JQW、XD、RCDD 和 YY 得到了国立卫生研究院 R01 NS088353 的赠款的支持;NIH 赠款 1R21AR071583-01;Staman 奥格尔维基金-纪念赫尔曼基金会;UTHealth 脑主动和 CTSA UL1 TR000371;还有德克萨斯大学神经系统科学和 Neurotechnology 研究所的补助金 (赠款 #362469)。

所有动物的使用和实验性的协议都是按照《动物护理和使用指南》进行的, 并由德克萨斯大学卫生科学中心的机构生物安全委员会和动物福利委员会批准。休斯顿。
1. 从小鼠脑中纯化 PDGFRα阳性少突胶质谱系细胞 (从先前描述的 immunopanning 协议修改为5, 6, 7)
<ol><li>
PDGFRα阳性细胞选择 immunopanning 板的制备及2片用于内皮细胞和小胶质的损耗。 注: 请注意, 培养皿而不是细胞培养皿为 immunopanning 实验工作;如果纯化细胞将用于培养, immunopanning 步骤必须在生物安全柜中进行<ol>
<li>10厘米培养板与30µL 山羊抗鼠 IgG 在10毫升 pH 9.5, 50 毫米三 HCl 隔夜在4°c。搅拌板, 确保板材表面均匀, 完全覆盖的涂层解决方案。</li>
		<li>用12毫升 Dulbecco 磷酸盐缓冲盐水 (DPBS) 稀释40µL 抗 PDGFRα抗体, 制备 PDGFRα抗体溶液, 含 0.2% BSA。</li>
		<li>用10毫升 1x DPBS 的 igg 涂敷板3洗净后, 室温下用 PDGFRα抗体溶液孵育4小时的 igg 涂层。</li>
		<li>用10毫升 1x DPBS 的 PDGFRα抗体涂覆钢板3次。在板侧壁上轻轻地添加 DPBS 溶液, 不干扰涂层表面。</li>
		<li>涂层2新的 15 cm 培养板为内皮细胞和小胶质的耗尽与20毫升 DPBS 包含2.3 µg/毫升的Banderiaea 单叶蔓荆凝集素 1 (BSL-1) 2 h。</li>
		<li>用20毫升 1x DPBS BSL-1 涂层板3次。在板侧壁上轻轻地添加 DPBS 溶液, 不要扰乱涂层表面。</li>
	</ol>
</li>
	<li>
从以前发布的方法中修改 PDGFRα正少突胶质沿袭单元格的纯化 6,7, 8.
	<ol>
<li>根据以前发布的协议5,6, 从2出生日 7 (P7) 鼠脑中解剖皮质组织。</li>
		<li>根据详细制造商的说明, 将组织分离产生单细胞悬浮体, 并采用神经组织离解试剂盒 (P)。</li>
		<li>简要地, 用手术刀把解剖过的皮质组织切成片, 并将其与酶消化37摄氏度。消化后, 手工将工件与火抛光玻璃巴斯德吸管分离成单细胞悬浮。</li>
		<li>离心机的单细胞悬浮在 300 x g 10 分钟室温和悬浮细胞颗粒使用15毫升的 immunopanning 缓冲 (immunopanning 缓冲 DPBS 与 0.02% BSA 和5µg/毫升胰岛素)。</li>
		<li>在室温下, 每5分钟将2只小鼠脑细胞悬浮在 2 BSL-1 涂覆板上, 以温和的搅拌板, 以确保小胶质细胞和内皮干细胞的更好的耗竭。</li>
		<li>轻轻地旋转盘子, 收集细胞悬浮液中的非黏附细胞, 在室温下将其孵化在大鼠 PDGFRα抗体涂层板上45分钟。</li>
		<li>在对大鼠 PDGFRα抗体涂层板进行细胞悬浮培养后, 轻轻地旋转盘子收集细胞悬浮液, 用 DPBS 冲洗盘子8次以去除非黏附细胞。轻轻地在板侧壁上加洗液, 搅拌板几次, 以摆脱非黏附细胞。</li>
		<li>用4毫升细胞脱离溶液治疗10分钟37摄氏度, 从大鼠 PDGFRα抗体涂层板中分离出细胞。摇动盘子以去除黏附的细胞。</li>
		<li>在室温下以 300 x g 离心法收集纯化 OPCs, 用2毫升 OPC 细胞培养基悬浮细胞, 用台盼蓝和 hemocytometer (500 毫升细胞培养培养基 DMEM/F12, 含5毫升的培养基来计数细胞;青霉素-链霉素溶液 (P/S), 5 毫升 N2, 10 毫升 B27, 5 µg/毫升胰岛素, 0.1% BSA, 20 ng/毫升 bFGF 和 10 ng/毫升 PDGFRα)。</li>
	</ol>
</li>
	<li>
immunopanning 后 OPCs 纯度的验证。
	<ol>
<li>为了评价 immunopanning 后 OPCs 的富集情况, 应根据制造商的指示, 使用一些纯化的 OPCs 进行 RNA 提取, 以 guanidium 硫氰酸盐为基础提取。</li>
		<li>采用荧光绿色染料 qRT 进行 PCR 检测, 以检查纯化 OPCs 中 PDGFRα表达的富集度, 并根据先前发布的材料4与离解脑细胞进行比较。</li>
		<li>另外, 用 anti-NG2 硫酸软骨素蛋白多糖 (NG2) 抗体作为先前发布的材料9, 将一些纯化的 OPCs 加入聚 d-赖氨酸涂层的24井板中染色。</li>
	</ol>
</li>
</ol>2. 高通量测序的低细胞芯片制备和芯片库建设
<ol><li>
Olig2 低细胞芯片的准备与商业上可用的超声波系统和一个商业上可用的低细胞数芯片套件 (见材料表), 按照详细的标准程序, 从制造商的指示。
	<ol>
<li>分离后的大鼠抗 PDGFRα抗体涂层板和细胞计数的台盼蓝和 hemocytometer 投入2万纯化 OPCs 1 毫升 OPC 细胞培养基为每个芯片的反应。</li>
		<li>添加27µL 36.5% 甲醛固定细胞悬浮在室温下10分钟。 警告: 请注意, 由于安全原因, 必须在化学油烟机中使用甲醛。</li>
		<li>在室温下停止 DNA 蛋白交联, 50 µL 2.5 米甘氨酸5分钟。 注: 所有步骤必须在冰或4摄氏度冷室从这一点上进行。</li>
		<li>将1毫升的冰冷汉克斯平衡盐溶液 (HBSS) 与蛋白酶抑制剂鸡尾酒洗净, 用 300 x 克4摄氏度的预冷离心机把细胞颗粒化。</li>
		<li>溶解细胞颗粒在25µL 完全溶解缓冲5分钟冰。 注: 搅动管在裂解缓冲液中悬浮细胞。</li>
		<li>用75µL 冰冷 HBSS 含蛋白酶抑制剂鸡尾酒补充细胞裂解物, 用预冷超声波系统剪切细胞裂解物的染色质, 并在三十年代和三十年代关闭程序。总是油脂实验6管一起, 并确保平衡管包含100µL 水。</li>
		<li>剪切后, 离心机在 1.4万 x g 10 分钟4°c, 离心后在新管中收集上清。</li>
		<li>用蛋白酶抑制剂稀释100µL 的剪切染色质, 同体积的冰冷完全芯片缓冲器。</li>
		<li>保存20µL 稀释剪切染色质作为输入控制样品。 注: 输入控制样本也需要作为比较 Olig2 immunoprecipitated 样本, 以识别 Olig2 抗体 immunoprecipitated DNA。</li>
		<li>添加1µL 兔 anti-olig2 抗体180µL 稀释剪切染色质和孵化反应管在一个旋转轮 40 rpm 16 小时在4°c。 注: 在冷室中执行此步骤。</li>
		<li>对于每个芯片的反应, 洗涤11µL 磁性蛋白 A 涂层珠与55µL 珠洗涤缓冲器, 并把珠丸在11µL 的珠洗涤缓冲区后2洗涤。 注: 在冷室中执行此步骤。</li>
		<li>对于每个芯片的反应, 添加10µL 的预水洗蛋白 A 涂层珠, 以芯片反应管。在寒冷的房间里执行这一步骤。</li>
		<li>在旋转轮上将芯片反应管孵化为4摄氏度, 另2小时。</li>
		<li>将芯片反应管放在磁架上1分钟。 注: 在冷室中执行此步骤。</li>
		<li>移除上清, 不要将珠粒取出。</li>
		<li>用100µL 为4个洗涤缓冲器分别在4摄氏度的旋转轮上清洗珠粒。</li>
		<li>洗净后, 将200µL 的洗脱缓冲液添加到珠状颗粒中, 在65°c 处孵化出芯片反应管4小时, 以逆转交联蛋白 DNA。</li>
		<li>另外, 添加180µL 的洗脱缓冲到20µL 输入控制样本和孵化65°c 4 小时, 以逆转交叉链蛋白 DNA 以及。</li>
		<li>添加200µL 25:24:1 (v/v) 混合苯酚, 氯仿和异戊醇的每个芯片反应管。</li>
		<li>涡流为1分钟, 离心机在室温下以 1.3万 x g 为15分钟。将上相转移到新管上。</li>
		<li>添加40µL 3 米醋酸钠溶液, 1000 µL 100% 乙醇和2µL 糖原沉淀共价键链接到每个芯片反应管隔夜在-20 °c。</li>
		<li>离心机在 1.3万 x g 20 分钟在4摄氏度。</li>
		<li>丢弃上清液, 用500µL 冷70% 乙醇和离心机在 1.3万 x g 处洗涤20分钟4摄氏度。</li>
		<li>丢弃上清液, 保持颗粒空气干燥10分钟, 用50µL 水溶解颗粒。</li>
	</ol>
</li>
	<li>
用于高通量测序的芯片库构建
	<ol>
<li>根据制造商的指示, 清洁和浓缩芯片 DNA 和清洁套件。</li>
		<li>根据制造商的说明, 用微微绿色量化芯片样品。</li>
		<li>根据制造商的说明, 使用一个芯片序列套件, 用于 T 尾跟踪、复制和跟踪、模板切换和扩展、添加适配器和放大、库大小选择和纯化。<ol>
<li>dsDNA 变性后, dephosphorylate 3 ' 末端 ssDNA 虾碱性磷酸酶, 并添加一吨 (T) 尾到 ssDNA 由终端 deoxynucleotidyl 转移转移。</li>
			<li>将 dna 聚 (dA) 底漆退火到 ssDNA 模板进行 dna 复制和模板交换。</li>
			<li>模板切换后, 用正向和反向引物进行 PCR 放大芯片序列库, 进行索引。</li>
			<li>选择 PCR 放大芯片序列库, 其片段范围从250到 500 bp 由顺磁珠的选择2为双尺寸选择。</li>
			<li>使用微流控芯片-毛细管电泳装置检查所选芯片序列库的质量。</li>
		</ol>
</li>
	</ol>
</li>
</ol>3. 数据分析
<ol><li>准备目录结构。
	<ol>
<li>创建一个用于执行芯片序列数据分析的目录。在新创建的目录内, 创建六个具有以下名称的子目录: 原始文件、fastqc 输出、领结输出、荷马输出、macs2 输出、主题分析和报告。</li>
	</ol></li>
	<li>准备数据并对原始序列数据进行质量控制分析。
	<ol>
<li>移动到 "原始文件" 目录并下载芯片序列数据文件。</li>
		<li>验证文件名。如果排序是配对结束, 将有4个文件 (两个用于治疗和两个输入) 具有相似的基本名称: PDGFRα_Olig2. read1 fastq, PDGFRα_Olig2 read2. fastq, PDGFRα_input. read1 fastq, PDGFRα_input. read2. fastq。如果文件名不同, 请使用 "mv" 命令重命名它们。</li>
		<li>移动到 "fastqc 输出" 目录。</li>
		<li>使用 fastq 文件质量控制软件10从原始读取中获取质量控制指标。使用图 S1A中的命令分别为每个 fastq 文件运行质量控制过程。该软件将在 html 文件中显示几个质量控制指标。</li>
		<li>打开 html 质量控制文件并验证读取次数、读取长度、每基序列质量、每个序列质量分数、序列 GC 内容、序列复制级别、适配器内容和 kmer 内容。</li>
	</ol></li>
	<li>修剪尾随的低质量读取和适配器内容。
	<ol>
<li>观察 "每基序列质量" 和 "适配器内容" 地块。确定每个读取的头部和尾部的修剪长度。修剪的适当长度是基础质量低于 30, 并且有适配器内容的证据。</li>
		<li>移动到 "原始文件" 目录。</li>
		<li>下载并安装用于修剪读取11的软件。使用图 S1B中的命令分别修剪处理和输入。参数 "裁剪" 指示从末尾修剪基础后的剩余读取长度, "HEADCROP" 指定从读取开始时要消除的基数。</li>
		<li>修剪命令还包括在参数 "MINLEN" 中筛选后接受的最小读取长度。如果读数至少为 50 bp, 请使用 35 bp 作为读取长度阈值。</li>
		<li>移动到 "fastqc 输出" 目录。</li>
		<li>在修剪读取后执行 fastq 文件质量控制分析。验证质量控制问题是否已解决。使用图 S1C中的命令获取质量控制指标。</li>
	</ol></li>
	<li>将单端或配对端的芯片序列读数与鼠标参考基因组对齐。
	<ol>
<li>移动到 "领结输出" 目录进行映射。</li>
		<li>下载 GENCODE mm10 鼠标参考基因组。将 mm10 鼠标参考基因组重命名为 "mm10"。</li>
		<li>下载读取映射器12并在系统中安装它。</li>
		<li>使用图 S2A中的命令创建下载的参考基因组的索引文件。将自动创建六个文件: mm10.1 bt2、mm10.2 bt2、mm10.3 bt2、mm10.4 bt2、mm10. 启 1. bt2 和 mm10. 启 2. bt2。</li>
		<li>使用图 S2B中的命令执行对齐方式, 并根据系统设置将参数 "-p" 调整为处理内核的数目。如果运行配对结束模式, 参数 "-1" 和 "-2" 表示修剪的 fastq 文件的名称。 注意: 为处理和输入示例分别执行映射, 并为每个示例创建输出度量日志文件。</li>
		<li>下载用于操作 SAM 格式13中的文件并将其安装到系统中的软件。使用图 S2C中的命令将对齐的 SAM 文件转换为 BAM 文件。</li>
	</ol></li>
	<li>获得对配对端处理和控制样本的峰值调用前映射读取的质量控制指标。
	<ol>
<li>要解决的最重要的质量控制指标之一是排序深度。在领结. 输出目录中打开文件 "领结 PDGFRα_Olig2 .txt" 和 "日志领结. .txt", 并验证每个示例中唯一映射的读对数是否大于1000万。</li>
		<li>移动到 "荷马输出" 目录。</li>
		<li>下载用于主题发现和下一代排序分析的软件14并将其安装在系统中。</li>
		<li>创建名为 "tagDir" 的目录。</li>
		<li>要验证标记克隆, 请使用图 S3A中描述的命令。"makeTagDirectory" 命令将生成四质量控制输出 files:tagAutocorrelation.txt、tagCountDistribution.txt、tagInfo.txt 和 tagLengthDistribution.txt。</li>
		<li>移动到 "tagDir" 目录。</li>
		<li>将 "tagCountDistribution.txt" 文件复制到 "报告" 目录中。</li>
		<li>移动到 "报告" 目录。</li>
		<li>使用电子表格程序打开 tagCountDistribution.txt 文件, 并创建每个基因组位置标记数的条形图。存储直方图文件的名称为 "tag.clonality.xlsx"。</li>
		<li>在系统中安装 R 编程语言15 。</li>
		<li>在终端中, 键入 "r", 然后按 enter 键访问 R 编程环境。</li>
		<li>下载用于处理芯片序列数据16的 R 包, 并使用图 S3B中描述的命令安装它。</li>
		<li>使用图 S3C中的 R 脚本绘制链的交叉关联。</li>
	</ol></li>
	<li>使用峰值映射器检测峰值。
	<ol>
<li>移动到 "macs2 输出" 目录。在系统中下载并安装峰值映射器17 。</li>
		<li>使用函数 "callpeak" 与处理和控制在步骤3.4.6 中生成的 BAM 文件。 注: 其他参数包括 "-f" 输入文件格式, "-g" 用于基因组大小, "-名称" 表示所有输出文件的基名称, "-B" 用于存储 bedgraph 格式的碎片堆积。完整的峰值调用命令包括在图 S4A中。对配对端样品使用 BEDPE。</li>
		<li>验证峰值映射程序生成了六个文件: PDGFRα_Olig2_vs_PDGFRα_input_peaks、PDGFRα_Olig2_vs_PDGFRα_input_peaks narrowPeak、PDGFRα_Olig2_vs_PDGFRα_input_summits 床、PDGFRα_Olig2_vs_PDGFRα_input_model r、PDGFRα_Olig2_vs _PDGFRα_input_control_lambda. bdg 和 PDGFRα_Olig2_vs_PDGFRα_input_treat_pileup bdg。</li>
		<li>打开文件 PDGFRα_Olig2_vs_PDGFRα_input_peaks. xls 查看被称为峰值, 位置, 长度, 山顶位置, 交通, 和峰值浓缩指标:-log10 (pvalue), 折叠浓缩, 和-log10 (q 值)。</li>
	</ol></li>
	<li>筛选和注释称为峰值。
	<ol>
<li>使用上一步中打开的文件, 根据折叠富集、p 值和/或 q 值对生成的峰值进行筛选。要确定适当的过滤阈值, 请制作一个直方图图来决定每个指标的密度。筛选出选定阈值之下的数值以获得显著峰值。</li>
		<li>下载 mm10 黑名单 (已知有人为地高信号) 和过滤出芯片序列峰值, 这是在任何一个工件区域内。</li>
		<li>创建包含以下列的筛选峰值的床文件: 染色体、开始、结束、peakID、模拟和链。用点 "." 填充模拟列, 因为它没有使用。在 "链" 列中, 将所有值设置为 "+"。将床文件保存为 "filtered.peakData2.bed"。</li>
		<li>在 "报告" 目录中复制 "filtered.peakData2.bed" 文件。</li>
		<li>移动到 "报告" 目录。</li>
		<li>若要将筛选的峰值批注到特定的基因区域, 请使用在上一步中创建的床文件的函数 "annotatePeaks"。使用的命令显示在图 S5A中。</li>
		<li>使用统计软件打开 "filtered.annotatedPeaks.txt" 文件。</li>
		<li>过滤产生的峰值位于基因间区域的距离大于 5 kb, 从注释的 TSS。使用文件中的 "距离到 TSS" 列作为筛选距离。将已筛选的峰值存储在名为 "5 kbup geneBody peakData .txt" 的 excel 文件中。</li>
		<li>将生成的筛选峰值存储在具有列的 bedGraph 文件中: 染色体、开始、结束和-log10 (q 值)。命名文件 "5 kbup geneBody peakData bedGraph"。</li>
	</ol></li>
	<li>为浏览器可视化生成要人文件。
	<ol>
<li>下载并安装用于基因组算术18的软件。</li>
		<li>下载用于将 bedgraph 转换为要人并安装在系统中的工具。下载 "mm10.chrom.sizes" 文件。</li>
		<li>使用图 S6A中的命令生成要人文件。</li>
		<li>在系统中下载并安装一个基因组浏览器19 。</li>
		<li>打开基因组浏览器并加载文件 "5 kbup, geneBody, peakData", 以可视化的显著峰值和他们的位置有关已知的基因。</li>
	</ol></li>
	<li>主题搜索。
	<ol>
<li>移动到 "主题分析" 目录。</li>
		<li>下载一个从头主题扫描和主题浓缩程序, 并将其安装到您的系统20中。</li>
		<li>下载一个包含 Olig2 主题的综合主题数据库。</li>
		<li>获得500个以重大高峰峰会为中心的 bp 地区的基因组序列。将序列存储为 peak.sequences.txt。使用图 S7中的命令在500个 bp 峰值区域中搜索 Olig2 图案。</li>
		<li>使用适当的电子值筛选生成的图案 (默认为 0.05)。</li>
	</ol></li>
</ol>

<ol>
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作者声明他们没有竞争的财政利益。

哺乳动物基因调控网络是非常复杂的。芯片序列是研究全基因组蛋白质-DNA 相互作用的有力方法。该协议包括如何使用小鼠大脑中的少量纯化 OPCs (每反应低2万细胞) 来执行 Olig2 芯片序列。该协议的第一个关键步骤是用 PDGFRα抗体 immunopanning 纯化小鼠大脑中的 OPCs。对于 PDGFRα涂覆板的 OPCs 的阳性选择, OPCs 通常与 PDGFRα涂层板紧密结合。即使经过几次洗涤, 仍然有一些非黏附的细胞留下。在显微镜下,...

重要的是研究蛋白质 (或蛋白质复合物) DNA 绑定和表观遗传标记建立转录调控网络参与各种生物过程。特别是转录因子与基因组 DNA 的绑定, 在基因调控、细胞分化、组织发育等方面都有重要作用。研究转录调节和表观遗传学机制的有力工具是染色质免疫沉淀 (芯片)。由于下一代测序技术的飞速进步, 芯片结合高通量 dna 测序 (芯片序列) 被用于蛋白质 DNA 绑定和表观遗传标记的分析1。然而, 一个标准的芯片序列协议需要大约2000万细胞每反应, 这使得这种技术的应用困难, 当细胞数量有限, 如孤立的原生细胞和稀有细胞群体。
少突胶质谱系细胞包括少突胶质前体细胞 (OPCs) 和少突胶质广泛分布于整个大脑, 对大脑的发育和功能至关重要。OPCs 作为前体细胞的一种, 具有自我更新和分化的能力。OPCs 不仅作为少突胶质的始祖, 而且在神经元信号的传播中起着重要作用, 通过与其他类型的脑细胞进行交流2。以前的研究表明, 少突胶质的发展是由沿袭特定的转录因子 (如 Olig2 和Sox103, 4) 调节的.这些转录因子被发现绑定到一些关键基因的启动子或增强区, 以影响其表达在少突胶质规范和分化。然而, 要确定蛋白质 (或蛋白质复合物) 在急性纯化的初级 OPCs 中的 DNA 结合有很大的挑战性, 细胞数量非常有限。
本协议描述了如何利用芯片序列技术对纯化小鼠 OPCs 中 Olig2 基因组 DNA immunoprecipitated 进行系统的研究。OPCs 从小鼠的大脑被 immunopanning 的敏锐纯化, 并用于芯片实验, 不扩散在体外。有限数量的 OPCs 可以得到 immunopanning, 是不够的标准芯片序列实验。本文介绍了一种低细胞芯片序列协议, 其每片反应的转录因子为2万细胞。简而言之, 交联细胞裂解和微气泡由超声波装置剪切染色质。用 Olig2 抗体和蛋白 A 包覆的微珠对剪切的染色质进行孵化, 沉淀 Olig2 抗体绑定基因组 DNA。从蛋白 A 包膜珠和反向交联洗脱后, 用苯酚-氯仿萃取纯化 Olig2 抗体沉淀的基因组 DNA。该产品经过定量化, 并受到 T 尾矿、底漆退火模板的切换和扩展, 增加了适配器和放大器, 库的大小选择和净化步骤, 为芯片序列库的建设。
在测序后, 分析了用 Olig2 转录因子抗体和对照样品制备的样品的原始读数质量。剪裁了包含读取片段的低质量基对和适配器。接下来, 修剪的读数与鼠标参考基因组对齐。与对照样品相比, 对芯片读数显著丰富的基因组区域被检测为峰值。显著的峰值, 代表潜在的转录因子结合点, 被过滤和可视化的基因组浏览器。
值得注意的是, 本协议中描述的方法可以广泛地用于其他转录因子的芯片序列, 并具有任何细胞类型的有限数目。

<table>
	<tbody>
		<tr>
			<td>Reagent/ Equipment</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>Banderiaea simplicifolia lectin 1</td>
			<td>Vector Laboratories</td>
			<td># L-1100</td>
			<td></td>
		</tr>
		<tr>
			<td>Rat anti-PDGFRa antibody</td>
			<td>BD Bioscience</td>
			<td># 558774</td>
			<td></td>
		</tr>
		<tr>
			<td>Neural tissue dissociation Kit (P)</td>
			<td>MACS Miltenyi Biotec</td>
			<td># 130-092-628</td>
			<td></td>
		</tr>
		<tr>
			<td>Accutase</td>
			<td>STEMCELL technologies</td>
			<td># 07920</td>
			<td></td>
		</tr>
		<tr>
			<td>TRIzol</td>
			<td>Thermo Fisher</td>
			<td># 15596026</td>
			<td></td>
		</tr>
		<tr>
			<td>Anti-NG2 Chondroitin Sulfate Proteoglycan Antibody</td>
			<td>Millipore</td>
			<td># AB5320</td>
			<td></td>
		</tr>
		<tr>
			<td>Bioruptor Pico sonication device</td>
			<td>Diagenode</td>
			<td># B01060001</td>
			<td></td>
		</tr>
		<tr>
			<td>True MicroChIP kit</td>
			<td>Diagenode</td>
			<td># C01010130</td>
			<td></td>
		</tr>
		<tr>
			<td>Phenol:Chloroform:Isoamyl Alcohol (25:24:1, v/v)</td>
			<td>Thermo Fisher</td>
			<td># 15593031</td>
			<td></td>
		</tr>
		<tr>
			<td>NucleoSpin Gel and PCR Clean-Up kit</td>
			<td>MACHEREY-NAGEL</td>
			<td># 740609</td>
			<td></td>
		</tr>
		<tr>
			<td>Quant-iT PicoGreen dsDNA Assay Kit</td>
			<td>Thermo Fisher</td>
			<td># P11496</td>
			<td></td>
		</tr>
		<tr>
			<td>DNA SMART ChIP-Seq kit</td>
			<td>Clontech Laboratories</td>
			<td># 634865</td>
			<td></td>
		</tr>
		<tr>
			<td>Agencourt AMPure XP</td>
			<td>Beckman Voulter</td>
			<td># A63880</td>
			<td></td>
		</tr>
		<tr>
			<td>GlycoBlue</td>
			<td>Thermo Fisher</td>
			<td># AM9516</td>
			<td></td>
		</tr>
		<tr>
			<td>pico-green</td>
			<td>Thermo Fisher</td>
			<td># P11496</td>
			<td></td>
		</tr>
		<tr>
			<td>D-PBS</td>
			<td>Thermo Fisher</td>
			<td># 14190-144</td>
			<td></td>
		</tr>
		<tr>
			<td>SYBR Green master mix</td>
			<td>Bio-Rad Laboratories</td>
			<td># 1725124</td>
			<td></td>
		</tr>
		<tr>
			<td>DMEM/F12</td>
			<td>Fisher Scientific</td>
			<td># 11-320-033</td>
			<td></td>
		</tr>
		<tr>
			<td>Penicillin-Streptomycin (P/S)</td>
			<td>Fisher Scientific</td>
			<td># 15140122</td>
			<td></td>
		</tr>
		<tr>
			<td>N-2 Supplement</td>
			<td>Fisher Scientific</td>
			<td># 17502048</td>
			<td></td>
		</tr>
		<tr>
			<td>B-27 Supplement</td>
			<td>Fisher Scientific</td>
			<td># 17504044</td>
			<td></td>
		</tr>
		<tr>
			<td>insulin</td>
			<td>Sigma</td>
			<td># I6634</td>
			<td>Prepare 0.5 mg/ml insulin solution by dissolving 5 mg insulin in 10 ml water and 50 &#956;l of 1 N HCl.</td>
		</tr>
		<tr>
			<td>Bovine Serum Albumin, suitable for cell culture (BSA)</td>
			<td>Sigma</td>
			<td># A4161</td>
			<td>Prepare 4% BSA solution by dissolving 4 g BSA in 100 ml D-PBS and adjust the pH to 7.4</td>
		</tr>
		<tr>
			<td>Fibroblast Growth Factor basic Protein, Human recombinant (bFGF)</td>
			<td>EMD Millipore</td>
			<td># GF003</td>
			<td></td>
		</tr>
		<tr>
			<td>HUMAN PDGF-AA</td>
			<td>VWR</td>
			<td># 102061-188</td>
			<td></td>
		</tr>
		<tr>
			<td>poly-D-lysine</td>
			<td>VWR</td>
			<td># IC15017510</td>
			<td>Prepare 1 mg/ml poly-D-lysine solution by dissolving 10 mg poly-D-lysine in 10 ml water and dilute 100 times when using.</td>
		</tr>
		<tr>
			<td>Falcon Disposable Petri Dishes, Sterile, Corning, 100x15mm</td>
			<td>VWR</td>
			<td># 25373-100</td>
			<td></td>
		</tr>
		<tr>
			<td>Falcon Disposable Petri Dishes, Sterile, Corning, 150x15mm</td>
			<td>VWR</td>
			<td># 25373-187</td>
			<td></td>
		</tr>
		<tr>
			<td>HBSS, 10X, no Calcium, no Magnesium, no Phenol Red</td>
			<td>Fisher Scientific</td>
			<td># 14185-052</td>
			<td></td>
		</tr>
		<tr>
			<td>Trypan Blue</td>
			<td>Stemcell Technologies</td>
			<td># 07050</td>
			<td></td>
		</tr>
		<tr>
			<td>protease inhibitor cocktail</td>
			<td>Sigma</td>
			<td># 11697498001</td>
			<td></td>
		</tr>
		<tr>
			<td>Software</td>
			<td></td>
			<td></td>
			<td></td>
		</tr>
		<tr>
			<td>FastQC</td>
			<td>[10] http://www.bioinformatics.babraham.ac.uk/projects/fastqc/</td>
			<td></td>
			<td>Obtaining quality control metrics of raw and trimmed reads</td>
		</tr>
		<tr>
			<td>Trimmomatic 0.33</td>
			<td>[11] http://www.usadellab.org/cms/?page=trimmomatic</td>
			<td></td>
			<td>Trimming and filtering raw reads</td>
		</tr>
		<tr>
			<td>GENCODE mm10</td>
			<td>ftp://ftp.sanger.ac.uk/pub/gencode/Gencode_mouse/release_M15/GRCm38.primary_assembly.genome.fa.gz</td>
			<td></td>
			<td>Mouse reference genome</td>
		</tr>
		<tr>
			<td>Bowtie2 2.2.4</td>
			<td>[12] http://bowtie-bio.sourceforge.net/bowtie2/index.shtml</td>
			<td></td>
			<td>Aligning reads to a reference genome</td>
		</tr>
		<tr>
			<td>SAMTools 1.5</td>
			<td>[13] http://samtools.sourceforge.net/</td>
			<td></td>
			<td>Converting SAM file into BAM format</td>
		</tr>
		<tr>
			<td>HOMER 4.9.1</td>
			<td>[14] http://homer.ucsd.edu/homer/</td>
			<td></td>
			<td>Creating a tag directory and annotating enriched genomic regions with gene symbols</td>
		</tr>
		<tr>
			<td>R 3.2.2</td>
			<td>[15] https://www.R-project.org/</td>
			<td></td>
			<td>Programming scripts and running functions</td>
		</tr>
		<tr>
			<td>SPP 1.13</td>
			<td>[16] https://github.com/hms-dbmi/spp</td>
			<td></td>
			<td>Creating a strand cross-correlation plot</td>
		</tr>
		<tr>
			<td>MACS2 2.2.4</td>
			<td>[17] https://github.com/taoliu/MACS</td>
			<td></td>
			<td>Finding regions of ChIP enrichment over control</td>
		</tr>
		<tr>
			<td>BEDTools 2.25</td>
			<td>[18] http://bedtools.readthedocs.io/en/latest/</td>
			<td></td>
			<td>Genome arithmetics</td>
		</tr>
		<tr>
			<td>bedGraphToBigWig</td>
			<td>http://hgdownload.cse.ucsc.edu/admin/exe/</td>
			<td></td>
			<td>Converting bedGraph file into bigwig</td>
		</tr>
		<tr>
			<td>IGV browser 2.3.58</td>
			<td>[19] http://software.broadinstitute.org/software/igv/</td>
			<td></td>
			<td>Visualization and browsing of significant ChIP-seq peaks</td>
		</tr>
		<tr>
			<td>Microsoft Excel</td>
			<td></td>
			<td></td>
			<td>Spreasheet program</td>
		</tr>
		<tr>
			<td>ENCODE&#39;s blacklist</td>
			<td>https://sites.google.com/site/anshulkundaje/projects/blacklists</td>
			<td></td>
			<td>Filtering peaks</td>
		</tr>
		<tr>
			<td>mm10.chrom.sizes</td>
			<td>http://hgdownload.cse.ucsc.edu/goldenPath/mm10/bigZips/mm10.chrom.sizes.</td>
			<td></td>
			<td>Converting bedGraph file into bigwig</td>
		</tr>
		<tr>
			<td>ENCODE&#39;s motif database</td>
			<td>[25] http://compbio.mit.edu/encode-motifs/</td>
			<td></td>
			<td>Comprehensive motif database required for motif enrichment</td>
		</tr>
		<tr>
			<td>MEME-ChIP</td>
			<td>[20] http://meme-suite.org/index.html</td>
			<td></td>
			<td>Motif enrichment analysis and motif discovery</td>
		</tr>
		<tr>
			<td>Primer names used for qPCR</td>
			<td></td>
			<td></td>
			<td>Primer sequences used for qPCR</td>
		</tr>
		<tr>
			<td>Mbp-F:</td>
			<td></td>
			<td></td>
			<td>CTATAAATCGGCTCACAAGG</td>
		</tr>
		<tr>
			<td>Mbp-R:</td>
			<td></td>
			<td></td>
			<td>AGGCGGTTATATTAAGAAGC</td>
		</tr>
		<tr>
			<td>Iba1-F:</td>
			<td></td>
			<td></td>
			<td>ACTGCCAGCCTAAGACAACC</td>
		</tr>
		<tr>
			<td>Iba1-R:</td>
			<td></td>
			<td></td>
			<td>GCTTTTCCTCCCTGCAAATCC</td>
		</tr>
		<tr>
			<td>Mog-F:</td>
			<td></td>
			<td></td>
			<td>GGCTTCTTGGAGGAAGGGAC</td>
		</tr>
		<tr>
			<td>Mog-R:</td>
			<td></td>
			<td></td>
			<td>TGAATTGTCCTGCATAGCTGC</td>
		</tr>
		<tr>
			<td>GAPDH-F</td>
			<td></td>
			<td></td>
			<td>ATGACATCAAGAAGGTGGTG</td>
		</tr>
		<tr>
			<td>GAPDH-R</td>
			<td></td>
			<td></td>
			<td>CATACCAGGAAATGAGCTTG</td>
		</tr>
		<tr>
			<td>Tuj1-F</td>
			<td></td>
			<td></td>
			<td>TTTTCGTCTCTAGCCGCGTG</td>
		</tr>
		<tr>
			<td>Tuj1-R</td>
			<td></td>
			<td></td>
			<td>GATGACCTCCCAGAACTTGGC</td>
		</tr>
		<tr>
			<td>PDGFR&#945;-F</td>
			<td></td>
			<td></td>
			<td>AGAGTTACACGTTTGAGCTGTC</td>
		</tr>
		<tr>
			<td>PDGFR&#945;-R</td>
			<td></td>
			<td></td>
			<td>GTCCCTCCACGGTACTCCT</td>
		</tr>
	</tbody>
</table>

identifying transcription factor olig2 genomic binding sites in acutely purified pdgfr&#945;+ cells by low-cell chromatin immunoprecipitation sequencing analysis

在哺乳动物细胞中, 基因转录通过转录因子与基因组 DNA 的相互作用来调节细胞类型的特定方式。谱系特异转录因子被认为在细胞规格和分化过程中发挥重要作用。芯片结合高通量 DNA 测序 (芯片序列) 被广泛用于分析基因组范围内转录因子 (或其关联的复合体) 与基因组 DNA 的结合点。然而, 一个标准的芯片反应需要大量的细胞, 这使得研究孤立的原生细胞或稀有细胞群体的数量有限是困难的。为了了解 Olig2 在急性纯化小鼠 OPCs 中少突胶质谱系特异转录因子的调控机制, 本文给出了用芯片序列识别 Olig2 (或 Olig2 复合体) 全基因组结合点的详细方法。首先, 该协议解释了如何从小鼠脑中纯化血小板衍生生长因子受体α (PDGFRα) 阳性 OPCs。其次, 对 Olig2 抗体介导芯片和库结构进行了实验。最后一部分介绍了用于 Olig2 芯片序列分析的生物信息学软件和程序。总之, 本文报道了一种分析急性纯化脑 OPCs 转录因子 Olig2 的全基因组绑定的方法。

采用低细胞芯片序列进行生物信息学分析, 研究转录因子 Olig2 与基因组 DNA 在急性纯化脑 OPCs 中的潜在相互作用。图 1显示了实验和数据分析过程的一般工作流。在这个协议中, 产后老鼠的大脑被分离成单细胞悬浮。组织分离后, 采用 PDGFRα抗体对 OPCs 进行纯化, immunopanning。图 2显示了 PDGFRα的显著丰富性、神经元标记 Tuj1 的小表?...

Watch this Scientific Journal Video about Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRÎ±+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysi

通过低细胞染色质免疫沉淀测序分析鉴定急性纯化的 PDGFRα+ 细胞中的转录因子 Olig2 基因组结合位点 |神经科学 |视频

Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFR&#945;+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

在这里, 我们提出一个协议, 旨在分析少突胶质转录因子 2 (Olig2) 在急性纯化脑少突胶质前体细胞 (OPCs) 的全基因组结合执行低细胞染色质免疫沉淀 (芯片)、库准备、高通量测序和生物信息学数据分析。

低细胞染色质免疫沉淀测序分析 Olig2 基因组结合位点在急性纯化 PDGFRα + 细胞中的转录因子鉴定

In mammalian cells, gene transcription is regulated in a cell type specific manner by the interactions of transcriptional factors with genomic DNA. ...

identifying-transcription-factor-olig2-genomic-binding-sites-acutely

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Unit 1

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Gene Expression

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9.2K 次观看.  University of Texas Health Science Center. 在这里, 我们提出一个协议, 旨在分析少突胶质转录因子 2 (Olig2) 在急性纯化脑少突胶质前体细胞 (OPCs) 的全基因组结合执行低细胞染色质免疫沉淀 (芯片)、库准备、高通量测序和生物信息学数据分析。

视频: Identifying Transcription Factor Olig2 Genomic Binding Sites in Acutely Purified PDGFRα+ Cells by Low-cell Chromatin Immunoprecipitation Sequencing Analysis

Transfection of Mouse Retinal Ganglion Cells by in vivo Electroporation

The targeting and refinement of RGC projections to the midbrain is a popular and powerful model system for studying how precise patterns of neural connectivity form during development. In mice, retinofugal projections are arranged in a topographic manner and form eye-specific layers in the Lateral Geniculate Nucleus (dLGN) of the thalamus and the Superior Colliculus (SC). The development of these precise patterns of retinofugal projections has typically been studied by labeling populations of RGCs with fluorescent dyes and tracers, such as horseradish peroxidase1-4. However, these methods are too coarse to provide insight into developmental changes in individual RGC axonal arbor morphology that are the basis of retinotopic map formation. They also do not allow for the genetic manipulation of RGCs.
Recently, electroporation has become an effective method for providing precise spatial and temporal control for delivery of charged molecules into the retina5-11. Current retinal electroporation protocols do not allow for genetic manipulation and tracing of retinofugal projections of a single or small cluster of RGCs in postnatal mice. It has been argued that postnatal in vivo electroporation is not a viable method for transfecting RGCs since the labeling efficiency is extremely low and hence requires targeting at embryonic ages when RGC progenitors are undergoing differentiation and proliferation6. 
In this video we describe an in vivo electroporation protocol for targeted delivery of genes, shRNA, and fluorescent dextrans to murine RGCs postnatally. This technique provides a cost effective, fast and relatively easy platform for efficient screening of candidate genes involved in several aspects of neural development including axon retraction, branching, lamination, regeneration and synapse formation at various stages of circuit development. In summary we describe here a valuable tool which will provide further insights into the molecular mechanisms underlying sensory map development.

Transfection of Mouse Retinal Ganglion Cells by in vivo Electroporation

We demonstrate an in vivo electroporation protocol for transfecting single or small clusters of retinal ganglion cells (RGCs) and other retinal cell types in postnatal mice over a wide range of ages. The ability to label and genetically manipulate postnatal RGCs in vivo is a powerful tool for developmental studies.

小鼠视网膜神经节细胞的转染在体内电穿孔

The targeting and refinement of RGC projections to the midbrain is a popular and powerful model system for studying how precise patterns of neural ...

科研

神经科学

Live-cell Imaging of Sensory Organ Precursor Cells in Intact Drosophila Pupae

Since the discovery of Green Fluorescent Protein (GFP), there has been a revolutionary change in the use of live-cell imaging as a tool for understanding fundamental biological mechanisms. Striking progress has been particularly evident in Drosophila, whose extensive toolkit of mutants and transgenic lines provides a convenient model to study evolutionarily-conserved developmental and cell biological mechanisms. We are interested in understanding the mechanisms that control cell fate specification in the adult peripheral nervous system (PNS) in Drosophila. Bristles that cover the head, thorax, abdomen, legs and wings of the adult fly are individual mechanosensory organs, and have been studied as a model system for understanding mechanisms of Notch-dependent cell fate decisions. Sensory organ precursor (SOP) cells of the microchaetes (or small bristles), are distributed throughout the epithelium of the pupal thorax, and are specified during the first 12 hours after the onset of pupariation. After specification, the SOP cells begin to divide, segregating the cell fate determinant Numb to one daughter cell during mitosis. Numb functions as a cell-autonomous inhibitor of the Notch signaling pathway.
Here, we show a method to follow protein dynamics in SOP cell and its progeny within the intact pupal thorax using a combination of tissue-specific Gal4 drivers and GFP-tagged fusion proteins 1,2.This technique has the advantage over fixed tissue or cultured explants because it allows us to follow the entire development of an organ from specification of the neural precursor to growth and terminal differentiation of the organ. We can therefore directly correlate changes in cell behavior to changes in terminal differentiation. Moreover, we can combine the live imaging technique with mosaic analysis with a repressible cell marker (MARCM) system to assess the dynamics of tagged proteins in mitotic SOPs under mutant or wildtype conditions. Using this technique, we and others have revealed novel insights into regulation of asymmetric cell division and the control of Notch signaling activation in SOP cells (examples include references 1-6,7 ,8).

Live-cell Imaging of Sensory Organ Precursor Cells in Intact Drosophila Pupae

In this video, we describe a method for live cell imaging of asymmetrically dividing sensory organ progenitor cells and epidermal cells in intact Drosophila pupae

在完整的感觉器官前体细胞的活细胞成像果蝇蛹

Since the discovery of Green Fluorescent Protein (GFP), there has been a revolutionary change in the use of live-cell imaging as a tool for understanding ...

Chromatin Immunoprecipitation from Dorsal Root Ganglia Tissue following Axonal Injury

Axons in the central nervous system (CNS) do not regenerate while those in the peripheral nervous system (PNS) do regenerate 
to a limited extent after injury (Teng et al., 2006). It is recognized that transcriptional programs essential for neurite and axonal outgrowth are 
reactivated upon injury in the PNS (Makwana et al., 2005). However the tools available to analyze neuronal gene regulation in vivo are limited and 
often challenging.
The dorsal root ganglia (DRG) offer an excellent injury model system because both the CNS and PNS are innervated by a 
bifurcated axon originating from the same soma. The ganglia represent a discrete collection of cell bodies where all transcriptional events occur, 
and thus provide a clearly defined region of transcriptional activity that can be easily and reproducibly removed from the animal. Injury of nerve 
fibers in the PNS (e.g. sciatic nerve), where axonal regeneration does occur, should reveal a set of transcriptional programs that are distinct from 
those responding to a similar injury in the CNS, where regeneration does not take place (e.g. spinal cord). Sites for transcription factor binding, 
histone and DNA modification resulting from injury to either PNS or CNS can be characterized using chromatin immunoprecipitation (ChIP). 
Here, we describe a ChIP protocol using fixed mouse DRG tissue following axonal injury. This powerful combination provides a means for characterizing the pro-regeneration chromatin environment necessary for promoting axonal regeneration.

We present a method for chromatin immunoprecipitation from dorsal root ganglia tissue following axonal injury. The approach can be used to identify specific transcription factor binding sites and epigenetic modification of histone and DNA important for the regeneration of injured axons in both the peripheral and central nervous system.

染色质免疫沉淀从以下轴索损伤的背根神经节组织

Axons in the central nervous system (CNS) do not regenerate while those in the peripheral nervous system (PNS) do regenerate 
to a limited extent after ...

Detection of Protein Palmitoylation in Cultured Hippocampal Neurons by Immunoprecipitation and Acyl-Biotin Exchange (ABE)

Palmitoylation is a post-translational lipid modification involving the attachment of a 16-carbon saturated fatty acid, palmitate, to cysteine residues of substrate proteins through a labile thioester bond [reviewed in1]. Palmitoylation of a substrate protein increases its hydrophobicity, and typically facilitates its trafficking toward cellular membranes. Recent studies have shown palmitoylation to be one of the most common lipid modifications in neurons1, 2, suggesting that palmitate turnover is an important mechanism by which these cells regulate the targeting and trafficking of proteins. The identification and detection of palmitoylated substrates can therefore better our understanding of protein trafficking in neurons.
Detection of protein palmitoylation in the past has been technically hindered due to the lack of a consensus sequence among substrate proteins, and the reliance on metabolic labeling of palmitoyl-proteins with 3H-palmitate, a time-consuming biochemical assay with low sensitivity. Development of the Acyl-Biotin Exchange (ABE) assay enables more rapid and high sensitivity detection of palmitoylated proteins2-4, and is optimal for measuring the dynamic turnover of palmitate on neuronal proteins. The ABE assay is comprised of three biochemical steps (Figure 1): 1) irreversible blockade of unmodified cysteine thiol groups using N-ethylmaliemide (NEM), 2) specific cleavage and unmasking of the palmitoylated cysteine's thiol group by hydroxylamine (HAM), and 3) selective labeling of the palmitoylated cysteine using a thiol-reactive biotinylation reagent, biotin-BMCC. Purification of the thiol-biotinylated proteins following the ABE steps has differed, depending on the overall goal of the experiment.
Here, we describe a method to purify a palmitoylated protein of interest in primary hippocampal neurons by an initial immunoprecipitation (IP) step using an antibody directed against the protein, followed by the ABE assay and western blotting to directly measure palmitoylation levels of that protein, which is termed the IP-ABE assay. Low-density cultures of embryonic rat hippocampal neurons have been widely used to study the localization, function, and trafficking of neuronal proteins, making them ideally suited for studying neuronal protein palmitoylation using the IP-ABE assay. The IP-ABE assay mainly requires standard IP and western blotting reagents, and is only limited by the availability of antibodies against the target substrate. This assay can easily be adapted for the purification and detection of transfected palmitoylated proteins in heterologous cell cultures, primary neuronal cultures derived from various brain tissues of both mouse and rat, and even primary brain tissue itself.

Detection of Protein Palmitoylation in Cultured Hippocampal Neurons by Immunoprecipitation and Acyl-Biotin Exchange ABE

The reversible addition of palmitate to proteins is an important regulator of intracellular protein trafficking. This is of particular interest in neurons where many synaptic proteins are palmitoylated. We utilize a simple biochemical method to detect palmitoylated proteins in cultured neurons, which can be adapted for multiple cell types and tissues.

检测蛋白质棕榈酰化培养海马神经元通过免疫沉淀和酰基-生物素交易所（ABE）

Palmitoylation is a post-translational lipid  modification involving the attachment of a 16-carbon saturated fatty acid,  palmitate, to cysteine residues ...

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.
This extracellular technique has advantages. First, extracellular electrodes can stimulate and record neurons through the sheath5, so it does not need to be removed. Thus, neurons will be healthier in extracellular experiments than in intracellular ones. Second, if ganglia are rotated by appropriate pinning of the sheath, extracellular electrodes can access neurons on both sides of the ganglion, which makes it easier and more efficient to identify multiple neurons in the same preparation. Third, extracellular electrodes do not need to penetrate cells, and thus can be easily moved back and forth among neurons, causing less damage to them. This is especially useful when one tries to record multiple neurons during repeating motor patterns that may only persist for minutes. Fourth, extracellular electrodes are more flexible than intracellular ones during muscle movements. Intracellular electrodes may pull out and damage neurons during muscle contractions. In contrast, since extracellular electrodes are gently pressed onto the sheath above neurons, they usually stay above the same neuron during muscle contractions, and thus can be used in more intact preparations.
To uniquely identify motor neurons for a motor pool (in particular, the I1/I3 muscle in Aplysia) using extracellular electrodes, one can use features that do not require intracellular measurements as criteria: soma size and location, axonal projection, and muscle innervation4,6,7. For the particular motor pool used to illustrate the technique, we recorded from buccal nerves 2 and 3 to measure axonal projections, and measured the contraction forces of the I1/I3 muscle to determine the pattern of muscle innervation for the individual motor neurons.
We demonstrate the complete process of first identifying motor neurons using muscle innervation, then characterizing their timing during motor patterns, creating a simplified diagnostic method for rapid identification. The simplified and more rapid diagnostic method is superior for more intact preparations, e.g. in the suspended buccal mass preparation8 or in vivo9. This process can also be applied in other motor pools10,11,12 in Aplysia or in other animal systems2,3,13,14.

Extracellularly Identifying Motor Neurons for a Muscle Motor Pool in Aplysia californica

In animals with large identified neurons (e.g. mollusks), analysis of motor pools is done using intracellular techniques1,2,3,4. Recently, we developed a technique to extracellularly stimulate and record individual neurons in Aplysia californica5. We now describe a protocol for using this technique to uniquely identify and characterize motor neurons within a motor pool.

运动神经元细胞外识别的肌肉运动游泳池<em&gt;海兔夜蛾</em

In animals with large  identified neurons (e.g. mollusks), analysis of motor pools is done using  intracellular techniques1,2,3,4. Recently,  we developed ...

Intracerebroventricular Injection of Amyloid-&#946; Peptides in Normal Mice to Acutely Induce Alzheimer-like Cognitive Deficits

Amyloid-&#946; (A&#946;) is a major pathological mediator of both familial and sporadic Alzheimer's disease (AD). In the brains of AD patients, progressive accumulation of A&#946; oligomers and plaques is observed. Such A&#946; abnormalities are believed to block long-term potentiation, impair synaptic function, and induce cognitive deficits. Clinical and experimental evidences have revealed that the acute increase of A&#946; levels in the brain allows development of Alzheimer-like phenotypes. Hence, a detailed protocol describing how to acutely generate an AD mouse model via the intracerebroventricular (ICV) injection of A&#946; is necessary in many cases. In this protocol, the steps of the experiment with an A&#946;-injected mouse are included, from the preparation of peptides to the testing of behavioral abnormalities. The process of preparing the tools and animal subjects before the injection, of injecting the A&#946; into the mouse brain via ICV injection, and of assessing the degree of cognitive impairment are easily explained throughout the protocol, with an emphasis on tips for effective ICV injection of A&#946;. By mimicking certain aspects of AD with a designated injection of A&#946;, researchers can bypass the aging process and focus on the downstream pathology of A&#946; abnormalities.

The amyloid-&#946; (A&#946;)-injected animal model enables the administration of a defined quantity and species of A&#946; fragments and reduces individual differences within each study group. This protocol describes the intracerebroventricular (ICV) injection of A&#946; without stereotactic instruments, enabling the production of Alzheimer-like behavioral abnormalities in normal mice.

正常小鼠β淀粉样肽侧脑室注射敏锐地诱导老年痴呆样认知缺陷

Amyloid-&#946; (A&#946;) is a major pathological mediator of both familial and sporadic Alzheimer's disease (AD). In the brains of AD patients, ...

Assessment of Dopaminergic Homeostasis in Mice by Use of High-performance Liquid Chromatography Analysis and Synaptosomal Dopamine Uptake

Dopamine (DA) is a modulatory neurotransmitter controlling motor activity, reward processes and cognitive function. Impairment of dopaminergic (DAergic) neurotransmission is strongly associated with several central nervous system-associated diseases such as Parkinson&#39;s disease, attention-deficit-hyperactivity disorder and drug addiction1,2,3,4. Delineating disease mechanisms involving DA imbalance is critically dependent on animal models to mimic aspects of the diseases, and thus protocols that assess specific parts of the DA homeostasis are important to provide novel insights and possible therapeutic targets for these diseases.
Here, we present two useful experimental protocols that when combined provide a functional read-out of the DAergic system in mice. Biochemical and functional parameters on DA homeostasis are obtained through assessment of DA levels and dopamine transporter (DAT) functionality5. When investigating the DA system, the ability to reliably measure endogenous levels of DA from adult brain is essential. Therefore, we present how to perform high-performance liquid chromatography (HPLC) on brain tissue from mice to determine levels of DA. We perform the experiment on tissue from dorsal striatum (dStr) and nucleus accumbens (NAc), but the method is also suitable for other DA-innervated brain areas.
DAT is essential for reuptake of DA into the presynaptic terminal, thereby controlling the temporal and spatial activity of released DA. Knowing the levels and functionality of DAT in the striatum is of major importance when assessing DA homeostasis. Here, we provide a protocol that allows to simultaneously deduce information on surface levels and function using a synaptosomal6 DA uptake assay.
Current methods combined with standard immunoblotting protocols provide the researcher with relevant tools to characterize the DAergic system.

Synaptosomal dopamine uptake and high-performance liquid chromatography analysis represent experimental tools to investigate dopamine homeostasis in mice by assessing the function of the dopamine transporter and levels of dopamine in striatal tissue, respectively. Here we present protocols to measure dopamine tissue content and assess the functionality of the dopamine transporter.

高效液相色谱分析和触多巴胺摄取对小鼠多巴胺能稳态的评价

Dopamine (DA) is a modulatory neurotransmitter controlling motor activity, reward processes and cognitive function. Impairment of dopaminergic (DAergic) ...

Isolation and Cultivation of Neural Progenitors Followed by Chromatin-Immunoprecipitation of Histone 3 Lysine 79 Dimethylation Mark

Brain development is a complex process, which is controlled in a temporo-spatial manner by gradients of morphogens and different transcriptional programs. Additionally, epigenetic chromatin modifications, like histone methylation, have an important role for establishing and maintaining specific cell fates within this process. The vast majority of histone methylation occurs on the flexible histone tail, which is accessible to histone modifiers, erasers, and histone reader proteins. In contrast, H3K79 methylation is located in the globular domain of histone 3 and is implicated in different developmental functions. H3K79 methylation is evolutionarily conserved and can be found in a wide range of species from Homo sapiens to Saccharomyces cerevisiae. The modification occurs in different cell populations within organisms, including neural progenitors. The location of H3K79 methylation in the globular domain of histone 3 makes it difficult to assess. Here, we present methods to isolate and culture cortical progenitor cells (CPCs) from embryonic cortical brain tissue (E11.5-E14.5) or cerebellar granular neuron progenitors (CGNPs) from postnatal tissue (P5-P7), and to efficiently immunoprecipitate H3K79me2 for quantitative PCR (qPCR) and genome-wide sequencing.

We present an effective and reproducible method to isolate and culture neural progenitor cells from embryonic and postnatal brain tissue for chromatin immunoprecipitation (ChIP) of histone 3 lysine 79 dimethylation (H3K79me2) - a histone mark located within the globular domain of histone 3.

组蛋白3赖氨酸 79 Dimethylation 标记沉淀后神经祖细胞的分离培养

Brain development is a complex process, which is controlled in a temporo-spatial manner by gradients of morphogens and different transcriptional programs. ...

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Probing an individual cell&#39;s gene expression enables the identification of cell type and cell state. Single-cell RNA sequencing has emerged as a powerful tool for studying transcriptional profiles of cells, particularly in heterogeneous tissues such as the central nervous system. However, dissociation methods required for single cell sequencing can lead to experimental changes in the gene expression and cell death. Furthermore, these methods are generally restricted to fresh tissue, thus limiting studies on archival and bio-bank material. Single nucleus RNA sequencing (snRNA-Seq) is an appealing alternative for transcriptional studies, given that it accurately identifies cell types, permits the study of tissue that is frozen or difficult to dissociate, and reduces dissociation-induced transcription. Here, we present a high-throughput protocol for rapid isolation of nuclei for downstream snRNA-Seq. This method enables isolation of nuclei from fresh or frozen spinal cord samples and can be combined with two massively parallel droplet encapsulation platforms.

Here, we present a protocol to rapidly isolate high-quality nuclei from the fresh or frozen tissue for downstream massively parallel RNA sequencing. We include detergent-mechanical and hypotonic-mechanical tissue disruption and cell lysis options, both of which can be used for isolation of nuclei.

成人脊髓细胞核在大规模平行单核 RNA 测序中的分离

Probing an individual cell&#39;s gene expression enables the identification of cell type and cell state. Single-cell RNA sequencing has emerged as a ...

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing (DIVA-Seq)

Single-cell RNA sequencing (RNA-seq) is now a widely implemented tool for assaying gene expression. Commercially available single-cell RNA-sequencing platforms process all input cells indiscriminately. Sometimes, fluorescence-activated cell sorting (FACS) is used upstream to isolate a specifically labeled population of interest. A limitation of FACS is the need for high numbers of input cells with significantly labeled fractions, which is impractical for collecting and profiling rare or sparsely labeled neuron populations from the mouse brain. Here, we describe a method for manually collecting sparse fluorescently labeled single neurons from freshly dissociated mouse brain tissue. This process allows for capturing single-labeled neurons with high purity and subsequent integration with an in vitro transcription-based amplification protocol that preserves endogenous transcript ratios. We describe a double linear amplification method that uses unique molecule identifiers (UMIs) to generate individual mRNA counts. Two rounds of amplification results in a high degree of gene detection per single cell.

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

This protocol describes the manual sorting procedure to isolate single fluorescently labeled neurons followed by in vitro transcription-based mRNA amplification for high-depth single-cell RNA sequencing.

荧光标记小鼠神经元的单细胞 RNA 序列的人工分选和双体外转录与绝对计数测序 (天后-Seq)

Single-cell RNA sequencing (RNA-seq) is now a widely implemented tool for assaying gene expression. Commercially available single-cell RNA-sequencing ...