来自用户 白鸟 的文献。
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21.
白鸟
(2023-01-28 22:01):
#paper https://doi.org/10.1038/s41591-022-02104-7 nature medicine 2022. Impact of the Human Cell Atlas on medicine.
疾病只有在健康样本的参照下才能被充分理解,实现这一愿景需要构建人类所有细胞的综合参考图谱。单细胞图谱有望填补基因、疾病和疗法之间“缺失的一环”。
图谱的意义在于:1.提高我们对疾病的认知,通过识别特定的细胞类型、状态、程序和与疾病相关基因起作用的环境,我们可以从细胞和组织层面了解疾病机制。2.诊断和治疗的应用:单细胞图谱和空间图谱改变我们对不同疾病在细胞和组织层面的理解,为了解诊断学、药物发现和新的治疗途径的发展提供信息。利用这些发现来开发强大的疾病诊断;确定有前途的新药物靶标;预测它们的功效、毒性和耐药机制;从癌症疗法到再生医学方领域授予新的疗法。
总结:人类细胞图谱的使命是形成一个参考图谱,作为了解人类健康以及诊断、监测和治疗疾病的基础。类似于人类基因组计划,基因组计划本身并没有“解决”疾病,但为生物医学的许多领域奠定了重要基础。绘制人类细胞图谱同时也带来了巨大的后期工作和技术挑战,路漫漫兮,但是它对医学的潜力也是巨大的。
Abstract:
Single-cell atlases promise to provide a 'missing link' between genes, diseases and therapies. By identifying the specific cell types, states, programs and contexts where disease-implicated genes act, we will understand …
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Single-cell atlases promise to provide a 'missing link' between genes, diseases and therapies. By identifying the specific cell types, states, programs and contexts where disease-implicated genes act, we will understand the mechanisms of disease at the cellular and tissue levels and can use this understanding to develop powerful disease diagnostics; identify promising new drug targets; predict their efficacy, toxicity and resistance mechanisms; and empower new kinds of therapies, from cancer therapies to regenerative medicine. Here, we lay out a vision for the potential of cell atlases to impact the future of medicine, and describe how advances over the past decade have begun to realize this potential in common complex diseases, infectious diseases (including COVID-19), rare diseases and cancer.
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22.
白鸟
(2022-12-31 23:22):
#paper https://doi.org/10.1016/j.csbj.2020.06.012 Computational and Structural Biotechnology Journal 2020. Single-cell ATAC sequencing analysis: From data preprocessing to hypothesis generation.
关注点:这是一篇关于单细胞ATAC-seq分析的综述文章,比较系统地从数据的预处理到生成科学假设的过程进行了详细方法论的说明和基准测试,使用适当的软件工具和数据库,提供有价值的分析方法指导。
研究背景:与人类复杂性状相关的大多数遗传变异位于基因组非编码区域。因此,了解基因型到表型之间的生物学机理机制的研究,大多涉及基因表达的表观遗传调控。开放染色质区域的全基因组图谱可以通过顺式和反式调控元件与性状相关序列变异的关联分析,促进顺式和跨式调控元件的功能分析。ATAC-seq测序 技术,转座酶可及染色质分析被认为是染色质可及性全基因组分析中最容易获得且最具成本效益的策略。
研究不足:目前,还开发了单细胞 ATAC-seq (scATAC-seq) 技术,来研究不同异质细胞群的组织样本中细胞类型特异性染色质的可及性差异。但是,由于 scATAC-seq 数据的固有特性,高噪声和稀疏性,很难准确提取生物信号并设计有效的生物学假设。为了克服 scATAC-seq 数据分析中的这些限制,过去几年研究者开发了一些新的方法和软件工具。然而,scATAC-seq 数据分析的最佳和标准分析流程并未达成共识。
内容大纲:1.阐述scATAC-seq 分析工作流程:数据的预处理,测序read的预处理->过滤掉低质量细胞或双细胞->生成细胞-特征矩阵->多样本的批次校正和数据整合->数据转换,包括归一化->降维、可视化和聚类。以上跟scrna-seq的步骤很相似,又有其特殊性。2.scATAC-seq生成科学假设的下游分析:包括细胞类型注释,染色质可及性动力学研究,基于TF motif,基于基因,增强子,基因-疾病相关遗传变异的研究促进假说的生成。以阐明顺式调控元件(例如启动子和增强子)与反式调控元件(例如转录因子 (TF))之间的网络。还可以使用 scATAC-seq 数据分析基因活性和遗传变异的可及性。3.多模态分析:scATAC-seq 可以与单细胞 RNA 测序 (scRNA-seq) 数据 和其他组学数据相结合,用于多组学研究。这种综合多模态分析将有助于识别参与疾病进展的关键调节因子,这些调节因子通常是潜在的治疗靶点和诊断生物标志物。
IF:4.400Q2
Computational and structural biotechnology journal,
2020.
DOI: 10.1016/j.csbj.2020.06.012
PMID: 32637041
Abstract:
Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of …
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Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.
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23.
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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24.
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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