Comparative advantages of mechanical biosensors

Abstract

Mechanical interactions are fundamental to biology. Mechanical forces of chemical origin determine motility and adhesion on the cellular scale, and govern transport and affinity on the molecular scale. Biological sensing in the mechanical domain provides unique opportunities to measure forces, displacements and mass changes from cellular and subcellular processes. Nanomechanical systems are particularly well matched in size with molecular interactions, and provide a basis for biological probes with single-molecule sensitivity. Here we review micro- and nanoscale biosensors, with a particular focus on fast mechanical biosensing in fluid by mass- and force-based methods, and the challenges presented by non-specific interactions. We explain the general issues that will be critical to the success of any type of next-generation mechanical biosensor, such as the need to improve intrinsic device performance, fabrication reproducibility and system integration. We also discuss the need for a greater understanding of analyte-sensor interactions on the nanoscale and of stochastic processes in the sensing environment.

Figure 1. Fluidic detection limits for protein sensing

The limit of detection in moles (left axis) and grams per millilitre (right axis) versus the analysis time for the different types of biosensor (both mechanical and non-mechanical) shown in the panels at the top of the figure and listed in Table 1. Note that both axes are logarithmic. The black dashed line shows the present state-of-the-art (with longer analysis times leading to lower limits of detection); the ideal biosensor would offer low limits of detection and short analysis time (that is, it would be found in the bottom left region of this graph). For many biomarkers the diagnostic level of significance is within the picomolar to nanomolar range, which can be accessed by conventional immunofluorescence assays (IFAs): the challenge for new biosensors is to achieve this sensitivity while also achieving shorter analysis times than the IFA approach. However, detector performance is frequently limited by non-specific binding effects rather than the intrinsic biosensor performance (see text). Non-specific binding effects lead to a ‘biological noise floor’ below which the analyte of interest cannot be detected. The figure shows the biological noise floors (horizontal blue lines) for target–receptor affinities of 1 nM⁻¹ and 100 nM⁻¹ and a non-specific binding association rate of 10⁴ M⁻¹; this noise floor is less of a problem when the target–receptor affinity is high. Such limitations do not apply to sandwich-type assays (see text). Many microfluidic sensors are now approaching the level of sensitivity that will permit real-time measurements on proteins secreted from individual cells. The figure shows the biosensor performance (solid black sloping lines) needed to detect the secretion of TNF-α from a single human monomyelocytic cell in a 1-nl volume for both native single cell (SC) secretion and stimulated SC secretion (in which the rate of secretion is increased by a factor of ∼80); a mass of 34 kDa was used to relate concentration to density. SPR: surface-plasmon resonance; SMR: suspended microchannel resonantor; fNW: nanowire; LFA: lateral flow assay; MRR: microring resonantor; QCM: quartz crystal microbalance; BBA: biobarcode amplification assay; IFA: immunofluorescent assay; MC: microcantilever. Panels at top of figure reproduced with permission from: SMR, ref. , © 2007 NPG; NW, ref. , © 2005 NPG; MRR, ref. © 2009 ACS; IFA, ref. , © 2004 RSC.

Figure 2. Fluidic micromechanical biosensors

a, Schematic of static-mode surface-stress sensing MEMS device. Binding of target molecules generates a surface stress, which leads to a quasistatic defection of the cantilever (bottom). b, Scanning electron micrograph (SEM) of a dynamic mode MEMS device. Target molecules are detected through their influence on the resonance frequency of the cantilever: when the molecules land on the cantilever, they increase its mass and therefore reduce its resonance frequency. c, Suspended microchannel resonator (SMR). The fluid containing the target molecules flows through a channel inside the device (the top of the device is not shown in this cutaway schematic) and bind to the inner flow-channel walls, while the resonator oscillates in air or vacuum. d, Resonance spectrum (oscillation amplitude versus frequency) of a SMR. The quality factor of the device is normally unaffected when the channel is filled with water (red line). Figure reproduced with permission from: a, ref. , © 2001 NPG; b, ref. , © 2004 RSC; c, ref. , © 2010 ACS; d, ref. , © 2007 NPG.

Figure 3. Depletion in microfluidic structures

The length needed for 50% depletion L* versus the rate of association k_on in an open-loop fluidic configuration for five different combinations of flow rate and microchannel geometry: details of four of these combinations are shown in Table 2; for the fifth combination (black line) t = 700 nm, w = 4 μm and l = 2.05 cm. The dotted vertical lines show the values of k_on for the six target–receptor pairs listed in Table 2. Significant depletion can be achieved for lengths of hundreds of nanometres for very small channels (in which the flow rate is reduced) for the highest values of k_on (such as for biotin–streptavidin binding), but tens of micrometres or more are needed to achieve Significant depletion for larger channels (with much greater flow rates), even for the highest values of k_on. For much lower values of k_on (such as IL-6 binding to its receptor) it is not possible to achieve Significant depletion within practical length scales for microfluidic sensors, implying that the kinetics are always reaction limited. Depletion length scales shown here are for short timescales, that is, far from equilibrium. Near equilibrium the kinetics are always dominated by reaction kinetics (see Table 2). Depletion is strongly dependent on the flux of molecules to the surface, which depends on both the flow rate and the channel geometry; here depletion has the greatest role for the combination shown by the black line.

Figure 4. Effect of surface-area: volume ratio on bulk target depletion

The fraction of receptors bound at 10 min versus the surface-area: volume ratio for the six target–receptor pairs listed in Table 2 under reaction-limited conditions (Box 1): the affinity K_a of the pairs decreases from top to bottom. The fraction of bound receptors can be increased by reducing the surface-area: volume ratio. However, below a threshold (determined by K_a), there is no further gain.

Figure 5. Fluidic nanomechanical biosensors

Demonstration of reduction in force noise through the overall reduction of cantilever dimensions. a, Noise versus time for a large cantilever (length = 200 μm; spring constant k = 0.060 N m⁻¹; top trace) and a small cantilever (length = 10 μm; k = 0.060 N m⁻¹; bottom trace). b, Theoretical predictions for total force sensitivity (including thermomechanical Brownian noise, Johnson noise and typical readout amplifier noise) on a logarithmic scale versus frequency for a silicon piezoresistive cantilever immersed in water and operating at room temperature for three different sets of conditions; the thermodynamic limit (that is, just Brownian noise) is also shown for reference. The sensitivity depends on the maximum tolerable temperature rise both at the tip ΔT_tip and the position of maximum heating ΔT_max. As the bias voltage V_dev and bias current I increase, both ΔT_tip and ΔT_max also increase, and the sensitivity improves, approaching the thermodynamic limit. The cantilever device dimensions are: t = 130 nm, w = 2.5 μm, l = 15 μm.c, Analogous plot to b for a smaller cantilever showing qualitatively similar behaviour but substantially higher sensitivity (note that the scale on the y-axis is different): t = 30 nm, w = 100 nm, l = 3 μm. At frequencies below 1 MHz, the system approaches the thermodynamic limit, and the sensitivity remains within about 20% of the fluidic noise floor at the relatively low bias voltage of 0.5 V. Below 0.25 MHz, the total sensitivity is $~5\mathrm{f}\mathrm{N}\sqrt{{\text{Hz}}^{-1}}$ (for reasonable bias voltages). Figure reproduced with permission from: a, ref. , © 1999 AIP; b,c, ref. , © 2007 Springer

Figure 6. NEMS arrays and system integration

a, False-colour SEM image of an array of 20 silicon nitride nanomechanical resonators (in two separately biased banks) with capacitive readout and actuation; the resonant frequencies of the resonators are ∼12 MHz. b, Resonance spectrum (oscillation amplitude (S₁₁) versus frequency) of the array in a. It is possible to read out the array with a single radiofrequency readout circuit. Seven resonators in bank 1 (blue trace) and three resonators in bank 2 (red trace) were detected in this frequency range. c, Array of silicon cantilevers: each cantilever is 2.8-μm long and 0.7-μm wide, with the ‘legs’ being 200-nm wide. A piezoresistive approach was used for readout. Image courtesy of P. Andreucci (Minatec, Leti, CEA). d, SEM of a section of a 4,096 silicon cantilever array, transferred onto a wiring wafer. The transfer is done on the 100-mm wafer scale, with approximately 50 such arrays per wafer. These cantilevers were designed for memory storage applications, with resistors at the base to induce (the write step) and measure (read step) the defection. e, Multiplexed microfluidics. PDMS microvalves enable independent compartmentalization, purging and pairwise mixing for each of the 256 chambers on the chip. Figure reproduced with permission from: a,b, ref. , © 2007 ACS; d, ref. , © 2004 IEEE; e, ref. , © 2002 AAAS.