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

Summary

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

Abstract

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

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 properties^3,4. 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 cells^7,8. 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 biosensors^9,11. 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 uptake^13,14. Despite significant progress, these technologies have not yet reached their full potential for investigating critical biological questions involving small-molecule dynamics in vivo¹⁵. 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.

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. 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 (cpGFP), which is sensitive to conformational changes, responds to ligand binding-induced rearrangements of the sensory domain.
   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.
   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.
   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.
   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^12,13,14.
   5. Following cloning, transform the recombinant plasmid into bacterial cells for amplification of the plasmid DNA. Use purified plasmid DNA for yeast transformation.
   6. Introduce the purified plasmid into yeast cells through polyethylene glycol (PEG)-mediated transformation as published¹⁶. Plate transformed yeast cells on uracil-deficient media, which selects for positive clones containing the plasmid carrying the URA3 selection marker.

2. Sample preparation

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 ura3auxotrophy, 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. 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 pAG416¹².
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.
4. Prepare competent yeast cells and perform PEG 3350-mediated transformation according to an established protocol¹⁶.
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)

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).
   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.
   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. 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.
   4. Cell loading in well 8: Add 50 µL of yeast cell suspension (OD600=0.5-1.0).
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.
   1. Align the plate onto the manifold. Turn on the vacuum. Switch on the microfluidic Platform.
   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. 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.
   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. 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)

1. Assess cell growth and fluorescence through a rapid microscopic visualization.
   1. Place the microfluidic plate onto the stage of an inverted microscope. Focus on the center of the viewing area to locate the cells.
   2. Use brightfield and/or fluorescence microscopy to assess cell morphology, viability, and biosensor expression.
2. Investigate calcium steady-state dynamics under precisely controlled conditions using the microfluidic system and compatible microfluidic plates^4,12,13,14.
3. Perform live-cell imaging using an inverted laser scanning confocal microscope with a100x magnification and a numerical aperture (NA) of 1.40. Monitor the dual-fluorophore biosensors by exciting GFP and LSSmApple at 488 nm using an argon laser. Collect emission signals at 520 nm for GFP and 610 nm for LSSmApple (Figure 5) and conduct ratiometric fluorescence imaging experiments to quantify dynamic responses of the biosensor. Measure the ratio of emission intensities (GFP/LSSmApple) over time to monitor analyte-induced changes.

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

1. Obtain the CellProfiler or any other similar software from the official repository and install it on a local workstation.
2. Organize the time-lapse fluorescence microscopy images in sequential order (e.g., img_001.tif, img_002.tif, etc.) to ensure compatibility with automated batch processing.
3. Launch the software and initialize a new analysis pipeline by selecting New Project. Import the time-lapse image sequence into the pipeline workspace.
4. Incorporate the IdentifyPrimaryObjects module to detect individual cells within each frame based on fluorescence signal or morphological criteria.
5. Add the TrackObjects module to establish temporal correspondences between cells across frames, enabling single-cell tracking over time.
6. Include the MeasureObjectIntensity module to quantify fluorescence intensity parameters (e.g., mean, median, and integrated intensity) for each tracked cell.
7. Use the ExportToSpreadsheet module to compile and export quantitative data into a tabular format (e.g., CSV) for downstream analysis.
8. Analyze the exported data using statistical or data visualization tools such as spreadsheet software.

Results

To achieve successful ratiometric imaging of biosensors in microbial systems, several technical challenges must be addressed.

Expression System: The first challenge lies in the selection of the expression system; whether plasmid-based, chromosomally integrated, driven by constitutive or inducible promoters, and with or without subcellular targeting tags. These factors can significantly affect the biosensor's stability, toxicity, and localization within the cells (Figure 1<...

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 ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Agar	Sigma-Aldrich Co.	A5306-250G
CellASIC ONIX plate for haploid yeast cells	Merck KGaA	Y04C-02-5PK
CellASIC ONIX2	Merck KGaA	CAX2-S0000
CSM-Ura	Thermo Fisher Scientific	11387899
D-glucose	Sigma-Aldrich Co.	G8270-100G
D-Sorbitol	Sigma-Aldrich Co.	S1876-10MG
NaCl	Sigma-Aldrich Co.	S9888-25G
pAG416 GPD GA-MatryoshCaMP6s	NA	7	For expression of the GA-MatryoshCaMP6s in yeast
TCS SP8 STED	Leica	NA
Yeast strain K601/ W303-1A MAT-a	NA	8	ade2-1 ura3-1 his3-11 trp1- 1 leu2-3 leu2-112 can1-100
YNB	BocaScientific	GCM16.0500
α-Mating Factor acetate salt	Sigma-Aldrich Co.	T6901-.5MG