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Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes - Combined MGE and taxonomic mapping

<p>We used rRNA-FISH to stain five common oral genera, <em>Veillonella, Streptococcus, Corynebacterium, Lautropia, </em>and <em>Neisseria, </em>each with a different fluorophore, and we used MGE-FISH to stain the <em>termL</em> gene of the active prophage with a sixth fluorophore.</p> <p>We assembled contigs using combined long- and short-read sequencing and identified a highly abundant plasmid. Alignment of this contig to the plasmid database (PLSDB) showed that the plasmid had previously been observed in <em>Prevotella nigrescens</em> (https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_018127865.1/). We selected two genes from the contig with metallo-&beta;-lactamase (MBL) domains as targets for MGE-FISH (https://www.uniprot.org/uniprotkb/V8CNR4/entry, https://www.uniprot.org/uniprotkb/V8CNR9/entry). We stained both putative MBL genes (<em>pMBL</em>) with the same color using MGE-FISH. For taxonomic mapping, we broadened our target panel by employing HIPR-FISH. We selected a target panel of 18 genera that are highly abundant and prevalent in human plaque.&nbsp;We designed a HiPR-FISH spectral encoding using a 5-fluorophore combinatorial barcoding scheme, whereby each fluorophore represents a binary bit, providing 31 possible barcodes (2^5 - 1 = 31).&nbsp;The fluorophore for MGE-FISH was spectrally distinct from those of HiPR-FISH, enabling simultaneous implementation of both methods.</p> <p>Images are labeled by collection time such that the laser order for a given field of view (fov) is: 488nm Lambda, 514nm Lambda, 561nm Lambda, 633nm Airyscan, 405nm Lambda. We used OPERA-MS (https://github.com/CSB5/OPERA-MS) to do hybrid assembly with Illumina short reads and Nanopore long reads. The assemblies are in the fasta files and the reads that map to the assemblies are in the fastq files.&nbsp;</p>

ShareScore

36/100

Overall dataset sharing score

Score breakdown

These five areas show where the dataset supports — or may limit — practical reuse.

Stewardship
4
Harmonization
4
Access
20
Reuse readiness
8
Engagement
0

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