Rapid diagnostics are critical infectious disease tools that are designed to detect a known biomarker using antibodies specific to that biomarker. However, a way to detect unknown disease variants has not yet been achieved in a paper test format. We describe here a route to make an adaptable paper immunoassay that can detect an unknown biomarker, demonstrating it on SARS-CoV-2 variants. The immunoassay repurposes cross reactive antibodies raised against the alpha variant. Gold nanoparticles of two different colors conjugated to two different antibodies create a colorimetric signal, and machine learning of the resulting colorimetric pattern is used to train the assay to discriminate between variants of alpha and Omicron BA.5. By using principal component analysis, the colorimetric test patterns can pick up and discriminate an unknown variant that it has not encountered before, Omicron BA.1. The test has an accuracy of 100% and a potential calculated discriminatory power of 900. We show that it can be used adaptively and that it can be used to pick up emerging variants without the need to raise new antibodies.
Infectious diseases are a global health threat that impacts the entire world. Diagnostics are important tools that are at the front line of defense for infectious diseases, enabling decision making for patient treatment and disease surveillance. They are especially critical when a disease is newly emerging, as this is when tools for identification and treatment are not yet in place and stopping the spread is essential. As soon as point of care (POC) tests were widely available for the COVID-19 pandemic, they proved to be vital in disease control because they allowed end users to determine if they were infected. One of the most useful formats for POC diagnostics has been rapid paper tests, such as lateral flow assays (LFAs) and dipstick assays. These are paper strips embedded with gold nanoparticles and antibodies specific to the target. The tests can go from sample to answer within minutes, do not require external instrumentation or power, and can be easily read out by eye or mobile phone cameras. Because of their low cost, they can be manufactured in large numbers and be widely distributed, enabling users to test themselves in a variety of environments.
As the infectious disease landscape is constantly evolving, it is urgent that we be ready for the next global pandemic. However, one challenge that has always remained is that we have never come up with a broadly accessible way to diagnose newly emerging infectious diseases. Viruses undergo continual mutations across various species, and zoonotic spillovers events, involving the transmission from animals to human, exert a substantial influence on the emergence of new outbreaks. An estimated 60–75% of all emerging human infectious diseases are thought to be zoonotic in origin. The heightened frequency of zoonotic spillover events is attributed to increasing levels of globalization, travel, urbanization, and shipping; these occurrences are further exacerbated by climate change, posing a persistent threat. Any given unknown pathogen might become the next big global pandemic if left unchecked, so the ability to rapidly identify unknowns circulating in the human population is of extreme importance. While complete identification in real time is impossible, information on what virus class it is, vector type (i.e., tick vs. mosquito borne), or what it is most similar to would be valuable in mounting a public health response to contain transmission, treat patients, and allocate resources.
Conventional LFAs require highly specific antibodies, but in order to produce them, the antigen target must first be identified before antibodies can be raised against it, thus preventing the ability to raise antibodies for an unknown. The only tool that can identify pathogens without prior knowledge of the target identity is DNA sequencing. However, sequencing is expensive, as it requires sophisticated equipment, reagents, and personnel, and is performed after PCR registers a positive. As a consequence, sequencing is commonly conducted in a centralized lab, with turnaround times spanning weeks or more. The COVID-19 pandemic spurred the opening of distributed sequencing centers that greatly enhanced the identification of new variants. However, these facilities were disproportionately concentrated in high-income countries, creating a significant disparity in genomic surveillance capabilities between high-income and low- to middle-income countries, primarily attributable to socioeconomic inequalities. Eventually, governments ultimately shuttered them because of high associated costs. Moreover, sequencing can still miss detection of the target if reference sequence or primers are not well suited to the divergent sequence, or the virus is present at low abundance, or the window in which the virus is present in the patient is short. Many infections by unknowns go undiagnosed. Therefore, we have a major flaw in our strategies to respond to newly emerging infectious diseases, and we urgently need to come up with a different tactic because the emergence of new diseases, strains, variants, and subtypes are now occurring with greater frequency. As it stands, diagnosing unknowns remains a major unmet need in global health.
However, chemists have determined a way to detect unknowns via olfactory arrays. To detect a range of previously unencountered analytes, they have developed novel sensors that act as a “chemical nose” or “chemical tongue” by employing a selective array rather than a specific binary (yes/no) sensor. In these cases, the signal is read out as a multidimensional pattern, or fingerprint, via principal component analysis (PCA). More importantly, chemical olfaction can classify these analytes that have never been encountered by the assay before. As selective arrays, they are not constrained to the detection of a limited number of target analytes, but instead can detect hundreds if not thousands of different species in pure form or in complex mixtures. Moreover, the format for olfaction sensors is highly versatile, with many ways to achieve readouts (e.g., electrochemical, fluorescence, colorimetric, nanoplasmonic, and others). It has been demonstrated to be powerful for a variety of analytes, ranging from small molecules, beverages, and disease cell types. Additionally, the sensor itself does not need to be expensive or difficult to operate, as they have been successfully implemented with colorimetric readouts on paper that can be imaged by a desktop scanner. The development of these sensors has given rise to overarching principles for constructing an olfactory array, where a salient feature is that it possesses some degree of cross-reactivity.
Recognizing the power of chemical olfaction, we created a selective array in an immunoassay format to result in an adaptive diagnostic that can detect a biomarker that it has not encountered before. We chose to demonstrate it on variants of SARS-CoV-2 because the virus is a compelling model for a pathogen that evolves into new variants. COVID-19 disease evolution has occurred on an exceedingly accelerated timescale. In the last 4 years, we have been faced with more than 35 variants, with intervals between some variants spanning just a few weeks. (e.g., BA.1 and BA.5 in 2022). Each variant is effectively a new disease, where immunization or vaccination against one does not fully protect against future variants, and the nature of the disease has inflicted grave public health consequences and led to increasing disease burdens. The virus has mostly leveraged mutations on the receptor binding domain (RBD) in the spike protein (S) where it interacts with the angiotensin-converting enzyme 2 (ACE2) receptor on cells. However, nucleocapsid mutations have also been shown to be significant, contributing to increased infectivity, heightened transmission, and, notably, the ability to evade detection in rapid tests. While there are many available rapid tests that can diagnose SARS-CoV-2 infections, they cannot discriminate between variants.
The selective array also allowed us to leverage antibody cross-reactivity, as specificity is not required. By doing so, antibodies for another target can be repurposed. We utilized reagents that were available at a hypothetical time point of 2021–2022 after the alpha variant had emerged, and assumed we would have antibodies for alpha S RBD but not for what was newly emerging, Omicron BA.1 and Omicron BA.5 (timeline, Fig. 1). Starting with a pool of two antibodies from the alpha variant, we constructed a multicolor array and trained it with machine learning to detect the new variant BA.5 based on its colorimetric pattern. Then, using PCA, we show that the assay could pick up an unknown, BA.1, and classify it as something different from the previous variants it has encountered. We demonstrate that a limited set of just two cross-reactive antibodies can be repurposed to identify a newer variant if the array follows basic rules from chemical olfactory arrays. These results illustrate that rethinking how rapid paper tests are used can yield capabilities that go beyond yes/no answers. Furthermore, they show that strategic use of cross-reactive antibodies have the potential to pick up newly emerging pathogenic threats in a point of care format, ultimately aiding emergency preparedness.
Gold chloride trihydrate (CAS: 16961-25-4), N-(2-hydroxyethyl) piperazine-N′-(2-ethanesulfonic acid) (HEPES) (CAS: 7365-45-9), bis(sulphatophenyl) phenylphosphine dehydrate (BPS) (CAS: 308103-66-4), sodium citrate tribasic trihydrate (CAS: 6132-04-3), tween 20 (CAS: 9005-64-5), and sucrose (CAS: 57-50-1) were all purchased from Sigma-Aldrich. Phosphate buffer saline (PBS) was purchased from Fisher Scientific, and tris-buffered saline (TBS) (10×, pH 7.4) from Boston BioProducts. Thiolated mPEG (5 kDa) was purchased from Nanocs. Casein hydrolysate was purchased from Sigma.
From the biological reagents used, the antibodies for the spike receptor binding domain (RBD) were rabbit anti-spike IgG (Ab1) and human anti-spike IgG (Ab2), which were purchased from Sino Biological. Anti-mouse and anti-rabbit IgG antibodies (anti-Fc) for the control line were purchased from Sino Biological. The S RBDs used in the test pertained to the alpha, BA.1, and BA.5 variants, all from Sino Biological.
Blue-colored gold NPs were synthesized according to established methods in the literature. Gold nanostars (GNS), characterized by their star-shaped morphology and blue color, were generated by combining 1100 μL of Milli-Q (MQ) H 2 O, 900 μL of 140 mM HEPES (pH 7.4), and 32 μL of 25 mM gold chloride trihydrate. Antibody conjugation to GNS was achieved through physisorption. The synthesized GNS underwent centrifugation at 3381 g for 12 min to form a pellet, and the supernatant was subsequently removed. The pellet was then resuspended in a solution comprising 140 mM HEPES (pH 7.48) and MQ H 2 O. Subsequently, Ab1 (10 μg) was added to the solution, and the mixture was incubated for 60 min at room temperature. Following this incubation period, thiolated polyethylene glycol (PEG-SH) (5 kDa) was introduced (5 × 10−10 mol) and allowed to mix with the GNS–Ab1 for 10 min. The PEGylated GNS–Ab1 complex was separated by centrifugation at 2348 g for 10 min, and the supernatant containing f
