Sc-Splash Provides Ultra-Efficient Reference-Free Discovery in Barcoded Single-Cell Sequencing

Roozbeh Dehghannasiri,Marek Kokot,Alexander L Starr,Jamie Maziarz,Tal Gordon,Serena Y Tan,Peter L Wang,Ayelet Voskoboynik,Jacob M Musser,Sebastian Deorowicz,Julia Salzman

bioRxiv the preprint server for biology（2024）

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Abstract

Typical high-throughput single-cell RNA-sequencing (scRNA-seq) analyses are primarily conducted by (pseudo)alignment, through the lens of annotated gene models, and aimed at detecting differential gene expression. This misses diversity generated by other mechanisms that diversify the transcriptome such as splicing and V(D)J recombination, and is blind to sequences missing from imperfect reference genomes. Here, we present sc-SPLASH, a highly efficient pipeline that extends our SPLASH framework for statistics-first, reference-free discovery to barcoded scRNA-seq (10x Chromium) and spatial transcriptomics (10x Visium); we also provide its optimized module for preprocessing and k-mer counting in barcoded data, BKC, as a standalone tool. sc-SPLASH rediscovers known biology including V(D)J recombination and cell-type-specific alternative splicing in human and trans-splicing in tunicate (Ciona) and when applied to spatial datasets, detects sequence variation including tumor-specific somatic mutation. In sponge (Spongilla) and tunicate (Ciona), we uncover secreted repeat proteins expressed in immune-type cells and regulated during development; the sponge genes were absent from the reference assembly. sc-SPLASH provides a powerful alternative tool for exploring transcriptomes that is applicable to the breadth of life's diversity.

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【要点】：论文提出了一种高效的参考基因组无关的scRNA-seq数据分析方法sc-SPLASH，能够发现由非基因表达差异机制产生的多样性，如剪接和V(D)J重组。

【方法】：sc-SPLASH是基于统计优先、无参考基因组的SPLASH框架，并针对条形码scRNA-seq（10x Chromium）和空间转录组学（10x Visium）进行了扩展。

【实验】：研究者使用sc-SPLASH在人类和小膜菌（Ciona）中重新发现了已知生物学现象，如V(D)J重组和细胞类型特异性可替代剪接，并在空间数据集中检测到序列变异，包括肿瘤特异性体细胞突变。在小膜菌（Ciona）和海绵（Spongilla）中，发现了在免疫型细胞中表达的分泌重复蛋白，并在发育过程中调控，其中海绵基因在参考组装中缺失。实验使用了10x Chromium和10x Visium平台产生的数据集。

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