@article{WarmtFenzelHenkeletal.2021, author = {Warmt, Christian and Fenzel, Carolin Kornelia and Henkel, J{\"o}rg and Bier, Frank Fabian}, title = {Using Cy5-dUTP labelling of RPA-amplicons with downstream microarray analysis for the detection of antibiotic resistance genes}, series = {Scientific reports}, volume = {11}, journal = {Scientific reports}, number = {1}, publisher = {Macmillan Publishers Limited, part of Springer Nature}, address = {[London]}, issn = {2045-2322}, doi = {10.1038/s41598-021-99774-z}, pages = {9}, year = {2021}, abstract = {In this report we describe Cy5-dUTP labelling of recombinase-polymerase-amplification (RPA) products directly during the amplification process for the first time. Nucleic acid amplification techniques, especially polymerase-chain-reaction as well as various isothermal amplification methods such as RPA, becomes a promising tool in the detection of pathogens and target specific genes. Actually, RPA even provides more advantages. This isothermal method got popular in point of care diagnostics because of its speed and sensitivity but requires pre-labelled primer or probes for a following detection of the amplicons. To overcome this disadvantages, we performed an labelling of RPA-amplicons with Cy5-dUTP without the need of pre-labelled primers. The amplification results of various multiple antibiotic resistance genes indicating great potential as a flexible and promising tool with high specific and sensitive detection capabilities of the target genes. After the determination of an appropriate rate of 1\% Cy5-dUTP and 99\% unlabelled dTTP we were able to detect the bla(CTX-M15) gene in less than 1.6E-03 ng genomic DNA corresponding to approximately 200 cfu of Escherichia coli cells in only 40 min amplification time.}, language = {en} } @article{AgarwalWarmtHenkeletal.2022, author = {Agarwal, Saloni and Warmt, Christian and Henkel, J{\"o}rg and Schrick, Livia and Nitsche, Andreas and Bier, Frank Fabian}, title = {Lateral flow-based nucleic acid detection of SARS-CoV-2 using enzymatic incorporation of biotin-labeled dUTP for POCT use}, series = {Analytical and bioanalytical chemistry : a merger of Fresenius' journal of analytical chemistry, Analusis and Quimica analitica}, volume = {414}, journal = {Analytical and bioanalytical chemistry : a merger of Fresenius' journal of analytical chemistry, Analusis and Quimica analitica}, number = {10}, publisher = {Springer}, address = {Heidelberg}, issn = {1618-2642}, doi = {10.1007/s00216-022-03880-4}, pages = {3177 -- 3186}, year = {2022}, abstract = {The degree of detrimental effects inflicted on mankind by the COVID-19 pandemic increased the need to develop ASSURED (Affordable, Sensitive, Specific, User-friendly, Rapid and Robust, Equipment-free, and Deliverable) POCT (point of care testing) to overcome the current and any future pandemics. Much effort in research and development is currently advancing the progress to overcome the diagnostic pressure built up by emerging new pathogens. LAMP (loop-mediated isothermal amplification) is a well-researched isothermal technique for specific nucleic acid amplification which can be combined with a highly sensitive immunochromatographic readout via lateral flow assays (LFA). Here we discuss LAMP-LFA robustness, sensitivity, and specificity for SARS-CoV-2 N-gene detection in cDNA and clinical swab-extracted RNA samples. The LFA readout is designed to produce highly specific results by incorporation of biotin and FITC labels to 11-dUTP and LF (loop forming forward) primer, respectively. The LAMP-LFA assay was established using cDNA for N-gene with an accuracy of 95.65\%. To validate the study, 82 SARS-CoV-2-positive RNA samples were tested. Reverse transcriptase (RT)-LAMP-LFA was positive for the RNA samples with an accuracy of 81.66\%; SARS-CoV-2 viral RNA was detected by RT-LAMP-LFA for as low as CT-33. Our method reduced the detection time to 15 min and indicates therefore that RT-LAMP in combination with LFA represents a promising nucleic acid biosensing POCT platform that combines with smartphone based semi-quantitative data analysis.}, language = {en} }