A digital protein microarray for COVID-19 cytokine storm monitoring

Abstract

Despite widespread concern regarding cytokine storms leading to severe morbidity in COVID-19, rapid cytokine assays are not routinely available for monitoring critically ill patients. We report the clinical application of a digital protein microarray platform for rapid multiplex quantification of cytokines from critically ill COVID-19 patients admitted to the intensive care unit (ICU) at the University of Michigan Hospital. The platform comprises two low-cost modules: (i) a semi-automated fluidic dispensing/mixing module that can be operated inside a biosafety cabinet to minimize the exposure of the technician to the virus infection and (ii) a 12-12-15 inch compact fluorescence optical scanner for the potential near-bedside readout. The platform enabled daily cytokine analysis in clinical practice with high sensitivity (<0.4 pg mL^-1), inter-assay repeatability (∼10% CV), and rapid operation providing feedback on the progress of therapy within 4 hours. This test allowed us to perform serial monitoring of two critically ill patients with respiratory failure and to support immunomodulatory therapy using the selective cytopheretic device (SCD). We also observed clear interleukin-6 (IL-6) elevations after receiving tocilizumab (IL-6 inhibitor) while significant cytokine profile variability exists across all critically ill COVID-19 patients and to discover a weak correlation between IL-6 to clinical biomarkers, such as ferritin and C-reactive protein (CRP). Our data revealed large subject-to-subject variability in patients' response to COVID-19, reaffirming the need for a personalized strategy guided by rapid cytokine assays.

Figure 1.

PEdELISA microarray assay platform for COVID-19 patient cytokine storm profiling. (A)-(C) Schematic and photo image of the assay system in a biosafety cabinet. The platform comprises a cartridge holding a disposable microfluidic chip (inset), an automated fluidic dispensing and mixing module, and a 2D inverted fluorescence scanning module. Each channel of the chip has 8 circular biosensor patterns formed by a cluster of 66,724 arrayed microwells. Each biosensor pattern detects one of four target analytes. (D) The 5-step assay procedure includes (i) automated injection and subsequent on-chip mixing of serum and a detection antibody solution with capture antibody-coated magnetic beads pre-deposited in microwell arrays, which is accompanied by a short incubation (9-min) and followed by washing (2-min), (ii) HRP enzyme labeling (1-min), followed by washing (5-min), (iii) fluorescence substrate loading and oil sealing (2-min), (iv) x-y optical scanning and imaging (5-min), and (v) Data analysis using a convolutional neural network-guided image processing algorithm (4sec/image) for high throughput and accurate single-molecule counting (5–7min). The total sample to answer time is around 30 min for 8 samples in 4-plex. Both the fluorescence substrate channel (Qred CH) and brightfield channel (BF CH) are analyzed to calculate the average number of immune-complexes formed on each bead surface.

Figure 2.

(A) Architecture of the PEdELISA data analysis algorithm using convolutional neural network (CNN) and parallel computing by MATLAB. Two CNN networks were trained and executed in parallel to read in the QuantaRedTM (Qred) fluorescence mirowell images, and sequentially performed image pre-processing (including image cropping, contrast enhancement, and noise filtering), category classification, image segmentation, and post-processing (image overlay, visualization, defect compensation, microwell counting). (B) Representative snapshot images of enzyme active “On” microwells on a biosensing pattern (66,724 wells/biosensor) for various analyte concentrations of IL-6. For clear visualization, images of 3600×3600 pixels were cropped from original raw images of 6000×4000 pixels with 80% brightness enhancement and 80% contrast enhancement. All of the scale bars are 200 μm. See Fig. S4 for images taken for IL-1β, TNF-α, IL-10, and IL-6.

Figure 3.

Assay characterization of the PEdELISA. (A) Limit of detection (LOD) of the assay as a function of sample/detection antibody reaction incubation time for the four cytokines. Each LOD value was estimated by taking the intercept of the fitted standard curve with the mean value of the negative control signal plus three times the standard deviation. (B) Statistical analysis of the bead array filling rate for cytokine antibody-conjugated magnetic beads. IL-1β: 56.7±3.9%; TNF-α: 68.7±7.3%; IL-10: 56.2±4.4%; and IL-6: 53.1±4.7%. Each data point represents the bead filling rate obtained across 66,724 microwells in each circular biosensor pattern on the PEdELISA chip. We examined 40 representative biosensor patterns from five different chips to obtain the statistically averaged value for each analyte. (C) Assay specificity test with “all-spike-in,” “single-spike-in,” and “no-spike-in” (negative) samples of recombinant cytokine marker(s) at 200 pg/mL in fetal bovine serum (FBS) buffer. The labels on the horizontal axis represent the cytokine analytes targeted by 4 biosensors in each detection channel on a 4-plex PEdELISA chip. The top label of each graph represents the recombinant cytokine marker(s) loaded to the chip.

Figure 4.

(A) Daily COVID-19 patient assay standard curves for four cytokines from 0.32 pg/mL to 1000 pg/mL in FBS (10 curves for each cytokine obtained over 10 workdays). The data points were fitted with four-parameter logistic (4PL) curves. The black dotted line represents the signal level from a blank solution. The blue dotted line shows 3σ above the blank signal, which is used to estimate the limit of detection (LOD) for each cytokine. (B) Linear correlation (R²=0.99, P<0.0001) between rapid measurements of fresh samples and retrospective measurements of samples stored for more than 30 days (1 freeze-and-thaw at −80 °C) in quadruplicate for 5 representative COVID-19 patients. (C) Good agreement was observed between single-plex IL-6 ELISA (R²=0.95, P<0.0001) and multiplex PEdELISA measurements for 15 COVID-19 patients. The inset shows the circled region and k is the slope of the linear regression for (B)-(C).

Figure 5.

(A) Timeline of daily COVID-19 cytokine measurement. (B)-(C) Two-week serial monitoring of two critically ill COVID-19 patients with respiratory failure in the ICU. Both patients experienced severe cytokine storm and were under the emergency use of a selective cytophoretic device (SCD) by cytokine pre-screening (first data point). The patients demonstrated decreases in IL-6 and IL-6/IL-10 ratio and clinically improved treatment outcome over the course of therapy. (D) Statistical group analysis of patients that are dosed/undosed with Tocilizumab. Significant elevations of IL-6 levels were observed after the treatment of Tocilizumab (P<0.0001). (E)-(F) Correlation of cytokine IL-6 to Ferritin and C-Reactive Protein (CRP), standard clinical inflammatory biomarkers. Ferritin does not correlate well with IL-6 (R² = 0.066, P=0.261). CRP correlates with IL-6 (R²=0.394, P=0.0013) better, but the IL-6 levels were widely distributed for patients with high levels of CRP.