Matrix Inversion and Subset Selection (MISS): A pipeline for mapping of diverse cell types across the murine brain.
Matrix Inversion and Subset Selection (MISS): A pipeline for mapping of diverse cell types across the murine brain.
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DOI：
10.1073/pnas.2111786119
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发表时间：
2022-04-05
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
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作者：
目前最先进的细胞类型图谱在精细解析神经细胞亚型方面存在不足，尤其是γ-氨基丁酸能和谷氨酸能亚型。大多数此类图谱都会损害每项研究中量化的独特细胞类型的数量或特异性。其他人只对他们的图谱进行定性验证，而未能解决基因子集选择对于最佳图谱是否是必要的。矩阵反转和子集选择流程使用公开的原位杂交和单细胞 RNA 测序基因表达数据来推断细胞类型分布，以绘制小鼠大脑中不同细胞类型的图谱。最重要的是，我们证明数据驱动的特征选择对于使用基于反演、反卷积和相关的映射方法获得定量最佳的细胞类型图是必要的。日益复杂的成像平台的出现使得小鼠神经系统能够以单细胞分辨率可视化。然而，目前的实验方法尚未产生接近哺乳动物皮质细胞多样性的一组全面的神经元和非神经元类型的全脑图。在这里，我们的目标是通过开源计算管道“矩阵求逆和子集选择”(MISS) 来填补这一知识空白，该管道可以结合单细胞 RNA 测序 (RNAseq) 和原位杂交数据集，在 200 μm 分辨率下定量推断不同神经细胞类型集合的分布。我们根据文献预期严格证明了 MISS 的准确性。重要的是，我们表明，基因子集选择是我们在执行反卷积之前过滤掉低信息基因的过程，是一个关键的预处理步骤，它将 MISS 与其前辈区分开来，并有助于以更高的精度生成细胞类型图谱。我们还表明，通过从第二个独立策划的单细胞 RNAseq 数据集生成高质量的细胞类型图谱，MISS 是可推广的。总之，我们的结果说明了仅从遗传数据确定多种细胞类型的空间分布的计算方法的可行性。
The current state-of-the-art mappings of cell types fall short regarding finely resolved subtypes of neural cells, especially γ-aminobutyric acidergic and glutamatergic subtypes. Most such maps compromise on either the number or specificity of unique cell types quantified in each study. Others only use qualitative validation for their maps and fail to address whether gene subset selection is necessary for optimal maps. The Matrix Inversion and Subset Selection pipeline uses publicly available in situ hybridization and single-cell RNA sequencing gene expression data to infer cell-type distributions to map diverse cell types across the murine brain. Most importantly, we demonstrate that data-driven feature selection is necessary to arrive at quantitatively optimal cell-type maps using inversion-, deconvolution-, and correlation-based mapping approaches. The advent of increasingly sophisticated imaging platforms has allowed for the visualization of the murine nervous system at single-cell resolution. However, current experimental approaches have not yet produced whole-brain maps of a comprehensive set of neuronal and nonneuronal types that approaches the cellular diversity of the mammalian cortex. Here, we aim to fill in this gap in knowledge with an open-source computational pipeline, Matrix Inversion and Subset Selection (MISS), that can infer quantitatively validated distributions of diverse collections of neural cell types at 200-μm resolution using a combination of single-cell RNA sequencing (RNAseq) and in situ hybridization datasets. We rigorously demonstrate the accuracy of MISS against literature expectations. Importantly, we show that gene subset selection, a procedure by which we filter out low-information genes prior to performing deconvolution, is a critical preprocessing step that distinguishes MISS from its predecessors and facilitates the production of cell-type maps with significantly higher accuracy. We also show that MISS is generalizable by generating high-quality cell-type maps from a second independently curated single-cell RNAseq dataset. Together, our results illustrate the viability of computational approaches for determining the spatial distributions of a wide variety of cell types from genetic data alone.
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DOI：
10.1073/pnas.1312098111
发表时间：
2014-04-08
影响因子：
11.1
作者：
Grange, Pascal;Bohland, Jason W.;Mitra, Partha P.
通讯作者：
Mitra, Partha P.
下丘脑前区域的分子，空间和功能性单细胞分析。
DOI：
10.1126/science.aau5324
发表时间：
2018-11-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Moffitt JR;Bambah-Mukku D;Eichhorn SW;Vaughn E;Shekhar K;Perez JD;Rubinstein ND;Hao J;Regev A;Dulac C;Zhuang X
通讯作者：
Zhuang X
影响因子：
16.2
作者：
Pinto L;Dan Y
通讯作者：
Dan Y
影响因子：
4.2
作者：
Rama Rao KV;Iring S;Younger D;Kuriakose M;Skotak M;Alay E;Gupta RK;Chandra N
通讯作者：
Chandra N
影响因子：
25
作者：
Fu H;Hardy J;Duff KE
通讯作者：
Duff KE