Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa

José M.R. Flauzino; Abdulkadir Sanli; Rudolph R. Shirima; Yuanjun Cai; Tinghao Hu; Leyang Li; Taliya Weinstein; Hong Seok Lee; Tayeb Tadlaoui; Selin Olenik; Abdülkadir Gümüşçü; Laura Gonzalez-Macia; George Mahuku; James Legg; Anthony E.G. Cass; Firat Güder

doi:10.1073/pnas.2602947123

Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa

José M.R. Flauzino
, Abdulkadir Sanli
, Rudolph R. Shirima
, Yuanjun Cai
, Tinghao Hu
, Leyang Li
, Taliya Weinstein
, Hong Seok Lee
, Tayeb Tadlaoui
, Selin Olenik
, Abdülkadir Gümüşçü
, Laura Gonzalez-Macia
, George Mahuku
, James Legg
, Anthony E.G. Cass
, Firat Güder

Imperial College London
International Institute of Tropical Agriculture (IITA-Tanzania)
Harran University
Department of Bioengineering

Research output: Contribution to journal › Article › peer-review

Abstract

Cassava brown streak disease (CBSD) threatens food security for millions in East Africa, yet its control remains limited by the absence of field-deployable molecular diagnostics. Here, we introduce ELLA (Electrochemical Lateral flow assay with Linked Analytics), a battery-free, smartphone-powered electrochemical lateral flow assay that delivers enzyme-linked immunosorbent assay (ELISA)-grade protein detection directly in the field. ELLA integrates near-field communication, a single-chip potentiostat, metal-pin electrodes, and ferrocene-labeled nanoparticles into a fully disposable cassette, enabling quantitative immunoassays without optical instrumentation or centralized laboratory infrastructure. Validated across laboratory studies and extensive field trials in Tanzania, ELLA achieved 95% agreement with ELISA and 89% agreement with RT-qPCR, outperforming ELISA’s limit of detection while maintaining a material cost below US$1. By coupling molecular test results with cloud-linked analytics, we further trained DeepELLA, a smartphone-based image classification model that enables scalable surveillance from field-acquired leaf images. Together, these advances unify electrochemical sensing, digital connectivity, and AI-assisted interpretation, enabling portable, ELISA-level diagnostics for plant, environmental, and health monitoring in resource-limited regions.

Original language	English
Article number	e2602947123
Number of pages	12
Journal	Proceedings of the National Academy of Sciences of the United States of America
Volume	123
Issue number	30
Early online date	20 Jul 2026
DOIs	https://doi.org/10.1073/pnas.2602947123
Publication status	Published - 28 Jul 2026

Data Availability Statement

Study data are included in the article and/or supporting information.

Acknowledgements

We acknowledge the Gates Foundation (GrandChallenges Explorations scheme under grant numbers OPP1212574 andINV-038695) for financial support. We are especially grateful to Jim Lorenzenand Neil Hausmann for their invaluable guidance and continued encourage-ment during this project. F.G. and J.M.R.F. acknowledge Research EnglandInternational Science Partnerships Fund (Reference: G08259) and theBiotechnology and Biological Sciences Research Council Impact AccelerationAccount (Reference: PSR417). F.G. and A.S. acknowledge Research EnglandInternational Science Partnerships Fund (Reference: G08288) and Innovate UKBMC (Reference: PA2131). F.G. and L.G.-M. thank the European Union’s Horizon2020 research and innovation program under the Marie Sklodowska-Curiegrant agreement No 101025390. F.G. and S.O. acknowledge Bezos Earth Fundthrough the Bezos Centre for Sustainable Protein (BCSP/IC/001). A.G. wouldlike to thank the TUBITAK (The Scientific and Technological Research Councilof Türkiye), for their support under grant number: 1059B192400999. We alsothank Professor Susan Seal, Gonçalo Ramalho e Silva, and Sophie Bouvainefrom the University of Greenwich for kindly providing some of the cassavasamples used in this study

Keywords

biosensors
cassava brown streak disease
digital diagnostics
near-field communication

ASJC Scopus subject areas

General

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10.1073/pnas.2602947123Licence: CC BY

Cite this

Flauzino, J. M. R., Sanli, A., Shirima, R. R., Cai, Y., Hu, T., Li, L., Weinstein, T., Lee, H. S., Tadlaoui, T., Olenik, S., Gümüşçü, A., Gonzalez-Macia, L., Mahuku, G., Legg, J., Cass, A. E. G., & Güder, F. (2026). Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa. Proceedings of the National Academy of Sciences of the United States of America, 123(30), Article e2602947123. https://doi.org/10.1073/pnas.2602947123

Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa. / Flauzino, José M.R.; Sanli, Abdulkadir; Shirima, Rudolph R. et al.
In: Proceedings of the National Academy of Sciences of the United States of America, Vol. 123, No. 30, e2602947123, 28.07.2026.

Research output: Contribution to journal › Article › peer-review

Flauzino, JMR, Sanli, A, Shirima, RR, Cai, Y, Hu, T, Li, L, Weinstein, T, Lee, HS, Tadlaoui, T, Olenik, S, Gümüşçü, A, Gonzalez-Macia, L, Mahuku, G, Legg, J, Cass, AEG & Güder, F 2026, 'Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa', Proceedings of the National Academy of Sciences of the United States of America, vol. 123, no. 30, e2602947123. https://doi.org/10.1073/pnas.2602947123

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Electrochemical lateral flow assay with ELISA-level performance for detecting plant diseases in East Africa

Abstract

Data Availability Statement

Acknowledgements

Keywords

ASJC Scopus subject areas

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