure, and state in both spatial and spectral domains46-48. This high-dimensional data confers a unique advantage on hyperspectral imaging in analyzing multidimensional color-coded solutions. In absorption spectrum analysis, due to its characteristic of single-hole detection, only a portion of absorbance information can be obtained to improve detection speed. Hyperspectral imaging, on the other hand, simultaneously acquires full image and spectral data for multiple samples within 3-5 minutes per shot, functioning akin to Polaroid-style instant photography, significantly increasing the detection throughput and speed without compromising information density. Its vast information content lays the groundwork for decoding higher-dimensional colorimetric encoding, and has immense potential for subsequent computer-assisted intelligent decoding28. Portable hyperspectral imaging cameras also can be integrated with portable devices (e.g., smartphones) to enable true real-time in situ detection (Figure 6g). The reflectance spectra of the red, yellow, and green solutions reveal a high degree of consistency with their respective absorption spectra (Figure 6c, h). Also, consistent with the absorption spectrum, increasing the concentration of a raw solution in the mixture induces the reflectance spectrum shifts that align with the characteristic reflectance profile of the raw solution (Figure 6i). This suggests that the reflectance spectrum possesses the same performance as the absorption spectrum, and also has the ability to encode and decode, capable of releasing the rich information contained in the spectrum.

Methods
Ethics Statement
This study was conducted in accordance with the Declaration of Helsinki (as revised in 2024) and was approved by the Ethics Committee of Shanghai Tenth People’s Hospital (Approval No. 24K210), the Ethics Committee of Shanghai Fourth People’s Hospital (Approval No. 2026008-001), and the Ethics Committee of Wuxi People’s Hospital (Approval No. KY24166). The requirement for informed consent was waived by the ethics committees because this retrospective study used de-identified excess blood samples collected during routine clinical care, in accordance with relevant ethical regulations.

Chemicals and Reagents
The oligonucleotides used in this study were custom-synthesized and purified by Huzhou Hippo Biotech (HuZhou, China) and Sangon Biotech (Shanghai, China) and purified by high-performance liquid chromatography (HPLC), as listed in Supplementary Table 6-11. Horseradish peroxidase (HRP) was purchased from Dalian Bergolin Biotech (Dalian, China). Alkaline phosphatase (AP), dNTP mixture, DNA maker-A, 5× sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) protein sample loading buffer, regular agarose G-10, tetramethylethylenediamine (TEMED), ammonium persulfate, 30% acrylamide/methylene bisacrylamide, phosphate-buffered saline (PBS), tris-acetate-EDTA (TAE) buffer, Tris-borate-EDTA (TBE) buffer and 2X TaqMan fast qPCR master mix were obtained from Sangon Biotech (Shanghai, China). 2,2'-Azinobis-(3-ethylbenzthiazoline-6-sulphonate) (ABTS) and p-nitrophenylphosphate (pNPP) were purchased from Aladdin (Shanghai, China). 30% hydrogen peroxide (H2O2) was acquired from Sinopharm (Shanghai, China). 5× DNA sample loading buffer (with GelRed) was obtained from Generay (Shanghai China). 10% PAGE gel quick preparation kit was acquired from Epizyme (Shanghai, China). Amplex Red, color prestained protein standard (6.5 - 270 kDa), Coomassie Brilliant Blue stain, and phi29 DNA ligase were purchased from Beyotime Biotech (Shanghai, China). SplintR Ligase, exonuclease I (E. coli), exonuclease III (E. coli), and hydrophilic streptavidin magnetic beads were obtained from New England BioLabs Inc (USA). NHS-PEG4-N3 and streptavidin high binding capacity coated 96-well plates were sourced from Thermo Fisher Scientific (USA). 30 kDa ultrafiltration centrifugal tubes were acquired from Merck Millipore (Germany). miRcute Serum/Plasma miRNA Extraction kits were purchased from TianGen Biotech (Beijing, China). Real Time PCR kits and Mir-X miRNA First-Strand Synthesis kits were purchased from TaKaRa Bio (Japan). Ultrapure water used in the experiments was sourced from the Milli-Q ultrapure water system (resistivity 18.2 MΩ/cm). All reagents were of analytical grade and no further purification was done. All reagents are stored strictly according to the supplier's instructions. Nucleic acids are stored at -20°C.

Sample Source
All serum/plasma samples were collected at the tenth People's Hospital of Shanghai (Shanghai, China), Shanghai Fourth People’s Hospital (Shanghai, China) and Wuxi People's Hospital (Wuxi, China). Participant demographics, including sex and age, are provided in the Supplementary Tables 4-5. During sample collection, efforts were made to maintain a balanced sex distribution between males and females. After completion of the analyses, any remaining human specimens were disposed of in accordance with the biosafety and institutional regulations of the participating hospitals.

Oligonucleotides
All oligonucleotides (including primers and probes) used in this study were custom-designed for this work, custom-synthesized, and HPLC-purified by Huzhou Hippo Biotech (Huzhou, China) and Sangon Biotech (Shanghai, China). The sequences of all oligonucleotides are provided in Supplementary Information (Supplementary Tables 6–11). To facilitate reuse and reproducibility, newly synthesized oligonucleotides were aliquoted and stored at −20 °C for repeat experiments.

Validation of Optimal Enzyme Reaction Conditions
The exploration of optimal reaction conditions for HRP involved incubating various concentrations of ABTS and H2O2 with 2 nM HRP at room temperature to initiate the reaction. The reaction kinetics and HRP activity were evaluated by monitoring the change in absorbance at a wavelength of 400 nm.

Concurrently, to investigate the optimal conditions for AP, different concentrations of pNPP were combined with 2 nM AP at room temperature to initiate the reaction. The reaction kinetics and AP activity were assessed by monitoring the change in absorbance at a wavelength of 400 nm.

HRP and AP were stored at 4 °C in PBS containing 50% (v/v) glycerol. The reaction buffer consisted of 500 mM Tris–HCl (pH 8.0), 100 mM MgCl2, 0.1 mM ZnCl2, and 100 mM KCl. Diluted enzyme solutions were stable at 4 °C for up to 1 week.

Enzymatic Color Reaction and Color Mixing of the Color Development Solution
First, prepare a color substrate mixture by combining 2 mM pNPP, 2mM ABTS, and 0.5 mM H2O2. Next, add 2 nM HRP to this mixture and allow the reaction to proceed at room temperature for 24 hours, until the color stabilizes, yielding a green solution. Subsequently, introduce 2 nM AP into the color substrate mixture and again incubate at room temperature for 24 hours, until the color stabilizes, producing a yellow solution. Mix the yellow and green solutions in various proportions. Utilize a multi-mode microplate reader (Infinite 200 PRO, Tecan, Switzerland) to measure the absorption spectrum of the resulting mixtures within a 96-well plate. Capture photographs of the solutions and analyze the average RGB values of the colors using Photoshop software for further evaluation.

Enzyme-Catalyzed Colorimetric Reaction
Initially, prepare a chromogenic substrate mixture by combining 2 mM pNPP, 2 mM ABTS, and 0.5 mM H2O2. The enzyme-catalyzed colorimetric reaction is realized by precisely adjusting the concentrations of AP and HRP. After allowing the reaction to proceed for 30 minutes at room temperature, utilize a multi-mode microplate reader (Infinite 200 PRO, Tecan, Switzerland) to measure the absorption spectrum of the resulting mixtures within a 96-well plate. Furthermore, capture photographs of the solution and employ Photoshop software to analyze the average RGB values of the solution. All assays were conducted under controlled ambient conditions (25 ± 1 °C, 50 ± 5% relative humidity). For image acquisition, a fixed-distance holder was used to standardize the camera-to-plate geometry, together with uniform diffuse illumination (LED ring light or backlight lightbox) to reduce shadows and spatial inhomogeneity. Each run included standardized reference wells for plate-to-plate calibration.

Circularization of Template DNA
The template DNAs used in this study were custom-synthesized and purified by Huzhou Hippo Biotech (Huzhou, China) and Sangon Biotech (Shanghai, China). Purification was performed by HPLC, and mass spectrometry confirmed a single dominant peak.

Initially, combine 10 μM of linear template DNA with 20 μM of the target. Subsequently, subject this mixture to heat annealing by incubating it at 95 °C for 3 minutes, followed by a gradual cooling to 12 °C at a rate of 1 °C per minute to form a linear DNA template-target heterodimer. Next, incubate the 2 μM linear template DNA-primer heterodimer with 40 U/μL SplintR Ligase in 1× SplintR Ligase buffer at 16 °C for a minimum of 1 hour to yield a circular template DNA-primer heterodimer. To confirm the successful circularization of the template DNA, treat the system with 0.2 U/μL of Deoxyribonuclease I and 2 U/μL of Deoxyribonuclease III in 1× appropriate buffer at 37 °C for 1 hour to completely remove the primers and linear template. Verify the results through polyacrylamide gel electrophoresis (PAGE).

Verification of Rolling Circle Amplification
In a 1× phi29 DNA polymerase buffer, combine a specific concentration of primers with 1 μM of circular template, 0.4 mM dNTP mixture, and 0.2 U/μL phi29 DNA polymerase. Incubate the mixture at 30 °C for a predetermined duration. The rolling circle amplification products are confirmed through agarose gel electrophoresis.

Preparation of Enzyme-DNA Barcode Conjugates
Initially, a 30 μM solution of enzyme (Amylase or Horseradish Peroxidase) was combined with 1.5 mM of the bifunctional crosslinker NHS-PEG4-N3 in 1× PBS buffer. The mixture was gently stirred for 1 hour at 300 rpm and 25 °C to convert the amino groups of the enzyme into azide groups. Once the reaction was complete, the enzyme was purified using a 30 kDa ultrafiltration centrifuge tube and washed 6 times with 1× PBS buffer (pH 7.4) to remove excess NHS-PEG4-N3. Subsequently, the azide-modified enzyme was mixed with DBCO-modified DNA barcodes at a concentration ratio of 10 μM:100 μM and incubated at 4 °C for 24 hours to produce the enzyme-DNA barcode conjugates. To eliminate excess DBCO-DNA, the product was filtered again using a 30 kDa ultrafiltration centrifuge tube and washed with 1× PBS buffer (pH 7.2). Finally, The conjugated enzymes were then stored in PBS containing 50% glycerol at -20 °C for future use.

All conjugates were verified for successful enzyme-DNA conjugation using SDS-PAGE to confirm the expected molecular weight shift. Enzyme activity was assessed using HRP or AP activity assays to ensure that the conjugates retained sufficient enzymatic activity.

Polyacrylamide Gel Electrophoresis (PAGE)
A 15% PAGE was selected for assessing the preparation of the circular template. Initially, the samples were mixed with 5× DNA loading buffer at a 4:1 volume ratio. Subsequently, 10 μL of the prepared mixture was loaded onto the 15% PAGE for electrophoresis, using 1× TBE buffer. The gel electrophoresis was conducted at 80 V for 20 minutes, followed by a voltage increase to 120 V for an additional 45 minutes. Following electrophoresis, the gel was imaged and analyzed using the Bio-Rad Gel Doc XR imaging system.

Agarose Gel Electrophoresis
The efficiency of Rolling Circle Amplification (RCA) was evaluated through 2% agarose gel electrophoresis. Prior to loading, all samples were mixed with 5× DNA loading buffer at a 4:1 volume ratio. Subsequently, 6 μL of the prepared mixture was loaded into the 2% agarose gel. In 1× TAE buffer, the gel electrophoresis was conducted at 120 V for 40 minutes. Following electrophoresis, the agarose gel was imaged and analyzed using the Bio-Rad Gel Doc XR imaging system.

SDS-PAGE
To verify the preparation of enzyme-primer conjugates, 10% SDS-PAGE gel electrophoresis was employed. Typically, samples containing 10 μM enzyme were mixed with 5× loading buffer in a 4:1 volume ratio and heated at 100 °C for 10 minutes to denature the enzyme. A 10 μL aliquot of the mixture was loaded onto a 10% SDS-PAGE gel, with a pre-stained protein standard (6.5 - 270 kD) serving as a reference marker. The gel was run at 80 V for 20 minutes, followed by an additional 60 minutes at 120 V to complete the electrophoresis. The gel was then placed in a microwave and heated for 3 minutes, repeated twice. Subsequently, the gel was stained with Coomassie Brilliant Blue. After washing with H2O, imaging and analysis of the samples were conducted using the Bio-Rad Gel Doc XR gel imaging system and ImageJ software.

Application of Enzymatic Colorimetric Coding Detection Process for Target miRNA
First, the circularization of the template DNA is achieved by preparing a 100 μL reaction mixture that includes 1× SplintR Ligase buffer, miRNA samples, 50 nM of the corresponding template DNA, and 40 U/μL of SplintR Ligase. This mixture is incubated at 25°C for 30 minutes to form the circular template DNA-target heterodimer.

Concurrently, the capture strand is immobilized on a streptavidin-coated 96-well enzyme plate by adding 10 μM of 5'-biotinylated capture strand to each well and incubating at room temperature for 2 hours. The wells are washed three times with PBS to eliminate excess capture strand, ensuring complete immobilization.

The circular template DNA-target complex is then incubated at room temperature for 30 minutes within the wells coated with the capture strand, followed by three washes. Subsequently, rolling circle amplification (RCA) is performed by mixing 200 μL of 0.4 mM dNTP, 0.2 U/μL phi29 DNA polymerase, 100 nM HRP-DNA barcode 1, and 100 nM AP-DNA barcode 2 in a 200 μL 1× phi29 DNA polymerase buffer system within the microplate wells and incubating at 30°C for 30 minutes.

Lastly, an enzyme-catalyzed colorimetric reaction is conducted by adding 200 μL of the color development solution, which consists of 5 mM pNPP, 5 mM ABTS, and 0.5 mM H2O2 mixed in 1× PBS, to the enzyme plate. The reaction is allowed to proceed at room temperature for 30 minutes. The absorbance measurements are taken using a multi-mode microplate reader (Infinite 200 PRO, Tecan, Switzerland), and the color development results are photographed and analyzed. All assays were conducted under controlled ambient conditions (25 ± 1 °C, 50 ± 5% relative humidity). For image acquisition, a fixed-distance holder was used to standardize the camera-to-plate geometry, together with uniform diffuse illumination (LED ring light or backlight lightbox) to reduce shadows and spatial inhomogeneity.

By incorporating various template DNAs corresponding to multiple miRNA targets, multiplex detection of these targets is enabled. Through the inclusion of blank template DNAs corresponding to the miRNA targets in appropriate proportions, weighted detection of the targets is achieved.

For calibration and robustness evaluation, a panel of score-referenced standard samples spanning the intended score range was processed in parallel with each assay plate. These standards were measured in triplicate and served as internal controls to correct for run-to-run variations in enzyme activity and optical readouts, enabling consistent transformation of colorimetric signals into decoded scores.

Simulated Fluorescence Validation of Target Detection
First, fluorescence validation of miRNA detection is performed on an enzyme-linked plate. In the RCA process described, a fluorescence-labeled DNA barcode is substituted for the enzyme-DNA barcode polymer. After 1 hour of RCA, the microplate is subjected to fluorescence imaging and analysis using an inverted fluorescence microscope.

Next, fluorescence validation for the dual detection of miRNA targets is carried out on magnetic beads. Streptavidin-coated magnetic beads are incubated with 5' biotin-modified capture DNA at concentrations of 4 mg/mL and 10 μM at room temperature for 2 hours. Following this, the beads are washed three times with PBS to remove any excess capture strands. After the magnetic beads are coated with different capture DNA, they are mixed in equal proportions. Then, the two miRNA targets are mixed with their respective template DNA for circularization. The resulting circular template DNA-target polymer product is mixed with the capture DNA-modified magnetic beads. This mixture is incubated at room temperature for 0.5 hours and then washed three times with 1× PBS. Finally, the magnetic beads are mixed with 0.4 mM dNTPs, 0.2 U/μL phi29 DNA polymerase, 100 nM FAM-labeled DNA barcode 1, and 100 nM Cy3-labeled DNA barcode 2 in a total volume of 200 μL of 1× phi29 DNA polymerase buffer, and rolling circle amplification is performed for 1 hour. The samples are analyzed through fluorescence imaging using an LSM 900 confocal laser scanning microscope (Zeiss, Germany).

The Construction and Validation of the Pancreatic Cancer Prediction Model
The construction and validation of the pancreatic cancer prediction model was conducted using microarray datasets GSE21169250, GSE163031, and GSE10681751 downloaded from the Gene Expression Omnibus (GEO) database. The GSE211692 dataset included 851 serum samples from pancreatic cancer patients and 1972 serum samples from healthy individuals. The GSE163031 dataset comprised 25 tissue samples from pancreatic cancer patients and 13 samples from non-cancerous pancreas. The GSE106817 dataset consisted of 115 serum samples from pancreatic cancer patients and 2759 serum samples from healthy individuals.

The LIMMA package from the Bioconductor project in R software52 was utilized to identify differentially expressed miRNAs. Given our objective to analyze serum miRNAs for subsequent detection, we aimed to find miRNAs with greater expression differences in serum samples. The cutoff for differentially expressed miRNAs in serum samples from the GSE211692 dataset was set as log2 fold change (FC) ≥ 3.5 or log2 fold change (FC) ≤ -2.5 and an error discovery rate (FDR) < 0.05, while in the tissue samples from the GSE163031 dataset, the cutoff was set as |log2 fold change (FC)| ≥ 1 and an FDR < 0.05. Overlapping miRNA expression profiles between the GSE211692 and GSE163031 datasets were retained for further study.

Binary logistic regression was used to construct a probability model for pancreatic cancer using the selected miRNAs as predictors. Prior to model fitting, miRNA concentrations were log10-transformed to reduce skewness and improve numerical stability across the wide dynamic range introduced by the pre-amplification step. Regression coefficients were estimated by maximum likelihood using the standard iterative algorithm implemented in SPSS (Version 20). A parsimonious model was derived using forward stepwise selection in SPSS (Forward: Wald), with candidate variables sequentially entered according to prespecified significance criteria (entry p < 0.05). Model discrimination was evaluated by receiver operating characteristic (ROC) analysis and summarized by the area under the curve (AUC). The final score was reported as a linear combination of the retained miRNAs with their estimated coefficients and directions of effect.

miRNA Extraction in Serum Samples
All methods were performed in accordance with the approved guidelines. Unless otherwise specified, miRNAs in serum samples were extracted using the column-based the miRcute Serum/Plasma miRNA Extraction Kit (TianGen Biotech, Beijing, China). All samples were purified according to the manufacturer's instructions and the eluted RNA was stored in nucleic acid-free water at -80 °C until required.

miRNA RT-qPCR Quantification
Following the extraction of miRNAs from serum samples, unless otherwise specified, the miRNAs were reverse-transcribed into cDNA using the Mir-X miRNA First-Strand Synthesis Kit (Takara Bio, Japan). Subsequently, quantitative PCR (qPCR) detection was performed using Real Time PCR kits from TaKaRa Bio (Japan). All samples were processed according to the manufacturer's instructions.

Pre-amplification of Serum miRNA for Enzymatic Colorimetric Coding Detection
Pre-amplification of serum miRNAs using LATE-PCR (Linear-After-The-Exponential PCR) prior to enzymatic colorimetric coding detection. Following the reverse transcription of miRNAs into cDNA, the 2X TaqMan fast qPCR master mix from Sangon Biotech (Shanghai, China) was used for the LATE-PCR amplification. The concentrations of the forward and reverse primers were adjusted to be excess and limiting, respectively. The excess primer was set at 2 μM, and the limiting primer at 50 nM. The limiting primer was derived from the universal primers provided in the one-step miRNA reverse transcription kit from TaKaRa Bio (Japan). All other steps were performed according to the manufacturer's instructions.

Overview of the EnCODE Assay Workflow
The entire EnCODE assay, from sample preparation to final colorimetric readout, is completed within 7 hours, with the following breakdown:
Serum/Plasma miRNA Extraction: Serum/plasma samples are processed using the miRcute Serum/Plasma miRNA Extraction Kit (TianGen Biotech, Beijing, China). This step takes approximately 1 hour. Using a refrigerated high-speed centrifuge (Eppendorf 5424), 24 serum/plasma samples can be processed per batch. Extracted miRNAs are stored in nuclease-free water at -80°C until use.
miRNA Pre-amplification: miRNAs are reverse transcribed into cDNA using the Mir-X miRNA First-Strand Synthesis Kit (Takara Bio, Japan), followed by LATE-PCR amplification using the 2X TaqMan Fast qPCR Master Mix (Sangon Biotech, Shanghai, China). This step requires 30–40 minutes for reverse transcription and 1–1.5 hours for PCR amplification. The sample processing capacity per batch is limited to the capacity of the PCR machine, typically 96 samples per batch.
Circularization of DNA Templates: Using the pre-amplified miRNA product as a primer, DNA templates are circularized via SplintR ligase. This step takes approximately 30–40 minutes, with no theoretical limit to the number of samples per batch.
Capture Probe Coating: The capture probe is immobilized onto a streptavidin-coated enzyme plate in 2 hours, which can be done in parallel with steps 1, 2, and 3.
Template Capture: Circularized DNA templates are captured by incubating the miRNA-pre-amplified DNA-template complex with the streptavidin-coated enzyme plate. This step takes 30 minutes. The batch processing capacity is theoretically unlimited.
Rolling Circle Amplification and Signal Encoding: Phi29 polymerase is used for amplification of the circular template, while simultaneously introducing DNA-enzyme complexes for signal encoding. This step takes 30 minutes and can handle an unlimited number of samples per batch.
Enzyme-Catalyzed Colorimetric Reaction: Substrates for the enzyme-catalyzed reaction are added, producing detectable colorimetric signals. This step requires 30 minutes. While theoretically unlimited in terms of sample processing per batch, for reducing batch-to-batch variation, it is recommended to process no more than 96 samples per batch.
Signal Detection: Signal detection takes 3–5 minutes, with the capacity to process 96 samples per batch.
The total hands-on processing time is approximately 5–6 hours. With additional error allowances for calculation and operational setup, the full process from clinical sample to result output can be completed in approximately 6–7 hours.
All steps in the assay can be automated with robotic arms, reducing processing time and minimizing batch-to-batch variation.

Three-dimensional Colorimetric Coding and Hyperspectral Imaging Detection
In a mixture solution containing 5 mM ABTS and 0.5 mM H2O2, HRP (2 nM) was added and fully catalyzed until the color became stable, resulting in a green solution. Similarly, in a mixture solution containing 5 mM Amplex Red and 0.5 mM H2O2, HRP (2 nM) was added and fully catalyzed until the color became stable, resulting in a red solution. In a solution containing 5 mM pNPP, AP (2 nM) was added and fully catalyzed until the color became stable, resulting in a YELLOW solution. The three basic color solutions were mixed in a series of different volumes and adjusted to maintain a final solution volume of 200 μL. The absorption spectra of the final solutions were measured in a 96-well microplate reader and the solutions were photographed. The average RGB values of the solutions were analyzed using Photoshop software.

Additionally, the three-dimensional Colorimetric coding results were photographed using a hyperspectral camera (Specim IQ, Finland), and the reflectance spectra were analyzed using ENVI software (v 5.6).

Statistics & Reproducibility
Unless otherwise specified, data were analyzed using GraphPad Prism 8.2.1. All data were statistically analyzed using Two-tailed Student's t-test, and are presented as the standard deviation (SD) ± mean. Normality was assessed using the Shapiro–Wilk test and equality of variances using an F test; when assumptions were not met, Welch’s t-test or the Mann–Whitney U test was used as appropriate.

We set the target sensitivity (Se) at 90% and specificity (Sp) at 85%. The required numbers of disease-positive (N_pos) and disease-negative (N_neg) samples were approximated using the standard normal approximation for estimating a proportion with a desired precision:
N = (Z^2 * (1-alpha/2) * p * (1-p)) / d^2 (2)
where Z =1.96 for a two-sided 95% confidence interval, p represents the expected Se or Sp, and d is the allowable absolute error (half-width of the CI).The estimated minimum sample size is approximately 62 pancreatic cancer cases (for Se = 0.90) and 87 controls (for Sp = 0.85), resulting in a total cohort size of ~149 samples to achieve ±7.5% precision for both sensitivity and specificity.

Unless otherwise stated in the figure legends, key bench experiments were repeated in independent experimental runs. No data were excluded from the analyses. The sample were randomly assigned during testing. The Investigators were blinded to allocation during experiments and outcome assessment.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors propose and systematically validate a digital medicine platform, EnCODE, based on enzyme-driven colorimetric encoding. Using multiple miRNAs as an example, the platform maps multidimensional biomarkers into 2D/3D color signals via programmable DNA reaction and enzyme-catalyzed chromogenesis, and establishes a rigorous encode–decode framework with continuous weighting. This enables weighted computation of multiple targets and intuitive readout within a single tube. Diagnostic performance is also evaluated on clinical samples, demonstrating potential for POCT applications and multi-disease classification. Overall, the study shows strong innovation and engineering feasibility with a largely complete data chain. However, several methodological and translational aspects require further strengthening. Before acceptance, we recommend addressing the following:\n1) The current encoding/decoding relies on two premises: (i) the mixed spectrum ≈ a proportional linear combination of individual enzyme spectra minus the blank; and (ii) RGB signals are linearly additive. However, validation to date covers only a limited concentration range under ideal background conditions. We recommend extending tests to both high and low extremes and quantitatively determining the conditions under which these linearity assumptions hold and where they break down—i.e., clearly defining the applicability limits of spectral linearity and RGB additivity.\n2) The current weighting strategy controls weights via the Tem1/Tem0 ratio. In essence, this approach hinges on the fraction of binding sites on RCA products that are actually accessible. That accessibility can be affected by multiple factors—e.g., template folding, variability in RCA fragment length distribution, and barcode crowding that introduces steric hindrance. Consequently, non-negligible drift may arise across batches and even between wells within the same batch. In parallel, we recommend a quantitative comparison of the three weighting schemes (dynamic range, sensitivity, robustness, and CV), providing statistical evidence for the superiority of scheme (i) rather than relying on qualitative claims.\n3) The manuscript states “2 mL of serum with a report within 7 hours,” but it does not specify per-step durations, which steps can run in parallel, the per-run/sample throughput, or the degree of automation.\n4) It is necessary to clarify how much hemolysis and common drugs/anticoagulants affect the HRP/AP reactions and the optical readouts. Based on these results, develop actionable rules that specify when to reject a sample and when—and how—to apply correction.\n5) The authors use NHS‑PEG4‑N3/DBCO click chemistry to conjugate enzymes to DNA and claim that “enzyme activity is unaffected.” However, key quantitative evidence is still lacking to substantiate this claim—particularly the distribution of modification degrees, the enzyme load per RCA product, the conjugation yield, and the specific activity per barcode. These parameters directly determine the metrological stability of the miRNA-to-enzyme signal transfer.", "reviewer_2": "This study proposes an innovative enzymatic colorimetric encoding (EnCODE) platform that enables weighted calculation of multiple miRNA biomarkers within a single tube and translates the results into a visual color signal. The concept is novel, and the technology demonstrates diagnostic performance in pancreatic cancer comparable to qRT-PCR (AUC = 0.97), suggesting both academic value and potential for clinical translation. However, the manuscript requires major revisions in several critical areas\nMajor Revisions\n1. The transformation of color signals into risk scores may be influenced by multiple confounding factors (e.g., sample handling time, batch effects, environmental temperature and humidity). A detailed description of the calibration strategy, including whether standardized reference materials were used, as well as data on robustness, is required.\n2. The rationale behind selecting the five miRNA markers is insufficient. The manuscript would benefit from a detailed description of the selection pipeline, supporting evidence from the literature, and a discussion of their biological roles in pancreatic cancer development.\n3. The process for determining the coefficients in the weighted calculation formula is unclear. The authors should describe the optimization strategy in detail (e.g., machine learning algorithms, statistical modeling), and provide justification for the chosen approach.\n4. A direct comparison with existing point-of-care testing (POCT) platforms is missing. To highlight the novelty and translational value of EnCODE, please provide comparative data with at least two commercially available rapid detection methods, including cost-effectiveness and operational complexity assessments.\n5. The current study includes only 32 samples (12 pancreatic cancer patients), which is insufficient to draw reliable conclusions. The authors are strongly encouraged to increase the sample size to a statistically meaningful scale and provide a clear rationale with sample size calculations.\n6. An independent external validation cohort is missing. To establish the robustness and generalizability of the EnCODE platform, data from additional medical centers and different populations should be included.\n7. The control group design is suboptimal, as it does not incorporate benign pancreatic diseases (e.g., chronic pancreatitis). Including such cases is essential for accurately evaluating diagnostic specificity.\n8. Critical clinical information (e.g., disease stage, pathological type) is lacking for the enrolled patients. Please provide complete baseline clinical characteristics.\n9. The GSE163031 dataset used for validation contains too few samples, raising concerns about potential overfitting. A larger external dataset should be used for validation, or appropriate overfitting tests should be presented.\nMinor Revisions\n10. The manuscript should include a detailed description of how the concentrations of EnCODE components (primers, templates, enzymes) were optimized and determined.\n11. Please clarify the optimization strategy for the coefficients in the weighted calculation formula, and indicate whether alternative mathematical models were tested and how their performance compared.\n12. Additional validation data demonstrating the linear relationship between color signals and miRNA concentrations, including the linear detection range, should be provided.\n13. The effects of sample storage conditions (e.g., temperature, storage time) on assay performance should be systematically assessed.\n14. Please provide a quantitative analysis of how common interfering factors (e.g., hemolysis, lipemia) affect assay accuracy.\n15. Variability in subjective interpretation of colorimetric results should be evaluated, and strategies to improve consistency should be proposed.\n16. Finally, please describe the pathway and technical feasibility of smartphone integration with the EnCODE platform.", "reviewer_3": "This manuscript presents EnCODE, an enzymatic colorimetric encoding system for detecting multiple miRNA biomarkers in clinical samples. While the core technical concept demonstrates some merit, substantial concerns limit the work's current contribution and reliability.\n\nThe authors report several noteworthy results, including the development of a dual-enzyme system that mathematically encodes biomarker concentrations into interpretable color patterns, demonstration of linear relationships between enzyme concentrations and spectral outputs, and extension to three-dimensional encoding. In a clinical study of 32 samples, they achieved reported sensitivity of 100% and overall accuracy of 91% for pancreatic cancer detection. However, these results require critical examination.\n\nRegarding significance to the field, the work makes a meaningful contribution to colorimetric biosensing and multiplexed nucleic acid detection. The mathematical encoding framework represents a novel approach - while colorimetric detection exists widely, the systematic use of enzymatic color mixing as a mathematically rigorous information encoding system with reversible decoding capabilities is innovative. This differs substantially from conventional colorimetric approaches that typically provide simple positive/negative or semi-quantitative readouts. However, the clinical diagnostic performance does not clearly exceed established miRNA detection methods, and without comparative studies, the practical advantages over qRT-PCR or digital PCR remain unclear. The clinical application, while demonstrating feasibility, requires more robust validation to assess its true diagnostic value.\n\nThe work does not adequately support its major conclusions and claims. The 32-sample clinical validation is insufficient for reliable diagnostic performance estimates, requiring independent validation cohorts and multi-site studies. Claims of \"Polaroid-like simplicity\" contradict the multi-step, 7-hour protocol described, and no evidence supports visual interpretation by non-specialists. Assertions of superior performance over established methods lack supporting comparative data, and the biomarker selection process from existing datasets may involve circularity between discovery and validation.\n\nSeveral significant flaws in data analysis, interpretation, and conclusions prohibit publication in the current form. Statistical issues include inadequate sample size for diagnostic performance claims, wide confidence intervals indicating high uncertainty, potential overfitting with five variables and only twelve positive cases, and unclear validation methodology. Analytical concerns encompass missing validation of linearity under real-world conditions, insufficient assessment of matrix effects, limited stability data, and no inter-operator validation. The interpretation problems include claims exceeding supporting evidence, premature clinical readiness assertions, and oversimplified presentation of the complex protocol.\n\nThe methodology falls below expected standards for diagnostic test evaluation. The clinical study lacks proper power analysis and adequate controls, biomarker selection may involve methodological circularity, and critical validation studies for stability, reproducibility, and specificity are missing. Environmental variables affecting colorimetric detection are insufficiently controlled, and potential confounding factors receive limited assessment. While the basic chemistry appears sound, the clinical validation methodology is inadequate for the diagnostic claims presented.\n\nCritical details necessary for reproduction are missing throughout the methods section. These include enzyme standardization and quality control procedures, environmental control requirements, specific protocols for visual interpretation, statistical analysis software parameters, detailed preparation protocols for DNA templates and conjugates, and storage requirements for reagents. Importantly, despite stating that code is available \"upon reasonable request,\" no data analysis scripts, algorithms for spectral decoding, or computational workflows are provided or deposited in public repositories. This lack of computational transparency significantly hampers reproducibility, particularly given the mathematical encoding/decoding framework central to the method. Although supplementary methods provide some additional detail, key aspects remain insufficiently described for independent reproduction.\n\nThe manuscript requires major revision before publication consideration. The core technical approach has merit as a proof-of-concept study, but clinical claims are premature and inadequately supported. The authors should reframe this as a methods development study rather than a clinical diagnostic platform, conduct properly powered clinical validation with independent cohorts, provide comprehensive reproducibility and stability data, include direct comparisons with established detection methods, make all analysis code and algorithms publicly available, and correct the numerous presentation errors throughout the manuscript. Additionally, the pervasive spelling and grammatical errors, including technical terms in figures, require professional editing to meet publication standards.\n\nWith appropriate scope and validation, this work could potentially contribute to the biosensing literature. However, substantial additional evidence and methodological improvements are needed to support the current conclusions and clinical claims.\n\nMinor Issues\nThe manuscript also needs professional editing - there are numerous spelling errors and grammatical mistakes throughout, including in figure labels (e.g., \"spectural\" instead of \"spectral\", \"competitive binging\" instead of \"binding\")."}, "author_response": "Dear Editor and Reviewers,\nWe are sincerely grateful for the thoughtful feedback on our manuscript titled “Enzymatic Colorimetric Encoding-Based Digital Medicine for Pancreatic Cancer Diagnosis” (Manuscript ID: NCOMMS-25-68381). The reviewers’ comments have not only helped us in enhancing the clarity and rigor of this work but also provided valuable perspectives that will inspire our future research directions. We have carefully addressed all comments, as outlined in the point-by-point responses below, with corresponding revisions highlighted in red font within the revised manuscript.\n\n[... Author response text continues for all points ...]"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I think the authors have fully addressed my previous concerns. I have no further comments.", "reviewer_2": "The authors did sufficient job to address my critics.", "reviewer_3": "The authors have substantially improved their manuscript which now meets the standard. This is a very good and novel study which should be interested for a broad readership."}, "author_response": ""}]

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