Fingerprint profiling human gut microbiota through boronic acid-based sensor array and its application on colorectal cancer classification

Domain: neuroscience
Published: 2026-04-08
Editorial outcome: minor_revision

Abstract
The interplay between gut microbiota and host plays a crucial role in human health and disease
pathogenesis. However, current profiling methods for gut microbiota are costly, time-intensive and
require specialized instrumentation. Herein, we develop a boronic acid-based sensor array, GUT-SCAN
(Gut Unique Typing via Spectral and Chemical Analysis using Boronic Acid Nanosensor), for rapid
profiling of human gut microbiota. The sensor array consists of six pH-responsive boronic acid derivatives
and three pH environments (6.0, 7.4, and 8.2), forming an 18-channel sensor array. This array
successfully distinguishes gut-derived bacteria with 100% accuracy. Additionally, it enables
quantification of bacterial species associated with colorectal cancer (CRC) and identification of distinct
bacterial compositions at varying mixing ratios. Notably, this sensor array is also capable of
discriminating CRC at different stages. The simplicity, rapidity and affordability of this platform highlight
its potential for microbiota analysis and its applicability in disease diagnostics, particularly for CRC.

Introduction
The gut microbiota is integral to human health, influencing immune regulation, metabolism and
neurological functions1,2. As a complex microbial ecosystem, it facilitates nutrient digestion and provides
a critical defense against pathogenic invasion3. However, disruption of the gut microbiota composition,
termed dysbiosis, has been implicated in various diseases, including metabolic disorders4, inflammatory
diseases5, inflammatory bowel disease (IBD)6 and colorectal cancer (CRC)7,8. Among these, CRC is the
third most prevalent malignancy worldwide and constitutes the second leading cause of cancer-related
mortality9. Early detection and precise screening are essential for improving CRC prognosis and clinical
outcomes.

Emerging evidence indicates that gut microbiota contributes to CRC pathogenesis by modulating
inflammation, inducing DNA damage and regulating apoptosis10,11. Specifically, the alterations in
microbial composition can exacerbate intestinal inflammation, promote carcinogen production and
facilitate tumorigenesis. These findings highlight the potential of gut microbiota as a diagnostic and
prognostic biomarker for CRC12,13. Therefore, comprehensive profiling and dynamic monitoring of gut
microbiota are of significant interest, offering promising avenues for CRC prevention, early diagnosis and
treatment through microbiota-based therapeutics and precision medicine.

Despite its clinical potential, gut microbiota detection remains challenging for two primary reasons. Firstly,
the gut microbiota constitutes an intricate and highly diverse ecosystem, necessitating robust and reliable
detection methods14,15. Current profiling techniques, such as metagenomic sequencing (e.g., 16S rRNA
analysis), rely on next-generation sequencing (NGS) platforms and require specialized bioinformatics
expertise16. However, these methods are expensive, labor-intensive and time-consuming, limiting their
feasibility for routine clinical application. Secondly, bacterial species exhibit substantial morphological
and structural diversity, further complicating detection17,18. Molecular methods including multiplex PCR19,
CRISPR-based diagnostics (CRISPR-Dx)20 and DNA microarray21, while highly specific, are similarly
constrained by high costs, prolonged processing times, and the need for trained operators. These
constraints significantly underscore the need for a rapid, high-throughput and cost-efficient sensing
platform for gut microbiota detection.

Fluorescence-based methods have gained significant attention due to their high sensitivity, specificity
and operational simplicity. Traditional fluorescence-based probes often employ a fluorophore-ligand
conjugate22,23, enabling selective bacterial recognition (Fig. 1a). For instance, fluorescence in situ
hybridization (FISH) has been used to analyze the spatial organization of gut microbiota24. However,
these approaches generally rely on a “lock-and-key” strategy, which lacks versatility in complex
environments with diverse bacterial populations. In contrast, chemical sensor arrays inspired by the
mammalian olfactory system offer a versatile platform for bacterial detection25-28. These arrays leverage
cross-reactive interactions among various probes and bacterial species to generate distinct response
patterns. While widely applied for microbial discrimination, current sensor arrays primarily rely on the
electrostatic interaction and/or hydrophobic/hydrophilic interaction (Fig. 1b), which can be affected by
environmental conditions, potentially compromising detection accuracy, especially in complex biological
matrices such as the gut microbiota. Thus, the development of a robust, reliable sensor array capable of
accurate microbiota detection remains an urgent challenge.

Boronic acid derivatives are an ideal candidate for this task due to their unique capacity for selective
binding to cis-diol-containing molecules, which forms the fundamental principle of our sensing platform.
This capability is particularly advantageous for bacterial profiling, as bacterial surfaces are rich in
carbohydrate moieties including cis-diols29, and boronic acid-based sensors provide a promising
approach for bacterial identification30,31. The binding is characteristically pH-dependent, which is
modulated by their molecular structure and the pH of the medium32. When the surrounding pH is higher
than the pKa value of boronic acids, the boronic acid readily forms five- or six-membered cyclic esters
with cis-diols, resulting in the binding affinity between boronic acid derivative and cis-diol structures. Fine-
tuning the electronic properties of boronic acid derivatives through structural modifications enables
optimization of their pKa and binding properties33,34. Moreover, the structural variations in bacterial
surface oligosaccharides and polysaccharides offer an opportunity for differential binding, facilitating
species-specific detection. To exploit this, we construct a sensor array composed of structurally diverse
boronic acid probes. This system effectively leverages the intrinsic variability of surface glycans in
conjunction with the boronic acid probes' key attributes—pH-dependent binding affinity and structural
tailorability—to produce complex, multi-dimensional response patterns. Thus, the construction of a
boronic acid-based sensor array holds a highly promising strategy for accurate gut microbiota profiling
in complex biological environment.

Herein, we introduce GUT-SCAN (Gut Unique Typing via Spectral and Chemical Analysis using Boronic
Acid Nanosensor), a boronic acid-based sensor array for rapid and robust profiling of human gut
microbiota. The GUT-SCAN system consists of six engineered boronic acid derivatives (BP1-BP6) with
varying affinities for cis-diol structures, exhibiting pH-dependent fluorescence responses. By
incorporating three distinct pH environments (6.0, 7.4, and 8.2), we generate an 18-channel detection
platform, significantly enhancing detection resolution. Pyrene (Py), a polycyclic aromatic hydrocarbon
with an extended conjugated system, is employed as the fluorescent signal reporter. Upon exposure to
gut-derived bacteria, the fluorescence intensity increases due to the adhesion of boronic acid probes on
the bacterial surfaces, generating unique fluorescence “fingerprints” for different bacterial species (Fig.
1c). This 18-channel sensor array achieves 100% classification accuracy and 98% correct identification
of unknown samples. In addition, the proposed GUT-SCAN system successfully detects and
differentiates human gut microbiota from human fecal samples and demonstrated robust discrimination
of CRC patients at different disease stages. These findings establish GUT-SCAN as a powerful, cost-
effective tool for gut microbiota analysis, with potential for advancing microbiota-based diagnostics and
CRC detection.

Results
Design and synthesis of the probe library
Boronic acids can form reversible covalent bonds with cis-diols and have been extensively utilized
for detecting cis-diol-containing molecules such as saccharides,35 nucleosides,36 glycoproteins,37 and so
forth.38,39 The binding affinity between boronic acids and cis-diols is strongly influenced by the pKa of the
boronic acid (Supplementary Fig. 1). The pKa of boronic acid can be precisely tuned by modifying the
microenvironment of the boron atom40. In this work, the core design principle is to precisely tune the
electron density of the boron center via phenyl substituents to optimize probe performance. For example,
phenylboronic acids with electron-withdrawing groups possess increased acidity and lowered pKa
value41. On the contrary, the presence of the electron-donating group may increase the pKa value42.
Hence, we aimed to regulate the microenvironment of boron atoms in boronic acids, adjusting their pKa
and thus influencing their binding affinity with cis-diol structures. The boronic acid derivatives are
expected to interact with the cis-diols structures present in bacterial cell walls. Each probe is endowed
with a different binding affinity and is expected to produce distinct binding affinity to cis-diols on the
surface of gut-derived bacteria. Due to the signatures of the surface characteristics, size and morphology
of the gut-derived bacteria, leading to differential fluorescence signals from the sensor array. Employing
machine-learning algorithms to analyze fingerprints, effective classification of gut microbiota samples
can be achieved.

The sensor array of the present study relies on the use of six different probes, BP1-BP6, each
probe incorporates a Py and a modified boronic acid unit (Fig. 1a). Among them, the boronic acid unit
exhibits varying binding affinities for cis-diols groups, which are expected to produce distinct binding
affinity to cis-diols on the surface of gut-derived bacteria43. The Py, a polycyclic aromatic hydrocarbon
with a large conjugated structure, exhibits excellent optical properties and exerts an electron-withdrawing
effect44. The six probes were synthesized via straightforward synthetic routes with satisfactory yields
(Supplementary Fig. 2). The chemical structures of the probes were fully characterized by 1H NMR, 13C
NMR, and high-resolution mass spectrometry (HRMS) (Supplementary Figs. 3-15).

To further understand the electronic effects of these modifications, electrostatic energy
calculations were performed on the molecular structures. The calculations demonstrated that the electron
cloud density distribution was significantly altered by the functional group modifications (Fig. 2b), further
corroborating the ability to fine-tune pKa through structural modifications. Additionally, the hydrophobicity
of the probes was also influenced by these modifications, which could further impact their solubility and
interaction with biological environments. To experimentally validate these effects, we detected the pKa
values and ClogP of BP1-BP6 shown in Fig. 2c and Supplemenatry Fig. 16. The results indicated that
the boronic acid derivatives with the electron-withdrawing groups possess increased acidity and lowered
pKa value, while the introduction of the electron-donating group slightly increases the pKa value.
Moreover, structural modifications also influenced the overall hydrophobicity of the probes, which could
affect the binding affinity toward the target biomolecules. The rational design of these probes allows for
precise control over pKa, enabling their potential be able to recognize the complex features of bacterial
surfaces.

Additionally, the binding affinities between boronic acid derivatives and cis-diol-containing
molecules also show pH dependence (Supplementary Fig. 1). To validate the pH responsiveness of
binding between BP1-BP6 probes and cis-diol-containing saccharides, we test the binding constants
between a series of saccharides and BP1-BP6 at different surrounding pH values (see detail in the
Supporting Information). Herein, we selected five representative monosaccharides (Fig. 2d) as target
analytes because of their biological relevance, especially their presence on the surface of bacterial cell
surfaces.45,46 As expected, the binding affinities (i. e., K ) between the BP1-BP6 and different
eq
monosaccharides were found to range from 12.1 to 3985.5 M−1 (Supplementary Figs. 17-34, and
Supplementary Table 1). The value of K is affected by the surrounding pH. At lower pH levels, boronic
eq
acids are less ionized, binding affinities tend to decrease. In contrast, at higher pH levels, boronic acids
are more fully ionized and capable of forming boronate esters, and the binding constants increase (Fig.
2e). The binding affinities of boronic acid derivatives can be effectively modulated by adjusting the pKa
through structural modifications. Additionally, pH serves as an important environmental variable that
further fine-tunes these interactions. Together, these strategies offer valuable insights for the design of
a boronic acid-based sensor array for pattern recognition of gut-derived bacterial strains.

Fluorescence response of gut-derived bacterium
With the BP1-BP6 probes in hand, the photophysical properties of the probes were investigated.
UV-vis absorption peaks centered at 320-350 nm were observed for all BP probes (Supplementary Fig.
35), indicating characteristics of Py compounds47. A peak at ~400 nm was observed in the fluorescence
spectra of all BP probes (Supplementary Fig. 36), serving as the signal reporting units. The bacterial
cell walls consist of amounts of peptidoglycans and glycopolymers, containing a lot of cis-diols
structures48,49. We hypothesize that the interaction between our BP probes and gut-derived bacterium
can be driven through binding to the cis-diols structure of the bacterial cell wall. Thus, we tested the
effect of bacteria on the fluorescence emission intensity of BP1-BP6. A typical gut-derived probiotic
Lactobacillus plantarum (L. pl)50 was used as target analyte (Fig. 3a). Increasing concentrations of L. pl
(OD = 0.4-1.0) to the solutions of the probes, gradual increase in the fluorescence intensity was
600
observed in all cases (Supplementary Figs. 37-39). The result can be interpreted in terms of interactions
between the BP probes and the cis-diols structure in the bacteria, leading to adhesion of probes on
bacterial surface and enhanced fluorescence intensity.

To facilitate the study of pH-binding relationships, we compared the fluorescence response of L.
pl at various concentrations and different surrounding pH values (pH 6.0, 7.4, and 8.2). As shown in Fig.
3b, a gradual increase in fluorescence response of L. pl compared to additional concentration at different
pH was observed. The fluorescence response of L. pl in different BP probes disappear different response
intensity at various pH values. Specifically, the probes BP1-BP3 disappear a greater response at pH 6.0,
while the probe BP4-BP6 shows a greater response at pH 7.4, which can be explained by the
modification of the boron atom, directly the binding affinity to the cis-diol. These findings provide
compelling evidence that the modification of boron atom in boronic acid can mediate its interactions to
cis-diol and that pH serves as an important environmental variable that further fine-tunes these
interactions. Then, we investigated the effect of pH to bacterial physiology, subsequent growth in
standard medium was identical across groups (pH 6.0, 7.4, and 8.2), confirming that short-term pH
exposure does not affect bacterial viability or metabolic state under our experimental conditions
(Supplementary Fig. 40).

To validate the binding of BP probes on the surface of bacteria, the diverse surface properties
among L. pl were investigated through testing the surface hydrophilicity and fluorescence imaging.
Accordingly, bacterial adherence to hydrocarbons (BATH) were used to assess the changes of bacterial
surface hydrophobicity (Fig. 3c). The results indicated that a significant increase in BATH levels of L. pl
were observed following incubation with the BP probes, suggesting the successful adhesion of probes
to the bacterial surface51. The enhanced hydrophobicity should be due to the interaction between the
boronic acid and cis-diol-containing compounds on the surface of bacterial cell, facilitating stronger probe
adhesion. Consequently, heat-inactivated or fixed bacteria is used to effectively distinguish the specific
surface binding of the BP probes from active biological responses of bacteria. As our results reveal that
the BP probes primarily bind to surface cis-diol structures independently of bacterial viability or metabolic
activity. (Supplementary Fig. 41). To further validate the adhesion mechanism between bacteria and
BP probes, L. pl was treated with tunicamycin (which degrades the glycosyl)52 and lysozyme (which
degrades the cell wall)53 and the fluorescence intensity showed varying degrees of decrease following
these treatments (Supplementary Fig. 42). In addition, after treatment with concanavalin A (binds to
glycoproteins in the cell wall)54 and urea (disrupts hydrophobic interactions)55, the fluorescence intensity
of L. pl was more negative than that without (Supplementary Figs. 43 and 44), suggesting the
fluorescence features of the boronic acid-based probes are mainly dependent on the interaction with the
cis-diol-containing bacteria. Furthermore, confocal laser scanning microscopy (CLSM) was employed to
further confirm the binding of the probes to the bacterial surface. As shown in Fig. 3d, the BP probes not
only can efficiently stain the L. pl but also show diverse response signals. Diverse fluorescence
responses were presented when one strain was incubated with different probes (Fig. 3e), which is a
prerequisite for the construction of a sensor array for gut microbiota discrimination.

Application of sensor arrays for gut-derived bacterial strains
Due to the differences in surface glycan composition of various bacterial species, we hypothesize
that the interaction between the BP probes and bacteria varies. We developed a BP probes-based array
that distinguishes gut-derived bacteria. The array was deliberately designed with 18 channels
constituting an orthogonal design combination of six boronic acid probes (BP1-BP6) and three pH
conditions (6.0, 7.4 and 8.2) (Fig. 4a and Supplementary Table 2). According to the features of gut
microbiota, six representational bacteria, including four probiotics (B. longum, B. breve, L. a and L. pl),
two commensal bacteria (E. coli and E. cloacae), and four pathogenic bacteria (F. nucleatum, B. fragilis,
S. aureus and C. k), were selected as the target analyte (Supplementary Table 3). Among them, F.
nucleatum, B. fragilis and E. coli linked to tumorigenesis through inflammation, DNA damage and
immune modulation. The BP probes were orthogonally incubated with the gut-derived bacteria in a 96-
well microplate, and the resulting fluorescence response of each well was recorded. As shown in Fig. 4b
and Supplementary Fig. 45, after incubation with the BP probes, the gut-derived bacteria showed an
enhanced trend in fluorescence intensity, and fluorescence response patterns were produced.

Hierarchical clustering analysis (HCA) of the fluorescence responses indicated that each gut-derived
bacterium exhibited well-separated clusters, suggesting the differences among the patterns of the ten
bacterial strains evaluated are statistically significant (Fig. 4b). The fluorescence responses were
analyzed through a supervised pattern recognition algorithm linear discriminant analysis (LDA). As
shown in Fig. 4c, all ten bacteria are well spatially separated in the 2D space. Notably, all Gram-negative
and Gram-positive bacteria formed distinct clusters on both sides of the LDA plot, with no regions of
overlap. Intriguingly, all probiotics can cluster well together, which can distinguish the other pathogenic
bacteria from opposite ends. In addition, our BP probes-based array can also discriminate the gut-derived
bacteria at different concentrations (OD = 0.4 and OD = 0.6) (Supplementary Fig. 46). All the results
600 600
revealed that this sensing system successfully identified the gut-derived bacteria.

Further, we compared the prediction performance of machine learning (ML) algorithms for
classifying the fluorescence response. The ML algorithms include GPC (Gaussian Process Classifier),
KNN (K-Nearest Neighbour), LDA (Linear Discriminant Analysis), SVM (Support Vector Machine), DT
(Decision Tree), and LR (Logistic Regression). LDA, GPC, and LR exhibited the highest model accuracy
(100%) (Fig. 4d). Meanwhile, Jackknife analysis (Fig. 4e) on the training data set (6 probes × 3 pH
values × 10 bacterial strains × 5 replicates) revealed 100% between-group cross-validation accuracy,
indicating the LDA method as a robust statistical tool for this system. Furthermore, to evaluate the BP
sensor array's reproducibility, 42 unknown samples underwent further validation. The reproducibility
assessment indicated that the accuracy of only one pH value was 83% (pH 6.0), 76% (pH 7.4), and 81%
(pH 8.2), respectively (Fig. 4f, Supplementary Fig. 47, and Supplementary Data 1). However,
combining the three pH values, 41 blind samples were predicted with 98% correct unknown identification
by the 18-channel sensor array (Supplementary Fig. 47 and Supplementary Data 1). The result
showed that our BP probe-based array accurately and reliably distinguished gut-derived bacteria.

Previous studies have demonstrated that the imbalance between probiotics and pathogenic
bacteria directly disrupts the microbial environment and mediates the CRC process56,57. Compared to
healthy controls, CRC patients exhibit significantly increased E. coli abundance and reduced probiotic
populations58. Among probiotics, the most frequently used probiotic species is L. pl59. Thus, the L. pl and
E. coli were chosen to validate the quantitative analysis capabilities of the BP sensor array. As illustrated
in Fig. 4g, Supplementary Figs. 48 and 49, the 2D LDA scatterplots depicted a significant variation
between bacteria at different concentrations (OD ranging from 0.4 to 1.0). Moreover, the Factor 1
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values exhibited a linear relationship with the concentrations of L. pl and E. coli (Supplementary Figs.
48 and 49). The results indicate that the proposed sensor array can effectively quantify individual gut-
derived bacteria.

Moreover, to mimic the gut microbiota composition of CRC patients, accurately identifying the
mixed ratios of probiotics to pathogenic bacteria is crucial. Herein, L. pl and E. coli. were selected as the
models of gut microbiota to verify the accuracy of the sensor array. The model samples were prepared
by mixing L. pl and E. coli in different ratios (9:1, 8:2, 7:3, 6:4, 5:5, 4:6, 3:7, 2:8, 1:9), designed to span
microbial profiles from CRC-affected to healthy populations60. The fluorescence response of these nine
binary mixtures (the total concentration of bacteria is OD = 0.5) was recorded (Supplementary Fig.
600
50). LDA analysis revealed a clear clustering of all nine combinations in the score plot (Fig. 4h). In
addition, using E. coli, S. aureus and L. pl blends in varying ratios as a ternary mixture, the BP sensor
array successfully resolved nine distinct clusters in 2D LDA space (Fig. 4i and Supplementary Fig. 51).
These results demonstrate our BP probe array's ability to differentiate bacterial consortia with subtle
compositional differences, highlighting its potential for analyzing complex gut microbiota in clinical
samples.

To further verify the practicability of the proposed sensor array, an analysis of gut-derived bacteria
in real samples (human urine, fetal bovine serum samples, Intestinal extract and Simulated intestinal
fluid) was performed. As a result, urine and serum produce unique response patterns, and the same type
of bacteria aggregate into seven non-overlapping groups, meanwhile, Intestinal extract and Simulated
intestinal fluid produce unique response patterns, and the same type of bacteria aggregate into eleven
non-overlapping groups (Supplementary Fig. 52). The result indicated that the BP sensor array has
excellent selectivity for gut-derived bacteria, demonstrating the sensor array's discrimination capability
in real samples and its potential for practical clinical applications.

Application for the CRC diagnosis
Recent studies show that CRC patients have a unique microbial composition compared to healthy
individuals, with significant variations in microbiota diversity at different stages of disease progression61,62.
Assessing gut microbiota differences presents a promising strategy for CRC diagnosis and staging63.
Therefore, to explore the proposed sensor array's diagnostic potential, fecal microbiota from healthy
volunteers and CRC patients at different pathological stages were analyzed (Fig. 5a). The identification
focused on the whole gut microbiome communities rather than individual bacterial species. Clinical feces
samples from CRC patients were collected at Hunan Cancer Hospital (Changsha, Hunan) under
informed consent and approval from the local Scientific Ethics Committee.

The feces samples of CRC patients were stratified into two groups according to the tumor node
metastasis (TNM) staging system: early-stage CRC (I, II) and advanced-stage CRC (III, IV)64.
Histopathological analysis, the gold standard diagnostic method for CRC staging, confirmed the clinical
classification of samples (Fig. 5b). Microbiota suspensions were prepared following the detailed
protocols described in the Supporting Information. Subsequently, the sensor array assessed gut
microbiome samples at the same concentration (OD = 0.5). The fluorescence response of 15 samples
600
(volunteer information in Supplementary Table 4) could effectively generate a fluorescent fingerprint
(Supplementary Fig. 53). After being analyzed through LDA algorithms, healthy volunteer, early and
advanced CRC patients were separated in a 2D score plot (Fig. 5c). Moreover, the score of Factor 1 in
the LDA plot significantly differs among healthy volunteers, early and advanced CRC patients (Fig. 5d).
The results suggest that our BP probe-based array may be effective for clinical CRC diagnosis.

To further verify the BP probes-based array method, the gut microbiome composition of healthy
volunteers and CRC patients at different stages was investigated by 16S rRNA sequencing technology.
As shown in Fig. 5e, both the bacterial richness and diversity of microbiota in the feces of CRC patients
were drastically reduced. Moreover, there were noticeable changes in the relative abundance of
microbiota between the early and advanced-stage CRC fecal samples. The results showed variations in
microbiota diversity across different stages of CRC development. At the phylum level, the CRC group
significantly decreased the relative abundance of Firmicutes while increasing the relative abundance of
Proteobacteria (Fig. 5f), indicating dysbiosis in CRC patients. Additionally, the distinct clustering
observed in the partial least squares discriminant analysis (PLS-DA) plots (Fig. 5g) indicated significant
alterations in the microbiota composition between the early and advanced stage CRC fecal samples,
allowing us to identify distinct microbial patterns associated with different stages of CRC. Orthogonal
validation via lectin microarray analysis65 with FITC-ConA confirmed significant differences in the surface
glycome among the samples (Supplementary Fig. 54). All the results support the hypothesis that
specific microbiota variations correspond to CRC progression, reinforcing the potential of microbial
composition as a biomarker for CRC diagnosis and staging and highlighting both the consistency and
reliability of microbiota-based differentiation in CRC classification using our sensor array.

Our research aimed to create a portable, low-cost and noninvasive method for identifying healthy
individuals and CRC patients at various stages. To assess the clinical reliability and diagnostic accuracy
of the BP probe-based array, 147 blind samples (40 advanced-stage CRC (Adv.), 50 early-stage CRC
(Ear.), 30 healthy controls (Hea.), 12 inflammatory and 15 adenoma controls; details in Supplementary
Table 5) were analyzed. As a result, the BP sensor array generated unique fluorescent fingerprints for
each sample (Supplementary Fig. 55, Supplementary Data 2). LDA analysis reveals that individual
samples are identifiable in the 2D score plot with minimal overlap, while an initial separation is observed
between the CRC samples and the inflammatory/adenoma controls (Fig. 5h). Although some minor
overlaps existed between CRC stages, their distributions showed statistically significant separation as
anticipated. Factor 1, explaining over 60% of the variance in the LDA model, enabled effective
differentiation between early and advanced CRC samples (Fig. 5i). To further validate diagnostic
performance, receiver operating characteristic (ROC) curve analysis66 was conducted on three
constructed models: (Model 1: Hea. VS. Adv., Model 2: Hea. VS. Ear., and Model 3: Ear. VS. Adv.). As
illustrated in Fig. 5j, these models demonstrated exceptional discriminatory power with area under the
curve (AUC) values of 1.00, 0.99, and 0.92, respectively—well above the clinical significance threshold
of 0.8067. These results establish the BP sensor array as a promising tool for non-invasive early CRC
detection and staging.

Methods
Ethical statement
This study was approved by the Medical Ethics Committee of The Affiliated Cancer Hospital of Xiangya
School of Medicine, China (Approval No: KYJJ-2030-080). All patients provided written informed consent
for sample collection prior to enrollment. The research complies with all relevant ethical regulations.

Materials
4-boronobenzoic acid, 1-hydroxy-1,3-dihydrobenzo[c]-[1,2]oxaborole-6-carboxylic acid, 4-borono-3-
methoxybenzoic acid, 4-borono-3-fluorobenzoic acid, 4-borono-3-chlorobenzoic acid, and 3-borono-5-
fluorobenzoic acid were purchased from commercial laboratory suppliers (Bide Pharm.). Pyren-1-
ylmethanol was purchased from commercial laboratory suppliers (Energy Chemical). All chemicals and
reagents were purchased commercially at analytical grade unless otherwise noted. Staphylococcus
aureus (S. aureus), Escherichia coli (E. coli) and Citrobacter koseri (C. k) were obtained from China
General Microbiological Culture Collection Center. Lactobacillus acidophilus (L. a) was purchased from
Guangdong Microbial Culture Collection Center. Enterobacter cloacae (E. cloacae) and Lactobacillus
plantarum (L. pl) were obtained from the Central Laboratory at Hunan Agricultural University (Changsha,
China). All solutions were prepared with ultrapure water (18.25 MΩ·cm-1) from the Millipore system.

Materials characterization
1H NMR and 13C NMR spectra were recorded on a Bruker Avance-500 spectrometer (Bruker, Switzerland)
in DMSO-d with TMS as an internal standard. Absorption and fluorescence spectra were acquired on a
6
2600 UV-vis spectrometer (Shimadzu, Japan) and a F-7100 spectrometer (Hitachi, Japan), respectively.
All imaging experiments were performed on a TCS SP8 confocal microscope (Leica, Germany). The
fluorescence intensity of the BRP sensor array for bacterial response were recorded on a Biotek microplate
reader (SynergyMX, USA). High resolution mass spectroscopy (HRMS) was recorded on TOF MS ES+
mass spectrometer and acetonitrile was used to dissolve the sample.

General procedure for synthesis of phenylboronic acid (BP1-BP6)
Synthesis of BP1-BP6: A mixture of the corresponding phenylboronic acid (0.10 mmol), EDC (77.60 mg,
0.50 mmol) and DMAP (24.43 mg, 0.20 mmol) was stirred in the co-solvent of DMF for 0.5 h, and then
the pyren-1-ylmethanol (46.46 mg, 0.20 mmol) was added into the above solution and stirred for 24 h at
25 ℃. After the reaction completed, ultrapure water was added into the solution and white crude solids
were filtered and washed with copious amounts of MeOH. The crude solids were further purified by
column chromatography of Al O and then white solids were the corresponding probes BP1-BP6. BP1:
2 3
Yield: 66.4%. 1H NMR (500 MHz, DMSO-d ) δ 7.91 (d, J = 7.7 Hz, 2H), 7.78 (d, J = 7.1 Hz, 1H), 7.72 (d,
6
J = 8.4 Hz, 1H), 7.62 (t, J = 7.4 Hz, 2H), 7.54 (s, 2H), 7.47 (t, J = 7.4 Hz, 1H), 7.36 (d, J = 8.3 Hz, 2H),
7.24 (d, J = 8.2 Hz, 2H), 6.84-6.75 (m, 2H), 4.95 (s, 2H). 13C NMR (126 MHz, DMSO-d ) δ 166.31, 134.80,
6
131.66, 131.24, 131.19, 130.71, 129.79, 129.49, 128.68, 128.54, 128.43, 128.27, 127.82, 126.96, 126.15,
126.06, 125.26, 124.50, 124.26, 123.64, 65.35. HRMS (m/z): [M+ H]+ calcd. for C24H18BO4+, 381.1293;
found, 381.2936. BP2: Yield: 69.2%. 1H NMR (500 MHz, DMSO-d ) δ 8.48-8.38 (m, 2H), 8.37-8.29 (m,
6
5H), 8.25 (d, J = 7.8 Hz, 1H), 8.21 (d, J = 3.5 Hz, 2H), 8.11 (d, J = 7.6 Hz, 1H), 8.09-8.06 (m, 1H), 7.54-
7.49 (m, 1H), 6.10 (s, 2H), 5.02 (s, 2H). 13C NMR (126 MHz, DMSO-d ) δ 166.38, 159.62, 132.38, 132.00,
6
131.69, 131.18, 130.70, 129.77, 129.52, 128.89, 128.89, 128.74, 128.49, 128.30, 127.82, 126.99, 126.18,
126.08, 125.29, 124.50, 124.24, 123.59, 122.49, 70.49, 65.35. HRMS (m/z): [M+ H]+ calcd. for
C25H18BO4+ [M+ H]+, 395.1449; found, 395.1601. BP3: Yield: 80.4%. 1H NMR (500 MHz, DMSO-d )
6
δ 8.47 (d, J = 9.3 Hz, 1H), 8.34 (ddd, J = 12.6, 8.4, 4.9 Hz, 4H), 8.23 (dd, J = 8.4, 6.0 Hz, 3H), 8.11 (t, J
= 7.6 Hz, 1H), 7.96 (s, 2H), 7.59-7.51 (m, 2H), 7.48 (d, J = 1.2 Hz, 1H), 6.11 (s, 2H), 3.80 (s, 3H). 13C
NMR (126 MHz, DMSO-d ) δ 166.15, 163.35, 135.44, 132.42, 131.65, 131.20, 130.71, 129.80, 129.44,
6
128.64, 128.34, 128.27, 127.82, 126.97, 126.15, 126.07, 125.26, 124.51, 124.25, 123.65, 121.60, 110.58,
65.46, 55.84. HRMS (m/z): [M+ H]+ calcd. for C25H20BO5+, 411.1398; found, 411.4252. BP4: Yield:
85.1%. 1H NMR (500 MHz, DMSO-d ) δ 8.47 (d, J = 9.2 Hz, 2H), 8.34 (dd, J = 10.1, 6.8 Hz, 3H), 8.30 (s,
6
1H), 8.26 (s, 2H), 8.21 (d, J = 4.1 Hz, 2H), 8.15-8.08 (m, 2H), 7.73 (d, J = 9.3 Hz, 3H), 6.12 (s, 2H). 13C
NMR (126 MHz, DMSO-d ) δ 166.36, 165.21, 136.22, 131.71, 131.18, 130.69, 129.53, 129.49, 128.71,
6
128.48, 128.31, 127.81, 126.98, 126.17, 126.09, 125.26, 124.88, 124.86, 124.49, 124.24, 123.62, 115.70,
65.72. HRMS (m/z): [M+ K]+ calcd. for C24H16BFKO4+, 437.0757; found, 437.2306. BP5: Yield: 69.2%.
1H NMR (500 MHz, DMSO-d6) δ 8.55 (s, 2H), 8.46 (d, J = 9.1 Hz, 1H), 8.33 (d, J = 6.8 Hz, 4H), 8.27-
8.15 (m, 4H), 8.11 (t, J = 7.2 Hz, 1H), 7.91-7.81 (m, 2H), 7.53 (d, J = 7.4 Hz, 1H), 6.10 (s, 2H). 13C NMR
(126 MHz, DMSO-d ) δ 165.13, 136.63, 134.46, 131.72, 131.63, 131.19, 131.19, 130.69, 129.51, 128.87,
6
128.71, 128.51, 128.33, 127.82, 127.21, 126.99, 126.19, 126.10, 125.27, 124.49, 124.23, 123.63, 65.77.
HRMS (m/z): [M+ Na]+ calcd. for C24H16BClNaO4+, 437.0722; found, 437.2305. BP6: Yield: 73.8%. 1H
NMR (500 MHz, DMSO-d ) δ 8.48 (s, 1H), 8.46 (s, 2H), 8.37-8.33 (m, 2H), 8.31 (d, J = 8.7 Hz, 2H), 8.26
6
(s, 1H), 8.24 (s, 1H), 8.22-8.17 (m, 2H), 8.11 (t, J = 7.6 Hz, 1H), 7.79 (d, J = 9.1 Hz, 1H), 7.73 (d, J = 9.1
Hz, 1H), 6.12 (s, 2H). 13C NMR (126 MHz, DMSO-d ) δ 165.40, 161.22, 131.69, 131.65, 131.42, 131.18,
6
130.69, 129.47, 128.71, 128.46, 128.30, 127.82, 126.96, 126.16, 126.08, 125.71, 125.56, 125.27, 124.47,
124.23, 123.62, 117.81, 117.62, 65.64. HRMS (m/z): ([M + K]+ calcd. for C24H16BFKO4+, 437.0757;
found, 437.2291.

Determination of apparent ARS-boronic acid association constants (K )
ARS
To spectroscopically determine the association constant of ARS with a boronic acid,73 each boronic acid
was titrated into solutions of constant ARS concentration. A stock solution of ARS was prepared in DI
water (9.0 × 10-5 M), along with stock sodium phosphate buffers (PBS, 0.1 M, pH 6.0, pH 7.4, or pH 8.2).
Stock solutions were prepared in DMSO for each boronic acid (2.0 × 10-2 M). From these solutions, two
separate solutions were prepared in DI water, one containing a boronic acid (2.0 × 10-3 M), ARS (9.0 ×
10-6 M), and PBS (0.1 M) with the second solution containing the same. These solutions were used to
perform titrations of constant ARS concentration and decreasing boronic acid concentration. A non-
fluorescent ARS standard was prepared as a solution of ARS and PBS in the absence of boronic acid
and was used as a fluorescence blank. Fluorescence measurements were acquired with the F-7100
spectrometer, with the samples being excited at 468 nm and emission being measured at 570 nm. A full
presentation of component concentrations for every measurement can be found in Supplementary
Table1.

Determination of apparent diol-boronic acid association constants (Keq)
To determine the diol-boronic acid association constant of each diol and boronic acid,73 three solutions
were prepared from the previously mentioned stock solutions. The first solution contained the boronic
acid (2.0 × 10-3 M), ARS (9.0 × 10-6 M), and PBS (0.1 M), while the second solution also contained the
diol in question. The concentration of the diol varied with each boronic acid-diol combination and at each
pH. A third ARS standard solution was prepared containing only ARS (9.0 × 10-6 M) and PBS (0.1 M).
The first solution was then titrated into the second to prepare solutions of constant ARS and boronic acid
concentrations but varying diol concentration. The ARS only solution was used as a fluorescence blank.
Fluorescence measurements were acquired with the F-7100 spectrometer, with the samples being
excited at 468 nm and emission being measured at 570 nm.

Bacteria strains and culture
Luria-Bertani broth medium: The containers and glassware were sterilized at 121 ℃ for 30 min. 0.5 g
sodium chloride, 0.5 g tryptone, and 0.25 g yeast extract were dissolved in 50 mL ultrapure water to
prepare Luria-Bertani broth medium (pH 7.0). MRS nutrient broth: 1 g peptone, 0.5 g beef extract, 0.1
mL polysorbate 80, 0.2 g dipotassium phosphate, 0.4 g yeast extract, 0.02 g magnesium sulfate, 0.2 g
triammonium citrate, 2 g glucose, 0.005 g manganese sulfate and 0.5 g sodium acetate were dissolved
in 100 mL ultrapure water to prepare MRS nutrient broth (pH 7.0).

E. coli, S. aureus, C. k and E. cloacae were grown in LB broth medium at 37 ℃ with continuous shaking
12 h. L. pl. was grown in MRS nutrient broth medium at 37 ℃ with continuous shaking 48 h. L. a. was
grown in MRS nutrient broth medium at 37 ℃ and incubated for 48 h at rest.

Fluorescence response to bacteria
Fluorescence measure: A DMSO solution of BP (1 mM) was diluted into deionized water to afford a final
concentration of 5 μM, and the respective UV-vis spectra recorded using a 2600 UV-vis spectrometer
with the wavelength of 280-360 nm, and the fluorescence spectra of the resulting solutions were recorded
on F-7100 Fluorescence spectrophotometer with excitation at 347 nm.

Bacteria response: The as-obtained cultured L.pl was centrifuged at 2170 g for 5 min, and then the
supernatant was removed, the bacterial pellets obtained were reconstituted in 4 mL of PBS, aiming for
different OD . For fluorescence response, BP solutions with different concentrations suspensions of
600
bacterial cells (OD = 0.4-1.0) for 30 min or without, were recorded with excitation at 347 nm.
600

Bacterial adhesion to hydrocarbons (BATH) of bacteria
The bacterial surface hydrophobicity was measured by bacterial adhesion to hydrocarbons (BATH)
assay.75 Specifically, 0.1 mL of bacterial suspension, 0.9 mL of water, and 1.0 mL of xylene were added
into the 5 mL tubes and the mixture was shaken on a vortex for 15 min. After standing still for 5 min, the
bacteria in xylene and water were then quantified by the absorbance of microorganism at 600 nm. The
BATH value can be obtained as
BATH = (C (Water ) - C (Water )) / (C (Water ) - C (Blank))
Bacteria Before Bacteria After Bacteria Before Bacteria
C (Water ): the absorbance of solution at 600 nm the bacteria solution before xylene treatment;
Bacteria Before
C (Water ): the absorbance of solution at 600 nm the bacteria solution after xylene treatment;
Bacteria After
C (Water ): the absorbance of solution at 600 nm the blank solution without bacteria;
Bacteria Blank

Bacterial staining and imaging by microscopy
The as-obtained cultured L.pl was centrifuged at 2170 g for 5 min, and the supernatant was removed.
Bacteria were re-suspended in deionized water to obtain a density of OD = 0.5. For imaging, the
600
bacteria were washed three times with PBS, then incubated with BP1-BP6 (5 μM) for 30 min, respectively.
After incubation, each suspension was centrifuged at 2170 g for 5 min. The supernatant was then
removed. The bacteria obtained in this way were washed three times with deionized water, suspended
in deionized water, placed 10 μL of solutions onto confocal culture dish, and then used to imaging.
Microscopy images were recorded on a Leica TCS-SP8 super resolution microscope. The excitation and
emission channels used for the BP were 405 nm and 415-520 nm.

Investigation of bacterial binding force
The as-obtained cultured L.pl was centrifuged at 2170 g for 5 min, and then the supernatant was removed.
L.pl was re-suspended in deionized water to obtain a density of OD = 0.5. For binding force
600
investigation, suspensions of bacterial cells, either pre-treated with tunicamycin (2 μg mL-1), lysozyme
(10 mg mL-1) for 1 h, 4 h, or without, were centrifuged at 2010 g for 5 min. The bacteria were washed
three times with PBS, then incubated with BP1-BP6 (5 μM) for 30 min, respectively. After incubation,
each suspension was centrifuged at 2170 g for 5 min. The supernatant was then removed. The bacteria
obtained in this way were measured and the fluorescence intensity was recorded on F-7100
Fluorescence spectrophotometer with excitation at 347 nm.

Investigation: For further binding force investigation, suspensions of bacterial cells, either pre-treated
with Concanavalin A (0.5, 1.0 mg mL-1), Urea (5%, 10%), 1,6-Hexanediol (1 M, 2 M) and NaCl (1 M, 2
M) for 1 h or without, were centrifuged at 2010 g for 5 min. The bacteria were washed three times with
PBS, then incubated with BP1-BP6 (5 μM) for 30 min, respectively. After incubation, each suspension
was centrifuged at 2170 g for 5 min. The supernatant was then removed. The bacteria obtained in this
way were measured and the fluorescence intensity was recorded on a F-7100 Fluorescence
spectrophotometer with excitation at 347 nm.

Machine learning algorithms
Linear Discriminant Analysis: Linear discriminant analysis (LDA) was performed using the Linear
Discriminant Analysis class from the scikit-learn library (version 1.0.2) in Python 3.8 (Anaconda
distribution). All variables were included in the model (full model) with a tolerance of 0.001. The
fluorescence response patterns were transformed into canonical variables. Mahalanobis distances of
each sample to the centroid of each group in the LDA-transformed space were calculated, and
classification was based on the shortest distance.

Decision tree classifier: Decision tree, a supervised learning algorithm, employs a tree structure to
facilitate decision-making. Within this structure, every node corresponded to a feature, while the edges
symbolized decision rules. The terminal nodes, or leaves, represented the output results. In our work,
the decision tree utilized “entropy” as a criterion, enabling each tree to make decisions regarding feature
values. This criterion guided the decisionmaking process, ultimately assigning decision labels as the
results. Furthermore, it allocated scores based on the accuracy of classification.

Support vector machine: Support vector machine (SVM) serves as a supervised learning algorithm used
for classification and regression. It functioned by finding the hyperplane that maximized the margin
between the two classes. Upon data ingestion, the SVM is trained using a polynomial kernel with the
support vector classifier (SVC), enabling it to identify the appropriate hyperplane for each classification.

Logistic regression: Logistic regression, a statistical method used for binary classification problems,
estimated the probability of an event occurring. In this context, the logistic regression model was trained
using default parameters.

K-neighbors classifier: The k-nearest neighbors (KNN) algorithm is a simple instance-based learning
method. It predicted the category of a new data point by selecting the k closest training samples to that
point.

Gaussian process classifier: The Gaussian process classifier uses Gaussian processes for probabilistic
classification. A Gaussian process is an infinite-dimensional stochastic process, used for non-parametric
Bayesian inference of functions. In classification tasks, the Gaussian process is utilized to predict the
probability of a data point belonging to a specific category. It offered a flexible, kernel-driven approach,
allowing the model to automatically adapt to the complexity of the data. The code for the analysis is
available as Supplementary Code.

Array-based for multi-bacterial detections
Construction of the sensor array: BP1, BP2, BP3, BP4 , BP5 and BP6 were dissolved in DMSO solution,
respectively, to form a stock with a final concentration of 1 mM, and diluted by adding PBS (pH = 7.4)
buffer to obtain 5 dilutions and the concentration of BP1-BP6 was 5 μM, namely, six fluorescence
channels BP1-BP6-7.4. Then the PBS (pH =7.4) buffer was adjusted by the PBS (pH = 6.0 and 8.2)
buffer, in this way, BP1-BP6-6.0 and BP1-BP6-8.2 was obtained. The six probes (BP1-BP6) combined
3 pH (6.0, 7.4, and 8.2) were excited respectively at wavelengths of 347 nm, resulting in the collection of
the distinct fluorescence signal at 400 nm, specifically. Therefore, for each bacterium analyzed, the 6
probes combined 3 pH collectively produced a total of 18 fluorescent readouts. The data analysis was
performed using linear discriminant analysis (LDA) and Hierarchical cluster analysis (HCA) in Anaconda
Prompt.

Detection of different types of bacteria: The six distinct bacterial strains were subjected to multiple
washing steps and subsequently diluted to varying concentrations of OD = 0.5 with PBS. For bacterial
600
detection, 100 μL bacterial suspensions of each bacterium were mixed with 100 μL BP probes in the
black 96-well plates (5 replicates). Under the excitation wavelength at 347 nm, the fluorescence changes
of BP were obtained at 400 nm. After the addition of bacteria, the change in fluorescence intensity was
expressed as F.

Bacterial detection at different concentrations: the cultured L. pl and E. coli were diluted to various
concentrations (OD = 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0). Subsequently, 100 μL bacterial suspensions
600
of two different concentrations of bacteria were mixed with 100 μL probes BP in the black 96-well plates
(5 replicates). The parameters for the instrument set up were maintained as described previously.

Mixed bacteria identification: binary mixed bacteria models: by blending L. pl and E. coli (each with OD
600
= 0.5) in various ratios, distinct bacterial mixtures were prepared. 100 μL of bacterial mixes with 100 μL
of probes BP were added to a black 96-well plates (5 replicates).

Ternary mixed bacteria models: by blending L. pl, E. coli and S. aureus (each with OD = 0.5) in various
600
ratios, distinct bacterial mixtures were prepared. 100 μL of bacterial mixes with 100 μL of probes BP
were added to a black 96-well plates (5 replicates). The parameters for the instrument set up were
maintained as described previously. The parameters for the instrument set up were maintained as
described previously.

Application for diagnosis of CRC.
The fecal samples of CRC patients were collected from Hunan Cancer Hospital (Changsha, Hunan). The
gut microbiota suspension76 was prepared with minor modifications. Collected samples were
resuspended in PBS, vortexed for 1 min and this washing procedure was repeated 5 times. The bacterial
fraction was pelleted by centrifugation at 2850 g for 10 min, with supernatants discarded after each spin.
The resulting white intermediate layer was carefully collected, re-suspended in fresh PBS, and filtered
through a 40-μm mesh to eliminate large aggregates, yielding a homogeneous microbial suspension.
The obtained gut microbiome samples were adjusted to OD = 0.5 using PBS prior to experimental use.
600
For fluorescence measurements, 100 μL of the gut microbiota suspension was blended with 5 μL of BP
probes and 95 μL of DMSO in a black 96-well plate for 30 min (5 biological replicates), following the
same protocol as the bacterial culture assay.

16S rRNA gene sequencing of fecal samples
Fecal sample collection and processing: fresh fecal samples (0.25-0.5 g) were placed into 2 mL sterile
centrifuge tubes; 200 μL was used for liquid samples. Then 500 μL buffer SA, 100 μL buffer SC, and
0.25 g grinding beads were added. For RNA removal, 10 μL RNase A (TIANGEN, RT405-02) was added
(Supplementary Data 3). The mixture was homogenized, heated at 70 ℃ for 15 min, centrifuged at
13400 g for 1 min, and ~500 μL supernatant was transferred to a new tube. Unprocessed samples were
stored at -80 ℃ to avoid repeated freeze-thaw.

Total fecal DNA extraction: total DNA was extracted using TGuide S96 Magnetic Universal DNA Kit
(Tiangen, DP812) with minor modifications: 200 μL buffer SH was added to the supernatant, vortexed
for 5 s, and incubated at 4 ℃ for 10 min. After centrifugation at 13400 g for 3 min, the supernatant was
mixed with 500 μL buffer GFA (confirmed isopropanol added). Then 10 μL magnetic bead suspension G
was added, shaken for 5 min, and magnetic beads were adsorbed on a magnetic rack to remove liquid.
After washing with 700 μL proteinase K buffer RID (ethanol checked) and twice with 700 μL wash buffer
PWD (ethanol checked), beads were air-dried for 5-10 min. Finally, 50-100 μL elution buffer TB was
added, incubated at 56 ℃ for 5 min (shaken 3 times), and DNA was collected. DNA quality
(A260/A280=1.8-2.0) and integrity were detected by ELISA reader (GeneCompany, Synergy HTX) and
1.8% agarose gel electrophoresis, respectively, and stored at -20 ℃.

PCR amplification of 16S rRNA Gene: the V3-V4 region of 16S rRNA gene was PCR-amplified using
purified DNA as template with primers 338F (5'-ACTCCTACGGGAGGCAGCA-3') and 806R (5'-
GGACTACHVGGGTWTCTAAT-3'). The 10 μL system contained 2.5-4 ng genomic DNA, 0.3 μL each of
10 μM VnF/VnR, 5 μL KOD FX Neo Buffer, 2 μL dNTP (2 mM each), 0.2 μL KOD FX Neo (TOYOBO),
and ddH₂O. Each sample had 3 replicates and a blank control. PCR conditions: 95 ℃ pre-denaturation
for 5 min; 25 cycles of 95 ℃ (30 s), 50 ℃ (30 s), 72 ℃ (40 s); 72 ℃ final extension for 7 min; 4 ℃ hold.
PCR products were verified by 1.8% agarose gel electrophoresis (target band ~468 bp).

PCR product purification: PCR products from the same sample were pooled and purified with VAHTSTM
DNA Clean Beads (Vazyme) at 10:8 (product:beads) ratio. After 5 min incubation at room temperature,
beads were adsorbed, supernatant removed, washed twice with 200 μL 80% ethanol, air-dried for 3 min,
and eluted with 8-10 μL ddH₂O. Purified products were quantified by ImageJ and stored at -20 ℃.

Library construction and sequencing: libraries were constructed from purified PCR products via end
repair, 3'-A tailing, and adapter ligation using KOD FX Neo (TOYOBO), Phusion HF MM (Biolink), and
VnF/VnR primers (SYNBIO). Target fragments (500-600 bp) were selected by 1.8% agarose gel
electrophoresis and purified with Monarch DNA Gel Extraction Kit (Hongyue). After Solexa PCR
enrichment, library quality was detected, and sequencing was performed on Illumina NovaSeq 6000 (PE
250).

Sequencing data processing and analysis: raw sequencing data were filtered to remove low-quality reads
(Q<20 >50%), adapters, and N-rich reads to get clean reads. Paired-end reads were spliced by FLASH
(unsuccessful ones discarded). Sequences with ≥97% similarity were clustered into OTUs (singletons
removed). Species annotation was done via SiIlva database alignment. α-diversity (Shannon, Chao1)
and β-diversity (PCoA, NMDS) were calculated to analyze microbial community composition.

Statistics and reproducibility
Sample size was determined by power analysis (G*Power, α = 0.05, power = 0.80, effect size d = 0.6),
requiring ≥30 samples per group. No data were excluded. All key experiments were independently
replicated at least three times with consistent results. Random assignment and blinding were applied
during data collection and analysis. Data analysis and illustrations were performed using Origin 2021.
The fluorescence intensities were quantified using ImageJ software for the region of interest analysis.
Statistical comparisons between two groups were determined by two-tailed Student’s t-test. Statistical
analysis was performed using GraphPad Prism 9.0. P < 0.05 was considered significant and the P-values
are provided as follows: **** P < 0.0001, *** P < 0.001, ** P < 0.01, * P < 0.05, ns = not significant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper reports that boronic acids modified with various fluorescent dyes can recognize sugar chains on microbial surfaces and distinguish the presence or absence of cancer through multivariate analysis. The experiments were conducted appropriately, and the results appear to be reliable. However, there are a few points of concern, which are listed below.\nDespite being a paper proposing a method for detecting intestinal microbiota, why are microorganisms in serum samples being detected?(Line 252)\nThis is a method for identifying sugar chain modifications on the surface of microorganisms. However, the authors only use 16S rRNA analysis to compare their analysis methods. It is natural to compare with 16S rRNA analysis, which is a classic gene sequence analysis, but differences in sugar chain modifications on the cell surface should also be investigated. Cell surface sugar chain analysis methods have been established using MALDI-TOF-MS and other techniques.", "reviewer_2": "The manuscript “Fingerprint profiling human gut microbiota through boronic acid-based sensor array for colorectal cancer classification diagnostics” described a new analytical tool for profiling human gut microbiota, as well as the diagnosis of colorectal cancer at different stages. After reading the manuscript carefully, I found this study focused on a hot topic in chemosensors and analytical chemistry. The authors utilized the pH-responsive property of boronic acid-carbohydrate interaction. By establishing an array of boronic acid fluorescence probes and monitoring its response under different pH, the distinction of different species of gut microbiota was accomplished. The novelty of this study is high, and the potential impact is also significant, judging by the excellent outcomes of the assay. The conclusion was well supported by the data, and an in-depth discussion was provided. The logic and structure of the manuscript were also well organized. Some expressions need to be improved. Overall, I’d like to recommend the publication of this work on Nature Communications after minor revision. Here are several detailed comments.\n1. I suggest the authors to slightly modify the title. The current title emphasized on colorectal cancer classification. However, the profiling of gut microbiota is more meaningful as it covers much wider applications. “Fingerprint profiling human gut microbiota through boronic acid-based sensor array and its application on colorectal cancer classification” might be better. In this case, the readers understand that the diagnosis of CRC is just one application of the proposed method.\n2. The abbreviation \"GUT-SCAN\" should be explicitly defined at its first appearance in Abstract and Introduction, to make it clearer to the readers.\n3. The reason to choose boronic acid probes to fulfill this task needs to be further explained in Introduction. Because it relates to the fundamental principle and the major advantages of the proposed sensing platform.\n4. The design of the selected boronic acid probes (BP1–BP6) needs to be further elaborated. Especially, the role of different substituted groups on phenylboronic acid should be explained in detail. How do they affect the electronic properties of the probes, and modulate the binding affinity towards carbohydrates. This information is quite important.\n5. The design of the pH sensor array and the selection of 18 sensing channels should be further explained.\n6. In line 73, “Boronic acid derivatives exhibit selective binding to cis-diol-containing biomolecules”. The expression is not very accurate. Boronic acid binds with diol molecules, not just “biomolecules”. “an interaction modulated by their molecular structure and the dissociation constant (pKa)”. pKa is a property of the molecule, it is decided by the molecular structure. Thus, it is not appropriate to say pKa modulates the interaction. Change the sentence as “Boronic acid derivatives exhibit selective binding to cis-diol molecules, an interaction modulated by their molecular structure and the pH of the medium.”\n7. In line 79 and 80, “bacterial carbohydrate” should be changed as “bacterial oligosaccharides” or “bacterial polysaccharides”, to make it more accurate.\n8. In line 107, “boronic acids with electron-withdrawing groups” should be “phenylboronic acids with electron-withdrawing groups”. The substituted groups are on the benzene ring.\n9. In line 423-450, the information of MS for each product should be included.", "reviewer_3": "In the current study, Zhao et al. reported a boronic acid–based fluorescent sensor array, “GUT-SCAN”, for rapid profiling of microbiota and discrimination of colorectal cancer (CRC) stages. The authors synthesized six pyrene–boronic acid probes (BP1–BP6) with tunable pKa values and used multichannel fluorescence signals across three pH conditions to generate “distinct fingerprints” of gut-derived bacterial species. They then developed a Machine-learning analysis (LDA, HCA) that yielded apparently high classification accuracy. The same platform was applied to examine fecal samples from CRC patients and healthy controls. The concept of the study is novel. It has potential to apply the recently developed knowledge in microbiome to translational studies for a rapid, high-throughput and cost-efficient sensing platform for gut microbiota detection. However, better study design with rationale and application of microbiome significantly associated with CRC are needed. Specifically, the authors need address the following concerns:\n1. Few data in Fig. 1 and 2 but the study design/Schematic illustration.\n2. The rationale to choose bacteria is not well presented. The top CRC associated bacteria are not specifically tested.\n3. Figure 4 shows the application to human fecal samples from CRC. The cohort (n = 75) is small. There was no discussion on demographic or dietary and did not include inflammatory or adenoma controls.\n4. The complexity of microbiome in CRC or other diseases is not well considered.", "reviewer_4": "This study aims to develop a boronic acid-based sensor array for analyzing the human gut microbiota, with a particular focus on colorectal cancer (CRC). stablishing rapid and reliable techniques to capture the full spectrum of microbial diversity is essential. This manuscript is working on a timely topic. However, the current design and data still have limits. Specifically, the authors need to address the following concerns:\n1. Title: “Fingerprint Profiling Human Gut Microbiota through Boronic Acid-Based Sensor Array for Colorectal Cancer Classification Diagnostics” is too broad and overstates the presented data in this study.\n2. The authors employed three pH conditions to expand channel diversity. However, the different pH environment itself can influence bacterial physiology and probe ionization. Further clarification and study is needed.\n3. Control experiments should be added in several experiments. For example, in Fig. 2b, the authors need consider using heat-inactivated or fixed bacteria, as well as buffer-only measurements to clarify whether the observed differences truly arise from binding specificity.\n4. Fig. 4 is to verify the efficacy of this assay with the human fecal samples. However, the cohort is too small and does not include any inflammatory or adenoma controls. These limitations make it difficult to assess disease specificity for CRC.\n5. The in vitro model was overly simplified and did not mimic the real intestinal environment. And the single bacterial culture was completed in PBS, which is completely disconnected from the real gut environment.\n6. The authors indicated that “According to the features of gut microbiota, six representational bacteria, including two probiotics (L. a and L. pl), two commensals bacteria (E. coli and E. cloacae), and two pathogenic bacteria (S. aureus and C. k), were selected as the target analyte” is oversimplified the complexity of gut microbiota."}, "author_response": "Point-by-point response to the reviewer’s comments\nResponse to reviewer 1:\nReviewer 1’s comment 1-1:\nThis paper reports that boronic acids modified with various fluorescent dyes can\nrecognize sugar chains on microbial surfaces and distinguish the presence or absence\nof cancer through multivariate analysis. The experiments were conducted appropriately,\nand the results appear to be reliable. However, there are a few points of concern, which\nare listed below.\nResponse 1-1: We appreciate very much the reviewer’s encouraging and constructive\ncomments. Following the reviewer’s advice, we have performed supplementary\nexperiments to substantiate our novel conclusions. Point-by-point responses to\nreviewer’s comments are showed as follows.\nComments 1-2:\nDespite being a paper proposing a method for detecting intestinal microbiota, why\nare microorganisms in serum samples being detected? (Line 252)\nResponse 1-2: Thank you for raising this critical question. The serum sample is often\nused for various tests to verify the feasibility of the prepared assay (Stern, E., et al. Nat.\nNanotech. 2010, 5, 138-142; De Silva, T., et al. Nat. Commun. 2022, 13, 7593-7603;\nFan, R., et al. Nat. Biotechnol. 2008, 26, 1373-1378; Wu A. H B, et al. Clin. Chem.\n2009, 55, 2057-2059; Mu, L., et al. Anal. Chem. 2017, 89, 21, 11325-11331). Thus, to\nevaluate the robustness and practicability of the proposed sensor array, the serum\nsamples were selected for detection.\nComments 1-3:\nThis is a method for identifying sugar chain modifications on the surface of\nmicroorganisms. However, the authors only use 16S rRNA analysis to compare their\nanalysis methods. It is natural to compare with 16S rRNA analysis, which is a classic\ngene sequence analysis, but differences in sugar chain modifications on the cell surface\nshould also be investigated. Cell surface sugar chain analysis methods have been\nestablished using MALDI-TOF-MS and other techniques.\nResponse 1-3: Thank you very much for this insightful comment. We fully agree that\ndirectly comparing our method with established techniques for cell surface glycan\nanalysis would provide deeper mechanistic validation. We appreciate the opportunity\nto clarify the focus of our current study and outline our plans in response to this valuable\nsuggestion. In our current work, the primary objective was to establish and validate the\nfunctional output of our boronic acid-based sensor array for bacterial discrimination.\nFor this purpose, we used 16S rRNA gene sequencing as the benchmark because it is\nthe gold-standard method for species-level identification in complex microbiomes. The\nconcordance between our sensor's clustering and the phylogenetic tree demonstrates of\nthe 16S rRNA gene sequencing, demonstrating that our platform with great accuracy\nfor evaluation of cancer subtypes.\nWe fully concur with the reviewer that investigating the structural input,\nspecifically glycan modifications, is indeed crucial. The suggestion to employ MALDI-\nTOF-MS is highly pertinent for profiling the surface glycome of intact cells. However,\nthe analysis of complex microbial communities presents significant technical\nchallenges, including the necessity for glycan release and purification. To directly\naddress your point, we employed lectin microarray profiling, a technique well-suited\nfor high-throughput, functional profiling of accessible surface glycans (Liu, et al, Nat.\nCommun. 2025, 16, 1000-1014). As shown in Supplementary Fig. 52, this analysis\nconfirmed distinct surface glycan signatures among samples, providing orthogonal\nvalidation that our method's output correlates with genuine differences in surface\nglycosylation. According to the suggestion, we have added the following statement in\nthe revised manuscript (Page 12, Lines 306-308): “Orthogonal validation via lectin\nmicroarray analysis confirmed significant differences in the surface glycome\namong the samples (Supplementary Fig. 52)”. We believe this addition strengthens\nthe study by directly validating the method's functional readout, and we thank you for\nthe suggestion.\nSupplementary Fig. 52. The comparative flow cytometry analysis (A) and mean\nfluorescent intensity (B) of fecal samples from the Adv., Ear., and Hea. cohorts (n=3\neach) stained with FITC-ConA. Data points are expressed as mean ± standard deviation.\nStatistical analysis was calculated by Student’s t-test.\nResponse to reviewer 2:\nReviewer 2’s comment 2-1:\nThe manuscript “Fingerprint profiling human gut microbiota through boronic\nacid-based sensor array for colorectal cancer classification diagnostics” described a\nnew analytical tool for profiling human gut microbiota, as well as the diagnosis of\ncolorectal cancer at different stages. After reading the manuscript carefully, I found this\nstudy focused on a hot topic in chemosensors and analytical chemistry. The authors\nutilized the pH-responsive property of boronic acid-carbohydrate interaction. By\nestablishing an array of boronic acid fluorescence probes and monitoring its response\nunder different pH, the distinction of different species of gut microbiota was\naccomplished. The novelty of this study is high, and the potential impact is also\nsignificant, judging by the excellent outcomes of the assay. The conclusion was well\nsupported by the data, and an in-depth discussion was provided. The logic and structure\nof the manuscript were also well organized. Some expressions need to be improved.\nOverall, I’d like to recommend the publication of this work on Nature Communications\nafter minor revision. Here are several detailed comments.\nResponse 2-1: We appreciate very much the reviewer’s encouraging and constructive\ncomments. Following the reviewer’s advice, we have performed supplementary\nexperiments to substantiate our novel conclusions. Point-by-point responses to the\nreviewer’s comments are shown as follows.\nComments 2-2:\nI suggest the authors to slightly modify the title. The current title emphasized on\ncolorectal cancer classification. However, the profiling of gut microbiota is more\nmeaningful as it covers much wider applications. “Fingerprint profiling human gut\nmicrobiota through boronic acid-based sensor array and its application on colorectal\ncancer classification” might be better. In this case, the readers understand that the\ndiagnosis of CRC is just one application of the proposed method.\nResponse 2-2: We are grateful to the reviewer for inviting us to clarify this important\npoint. We adapt the reviewer's suggested title and modify the title to “Fingerprint\nprofiling human gut microbiota through boronic acid-based sensor array and its\napplication on colorectal cancer classification”.\nComments 2-3:\nThe abbreviation “GUT-SCAN” should be explicitly defined at its first appearance\nin Abstract and Introduction, to make it clearer to the readers.\nResponse 2-3: We thank the reviewer for this helpful reminder. According to the\nsuggestion, we have explicitly defined the abbreviation “GUT-SCAN” upon its first\nappearance in both the Abstract and the Introduction in the revised manuscript (Page 2,\nLines 24-25). The revised text now reads: “...we developed a boronic acid-based\nsensor array named GUT-SCAN (Gut Unique Typing via Spectral and Chemical\nAnalysis using Boronic Acid Nanosensor) for...”.\nComments 2-4:\nThe reason to choose boronic acid probes to fulfill this task needs to be further\nexplained in Introduction. Because it relates to the fundamental principle and the major\nadvantages of the proposed sensing platform.\nResponse 2-4: We sincerely thank the reviewer for this insightful and constructive\nsuggestion. We fully agree that a more thorough explanation of the rationale behind\nchoosing boronic acid probes is crucial for establishing the fundamental principle and\nhighlighting the advantages of our sensing platform. We have carefully considered this\nsuggestion and substantially revised the Introduction section to explicitly address this\npoint (Page 4, Lines 74-79). See detail as “Boronic acid derivatives are an ideal\ncandidate for this task due to their unique capacity for selective binding to cis-diol-\ncontaining molecules, which forms the fundamental principle of our sensing\nplatform. This capability is particularly advantageous for bacterial profiling, as\nbacterial surfaces are rich in carbohydrate moieties including cis-diols, and\nboronic acid-based sensors provide a promising approach for bacterial\nidentification. The binding is characteristically pH-dependent, which is modulated\nby their molecular structure and the pH of the medium.”\nComments 2-5:\nThe design of the selected boronic acid probes (BP1–BP6) needs to be further\nelaborated. Especially, the role of different substituted groups on phenylboronic acid\nshould be explained in detail. How do they affect the electronic properties of the probes,\nand modulate the binding affinity towards carbohydrates. This information is quite\nimportant.\nResponse 2-5: We thank the reviewer for this insightful comment regarding the\nmolecular design of our boronic acid probes (BP1-BP6). The core design principle is\nto precisely tune the electron density of the boron center via phenyl substituents to\noptimize probe performance. The strategic introduction of substituents with distinct\nelectronic properties onto the phenylboronic acid scaffold was indeed central to our\nprobe design, enabling systematic modulation of their binding affinity toward\ncarbohydrates. The molecular design specifically targeted the para-position of the\nphenyl ring to maximize electronic effects on the boron atom. We incorporated both\nelectron-donating groups (EDGs, e.g., -OCH₃) and electron-withdrawing groups\n(EWGs, e.g., -F, -Cl). EDGs increase the electron density around the boron atom,\nraising its pKa. Conversely, EWGs decrease the electron density, lowering the pKa. To\nvalidate this rational design, we quantitatively evaluated the binding constants of\nrepresentative probes with various cis-diol-containing sugars across different pH value.\nAs summarized in Fig. 1e, the modified boronic acids exhibited distinctly different\nbinding affinities toward specific cis-diol configurations, directly confirming that our\nstructural modifications successfully programmed the desired binding selectivity. This\nstructure-activity relationship forms the fundamental basis for the discriminative\ncapacity of our sensing platform.\nWe have carefully considered this suggestion and substantially revised the Results\nsection to explicitly address this point (Page 5, Lines 112-113). See detail as “In this\nwork, the core design principle is to precisely tune the electron density of the boron\ncenter via phenyl substituents to optimize probe performance.”.\nComments 2-6:\nThe design of the pH sensor array and the selection of 18 sensing channels should\nbe further explained.\nResponse 2-6: We thank the reviewer for this insightful comment regarding the design\nof our sensor array. The core principle of our design is to leverage two key attributes of\nboronic acid probes--their pH-dependent binding affinity and structural tailorability--to\ncapture the inherent heterogeneity of bacterial surface glycans, thereby generating\nmultidimensional response patterns. The configuration of 18 sensing channels (6\nboronic acid derivatives × 3 pH) was a deliberate and systematic choice to maximize\nthe discriminatory power of our platform. The rationale is twofold: (1) Six Boronic\nAcid Probes: We synthesized a library of boronic acid compounds with varied\nelectronic and steric properties. These modifications were designed to fine-tune their\npKa values and their binding affinities toward the diverse cis-diol configurations\npresent on bacterial surface glycans. Each probe serves as a unique recognition element,\ncontributing to a distinct binding profile across different bacterial species. (2) Three pH\nConditions: The binding interaction between boronic acids and cis-diols is highly pH-\ndependent. By performing assays under three pH levels, we can selectively modulate\nthe binding activity of each probe based on its individual pKa. This strategy provides\nan additional dimension for discrimination, as the binding profile of each bacterium\nevolves characteristically with pH. This integrated approach effectively translates the\nstructural variability of bacterial glycans and the programmable binding properties of\nour probes into discriminative, multi-dimensional fingerprints, enabling accurate and\ninformative profiling of gut microbiota.\nTo improve clarity, we have carefully revised the section (Page 9, Lines 212-214).\nSee detail as “The array was deliberately designed with 18 channels constituting\nan orthogonal design combination of six boronic acid probes (BP1-BP6) and three\npH conditions (6.0, 7.4 and 8.2)”.\nComments 2-7:\nIn line 73, “Boronic acid derivatives exhibit selective binding to cis-diol-\ncontaining biomolecules”. The expression is not very accurate. Boronic acid binds with\ndiol molecules, not just “biomolecules”. “an interaction modulated by their molecular\nstructure and the dissociation constant (pKa)”. pKa is a property of the molecule, it is\ndecided by the molecular structure. Thus, it is not appropriate to say pKa modulates the\ninteraction. Change the sentence as “Boronic acid derivatives exhibit selective binding\nto cis-diol molecules, an interaction modulated by their molecular structure and the pH\nof the medium.”\nResponse 2-7: We thank the reviewer for this insightful comment. We agree that the\noriginal phrasing was scientifically imprecise. The binding specificity is indeed for cis-\ndiol moieties in general, not exclusively within biomolecules. We have revised the word\nin the revised manuscript (Page 4, Line 75). See detail as “molecules”. Furthermore,\nwe acknowledge that the pH of the medium, rather than the pKa itself, is the direct\nmodulator of the interaction, as the pKa is an intrinsic molecular property. According\nto the suggestion, we have revised the sentence in the revised manuscript (Page 4, Lines\n78-79). See detail as “The binding is characteristically pH-dependent, which is\nmodulated by their molecular structure and the pH of the medium”.\nComments 2-8:\nIn line 79 and 80, “bacterial carbohydrate” should be changed as “bacterial\noligosaccharides” or “bacterial polysaccharides”, to make it more accurate.\nResponse 2-8: We thank the reviewer for this precise correction. We agree that the\noriginal term “bacterial carbohydrate” is too general. As the reviewer rightly points out,\nthe primary targets on the bacterial cell surface are oligo- and poly-saccharides. We\nhave revised the text on Page 4, Lines 83-84 to the more accurate and specific term:\n“bacterial surface oligosaccharides and polysaccharides” This change better reflects\nthe actual molecular targets of our boronic acid probes.\nComments 2-9:\nIn line 107, “boronic acids with electron-withdrawing groups” should be\n“phenylboronic acids with electron-withdrawing groups”. The substituted groups are\non the benzene ring.\nResponse 2-9: We thank the reviewer for this precise correction. Our discussion of\nsubstituent effects is specific to the para-substituted phenylboronic acid scaffold used\nin this study. We have revised the text on Page 5, Line 114 accordingly to specify:\n“phenylboronic acids with electron-withdrawing groups”.\nComments 2-10:\nIn line 423-450, the information of MS for each product should be included.\nResponse 2-10: We thank the reviewer for this suggestion. In response, we have\nincorporated the MS characterization results for all synthesized boronic acid probes\n(BP1-BP6) into the Supporting Information. Additionally, we have revised the\ncorresponding section in the main text (Page 18, Lines 452-476) to explicitly reference\nthese mass spectrometry data. The specific data for each compound are presented as\nfollows:\n“BP1: HRMS (ESI): m/z = 381.2936 ([M + H]+); BP2: HRMS (ESI): m/z = 395.1601\n([M + H]+); BP3: HRMS (ESI): m/z = 411.4252 ([M + H]+); BP4: HRMS (ESI): m/z =\n437.2306 ([M + K]+); BP5: HRMS (ESI): m/z = 437.2305 ([M + Na]+); BP6: HRMS\n(ESI): m/z = 437.0757 ([M + H]+)”.\nResponse to reviewer 3:\nReviewer 3’s comment 3-1:\nIn the current study, Zhao et al. reported a boronic acid–based fluorescent sensor\narray, “GUT-SCAN”, for rapid profiling of microbiota and discrimination of colorectal\ncancer (CRC) stages. The authors synthesized six pyrene–boronic acid probes (BP1-\nBP6) with tunable pKa values and used multichannel fluorescence signals across three\npH conditions to generate “distinct fingerprints” of gut-derived bacterial species. They\nthen developed a Machine-learning analysis (LDA, HCA) that yielded apparently high\nclassification accuracy. The same platform was applied to examine fecal samples from\nCRC patients and healthy controls. The concept of the study is novel. It has potential to\napply the recently developed knowledge in microbiome to translational studies for a\nrapid, high-throughput and cost-efficient sensing platform for gut microbiota detection.\nHowever, better study design with rationale and application of microbiome\nsignificantly associated with CRC are needed. Specifically, the authors need address\nthe following concerns:\nResponse 3-1: We appreciate the reviewer’s encouraging and constructive comments.\nFollowing the reviewer’s advice, we have performed supplementary experiments to\nsubstantiate our novel conclusions. Point-by-point responses to reviewer’s comments\nare shown as follows.\nComments 3-2:\nFew data in Fig. 1 and 2 but the study design/Schematic illustration.\nResponse 3-2: Thank you very much for the valuable comments regarding Figs. 1 and\n2. We understand the concern about the relatively limited experimental data and the\ngreater emphasis on research design and schematic illustrations in the figures. We want\nto clarify our design rationale and highlight the key additions. In this work, Figs. 1 and\n2 serve the critical role of establishing the methodological foundation. The primary\nobjective of Fig. 1 is to visually present the design and structural modification of\nboronic acid probes, thereby establishing the conceptual framework of our work. We\nhave supplemented this with binding constant measurements with various sugar\nmolecules to validate how structural modifications of the boronic acids regulate their\nbinding capabilities. Fig. 2 systematically demonstrates the molecular structures of the\nsix boronic acid probes and their binding interactions with cis-diol structures on\nbacterial surfaces, exploring the differences and underlying principles of their binding\nto bacteria. These data fully demonstrate that our designed probe library possesses the\nrequired differentiated binding properties.\nWe agree that these figures focus more on methodological validation, which is\nprecisely their intended role in this study--to establish a reliable analytical foundation\nfor the more complex bacterial discrimination (Fig. 3) and clinical sample detection\n(Fig. 4). Without the principal verification and probe screening established in Figs. 1\nand 2, the subsequent analysis of complex samples would lack scientific basis. In the\nrevised manuscript, we have further clarified the positioning of these figures and\nprovided more detailed descriptions of the relevant experimental data in the figure\ncaptions.\nAdditionally, we have refined Figs. 1 and 2 to enhance the clarity and scientific\nrigor, ensuring they more effectively communicate the foundational work underpinning\nour study. We believe that these explanations and corresponding revisions will more\nclearly demonstrate the important role of Figs. 1 and 2 within the research framework.\nWe thank the reviewer for providing us with the opportunity to better articulate this\naspect.\nFig. 1 | Structures of boronic acid derivatives (BP1-BP6) and experimental\nresults using different derivatives.\na, The structures of BP1-BP6 after modification to the phenylboronic acid.\nb, Electrostatic potential distribution of BP1-BP6.\nc, The pKa value of BP1-BP6.\nd, The structures of monosaccharides were investigated in this study.\ne, The heat map of the association constant K (M−1) for BP1-BP6 with Glucose,\nep\nFructose, Mannose, GlcNAc, and Glc at pH 6.0, 7.4, and 8.2, respectively.\nFig. 2 | Response and mechanism validation of BP1-BP6 with the gut-derived\nbacterium.\na, Schematics illustration of probes with the cis-diol structure in the cell wall of the\nbacterium.\nb, Fluorescence responses of BP1-BP6 after incubation with L.pl at pH 6.0, 7.4, and\n8.2.\nc, Bacterial adhesion to hydrocarbon (BATH) value without incubation and incubation\nwith BP1-BP6 in L.pl.\nd-e, Confocal fluorescence images and semi-quantitative analysis of bacteria\nincubated with BP1-BP6.\nData points are expressed as mean ± standard deviation. Statistical analysis was\ncalculated by Student’s t-test. ns: no significance, **P<0.01, ***P<0.001\nComments 3-3:\nThe rationale to choose bacteria is not well presented. The top CRC associated\nbacteria are not specifically tested.\nResponse 3-3: Thank you very much for this insightful and critical comment. We fully\nacknowledge that the initial rationale for selecting bacterial strains was inadequately\nconnected to the colorectal cancer (CRC) context. In response, we have\ncomprehensively revised both the narrative and the experimental design to anchor our\nmodel system firmly within the pathophysiological framework of CRC. Our revised\nexperimental design and manuscript narrative now explicitly anchor the choice of\nbacterial strains within the pathophysiological framework of CRC. This shift moves\nbeyond generic taxonomic representation to focus on species with established,\nmechanistically defined roles in CRC pathogenesis or protection.\nThe strategic expansion of our model system includes the following key species,\nselected based on robust clinical and molecular evidence:\nFusobacterium nucleatum (F. nucleatum): this obligate anaerobe is one of the most\nconsistently and significantly enriched bacterial species in CRC tissues. It was included\ndue to its direct, multi-faceted role in tumorigenesis, which includes (1) adhesion and\nproliferation, mediated by its FadA adhesin binding to E-cadherin, leading to activation\nof β-catenin signaling and stimulation of cancer cell proliferation (Rubinstein, M.R.,\net al. Cell Host & Microbe. 2013, 14, 195-206); (2) immune modulation: shaping a pro-\ntumorigenic microenvironment by recruiting immunosuppressive myeloid cells (Kostic,\nA.D., et al. Cell Host & Microbe. 2013, 14, 207-215). Its selection allows us to test our\nsensor against a classic “oncobacterium” with a distinct surface architecture.\nBacteroides fragilis (B. fragilis): we specifically refer to the pathogenic subset\nproducing the B. fragilis toxin (BFT). Enterotoxigenic B. fragilis (ETBF) is a key driver\nof colitis and CRC in experimental models and is implicated in human carcinogenesis.\nToxin-driven inflammation: BFT induces a potent and persistent IL-17-driven\ninflammatory response, a known promoter of colorectal tumorigenesis (Wu, S., et al.\nNat. Med. 2009, 15, 1016-1022). Direct cellular effects: BFT causes cleavage of E-\ncadherin and activation of STAT3 in colonic epithelial cells (Chung, L., et al. Cell Host\n& Microbe. 2018, 23, 203-214). Including ETBF enables our platform to target surface\nfeatures associated with a toxin-producing, inflammation-driving pathobiont.\nBifidobacterium longum (B. longum) and Bifidobacterium breve (B. breve): these\nprobiotic species were selected to represent the protective axis of the gut microbiome,\noften found depleted in CRC settings. Their inclusion is justified by their mechanisms\nof action, which counteract carcinogenesis (1) barrier enhancement and anti-\ninflammatory activity, they reinforce intestinal barrier integrity and produce anti-\ninflammatory metabolites like short-chain fatty acids (Veiga, P., et. al. Nat. Microbiol.\n2020, 5, 878-880); (2) immune regulation: certain strains can potentiate anti-tumor\nimmunity and improve responses to immunotherapy (Sivan, A. et al. Science 2015, 350,\n1084-1089).\nTheir presence tests the ability of our sensor array to discriminate beneficial\ncommensals from pathogens based on surface glycan differences. This curated panel of\nten strains is deliberately heterogeneous, encompassing Gram-positive and Gram-\nnegative bacteria, aerobes, facultative and obligate anaerobes. This diversity serves a\ndual purpose (1) it models key ecological and structural players in the CRC-associated\nmicrobiota, and (2) it provides a rigorous, biologically complex testbed to challenge the\nbroad-spectrum discriminative capacity of our boronic acid-based sensor array, which\ntargets universal cis-diol motifs expressed in varied chemical contexts.\nAs shown in Figs. R1b and c, the refined panel is highly distinguishable by our\nsensor array, with unsupervised analysis showing clear separation of all ten strains,\nincluding functional clustering (e.g., CRC-associated vs. probiotic groups). We have\nrevised the manuscript to include a dedicated subsection outlining this rationale and\nhave incorporated the supporting references cited above. We also added the new results\nin the revised manuscript (Page 9, Lines 217-219) “Among them, F. nucleatum, B.\nfragilis and E. coli linked to tumorigenesis through inflammation, DNA damage\nand immune modulation.” We believe this revision solidly grounds our model in\ndisease-specific biology and thank the reviewer for prompting this essential\nimprovement.\nFig. 1. Performance of the BP-based sensor array for bacteria discrimination.\nb, Heat map of the fluorescence response patterns for the different bacteria upon\nincubation with the sensor array under 3 pH (6.0, 7.4, and 8.2).\nc, LDA plot demonstrating the BP sensor array’s discrimination of the ten bacteria\nbased on their fluorescence response profiles.\nComments 3-4:\nFigure 4 shows the application to human fecal samples from CRC. The cohort (n\n= 75) is small. There was no discussion on demographic or dietary and did not include\ninflammatory or adenoma controls.\nResponse 3-4: Thank you for these critical and insightful comments regarding the\nclinical validation aspect of our study. We fully acknowledge the limitations initially\npresent in our cohort design. To comprehensively address these points, we have\nsignificantly expanded our clinical validation using larger cohorts, and the revised\nresults are summarized as follows (1) cohort expansion with critical control groups: our\nrevised validation now comprises 147 clinical samples across two complementary\nanalyses. Cohort A (Disease Staging Focus): 120 samples, including 50 early-stage\ncolorectal cancer (CRC), 40 advanced-stage CRC, and 30 healthy controls. This cohort\nevaluates the performance of our assay across various stages of disease progression. As\na result, the assay also robustly distinguished CRC patients (both early and late stage)\nfrom healthy controls (as shown in Fig. R2). The new results were added in the revised\nmanuscript.\nFig. R1. Unknown identification of Adv., Ear. and Hea. fecal samples by using the BP\nsensor assay.\nDiscussion of demographic and dietary factors: We acknowledge that detailed\ndietary information was not systematically collected in this initial. However, this is a\nsingle-center retrospective analysis. All volunteers were recruited from a single\ngeographic region (Hunan Province, China), which may minimize significant\nvariability arising from regional dietary differences. To address this transparently, we\nhave added the following clarification to the discussion section (Page 16, Lines 391-\n395): “The other is that detailed individual dietary information was not collected.\nHowever, all volunteers were recruited from a single geographic region (Hunan\nProvince, China), which may minimize significant variability arising from\nregional dietary differences. Nevertheless, individual dietary habits remain a\npotential confounding factor. Future prospective studies should systematically\ncollect such data for a more comprehensive analysis.” Moreover, basic demographic\ninformation (age, sex) for all participants is now included in Supplementary\nInformation.\nDiscussion of differential diagnosis: We are grateful for the reviewer's suggestion,\nwhich has significantly strengthened the clinical validation of our work. According to\nthe suggestion, we recruited fecal samples from inflammatory disease (n = 12) and\nadenoma (n = 15) patients as controls. As a result, our sensor array generated distinct\nresponse patterns (Fig. 4 h). Multivariate statistical analysis (e.g., LDA) shows clear\nand separate clustering among the CRC, inflammatory and adenoma groups. The results\nshow that our sensor array platform is more specific to CRC rather than inflammatory\nand adenoma, demonstrating the feasible prospects of a sensor array in clinical auxiliary\ndiagnosis. See detail as “while an initial separation is observed between the CRC\nsamples and the inflammatory/adenoma controls” (Page 13, Lines 318-319).\nFig. 4. (h) Identification of Hea., Adv., Ear., IBD and Adenoma fecal samples controls\nby using the BP sensor assay.\nIn summary, through substantial cohort expansion, inclusion of critical\ninflammatory and adenoma control groups, and transparent discussion of limitations,\nwe have significantly strengthened the clinical validation and specificity analysis of our\nstudy.\nComments 3-5:\nThe complexity of microbiome in CRC or other diseases is not well considered.\nResponse 3-5: Thank you very much for raising this important point regarding the\nconsideration of microbiome complexity in the context of disease. We fully agree that\na rigorous study must engage with the multidimensional complexity of the gut\necosystem-spanning taxonomic diversity, functional potential and dynamic host-\nmicrobe interactions. To address this, our study was deliberately designed across\nseveral layers to capture and parse this complexity rather than oversimplify it:\nModel system: we moved beyond a simplistic “representative” panel. Our curated\nmodel now includes 10 bacterial strains selected based on their established,\nmechanistically distinct roles in the CRC context (e.g., F. nucleatum as a pathobiont, B.\nfragilis as a toxin-driven driver, and B. longum and B. breve as protective symbionts).\nThis design captures key ecological actors (pathogens, commensals and probiotics)\nwith diverse cell wall structures (Gram-positive/negative) and metabolic states\n(aerobic/anaerobic). By testing our sensor array against this structurally and\nfunctionally heterogeneous panel, we directly challenge its ability to parse complexity\nrooted in biological relevance.\nClinical validation: we specifically tested the capacity of our sensor array to\ndiscriminate CRC from key clinical confounders-adenoma and inflammatory. The\nsuccessful differentiation of these groups demonstrates that the platform captures\ndisease-specific ecological shifts, not mere nonspecific dysbiosis or inflammation.\nIn summary, our work systematically addresses complexity through a functionally-\ngrounded model, a multi-parametric sensing strategy and specific clinical validation.\nWe have revised the manuscript to make this rationale more explicit.\nResponse to reviewer 4:\nReviewer 4’s comment 4-1:\nThis study aims to develop a boronic acid-based sensor array for analyzing the\nhuman gut microbiota, with a particular focus on colorectal cancer (CRC). Stablishing\nrapid and reliable techniques to capture the full spectrum of microbial diversity is\nessential. This manuscript is working on a timely topic. However, the current design\nand data still have limits. Specifically, the authors need to address the following\nconcerns:\nResponse 4-1: We appreciate the reviewer’s encouraging and constructive comments.\nFollowing the reviewer’s advice, we have performed supplementary experiments to\nsubstantiate our novel conclusions. Point-by-point responses to the reviewer’s\ncomments are shown as follows.\nComments 4-2:\nTitle: “Fingerprint Profiling Human Gut Microbiota through Boronic Acid-Based\nSensor Array for Colorectal Cancer Classification Diagnostics” is too broad and\noverstates the presented data in this study.\nResponse 4-2: We thank the reviewer for this critical and fair assessment. We agree\nthat the original title overstated the clinical maturity of our findings. We have revised\nthe title to more accurately reflect the scope and nature of our work. The new title is:\n“Fingerprint profiling of human gut microbiota through a boronic acid-based\nsensor array and its application in colorectal cancer classification”. This revised\ntitle replaces “colorectal cancer classification diagnostics” with the more accurate “its\napplication in colorectal cancer classification,” which better aligns with our study’s\nexploratory and methodological focus. We believe this change ensures the title is both\ncompelling and scientifically precise.\nComments 4-3:\nThe authors employed three pH conditions to expand channel diversity. However,\nthe different pH environment itself can influence bacterial physiology and probe\nionization. Further clarification and study is needed.\nResponse 4-3: Thank you for raising this important point regarding the potential\ninfluence of pH on bacterial physiology and probe ionization. We agree that these\nfactors require careful consideration and are pleased to clarify that the systematic\nvariation of pH is a deliberate and integral feature of our sensor design, rather than a\nconfounding variable. Below, we address the concern directly and provide experimental\nand mechanistic justification.\n(1) Physiological relevance of selected pH range. The pH conditions used (6.0, 7.4,\nand 8.2) span a range pertinent to host-microbe interfaces such as the intestinal tract,\nwhere pH can vary spatially and with physiological state (Brinck, J.E., et al. Nat. Rev.\nGastroenterol. Hepatol. 2025, 22, 639-656). Testing across this range thus increases the\nbiological relevance and discriminative capacity of the array.\n(2) Bacterial physiology remains unaffected during the assay window. To rule out\nsignificant pH-induced physiological changes that could artifactually alter binding, we\ntest the growth-curve assays of the gut-derived bacterial strains after exposing bacteria\nto each pH for 30 min (the incubation time used in sensing). As shown in Fig. R5,\nsubsequent growth in standard medium was identical across groups, confirming that\nshort-term pH exposure does not affect bacterial viability or metabolic state under our\nexperimental conditions.\n(3) Assay design isolates specific binding signals. Following incubation with\nprobes, cells were pelleted and washed prior to fluorescence measurement. This step\nensures that the recorded signal originates from probe molecules bound to the bacterial\nsurface, effectively removing background contribution from free probes whose\nfluorescence might be influenced by bulk pH.\n(4) pH-modulated probe ionization is the core sensing mechanism. The design\nharnesses the well-established pH-dependent binding of boronic acids to cis-diols.\nBinding is favored when the boronic acid is in its anionic tetrahedral form, the\nproportion of which is controlled by the solution pH relative to the probe’s pKa. Our\nprobe library was synthesized to display a range of pKa values through deliberate\nsubstitution. Consequently, at a given pH, each probe exhibits a distinct degree of\nionization and thus differential binding affinity toward bacterial surface cis-diols. By\nemploying three pH values, we systematically shift the binding profile of each probe,\neffectively generating 18 functionally distinct sensing channels (6 probes × 3 pH\nconditions). This “pH-activation profile” is engineered to maximize cross-reactivity\nand information output, forming the basis of our platform’s discriminatory power.\nIn summary, the use of multiple pH conditions is a fundamental aspect of our\nsensor array design. It is physiologically pertinent, does not perturb bacterial\nphysiology in the assay timeframe, is accounted for in our measurement protocol, and\nserves as the key parameter that modulates probe binding, thereby creating the\nmultidimensional data necessary for high-resolution bacterial identification.\nWe included the growth-curve data and corresponding references in the revised\nmanuscript. See detail as: “Then, we investigated the effect of pH to bacterial\nphysiology, subsequent growth in standard medium was identical across groups\n(pH 6.0, 7.4, and 8.2), confirming that short-term pH exposure does not affect\nbacterial viability or metabolic state under our experimental conditions\n(Supplementary Figs. 38).” (Page 8, Lines 183-186). We thank the reviewer again for\nallowing us to clarify this central point.\nSupplementary Fig. 38. Growth curves of E. coli under specified conditions. OD\n600\nmeasurements commenced after a 30 min incubation (control and pH 6.0, 7.4 and 8.2)\nand continued at regular intervals for a duration of 24 h.\nComments 4-4:\nControl experiments should be added in several experiments. For example, in Fig.\n2b, the authors need consider using heat-inactivated or fixed bacteria, as well as buffer-\nonly measurements to clarify whether the observed differences truly arise from binding\nspecificity.\nResponse 4-4: Thanks very much for this critical suggestion to strengthen the\nexperimental validation. We fully agree that rigorous controls are essential to confirm\nthe specificity of the observed binding signals. According to the suggestion, we have\nperformed a comprehensive set of control experiments (Supplementary Figs. 39). The\nresults have been incorporated into the revised Supplementary Figures.\n(1) Heat-inactivated bacteria: Bacterial samples were heated at 95℃ for 30 min to\ndenature proteins/enzymes while preserving cell wall carbohydrate structures. The\nbinding patterns remained clear and highly correlated with those of live bacteria, with\nonly a modest reduction in signal intensity. This strongly indicates that the primary\ninteraction targets are the thermally stable glycan structures on the bacterial surface.\n(2) Glutaraldehyde-fixed bacteria: Fixation with glutaraldehyde maintains\nmorphological integrity and cross-links surface components. The binding profiles of\nfixed cells were consistent with both live and heat-inactivated samples. This confirms\nthat the differential signals are not artefacts from bacterial metabolism or dynamic\nsurface changes.\n(3) Buffer-only controls: Measurements with assay buffer alone yielded negligible\nbackground signals, ruling out probe autofluorescence or non-specific solution effects.\nThe core mechanism of our assay is molecular recognition between boronic acids\nand surface-exposed cis-diols on bacterial glycans/polysaccharides. These\ncarbohydrate structures are largely unaffected by heat denaturation or chemical fixation,\nwhich primarily impact proteins. Therefore, the preservation of specific, pH-dependent\nbinding patterns across all control conditions robustly validates that our signals arise\nfrom selective probe-glycan interactions, not from non-specific adsorption or\nphysiological activity. These new data provide a solid, multi-layered foundation for the\nspecificity claims in our study.\nSee detail as: “Consequently, heat-inactivated or fixed bacteria are used to\neffectively distinguish the specific surface binding of the BP probes from active\nbiological responses of bacteria. Our results reveal that the BP probes primarily\nbind to surface cis-diol structures independently of bacterial viability or metabolic\nactivity (Supplementary Figs. 39).” (Page 8, Lines 194-197).\nSupplementary Figs. 39. Fluorescence responses of BP1-BP6 for L. pl under control,\nFixed, Heat-inactivated interaction and PBS.\nComments 4-5:\nFig. 4 is to verify the efficacy of this assay with the human fecal samples. However,\nthe cohort is too small and does not include any inflammatory or adenoma controls.\nThese limitations make it difficult to assess disease specificity for CRC.\nResponse 4-5: Thank you very much for this critical and insightful comment regarding\nthe clinical validation of our assay using human fecal samples. We fully acknowledge\nthe limitations highlighted in the original submission. In direct response, we have\nsignificantly expanded our clinical study to address these specific concerns regarding\ncohort size, the inclusion of essential disease control groups, and the assessment of\ndisease specificity for CRC. Our revised approach and key findings are summarized\nbelow.\n1. Substantial Cohort Expansion with Key Disease Control Groups.\nTo rigorously evaluate the specificity of our assay, we have expanded our analysis\nto include 147 clinical samples across two complementary cohorts designed to answer\ndistinct clinical questions:\nCohort A (Disease Staging): This cohort consists of 120 samples, including 50\nearly-stage CRC, 40 late-stage CRC, and 30 healthy controls. It is designed to assess\nthe assay's performance across the spectrum of disease progression.\nCohort B (Differential Diagnosis): This cohort is pivotal for addressing the\nreviewer's core concern regarding specificity. It adds 15 adenoma, and 12 inflammatory\nsamples. The inclusion of adenoma and inflammatory groups allows for a direct test of\nwhether our assay can distinguish CRC from its primary pre-malignant and\ninflammatory mimics.\n2. Results Demonstrating Specificity for CRC.\nAnalysis of Cohort B (Differential Diagnosis) provides the critical evidence for\ndisease specificity. Our boronic acid sensor array generated distinct, multi-dimensional\nresponse patterns for the three patient groups. Multivariate analysis (e.g., LDA)\ndemonstrated clear and separate clustering of CRC, adenoma, and inflammatory\nsamples. This successful discrimination indicates that the microbial surface glycan\nsignatures captured by our platform are associated with CRC-specific pathology rather\nthan general intestinal inflammation or benign mucosal growth. The assay also\nperformed robustly in Cohort A, distinguishing CRC patients from healthy controls.\nIn conclusion, by substantially expanding the cohort size and, most importantly,\nby including and validating the assay against adenoma and inflammatory control groups,\nwe have directly addressed the reviewer's concerns. The results confirm that our sensing\nplatform can effectively differentiate CRC from relevant confounding conditions,\nthereby strongly supporting its disease specificity. We are grateful for the reviewer's\nsuggestion, which has significantly strengthened the clinical validation of our work.\nComments 4-6:\nThe in vitro model was overly simplified and did not mimic the real intestinal\nenvironment. And the single bacterial culture was completed in PBS, which is\ncompletely disconnected from the real gut environment.\nResponse 4-6: Thank you very much for this compelling point, which rightly highlights\nthe complexity of the in vivo gut environment. We fully acknowledge that cultivating\nsingle bacterial strains in PBS is a highly simplified model that lacks the nutritional\ncomplexity, microbial interactions, and host factors of the human gut.\nTo directly address this concern and substantively enhance the physiological\nrelevance of our in vitro model, we have systematically refined our experimental system\nacross two key dimensions:\n1. Development of a More Ecologically and Disease-Relevant Bacterial Model.\nWe expanded the tested bacterial panel from six to ten strains. This expansion\nstrategically includes species with established functional roles in the context of\ncolorectal cancer (CRC), such as the CRC-associated F. nucleatum and enterotoxigenic\nB. fragilis, as well as protective probiotics like B. longum and B. breve. This approach\naims to model the compositional and functional dysbiosis associated with a disease state,\nmoving beyond merely increasing taxonomic diversity.\n2. Adoption of Physiologically Relevant Culture Media to Simulate Key Gut\nEnvironmental Features.\n. We employed sterile filtrates from mouse intestinal contents as a culture medium.\nThis extract contains host-derived proteins, mucus components, metabolites, and\nendogenous microbial products, offering a more comprehensive simulation of the\nnutritional and chemical microenvironment bacteria encounter in vivo. It is\nencouraging that even within these complex matrices--characterized by intricate\ncompositions and significant background interference--our boronic acid-based sensor\narray successfully discriminated all ten bacterial strains, yielding clear and distinct\nclustering patterns (Supplementary Fig. 50 C).\nSimulated intestinal fluid (SIF). We prepared in accordance with pharmacopeial\nstandards (United States Pharmacopeia; Chinese Pharmacopoeia) for a selected set of\nexperiments. Its composition better approximates the chemical environment of the\nintestinal lumen, providing a more relevant condition to evaluate probe performance\namidst complex ionic and buffering systems (Supplementary Fig. 50 D).\nThese refinements transition our validation platform from a highly simplified\nsystem to a significantly enhanced test bed with greater species complexity,\nenvironmental relevance, and chemically challenging background. We fully\nacknowledge that no in vitro model can entirely recapitulate the full complexity of the\nin vivo setting. However, the implemented upgrades establish a foundation for\nvalidating our sensor’s discriminatory power, stability, and interference resistance that\nis substantially more physiologically relevant and rigorous than our initial design. We\nhave elaborated on these improvements and their scientific rationale in the revised\nmanuscript.\nSee detail as: “meanwhile, Intestinal extract and Simulated intestinal fluid\nproduce unique response patterns, and the same type of bacteria aggregate into\neleven non-overlapping groups” (Page 11, Lines 271-273).\nSupplementary Fig. 50. Discrimination of bacteria in the real samples. LDA plot of\nthe sensor array identification of bacteria (OD = 0.5) in fetal bovine serum (A), urine\n600\n(B), intestinal extract (C) and simulated intestinal fluid (D).\nComments 4-7:\nThe authors indicated that “According to the features of gut microbiota, six\nrepresentational bacteria, including two probiotics (L. a and L. pl), two commensals\nbacteria (E. coli and E. cloacae), and two pathogenic bacteria (S. aureus and C. k), were\nselected as the target analyte” is oversimplified the complexity of gut microbiota.\nResponse 4-7: Thank you very much for this critical and constructive comment. We\nfully acknowledge and agree that our original statement selecting six bacteria as\n“representative” constituted a significant oversimplification of the immense taxonomic\nand functional complexity of the gut microbiota. We fully agree that such a framework\nlacks the necessary biological depth. In direct response to this critique, we have\nsignificantly expanded our bacterial panel to enhance its representational breadth and\nfunctional diversity. In addition to the original six gut-derived bacterial strains, we have\nnow included four additional bacterial species selected based on their distinct and\necologically relevant roles in the gut ecosystem:\nFusobacterium nucleatum (F. nucleatum), a Gram-negative anaerobe consistently\nenriched in CRC tissues that promotes tumor progression through direct adhesion (e.g.,\nvia FadA) and immunomodulation (Rubinstein, M.R., et al. Cell Host & Microbe. 2013,\n14, 195-206); Bacteroides fragilis (B. fragilis), whose secretion of the B. fragilis toxin\n(BFT) drives a pro-carcinogenic inflammatory response (primarily Th17-mediated) and\nactivates oncogenic signaling in colonocytes (Wu, S., et al. Nat. Med. 2009, 15, 1016-\n1022); Bifidobacterium longum (B. longum) and Bifidobacterium breve (B. breve),\nwhich exert potential protective effects by reinforcing intestinal barrier integrity,\nproducing anti-inflammatory metabolites like short-chain fatty acids, and modulating\nhost immunity (Veiga, P., et. al. Nat. Microbiol. 2020, 5, 878-880; Sivan, A. et al.\nScience 2015, 350, 1084-1089).\nProfiling this refined panel with our boronic acid-based sensor array resulted in\nten distinct, well-separated clusters in unsupervised analysis, which clearly\ndifferentiated functional categories (e.g., CRC-associated from protective bacteria).\nThis revision transforms our model from an abstract simplification into a functionally\ninformed system that captures key microbial actors within a specific disease framework,\nthereby providing a substantially more robust and biologically meaningful basis for\nvalidating the discriminative power and pathophysiological relevance of our sensing\nplatform. We have updated the manuscript text and rationale accordingly and are\ngrateful for the reviewer's insight, which has significantly strengthened the scientific\nfoundation of our work.\nThis expanded panel of ten strains now captures a wider spectrum of: (1)\nphylogenetic diversity (spanning multiple phyla: Firmicutes, Bacteroidetes,\nProteobacteria, etc.); metabolic and aerobic diversity (strict anaerobes to facultative\nanaerobes). Profiling this expanded and functionally curated panel with our boronic\nacid-based sensor array yielded clear experimental validation. Unsupervised\nmultivariate analysis (LDA) demonstrated that all ten bacterial strains formed distinct,\nwell-separated clusters (Fig. 1 b and c). Importantly, the sensor's response patterns\nsuccessfully differentiated overarching functional categories, such as clearly separating\nCRC-associated pathobionts (e.g., F. nucleatum) from protective probiotics (e.g., B.\nlongum). We believe these additions have strengthened our study by directly addressing\nthe concern of oversimplification and providing a more comprehensive validation of\nour platform.\nFig. 1. Performance of the BP-based sensor array for bacteria discrimination.\nb, Heat map of the fluorescence response patterns for the different bacteria upon\nincubation with the sensor array under 3 pH (6.0, 7.4, and 8.2).\nc, LDA plot demonstrating the BP sensor array’s discrimination of the ten bacteria\nbased on their fluorescence response profiles."}, {"round": 2, "reviewer_comments": {"reviewer_1": "This paper was previously requested for revision. I have confirmed that the authors have appropriately revised the paper in response to comments from two reviewers. I recommend to be publised in this journal.", "reviewer_2": "After reading the revised manuscript and the response to all the reviewers' comments, I think the authors did a good job to adopt the advices from the reviewers, and a comprehensive revision was conducted. The quality of the presentation was obviously improved, and extra experiments were carried out to address some reviewers' concerns. Moreover, I appreciate the authors to fully accept suggestions provided by myself. Overall, I'd like to support the publication of this work in the current form.", "reviewer_3": "The authors have addressed this reviewer's concerns.", "reviewer_4": "The authors have addressed my previous comments satisfactorily.\nThe title has been revised to better reflect the scope of the study and no longer overstates the level of clinical validation.\nConcerns regarding the use of multiple pH conditions have been appropriately clarified, and the added control experiments demonstrate that short-term pH exposure does not measurably affect bacterial viability under the assay conditions.\nImportantly, the authors have introduced additional control experiments, including heat-inactivated and fixed bacteria as well as buffer-only measurements, which strengthen the conclusion that the observed fluorescence responses arise primarily from surface binding interactions rather than bacterial metabolic activity.\nThe biological relevance of the study has been improved by expanding both the bacterial panel and the human fecal sample cohort. The inclusion of inflammatory and adenoma controls, together with a substantially increased sample size, allows a more meaningful assessment of disease specificity in the context of colorectal cancer.\nWhile any in vitro model remains a simplification of the intestinal environment, the use of intestinal extracts and simulated intestinal fluid represents a reasonable and useful step toward greater physiological relevance compared with the original PBS-based system.\nOverall, the revisions have significantly strengthened the manuscript, and the key concerns raised in the previous review have been adequately addressed."}, "author_response": ""}]

Decision letter