A nanoelectronics-blood-based diagnostic biomarker for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS)

Abstract

There is not currently a well-established, if any, biological test to diagnose myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). The molecular aberrations observed in numerous studies of ME/CFS blood cells offer the opportunity to develop a diagnostic assay from blood samples. Here we developed a nanoelectronics assay designed as an ultrasensitive assay capable of directly measuring biomolecular interactions in real time, at low cost, and in a multiplex format. To pursue the goal of developing a reliable biomarker for ME/CFS and to demonstrate the utility of our platform for point-of-care diagnostics, we validated the array by testing patients with moderate to severe ME/CFS patients and healthy controls. The ME/CFS samples' response to the hyperosmotic stressor observed as a unique characteristic of the impedance pattern and dramatically different from the response observed among the control samples. We believe the observed robust impedance modulation difference of the samples in response to hyperosmotic stress can potentially provide us with a unique indicator of ME/CFS. Moreover, using supervised machine learning algorithms, we developed a classifier for ME/CFS patients capable of identifying new patients, required for a robust diagnostic tool.

Keywords: artificial intelligence; diagnostic biomarker; machine learning; myalgic encephalomyelitis/chronic fatigue syndrome; nanoelectronics biosensor.

Fig. 1.

(A) Schematic of a single nanoelectronic sensor (not to scale). (B) Circuit model of a sensor−solution interface, where Z_m-s is media−sensor surface interactions, Z_c-c is cell−cell interactions, Z_c-s is cell−sensor surface adhesion, Z_c is a cell impedance (membrane capacitance C_m, and cytoplasm conductivity of the cells, σ_cp), and R_s is resistance of the solution. (C) The experimentally obtained impedance versus time curves illustrating the electrical response of hyperosmotic-stressed samples of a bed-bound ME/CFS patient and a healthy control in real time. A gray region is defined experimentally and its top and bottom borders are shown with orange and green lines, respectively (for further details see Trial Population and Statistical Analysis). (D) Array of nanoneedle sensors fabricated on a 4-in wafer. (E and F) SEM images of a nanoelectronic sensor tips, (E) top view and (F) from the microfluidics channel side.

Fig. 2.

Trial population and statistical analysis. (A) The experimentally obtained impedance versus time curves of 40 ME/CFS and healthy control samples used in this study with an experimentally defined gray region. Top and bottom borders of the gray region are shown with orange and green lines, respectively. To generate each plot, about ∼40,000 data points per experiment were collected. (B−E) Analyzed percentage change of (B) minimum-to-plateau and (C) baseline-to-plateau impedance signals, in which both showed a strong separability, (D) with P = 7.27E-9 for minimum-to-plateau and (E) P = 4.48E-9 for baseline-to-plateau impedance signals. (F and G) Repeatability and reproducibility validation of the assay for both ME/CFS patients and healthy controls. (H) Primary ME/CFS classifier created by applying supervised SVM machine learning algorithm to our experimental datasets. (I) Perfect linearly separable dataset in PCA space after performing PCA on a data matrix comprising six features of impedance change signals from the baseline and minimum to the plateau for all three components of impedance (|Z|, Z_re, and Z_im). *P < 1e-8.