我们感谢加拿大卫生部 Dr. 放射和保护司, 以及法拉 Flegal、加拿大核实验室及其实验室人员从其细胞遗传学剂量实验室获取中期图像数据。本文得到了加拿大创新项目 CytoGnomix (串行 No。EN579-172270/001/SC)。初始版本的 ADCI 和算法的发展得到了西方创新基金的支持;加拿大自然科学与工程研究理事会 (NSERC 发现赠款 371758-2009);美国公共卫生服务 (飞镖剂量 CMCR, 5U01AI091173-0);加拿大创新基金会;加拿大研究椅和 CytoGnomix Inc。

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PKR 和 JHMK 创建 CytoGnomix, 这是商业化 ADCI 和持有相关专利。基和 BCS 是 CytoGnomix 的雇员。ADCI 是受版权保护的, 在 ADCI 的丝本地化方法是专利的 (美国帕特。No. 8605981;德国帕特。No. 112011103687)。

软件的功能和限制
本文所述的协议介绍了 ADCI 中用于导入和处理细胞遗传学中期图像的典型逐步程序, 创建辐射校准曲线, 并估计暴露于未知的个体或样本中的生物剂量辐射水平。但是, 没有必要按顺序执行这些指令。例如, 可以使用相同的算校准曲线来处理和分析未知剂量的许多测试样本。此外, 处理完成后, 用户可以对图像选择和 DC 过滤模型进行迭代。适当的图像选择模型的应用取决于中期图像数据的特征和质量, 这反过来又依赖于用于准备细胞的实验室协议和用于选择自动的细胞的严格标准。中期捕获系统。剂量和细胞遗传学实验室的染色体形态会有所不同, 因此, 用户应评估图像选择模型, 以确定随软件提供的预定义图像选择模型是否足以生成精确的剂量估计, 或者是否需要创建具有用户定义阈值的自定义模型。根据我们的经验, 图像选择模型的有效性受到细胞图像的来源和质量的影响。用户可以设计自己的图像选择标准, 使用不同组合的过滤器来消除误报和图像选择模型, 以及相应的阈值选择所需的图像。由于线性二次曲线和直流频率的系数可以被修改或手动输入, 所以在校准曲线和剂量估计的投入上有很大的灵活性。
虽然软件是全自动的, 但图像可以手动审阅和选择。此功能可通过主 GUI 中的显微镜查看器功能来包括或删除单独处理的图像。然而, 由于自动化, 与中期图像和计数 DCs 的手工评分相比, 该软件的效率明显提高。一个由1000年图像组成的样本可以在多核性能工作站上以 20 (tiff) 到 40 (jpg) 最小值进行处理。这一软件将在时间紧迫或 labor-intensive 的情况下特别有用, 例如, 多个人暴露或被怀疑暴露在辐射中的事件, 或时间敏感的诊断和治疗决定是至关重要的。
对于无人值守的辐射评估, 需要精确准确的 DCs 高通量检测以及剂量估计。其他可供选择的软件不满足这两个要求。用户辅助的基于图像的细胞遗传学分析 (DCScore, Metasystems17) 系统需要对候选 DCs 进行手动验证, 这是由于染色体间的未更正重叠导致的高错误率, 而系统不确定辐射剂量在涉及大量潜在暴露个体的辐射事件中, DCScore 不会像 ADCI 那样有效。大口径显微镜系统可以采集多个中期细胞的图像18, 但是, 它们不分析它们。"漆皮"19和 "剂量估计"20软件可以生成校准曲线和估计剂量, 但不分 DCs。其他不基于 DC 分析的剂量检测包括 H2AX 荧光、荧光原位杂交和针对特定染色体的 DNA 探针、基因表达、微核检测、尿液和呼吸道生物标志物。这些方法不太具体, 对电离辐射的敏感度更低, 在某些情况下, 成本会更高, 而且在多个参考实验室中通常没有标准化。大多数这些技术不能检测到稳定的辐射反应, 因此不能用于长期评估 (#62; 7 天后) 的辐射剂量。相比之下, 这可以评估个人多达90天后, 并可以处理数据从任何细胞遗传学实验室显微镜成像系统。然而, 如果样本是绘制和 #62; 4 周后, 灵敏度下降, 由于衰变的 dicentric 像差1,2,3和软件目前没有正确的 DC 频率的延迟取样暴露的个人。
该软件有一定的局限性。现有的图像选择模型大多选择可接受的中期图像, 但在某些情况下, 无法消除图像不理想, 从而降低了 DC 检测的准确度。如何设计出一个令人满意的图像选择模型, 消除了所有不合适的中期细胞, 这仍然是一个开放性的问题。该软件为暴露于较高辐射剂量 (≥ 2) 的样品提供准确的估计。尽管在减少假阳性 dc 的数量方面取得了很大的进展16, 但这些对象还没有被消除。低辐射剂量的中期细胞 (特别是 #60; 1) 更容易出现假阳性 DC 检测。因此, 在生成 HC 试验样品剂量估算的校准曲线时, 不包括低剂量样品。但是, 如果需要一个包含低剂量样本的曲线, 一个较低的 SVM 西格玛值可以减少低剂量样品中的假阳性计数, 但可能会导致高剂量样品的 DC 产量降低。图8比较了用于剂量估计 (西格玛 = 1.5) 的 HC 曲线与在较低支持向量机西格玛值 (1.0) 中附加的低剂量样本相匹配的校准曲线。在中期细胞数量不足和/或中期图像质量较差的样本中, 不可能精确估计低剂量的生物照射量, 可能导致物理剂量超过0.5。
如果其剂量响应曲线最适合线性或近线性模型, 则该软件可能无法准确地评估辐射类型。到目前为止, 它只测试了暴露于 X 射线和伽马射线的样品。如果检查另一个辐射源, 用户必须确保校准和测试样品都暴露在同一类型的辐射中。该软件采用最大似然或最小二乘法拟合, 用线性二次模型构造剂量响应曲线。目前没有选择强加一个严格的线性曲线拟合, 适用于高能粒子曝光, 但这样的功能将是可用的未来。
未来发展
我们正在努力改善图像选择模型和准确的剂量测量, 特别是暴露于低辐射剂量的样品。随后的软件版本将提供标准误差测量的剂量估计和置信区间的校准曲线。此外, 一个高性能计算版本的软件的蓝色基因 (BG/Q, IBM) 超级计算机正在开发, 以及时评估个人暴露在大规模伤亡辐射事件。软件的某些组件已经在这个平台上进行了测试和部署少女 = "xref" > 11。

辐射剂量使用生物标记, 主要是染色体畸变, 如 dicentric 染色体 (DCs) 和染色体易位, 以测量个人接触到的辐射剂量。由于个体间的变异性, 生物吸收剂量可能与仪器测量的物理剂量不同。同样地, 某种物理剂量的辐射也会因潜在的生理或环境条件而产生不同的生物暴露。对生物剂量的认识对于诊断和治疗都特别重要。
DC 化验是世界卫生组织 (世卫组织) 和国际原子能机构 (原子能机构) 的金标准, 用于评估人体内的生物辐射暴露情况。这是原子能机构和世卫组织对辐射剂量评估所建议的第一次化验。在辐射照射后约4周内, dc 频率相对稳定, 与发射辐射剂量的定量相关性比较准确, 使 DCs 成为理想的生物标志物.辐射剂量 (被引用在灰色的单位) 和 dc 频率 (被引用为每个单元的 dc 数) 之间的关系可以表示为线性二次函数。
细胞遗传学 DC 检测已成为行业标准约55年2。它已经手动执行, 需要 1-2 天的时间来分析显微镜数据从一个单一的血液样本。根据剂量3的不同, 需要几百到几千个图像来准确估计辐射照射。在超过1的剂量时, 原子能机构建议至少检测 100 dc。检查 250-500 中期图像是常见的做法在剂量细胞遗传学实验室。对于具有曝光度和 #60 的样品, 由于 DC 形成的概率较低, 建议使用1的 3000-5000 图像。无论哪种情况, 这都是一项劳动密集的任务。
细胞遗传学剂量实验室创建自己的在体外辐射剂量校准曲线之前, 评估生物剂量的测试样本。血液样本从正常, 控制个体暴露在辐射和淋巴细胞, 然后培养和准备中期染色体分析。使用这些样本, 收到的生物剂量校准到已知的物理剂量的标准辐射源发射。在中期细胞图像记录后, 专家检查图像, 计数 DCs 和计算每个样本的直流频率。通过将线性二次曲线拟合为所有剂量的直流频率来建立校准曲线。然后, 通过将直流频率与曲线上的校准剂量匹配, 或者在相应的线性二次型公式中指定它们, 可以推断出来自个人的测试样本的曝光。
我们已经自动化的检测 DCs 和剂量的决心, 以加快这一程序使用软件。自动 Dicentric 染色体标识符和剂量估计器 (ADCI) 使用机器学习图像处理技术, 以检测和鉴别 Dicentric 染色体 (DCs) 从 monocentric 染色体 (MCs) 和其他对象和自动化辐射剂量估计。该软件旨在大大减少或消除手动验证 DC 计数的必要性, 并通过自动化加速剂量估计。它是在加拿大卫生部 (HC) 和加拿大核实验室 (CNL) 的参考剂量实验室的参与下开发的。他们的反馈将确保业绩将继续符合原子能机构对这种化验的标准。
该软件执行以下功能: 1) 过滤 DCs 和选择最佳中期细胞图像进行分析, 2) 染色体识别, dc 检测, 和直流频率测定, 和 3) 估计辐射剂量的剂量校准,细胞遗传学辐射数据。该软件处理来自同一个体的中期图像组 (称为样本), 使用图像处理技术计算各区的 dc 数, 并返回每个样本在灰度单位接收的估计辐射剂量。
该软件被设计来处理一系列的染色体结构, 计数和密度。然而, 该算法的最佳表现在中期图像中包含一个近乎完整的补充分离, 线性染色体4。图像含有高度重叠的染色体组、多细胞、不完全中期细胞、姊妹染色单体分离、细胞核、non-chromosomal 物体等缺陷, 可降低算法的准确度。专用的图像选择模型和其他目标分割阈值可以滤除大多数的次优图像和伪正则 dc。
Dicentric 染色体检测是在处理图像时进行的。该算法试图确定图像中的哪些对象是染色体, 然后找到最有可能在每个染色体上丝的两个区域。然后, 一系列不同的支持向量机 (SVM) 学习模型将染色体区分为 DCs 或正常的、monocentric 的染色体。支持向量机模型在 dc 检测的灵敏度和特异性上有不同 (见下面的步骤 3.1.4), 这会影响在样本中确定的直流频率。
ADCI 处理一套姬姆萨 (或 DAPI) 染色中期数字图像 (TIFF 或 JPG 格式) 的一个或多个样本。该软件在校准样本和测试样本中分析 DCs。校准样品的物理剂量是已知的, 并用于校准曲线的生成。该软件从机器生成的校准曲线中推断出具有未知暴露的个体的物理和生物剂量。尽管实验室使用可比技术, 但来自不同实验室的校准曲线通常会有所不同3。在测试样品中, 应对同一实验室的校准曲线和测试样本进行精确的剂量估计。
该软件提供了速度、准确性和可伸缩性, 它解决了处理事件所需的生产率, 其中许多人必须同时进行测试。它被开发了从 2008年-20174,5,6,7,8,9,10,11,12 ,13。使用最近的计算机硬件, 此桌面PC 软件可以处理和估计的辐射剂量在500中期基因组等效的 10-20 min 4的病人样本。该代码是基于一组专有的图像分割和机器学习算法的染色体分析。专家分析的每一个染色体暴露于3辐射给出了相当的准确性 ADCI。在一套6样本的未知曝光 (以前用于国际熟练程度的演习), 软件估计剂量在 0.5, 通过手工审查相同的数据, 由 HC 和 CNL, 满足国际原子能机构的要求会审剂量.此外, 实验室标准化和最终重现剂量估计受益于有一个共同的, 自动 DC 评分算法。然而, 该软件允许定制的图像过滤和选择标准, 使不同的染色体制备方法和辐射校准源要考虑。
该软件是一个基于图形用户界面 (GUI) 的系统, 它分析了含有姬姆萨 (或 DAPI) 染色的中期细胞的染色体图像的集合, 因为电离辐射造成的异常。图像集用光 (或 epifluorescent) 显微镜系统进行数字拍摄, 每组都对应不同的样本。该软件利用图像处理技术, 从 MCs 和其他对象中检测和鉴别 DCs。经验派生的分割过滤器, 然后自动消除假阳性 dcs, 而不影响真正的 dcs。最后, 软件根据不同的图像属性自动过滤出不良图像, 发现算 (或用户指定的) 图像选择模型的低质量中期图像。这些图像包括含有过量或不足的 "嘈杂" 对象、多重重叠的染色体、缺乏中期染色体的图像、过多的姐妹染色4。自动策划的图像数据用于生成已知辐射剂量样本的剂量校准曲线, 用于估计暴露于未知剂量的试验样品的暴露情况。
该软件的输出可以查看并保存为: 1) 基于文本的输出在控制台, 2) 图可以保存为图像, 和 3) 的 HTML 格式的报告。软件的许多方面都是可定制的, 以适应不同实验室的特殊需要。个别实验室通常提供的校准和测试样本的准备和收集的基础上的细胞遗传学的协议在该实验室验证。这保持了样品制备的均匀性, 并允许从校准样品中生成的校准曲线有意义地应用于使用同一协议导出的测试样本。校准曲线也可以创建从曲线系数或直流频率的定义剂量。通过滤除质量较低的图像和伪正 dc (FPs), 得到最准确的剂量估计。每个样本中的最佳图像子集的选择是通过使用 "图像选择模型" 来完成的, 它可以消除那些倾向于引入 FPs 的图像。软件中包含了一系列的 pre-validated 模型, 但是用户可以创建和保存具有自定义阈值和筛选器的附加模型。
软件成功加载后, 将显示主图形用户界面 (GUI) (请参见图 1)。从这个界面, 每个样本, 包括一个中期细胞图像文件的文件夹, 可以选择和处理, 以确定 DCs, 可以创建和比较的校准曲线, 和辐射照射剂量的样品可以确定。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56245/56245fig1.jpg"> 
图 1:图形用户界面的主要扇区包括:示例列表(1)、校准曲线(2)列表、进程队列(3), 它监视每个示例的每组图像中的 DC 检测状态, 一个情节显示(4), 它汇总了样本或校准曲线中一组图像的统计或其他定量属性, 以及一个控制台(5) , 其中包含作为程序执行的每个操作的输出的描述性文本。<a href="//ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56245/56245fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

<table>
	<tbody>
		<tr>
			<td>Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI)</td>
			<td>CytoGnomix</td>
			<td>NA</td>
			<td>ADCI software is released in a binary installation package file for Microsoft Windows 7, 8, 8.1 and 10; 235 Mb of disk storage are required for a typical installation. The software has been tested with Intel or AMD x86-64 processors; at least 1 Gb RAM is recommended. Analyses have been benchmarked on a computer configured with an Intel I7 processor and 16 Gb RAM. Operation of ADCI requires an active license and a USB-based hardware dongle, which must remain plugged in while the software is executing. The dongle encodes the software expiry date. Each time the software is started, this date is read. The software will allow access to the program if the current date and time precedes the expiration time-date stamp. Extending an expired software license can be accomplished by obtaining a new dongle or by renewing the license with an updated key at startup.</td>
		</tr>
		<tr>
			<td>Digital images of metaphase cell nuclei</td>
			<td>Examples: Metasystems, Leica Microsystems</td>
			<td>M-Search (Metasystems), Cytovision (Leica) software</td>
			<td>High resolution TIFF format; typically &#62;250 digital images generated with a microscope imaging capture system (minimum 63x magnification objective, 10x magnification ocular).</td>
		</tr>
		<tr>
			<td>MSI Leopard Pro (recommended, optional)</td>
			<td>Micro-Star International</td>
			<td>MSI GP62 6QF 480CA Leopard Pro</td>
			<td>Multi-core performance workstation.</td>
		</tr>
	</tbody>
</table>

expedited radiation biodosimetry by automated dicentric chromosome identification (adci) and dose estimation

生物辐射剂量可从中期细胞的 dicentric 染色体频率估计。执行这些细胞遗传学 dicentric 染色体检测传统上是一个手册, labor-intensive 过程不太适合处理的样本量可能需要检查后, 大规模伤亡事件。自动 Dicentric 染色体标识符和剂量估计器 (ADCI) 软件自动化这一过程, 通过检查一套中期图像使用机器学习图像处理技术。该软件通过删除不合适的图像来选择合适的图像进行分析, 将每个对象归类为含有丝的染色体或 non-chromosome, 进一步区分染色体为 monocentric 染色体 (MCs) 或 dicentric染色体 (DCs), 确定一个样本中的直流频率, 并估计的生物辐射剂量比较采样直流频率与校准曲线计算使用校准样本。本协议描述了 ADCI 软件的使用。通常, 两个校准 (已知剂量) 和测试 (未知剂量) 的中期图像的设置, 以执行准确的剂量估计。最佳的分析图像可以通过预设的图像过滤器自动找到, 也可以通过人工检查进行过滤。该软件处理每个样本中的图像和 dc 频率在不同级别的严格计算调用 DCs, 使用机器学习方法。在已知物理剂量的标定样品中, 根据直流频率生成线性二次校准曲线。使用这些校准曲线, 从其直流频率估计暴露于不确定辐射水平的试样剂量。报告可以根据请求生成, 并提供一个或多个样本的结果汇总, 一个或多个校准曲线, 或剂量估计。

利用 HC 和 CNL 的中期染色体图像数据进行了软件测试。血液样本由一个 XRAD-320 单位 (250 伏 X 射线, 12.5 毫安, 2mm 铝过滤, 剂量率: 0.92 或1.7 的/分钟), 在 HC 的离子室进行校准, 并在两个实验室进行处理。根据已建立的协议3,15, 在每个设施上培养、固定和染色外周血淋巴细胞样本。姬姆萨的中期图像由每个实验室使用自动显微镜系统独立捕获。每个实验室的专家都在这些样本中手工进行了 DCs, 构建了他们自己的校准曲线和未知暴露的测试样本的估计剂量。表1提供了这些数据集的详细说明。
<table border="1">
	<tbody>
		<tr>
			<td>物理剂量</td>
			<td>目的</td>
			<td colspan="2">HC 制备</td>
			<td colspan="2">CNL 制剂</td>
		</tr>
		<tr>
			<td></td>
			<td></td>
			<td>引用的名称</td>
			<td>图像的 #</td>
			<td>引用的名称</td>
			<td>图像的 #</td>
		</tr>
		<tr>
			<td>0</td>
			<td>校准</td>
			<td>HC0Gy</td>
			<td>731</td>
			<td>CNL0Gy</td>
			<td>798</td>
		</tr>
		<tr>
			<td>0。1</td>
			<td>校准</td>
			<td>HC01Gy</td>
			<td>2162</td>
			<td>那</td>
			<td>那</td>
		</tr>
		<tr>
			<td>0.25</td>
			<td>校准</td>
			<td>HC025Gy</td>
			<td>1826</td>
			<td>那</td>
			<td>那</td>
		</tr>
		<tr>
			<td>0。5</td>
			<td>校准</td>
			<td>HC05Gy</td>
			<td>1054</td>
			<td>CNL05Gy</td>
			<td>1532</td>
		</tr>
		<tr>
			<td>0.75</td>
			<td>校准</td>
			<td>HC075Gy</td>
			<td>1233</td>
			<td>那</td>
			<td>那</td>
		</tr>
		<tr>
			<td>1</td>
			<td>校准</td>
			<td>HC1Gy</td>
			<td>1566</td>
			<td>CNL1Gy</td>
			<td>841</td>
		</tr>
		<tr>
			<td>2</td>
			<td>校准</td>
			<td>HC2Gy</td>
			<td>1147</td>
			<td>CNL2Gy</td>
			<td>996</td>
		</tr>
		<tr>
			<td>3</td>
			<td>校准</td>
			<td>HC3Gy</td>
			<td>1212</td>
			<td>CNL3Gy</td>
			<td>1188</td>
		</tr>
		<tr>
			<td>4</td>
			<td>校准</td>
			<td>HC4Gy</td>
			<td>909</td>
			<td>CNL4Gy</td>
			<td>1635</td>
		</tr>
		<tr>
			<td>5</td>
			<td>校准</td>
			<td>HC5Gy</td>
			<td>1019</td>
			<td>那</td>
			<td>那</td>
		</tr>
		<tr>
			<td>3。1</td>
			<td>测试</td>
			<td>HCS01</td>
			<td>540</td>
			<td>CNLS01</td>
			<td>500</td>
		</tr>
		<tr>
			<td>2。3</td>
			<td>测试</td>
			<td>HCS08</td>
			<td>637</td>
			<td>CNLS08</td>
			<td>500</td>
		</tr>
		<tr>
			<td>1。4</td>
			<td>测试</td>
			<td>HCS10</td>
			<td>708</td>
			<td>那</td>
			<td>那</td>
		</tr>
		<tr>
			<td>1。8</td>
			<td>测试</td>
			<td>HCS04</td>
			<td>600</td>
			<td>CNLS04</td>
			<td>957</td>
		</tr>
		<tr>
			<td>2。8</td>
			<td>测试</td>
			<td>HCS05</td>
			<td>1136</td>
			<td>CNLS05</td>
			<td>1527</td>
		</tr>
		<tr>
			<td>3。4</td>
			<td>测试</td>
			<td>HCS07</td>
			<td>477</td>
			<td>CNLS07</td>
			<td>735</td>
		</tr>
	</tbody>
</table>
表 1:由 HC 和 CNL 提供的图像数据源, 用于评估软件. 脚注: 从表 1中修改了罗根et al., 20164。只有手动预选的图像之前, 我们可以从 CNL。未筛选的图像已变为可用, 并相应更新图像计数。此外, 新获得的 HC 样本 (0.25Gy、0.75Gy 和 5Gy) 在这里介绍。
样本中的自动图像选择
图像质量是直流分析中纠正直流检测的关键。传统的 DC 分析通常手动进行细胞遗传学专家的图像选择。ADCI 使用定量图像标准在直流频率计算16之前自动选择图像。用户可以过滤出基于特定染色体形态的图像和/或根据对象长度的相对比例对细胞进行分类, 根据已知的染色体在正常的人核型中的细胞群定义的长度 (称为组-bin 距离方法)。可用的形态学过滤器使用缩放不变阈值来拒绝不完整的染色体组或多 metaphases 的细胞图像, 与中期染色体, 与突出的姐妹染色单体分离, 高度弯曲和扭曲染色体, 具有平滑的轮廓特征的完整的核, 以及那些较少的物体被识别为染色体的物体。图 3(a) 和 (b) 显示所选图像的示例, 而图 3 (c) 和 (d) 是软件筛选出的图像的示例。这些图像是从样本 HCS05 (如表1所述), 并由预定义的图像选择模型, 按组 bin 距离排列所有图像, 然后选择最好的250图像。在图 3 (a)、(b) 中的染色体分离良好, 并表现出令人满意的形态学。图 3(c) 含有过多的重叠染色体簇。图 3(d) 显示严重的姐妹染色单体分离。姐妹染色是完全分离的至少8的染色体和丝约束是模棱两可的大多数其他染色体。
 
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56245/56245fig3.jpg"> 
图 3: 示例 HCS05 中中期图像的示例 (放大倍数: 63X),由模型 "组 Bin 距离, 前250图像" 选定并选择.(A)和(B)是选定的图像。(C)和(D)是模型已消除的图像。(C) 被排除在外, 因为它含有太多的重叠染色体, (D) 有过多的失散的姐妹染色。<a href="//ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56245/56245fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
通过对样本中直流检测的置信度进行检验, 验证了这些图像选择模型的应用效果。在一个受照射样本的细胞群中, DCs 的出现遵循泊松分布。chi-square 优试验将观测到的直流频率分布与泊松分布的期望拟合进行了比较。正确筛选样本数据的模型显示的 DC 频率与预期的泊松值 (通常意义级别和 #62; 0.01) 没有显著的不同。图 4显示 DC 发生的情况以及对应于所有图像的 HC4Gy 样本的泊松分布, 而不是 "组 bin 距离、前250图像" 模型所选择的图像。图 4(b) 显示出更适合泊松分布的情况。的 p筛选的图像集的值 (0.36) 明显超过图 4 (a) 中未筛选的 DC 分布。在5% 或1% 意义级别上,图 4 (a) 中未筛选的样本不太可靠, 因为它包含较低质量的 dc 数据, 因为 dc 的泊松分布的 null 假设被拒绝。

<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/56245/56245fig4.jpg"> 
图 4:按比例的 DC 频率的截图适合软件中样品 HC4Gy 的泊松 Dstributions.(A)包括所有图像, (B)只包含由模型选择的图像 (组 bin 距离、前250图像)。图例 (右上方) 表示适合于泊松分布 (色散指数、Mu 测试和 Lambda) 的统计信息和拟合测试 (p 值) 的 Chi 平方的优点<a href="//ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56245/56245fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
Dicentric 染色体 (DC) 检测
准确的直流检测是 ADCI 的关键前提要求。正确检测到的 DCs 和软件遗漏的都被分别定义为真阳性 (TPs) 和假阴性 (FNs)。不是 dc 但被错误地检测为 dc 的对象称为误报 (FPs)。FPs 包括 monocentric 染色体, 染色体片段, 分离的姐妹染色, 重叠的染色体簇, 和 non-chromosomal 的对象。图 5显示了两个中期图像中的 DC 检测结果。对象1和3是 TPs, 而对象4是一个 FP, 包括两个不同的 monocentric 染色体, 沿着它们的短臂。在图 5 (a) 中, 对象2最初是 fp, 但随后由软件中的 fp 筛选器更正。图 5 (b) 中的对象5和对象6可能是 FNs 的示例。

<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/56245/56245fig5.jpg"> 
图 5:截图显示潜在 DCs 的中期染色体分类. (A)在样品 CNL1Gy (放大: 63X) 显示 1 TP, 对象 "1" 的图像;和1更正 FP, 对象 "2"。(B)示例 CNL4Gy 中的图像 (放大倍数: 63X) 显示 1 TP, 对象 "3";1 FP, 对象 "4";和2潜在的 FNs, 对象 "5" 和 "6"。TPs、修正的 FPs、普通 monocentric 和未分类的染色体分别用红色、黄色、绿色和蓝色轮廓勾勒出来。<a href="//ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56245/56245fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
测试样本的剂量估计
ADCI 分析的最终结果是从校准曲线推断出的样品的剂量估计。软件为表 1中的测试样本所做的剂量估计在表 2和3中表示。为了比较, HCS01, HCS08 和 HCS10 的物理辐射剂量和人工评分的专家在 HC 的剂量。同样, CNL 专家的物理和人工评分的剂量显示为 CNLS04, CNLS05 和 CNLS07。
图 6演示了用于加拿大卫生部剂量实验室样本 HCS01、HCS08、HCS10、HCS04、HCS05 和 HCS07 的辐射剂量估计的校准曲线。使用 HC0Gy、HC1Gy、HC2Gy、HC3Gy 和 HC4Gy 等样品生成标定曲线。所有样本都应用了包含 3 Z score-based 滤镜 + "组 bin 距离、前250图像" 的图像选择模型。剂量估计以及相关的统计分析显示在表 2中。
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/56245/56245fig6.jpg"> 
图 6:HC 测试样本剂量估计的截图黑色方块代表校准样品。测试样本和校准样本中的图像由模型 (3 FP 过滤器 + 组 bin 距离, 前250图像) 选择。粗虚线表示 DCs/中期通过校准曲线到估计剂量的映射。细虚线表示 DCs/中期的上限和下限95% 置信限。测试样品的颜色代码: 亮红色, hc S01 (物理剂量: 3.1Gy, hc 推断剂量: 3.4Gy, ADCI: 3Gy);深绿色, HC S04 (物理剂量: 1.8Gy, ADCI: 1.85Gy);明亮的蓝色, HC S05 (物理药量: 2.8Gy, ADCI: 2.95Gy);深蓝色, HC S07 (物理剂量: 3.4Gy, ADCI: 2.35Gy);暗红色, hc S08 (物理剂量: 2.3Gy, hc 推断剂量: 2.5Gy, ADCI: 2Gy);亮绿色, hc S10 (物理剂量: 1.4Gy, hc 推断剂量: 1.4Gy, ADCI: 0.95Gy)。<a href="//ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56245/56245fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table border="1">
	<tbody>
		<tr>
			<td>样品</td>
			<td>物理剂量</td>
			<td>HC 推断剂量</td>
			<td>ADCI 估计剂量</td>
			<td>估计剂量拼箱</td>
			<td>估计剂量</td>
			<td>p-值 *</td>
		</tr>
		<tr>
			<td>HCS01</td>
			<td>3。1</td>
			<td>3。4</td>
			<td>3</td>
			<td>2。3</td>
			<td>3。8</td>
			<td>0.117</td>
		</tr>
		<tr>
			<td>HCS08</td>
			<td>2。3</td>
			<td>2。5</td>
			<td>2</td>
			<td>1。4</td>
			<td>2。7</td>
			<td>0.815</td>
		</tr>
		<tr>
			<td>HCS10</td>
			<td>1。4</td>
			<td>1。4</td>
			<td>0.95</td>
			<td>0。5</td>
			<td>1.55</td>
			<td>0.211</td>
		</tr>
		<tr>
			<td>HCS04</td>
			<td>1。8</td>
			<td>那</td>
			<td>1.85</td>
			<td>1.25</td>
			<td>2.55</td>
			<td>0.0293</td>
		</tr>
		<tr>
			<td>HCS05</td>
			<td>2。8</td>
			<td>那</td>
			<td>2.95</td>
			<td>2.25</td>
			<td>3.75</td>
			<td>0.00354</td>
		</tr>
		<tr>
			<td>HCS07</td>
			<td>3。4</td>
			<td>那</td>
			<td>2.35</td>
			<td>1。7</td>
			<td>3。1</td>
			<td>0.0002</td>
		</tr>
	</tbody>
</table>
表 2:HC 测试样本的剂量估计结果. 脚注: 从表 3中修改了罗根et al., 20164。先前报告的 ADCI 剂量估计是基于未过滤的图像, 并使用最小二乘法进行曲线拟合。在此, 用最大似然法拟合了标定曲线, 并在剂量估计前应用了包含 3 FP 滤波器 + "群 bin 距离, 前250图像" 的图像选择模型。估计剂量而拼箱则根据直流屈服的泊松特性, 参考剂量估计上限和下限95% 置信限。* 符合理论泊松分布的 Chi 平方善;NA: 未提供人工推断剂量的结果。
加拿大核实验室 CNLS04、CNLS05、CNLS07、CNLS01 和 CNLS08 的辐射剂量估计数见图 7。使用 CNL0Gy、CNL0.5Gy、CNL1Gy、CNL2Gy、CNL3Gy 和 CNL4Gy 等样品生成标定曲线。我们应用了一个由 6 FP 滤波器组成的图像选择模型。统计分析的结果显示在表 3中。

<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/56245/56245fig7.jpg"> 
图 7:CNL 测试样本的剂量估计的截图黑色方块代表校准样品。测试样本和校准样本中的图像使用 6 FP 滤波器进行选择。粗虚线表示 DCs/中期通过校准曲线到估计剂量的映射。细虚线表示 DCs/中期的上限和下限95% 置信限。测试样品的颜色代码: 亮红色, CNL S04 (物理剂量: 1.8Gy, CNL 推断剂量: 1.7Gy, ADCI: 1.95Gy);暗红色, CNL S05 (物理剂量: 2.8Gy, CNL 推断剂量: 2.7Gy, ADCI: 3.05Gy);亮绿色, CNL S07 (物理剂量: 3.4Gy, CNL 推断剂量: 3.1Gy, ADCI: 3.4Gy);深绿色, CNL S01 (物理剂量: 3.1Gy, ADCI: 3.75Gy);蓝色, CNL S08 (物理剂量: 2.3Gy, ADCI: 2.8Gy)。<a href="//ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56245/56245fig7large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<table border="1">
	<tbody>
		<tr>
			<td>样品</td>
			<td>物理剂量</td>
			<td>CNL 推断剂量</td>
			<td>ADCI 估计剂量</td>
			<td>估计剂量拼箱</td>
			<td>估计剂量</td>
			<td>p-值 *</td>
		</tr>
		<tr>
			<td>CNLS04</td>
			<td>1。8</td>
			<td>1。7</td>
			<td>1.95</td>
			<td>1.25</td>
			<td>2.45</td>
			<td>0.0545</td>
		</tr>
		<tr>
			<td>CNLS05</td>
			<td>2。8</td>
			<td>2。7</td>
			<td>3.05</td>
			<td>2.75</td>
			<td>3.35</td>
			<td>0.325</td>
		</tr>
		<tr>
			<td>CNLS07</td>
			<td>3。4</td>
			<td>3。1</td>
			<td>3。4</td>
			<td>3</td>
			<td>3.75</td>
			<td>0.473</td>
		</tr>
		<tr>
			<td>CNLS01</td>
			<td>3。1</td>
			<td>那</td>
			<td>3.75</td>
			<td>3.35</td>
			<td>和 #62; 4</td>
			<td>7.63E-11</td>
		</tr>
		<tr>
			<td>CNLS08</td>
			<td>2。3</td>
			<td>那</td>
			<td>2。8</td>
			<td>2.25</td>
			<td>3。3</td>
			<td>0.777</td>
		</tr>
	</tbody>
</table>
表 3:CNL 测试样本的剂量估计结果. 脚注: 从表 3中修改, 罗根et al., 20164。先前报告的 ADCI 剂量估计是基于未过滤 (HC) 或手动选择的 (CNL) 图像, 并使用最小二乘法进行曲线拟合。在此, 用最大似然法拟合了标定曲线, 并在剂量估计前应用了包含 3 FP 滤波器 + "群 bin 距离, 前250图像" 的图像选择模型。根据直流屈服的泊松特性, 估计剂量和拼箱分别参考剂量估计上限和下限95% 置信限。 * 符合理论泊松分布的 Chi 平方善;NA: 人工推断出的剂量的结果是不可用的。
估计的辐射剂量范围内的校准曲线 (和 #60; 1) 可以执行的软件, 但西格玛值1.0 建议是进一步减少频率的归类 DCs (图 8)。
 
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/56245/56245fig8.jpg"> 
图 8:屏幕截图的两个校准曲线衍生的 HC 校准样品在不同的西格玛值。(A) HC 校准示例: 0Gy、2Gy、3Gy、4Gy 和5Gy 在西格玛 = 1.5。(B) HC 校准示例: 0Gy、0.25Gy、0.5Gy、0.75Gy、1Gy、2Gy、3Gy、4Gy 和5Gy 使用支持向量机西格玛 = 1.0。<a href="//ecsource-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/56245/56245fig8large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
这些分析表明, 专家和软件解释的物理和生物学推断剂量之间存在着微小但可接受的差异。手动或软件估计从物理剂量的区别被称为 "错误"。HC 和 CNL 对样品的推断剂量误差≤0.3。软件的自动处理比专家的准确性低, 但一般在原子能机构指定的会审限制范围内± 0.53。对于表 2和3中的大多数测试示例, 软件在这个阈值内生成了正确的结果。然而, HCS07 和 CNLS01 在泊松分布上表现出较差的优, 表明这些样本中的图像和直流质量存在潜在的问题, 而图像和 FP 选择模型的应用并没有得到解决。在 HCS05 的情况下, p 值意义阈值显得过于严格, 软件准确地确定了正确的剂量。

Watch this Scientific Journal Video about Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation at JoVE.com

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

自动 Dicentric 染色体识别 (ADCI) 和剂量估计加速辐射剂量

细胞遗传学 dicentric 染色体 (DC) 检测量化暴露于电离辐射。自动 Dicentric 染色体标识符和剂量估计软件准确快速地估计了中期细胞中 DCs 的生物剂量。它区别 monocentric 染色体和其他对象从 dcs, 并且估计生物辐射剂量从 dcs 的频率。

Biological radiation dose can be estimated from dicentric chromosome frequencies in metaphase cells. Performing these cytogenetic dicentric chromosome ...

expedited-radiation-biodosimetry-automated-dicentric-chromosome

Nanyang Technological University

Research

JoVE Journal

Genetics

Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation

15.6K 次观看.  CytoGnomix Inc.. 细胞遗传学 dicentric 染色体 (DC) 检测量化暴露于电离辐射。自动 Dicentric 染色体标识符和剂量估计软件准确快速地估计了中期细胞中 DCs 的生物剂量。它区别 monocentric 染色体和其他对象从 dcs, 并且估计生物辐射剂量从 dcs 的频率。

视频: Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification ADCI and Dose Estimation

Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization (mFISH) and Spectral Karyotyping (SKY) in Irradiated Mice

Ionizing radiation (IR) induces numerous stable and unstable chromosomal aberrations. Unstable aberrations, where chromosome morphology is substantially compromised, can easily be identified by conventional chromosome staining techniques. However, detection of stable aberrations, which involve exchange or translocation of genetic materials without considerable modification in the chromosome morphology, requires sophisticated chromosome painting techniques that rely on in situ hybridization of fluorescently labeled DNA probes, a chromosome painting technique popularly known as fluorescence in situ hybridization (FISH). FISH probes can be specific for whole chromosome/s or precise sub-region on chromosome/s. The method not only allows visualization of stable aberrations, but it can also allow detection of the chromosome/s or specific DNA sequence/s involved in a particular aberration formation. A variety of chromosome painting techniques are available in cytogenetics; here two highly sensitive methods, multiple fluorescence in situ hybridization (mFISH) and spectral karyotyping (SKY), are discussed to identify inter-chromosomal stable aberrations that form in the bone marrow cells of mice after exposure to total body irradiation. Although both techniques rely on fluorescent labeled DNA probes, the method of detection and the process of image acquisition of the fluorescent signals are different. These two techniques have been used in various research areas, such as radiation biology, cancer cytogenetics, retrospective radiation biodosimetry, clinical cytogenetics, evolutionary cytogenetics, and comparative cytogenetics.

Detection of Inter-chromosomal Stable Aberrations by Multiple Fluorescence In Situ Hybridization mFISH and Spectral Karyotyping SKY in Irradiated Mice

The present protocol describes the usefulness of multiple fluorescence in situ hybridization (mFISH) and spectral karyotyping (SKY) in identifying inter-chromosomal stable aberrations in the bone marrow cells of mice after exposure to total body irradiation.

通过多色荧光跨染色体畸变稳定的检测<em&gt;原位</em&gt;杂交（渔业部）和光谱核型分析（SKY）的照射小鼠

Ionizing radiation (IR) induces numerous stable and unstable chromosomal aberrations. Unstable aberrations, where chromosome morphology is substantially ...

科研

遗传学

Pooled shRNA Screen for Reactivation of MeCP2 on the Inactive X Chromosome

Forward genetic screens using reporter genes inserted into the heterochromatin have been extensively used to investigate mechanisms of epigenetic control in model organisms. Technologies including short hairpin RNAs (shRNAs) and clustered regularly interspaced short palindromic repeats (CRISPR) have enabled such screens in diploid mammalian cells. Here we describe a large-scale shRNA screen for regulators of X-chromosome inactivation (XCI), using a murine cell line with firefly luciferase and hygromycin resistance genes knocked in at the C-terminus of the methyl CpG binding protein 2 (MeCP2) gene on the inactive X-chromosome (Xi). Reactivation of the construct in the reporter cell line conferred survival advantage under hygromycin B selection, enabling us to screen a large shRNA library and identify hairpins that reactivated the reporter by measuring their post-selection enrichment using next-generation sequencing. The enriched hairpins were then individually validated by testing their ability to activate the luciferase reporter on Xi.

We report a small hairpin RNA (shRNA) and next generation sequencing-based protocol for identifying regulators of X-chromosome inactivation in a murine cell line with firefly luciferase and hygromycin resistance genes fused to the methyl CpG binding protein 2 (MeCP2) gene on the inactive X chromosome.

在非活动 X 染色体上重新激活 MeCP2 的池 shRNA 屏幕

Forward genetic screens using reporter genes inserted into the heterochromatin have been extensively used to investigate mechanisms of epigenetic control ...

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our entire society's ability to treat systemic infections. In the United States alone, antibiotic-resistant infections kill approximately 23,000 people a year and cost around 20 billion USD in additional healthcare. One approach to combat antimicrobial resistance is combination therapy, which is particularly useful in the critical early stage of infection, before the infecting organism and its drug resistance profile have been identified. Many antimicrobial treatments use combination therapies. However, most of these combinations are additive, meaning that the combined efficacy is the same as the sum of the individual antibiotic efficacy. Some combination therapies are synergistic: the combined efficacy is much greater than additive. Synergistic combinations are particularly useful because they can inhibit the growth of antimicrobial drug resistant strains. However, these combinations are rare and difficult to identify. This is due to the sheer number of molecules needed to be tested in a pairwise manner: a library of 1,000 molecules has 1 million potential combinations. Thus, efforts have been made to predict molecules for synergy. This article describes our high-throughput method for predicting synergistic small molecule pairs known as the Overlap2 Method (O2M). O2M uses patterns from chemical-genetic datasets to identify mutants that are hypersensitive to each molecule in a synergistic pair but not to other molecules. The Brown lab exploits this growth difference by performing a high-throughput screen for molecules that inhibit the growth of mutant but not wild-type cells. The lab's work previously identified molecules that synergize with the antibiotic trimethoprim and the antifungal drug fluconazole using this strategy. Here, the authors present a method to screen for novel synergistic combinations, which can be altered for multiple microorganisms.

High-throughput Identification of Synergistic Drug Combinations by the Overlap2 Method

Synergistic drug combinations are difficult and time-consuming to identify empirically. Here, we describe a method for identifying and validating synergistic small molecules.

通过重叠2方法高通量识别协同药物组合

Although antimicrobial drugs have dramatically increased the lifespan and quality of life in the 20th century, antimicrobial resistance threatens our ...

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and effect sizes of regulatory elements to a target gene. Some progress has been made with the bioinformatic prediction of the existence and function of proximal epigenetic modifications associated with activated gene expression using conserved transcription factor binding sites. Chromatin conformation capture studies have revolutionized our ability to discover physical chromatin contacts between sequences and even within an entire genome. Circular chromatin conformation capture coupled with next-generation sequencing (4C-seq), in particular, is designed to discover all possible physical chromatin interactions for a given sequence of interest (viewpoint), such as a target gene or a regulatory enhancer. Current 4C-seq strategies directly sequence from within the viewpoint but require numerous and diverse viewpoints to be simultaneously sequenced to avoid the technical challenges of uniform base calling (imaging) with next generation sequencing platforms. This volume of experiments may not be practical for many laboratories. Here, we report a modified approach to the 4C-seq protocol that incorporates both an additional restriction enzyme digest and qPCR-based amplification steps that are designed to facilitate a greater capture of diverse sequence reads and mitigate the potential for PCR bias, respectively. Our modified 4C method is amenable to the standard molecular biology lab for assessing chromatin architecture.

High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture 4C-seq

The identification of physical interactions between genes and regulatory elements is challenging but has been facilitated by chromosome conformation capture methods. This modification to the 4C-seq protocol mitigates PCR bias by minimizing over-amplification of PCR templates and maximizes the mappability of reads by incorporating an addition restriction enzyme digest step.

利用下一代序列染色体构象捕获 (4 c 序列) 的高通量识别基因调控数列

The identification of regulatory elements for a given target gene poses a significant technical challenge owing to the variability in the positioning and ...

A Non-random Mouse Model for Pharmacological Reactivation of Mecp2 on the Inactive X Chromosome

X chromosome inactivation (XCI) is the random silencing of one X chromosome in females to achieve gene dosage balance between the sexes. As a result, all females are heterozygous for X-linked gene expression. One of the key regulators of XCI is Xist, which is essential for the initiation and maintenance of XCI. Previous studies have identified 13 trans acting X chromosome inactivation factors (XCIFs) using a large-scale, loss-of-function genetic screen. Inhibition of XCIFs, such as ACVR1 and PDPK1, using short-hairpin RNA or small molecule inhibitors, reactivates X chromosome-linked genes in cultured cells. But the feasibility and tolerability of reactivating the inactive X chromosome in vivo remains to be determined. Towards this goal, a Xist&#916;:Mecp2/Xist:Mecp2-Gfp mouse model has been generated with non-random XCI due to deletion of Xist on one X chromosome. Using this model, the extent of inactive X reactivation was quantitated in the mouse brain following treatment with XCIF inhibitors. Recently published results show, for the first time, that pharmacological inhibition of XCIFs reactivates Mecp2 from the inactive X chromosome in cortical neurons of the living mouse brain.

Here, we describe a protocol to generate a viable female murine model with non-random X chromosome inactivation, i.e., the maternally-inherited X chromosome is inactive in 100% of the cells. We also describe a protocol to test feasibility, tolerability, and safety of pharmacological reactivation of the inactive X chromosome in vivo.

非随机小鼠模型在非活性 X 染色体上对 Mcp2 的药理再激活

X chromosome inactivation (XCI) is the random silencing of one X chromosome in females to achieve gene dosage balance between the sexes. As a result, all ...

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing (RIPiT-Seq)

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT employs two purification steps. First, immunoprecipitation of a tagged RNP subunit is followed by mild RNase digestion and subsequent non-denaturing affinity elution. A second immunoprecipitation of another RNP subunit allows for enrichment of a defined complex. Following a denaturing elution of RNAs and proteins, the RNA footprints are converted into high-throughput DNA sequencing libraries. Unlike the more popular ultraviolet (UV) crosslinking followed by immunoprecipitation (CLIP) approach to enrich RBP binding sites, RIPiT is UV-crosslinking independent. Hence RIPiT can be applied to numerous proteins present in the RNA interactome and beyond that are essential to RNA regulation but do not directly contact the RNA or UV-crosslink poorly to RNA. The two purification steps in RIPiT provide an additional advantage of identifying binding sites where a protein of interest acts in partnership with another cofactor. The double purification strategy also serves to enhance signal by limiting background. Here, we provide a step-wise procedure to perform RIPiT and to generate high-throughput sequencing libraries from isolated RNA footprints. We also outline RIPiT&#39;s advantages and applications and discuss some of its limitations.

Identification of Footprints of RNA:Protein Complexes via RNA Immunoprecipitation in Tandem Followed by Sequencing RIPiT-Seq

Here, we present a protocol to enrich endogenous RNA binding sites or &#34;footprints&#34;&#160;of RNA:protein (RNP) complexes from mammalian cells. This approach involves two immunoprecipitations of RNP subunits and is therefore dubbed RNA immunoprecipitation in tandem (RIPiT).

RNA足迹的识别:通过RNA免疫沉淀在串联中的蛋白质复合物,然后测序(RIPiT-Seq)

RNA immunoprecipitation in tandem (RIPiT) is a method for enriching RNA footprints of a pair of proteins within an RNA:protein (RNP) complex. RIPiT ...

Transforming, Genome Editing and Phenotyping the Nitrogen-fixing Tropical Cannabaceae Tree Parasponia andersonii

Parasponia andersonii is a fast-growing tropical tree that belongs to the Cannabis family (Cannabaceae). Together with 4 additional species, it forms the only known non-legume lineage able to establish a nitrogen-fixing nodule symbiosis with rhizobium. Comparative studies between legumes and P. andersonii could provide valuable insight into the genetic networks underlying root nodule formation. To facilitate comparative studies, we recently sequenced the P. andersonii genome and established Agrobacterium tumefaciens-mediated stable transformation and CRISPR/Cas9-based genome editing. Here, we provide a detailed description of the transformation and genome editing procedures developed for P. andersonii. In addition, we describe procedures for the seed germination and characterization of symbiotic phenotypes. Using this protocol, stable transgenic mutant lines can be generated in a period of 2-3 months. Vegetative in vitro propagation of T0 transgenic lines allows phenotyping experiments to be initiated at 4 months after A. tumefaciens co-cultivation. Therefore, this protocol takes only marginally longer than the transient Agrobacterium rhizogenes-based root transformation method available for P. andersonii, though offers several clear advantages. Together, the procedures described here permit P. andersonii to be used as a research model for studies aimed at understanding symbiotic associations as well as potentially other aspects of the biology of this tropical tree.

Transforming, Genome Editing and Phenotyping the Nitrogen-fixing Tropical Cannabaceae Tree Parasponia andersonii

Parasponia andersonii is a fast-growing tropical tree that belongs to the Cannabis family (Cannabaceae) and can form nitrogen-fixing root nodules in association with the rhizobium. Here, we describe a detailed protocol for reverse genetic analyses in P. andersonii based on Agrobacterium tumefaciens-mediated stable transformation and CRISPR/Cas9-based genome editing.

转化、基因组编辑和拟合氮的热带大麻树帕拉斯波尼亚安德森

Parasponia andersonii is a fast-growing tropical tree that belongs to the Cannabis family (Cannabaceae). Together with 4 additional species, it forms the ...

Identification of Circular RNAs using RNA Sequencing

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein interactions, and regulation of parental gene transcription. In classical next generation RNA sequencing (RNA-seq), circRNAs are typically overlooked as a result of poly-A selection during construction of mRNA libraries, or are found at very low abundance, and are therefore difficult to isolate and detect. Here, a circRNA library construction protocol was optimized by comparing library preparation kits, pre-treatment options and various total RNA input amounts. Two commercially available whole transcriptome library preparation kits, with and without RNase R pre-treatment, and using variable amounts of total RNA input (1 to 4 &#181;g), were tested. Lastly, multiple tissue types; including liver, lung, lymph node, and pancreas; as well as multiple brain regions; including the cerebellum, inferior parietal lobe, middle temporal gyrus, occipital cortex, and superior frontal gyrus; were compared to evaluate circRNA abundance across tissue types. Analysis of the generated RNA-seq data using six different circRNA detection tools (find_circ, CIRI, Mapsplice, KNIFE, DCC, and CIRCexplorer) revealed that a stranded total RNA library preparation kit with RNase R pre-treatment and 4 &#181;g RNA input is the optimal method for identifying the highest relative number of circRNAs. Consistent with previous findings, the highest enrichment of circRNAs was observed in brain tissues compared to other tissue types.

Circular RNAs (circRNAs) are non-coding RNAs that may have roles in transcriptional regulation and mediating interactions between proteins. Following assessment of different parameters for construction of circRNA sequencing libraries, a protocol was compiled utilizing stranded total RNA library preparation with RNase R pre-treatment and is presented here.

使用RNA测序识别循环RNA

Circular RNAs (circRNAs) are a class of non-coding RNAs involved in functions including micro-RNA (miRNA) regulation, mediation of protein-protein ...

Identification of Host Pathways Targeted by Bacterial Effector Proteins using Yeast Toxicity and Suppressor Screens

Intracellular bacteria secrete virulence factors called effector proteins into the host cytosol that act to subvert host proteins and/or their associated biological pathways to the benefit of the bacterium. Identification of putative bacterial effector proteins has become more manageable due to advances in bacterial genome sequencing and the advent of algorithms that allow in silico identification of genes encoding secretion candidates and/or eukaryotic-like domains. However, identification of these important virulence factors is only an initial step. Naturally, the goal is to determine the molecular function of effector proteins and elucidate how they interact with the host. In recent years, techniques like the yeast two-hybrid screen and large-scale immunoprecipitations coupled with mass spectrometry have aided in the identification of protein-protein interactions. Although identification of a host binding partner is the crucial first step toward elucidating the molecular function of a bacterial effector protein, sometimes the host protein is found to have multiple biological functions (e.g., actin, clathrin, tubulin), or the bacterial protein may not physically bind host proteins, depriving the researcher of crucial information about the precise host pathway being manipulated. A modified yeast toxicity screen coupled with a suppressor screen has been adapted to identify host pathways impacted by bacterial effector proteins. The toxicity screen relies on a toxic effect in yeast caused by the effector protein interfering with the host biological pathways, which often manifests as a growth defect. Expression of a yeast genomic library is used to identify host factors that suppress the toxicity of the bacterial effector protein and thus identify proteins in the pathway that the effector protein targets. This protocol contains detailed instructions for both the toxicity and suppressor screens. These techniques can be performed in any lab capable of molecular cloning and cultivation of yeast and Escherichia coli.

Bacterial pathogens secrete proteins into the host that target crucial biological processes. Identifying the host pathways targeted by bacterial effector proteins is key to addressing molecular pathogenesis. Here, a method using a modified yeast suppressor and toxicity screen to elucidate host pathways targeted by toxic bacterial effector proteins is described.

使用酵母毒性和抑制器屏幕识别细菌效应蛋白的宿主通路

Intracellular bacteria secrete virulence factors called effector proteins into the host cytosol that act to subvert host proteins and/or their associated ...

Preparation of Meiotic Chromosome Spreads from Zebrafish Spermatocytes

Meiosis is the key cellular process required to create haploid gametes for sexual reproduction. Model organisms have been instrumental in understanding the chromosome events that take place during meiotic prophase, including the pairing, synapsis, and recombination events that ensure proper chromosome segregation. While the mouse has been an important model for understanding the molecular mechanisms underlying these processes, not all meiotic events in this system are analogous to human meiosis. We recently demonstrated the exciting potential of the zebrafish as a model of human spermatogenesis. Here we describe, in detail, our methods to visualize meiotic chromosomes and associated proteins in chromosome spread preparations. These preparations have the advantage of allowing high resolution analysis of chromosome structures. First, we describe the procedure for dissecting testes from adult zebrafish, followed by cell dissociation, lysis, and spreading of the chromosomes. Next, we describe the procedure for detecting the localization of meiotic chromosome proteins, by immunofluorescence detection, and nucleic acid sequences, by fluorescence in situ hybridization (FISH). These techniques comprise a useful set of tools for the cytological analysis of meiotic chromatin architecture in the zebrafish system. Researchers in the zebrafish community should be able to quickly master these techniques and incorporate them into their standard analyses of reproductive function.

Nuclear surface spreads are an indispensable tool for studying chromosome events during meiosis. Here we demonstrate a method to prepare and visualize meiotic chromosomes during prophase I from zebrafish spermatocytes.

从斑马鱼精子细胞中培养染色体传播

Meiosis is the key cellular process required to create haploid gametes for sexual reproduction. Model organisms have been instrumental in understanding ...