这项工作部分是由国家科学基金会授予玛丽 e. Konkle 和迈克尔. Menze 的 CHE-160944 资助的。

1. 细胞培养
<ol><li>维持人肝癌 (HepG2) 细胞在 Dulbecco 的改良鹰的培养基 (DMEM) 含有10% 胎牛血清 (血清) 和附加基质 (10 毫米葡萄糖, 2 毫米谷氨酰胺, 1 毫米丙酮酸盐) 在37°c 在细胞培养孵化器 (5% CO2, 95% 空气, 100% 湿度)。 注: 在 respirometry 和量热仪的后续步骤中引用 DMEM 时, 请使用上述 DMEM 公式。</li>
	<li>将单元格在 1 x 106细胞每100毫米培养板上, 每7天或在到达90% 融合之前, 每 3-4 天更新培养基。</li>
	<li>在细胞 60-80% 汇合时进行实验。</li>
</ol>2. 呼吸和热量计的制备
<ol><li>吸管2.2 毫升的 DMEM 进入呼吸室, 充分插入塞子, 并调整与塞子间隔工具, 以使最佳的氧气扩散从空气到介质。</li>
	<li>连续搅拌37摄氏度, 直至氧浓度 (蓝迹) 和氧通量 (红迹) 稳定。确保呼吸的软件被编程来解释实验中所用介质的氧溶解度。 注: 不同介质有不同的氧溶解度系数 (例如, DMEM = 0.92)。</li>
	<li>在 DMEM 氧浓度稳定后, 用燃烧室打开, 选择氧浓度跟踪的稳定部分, 并校准100% 空气饱和度。</li>
	<li>校准0% 氧气选择稳定部分的氧气浓度的痕迹时, 没有氧气存在的房间。执行此步骤后, 20 µL 滴定230毫米硫酸钠钠或在所有氧气消耗的生物样品在实验结束。</li>
	<li>在100% 的空气校准呼吸, 关闭塞子, 并确保没有气泡出现在会议厅内。 注: 当极谱氧传感器 (POS) 消耗氧气以测量氧分压时, 密闭腔内的氧通量会轻微增加。</li>
	<li>在10分钟的塞子被关闭, 确认呼吸是准备 HepG2 细胞通过观察稳定的氧气流量。 注: 这里使用的热量计采用不锈钢安瓿, 需要一个安瓿作为参考。</li>
	<li>将2.5 毫升的 H2o (dH2o) [相等体积作为样本 (步骤 4)] 添加到热量计的参考安瓿并拧紧瓶盖, 如有必要, 使用钳子。清洁密封安瓿与气流, 并慢慢降低安瓿到位置1在参考通道的热量计。保持在位置1直到生物样品准备好。</li>
</ol>3. Respirometry 和量热法制备 HepG2 细胞
<ol><li>确认呼吸和热量计的制备和校准。温暖的 DMEM 和胰蛋白酶到37°c 在水浴。</li>
	<li>添加 DMEM 到一个新鲜的100毫米板和放置在孵化器, 以允许平衡的媒介为 CO2和温度。 注: 此介质将用于量热实验, 因此适当的体积取决于将使用的安瓿的数量。</li>
	<li>用倒置光显微镜确认细胞在 60-80% 汇合处, 然后用10毫升室温磷酸盐缓冲盐水 (PBS) 洗涤100毫米板2x。</li>
	<li>添加3毫升的37°c 胰蛋白酶到100毫米细胞和孵化他们为 7-10 分钟在37°c 在细胞培养孵化器。将胰蛋白酶与3毫升的37°c DMEM 中和, 通过轻轻吹打20倍于5毫升吸管。</li>
	<li>将 DMEM 和胰蛋白酶中的6毫升悬浮细胞转移到无菌的15毫升圆锥管中, 在 175 x g 处将其离心为5分钟. 取出液体而不干扰细胞颗粒。</li>
	<li>并用重悬细胞颗粒在 ~ 5 毫升的 DMEM 和吸管它彻底地确保细胞彼此充分分离。</li>
	<li>删除100µL 的混合样本, 并使用 hemocytometer 上的所有8个字段进行彻底的单元格计数, 以确定单元格的总数。然后计算单元格泥沙的音量, 以便每50µL 生成 ~ 2 x 106单元格。 注意: 这将确保将 2 x 106单元格添加到呼吸的每个分室中, 且介质位移最小。</li>
	<li>将细胞离心5分钟, 在 175 x g 并用重悬从步骤3.7 中计算出的 DMEM 量中的颗粒。</li>
	<li>执行单元格计数以确定在呼吸中每室注入 2 x 106单元格所需的卷 (这应该是 ~ 50 µL)。储存在冰上的 respirometric 测量的细胞, 直到准备就绪。</li>
	<li>当浓缩的细胞样品在冰上时, 确定稀释量为 1 x 105细胞/毫升的热量。</li>
	<li>将样本进行混合, 并在每个安瓿的 DMEM 中为 1 x 105细胞/毫升准备3毫升的细胞。 注: 样品稀释用的 DMEM 应为步骤3.3 中制备的平衡培养基。</li>
</ol>4. 量热法
<ol><li>确保热量计与计算机连接, µW 中的热信号是可观测和稳定的。</li>
	<li>将2.5 毫升的细胞在 1 x 105细胞/mL (~ 2.5 x 105细胞) 中添加到每个安瓿, 确保足够的空气在安瓿和介质的盖子之间, 以允许气体扩散。</li>
	<li>立即拧紧瓶盖, 必要时使用钳子。清洁密封安瓿, 使用气流去除任何外部碎片, 并慢慢降低安瓿的位置1的热量计。记录安瓿降低到位置1的时间。</li>
	<li>允许安瓿在位置1处坐15分钟。 注意: 在这15分钟期间, 细胞将被注射入呼吸 (步骤 5)。这确保在确定 CR 比时, 同样的时间间隔用于热量生产和氧气消耗。</li>
	<li>15分钟在位置 1, 慢慢地降低参考和样品安瓿到位置2。记录安瓿降低的时间, 并测量至少30分钟。</li>
</ol>5. Respirometry
<ol><li>在15分钟间隔内, 安瓿在热量计中的位置 1, 开始 respirometric 研究。</li>
	<li>通过吹打彻底混合细胞样本, 以确保一个均匀的溶液, 并注入 < 50 µL 细胞样本到呼吸的每个腔内, 最终浓度为 ~ 2 x 106细胞/室。记录单元格注入呼吸的时间。</li>
	<li>注射后, HepG2 细胞连续搅拌37摄氏度。至少等待 20-30 分钟的氧气流量稳定和氧浓度的稳定下降。</li>
	<li>选中屏幕右侧的O2每 V 的通量选项卡, 并突出显示氧气流量的稳定部分。选择标记, 然后统计和记录 O2通量 (每 v) 的数据。此记录将用于计算 CR 比率。 注意: 步骤5.5 和5.6 是可选的。寡霉素和 FCCP 的滴定揭示了漏呼吸和最大不耦合呼吸的情况。</li>
	<li>注入1µL 4 毫克/毫升寡霉素到每个房间, 并允许〜10分钟的氧气流量稳定。通过以下步骤记录稳定的漏呼吸率5.4 通过突出一个稳定部分的氧气流量的痕迹后寡霉素添加。</li>
	<li>滴定 2-µL 注射0.2 毫米 FCCP 进入每个室, 允许氧通量稳定后每次注射。继续滴定直到达到最大通量, 如随后的 FCCP 滴定略有下降所示。在最大通量期间记录稳定的呼吸速率。</li>
</ol>6. Calorespirometric 比的计算
<ol><li>对使用 hemocytometer 的所有8个字段的 1 x 105单元格/mL 中准备的样本进行最终单元格计数, 以确定添加到安瓿 (2.5 mL) 中的单元格总数。</li>
	<li>确定一个时间来记录测量, 从热量计和呼吸, 稳定的信号在同一时间存在。参考当安瓿被降低到位置2和注入细胞进入呼吸时记录的时间。</li>
	<li>通过将光标放在显示热量输出的迹线上, 在步骤确定的时间和表达为µW/百万细胞时, 记录热生产。收集氧气消耗数据, 并确保它被表示为 pmol O2 x s-1 x 万元单元格。</li>
	<li>要计算 CR 比率, 首先将µW 转换为焦/秒, 然后除以 O2/秒的摩尔上的焦/秒, 以获取表示为焦/摩尔 O2的 CR 比率。注意: CR 比率在焦/摩尔 O2中显示。 (请参见<a href="/files/ftp_upload/57724/JoVE_Supplemental_File.docx" target="_blank">辅助文件 1</a>)</li>
</ol>

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作者没有什么可透露的。

calorespirometry 的目的是定量评价有氧和厌氧途径对代谢活动的贡献, 并获得细胞能量通量的综合观点。这是通过同时测量散热和氧通量, 然后用计算的 CR 比与理论 oxycaloric 当量的比较来完成的。对于可重现和可靠的数据, 必须考虑几个关键步骤。维持一种无菌、健康的细胞培养是至关重要的。细菌或真菌污染可能导致被挪用的热量生产或氧气消耗量, 使 CR 比率没有意义。此外, 细胞计数不当, 样本混合不良, 或丛生细胞也可能导致不准确的 CR 比率。对于呼吸的一个关键步骤是适当的样品准备, 以便注射量低于50µL。此卷确保从腔室中偏移的介质数量最少, 并且氧气消耗量正确归因于 2 x 106单元格。一旦例行呼吸观察, 建议的协议是兼容两个额外的滴定 (寡霉素和 FCCP), 这说明了泄漏呼吸和 ETS 能力的线粒体。对于这种应用, 过量添加 FCCP 可以抑制呼吸, 防止最大流量;因此, 在达到最大不耦合呼吸之前, 至少要进行 3-5 滴定。
呼吸和热量计的维护和正确校准对 calorespirometry 至关重要。对背景耗氧量的滴定方案提出了强烈建议, 并采用步进滴定的硫酸钠, 达到了腔内氧含量的降低。这是一个必要的措施, 以确认密闭室和正确的氧气回扩散到商会, 特别是如果测量是在低氧紧张。另外, 在添加单元格之前执行搅拌器测试可以提供有关 POS 响应的信息。如果响应能力较差或氧气流量不稳定 (图 2), 则需要对 POS 服务。不适当的清洁呼吸室可以留下残留量的抑制剂或 uncouplers, 这可能会影响后续实验。密封腔内的气泡也能干扰测量, 并且可以在腔室关闭时或在注射过程中, 如果注射器内存在气泡, 则可进行取样。热量计的协议比较直接, 但也容易受到实验误差的影响。例如, 安瓿的不适当密封会使蒸发发生, 从而极大地改变热流。
这种方法的一个局限性是, 并非所有细胞都能在悬浮体中培养, 许多细胞系附着在细胞培养板或基质上。为了进行实验, 附着细胞必须通过使用胰蛋白酶等离解剂酶脱落。这种状态下的细胞可能不处于对数生长阶段, 其生理学可能会改变。因此, 随后的分析通过最近的 trypsinized 细胞的热量计和呼吸可能评估中断的细胞活动。此外, 在所提出的热协议的限制是在封闭的安瓿细胞的沉淀, 这可能导致局部缺氧的细胞周围由于缓慢的氧气扩散的水溶液。我们已经确定 ~ 3 x 105单元格是与本协议中使用的安瓿兼容的上限。增加细胞数量可以从细胞的沉淀中产生缺氧环境, 从而损害结果。因此, 如果对不同的细胞线进行调查, 并考虑其他方法, 例如增加介质密度以减少单元格的沉积, 则优化是至关重要的, 如24。安瓿可供搅拌;然而, 在搅拌过程中, 保证最小信噪比是至关重要的。此外, 热量计除了在本协议中使用的人与 calorespirometry 兼容, 他们的能力可能超出我们所提出的范围。calorespirometric 分析通过的另一个限制是两室呼吸是无法进行高通量分析, 这将与药物开发最相关。然而, 这一限制, 是抵消了高分辨率的特点, 对呼吸链和线粒体生理学的化合物的影响的能力。
同一样品的热输出和氧通量的高分辨和同时测量的能力是该方法的一个重要强度。一个潜在的黄金标准, 当使用黏附细胞将是两种文化和分析细胞生长在载体珠悬浮。这种方法可以避免从细胞培养表面的分离的负面影响, 并允许分析生长曲线的不同阶段的细胞功能 (对数相vs.接触抑制阶段7)。不幸的是, 使用载体珠子可能会有问题, 因为呼吸所需的高搅拌速度, 以及在搅拌杆下方的单元格磨削的潜力。尽管有这些限制, 但广泛的应用程序仍然与 calorespirometry 兼容。特别是, 这种方法可以通过快速识别培养细胞中的 mitotoxic 化合物来更好地促进药物的发展。

在生物系统中, 新陈代谢过程中的热释放或焓变化通常直接通过 O 2消耗量和/或 CO2 (通过 respirometry) 来监测。不幸的是, 当这些技术被隔离使用时, 关键信息就会丢失, 例如厌氧途径对细胞代谢的贡献。Calorespirometry 是一种强大的技术, 依赖于同时测量散热和呼吸。开创性的 calorespirometric 工作研究了完全氧合哺乳动物细胞的厌氧代谢, 并证明了有氧和厌氧途径同时贡献能量动态平衡, 尽管转化的细胞在完全氧合环境1。Calorespirometry 已经被应用到各种各样的生物学问题上。一些例子包括研究低氧水平的动物热力学, 除草剂和雌激素对双壳贝类鳃的影响, 陆地有机体的新陈代谢, 以及有机土壤物质的微生物分解2,3,4,5,6. 此外, calorespirometry 还揭示了冷冻前的代谢预处理改善了哺乳动物细胞的冷冻保存7。每种方法, 无论是量热和 respirometry, 都独立增加了我们对细胞和生物体生物能学的认识。然而, 可以通过使用 calorespirometry 来回答的基本生物学问题仍然是相对未探索的。
赫斯定律指出, 反应的总焓变化与初始和最终状态之间的通路无关。例如, 生化通路的总焓变化是通路内所有反应焓的变化总和。量热法提供了一种测量细胞热量的实时方法, 它不加区别地检测有氧和厌氧通路。这是基于没有在系统中交换能量的基础, 除非通过实验安瓿8的墙壁。散热的变化等于安瓿中所有代谢反应释放出的焓的变化。因此, 负焓与系统的热量损失相关。在过去四年里, 详尽的研究描绘了分解代谢和代谢的热力学景观。这是由美国国立医学图书馆 (NLM) 在国家卫生研究院 (PubMed) 索引的 "生物" 和 "量热" 搜索词下的研究文章的稳步增加所代表的。搜索结果显示, 在1970之前, 共有27份出版物参考生物量热仪;与此同时, 仅在 2016年, 就有546份出版物利用了这项技术。
热方法的建立, 以确定热生产。但是, 对于解决 respirometric 值, 还是给予了更大的灵活性。respirometric 度量可以由 o2、CO2或 o2和 co2组成。此外, O2或 CO2的测量可以通过各种技术完成, 其中包括 optrodes、克拉克型电极和可调谐二极管激光吸收光谱7、9、10 ,11。虽然 CO2生产在许多 respirometric 研究中是一个有价值的指标, 但培养细胞的培养基通常利用 bicarbonic 缓冲系统来控制 pH 值1213。为避免在碳酸氢钠系统中 CO2测量的并发症, 以下用于区域性单元格 calorespirometry 的协议利用 O2作为唯一 respirometric 参数。
在测量氧通量的同时, 某些 respirometers (参见材料表) 是为了详细评估线粒体功能而设计的。衬底-uncoupler 抑制剂-滴定 (套装) 协议是很好的建立和兼容的实验设计, 以测量膜电位或活性氧 (ROS) 形成14。所提出的完整细胞 calorespirometry 协议与化学 uncouplers 的引入相兼容, 如羰基氰化物-trifluoromethoxyphenylhydrazone (FCCP) 和 f0f1-ATP 合成酶抑制剂寡霉素。通过添加 FCCP, 氧消耗可以从 ATP 生产中拆解, 这有助于评估潜在疗法对线粒体绩效15的影响。此外, 寡霉素的添加也照亮了泄漏呼吸的程度。因此, 在 calorespirometry 期间进行的 respirometric 测量与广泛的协议兼容, 旨在进一步阐明线粒体生理学。
同时测量散热和氧通量可以计算 calorespirometric (CR) 比。这个比率然后被比较对桑顿的恒定或理论 oxycaloric 等值, 范围在-430 到-480 焦摩尔-1之间根据细胞线或组织利益和被补充的碳基体1, 16. 因此, 一个更负的 CR 比率揭示了从厌氧途径到整体新陈代谢活动的贡献增加。例如, 常规肌肉组织呼吸的 CR 比率没有工作的活动性能范围从-448 到-468 焦 mol-1, 这是在理论 oxycaloric 等效17,18的范围内。同时, 在高葡萄糖的培养基中培养的哺乳动物癌细胞, 在糖酵解和相对低线粒体接触19的情况下增强了乳酸发酵。这种表型结果在-490 至-800 焦摩尔-1范围内的 CR 比值, 显示了更多的负 CR 比值1,7, 在细胞代谢中的厌氧通路的增加参与, 16,20。
商业和非营利细胞和组织分销商目前提供150多个物种的细胞线, 近4000个细胞系来源于人类。永生化细胞系是快速评估潜在疗法毒性的方便工具, 其中许多可能直接或间接干扰线粒体功能。在药物筛选过程中使用转换后的细胞可能具有有限的预测价值, 部分原因是因为华宝效应是许多癌症的标志。通常, 癌症产生 ATP 从基质层磷酸化和保持氧化还原平衡, 通过生产乳酸, 而不充分参与线粒体下有氧条件下19。制药业的发展是众所周知的昂贵和低效的, 约8的9化合物测试在人体临床试验未能达到市场批准21。虽然潜在的治疗可能通过初步筛选由于细胞株的细胞毒性低, 有可能这些化合物中的一些 mitotoxic。如果没有一个合适的方法来检测这些毒素如何损害不显示华宝效应的初级细胞的能量平衡, 关键信息往往是过度观察, 瓶颈治疗发展的早期阶段。
Calorespirometry 是一种实用的、无创的方法来分析各种生物样品中的代谢活动, 包括细胞和组织。所提出的协议的核心与一系列应用程序兼容。然而, 已经发现了一个并发症。永生化细胞通常培养在无葡萄糖培养基中补充半乳糖, 以增加氧化磷酸 (OXPHOS) 对能源生产的贡献, 以便使细胞敏感到潜在的 mitotoxins22, 23。当样品放在热量计15使用的不锈钢安瓿中时, 这种代谢重新编程似乎不清楚分析。在葡萄糖培养基中培养的细胞继续从事高代谢活动几个小时。同时, 在半乳糖培养基中培养的细胞在安瓿的30分钟内减少了热量的产生, 使测量仅限于早期实验时间点。不幸的是, 这种行为阻碍了评估细胞增殖的机会。尽管有这一特定限制, 大多数应用都与 calorespirometric 分析兼容, 并且可以通过这种方法获得详细的代谢信息。

<table border="0" cellpadding="0" cellspacing="0" width="627">
	<tbody>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">HepG2 Cells</td>
			<td style="width:125px;">American Type Culture Collection</td>
			<td style="width:119px;">HB-8065</td>
			<td style="width:167px;">Cells used for calorespirometry</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">O2k-Respirometer</td>
			<td style="width:125px;">Oroboros Instruments</td>
			<td style="width:119px;">10022-02</td>
			<td>Respirometer</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">LKB 2277 thermal activity monitor (TAM)</td>
			<td style="width:125px;">Thermometric AB</td>
			<td style="width:119px;"></td>
			<td>Thermometric was purchased by TA Instruments</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Sodium Pyruvate (100 mM)</td>
			<td style="width:125px;">Thermofisher Scientific</td>
			<td style="width:119px;">11360070</td>
			<td>100x solution added to DMEM medium</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Fetal Bovine Serum - Premiuim Select</td>
			<td style="width:125px;">Atlanta Biologicals</td>
			<td style="width:119px;">S11550</td>
			<td>Added to 10% in DMEM medium</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Trypsn-EDTA (0.25%)</td>
			<td style="width:125px;">Thermofisher Scientific</td>
			<td style="width:119px;">25200072</td>
			<td>Cell dissociation reagent</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Oligomycin from Streptomyces diastatochromogenes</td>
			<td style="width:125px;">Sigma Aldrich&#160;</td>
			<td style="width:119px;">O4876</td>
			<td>Mitochondrial Inhibitor</td>
		</tr>
		<tr height="63">
			<td height="63" style="height:63px;width:216px;">Carbonyl cyanide 4-(trifluoromethoxy)phenylhydrazone</td>
			<td style="width:125px;">Sigma Aldrich</td>
			<td style="width:119px;">C2920</td>
			<td>Mitochondrial Uncoupler</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Corning 100 mm TC-Treated Culture Dish</td>
			<td style="width:125px;">Corning Corporation</td>
			<td style="width:119px;">430167</td>
			<td>Tissue culture dish</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">Glucose, powder</td>
			<td style="width:125px;">Thermofisher Scientific</td>
			<td style="width:119px;">15023021</td>
			<td>Glucose for DMEM medium</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:216px;">Galactose, powder</td>
			<td style="width:125px;">Fischer Scientific</td>
			<td style="width:119px;">BP656500</td>
			<td>Galactose for DMEM medium</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">L-Glutamine (200 mM)</td>
			<td style="width:125px;">Thermofisher Scientific</td>
			<td style="width:119px;">25030081</td>
			<td>Glutamine for DMEM medium</td>
		</tr>
		<tr height="42">
			<td height="42" style="height:42px;width:216px;">DMEM, no glucose</td>
			<td style="width:125px;">Thermofisher Scientific</td>
			<td style="width:119px;">11966025</td>
			<td>Cell culture medium</td>
		</tr>
	</tbody>
</table>

calorespirometry: a powerful, noninvasive approach to investigate cellular energy metabolism

在基本生物和生物医学研究中使用的许多细胞系通过有氧和厌氧呼吸的结合来维持能量平衡。然而, 这两种途径在多大程度上促进了细胞能源生产的前景一直被忽视。在饱和葡萄糖中培养的转化细胞往往会减少对 ATP 生产的氧化磷酸化的依赖性, 这是由于基质层磷酸化的增加而得到补偿的。这种新陈代谢平衡的转变允许细胞增殖, 尽管存在线粒体毒素。在忽视转化细胞的新陈代谢平衡时, 药物筛选的结果可能会被误解, 因为在高葡萄糖存在下培养的模型细胞株可能无法检测到潜在的 mitotoxic 效应。浓度。该协议描述了两种强大的技术, respirometry 和量热, 这使得对细胞 ATP 生产的有氧和厌氧贡献的定量和无创评估。有氧和厌氧呼吸产生热量, 可以监测通过量热。同时, 测定氧耗率可以评估有氧呼吸的程度。当同时测量散热和耗氧量时, 可以确定 calorespirometric 比。实验得到的值可以与理论 oxycaloric 等效, 可判断厌氧呼吸的程度。因此, calorespirometry 提供了一种独特的方法来分析广泛的生物学问题, 包括药物的发展, 微生物的生长, 和基本生物能学在常氧和缺氧条件下。

calorespirometric 测量的重现性取决于适当和一致的样品准备。如果板块生长过度, 细胞培养所制备的样品不应使用, 因为细胞计数可能由于结块而变得不准确。此外, 由于在丛生细胞中的基底扩散有限, 可能会发生热量流动减少。因此, 在使用粘附细胞时, 选择一个融合在 60-80% 之间的板并在实验之前改变中等 24 h 是至关重要的。
适当的仪器校准和维护对于信息和可重现的 calorespirometric 测量也是至关重要的。按照制造商为呼吸制定的清洁协议, 实施了广泛的乙醇清洗, 以确保去除残留疏水 uncouplers 和抑制剂, 不损害测量部分抑制或解耦线粒体。当呼吸室在取样测量之前运行时, 氧浓度和氧通量的痕迹都应该稳定, 如图 1所示。如果 POS 需要传感器服务, 则氧气流量不会稳定, 如图 2所示。当 POS 未得到适当的服务时, 不应执行测量。此外, 热量计中所使用的安瓿应经过清洗、灭菌和干燥, 然后再用, 因为生物污染会干扰样品的热生产。
细胞样品在适当的校准和准备呼吸和热量计之后准备。首先, 在呼吸的塞子关闭, 并检查房间, 以确保没有气泡存在。然后, HepG2 细胞集中到 ~ 2 x 106/50 µL respirometry, 并立即放置在冰上。对于量热仪, 浓缩样品中的 HepG2 细胞在平衡 DMEM 中稀释 1 x 105细胞/毫升。细胞经过吹打彻底混合后, 2.5 毫升的悬浮液添加到无菌安瓿。安瓿被严密密封, 并且任何外部残骸被去除通过吹安瓿与空气气流从空气泵。安瓿然后慢慢地被降下到位置 1, 它保持15分钟。经过15分钟后, 引用和示例安瓿都将慢慢降低到位置 2, 并且一旦热量输出稳定, 测量就会开始记录 (图 3)。
最后的细胞计数是在将细胞添加到热量计和呼吸中后进行的, 以确定在热量计中引入的细胞的精确数量。热量生产然后被表达每百万细胞。如果 HepG2 细胞在缺乏葡萄糖和半乳糖的情况下培养, 增加线粒体的参与, 细胞不会在热量计内增殖, 而是在安瓿中减少30分钟后的新陈代谢活动。这就将 CR 计算限制为测量散热的最早时间点 (图 4)。
记录细胞添加到安瓿的时间是至关重要的, 以便在相同的时间点收集热量产生和耗氧量的测量。如果不采取这种预防措施, 就可以在 CR 比值的确定中引入重要的实验误差。在15分钟期间, 当安瓿位于热量计中的位置1时, 单元格通过吹打它们得到充分的混合, 2 x 106细胞被注入到呼吸通过a 哈密尔顿注射器中。氧气流量稳定后约15分钟和氧气浓度稳步下降 (图 5)。同样, 对采样注入的时间进行记录以进行数据分析是至关重要的。在计算 CR 比时, 细胞的稳定氧通量作为氧气消耗的替代品。额外的滴定, 以评估泄漏呼吸 (1 µL 寡霉素) 表明由下降到较低的氧气流量和最大的不耦合呼吸 (3-5 滴定2µL FCCP) 表明, 没有增加呼吸后, 连续FCCP 滴定。如果 FCCP 库存已过期 (图 6), 则可能无法观察到不完全拆解或抑制呼吸。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57724/57724fig1.jpg"> 图 1: 100% 呼吸的空气校准和极谱氧传感器 (POS) 的状态.信号为氧气浓度 (蓝色线) 和氧气通量 (红线) 在100% 空气校准之前都稳定。稳定线表明呼吸已准备好进行校准。校准应在 DMEM 中执行, 而不是在 H2O.<a href="/files/ftp_upload/57724/57724fig1large.jpg" target="_blank">请单击此处查看此图的更大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57724/57724fig2.jpg"> 图 2: 需要服务的 POS.氧浓度 (蓝迹) 稳定后, 氧通量 (红迹) 无法稳定。POS 不应用于试验, 服务应按制造商描述的那样应用。增加的氧气流量不稳定与增加的 POS 服务需求相关。<a href="/files/ftp_upload/57724/57724fig2large.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/57724/57724fig3v2.jpg"> 图 3: 在葡萄糖培养基中培养的大约 2.5 x 105 HepG2 细胞显示了热量计的增殖迹象.加载 ~ 2.5 x 105在每安瓿葡萄糖存在下培养的 HepG2 细胞, 利用仪器上的-28-µW 设置, 产生-20 到30µW 的恒定热量, 随着时间的推移, 随着细胞增殖而增加的热量产生。在安瓿。<a href="/files/ftp_upload/57724/57724fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57724/57724fig4v2.jpg"> 图 4: 大约 2.5 x 105在半乳糖培养基中培养的 HepG2 细胞不会增殖, 随着时间的推移, 热量产生减少.加载 ~ 2.5 x 105 HepG2 细胞每安瓿培养的半乳糖导致立即热生产的 ~ 20 µW。然而, 随着时间的推移, 热量产生下降, 并限制了 CR 比测量的早期时间点的实验。<a href="/files/ftp_upload/57724/57724fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/57724/57724fig5.jpg"> 图 5: HepG2 细胞的常规、解耦和泄漏呼吸.一旦细胞注入呼吸, 氧浓度稳步下降 (蓝迹)。在正常细胞的常规呼吸过程中, 氧通量 (红迹) 稳定后, 寡霉素添加以抑制f0f1-ATP 合酶。这是由氧通量稳定后的寡霉素添加, 这表明泄漏呼吸。大约 3-5 滴定 FCCP 应进行拆呼吸从 ATP 合成和揭示电子运输系统 (ETS) 能力。<a href="/files/ftp_upload/57724/57724fig5large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/57724/57724fig6.jpg"> 图 6: 不成功解耦 HepG2 细胞呼吸.添加细胞后, 在常规呼吸过程中氧通量稳定 (红迹) 和氧浓度稳步下降 (蓝线)。FCCP 滴定法导致不完全解耦和最终抑制, 防止最大的 ETS 容量被到达。FCCP 从长期贮存中可能受到污染或退化。如果观察到, 必须准备一个新的 FCCP 库存。另外, 确认寡霉素在拆解过程中不会抑制 ETS 容量。<a href="/files/ftp_upload/57724/57724fig6large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Calorespirometry: A Powerful, Noninvasive Approach to Investigate Cellular Energy Metabolism at JoVE.com

Calorespirometry: A Powerful, Noninvasive Approach to Investigate Cellular Energy Metabolism

Calorespirometry: 一种功能强大、无创的研究细胞能量代谢的方法

该协议描述 calorespirometry, 直接和同时测量散热和呼吸, 这提供了一种无创的方法来评估能量代谢。该技术用于通过监测总的细胞能量流来评估有氧和厌氧途径对能源利用的贡献。

Many cell lines used in basic biological and biomedical research maintain energy homeostasis through a combination of both aerobic and anaerobic ...

calorespirometry-powerful-noninvasive-approach-to-investigate

Nanyang Technological University

Research

JoVE Journal

Bioengineering

7.5K 次观看.  University of Louisville. 该协议描述 calorespirometry, 直接和同时测量散热和呼吸, 这提供了一种无创的方法来评估能量代谢。该技术用于通过监测总的细胞能量流来评估有氧和厌氧途径对能源利用的贡献。

视频: Calorespirometry: A Powerful, Noninvasive Approach to Investigate Cellular Energy Metabolism

The Three-Dimensional Human Skin Reconstruct Model: a Tool to Study Normal  Skin and Melanoma Progression

Most in vitro studies in experimental skin biology have been done in 2-dimensional (2D) monocultures, while accumulating evidence suggests that cells behave differently when they are grown within a 3D extra-cellular matrix and also interact with other cells (1-5). Mouse models have been broadly utilized to study tissue morphogenesis in vivo. However mouse and human skin have significant differences in cellular architecture and physiology, which makes it difficult to extrapolate mouse studies to humans. Since melanocytes in mouse skin are mostly localized in hair follicles, they have distinct biological properties from those of humans, which locate primarily at the basal layer of the epidermis. The recent development of 3D human skin reconstruct models has enabled the field to investigate cell-matrix and cell-cell interactions between different cell types. The reconstructs consist of a "dermis" with fibroblasts embedded in a collagen I matrix, an "epidermis", which is comprised of stratified, differentiated keratinocytes and a functional basement membrane, which separates epidermis from dermis. Collagen provides scaffolding, nutrient delivery, and potential for cell-to-cell interaction. The 3D skin models incorporating melanocytic cells recapitulate natural features of melanocyte homeostasis and melanoma progression in human skin. As in vivo, melanocytes in reconstructed skin are localized at the basement membrane interspersed with basal layer keratinocytes. Melanoma cells exhibit the same characteristics reflecting the original tumor stage (RGP, VGP and metastatic melanoma cells) in vivo. Recently, dermal stem cells have been identified in the human dermis (6). These multi-potent stem cells can migrate to the epidermis and differentiate to melanocytes.

In this report, we describe the three-dimensional skin reconstruct model which mimics human skin in architecture and composition. Melanocyte physiology, melanoma progression and the fate of dermal stem cells have been investigated using the skin reconstruct model. The model is also useful as a preclinical tool for drug assessment.

三维人体皮肤重建模型:一种工具来研究正常皮肤恶性黑色素瘤进展

Most in vitro studies in experimental skin biology have been done in 2-dimensional (2D) monocultures, while accumulating evidence suggests that cells ...

科研

生物工程

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic valve leaflets are composed of interstitial cells suspended within an extracellular matrix (ECM) and are lined with an endothelial cell monolayer. The valve withstands a harsh, dynamic environment and is constantly exposed to shear, flexion, tension, and compression. Research has shown calcific lesions in diseased valves occur in areas of high mechanical stress as a result of endothelial disruption or interstitial matrix damage1-3. Hence, it is not surprising that epidemiological studies have shown high blood pressure to be a leading risk factor in the onset of aortic valve disease4. 
The only treatment option currently available for valve disease is surgical replacement of the diseased valve with a bioprosthetic or mechanical valve5. Improved understanding of valve biology in response to physical stresses would help elucidate the mechanisms of valve pathogenesis. In turn, this could help in the development of non-invasive therapies such as pharmaceutical intervention or prevention. Several bioreactors have been previously developed to study the mechanobiology of native or engineered heart valves6-9. Pulsatile bioreactors have also been developed to study a range of tissues including cartilage10, bone11 and bladder12. The aim of this work was to develop a cyclic pressure system that could be used to elucidate the biological response of aortic valve leaflets to increased pressure loads. 
The system consisted of an acrylic chamber in which to place samples and produce cyclic pressure, viton diaphragm solenoid valves to control the timing of the pressure cycle, and a computer to control electrical devices. The pressure was monitored using a pressure transducer, and the signal was conditioned using a load cell conditioner. A LabVIEW program regulated the pressure using an analog device to pump compressed air into the system at the appropriate rate. The system mimicked the dynamic transvalvular pressure levels associated with the aortic valve; a saw tooth wave produced a gradual increase in pressure, typical of the transvalvular pressure gradient that is present across the valve during diastole, followed by a sharp pressure drop depicting valve opening in systole. The LabVIEW program allowed users to control the magnitude and frequency of cyclic pressure. The system was able to subject tissue samples to physiological and pathological pressure conditions. This device can be used to increase our understanding of how heart valves respond to changes in the local mechanical environment.

Design of a Cyclic Pressure Bioreactor for the Ex Vivo Study of Aortic Heart Valves

A cyclic pressure bioreactor capable of subjecting heart valve tissue to physiological and pathological pressure conditions has been designed. A LabVIEW program allows users to control pressure magnitude, amplitude and frequency. This device can be used to study the mechanobiology of heart valve tissue or isolated cells.

循环压力为生物反应器的设计体外主动脉心脏瓣膜的研究

The aortic valve, located between the left ventricle and the aorta, allows for unidirectional blood flow, preventing backflow into the ventricle. Aortic ...

Directed Cellular Self-Assembly to Fabricate Cell-Derived Tissue Rings for Biomechanical Analysis and Tissue Engineering

Each year, hundreds of thousands of patients undergo coronary artery bypass surgery in the United States.1 Approximately one third of these patients do not have suitable autologous donor vessels due to disease progression or previous harvest. The aim of vascular tissue engineering is to develop a suitable alternative source for these bypass grafts. In addition, engineered vascular tissue may prove valuable as living vascular models to study cardiovascular diseases. Several promising approaches to engineering blood vessels have been explored, with many recent studies focusing on development and analysis of cell-based methods.2-5 Herein, we present a method to rapidly self-assemble cells into 3D tissue rings that can be used in vitro to model vascular tissues.
To do this, suspensions of smooth muscle cells are seeded into round-bottomed annular agarose wells. The non-adhesive properties of the agarose allow the cells to settle, aggregate and contract around a post at the center of the well to form a cohesive tissue ring.6,7 These rings can be cultured for several days prior to harvesting for mechanical, physiological, biochemical, or histological analysis. We have shown that these cell-derived tissue rings yield at 100-500 kPa ultimate tensile strength8 which exceeds the value reported for other tissue engineered vascular constructs cultured for similar durations (&lt;30 kPa).9,10 Our results demonstrate that robust cell-derived vascular tissue ring generation can be achieved within a short time period, and offers the opportunity for direct and quantitative assessment of the contributions of cells and cell-derived matrix (CDM) to vascular tissue structure and function.

This article outlines a versatile method to create cell-derived tissue rings by cellular self-assembly. Smooth muscle cells seeded into ring-shaped agarose wells aggregate and contract to form robust three-dimensional (3D) tissues within 7 days. Millimeter-scale tissue rings are conducive to mechanical testing and serve as building blocks for tissue assembly.

定向细胞自我组装制造，为生物力学分析与组织工程细胞衍生的组织环

Each year, hundreds of thousands of patients undergo coronary artery bypass surgery in the United States.1 Approximately one third of these patients do ...

FIBS-enabled Noninvasive Metabolic Profiling

In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be validated for predictive purposes. Ideally such systems would be low volume, which precludes sampling and destructive analyses when time course data are to be obtained. What is needed is an in situ monitoring tool which can report the necessary information in real-time and noninvasively. An interesting option is the use of fluorescent, protein-based in vivo biological sensors as reporters of intracellular concentrations. One particular class of in vivo biosensors that has found applications in metabolite quantification is based on F&#246;rster Resonance Energy Transfer (FRET) between two fluorescent proteins connected by a ligand binding domain. FRET integrated biological sensors (FIBS) are constitutively produced within the cell line, they have fast response times and their spectral characteristics change based on the concentration of metabolite within the cell. In this paper, the method for constructing Chinese hamster ovary (CHO) cell lines that constitutively express a FIBS for glucose and glutamine and calibrating the FIBS in vivo in batch cell culture in order to enable future quantification of intracellular metabolite concentration is described. Data from fed-batch CHO cell cultures demonstrates that the FIBS was able in each case to detect the resulting change in the intracellular concentration. Using the fluorescent signal from the FIBS and the previously constructed calibration curve, the intracellular concentration was accurately determined as confirmed by an independent enzymatic assay.

A description of how to calibrate F&#246;rster Resonance Energy Transfer integrated biological sensors (FIBS) for in situ metabolic profiling is presented.&#160;The FIBS can be used to measure intracellular levels of metabolites noninvasively aiding in the development of metabolic models and high throughput screening of bioprocess conditions.

FIBS功能的无创性代谢轮廓

In the era of computational biology, new high throughput experimental systems are necessary in order to populate and refine models so that they can be ...

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, enabling the visualization of large macromolecular machines, such as adhesion complexes, as well as higher-order structures, such as the cytoskeleton and cellular organelles in their respective cell and tissue context. Given the inherent complexity of cellular volumes, it is essential to first extract the features of interest in order to allow visualization, quantification, and therefore comprehension of their 3D organization. Each data set is defined by distinct characteristics, e.g., signal-to-noise ratio, crispness (sharpness) of the data, heterogeneity of its features, crowdedness of features, presence or absence of characteristic shapes that allow for easy identification, and the percentage of the entire volume that a specific region of interest occupies. All these characteristics need to be considered when deciding on which approach to take for segmentation.
The six different 3D ultrastructural data sets presented were obtained by three different imaging approaches: resin embedded stained electron tomography, focused ion beam- and serial block face- scanning electron microscopy (FIB-SEM, SBF-SEM) of mildly stained and heavily stained samples, respectively. For these data sets, four different segmentation approaches have been applied: (1) fully manual model building followed solely by visualization of the model, (2) manual tracing segmentation of the data followed by surface rendering, (3) semi-automated approaches followed by surface rendering, or (4) automated custom-designed segmentation algorithms followed by surface rendering and quantitative analysis. Depending on the combination of data set characteristics, it was found that typically one of these four categorical approaches outperforms the others, but depending on the exact sequence of criteria, more than one approach may be successful. Based on these data, we propose a triage scheme that categorizes both objective data set characteristics and subjective personal criteria for the analysis of the different data sets.

The bottleneck for cellular 3D electron microscopy is feature extraction (segmentation) in highly complex 3D density maps. We have developed a set of criteria, which provides guidance regarding which segmentation approach (manual, semi-automated, or automated) is best suited for different data types, thus providing a starting point for effective segmentation.

从体素知识:实用指南复电镜三维数据的分割

Modern 3D electron microscopy approaches have recently allowed unprecedented insight into the 3D ultrastructural organization of cells and tissues, ...

Planar Gradient Diffusion System to Investigate Chemotaxis in a 3D Collagen Matrix

The importance of cell migration can be seen through the development of human life. When cells migrate, they generate forces and transfer these forces to their surrounding area, leading to cell movement and migration. In order to understand the mechanisms that can alter and/or affect cell migration, one can study these forces. In theory, understanding the fundamental mechanisms and forces underlying cell migration holds the promise of effective approaches for treating diseases and promoting cellular transplantation. Unfortunately, modern chemotaxis chambers that have been developed are usually restricted to two dimensions (2D) and have complex diffusion gradients that make the experiment difficult to interpret. To this end, we have developed, and describe in this paper, a direct-viewing chamber for chemotaxis studies, which allows one to overcome modern chemotaxis chamber obstacles able to measure cell forces and specific concentration within the chamber in a 3D environment to study cell 3D migration. More compelling, this approach allows one to successfully model diffusion through 3D collagen matrices and calculate the coefficient of diffusion of a chemoattractant through multiple different concentrations of collagen, while keeping the system simple and user friendly for traction force microscopy (TFM) and digital volume correlation (DVC) analysis.

Cell migration is an important part of human development and life. In order to understand the mechanisms that can alter cell migration, we present a planar gradient diffusion system to investigate chemotaxis in a 3D collagen matrix, which allows one to overcome modern diffusion chamber limitations of existing assays.

平面梯度扩散系统的调查趋化三维胶原基质

The importance of cell migration can be seen through the development of human life. When cells migrate, they generate forces and transfer these forces to ...

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular cartilage impairs its load-bearing function substantially as it experiences tremendous chemical degradation, i.e. proteoglycan loss and collagen fibril disruption. One promising way to investigate chemical damage mechanisms during OA is to expose the cartilage specimens to an external solute and monitor the diffusion of the molecules. The degree of cartilage damage (i.e. concentration and configuration of essential macromolecules) is associated with collisional energy loss of external solutes while moving across articular cartilage creates different diffusion characteristics compared to healthy cartilage. In this study, we introduce a protocol, which consists of several steps and is based on previously developed experimental micro-Computed Tomography (micro-CT) and finite element modeling. The transport of charged and uncharged iodinated molecules is first recorded using micro-CT, which is followed by applying biphasic-solute and multiphasic finite element models to obtain diffusion coefficients and fixed charge densities across cartilage zones.

We propose a protocol to investigate the transport of charged and uncharged molecules across articular cartilage with the aid of recently developed experimental and numerical methods.

实验和有限元议定书调查中性和带电溶质的跨关节软骨运输

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular ...

Workflow Based on the Combination of Isotopic Tracer Experiments to Investigate Microbial Metabolism of Multiple Nutrient Sources

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods substantially contributes to providing extensive knowledge of the metabolism of microorganisms growing in chemically defined media containing unique nitrogen and carbon sources. In contrast, the management through metabolism of multiple nutrient sources, despite their broad presence in natural or industrial environments, remains virtually unexplored. This situation is mainly due to the lack of suitable methodologies, which hinders investigations.
We report an experimental strategy to quantitatively and comprehensively explore how metabolism operates when a nutrient is provided as a mixture of different molecules, i.e., a complex resource. Here, we describe its application for assessing the partitioning of multiple nitrogen sources through the yeast metabolic network. The workflow combines information obtained during stable isotope tracer experiments using selected 13C- or 15N-labeled substrates. It first consists of parallel and reproducible fermentations in the same medium, which includes a mixture of N-containing molecules; however,a selected nitrogen source is labeled each time. A combination of analytical procedures (HPLC, GC-MS) is implemented to assess the labeling patterns of targeted compounds and to quantify the consumption and recovery of substrates in other metabolites. An integrated analysis of the complete dataset provides an overview of the fate of consumed substrates within cells. This approach requires an accurate protocol for the collection of samples&#8211;facilitated by a robot-assisted system for online monitoring of fermentations&#8211;and the achievement of numerous time-consuming analyses. Despite these constraints, it allowed understanding, for the first time, the partitioning of multiple nitrogen sources throughout the yeast metabolic network. We elucidated the redistribution of nitrogen from more abundant sources toward other N-compounds and determined the metabolic origins of volatile molecules and proteinogenic amino acids.

This protocol describes an experimental procedure to quantitatively and comprehensively investigate the metabolism of multiple nutrient sources. This workflow, based on a combination of isotopic tracer experiments and an analytical procedure, allows the fate of consumed nutrients and the metabolic origin of molecules synthetized by microorganisms to be determined.

基于同位素示踪实验结合的工作流研究多营养源的微生物代谢

Studies in the field of microbiology rely on the implementation of a wide range of methodologies. In particular, the development of appropriate methods ...

Fabrication of a Multiplexed Artificial Cellular MicroEnvironment Array

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are well orchestrated and are crucial in regulating cell functions in a living system. Although a number of researchers have attempted to investigate the correlation between environmental factors and desired cellular functions, much remains unknown. This is largely due to the lack of a proper methodology to mimic such environmental cues in vitro, and simultaneously test different environmental cues on cells. Here, we report an integrated platform of microfluidic channels and a nanofiber array, followed by high-content single-cell analysis, to examine stem cell phenotypes altered by distinct environmental factors. To demonstrate the application of this platform, this study focuses on the phenotypes of self-renewing human pluripotent stem cells (hPSCs). Here, we present the preparation procedures for a nanofiber array and the microfluidic structure in the fabrication of a Multiplexed Artificial Cellular MicroEnvironment (MACME) array. Moreover, overall steps of the single-cell profiling, cell staining with multiple fluorescent markers, multiple fluorescence imaging, and statistical analyses, are described.

This article describes the detailed methodology to prepare a Multiplexed Artificial Cellular MicroEnvironment (MACME) array for high-throughput manipulation of physical and chemical cues mimicking in vivo cellular microenvironments and to identify the optimal cellular environment for human pluripotent stem cells (hPSCs) with single-cell profiling.

多路人工细胞微环境阵列的制备

Cellular microenvironments consist of a variety of cues, such as growth factors, extracellular matrices, and intercellular interactions. These cues are ...

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Proteases are multi-functional enzymes that specialize in the hydrolysis of peptide-bonds and control broad biological processes including homeostasis and allostasis. Moreover, dysregulated protease activity drives pathogenesis and is a functional biomarker of diseases such as cancer; therefore, the ability to detect protease activity in vivo may provide clinically relevant information for biomedical diagnostics. The goal of this protocol is to create nanosensors that probe for protease activity in vivo by producing a quantifiable signal in urine. These protease nanosensors consist of two components: a nanoparticle and substrate. The nanoparticle functions to increase circulation half-life and substrate delivery to target disease sites. The substrate is a short peptide sequence (6-8 AA), which is designed to be specific to a target protease or group of proteases. The substrate is conjugated to the surface of the nanoparticle and is terminated by a reporter, such as a fluorescent marker, for detection. As dysregulated proteases cleave the peptide substrate, the reporter is filtered into urine for quantification as a biomarker of protease activity. Herein we describe construction of a nanosensor for matrix metalloproteinase 9 (MMP9), which is associated with tumor progression and metastasis, for detection of colorectal cancer in a mouse model.

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics

Proteases are tightly regulated enzymes involved in fundamental biological processes, and dysregulated protease activity drives progression of complex diseases such as cancer. This method&#39;s goal is to create nanosensors that measure protease activity in vivo by producing a cleavage signal that is detectable from host urine and discriminates disease.

纳米传感器检测体内蛋白酶活性的无创诊断

Proteases are multi-functional enzymes that specialize in the hydrolysis of peptide-bonds and control broad biological processes including homeostasis and ...