Discrimination of bacterial and viral infection using host-RNA signatures integrated in a lab-on-chip platform

• Published in: Biosensors & bioelectronics Vol. 216; p. 114633
• Main Authors: Pennisi, Ivana, Moniri, Ahmad, Miscourides, Nicholas, Miglietta, Luca, Moser, Nicolas, Habgood-Coote, Dominic, Herberg, Jethro A, Levin, Michael, Kaforou, Myrsini, Rodriguez-Manzano, Jesus, Georgiou, Pantelis
• Format: Journal Article
• Language: English
• Published: England 15-11-2022
• Subjects: Anti-Infective Agents; Bacteria - genetics; Biosensing Techniques; Communicable Diseases; Humans; Lab-On-A-Chip Devices; Nucleic Acid Amplification Techniques - methods; Point-of-Care Systems; RNA; Virus Diseases; Point-of-care diagnostics; Infectious disease; Host-response signatures; Data-driven algorithm; Electronic LAMP (eLAMP); ISFET Arrays; Antimicrobial resistance; Lab-on-chip
• ISSN: 0956-5663; 1873-4235
• DOI: 10.1016/j.bios.2022.114633

The unmet clinical need for accurate point-of-care (POC) diagnostic tests able to discriminate bacterial from viral infection demands a solution that can be used both within healthcare settings and in the field, and that can also stem the tide of antimicrobial resistance. Our approach to solve this problem combine the use of host gene signatures with our Lab-on-a-Chip (LoC) technology enabling low-cost POC expression analysis to detect Infectious Disease. Transcriptomics have been extensively investigated as a potential tool to be implemented in the diagnosis of infectious disease. On the other hand, LoC technologies using ion-sensitive field-effect transistor (ISFET), in conjunction with isothermal chemistries, are offering a promising alternative to conventional amplification instruments, owing to their portable and affordable nature. Currently, the data analysis of ISFET arrays are restricted to established methods by averaging the output of every sensor to give a single time-series. This simple approach makes unrealistic assumptions, leading to insufficient performance for applications that require accurate quantification such as Host-Transcriptomics. In order to reliably quantify transcripts on our LoC platform enabling the classification of infectious disease on-chip, we propose a novel data-driven algorithm for extracting time-to-positive values from ISFET arrays. The algorithm proposed correctly outputs a time-to-positive for all the reactions, with a high correlation to RT-qLAMP (0.85, R
 = 0.98, p < 0.01), resulting in a classification accuracy of 100% (CI, 95-100%). This work aims to bridge the gap between translating assays from microarray analysis to ISFET arrays providing benefits on tackling infectious disease and diagnostic testing in hard-to-reach areas of the world.