Cole Wunderlich

Transposable elements (TEs) are mobile genetic sequences that can generate new copies of themselves via insertional mutations. These viral-like sequences comprise nearly half the human genome and are present in most genome wide sequencing assays. While only a small fraction of genomic TEs have retained their ability to transpose, TE sequences are often transcribed from their own promoters or as part of larger gene transcripts. Accurately assessing TE expression from each individual genomic TE locus remains an open problem in the field, due to the highly repetitive nature of these multi-copy sequences. These issues are compounded in single-cell and single-nucleus transcriptome experiments, where additional complications arise due to sparse read coverage and unprocessed mRNA introns. Here we present our tool for single-cell TE and gene expression analysis, TEsingle. Using synthetic datasets, we show the problems that arise when not properly accounting for intron retention events, failing to address uncertainty in alignment scoring, and failing to make use of unique molecular identifiers for transcript resolution. Addressing these challenges has enabled an accurate TE analysis suite that simultaneously tracks gene expression as well as locus-specific resolution of expressed TEs. We showcase the performance of TEsingle using single-nucleus profiles from substantia nigra (SN) tissues of Parkinsons Disease (PD) patients. We find examples of young and intact TEs that mark dopaminergic neurons (DA) as well as many young TEs from the LINE and ERV families that are elevated in PD neurons and glia. These results demonstrate that TE expression is highly cell-type and cellular-state specific and elevated in particular subsets of neurons, astrocytes, and microglia from PD patients.

The optimal extraction of information from untargeted metabolomics analyses is a continuing challenge. Here, we describe an approach that combines stable isotope labeling, liquid chromatography– mass spectrometry (LC–MS), and a computational pipeline to automatically identify metabolites produced from a selected metabolic precursor. We identified the subset of the soluble metabolome generated from phenylalanine (Phe) in Arabidopsis thaliana, which we refer to as the Phe-derived metabolome (FDM) In addition to identifying Phe-derived metabolites present in a single wild-type reference accession, the FDM was established in nine enzymatic and regulatory mutants in the phenylpropanoid pathway. To identify genes associated with variation in Phe-derived metabolites in Arabidopsis, MS features collected by untargeted metabolite profiling of an Arabidopsis diversity panel were retrospectively annotated to the FDM and natural genetic variants responsible for differences in accumulation of FDM features were identified by genome-wide association. Large differences in Phe-derived metabolite accumulation and presence/absence variation of abundant metabolites were observed in the nine mutants as well as between accessions from the diversity panel. Many Phe-derived metabolites that accumulated in mutants also accumulated in non-Col-0 accessions and was associated to genes with known or suspected functions in the phenylpropanoid pathway as well as genes with no known functions. Overall, we show that cataloguing a biochemical pathway’s products through isotopic labeling across genetic variants can substantially contribute to the identification of metabolites and genes associated with their biosynthesis.