一种用于在复杂的细胞群落稳定性同位素示踪排序亚群测量方法代谢
概要
This article describes a method for studying cellular metabolism in complex communities of multiple cell types, using a combination of stable isotope tracing, cell sorting to isolate specific cell types, and mass spectrometry.
Abstract
Mammalian cell types exhibit specialized metabolism, and there is ample evidence that various co-existing cell types engage in metabolic cooperation. Moreover, even cultures of a single cell type may contain cells in distinct metabolic states, such as resting or cycling cells. Methods for measuring metabolic activities of such subpopulations are valuable tools for understanding cellular metabolism. Complex cell populations are most commonly separated using a cell sorter, and subpopulations isolated by this method can be analyzed by metabolomics methods. However, a problem with this approach is that the cell sorting procedure subjects cells to stresses that may distort their metabolism.
To overcome these issues, we reasoned that the mass isotopomer distributions (MIDs) of metabolites from cells cultured with stable isotope-labeled nutrients are likely to be more stable than absolute metabolite concentrations, because MIDs are formed over longer time scales and should be less affected by short-term exposure to cell sorting conditions. Here, we describe a method based on this principle, combining cell sorting with liquid chromatography-high resolution mass spectrometry (LC-HRMS). The procedure involves analyzing three types of samples: (1) metabolite extracts obtained directly from the complex population; (2) extracts of “mock sorted” cells passed through the cell sorter instrument without gating any specific population; and (3) extracts of the actual sorted populations. The mock sorted cells are compared against direct extraction to verify that MIDs are indeed not altered by the cell sorting procedure itself, prior to analyzing the actual sorted populations. We show example results from HeLa cells sorted according to cell cycle phase, revealing changes in nucleotide metabolism.
Introduction
高等生物包含协作带来更复杂的功能不同的细胞类型的复杂的社区。例如，肿瘤不仅含有癌细胞，而且成纤维细胞，在构成血管，并经常免疫细胞浸润1细胞;血液中含有数十免疫细胞亚型2的复杂混合物;甚至培养的细胞系可以由多个亚群，如管腔和乳腺癌细胞3的基底亚型。此外，不同的细胞类型共存能够表现出代谢“合作”。例如，在大脑中，星形胶质细胞被认为是葡萄糖转化为乳酸，然后将其“喂”到氧化该衬底4的神经元; T淋巴细胞是在一些依赖于相邻的树突细胞作为半胱氨酸5的源上下文;和癌细胞可能与阿索协作肿瘤6 ciated成纤维细胞。为了理解这种系统的代谢行为，至关重要的是，以分离和测量存在于各种细胞类型的代谢活动。
迄今为止用于分离细胞类型的最常用的方法是荧光激活细胞分选。此方法是广泛适用的，只要该细胞类型或感兴趣的状态可以“标记”使用荧光抗体，工程化的荧光蛋白的表达，或其他的染料。一种选择是最初分离的细胞类型通过细胞分选仪，再培养得到的个别细胞类型，然后执行这些培养7的代谢研究。然而，如果细胞类型或表型是在培养条件下稳定的，并且不能捕获瞬态行为，如细胞周期状态，也不会在共培养物的代谢合作，这是唯一可行的。对于这样的情况下，代谢必须直接上，以便测量rted细胞。这是具有挑战性的，因为细胞分选过程科目细胞可能扭曲他们的新陈代谢8的压力，而我们都知道只有少数的研究采用这种方法9,10。特别是，我们已经发现，主要代谢产物如氨基酸可以从保持在细胞分拣缓冲液细胞泄漏，使绝对代谢物丰度的测量结果不再可靠11（虽然排序级分之间的相对比较，仍可能是有价值的）。
为了规避这些问题，我们的标签细胞之前，分拣稳定同位素，并专注于细胞代谢产物，而不是代谢产物丰度的MID产品。由于MID产品则经过较长时间尺度上形成的，就应该少受短期接触到排序的条件。我们用量化的全扫描高分辨率质谱，这是非常敏感地提供移动互联网设备达TA数以百计的代谢物从约50选细胞出发，大约需要30-60分钟的细胞分选的时间。一个之间的比较“模拟排序”控制（通过细胞分选仪仪器没有选通任何特定人口传递细胞）和代谢物提取直接从培养皿制成，以确保所观察到的MID的是代表那些在原始培养物。取决于稳定同位素示踪剂的选择，各种代谢途径可以用这种方法进行研究。
Protocol
Representative Results
Discussion
我们的方法是基于这样的原则，在细胞代谢产物的MID反映的细胞代谢活动的“历史”。这使得有可能研究的代谢活性在细胞的亚群，因为它们发生在细胞的复杂社区，细胞分选过程之前。与此相反，代谢物的峰面积分选的细胞，并从培养皿11直接提取的提取物之间明显不同。这部分是因为不同的化学组成改变在质谱，一个所谓的“基体效应”的信号响应，但是我们还表明，氨基?…
開示
The authors have nothing to disclose.
Acknowledgements
The authors would like to thank Dr. Anas Kamleh for valuable help with optimizing mass spectrometry methods, and Annika von Vollenhoven for assistance with cell sorting. This research was supported by the Swedish Foundation for Strategic Research (grant no. FFL12-0220) and the Strategic Programme in Cancer Research (IR, RN); the Swedish Heart-Lung Foundation (CEW, HG); and Mary Kay Foundation (JW, MJ).
Materials
| HBSS | Sigma | H6648 | |
| INFLUX (inFlux v7 Sorter) | BD Biosciences | ||
| U-13C-Glucose | Cambridge isotopes | 40762-22-9 / GLC-018 | |
| U-13C,15N2-Glutamine | Cambridge isotopes | CNLM-1275-H-0.1 | |
| Methanol (JT Baker), HPLC grade | VWR | BAKR8402.2500 | |
| Ultrafree – MC – VV centrifugal Filters. Durapore PVDF 0.1 µm | Millipore | UFC30VV00 | |
| Ultimate 3,000 UHPLC | Thermo Fisher scientific | ||
| Q-Exactive Orbitrap Mass spectrometer | Thermo Fisher scientific | ||
| Merk-Sequant ZIC HILIC column (150 mm x 4.6 mm, 5 µm) | Merck KGaA | 1.50444.0001 | |
| Merk-Sequant ZIC HILIC guard column (20×2.1 mm) | Merck KGaA | ||
| Acetonitrile Optima LC-MS, amber glass | Fisher Scientific | A955-212 | |
| Milli-Q water | Millipore | Produced with a Milli-Q Gradient system | |
| MYRSYRA 99.5% OPTIMA (Formic acid) | Fisher Scientific | 11423423 | |
| X100 Screw Vial 1.5ml, 8-425 32×11.6mm,amber, 100 units | Thermo Fisher scientific | 10560053 | |
| X100 LOCK SKRUV VITT PTFE PACKNING 8-425 (Screw caps) | Thermo Fisher scientific | 12458636 | |
| ProteoMass LTQ/FT-Hybrid ESI Pos. Mode Cal Mix | Sigma-Aldrich | MSCAL5 | Calibration kit |
| SNAKESKIN 10K MWCO | Thermo Fisher scientific | 88245 | |
| Mathematica v.10 | Wolfram Research |
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