Fedrigo, O. — Page 3 | Search bioRxiv

bioRxiv2019

Six new reference-quality bat genomes illuminate the molecular basis and evolution of bat adaptations

Bats account for ~20% of all extant mammal species and are considered exceptional given their extraordinary adaptations, including biosonar, true flight, extreme longevity, and unparalleled immune systems. To understand these adaptations, we generated reference-quality genomes of six species representing the key divergent lineages. We assembled these genomes with a novel pipeline incorporating state-of-the-art long-read and long-range sequencing and assembly techniques. The genomes were annotated using a maximal evidence approach, de novo predictions, protein/mRNA alignments, Iso-seq long read and RNA-seq short read transcripts, and gene projections from our new TOGA pipeline, retrieving virtually all (>99%) mammalian BUSCO genes. Phylogenetic analyses of 12,931 protein coding-genes and 10,857 conserved non-coding elements identified across 48 mammalian genomes helped to resolve bats closest extant relatives within Laurasiatheria, supporting a basal position for bats within Scrotifera. Genome-wide screens along the bat ancestral branch revealed (a) selection on hearing-involved genes (e.g LRP2, SERPINB6, TJP2), which suggest that laryngeal echolocation is a shared ancestral trait of bats; (b) selection (e.g INAVA, CXCL13, NPSR1) and loss of immunity related proteins (e.g. LRRC70, IL36G), including pro-inflammatory NF-kB signalling; and (c) expansion of the APOBEC family, associated with restricting viral infection, transposon activity and interferon signalling. We also identified unique integrated viruses, indicating that bats have a history of tolerating viral pathogens, lethal to other mammal species. Non-coding RNA analyses identified variant and novel microRNAs, revealing regulatory relationships that may contribute to phenotypic diversity in bats. Together, our reference-quality genomes, high-quality annotations, genome-wide screens and in-vitro tests revealed previously unknown genomic adaptations in bats that may explain their extraordinary traits.

genomics↗

bioRxiv2018

Single-cell isoform RNA sequencing (ScISOr-Seq) across thousands of cells reveals isoforms of cerebellar cell types.

Full-length isoform sequencing has advanced our knowledge of isoform biology1-11. However, apart from applying full-length isoform sequencing to very few single cells12,13, isoform sequencing has been limited to bulk tissue, cell lines, or sorted cells. Single splicing events have been described for <=200 single cells with great statistical success14,15, but these methods do not describe full-length mRNAs. Single cell short-read 3 sequencing has allowed identification of many cell sub-types16-23, but full-length isoforms for these cell types have not been profiled. Using our new method of single-cell-isoform-RNA-sequencing (ScISOr-Seq) we determine isoform-expression in thousands of individual cells from a heterogeneous bulk tissue (cerebellum), without specific antibody-fluorescence activated cell sorting. We elucidate isoform usage in high-level cell types such as neurons, astrocytes and microglia and finer sub-types, such as Purkinje cells and Granule cells, including the combination patterns of distant splice sites6-9,24,25, which for individual molecules requires long reads. We produce an enhanced genome annotation revealing cell-type specific expression of known and 16,872 novel (with respect to mouse Gencode version 10) isoforms (see isoformatlas.com).\n\nScISOr-Seq describes isoforms from >1,000 single cells from bulk tissue without cell sorting by leveraging two technologies in three steps: In step one, we employ microfluidics to produce amplified full-length cDNAs barcoded for their cell of origin. This cDNA is split into two pools: one pool for 3 sequencing to measure gene expression (step 2) and another pool for long-read sequencing and isoform expression (step 3). In step two, short-read 3-sequencing provides molecular counts for each gene and cell, which allows clustering cells and assigning a cell type using cell-type specific markers. In step three, an aliquot of the same cDNAs (each barcoded for the individual cell of origin) is sequenced using Pacific Biosciences (\"PacBio\")1,2,4,5,26 or Oxford Nanopore3. Since these long reads carry the single-cell barcodes identified in step two, one can determine the individual cell from which each long read originates. Since most single cells are assigned to a named cluster, we can also assign the cells cluster name (e.g. \"Purkinje cell\" or \"astrocyte\") to the long read in question (Fig 1A) - without losing the cell of origin of each long read.\n\nO_FIG O_LINKSMALLFIG WIDTH=180 HEIGHT=200 SRC=\"FIGDIR/small/364950_fig1.gif\" ALT=\"Figure 1\">\nView larger version (66K):\norg.highwire.dtl.DTLVardef@4df0cdorg.highwire.dtl.DTLVardef@fc4beborg.highwire.dtl.DTLVardef@1dc485forg.highwire.dtl.DTLVardef@1138a3e_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO (A) Outline of our ScISOr-Seq approach. (B) TSNE-plot depicting cell clusters, marker genes and names given to clusters, including: Bergman glia (BG), External granule cell layer neurons (EGL), Internal granule cell layer and other neurons in the interior of the cerebellum (IGL), two clusters of Purkinje cell layer neurons (PCL), oligodendrocyte progenitor cells (OPCs), Atoh1+ neuronal progenitors, Ptf1a+ neuronal progenitors and other neuronal progenitors (NPCs) (C) In-situ hybridization images from the Allen Brain Atlas depicting expression of marker genes in specific layers. (D) Expression patterns of selected marker genes across cell types.\n\nC_FIG

molecular biology↗