Prasanta
Kalita
a,
Ashwin
Bhola
a,
Nitish
Goel
a,
Venkataraman
Sritharan
b and
Shalini
Gupta
*a
aDept. of Chemical Engineering, Indian Institute of Technology Delhi, Hauz Khas 110016, India. E-mail: shalinig@chemical.iitd.ac.in; Tel: +91 (011) 2659 1070
bMolecular Diagnostics & Biomarkers Lab, Global Medical Education and Research Foundation, Hyderabad, 500004, India
First published on 25th July 2017
We present a detailed study of an endotoxin detection bioassay performed in a sandwich format on solid substrates. Self-assembled monolayers (SAMs) of C-18 alkyl silanes were immobilized on glass to heterogeneously entrap lipopolysaccharides (LPS) from human serum. The captured endotoxin was then tagged with polymyxin B sulfate–gold nanoparticle (GNP) conjugates followed by silver enhancement. The intensity of the obtained color spots were quantified using a mobile phone camera and the assay performance was optimized by studying the effects of various experimental parameters like silane concentration, temperature, humidity and incubation time. The assay gave a linear dynamic range of 104 with a lower limit of detection (LOD) at 1 pg mL−1. This LOD was further improved by an LPS pre-enrichment step prior to the assay. Rudimentary thermodynamic and kinetic models fitted to our experimental data suggest that LPS binding is an entropic process and LPS/GNP binding is mainly adsorption-controlled.
Design, System, ApplicationWe have developed a bioassay for the detection of endotoxins or lipopolysaccharides (LPS) in human serum using a solid phase. For this, the molecular structure of LPS has been taken into consideration in order to capture the molecule from its tail end. This molecular orientation not only helps in immobilizing the molecules using simple hydrophobic interactions but also provides advantages in terms of better availability of epitopes for more efficient targeting. The entire assay has been optimized empirically in order to obtain the most suitable conditions for its best performance. We have also estimated how the free energy of interaction between the molecules depends on the length of the carbon chains in the lipid chain. Further, a simple kinetic model has been developed which shows that the binding process is mainly adsorption-controlled. This is not surprising as the LPS molecules are large (∼15 kDa) and bind in a specific orientation. The assay has great utility for the medical diagnosis of septicemia where the LPS concentration levels in blood are low (subnanomolar) especially at the onset of infection. |
Presently, the most widely used endotoxin detection or quantification assays (LAL tests) are based on amebocyte lysate from Limulus polyphemus or the rare horseshoe crab. Many formats of these assays exist, namely, the gel clot, turbidimetric and chromogenic, which work on the principle of liquid to solid phase transition (of the hemolymph), endogenous substrate cleavage-induced turbidity variation and a synthetic peptide–chromogen complex cleavage-induced color change, respectively.5–7 All the assays are FDA approved to measure endotoxin levels in parenteral drugs, biologicals and medical devices. However, they cannot be directly applied to biological samples because of the presence of circulating inhibitors of the coagulation/gel clot reaction. In addition, other microbial products, notably fungi, can also activate the Limulus reaction, so the assay is not specific for endotoxin. Therefore, designing an endotoxin assay that works efficiently in a complex matrix such as serum still remains a challenge.
One way to overcome the problem of serum complexity is to selectively capture and remove LPS from biological fluids using a solid phase. This approach requires an understanding of the LPS molecular structure such that site-specific binding receptors may be appropriately designed or selected for LPS targeting. For instance, one end of the LPS molecule, known as the outer O-antigen domain, consists of a polysaccharide chain that varies from species to species. This portion is the exposed region in a bacterial cell wall and thus, can be specifically targeted using antibodies. Many commercial ELISAs (enzyme linked immunosorbent assays) use this principle to immobilize LPS on their assay plates.8,9 Similarly, the middle oligosaccharide core region comprises heptose and 2-keto 3-deoxy-D-manno-octolusonic acid (KDO) sugars with phosphate moieties that can be targeted via electrostatic ligand interactions. The third portion, called the lipid A region, is the conserved domain that makes LPS highly toxic and responsible for biological activation inside a host.10,11 It comprises phosphorylated glucosamine disaccharides joined together with multiple fatty acid chains that are each 12 to 18 carbon atoms long. Therefore, lipid bilayer membranes of synthetic phospholipids mimicking the outer wall of the Gram-negative bacteria can serve as suitable supports for asymmetric capture of LPS. The many receptor–ligand based strategies reported in the literature for specific LPS binding are summarized in Table 1.
| Receptor | LPS binding region | Mode of interaction | Ref. |
|---|---|---|---|
| a Note: DOPC: 1,2-dioleoyl-sn-glycero-3-phosphocholine, POPC: 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine and DSPC: 1,2-distearoyl-sn-glycero-3-phosphocholine. | |||
| Mono- and polyclonal antibodies | Core (R1, R2, R3, R4 and K12) | Biological | 8, 9, 12–14 |
| gp9 protein on p22 bacteriophage | O-antigen | Biological | 15–19 |
| Lipid binding proteins (LBPs) and mannose binding lectins (MBLs) | (O-antigen) N-acetyl-D-glucosamine, mannose and N-acetyl-mannosamine | Biological | 20–23 |
| Aptamers | Phosphate moieties (lipid A) | Electrostatic | 24, 25 |
| Antibiotics (PMB, bacitracin) | Phosphate moieties (lipid A) | Electrostatic and hydrophobic | 26–28 |
| Antimicrobial peptides (AMPs) (cathelicidins, indolicidine, sushi tachyplesin) | Phosphate moieties (lipid A) | Electrostatic | 29–32 |
| Phospholipids (DOPC, POPC, DSPC etc.)a | Lipid A tail | Hydrophobic | 33–35 |
| C-18 alkyl chains | Lipid A tail | Hydrophobic | 36, 37 |
Our group recently demonstrated a facile strategy for entrapping endotoxins from human sera using C-18 alkyl silane SAMs on glass substrates (Fig. 1).38 The captured LPS molecules were labeled with GNPs conjugated to an antibiotic drug called polymyxin B sulfate (PMB) enabling ultrasensitive detection of endotoxins down to sub-pM levels with the naked eye within 2.5 h. PMB is known to interact electrostatically with the two negatively charged phosphate moieties in the lipid-A region of LPS via its three positively charged amine groups in the cyclic hepta-peptide ring.39,40 Its use in the GNP detection probe as a tag (say, in place of an antibody) allows improving the cost and thermostability of the assay. In the current work, we present a detailed optimization study of the various experimental parameters involved in this detection process with the aim of developing a better understanding of their role in the signal response and to create a more robust assay that can work efficiently under any clinical setting. We also report a significant improvement in the signal collection and data analysis procedure that further improves the speed of the assay. In addition, kinetic and thermodynamic models have been applied to our experimental system which have not been reported earlier. With the optimized conditions, the variability in the results has been reduced and the sensitivity enhanced.
000× molar excess of DTH for 6 h at room temperature (∼25 °C) with constant stirring in a shaker incubator (Orbitek, India). The particles were then washed thrice with 40 mM HEPES at pH 7.4 at 8690, 11
175 and 14
900 rcf for 20 min each, respectively, to remove excess DTH. The final pellet was resuspended in 20 mL of 2.5% (v/v) GLA solution in HEPES buffer and incubated overnight with constant stirring at room temperature. The amine-reactive NP conjugates were centrifuged the next day using the settings mentioned above and the final pellet was resuspended in 8 mL of 20 nM PMB in HEPES followed by 16 h of incubation with constant stirring. The unreacted aldehyde groups were passivated with 0.05 mL of 20 mM glycine in HEPES to minimize nonspecific binding. The final conjugate was washed and resuspended in 6 mL of 40 mM HEPES buffer at pH 7.4.38 The particles were stored at 4 °C until further use.
:
1
:
5
:
H2O2
:
NH3
:
H2O (v/v) for 1 h at 70 °C. The slides were then washed extensively with DI water and dried under a purge of nitrogen. The dried glass slides were then dipped in 10 mM OTS solution prepared in toluene and incubated at 60 °C for at least 14 h.42 The slides were finally washed with excess toluene to remove any unbound OTS and stored in a desiccator until further use. The entire silanization protocol was performed in a clean and dry environment in the presence of nitrogen gas to avoid silane self-polymerization.42,43
:
1 v/v chloroform–phenol mixture inside an eppendorf tube. The tube was vigorously vortexed for 20 s followed by 5 min of incubation at 65 °C. The sample was centrifuged at 1660 rcf for 10 min and the aqueous supernatant (∼300 μL) was pipetted out carefully into three equal fractions (top: SN1, middle: SN2 and lower: SN3) avoiding the inclusion of the white precipitate and chloroform layer at the bottom of the tube.

| ΔGT = (ΔGF) × (A) × (n) + (kBT) × ln(Vf/Vb). | (1) |
Here, ΔGF is the transfer free energy per unit area of the hydrocarbon, A is the area of one C-18 chain, n is the number of chains per LPS molecule, kB is the Boltzmann constant, T is the temperature, and Vb and Vf are the bound and free volumes, respectively. Now, A = π(r2 + 2rl), where r is the axial cross-sectional radius of the carbon chain (∼0.26 nm) and l is length of the carbon chain interacting with one alkyl silane chain. Further, l = 0.154 + 0.1265m, where m is the number of C atoms per LPS chain. Taking Vf = 10 μL (analyte volume taken for the assay), Vb = 1.71 × 10−15 m3 (i.e., disk volume with height l = 2.4 nm × base area of the micro droplet with 1 mm diameter), T = 298 K, ΔGF = −41 mJ m−2, A = 4.17 nm2 and n = 4, ΔGT was computed to be −6.2 × 10−19 J (per LPS molecule). In other words, LPS binding is a thermodynamically feasible process. Interestingly, ΔGT increases with the decrease in carbon chain length and becomes zero below C-6. This trend was also observed experimentally using C-12 alkyl silanes (data not shown). Thus, this simple thermodynamic model suggests that the surface area overlap between the hydrophobic surface and the endotoxin molecules is crucial and leads to an orientational binding of LPS molecules over the surface. This feature additionally allows targeting of the phosphate groups in the conserved region and thus, a common detection strategy for all LPS molecules across different bacterial strains.
These results suggested that at higher silane concentrations, the process was either operating away from equilibrium as in a kinetically-controlled process (i.e., the total time given for LPS immobilization was insufficient) or there were not enough LPS molecules present in the bulk to saturate all the silane receptor sites. To confirm this, we performed another set of experiments in which the incubation time of LPS was systematically varied up to 35 min for a fixed silane and LPS concentration in the range of interest. The results showed that an LPS incubation time of 20 min was sufficient (>90% of the saturation value or 2τ) to get a reasonably stable spot intensity (Fig. 2b), further implying that it was the silane receptors that were in excess and the LPS binding time was not a limiting factor.
Next, we looked into the effect of GNP incubation time. As the contact time for NPs was increased, the final spot intensities came out darker and eventually saturated (Fig. 2c). The saturation time increased with the number of underlying receptor sites for gold (LPS in this case), suggesting that this was most likely an adsorption-controlled process. For instance, 40 min were sufficient to saturate 100 fg mL−1 of LPS, whereas this time was found to be >60 min in the case of 100 ng ml−1 of LPS and more. Since the sensitivity of the assay generally comes at the cost of its running time, one may choose the operating conditions appropriately depending on their process requirement.
In addition to the above experimental conditions, temperature and humidity were also expected to impact the assay performance significantly. Higher temperatures were anticipated to speed up the process kinetics, whereas a higher relative humidity was expected to minimize the rate of evaporation of the sessile serum droplet in which the reaction took place. Minimizing solvent loss was important for many reasons – (i) to avoid change in “reactor” volume (ii) to prevent generation of convective currents that would make the process unsteady and difficult to model and, (iii) to preclude any changes in viscosity and ultimately cake formation by the proteins present in serum. Since in general, temperature and relative humidity vary inversely for a fixed water content in the air, all our experiments were performed inside a humidifying chamber maintained at a high relative humidity of 85% at 37 °C. When the relative humidity was reduced to ∼46% by carrying out the incubation steps inside a 37 °C incubator instead, the bioassay indeed showed a discernibly poor performance, especially at higher LPS concentrations (Fig. 3b). Similarly, when the bioassays were performed below 37 °C, the spot intensities also decreased as expected (Fig. 3a).
After developing a fairly good understanding of the process parameters, finally a standard calibration curve was prepared for the LPS concentrations using the optimized experimental conditions (Fig. 4a). The dynamic range of the assay was seen to vary over four orders of magnitude and 1 pg mL−1 was found to be the lowest LOD. These values lie right in the clinically relevant range for correlating to the severity of sepsis.47 The assay also gave an equally good performance in aqueous samples (data not shown).
The results showed an interesting trend. A clear upward concentration gradient was observed as we moved away from the water–debris interface towards air (i.e., from SN3 to SN1) (Fig. 4b). This was not entirely unexpected given the amphiphilic disposition of the LPS molecules to stay near the hydrophobic–hydrophilic interface. Consequently, SN3 was slightly depleted of LPS as compared to the original sample. Something, however, that could not be explained by mass conservation was the slight increase in the overall LPS amount in the system (obtained by averaging the concentrations of the three fractions). We attributed this variation to the change in the matrix composition and viscosity upon removal of the proteins from the serum which may lead to faster binding kinetics and hence, a higher signal.
The absolute amount of LPS in SN1 was estimated to be ∼1 pg mL−1 from the calibration curve, which was an order of magnitude higher than the original sample, whereas the SN2 and SN3 fractions lay below the detectable limit. Although this pre-enrichment approach adds an additional step to our bioassay, it may be used as a valuable option in cases where the LPS concentration in clinical samples poses a limitation to its diagnostic utility.
![]() | (2) |
| radsorption = kcscv | (3) |
For the analysis of GNP binding, we assumed the GNP
:
LPS binding stoichiometry to be 1
:
1 and the spot intensity I to vary linearly with the bound GNP concentration at any time.48 This gave us the relation (eqn (4))
![]() | (4) |
Here, I0 is the highest intensity (maximum darkness) achieved and ct is the total number of binding sites at the beginning. In the case of the adsorption-limited process, the concentration of molecules near the surface matches that of the bulk (cs = cb) as the molecules cannot get adsorbed that quickly and get accumulated. In other words (eqn (5)),
| I = I0(1 − exp(−k*cb*t)). | (5) |
Conversely, if the process is diffusion-controlled, the molecules near the surface bind instantaneously (cs = 0) giving the final form of the equation as (eqn (6))
![]() | (6) |
When we modeled the data in Fig. 2b using both eqn (5) and (6), the data fitted better using the adsorption-controlled process (coefficient of determination with the regression value R2 = 0.99) giving a k value of 0.21 nM−1 min−1. Similarly, the data in Fig. 2c also fitted more effectively assuming the rate to be adsorption-limited (R2 > 0.96 for all the 3 cases), but it was not possible to fit all the experimental data with a single rate constant. The value of k decreased from 0.040 nM−1 min−1 to 0.028 nM−1 min−1 to 0.026 nM−1 min−1 as the LPS concentration was raised from 100 fg mL−1 to 100 pg mL−1 to 100 ng mL−1. This lowering of effective rate constants was linked to the steric hindrance produced at a higher receptor packing density, due to which GNPs take more time to orient and find a more favorable configuration for binding.
Based on the literature, the values of the rate constants obtained for LPS and GNP binding seemed fairly reasonable but to further confirm that our model was indeed a true fit, we looked into the effect of temperature on our rate constants. The results showed an exponential dependence of k on temperature (see Fig. 3a), as predicted by the Arrhenius law which confirmed that our model was correct and the binding was dominated by surface reaction kinetics. On the other hand, the dependence of signal intensity on temperature assuming the process to be diffusion-controlled did not show any correlation at all (see the ESI†), thus supporting our claims. The relation between diffusion rate and temperature was obtained from the Stokes–Einstein equation which states that
, where μ is the viscosity of the solution and r is the molecular radius.49 Since viscosity follows the relation μ = μ0*10b/T, D varies directly proportional to T*10−b/T.
Footnote |
| † Electronic supplementary information (ESI) available. See DOI: 10.1039/c7me00037e |
| This journal is © The Royal Society of Chemistry 2017 |