Precise and reproducible measurement lies at the heart of our project.

Our modular aptamer-based transcriptional biosensor relies on quantifying fluorescent signals that report molecular recognition and amplification.

To ensure the reliability of every result, we established a systematic, quantitative, and well-calibrated measurement workflow — from DNA hybridization to time-resolved fluorescence detection in 384-well plates.

This framework not only supports our own sensor characterization, but also provides a standardized pipeline that other iGEM teams can easily adapt for quantitative analysis of RNA transcription and Cas12a trans-cleavage activity.

Synthetic biology thrives on quantifiable data.

For a fluorescence-based biosensor, fluorescence intensity is the primary signal — but fluorescence can vary with reporter's concentration, enzyme activity, temperature, and background noise.

Therefore, we defined three major measurement goals:

1. Accuracy: Each fluorescence signal must directly reflect transcription output.
2. Repeatability: Measurements must remain consistent across replicates and batches.
3. Comparability: Results from different designs must be normalized and comparable through a shared calibration scale.

Mechanism of Fluorescence Generation

At the core of our measurement system lies a signal-transduction cascade that converts molecular binding into light.

1. Target recognition (Aptamer binding):
   The DNA aptamer strand (LBO or SSS-type) specifically binds vancomycin through its folded structure. Upon vancomycin binding, the aptamer undergoes a conformational change that exposes or releases the complementary SRO containing a T7 promoter sequence.
2. Transcriptional amplification:
   The exposed T7 promoter allows T7 RNA polymerase to initiate in-vitro transcription of a downstream RNA sequence that encodes the Squash RNA aptamer — an RNA fluorogenic tag capable of binding the small-molecule dye DFHBSI.
3. Fluorescence activation:
   The newly transcribed Squash RNA rapidly binds DFHBSI (or DFHBI-1T) present in the reaction mixture. The dye becomes fluorescent only when bound to the RNA, producing bright green emission at 560 nm upon 506 nm excitation.
4. Quantitative output:
   Therefore, fluorescence intensity directly represents the amount of transcribed Squash RNA, which in turn reflects the extent of aptamer activation by vancomycin.
   Each step — molecular binding, transcription, fluorescence — converts qualitative molecular recognition into a quantitative optical signal that can be continuously measured.

Measurement Framework

To achieve those goals, we built a comprehensive workflow that integrates physical calibration, biological controls, and real-time monitoring.

1. Time-Resolved Fluorescence Monitoring

All reactions were carried out in 384-well black plates (50 µL per well).
Fluorescence (Ex = 506 nm, Em = 560 nm) was measured for 10 h using a microplate reader.
This high-resolution time course allows kinetic fitting, slope extraction, and area-under-curve (AUC) quantification, enabling precise comparison across conditions.

2. System Calibration

To ensure accurate optical readings:

• Background correction was performed using negative wells containing DFHBSI only (no DNA or T7 polymerase).
• Instrumental linearity was verified using serial dilutions of DFHBSI–Squash complexes (data pending).
• Replicates were used to calculate standard deviation and coefficient of variation (CV).
• Results were reported in ΔF/F₀, the fold-change of fluorescence relative to 0 µM vancomycin baseline.

3. Reaction Standardization

Each transcription reaction contained identical reagent concentrations except for the variable of interest (e.g., VAN, T7 polymerase, or template DNA).
All buffers were prepared freshly using RNase-free conditions.
Temperature (37 °C), reaction volume (50 µL), and plate type were strictly controlled to minimize systematic errors.

Targets

Each of these tests was performed in triplicate with identical control groups (PBS = 0 µM VAN) to ensure reproducibility.

Example: Quantifying S1 + L1–L6 Duplex Sensors

In this validation experiment, we tested six duplex-type biosensors (S1 + L1~L6) to examine how structural sealing affects vancomycin responsiveness.
Each duplex controls the exposure of the T7 promoter and thus regulates transcription-driven fluorescence.

All reactions were performed at 37 °C for 10 h, with fluorescence (Ex = 506 nm, Em = 560 nm) recorded every minute.
Each system contained 100 nM template, 200 nM L-n, 100 nM S1, 2 mM NTPs, 1 µM DFHBSI, and 2.5 µM T7 polymerase, under 0 µM vs 100 µM VAN conditions.

To quantify the fluorescence increase induced by vancomycin, we calculated the relative fluorescence change (ΔF/F₀) between the 100 µM and 0 µM VAN groups for the S1 + L2 duplex sensor.

Step 1 — Raw Data and Descriptive Statistics

At the 10-hour endpoint, the fluorescence intensities (arbitrary units) were:

Step 2 — Fold-Change and ΔF/F₀

The fold-change (R) was computed as the ratio of the two means:

The corresponding relative fluorescence increase was therefore:

indicating a 21 % fluorescence increase in the presence of vancomycin.

Step 3 — Uncertainty Estimation

Assuming the two groups are independent and each measured in triplicate, the propagated variance of R was estimated as:

Substituting the measured values gave

SE_R = 0.025

Using t₄ = 2.78 (95 % confidence),

Step 4 — Interpretation

The S1 + L2 duplex exhibited a 1.21 ± 0.07-fold fluorescence enhancement, corresponding to a 21 % ± 7 % increase (ΔF/F₀ = 0.21 ± 0.07, 95 % CI = [0.14, 0.28], n = 3).
This quantitative result confirms a reproducible, vancomycin-dependent activation of transcriptional fluorescence within our standardized measurement framework.

These measurements confirm that the duplex architecture directly modulates transcriptional leakage and responsiveness.
Although the signal-to-background ratio remains modest (~1.2×), the result demonstrates a measurable and reproducible vancomycin-dependent activation within our standardized fluorescence assay.

Error Analysis and Reproducibility

To evaluate reliability, we analyzed triplicate wells for each condition:

• Mean ± standard deviation (SD) was reported at each time point.
• Relative standard deviation (RSD %) was maintained below 10 % for all major datasets.
• Cross-plate tests showed < 5 % variation after normalization by background control.

This reproducibility confirms that the measurement pipeline — including plate layout, reagent composition, and instrument calibration — provides robust, quantitative readouts.

Contribution to the iGEM Community

By building a standardized transcriptional measurement framework, we provide:

1. A reproducible method for quantifying transcription-based aptamer sensors.
2. A dataset demonstrating how structural variants (single-, double-, triple-strand designs) influence quantitative response.
3. A template protocol that others can reuse to characterize their own RNA-producing systems.

We hope our Measurement Framework can serve as a reference for teams working on DNA sensors, transcriptional amplifiers, and fluorescence-based reporters.

Future Measurement Improvements

We are currently developing:

• Absolute quantification of transcribed RNA via qPCR to correlate with fluorescence.
• Cross-validation of DFHBSI and DFHBI-1T fluorescence against known Squash RNA concentrations.
• Automated data-processing scripts for real-time normalization and kinetic fitting.

These improvements will further enhance the reliability and transferability of our measurement framework across biosensor platforms.

Mechanism of Fluorescence Generation

1. Target recognition (Aptamer binding)
   LBO-Cas specifically binds vancomycin through its folded structure. Upon vancomycin binding, the aptamer undergoes a conformational change that exposes or releases the complementary SRO containing a target sequence of crRNA.
2. Cas12a amplification
   Upon binding with the Cas12a protein, the crRNA first identifies the target sequence on SRO-Cas, triggering a conformational change in Cas12a that activates its cis-cleavage activity. After Cas12a completes the cis-cleavage of the target DNA, it unlocks another active site, enabling its trans-cleavage capability. This allows Cas12a to non-specifically cleave the single-stranded reporter sequence (FAM-CCCCCC-BHQ) present in the system.
3. Fluorescence activation
   Following reporter cleavage, the quencher group BHQ becomes separated from the fluorophore FAM, producing bright green emission at 520 nm upon 490 nm excitation.
4. Quantitative output
   The signal we directly obtain is fluorescence intensity, but this parameter is susceptible to multiple factors such as reaction equilibrium, detection time window, and instrument sensitivity, which may lead to quantification inaccuracy or narrowed dynamic range.

In contrast, the fluorescence increase rate reflects the kinetic process of reporter cleavage per unit time and directly correlates with the target's efficiency in activating Cas12a. This kinetic parameter consequently demonstrates superior accuracy, linearity, and anti-interference capability in quantitative analysis, making it more suitable for precise determination of target concentration. We therefore selected the fluorescence increase rate as our quantitative metric.

Measurement Framework

In the T7 transcription measurement, we analyzed the converted signal. For the Cas12a amplification system, we performed simultaneous quantitative analysis of the strand displacement reaction and the Cas12a amplification reaction to better evaluate the system's amplification efficiency.

1. Strand Displacement Reaction

• Objective: To directly detect the released SRO-Cas. We labeled the SRO-Cas with a fluorophore and the corresponding position on the LBO-Cas with a quencher. Fluorescence intensity increases upon SRO-Cas release.
• Parameters Used: Excitation wavelength = 490 nm, Emission wavelength = 520 nm.
• Measurement Duration: 30 min.
• Time Interval Between Measurements: 1 min.
• Quantity Representing Reacting Antibiotic Concentration: Intensity of the normalized fluorescence signal.

2. Cas12a Amplification Reaction

• Objective: During the measurement of the strand displacement reaction, we found that the reaction completes within a short time, making it difficult to obtain kinetic data. We could only obtain a relatively constant fluorescence intensity correlated with antibiotic concentration, and measurements were impossible for low antibiotic concentrations (less than 20 µM). We aimed to address this issue through measurement of the Cas12a amplification reaction.
• Parameters Used: Excitation wavelength = 490 nm, Emission wavelength = 520 nm.
• Measurement Duration: 30 min.
• Time Interval Between Measurements: 1 min.
• Quantity Representing Reacting SRO-Cas Concentration: Growth rate of the normalized fluorescence signal intensity.

Example: Quantifying LBO1-SRO2-Cas Sensor Response

1. Strand Displacement Reaction

We first performed a fluorescence quantitative analysis of the strand displacement reaction involving LBO1-SRO2-Cas and calculated its normalized average fluorescence intensity over a 30-minute period. Each concentration had five replicates. For each well fluorescence intensity was recorded every minute. The data from experimental groups with the highest and lowest values at each concentration were excluded.

Normalization

We set the average fluorescence intensity within 30 minutes of the group with zero vancomycin concentration as the baseline, assigned it a value of 1, and normalized the data from other concentration groups accordingly. The results are as follows:

Construction

Unit: μM

2. Cas12a Amplification Reaction

In the strand displacement module, the antibiotic target triggered a strand displacement reaction, releasing the SRO-Cas strand, which served as the direct target of the CRISPR-Cas12a system. We analyzed the reaction kinetics of the CRISPR-Cas12a system and investigated the relationship between antibiotic target concentration and signal intensity of the system.

The fluorescence time-course curves (0-30 min) of the Cas12a trans-cleavage system were obtained from enzyme-plate readings at different antibiotic concentrations (0-50 µM). Each concentration had five replicates. For each well fluorescence intensity was recorded every minute. The data from experimental groups with the highest and lowest values at each concentration were excluded.

(1) Logarithmic curve fitting

By observing the data, we first chose to fit the antibiotic concentration and reaction rate using a logarithmic curve.

Normalization

We set the average fluorescence intensity increase per minute of the group with zero vancomycin concentration as the baseline, assigned it a value of 1, and normalized the data from other concentration groups accordingly. The results are as follows:

Fpm (Fluorescence intensity growth rate per minute)

(2) Michaelis-Menten equation fitting

We learned through literature review that the Michaelis-Menten equation is more suitable for characterizing enzyme kinetics^[1]. So we tried to use Michaelis-Menten equation for analysis.

Initial-Rate Extraction

To capture the true catalytic rate, the 0-6 min region of each fluorescence curve—where the signal increased linearly—was used to perform least-squares linear regression:

The slope b represents the initial rate v₀ (in a.u./min).

Normalization

Because raw fluorescence depends on instrument gain and probe concentration, the rate was normalized by the terminal fluorescence F₃₀ (the fluorescence value at the 30-minute mark):

Normalizing the initial rate in this way provided a more robust and physically meaningful metric than scaling all values relative to the blank (0 µM = 1). This normalization compensated for differences in fluorescence intensity and instrument gain, allowing data from different wells or plates to be directly compared. Moreover, v₀/F₃₀ represented the relative rate of fluorescence increase within each reaction.

This gives a dimensionless normalized rate:

Construction

The antibiotic-dependent activation kinetics were modeled using a pseudo-Michaelis-Menten equation:

Where:

• V_max: maximum normalized rate
• K_m: apparent half-saturation concentration
• [V]: vancomycin concentration

Statistical analysis indicated no significant difference (p > 0.05) between the 0 µM and 2.5 µM antibiotic groups, suggesting that the system remains at baseline within this low concentration range. Therefore, the 2.5 µM group was taken as the baseline, and data from 2.5-50 µM were used for kinetic fitting and plotting to better visualize the concentration-dependent response.

The fit shows excellent agreement (R² = 0.985), indicating that the Cas12a amplification follows Michaelis-type kinetic saturation.

3. Conclusion

Through measurement, data normalization, and curve fitting, we found that in the absence of the CRISPR-Cas12a system, antibiotic concentrations above 20 μM produced a concentration-dependent fluorescence intensity, with a linear relationship between concentration and fluorescence intensity. In the presence of the CRISPR-Cas12a system, no response was observed at antibiotic concentrations below 2.5 μM, while concentrations above 5 μM produced a concentration-dependent reaction rate. The relationship between antibiotic concentration and reaction rate was well-fitted by the Michaelis-Menten equation. Our measurements demonstrate the signal amplification effect of the system.

Reference

[1] Wei Feng, Hongquan Zhang, X. Chris Le. Signal Amplification by the trans-Cleavage Activity of CRISPR-Cas Systems: Kinetics and Performance. Anal. Chem. 2023, 95, 1, 206-217