Introduction
Our objective was to establish a fluorescence-based biosensor to detect fluoride ions released during the defluorination of short-chain PFAS by the enzyme DeHa2 (Figure 1). Fluorescence intensity served as a direct readout of the activity of DeHa2 variants generated through orthogonal replication, enabling the selection of the most efficient enzymes. Inspired by recent advances in RNA aptamers, we turned to the FluorMango aptamer (Husser et al., 2023), which produces a specific fluorescent signal upon fluoride binding. However, its extracellular nature limited its use for in-medium screening of your variant. To overcome this challenge and enable high-throughput applications, we developed a microfluidic-compatible screening protocol.
Beyond our project, FluorMango stands out as a simple, yet powerful tool for quantitative and specific fluoride detection. Its versatility goes far beyond enzymatic screening; it could for instance be used for monitoring environmental fluoride contamination in water and soil, for medical diagnostics to track chronic exposure in patients, or even for food safety analysis to control fluoride levels in consumer products.
To demonstrate the broad applicability of FluorMango, we performed measurements on two different platforms. We conducted microplate assays in the presence or absence of bacteria, testing various molecules capable of binding the aptamer and at different fluoride concentrations. In addition, we carried out droplet-microfluidic experiments to show that FluorMango can be adapted to other technologies, where low-volume reactions and high throughput are important.
Measurements in microplates
1- FluorMango fluorescence is sensitive to NaF concentration variations
Our first goal was to quantify the responsiveness of FluorMango to varying amounts of fluoride ions. We produced the aptamer and showed it could be stored in the fridge for at least 5 days, which proved very convenient to reduce lab work. Measurements were carried out in 384-well plates to minimize the reaction volumes and enable multiplexing. The results clearly demonstrate that the fluorescence intensity increases with the fluoride concentration and that the aptamer is sensitive to 0.1 mM NaF (Figure 2).
FluorMango (400 nM) aptamer was incubated for 1 hour with TO1-biotin (700 nM), 1X refolding buffer and increasing NaF concentrations. Fluorescence of FluorMango/TO1-biotin complex was directly measured at 𝜆excitation=510 nm and 𝜆emission=550 nm. The x-axis represents NaF concentration and is displayed on a logarithmic scale. Grey points represent individual measurements (n=6), and black symbols indicate the mean values. The shaded area represents the confidence interval at 95%.
2- FluorMango responds specifically to fluoride ions
Our next objective was to examine the specificity of FluorMango. We exposed the aptamer to a variety of molecules relevant to our experiments and measured its fluorescence. A strong fluorescence signal was observed exclusively in the presence of fluoride ions, whereas fluorescence remained low with NaCl, TFA, or the fluorinated oil used in microfluidics (Figure 3). This remarkable specificity, combined with its quantitative responsiveness, validates FluorMango as a reliable biosensor for fluoride ions, including for the diverse applications discussed earlier.
FluorMango aptamer (400 nM) was incubated for 1 hour with TO1-biotin (700 nM), 1X refolding buffer and 1 mM of NaF, NaCl, TFA or fluorinated microfluidics oil (Fluo-oil 135) with or without surfactant (FluoroSurf O at 2%). Fluorescence of FluorMango/TO1-biotin complex was directly measured at 𝜆excitation=510 nm and 𝜆emission=550 nm. Values are the mean of three independent experiments (n=3), and errors bars correspond to standard deviation. The individual data points are also displayed in grey.
3- FluorMango detects fluoride release from defluorination in real time
Next, we investigated if FluorMango was compatible with the detection of fluoride in the presence of bacteria, i.e., under conditions close to the screening of DeHa2 variants with ORep E. coli cells. This was also important to show that this technology could be applied far beyond our project, including in industrial processes, bioproduction, or fluoride release monitoring. To this end, we exposed our aptamer to the E. coli strain modified with the IPTG-inducible dehalogenase gene to study if FluorMango could track the degradation of fluorinated molecules over time, even at low levels. We found that the degradation of 2-FP and the accompanying production of F- by DeHa2 (with IPTG) could successfully be detected by FluorMango (upper curve in Figure 4), while the fluorescence signal remained low in the control samples (no IPTG induction, only bacteria, only 2-FP).
Cultures were grown overnight in LB at 37 °C, washed in PBS, and incubated in a microplate reader at 25 °C with IPTG (0.1 mM), 2-FP (50 mM), FluorMango (400 nM), and 1X refolding buffer. Fluorescence was recorded with λexcitation = 510 nm and λemission = 550 nm. Values are the mean of two independent experiments (n=2), and errors bars correspond to the standard deviation.
Summarizing, we provided a detailed characterization of FluorMango responsiveness, establishing this biosensor as a quantitative tool for the directed evolution of DeHa2 and more generally for the detection of fluoride ions in laboratory conditions and potentially in more complex microbial environments.
Measurements in droplet-microfluidics
Having validated FluorMango as a sensitive and specific biosensor in microplate assays and even in the presence of living bacteria, the next step was to test whether this technology could be translated into a droplet-based format. Microfluidic encapsulation not only provides the high-throughput capacity required for directed evolution but also allows linking enzymatic activity to individual genetic variants. We therefore explored whether FluorMango retains its functionality inside droplets and whether it can support the sorting of active versus inactive variants in a pooled screening setup.
Pooled genetic screens of DeHa2 variants require a method to efficiently link the gene sequence to enzymatic activity. During hypermutagenesis in the ORep E. coli strain, a large diversity of the DeHa2 gene is created within the bacterial population (Figure 1). Isolation of genetic variants and their phenotypic outputs is then essential for directed evolution.
Our strategy consisted in compartmentalizing individual ORep cells along with FluorMango inside water-in-oil emulsion droplets generated by microfluidics. We performed a series of measurements to answer the following questions:
- Can we generate microfluidic droplets for the detection of fluoride ions with FluorMango?
- Can we sort active droplets based on fluorescence intensity thresholding from a mixed population of positive (with fluoride → fluorescent) and negative (no fluoride → nonfluorescent) droplets? This ‘mock library’ assay would validate the method for the prospective screening and selection of DeHa2 variants.
1- Mock library preparation
Method 1: Separate component preparation
A solution containing FluorMango (400 nM), TO1-biotin (700 nM), and refolding buffer (1X) was mixed in the microfluidic device with a solution consisting either of NaF (1 mM) for the positive droplets or RNAse-free water for the negative droplets, at the indicated final concentrations. All solutions were prepared at 2X to account for the 1:1 dilution during droplet generation (see 2- Droplet Generation, Figure 5).
Method 2: Pre-incubation mix
In a second approach, droplets were prepared directly from pre-mixed and incubated solutions (1 hour at 25°C) containing FluorMango (400 nM), TO1-biotin (700 nM), refolding buffer (1X), and either NaF (1 mM, for positive droplets) or RNAse-free water (for negative droplets) at the indicated final concentrations (see 2- Droplet Generation, Figure 6).
2- Droplet generation
Method 1: Two-inlet device
Droplets were generated using a two-inlet chip Drop-100 (Figure 5) with an oil pressure of 1250 mbar and sample pressure of 1100 mbar. With this design, FluorMango and TO1-biotin were kept separate from NaF, allowing activation of FluorMango only upon encapsulation.
Video 1: Droplets generation-microfluidics
Method 2: Single-inlet device
For this method, the droplet content was pre-incubated before transfer to a single-inlet microfluidic chip for droplet generation. Here, FluorMango was already activated by fluoride (positive droplets) allowing us to screen the droplets using an integrated fluorescence detector at 𝜆excitation = 488 nm and 𝜆emission = 525 nm (Figure 6). A single-inlet Drop100 chip was used with an oil pressure of 1250 mbar and sample pressure of 1100 mbar.
For both methods, the two populations of droplets were generated sequentially, starting with the negative droplets followed by the positive droplets. Each type of droplet was collected in separate Eppendorf tubes. Droplet generation was carried out using FluoroSurf O surfactant at 2% (w/w) in fluorinated oil Fluo-Oil 135 from Emulseo.
3- FluorMango enables fluorescence detection of fluoride in microfluidic droplets
We visualized the positive and negative droplets collected from the microfluidic chip by fluorescence microscopy. With both sample preparation and droplet generation methods, no fluorescence was detected in negative control droplets, whereas positive control droplets consistently exhibited strong fluorescence (Figure 7). These results demonstrate the successful detection of fluoride ions by FluorMango in microfluidic droplets. We conclude that its implementation in microfluidics does not impair biosensor functionality.
Bright-field (top) and fluorescence (bottom) microscopy images of negative (+RNAse-free water) and positive (+NaF) droplets generated with A) method 1 where FluorMango activation occurred after encapsulation, B) method 2 where FluorMango activation occurred prior encapsulation. Fluorescence measured at 𝜆excitation=480/40 nm and 𝜆emission=527/30 nm.
4- Droplet sorting
Positive droplets and negative droplets generated using Method 2 were mixed at a 2:8 ratio (v/v) to create a mock library of active and inactive variants. The mixture was loaded into the R-sort100 microfluidic chip. Droplets were screened using the integrated fluorescence detector with 𝜆excitation = 488 nm and 𝜆emission = 525 nm. A fluorescence threshold of 0.15 a.u. was set to distinguish positive droplets from negative ones. Other sorting parameters, such as applied voltage, can be found in Figure 8.
Figure 9 shows a droplet sorting experiment which was performed under an oil pressure of 500 mbar and a sample pressure of 360 mbar to ensure efficient separation of droplets. When a droplet passing the detector exceeds the fluorescence threshold, the electrode is activated, deflecting the droplet toward the lower outlet for collection of the fluorescent droplets. Droplets with fluorescence below the threshold are directed to the upper outlet as waste.
The flow direction and position of the fluorescence detector are indicated. Droplets are directed to one of the two outlets depending on their fluorescence intensity.
Video 2: Droplets sorting
Droplet screening was performed with samples prepared according to Method 2 using the chip-integrated fluorescence detector. Negative droplets led to basal fluorescence level (Figure 11, left), while events of higher amplitude (not resolved in the figure) were observed for positive droplets (Figure 11, right). These results demonstrate that FluorMango provides a robust and specific fluorescence signal directly compatible with real-time detection in microfluidic systems.
The x-axis is the time (s) and the y-axis is the voltage (V). A) Fluorescence of negative droplets. B) Fluorescence of positive-fluorescent droplets showing an increase of the amplitude. Time-resolved events are shown in Figure 12. Fluorescence measured at 𝜆excitation = 488 nm and 𝜆emission = 525 nm.
5-FluorMango allows for fluorescence-based sorting in microfluidics
Fluorescence-based sorting of the mixed droplet population (‘mock library’) yielded time-resolved events of screened positive droplets (Figure 11). These detection hits activated the sorting of droplets that were collected in an Eppendorf tube. We assessed the sorting efficiency by imaging the collected droplets under a fluorescence microscope. The images (bright field and fluorescence) revealed an enrichment of the positive droplets from 20% (pre-sort) to 80% after sorting (Figure 12).
Events above threshold are represented as red lines on top of the graph.
All the droplets are visualized with the bright-field mode (top), while active droplets (+NaF) are observed in the fluorescence channel (bottom). Fluorescence measured at 𝜆excitation=480/40 nm and 𝜆emission=527/30 nm.
We conclude that FluorMango, combined with fluorescence-based droplet microfluidic sorting, enables successful enrichment of positive droplets in a mock library containing an excess of inactive droplets.
Conclusion
We established FluorMango as a sensitive, specific, and robust fluorescent biosensor for the detection of fluoride ions. Our results demonstrate its versatility across two complementary measurement platforms: microplate assays, where it quantitatively reports fluoride release from enzymatic reactions in the presence of living cells, and droplet-based microfluidics, where it maintains full functionality and enables high-throughput sorting of active variants from mixed populations.
The combination of sensitivity, selectivity, and technical compatibility positions FluorMango as a promising measurement tool for directed enzyme evolution, with potential for broader applications such as environmental monitoring, medical diagnostics, and food safety. The successful enrichment of positive droplets in a mock library provides proof of principle for its integration into large-scale genetic screening.
In summary, we established an end-to-end measurement pipeline, from bulk assays to microfluidic screening, that introduces FluorMango as a versatile and quantitative biosensor with strong potential for deployment well beyond the scope of our project.
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