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PCR-based pooling of dried blood spots for detection of malaria parasites: optimization and application to a cohort of Ugandan children.

ABSTRACT: Sensitive, high-throughput methods to detect malaria parasites in low-transmission settings are needed. PCR-based pooling strategies may offer a solution. We first used laboratory-prepared samples to compare 2 DNA extraction and 4 PCR detection methods across a range of pool sizes and parasite densities. Pooled Chelex extraction of DNA, followed by nested PCR of cytochrome b, was the optimal strategy, allowing reliable detection of a single low-parasitemic sample (100 parasites/?l) in pool sizes up to 50. This PCR-based pooling strategy was then compared with microscopy using 891 dried blood spots from a cohort of 77 Ugandan children followed for 2 years in an urban setting of low endemicity. Among 419 febrile episodes, 35 cases of malaria were detected using the PCR-based pooling strategy and 40 cases using microscopy. All five cases of malaria not detected by PCR were from samples stored for >2 years with parasitemia of <6,000/?l, highlighting the issue of possible DNA degradation with long-term storage of samples. Among 472 samples collected from asymptomatic children as part of routine surveillance, 15 (3.2%) were positive by PCR-based pooling compared to 4 (0.8%) by microscopy (P = 0.01). Thus, this PCR-based pooling strategy for detection of malaria parasites using dried blood spots offers a sensitive and efficient approach for malaria surveillance in low-transmission settings, enabling improved detection of asymptomatic submicroscopic infections and dramatic savings in labor and costs.

SUBMITTER: Hsiang MS

PROVIDER: S-EPMC2953134 | biostudies-literature | 2010 Oct

REPOSITORIES: biostudies-literature

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PCR-based pooling of dried blood spots for detection of malaria parasites: optimization and application to a cohort of Ugandan children.

Hsiang Michelle S MS Lin Michael M Dokomajilar Christian C Kemere Jordan J Pilcher Christopher D CD Dorsey Grant G Greenhouse Bryan B

Journal of clinical microbiology 20100804 10

Sensitive, high-throughput methods to detect malaria parasites in low-transmission settings are needed. PCR-based pooling strategies may offer a solution. We first used laboratory-prepared samples to compare 2 DNA extraction and 4 PCR detection methods across a range of pool sizes and parasite densities. Pooled Chelex extraction of DNA, followed by nested PCR of cytochrome b, was the optimal strategy, allowing reliable detection of a single low-parasitemic sample (100 parasites/μl) in pool sizes ...[more]

PMID: 20686079

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Project description:BackgroundEpidemiological surveys of malaria currently rely on microscopy, polymerase chain reaction assays (PCR) or rapid diagnostic test kits for Plasmodium infections (RDTs). This study investigated whether mid-infrared (MIR) spectroscopy coupled with supervised machine learning could constitute an alternative method for rapid malaria screening, directly from dried human blood spots.MethodsFilter papers containing dried blood spots (DBS) were obtained from a cross-sectional malaria survey in 12 wards in southeastern Tanzania in 2018/19. The DBS were scanned using attenuated total reflection-Fourier Transform Infrared (ATR-FTIR) spectrometer to obtain high-resolution MIR spectra in the range 4000 cm-1 to 500 cm-1. The spectra were cleaned to compensate for atmospheric water vapour and CO2 interference bands and used to train different classification algorithms to distinguish between malaria-positive and malaria-negative DBS papers based on PCR test results as reference. The analysis considered 296 individuals, including 123 PCR-confirmed malaria positives and 173 negatives. Model training was done using 80% of the dataset, after which the best-fitting model was optimized by bootstrapping of 80/20 train/test-stratified splits. The trained models were evaluated by predicting Plasmodium falciparum positivity in the 20% validation set of DBS.ResultsLogistic regression was the best-performing model. Considering PCR as reference, the models attained overall accuracies of 92% for predicting P. falciparum infections (specificity = 91.7%; sensitivity = 92.8%) and 85% for predicting mixed infections of P. falciparum and Plasmodium ovale (specificity = 85%, sensitivity = 85%) in the field-collected specimen.ConclusionThese results demonstrate that mid-infrared spectroscopy coupled with supervised machine learning (MIR-ML) could be used to screen for malaria parasites in human DBS. The approach could have potential for rapid and high-throughput screening of Plasmodium in both non-clinical settings (e.g., field surveys) and clinical settings (diagnosis to aid case management). However, before the approach can be used, we need additional field validation in other study sites with different parasite populations, and in-depth evaluation of the biological basis of the MIR signals. Improving the classification algorithms, and model training on larger datasets could also improve specificity and sensitivity. The MIR-ML spectroscopy system is physically robust, low-cost, and requires minimum maintenance.

Project description:BackgroundScreening malaria-specific antibody responses on protein microarrays can help identify immune factors that mediate protection against malaria infection, disease, and transmission, as well as markers of past exposure to both malaria parasites and mosquito vectors. Most malaria protein microarray work has used serum as the sample matrix, requiring prompt laboratory processing and a continuous cold chain, thus limiting applications in remote locations. Dried blood spots (DBS) pose minimal biohazard, do not require immediate laboratory processing, and are stable at room temperature for transport, making them potentially superior alternatives to serum. The goals of this study were to assess the viability of DBS as a source for antibody profiling and to use DBS to identify serological signatures of low-density Plasmodium falciparum infections in malaria-endemic regions of Myanmar.MethodsMatched DBS and serum samples from a cross-sectional study in Ingapu Township, Myanmar were probed on protein microarrays populated with P. falciparum antigen fragments. Signal and trends in both sample matrices were compared. A case-control study was then performed using banked DBS samples from malaria-endemic regions of Myanmar, and a regularized logistic regression model was used to identify antibody signatures of ultrasensitive PCR-positive P. falciparum infections.ResultsApproximately 30% of serum IgG activity was recovered from DBS. Despite this loss of antibody activity, antigen and population trends were well-matched between the two sample matrices. Responses to 18 protein fragments were associated with the odds of asymptomatic P. falciparum infection, albeit with modest diagnostic characteristics (sensitivity 58%, specificity 85%, negative predictive value 88%, and positive predictive value 52%).ConclusionsMalaria-specific antibody responses can be reliably detected, quantified, and analysed from DBS, opening the door to serological studies in populations where serum collection, transport, and storage would otherwise be impossible. While test characteristics of antibody signatures were insufficient for individual diagnosis, serological testing may be useful for identifying exposure to asymptomatic, low-density malaria infections, particularly if sero-surveillance strategies target individuals with low previous exposure as sentinels for population exposure.

Dataset Information

PCR-based pooling of dried blood spots for detection of malaria parasites: optimization and application to a cohort of Ugandan children.

Publications

PCR-based pooling of dried blood spots for detection of malaria parasites: optimization and application to a cohort of Ugandan children.

Similar Datasets

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