# Label-Free Electrochemical Microfluidic Sensing Platform for the Detection of miR-30b from Milk Samples

Martina Freisa, Hiu Mun Man, Sandrine Le Guillou, Johann Laubier, Fabienne Le Provost, David Bouville, Isabelle Le Potier, Laurent Thouin, Jean Gamby

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Martina Freisa, Hiu Mun Man, Sandrine Le Guillou, Johann Laubier, Fabienne Le Provost, et al. Label-Free Electrochemical Microfluidic Sensing Platform for the Detection of miR-30b from Milk Samples. Electroanalysis, 2025, 37 (10), 10.1002/elan.70066. hal-05340463

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Submitted on 31 Oct 2025

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# Label-free electrochemical microfluidic sensing platform for the detection of *miR-30b* from milk samples

Martina Freisa<sup>aΔ</sup>, Hiu Mun Man<sup>aΔ</sup>, Sandrine Le Guillou<sup>b</sup>, Johann Laubier<sup>b</sup>, Fabienne Le Provost<sup>b</sup>, David Bouville<sup>a</sup>, Isabelle Le Potier<sup>a</sup>, Laurent Thouin<sup>c,*</sup> and Jean Gamby<sup>a,*</sup>

<sup>a</sup> Université Paris-Saclay, CNRS, Centre de Nanosciences et de Nanotechnologies, 91120, Palaiseau, France

<sup>b</sup> Université Paris-Saclay, INRAE, AgroParisTech, GABI, 78350, Jouy-en-Josas, France

<sup>c</sup> CPCV, Département de chimie, Ecole normale supérieure, PSL University, Sorbonne Université, CNRS, 75005, Paris, France

<sup>a</sup> These authors contributed equally to this work.

* Corresponding authors: *Laurent.thouin@ens.psl.eu*, *jean.gamby@universite-paris-saclay.fr*

## Abstract

Circulating microRNAs (miRNAs) in biological fluids are small non-coding RNAs identified as promising biomarkers. They are particularly abundant in milk, a fluid easily accessible without invasive techniques. However, detection methods are currently performed by specialized personnel in centralized laboratories, which greatly lengthens analysis times. This study addresses some challenges by proposing a portable microfluidic electrochemical platform for the detection of *miR-30b*, a miRNA overexpressed by a previously developed transgenic mouse model. The sensors integrated into the platform were surface-modified with oligonucleotides. Through dual-mode detection by cyclic voltammetry and electrochemical impedance spectroscopy, they exhibit high specificity and sensitivity with a detection range from $10^{-16}$ to $10^{-8}$ M. Tests on real samples show that the platform is able to distinguish between *miR-30b* extracted from milk of transgenic mice and that of wild-type
mice. These results suggest a strong potential of this sensing platform to carry out early and non-
invasive detections. They open the way to precise monitoring of the physiological state of individuals
while improving diagnostic options for pathologies in clinical or veterinary settings.

**Keywords:** microRNA, microfluidics, nucleic acid analysis, electrochemistry, milk
# 1 INTRODUCTION

MicroRNAs (miRNAs) are small, single-stranded non-coding RNAs of around 22 nucleotides in model species, livestock species and humans. They are highly conserved between species and act on the post-transcriptional regulation of genes by impacting messenger RNAs (mRNAs) [1]. Their pairing with mRNA leads to the inhibition of their translation or induces their degradation. This involves them in the regulation of numerous physiological processes, including potentially pathological ones [2]. They are highly stable, present in varied and large quantities in biological fluids, particularly in milk, which has the added advantage of making them easily accessible [3]. In addition, the profile of circulating miRNAs is partly specific to the cell that secretes them and varies according to its physiological or pathological state. The development of transcriptomic technologies, particularly high-throughput sequencing, has made it possible to comprehensively characterize the miRNA composition of cells and biological fluids under a wide range of conditions. A growing number of candidate biomarkers have been identified. Indeed, circulating miRNAs have demonstrated their potential as biomarkers for the early detection and diagnosis of a wide range of diseases in animal species and humans. This explains the growing interest in their identification and precise screening for veterinary or medical purposes [4-7]. In this context, their early and accurate detection constitutes a major challenge, where it is crucial to develop appropriate methods and tools. Their direct analysis on patients or animals are not currently possible because they require the systematic use of laboratory manipulations. These later are only carried out in centralized biomedical laboratories with specific equipment and specialized skills, leading to high operational cost with time-consuming analysis [7-13].

The traditional detection methods of miRNAs include microarray [14], quantitative PCR (polymerase-chain reaction) [15] or digital PCR [16], and sequencing [17]. Electrochemical-based sensors present to be a promising alternative as they are highly sensitive and specific, portable, cheap and can be easily miniaturized [18]. Table 1 highlights the advantages and limitations of conventional miRNA detection methods. Each method has unique strengths and limitations. RT-qPCR and digital droplet PCR (ddPCR) provide high sensitivity and reliability, but they are limited to targeted analyses
and require costly equipment. Microarrays offer genome-wide coverage at low cost and high
throughput but with relatively low sensitivity and specificity that lead to semi-quantitative results.
Next-generation sequencing enables comprehensive profiling and discovery of novel biomarkers but
is expensive, complex, and time-intensive. In contrast, electrochemical sensors can offer low cost,
rapid analysis, portability, absolute quantification, and high sensitivity, despite requiring known probe
sequences and single-use devices. Several amplification approaches have been reported on the
development of electrochemical miRNA sensing, including the use of cyclic enzymatic amplification
[19], catalyzed hairpin assembly [20,21], intercalating redox mediators such as methylene blue [22],
and nanomaterials [23,24]. A dual mode sensing platform for the detection of *miR-145* by
electrochemical impedance spectroscopy and square wave voltammetry was proposed, using peptide
nucleic acids as probe [25]. Positively charged gold nanoparticles have been also employed for signal
amplification with a detection limit down to 0.37 fM. Among these approaches, an innovative strategy
lies in the development of electrochemical microfluidic sensors, which represent a practical and
promising option for point-of-care miRNA detection (see table 1). In this case, very small volumes are
required allowing on chip parallelization of samples coupled with electrochemical detections. Indeed,
microelectrodes are easily integrated within microchannels while laminar flow rates above
electrochemical sensors can significantly enhance their signal.

In this work, we wanted to illustrate the performance of such electrochemical microfluidic
sensors in real samples based on studies we initiated on label-free detections [26]. Indeed, the
characteristics mentioned above make electrochemical sensors particularly interesting for several
applications, including medical research, diagnostic and food safety. Nevertheless, despite these
advantages, the use of miRNA sensors in real samples is still evolving and presents significant
challenges [27]. One of the main difficulties lies in the complex and often variable composition of real
samples, which can interfere with sensor performance. As a result, robust and adaptable detection
protocols are needed, ensuring accurate and reliable measurements. In this framework, our main
objective was to implement a dual-mode detection by cyclic voltammetry (CV) and electrochemical
impedance spectroscopy (EIS) in order to analytically test the detection performance of miRNA in
complex biological matrixes such as milk. In the following, we investigated the microfluidic sensing platform described in Figure 1 to detect *miR-30b* directly in milk samples. To implement and validate this platform, we employed a transgenic mouse model developed in our laboratory, which overexpresses *miR-30b* specifically in the mammary gland during lactation. This model produces milk containing *miR-30b* at levels approximatively 134-fold higher than wild-type (WT) controls [28,29], providing a unique biological resource to rigorously test and optimize our sensing system. *miR-30b* is part of a family of 7 closely related mouse miRNAs sharing the same seed sequence (nucleotides 2 to 7). While its role in mammary gland development and function is well-established, *miR-30b* is also gaining prominence as a critical regulator in various diseases and cancers, positioning it as a promising biomarker for early detection and monitoring [30–34]. Our transgenic model offers a distinct advantage: access to authentic biological samples with precisely characterized *miR-30b* concentrations, enabling robust benchmarking of the microfluidic platform’s performance. This facilitates not only accurate detection but also reproducible validation through repeated biological replicates.

Therefore, to establish the specificity and sensitivity of the microfluidic sensing platform, real biological samples (milk RNA from both WT and *miR-30b* transgenic (TG) mice) were analyzed without relying on PCR amplification. This approach was to test the ability of the platform to accurately quantify *miR-30b* directly from complex biofluids. By combining electrochemistry and microfluidic techniques for advanced biological studies, these results should help establish the necessary conditions for the direct monitoring and analysis of miRNA biomarkers in milk. This could contribute to the development of innovative tools and expand the scope of biomarker applications.

# 2 EXPERIMENTAL SECTION

## 2.1 Chemicals and synthetic miRNAs

Potassium hexacyanoferrate (III) and potassium hexacyanoferrate (II) trihydrate, sodium chloride, potassium hydroxide, 6-mercaptohexanol (MCH) were purchased from Sigma Aldrich
(France). Tris(2-carboxyethyl)phosphine (TCEP) was acquired from Thermo Fisher Scientific
(France).

The synthetic miR-30b probe and complementary miR-30b target (mimicking the gene mmu-miR-30b-5p), as well as the miR-122 target (mimicking the gene miR-122) and miR-30d target were purchased from Eurogentec (Belgium). The sequences for probe and targets are described in Table 2 respectively. The 5' end of the probe sequence was modified with a thiol (-SH) function to enhance the immobilization on the platinum electrode surface. Note that the miR-30d target is from the same family of miR-30b, so sharing exactly the same seed sequence, but with an 8 base difference at the 3' end.

## 2.2 Microfluidic chip design and fabrication

The microfluidic sensing platform is described in Figure 1A. It comprises two parallel and independent fluidic circuits and four electrochemical sensors. Each sensor has a two-electrodes electrochemical cell implementing upstream a working electrode (30 µm x 300 µm) and downstream a counter electrode (2 mm x 300 µm).

This platform was fabricated in the clean room following previous works done in our group [26,35,36]. Briefly, optical lithography was used to pattern the electrode designs onto glass wafers with a negative photoresist AZ2026. A thin film of Ti/Pt was deposited by a thermal evaporator achieving the thickness of 10/100 nm. The microfluidic channels (~60 µm in height) were fabricated in polydimethylsiloxane (PDMS). PDMS (10:1 w/w base to curing agent) was cast on an SU-8 master mold (patterned via UV-lithography) at 60°C for overnight. The fluidic inlets and outlets were created using a 0.5 mm biopsy punch. Oxygen plasma treatment was performed to assemble the PDMS blocks and the electrode-patterned glass together. The fabricated device was cured at 60 °C for at least 4 hours.

## 2.3 Electrochemical measurements
### 2.3.1 Electrochemical characterizations

The electrode responses were systematically characterized in CV and EIS after each step, i.e., probe functionalization and target hybridization (Figure 1B and procedures described hereafter). Measurements were performed in an equimolar solution of 20 mM hexacyanoferrate (III) / hexacyanoferrate (II) in 0.5 M NaCl under fluidic conditions. A flow rate of 0.5 µL s⁻¹ was set by means of a syringe micropump (neMESYS S, CETONI). According to the geometries of the microchannels and electrodes, this flow rate corresponds to a purely convective regime (i.e., Levich regime) where convection is largely predominant over diffusion, in the mass transport of redox species to the electrode surface. CV and EIS measurements were carried out using a potentiostat (SP-300, Bio-Logic) controlled by the EC-lab software. CV measurements were performed at the scan rate of 20 mVs⁻¹ in the potential window between 0.2 V and -0.2 V. For repeatability testing, three potential cycles were recorded for each experimental condition. EIS measurements were carried out at VDC = 0 V and AC = 10 mV over the frequency range of 1 MHz to 0.1 Hz. For reproducibility testing, CV and EIS data were averaged from the individual responses of the four sensors.

### 2.3.2 Model and numerical simulations

To simulate the linear sweep voltammograms, the mass transport of the redox species (i.e., hexacyanoferrate (III) and hexacyanoferrate (II)) was considered under laminar flow condition in association with Butler-Volmer kinetics at the electrode. The differential equations were solved numerically for each species by finite elements based on a previously described model [26]. Some details about the model, but also on the simulations and fitting procedures, were recalled in the Supporting Information (SI). Simulations were carried out in a 2D space using the laminar flow and electroanalysis components of COMSOL Software 6.3.

In EIS, the apparent charge transfer resistance of electrode $R_{ct}^{ap}$ was determined based on a circuit model proposed by Freisa *et al* [26] by fitting the data using a python-based software [37,38] or by using SIMAD software, initially developed at the LISE laboratory. The circuit model and other fitting parameters are described and provided in SI. The model was derived from a Randles circuit and
adapted to the modification of electrode surfaces by blocking layers. The key components of this
circuit include a global ohmic resistance (Re), a local ohmic resistance (Re'), an apparent charge
transfer resistance R<sub>ct</sub><sup>ap</sup>, a convection diffusion resistance (R<sub>D</sub>, when Z<sub>D</sub> (f→ 0)), an equivalent double
layer capacitance (C<sub>dl,eq</sub>), and a blocking layer capacitance (C<sub>b</sub>) (Table S1).
## 2.4 miRNA detections

### 2.4.1 Synthetic target detection

Before functionalizing, the Pt working electrodes were first submitted in 0.5 M KOH to a few potential sweeps between -1.2 V and -0.2 V (for at least 10 cycles at 50 mV s⁻¹ scan rate) in order to clean and electrochemically activate their surface. The functionalization process was then carried out by incubating the electrodes for 2 hours in the probe/MCH mixture injected into the microchannel. The concentrations in the mixture were 10⁻⁶ M *miR-30b* probe, 10⁻⁵ M MCH and 5 mM TCEP. Note that TCEP was used to reduce disulfide bonds [37]. Similarly, the hybridization processes were carried out by sequentially incubating the electrodes for 30 minutes in solutions of increasing concentrations of complementary *miR-30b* targets from 10⁻¹⁶ M to 10⁻⁸ M. Specificity testing were performed after functionalization by incubating the electrodes in solutions of non-complementary *miR-30b* targets, i.e., *miR-122* and *miR-30d*, at the concentrations 10⁻¹⁴, 10⁻¹⁰ and 10⁻⁶ M. For all experimental conditions, the electrochemical responses of the four sensors were analyzed by averaging the data.

### 2.4.2 Mouse milk extracts and miR-30b detection

Milk RNA extracts from wild-type (WT) control and transgenic (TG) female mice were provided by the Unit of Animal Genetics and Integrative Biology (GABI), in the French National Research Institute for Agriculture, Food, and the Environment (INRAe), who developed the mouse model [28]. Transgenic mice overexpress *mmu-miR-30b-5p* in their mammary epithelial cells and present a significantly higher level of *miR-30b* in milk [29]. Milk samples were collected at mid-lactation (lactation day-12 from parturition) from WT and TG mice as previously described [29]. All experiments involving animals were performed in strict accordance with the guidelines of the Code for Methods and Welfare Considerations in Behavioral Research with Animals (Directive 86/609EC) and the recommendations of the French Commission de Génie Génétique (Permit number 12931 (01.06.2003)) which approved this study. Briefly, the pups, limited to 8 per litter, were removed from their mothers 4h beforehand to ensure sufficient milk. Then females received an intraperitoneal
injection of 150 µL oxytocin (10 IU/mL, Ocytovem, Med'Vet) to stimulate milk ejection and were anesthetized after 10 min using a 0.12% xylazine (Rompun, Bayer)/10 mg/mL ketamine (Imalgène, Merial) mix. Milking was facilitated by applying manual pressure, and samples were collected and frozen at -80°C until use for each individual as aliquots of 20 to 100 µL of whole milk.

Total RNAs were isolated from frozen milk samples using the RNA Now kit® (Ozyme), with an overnight isopropanol precipitation, to guarantee a maximum yield of miRNAs, and quantified by Nanodrop ND1000. The RNA samples were stored at -80°C until further processing.

For the electrochemical detections, two series of milk total RNA samples were analyzed: one with an initial concentration adjusted to 1 µg/µL (samples named WT.72, WT.76, TG.75, TG.91) and the other to 2 µg/µL (samples named WT.94, TG.92). After thawing on ice, the samples (~25 µL) were injected to fill the microchannel completely, and incubated for 30 minutes under static conditions. Similar to synthetic target detections, the response of each sensor was characterized after surface cleaning and activation, after probe/MCH functionalization, and finally after sample hybridization.

# 3 RESULTS AND DISCUSSION

## 3.1 Cyclic voltammetry with synthetic miR-30b

The microfluidic miRNA sensors were first characterized through the detection of synthetic *miR-30b* targets by performing CV measurements in solutions containing 20 mM equimolar hexacyanoferrate (III) / hexacyanoferrate (II) and 0.5 M NaCl. Before functionalization, the bare Pt electrodes were pretreated in KOH-solution to electrochemically clean and activate the electrodes (see experimental section). Figure 2A shows an example of cyclic voltammograms initially recorded at bare Pt electrode, then after probe/MCH functionalization and target hybridization. The bare Pt electrodes demonstrated a sigmoidal shape of cyclic voltammograms characteristic of steady-state regime achieved under fluidic conditions as previously reported for channel-electrodes [26,35]. Values of the limiting currents on both plateaus were very close to those predicted in convective
regime, taking in account the geometry of the device, the flow rate, the concentration and the diffusion coefficients of hexacyanoferrate (III) / hexacyanoferrate (II) [39–41]. Accordingly, the cyclic voltammograms indicated a rapid and efficient electron transfer after KOH activation (see below numerical simulations of the cyclic voltammograms and corresponding parameters).

After probe/MCH functionalization, the voltammograms could show a clear decrease in the absolute current due to the immobilization of the thiol-ended probes on the electrodes (Figure 2A) [26]. After hybridization of the probes with synthetic *miR-30b* targets, a gradual decrease in current of a few hundred of nanoamperes was then observed with increasing target concentrations from 10⁻¹⁶ to 10⁻⁸ M. (see inset of Figure 2A), Indeed, immobilization of the nucleic acids on the electrode surface leads to the formation of self-assembled monolayers (SAM) that reduce the electrode accessibility of the redox species to the electrode. These variations were better illustrated in Figure 2B for the four sensors where the limiting currents *i*<sup>L</sup> recorded at -0.2 V were plotted as function of the logarithm of target concentration. In comparison, very similar behaviours were noticed, with good repeatability between measurements. Each value was obtained from three successive potential scans. However, deviations in current amplitudes were noted between the sensor responses upon electrode functionalization. This indicates that the electrode modification led to a different structuring of the interface on each sensor. Nevertheless, the systematic decrease in current after successive incubations with synthetic targets demonstrated a clear dependence of the sensor response on their concentration.

In order to better analyze the influence of the target concentration and to attempt to eliminate deviations in sensor responses due to the functionalization step, the limiting currents *i*<sup>L</sup> were normalized with respect to those measured after probe/MCH functionalization according to the equation:

$$i_{\text{norm}}^{L} = \left| \frac{i_{\text{probe}}^{L} - i_{\text{target}}^{L}}{i_{\text{probe}}^{L}} \right| \qquad (1)$$

where *i*<sup>L</sup><sub>probe</sub> and *i*<sup>L</sup><sub>target</sub> are the limiting currents monitored after probe functionalization and target hybridization respectively. Under these conditions, the values of *i*<sup>L</sup><sub>norm</sub> derived from the cyclic
voltammograms were further analyzed in Figure 3 for the four sensors. Linear relationships could thus be drawn between $i_{norm}^L$ and the logarithm of target concentration (Figures 3A-D). It should be noted that the current normalization led here to a six-fold increase in $i_{norm}^L$, which makes this parameter particularly relevant for miRNA sensing under fluidic conditions. Despite the normalization procedure, differences between sensors were still observed, thus reflecting inherent intrinsic variability. In order to evaluate the average performance resulting from the four sensors, values were then averaged and plotted in Figure 3E. A linear relation could be established within the experimental uncertainties, with an R-squared value of 0.992, demonstrating the reliability of the determinations by CV.

## 3.2 EIS with synthetic miR-30b

Complementary to CV characterization, EIS measurements were carried out with the same microfluidic sensors in order to investigate variations in the apparent charge transfer resistance $R_{ct}^{ap}$ after each step. The apparent $R_{ct}$ values were extracted through numerical simulation and curve fitting of the experimental impedance spectra, enabling precise quantification of the sensor response even when absolute changes in impedance (Z) are relatively small. Indeed, an increase in $R_{ct}^{ap}$ upon probe functionalization and target hybridization can be also related to the SAM-induced micro-structuration of the interface owing to the inherent sensitivity of EIS to changes at the electrode surface. The increase in apparent charge transfer resistance during miRNA hybridization is also explained by the contribution of steric and electrostatic effects (i.e., steric hindrance and increased negative charge of the RNA backbone) that may limit the accessibility of the redox mediator to the electrode surface. Figures 4A-G shows the Nyquist plots obtained from a sensor for various miR-30b target concentrations. An increase in $R_{ct}^{ap}$ value by one order of magnitude was observed from bare Pt to probe/MCH functionalization of electrode, confirming the adsorption of thiol-ended probes. Moreover, after the incubation of 0.1 fM miR-30b synthetic target, an increase in $R_{ct}^{ap}$ was still recorded. As for
the limiting currents, variations of $R_{ct}^{ap}$ were analyzed after normalizing the values against that obtained after probe/MCH functionalization by:

$$R_{ct\_norm}^{ap} = \frac{R_{ct\_target}^{ap} - R_{ct\_probe}^{ap}}{R_{ct\_probe}^{ap}} \qquad (2)$$

where $R_{ct\_probe}^{ap}$ and $R_{ct\_target}^{ap}$ are $R_{ct}^{ap}$ values monitored after probe functionalization and target hybridization respectively. Figure 4H shows the average $R_{ct\_norm}^{ap}$ values resulting from the four sensors as a function of the logarithm of target concentration. A linear relation could also be established with an R-squared value of 0.990. Additional details regarding the results are reported in SI, in particular, the fitting procedure (Figure S1), the EIS data from the other sensors (Figures S2-4) and a summary of all the parameters derived from one sensor (Table S2).

### 3.3 Comparison between CV and EIS performance with synthetic miR-30b

The performances evaluated from CV (Figure 3E) and EIS (Figure 4H) measurements were found to be similar. It is interesting to point out that these two determinations are intrinsically different since $R_{ct}^{ap}$ is evaluated at the open circuit potential where the contribution of mass transfer is less significant, while $i^L$ is measured at -0.2 V on the diffusion plateau of the voltammograms. Despite this difference, the sensitivities of the sensor responses remain comparable considering the experimental uncertainties, and consistent with the responses of other electrochemical miRNA biosensors described elsewhere [42].

In this context, the surface coverage ratio of electrode named $\theta$ is also a relevant parameter to characterize and monitor the electrode modification after each step. It can be evaluated both from CV and EIS measurements. In CV, $\theta_{volta}$ is determined by simulating and fitting the voltammograms according to a previously described model [26] and a procedure recalled in SI. In EIS, we have also also showed that $\theta_{EIS}$ is derived from $R_{ct}^{ap}$ via the following equation:

$$\theta_{EIS} = \frac{R_{ct}^{ap} - R_{ct,bare}^{ap}}{R_{ct}^{ap}} \qquad (3)$$
where $R_{ct,bare}^{ap}$ is the apparent charge transfer resistance at bare-Pt electrode.

Both $\theta_{volta}$ and $\theta_{EIS}$ were determined from the previous measurements. Figure 5 gives an example of $\theta_{volta}$ evaluations performed by fitting the cyclic voltammograms of one sensor. All $\theta$ values obtained from the four sensors were then averaged and compared in Figure 6 for each target concentration. This approach, complementary to the previous one, confirmed a good correlation between the CV and EIS measurements. Under these experimental conditions, $\theta$ values were found between 0.92 and 0.98 as expected for such sensors. It is worth noting that $\theta_{EIS}$ varied less than $\theta_{volta}$ but the trend clearly demonstrated the influence of the target concentration. Note that $\theta_{volta}$ and $\theta_{EIS}$ are parameters that can be considered for miRNA detections. However, unlike $i_{norm}^{L}$ and $R_{ct,norm}^{ap}$ where the functionalization step is the reference for normalization, $\theta$ remains absolute determinations submitted to experimental uncertainties resulting from surface activation of bare electrodes and electrode functionalization. For this reason, $R_{ct,norm}^{ap}$ and $i_{norm}^{L}$ appear as more appropriate parameters for performing miRNA detections. Like illustrated in Figures 3E and 4H, they allow for consistent determinations in the concentration range ($10^{-16}$ – $10^{-8}$ M) typically used for miRNA detections [43].

## 3.4 Specificity tests with synthetic miR-30d and miR-122

The specificity of the miR-30b sensors was checked by performing hybridization experiments against non-complementary sequence targets, miR-30d and miR-122. Experiments were conducted in CV and EIS by analyzing $i_{norm}^{L}$ and $R_{ct,norm}^{ap}$ variations respectively (Figure 7). As expected, compared to miR-30b, the sensor response in presence of both targets did not significantly increase with their concentration (from $10^{-14}$ to $10^{-6}$ M), within the experimental uncertainties and regardless the techniques used. Under these conditions, the sensors were not only able to distinguish miR-122 from miR-30b (which comprises a completely mismatched sequence), but they were also discriminating against miR-30d. It is noteworthy that miR-30d belongs to the same miR family than miR-30b, but have an 8-base mismatch at the 3' end of their sequence. These results clearly demonstrate the specificity of the sensors for the target sequence matching the probe. They also show
that nonspecific adsorption of miRNA does not occur, as the presence of non-complementary
sequences does not affect the initial signals with target concentration. Confirmation of these
properties then open the door to interesting applications in the field of diagnostics. In the following,
the sensors were tested for *miR-30b* detection in milk.

**3.5 Detections in control wild-type and transgenic milks**

Electrochemical measurements were performed on a probe-functionalized electrode surface after a 30-min incubation of RNA extracts from control wild-type (WT) and transgenic (TG) milk. Three WT samples (named WT.72, WT.76, WT.94), and three TG samples (named TG.75, TG.91, TG.92) were investigated, each extract being analyzed by a microfluidic sensor. Figure 8 displays CV and EIS responses for WT.76, and TG.91. The fitting parameters of these spectra are reported in Table S3 while the EIS spectra for the other samples are given in Figures S5 to S8. It is interesting to note the different shapes of EIS spectra in Figures 8B and D. This can be attributed to variations between individual electrodes, including between bare and thiol-functionalized functionalized surfaces. This leads to a different organization of the DNA probe at the electrode surface, which once again demonstrates the importance of normalizing target signals in the dual-mode detection with the corresponding probe signals.

Attempts to quantify miRNA extracts were performed from $i_{norm}^L$ and $R_{ct\_norm}^{ap}$ values, based on the previously established curves (Figures 3E and 4H). As summarized in Table 3, the concentration of *miR-30b* evaluated in control WT samples were found of picomolar levels ($10^{-12}$ – $10^{-10}$ M) and about two orders of magnitude lower than that of transgenic samples ($>10^{-8}$ M). The observed difference in concentration between the two groups of biological samples was consistent with previous abundance measurements in milk by conventional RT-qPCR technique, where a $10^2$-fold variation was also reported [29]. Notably, in both methods, the transgenic samples yielded values exceeding the upper limit of concentrations investigated. The ability of the microfluidic platform to reproduce this order or magnitude demonstrate the precision and the reliability of this innovative approach. However, an apparent and constant discrepancy between concentrations evaluated in CV
and EIS was observed. Concentrations in CV remain two orders of magnitude higher than those in EIS. This observation may point out a difference in performance related to the method employed and nature of medium used. Although CV and EIS measurements were initially assumed to be independent of the medium, it seems that they performance may be slightly and differently altered in milk. It should also be stressed that the uncertainties related to curves presenting logarithm scales may partly explain this difference (see errors bars in Figures 3E and 4H). All these observations can justify the need here to implement the dual-mode detection on a microfluidic platform in order to overcome experimental uncertainties and the influence of complex matrices on both type of measurements. In addition, the dual-output by CV and EIS allows the control of the reliability of the sensors after each operation (i.e., from bare electrode and probe immobilization to target hybridization).

Noteworthy, in both methods, the microfluidic sensors could successfully distinguish between the transgenic and control samples. They could also quantitatively discriminate between the control wild type and transgenic samples, with the significant advantage of enabling direct quantification of microRNA without the need for amplification, in contrast to conventional molecular biology approaches. Biological fluids such as milk contain numerous interfering substances (including proteins, salts, and other organic molecules) that can cause non-specific binding or generate unwanted electrochemical signals, thus compromising measurement reliability. Additionally, variations in factors such as pH and ionic strength between biological samples further complicate consistent sensor performance. Despite these challenges, the microfluidics-based electrochemical sensors maintained stable and reliable performance for *miR-30b* detection in real milk samples.

## 4 CONCLUSION

In this study, we reported the performance of a microfluidic platform integrating electrochemical biosensors to detect *miR-30b* derived from mouse milk samples. By using synthetic targets, the implemented dual-mode detection by CV and EIS, under flow conditions, enabled determinations over linear concentration ranges from 10<sup>-16</sup> M to 10<sup>-8</sup> M, thus covering 8 orders of magnitude. These
characteristics appear promising, especially compared to detection ranges commonly reported in the literature. They make the microfluidic sensing platform particularly suitable for applications requiring accurate quantification over wide concentration ranges. We also demonstrated that the dual-mode detection offered high specificity in miRNA detection by reliably distinguishing closely related sequences differing by only 8 bases, (i.e., between *miR-30b* and *miR-30d*). This property is particularly attractive for applications requiring the discrimination of similar miRNA sequences. Finally, tests performed on real samples clearly differentiated milk from transgenic and wild-type mice with possible quantification of *miR-30b*. In the microfluidic platform, the number of sensors thus reduces the experimental uncertainties resulting from each step (i.e., from probe immobilization to target hybridization), while dual-mode detection can overcome the influence of complex matrices on each type of measurement. All these results already highlight the robustness and detection potential of the microfluidic sensing platform in real biological matrices, making it promising for future diagnostic applications.

## Acknowledgements

The authors thank the DIMELEC project ANR-19-CE09-0016 for funding and RENATECH clean room facilities at C2N, Palaiseau, France. M.F. thanks the doctoral school "Electrical, Optical, Bio-Physics and Engineering" (ED575) and Paris-Saclay University for PhD grants. We wish to thank the staff in UE0907 IERP Infectiologie Expérimentale des Rongeurs et Poissons (INRAe, Jouy-en-Josas, France) for their care of the mice used in this study. We particularly thank J. Castille (UMR GABI, Université Paris-Saclay, INRAE, AgroParisTech) for its technical assistance in managing and milking the mice.
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<table><caption>**Table 1.** Comparison of conventional miRNA detection strategies with the developed on-chip electrochemical miRNA sensor.</caption><thead><tr><th></th><th>RT-qPCR / ddPCR [44–47]</th><th>Microarray [9,14,48]</th><th>Next generation sequencing [49,50]</th><th>Electrochemistry on-chip [51,52]</th></tr></thead><tbody><tr><td>Amplification</td><td>Required</td><td>Required</td><td>Required, during libraries production</td><td>Not required</td></tr><tr><td>Detection limit</td><td>ddPCR: few copies/µL (20 aM [53])</td><td>0.4 fM [54]</td><td>One read</td><td>50 fM in CV (this work)<br>1 fM in EIS (this work)</td></tr><tr><td>Analysis time</td><td>hours</td><td>days</td><td>weeks</td><td>30 min</td></tr><tr><td>Cost</td><td>High</td><td>Low</td><td>High per sample, low per miRNA</td><td>Low</td></tr><tr><td>Advantages</td><td>High sensitivity and specificity<br>High reproducibility, reliable<br>Large dynamic range<br>ddPCR: absolute quantification</td><td>Low cost<br>High throughput<br>Genome-wide coverage</td><td>High throughput<br>Genome-wild coverage<br>High sensitivity and very high specificity<br>Global expression profile can be obtained<br>Allows the identification of new</td><td>Low cost<br>Fast analysis time<br>Absolute quantification<br>High sensitivity and specificity<br>Portability</td></tr></tbody></table>
<table><tr><td></td><td></td><td>miRNAs and isomiRs (sequence variants), allow the identification of new biomarkers massively and in parallel<br/>Do not required knowledge of target miRNAs or specific probes</td><td>Facile to use</td></tr><tr><td rowspan="2">Limitations</td><td>Restricted to a targeted microRNA by analysis, lack of multiplexing and genome-wide coverage</td><td>Relatively low sensitivity</td><td>High cost</td><td>Known probe sequences required</td></tr><tr><td>Complex primer design is required [55]</td><td>Specificity may be compromised for miRNAs from the same family, due to the nature of short miRNA hybridization sequences</td><td>Highly complex technique, requiring intricate steps and specialized equipment, not suitable for routine laboratory use</td><td>Non-reusable devices</td></tr><tr><td></td><td>qPCR: relative quantification requiring reference genes</td><td>Semi-quantitative, relative abundance measurement only, with appropriate data normalization</td><td>Requires thorough bioinformatics data analysis, demanding time and skills</td><td></td></tr><tr><td></td><td>Requires large and expensive thermal cycling equipment</td><td>Specific probes required</td><td>Not suitable for large-scale application in early diagnosis because of the long time and high cost</td><td></td></tr></table>
<table><caption>**Table 2:** Sequences of synthetic oligonucleotides probes and targets.</caption><thead><tr><td>Name</td><td>Nucleotide sequence</td></tr></thead><tbody><tr><td>Thiol-labeled probe miR-30b</td><td>5'*AGC-TGA-GTG-TAG-GAT-GTT-TAC-A 3'</td></tr><tr><td>Complementary target miR-30b</td><td>5' UGU-AAA-CAU-CCU-ACA-CUC-AGC-U 3'</td></tr><tr><td>Target miR-30d</td><td>5' UGU-AAA-CAU-CCC-CGA-CUG-GAA-G 3'</td></tr><tr><td>Target miR-122</td><td>5' GCA-GUG-UGA-CAA-UGG-UGU-UUG 3'</td></tr></tbody></table>

*(Differences in complementary versus non-complementary nucleotides are marked in bold.)*

<footnote>*The 5' end of the probe is modified with a thiol (-SH) C6 modifier.</footnote>
<table><caption>**Table 3.** Concentrations of *miR-30b* in wild type (WT) and transgenic (TG) mice milk samples evaluated by cyclic voltammetry and electrochemical impedance spectroscopy. The last column displays RT-qPCR data from a previous study [29].</caption><thead><tr><th>Sample - Wild Type</th><th>i<sup>L</sup><sub>norm</sub></th><th>R<sup>ap</sup><sub>ct_norm</sub></th><th>Concentration from i<sup>L</sup><sub>norm</sub> (M)</th><th>Concentration from R<sup>ap</sup><sub>ct_norm</sub> (M)</th><th>RT-qPCR miR-30b <sup>[29]</sup><br>(miR-30b/U6)</th></tr></thead><tbody><tr><td>WT.94 (n=2)</td><td>0.05 ± 0.01</td><td>0.28 ± 0.09</td><td>&lt;10<sup>-16</sup></td><td>(5 ± 3) × 10<sup>-16</sup></td><td>13769</td></tr><tr><td>WT.72 (n=4)</td><td>0.26 ± 0.03</td><td>0.93 ± 0.17</td><td>(6 ± 6) × 10<sup>-10</sup></td><td>(8 ± 9) × 10<sup>-12</sup></td><td>47946</td></tr><tr><td>WT.76 (n=4)</td><td>0.19 ± 0.05</td><td>0.53 ± 0.12</td><td>(2 ± 4) × 10<sup>-12</sup></td><td>(2 ± 2) × 10<sup>-14</sup></td><td>810</td></tr></tbody></table><table><thead><tr><th>Sample - Transgenic</th><th>i<sup>L</sup><sub>norm</sub></th><th>R<sup>ap</sup><sub>ct_norm</sub></th><th>Concentration from i<sup>L</sup><sub>norm</sub> (M)</th><th>Concentration from R<sup>ap</sup><sub>ct_norm</sub> (M)</th><th>RT-qPCR miR-30b<br>(miR-30b/U6)</th></tr></thead><tbody><tr><td>TG.92 (n=1)</td><td>0.30</td><td>1.51</td><td>2 × 10<sup>-08</sup></td><td>4 × 10<sup>-08</sup></td><td>512000</td></tr><tr><td>TG.75 (n=4)</td><td>0.31 ± 0.05</td><td>0.84 ± 0.28</td><td>(3 ± 11) × 10<sup>-08</sup></td><td>(2 ± 4) × 10<sup>-12</sup></td><td>1614271</td></tr><tr><td>TG.91 (n=4)</td><td>0.42 ± 0.03</td><td>1.23 ± 0.53</td><td>&gt; 10<sup>-08</sup></td><td>(6 ± 22) × 10<sup>-10</sup></td><td>167730</td></tr></tbody></table>
<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /><figcaption>**Fig. 1:** A) Design of the microfluidic platform comprising two fluidic circuits and four sensors. The inset is the zoom-in view of a sensor with its electrochemical cell composed of a working electrode (WE, 30 µm x 300 µm) and a counter electrode (CE, 2 mm x 300 µm). B) Scheme showing the immobilization of 6-mercaptohexanol and thiol-modified DNA probes on the platinum surface, followed by the detection of *miR-30* in mouse milk total RNA extracts.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Fig. 2:** A) Cyclic voltammograms recorded at sensor 1 after probe immobilization and target hybridization with synthetic *miR-30b*. B) Variation of limiting currents $i^L$ at -0.2 V after electrode functionalization (open symbols) and hybridization (solid symbols): sensor 1 (diamond), sensor 2 (circle), sensor 3 (square) and sensor 4 (triangle); each point corresponds to an average value of 3 measurements.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Fig. 3:** Variations of normalized limiting currents $i^L_{norm}$ at -0.2 V as a function of synthetic *miR-30b* concentration: sensor 1 (A), sensor 2 (B), sensor 3 (C), sensor 4 (D), and average values from the 4 sensors (E).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 4:** A-G) Example of EIS responses recorded at Sensor 2 for synthetic *miR-30b* detection: Bare Pt electrode (from 1MHz to 0.1 Hz), after probe immobilization and target hybridization (from 1 MHz to 260 or 844 Hz). H) Variation of $R_{ct\_norm}^{ap}$ against the logarithm of *miR-30b* target concentration; each point corresponds to an average value obtained from the 4 sensors.</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>**Fig. 5:** Experimental (symbols) and simulated (lines) voltammograms for sensor 2. A) Bare electrode. B) After functionalization with probes. C-E) After hybridization with increasing concentrations of targets. F) Simulated voltammograms of (A-E).</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>**Fig. 6:** Correlation between the surface coverage ratios $\theta_{\text{volta}}$ and $\theta_{\text{EIS}}$ after electrode functionalization (open diamond) and hybridization for -log [miR-30b] = 16 (solid diamond), 14 (solid circle), 12 (solid square), 10 (solid triangle) and 8 (inverted triangle).</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>**Fig. 7:** Specificity tests from the 4 sensors for *miR-30b* and non-complementary sequences *miR-122* and *miR-30d*. A) Variation of $i_{norm}^L$ versus the logarithm of target concentration. B) Variation of $R_{ct\_norm}^{ap}$ versus the logarithm of target concentration. In (A-B), *miR-30b* (black), *miR-30d* (blue), and *miR-122* (red) with data of *miR-30b* from Figure 3E and Figure 4H.</figcaption></figure>
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /><figcaption>**Fig. 8:** Real sample detections of *miR-30b*. Detection in wild type mice milk sample (WT.76) by CV (A) and EIS (B). Detection in transgenic mice milk sample (TG.91) by CV (C) and EIS (D).</figcaption></figure>
Graphical abstract

<figure><img src="image_14.png" /></figure>