Assessment of Automated Nucleic Acid Extraction Systems in Combination with MinION Sequencing As Potential Tools for the Detection of Microbial Biosignatures

Sample-to-answer sensing technologies for nucleic acid preparation and detection in the field

Efficient sample preparation and accurate disease diagnosis under field conditions are of great importance for the early intervention of diseases in humans, animals, and plants. However, in-field preparation of high-quality nucleic acids from various specimens for downstream analyses, such as amplification and sequencing, is challenging. Thus, developing and adapting sample lysis and nucleic acid extraction protocols suitable for portable formats have drawn significant attention. Similarly, various nucleic acid amplification techniques and detection methods have also been explored. Combining these functions in an integrated platform has resulted in emergent sample-to-answer sensing systems that allow effective disease detection and analyses outside a laboratory. Such devices have a vast potential to improve healthcare in resource-limited settings, low-cost and distributed surveillance of diseases in food and agriculture industries, environmental monitoring, and defense against biological warfare and terrorism. This paper reviews recent advances in portable sample preparation technologies and facile detection methods that have been / or could be adopted into novel sample-to-answer devices. In addition, recent developments and challenges of commercial kits and devices targeting on-site diagnosis of various plant diseases are discussed.

Microbial Characterization of Arctic Glacial Ice Cores with a Semiautomated Life Detection System

The search for extant microbial life will be a major focus of future astrobiology missions; however, no direct extant life detection instrumentation is included in current missions to Mars. In this study, we developed the semiautomated MicroLife detection platform that collects and processes environmental samples, detects biosignatures, and characterizes microbial activity. This platform is composed of a drill for sample collection, a redox dye colorimetric system for microbial metabolic activity detection and assessment (μMAMA [microfluidics Microbial Activity MicroAssay]), and a MinION sequencer for biosignature detection and characterization of microbial communities. The MicroLife platform was field-tested on White Glacier on Axel Heiberg Island in the Canadian high Arctic, with two extracted ice cores. The μMAMA successfully detected microbial metabolism from the ice cores within 1 day of incubation. The MinION sequencing of the ice cores and the positive μMAMA card identified a microbial community consistent with cold and oligotrophic environments. Furthermore, isolation and identification of microbial isolates from the μMAMA card corroborated the MinION sequencing. Together, these analyses support the MicroLife platform's efficacy in identifying microbes natively present in cryoenvironments and detecting their metabolic activity. Given our MicroLife platform's size and low energy requirements, it could be incorporated into a future landed platform or rovers for life detection.

A primer on pollen assignment by nanopore-based DNA sequencing

The possibility to identify plants based on the taxonomic information coming from their pollen grains offers many applications within various biological disciplines. In the past and depending on the application or research in question, pollen origin was analyzed by microscopy, usually preceded by chemical treatment methods. This procedure for identification of pollen grains is both time-consuming and requires expert knowledge of morphological features. Additionally, these microscopically recognizable features usually have a low resolution at species-level. Since a few decades, DNA has been used for the identification of pollen taxa, as sequencing technologies evolved both in their handling and affordability. We discuss advantages and challenges of pollen DNA analyses compared to traditional methods. With readers with little experience in this field in mind, we present a hands-on primer for genetic pollen analysis by nanopore sequencing. As our lab mainly works with pollen collected within agroecological research projects, we focus on pollen collected by pollinating insects. We briefly consider sample collection, storage and processing in the laboratory as well as bioinformatic aspects. Currently, pollen metabarcoding is mostly conducted with next-generation sequencing methods that generate short sequence reads (<1 kb). Increasingly, however, pollen DNA analysis is carried out using the long-read generating (several kb), low-budget and mobile MinION nanopore sequencing platform by Oxford Nanopore Technologies. Therefore, we are focusing on aspects for palynology with the MinION DNA sequencing device.

Machine Learning-Based Models for Detection of Biomarkers of Autoimmune Diseases by Fragmentation and Analysis of miRNA Sequences

Thanks to high-throughput data technology, microRNA analysis studies have evolved in early disease detection. This work introduces two complete models to detect the biomarkers of two autoimmune diseases, multiple sclerosis and rheumatoid arthritis, via miRNA analysis. Based on work the authors published previously, both introduced models involve complete pipelines of text mining methods, integrated with traditional machine learning methods, and LSTM deep learning. This work also studies the fragmentation of miRNA sequences to reduce the needed processing time and computational power. Moreover, this work studies the impact of obtaining two different library preparation kits (NEBNEXT and NEXTFLEX) on the detection accuracy for rheumatoid arthritis. Additional experiments are applied to the proposed models based on three different transcriptomic datasets. The results denote that the transcriptomic fragmentation model reported a biomarker detection accuracy of 96.45% on a sequence fragment size of 0.2, indicating a significant reduction in execution power while retaining biomarker detection accuracy. On the other hand, the LSTM model obtained a promising detection accuracy of 72%, implying savings in feature engineering processing. Additionally, the fragmentation model and the LSTM model reported 22.4% and 87.5% less execution time than work in the literature, respectively, denoting a considerable execution power reduction.