Chemical Analog-to-Digital Signal Conversion Based on Robust Threshold Chemistry and Its Evaluation in the Context of Microfluidics-Based Quantitative Assays

Abstract

In this paper, we describe a nonlinear threshold chemistry based on enzymatic inhibition and demonstrate how it can be coupled with microfluidics to convert a chemical concentration (analog input) into patterns of ON or OFF reaction outcomes (chemical digital readout). Quantification of small changes in concentration is needed in a number of assays, such as that for cystatin C, where a 1.5-fold increase in concentration may indicate the presence of acute kidney injury or the progression of chronic kidney disease. We developed an analog-to-digital chemical signal conversion that gives visual readout and applied it to an assay for cystatin C as a model target. The threshold chemistry is based on enzymatic inhibition and gives sharper responses with tighter inhibition. The chemistry described here uses acetylcholinesterase (AChE) and produces an unambiguous color change when the input is above a pre-determined threshold concentration. An input gives a pattern of ON/OFF responses when subjected to a monotonic sequence of threshold concentrations, revealing the input concentration at the point of transition from OFF to ON outcomes. We demonstrated that this threshold chemistry can detect a 1.30–fold increase in concentration at 22 °C, and that it is robust to experimental fluctuations: it provided the same output despite changes in temperature (22–34 °C) and readout time (10-fold range). We applied this threshold chemistry to diagnostics by coupling it with a traditional sandwich immunoassay for serum cystatin C. Because one quantitative measurement comprises several assays, each with its own threshold concentration, we used a microfluidic SlipChip device to process 12 assays in parallel, detecting a 1.5-fold increase (0.64 mg/L (49 nM) to 0.96 mg/L (74 nM)) of cystatin C in serum. We also demonstrated applicability to analysis of patient serum samples and the ability to image results using a cell phone camera. This work indicates that combining developments in nonlinear chemistries with microfluidics may lead to the development of user-friendly diagnostic assays with simple readouts.

A microfluidic platform with digital readout and ultra-low detection limit for quantitative point-of-care diagnostics

“…The result demonstrates that the DV-chip possesses a detection limit of less than 5 pM for BNP in patient plasma samples, which is three orders of magnitude lower than that of the previously described readerless digital method. 27 The data in Figure 4b and c also show that BNP level is correlated with clinical outcome (Table S1), proving that this biomarker is of great value for the diagnosis of HF. 5, 16 In addition, the sensitivity and specificity of BNP for HF at a cutoff of 100 pg/ml was 62.5% and 83.3%, respectively; if taking 200 pg/mL as the cutoff value, the sensitivity and specificity would be 87.5% and 66.6%, respectively.…”

“…26, 27 However, very few papers reported equipment-free digital platforms for POC diagnostics because it is difficult to accurately control the transit between positive and negative. By employing a hydrophobic-to-hydrophilic reaction, Phillips' group developed a novel digital assay carried out on paper-based devices to detect hydrogen peroxide in the millimolar range.…”

Effects of porous media on extraction kinetics: Is the membrane really a limiting factor?

“…Microfluidics is an advantageous way of implementing assays for membrane-based extraction, whether for gas-liquid [21] or liquid-liquid interface [17,[22][23][24][25][26]. With regard to a pertraction device, a microfluidic chip offers the advantage of small sample consumption, faster equilibrium and better control over extraction kinetics [22].…”

Digital biology and chemistry

“…In this example, switching occurs when the concentration of signals exceeds a certain threshold by a value of concentration equal to α, resulting in a drastic change in output given by the value of γ. Sharpness of switching can be defined via α and γ. 2 Right: schematic showing a gradient range of input translated into digital output. When studied using microfluidics, switching systems can be precisely characterized.…”