Next-generation nanophotonic-enabled biosensors for intelligent diagnosis of SARS-CoV-2 variants

Abstract

Constantly mutating SARS-CoV-2 is a global concern resulting in COVID-19 infectious waves from time to time in different regions, challenging present-day diagnostics and therapeutics. Early-stage point-of-care diagnostic (POC) biosensors are a crucial vector for the timely management of morbidity and mortalities caused due to COVID-19. The state-of-the-art SARS-CoV-2 biosensors depend upon developing a single platform for its diverse variants/biomarkers, enabling precise detection and monitoring. Nanophotonic-enabled biosensors have emerged as 'one platform' to diagnose COVID-19, addressing the concern of constant viral mutation. This review assesses the evolution of current and future variants of the SARS-CoV-2 and critically summarizes the current state of biosensor approaches for detecting SARS-CoV-2 variants/biomarkers employing nanophotonic-enabled diagnostics. It discusses the integration of modern-age technologies, including artificial intelligence, machine learning and 5G communication with nanophotonic biosensors for intelligent COVID-19 monitoring and management. It also highlights the challenges and potential opportunities for developing intelligent biosensors for diagnosing future SARS-CoV-2 variants. This review will guide future research and development on nano-enabled intelligent photonic-biosensor strategies for early-stage diagnosing of highly infectious diseases to prevent repeated outbreaks and save associated human mortalities.

Keywords: Artificial intelligence; Biosensors; Mutations evolution; Nanophononics; SARS-COV-2 variants.

Graphical abstract

Fig. 1

Taxonomy of studies for evaluation, structures, and biosensors diagnosis of SARS-COV-2 variants.

Fig. 2

Compares the population (healthy, infected, symptomatic) across the six variants of SARS-CoV-2 strains per day, with data obtained from https://ourworldindata.org.

Fig. 3

Illustrates the impact of the D614G mutation on the amino acid composition of the viral spike protein at position 614, which has been changed from aspartate (D) to glycine (G).

Fig. 4

Showcases the latest advancements in optical sensors for detecting SARS-CoV-2 variants. These include: A. a SERS biosensor, reprinted with permission of Payne et al. (2021) Copyright 2021, ACS sensors. B. a silicon photonic crystal microarray structure for multiplexed sensing, reprinted with permission of Asghari et al. (2021) Copyright 2021, Applied Physics Reviews. C. a microfluidic particle dam for directly visualizing SARS-CoV-2 antibody levels in COVID-19 vaccinees, reprinted with permission of Wu et al. (2022) Copyright 2022, Science Advances, and D. a standard SPR-based biosensor configuration. Reprinted with permission of Asghari et al. (2021) Copyright 2021, Applied Physics Reviews.

Fig. 5

Represented A portable electrical biosensor is built using virus receptors, such as replication and infection of SARS-CoV-2, and synthesis of the synthetic virus using microfluidics and the portable electrical biosensor. A synthetic SARS-COV-2 with the SP1 spike protein bound to the avidin molecule is available for cryo-electron microscopy. Reprinted with permission of Park et al. (2022), Copyright 2022, Nano Letters.

Fig. 6

Illustrate of the SARS-COV-2 variant electrochemical sensors instrument: A. Infected samples are collected from nasal swabs or patient saliva under observation, B. SARS-CoV-2 RNA is extracted, C. Viral RNA is added to graphene-ssDNA-AuNP, D. Incubation is allowed for 5 min, and E. Electrochemical output is recorded digitally. Reprinted with permission of Alafeef et al. (2020), Copyright 2020, ACS Nano.

Fig. 7

Detection of SARS-COV-2 variants based on CRISPR: A. Mobile phone-based microscope for fluorescence detection and images of CRISPR-Cas13a-based mobile phone microscopy. Reprinted with permission of Fozouni et al. (2021), Copyright 2021, Cell. B. CRISPR-LAMP platform that is automated, Reprinted with permission of Zhang et al. (2022), Copyright 2022, Analytical Chemistry.

Fig. 8

Portable variants of the SARS-COV-2 virus tested based on the smart phone: A. MiSHERLOCK schematic showing the integration of instrument-free viral RNA extraction and concentration from raw saliva, reactions that identify SARS-CoV-2 and variations, fluorescence output and an auxiliary mobile phone app for automatic result interpretation. Reprinted with permission of De Puig et al. (2021), Copyright 2021, Science Advances. B. Smartphone-based imaging device for quantum dot barcodes scan immunoassay. Reprinted with permission from Zhang et al. (2021), Copyright 2021, ACS Nano Letters.

Fig. 9

Showcases the potential of integrated nanophotonics biosensors and artificial intelligence (AI) for creating multi-attribute decision support systems capable of detecting analytes in diverse environments.