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Biology subjects

Reinecke, F.

Publications and source records attributed to Reinecke, F..

2 recordsLinked to original sources

Highly resolved spatial transcriptomics for detection of rare events in cells

Single-cell spatial transcriptomics technologies leveraged the potential to transcriptionally landscape sophisticated reactions in cells. Current methods to delineate such complex interplay lack the flexibility in rapid target adaptation and are particularly restricted in detecting rare transcripts. We developed a multiplex single-cell RNA In-situ hybridization technique, called Molecular Cartography (MC) that can be easily tailored to specific applications and, by providing unprecedented sensitivity, specificity and resolution, is particularly suitable in tracing rare events at a subcellular level. Using a SARS-CoV-2 infection model, MC allows the discernment of single events in host-pathogen interactions, dissects primary from secondary responses, and illustrates differences in antiviral signaling pathways affected by SARS-CoV-2, simultaneously in various cell types.

molecular biology

Predicting primer and panel off-target rate in QIAseq targeted DNA panels using convolutional neural networks

In QIAseq targeted DNA panels, synthetic primers (short single-strand DNA sequences) are used for target enrichment via complementary DNA binding. Off-target priming could occur in this process when a primer binds to some loci where the DNA sequences are identical or very similar to the target template. These off-target DNA segments go through the rest of the workflow, wasting sequencing resources in unwanted regions. Off-target cannot be avoided if some segments of the target region are repetitive throughout the genome, nor can it be quantified until after sequencing. But if off-target rates can be prospectively predicted, scientists can make informed decisions about investment on high off-target panels. We developed pordle (predicting off-target rate with deep learning and epcr07), a convolutional neural network (CNN) model to predict off-target binding events of a given primer. The neural network was trained using 10 QIAseq DNA panels with 29,274 unique primers and then tested on an independent QIAseq panel with 7,576 primers. The model predicted a 10.5% off-target rate for the test panel, a -0.1% bias from the true value of 10.6%. The model successfully selected the better primer (in terms of off-target rate) for 89.2% of 3,835 pairs of close-by primers in the test panel whose off-target rates differ by at least 10%. The order-preserving property may help panel developers select the optimal primer from a group of candidates, which is a common task in panel design.

bioinformatics