来自用户 白鸟 的文献。
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41.
白鸟
(2022-11-30 11:26):
#paper https://doi.org/10.1016/j.cell.2022.05.013 Cell 2022. Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq. 遗传学的一个核心目标是研究遗传变化(基因型)和表型之间的关系。主要有两种研究思路，正向遗传学和反向遗传学。正向遗传以表型为中心的“正向遗传”，即揭示驱动表型的基因变化（果因论）；而反向遗传是以基因为中心，对确定的遗传变化引起的不同表型进行解析（因果论）。为了揭示基因扰动的功能后果和基因型-表型关系，文章团队构建了一套可实践的方法论。本文利用单细胞高通量CRISPR 筛选技术Perturb-seq，针对对K562和RPE1细胞系超过250万个细胞进行了单个基因的CRISPR扰动（即1个细胞只包含一种基因的 sgRNA），通过单一基因型的变化，查看在转录组层面表型的变化，构建了一个基因型-表型综合图谱。研究团队根据基因的共同调控将其聚类到特定表达程序中，并计算每个扰动簇中每个基因表达程序的平均活性。分析结果包含多个与基因干扰相关的已知表达程序，包括蛋白酶体功能障碍导致的蛋白酶体亚基上调、 ESCRT蛋白缺失时NF-kB信号通路的激活，以及胆固醇生物合成上调对囊泡运输缺陷的反应等。它的意义在于单细胞CRISPR筛选为系统探索遗传和细胞功能提供了一个研究工具，构建和分析丰富的基因型-表现型图谱,以作为系统探索遗传和细胞功能的驱动力。可以构建全基因组的基因敲除细胞池，定向的研究，关键基因的敲除对下游转录调控表型的生物学功能。重点学习文章中grna的数据质控和归一化等细节处理。
Abstract:
A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional …
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A central goal of genetics is to define the relationships between genotypes and phenotypes. High-content phenotypic screens such as Perturb-seq (CRISPR-based screens with single-cell RNA-sequencing readouts) enable massively parallel functional genomic mapping but, to date, have been used at limited scales. Here, we perform genome-scale Perturb-seq targeting all expressed genes with CRISPR interference (CRISPRi) across >2.5 million human cells. We use transcriptional phenotypes to predict the function of poorly characterized genes, uncovering new regulators of ribosome biogenesis (including CCDC86, ZNF236, and SPATA5L1), transcription (C7orf26), and mitochondrial respiration (TMEM242). In addition to assigning gene function, single-cell transcriptional phenotypes allow for in-depth dissection of complex cellular phenomena—from RNA processing to differentiation. We leverage this ability to systematically identify genetic drivers and consequences of aneuploidy and to discover an unanticipated layer of stress-specific regulation of the mitochondrial genome. Our information-rich genotype-phenotype map reveals a multidimensional portrait of gene and cellular function.
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42.
白鸟
(2022-10-27 09:36):
#paper doi:#paper doi:https://doi.org/10.1038/s41587-022-01468-y Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes.
单细胞转录组体细胞拷贝数变异的单倍型感知分析
基因组不稳定性和转录程序的异常改变都在癌症中发挥重要作用。单细胞 RNA 测序 (scRNA-seq) 在一次检测中能够同时研究肿瘤异质性的遗传和非遗传来源。虽然有许多工具可以从外显子组和全基因组测序数据中识别CNV，针对单细胞RNA-seq数据中检测CNV的方法非常稀缺。常用的inferCNV和copyKAT都只是利用转录组的基因表达信息进行CNV推断。最近，哈佛医学院的研究者提出了一种计算方法，Numbat，它将基于群体的定相(population-based phasing)获得的单倍型信息与等位基因和表达信号相结合，能准确推断单个细胞中的等位基因特异性CNV并重建它们的谱系关系。也就是说它通过基因表达和等位基因两个证据链，进行联合推断，避免CNV推断误判。Numbat利用亚克隆之间的进化关系来迭代推断单细胞拷贝数分布和肿瘤克隆系统发育。比其他工具进行基准测试，对包括多发性骨髓瘤、胃癌、乳腺癌和甲状腺癌在内的 22 个肿瘤样本的分析表明，Numbat可以重建肿瘤拷贝数分布，并准确识别肿瘤微环境中的恶性细胞。Numbat 不需要样本匹配的 DNA 数据，也不需要先验基因分型，适用于广泛的实验环境和癌症类型。总之，Numbat 可以扩展单细胞RNA-seq数据来探测细胞的CNV景观以及转录组景观。需要思考的是我们可能需要更多不同遗传背景的人群定相单倍型信息来辅助推断。另外，肿瘤基线倍性估计仍是拷贝数分析中的有挑战性的问题。
Abstract:
Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor …
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Genome instability and aberrant alterations of transcriptional programs both play important roles in cancer. Single-cell RNA sequencing (scRNA-seq) has the potential to investigate both genetic and nongenetic sources of tumor heterogeneity in a single assay. Here we present a computational method, Numbat, that integrates haplotype information obtained from population-based phasing with allele and expression signals to enhance detection of copy number variations from scRNA-seq. Numbat exploits the evolutionary relationships between subclones to iteratively infer single-cell copy number profiles and tumor clonal phylogeny. Analysis of 22 tumor samples, including multiple myeloma, gastric, breast and thyroid cancers, shows that Numbat can reconstruct the tumor copy number profile and precisely identify malignant cells in the tumor microenvironment. We identify genetic subpopulations with transcriptional signatures relevant to tumor progression and therapy resistance. Numbat requires neither sample-matched DNA data nor a priori genotyping, and is applicable to a wide range of experimental settings and cancer types.
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