Project description:Long-read sequencing has become a powerful tool for alternative splicing analysis. However, technical and computational challenges have limited our ability to couple long-read sequencing with single cell and spatial barcoding to explore alternative splicing in the single cell and spatial setting. Though Nanopore-based long reads sequencing are widelyhave been adopted applied to explore single cell alternative and spatially barcoded librariessplicing in recent research, there still exist technical issues have problems which could bias the hindered accurate single cell isoform-level quantification, which are not well addressed in such settings. First, Tthe relatively higher sequencing error of Nanopore long reads, despite the recent improvements, has limited the accuracy ofhinder cell barcode and unique molecular identifier (UMI) recovery, a necessary first step in the analysis of single cell/spatial sequencing data. Then Rread truncation and mapping errors, the latter exacerbated by the higher sequencing error rates, further leads to the false detection of spurious new isoformsdegrade quantification accuracy. We show that these technical issues persist despite the recent improvements in long read sequencing accuracy. Beyond the initial data pre-processing, in downstream analysis we are lacking a statistical framework to quantify splicing variation within and between cells/spots. In light of these multiple challenges, we developed Longcell, a statistical framework and computational pipeline for isoform quantification using single cell and spatial spot barcoded Nanopore long read sequencing data. Longcell performs computationally efficient cell/spot barcode extraction, UMI recovery, and UMI-based truncation- and mapping-error correction. Through a statistical model that accounts for varying read coverage across cells/spots, Longcell rigorously quantifies the level of inter-cell/spot versus intra-cell/ spot diversity in exon-usage and detects changes in splicing distributions between cell populations. Applying Longcell to single cell long-read data from multiple contexts, we found that intra-cell splicing heterogeneity, where multiple isoforms co-exist within the same cell, is ubiquitous for highly expressed genes. On matched single cell and Visium long read sequencing for a tissue of colorectal cancer metastasis to the liver, Longcell found concordant signals between the single cell and spatial data modalities. On Visium long read sequencing data for multiple tissues, Longcell allows accurate identification of spatial isoform switching. Finally, on a perturbation experiment for 9 splicing factors, Longcell identified regulatory targets that are validated by targeted sequencing.

Project description:Over the past 15+ years, genetic studies have been notably successful in revealing the architecture of multiple psychiatric disorders, including schizophrenia. It is now widely acknowledged that schizophrenia is highly polygenic, consisting of both common variants of unknown significance, identified through genome-wide association studies (GWAS), and rare CNVs and coding variants. Despite these advances, the underlying mechanisms of specific genes implicated by GWAS are not well understood. Multiple lines of evidence implicate alternative splicing in the pathophysiology of schizophrenia. Single-nucleotide polymorphisms (SNPs) within implicated schizophrenia loci could alter isoform diversity and abundances which may not be reflected in a typical differential expression study. Hence, we generated a comprehensive isoform survey of postmortem human dorsolateral prefrontal cortex (DLPFC) from schizophrenia cases and neurotypical controls to identify case-control isoform-level differences. We developed an analysis pipeline that combines the strengths of PacBio SMRT long-read RNA sequencing in conducting a detailed isoform census with the capacity of short-read RNA sequencing for the quantification of isoform abundances. From several hundred thousand discovered long-read isoforms we curated a transcriptome with tens of thousands of high confidence novel isoforms. We then identified differential isoform usage (DIU) genes using a combination of established and in-house pipelines that enables case-control comparisons. Many of these novel isoforms are differentially expressed in schizophrenia DLPFC vs neurotypical controls. Differentially expressed genes are enriched in gene sets related to synaptic structure and function, RNA binding and splicing, as well as cell types previously implicated in schizophrenia, including cortical excitatory neurons, medium spiny neurons, and pyramidal CA1 neurons. Publicly available splicing data, genotyping, proteomics, and single nucleus sequencing results verify and support our results.

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Short-read and long-read single-cell sequencing capture distinct perturbation effects in CRISPR screens

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