Imperial College London

DrJesusRodriguez Manzano

Faculty of MedicineDepartment of Infectious Disease

Senior Lecturer in Diagnostics for Infectious Disease
 
 
 
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Contact

 

j.rodriguez-manzano

 
 
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Location

 

Commonwealth BuildingHammersmith Campus

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Summary

 

Publications

Publication Type
Year
to

105 results found

Miglietta L, Chen Y, Luo Z, Xu K, Ding N, Peng T, Moniri A, Kreitmann L, Cacho-Soblechero M, Holmes A, Georgiou P, Rodriguez-Manzano Jet al., 2023, Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays., Communications Biology, Vol: 6, ISSN: 2399-3642

Developing multiplex PCR assays requires extensive experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays ensuring specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the process of developing and designing multiplex assays using a hybrid, simple workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation to develop optimised multiplex combinations. The Smart-Plexer analyses kinetic inter-target distances between amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. In this study, the Smart-Plexer method is applied and evaluated for seven respiratory infection target detection using an optimised multiplexed PCR assay. Single-channel multiplex assays, together with the recently published data-driven methodology, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.

Journal article

Malpartida-Cardenas K, Baum J, Cunnington A, Georgiou P, Rodriguez-Manzano Jet al., 2023, A dual paper-based nucleic acid extraction method from blood in under ten minutes for point-of-care diagnostics, The Analyst, Vol: 148, Pages: 3036-3044, ISSN: 0003-2654

Nucleic acid extraction (NAE) plays a crucial role for diagnostic testing procedures. For decades, dried blood spots (DBS) have been used for serology, drug monitoring, and molecular studies. However, extracting nucleic acids from DBS remains a significant challenge, especially when attempting to implement these applications to the point-of-care (POC). To address this issue, we have developed a paper-based NAE method using cellulose filter papers (DBSFP) that operates without the need for electricity (at room temperature). Our method allows for NAE in less than 7 min, and it involves grade 3 filter paper pre-treated with 8% (v/v) igepal surfactant, 1 min washing step with 1× PBS, and 5 min incubation at room temperature in 1× TE buffer. The performance of the methodology was assessed with loop-mediated isothermal amplification (LAMP), targeting the human reference gene beta-actin and the kelch 13 gene from P. falciparum. The developed method was evaluated against FTA cards and magnetic bead-based purification, using time-to-positive (min) for comparative analysis. Furthermore, we optimised our approach to take advantage of the dual functionality of the paper-based extraction, allowing for elution (eluted disk) as well as direct placement of the disk in the LAMP reaction (in situ disk). This flexibility extends to eukaryotic cells, bacterial cells, and viral particles. We successfully validated the method for RNA/DNA detection and demonstrated its compatibility with whole blood stored in anticoagulants. Additionally, we studied the compatibility of DBSFP with colorimetric and lateral flow detection, showcasing its potential for POC applications. Across various tested matrices, targets, and experimental conditions, our results were comparable to those obtained using gold standard methods, highlighting the versatility of our methodology. In summary, this manuscript presents a cost-effective solution for NAE from DBS, enabling molecular testing in virtually

Journal article

Jackson HR, Miglietta L, Habgood-Coote D, D'Souza G, Shah P, Nichols S, Vito O, Powell O, Davidson MS, Shimizu C, Agyeman PKA, Beudeker CR, Brengel-Pesce K, Carrol ED, Carter MJ, De T, Eleftheriou I, Emonts M, Epalza C, Georgiou P, De Groot R, Fidler K, Fink C, van Keulen D, Kuijpers T, Moll H, Papatheodorou I, Paulus S, Pokorn M, Pollard AJ, Rivero-Calle I, Rojo P, Secka F, Schlapbach LJ, Tremoulet AH, Tsolia M, Usuf E, Van Der Flier M, Von Both U, Vermont C, Yeung S, Zavadska D, Zenz W, Coin LJM, Cunnington A, Burns JC, Wright V, Martinon-Torres F, Herberg JA, Rodriguez-Manzano J, Kaforou M, Levin Met al., 2023, Diagnosis of multisystem inflammatory syndrome in children by a whole-blood transcriptional signature, Journal of the Pediatric Infectious Diseases Society, Vol: 12, Pages: 322-331, ISSN: 2048-7207

BACKGROUND: To identify a diagnostic blood transcriptomic signature that distinguishes multisystem inflammatory syndrome in children (MIS-C) from Kawasaki disease (KD), bacterial infections, and viral infections. METHODS: Children presenting with MIS-C to participating hospitals in the United Kingdom and the European Union between April 2020 and April 2021 were prospectively recruited. Whole-blood RNA Sequencing was performed, contrasting the transcriptomes of children with MIS-C (n = 38) to those from children with KD (n = 136), definite bacterial (DB; n = 188) and viral infections (DV; n = 138). Genes significantly differentially expressed (SDE) between MIS-C and comparator groups were identified. Feature selection was used to identify genes that optimally distinguish MIS-C from other diseases, which were subsequently translated into RT-qPCR assays and evaluated in an independent validation set comprising MIS-C (n = 37), KD (n = 19), DB (n = 56), DV (n = 43), and COVID-19 (n = 39). RESULTS: In the discovery set, 5696 genes were SDE between MIS-C and combined comparator disease groups. Five genes were identified as potential MIS-C diagnostic biomarkers (HSPBAP1, VPS37C, TGFB1, MX2, and TRBV11-2), achieving an AUC of 96.8% (95% CI: 94.6%-98.9%) in the discovery set, and were translated into RT-qPCR assays. The RT-qPCR 5-gene signature achieved an AUC of 93.2% (95% CI: 88.3%-97.7%) in the independent validation set when distinguishing MIS-C from KD, DB, and DV. CONCLUSIONS: MIS-C can be distinguished from KD, DB, and DV groups using a 5-gene blood RNA expression signature. The small number of genes in the signature and good performance in both discovery and validation sets should enable the development of a diagnostic test for MIS-C.

Journal article

Malpartida Cardenas K, Moser N, Ansah F, Pennisi I, Ahu Prah D, Eva Amoah L, Awandare G, Hafalla JC, Cunnington A, Baum J, Rodriguez Manzano J, Georgiou Pet al., 2023, Sensitive detection of asymptomatic and symptomatic malaria with seven novel parasite-specific LAMP assays and translation for use at point-of-care, Microbiology Spectrum, Vol: 11, Pages: 1-12, ISSN: 2165-0497

Human malaria is a life-threatening parasitic disease with high impact in the sub-Saharan Africa region, where 95% of global cases occurred in 2021. While most malaria diagnostic tools are focused on Plasmodium falciparum, there is a current lack of testing non-P. falciparum cases, which may be underreported and, if undiagnosed or untreated, may lead to severe consequences. In this work, seven species-specific loop-mediated isothermal amplification (LAMP) assays were designed and evaluated against TaqMan quantitative PCR (qPCR), microscopy, and enzyme-linked immunosorbent assays (ELISAs). Their clinical performance was assessed with a cohort of 164 samples of symptomatic and asymptomatic patients from Ghana. All asymptomatic samples with a parasite load above 80 genomic DNA (gDNA) copies per μL of extracted sample were detected with the Plasmodium falciparum LAMP assay, reporting 95.6% (95% confidence interval [95% CI] of 89.9 to 98.5) sensitivity and 100% (95% CI of 87.2 to 100) specificity. This assay showed higher sensitivity than microscopy and ELISA, which were 52.7% (95% CI of 39.7 to 67%) and 67.3% (95% CI of 53.3 to 79.3%), respectively. Nine samples were positive for P. malariae, indicating coinfections with P. falciparum, which represented 5.5% of the tested population. No samples were detected as positive for P. vivax, P. ovale, P. knowlesi, or P. cynomolgi by any method. Furthermore, translation to the point-of-care was demonstrated with a subcohort of 18 samples tested locally in Ghana using our handheld lab-on-chip platform, Lacewing, showing comparable results to a conventional fluorescence-based instrument. The developed molecular diagnostic test could detect asymptomatic malaria cases, including submicroscopic parasitemia, and it has the potential to be used for point-of-care applications.

Journal article

Malpartida-Cardenas K, Moser N, Ansah F, Prah DA, Awandare G, Cunnington A, Baum J, Rodriguez-Manzano J, Georgiou Pet al., 2023, EVALUATION OF SYMPTOMATIC AND ASYMPTOMATIC P. FALCIPARUM WITH SPECIES-SPECIFIC LAMP ASSAY AND TRANSLATION TO THE POINT-OF-CARE, Publisher: ELSEVIER SCI LTD, Pages: S133-S133, ISSN: 1201-9712

Conference paper

Mao Y, Miglietta L, Kreitmann L, Moser N, Georgiou P, Holmes A, Rodriguez Manzano Jet al., 2023, Deep domain adaptation enhances Amplification Curve Analysis for single-channel multiplexing in real-time PCR, IEEE Journal of Biomedical and Health Informatics, Vol: 27, Pages: 3093-3103, ISSN: 2168-2208

Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes currently faces several challenges, such as distribution discrepancies between different data sources of synthetic DNA and clinical samples (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes ( bla NDM , bla IMP and bla OXA-48 ) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively, compared with previous methods. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities without hardware modification in real-world cli

Journal article

Kreitmann L, Miglietta L, Xu K, Malpartida-Cardenas K, D'Souza G, Kaforou M, Brengel-Pesce K, Drazek L, Holmes A, Rodriguez-Manzano Jet al., 2023, Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR, TrAC Trends in Analytical Chemistry, Vol: 160, Pages: 1-11, ISSN: 0165-9936

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner.To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) – two of the most standard bio-signals emitted during qPCR – for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR.Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent

Journal article

Kuiper R, Wright VJ, Habgood-Coote D, Shimizu C, Huigh D, Tremoulet AH, van Keulen D, Hoggart CJ, Rodriguez-Manzano J, Herberg JA, Kaforou M, Tempel D, Burns JC, Levin Met al., 2023, Bridging a diagnostic Kawasaki disease classifier from a microarray platform to a qRT-PCR assay, Pediatric Research, Vol: 93, Pages: 559-569, ISSN: 0031-3998

BACKGROUND: Kawasaki disease (KD) is a systemic vasculitis that mainly affects children under 5 years of age. Up to 30% of patients develop coronary artery abnormalities, which are reduced with early treatment. Timely diagnosis of KD is challenging but may become more straightforward with the recent discovery of a whole-blood host response classifier that discriminates KD patients from patients with other febrile conditions. Here, we bridged this microarray-based classifier to a clinically applicable quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay: the Kawasaki Disease Gene Expression Profiling (KiDs-GEP) classifier. METHODS: We designed and optimized a qRT-PCR assay and applied it to a subset of samples previously used for the classifier discovery to reweight the original classifier. RESULTS: The performance of the KiDs-GEP classifier was comparable to the original classifier with a cross-validated area under the ROC curve of 0.964 [95% CI: 0.924-1.00] vs 0.992 [95% CI: 0.978-1.00], respectively. Both classifiers demonstrated similar trends over various disease conditions, with the clearest distinction between individuals diagnosed with KD vs viral infections. CONCLUSION: We successfully bridged the microarray-based classifier into the KiDs-GEP classifier, a more rapid and more cost-efficient qRT-PCR assay, bringing a diagnostic test for KD closer to the hospital clinical laboratory. IMPACT: A diagnostic test is needed for Kawasaki disease and is currently not available. We describe the development of a One-Step multiplex qRT-PCR assay and the subsequent modification (i.e., bridging) of the microarray-based host response classifier previously described by Wright et al. The bridged KiDs-GEP classifier performs well in discriminating Kawasaki disease patients from febrile controls. This host response clinical test for Kawasaki disease can be adapted to the hospital clinical laboratory.

Journal article

Arkell P, Mairiang D, Songjaeng A, Malpartida-Cardenas K, Hill-Cawthorne K, Avirutnan P, Georgiou P, Holmes A, Rodriguez-Manzano Jet al., 2023, Analytical and diagnostic performance characteristics of reverse-transcriptase loop-mediated isothermal amplification assays for dengue virus serotypes 1-4: A scoping review to inform potential use in portable molecular diagnostic devices., PLOS Glob Public Health, Vol: 3

Dengue is a mosquito-borne disease caused by dengue virus (DENV) serotypes 1-4 which affects 100-400 million adults and children each year. Reverse-transcriptase (RT) quantitative polymerase chain reaction (qPCR) assays are the current gold-standard in diagnosis and serotyping of infections, but their use in low-middle income countries (LMICs) has been limited by laboratory infrastructure requirements. Loop-mediated isothermal amplification (LAMP) assays do not require thermocycling equipment and therefore could potentially be deployed outside laboratories and/or miniaturised. This scoping literature review aimed to describe the analytical and diagnostic performance characteristics of previously developed serotype-specific dengue RT-LAMP assays and evaluate potential for use in portable molecular diagnostic devices. A literature search in Medline was conducted. Studies were included if they were listed before 4th May 2022 (no prior time limit set) and described the development of any serotype-specific DENV RT-LAMP assay ('original assays') or described the further evaluation, adaption or implementation of these assays. Technical features, analytical and diagnostic performance characteristics were collected for each assay. Eight original assays were identified. These were heterogenous in design and reporting. Assays' lower limit of detection (LLOD) and linear range of quantification were comparable to RT-qPCR (with lowest reported values 2.2x101 and 1.98x102 copies/ml, respectively, for studies which quantified target RNA copies) and analytical specificity was high. When evaluated, diagnostic performance was also high, though reference diagnostic criteria varied widely, prohibiting comparison between assays. Fourteen studies using previously described assays were identified, including those where reagents were lyophilised or 'printed' into microfluidic channels and where several novel detection methods were used. Serotype-specific DENV RT-LAMP assays are high-perform

Journal article

Pennisi I, Moniri A, Miscourides N, Miglietta L, Moser N, Habgood-Coote D, Herberg JA, Levin M, Kaforou M, Rodriguez-Manzano J, Georgiou Pet al., 2022, Discrimination of bacterial and viral infection using host-RNA signatures integrated in a lab-on-chip platform, BIOSENSORS & BIOELECTRONICS, Vol: 216, ISSN: 0956-5663

Journal article

Miglietta L, Xu K, Chhaya P, Kreitmann L, Hill-Cawthorne K, Bolt F, Holmes A, Georgiou P, Rodriguez-Manzano Jet al., 2022, Adaptive filtering framework to remove nonspecific and low-efficiency reactions in multiplex digital PCR based on sigmoidal trends., Analytical Chemistry, Vol: 94, Pages: 14159-14168, ISSN: 0003-2700

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics

Journal article

Moser N, Yu L-S, Rodriguez Manzano J, Malpartida Cardenas K, Au A, Arkell P, Cicatiello C, Moniri A, Miglietta L, Wang W-H, Wang S-F, Holmes A, Chen Y-H, Georgiou Pet al., 2022, Quantitative detection of dengue serotypes using a smartphone-connected handheld Lab-on-Chip platform, Frontiers in Bioengineering and Biotechnology, Vol: 10, Pages: 1-14, ISSN: 2296-4185

Dengue is one of the most prevalent infectious diseases in the world. Rapid, accurate and scalable diagnostics are key to patient management and epidemiological surveillance of the dengue virus (DENV), however current technologies do not match required clinical sensitivity and specificity or rely on large laboratory equipment. In this work, we report the translation of our smartphone-connected handheld Lab-on-Chip (LoC) platform for the quantitative detection of twodengue serotypes. At its core, the approach relies on the combination of Complementary Metal Oxide-Semiconductor (CMOS) microchip technology to integrate an array of 78x56 potentiometric sensors, and a label-free reverse-transcriptase loop mediated isothermal amplification (RT-LAMP) assay. The platform communicates to a smartphone app which synchronises results in real time with a secure cloud server hosted by Amazon Web Services (AWS) for epidemiological surveillance. The assay on our LoC platform (RT-eLAMP) was shown to match performance on a gold-standard fluorescence-based real-time instrument (RT-qLAMP) with synthetic DENV-1 and DENV-2 RNA and extracted RNA from 9 DENV-2 clinical isolates, achieving quantitative detection in under 15 minutes. To validate the portability of the platform and the geo-tagging capabilities, we led our study in the laboratories at Imperial College London, UK, and Kaohsiung Medical Hospital, Taiwan. This approach carries high potential for application in low resource settings at the point-of-care (PoC).

Journal article

Pennisi I, Moniri A, Miscourides N, Miglietta L, Moser N, Habgood-Coote D, Herberg J, Levin M, Kaforou M, Rodriguez-Manzano J, Georgiou Pet al., 2022, Discrimination of bacterial and viral infection using host-RNA signatures integrated in a lab-on-a-chip technology, Publisher: MedRxiv

<h4>ABSTRACT</h4> 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 is to combine the use of Host-gene signatures with our Lab-on-a-chip (LoC) technology enabling low-cost LoC expression analysis to detect Infectious Disease.Host-gene expression signatures have been extensively study 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) arrays, in conjunction with isothermal chemistries, are offering a promising alternative to conventional lab-based nucleic acid 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 RNA host transcriptomics. In order to reliably quantify host-gene expression on our LoC platform enabling the classification of bacterial and viral infection on chip, we propose a novel data-driven algorithm for extracting time-to-positive values from ISFET arrays. The algorithm proposed is based on modelling sensor drift with adaptive signal processing and clustering sensors based on their behaviour with unsupervised learning methods. Results show that the approach correctly outputs a time-to-positive for all the reactions, with a high correlation to RT-qLAMP (0.85, R2 = 0.98, p < 0.01), resulting in a classification accuracy of 100 % (CI, 95 - 100). By leveraging more advanced data processing methods for ISFET arrays, this work aims to bridge the gap between tr

Working paper

Cavallo FR, Mirza KB, de Mateo S, Miglietta L, Rodriguez-Manzano J, Nikolic K, Toumazou Cet al., 2022, A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR, BIOSENSORS-BASEL, Vol: 12

Journal article

Wormald BW, Moser N, deSouza NM, Mantikas K-T, Malpartida-Cardenas K, Pennisi I, Ind TEJ, Vroobel K, Kalofonou M, Rodriguez-Manzano J, Georgiou Pet al., 2022, Lab-on-Chip assay of tumour markers and Human Papilloma Virus for cervical cancer detection at the point-of-care, Scientific Reports, Vol: 12, ISSN: 2045-2322

Cervical cancer affects over half a million people worldwide each year, the majority of whom are in resource-limited settingswhere cytology screening is not available. As persistent human papilloma virus (HPV) infections are a key causative factor,detection of HPV strains now complements cytology where screening services exist. This work demonstrates the efficacy ofa handheld Lab-on-Chip (LoC) device, with an external sample extraction process, in detecting cervical cancer from biopsysamples. The device is based on Ion-Sensitive Field-Effect Transistor (ISFET) sensors used in combination with loop-mediatedisothermal amplification (LAMP) assays, to amplify HPV DNA and human telomerase reverse transcriptase (hTERT) mRNA.These markers were selected because of their high levels of expression in cervical cancer cells, but low to nil expression innormal cervical tissue. The achieved analytical sensitivity for the molecular targets resolved down to a single copy per reactionfor the mRNA markers, achieving a limit of detection of 102for hTERT. In the tissue samples, HPV-16 DNA was present in 4/5malignant and 2/5 benign tissues, with HPV-18 DNA being present in 1/5 malignant and 1/5 benign tissues. hTERT mRNA wasdetected in all malignant and no benign tissues, with the demonstrated pilot data to indicate the potential for using the LoC incervical cancer screening in resource-limited settings on a large scale.

Journal article

Miglietta L, Xu K, Chhaya P, Kreitmann L, Hill-Cawthorne K, Bolt F, Holmes A, Georgiou P, Rodriguez-Manzano Jet al., 2022, An adaptive filtering framework for non-specific and inefficient reactions in multiplex digital PCR based on sigmoidal trends

<jats:title>ABSTRACT</jats:title><jats:p>Real-time digital PCR (qdPCR) coupled with artificial intelligence has shown the potential of unlocking scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One of the most promising applications is the use of machine learning (ML) methods to enable single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves. However, the robustness of such methods can be affected by the presence of undesired amplification events and nonideal reaction conditions. Therefore, here we proposed a novel framework to filter non-specific and low efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported ML-based Amplification Curve Analysis (ACA), using available data from a previous publication where the ACA method was used to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named Adaptive Mapping Filter (AMF), to consider the variability of positive counts in digital PCR. Over 152,000 amplification events were analyzed. For the positive reactions, filtered and unfiltered amplification curves were evaluated by comparing against melting peak distribution, proving that abnormalities (filtered out data) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to compare classification accuracies before and after AMF, showing an improved sensitivity of 1.18% for inliers and 20% for outliers (p-value &lt; 0.0001). This work explores the correlation between kinetics of amplification curves and thermodynamics of melting curves and it demonstrates that filtering out non-specific or low efficient reactions can significantly impr

Journal article

Malpartida-Cardenas K, Miglietta L, Peng T, Moniri A, Holmes A, Georgiou P, Rodriguez-Manzano Jet al., 2022, Single-channel digital LAMP multiplexing using Amplification Curve Analysis, Sensors and Diagnostics, Vol: 1, Pages: 465-468, ISSN: 2635-0998

Loop-mediated isothermal amplification assays are currently limited to one target per reaction in the absence of melting curve analysis, molecular probes or restriction enzyme digestion. Here, we demonstrate multiplexing of five targets in a single fluorescent channel using digital LAMP and the machine learning-based method amplification curve analysis, resulting in a classification accuracy of 91.33% on 54 186 positive amplification events.

Journal article

Stringer OW, Li Y, Bosse JT, Forrest MS, Hernandez-Garcia J, Tucker AW, Nunes T, Costa F, Mortensen P, Velazquez E, Penny P, Rodriguez Manzano J, Georgiou P, Langford Pet al., 2022, Rapid detection of A. pleuropneumoniae from clinical samples using recombinase polymerase amplification, Frontiers in Veterinary Science, Vol: 9, ISSN: 2297-1769

Actinobacillus pleuropneumoniae (APP) is the causative agent of porcine pleuropneumonia, resulting in high economic impact worldwide. There are currently 19 known serovars of APP, with different ones being predominant in specific geographic regions. Outbreaks of pleuropneumonia, characterized by sudden respiratory difficulties and high mortality, can occur when infected pigs are brought into naïve herds, or by those carrying different serovars. Good biosecurity measures include regular diagnostic testing for surveillance purposes. Current gold standard diagnostic techniques lacksensitivity (bacterial culture), require expensive thermocycling machinery (PCR) and are time consuming (culture and PCR). Here we describe the development of an isothermal point-of-care diagnostic test - utilizing recombinase polymerase amplification (RPA) for the detection of APP,targeting the species-specific apxIVA gene. Our APP-RPA diagnostic test achieved a sensitivity of 10 copies/µL using a strain of APP serovar 8, which is the most prevalent serovar in the UK. Additionally, our APP-RPA assay achieved a clinical sensitivity and specificity of 84.3% and 100%, respectively,across 61 extracted clinical samples obtained from farms located in England and Portugal. Using a small subset (n = 14) of the lung tissue samples, we achieved a clinical sensitivity and specificity of 76.9% and 100%, respectively) using lung imprints made on FTA cards tested directly in the APP- RPA reaction. Our results demonstrate that our APP-RPA assay enables a suitable rapid and sensitive screening tool for this important veterinary pathogen.

Journal article

Fernandez-Cassi X, Timoneda N, Martinez-Puchol S, Rusinol M, Rodriguez-Manzano J, Figuerola N, Bofill-Mas S, Abril JF, Girones Ret al., 2022, Metagenomics for the study of viruses in urban sewage as a tool for public health surveillance (vol 618, pg 870, 2016), SCIENCE OF THE TOTAL ENVIRONMENT, Vol: 814, ISSN: 0048-9697

Journal article

Wormald BW, Moser N, deSouza NM, Mantikas K-T, Malpartida-Cardenas K, Pennisi I, Ind TEJ, Vroobel K, Kalofonou M, Rodriguez-Manzano J, Georgiou Pet al., 2021, Lab-on-Chip assay of tumour markers and Human Papilloma Virus for cervical cancer detection at point-of-care

<jats:title>Abstract</jats:title> <jats:p>Cervical cancer affects over half a million people worldwide each year, the majority of whom are in resource-limited settingswhere cytology screening is not available. As persistent human papilloma virus (HPV) infections are a key causative factor,detection of HPV strains now complements cytology where screening services exist. This work demonstrates the efficacy of ahandheld Lab-on-Chip (LoC) device in detecting cervical cancer from biopsy samples. The device is based on Ion-SensitiveField-Effect Transistor (ISFET) sensors used in combination with loop-mediated isothermal amplification (LAMP) assays, toamplify HPV DNA and human telomerase reverse transcriptase (hTERT) mRNA. These markers were selected because of theirhigh levels of expression in cervical cancer cells, but low to nil expression in normal cervical tissue. The achieved analyticalsensitivity for the molecular targets resolved down to a single copy per reaction for the mRNA markers, achieving a limit ofdetection of 102for hTERT. In the tissue samples, HPV-16 DNA was present in 4/5 malignant and 2/5 benign tissues, withHPV-18 DNA being present in 1/5 malignant and 1/5 benign tissues. hTERT mRNA was detected in all malignant and nobenign tissues, with the demonstrated pilot data to indicate the potential for using the LoC in cervical cancer screening inresource-limited settings on a large scale</jats:p>

Journal article

Wormald B, Rodriguez-Manzano J, Moser N, Pennisi I, Ind TEJ, Vroobel K, Attygalle A, Georgiou P, deSouza NMet al., 2021, Loop-mediated isothermal amplification assay for detecting tumor markers and human papillomavirus: accuracy and supplemental diagnostic value to endovaginal MRI in cervical cancer, Frontiers in Oncology, Vol: 11, Pages: 1-13, ISSN: 2234-943X

Objective: To establish the sensitivity and specificity of a human papillomavirus (HPV) and tumor marker DNA/mRNA assay for detecting cervical cancer that is transferrable to a Lab-on-a-chip platform and determine its diagnostic benefit in early stage disease when used in conjunction with high-resolution endovaginal magnetic resonance imaging (MRI).Methods: Forty-one patients (27 with Stage1 cervical cancer [Group1] and 14 non-cancer HPV negative controls [Group2]) had DNA and RNA extracted from cervical cytology swab samples. HPV16, HPV18, hTERT, TERC/GAPDH and MYC/GAPDH concentration was established using a loop mediated isothermal amplification (LAMP) assay. Thresholds for tumor marker detection for Group1 were set from Group2 analysis (any hTERT, TERC/GAPDH 3.12, MYC/GAPDH 0.155). Group 1 participants underwent endovaginal MRI. Sensitivity and specificity for cancer detection by LAMP and MRI individually and combined was documented by comparison to pathology.Results: Sensitivity and specificity for cancer detection was 68.8% and 77.8% if any tumor marker was positive regardless of HPV status (scenario1), and 93.8% and 55.8% if tumor marker or HPV were positive (scenario 2). Adding endovaginal MRI improved specificity to 88.9% in scenario 1 (sensitivity 68.8%) and to 77.8%% in scenario2 (sensitivity 93.8%).Conclusion: Specificity for cervical cancer detection using a LAMP assay is superior with tumor markers; low sensitivity is improved by HPV detection. Accuracy for early stage cervical cancer detection is optimal using a spatially multiplexed tumor marker/HPV LAMP assay together with endovaginal MRI.

Journal article

Li HK, Kaforou M, Rodriguez-Manzano J, Channon-Wells S, Monir A, Habgood-Coote D, Gupta RK, Mills EA, Lin J, Chiu Y-H, Pennisi I, Miglietta L, Mehta R, Obaray N, Herberg JA, Wright VJ, Georgiou P, Shallcross LJ, Mentzer AJ, Levin M, Cooke GS, Noursadeghi M, Sriskandan Set al., 2021, Discovery and validation of a 3-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations; a case-control then observational cohort study, The Lancet Microbe, Vol: 2, Pages: 594-603, ISSN: 2666-5247

Background: Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We sought to derive and validate a blood transcriptional signature to detect viral infections including COVID-19 among adults with suspected infection presenting to the Emergency Department (ED).Methods: Blood RNA sequencing was performed on a discovery cohort of adults attending the ED with suspected infection who had subsequently-confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes were subjected to feature selection to derive the most parsimonious discriminating signature. RT-qPCR validation of the signature was then performed in a prospective cohort of ED patients presenting with undifferentiated fever, and a second case-control cohort of ED patients with COVID-19 or bacterial infection. Signature performance was assessed by calculating area under receiver-operating characteristic curves (AUC-ROCs), sensitivities, and specificities.Findings: A 3-gene transcript signature was derived from the discovery cohort of 56 bacterial and 27 viral infection cases. In the validation cohort of 200 cases, the signature differentiated bacterial from viral infections with an AUC-ROC of 0.976 (95% CI: 0.919-1.000), sensitivity 97.3% and specificity of 100%. The AUC-ROC for C-reactive protein (CRP) and leucocyte count (WCC) was 0.833 (95% CI: 0.694-0.944) and 0.938 (95% CI: 0.840-0.986) respectively. The signature achieved higher net benefit in decision curve analysis than either CRP or WCC for discriminating viral infections from all other cases. In the second validation analysis the signature discriminated 35 bacterial infections from 34 SARS-CoV-2 positive COVID-19 infections with AUC-ROC of 0.953 (95% CI: 0.893-0.992), sensitivity 88.6% and specificity of 94.1%.Interpretation: This novel 3-gene signature discriminates viral i

Journal article

Miglietta L, Moniri A, Pennisi I, Malpartida-Cardenas K, Abbas H, Hill-Cawthorne K, Bolt F, Jauneikaite E, Davies F, Holmes A, Georgiou P, Rodriguez Manzano Jet al., 2021, Coupling machine learning and high throughput multiplex digital PCR enables accurate detection of carbapenem-resistant genes in clinical isolates, Frontiers in Molecular Biosciences, Vol: 8, Pages: 1-11, ISSN: 2296-889X

Rapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays when using synthetic DNA templates. We sought to determine if this novel methodology could be applied to improve identification of the five major carbapenem-resistant genes in clinical CPO-isolates, which would represent a leap forward in the use of PCR-based data-driven diagnostics for clinical applications. We collected 3 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex PCR assay for detection of blaIMP, blaKPC, blaNDM, blaOXA-48 and blaVIM. Combining the recently reported ML method ‘Amplification and Melting Curve Analysis’ (AMCA) with the abovementioned multiplex assay, we assessed the performance of the AMCA methodology in detecting these genes. The improved classification accuracy of AMCA relies on the usage of real-time data from a single fluorescent channel and benefits from the kinetic/thermodynamic information encoded in the thousands of amplification events produced by high throughput real-time dPCR. The 5-plex showed a lower limit of detection of 10 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellentpredictive performance with 99.6% (CI 97.8-99.9%) accuracy (only one misclassified sample out of the 253, with a total of 160,041 positive amplification events), which represents a 7.9% increase (p value < 0.05) compared to conventional melting curve analysis. This work demonstrates the use of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, without hardware modifications and additiona

Journal article

Rodriguez Manzano J, Moniri A, Miglietta L, Georgiou Pet al., 2021, Method of assay design

Patent

Charani E, McKee M, Ahmad R, Balasegaram M, Bonaconsa C, Merrett GB, Busse R, Carter V, Castro-Sanchez E, Franklin BD, Georgiou P, Hill-Cawthorne K, Hope W, Imanaka Y, Kambugu A, Leather AJM, Mbamalu O, McLeod M, Mendelson M, Mpundu M, Rawson TM, Ricciardi W, Rodriguez-Manzano J, Singh S, Tsioutis C, Uchea C, Zhu N, Holmes AHet al., 2021, Optimising antimicrobial use in humans-review of current evidence and an interdisciplinary consensus on key priorities for research, The Lancet Regional Health - Europe, Vol: 7, Pages: 1-10, ISSN: 2666-7762

Addressing the silent pandemic of antimicrobial resistance (AMR) is a focus of the 2021 G7 meeting. A major driver of AMR and poor clinical outcomes is suboptimal antimicrobial use. Current research in AMR is inequitably focused on new drug development. To achieve antimicrobial security we need to balance AMR research efforts between development of new agents and strategies to preserve the efficacy and maximise effectiveness of existing agents.Combining a review of current evidence and multistage engagement with diverse international stakeholders (including those in healthcare, public health, research, patient advocacy and policy) we identified research priorities for optimising antimicrobial use in humans across four broad themes: policy and strategic planning; medicines management and prescribing systems; technology to optimise prescribing; and context, culture and behaviours. Sustainable progress depends on: developing economic and contextually appropriate interventions; facilitating better use of data and prescribing systems across healthcare settings; supporting appropriate and scalable technological innovation. Implementing this strategy for AMR research on the optimisation of antimicrobial use in humans could contribute to equitable global health security.

Journal article

Alexandrou G, Moser N, Mantikas K-T, Rodriguez-Manzano J, Ali S, Coombes RC, Shaw J, Georgiou P, Toumazou C, Kalofonou Met al., 2021, Detection of Multiple Breast Cancer ESR1 mutations on an ISFET based Lab-on-Chip Platform., IEEE Trans Biomed Circuits Syst, Vol: PP

ESR1 mutations are important biomarkers in metastatic breast cancer. Specifically, p.E380Q and p.Y537S mu- tations arise in response to hormonal therapies given to patients with hormone receptor positive (HR+) breast cancer (BC). This paper demonstrates the efficacy of an ISFET based CMOS integrated Lab-on-Chip (LoC) system, coupled with variant- specific isothermal amplification chemistries, for detection and discrimination of wild type (WT) from mutant (MT) copies of the ESR1 gene. Hormonal resistant cancers often lead to increased chances of metastatic disease which leads to high mortality rates, especially in low-income regions and areas with low healthcare coverage. Design and optimization of bespoke primers was carried out and tested on a qPCR instrument and then benchmarked versus the LoC platform. Assays for detection of p.Y537S and p.E380Q were developed and tested on the LoC platform, achieving amplification in under 25 minutes and sensitivity of down to 1000 copies of DNA per reaction for both target assays. The LoC system hereby presented, is cheaper and smaller than other standard industry equivalent technologies such as qPCR and sequencing. The LoC platform proposed, has the potential to be used at a breast cancer point-of-care testing setting, offering mutational tracking of circulating tumour DNA in liquid biopsies to assist patient stratification and metastatic monitoring.

Journal article

Miglietta L, Moniri A, Pennisi I, Malpartida Cardenas K, Abbas H, Hill-Cawthorne K, Bolt F, Davies F, Holmes AH, Georgiou P, Rodriguez Manzano Jet al., 2021, Coupling machine learning and high throughput multiplex digital PCR enables accurate detection of carbapenem-resistant genes in clinical isolates, Publisher: Cold Spring Harbor Laboratory

<jats:p>Background: The emergence and spread of carbapenemase-producing organisms (CPO) are a significant clinical and public health concern. Rapid and accurate identification of patients colonised with CPO is essential to adopt prompt prevention measures in order to reduce the risk of transmission. Recent proof-of-concept studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex assays. From this, we sought to determine if this ML based methodology could accurately identify five major carbapenem-resistant genes in clinical CPO-isolates.Methods: We collected 253 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex assay for detection of blaVIM, blaOXA-48, blaNDM, blaIMP and blaKPC. Combining the recently reported ML method "Amplification and Melting Curve Analysis" (AMCA) with the abovementioned multiplex assay, we assessed the performance of the methodology in detecting these five carbapenem-resistant genes. The classification accuracy relies on the usage of real-time data from a single fluorescent channel and benefits from the kinetic and thermodynamic information encoded in the thousands of amplification events produced by high throughput dPCR.Results: The 5-plex showed a lower limit of detection of 100 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellent predictive performance with 99.6% (CI 97.8-99.9%) accuracy (only one misclassified sample out of the 253, with a total of 163,966 positive amplification events), which represents a 7.9% increase compared to the conventional ML-based melting curve analysis (MCA) method.Conclusion: This work demonstrates the utility of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, reducing costs without any changes

Working paper

Cavallo FR, Mirza KB, de Mateo S, Nikolic K, Rodriguez-Manzano J, Toumazou Cet al., 2021, Aptasensor for quantification of leptin through PCR amplification of short DNA-aptamers., ACS Sensors, Vol: 6, Pages: 709-715, ISSN: 2379-3694

Protein quantification is traditionally performed through enzyme-linked immunosorbent assay (ELISA), which involves long preparation times. To overcome this, new approaches use aptamers as an alternative to antibodies. In this paper, we present a new approach to quantify proteins with short DNA aptamers through polymerase chain reaction (PCR) resulting in shorter protocol times with comparatively improved limits of detection. The proposed method includes a novel way to quantify both the target protein and the corresponding short DNA-aptamers simultaneously, which also allows us to fully characterize the performance of aptasensors. Human leptin is used as a target protein to validate this technique, because it is considered an important biomarker for obesity-related studies. In our experiments, we achieved the lowest limit of detection of 100 pg/mL within less than 2 h, a limit affected by the dissociation constant of the leptin aptamer, which could be improved by selecting a more specific aptamer. Because of the simple and inexpensive approach, this technique can be employed for Lab-On-Chip implementations and for rapid "on-site" quantification of proteins.

Journal article

Rodriguez-Manzano J, Malpartida-Cardenas K, Moser N, Pennisi I, Cavuto M, Miglietta L, Moniri A, Penn R, Satta G, Randell P, Davies F, Bolt F, Barclay W, Holmes A, Georgiou Pet al., 2021, Handheld point-of-care system for rapid detection of SARS-CoV-2 extracted RNA in under 20 min, ACS Central Science, Vol: 7, Pages: 307-317, ISSN: 2374-7943

The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (<20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against extracted RNA from 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 91% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 ± 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n = 52), showing average detection times of 12.68 ± 2.56 min for positive samples (n = 34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geolocalization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.

Journal article

Cavuto M, Pennisi I, Rodriguez Manzano J, Georgiou Pet al., 2021, Lid assembly for a sample tube, method of using the same to collect magnetic beads, and sample processing kit

Patent

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