哺乳动物初级运动皮层的多模式细胞普查和地图集

　　在本手稿中，我们采用了一份术语，用于对皮质谷氨酸能兴奋性神经元的主要子类，这些神经元在皮质内外都有长期的投影，遵循了长期的命名惯例，这些惯例经常基于其投射目标对神经元进行分类。该命名法是基于我们的从头转录组分类法（图9），该分类法（图9）通过其主要的远距离投影靶标验证了细胞类型，并验证了谷氨酸能神经元的主要分支的命名。在这些水平上，谷氨酸辅助神经元明确分为几个子类，皮质皮质和皮质纹状体仅投影在几乎所有层上分布的IT神经元（L2/3 IT，L4/5 IT，L5 IT，L5 IT，L6 IT和L6 IT CAR3），l6 it car3），car3 extrosing tots tots trogs to fromists to fromists Injectsiment-promists Ingrance lightsInging lightsInge lightsInging lightsInging lightsimins lightsInging lightsIngions lighinsimentions lightsInging lightsIngions（在第6层（L6 CT）中，NP神经元在5和6层中发现的NP神经元，而投影模式的L6B神经元在很大程度上未知。 　　尽管IT，CT，NP和L6B神经元在该领域一直始终如一地标记，但在文献中，L5等神经元并未始终如一地命名，这在很大程度上是由于它们的各种投影靶标和其他根据皮质区域和物种而变化的表型特征。在这里，我们使用术语L5 ET（第5层外脑外脑）将这种突出和独特的神经元子类作为标准名称，可以在皮质区域和跨物种之间准确使用，并在下面提供我们的理由。 　　长期以来，人们一直认为，皮质层5包含两个不同的神经元种群，这些神经元不仅基于对ET靶标（ET和IT细胞）的投影的存在或不存在，而且还基于其主要的躯体位置，树突状形态和内在物理学48。因此，已经采用了各种包含这些特征的名称，以指L5 ET与L5 IT细胞，例如L5B与L5A，厚tuft tuft the tuft the-Tuft and Thuft-Tuft and Burst forting frot frots frots frots frots frots frots frots frots frots frots。用于指代在运动皮质区域中的L5 ET细胞的最常见术语是PT，它是指投射到金字塔道的神经元。As accurately stated in Wikipedia, “The pyramidal tracts include both the corticobulbar tract and the corticospinal tract. These are aggregations of efferent nerve fibers from the upper motor neurons that travel from the cerebral cortex and terminate either in the brainstem (corticobulbar) or spinal cord (corticospinal) and are involved in the control of motor functions of the身体。” 　　由于过去广泛使用PT，我们不决定使用L5 ET而不是轻轻使用PT。然而，面对过去几年积累的多种证据，在本手稿中突出显示了72,73，现在很明显，PT仅代表L5 ET细胞的一个子集，因此无法准确地包含整个L5 ET子类。通过对物种和皮质区域之间的比较以及单细胞转录组学和单个神经元投影的描述以及将转录簇与投影靶标联系起来的研究来了解这种认识。 　　如上所述，随着神经元在成人的命运中逐渐限制37,38，皮质神经元之间的总体转录组关系与一棵层次树相匹配，与发育谱系的关系非常相匹配（图9）。皮质兴奋性神经元是一个主要分支，与抑制性，神经胶质和上皮细胞不同。随后的兴奋性神经元的分裂揭示了几个主要的兴奋性神经元子类 - IT，L5 ET，L6 CT，NP和L6B。这些主要的亚类在哺乳动物物种中保守9,10，以及在所有皮质区域中，如Mouse11所示。因此，很明显，需要精确合并并准确区分这些子类中的神经元，并且在所有皮质区域中都适用。 　　另外，如上所述，L5 ET的广泛使用的替代方案是Pt。此外，该术语传统上与CT一起使用，以区分这些不同的投影。使这些替代术语无法实现的两个主要观察结果是：（1）PT指的是将运动神经元投射到我或脊髓中的运动神经元，但在许多皮质区域（例如，视觉和听觉区域）中，L5 ET细胞都不是运动神经元；（2）即使在运动皮层中，L5 ET子类中的许多细胞也不会投射到锥体区域，而是仅投射到TH（或TH和其他非PPT目标）。单神经元重建18,46,53（图6、8），Barseq64揭示了这一点。因此，PT一词不包括整个L5 ET子类。此外，L5 ET亚类中的L5 CT细胞在很大程度上与PT细胞（或“ PT样细胞”）连续连续，不仅在遗传学上而且在解剖学上也是1,1,42（图2，3）（图2，3），作为大多数L5 ET细胞投射到多个目标上，通常包括TH和PT结构，例如3和Spinter 3（例如，图3）（例如，均为图3）。8）。因此，L5 ET子类既不应分为PT和CT，也不应使用术语PT省略仅CT的细胞。这些事实还告知我们，重要的是要保持L5 CT（一种L5 ET）和L6 CT（一种主要的皮质兴奋性神经元的主要子类，与L5 ET极为不同，尽管有一些L6 CT细胞在第5层的底部存在某些L6 CT细胞，但这很重要。CT可以准确地用作通用术语，但CT神经元不属于皮质兴奋性神经元的单个子类。 　　我们认识到，用来描述L5 ET细胞的另一个名称是脑脑投射神经元（SCPN）49。鉴于尾脑相当于大脑，ET和脑脑具有相同的含义，而术语L5-SCPN将是准确且等效的替代方案。但是，在任何一种情况下，“ L5”预选赛对于将这些细胞与L6 CT子类区分开来都是至关重要的。我们赞成使用ET，因为SCPN尚未被广泛采用，并且由于与广泛使用的“ IT”命名法的对称性。另外，鉴于他们的证据表明，“与运动皮层中的锥体道神经元不同，听觉皮层中的这些神经元不会向脊髓投射出来”，Chen等人64使用了“ pyramidal tract-like like”（pt- l）。我们还偏爱L5 ET，而不是L5 PT-L，该L5 PT-L依靠不准确而现在过时的命名法。 　　为了鉴定物种跨物种的同源细胞类型，使用Seurat的Sctransform工作流程集成了人，摩尔果和小鼠10x v3 snRNA-seq数据集。每个主要的细胞类别（谷氨酸能，GABA能和非神经元细胞）均分别整合了跨物种。在这三种物种中，表达矩阵降低至14,870一对直系同源物（NCBI同源元； 2019年11月22日）。核被缩减采样，在跨物种的亚类水平上具有大约等效的数字。使用Seurat的Findallmarkers功能鉴定出每个物种簇的标记基因，并将其设置为“ ROC”，> 0.7分类能力。在集成步骤期间，将标记用作指导对准和锚定调查的输入。有关完整方法，请参见参考。38。生成无花果的代码。1b – h，3，扩展数据图2可在http://data.nemoarchive.org/publication_release/lein_2020_m1_study_analysis/transcriptomics/flagships/。分析使用R版本3.5.3，R软件包在RSTUDIO中进行：Seurat 3.1.1，GGPLOT2 3.2.1和Scrattch.HICAT 0.0.22。 　　为了建立强大的跨物种单元格分类法，我们在集成的类级数据集（https://github.com/alleninstitute/bicccn_m1_evo）上应用了基于树的聚类方法。对于每个类别的高度相似核（每类约500个簇），整合的空间（来自前面提到的Seurat集成）过度群集分为高度相似的核。将簇聚集到元中，然后使用Ward的方法根据Metacell基因表达矩阵进行层次聚类。然后，通过用95％的核对数据集进行100倍的次采样来评估层次树的簇大小，物种混合和分支稳定性。最后，我们递归地搜索了树的每个节点，如果某些启发式标准不足以容纳上部节点下方的节点，则将上部节点下方的所有节点修剪，并且属于该子树的核被合并为一个同源组。我们确定了24个GABA能，13个谷氨酸能和8个非神经跨物种共识簇，它们在物种之间高度混合并稳健。有关完整方法，请参见参考。38。通过将原始唯一分子标识符（UMI）计数转换为log2（每百万（CPM））归一化计数来构建共有细胞类型的最终树状图。通过使用scrattch.hicat（v0.0.22）（https://github.com/alleninstitute/scrattch.hicat）display_cl和select_markers函数，使用以下参数;q1.th = 0.4，q.diff.th = 0.5，de.score.th = 80。然后将这些基因的中位数群log2 cpm表达式用作scrattch.hicat.hicat的build_dend函数的输入。该分析以分支颜色表示置信度10,000次。通过重建树状图10来评估分支鲁棒性000倍在群集中随机80％的可变基因子集，并计算出同一分支上存在簇的迭代比例。图9E中的共识分类学协议是通过选择最大频率叶片匹配的堆叠条来确定的。 　　将表达矩阵子群纳入所有三个物种中的一对一直系同源基因。图1D所示的Spearman相关性是通过比较跨物种群集中位数log2 cpm表达每个物种对的所有直系同源基因表达的。为了计算每个跨物种簇之间每个物种对之间差异表达的基因的数量，我们使用了deseq2（v1.30.0）的伪库克比较方法74。对于给定的跨物种群集，每个样品按物种和供体分开，然后在每个物种对之间进行WALD测试。具有调整后P值的基因< 0.05 and log2 fold-changes greater than 2 in either direction were counted and reported in Fig. 1e. 　　We injected retrograde tracer rAAV2-retro-Cre75 into a target region in INTACT mice76, which turned on Cre-dependent GFP expression in the nuclei of MOp neurons projecting to the injected target region. Individual GFP-labelled nuclei of MOp projection neurons were then isolated using fluorescence-activated nucleus sorting (FANS) (box outlines selected cells in Fig. 4a). snmC-seq277 was performed to profile the DNA methylation (mC) of each single nucleus. 　　The methods used to evaluate contamination level and potential reasons are described in detail in ref. 45. Specifically, we quantified the ratio between the number of cells in expected on-target subclasses (for example, L5 ET cluster for ET-projecting neurons) versus in expected off-target subclasses (for example, IT clusters for ET-projecting neurons), denoted as rp, and compared the ratio with the one expected from the unbiased data without enrichment for specific projections, denoted as ru. This provides an estimation of signal-to-noise ratio of each FANS experiment. For IT projections, we used IT subclasses as on-target and L6 CT + inhibitory as off-target, and for ET projections, we used L5 ET as on-target and IT + inhibitory as off-target. For the MOp neurons without enrichment of projections, the expected ratio between cells in IT subclasses and in L6 CT + inhibitory are ru = 2,652:1,775, whereas the expected ratio between cells in L5 ET subclass and in IT + inhibitory are ru = 202:3,434. The fold enrichment in the text was computed by rp/ru for each FANS run separately and averaged across IT or ET targets respectively. 　　We want to point out that, in addition to this computational method, other methods are available to evaluate and minimize potential contamination in Epi-retro-seq. In cases in which differences in expected results from on- versus off-target populations are unknown, other available methods would need to be used to eliminate cases in which injections might have directly labelled cells outside the intended target region, such as examination of labelling along the injection electrode track. 　　For snRNA-seq, the 4,515 cells from 10x v3 B dataset labelled as L5 ET by SCF were selected37. The read counts were normalized by the total read counts per cell and log transformed. Top 5,000 highly variable genes were identified with Scanpy78 (v1.8.1) and z-score was scaled across all the cells. For Epi-retro-seq, the posterior methylation levels of 12,261 genes in the 848 L5 ET cells were computed45. Top 5,000 highly variable genes were identified with AllCools79 and z-score was scaled across all the cells. The 1,512 genes as the intersection between the two highly variable gene lists were used in Scanorama80 (v1.7.1) to integrate the z-scored expression matrix and minus z-scored methylation matrix with sigma equal to 100. 　　To integrate IT cell types from different mouse datasets, we first take all cells that are labelled as IT, except for L6_IT_Car3, from the 11 datasets as listed in Fig. 7a. These cell labels are either from dataset-specific analyses41,45, or from the integrated clustering of multiple datasets37. The integrated clustering and embedding of the 11 datasets are then generated by projecting all datasets into the 10x v2 scRNA-seq dataset using SingleCellFusion37,79. Genome browser views of IT and ET cell types (Figs. 7b, 8c) are taken from the corresponding cell types of the brainome portal37 (https://brainome.ucsd.edu/BICCN_MOp). MERFISH data were analysed using custom Python code, which is available at https://github.com/ZhuangLab/MERlin. 　　For peak calling in the snATAC-seq data, we extracted all the fragments for each cluster, and then performed peak calling on each aggregate profile using MACS281 v2.2.7.1. using Python 3.6 with parameter: “--nomodel --shift −100 --ext 200 --qval 1e-2 –B --SPMR”. First, we extended peak summits by 250 bp on either side to a final width of 501 bp. Then, to account for differences in performance of MACS2 based on read depth and/or number of nuclei in individual clusters, we converted MACS2 peak scores (−log10(q-value)) to ‘score per million’82. Next, a union peak set was obtained by applying an iterative overlap peak-merging procedure, which avoids daisy-chaining and still allows for use of fixed-width peaks. Finally, we filtered peaks by choosing a score per million cut-off of 5 as cCREs for downstream analysis. 　　First, co-accessible cCREs are identified for all open regions in all neuron types (cell clusters with less than 100 nuclei from snATAC-seq are excluded) using Cicero83 with the following parameters: aggregation k = 50, window size = 500 kb, distance constraint = 250 kb. In order to find an optimal co-accessibility threshold, we generated a random shuffled cCRE-by-cell matrix as background and calculated co-accessible scores from this shuffled matrix. We fitted the distribution of co-accessibility scores from random shuffled background into a normal distribution model by using the R package fitdistrplus84. Next, we tested every co-accessible cCRE pair and set the cut-off at co-accessibility score with an empirically defined significance threshold of FDR < 0.01. The cCREs outside of ±1 kb of transcriptional start sites in GENCODE mm10 (v16) were considered distal. Next, we assigned co-accessibility pairs to three groups: proximal-to-proximal, distal-to-distal and distal-to-proximal. In this study, we focus only on distal-to-proximal pairs. We calculated the Pearson’s correlation coefficient (PCC) between gene expression (scRNA SMART-seq) and cCRE accessibility across the joint clusters to examine the relationships between the distal cCREs and target genes as predicted by the co-accessibility pairs. To do so, we first aggregated all nuclei or cells from scRNA-seq and snATAC-seq for every joint cluster to calculate accessibility scores (log2 CPM) and relative expression levels (log2 transcripts per million). Then, PCC was calculated for every gene-cCRE pair within a 1-Mbp window centred on the transcriptional start sites for every gene. We also generated a set of background pairs by randomly selecting regions from different chromosomes and shuffling the cluster labels. Finally, we fit a normal distribution model on background and defined a cut-off at PCC score with an empirically defined significance threshold of FDR < 0.01, in order to select significant positively correlated cCRE-gene pairs. 　　We used nonnegative matrix factorization (NMF) to group cCREs into cis-regulatory modules based on their relative accessibility across cell clusters. We adapted NMF (Python package: sklearn v.0.24.2) to decompose the cluster-by-cCRE matrix V (N × M, N rows: cCRE, M columns: cell clusters) into a coefficient matrix H (R × M, R rows: number of modules) and a basis matrix W (N × R), with a given rank R: V ≈ WH. 　　The basis matrix defines module related accessible cCREs, and the coefficient matrix defines the cell cluster components and their weights in each module. The key issue to decompose the occupancy profile matrix was to find a reasonable value for the rank R (that is, the number of modules). Several criteria have been proposed to decide whether a given rank R decomposes the occupancy profile matrix into meaningful clusters. Here we applied a measurement called sparseness85 to evaluate the clustering result. Median values were calculated from 100 times for NMF runs at each given rank with a random seed, which will ensure the measurements are stable. Next, we used the coefficient matrix to associate modules with distinct cell clusters. In the coefficient matrix, each row represents a module and each column represents a cell cluster. The values in the matrix indicate the weights of clusters in their corresponding module. The coefficient matrix was then scaled by column (cluster) from 0 to 1. Subsequently, we used a coefficient >0.1（〜95个百分位数的整个矩阵）是将群集与模块相关联的阈值。同样，我们使用基矩阵将每个模块与可访问元素关联。对于每个元素和每个模块，我们得出一个基数系数分数，该分数代表了定义模块中所有群集贡献的可访问信号。 　　本节的所有分析均处于子类级别。对于RNA表达，我们使用了SCSMART-SEQ数据集，并通过单尾Wilcoxon检验和FDR校正将每个子类与其他人群进行了比较，以选择显着差异表达的转录因子（调整后的P值调整后< 0.05, cluster average fold change > 2）。为了执行基序富集分析，我们使用了Jaspar 2020 Database86的已知基序以及在Yao等人37中鉴定出的子类比例的Hypo-CG-DMR。模因套件（v5.1.1）87的AME软件用于识别明显的主题富集（调整后的P值 < 10−3, odds ratio > 1.3）使用默认参数和与所述37相同的背景区域集。扩展数据中的所有基因均显着表达，并在至少一个亚类中富集了其基序。 　　所有实验程序均由美国国立卫生研究院（NIH）指南批准了加利福尼亚大学伯克利分校和艾伦学院的冷泉港实验室的机构动物护理和使用委员会（IACUC）。小鼠敲击驱动器线正在沉积到杰克逊实验室以进行广泛的分布。 　　使用基于PCR的克隆生成驱动器和记者鼠标线。敲击小鼠系TLE4-2A-creer，FeZF2-2A-CREER，PLEXIND1-2A-CREER，PLEXIND1-2A-FLP和TBR2-2A-CREER是通过在固定基因的固定密码中插入2A-Creer或2A-FLP盒，从而产生2A-Creer或2A-FLP盒。使用基于PCR的克隆方法27,47生成靶向矢量。简而言之，对于每个感兴趣的基因，从RPCI-23和24库中的两个部分重叠的BAC克隆（由C57BL/B小鼠制成）是从小鼠基因组浏览器中选择的。使用BAC DNA作为模板，将5'和3'同源臂放大（分别为2-5 kb上游和下游），并将其克隆到建筑物向量中，以侧翼2a-creert2或2a-flp表达盒式盒，如所述27。这些靶向矢量被纯化，通过酶限制和PCR测序对完整性进行了测试。将线性化靶向矢量电穿孔到129SVJ/B6杂交ES细胞系中（v.6.5）。首先通过PCR筛选ES细胞克隆，然后使用适当的探针通过Southern印迹确认。通过PCR生成挖掘标记的南部探针，亚克隆并在野生型基因组DNA上进行了测试，以验证它们给出了明确且预期的结果。阳性V6.5 ES细胞克隆用于四倍体互补，按照标准程序获得雄性杂合小鼠。F0雄性和随后的世代繁殖了记者线（AI14，SNAP25-LSL-EGFP，AI65），并在适当的年龄诱导了他莫昔芬诱导，以表征所得的基因靶向重组模式。Drivers Tle4-2A-CreER, Fezf2-2A-CreER and PlexinD1-2A-CreER were additionally crossed with reporter Rosa26-CAG-LSL-Flp and Tbr2-2A-CreER;PlexinD1-2A-Flp with reporter dual-tTA, and induced with tamoxifen at the appropriate age to perform anterograde viral tracing, with Flp- or tTA-dependent AAV表达EGFP（AAV8-CAG-FDIO-TVA-EGFP或AAV-TRE-3G-TVA-EGFP）的向量表征所得的轴突投影模式。 　　为了生成P2A-FLPO或P2A-CRE的框内基因组插入的线，我们分别使用核糖核蛋白（RNP）复合物组成的NPNT和SLCO2A1的终止密码子在NPNT和SLCO2A1的停止密码子上进行了设计，由SPCAS9-NLS蛋白和VITRO controcrece sgragnna（npgag）（rnp）组成SLCO2A1：CAGTCTGCAGGAGAATGCCT）。将这些RNP复合物核包装成106 V6.5小鼠胚胎干细胞（C57/BL6; 129/SV；来自R. Jaenisch的礼物），以及修复构建体，其中P2A-FLPO或P2A-CRE与以下序列的序列与目标部位的序列侧翼，因此，该序列与目标部位同源，从而使启用的人体介导的人体介导了较高的人体介导的修复。 　　NPNT-P2A-FLPO：tggcccttgagctctagtgttcccacttgccatagaaatctgatcttcggtttcggtttgggggggggggagggttgccttaccatgctccatgctccatgagtgagcactgcactggaaaaggggcaggcagaggaggaggaggcgcctgaccagtgtataCgtctctcctaggtcatcttcaaaggtgaaaaaaaaaaaaaaaaaaaaaaaggcgtggtggtcacaccacggggggggatgatgatgatgatgatgatgatgatgatgtgagcttgaagcgcggcggaagagagatgtggtggaagCGGAGCTACTACTTCCTGCTGAAGCAGGCTGGACGACGACGAGACCCTGGACCTATGCTATGCTCCTCCTAAGAAGAAGAAGAAGAAGGAGGTGAGTGAGTGAGCCCCAGTTCGACATCTCCTGTGCAAgacccccgccgacgcgcgtggtgcggcggcagttcgtgagagagagagagagaggccagcgcgcgcgcgagcgaaaagatcgcccagccccagccctgcccgccgccgccgccgccgctcctcctcctgtgtgctgctggtgctgatgatgatcacccacaacggcagcgcgcgatcaagggccccttcatgagttataaccatcatcatcatcagcaagcctgagttttgacattgacatcgtgaacaagagagagcctgcagcagttcaagtacaagacccccagaaGGCCACCATCCTGGGCCCTGAGAAGAAGCCCCCCCCCCCCCCATGGGAGTTCACGATTCCCTTACACGCAGGCCAGAAGCAGCAGCAGCAGCAGCAGCACCACCACCGACCGACATCGTCGTCCCAGCCTgcagctgcagttcgaaagcagcagggccgccagcaggcaggggaaggccacagccacagaagaagatgctgagccctgcctgctgtgtcccccagcgagcgcgagagagagagagcatctgggagagatccgagagaagatCCTGAACAGCTTCGAGTACACCAGCAGATTACCAAAAACGAAGACCCTGTACCAGTCCTGTCTCCTGGCCACCACATCATCATCATCATCATCATCACTGCGGCGGTTCAGCGACGACATCAAGAACGACGTGGACccgaagagcttcaagctcgtccagaacaagtatctgggcgcgtgcgtgatcattcagtgcctggtcacggagaccaagacaagcagcgtgtgtgtccaggcacatctacttttttttcagcgcgcccagaggcaggatcgacccccctggtgtgtaCctggAgttcctgaggaAcagcgcgcgccgtgccgtgctgaagagagagtgaAcaggacgacgcgcgcgcagcagcagcagcaagcaagcaagcaagcaggaggaggaggagtaccagctgctgAaggacaacctggtgcgcgcagctacaaCaAcaAggcctgaAagaAgaAgaAcgccccccctaccccccccatcccattcgcttcctattaaaaaaaaaaaaaaaAcggcctaagccctaagccacatcagcaggcagcagcagcagcagcaccctgatgaccagcccagctttctgagcatgaagggcctgaccgagctgactgcaaaacgtggtggtgggcaactggagcgacaaggggcctccccccgccgccgcgcgtggccagcaccaccaccccccccccaccaccaccaccaccacccgcccatccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccccced，ccccccccaccaccaccaccaccaccaccccccccccccc-c-c一下一下一下，gaccactacttcgccctggtgtgtccaggtactacgcctacgcccccatcagtaaggagatgatgatgccccccctgagacgacgacgagagagaccaaccccccatcaggaggaggaggaggagtggcacagcacatcagagagcaggagcagctgaagggcagcgccgcgcaggcagcagcagataccccgcccccccctggaacggcattataagccaggggtgctgctgctggctgctactacctgagcagcatcatcataCatCaaCaggCggCggatCggatctgaAagagggtcgctgctgctgagaagacccctggcctcccccgagctagcagtgaattgtgtcgctctcctcctcctcatccaatccaatgcttgcccttgcctttgtgtctctctttatatcaggcctaggcctaggggggcaggaggagtgggtcaggaggaaggttgcttggtgatgtgatcgggtcgtcggtggtgcctgtttgtgtgtgtgtgtgcaatccagtgaaacagtgacactgacactctcgaagtacaggaggagcatctggagcatctggagacaccctccggcgcccttctg 　　SLCO2A1-P2A-CRE： 　　tgccccctgggcctcactcatcctgtcttcttcctgcctcctcataggtacctgggcctcctacgtaatctaatctacaaggtcttgggcacactgcacactgctgctttcttcattcatcatcaGCTGGGGGGTGAAGAAGAACAGGGAATCAGTCTGCAGGAGAATGCTTCCGGATCGATTGATTGATGAGAGCGGAGCTACTACTACTTCTCCTGTGTTGAAACAAGCAGGGGGatgtcgaagaagaatcctggactatgctcctcctaagaagaagaagaaggaaggtgatgatgagccagcagttcgacattcctgtgcctgcaagcctcccccaaaggtgctgctggcggcagttcgtggagagatcgaggccagcgcgcgagaagaagagagcccagcccgcctgccgccgccgccgccgccgctgctcctcctgtgctgctggatgatgatgatgatcacccaccacaacgggcaccgccatcaagagggccacttcatgagctacaaccatcatcatcagcaagcctgagcttcgacatcgtcgtgaacaagagagcctgcagttcaagtcaagtacaagaccccagaaGGCCACCATCCTGGGCCCTGAGAAGAAGCCCCCCCCCCCCCCCCCCCCCCCATTCACCATCATCATCCTCCTTACAACGGCCAGAAGCAGCAGCAGAGCAGCGACACATCACCCGACATCGTGTCCAGCCTGCAGCTGCAGTTCGAGCAGCGAGGCCCGCCAGGCAGGAGGACAGCCAGCACAGCAAGAAGAAGAAGAAGAGAGCTGAGCCCTGCCTGCTGTCCCGAGGGGGGGGGGGGGGGGAGAgcatctgggagatcacccagaagatcctgaagcttcgagtaccagcagcaggttccaccaagaccaagacccctgtaccagtcctgttcctgttcctgcccacattcaTCAACTGCGGCAGGTTCAGCGACATCAAGAACGTGGACCCCAGAGCTTCAAGCTGGTGCAGAACAAGTACCTGGGCGCGCGTGCTGATGATCATTCATCAGTGCCTGGTGACCgagaccaagacaagcgtgtgtccaggcacatctttttttttcagcgccaggcaggcaggcaggacgacccccccctggtgtgtaCctggacgagttccttcctgaggaAcagcgagcgagcccGTGCTGAAGAGAGTGAACAGGACGCGCAGCAGCAGCAACAAGCAGGAGGAGTACCAGCTGCTGAGGAGGACACCTGGGCGCGCGCAGCTACACAACAACAAGGCCCTGACCTGAAgaagaacgcccccctacccccatcttcgctatcaagaacgcctaagcctaagccacatcggcaggcagcactgacctgaccagcagctttcttctgagcatgagcatgaaggggggcctgaccgaGCTGACAAACGTGGTGGTGGGCAACTGGCGAGGGCCTCCCGCCGCCGCGCCGCCAGGACCCCTACCCCCCCCCACCACCACCCACCCCATCCCCCCCCCCGACCACTACTACTCGccctggtgtccaggtactacgcctacgccccatcagcaggagagatgatgcccctgagacgacgacgagagagaccaaccccccatcgaggaggaggaggagtggcagcacatcagagcagcagcctgaagggcagcgccgcggggcagcagcagatacccccgccccccctggaacggcatcatcagcagcaggggggtgctgctggctactaCctAcctgagcagctacataCataCatCaaCaggCggCggatctgaccttcagctgctgccctgccctgccccccccagactgatgatcctaccccctccccccccaccactactatatataattaactaattaactaatgttagcatgccttccttcctcctccttcc 　　培养转染的细胞，并通过PCR直接筛选出菌落，使用以下基因分型引物正确整合：侧翼底漆Atgcattgcttcatgccata和内部重组酶引物CCTTCAGCAGCAGCTGGTACTCC，用于NPNT-P2A-P2A-FLPO左POLPO左同源物臂；gattgaggtcaggccagaag和tcgacatcgtgaacaagagc，用于npnt-p2a-flpo右同源部门；ctggtgaaaggggaactcttgct和gatccctgaacatgtccatcagg slco2a1-p2a-cre左同源部门；tacagcatccctgacaaacacca和tagcaccgcaggtgtgtagagaagg，用于slco2a1-p2a-cre右同源部门。 　　对插入的转基因进行了完全测序，并分析了候选线的正常核型。传递质量控制的线与白化病莫拉氏菌聚集在一起，并植入伪久的雌性中，产生了属于种系的嵌合创始人，而这些嵌合创始人又与C57/BL6背景上的适当记者线交叉。 　　所有使用活动物的实验程序均根据所有参与机构的机构动物护理和使用委员会（IACUC）批准进行的所有实验程序。通过华盛顿国家灵长类动物研究中心的组织分配计划，对指定安乐死的动物进行了猕猴实验。 　　验尸后人类脑组织收集是根据《 2006年美国统一的解剖学礼物法》第7150条（2008年1月1日生效）以及其他适用的州和联邦法律和法规的规定进行的。西方机构审查委员会审查了组织收集过程，并确定它们不构成需要机构审查委员会（IRB）审查的人类主题研究。在开始人类补丁序列之前，捐助者提供了知情同意，并获得了医院研究所审查委员会的批准。 　　有关研究设计的更多信息可在与本文有关的自然研究报告摘要中获得。