The Combination of Droplet Extraction and Pico-ESI-MS Allows the

Subscriber access provided by Kaohsiung Medical University

Article

The Combination of Droplet Extraction and Pico-ESI-MS Allows the Identification of Metabolites from Single Cancer Cells Xiao-Chao Zhang, Qingce Zang, Hansen Zhao, Xiaoxiao Ma, Xingyu Pan, Jiaxin Feng, Sichun Zhang, Ruiping Zhang, Zeper Abliz, and Xinrong Zhang Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b02098 • Publication Date (Web): 24 Jul 2018 Downloaded from http://pubs.acs.org on July 25, 2018

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

The Combination of Droplet Extraction and Pico-ESI-MS Allows the Identification of Metabolites from Single Cancer Cells Xiao-Chao Zhang,†,⊥ Qingce Zang,‡,⊥ Hansen Zhao,† Xiaoxiao Ma,§ Xingyu Pan,† Jiaxin Feng,† Sichun Zhang,† Ruiping Zhang,‡ Zeper Abliz,*,‡,ǁ Xinrong Zhang*,† †

Beijing Key Laboratory of Microanalytical Methods and Instrumentation, Department of Chemistry, Tsinghua University, Beijing 100084, China. ‡ State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100050, China. § Department of Precision Instrument, Tsinghua University, Beijing 100084, China. ǁ Centre for Bioimaging and Systems Biology, Minzu University of China, Beijing 100081, China. ABSTRACT: We have combined droplet extraction and a pulsed direct current electrospray ionization mass spectrometry method (Pico-ESI-MS) to obtain information-rich metabolite profiling from single cells. We studied normal human astrocyte cells and glioblastoma cancer cells. Over 600 tandem mass spectra (MS2) of metabolites from a single cell were recorded allowing the successful identification of more than 300 phospholipids. We found the ratios of unsaturated phosphatidylcholines (PCs) to saturated PCs were significantly higher in glioblastoma cells compared to normal cells. In addition, both isomeric PC (17:1) and (phosphatidylethanolamine) PE (20:1) were found in glioblastoma cells, whereas only PC (17:1) was observed in astrocyte cells. Our method paves the way to characterize the chemical contents of single cells, providing rich metabolome information. We suggest that this technique is general and can be applied to other life science studies such as differentiation and drug resistance of individual cells.

Substantial achievement has been made in “omics” studies over the past decades.1-3 However, most bio-information is acquired from bulk bio-samples that consist of different types of cells. Some of the fine-grained complexity of the disease might be masked due to the cell heterogeneity.4 Recently, single-cell genetic sequencing-based technologies have made significant advances in revealing the heterogeneity across different cell lines, as well as in correlating genetic information with phenotype.5-8 The development of single-cell transcriptomics has led to new discoveries in biology, from identifying new cell types9 to studying global patterns of random gene expression.10 It also revealed single-cell heterogeneity in human samples.11,12 However, as transcript and protein levels are only modestly correlated,13,14 and metabolites can be extensively modified by enzymatic processes and internal/external stimuli,15 it is necessary to develop analytical protocols for single-cell metabolomics. These would provide a more direct and integrated view of various “omics” analyses for a comprehensive understanding of the biological consequences of metabolites. Such approaches will not only assist in unravelling the relationships between gene/protein expression levels and metabolite concentrations, but also lead to breakthroughs in clinical research, where metabolic substrates/products that directly drive essential cellular functions are analyzed.13,16 Developing analytical tools for single-cell metabolomics is challenging, because no amplification strategies exist.17 Mass

spectrometry (MS) has been the major analytical platform to investigate unicellular metabolite profiles due to its exquisite sensitivity and multiplexing capabilities.17 Many MS-based techniques have been extensively used in single cell analysis, such as capillary electrophoresis-mass spectrometry (CEMS),18 liquid chromatography-mass spectrometry (LC-MS),19 matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS),20,21 and secondary ion mass spectrometry (SIMS).22 Additionally, nanoelectrospray ionization mass spectrometry (nano-ESI-MS) has become one of the most popular MS techniques for metabolomics studies.23,24 Amino acids,25,26 lipids27,28 and other energy-related metabolites29,30 have been successfully detected in single cells by using this technique. However, when researchers want to perform MS2 analysis of the detected metabolites for structural identification, multiple cells27,29 or cell clusters28 are commonly used. This is due to the fact that the sample flow rate of nano-ESI is around 10-100 nL/min, which is ~3-5 orders of magnitude higher than the volume of a mammalian cell (~1-10 pL).16,31,32 As a result, the available time for mass analysis and data acquisition is too short to perform abundant MS2 analysis in a single-cell sample, which limits the identification of unicellular metabolites.33,34 Other researchers have considered the use of spray solvent to dilute the contents of a single cell to increase the volume for electrospray.18,35 However, the sensitivity of detection would be significantly reduced, introducing difficulties for metabolite characterization.

ACS Paragon Plus Environment

Analytical Chemistry 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

To separately solve the above problems around sensitivity and MS2 analysis, our group has previously developed two individual techniques: one is droplet extraction36 and the other is pulsed direct current electrospray ionization (pico-ESI)32. The droplet extraction technique36 can selectively extract unicellular metabolites by using a droplet (2 nL). Although the sensitive detection of metabolites was stably achieved by using this technique, the spray time was still not enough to acquire abundant MS2 spectra. The pico-ESI technique32 can extend the electrospray time of an Allium cepa cell over 100 seconds by generating a pulsed electrospray, allowing the user to perform abundant MS2 analysis. Nevertheless, the sensitivity and stability need to be further improved when analyzing smaller-volume samples such as single cancer cells. Aiming to simultaneously achieve sensitive, stable and information-rich metabolite analysis from single cancer cells, the combination of droplet extraction and pico-ESI is necessary. Herein, we report the single-cell metabolomics study of normal human astrocyte cells and glioblastoma cells by using the droplet extraction technique coupled to a home-built picoESI source. With the use of droplet extraction, the stability of the method was remarkably enhanced, and the RSD of analyzing cancer cells was no more than 12%. Owing to the extended electrospray time offered by pico-ESI, over 600 MS2 spectra were acquired from a single cell. Both full-scan MS profiles and MS2 profiles were obtained to enable untargeted analysis of unknown metabolites in these cells. The combination of droplet extraction and pico-ESI allowed hundreds of metabolites to be detected and identified via their unique fragmentation patterns and accurate mass measurement, which enables more sensitive, accurate and comprehensive single-cell metabolomics analysis.

MATERIALS AND METHODS Chemicals and materials. D-(+)-glucose, D-13C6-glucose, L-glutathione reduced (GSH), adenosine 5'-triphosphate (ATP) disodium trihydrate, adenosine 5'-diphosphate (ADP) disodium dihydrate, ammonium formate, and HPLC-grade methanol were purchased from Sigma Aldrich. Ultrapure water was made from a Milli-Q water purification system (Millipore, resistance ≥ 18 MΩ cm−1). Dulbecco’s modified eagle media (DMEM), Dulbecco’s phosphate buffered saline (DPBS), trypsin EDTA, fetal bovine serum (FBS), penicillin and streptomycin (100 U/mL) were purchased from Gibco (Life Technologies, Carlsbad, CA). Other materials used for cell culturing were purchased from Corning (NY, USA), unless otherwise noted. Solutions of glucose (5 µM), 13C6glucose (5 µM), and glutathione (10 µM) were prepared in ultrapure water and stored at -20 °C. Ammonium formate was dissolved in ultrapure water (0.9 wt.%) and stored at 4 °C. All PDMS chips of micropore array were purchased from Wen-hao Microfluidic Co. Ltd. (Suzhou, China). The PDMS chip was fabricated by standard soft lithographic and replica molding techniques. Oxygen plasma was used to treat the chip to generate a hydrophilic 16 × 16 micropore array. The diameter of each pore was 100 µm and the depth was 30 µm. The center-to-center distance between adjacent pores was 700 µm.

Preparation of the pico-ESI tip. Borosilicate glass microcapillaries (VitalSense Scientific Instruments Co. Ltd., Wuhan, China) were pulled using a micropipette puller (P-2000, Sutter Instrument, Novato, CA) for the preparation of the picoESI tips. The diameter of the tip’s opening was ~3 µm. Measurement of the diameter was performed under a microscope (YX20L20, Dayueweijia Science and Technology Co. Ltd., Beijing), and the result is shown in Figure S1. Cell Culture and Treatment. Human glioblastoma cell line (A172) and normal human astrocyte cell (HA) line were obtained from the American Type Culture Collection (ATCC) (Manassas, VA, USA). A172 cells were cultured in DMEM supplemented with 10% FBS and 1% penicillin-streptomycin. Human normal astrocyte cells were cultured in astrocyte medium (ScienCell, Carlsbad, CA, USA). All cells were cultured according to the guidelines recommended by the ATCC and were maintained at 37 °C in a humidified atmosphere containing 5% CO2. Cells were passaged by trypsinization when they reached 85~90% confluence in a 10 cm culture dish. Cell lines were not cultured for more than 6 months before the work described here was conducted. The Countstar automated cell counter (Inno-Alliance Biotech, USA) was used to count cells and measure the cellular diameters. The average diameters of A172 cells and HA cells were around 19 µm and 14 µm, respectively. A172 and HA cells were seeded in 10 cm culture dishes at the density of 1×106 cells/dish and cultured for 1 day and 5 days, respectively. After cell growth entered the logarithmic growth phase, the culture medium was removed and the cells were quickly quenched with 60% aqueous methanol at -40 °C and washed twice by ice-cold ammonium formate solution. This step was conducted carefully to prevent the cellular contents from leaking. Then the cells were dried in a vacuum oven for 10 minutes to rupture. Sampling single-cell metabolites. An inverted microscope (DX30, Dayueweijia Science and Technology Co. Ltd., Beijing) and a three-dimensional manipulator (MP-225, Sutter Instrument) were used throughout the experiment. Droplet extraction36 was used for cell sampling. The capillary tip (i.d. ~3 µm) was inserted into acetonitrile (ACN) for 1 s for solvent aspiration (~2 nL). The volume was measured and calculated under microscope, as shown in Figure S1. The tip was then connected to a syringe for subsequent operations. With the assistance of the manipulator and syringe, ACN was deposited onto a single cell for 10 s to extract cellular contents. Afterwards, the extract was aspirated into the capillary tip, where the ACN then evaporated. The extract was re-dissolved in 2 nL of assistant solvent (50% methanol aqueous solution, v/v, with 1% formic acid) for MS analysis. Mass Spectrometry. All the experiments for single-cell metabolomics analysis were performed on a QE-Orbitrap mass spectrometer (Thermo Scientific, San Jose, CA). The instrument was calibrated prior to measurements to minimize detection error in the accurate mass of metabolites. The calibration solution of positive and negative ion mode was purchased from Thermo Scientific. Both full MS scans and datadependent MS2 scans were used. The instrumental parameters were set as follows. Full MS scan: capillary temperature = 320 °C, resolution = 70,000, maximum inject time = 100 ms, AGC


Page 2 of 9

Page 3 of 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry = 3 × 106. Data-dependent MS2 scan: resolution = 35,000, ion inject time = 100 ms, AGC = 1 × 105, NCE = 40, loop count = 10, underfill ratio = 0.1%, dynamic exclusion = 300 s. Experiments to evaluate the stability of this analytical method were performed on a LTQ mass spectrometer (Thermo Scientific, San Jose CA). The instrument parameters were as follows: capillary temperature = 275 °C, max injection time = 100 ms, capillary voltage = 9 V, tube lens voltage = 100 V, microscans = 1. The commercial ionization source was removed before the experiment. Our home-built pico-ESI source was used throughout the experiments. The single-cell extract was loaded in the capillary tip. A stainless steel needle (diameter of 0.3 mm) was used as an electrode and inserted into the back of the capillary. The electrode and sample solution did not come in contact with each other; they were separated by 5 mm. The distance between capillary tip and MS inlet was also 5 mm. After applying DC voltage to the electrode, the single polarity pulsed electrospray was generated. The electrode voltage was +1.2 kV for positive mode analysis, and -1.3 kV for negative mode analysis. Data Analysis and Statistics. Original MS Data was processed by homemade software based on MATLAB scripting. The source code of this software can be found on the website (https://github.com/HansenZhao/PeakPicker). The program provided an easy-to-use interface to convert the original RAW data files to CSV files. The data from each MS scan was placed into an individual CSV file and named by scan number. For MS2 data, the Peak Searching function was used to find all parent ions with typical product ions, such as m/z = 86.10, 125.00 and 184.07 for phosphatidylcholine. The threshold was set as 10% (the relative intensity of product ions must exceed 10% to be considered). The accurate identification of metabolites depended on two factors. One was matching the accurate mass number with the standards in METLIN database (http://metlin.scripps.edu). The other was searching the typical product ions in MS2 spectrum. Data were collected randomly, and the distribution of data was hypothesized to be normal. All the average values in figures are expressed as the mean ± SEM. The data were statistically analyzed by an unpaired t test in GraphPad Prism (Version 6.0). P < 0.05 was considered as a significant difference in the t test. For multiple comparisons, P values were adjusted using the false discovery rate at q = 0.05 to distinguish statistically significant metabolites.34

methods for sampling.18,29,32-34 Firstly, the use of a small droplet avoids direct contact of the microcapillary with the cell, so that the fragile capillary tip will not be easily broken, which significantly improves the stability and speed of analysis. The sampling time of our method is just 15-20 s (10 s for extraction and 5-10 s for capillary repositioning). Secondly, the use of specific extraction solvents allows one to selectively extract unicellular components of interest while minimizing matrix effects, leading to improved sensitivity and selectivity of detection. Apart from using acetonitrile to extract phospholipids in single cells, we also used water as the extraction solvent (Figure S2). Cellular energy-related metabolites, such as ATP and ADP, and redox-related metabolites, such as glutathione (GSH), were extracted and detected in single cells (Figure S3). The advantage of using pico-ESI as the detection method is that it extends the electrospray time to allow for abundant MS2 measurements. Compared with nano-ESI, pico-ESI generates electrospray without contact between the sample solution and the electrode. It can reduce the dead volume of sample and decrease the flow rate to pico-liter levels. In addition, pico-ESI can generate varying frequencies of pulsed electrospray by adjusting the applied voltage. Compared to the continuous electrospray generated by nano-ESI, the pulsed electrospray can better match the frequency of the mass analyzer, further reducing the sample loss between adjacent MS scans. As a result, the duration of signal was extended to 2 min (Figure 1b). In contrast, the signal of nano-ESI was only maintained for 0.02 min (Figure 1c). Owing to the extended electrospray time by pico-ESI, more than 600 MS2 spectra were obtained from a single-cell sample (Figure S4). With the combination of droplet extraction and pico-ESI, more than 300 phospholipids were detected from a single glioblastoma cell based on the analysis of typical product ions (Figure S4). The MS spectra of the blank controls are shown in Figure S5. An additional group of 85 metabolites was identified via accurate mass measurement with a high-resolution mass spectrometer, as well as comparison of the acquired MS2 spectra with those of standard compounds in the METLIN database (http://metlin.scripps.edu) (Table S1).

RESULTS AND DISCUSSION Workflow of single-cell metabolomics analysis by droplet extraction and pico-ESI-MS. The overall workflow of single-cell analysis was shown in Figure 1a. Briefly, prequenched and dried single cells were placed under a microscope. The tip of a pulled glass capillary (containing 2 nL extract solvent) was controlled by a manipulator to deposit a droplet over the cell. Metabolites in the cell were then extracted into the solvent. The extract was subsequently sucked back into the capillary by a syringe for pico-ESI-MS analysis. There are two advantages of using the droplet extraction technique instead of the traditionally used patch clamp-like



Figure 1. (a) Workflow of single-cell metabolomics profiling. The pretreated single cells were placed under an inverted microscope. A pulled glass capillary’s tip (pre-loaded 2 nL ACN) was controlled by a manipulator to extrude a droplet on the surface of single cell. The cell was extracted for 10 s and then the solution was sucked back. The extract was detected by pico-ESI-MS. Data-dependent MS2 scan mode was used to circularly acquire primary MS spectrum and MS2 spectra during the spray process. Over 600 MS2 spectra were obtained from a single cell within 2 min. Total ion chronograms of (b) pico-ESI and (c) nano-ESI when analyzing single glioblastoma cells.

Evaluation of method stability by using an isotope as internal standard. To assure that single-cell differences truly originated from innate heterogeneities rather than detection errors, it is necessary to evaluate the stability of this method. Individual microcapillaries might vary in tip size. This could lead to variation in droplet volume, which might influence the dilution of the single-cell sample and ultimately influence the signal intensity. We performed a comparative experiment by using 13C6-glucose as an internal standard (Figure 2). In the control group (Figure 2a), a droplet of mixed glucose and 13C6glucose (1:1, v/v) was directly added into a micropore by our method. In the experimental group (Figure 2b), droplets of glucose and 13C6-glucose were separately added into the same position of a micropore, which resulted in the volume variation. The two groups were then extracted by water and detected by pico-ESI-MS. We performed 30 repetitions for each group over 3 consecutive days, and the obtained MS spectra are shown in Figure S6 and Figure S7. Two-tailed Student’s t test was performed. The result (Figure 2c) showed no statistically significant difference between the two groups (P = 0.0563) by comparing the intensity ratios of glucose and 13C6glucose. It indicated that any variations from manipulating the droplet did not significantly influence the result. We also calculated the intra-day RSDs and inter-day RSDs of the 30 repeated experiments performed over 3 consecutive days, which are shown in Table 1. The average RSD is no more than 11%, which indicates our method is stable when analyzing standards.

Figure 2. Evaluation of method stability. (a) The Scheme diagram of control group. Sample (glucose) and isotope (13C6-glucose) were pre-mixed (1:1, v/v), and the mixture solution was directly added into a micropore. (b) The Scheme diagram of experimental group. Solutions of glucose and 13C6-glucose were separately added into the same micropore. Both control group and experimental group were then extracted by water and detected by picoESI-MS. (c) Statistic results of control group (direct mixing sample and isotope, blue dots, n = 30) and experimental group (separate adding sample and isotope, red dots, n = 30).

To evaluate the stability of analyzing biological samples, we added internal standard (13C6-glucose) into the population-cell samples. The extract solution of a whole dish of cells was used as the sample. Droplets of population-cell sample and 13C6glucose were separately added into a micropore by using different microcapillaries. Then, the mixture was extracted by water and detected by pico-ESI-MS. After 30 replicates over 3 consecutive days, the obtained MS spectra are shown in Figure S8 and the RSDs are shown in Table 1. The average RSD is no more than 12%, which also indicates that combining droplet extraction and pico-ESI is a stable method for analyzing biological samples. Classifying normal and cancer cells by primary MS spectra. We detected single normal astrocyte cells and single glioblastoma cells and obtained primary MS spectra (Figures 3a and 3b). Principle component analysis (PCA) was applied to reveal the major differences between samples. We selected the first (76.64%) and second (7.00%) component to scatter each single-cell sample in the principle component space (Figure 3c). With the fingerprint information provided by thousands of MS peaks, the single glioblastoma cells and normal astrocyte cells were easily classified into two groups (Figure 3c). Meanwhile, the cells in the same group also show variations in the principle component space, which implies that intrinsic heterogeneity between single cells exists.


Page 4 of 9

Page 5 of 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry the loading diagram in Figure 3d, peaks contributing most to the classification of glioblastoma cells from normal cells were selected and subjected to MS2 analysis. These species were identified as phosphatidylcholines (PCs) according to the typical fragmentation information (Figures 4a, 4b, 4c, and Figure S9). Once the lipid type was identified, the total number of carbons and C=Cs of the fatty acyls can be inferred and their relative abundances can be directly read from the MS spectra.

Figure 3. Comparison of primary MS spectra between normal and cancer cells. (a) Representative MS spectrum of single normal astrocyte cell. (b) Representative MS spectrum of single glioblastoma cell (A172). (c) Clustering diagram of normal astrocyte cells (blue dots, n = 16 cells) and glioblastoma cells (red dots, n = 13 cells). (d) Loading diagram of PC1 (76.64%) and PC2 (7.00%). The dots show the contribution of different MS peaks to PC1 and PC2.

To investigate the origin of the variations, we characterized the correlation of the measured mass spectral peaks with each principle component in the 2D loading plot (Figure 3d). Each MS peak was scattered according to their contribution to PC1 (x-axis) or PC2 (y-axis). Higher loading magnitude (both positive and negative) of a certain MS peak implies higher contribution to the relative principle component. We found most peaks assembled near the origin, suggesting relatively low contributions to the cell variation. Several peaks have high loading in PC1 or PC2, which indicates that these components contribute most to differentiating cells. For example, the peak of m/z = 760.5847, phosphatidylcholine PC (34:1), has high contribution to PC1. The loading plot can help find the potential biomarkers that differentiate glioblastoma cells from the normal astrocyte cells.

After detailed metabolite analysis, we found that the ratios of PC (32:2)/PC (32:1), PC (32:2)/PC (32:0) and PC (32:1)/PC (32:0) were all significantly higher in glioblastoma cells than in normal cells (Figure 4d and Table S2). Similar results were observed for PCs containing more carbons in acyl chains, e.g. 33 C atoms (Figure 4e) or 34 C atoms (Figure 4f). The abundance of these nine PCs was analyzed (Figures 4g and 4h). The nine PCs could be classified into PC (X:2), PC (X:1) and PC (X:0) according to the number of C=C double bond. “X” represents the number of carbon atoms in the acyl chain, and was 32, 33, or 34, respectively. PC (X:2) and PC (X:1) were unsaturated while PC (X:0) was saturated. In single glioblastoma cells, the percentage of saturated PC was only 14%, and the other 86% was unsaturated PC. However, in the normal cells, saturated PC and unsaturated PC were each 50%. Similar phenomena were previously reported in breast, lung, prostate and thyroid cancers37-40 for cell populations. The increased glycolysis in cancer cells, i.e. the Warburg effect, supplies energy and precursors such as acetyl coenzyme A for lipid synthesis.41 Multiple lipogenic enzymes such as fatty acid synthase (FASN), which are required for de novo biogenesis of lipids, were found to be overexpressed and highly activated in cancer cells.39,42,43 Increased expression of lipid desaturase SCD1 and Ckɑ were reported to be responsible for the conversion of monounsaturated fatty acids to monounsaturated PCs.39 The upregulation of monounsaturated PC could supply biomembrane components for rapid proliferation of cancer cells, leading to more densely packed biomembranes and altered membrane fluidity to promote carcinogenesis.40 It is interesting that we have not observed the upregulation of polyunsaturated

A high degree of PC desaturation is associated with cancer cells. With primary MS and MS2 analysis, pico-ESI-MS offers high accuracy in the identification of lipid species. From Table 1. RSDs of analyzing glucose standards and population-cell samples Intra-day

Inter-day

No. of day

1

2

3

1&2

1&3

2&3

Control group

2.3%

2.2%

2.7%

2.2%

2.5%

2.5%

Experimental group

8.3%

11%

11%

2.1%

2.1%

0.064%

Population-cell samples

6.9%

12%

8.1%

9.3%

10%

12%



Figure 4. Increased desaturation degree of phospholipids in single glioblastoma cells compared to normal cells. (a)-(c) MS2 spectra of three parent ions with high loading. The three parent ions were identified as PCs. (d)-(f) Intensity ratios of PCs with different number of C=C double bond (2, 1, 0). The number of C atom in the acyl chain was (d) 32, (e) 33, and (f) 34. Analysis of different PC’s abundance in (g) glioblastoma cells and (h) normal cells. “X” represented for the number of C atom in the acyl chain and was 32, 33 or 34, respectively. Single glioblastoma cells (red, n = 13 cells) and single normal astrocyte cells (blue, n = 16 cells) were analyzed. *** denotes P < 0.001.

PCs (with above three C=C bonds in acyl chains) such as PC (35:6), PC (37:5) and PC (38:4) in single glioblastoma cells (Table S1). This can possibly be rationalized by the fact that polyunsaturated acyl chains are susceptible to peroxidation,44 leading to their degradation into small toxic, reactive and celldamaging molecules, such as malondialdehyde and 4hydroxyalkenal.45 To prevent oxidative stress-induced death, cancer cells preferentially synthesize monounsaturated PCs rather than polyunsaturated ones. To the best of our knowledge, this is the first study that reports the altered content of lipid desaturation in glioblastoma cells at a single-cell level. Distinguishing normal and cancer cells by MS2 analysis of phospholipid isomers. Phospholipids (PLs) are represented by a large variety of lipid types, and PLs of different types can be isomers.46 For instance, PC (17:1) and PE (20:1) are isomers, and they have the same accurate mass of m/z = 508.3398 (Figure 5a). The existence of PC (17:1) can be confirmed by the characteristic ion at m/z = 184.07 for PCs (phosphoryl choline group) while PE (20:1) can be identified by the characteristic neutral loss of 141 Da, with a fragment ion generated at m/z = 367.28.47 As to glioblastoma and normal astrocyte cells, we found a very interesting phenomenon that is potentially useful to differentiate glioblastoma cells from normal astrocyte cells at the single-cell level. In the glioblastoma cells, ions at m/z = 508.3398 is a mixture of PC (17:1) and PE (20:1) (Figure 5b), while only PC (17:1) was observed in normal cells (Figure 5c).

Figure 5. Difference of isomers between cancer and normal cells. (a) Structural formulas and typical MS2 product ions of PC (17:1) and PE (20:1). In the abbreviation of PC (17:1), “17” represents the number of C atom in the acyl chain, and “1” represents the number of C=C double bond. The exact position of C=C double bond has not been confirmed. (b) Representative MS2 spectrum of parent ion (m/z = 508.34) in single glioblastoma cells. Typical product ions of PC (17:1) and PE (20:1) were observed. (c) Representative MS2 spectrum of parent ion (m/z = 508.34) in single normal astrocyte cells. Typical product ions of PC (17:1) were observed. The region of m/z = 365-370 was magnified 10 times to show the difference of m/z = 367.28 more clearly.

PC and PE are synthesized from a famous biosynthesis pathway named Kennedy pathway48, which uses sn-1,2diradylglycerol and either CDP-choline or CDP-ethanolamine, respectively.49 They play major roles in the structure and function of biological membranes. Compared with PC, PE has a smaller polar head group and it can form reversed nonlamellar structure,49 which is required in membrane fusion and fission. It has been suggested that higher amount of PC and PE can enhance the synthesis of cellular membrane and accelerate the proliferation of cancer cells,50,51 which could account for


Page 6 of 9

Page 7 of 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry the co-existence of PC (17:1) and PE (20:1) in glioblastoma cells.

Notes

Recent studies have reported the differences of fatty acid and glycerophospholipid C=C isomers between normal and diseased samples, showing the significance of using the composition of lipid C=C isomers for disease diagnosis.43,47 In this work, we for the first time report that the composition of isomeric lipids of different types, i.e. PC (17:1) and PE (20:1), is also an efficient and important index capable of differentiating cancer cells from normal cells. More importantly, this is all done at the single-cell level. Without the capability of MS2 analysis, the deep characterization of isomeric lipid species would be impossible.

ACKNOWLEDGMENT

CONCLUSIONS In summary, we have successfully coupled droplet extraction to pico-ESI-MS for the highly sensitive, stable and extended metabolomics analysis of single glioblastoma cells and normal astrocyte cells. Significant advantages of our method include: (1) during the extended electrospray ionization, more than 600 MS2 spectra can be collected from a single-cell sample to allow both targeted and non-targeted analysis. (2) Stable and reliable detection result can be obtained by using isotope as internal standard. The RSD of our method is no more than 12%. (3) Structural characterization of the analyte is made possible. An important and useful example here is the use of MS2 spectra to distinguish PC/PE isomers, where the coexistence of PC (17:1) and PE (20:1) is a unique feature of glioblastoma cells, not normal astrocyte cells. By such a MS2 analyzing strategy, normal and cancer cells can be classified fairly easy and straightforward. It holds great potentials for new biomarkers to be developed to speed up disease diagnostics. It is therefore believed that a combined primary MS and MS2 analysis will become an indispensable tool to achieve more detailed metabolomics analysis of single cells, by enabling structural elucidation in addition to mass analysis. Moreover, chemical reactions can be introduced into the cellular contents to further improve the sensitivity of detection for species of interests. The developed methodology should find wide applications in circumstances where single-cell heterogeneities or cellular changes are concerned, such as developmental biology, drug resistance study of cancer cells, and cell differentiation.

ASSOCIATED CONTENT Supporting Information Details about experimental operation and results as noted in text. This material is available free of charge via the Internet at http://pubs.acs.org.

AUTHOR INFORMATION Corresponding Author *E-mail: [email protected] (Z.A.) *E-mail: [email protected] (X.Z.) Author Contributions ⊥

These authors contributed equally to this work. Z.A. and X.Z. designed research; X.C.Z., Q.Z., X.P. and J.F. performed research; X.C.Z., H.Z., S.Z. and R.Z. analyzed data; and X.C.Z., Q.Z., X.M., Z.A. and X.Z. wrote the paper.

The authors declare no competing financial interests.

We acknowledge Xin Yan and Sarah Elizabeth Noll (Stanford University, USA) for helpful suggestions to this research. This research was supported by the National Natural Science Foundation of China (No. 21390410, 21621003 and 21327902). Ministry of Science and Technology of China (No. 2013CB933804 and 2016YFF0100301), and the scholarship of QingFeng in Tsinghua university.

REFERENCES (1) Patti, G. J.; Yanes, O.; Siuzdak, G. Nat. Rev. Mol. Cell Biol. 2012, 13, 263-269. (2) Pandey, A.; Mann, M. Nature 2000, 405, 837-846. (3) Manning, G.; Whyte, D. B.; Martinez, R.; Hunter, T.; Sudarsanam, S. Science 2002, 298, 1912-1934. (4) Wang, D.; Bodovitz, S. Trends Biotechnol. 2010, 28, 281-290. (5) Shapiro, E.; Biezuner, T.; Linnarsson, S. Nat. Rev. Genet. 2013, 14, 618-630. (6) Saliba, A.-E.; Westermann, A. J.; Gorski, S. A.; Vogel, J. Nucleic Acids Res. 2014, 42, 8845-8860. (7) Eberwine, J.; Sul, J.-Y.; Bartfai, T.; Kim, J. Nat. Methods 2014, 11, 25-27. (8) Wang, Y.; Navin, N. E. Mol. Cell 2015, 58, 598-609. (9) Stegle, O.; Teichmann, S. A.; Marioni, J. C. Nat. Rev. Genet. 2015, 16, 133-145. (10) Deng, Q.; Ramsköld, D.; Reinius, B.; Sandberg, R. Science 2014, 343, 193-196. (11) Battich, N.; Stoeger, T.; Pelkmans, L. Nat. Methods 2013, 10, 1127-1133. (12) Thomsen, E. R.; Mich, J. K.; Yao, Z.; Hodge, R. D.; Doyle, A. M.; Jang, S.; Shehata, S. I.; Nelson, A. M.; Shapovalova, N. V.; Levi, B. P.; Ramanathan, S. Nat. Methods 2015, 13, 87-93. (13) Rubakhin, S. S.; Romanova, E. V.; Nemes, P.; Sweedler, J. V. Nat. Methods 2011, 8, S20-S29. (14) Knolhoff, A. M.; Nautiyal, K. M.; Nemes, P.; Kalachikov, S.; Morozova, I.; Silver, R.; Sweedler, J. V. Anal. Chem. 2013, 85, 31363143. (15) Qi, M.; Philip, M. C.; Yang, N.; Sweedler, J. V. ACS Chem. Neurosci. 2017, 9, 40-50. (16) Zenobi, R. Science 2013, 342, 1243259. (17) Comi, T. J.; Do, T. D.; Rubakhin, S. S.; Sweedler, J. V. J. Am. Chem. Soc. 2017, 139, 3920-3929. (18) Aerts, J. T.; Louis, K. R.; Crandall, S. R.; Govindaiah, G.; Cox, C. L.; Sweedler, J. V. Anal. Chem. 2014, 86, 3203-3208. (19) Hsieh, S.; Dreisewerd, K.; Van Der Schors, R. C.; Jiménez, C. R.; Stahl-Zeng, J.; Hillenkamp, F.; Jorgenson, J. W.; Geraerts, W. P.; Li, K. W. Anal. Chem. 1998, 70, 1847-1852. (20) Ong, T. H.; Kissick, D. J.; Jansson, E. T.; Comi, T. J.; Romanova, E. V.; Rubakhin, S. S.; Sweedler, J. V. Anal. Chem. 2015, 87, 7036-7042. (21) Ibanez, A. J.; Fagerer, S. R.; Schmidt, A. M.; Urban, P. L.; Jefimovs, K.; Geiger, P.; Dechant, R.; Heinemann, M.; Zenobi, R. Proc. Natl. Acad. Sci. U. S. A. 2013, 110, 8790-8794. (22) Passarelli, M. K.; Ewing, A. G.; Winograd, N. Anal. Chem. 2013, 85, 2231-2238. (23) Wei, Z. W.; Han, S.; Gong, X. Y.; Zhao, Y. Y.; Yang, C. D.; Zhang, S. C.; Zhang, X. R. Angew. Chem., Int. Ed. 2013, 52, 1102511028. (24) Kelly, R. T.; Page, J. S.; Marginean, I.; Tang, K. Q.; Smith, R. D. Angew. Chem., Int. Ed. 2009, 48, 6832-6835. (25) Fujii, T.; Matsuda, S.; Tejedor, M. L.; Esaki, T.; Sakane, I.; Mizuno, H.; Tsuyama, N.; Masujima, T. Nat. Protoc. 2015, 10, 14451456. (26) Onjiko, R. M.; Moody, S. A.; Nemes, P. Proc. Natl. Acad. Sci. U. S. A. 2015, 112, 6545-6550.



(27) Zhang, L.; Foreman, D. P.; Grant, P. A.; Shrestha, B.; Moody, S. A.; Villiers, F.; Kwak, J. M.; Vertes, A. Analyst 2014, 139, 50795085. (28) Bergman, H. M.; Lanekoff, I. Analyst 2017, 142, 3639-3647. (29) Zhang, L.; Vertes, A. Anal. Chem. 2015, 87, 10397-10405. (30) Pan, N.; Rao, W.; Kothapalli, N. R.; Liu, R.; Burgett, A. W. G.; Yang, Z. Anal. Chem. 2014, 86, 9376-9380. (31) Southam, A. D.; Payne, T. G.; Cooper, H. J.; Arvanitis, T. N.; Viant, M. R. Anal. Chem. 2007, 79, 4595-4602. (32) Wei, Z.; Xiong, X.; Guo, C.; Si, X.; Zhao, Y.; He, M.; Yang, C.; Xu, W.; Tang, F.; Fang, X.; Zhang, S.; Zhang, X. Anal. Chem. 2015, 87, 11242-11248. (33) Masujima, T. Anal. Sci. 2009, 25, 953-960. (34) Zhu, H.; Zou, G.; Wang, N.; Zhuang, M.; Xiong, W.; Huang, G. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 2586-2591. (35) Rubakhin, S. S.; Lanni, E. J.; Sweedler, J. V. Curr. Opin. Biotechnol. 2013, 24, 95-104. (36) Zhang, X. C.; Wei, Z. W.; Gong, X. Y.; Si, X. Y.; Zhao, Y. Y.; Yang, C. D.; Zhang, S. C.; Zhang, X. R. Sci. Rep. 2016, 6, 24730. (37) Banerjee, S.; Zare, R. N.; Tibshirani, R. J.; Kunder, C. A.; Nolley, R.; Fan, R.; Brooks, J. D.; Sonn, G. A. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 3334-3339. (38) Gouw, A. M.; Eberlin, L. S.; Margulis, K.; Sullivan, D. K.; Toal, G. G.; Tong, L.; Zare, R. N.; Felsher, D. W. Proc. Natl. Acad. Sci. U. S. A. 2017, 114, 4300-4305.

(39) Ide, Y.; Waki, M.; Hayasaka, T.; Nishio, T.; Morita, Y.; Tanaka, H.; Sasaki, T.; Koizumi, K.; Matsunuma, R.; Hosokawa, Y.; Ogura, H.; Shiiya, N.; Setou, M. PLoS One 2013, 8, e61204. (40) Guo, S.; Wang, Y.; Zhou, D.; Li, Z. Sci. Rep. 2014, 4, 5959. (41) Szutowicz, A.; Kwiatkowski, J.; Angielski, S. Br. J. Cancer 1979, 39, 681-687. (42) Menendez, J. A.; Lupu, R. Nat. Rev. Cancer 2007, 7, 763-777. (43) Li, J. J.; Condello, S.; Thomes-Pepin, J.; Ma, X. X.; Xia, Y.; Hurley, T. D.; Matei, D.; Cheng, J. X. Cell Stem Cell 2017, 20, 303314. (44) Deigner, H. P.; Hermetter, A. Curr. Opin. Lipidol. 2008, 19, 289-294. (45) Schneider, C.; Porter, N. A.; Brash, A. R. J. Biol. Chem. 2008, 283, 15539-15543. (46) Brugger, B. Annu. Rev. Biochem 2014, 83, 79-98. (47) Ma, X.; Chong, L.; Tian, R.; Shi, R.; Hu, T. Y.; Ouyang, Z.; Xia, Y. Proc. Natl. Acad. Sci. U. S. A. 2016, 113, 2573-2578. (48) Kennedy, E. P.; Weiss, S. B. J. Biol. Chem. 1956, 222, 193214. (49) Gibellini, F.; Smith, T. K. IUBMB life 2010, 62, 414-428. (50) Dobrzyńska, I.; Szachowicz-Petelska, B.; Sulkowski, S.; Figaszewski, Z. Mol. Cell. Biochem. 2005, 276, 113-119. (51) Monteggia, E.; Colombo, I.; Guerra, A.; Berra, B. Cancer Detect. Prev. 2000, 24, 207-211.


Page 8 of 9

Page 9 of 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry

For Table of Contents Only


The Combination of Droplet Extraction and Pico-ESI-MS Allows the

Recommend Documents