# Fluorophore-based Genetically Encoded Biosensors for Ratiometric Fluorescence Imaging in Microbes

Erdem Sunal, Vanessa Castro-Rodriguez, Mayuri Sadoine

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Erdem Sunal, Vanessa Castro-Rodriguez, Mayuri Sadoine. Fluorophore-based Genetically Encoded Biosensors for Ratiometric Fluorescence Imaging in Microbes. Journal of visualized experiments: JoVE, 2025, 222, 10.3791/68339. hal-05206336

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1 **TITLE:**

2 Fluorophore-based Genetically Encoded Biosensors for Ratiometric Fluorescence Imaging in

3 Microbes

4

5 **AUTHORS AND AFFILIATIONS:**

6 Erdem Sunal¹, Vanessa Castro-Rodriguez², Mayuri Sadoine³,⁴

7

8 ¹Department of Biology, Heinrich-Heine Universität Düsseldorf, Düsseldorf, Germany

9 ²Departamento de Biología Molecular y Bioquímica, Facultad de Ciencias, Universidad de Málaga,
Málaga, Spain

10 11 Institute of Cell and Interaction Biology, Heinrich-Heine Universität Düsseldorf, Düsseldorf,
12 Germany

13 ⁴Université de Lyon, INSA Lyon, CNRS, Université Claude Bernard Lyon 1, Lyon, France

14

15 Email address of co-author:

16 Erdem Sunal (erdem.94@web.de)

17

18 Corresponding authors:

19 Vanessa Castro-Rodriguez (vavicaro@uma.es)

20 Mayuri Sadoine (mayuri.sadoine@insa-lyon.fr)

21

22 **SUMMARY:**

23 This protocol describes the application of fluorophore-based genetically encoded biosensors in
24 microbes for quantitative ratiometric fluorescence imaging under controlled conditions. By
25 integrating these biosensing strategies with advanced imaging techniques, the method enables
26 high-resolution, real-time analysis at the single-cell level, providing deeper insights into small-
27 molecule dynamics.

28

29 **ABSTRACT:**

30 Investigating small-molecule dynamics within microbes is essential for comprehensive studies of
31 microbial function. Both intra-organism and inter-organism small molecule dynamics play critical
32 roles in microbial physiology, symbiosis, and disease. However, monitoring these dynamics
33 remains highly challenging using most existing techniques. Fluorophore-based genetically
34 encoded biosensors are powerful tools for tracking small-molecule dynamics in vivo and hold
35 high potential for driving new discoveries. These biosensors are most commonly used in
36 fluorescence imaging, often in combination with perfusion devices that allow precise control over
37 environmental conditions. When integrated with advanced imaging techniques, this approach
38 provides high-resolution, spatially and temporally resolved data, enabling insights into single-cell
39 microbial responses. Despite their promise, implementing such biosensors remains technically
40 challenging. Understanding the key steps is crucial for broader adoption. Here, we present a
41 protocol designed to support the effective deployment of newly engineered biosensors into
42 microbes for quantitative ratiometric fluorescence imaging under controlled conditions.

43

44 **INTRODUCTION:**
Single-fluorophore intensity-based and dual-fluorophore biosensors are widely used by researchers for a variety of applications¹. Some studies have employed multiplex biosensor analyses to simultaneously track multiple signals within the same cells². Many of these biosensors rely on ligand-binding domains, where binding events induce conformational changes that alter fluorescence properties³,⁴. Genetically encoded biosensors have become essential tools for *in vivo* imaging across a range of systems⁵, enabling high-resolution monitoring of ion and metabolite levels in space and time¹. Early biosensors, however, were limited in dynamic range and Signal-to-Noise Ratio (SNR)⁶. Recent advances in single fluorophore intensiometric biosensors based on conformationally sensitive Fluorescent Proteins (csFPs) have significantly improved performance. Nonetheless, variations in protein expression levels can impact *in vivo* measurements using genetically encoded fluorophore-based biosensors⁶, which are commonly employed to visualize signaling molecules and metabolites in living cells⁷,⁸. Single fluorophore intensiometric biosensors do not allow normalization for biosensor expression levels, which may fluctuate *in vivo*. Incorporating a reference fluorescent protein, however, can convert single-fluorophore biosensors into dual-fluorophore ratiometric versions, enabling more reliable quantifications⁹. Due to energy loss from non-radiative processes, the fluorescent emission spectrum is shifted relative to the absorption spectrum, a phenomenon known as the Stokes shift¹⁰. Large Stokes Shift Fluorescent Protein (LSSFPs) have been employed as optimal reference fluorophores in dual-fluorophore ratiometric biosensors⁹,¹¹. A recent advancement is the development of monochromatically excitable Green-Apple (GA) Matryoshka biosensors, known as GA-MatryoshCaMP6s, which incorporates a red LSSFP, called LSSmApple¹². When combined with microdevices such as microfluidic systems, fluorophore-based genetically encoded biosensors offer a minimally invasive approach for quantitative ratiometric imaging using techniques like confocal microscopy. This integration enables high-speed imaging and real-time monitoring of small-molecule dynamics in single cells under environmental stress. For example, cAMP and glucose biosensors have been successfully applied in yeast. When used in conjunction with mutant strains, these biosensors have helped elucidate key regulatory networks governing sugar uptake¹³,¹⁴. Despite significant progress, these technologies have not yet reached their full potential for investigating critical biological questions involving small-molecule dynamics *in vivo*¹⁵. As a result, their widespread adoption across laboratories remains limited, and standardized methodologies for broader implementation are still lacking.

Compared to traditional methodologies relying on non-ratiometric single-fluorophore biosensors or bulk-level measurements, our protocol based on GA Matryoshka biosensors enables high-resolution, single-cell imaging with quantitative ratiometric analysis. By integrating the large Stokes shift fluorophore as an internal reference, the method compensates for variability in biosensor expression, a key limitation of earlier designs. Additionally, the combination with microfluidic devices allows precise control of environmental conditions, offering an improvement over conventional perfusion chambers or static culture systems¹². This approach also provides distinct advantages for studying dynamic interactions between microbes and their hosts, such as plant-pathogen interactions and symbiotic associations such as mycorrhizas, where cellular-level spatial and temporal resolution is essential for uncovering the molecular mechanisms of communication and nutrient exchange.

88
In this context, we present a detailed step-by-step protocol for ratiometric biosensor imaging in microbes. Although specifically optimized for the implementation in yeast, this protocol can be adapted for use in other microbes, including bacteria, and extended to non-model organisms such as plant-associated microbes. For example, biosensors can be used in conjunction with microfluidic systems for quantitative fluorescence imaging of calcium signaling in yeast, enabling real-time monitoring of calcium responses under environmental stress¹⁴.

This protocol is intended to guide researchers in effectively deploying newly designed biosensors, thereby advancing the exploration of biological processes through biosensor-based fluorescent imaging in microbial systems. It includes practical recommendations on yeast culture conditions, optimal excitation/emission settings, and compatibility with commonly used yeast strains (e.g., K601 and W303-1A MAT-a). Notable limitations include photobleaching during prolonged imaging sessions and challenges related to biosensor expression in biological samples. Since numerous factors can influence the outcome of in vivo biosensor imaging experiments, this step-by-step protocol also aims to help researchers anticipate and overcome potential challenges, thereby facilitating reproducible implementations.

PROTOCOL:

1. Rational design of ratiometric biosensors (Figure 1)

NOTE: The binding protein of interest is selected as the sensory domain based on relevant criteria, including (i) specificity, (ii) affinity, (iii) structural information, and (iv) conformation rearrangement upon ligand binding. Specificity ensures that the biosensor selectively responds to the target molecule without cross-reactivity. Affinity is considered to align with the expected concentration range of the target molecule in the biological context. Structural information, derived from experimental data or computational models such as those generated by AlphaFold, provides insights into the three-dimensional organization of the sensory domain and the ligand-induced conformational changes. These insights are used to identify suitable insertion points for the fluorophores. A validation strategy is then developed to assess the biosensor's responsiveness and dynamic range, depending on the intended applications.

1.1. Construct the Matryoshka design *in silico* by inserting the Green-Apple Matryoshka¹² cassette into the sensory domain in such a way that the circularly permuted Green Fluorescent Protein (cGFP), which is sensitive to conformational changes, responds to ligand binding-induced rearrangements of the sensory domain.

1.1.1. Use molecular visualization software, such as Chimera, to analyze the structure of the binding domain. If the three-dimensional structure is not available, use AlphaFold to generate a reliable model.

1.1.2. Identify flexible loops within the protein structure as weak points and potential insertion sites for the dual-fluorophore cassette, chosen to avoid disrupting the protein's overall function or stability.
1.1.3. Use AlphaFold again to predict the structure of the chimeric protein containing the inserted dual-FP cassette, confirming that the insertion does not introduce unfavorable structural changes.

1.1.4. Clone the biosensor constructs into plasmid DNA that enables expression in yeast cells. Specifically, insert the gene encoding the newly designed biosensor into the yeast shuttle vector pAG416, which contains appropriate elements for yeast expression, using a suitable cloning technique<sup>12-14</sup>.

1.1.5. Following cloning, transform the recombinant plasmid into bacterial cells for amplification of the plasmid DNA. Use purified plasmid DNA for yeast transformation.

1.1.6. Introduce the purified plasmid into yeast cells through polyethylene glycol (PEG)-mediated transformation as published<sup>16</sup>. Plate transformed yeast cells on uracil-deficient media, which selects for positive clones containing the plasmid carrying the URA3 selection marker.

## 2. Sample preparation

2.1 Streak yeast transformants expressing GA-Matryoshka biosensors onto selective medium plates (i.e., SC-Ura) and incubate at 30 °C for 2-3 days to allow colony growth.

NOTE: The yeast strain carries a *ura3* auxotrophy, which is complemented by the URA3 gene on the plasmid, enabling selection on uracil-deficient media. The plasmid encoding the biosensor includes a cassette that complements the auxotrophy (e.g., pAG416 GPD GA-MatryoshCaMP6s).

2.2 Generate yeast transformants from the strain K601/W303-1A MAT-a (genotype: ade2-1 ura3-1 his3-11 trp1-1 leu2-3 leu2-112 can1-100). Perform transformation with an expression vector commonly used for *Saccharomyces cerevisiae*, such as pAG416<sup>12</sup>.

2.3 After transformation, culture yeast cells at 30 °C on SC-Ura medium, prepared per liter as follows, until independently transformed colonies appear: 0.77 g CSM-Ura, 6.7 g Yeast Nitrogen Base (YNB) with ammonium sulfate, 20 g glucose, 20 g agar, and adenine added to a final concentration of 150 µM.

2.4 Prepare competent yeast cells and perform PEG 3350-mediated transformation according to an established protocol<sup>16</sup>.

2.5 For microfluidic cell trapping experiments, streak 25% glycerol stocks onto SC-Ura plates and incubate at 30 °C for 2 days to allow the growth of independent single colonies. Resuspend individual colonies in 5 mL of liquid SC-Ura medium and incubate at 30 °C with shaking until the OD600 reaches 0.5-1.0, making them suitable for loading into the perfusion system.
### 3. Preparation of the perfusion system (Figure 2)

3.1. Prepare the microfluidic plate for haploid yeast cells by loading it with 50 µL of yeast cells expressing GA-Matryoshka biosensors, along with final SC-Ura media for growth and buffered solution for treatments (e.g., NaCl or sorbitol).

3.1.1. Remove storage solution from wells 1-8. Aspirate the Phosphate-buffered saline (PBS) solution from the wells and add 400 µL of sterile water.

3.1.2. Wash wells 1-6 and 8. Wash 1: Aspirate the sterile water solution from wells 1-8 and add 400 µL of sterile water. Wash 2: Repeat the wash by aspirating and adding 400 µL of sterile water. Wash 3: Repeat once more with 400 µL of sterile water.

3.1.3. Final fill for wells 1-6: Aspirate the water solution and add 300µL of final perfusion/growth solutions such as SC-Ura for yeast growth, or a buffered solution containing the analytes of interest for treatment.

3.1.4. Cell loading in well 8: Add 50 µL of yeast cell suspension (OD600=0.5-1.0).

3.2. Seal the plates with the temperature-controlled manifold of the microfluidic platform and connect it to the live-cell imaging system. The manifolds maintain a stable thermal environment at a temperature of 28 °C, while tubing and a pump ensure proper sealing and continuous perfusion.

3.2.1. Align the plate onto the manifold. Turn on the vacuum. Switch on the microfluidic Platform.

3.2.2. Press **Seal** and wait until the green light is illuminated. Open the microfluidic platform software, create a new experiment, and select the **Appropriate yeast plate** from the drop-down list.

3.2.3. Click on the **Run Liquid Priming Sequence** button. Click on the **Run Cell Loading Sequence** to load yeast cells from well 8 into the culture chamber, where they will be trapped and grown under continuous perfusion of selective medium SC-Ura overnight at 10 kPa at 28 °C.

3.2.4. Under the Manual Mode tab, click **Run a Custom Sequence** or use the Protocol Editor to set experimental parameters such as temperature at 30 °C, flow rate at 10 kPa, step duration (e.g., 10 min), and channels to open for perfusion.

3.3. Grow the cells overnight in the culture chamber under continuous perfusion with selective medium SC-Ura at 28 °C and a pressure of 10-17 kPa.

### 4. Ratiometric fluorescence imaging (Figure 3, Figure 4, and Video 1)
220 4.1. Assess cell growth and fluorescence through a rapid microscopic visualization.
221
222 4.1.1. Place the microfluidic plate onto the stage of an inverted microscope. Focus on the center
223 of the viewing area to locate the cells.
224
225 4.1.2. Use brightfield and/or fluorescence microscopy to assess cell morphology, viability, and
226 biosensor expression.
227
228 4.2. Investigate calcium steady-state dynamics under precisely controlled conditions using the
229 microfluidic system and compatible microfluidic plates<sup>4,12-14</sup>.
230

231 4.3. Perform live-cell imaging using an inverted laser scanning confocal microscope with a
232 100x magnification and a numerical aperture (NA) of 1.40. Monitor the dual-fluorophore
233 biosensors by exciting GFP and LSSmApple at 488 nm using an argon laser. Collect emission
234 signals at 520 nm for GFP and 610 nm for LSSmApple (Figure 5) and conduct ratiometric
235 fluorescence imaging experiments to quantify dynamic responses of the biosensor. Measure the
236 ratio of emission intensities (GFP/LSSmApple) over time to monitor analyte-induced changes.
237

238 5. Image analysis using open-source software (Figure 6)
239

240 5.1. Obtain the CellProfiler or any other similar software from the official repository and install
241 it on a local workstation.

243 5.2. Organize the time-lapse fluorescence microscopy images in sequential order (e.g.,
244 img_001.tif, img_002.tif, etc.) to ensure compatibility with automated batch processing.

245 5.3. Launch the software and initialize a new analysis pipeline by selecting **New Project**.
246 Import the time-lapse image sequence into the pipeline workspace.

248 5.4. Incorporate the IdentifyPrimaryObjects module to detect individual cells within each
250 frame based on fluorescence signal or morphological criteria.

252 5.5. Add the TrackObjects module to establish temporal correspondences between cells
253 across frames, enabling single-cell tracking over time.

255 5.6. Include the MeasureObjectIntensity module to quantify fluorescence intensity
256 parameters (e.g., mean, median, and integrated intensity) for each tracked cell.

258 5.7. Use the ExportToSpreadsheet module to compile and export quantitative data into a
259 tabular format (e.g., CSV) for downstream analysis.

260 5.8. Analyze the exported data using statistical or data visualization tools such as spreadsheet
262 software.

263
264 **RESULTS:**
265 To achieve successful ratiometric imaging of biosensors in microbial systems, several technical
266 challenges must be addressed.
267 Expression System: The first challenge lies in the selection of the expression system; whether
268 plasmid-based, chromosomally integrated, driven by constitutive or inducible promoters, and
269 with or without subcellular targeting tags. These factors can significantly affect the biosensor's
270 stability, toxicity, and localization within the cells (Figure 1). It is essential to ensure optimal
271 expression levels to avoid artifacts caused by overexpression or, conversely, insufficient signal.
272 Moreover, the biosensor must be expressed in a manner that preserves the physiological
273 function of the host cell.
274
275 Culture Density: The second bottleneck is culture density. The microbial growth phase and cell
276 concentration directly influence imaging quality. Overcrowded chambers may lead to
277 overlapping cells and signal interference, resulting in poor resolution. Conversely, very low
278 densities can yield insufficient signal-to-noise ratios. Achieving an optimal density that enables
279 clear imaging of individual, non-overlapping cells is crucial. This requires careful optimization of
280 culture conditions and continuous monitoring of cell growth (Figure 2).
281
282 Perfusion System: The third consideration is the perfusion system used during imaging (Figure
283 2). Long-term live-cell imaging often requires a continuous supply of fresh nutrients and the
284 removal of metabolic waste. An effective perfusion system helps maintain cell viability and
285 prevents shifts in metabolism that may alter biosensor expression or performance. A robust and
286 stable perfusion setup is especially critical for prolonged experiments where biosensor behavior
287 may vary over time (Figure 3).
288
289 Imaging System: The fourth bottleneck involves the imaging system itself. While resolution is
290 important, sensitivity is often more critical for detecting weak or transient biosensor responses,
291 particularly in live-cell imaging of dynamic processes. For fluorophore-based biosensors,
292 choosing an imaging system with high quantum efficiency, low background noise, and
293 appropriate filters is essential. In many cases, improving sensitivity yields more reliable results
294 than simply increasing resolution, as subtle biosensor responses otherwise go undetected (Figure
295 4 and Video 1).
296
297 Image analysis and data processing: Imaging analysis can mitigate certain limitations of the
298 acquisition system (Figure 5 and Figure 6). ImageJ or any other widely used open-source image
299 analysis tool allows users to process and enhance images, perform background subtraction, and
300 correct for drift or distortions. With careful application, these tools improve the reliability of data
301 interpretation¹⁷. Once imaging is completed, a critical next step is data analysis (Figure 6).
302 Monitoring the stability of the reference fluorescent protein over time is essential, as it serves as
303 an internal control to account for variability in signal intensity caused by experimental
304 fluctuations. Instability in reference FP may indicate photobleaching, changes in expression
305 levels, or system drift, all of which can affect data interpretation.
306
Results and interpretation: The data illustrate the effectiveness of the ratiometric imaging protocol and highlight how each technical component contributes to producing interpretable results. For example, fluorescence images from yeast expressing biosensor constructs (Figure 1) demonstrate the influence of promoter strength and subcellular localization on biosensor distribution, both of which are key parameters for biosensor validation. Culture density optimization (Figure 2) reveals the impact of over- or under-populated samples on signal clarity, underscoring the importance of maintaining cultures in the mid-log growth phase. Time-lapse experiments with a perfusion system (Figure 3) show the stability of fluorescence signals during extended imaging, demonstrating how continuous nutrient supply and waste removal preserve cellular homeostasis. For accurate interpretation, background subtraction and signal normalization using the reference fluorophore are essential (Figure 5 and Figure 6). Tracking the reference signal over time also helps identify potential issues such as photobleaching or drift.

In summary, successful ratiometric imaging of biosensors in microbes requires careful consideration of multiple technical factors, including the expression system, culture density, perfusion setup, imaging hardware, and data analysis strategies. Regular monitoring of the reference fluorophore and continuous adjustment of imaging conditions are essential for maintaining accuracy and reproducibility in long-term experiments. By addressing these bottlenecks, researchers can generate meaningful, high-quality biosensor data.

## FIGURE AND TABLE LEGENDS:

**Figure 1:** Rational design of ratiometric Matryoshka biosensors. Ratiometric Matryoshka biosensors, such as GA-CaMP6s⁹, are (A) rationally designed chimeric proteins composed of several components that (B) when fused together, enable the monitoring of small molecule dynamics (e.g., calcium ions), with high spatial and temporal resolution. These components here include RS20, circularly permuted Enhanced Green Fluorescent Protein (cpEGFP), and a Large Stokes Shift (LSS) fluorescent protein.

**Figure 2:** Combining confocal microscopy and microdevices for live-cell fluorescence imaging. The system consists of several components: (A) an inverted confocal microscope; (B) an automated perfusion control system; (C) a micro-device, such as the microfluidic system, which contains (D) four independent culture chambers where cells expressing biosensors are trapped and treated with six possible treatment solutions. These chambers are connected to flow inlets and a waste outlet.

**Figure 3:** Expression of biosensors in yeast cells. Cells expressing biosensors, grown in the chambers of the microfluidic system, are visualized to assess both growth (left) and biosensor expression under test treatment (right). Green indicates cpGFP, while magenta represents LSSmApple. A 488 nm laser light is used for excitation of both fluorophores, and detection is performed using HyD (Hybrid Detectors), with spectral windows set to 510–550 nm for GFP and 570–620 nm for LSSmApple.

**Figure 4:** Live-cell imaging. Ratiometric fluorescence imaging of microbes expressing GA-
Matryoshka biosensors is performed using time-lapse measurements. Cells are treated with a stimulus at a defined time point, and imaging data capture biosensor activity before, during, and after treatment. Green represents cpGFP, and magenta represents LSSmApple. A 488-nm laser light is used for excitation of both fluorophores, and detection is performed using HyD (Hybrid Detectors), with spectral windows set to 510–550 nm for GFP and 570–620 nm for LSSmApple.

**Figure 5: Biosensor imaging data.** (A) Imaging videos of ratiometric fluorescence in microbes are analyzed using Fiji to determine (B) the ratiometric responses of cells expressing biosensors.

**Figure 6: Data analysis using CellProfiler.** CellProfiler analysis workflow (A) object identification, (B) object tracking across frames, (C) measurement of object intensities, and (D) data export to a spreadsheet. Refer to the main text for additional details.

**Video 1: Time-lapse imaging of microbes.** Live-cell imaging of yeast expressing biosensors is performed while a treatment is applied at a defined time point. A 488-nm laser is used to excite both fluorophores (i.e., cpGFP and LSSmApple), and detection is carried out using HyD (Hybrid Detectors), with spectral windows set to 510–550 nm for GFP and 570–620 nm for LSSmApple. The green channel is shown here.

**DISCUSSION:**

Genetically encoded fluorophore-based biosensors enable the real-time monitoring of intracellular analyte concentrations in living cells¹. These biosensors typically function by converting conformational changes into optical signals, which can be detected either ratiometrically or intensiometrically. Intensiometric biosensors rely on fluorescence intensity changes from a single FP, making them sensitive to variations in biosensor abundance, leading to potential measurement bias. This issue can be mitigated by using ratiometric biosensors, which simultaneously measure fluorescence from two spectrally distinct FPs. The ratio of these emissions provides a more reliable readout, independent of biosensor abundance⁹,¹². However, ratiometric approaches are constrained by the limited spectral space, which restricts the co-expression of multiple biosensors or additional fluorescent markers.

In microbial systems, genetically encoded biosensors offer high spatiotemporal resolution with minimal invasiveness. Despite the wide range of biosensors available, their implementation in microbes remains limited. One of the most sensitive steps in using these tools is ensuring appropriate biosensor expression; both overexpression and underexpression can compromise the experiment. During the development of our protocol, several adaptations were required to address these issues and improve reproducibility.

Over the past three decades, numerous biosensors have been engineered to detect a variety of nutrients and metabolites in living cells¹⁸. These tools have proven invaluable across many biological fields, particularly when applied rationally to well-defined research questions. Their low invasiveness makes them ideal for real-time monitoring of cellular processes without disrupting physiology, an advantage over conventional techniques such as mass spectrometry or chromatography, which require sample disruption, lack subcellular resolution, and cannot be
performed in real-time.

Recent advances in artificial intelligence and imaging technologies are beginning to address some of the barriers to the broader adoption of biosensors in microbial research. A key advantage of genetically encoded sensors is their ability to be targeted to specific subcellular compartments, allowing researchers to investigate metabolite dynamics with spatial precision. However, challenges remain. Endogenous autofluorescence can compromise signal detection, and the spectral properties of both biosensors and background fluorescence must be carefully considered. The use of affinity variants and non-binding controls is essential, especially when deploying biosensors across different cellular compartments.

Despite their advantages, genetically encoded biosensors are not without limitations. Fluorophores are often sensitive to pH, which can confound interpretation, particularly in environments with variable pH. Moreover, the spectral requirements of ratiometric imaging limit the simultaneous use of multiple biosensors. Additionally, *in vitro* dissociation constants (Kd values) may not accurately reflect *in vivo* affinities under physiological conditions. Caution is warranted when extrapolating *in vitro* calibration data to *in vivo* measurements, and the range of ligands that can be characterized *in vitro* is often restricted.

Nonetheless, genetically encoded fluorophore-based biosensors offer unparalleled advantages over traditional methods. Their ability to provide dynamic, compartment-specific, and continuous measurements makes them uniquely suited for studying the temporal and spatial aspects of cellular processes.

Emerging tools in synthetic biology and machine learning are promising tools to further facilitate biosensor design and application. These innovations may soon enable more systematic deployment of biosensors in non-model organisms, opening new avenues for research in areas such as plant-microbe symbiosis⁸.

### ACKNOWLEDGMENTS:

This research was funded by a Chair of Junior Professorship from the French "Agence National de la Recherche (ANR)- PROJET N° ANR-24-CPJ1-0014-01 to M.S. and supported by grants from the Spanish "Agencia Estatal de Investigación (AEI), Ministerio de Ciencia, Innovación y Universidades" (TED2021-129691B-100), as well as the University of Malaga Fellowship (B1-2022_03) awarded to V.C.R. We would like to acknowledge the Center for Advanced Imaging (CAI) at Heinrich Heine University and the Lyon Multiscale Imaging Center (LyMIC) from Lyon University for fruitful discussion about biosensor imaging and/or access to microscopy systems.

### DISCLOSURES:

The authors declare no conflict of interest.

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A GA-CaMP6s

<figure><img src="image_2.png" /></figure>

B Sensory Domain

<figure><img src="image_3.png" /><figcaption>**Figure 1:** Rational design of ratiometric Matryoshka biosensors. Ratiometric Matryoshka biosensors, such as GA-CaMP6s⁹, are (A) rationally designed chimeric proteins composed of several components that (B) when fused together, enable the monitoring of small molecule dynamics (e.g., calcium ions), with high spatial and temporal resolution. These components here include RS20, circularly permuted Enhanced Green Fluorescent Protein (cpEGFP), and a Large Stokes Shift (LSS) fluorescent protein.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 2:** Combining confocal microscopy and microdevices for live-cell fluorescence imaging.

The system consists of several components: (A) an inverted confocal microscope; (B) an automated perfusion control system; (C) a micro-device, such as the microfluidic system, which contains (D) four independent culture chambers where cells expressing biosensors are trapped and treated with six possible treatment solutions. These chambers are connected to flow inlets and a waste outlet.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 3: Expression of biosensors in yeast cells.** Cells expressing biosensors, grown in the chambers of the microfluidic system, are visualized to assess both growth (left) and biosensor expression under test treatment (right). Green indicates cpGFP, while magenta represents LSSmApple. A 488 nm laser light is used for excitation of both fluorophores, and detection is performed using HyD (Hybrid Detectors), with spectral windows set to 510–550 nm for GFP and 570–620 nm for LSSmApple.</figcaption></figure>

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<figure><img src="image_6.png" /><figcaption>**Figure 4: Live-cell imaging.** Ratiometric fluorescence imaging of microbes expressing GA- Matryoshka biosensors is performed using time-lapse measurements. Cells are treated with a stimulus at a defined time point, and imaging data capture biosensor activity before, during, and after treatment. Green represents cpGFP, and magenta represents LSSmApple. A 488-nm laser light is used for excitation of both fluorophores, and detection is performed using HyD (Hybrid Detectors), with spectral windows set to 510–550 nm for GFP and 570–620 nm for LSSmApple.</figcaption></figure>

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<figure><img src="image_7.png" /><figcaption>**Figure 5:** Biosensor imaging data. (A) Imaging videos of ratiometric fluorescence in microbes are analyzed using Fiji to determine (B) the ratiometric responses of cells expressing biosensors.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>**Figure 6:** Data analysis using CellProfiler. CellProfiler analysis workflow (A) object identification, (B) object tracking across frames, (C) measurement of object intensities, and (D) data export to a spreadsheet. Refer to the main text for additional details.</figcaption></figure>

**Video 1:** Time-lapse imaging of microbes. Live-cell imaging of yeast expressing biosensors is performed while a treatment is applied at a defined time point. A 488-nm laser is used to excite both fluorophores (i.e., cpGFP and LSSmApple), and detection is carried out using HyD (Hybrid Detectors), with spectral windows set to 510–550 nm for GFP and 570–620 nm for LSSmApple. The green channel is shown here.