Waterston, R. H. | Search bioRxiv

bioRxiv2017

MIP-MAP: High-Throughput Mapping of Caenorhabditis elegans Temperature Sensitive Mutants via Molecular Inversion Probes

Temperature sensitive (TS) alleles are important tools for the genetic and functional analysis of essential genes in many model organisms. While isolating TS alleles is not difficult, determining the TS-conferring mutation can be problematic. Even with whole-genome sequencing (WGS) data there is a paucity of predictive methods for identifying TS alleles from DNA sequence alone. We assembled 173 TS lethal mutants of Caenorhabditis elegans and used WGS to identify several hundred mutations per strain. We leveraged single molecule molecular inversion probes (MIPs) to sequence variant sites at high depth in the cross-progeny of TS mutants and a mapping strain with identified sequence variants but no apparent phenotypic differences from the reference N2 strain. By sampling for variants at ~1Mb intervals across the genome we genetically mapped mutant alleles at a resolution comparable to current standards in a process we call MIP-MAP. The MIP-MAP protocol, however, permits high-throughput sequencing of multiple TS mutation mapping libraries at less than 200K reads per library. Using MIP-MAP on a subset of TS mutants, via a competitive selection assay and standard recombinant mutant selection, we defined TS-associated intervals of 3Mb or less. Our results suggest this collection of strains contains a diverse library of TS alleles for genes involved in development and reproduction. MIP-MAP is a robust method to genetically map mutations in both viable and essential genes. The MIPs protocol should allow high-throughput tracking of genetic variants in any mixed population.

genomics↗

bioRxiv2017

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Conventional methods for profiling the molecular content of biological samples fail to resolve heterogeneity that is present at the level of single cells. In the past few years, single cell RNA sequencing has emerged as a powerful strategy for overcoming this challenge. However, its adoption has been limited by a paucity of methods that are at once simple to implement and cost effective to scale massively. Here, we describe a combinatorial indexing strategy to profile the transcriptomes of large numbers of single cells or single nuclei without requiring the physical isolation of each cell (Single cell Combinatorial Indexing RNA-seq or sci-RNA-seq). We show that sci-RNA-seq can be used to efficiently profile the transcriptomes of tens-of-thousands of single cells per experiment, and demonstrate that we can stratify cell types from these data. Key advantages of sci-RNA-seq over contemporary alternatives such as droplet-based single cell RNA-seq include sublinear cost scaling, a reliance on widely available reagents and equipment, the ability to concurrently process many samples within a single workflow, compatibility with methanol fixation of cells, cell capture based on DNA content rather than cell size, and the flexibility to profile either cells or nuclei. As a demonstration of sci-RNA-seq, we profile the transcriptomes of 42,035 single cells from C. elegans at the L2 stage, effectively 50-fold \"shotgun cellular coverage\" of the somatic cell composition of this organism at this stage. We identify 27 distinct cell types, including rare cell types such as the two distal tip cells of the developing gonad, estimate consensus expression profiles and define cell-type specific and selective genes. Given that C. elegans is the only organism with a fully mapped cellular lineage, these data represent a rich resource for future methods aimed at defining cell types and states. They will advance our understanding of developmental biology, and constitute a major step towards a comprehensive, single-cell molecular atlas of a whole animal.

genomics↗