作者感谢 Dr. 安福和约瑟夫 Canzano 的技术援助, Dr. 詹妮弗 Treweek 的宝贵意见, Dr. 克里斯汀顿和 Dr. 卡尔 Deisseroth 培训通过斯坦福大学的清晰度研讨会。JDRF nPOD 和器官采购组织提供了组织样本。这项工作是由 JDRF (47-2014-1), 太平洋慈善信托基金 (2015-PG-T1D052) 和 NIDDK 1OT2 TR001773 到交通部。

所有实验都是根据佛罗里达大学机构审查委员会和联邦指导方针进行的。
警告: 甲醛和丙烯酰胺是有毒的刺激物。用适当的个人防护设备 (实验室大衣, 手套, 眼睛保护) 在油烟罩中处理试剂。
1. Deparaffinization 的甲醛固定组织 (如果使用新鲜的组织, 跳到步骤 2)
<ol><li>使用新的刀片或手术刀, 以削减与组织的表面垂直的石蜡。在组织边缘切石蜡, 以完成松解。使用镊子或刮刀轻轻松脱, 去除组织, 以光学清除。用刮刀轻轻刮掉组织中多余的石蜡 (图 1A)。</li>
	<li>用二甲苯填充一个玻璃容器 (约30毫升用于 3 x 3 x 3 mm 的组织), 并在室温下孵育该溶液中的组织为 24 h (RT)。</li>
	<li>用与1.2.1 相同体积的新鲜二甲苯填充另一个玻璃容器。在 RT 中将该溶液中的组织转移并孵育24小时。</li>
	<li>将100% 乙醇放入与1.2.1 相同体积的锥形管中。在 RT 中将该溶液中的组织转移并孵育24小时。</li>
	<li>将95% 乙醇放入与1.2.1 相同体积的锥形管中。在 RT 中将该溶液中的组织转移并孵育24小时。</li>
	<li>将70% 乙醇放入与1.2.1 相同体积的锥形管中。在 RT 中将该溶液中的组织转移并孵育24小时。</li>
	<li>将0.01 米磷酸缓冲盐水 (pbs) 中的组织冲洗, 并将其放置在0.01 米 pbs 中的锥形管中, 在 RT 处平衡24小时. 确保组织没有石蜡 (图 1B, 右面板)。</li>
</ol>2. 准备4% 甲醛 (粉煤灰) 固色剂
<ol><li>吸管10毫升16% 粉煤灰到50毫升锥形管, 添加4毫升 0.1 M PBS, 并添加26毫升蒸馏水去离子水 (ddH2O)。合上盖子, 然后简单地混合。</li>
	<li>大容量的4% 粉煤灰可以提前和冻结在等分。等分是良好的1天室温 (RT), 一个星期在4° c, 1 月在-20 ° c。</li>
</ol>3. 胰腺固定
<ol><li>修正胰腺样品 (≤ 1 x 1 x 2 cm) 在新鲜地准备的4% 煤灰在4° c 为 48 h。如果样品大于 1 x 1 x 2 cm, 使用手术刀或刀片解剖成不超过1厘米厚的小块。洗涤组织样品在三变动 0.01 M PBS 为至少15分钟每洗涤和存放在15毫升离心管在0.01% 煤灰或 0.01 m pbs 或0.5% 钠叠氮化物或 0.01 m pbs 直到用途。</li>
	<li>固定后, 将组织分成 1-2 毫米厚的部分进行进一步处理。使用 vibratome, 以协助甚至剖切。 注意: 部分的最终数量将取决于起始样本的大小。</li>
</ol>4. 在水凝胶中嵌入组织
<ol><li>准备200毫升的 4% acrylamide/1% 甲醛 (A4P1) 水凝胶单体溶液如下:<ol>
<li>把一个瓶子放在冰上, 放在磁搅拌盘的顶部。确保瓶子是坐在平坦, 并添加一个磁性搅拌棒。</li>
		<li>添加以下顺序: 147.8 毫升冷 (4-8 ° c) ddH2O, 20 毫升 0.1 M PBS, 20 毫升冷 (4-8 ° c) 40% 丙烯酰胺溶液, 12.2 毫升16% 的煤灰溶液, 和250毫克 VA-044 引发剂。将整个水凝胶溶液与磁性搅拌棒混合, 至少10分钟, 然后将溶液留在冰上进行下一步。</li>
	</ol>
</li>
	<li>在装有水凝胶溶液的烧瓶旁边的冰上放置一个15毫升的锥形管。将14毫升的单体溶液注入管内, 加入 1-2 毫米厚的固定组织样品中的一条。然后把管子盖上。</li>
	<li>将样品在单体溶液中孵育3天, 在4° c 下, 免受光照。分任何剩余单体溶液, 贮存在-20 ° c 供将来使用。</li>
</ol>5. 德加单体溶液和聚合水凝胶
<ol><li>使用气态 N28从水凝胶单体溶液中除去氧气。<ol>
<li>准备一桶冰, 并将样品放入水凝胶单体溶液中的冰上。</li>
		<li>收集 2-3, 18 口径的皮下针每样, 石蜡膜, 和一个定时器。将油管连接到氮气罐, 这样氮气就能流动。在将样品保存在冰上时, 小心地刺穿一个装有样品的锥形管的盖子, 然后按压一根皮下注射针, 直到它在液态单体溶液的表面下。</li>
		<li>用另一根皮下注射针头刺穿帽子的反面, 但不允许它被淹没。 注: 第二针将排气管。</li>
		<li>将氮气罐中的油管连接到水凝胶下面的皮下注射针头上, 慢慢地打开氮气, 直到它通过液体稳定地起泡。</li>
		<li>允许氮气在液体中泡10分钟。</li>
	</ol>
</li>
	<li>一旦氧气被去除, 快速地去除两根针并且盖盖子与石蜡影片防止气体的进一步交换在管子和环境之间。将脱样品放在37° c 为3小时的恒温箱中, 聚合水凝胶。</li>
</ol>6. 组织清理
<ol><li>准备500毫升清除溶液 (4% SDS 在 pH 8.5)。到〜300毫升的 ddH2O, 添加50毫升 0.1 M PBS 和20克 SDS 粉, 同时搅拌与磁搅拌棒。用氢氧化钠和盐酸调节溶液 pH 值8.5。添加 ddH2O, 直到最终卷为 500 mL。</li>
	<li>聚合后, 倒掉多余的水凝胶, 并将其丢弃在化学废料容器中。用纸巾轻轻拭去样品中的水凝胶, 然后丢弃到化学废料容器中。</li>
	<li>洗涤样品在 3-5 交换 0.01 M PBS (丢弃洗涤液入化工废物) 为15分钟每洗涤步。将样品转化为50毫升的锥管, 40 毫升的清洗缓冲器。</li>
	<li>在37° c 的清洁缓冲液中孵育样品, 并每隔一天将样品转换为新鲜的清洁缓冲器。<ol>
<li>根据样本大小 (〜8周为 3 mm x 3 mm x 3 mm 样本) 将样品留在清理缓冲区中 2-8 周, 以确保正确的清除。</li>
		<li>监测组织清理和停止时完成。确保样品是足够透明的, 把它保持到光检查适当的清除 (通常一些棕褐色着色将留在外分泌区)。 注意: 一个过清除的样本将出现磨损的边缘和质地将是非常柔软的, 当拿起钳子。这是常见的样品, 以清除不均匀。此外, 组织将不完全透明, 直到放置在安装介质 (插入,图 1C)。</li>
	</ol>
</li>
</ol>7. 多重免疫荧光
<ol><li>用40毫升 0.01 M PBS 在 RT 上以 60 rpm 的频率将样品洗涤一天 (4-5 的缓冲变化总, 40 毫升每次洗涤, 改变每小时直到最后的洗涤)。让最后的洗涤在 RT 继续过夜。</li>
	<li>准备协议染色缓冲器。到500毫升 0.01 M PBS, 加入50毫克的叠氮化钠和0.5 毫升 TritonX-100。拌匀。</li>
	<li>用主抗体孵育样品。<ol>
<li>在2毫升扁底管中加入2% 正常血清 (与二次抗体相同的物种) 到基协议染色缓冲器 (至少1毫升的总体积是建议每样/管)。</li>
		<li>在2% 血清/条约染色缓冲液中加入初级抗体。使用约5x 的原始抗体量的协议染色将用于标准免疫组织化学 (即如果抗体稀释1:500 的标准免疫组化, 使用1:100 的条约染色)。</li>
		<li>使用刮刀将样品从洗涤缓冲液中取出, 然后将多余的缓冲器移到纸巾上, 然后在试管中放置一个主抗体溶液。</li>
	</ol>
</li>
	<li>在 RT 上孵育2-4 天, 在 60 rpm。在 RT 上彻底清洗样品在 60 rpm 在 0.01 M PBS 转换为新鲜的缓冲4-5 次, 并留下最后的洗涤过夜如步骤7.1。</li>
	<li>用二次抗体孵育样品。<ol>
<li>在2毫升扁底管中加入2% 正常血清 (与二次抗体相同的种类) 到基协议染色缓冲器 (1 毫升总容积为每样/管)。</li>
		<li>在浓度为 1:200 (5 µl 的1毫升缓冲) 中添加二次抗体。 注意: 小格式抗体是首选, 以及高度交叉吸附抗体, 如果使用一个以上的主要抗体。</li>
		<li>使用刮刀将样品从洗涤缓冲液中取出, 然后将多余的缓冲器移到纸巾上, 然后在试管中放入二次抗体溶液。</li>
	</ol>
</li>
	<li>在 RT 上孵育 60 rpm 2 天, 并保护样品免受光照。</li>
	<li>彻底清洗样品, 如步骤 7.1, 在 RT 上的一个振动筛在 60 rpm 在 0.01 M PBS 改变到新鲜的缓冲 4-5 次, 并留下最后的洗涤隔夜, 保护免受光在洗。</li>
</ol>8. 用于成像的安装样品
<ol><li>准备折射率匹配解 (轮辋) 缓冲器。<ol>
<li>称11克非离子密度梯度介质 (如 Histodenz), 并小心地将其转移到50毫升的锥形管上。</li>
		<li>添加 ~ 5 毫升0.02 米磷酸缓冲液 (PB)8使用刮刀从粉末非离子密度梯度介质中释放空气。 注: 溶液将非常粘稠, 拌匀。</li>
		<li>将体积提高到10毫升使用更多的 PB, 与刮刀混合, 刮掉多余的刮刀入管。</li>
		<li>在37° c 下孵育轮辋, 直到溶解, 倒置, 并根据需要轻轻混合。</li>
	</ol>
</li>
	<li>将样品转移到轮辋。为此, 吸管1毫升轮辋成一个2毫升的扁底管。使用刮刀将样品从洗涤缓冲液中取出, 将多余的缓冲器涂在纸巾上, 然后将其放在带轮辋的管子中。<ol>
<li>轻轻拍打管子, 将样品浸入轮辋中。在成像前的 2-4 天内, 将样品放在长凳上的轮辋上, 保护其免受光照。</li>
	</ol>
</li>
	<li>对图像, 放置少量的轮辋到一个8井片底部室滑动。只添加足够的外衣底部, 更多的将导致样品漂浮使它更难以在一个倒置的范围图像。将样本添加到井中, 并将幻灯片盖好以进行成像。</li>
</ol>9. 成像
<ol><li>共焦显微镜和成像软件<ol>
<li>选择合适的激光器的激发和发射光谱的荧光用于染色的条约样本。调整购置软件的设置, 以消除通道之间的任何重叠。必要时使用单独的轨道 (当两个荧光有相似的激发谱时)。</li>
		<li>建立图像采集<ol>
<li>选择最大采集速度, 16 位, 4 或更多的平均值, 1024 x 1024 的分辨率或更好。</li>
			<li>放大要成像的对象。 注意: 这将减少采集时间, 减少照片漂白, 同时减少文件大小和下游编辑。</li>
		</ol>
</li>
		<li>设置 z 堆栈。<ol>
<li>选择要使用的目标的最佳剖切。如果以后需要反褶积, 请使用比优化更小的 z 步骤, 如1µm。</li>
			<li>使用 z 堆栈更正。<ol>
<li>开始最近的组织的表面和增加增益, 因为目标重点通过 z 平面和增加校正。不要改变激光设置的校正!</li>
				<li>获取一个平均、512 x 512 分辨率和最大捕获速度的测试堆栈。检查3D 中的图像, 确保在获得最终的高分辨率 z 堆栈之前, 每个颜色的堆栈中都有相等的亮度。</li>
			</ol>
</li>
		</ol>
</li>
	</ol>
</li>
	<li>Lightsheet 成像<ol>
<li>确保样品在轮辋内平衡至少24小时. 理想情况下, 将样品转移到成像室的新轮辋, 并允许在室内平衡至少一天。</li>
		<li>将示例装入图像<ol>
<li>选择最小 (黑色) 毛细管来装入样品</li>
			<li>使用腻子或一个盘子来保持样品, 而应用超级胶水的毛细管结束。将组织粘附到毛细血管上, 尽可能少地接触组织表面。</li>
			<li>将示例插入图像。</li>
		</ol>
</li>
		<li>使用5X 或25X 目标的图像, 适用于使用透视扫描选项的光学清除样品。如果发生阴影或模糊, 请旋转该示例, 再试一次, 或者让样品继续平衡在轮辋中。 注: 1 mm 堆栈通常可以在不到五分钟内获得, 具体取决于设置。</li>
		<li>使用图像分析软件查看和编辑图像堆栈。</li>
	</ol>
</li>
</ol>

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作者没有什么可透露的。

新的光学清除方法的可用性, 允许对动物模型中的中枢神经系统和周围神经系统进行空前大规模的检查。人类胰腺的总体神经支配模式在很大程度上是由于组织密度和难以获得高质量的 biospecimens。本协议提供了一个优化的组织清除协议的人胰腺组织从固定或石蜡嵌入的档案样本。
清除时间取决于样本大小、固定类型、持续时间、存储持续时间, 可能需要 1-8 周, 因此以下考虑因素是至关重要的。最好是在4% 粉煤灰固定后尽快清除组织, 因为长期贮存会增加清理时间。为了减少清洁时间, 水凝胶单体中的粉煤灰含量从4% 减少到 1%, 用于人类胰腺的固定步骤。对于其他组织, 特别是小鼠组织, 必须根据经验确定提供足够的水凝胶刚性来保存抗原所需的煤灰数量。充分的清除也是必要的, 因为在清除的组织中抗体的渗透率降低, 二级抗体的表面染色增加。每隔一天 (甚至每天) 更改结算解决方案, 最好保持 pH 值恒定和洗涤剂新鲜。反之, 过度清除会对细胞形态产生负面影响, 增加组织碎。由于一些胰腺样本由于固有的病症, 如慢性胰腺炎或其他病症的纤维化区域的不均匀性, 所以经常监测这一过程是很重要的。使用 vibratome 厚剖面也可用于制造均匀厚度, 但不需要。监测清理过程, 因此, 只要光通过样品, 样品就会从洗涤剂中除去, 确保样品被充分清除。
抗体穿透和表面染色是重要的问题, 考虑与条约样本。为了确保良好的抗体渗透, 它的关键是孵化的样品与主抗体至少2天。各种抗体有不同的扩散速率, 并应单独进行优化, 但对于大多数抗体, 4 天是足够的持续时间, 充分渗透在 1 mm3样本。如果表面染色是一个问题, 特别是对 Lightsheet 成像, 样品可以被切成一半, 或表面剥离后染色。小格式的抗体时, 可用的将有助于组织渗透8。Preconjugated 抗体似乎没有工作, 以及相同的游离克隆和更高的背景, 从非特异性结合和较差的组织渗透可以观察, 如果他们的工作在所有。为了提高 preconjugated 抗体的成功率, 在染色缓冲液中增加血清和 TritonX-100 浓度, 并使用更长的孵化 (> 4 天)。染色后, 在轮辋的样品孵化数天是至关重要的。清除组织膨胀的 PBS 和轮辋孵化使他们收缩回原来的大小。特别是 lightsheet 成像, 样品应充分平衡前成像。
当在共焦显微镜上成像样品时, 每次选择新的位置时必须设置校正。不同的采集深度和染色强度的变化在整个组织需要调整的每个区域的组织, 以最佳成像。同样, 必须仔细考虑所使用的二次抗体。光谱分解是具有挑战性的协议样本, 所以荧光必须适当选择低激发或发射重叠, 以确保一个明亮的信号时, 过滤在共焦显微镜的排放。对于每个应用程序, 必须在分辨率、成像速度和降噪之间达成平衡, 以便在不进行光漂白的情况下获得最佳质量的图像。分辨率大于 1024 x 1024 是不可探测的人眼, 但可能是必要的某些应用, 如果量化的污渍是必要的。
在选择光交换技术和选择光学清除方法时, 有很多事情需要考虑。低丰度抗原可能无法被检测使用公约的方法, 因此有限制抗原检测。某些抗原, 如免疫抗原 (CD3, CD4, CD8,等) 被公约方法破坏, 尽管有高质量的抗体可以检测到它们, 但它们是无法察觉的。这种方法的另一个局限性是在清除染色后难以辨别的施主胰腺之间的固有变异性。每个供体组织在手术过程中都趋向于清除和染色, 但不同的供体组织在清除后的清除时间和组织形态学质量有很大差异。使用档案组织的能力可以减轻这种限制, 如果一个人可以有足够的机会获得大量的病人胰腺样本, 虽然这尚未彻底调查。这里所报告的《公约》议定书被认为是廉价的, 而且很容易用标准的实验室设备来实施。清除样品适合通过传统的共焦显微镜成像, 以及2光子和 Lightsheet 技术, 并提供高分辨率3D 图像的神经和胰岛大段的人类胰腺。该程序的未来应用包括研究胎儿胰腺的外分泌和内分泌隔间和胰岛研究1型和2型糖尿病。

直到最近, 3 维 (3D) 大组织或整个器官的重建是通过费力的序列切片, 染色, 和图像重建的多个部分。这些方法有几个缺点, 包括依赖于大量的串行部分, 不良的组织渗透与抗体, 和光散射防止深层成像组织内。为了获得更强的光和抗体穿透力, 研究人员开发了化学方法来保存蛋白质抗原, 同时去除大部分的光散射脂。一些相关的方法是明确脂质交换丙烯酰胺-杂交刚性成像组织水凝胶 (透明), 被动透明技术 (协议), 和灌注辅助剂释放原位(部) (在1,2 中查看 ,3,4,5,6。清晰的方法是基于稳定的蛋白质使用甲醛, 丙烯酰胺和双水凝胶, 其次是脂质去除电泳在 SDS 溶液2。此方法后来被修改为被动清除和高级成像7。进一步的优化导致消除的双和不同百分比的甲醛在水凝胶溶液 (1-4%) 获得最佳的抗原稳定与最短的结算时间的技术称为协议8。早期的尝试开发这些光学清除技术涉及的有机或其他化合物, 难以使用和淬火内源性荧光蛋白的转基因小鼠模型。这些额外的方法包括鳞片, 无障碍的脑/体鸡尾酒和计算分析 (立方), 开关和尺寸成像的溶剂清除器官 (迪斯科) 方法, 所有具有各种优缺点9, 10、11、12。原清晰方法是在富含脂质的小鼠脑组织中开发的, 光学清除方法在人体组织中有限度的测试7,8,11,13,14.
光学清除方法是理想的跟踪神经过长的距离, 在完整的鼠标中枢神经系统。胰腺是由自主神经和感觉神经系统支配的。胰腺内分泌室, 胰岛, 包括一个非常小的部分整个器官 (1-2%) 和胰岛已知的大小异质性 (50-250 µm), 内分泌细胞比例, 密度特别是在糖尿病 (审查在15 ,16)。在开发一个协议, 几个光学清除方法被测试为人的胰腺和一个契约做法被发现提供最佳的平衡时间到清楚 (~ 2 星期) 以优秀形态学保存神经和小岛。最后的优化程序在这里描述的神经-岛屿网络的大尺度 (毫米距离) 和高分辨率3D 成像的多个完整的胰岛。该技术适用于人胰腺后立即固定或储存后, 以及标本固定在中性缓冲福尔马林和嵌入石蜡。样品适用于共焦或 lightsheet 显微镜成像。

<table><tbody><tr><td>10x phosphate buffered saline (PBS)</td><td>Fisher</td><td>BP399-1</td><td>Buffers</td></tr><tr><td>Sodium phosphate dibasic anhydrous</td><td>Fisher</td><td>S375-500</td><td>PB buffer (RIMS)</td></tr><tr><td>Sodium phosphate monobasic monohydrate</td><td>Sigma</td><td>71507-250</td><td>PB buffer (RIMS)</td></tr><tr><td>16% paraformaldehyde (PFA)</td><td>Electron Microscopy Sciences</td><td>15714-5</td><td>Immersion fixation, hydrogel, storage solution</td></tr><tr><td>40% acrylamide</td><td>Bio-Rad</td><td>161-0140</td><td>Hydrogel</td></tr><tr><td>2% bis-acrylamide</td><td>Bio-Rad</td><td>161-0142</td><td>Hydrogel</td></tr><tr><td>VA-044 initiator</td><td>Wako Pure Chemical Industries, Ltd.</td><td>VA044</td><td>Hydrogel</td></tr><tr><td>Sodium dodecyl sulfate (SDS)</td><td>Fisher</td><td>BP166-5</td><td>Clearing buffer</td></tr><tr><td>Sodium azide</td><td>Sigma</td><td>S8032</td><td>Sample storage buffer</td></tr><tr><td>18 gauge needles</td><td>Fisher</td><td>14-840-91</td><td>Degassing hydrogel solution</td></tr><tr><td>N2 tank</td><td>AirGas</td><td>various</td><td>Degassing hydrogel solution</td></tr><tr><td>Triton X-100</td><td>Sigma-Aldrich</td><td>100 ml</td><td>Buffers</td></tr><tr><td>Goat, normal serum</td><td>Vector</td><td>S-1000</td><td>Use as 2% in blocking buffer</td></tr><tr><td>Histodenz</td><td>Sigma</td><td>D2158-100G</td><td>RIMS</td></tr><tr><td>8-well chamber slides</td><td>Ibidi</td><td>80827</td><td>Imaging</td></tr><tr><td>Laser scanning confocal microscope</td><td>Zeiss</td><td>710</td><td>Imaging</td></tr><tr><td>LightSheet microscope</td><td>Zeiss</td><td>Z1</td><td>Imaging</td></tr><tr><td>&lt;strong&gt;Name&lt;/strong&gt;</td><td>&lt;strong&gt;Company&lt;/strong&gt;</td><td>&lt;strong&gt;Catalog Number&lt;/strong&gt;</td><td>&lt;strong&gt;Comments&lt;/strong&gt;</td></tr><tr><td>&lt;strong&gt;Primary Antibody&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>CD45</td><td>Bioss</td><td>bs-4820R-A488</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:100&lt;br/&gt; Comments: Did not work</td></tr><tr><td>CD45</td><td>DAKO</td><td>M0754</td><td>Host: Mouse&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: Did not work</td></tr><tr><td>GFAP</td><td>DAKO</td><td>Z0334</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:50&lt;br/&gt; Comments: Worked</td></tr><tr><td>Glucagon</td><td>BD Biosciences</td><td>565891</td><td>Host: Mouse&lt;br/&gt; Dilution: 1:50&lt;br/&gt; Comments: Worked</td></tr><tr><td>Glucagon</td><td>Cell Signaling</td><td>2760S</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: Did not work</td></tr><tr><td>Glucagon</td><td>Abcam</td><td>ab10988</td><td>Host: Mouse&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: Worked</td></tr><tr><td>Insulin</td><td>DAKO</td><td>A0564</td><td>Host: Guinea Pig&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: Worked</td></tr><tr><td>NCAM (CD56)</td><td>DAKO</td><td>M730429-2</td><td>Host: Mouse&lt;br/&gt; Dilution: 1:50&lt;br/&gt; Comments: Did not work</td></tr><tr><td>NCAM (CD56)-FITC conjugate</td><td>DAKO</td><td>M730429-2</td><td>Host: Mouse&lt;br/&gt; Dilution: 1:50&lt;br/&gt; Comments: Did not work</td></tr><tr><td>Peripherin</td><td>EnCor</td><td>RPCA-Peri</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:100&lt;br/&gt; Comments: Worked</td></tr><tr><td>PGP9.5</td><td>DAKO</td><td>Z5116</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:50&lt;br/&gt; Comments: Did not work</td></tr><tr><td>Secretogranin 3</td><td>Sigma</td><td>HPA006880</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: Worked</td></tr><tr><td>Smooth muscle actin</td><td>Sigma</td><td>A5228; C6198 (Cy5)</td><td>Host: Mouse&lt;br/&gt; Dilution: 1:200; 1:200&lt;br/&gt; Comments: Worked; Conjugated worked better than unconjugated</td></tr><tr><td>Substance P</td><td>BioRad</td><td>8450-0505</td><td>Host: Rat&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: Worked</td></tr><tr><td>Tyrosine Hydroxylase</td><td>Millipore</td><td>AB152</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: Worked</td></tr><tr><td>Tyrosine Hydroxylase</td><td>Abcam</td><td>Ab76442</td><td>Host: Chicken&lt;br/&gt; Dilution: 1:100&lt;br/&gt; Comments: Worked, but weak staining</td></tr><tr><td>Vasoactive Intestinal Peptide (VIP)</td><td>Immunostar</td><td>20077</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:100&lt;br/&gt; Comments: Worked</td></tr><tr><td>Vesicular Acetylcholine Transporter (VAChT)</td><td>Synaptic Systems</td><td>139103</td><td>Host: Rabbit&lt;br/&gt; Dilution: 1:50&lt;br/&gt; Comments: Worked</td></tr><tr><td>&lt;strong&gt;Secondary Antibody&lt;/strong&gt;</td><td></td><td></td><td></td></tr><tr><td>Guinea pig IgG</td><td>ThermoFisher Scientific</td><td>Various</td><td>Host: Goat&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: AlexaFluor conjugates</td></tr><tr><td>Mouse IgG</td><td>ThermoFisher Scientific</td><td>Various</td><td>Host: Goat&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: AlexaFluor conjugates</td></tr><tr><td>Rabbit IgG</td><td>ThermoFisher Scientific</td><td>Various</td><td>Host: Goat&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: AlexaFluor conjugates</td></tr><tr><td>Rat IgG</td><td>ThermoFisher Scientific</td><td>Various</td><td>Host: Goat, Donkey&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: AlexaFluor conjugates</td></tr><tr><td>Chicken IgG</td><td>ThermoFisher Scientific</td><td>Various</td><td>Host: Goat&lt;br/&gt; Dilution: 1:200&lt;br/&gt; Comments: AlexaFluor conjugates</td></tr></tbody></table>

high resolution 3d imaging of the human pancreas neuro-insular network

利用传统的组织学方法, 研究人员在大规模3D 的图像整体组织或器官的能力受到阻碍。组织学切片一般限于 < 20 µm 作为福尔马林固定石蜡切片的玻璃幻灯片或 < 500 µm 的自由浮动固定路段。因此, 需要对序列切片和大规模图像重建方法进行广泛的努力, 以使用传统方法重现3D 样本 > 500 µm。此外, 光从组织内的大分子中散射, 特别是脂质, 可以防止成像深度 > 150 µm 与大多数共聚焦显微镜。为了减少光散射和允许使用简单的共焦显微镜进行深层组织成像, 开发了各种光学清除方法, 它们与浸泡法固定的啮齿动物和人体组织样品有关。几种方法与丙烯酰胺和组织清除、十二烷基硫酸钠 (SDS) 的蛋白质交联有关。其他光学清洗技术使用各种溶剂, 虽然每一个修改都有不同的优缺点。本文介绍了一种优化的无源光清除方法, 用于研究人类胰腺的神经支配, 特别是对人类胰岛的神经支配进行审讯。

这些染色程序被开发, 以提供一个大规模的检查, 人类胰腺检查胰岛和相关的自主和感官网络。测试了几个过程, 包括清晰度7、iDISCO17和约定8 , 该协议方法最适合人类胰腺和共聚焦成像。
初级抗体最初是在固定的人胰腺标本上测试的, 有适当的阳性和阴性对照样本 (小鼠脑, 其他)。主要抗体和建议的稀释列在材料表中, 尽管每个实验室都可以根据批号和组织来源优化抗体稀释。初级抗体批次变异可影响染色强度和特异性, 并要求验证达到与前试剂相同程度的染色强度。
在人类胰腺中, 胰岛被描述为胰岛素、胰高血糖素和 secretogranin 3 (图 2)。雪旺细胞被描述使用胶质纤维酸性蛋白 (胶质,图 2和图 3A)。在 Lightsheet (图 3B) 上, 对副交感神经标记的神经血管活性肠肽 (VIP) 的抗体进行了成像。交感神经可以看到与染色酪氨酸羟化酶 (没有显示)。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/56859/56859fig1.jpg"> 图1。人类胰腺光学清除.(a)刀片用于在清除组织切片和从组织中刮掉多余的石蜡 (底部面板) 之前, 将部分石蜡切片组织 (顶部板) 切开并松开。有代表性的组织样品在(b 左, c 顶)和在(b 右, c 中)后显示石蜡嵌入组织的光清除, 如协议部分所述。清除后, 固定组织是不完全透明的清除(B 右, C 顶端)和经常组织肿胀可以欣赏(c, 中间面板)。清除组织成为完全透明后, 平衡在轮辋(C, 下面板)。缩放栏: 500 µm.<a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56859/56859fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/56859/56859fig2.jpg"> 图2。人类神经孤岛网络.从 nPOD 器官捐献者或档案石蜡包埋组织中所描述的人类胰腺标本被清除。(A-D)雪旺细胞 (胶质蛋白 +, 白色) 和内分泌细胞显示染色的胰岛素 (红色) 和胰高血糖素 (绿色)。(A)共聚焦显微镜和最大强度投影 (MIP) 显示了在胰岛周围的血管旁和延伸到胰岛的神经上的雪旺细胞 (胶质纤维 +) 的追踪。该插图显示高分辨率, 并表明接触之间的胶质蛋白 + 雪旺细胞和内分泌细胞。(B)面板的3D 图像 (比例: x = 50 µm、y = 20 µm、z = 25 µm) a。石蜡样品通过共聚焦显微镜进行成像, 并以最大强度投影(C)和 3D (D)显示, 比例: x、y = 50 µm、z = 25 µm)。刻度线a, C:50 µm.<a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56859/56859fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/56859/56859fig3.jpg"> 图 3. 3D 可视化和拼接.三缝合图像堆栈获得的雪旺细胞染色使用胶质和共聚焦显微镜和跟踪使用的突起示踪插件在图像分析软件。(A)将显示跟踪的3D 填充 (刻度线: x = 150 µm、y = 200 µm (0.2 mm)、z = 120 µm) (B)一个协议样本用 VIP 抗体染色, 用 lightsheet 显微镜成像。该栈是 > 1 毫米的深度和神经纤维清楚地看到高分辨率。纤维包裹管道 (D, 前景) 和一个神经节 (G, 背景) 可以看到 (大网格: x, y, z = 200 µm; 刻度线: x, y, z = 40 µm)。<a href="//cloudflare-jove-com.bdigitaluss.remotexs.co/files/ftp_upload/56859/56859fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network at JoVE.com

High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network

人类胰岛神经网络的高分辨率3D 成像

在这里, 我们提出了一个协议的图像人类胰腺切片三维 (3D) 使用优化的被动清除方法。这篇手稿演示了这些程序的被动光清除后, 多免疫荧光染色, 以确定关键元素的自主神经和感觉神经网络支配人类胰岛。

Using traditional histological methods, researchers are hampered in their ability to image whole tissues or organs in large-scale 3D. Histological ...

high-resolution-3d-imaging-of-the-human-pancreas-neuro-insular-network

Universidad San Sebastian

Research

JoVE Journal

Bioengineering

10.5K 次观看.  University of Florida. 在这里, 我们提出了一个协议的图像人类胰腺切片三维 (3D) 使用优化的被动清除方法。这篇手稿演示了这些程序的被动光清除后, 多免疫荧光染色, 以确定关键元素的自主神经和感觉神经网络支配人类胰岛。

视频: High Resolution 3D Imaging of the Human Pancreas Neuro-insular Network

Near Infrared Optical Projection Tomography for Assessments of &beta;-cell Mass Distribution in Diabetes Research

By adapting OPT to include the capability of imaging in the near infrared (NIR) spectrum, we here illustrate the possibility to image larger bodies of pancreatic tissue, such as the rat pancreas, and to increase the number of channels (cell types) that may be studied in a single specimen. We further describe the implementation of a number of computational tools that provide: 1/ accurate positioning of a specimen's (in our case the pancreas) centre of mass (COM) at the axis of rotation (AR)2; 2/ improved algorithms for post-alignment tuning which prevents geometric distortions during the tomographic reconstruction2 and 3/ a protocol for intensity equalization to increase signal to noise ratios in OPT-based BCM determinations3. In addition, we describe a sample holder that minimizes the risk for unintentional movements of the specimen during image acquisition. Together, these protocols enable assessments of BCM distribution and other features, to be performed throughout the volume of intact pancreata or other organs (e.g. in studies of islet transplantation), with a resolution down to the level of individual islets of Langerhans.

Near Infrared Optical Projection Tomography for Assessments of &#946;-cell Mass Distribution in Diabetes Research

We describe the adaptation of optical projection tomography (OPT)1 to imaging in the near infrared spectrum, and the implementation of a number of computational tools. These protocols enable assessments of pancreatic &beta;-cell mass (BCM) in larger specimens, increase the multichannel capacity of the technique and increase the quality of OPT data.

近红外光学投影层析成像技术在糖尿病研究β细胞质量分布的评估

By adapting OPT to include the capability of imaging in the near infrared (NIR) spectrum, we here illustrate the possibility to image larger bodies of ...

科研

医学

使用植入式稳定窗的小鼠胰腺成像

Stabilized Window for Intravital Imaging of the Murine Pancreas

The physiology and pathophysiology of the pancreas are complex. Diseases of the pancreas, such as pancreatitis and pancreatic adenocarcinoma (PDAC) have high morbidity and mortality. Intravital imaging (IVI) is a powerful technique enabling the high-resolution imaging of tissues in both healthy and diseased states, allowing for real-time observation of cell dynamics. IVI of the murine pancreas presents significant challenges due to the deep visceral and compliant nature of the organ, which make it highly prone to damage and motion artifacts. 
Described here is the process of implantation of the Stabilized Window for Intravital imaging of the murine Pancreas (SWIP). The SWIP allows IVI of the murine pancreas in normal healthy states, during the transformation from the healthy pancreas to acute pancreatitis induced by cerulein, and in malignant states such as pancreatic tumors. In conjunction with genetically labeled cells or the administration of fluorescent dyes, the SWIP enables the measurement of single-cell and subcellular dynamics (including single-cell and collective migration) as well as serial imaging of the same region of interest over multiple days. 
The ability to capture tumor cell migration is of particular importance as the primary cause of cancer-related mortality in PDAC is the overwhelming metastatic burden. Understanding the physiological dynamics of metastasis in PDAC is a critical unmet need and crucial for improving patient prognosis. Overall, the SWIP provides improved imaging stability and expands the application of IVI in the healthy pancreas and malignant pancreas diseases.

作者聚焦:通过先进的活体成像彻底改变胰腺疾病的理解 

We present a protocol for the surgical implantation of a stabilized indwelling optical window for subcellular-resolution imaging of the murine pancreas, allowing serial and longitudinal studies of the healthy and diseased pancreas.

用于小鼠胰腺活体成像的稳定窗口

The physiology and pathophysiology of the pancreas are complex. Diseases of the pancreas, such as pancreatitis and pancreatic adenocarcinoma (PDAC) have ...

癌症研究

In situ Quantification of Pancreatic Beta-cell Mass in Mice

Tracing changes of specific cell populations in health and disease is an important goal of biomedical research. The process of monitoring pancreatic beta-cell proliferation and islet growth is particularly challenging. We have developed a method to capture the distribution of beta-cells in the intact pancreas of transgenic mice with fluorescence-tagged beta-cells with a macro written for ImageJ (rsb.info.nih.gov/ij/). Following pancreatic dissection and tissue clearing, the entire pancreas is captured as a virtual slice, after which the GFP-tagged beta-cells are examined. The analysis includes the quantification of total beta-cell area, islet number and size distribution with reference to specific parameters and locations for each islet and for small clusters of beta-cells. The entire distribution of islets can be plotted in three dimensions, and the information from the distribution on the size and shape of each islet allows a quantitative and qualitative comparison of changes in overall beta-cell area at a glance.

In situ Quantification of Pancreatic Beta-cell Mass in Mice

The following protocol outlines the process of pancreatic dissection for virtual slice imaging, and the subsequent quantification of all GFP-tagged beta-cells in the entire pancreas.

在小鼠胰腺β-细胞团的原位定量

Tracing changes of specific cell populations in health and disease is an important goal of biomedical research. The process of monitoring pancreatic ...

生物学

Computer-assisted Large-scale Visualization and Quantification of Pancreatic Islet Mass, Size Distribution and Architecture

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), and delta-cells (somatostatin) that are embedded in the exocrine tissues and comprise 1-2% of the entire pancreas. There is a close correlation between body and pancreas weight. Total beta-cell mass also increases proportionately to compensate for the demand for insulin in the body. What escapes this proportionate expansion is the size distribution of islets. Large animals such as humans share similar islet size distributions with mice, suggesting that this micro-organ has a certain size limit to be functional. The inability of large animal pancreata to generate proportionately larger islets is compensated for by an increase in the number of islets and by an increase in the proportion of larger islets in their overall islet size distribution. Furthermore, islets exhibit a striking plasticity in cellular composition and architecture among different species and also within the same species under various pathophysiological conditions. In the present study, we describe novel approaches for the analysis of biological image data in order to facilitate the automation of analytic processes, which allow for the analysis of large and heterogeneous data collections in the study of such dynamic biological processes and complex structures. Such studies have been hampered due to technical difficulties of unbiased sampling and generating large-scale data sets to precisely capture the complexity of biological processes of islet biology. Here we show methods to collect unbiased "representative" data within the limited availability of samples (or to minimize the sample collection) and the standard experimental settings, and to precisely analyze the complex three-dimensional structure of the islet. Computer-assisted automation allows for the collection and analysis of large-scale data sets and also assures unbiased interpretation of the data. Furthermore, the precise quantification of islet size distribution and spatial coordinates (i.e. X, Y, Z-positions) not only leads to an accurate visualization of pancreatic islet structure and composition, but also allows us to identify patterns during development and adaptation to altering conditions through mathematical modeling. The methods developed in this study are applicable to studies of many other systems and organisms as well.

Novel computer-assisted methods of large-scale procurement and analysis of immunohistochemically stained pancreatic specimens are described: (1) Virtual Slice capture of the entire section; (2) Mass analysis of large-scale data; (3) Reconstruction of 2D Virtual Slices; (4) 3D islet mapping; and (5) Mathematical analysis.

计算机辅助大型胰岛质量，粒度分布和结构的可视化和量化

The pancreatic islet is a unique micro-organ composed of several hormone secreting endocrine cells such as beta-cells (insulin), alpha-cells (glucagon), ...

Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis

Structural properties of pancreatic islets are key for the functional response of insulin, glucagon, and somatostatin-secreting cells, due to their implications in intraislet communication via electric, paracrine, and autocrine signaling. In this protocol, the three-dimensional architecture of a pancreatic islet is firstly reconstructed from experimental data using a novel computational algorithm. Next, the morphological and connectivity properties of the reconstructed islet, such as the number and percentages of the different type of cells, cellular volume, and cell-to-cell contacts, are obtained. Then, network theory is used to describe the connectivity properties of the islet through network-derived metrics such as average degree, clustering coefficient, density, diameter, and efficiency. Finally, all these properties are functionally evaluated through computational simulations using a model of coupled oscillators. Overall, here we describe a step-by-step workflow, implemented in IsletLab, a multiplatform application developed specifically for the study and simulation of pancreatic islets, to apply a novel computational methodology to characterize and analyze pancreatic islets as a complement to the experimental work.

In this protocol, the pancreatic islets are reconstructed and analyzed using computational algorithms implemented in a dedicated multiplatform application.

胰岛的计算重建作为结构和功能分析的工具

Structural properties of pancreatic islets are key for the functional response of insulin, glucagon, and somatostatin-secreting cells, due to their ...

生物工程

Longitudinal In Vivo Imaging and Quantification of Human Pancreatic Islet Grafting and Contributing Host Cells in the Anterior Eye Chamber

Imaging beta cells is a key step towards understanding islet transplantation. Although different imaging platforms for the recording of beta cell biology have been developed and utilized in vivo, they are limited in terms of allowing single cell resolution and continuous longitudinal recordings. Because of the transparency of the cornea, the anterior chamber of the eye (ACE) in mice is well suited to study human and mouse pancreatic islet cell biology. Here is a description of how this approach can be used to perform continuous longitudinal recordings of grafting and revascularization of individual human islet grafts. Human islet grafts are inserted into the ACE, using NOD.(Cg)-Gt(ROSA)26Sortm4-Rag2-/-mice as recipients. This allows for the investigation of the expansion of recipient versus donor cells and the contribution of recipient cells in promoting the encapsulation and vascularization of the graft. Further, a step-by-step approach for image analysis and quantification of the islet volume or segmented vasculature and islet capsule forming recipient cells is outlined.

The goal of this protocol is to continuously monitor the dynamics of the human pancreatic islet engraftment process and the contributing host versus donor cells. This is accomplished by transplanting human islets into the anterior chamber of the eye (ACE) of an NOD.(Cg)-Gt(ROSA)26Sortm4-Rag2-/-mouse recipient followed by repeated 2-photon imaging.

前眼室人类胰岛移植和贡献宿主细胞的纵向维沃成像和定量

Imaging beta cells is a key step towards understanding islet transplantation. Although different imaging platforms for the recording of beta cell biology ...

Stabilized Longitudinal In Vivo Cellular-Level Visualization of the Pancreas in a Murine Model with a Pancreatic Intravital Imaging Window

Direct in vivo cellular-resolution imaging of the pancreas in a live small animal model has been technically challenging. A recent intravital imaging study, with an abdominal imaging window, enabled visualization of the cellular dynamics in abdominal organs in vivo. However, due to the soft sheet-like architecture of the mouse pancreas that can be easily influenced by physiologic movement (e.g., peristalsis and respiration), it was difficult to perform stabilized longitudinal in vivo imaging over several weeks at the cellular level to identify, track, and quantify islets or cancer cells in the mouse pancreas. Herein, we describe a method for implanting a novel supporting base, an integrated pancreatic intravital imaging window, that can spatially separate the pancreas from the bowel for longitudinal time-lapse intravital imaging of the pancreas microstructure. Longitudinal in vivo imaging with the imaging window enables stable visualization, allowing for the tracking of islets over a period of 3 weeks and high-resolution three-dimensional imaging of the microstructure, as evidenced here in an orthotopic pancreatic cancer model. With our method, further intravital imaging studies can elucidate the pathophysiology of various diseases involving the pancreas at the cellular level.

Stabilized Longitudinal In Vivo Cellular-Level Visualization of the Pancreas in a Murine Model with a Pancreatic Intravital Imaging Window

In vivo high-resolution imaging of the pancreas was facilitated with the pancreatic intravital imaging window.

稳定纵向 在Vivo 细胞级可视化胰腺在穆林模型与胰腺内活性成像窗口

Direct in vivo cellular-resolution imaging of the pancreas in a live small animal model has been technically challenging. A recent intravital imaging ...

Confocal Imaging of Neuropeptide Y-pHluorin: A Technique to Visualize Insulin Granule Exocytosis in Intact Murine and Human Islets

Insulin secretion plays a central role in glucose homeostasis under normal physiological conditions as well as in disease. Current approaches to study insulin granule exocytosis either use electrophysiology or microscopy coupled to the expression of fluorescent reporters. However most of these techniques have been optimized for clonal cell lines or require dissociating pancreatic islets. In contrast, the method presented here allows for real time visualization of insulin granule exocytosis in intact pancreatic islets. In this protocol, we first describe the viral infection of isolated pancreatic islets with adenovirus that encodes a pH-sensitive green fluorescent protein (GFP), pHluorin, coupled to neuropeptide Y (NPY). Second, we describe the confocal imaging of islets five days after viral infection and how to monitor the insulin granule secretion. Briefly, the infected islets are placed on a coverslip on an imaging chamber and imaged under an upright laser-scanning confocal microscope while being continuously perfused with extracellular solution containing various stimuli. Confocal images spanning 50 &#181;m of the islet are acquired as time-lapse recordings using a fast-resonant scanner. The fusion of insulin granules with the plasma membrane can be followed over time. This procedure also allows for testing a battery of stimuli in a single experiment, is compatible with both mouse and human islets, and can be combined with various dyes for functional imaging (e.g., membrane potential or cytosolic calcium dyes).

We describe a protocol for visualization of insulin exocytosis in intact islets using pHluorin, a pH-sensitive green fluorescent protein. Isolated islets are infected with adenovirus encoding pHluorin coupled to the vesicle cargo neuropeptide Y. This allows for the detection of insulin granule fusion events by confocal microscopy.

神经肽 Y-pHluorin 的共焦成像: 一种完整的小鼠和人胰岛胰岛素颗粒胞的可视化技术

Insulin secretion plays a central role in glucose homeostasis under normal physiological conditions as well as in disease. Current approaches to study ...

人类胰岛神经网络的高分辨率3D 成像

摘要

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此视频中的章节

近红外光学投影层析成像技术在糖尿病研究β细胞质量分布的评估

用于小鼠胰腺活体成像的稳定窗口

用于小鼠胰腺活体成像的稳定窗口

用于小鼠胰腺活体成像的稳定窗口

在小鼠胰腺β-细胞团的原位定量

计算机辅助大型胰岛质量，粒度分布和结构的可视化和量化

胰岛的计算重建作为结构和功能分析的工具

前眼室人类胰岛移植和贡献宿主细胞的纵向维沃成像和定量

稳定纵向在Vivo 细胞级可视化胰腺在穆林模型与胰腺内活性成像窗口

神经肽 Y-pHluorin 的共焦成像: 一种完整的小鼠和人胰岛胰岛素颗粒胞的可视化技术

人类胰岛神经网络的高分辨率3D 成像

摘要

探索更多视频

此视频中的章节

近红外光学投影层析成像技术在糖尿病研究β细胞质量分布的评估

用于小鼠胰腺活体成像的稳定窗口

用于小鼠胰腺活体成像的稳定窗口

用于小鼠胰腺活体成像的稳定窗口

在小鼠胰腺β-细胞团的原位定量

计算机辅助大型胰岛质量，粒度分布和结构的可视化和量化

胰岛的计算重建作为结构和功能分析的工具

前眼室人类胰岛移植和贡献宿主细胞的纵向维沃成像和定量

稳定纵向 在Vivo 细胞级可视化胰腺在穆林模型与胰腺内活性成像窗口

神经肽 Y-pHluorin 的共焦成像: 一种完整的小鼠和人胰岛胰岛素颗粒胞的可视化技术

稳定纵向在Vivo 细胞级可视化胰腺在穆林模型与胰腺内活性成像窗口