Carbon-nanotube field-effect transistors for resolving single-molecule aptamer-ligand binding kinetics

Abstract

Small molecules such as neurotransmitters are critical for biochemical functions in living systems. While conventional ultraviolet-visible spectroscopy and mass spectrometry lack portability and are unsuitable for time-resolved measurements in situ, techniques such as amperometry and traditional field-effect detection require a large ensemble of molecules to reach detectable signal levels. Here we demonstrate the potential of carbon-nanotube-based single-molecule field-effect transistors (smFETs), which can detect the charge on a single molecule, as a new platform for recognizing and assaying small molecules. smFETs are formed by the covalent attachment of a probe molecule, in our case a DNA aptamer, to a carbon nanotube. Conformation changes on binding are manifest as discrete changes in the nanotube electrical conductance. By monitoring the kinetics of conformational changes in a binding aptamer, we show that smFETs can detect and quantify serotonin at the single-molecule level, providing unique insights into the dynamics of the aptamer-ligand system. In particular, we show the involvement of G-quadruplex formation and the disruption of the native hairpin structure in the conformational changes of the serotonin-aptamer complex. The smFET is a label-free approach to analysing molecular interactions at the single-molecule level with high temporal resolution, providing additional insights into complex biological processes.

Extended Data Fig. 1 |. SEM images and transfer characteristics of two types of CNT FET fabrications used in the study.

The number of CNTs crossing the source–drain electrode pair is counted on the SEM images. Conducting FET devices transiting a single CNT are screened by back-gate transfer characteristics using a semiconductor parameter analyser (Agilent 4155 C). The back-gate voltage (V_BG) is applied to the heavily doped p + + silicon substrate and swept from −10 to +10 V with a step size of 500 mV. Constant source–drain voltage (V_SD) of 100 mV is applied to the devices, and the current at each V_BG is measured. a) The representative SEM images of a CNT FET produced by a CVD method. CNT nucleation features (4 μm by 4 μm) containing iron nanoparticles are patterned on the top of silicon substrates by electron beam lithography. CNTs with controlled density and length are grown from the sites by the CVD method (left). The Ti electrode pairs with a 2 μm gap are deposited on the defined CNT growth sites (right). Each chip (11 mm by 10 mm) contains 79 pairs of source–drain electrical contacts. b) The two representative SEM images of CNT FETs fabricated by spin-casting. After spin-coating the CNT solution (250 μg/ml) on the four-inch wafer, Ti electrode pairs with source–drain separation of 0.5 μm are patterned on top. Each chip (11 mm by 10 mm) contains 280 pairs of source–drain electrical contacts. c) The representative transfer curves of 22 CNT FETs, including a single-crossing CNT produced by the CVD-grown method. d) The transfer curves of 26 single-crossing spin-cast CNT FETs. Devices that have a single CNT bridge and exceed threshold I_D values of 1 nA at −10 V of V_BG are used in the subsequent experiments.

Extended Data Fig. 2 |. Electrically controlled diazonium chemistry for single-point CNT functionalization.

a) The example of I_D–t trace (black line) displaying serial single sp³ defect generations by aryl-diazonium salt at a fixed liquid gate potential (V_DS = 30 mV, V_LG = −200 mV, red line). Discrete downward current steps are detected (Inset, red arrows), and the I_D level is rapidly reduced after introducing 4-formylbenzene diazonium hexafluorophosphate (FBDP) solution (shaded in blue). b) The representative I_D–t trace (from Device A, which is discussed in the main text) displays controlled diazonium chemistry (V_DS = 50 mV). At −500 mV of V_LG, the downward current steps are not observed in the FBDP solution. The discrete current steps are detected when the VLG is tuned to −300 mV. The two I_D steps cause a resistance change of 4.7 kΩ and 5.1 kΩ, respectively. c) The diazonium cation is reduced by electrons supplied from CNT; subsequently, aryl-radical is coupled with CNT. The V_LG is then turned down to −0.5 V to reduce the electron density in CNT, halting the reaction. d) Examples of transfer curves of Device A before (grey squares) and after the controlled diazonium reaction (red circles) and after DNA aptamer conjugation (blue triangles). All transfer curves are obtained in the phosphate buffer. Error bars represent a standard deviation of the mean current value for three measurements. The measurement V_LG point (100 mV) for temporal dynamic serotonin sensing is chosen from the curve. After the functionalization, the average conductance is decreased, indicating that permanent DNA-CNT couplings are created on the sp³ defect site on CNT.

Extended Data Fig. 3 |. The temporal electric signals originated from the single-molecule binding events observed on serotonin-specific aptamer-functionalized smFET devices.

a-f) 30-s-length I_D-t traces displaying single-molecule events (displayed in the increasing order of liquid gate potentials). The right panel shows the I_D distributions of each time trace, and two Gaussian fits with peak values μ₁ and μ₂. (a) Device A, V_LG = 200 mV, V_DS = 50 mV. (b) Device B, V_LG = 100 mV, V_DS = 50 mV. (c) Device C, V_LG = 300 mV, V_DS = 25 mV. (d) Device D, V_LG = 400 mV, V_DS = 50 mV. (e) Device E, V_LG = −200 mV, V_DS = 50 mV. (f) Device F, V_LG = 0 mV, V_DS = 200 mV. g) Measured SNR values for six devices $\left(SNR=\left({\mu }_1-{\mu }_2\right)/\sqrt{0.5\left({\sigma }_1^2+{\sigma }_2^2\right)}\right)$.

Extended Data Fig. 4 |. Concentration dependence for the fraction of time spent in the lower conductance state (P_LOW) of Device B (a) and Device F (b).

The plots of P_LOW are fitted into Langmuir isotherm function (black line). Data points are the mean probability of the low conductance state calculated from all dwell times by bootstrapping (N_boot = 2,000). Error bars represent the 90% confidence interval from bootstrapped mean value of P_LOW.

Extended Data Fig. 5 |. Histograms showing temporal conductance profiles of smFET device in response to the target and non-target introduction.

a) The experimental sequence of introducing serotonin (5-HT) and dopamine solutions to the device (Device D used for the experiments). b) Histograms of I_D distributions before target introduction. c-i) Histograms of I_D distributions displayed chronologically after each sample is introduced. 600-s of I_D-t trace was sampled at each recording point. 400 mV of V_LG was applied during the experiment. The data recording time after introducing blank PBS buffer (b), serotonin (c-f), and dopamine-diluted PBS solution (g-i) is indicated in each graph.

Extended Data Fig. 6 |. Control experiments on the aptamer-conjugated smFET devices.

I_D–t traces measured on serotonin-specific aptamer-functionalized CNT FET (Device B) measured in the presence of pure buffer, dopamine (100 nM), and serotonin solution (50 nM), respectively.

Extended Data Fig. 7 |. Control experiments on the unfunctionalized CNT FET.

a) I_D–t trace of an unmodified CNT FET (Device G), measured in presence of pure buffer solution. b) Response of the device to a 10 nM serotonin solution in the buffer. c) Device response at a higher concentration of 50 nM serotonin in the buffer. d) Graph showing the average I_D values measured over 10 minutes at varying serotonin concentrations displaying the extent of the non-specific charge adsorption. Standard deviations of the I_D measurements are represented by error bars.

Extended Data Fig. 8 |. Normalized dwell time distributions and concentration dependency of the high and low conductance states.

The semi-logarithmic dwell time histograms for the low conductance state (left column) and high conductance state (right column) at various serotonin concentrations are shown. The counts are presented per second to account for variations in measurement time between experiments. a, b) Normalized dwell time distributions for the low and high conductance states obtained from Device A (a) and Device B (b). The histograms are overlaid with single-exponential (red lines) and double-exponential decay (orange lines) functions. The double-exponential fits reveal two distinct populations characterized by fast and slow rates, represented by orange dotted lines. c) Concentration-dependent number of counts of smFET transitions for Device A (left) and Device B (right). The plot illustrates the relationship between the number of counts per second and the serotonin concentration, addressing the concentration dependency of the smFET transitions for each device.

Extended Data Fig. 9 |. The rate constants determined by HMM analysis.

a) The dwell time analysis with HMM showing single-exponential distribution (data from Device A, 50 nM of [5-HT]). b) The HMM includes eight interconversion rates. c) The two rate constants (on-rate: pink, off-rate: green) calculated from the HMM at each pathway are plotted as a function of serotonin concentrations. The smFET data of Device B are used. Data points are calculated from fitting dwell-time distributions for each state transition to a single-exponential distribution. The error bars indicate the one-sigma confidence interval of the model fit.

Fig. 1 |. Device schematic and concentration-dependent current traces.

a, Schematics of the single aptamer immobilization onto the CNT and V_LG-controlled diazonium chemistry. A common V_LG is applied via a reference electrode in the buffer solution. A single functionalization site on the CNT is generated by sp³ addition controlled by V_LG-driven aryl radical generation from a diazonium salt (FBDP). The amine group of a functionalized DNA aptamer is covalently attached to the site by a Schiff base reaction. b, Representative baseline I_D–t trace of Device A after aptamer probe attachment in phosphate-buffered saline (pH 7.0). The V_LG was fixed at 200 mV, and a V_DS of 25 mV was applied. c–f, Representative I_D–t traces of Device A at different serotonin concentrations: 0.5 nM (c), 5 nM (d), 50 nM (e), 500 nM (f). The raw I_D–t traces (blue line) are overlaid with the idealized fit, revealing two conductance states (orange line). The histograms of I_D distributions are shown in the right panels. g, Concentration dependence for the fraction of time spent in the lower conductance state (P_low). The plots of P_low against serotonin concentrations are fitted to the Langmuir isotherm function. Data points are the mean probability of the low conductance state calculated from all dwell times by bootstrapping (N_boot = 2,000). Error bars represent the 90% confidence interval from the bootstrapped mean value of P_low.

Fig. 2 |. Single-molecule kinetic analysis.

a, Representative I_D–t trace at 50 nM serotonin concentration. The conformational state of the aptamer at the edge of the Debye layer (λ_D) on the CNT surface corresponds to the high-conductance (blue dashed line) and low-conductance states (orange dashed line). Two-state HMM analysis idealizes experimental I_D–t traces and defines the dwell times in each state (τ_high and τ_low). The rate constants of high-to-low transition (k_HL) and low-to-high transition (k_LH) are determined by 1/τ_high and 1/τ_low, respectively. b, Normalized dwell-time distributions for the low- and high-conductance states extracted from Device A. The distributions are normalized by 1 s and fitted with a double-exponential function (orange lines) and a single-exponential function (red lines). F-test confirmed that double-exponential functions provided a statistically better fit than the single-exponential functions. The most probable fast and slow dwell times are determined by double-exponential fit. c,d, Estimated rate constants: fast and slow rate constants of low-to-high state ($k_{\text{LH}}^{\text{fast}}$ and $k_{\text{LH}}^{\text{slow}}$) (c) and high-to-low state ($k_{\text{HL}}^{\text{fast}}$ and $k_{\text{HL}}^{\text{slow}}$) (d) are plotted as a function of serotonin concentration. Reported data points are mean values from the bootstrapped double-exponential fitting of the dwell-time distribution (N_boot = 2,000). The error bars indicate a 90% confidence interval from bootstrapping. e, The number of fast and slow high-to-low transitions in the recording time ($N_{\text{HL}}^{\text{slow}}$ and $N_{\text{HL}}^{\text{fast}}$) as a function of concentration. Reported data points are mean values from the bootstrapped double-exponential fitting of the dwell-time distribution (N_boot = 2,000) and error bars indicate the 90% confidence interval of the bootstrapping.

Fig. 3 |. Serotonin binding and kinetic observables analysed by HMMs.

a, Representation of serotonin binding using four kinetic observables, consisting of two high-conductance states (States 0 and 1, shown as blue circles) and two low-conductance states (States 2 and 3, shown as orange circles). b, Plot illustrating the two rate constants (pink, on-rate; green, off-rate) obtained from the HMM analysis for each binding pathway as a function of serotonin concentration. The data from Device A’s smFET experiments are utilized. Data points are calculated from fitting dwell-time distributions for each state transition to a single-exponential distribution. The error bars indicate the 1σ confidence interval of the model fit.

Fig. 4 |. Dose–response curves showing the bound state occupation estimated by HMM analysis.

Dose–response curves showing the bound state occupation (1 − P₀) estimated by HMM analysis for three independent devices: Device A (orange, V_LG = 200 mV), Device B (blue, V_LG = 100 mV) and Device F (green, V_LG = 0 mV). Inset: dissociation constants (K_D) obtained from Langmuir isotherm fitting, plotted as a function of V_LG. The measured K_D values are as follows: 31.5 ± 9.2 nM (V_LG = 0 mV, R² = 0.95, n = 6), 13.5 ± 7.4 nM (V_LG = 100 mV, R² = 0.87, n = 5) and 2.2 ± 1.3 nM (V_LG = 200 mV, R² = 0.96, n = 4). The error bars in the inset show the 1σ confidence interval of the Langmuir isotherm fitting.

Fig. 5 |. Schematic conformations of the serotonin–aptamer complex on the CNT FET.

In the presence of serotonin in the solution, the CNT-conjugated aptamer undergoes dynamic transitions between different conformational states. The serotonin (depicted as a green circle) binding involves G-quadruplex formation (State 1 and State 2) and disruption of native structure (State 3), leading to their corresponding characteristics in terms of conductance and FRET efficiency. The transitions from state 0 to both State 1 and State 3 occur rapidly at high serotonin concentrations. In State 1, a G-quadruplex stack is formed, involving eight guanines (G2, G3, G4, G5, G7, G8, G10, G11) while State 2 exhibits a larger G-quadruplex stack with 12 guanines (G2, G3, G4, G5, G7, G8, G9, G11, G15, G16, G17, G18), resulting in altered conductance and enhanced FRET efficiency. The guanine bases used to form G-quadruplex stacks are numbered as described in Supplementary Fig. 14a. State 3 acts as a potential intermediate state in ensemble measurements.