Biology subjects

Politza, A. J.

Publications and source records attributed to Politza, A. J..

4 recordsLinked to original sources

bioRxiv2025

Probe-Free Multiplexed RPA Detection Via Single-Molecule Nanopore Sensing and Deep Learning Classification

Recombinase Polymerase Amplification (RPA) is a rapid, sensitive, and isothermal method for nucleic acid amplification that has gained widespread use in diagnostic applications. While well-suited for single-target assays, extending RPA to multiplex detection remains technically challenging despite the multiplexed RPA being essential for tasks such as differential diagnosis and inclusion of internal controls. Existing multiplex RPA strategies rely on proprietary probes (e.g., Exo, Fpg, Nfo), which require complex design, are susceptible to cross-reactivity, and often depend on sophisticated optical instrumentation, limiting their scalability. To address these limitations, here, we developed a probe-free multiplex RPA assay that distinguishes targets by amplicon length, using solid-state nanopore detection of single molecules and deep neural network (DNN)-based classification. Using the Monkeypox (Mpox) as a model, we designed and validated a multiplex RPA assay targeting both the Mpox gene and the human RNase P gene as an internal control. We systematically evaluated the nanopore size requirement for detecting DNA amplicons ranging from 50 to 500 base pairs and found that [~]7 nm pores provided optimal performance for amplicons between 75 and 500 bp, balancing high event rates without pore clogging. Using single-molecule translocation events as input, we trained and optimized a DNN to classify amplicons by target. The model achieved 94.2% accuracy at the single-molecule event level, which translated to 100% accuracy at the population-level target call. While our current system demonstrates duplex detection, the strategy is inherently scalable to higher multiplex levels. These findings establish a new framework for multiplex RPA that eliminates the need for complex probe design and optical detection, paving the way for robust, scalable, and accessible molecular diagnostics.

molecular biology↗

bioRxiv2025

Solid-State Nanopore Sizing for cfDNA Sample Quality Control in Point-of-Need Sequencing

DNA sequencing is a powerful tool for diagnosing conditions like infectious diseases and cancers. Even though current workflows demand rigorous quality control (QC) of DNA samples, this QC is typically limited to lab settings, despite recent advances in portable nanopore sequencers. For personalized healthcare to truly benefit from the portable sequencer, QC must be performed right where the samples are processed. Here, we present a solid-state nanopore device that provides label-free, controlled quantification and qualification of cell-free DNA (cfDNA). We demonstrated the use of a 1 kbp double-stranded DNA internal marker at a known concentration to measure the concentration of a representative cfDNA target in the presence of genomic DNA. We also found that nanopores with diameters ranging from 6 to 19 nm yield consistent measurements, with a maximum coefficient of variation (CV) of less than 15%. Moreover, analyzing data from multiple nanopores over longer acquisition times can reduce the uncertainty to below 10% CV. Finally, we applied our nanopore QC assay to a plasma cfDNA sample and compared the results with those from a capillary electrophoresis (CE) assay. Both methods produced highly correlated measurements, demonstrating the potential of our nanopore QC assay for effective cfDNA assessment at the point of need. Table of Contents Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/643726v1_ufigl1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@1131713org.highwire.dtl.DTLVardef@4ffeb1org.highwire.dtl.DTLVardef@1da48e3org.highwire.dtl.DTLVardef@1e7697a_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2025

Point-Of-Need One-Pot Multiplexed RT-LAMP Test For Detecting Three Common Respiratory Viruses In Saliva

Respiratory viral infections pose a significant global public health challenge, partly due to the difficulty in rapidly and accurately distinguishing between viruses with similar symptoms at the point of care, hindering timely and appropriate treatment and limiting effective infection control and prevention efforts. Here, we developed a multiplexed, non- invasive saliva-based, reverse transcription loop-mediated isothermal amplification (RT- LAMP) test that enables the simultaneous detection of three of the most common respiratory infections, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), Influenza (Flu), and respiratory syncytial virus (RSV), in a single reaction via specific probes and monitored in real-time by a machine-learning-enabled compact analyzer. Our results demonstrate that the multiplexed assay can effectively detect three target RNAs with high accuracy. Further, testing with spiked saliva samples showed strong agreement with reverse transcription polymerase chain reaction (RT-PCR) assay, with area under the curve (AUC) values of 0.82, 0.93, and 0.96 for RSV, Influenza, and SARS-CoV-2, respectively. By enabling the rapid detection of respiratory infections from easily collected saliva samples at the point of care, the device presented here offers a practical and efficient tool for improving outcomes and helping prevent the spread of contagious diseases. SignificanceThis research presents an innovative approach to respiratory infection diagnostics by combining a one-pot isothermal molecular test with machine learning-based analysis to simultaneously detect SARS-CoV-2, Influenza, and RSV in saliva samples. The battery- powered portable analyzer features novel machine-learning-assisted fluorescence detection for multiplexed reporter quantification, eliminating the need for traditional filter- based optical components and enabling adaptation to new targets without hardware changes. The test demonstrates high accuracy in detecting single and co-infections in spiked saliva samples, providing a rapid, cost-effective point-of-need solution. This tool can expand testing access, improve patient outcomes, and support more effective disease control, particularly in resource-limited or decentralized healthcare settings.

bioengineering↗

bioRxiv2022

STAMP-Based Digital CRISPR-Cas13a (STAMP-dCRISPR) for Amplification-Free Quantification of HIV-1 Plasma Viral Load

The development of new nucleic acid techniques to quantify HIV RNA in plasma is critical for identifying the disease progression and monitoring the effectiveness of antiretroviral therapy. While RT-qPCR has been the gold standard for HIV viral load quantification, digital assays could provide an alternative calibration-free absolute quantification method. Here, we report the development of a self-digitalization through automated membrane-based partitioning (STAMP) technique to digitalize the CRISPR-Cas13 assay (dCRISPR) for amplification-free and absolute quantification of HIV-1 viral RNAs. The analytical performances of STAMP-dCRISPR were evaluated with synthetic HIV-1 RNA, and it was found samples spanning 4 orders of dynamic range between 100 aM to 1 pM can be quantified as fast as 30 min. We also examined the overall assay from RNA extraction to STAMP-dCRISPR quantification with spiked plasma samples. The overall assay showed a resolution of 42 aM at a 90% confidence level. Finally, a total of 20 clinical plasma samples from patients were evaluated with STAMP-dCRISPR. The obtained results agreed well with the RT-qPCR. Our result demonstrates a new type of easy-to-use, scalable, and highly specific digital platform that would offer a simple and accessible platform for amplification-free quantification of viral RNAs, which could be exploited for the quantitative determination of viral load for an array of infectious diseases.

bioengineering↗