Recent advances in single-cell analysis by mass spectrometry

Lei Yin ab, Zhi Zhang b, Yingze Liu c, Yin Gao *b and Jingkai Gu *abd
aResearch Institute of Translational Medicine, The First Hospital of Jilin University, Jilin University, Dongminzhu Street, Changchun 130061, PR China. E-mail:
bResearch Center for Drug Metabolism, School of Life Science, Jilin University, Qianjin Street, Changchun 130012, PR China. E-mail:
cSchool of Pharmaceutical Sciences, Jilin University, Changchun, 130012, P.R. China
dBeijing Institute of Modern Drug Metabolism, Beijing 102209, P.R. China

Received 28th June 2018 , Accepted 10th October 2018

First published on 11th October 2018

Cells are the most basic structural units that play vital roles in the functioning of living organisms. Analysis of the chemical composition and content of a single cell plays a vital role in ensuring precise investigations of cellular metabolism, and is a crucial aspect of lipidomic and proteomic studies. In addition, structural knowledge provides a better understanding of cell behavior as well as the cellular and subcellular mechanisms. However, single-cell analysis can be very challenging due to the very small size of each cell as well as the large variety and extremely low concentrations of substances found in individual cells. On account of its high sensitivity and selectivity, mass spectrometry holds great promise as an effective technique for single-cell analysis. Numerous mass spectrometric techniques have been developed to elucidate the molecular profiles at the cellular level, including electrospray ionization mass spectrometry (ESI-MS), secondary ion mass spectrometry (SIMS), laser-based mass spectrometry and inductively coupled plasma mass spectrometry (ICP-MS). In this review, the recent advances in single-cell analysis by mass spectrometry are summarized. The strategies of different ionization modes to achieve single-cell analysis are classified and discussed in detail.

1. Introduction

As the most basic structural and functional units found in biology, cells can be affected by various factors that can alter their proliferation, differentiation and metabolism, leading to a vast heterogeneity between each individual cell, even within the same organism.1–12 Despite their identical genetics, the composition and concentration of chemical substances found in two homologous cells can also differ.13–17 Population cell analysis only provides averaged results, and thus the information regarding cellular individuality is often lacking.18–21 Therefore, studying the cellular morphology and composition at the single-cell level can provide accurate information regarding individual cells in a particular microenvironment. The differential information presented by a large number of individual cells is crucial for revealing the differences among cells and is essential for the study of cell signaling, physiological pathologies of diseases, and facilitates the discovery of biomarkers that can enable the early diagnosis of major diseases.22–28

The main chemicals that are of interest in single-cell analysis include nucleic acids, proteins, peptides, small molecule metabolites, and trace elements. These substances play key roles in various cellular processes and in supporting life. However, the very limited sample volumes involved and the complexity of chemicals encountered in these cellular environments can make it very difficult to truly detect and analyze the compositions and contents existing within individual cells.5,6,29–36

Various analytical techniques have been applied to single-cell analysis,37–41 such as single-cell transcriptomics,42–47 flow cytometry,48–57 fluorescence microscopy,58–65 Raman spectroscopy66–73 and electrochemical assays,74–82 but these methods suffer from various disadvantages such as limited selectivity, poor reproducibility and low sensitivity. Moreover, many of these techniques require labeling, and are limited with respect to the number of molecules that can be analyzed. In some cases, electrochemistry or fluorescence detection could be combined with liquid chromatography or capillary electrophoresis to improve the selectivity of the analytical method,82–87 but the simultaneous detection of multiple cellular components by these combined methods is still limited. Single-cell RNA sequencing is becoming an important tool for biological and medical investigations. However, it is still a rapidly evolving technique, and many experimental and computational challenges regarding this technique remain unaddressed.42,88,89 Flow cytometry90–93 and fluorescence microscopy94–96 are the most commonly used methods for single-cell analysis, but they require fluorescent markers to enable specific labeling of biomolecules present within or on the surface of cells. The relevant information regarding these biomolecules can then be elucidated based on their fluorescence emission. However, this method has certain limitations. For example, different channels used to detect different dyes cannot be operated simultaneously due to the potential for spectral interference, and the linear range of the fluorescent signal is narrow, making it difficult to accurately quantify the cellular components. In addition, most drug molecules do not exhibit fluorescence, and thus a fluorescent tag is required for fluorescence-based assays. Unfortunately, the incorporation of fluorescent tags can potentially alter the properties of the target molecules, especially in the case of low molecular weight metabolites. As is the case with fluorescence methods, neither Raman spectroscopy71–73 nor electrochemical assays97,98 readily accommodate the simultaneous detection of multiple components. The sensitivities and selectivities of these methods are also limited. Therefore, a direct analysis of chemicals residing in individual cells is required to avoid the problems that arise with tag-labeling.

Mass spectrometry (MS) techniques can offer femtomolar sensitivity and have excellent potential for the detection of many molecules without the need for labeling.99–104 Other advantages such as low sample consumption, high throughput and multiplexed detection ensure that MS techniques are ideally suited for single-cell analysis, allowing quantitative, qualitative, multiplexed, and spatially-resolved investigations at the cellular level.100,103 MS encompasses various ionization modes, including electrospray ionization mass spectrometry (ESI-MS),105–111 secondary ion mass spectrometry (SIMS),112–121 laser desorption/ionization mass spectrometry (LDI-MS)122–135 and inductively coupled plasma mass spectrometry (ICP-MS).120,136–142 A key limitation of mass spectrometry is that a single ionization mode is not suitable for all compounds, and different ionization modes are required for the analysis of different chemicals. Schematic representations highlighting the basic principles of ESI-MS (I), LDI-MS (II), SIMS (III), and ICP-MS (IV) are shown in Fig. 1. In addition, these methods are discussed in the following sections.

image file: c8an01190g-f1.tif
Fig. 1 Schematic representation of the basic principles underlying different mass spectrometric techniques (I) ESI-MS. Reprinted with permission from Blades, et al., Anal. Chem., 1991, 63, 2109–2114. Copyright 1991 American Chemical Society. (II) LDI-MS. Reprinted with permission from N. Bergman, et al., Anal. Bioanal. Chem., 2014, 406, 49–61. Copyright Springer-Verlag Berlin Heidelberg 2013. (III) SI-MS, reprinted with permission from K. Wu, et al., J. Biol. Inorg. Chem., 2017, 22, 653–661. Copyright 2017 SBIC. (IV) ICP-MS. Reprinted with permission from Diane Beauchemin, Anal. Chem., 2010, 82, 4786–4810. Copyright 2010 American Chemical Society.


ESI is a soft and widely used ionization method for the detection and identification of molecules via MS/MS or MSn analysis.143 In cases utilizing positive ion analysis, the capillary is placed at a positive voltage, or the counter electrode can also be used to assist the electrospray ionization process at a negative voltage. The opposite settings are used for negative ion analysis (Fig. 1).144 This ionization mode allows the simultaneous detection of various components, and the structures of unknown molecules can also be identified. ESI-based MS methods have been utilized in single-cell metabolite analysis during the last decade.145–158 The most challenging issues encountered with this technique can be attributed to the matrix effect, which stems from the presence of other compounds in the biological samples. Therefore, the presence of proteins, lipids, nucleotides and inorganic salts in a culture medium and complex cellular matrix can greatly reduce the accuracy and precision of the results obtained via single cell MS analysis.146,159 In order to improve the detection limits achieved with ESI-MS, many derivatives of this technique have been developed and employed for single-cell analysis, including nano-ESI-MS, probe-ESI (PESI-MS), desorption ESI (DESI-MS), laser ablation ESI (LAESI-MS) and capillary ESI (CESI-MS).157,160,161 These strategies will be discussed in the subsequent subsections within section 2.

2.1 Nano-ESI-MS analysis

In recent years, nano-ESI has emerged as an efficient derivative of the traditional ESI technique that is capable of accommodating the ultrasmall volumes that are required for single-cell analysis. With its ability to operate at flow rates of <1 μL min−1, nano-ESI exhibits superior ionization efficiency and provides better sensitivity in a more cost-effective manner than that is offered by traditional ESI.154,162–164 Due to these characteristics, nano-ESI-MS is well-suited for the analysis of single cells with ultra small volumes and limited quantities of analytes.

A variety of microcapillary tips, such as capillaries, probe microsampling and microfluidic devices have been coupled with nano-ESI to further adapt this technique for single-cell analysis.150,155,165–168 Sirikatitham A. et al. reported that packing a nano-ESI needle with two types of resins enabled the simultaneous desalting and preconcentration of target molecules in mast cells, and with the use of a carefully chosen internal standard (5-methyltryptamine), they were able to successfully quantify the concentrations of secreted histamine and serotonin molecules in cell culture buffers.143 This method allowed the direct and timely analysis of secreted molecules in biological responses to stimuli. Mizuno et al. made further improvements to this method to develop a new technique known as “live single-cell video mass spectrometry”, which has been employed in single-cell analysis (Fig. 2I).165 This method consisted of two steps. The first step was video-microscopic observation of the cell behavior. When cells exhibited a response and/or one wished to analyze designated micro-regions within a cell, the contents of the cell or its organelles were drawn out directly into a nano-spray ionization tip. Along with the addition of 1 μL ionization solvent to the collected sample, the ionization tip was coupled to the nano-ESI probe and the samples were then ionized in a mass spectrometer (MS). The second step involved key component extraction and cell classification. Based on the obtained MS spectra, samples could be compared with each other via the paired t-test. The key components were then subjected to MS/MS analysis in order to identify the samples comprising these molecules. Through the use of this method, Mizuno identified the typical and specific molecules in different cell lines at the single cellular level, thus facilitating cell classification. Practically speaking, the normality of single-cell mass spectrometry data has a great influence on the reliability of statistical results. Nano-ESI provides a good technical platform for single-cell analysis, but further work is required to confirm its reproducibility and reliability.

image file: c8an01190g-f2.tif
Fig. 2 ESI-MS-based chemical analysis of single cells. (I) Scheme of live single-cell video – mass spectrometry for identification of the molecules residing in the cell. Reprinted with permission from Mizuno, et al., J. Mass Spectrom., 2008, 43, 1692–1770. Copyright Wiley Interscience. (II) Schematic depiction of probe electrospray ionization-mass spectrometer equipped with an internal electrode capillary (IEC-PPESI-MS) for detecting whole metabolites from an intact single-cell. Reprinted with permission from Nakashima, et al., Anal. Chem., 2016, 88, 3049–3057. Copyright 2016 American Chemical Society. (III) Schematic of a typical DESI experiment. Reprinted with permission from Takáts, et al., Science, 2004, 306, 471–473. Copyright the American association for the advancement of science. (IV) Schematic top view of LAESI with plume collimation for diverse group of cells analysis. Reprinted with permission from Stolee, et al., Anal. Chem., 2013, 85, 3592–3598. Copyright 2013 American Chemical Society.

2.2 Probe-ESI (PESI) analysis

As molecules of interest are usually at extremely low concentrations at the cellular and subcellular levels,102,153,156,169–176 some enrichment and separation steps are required to improve the sensitivity of single-cell MS analysis. LC (liquid chromatography) and capillary electrophoresis (CE) are effective techniques for compound enrichment and separation, but not suitable for direct single cell analysis. To solve this problem, Hiraoka proposed a concept known as “probe electrospray ionization (PESI)”.177 PESI was developed based on ESI-MS and incorporated an additional sample preparation step prior to injection. PESI can be performed on various solid substrates, such as paper, wooden tips as well as surface-modified glass rods,178–182 and this strategy has been employed for the analysis of various complex biological systems.183–187 For example, Gong et al. performed single-cell analysis of intra-cellular metabolites via this technique at cellular and subcellular levels.166 A probe with a 1 μm tip diameter made of tungsten was directly inserted into live A. cepa cells to enrich the metabolites. A hyphenated interface between the probe and MS detector was designed to allow direct desorption/ionization of the enriched analytes as they passed from the tip of the probe to the MS detector. Through the PESI method, various metabolites in different subcellular compartments in the same and different single A. cepa cell types were observed. This probe design greatly improved the sensitivity of this technique for single-cell analysis.

A further challenge encountered in single-cell metabolomics research is that the metabolites that are detected in a single cell cannot be fully identified, thus making it difficult to interpret the results.151,188,189 To address this problem, Nakashima et al. developed a pressure probe electrospray ionization MS technique (IEC-PPESI-MS) that utilized an additional internal electrode capillary, which offered high spatial-resolution in cell sampling, allowed precise post-sampling manipulation and provided high detection sensitivity.156 As shown in Fig. 2II, an internal electrode capillary (IEC) was used as a pressure probe to evaluate the structural integrity of the targeted cells based on their water status and the enriched multifarious metabolites. This ensured the detection of the metabolites from an intact individual cells, thus enhancing the integrity and reliability of single-cell metabonomic data. Through this technique, Nakashima compared in situ single-cell metabolite profiles between stalk and glandular cells, including those of amino acids, organic acids, carbohydrates and flavonoids. The results revealed that striking differences existed in the metabolite compositions between two adjacent cell types. PESI has thus emerged as a powerful offspring of ESI-MS that can offer a highly sensitive, reliable and high-throughput method for single-cell MS analysis.150,155,156,164,190–195


Desorption electrospray ionization (DESI) was first developed by Cooks et al. in 2004. The principle of DESI is to generate charged droplets and ions from a solvent via ESI and direct them to the surface of the targeted analyte,196 and these sprayed charged particles impacting the surface then produce gaseous ions along with the analytes.197 As shown in Fig. 2III, the sample solution is deposited onto a PTFE surface and subsequently dried, and methanol–water (1[thin space (1/6-em)]:[thin space (1/6-em)]1 containing 1% acetic acid or 0.1% aqueous acetic acid solution) is sprayed under the influence of a high (4 kV) voltage. Ferreira et al. developed a DESI-MS method for the structural characterization of lipids and metabolites in single oocytes and embryos, in which different compositions and contents were observed in response to changes in the embryonic metabolism.198 Gonzalez-Serrano et al. also employed this technique for the analysis of lipid compositions and dynamics in single oocytes and preimplantation embryos, and they demonstrated that the selection of viable and healthy embryos could be achieved based on their analysis of the in vivo embryonic lipid metabolism via this technique.199 DESI-MS has often been employed in the analysis at cellular levels, although it is difficult to detect metabolites at the subcellular level due to the limited spatial resolution.


Laser ablation electrospray ionization (LAESI) is a newly developed ionization technique that is particularly suited for the characterization of water-containing specimens. This technique utilizes a focused mid-IR laser beam with a wavelength of 2.94 μm to excite the water molecules in analytes.200–202 When the ablation occurs, the sample material is discharged from the surface of the analyte as particles, which are ejected for a distance of tens of millimeters above the analyte.203,204 Additionally, most of the neutral particulates will be intercepted by the electrospray to achieve ionization. Many single-cell investigations have utilized LAESI-MS.205 Shrestha et al. studied the in situ metabolic profiles of single cells by LAESI-MS.206 Small cell populations of A. cepa, N. pseudonarcissus bulb epidermis and single eggs of L. pictus were selected in this study as research models. With a fabricated etched tip of a GeO2-based glass fiber, mid-IR laser pulses were delivered to the analyte and oligosaccharides, anthocyanidins, flavonoids as well as glucosides were successfully detected. The results indicated that a relationship existed between the nature of the metabolites and aging. However, only a small portion of ionization was achieved by electrospray in the conventional LAESI-MS method due to the limited expansion of the ablation plume in three dimensions. Stolee et al. demonstrated that performing sample ablation within a capillary helped to confine the radial expansion of the plume.207 This strategy altered the expansion dynamics, offering a greater interaction with the electrospray plume, thus providing a higher ionization efficiency and greater dynamic range. As shown in Fig. 2IV, an etched optical fiber is inserted into a glass capillary with a certain inner diameter (ID) and length (L) that contains the sample. Upon ablation in the capillary, a collimated plume emerges (shown in blue) and is ionized by an electrospray. Ablation in the capillary leads to a well-directed plume. In addition, a diverse group of cells was selected to demonstrate the feasibility of this analyzing method. These cells included β-cell derivatives, epithelial cells, megakaryoblast cells and individual sea urchin eggs, in which the biologically relevant metabolites could be readily detected via this method.

2.5 CE-ESI-MS analysis

The composition of an individual cell is highly complex, and can include numerous types of saccharides, proteins, lipids and various small molecules.101,102,158,169,175,208–210 For the sake of selective analysis, chemical separation is often required to achieve meaningful mass spectrometry analysis. The coupling of mass spectrometry with capillary electrophoresis (CE), which can utilize nano-analytical volumes and offers a high separation efficiency (due to the differing charges of the analyte molecules), is ideally suited for single-cell analysis.154,170,175,211–226 Liu et al. utilized a CE-ESI-MS platform to quantitatively analyze anionic metabolites in an individual Aplysia R2 neuron cell; consequently they detected 15 nucleotides and derivatives and even calculated the thermodynamic energy balance of these cells.175 CE-ESI-MS is an effective and well-established technique for the analysis of single-cell metabolomics. Recently, Onjiko et al. exploited whole-cell dissection in combination with the CE-ESI-MS technique, and identified ∼52 single cell metabolites in the positive ion mode and revealed metabolic variations between individual cells in the 8- and 16-cell embryos of the South African clawed frog.162 In addition, Banek et al. quantified the proteins in individual embryonic cells of the cleavage-stage frog embryo using capillary electrophoresis electrospray ionization high-resolution mass spectrometry (CE-ESI-HR-MS).154 By using label-free quantification (LFQ) and fewer derivatization steps to enhance analytical sensitivity, Banek successfully identified 438 non-redundant protein groups in an individual 16-cell embryo, and 335 of these were quantified. The application of LFQ and CE-ESI-HR-MS has thus provided valuable insight regarding cellular metabolic processes.

3. Laser desorption/ionization mass spectrometry (LDI-MS)

Laser desorption/ionization mass spectrometry (LDI-MS) is an accurate surface analysis technique that was developed in 1960.227 The sample is irradiated by a laser beam at a specific wavelength to achieve desorption and ionization of the sample molecules, and the mass-to-charge ratios of the ions are subsequently analyzed by mass spectrometry.228–233 This process requires sample molecules to absorb energy at a specific wavelength matching that of the laser beam.192,234–238 The technique has been widely applied to analyze the composition and molecular structure of the tested substances.239–242 The optimized LDI-MS technique has high sensitivity and the detection limits can reach the fg level. LDI has a certain spatial resolution (4–50 μm), which can provide both qualitative and quantitative information regarding the spatial distribution of chemicals on a surface. Molecules that can be ionized and reach the range of detection usually have molecular weights that do not exceed 2000 Daltons,243–245 and thus LDI is usually used to analyze small molecular substances such as lipids, amino acids, small molecule metabolites, and other relevant species.246–253

3.1 Matrix-free laser desorption/ionization

Matrix-free laser desorption/ionization (LDI) provides an effective means to study plant and algae cells, because these cells are relatively large and they contain pigment molecules that can absorb laser light energy.254–258 In contrast, animal cells are relatively small, and they generally lack the pigment molecules that are found in plant cells. Consequently, the ionization and analysis of compounds in animal cells is relatively difficult. Extensive work has been undertaken to optimize LDI-MS systems to accommodate single animal cell analysis in order to facilitate the absorption of laser energy by these samples, and thus achieve the desorption/ionization of sample molecules.259–267

Urban et al.268 developed a label-free method for multidimensional chemical analysis of single cell organisms by combining optical, fluorescence and Raman microspectroscopy with LDI-MS. They observed protoplasts in unicellular algae by an optical microscope, and then used Raman spectroscopy to explore the distribution of β-carotene in the protoplast. They also observed the chloroplasts in the cells of unicellular algae by optical microscopy, and then utilized fluorescence imaging to explore the distribution of chlorophyll in chloroplasts. LDI was used to detect the chemical constituents of the unicellular algae cells, and to obtain information regarding the phospholipid composition. With the application of various detection methods, more comprehensive information can be obtained, which thus provides a good reference for biological research (Fig. 3I).

image file: c8an01190g-f3.tif
Fig. 3 LDI- and MALDI-based chemical analysis of single cells. (I) Negative ion mode laser desorption/ionization mass spectra of individual unicellular organisms. Reprinted with permission from P. L. Urban et al., Anal. Chem., 2011, 83, 1843–1849. Copyright © 2011 American Chemical Society. (II) LDI mass spectrum of a single yeast cell from NAPA in the mass range of metabolites. Reprinted with permission from B. N. Walker, et al., Anal. Chem., 2012, 84, 7756–7762. Copyright © 2012 American Chemical Society. (III) Ion image generated for a diglyceride-sodium adduct. Reprinted with permission from J. Niziol et al., Anal. Chem., 2016, 88, 7365–7371. Copyright © 2016 American Chemical Society.

Walker et al.125 designed an optimized structure as a matrix-free platform to improve the ionization efficiency of LDI. The optimized structural platform was made of silicon and its surface was finely processed, consisting of a columnar nanopost array structure (NAPA). Studies have indicated that NAPA structures exhibit ion yield resonances when posts with subwavelength diameters and the appropriate aspect ratios are selected. An optimized structural platform (H = 1200 nm, D = 150 nm) of H/D = 8 was selected for subsequent analysis and detection. With the use of the optimized structural platform, the detection limit of verapamil reached ∼800 zmol. This method was also used to detect the metabolites of individual yeast cells. A total of 24 metabolites, accounting for 4% of the yeast metabolomics, were detected by this technique. Walker and his coworkers referred to this modified LDI technique as nanophotonic ionization (Fig. 3II).

More recently, Walker et al.206 applied their improved nanophotonic ionization technology to investigate cerevisiae metabolomics. In the study of Saccharomyces cerevisiae, 584 metabolites were identified. These metabolites participate in 1175 reactions organized into 94 major biochemical pathways.269 Using this method, 24 metabolites in Saccharomyces cerevisiae were detected at the single cell level. More metabolites were detected as the number of cells increased. When the number of cells reached ∼80, 108 types of metabolites were detected, accounting for 18% of the known metabolomics, which involved 67% of the major metabolic pathways. They also studied the response of Brewer's yeast to environmental changes from the perspective of metabolomics. Their work demonstrates the usefulness of this technique for the study of cell metabolomics.

Jaschinski et al.123 used LDI-MS to perform single-cell investigations of the diatoms Coscinodsicus granii and Thalassiosira pseudonana. The unique biomineralized nanostructured surface supported the ionization of metabolites associated with the cellular surface. Two commonly known metabolites were detected in addition to chlorophyll, phospholipids and amino acids. Based on these data, they prepared specific MS images of individual cells at a resolution reaching down to 20 by 20 μm. They also utilized this method to image the larger C. granii cells and the smaller T. pseudonana cells. This technology is useful for the chemical classification of phytoplankton. In addition, it can be used to answer basic questions regarding plankton diversity.

Gao et al.270 analyzed the individual cells of paramecium with a highly irradiated femtosecond (10−15 s) laser at the elemental level. Paramecia cells are first heated and dried, and then directly subjected to desorption/ionization by a laser. The content of the paramecia cells at the fg level can be successfully detected via this approach. In addition, the correct isotope abundance ratio can be obtained via this method. Moreover, some nonmetallic elements that are difficult to detect via inductively coupled plasma mass spectrometry (ICP-MS) can be readily detected by this technique.

3.2 Matrix-assisted laser desorption/ionization (MALDI)

LDI is a powerful technique for the qualitative and quantitative analysis of a surface's composition.271–276 However, the structure of sample molecules is ever-changing. In many cases, sample molecules cannot directly absorb the energy at the wavelength of the laser. To address this problem, Karas and Hillenkamp first developed a technique now known as matrix-assisted laser desorption ionization mass spectrometry (MALDI-MS) in the 1980s.227,277–281 They used an organic compound that was strongly absorbed by a laser at a fixed wavelength as a substrate for LDI, thus increasing the rate of ionization. The auxiliary matrix absorbs the energy of the laser and then passes it to the sample molecule to desorb and ionize the compounds.282–288 As early as the 1990s, MALDI was used for single cell analysis, when researchers used this technique to analyze peptides and proteins in cells. Their groundbreaking work laid the foundation for single-cell analysis via MALDI.289–291 With the development of detectors, lasers and other technologies, the sensitivity and spatial resolution of MALDI have been greatly improved, thus enabling researchers to analyze more kinds of metabolites such as lipids, carbohydrates and small molecular metabolites through MALDI over a wider concentration range.292–296 MALDI techniques can be combined with other technologies and research results (such as various histological information, 3D technology), so as to achieve the differentiation of very similar cells (such as pancreatic islet α and β cells), 3D single-cell imaging and other valuable information, thus furthering our understanding of cellular biology.295,297,298 Because of the increased ionization efficiency provided by this approach, MALDI has become one of the most commonly used ionization methods in biological tissue imaging and single cell analysis.192,299–302 In recent years, some research groups have used optimized MALDI for single cell mass spectrometry imaging (MSI), which provides a high resolution reaching 4–50 μm.212,303–306

Ibáñez et al.296 designed a microarray as a platform for mass spectrometry. In this approach, a sample board served as a high-density “reservoir” array for high-throughput detection. During sample preparation, the cells were suspended in a low temperature buffer to inhibit the biochemical reactions that would otherwise occur in the cell. After centrifugation, a cell suspension with a certain concentration was obtained. The cell suspension was then spread onto the microarrays for mass spectrometry (MAMS), and the cells were scattered throughout the “reservoir”. The number of cells in each “reservoir” would obey the Poisson distribution. Controlling the concentration of the cell suspension could indirectly control the number of cells dispersed to the “reservoir”. Subsequently, the auxiliary matrix was sprayed onto the surface of MAMS for subsequent MALDI detection. This method was used to study the glycolysis metabolic pathway of yeast cells. 2-Deoxy-D-glucose was added to the culture medium to inhibit the glycolysis of yeast cells. They found that ATP/ADP in the yeast cells showed a downward trend. In addition, the ATP/ADP ratio of cells that knock out the pfk2 gene also decreased. The results obtained via single cell detection were compared with those found with groups of cells. It was discovered that the two trends were consistent. However, the results gained via single cell detection exhibited individual differences between each cell, which could not be observed via group cell detection.

Urban et al.307 investigated the synthesis and metabolism of ATP in yeast cells by means of MAMS. 13C-Labelled ethanol (with both carbon atoms replaced by 13C) was used to culture yeast cells. As the incubation time increased, the 12C atoms of the ATP molecules in the yeast cells were gradually replaced by 13C, until 48 h was reached, when most of the 12C-ATP had been converted to 13C-ATP. They compared the results obtained via single cell detection and group cell detection. After 10 h of cultivation, the number of 13C in the single cells of ATP showed a large degree of randomness. This reflected the differences between the individual cells, which could not be detected during investigations involving groups of cells. This also reflected the significance of single-cell detection.

Neupert et al.294 combined MALDI-MS with immunocytochemistry to establish a set of analytical methods for specific individual nerve cells in neural tissue. Due to differences at the individual cellular level, even the cells in the same tissue might differ significantly from one another in physiological function and material composition. The problem of finding target cells in multicellular hybrid systems and analyzing their metabolites was solved via this combined strategy. First, the enzymatic hydrolysis of the living tissue was performed to disperse the cells, and the cells were then cultured on slides before they were treated via immunocytochemistry to find the target cells, and lastly, the metabolites in the target cells were detected by MALDI-MS. Their method could thus be employed for most types of cells.

Guillaume-Gentil et al.293 designed a nondestructive and quantitative method for the extraction of intracellular liquids. Although the latest advances in mass spectrometry technology have reached the sensitivity required to study metabolites in single cells, the usual sample preparation method has been to isolate and sacrifice cells, generating an undesirable unstable state and preventing complementary analyses. With this in mind, Guillaume-Gentil and coworkers utilized fluidic force microscopy (FluidFM) to extract metabolites in a nondestructive manner from single living cells. FluidFM technology can be used not only for quantitative intracellular fluid sampling, but also for subsequent processing and sample recovery probe release. The cells were then analyzed via matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). This method enabled the detection and identification of 20 metabolites recovered from the cytoplasm of individual HeLa cells. They also used this method to verify the 13C culture experiment, as the marked carbon atoms were detected in different metabolites. Their method greatly improved the survival rate of the analyzed cells and preserved the physiological environment of the sample cells, thus ensuring that the same cell could be analyzed at different times, and providing an opportunity for complementary analysis of cell metabolism.

Fan et al.308 used MALDI-MS to detect secretions released from nerve cells of California sea hay after stimulation by secretin. They designed a capillary tube known as an octadecyl-modified silica nanoparticle deposited (OSND) capillary tube and filled it with nanoscale silicon spheres. The surfaces of the silicon spheres were modified by octadecyl groups to enrich the secretions of nerve cells. This capillary tube was placed within another larger capillary tube. A solution containing secretin flowed through a gap between the two capillary tubes to stimulate the cells to be analyzed. Subsequently, the solution flowed out of the inner capillary. The secretions released from the nerve cells were enriched on the nanocrystalline silicon spheres. Finally, the secretion was detected by MALDI after solvent elution. Their method can be used to stimulate and collect samples simultaneously, thus shortening the sample processing time. This strategy provides an effective approach for the determination of unstable analytes.

Do et al.309 used sequential MALDI-MS for optical guidance and high-throughput single cell analysis. Intact cells were enzymatically dissociated and stained with a fluorescent nuclear dye. The isolated cells were then coated onto a glass substrate. Finally, luminance and fluorescence-assisted localization were used to analyze the MALDI-MS spectra. Using this method, they analyzed more than 1000 mammalian cells which are located at the dorsal root ganglia containing many subgroups. They classified the cells according to the lipids, peptides and protein data measured in the cells. Their work achieved a high-throughput multicomponent analysis. Moreover, the classification of cells has been realized, and thus this approach may be suitable for research requiring the detailed analysis of cellular compositions.

Light-guided MALDI-MS provided a high-throughput assessment for the lipid and peptide contents of individual cells in large cell groups. However, this quantitative effect offered worse performance than is provided by ESI/APCI-based MS techniques, and many low levels of metabolites could not be detected because of the interference by the MALDI matrix. MS provided sufficient sensitivity with regard to the sample measurement, but each MS system revealed only partial chemical information about a single cell rather than complete information. In addition, complete information was essential for understanding the cellular functions of health and disease states. Therefore, some research groups have developed technology combining MALDI with other MS systems.

Comi et al.297 combined MALDI and CE-ESI to achieve complementarity between high-throughput and low detection limits. The two MS systems were connected to a custom liquid microjunction surface sampling probe. MALDI-MS was used to determine the hormone levels in numerous individual cells to reveal the extraction target. The liquid microjunction probe collected cell contents from the specified position on the ITO coating glass for subsequent CE-ESI analysis. This method was applied to analyze single rat islet cells. An optically guided MALDI-MS system was used to identify individual pancreatic islet α and β cells, which were then targeted for liquid microjunction extraction. The qualitative information regarding the metabolites of amino acids was obtained via the use of CE-ESI to analyze the extraction solution and individual cells. Each cell was categorized based on its peptide content. In addition, dopamine was consistently detected in both cell types. The method is suitable for extensive single cellular analysis of dissociated tissues. This work also provides an important means for the separation and characterization of complex tissue cells.

MALDI is one of the most commonly used ionization methods in biological tissue imaging. However, due to limitations with the spatial resolution, the application of MALDI in single cell imaging had encountered a bottleneck (Fig. 3III).310–315 The main factor limiting the spatial resolution obtained via MALDI was the size of the laser spot. If the imaging of individual cells is desired, improvements are required with regard to the spatial resolution of MALDI. In order to address this problem, many research groups have optimized MALDI.310,316–322

Feenstra et al.295 improved their previous laser optical system for matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). The actual laser spot size has been reduced from 9 to 4 μm, via a combination of spatial filtering, beam expansion, and reduction of the final focal length. The new laser optical system allows the spot size to be readily changed by simply swapping the beam extender components and yields a 5 μm resolution imaging without oversampling. Using 10×, 5×, and no beam expansion, they could routinely change the laser spot size between ∼4, ∼7, and ∼45 μm in less than 5 min. They applied this system to image corn root tissue sections, and the difference of image quality and signal sensitivity among the three different spatial resolutions were compared. The high-spatial resolution mass spectrometry imaging system reached the subcellular level and could be used for single cell imaging of plant tissue and analysis of single cell metabolites.323 Duenas et al.292 used a similar method to image the asymmetric Kranz anatomy of Zea mays leaves to study the differential localization of two major anionic lipids, namely sulfoquinovosyldiacylglycerol and phosphatidylglycerol. These differences in the localization could be readily observed via MSI.

Duenas et al.298 also designed a single cell three-dimensional spatial distribution imaging method through the use of high-spatial resolution MALDI-MSI. Cells were usually cryo-sectioned at a thickness of 10 μm. Slices of the cells were collected on pre-chilled glass slides with the use of Cryo-Jane tape. The samples were lyophilized under a moderate vacuum and stored in a desiccator prior to imaging. The 3D MALDI-MSI was built using the stack of collected data sets and 2D MALDI-MSI sets by the TrakEM2 module provided with Image J software.324–326 They also used this method to draw images of phospholipid and phospholipid derivatives in individual zebrafish fertilized eggs. This was the first time that MALDI-MSI had been used to achieve single cell 3D chemical imaging.

4. Secondary ion mass spectrometry (SI-MS)

Secondary ion mass spectrometry (SIMS),327–329 also known as ion probe analysis (IPA), was developed in the 1960s. SIMS is a very sensitive surface composition analysis technique. It uses a primary ion beam (Ar+, O2+, N2+, O, F, N or Cs+, etc.) to bombard the surface of a sample, thus sputtering the atoms on the surface of the sample into charged ions, and a mass spectrometer is subsequently employed to analyze the mass-to-charge ratio of the compound ions in order to determine the surface composition.330–332 SIMS can analyze all elements (even hydrogen), and can provide information regarding the isotopes, the composition of a compound and molecular structures existing in a sample. This technique has a very high sensitivity that can reach the ppm or even the ppb level. Moreover, SIMS has a high spatial resolution (<50 nm) and it can also provide qualitative and quantitative chemical information.333,334 In comparison with ESI or MALDI ion sources, SIMS is a relatively hard ionization technique. Detected ions are usually limited to a narrow molecular weight range of several hundred Da. Therefore, SIMS is currently used primarily for the analysis of lipids, small molecule metabolites, molecular fragments and elements.335–341

Depending on the ion dose and the extent of the surface damage imposed by the ion beam, SIMS includes two different modes, the static SIMS (TOF-SIMS) mode342–345 and the dynamic SIMS mode.329,346–354 In the case of static SIMS, the primary ion dose is <1013 cm−2 and the beam current is low (in the pA–nA range), resulting in sputtering of up to ∼0.1% of the top monolayer. For dynamic SIMS, the primary ion dose is >1013 cm−2 and a high beam current is used (in the mA range), producing 3D images that can offer valuable chemical information.355,356 SIMS is an effective technique for biological imaging, especially for single-cell analysis.342,357–361 It can provide information regarding the subcellular distribution of various chemicals in individual cells, from the membrane through to the cytoplasm and the nucleus without the need for labeling, and offers high spatial resolution as well as high selectivity.119,330,362 In this section, we focus on the MS analysis of chemicals in individual cells via SIMS, with primary emphasis on time-of-flight SIMS (TOF-SIMS)119,359,361,363–369 and nanoscale SIMS (NanoSIMS).370–378


The primary ion beam employed in TOF-SIMS is pulsed and a complete mass spectrum can be monitored during the analysis, resulting in an unlimited mass range, and this characterization technique can generate large fragment ions as well as molecular ions.379 TOF-SIMS can typically detect biomolecules with molecular weights below 1000 Da such as lipids, metabolites as well as small fragments, and can provide a high spatial resolution of 100 nm. Some representative studies are described in the subsequent paragraphs.

Henss et al. conducted a series of experiments involving the high resolution imaging and 3D analysis of silver nanoparticles (Ag NPs) in human mesenchymal stem cells via TOF-SIMS using the delayed extraction mode.380 They found that Ar1500+ (10 keV) could not sputter the Ag NPs to a significant degree inside the cells, but a model experiment with a defined Ag layer offered a higher Ag sputter yield for smaller Ar-clusters. The use of smaller cluster sizes was a promising compromise that could potentially enable the depth profiling of cells with inorganic nanoparticles. Hua et al. investigated Ag NP-induced changes in lipid characteristics on a single cell surface via TOF-SIMS (Fig. 4I).381 The delayed extraction mode was selected for the simultaneous acquisition of high resolution mass spectra and high spatial resolution chemical images of single cells. Their results were in close accordance with cytotoxicity assay measurements. Obvious distinctions were found between the cell groups that were treated with various doses of Ag NPs. The chemical mapping of single cell components revealed that cholesterol and diacylglycerol had a tendency to migrate to the surrounding cells after treatment with a high dose of Ag NPs. This study demonstrated the feasibility of utilizing TOF-SIMS to analyze the changes exhibited by lipids on the surface of an individual cell, thus providing a better understanding of the mechanism of cell–nanoparticle interactions at the molecular level.

image file: c8an01190g-f4.tif
Fig. 4 SI-MS-based chemical analysis of single cells. (I) Investigation of silver nanoparticle induced lipid changes on the surface of an individual cell surface. Reprinted with permission from X. Hua, et al., Anal. Chem., 2018, 90, 1072–1076. Copyright 2018, American Chemical Society. (II) Multimodal imaging of chemically-fixed cells in preparation. Reprinted with permission from J. Lovric, et al., Anal. Chem., 2016, 88, 8841–8848. Copyright 2018, American Chemical Society.

Winograd and coworkers developed a 3D imaging cluster time-of-flight secondary ion mass spectrometry (TOF-SIMS) assay to chemically map small molecular compounds in aggregated and single Escherichia coli (E. coli) cells, with a ∼300 nm spatial resolution and a high chemical sensitivity.116 This method was then validated by the observation of reduced tetracycline accumulation in an E. coli strain expressing a tetracycline-specific efflux pump (TetA) in comparison to the isogenic control. This work is a proof-of-concept for a new chemical imaging technique providing nanoscale resolution that has the potential to facilitate the discovery of new antibacterial agents. Moreover, their findings represent the first direct localization of unlabeled antibiotic molecules in single E. coli cells. The development of low energy gas cluster ion beams and further enhancement in ionization capabilities could expand the applicability of this technique as a valuable analytical tool for biological and pharmaceutical sciences.

Huang et al. developed a facile single-cell patterning (ScP) assay that was coupled with TOF-SIMS to study drug-induced changes in cellular phenotypes.382 Cell populations treated with cisplatin for different periods of time were examined by TOF-SIMS. Important molecular sources of variations in the data were successfully identified by principal component analysis (PCA). They observed a dramatic reduction in the quantity of ion species originating from cholesterol and fatty acids after cisplatin treatment, which were hallmarks of cellular apoptosis and consistent with the results obtained from other characterization technologies. In contrast, the quantities of ion fragments derived from DNA bases increased significantly, which implied that a higher degree of DNA ionization occurred due to chromosome disassembly and DNA fragmentation. This research demonstrated for the first time the powerful capabilities of TOF-SIMS as a high-throughput technique for single-cell analysis.

Vanbellingen et al. employed a 3D-MSI-TOF-SIMS strategy for the evaluation of chemotherapeutic drug delivery at the single-cell level.119 In their study, they revealed the advantages of label-free, tridimensional mass spectrometry imaging using dual beam analysis (25 keV Bi3+) and depth profiling (20 keV with a distribution centered at Ar1500+) coupled to 3D-MSI-TOF-SIMS for the characterization of A-172 human glioblastoma cell lines that were treated with the B-cell lymphoma 2 (Bcl-2) inhibitor ABT-737. The high spatial (∼250 nm) and high mass resolution (mm ∼10[thin space (1/6-em)]000) of TOF-SIMS permitted the localization and identification of intact, unlabeled molecular ions corresponding to drugs and characteristic fragment ions. Chemical maps prepared usingendogenous molecular markers showed that ABT-737 was primarily localized in subsurface regions and was absent from the nucleus. They proposed a semi-quantitative workflow that would enable rapid characterization and the possibility of interrogating numerous single cells while also accounting for the diversity of biological cells.

4.2 Nano-SIMS

Nano-SIMS, which is also known as dynamic SIMS,383 enables the rapid acquisition of depth profiles. Nano-SIMS is performed with a group of converging atomic ion beams that bombard the surface of a sample and produce monatomic, diatomic and other small molecular ions. Nano-SIMS can achieve a high spatial resolution of up to 50 nm in the Cs+ mode, and 100 nm in the O mode, and thus it is an effective strategy for the high-resolution mapping of unlabeled chemicals containing a unique element within the biological system under investigation.384 Some representative works involving this technique are discussed below.

Ewing and coworkers employed TOF-SIMS to image chemically fixed adrenal cells and then used Nano-SIMS for high-spatial-resolution imaging373 (Fig. 4II). In their study, an OsO4 species was localized in the lysosomes of cortical cells, a type of adrenal cell that was present in the culture sample. NanoSIMS imaging of the 190Os16O ion species in cortical cells revealed the same localization as was exhibited by a wide range of OsO4 ions according to TOF-SIMS characterization. This proof-of-concept study focused on gaining new insights into the capabilities offered by TOF-SIMS for imaging sub-cellular details and how, by probing a specimen via TOF-SIMS, possible insights into pertinent ion species can be obtained and applied for NanoSIMS imaging.

Penen et al. explored the use of NanoSIMS for subcellular imaging of macro and trace elements.385 The green algae C. reinhardtii was used as a model organism in their study. NanoSIMS was employed to localize the trace metals Fe and Cu in subcellular structures. Fe was found throughout the cell, but elevated concentrations were observed at the starch plates of the pyrenoids, as was the case with Cu. This study demonstrated that NanoSIMS is a powerful method for the chemical imaging of macro and trace elements with subcellular resolution.

Biesemeier et al. investigated the variations between individual neuromelanin-(NM)-containing organelles and granules using elemental maps of C, N, P, S, Cu, and Fe obtained via Nano-SIMS.386 In this study, the contents of C, N, S, Fe, and Cu were determined inside intact NM-containing organelles of dopamine neurons in sections of human SN for the first time. The in situ contents of each element in NM granules, lipid bodies, and protein matrices were quantitatively acquired. Significant quantities of S, Fe, and Cu were found in NM granules of the Substantia nigra collected from healthy subjects. A quantity of P consistent with the presence of phospholipids was also determined in lipid bodies. This study provided new insight regarding the changes in chemical composition that occur in NM pigments during healthy aging and disease development.

For SIMS applications, a duoplasmatron oxygen source is usually used as an ion microprobe. However, it cannot provide the same quality of images as the cesium primary ion source that is used to produce negative secondary ions (C, CN, S, P) due to a larger primary ion beam diameter. Malherbe and coworkers fitted a newly designed RF plasma oxygen ion source onto a Nano-SIMS 50L system.387 This new RF plasma oxygen primary ion source was employed to localize essential macroelements and trace metals at basal levels in two biological models, cells of Chlamydomonas reinhardtii and Arabidopsis thaliana. Compared to the conventional duoplasmatron and cesium sources, this ion source offered numerous improvements such as elevated primary beam current density, higher ultimate lateral resolutions and higher apparent sensitivity for electropositive elements, better long-term stability and reduced maintenance requirements.

Dekas et al. assessed the activity and interactions of methane seep microorganisms by parallel transcription and FISH-NanoSIMS analyses.388 This study revealed that Desulfosarcina/Desulfococcus (DSS) and Desulfobulbaceae (DSB) exhibited increased rRNA expression during incubation with methane, indicating that these samples exhibited methanotrophic archaea coupled activity. Their study offered a new understanding of the dynamics between methanotrophic archaea and seep Deltaproteobacteria.

5. Inductively coupled plasma mass spectrometry (ICP-MS)

The endogenous trace metals (Fe, Mn, Zn, Cu, Co and Ni) and non-metals (P, S, B and Cl) are essential and significant constituents of living systems. Many of these elements are bound with metalloproteins, metalloenzymes and some nucleic acids, and they play important roles in cell metabolism.389 Inductively coupled plasma mass spectrometry (ICP-MS) has been widely used for ultratrace element analysis because of its high sensitivity to multispecies elements.390–396 Using the ICP-MS technique, Meyer et al. investigated the cellular bioavailability of arsenite, which was incubated for 0–48 h.139 This method provided a lowest level of quantification reaching 0.35 fg per cell, as was validated via conventional bulk analysis.

5.1 Time-resolved ICP-MS

The effectiveness of classical ICP-MS as a means for cellular analysis was evaluated integrally using a mass of cells after they had been subjected to lysis, extraction and digestion.397 It is difficult to obtain any information regarding cell-to-cell variance between individual cells via classical ICP-MS. Consequently, a new platform known as time-resolved ICP-MS has emerged to meet the requirements for single-cell analysis.393,398–403 As shown in Fig. 5, in this technique a cell suspension is carried to a plasma torch by a nebulizer or microdroplet dispenser and each single-cell generates an ion cloud, which can be detected as an individual signal with a high time resolution.404 The short transient signal corresponds to a single-cell analyte of interest.
image file: c8an01190g-f5.tif
Fig. 5 Schematic image of time-resolved ICP-MS for elemental and multiparametric analysis of individual cells reprinted with permission from Miyashita, et al., Anal. Sci. 2014, 30, 219–224. Copyright 2014, the Japan Society for Analytical Chemistry.

Wang et al. developed a time-resolved ICP-MS method, analyzed mineral element contents and distribution patterns of single-cells. Single HeLa, 16HBE and A549 cells were chosen as model cells, and they found that the contents of Fe, Zn and Cu followed log-normal distributions and Poisson distributions with high λ values for Mn, P and S in single HeLa cells, while 16HBE cells exhibited a homogenous distribution.405 These results indicated that large cell-to-cell variance might arise in HeLa cells. Groombridge et al. developed a high efficiency cell introduction system (HECIS), which consisted of a high performance concentric nebulizer (HPCN) and a low-volume (15 mL) axis spray chamber utilizing a sheath gas flow.399 The improved device allowed the simultaneous detection of Mg, P, Ca, Mn, Fe, Cu, and Zn, with a time resolution of 1 ms, and offered a high degree of accuracy and as well as high-throughput performance. In addition, Wang et al. developed a facile droplet-chip-time-resolved ICP-MS online system for the determination of zinc in single cells.406 In this system, the aqueous cell suspension was ejected via a microflow nebulizer and divided by hexanol to generate droplets and the droplet-encapsulated single cells remained intact during the transportation into the ICP system for subsequent detection. The developed online droplet-chip-ICP-MS analysis system achieves stable single cell encapsulation and offers high-throughput performance for single cell analysis.406

5.2 Laser ablation (LA)-ICP-MS

Laser ablation (LA) is a highly effective sample introduction system and microprobe for ICP-MS.407–410 In contrast with conventional sample introduction, the whole cell samples are ablated by a laser in a line-by-line manner before they are introduced into the mass spectrometer.411–417 This special sampling mode provides a high spatial resolution and flexibility with regard to the sample type, greatly enhancing the detection sensitivity.412,418,419 Managh et al. labeled human CD4+ T cells with gadolinium-based magnetic resonance imaging (MRI) contrast agents, and they were able to monitor this sample at the single cell level by LA-ICP-MS.409 The analysis data demonstrated that the Gd-labeled human CD4+ T cells remained detectable for up to 10 days both in vitro and in vivo. This represented the first use of LA-ICP-MS for the single cell tracking of therapeutically-administrated cells. Herrmann et al. established a method for the detection of metals at a high lateral resolution in individual cells based on LA-ICP-MS.417 In this research, the cellular nucleus was stained with an iridium intercalator whereas the whole cell was stained by maleimido-mono-amide-DOTA (mDOTA) complexing lanthanide ions. The absolute quantity of metal staining agent per cell could subsequently be determined by LA-ICP-MS. The metal staining procedure and determination via ICP-MS allowed the direct identification and visualization at the single cell level. Malderen et al. achieved the quantitative determination and subcellular imaging of Cu in an individual cell via LA-ICP-MS coupled with high-density microarray gelatin standards.171 Through this coupled technique they overcame the limitations regarding the precision and throughput of existing LA-ICP-MS approaches and were able to achieve elemental quantification at the cellular level, thus evaluating the concentration of Cu in different cell states as well as its distribution within the cells.

5.3 Mass cytometry

Mass cytometry is a recently developed method merging time-of-flight ICP-MS with flow cytometry. Many researchers have used this technique for single-cell analysis.420–439 Guo et al. determined the concentration of silver in bacteria at the single-cell level.429 Ruthenium red was chosen as a marker for all cells of a population while a parallel application of cisplatin discriminated live from dead cells and live or dead subpopulations were analyzed by flow cytometry. By this method, Guo et al. successfully detected and quantified the silver in single bacterial cells of different physiological states. Additionally, Yang et al. employed mass cytometry to investigate the biodistribution of inorganic nanoparticles at the single-cell level.439 This technique achieved high-throughput quantification of inorganic nanoparticles, thus providing a new level of insight into the fate of nanoparticles in vivo. In addition, Orecchioni et al. used single-cell mass cytometry to investigate the effects of graphene oxide (GO) and amino group-functionalized GO (GONH2) on 15 immune cell populations, interrogating 30 markers at the single-cell level.427 Through this work they proposed an innovative approach for the analysis of the effects of nanomaterials on distinct immune cells.

6. Conclusions and future perspectives

Single-cell analysis has attracted growing attention from researchers in various fields of biology due to its effectiveness as a tool to study the precise mechanisms of cellular and molecular behavior. The development of sensitive and selective mass spectrometry techniques allows the study and quantification of chemicals in single cells. After years of development, the range of available mass spectrometry techniques for single-cell analysis has grown increasingly diverse. Herein, we have provided an overview of these analysis methods, including ESI-MS, MALDI-MS, SI-MS and ICP-MS, and summarized the latest progress that has been achieved with single-cell analysis and imaging research by these various mass spectrometry techniques. The diversified development of these methodologies is beneficial to researchers pursuing single-cell analysis, as each method has its own strengths and weaknesses. Notably, the different methods could complement each other to provide more comprehensive data and thus promote the development of single-cell mass spectrometry analysis as a well-developed discipline. In addition, the study of cellular environments and individual cells is accompanied by higher technological demands and thus demonstrates the need for mass spectrometry systems with improved capabilities, particularly with regard to the detection sensitivity, as well as the time and spatial resolution. As this technology continues to advance and yields even higher spatial resolution and sensitivity, mass spectrometry will play an increasingly crucial role in single-cell analysis, and in turn it will also promote the advancement of biological research. This technology may provide a powerful means of achieving early detection of various diseases and will yield valuable insight with regard to pathogenesis and cell metabolism.

Conflicts of interest

There are no conflicts to declare.


This research was supported by the National Natural Science Foundation of China (Grant No. 81603182, 81430087, 81673396, 81473142, 31700713 and 81102383), the Science and Technology Major Specialized Projects for “significant new drugs creation” of the 12th five-year plan (2012ZX09303-015, 2014ZX09303303) and the National Key Technology R&D Program of the Ministry of Science and Technology (2012BAI30B00), CERS-1-70 (CERS-China Equipment and Education Resources System).


  1. K. Paulsson, Cytogenet. Genome Res., 2013, 139, 174–180 CrossRef CAS PubMed.
  2. A. K. Gustavsson, D. D. van Niekerk, C. B. Adiels, M. Goksor and J. L. Snoep, FEBS Lett., 2014, 588, 3–7 CrossRef CAS PubMed.
  3. A. F. Coskun, U. Eser and S. Islam, Mol. BioSyst., 2016, 12, 2965–2979 RSC.
  4. D. T. Miyamoto, D. T. Ting, M. Toner, S. Maheswaran and D. A. Haber, Cold Spring Harbor Symp. Quant. Biol., 2016, 81, 269–274 CrossRef PubMed.
  5. L. Zhang and A. Vertes, Angew. Chem., Int. Ed., 2018, 57, 4466–4477 CrossRef CAS PubMed.
  6. J. A. Griffiths, A. Scialdone and J. C. Marioni, Mol. Syst. Biol., 2018, 14, e8046 CrossRef PubMed.
  7. G. D. Gutierrez, J. Gromada and L. Sussel, Front. Genet., 2017, 8, 22 CrossRef PubMed.
  8. E. Papalexi and R. Satija, Nat. Rev. Immunol., 2018, 18, 35–45 CrossRef CAS PubMed.
  9. I. Dagogo-Jack and A. T. Shaw, Nat. Rev. Clin. Oncol., 2018, 15, 81–94 CrossRef CAS PubMed.
  10. N. W. Bateman and T. P. Conrads, J. Pathol., 2018, 244, 628–637 CrossRef PubMed.
  11. K. A. Janes, Curr. Opin. Biotechnol., 2016, 39, 120–125 CrossRef CAS PubMed.
  12. I. C. Macaulay and T. Voet, PLoS Genet., 2014, 10, e1004126 CrossRef PubMed.
  13. S. C. Ren, M. Qu and Y. H. Sun, Asian J. Androl., 2013, 15, 729–734 CrossRef CAS PubMed.
  14. C. R. Evans and J. Ling, Curr. Genet., 2018, 64(3), 551–554 CrossRef CAS PubMed.
  15. Z. Cao and S. Wu, Int. J. Biochem. Cell Biol., 2017, 90, 167–171 CrossRef CAS PubMed.
  16. A. da Cruz Paula and C. Lopes, Anticancer Res., 2017, 37, 2173–2183 CrossRef PubMed.
  17. Y. Cheng and E. W. Newell, Adv. Immunol., 2016, 131, 101–134 CAS.
  18. T. Haselgrubler, M. Haider, B. Ji, K. Juhasz, A. Sonnleitner, Z. Balogi and J. Hesse, Anal. Bioanal. Chem., 2014, 406, 3279–3296 CrossRef PubMed.
  19. D. Petras, A. K. Jarmusch and P. C. Dorrestein, Curr. Opin. Chem. Biol., 2017, 36, 24–31 CrossRef CAS PubMed.
  20. I. C. Macaulay, C. P. Ponting and T. Voet, Trends Genet., 2017, 33, 155–168 CrossRef CAS PubMed.
  21. N. S. Barteneva and I. A. Vorobjev, Methods Mol. Biol., 2018, 1745, 3–23 CrossRef CAS PubMed.
  22. P. K. Chattopadhyay, T. M. Gierahn, M. Roederer and J. C. Love, Nat. Immunol., 2014, 15, 128–135 CrossRef CAS PubMed.
  23. G. Li, E. Dzilic, N. Flores, A. Shieh and S. M. Wu, J. Thorac. Dis., 2017, 9, S9–S16 CrossRef PubMed.
  24. G. Kelsey, O. Stegle and W. Reik, Science, 2017, 358, 69–75 CrossRef CAS PubMed.
  25. A. Jou and J. Hess, Oncol. Res. Treat., 2017, 40, 328–332 CrossRef CAS PubMed.
  26. A. Guillaumet-Adkins and H. Heyn, Adv. Exp. Med. Biol., 2017, 978, 393–407 CrossRef CAS PubMed.
  27. P. Abraham and T. T. Maliekal, Cell. Mol. Life Sci., 2017, 74, 1177–1189 CrossRef CAS PubMed.
  28. B. L. Khoo, P. K. Chaudhuri, N. Ramalingam, D. S. Tan, C. T. Lim and M. E. Warkiani, Int. J. Cancer, 2016, 139, 243–255 CrossRef CAS PubMed.
  29. L. Valihrach, P. Androvic and M. Kubista, Int. J. Mol. Sci., 2018, 19(3), pii: E807 CrossRef PubMed.
  30. R. Kodzius and T. Gojobori, Gene, 2016, 576, 701–707 CrossRef CAS PubMed.
  31. P. Hu, W. Zhang, H. Xin and G. Deng, Front. Cell Dev. Biol., 2016, 4, 116 Search PubMed.
  32. J. R. Heath, A. Ribas and P. S. Mischel, Nat. Rev. Drug Discovery, 2016, 15, 204–216 CrossRef CAS PubMed.
  33. A. Ciuffi, S. Rato and A. Telenti, Viruses, 2016, 8(5), 123 CrossRef PubMed.
  34. R. Kirchner and M. Alunni-Fabbroni, Methods Mol. Biol., 2015, 1347, 43–55 CrossRef CAS PubMed.
  35. J. Candia, J. R. Banavar and W. Losert, J. Phys.: Condens. Matter, 2014, 26, 073102 CrossRef PubMed.
  36. P. Bheda and R. Schneider, Trends Cell Biol., 2014, 24, 712–723 CrossRef PubMed.
  37. A. Grunberger, W. Wiechert and D. Kohlheyer, Curr. Opin. Biotechnol., 2014, 29, 15–23 CrossRef PubMed.
  38. V. Proserpio and B. Mahata, Immunology, 2016, 147, 133–140 CrossRef CAS PubMed.
  39. V. Proserpio and T. Lonnberg, Immunol. Cell Biol., 2016, 94, 225–229 CrossRef CAS PubMed.
  40. K. Kogermann, M. Putrins and T. Tenson, Eur. J. Pharm. Sci., 2016, 95, 2–16 CrossRef CAS PubMed.
  41. T. W. Murphy, Q. Zhang, L. B. Naler, S. Ma and C. Lu, Analyst, 2017, 143, 60–80 RSC.
  42. C. Ziegenhain, B. Vieth, S. Parekh, I. Hellmann and W. Enard, Briefings Funct. Genomics, 2018, 17, 220–232 CrossRef PubMed.
  43. Q. Zhao, A. Eichten, A. Parveen, C. Adler, Y. Huang, W. Wang, Y. Ding, A. Adler, T. Nevins, M. Ni, Y. Wei and G. Thurston, Cancer Res., 2018, 78, 2370–2382 CrossRef CAS PubMed.
  44. H. Zhao, T. Li, Y. Zhao, T. Tan, C. Liu, Y. Liu, L. Chang, N. Huang, C. Li, Y. Fan, Y. Yu, R. Li and J. Qiao, Antioxid. Redox Signaling, 2018 DOI:10.1089/ars.2017.7151.
  45. H. Wu, A. F. Malone, E. L. Donnelly, Y. Kirita, K. Uchimura, S. M. Ramakrishnan, J. P. Gaut and B. D. Humphreys, J. Am. Soc. Nephrol., 2018, 29, 2069–2080 CrossRef PubMed.
  46. B. Pijuan-Sala, C. Guibentif and B. Gottgens, Nat. Rev. Mol. Cell Biol., 2018, 19, 399–412 CrossRef CAS PubMed.
  47. L. Alberti-Servera, L. von Muenchow, P. Tsapogas, G. Capoferri, K. Eschbach, C. Beisel, R. Ceredig, R. Ivanek and A. Rolink, EMBO J., 2017, 36, 3619–3633 CrossRef CAS PubMed.
  48. P. Strang, A. Lindgren and U. Stendahl, Acta Radiol.: Oncol., 1985, 24, 337–341 CAS.
  49. S. Alberti, D. R. Parks and L. A. Herzenberg, Cytometry, 1987, 8, 114–119 CrossRef CAS PubMed.
  50. F. Miao, P. Todd and D. S. Kompala, Biotechnol. Bioeng., 1993, 42, 708–715 CrossRef CAS PubMed.
  51. H. M. Davey and D. B. Kell, Microbiol. Rev., 1996, 60, 641–696 CAS.
  52. H. Gaines, L. Andersson and G. Biberfeld, J. Immunol. Methods, 1996, 195, 63–72 CrossRef CAS PubMed.
  53. J. L. Collier, J. Phycol., 2000, 36, 628–644 CrossRef PubMed.
  54. B. G. Dorner, S. Steinbach, M. B. Huser, R. A. Kroczek and A. Scheffold, J. Immunol. Methods, 2003, 274, 83–91 CrossRef CAS PubMed.
  55. P. O. Krutzik, J. M. Crane, M. R. Clutter and G. P. Nolan, Nat. Chem. Biol., 2008, 4, 132–142 CrossRef CAS PubMed.
  56. Y. Li, B. Cornelis, A. Dusa, G. Vanmeerbeeck, D. Vercruysse, E. Sohn, K. Blaszkiewicz, D. Prodanov, P. Schelkens and L. Lagae, Comput. Biol. Med., 2018, 96, 147–156 CrossRef PubMed.
  57. J. Yeo, X. C. Wang and M. C. Hammond, Methods Mol. Biol., 2017, 1657, 111–130 CrossRef CAS PubMed.
  58. W. Pankow, K. Neumann, J. Ruschoff and P. von Wichert, Verh. Dtsch. Ges. Pathol., 1990, 74, 253–257 CAS.
  59. U. Kubitscheck, M. Kircheis, R. Schweitzer-Stenner, W. Dreybrodt, T. M. Jovin and I. Pecht, Biophys. J., 1991, 60, 307–318 CrossRef CAS PubMed.
  60. V. Sabado and E. Nagoshi, J. Visualized Exp., 2018, 131, e57015 Search PubMed.
  61. C. Guzman, X. Han, E. Shoguchi and S. N. Chormaic, Methods Appl. Fluoresc., 2018, 6, 035003 CrossRef PubMed.
  62. E. Alvarez, E. Nogueira and A. Lopez-Urrutia, Appl. Environ. Microbiol., 2017, 83(7), pii: e03317-16 CrossRef PubMed.
  63. A. M. Crawford, P. Kurecka, T. K. Yim, C. Kozemchak, A. Deb, L. Dostal, C. J. Sun, D. L. Brewe, R. Barrea and J. E. Penner-Hahn, J. Synchrotron Radiat., 2016, 23, 901–908 CrossRef CAS PubMed.
  64. A. Bridier, F. Hammes, A. Canette, T. Bouchez and R. Briandet, Int. J. Food Microbiol., 2015, 213, 2–16 CrossRef CAS PubMed.
  65. R. Dojcilovic, J. D. Pajovic, D. K. Bozanic, V. V. Vodnik, S. Dimitrijevic-Brankovic, A. R. Milosavljevic, S. Kascakova, M. Refregiers and V. Djokovic, Analyst, 2016, 141, 1988–1996 RSC.
  66. K. Majzner, S. Tott, L. Roussille, V. Deckert, S. Chlopicki and M. Baranska, Analyst, 2018, 143, 970–980 RSC.
  67. Y. Zhang, L. Jin, J. Xu, Y. Yu, L. Shen, J. Gao and A. Ye, Analyst, 2017, 143, 164–174 RSC.
  68. N. E. Dina, H. Zhou, A. Colnita, N. Leopold, T. Szoke-Nagy, C. Coman and C. Haisch, Analyst, 2017, 142, 1782–1789 RSC.
  69. I. W. Schie, R. Kiselev, C. Krafft and J. Popp, Analyst, 2016, 141, 6387–6395 RSC.
  70. A. Kaczor, K. Turnau and M. Baranska, Analyst, 2011, 136, 1109–1112 RSC.
  71. J. Zhang, S. Yan, Z. He, C. Ding, T. Zhai, Y. Chen, H. Li, G. Yang, X. Zhou and P. Wang, J. Phys. Chem. Lett., 2018, 9, 4679–4685 CrossRef CAS PubMed.
  72. G. Pezzotti, S. Horiguchi, F. Boschetto, T. Adachi, E. Marin, W. Zhu, T. Yamamoto, N. Kanamura, E. Ohgitani and O. Mazda, ACS Chem. Neurosci., 2018 DOI:10.1021/acschemneuro.8b00235.
  73. M. Navas-Moreno and J. W. Chan, Methods Mol. Biol., 2018, 1745, 219–257 CrossRef CAS PubMed.
  74. T. Kaya, Y. S. Torisawa, D. Oyamatsu, M. Nishizawa and T. Matsue, Biosens. Bioelectron., 2003, 18, 1379–1383 CrossRef CAS PubMed.
  75. P. Actis, S. Tokar, J. Clausmeyer, B. Babakinejad, S. Mikhaleva, R. Cornut, Y. Takahashi, A. Lopez Cordoba, P. Novak, A. I. Shevchuck, J. A. Dougan, S. G. Kazarian, P. V. Gorelkin, A. S. Erofeev, I. V. Yaminsky, P. R. Unwin, W. Schuhmann, D. Klenerman, D. A. Rusakov, E. V. Sviderskaya and Y. E. Korchev, ACS Nano, 2014, 8, 875–884 CrossRef CAS PubMed.
  76. T. Murata, T. Yasukawa, H. Shiku and T. Matsue, Biosens. Bioelectron., 2009, 25, 913–919 CrossRef CAS PubMed.
  77. D. Koley and A. J. Bard, Proc. Natl. Acad. Sci. U. S. A., 2010, 107, 16783–16787 CrossRef CAS PubMed.
  78. H. Liu, G. J. Newton, R. Nakamura, K. Hashimoto and S. Nakanishi, Angew. Chem., Int. Ed., 2010, 49, 6596–6599 CrossRef CAS PubMed.
  79. Y. Takahashi, T. Miyamoto, H. Shiku, R. Asano, T. Yasukawa, I. Kumagai and T. Matsue, Anal. Chem., 2009, 81, 2785–2790 CrossRef CAS PubMed.
  80. C. Amatore, S. Arbault, M. Guille and F. Lemaitre, Chem. Rev., 2008, 108, 2585–2621 CrossRef CAS PubMed.
  81. F. Xia, W. Jin, X. Yin and Z. Fang, J. Chromatogr. A, 2005, 1063, 227–233 CrossRef CAS PubMed.
  82. L. Zhang, S. Qv, Z. Wang and J. Cheng, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci., 2003, 792, 381–385 CrossRef CAS.
  83. R. B. Tjalkens, D. L. Carbone and G. Wu, Methods Mol. Biol., 2011, 758, 267–277 CrossRef CAS PubMed.
  84. L. Yu, T. Yao, S. Ni and S. Zeng, Biomed. Chromatogr., 2005, 19, 191–195 CrossRef CAS PubMed.
  85. J. Muhling, M. Fuchs, M. E. Campos, J. Gonter, J. M. Engel, A. Sablotzki, T. Menges, S. Weiss, M. G. Dehne, M. Krull and G. Hempelmann, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci., 2003, 789, 383–392 CrossRef CAS.
  86. J. L. Zabzdyr and S. J. Lillard, J. Chromatogr. A, 2001, 911, 269–276 CrossRef CAS PubMed.
  87. Q. Xue and E. S. Yeung, J. Chromatogr. B: Biomed. Sci. Appl., 1996, 677, 233–240 CrossRef PubMed.
  88. Z. Liu, L. Wang, J. D. Welch, H. Ma, Y. Zhou, H. R. Vaseghi, S. Yu, J. B. Wall, S. Alimohamadi, M. Zheng, C. Yin, W. Shen, J. F. Prins, J. Liu and L. Qian, Nature, 2017, 551, 100–104 CrossRef PubMed.
  89. Z. Liu, S. K. Hu, V. Campbell, A. O. Tatters, K. B. Heidelberg and D. A. Caron, ISME J., 2017, 11, 1282–1285 CrossRef CAS PubMed.
  90. H. E. Munoz, M. Li, C. T. Riche, N. Nitta, E. Diebold, J. Lin, K. Owsley, M. Bahr, K. Goda and D. Di Carlo, Anal. Chem., 2018, 90(19), 11280–11289 CrossRef CAS PubMed.
  91. T. Miura, H. Mikami, A. Isozaki, T. Ito, Y. Ozeki and K. Goda, Biomed. Opt. Express, 2018, 9, 3424–3433 CrossRef PubMed.
  92. K. M. McKinnon, Curr. Protoc. Immunol., 2018, 120, 511–5111 Search PubMed.
  93. A. L. Frelinger 3rd, Platelets, 2018, 1–7,  DOI:10.1080/09537104.2018.1478073.
  94. J. van Krugten and E. J. G. Peterman, Methods Mol. Biol., 2018, 1665, 145–154 CrossRef CAS PubMed.
  95. K. Taruc, C. Yin, D. G. Wootton and B. Heit, J. Visualized Exp., 2018, 138, e58149 Search PubMed.
  96. A. B. Alber and D. M. Suter, J. Visualized Exp., 2018, 132, e56604 Search PubMed.
  97. S. Chung, P. Chandra, J. P. Koo and Y. B. Shim, Biosens. Bioelectron., 2018, 100, 396–403 CrossRef CAS PubMed.
  98. M. Bozem, P. Knapp, V. Mirceski, E. J. Slowik, I. Bogeski, R. Kappl, C. Heinemann and M. Hoth, Antioxid. Redox Signaling, 2018, 29, 501–517 CrossRef CAS PubMed.
  99. T. Masujima, Anal. Sci., 2009, 25, 953–960 CrossRef CAS PubMed.
  100. T. H. Ong, E. G. Tillmaand, M. Makurath, S. S. Rubakhin and J. V. Sweedler, Biochim. Biophys. Acta, 2015, 1854, 732–740 CrossRef CAS PubMed.
  101. F. Yuan, D. W. Zhang, J. X. Liu, Y. L. Zhou and X. X. Zhang, Analyst, 2016, 141, 6338–6343 RSC.
  102. R. M. Onjiko, S. E. Morris, S. A. Moody and P. Nemes, Analyst, 2016, 141, 3648–3656 RSC.
  103. A. N. Polat and N. Ozlu, Analyst, 2014, 139, 4733–4749 RSC.
  104. W. Rao, N. Pan and Z. Yang, J. Visualized Exp., 2016, 112, e53911 Search PubMed.
  105. R. M. Onjiko, D. O. Plotnick, S. A. Moody and P. Nemes, Anal. Methods, 2017, 9, 4964–4970 RSC.
  106. X. C. Zhang, Z. W. Wei, X. Y. Gong, X. Y. Si, Y. Y. Zhao, C. D. Yang, S. C. Zhang and X. R. Zhang, Sci. Rep., 2016, 6, 24730 CrossRef PubMed.
  107. X. Cahours, T. T. Tran, N. Mesplet, C. Kieda, P. Morin and L. A. Agrofoglio, J. Pharm. Biomed. Anal., 2001, 26, 819–827 CrossRef CAS PubMed.
  108. R. Schneiter, B. Brugger, R. Sandhoff, G. Zellnig, A. Leber, M. Lampl, K. Athenstaedt, C. Hrastnik, S. Eder, G. Daum, F. Paltauf, F. T. Wieland and S. D. Kohlwein, J. Cell Biol., 1999, 146, 741–754 CrossRef CAS PubMed.
  109. L. Cui, K. Nithipatikom and W. B. Campbell, Anal. Biochem., 2007, 369, 27–33 CrossRef CAS PubMed.
  110. V. A. Tyurin, Y. Y. Tyurina, M. Y. Jung, M. A. Tungekar, K. J. Wasserloos, H. Bayir, J. S. Greenberger, P. M. Kochanek, A. A. Shvedova, B. Pitt and V. E. Kagan, J. Chromatogr. B: Anal. Technol. Biomed. Life Sci., 2009, 877, 2863–2872 CrossRef CAS PubMed.
  111. J. Yu, C. Li, S. Shen, X. Liu, Y. Peng and J. Zheng, Rapid Commun. Mass Spectrom., 2015, 29, 681–689 CrossRef CAS PubMed.
  112. J. Brison, M. A. Robinson, D. S. Benoit, S. Muramoto, P. S. Stayton and D. G. Castner, Anal. Chem., 2013, 85, 10869–10877 CrossRef CAS PubMed.
  113. J. Kokesch-Himmelreich, B. Woltmann, B. Torger, M. Rohnke, S. Arnhold, U. Hempel, M. Muller and J. Janek, Anal. Bioanal. Chem., 2015, 407, 4555–4565 CrossRef CAS PubMed.
  114. J. Bobrowska, J. Pabijan, J. Wiltowska-Zuber, B. R. Jany, F. Krok, K. Awsiuk, J. Rysz, A. Budkowski and M. Lekka, Anal. Biochem., 2016, 511, 52–60 CrossRef CAS PubMed.
  115. E. Gibbin, A. Gavish, I. Domart-Coulon, E. Kramarsky-Winter, O. Shapiro, A. Meibom and A. Vardi, BMC Microbiol., 2018, 18, 39 CrossRef CAS PubMed.
  116. H. Tian, D. A. Six, T. Krucker, J. A. Leeds and N. Winograd, Anal. Chem., 2017, 89, 5050–5057 CrossRef CAS PubMed.
  117. T. D. Do, T. J. Comi, S. J. Dunham, S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2017, 89, 3078–3086 CrossRef CAS PubMed.
  118. A. N. Bloom, H. Tian, C. Schoen and N. Winograd, Anal. Bioanal. Chem., 2017, 409, 3067–3076 CrossRef CAS PubMed.
  119. Q. P. Vanbellingen, A. Castellanos, M. Rodriguez-Silva, I. Paudel, J. W. Chambers and F. A. Fernandez-Lima, J. Am. Soc. Mass Spectrom., 2016, 27, 2033–2040 CrossRef CAS PubMed.
  120. J. Saatz, H. Stryhanyuk, D. Vetterlein, N. Musat, M. Otto, T. Reemtsma, H. H. Richnow and B. Daus, Environ. Pollut., 2016, 216, 245–252 CrossRef CAS PubMed.
  121. L. J. Gamble and C. R. Anderton, Microsc. Today, 2016, 24, 24–31 Search PubMed.
  122. L. Li, R. W. Garden and J. V. Sweedler, Trends Biotechnol., 2000, 18, 151–160 CrossRef CAS PubMed.
  123. T. Jaschinski, E. J. Helfrich, C. Bock, S. Wolfram, A. Svatos, C. Hertweck and G. Pohnert, J. Mass Spectrom., 2014, 49, 136–144 CrossRef CAS PubMed.
  124. D. C. Perdian, S. Cha, J. Oh, D. S. Sakaguchi, E. S. Yeung and Y. J. Lee, Rapid Commun. Mass Spectrom., 2010, 24, 1147–1154 CrossRef CAS PubMed.
  125. B. N. Walker, J. A. Stolee and A. Vertes, Anal. Chem., 2012, 84, 7756–7762 CrossRef CAS PubMed.
  126. A. Bondarenko, Y. Zhu, L. Qiao, F. Cortes Salazar, H. Pick and H. H. Girault, Analyst, 2016, 141, 3403–3410 RSC.
  127. Q. Fu, J. Tang, M. Cui, J. Xing, Z. Liu and S. Liu, J. Mass Spectrom., 2016, 51, 62–68 CrossRef CAS PubMed.
  128. A. Amantonico, P. L. Urban, S. R. Fagerer, R. M. Balabin and R. Zenobi, Anal. Chem., 2010, 82, 7394–7400 CrossRef CAS PubMed.
  129. Y. Gholipour, H. Nonami and R. Erra-Balsells, J. Am. Soc. Mass Spectrom., 2008, 19, 1841–1848 CrossRef CAS PubMed.
  130. S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2008, 80, 7128–7136 CrossRef CAS PubMed.
  131. M. E. Duenas, J. J. Essner and Y. J. Lee, Sci. Rep., 2017, 7, 14946 CrossRef PubMed.
  132. V. Labas, A. P. Teixeira-Gomes, L. Bouguereau, A. Gargaros, L. Spina, A. Marestaing and S. Uzbekova, J. Proteomics, 2018, 175, 56–74 CrossRef CAS PubMed.
  133. M. Shimizu, N. Ojima, H. Ohnishi, T. Shingaki, Y. Hirakawa and T. Masujima, Anal. Sci., 2003, 19, 49–53 CrossRef CAS PubMed.
  134. C. Xiong, X. Zhou, Q. He, X. Huang, J. Wang, W. P. Peng, H. C. Chang and Z. Nie, Anal. Chem., 2016, 88, 11913–11918 CrossRef CAS PubMed.
  135. J. Krismer, R. F. Steinhoff and R. Zenobi, Chimia, 2016, 70, 236–239 CrossRef CAS PubMed.
  136. L. Mueller, H. Traub, N. Jakubowski, D. Drescher, V. I. Baranov and J. Kneipp, Anal. Bioanal. Chem., 2014, 406, 6963–6977 CrossRef CAS PubMed.
  137. S. Miyashita, A. S. Groombridge, S. Fujii, A. Takatsu, K. Chiba and K. Inagaki, Anal. Sci., 2014, 30, 219–224 CrossRef CAS PubMed.
  138. R. C. Merrifield, C. Stephan and J. R. Lead, Environ. Sci. Technol., 2018, 52, 2271–2277 CrossRef CAS PubMed.
  139. S. Meyer, A. Lopez-Serrano, H. Mitze, N. Jakubowski and T. Schwerdtle, Metallomics, 2018, 10, 73–76 RSC.
  140. M. Corte Rodriguez, R. Alvarez-Fernandez Garcia, E. Blanco, J. Bettmer and M. Montes-Bayon, Anal. Chem., 2017, 89, 11491–11497 CrossRef CAS PubMed.
  141. H. Wang, M. Wang, B. Wang, L. Zheng, H. Chen, Z. Chai and W. Feng, Anal. Bioanal. Chem., 2017, 409, 1415–1423 CrossRef CAS PubMed.
  142. R. W. Hutchinson and J. A. Hutchinson, Transplantation, 2015, 99, 2237–2238 CrossRef PubMed.
  143. A. Sirikatitham, T. Yamamoto, M. Shimizu, T. Hasegawa, N. Tsuyama and T. Masujima, Rapid Commun. Mass Spectrom., 2007, 21, 385–390 CrossRef CAS PubMed.
  144. S. Crotti, R. Seraglia and P. Traldi, Eur. J. Mass Spectrom., 2011, 17, 85–99 CrossRef CAS PubMed.
  145. T. Lapainis, S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2009, 81, 5858–5864 CrossRef CAS PubMed.
  146. J. X. Liu, J. T. Aerts, S. S. Rubakhin, X. X. Zhang and J. V. Sweedler, Analyst, 2014, 139, 5835–5842 RSC.
  147. P. Nemes, A. M. Knolhoff, S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2011, 83, 6810–6817 CrossRef CAS PubMed.
  148. Z. Yu, L. C. Chen, S. Ninomiya, M. K. Mandal, K. Hiraoka and H. Nonami, Analyst, 2014, 139, 5734–5739 RSC.
  149. H. Mizuno, N. Tsuyama, S. Date, T. Harada and T. Masujima, Anal. Sci., 2008, 24, 1525–1527 CrossRef CAS PubMed.
  150. N. Pan, W. Rao, N. R. Kothapalli, R. M. Liu, A. W. G. Burgett and Z. B. Yang, Anal. Chem., 2014, 86, 9376–9380 CrossRef CAS PubMed.
  151. Y. Gholipour, R. Erra-Balsells, K. Hiraoka and H. Nonami, Anal. Biochem., 2013, 433, 70–78 CrossRef CAS PubMed.
  152. S. M. Abed, X. Q. Zou, A. H. Ali, Q. Z. Jin and X. G. Wang, J. Food Compos. Anal., 2017, 62, 245–253 CrossRef CAS.
  153. X. T. Li, S. L. Zhao and Y. M. Liu, ChemistrySelect, 2016, 1, 5554–5560 CrossRef CAS.
  154. C. Lombard-Banek, S. Reddy, S. A. Moody and P. Nemes, Mol. Cell. Proteomics, 2016, 15, 2756–2768 CrossRef CAS PubMed.
  155. F. M. Chen, L. Y. Lin, J. Zhang, Z. Y. He, K. Uchiyama and J. M. Lin, Anal. Chem., 2016, 88, 4354–4360 CrossRef CAS PubMed.
  156. T. Nakashima, H. Wada, S. Morita, R. Erra-Balsells, K. Hiraoka and H. Nonami, Anal. Chem., 2016, 88, 3049–3057 CrossRef CAS PubMed.
  157. Z. Yu, L. C. Chen, S. Ninomiya, M. K. Mandal, K. Hiraoka and H. Nonami, Analyst, 2014, 139, 5734–5739 RSC.
  158. L. W. Zhang, D. P. Foreman, P. A. Grant, B. Shrestha, S. A. Moody, F. Villiers, J. M. Kwake and A. Vertes, Analyst, 2014, 139, 5079–5085 RSC.
  159. R. Zenobi, Science, 2013, 342, 1243259 CrossRef CAS PubMed.
  160. A. Oikawa and K. Saito, Plant J., 2012, 70, 30–38 CrossRef CAS PubMed.
  161. A. Schmid and P. Neubauer, Curr. Opin. Biotechnol., 2010, 21, 1–3 CrossRef CAS PubMed.
  162. R. M. Onjiko, D. O. Plotnick, S. A. Moody and P. Nemes, Anal. Methods, 2017, 9, 4964–4970 RSC.
  163. X. C. Zhang, Z. W. Wei, X. Y. Gong, X. Y. Si, Y. Y. Zhao, C. D. Yang, S. C. Zhang and X. R. Zhang, Sci. Rep., 2016, 6, 24730 CrossRef PubMed.
  164. X. Y. Gong, Y. Y. Zhao, S. Q. Cai, S. J. Fu, C. D. Yang, S. C. Zhang and X. R. Zhang, Anal. Chem., 2014, 86, 3809–3816 CrossRef CAS PubMed.
  165. H. Mizuno, N. Tsuyama, T. Harada and T. Masujima, J. Mass Spectrom., 2008, 43, 1692–1700 CrossRef CAS PubMed.
  166. X. Gong, Y. Zhao, S. Cai, S. Fu, C. Yang, S. Zhang and X. Zhang, Anal. Chem., 2014, 86, 3809–3816 CrossRef CAS PubMed.
  167. T. Fujii, S. Matsuda, M. L. Tejedor, T. Esaki, I. Sakane, H. Mizuno, N. Tsuyama and T. Masujima, Nat. Protoc., 2015, 10, 1445–1456 CrossRef CAS PubMed.
  168. X. F. Sun, R. T. Kelly, K. Q. Tang and R. D. Smith, Anal. Chem., 2011, 83, 5797–5803 CrossRef CAS PubMed.
  169. L. W. Zhang, C. J. Sevinsky, B. M. Davis and A. Vertes, Anal. Chem., 2018, 90, 4626–4634 CrossRef CAS PubMed.
  170. R. M. Onjiko, E. P. Portero, S. A. Moody and P. Nemes, Anal. Chem., 2017, 89, 7069–7076 CrossRef CAS PubMed.
  171. S. J. M. Van Malderen, E. Vergucht, M. De Rijcke, C. Janssen, L. Vincze and F. Vanhaecke, Anal. Chem., 2016, 88, 5783–5789 CrossRef CAS PubMed.
  172. J. Krismer, M. Tamminen, S. Fontana, R. Zenobi and A. Narwani, ISME J., 2017, 11, 988–998 CrossRef CAS PubMed.
  173. C. Lombard-Banek, S. A. Moody and P. Nemes, Angew. Chem., Int. Ed., 2016, 55, 2454–2458 CrossRef CAS PubMed.
  174. A. Ali, Y. Abouleila, S. Amer, R. Furushima, S. Emara, S. Equis, Y. Cotte and T. Masujima, Anal. Sci., 2016, 32, 125–127 CrossRef CAS PubMed.
  175. J. X. Liu, J. T. Aerts, S. S. Rubakhin, X. X. Zhang and J. V. Sweedler, Analyst, 2014, 139, 5835–5842 RSC.
  176. S. S. Rubakhin, E. J. Lanni and J. V. Sweedler, Curr. Opin. Biotechnol., 2013, 24, 95–104 CrossRef CAS PubMed.
  177. K. Hiraoka, K. Nishidate, K. Mori, D. Asakawa and S. Suzuki, Rapid Commun. Mass Spectrom., 2007, 21, 3139–3144 CrossRef CAS PubMed.
  178. J. J. Liu, H. Wang, N. E. Manicke, J. M. Lin, R. G. Cooks and Z. Ouyang, Anal. Chem., 2010, 82, 2463–2471 CrossRef CAS PubMed.
  179. H. Wang, J. J. Liu, R. G. Cooks and Z. Ouyang, Angew. Chem., Int. Ed., 2010, 49, 877–880 CrossRef CAS PubMed.
  180. B. Hu, P. K. So and Z. P. Yao, J. Am. Soc. Mass Spectrom., 2013, 24, 57–65 CrossRef CAS PubMed.
  181. B. Hu, P. K. So, H. W. Chen and Z. P. Yao, Anal. Chem., 2011, 83, 8201–8207 CrossRef CAS PubMed.
  182. J. Y. Jeng and J. Shiea, Rapid Commun. Mass Spectrom., 2003, 17, 1709–1713 CrossRef CAS PubMed.
  183. M. G. von Muhlen, N. D. Brault, S. M. Knudsen, S. Y. Jiang and S. R. Manalis, Anal. Chem., 2010, 82, 1905–1910 CrossRef CAS PubMed.
  184. M. K. Mandal, L. C. Chen and K. Hiraoka, J. Am. Soc. Mass Spectrom., 2011, 22, 1493–1500 CrossRef CAS PubMed.
  185. K. Yoshimura, L. C. Chen, D. Asakawa, K. Hiraoka and S. Takeda, J. Mass Spectrom., 2009, 44, 978–985 CrossRef CAS PubMed.
  186. M. K. Mandal, S. Saha, K. Yoshimura, Y. Shida, S. Takeda, H. Nonami and K. Hiraoka, Analyst, 2013, 138, 1682–1688 RSC.
  187. Z. Yu, L. C. Chen, H. Suzuki, O. Ariyada, R. Erra-Balsells, H. Nonami and K. Hiraoka, J. Am. Soc. Mass Spectrom., 2009, 20, 2304–2311 CrossRef CAS PubMed.
  188. Y. Gholipour, H. Nonami and R. Erra-Balsells, J. Am. Soc. Mass Spectrom., 2008, 19, 1841–1848 CrossRef CAS PubMed.
  189. Y. Gholipour, R. Erra-Balsells and H. Nonami, Mass Spectrom., 2012, 1, A0003–A0003 Search PubMed.
  190. N. Pan, W. Rao, S. J. Standke and Z. B. Yang, Anal. Chem., 2016, 88, 6812–6819 CrossRef CAS PubMed.
  191. W. Rao, N. Pan and Z. B. Yang, J. Visualized Exp., 2016, 112, e53911 Search PubMed.
  192. Q. Fu, J. Tang, M. Cui, J. P. Xing, Z. Q. Liu and S. Y. Liu, J. Mass Spectrom., 2016, 51, 62–68 CrossRef CAS PubMed.
  193. T. Esaki and T. Masujima, Anal. Sci., 2015, 31, 1211–1213 CrossRef CAS PubMed.
  194. N. Pan, W. Rao, R. Liu, N. R. Kothapalli, A. W. Burgett and Z. Yang, Planta Med., 2015, 81, 869–869 Search PubMed.
  195. N. Pan, W. Rao, N. R. Kothapalli, R. M. Vowcicefski, A. W. Burgett and Z. B. Yang, Abstr. Pap. Am. Chem. Soc., 2014, 248 Search PubMed.
  196. Z. Takats, J. M. Wiseman, B. Gologan and R. G. Cooks, Science, 2004, 306, 471–473 CrossRef CAS PubMed.
  197. M. Nazari, M. T. Bokhart, P. L. Loziuk and D. C. Muddiman, Analyst, 2018, 143, 654–661 RSC.
  198. C. R. Ferreira, L. S. Eberlin, J. E. Hallett and R. G. Cooks, J. Mass Spectrom., 2012, 47, 29–33 CrossRef CAS PubMed.
  199. A. F. Gonzalez-Serrano, V. Pirro, C. R. Ferreira, P. Oliveri, L. S. Eberlin, J. Heinzmann, A. Lucas-Hahn, H. Niemann and R. G. Cooks, PLoS One, 2013, 8, e74981 CrossRef CAS PubMed.
  200. P. Nemes, I. Marginean and A. Vertes, Anal. Chem., 2007, 79, 3105–3116 CrossRef CAS PubMed.
  201. P. Nemes and A. Vertes, Anal. Chem., 2007, 79, 8098–8106 CrossRef CAS PubMed.
  202. P. Nemes, A. A. Barton, Y. Li and A. Vertes, Anal. Chem., 2008, 80, 4575–4582 CrossRef CAS PubMed.
  203. A. Combes, S. J. Choi, C. Pimentel, H. Darbon, D. Waidelich, D. Mestivier and J. M. Camadro, Eur. J. Mass Spectrom., 2009, 15, 517–529 CrossRef CAS PubMed.
  204. B. H. Li and X. P. Yan, J. Sep. Sci., 2007, 30, 916–922 CrossRef CAS PubMed.
  205. J. A. Stolee, B. Shrestha, G. Mengistu and A. Vertes, Angew. Chem., Int. Ed., 2012, 51, 10386–10389 CrossRef CAS PubMed.
  206. B. Shrestha and A. Vertes, Anal. Chem., 2009, 81, 8265–8271 CrossRef CAS PubMed.
  207. J. A. Stolee and A. Vertes, Anal. Chem., 2013, 85, 3592–3598 CrossRef CAS PubMed.
  208. G. Y. Li, S. M. Yuan, S. H. Zheng, Y. Z. Liu and G. M. Huang, Anal. Chem., 2018, 90, 3409–3415 CrossRef CAS PubMed.
  209. S. R. Fagerer, T. Schmid, A. J. Ibáñez, M. Pabst, R. Steinhoff, K. Jefimovs, P. L. Urban and R. Zenobi, Analyst, 2013, 138, 6732–6736 RSC.
  210. C. Lombard-Banek, S. A. Moody and P. Nemes, Angew. Chem., Int. Ed., 2016, 55, 2454–2458 CrossRef CAS PubMed.
  211. R. M. Onjiko, E. P. Portero, S. A. Moody and P. Nemes, J. Visualized Exp., 2017, 130, e56956 Search PubMed.
  212. T. J. Comi, M. A. Makurath, M. C. Philip, S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2017, 89, 7765–7772 CrossRef CAS PubMed.
  213. X. L. Wang, Y. F. Ma, M. Zhao, M. F. Zhou, Y. Xiao, Z. F. Sun and L. L. Tong, J. Chromatogr. A, 2016, 1469, 128–134 CrossRef CAS PubMed.
  214. Z. X. Zhang, X. F. Guo, H. Wang and H. S. Zhang, Anal. Chem., 2015, 87, 3989–3995 CrossRef CAS PubMed.
  215. X. L. Wang, J. Wang, H. Y. Fu, D. J. Liu and Z. Z. Chen, Electrophoresis, 2014, 35, 3371–3378 CrossRef CAS PubMed.
  216. L. L. Sun, G. J. Zhu, S. Mou, Y. M. Zhao, M. M. Champion and N. J. Dovichi, J. Chromatogr. A, 2014, 1359, 303–308 CrossRef CAS PubMed.
  217. J. T. Aerts, K. R. Louis, S. R. Crandall, G. Govindaiah, C. L. Cox and J. V. Sweedler, Anal. Chem., 2014, 86, 3203–3208 CrossRef CAS PubMed.
  218. R. M. Phillips, E. Bair, D. S. Lawrence, C. E. Sims and N. L. Allbritton, Anal. Chem., 2013, 85, 6136–6142 CrossRef CAS PubMed.
  219. A. J. Dickinson, P. M. Armistead and N. L. Allbritton, Anal. Chem., 2013, 85, 4797–4804 CrossRef CAS PubMed.
  220. K. Kleparnik, Electrophoresis, 2013, 34, 70–85 CrossRef CAS PubMed.
  221. R. M. Phillips, D. S. Lawrence and N. L. Allbritton, Biophys. J., 2012, 102, 666a–667a CrossRef.
  222. C. Cecala, S. S. Rubakhin, J. W. Mitchell, M. U. Gillette and J. V. Sweedler, Analyst, 2012, 137, 2965–2972 RSC.
  223. B. Deng, J. R. Xu, Z. M. Wang and Y. X. Xiao, Prog. Chem., 2010, 22, 2215–2223 CAS.
  224. L. Ren, H. K. Kim and W. W. Zhong, Anal. Chem., 2009, 81, 5510–5516 CrossRef CAS PubMed.
  225. Z. Zhang, T. Albanetti, T. Linkous, C. J. Larkin, R. Schoner, J. B. t. McGivney and N. J. Dovichi, Electrophoresis, 2017, 38, 401–407 CrossRef CAS PubMed.
  226. G. Zhu, L. Sun, J. Heidbrink-Thompson, S. Kuntumalla, H. Y. Lin, C. J. Larkin, J. B. t. McGivney and N. J. Dovichi, Electrophoresis, 2016, 37, 616–622 CrossRef CAS PubMed.
  227. N. Bergman, D. Shevchenko and J. Bergquist, Anal. Bioanal. Chem., 2014, 406, 49–61 CrossRef CAS PubMed.
  228. H. N. Abdelhamid and H. F. Wu, Analyst, 2015, 140, 1555–1565 RSC.
  229. Z. Lam, L. Beynon, M. B. Comisarow, G. G. S. Dutton and A. Bjarnason, Biochem. Soc. Trans., 1991, 19, 922–929 CrossRef CAS PubMed.
  230. S. Bugovsky, W. Winkler, W. Balika, M. Koranda and G. Allmaier, Methods, 2016, 104, 182–193 CrossRef CAS PubMed.
  231. R. Calavia, F. E. Annanouch, X. Correig and O. Yanes, J. Proteomics, 2012, 75, 5061–5068 CrossRef CAS PubMed.
  232. W. T. Chen, I. Tomalova, J. Preisler and H. T. Chang, J. Chin. Chem. Soc., 2011, 58, 769–778 CrossRef CAS.
  233. Y. C. Cheng, K. H. Chen, J. S. Wang, W. L. Hsu, C. C. Chien, W. Y. Chen and C. W. Tsao, Analyst, 2012, 137, 654–661 RSC.
  234. J. Etxebarria, J. Calvo and N. C. Reichardt, Analyst, 2014, 139, 2873–2883 RSC.
  235. J. G. Forsythe, J. A. Broussard, J. L. Lawrie, M. Kliman, Y. Jiao, S. M. Weiss, D. J. Webb and J. A. McLean, Anal. Chem., 2012, 84, 10665–10670 CrossRef CAS PubMed.
  236. J. Gao, K. B. Louie, P. Steinke, B. P. Bowen, M. de Raad, R. N. Zuckermann, G. Siuzdak and T. R. Northen, Anal. Chem., 2017, 89, 6521–6526 CrossRef CAS PubMed.
  237. M. P. Greving, G. J. Patti and G. Siuzdak, Anal. Chem., 2011, 83, 2–7 CrossRef CAS PubMed.
  238. S. M. Huang, J. Q. Xu, X. Tao, X. Chen, F. Zhu, Y. Wang, R. F. Jiang and G. F. Ouyang, Talanta, 2017, 172, 155–161 CrossRef CAS PubMed.
  239. T. Jaschinski, K. Thume, M. Klein, P. Richter, J. Popp, A. Svatos and G. Pohnert, Rapid Commun. Mass Spectrom., 2014, 28, 1521–1529 CrossRef CAS PubMed.
  240. F. Jiang, X. Y. Zhou and J. Huang, Technol. Cancer Res. Treat., 2014, 13, 109–117 CrossRef CAS PubMed.
  241. J. M. Jin, S. Choi, Y. H. Kim, M. H. Choi, J. Kim and S. Kim, J. Am. Soc. Mass Spectrom., 2012, 23, 1450–1453 CrossRef CAS PubMed.
  242. K. Kang, H. Jang and Y. K. Kim, Analyst, 2017, 142, 2372–2377 RSC.
  243. N. Kang, J. M. Lee, A. Jeon, H. B. Han and B. Moon, Tetrahedron, 2016, 72, 5612–5619 CrossRef CAS.
  244. H. Kawasaki, K. Nakai, R. Arakawa, E. K. Athanassiou, R. N. Grass and W. J. Stark, Anal. Chem., 2012, 84, 9268–9275 CrossRef CAS PubMed.
  245. T. R. Kuo, Y. C. Chen, C. I. Wang, T. H. Shen, H. Y. Wang, X. Y. Pan, D. Y. Wang, C. C. Liou, Y. H. Chang, Y. C. Chen, Y. H. Wu, Y. R. Liu, Y. H. Lin, C. C. Hu and C. C. Chen, Nanoscale, 2017, 9, 11119–11125 RSC.
  246. I. Kurylo, M. Dupre, S. Cantel, C. Enjalbal, H. Drobecq, S. Szunerits, O. Melnyk, R. Boukherroub and Y. Coffinier, Analyst, 2017, 142, 969–978 RSC.
  247. P. A. Kuzema, J. Anal. Chem., 2011, 66, 1227–1242 CrossRef CAS.
  248. D. Y. Lee, V. Platt, B. Bowen, K. Louie, C. A. Canaria, C. T. McMurray and T. Northen, Integr. Biol., 2012, 4, 693–699 RSC.
  249. J. Lee, J. Lee, T. D. Chung and W. S. Yeo, Anal. Chim. Acta, 2012, 736, 1–6 CrossRef CAS PubMed.
  250. J. Li, X. K. Hu and R. H. Lipson, Anal. Chem., 2013, 85, 5499–5505 CrossRef CAS PubMed.
  251. C. W. Liu, M. W. Chien, C. Y. Su, H. Y. Chen, L. J. Li and C. C. Lai, Analyst, 2012, 137, 5809–5816 RSC.
  252. T. Lu and S. V. Olesik, Anal. Chem., 2013, 85, 4384–4391 CrossRef CAS PubMed.
  253. T. H. Ong, D. J. Kissick, E. T. Jansson, T. J. Comi, E. V. Romanova, S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2015, 87, 7036–7042 CrossRef CAS PubMed.
  254. C. Piotto, G. Guella and P. Bettotti, Rapid Commun. Mass Spectrom., 2017, 31, 1228–1230 CrossRef CAS PubMed.
  255. P. Pogam, A. Schinkovitz, B. Legouin, A. C. Le Lamer, J. Boustie and P. Richomme, Anal. Chem., 2015, 87, 10421–10428 CrossRef PubMed.
  256. M. N. Qureshi, G. Stecher, G. Abel and G. K. Bonn, Amino Acids, 2011, 41, S73–S73 CrossRef PubMed.
  257. M. N. Qureshi, G. Stecher and G. K. Bonn, Anal. Lett., 2013, 46, 29–34 CrossRef.
  258. M. Rainer, M. N. Qureshi and G. K. Bonn, Anal. Bioanal. Chem., 2011, 400, 2281–2288 CrossRef CAS PubMed.
  259. C. Y. Shi and C. H. Deng, Analyst, 2016, 141, 2816–2826 RSC.
  260. R. Singh, L. W. Bezuidenhout, A. Jemere, Z. Wang, M. Brett and D. J. Harrison, Rapid Commun. Mass Spectrom., 2017, 31, 631–638 CrossRef CAS PubMed.
  261. R. M. Sturm, T. Greer, R. B. Chen, B. Hensen and L. J. Li, Anal. Methods, 2013, 5, 1623–1628 RSC.
  262. A. Tata, A. M. A. P. Fernandes, V. G. Santos, R. M. Alberici, D. Araldi, C. A. Parada, W. Braguini, L. Veronez, G. S. Bisson, F. H. Z. Reis, L. C. Alberici and M. N. Eberlin, Anal. Chem., 2012, 84, 6341–6345 CrossRef CAS PubMed.
  263. S. Trimpin and E. D. Inutan, J. Am. Soc. Mass Spectrom., 2013, 24, 722–732 CrossRef CAS PubMed.
  264. C. W. Tsao, C. H. Lin, Y. C. Cheng, C. C. Chien, C. C. Chang and W. Y. Chen, Analyst, 2012, 137, 2643–2650 RSC.
  265. C. W. Tsao, Y. J. Lin, P. Y. Chen, Y. L. Yang and S. H. Tan, Analyst, 2016, 141, 4973–4981 RSC.
  266. D. Y. Wang, X. Huang, J. Li, B. He, Q. Liu, L. G. Hu and G. B. Jiang, Chem. Commun., 2018, 54, 2723–2726 RSC.
  267. J. L. Zhang, Z. Li, C. S. Zhang, B. S. Feng, Z. G. Zhou, Y. Bai and H. W. Liu, Anal. Chem., 2012, 84, 3296–3301 CrossRef CAS PubMed.
  268. P. L. Urban, T. Schmid, A. Amantonico and R. Zenobi, Anal. Chem., 2011, 83, 1843–1849 CrossRef CAS PubMed.
  269. J. Förster, I. Famili, P. Fu, B. Ø. Palsson and J. Nielsen, Genome-scale reconstruction of the Saccharomyces cerevisiae metabolic network, Cold Spring Harbor Laboratory Press, 2003 Search PubMed.
  270. Y. Gao, Y. Lin, B. Zhang, D. Zou, M. He, B. Dong, W. Hang and B. Huang, Anal. Chem., 2013, 85, 4268–4272 CrossRef CAS PubMed.
  271. T. Yang, D. Gao, F. Jin, Y. Y. Jiang and H. X. Liu, Rapid Commun. Mass Spectrom., 2016, 30, 73–79 CrossRef CAS PubMed.
  272. M. A. Yang, R. Nelson and A. Ros, Anal. Chem., 2016, 88, 6672–6679 CrossRef CAS PubMed.
  273. H. Yang and Q. Cheng, Analyst, 2017, 142, 2654–2662 RSC.
  274. C. Q. Xiong, X. Y. Zhou, Q. He, X. Huang, J. Y. Wang, W. P. Peng, H. C. Chang and Z. X. Nie, Anal. Chem., 2016, 88, 11913–11918 CrossRef CAS PubMed.
  275. W. Y. Xie, D. Gao, F. Jin, Y. Y. Jiang and H. X. Liu, Anal. Chem., 2015, 87, 7052–7059 CrossRef CAS PubMed.
  276. W. Wan, Q. Liang, X. Zhang, M. Yan and M. Ding, Analyst, 2016, 141, 4568–4572 RSC.
  277. M. Karas, D. Bachmann and F. Hillenkamp, Anal. Chem., 1985, 57, 2935–2939 CrossRef CAS.
  278. M. Karas, D. Bachmann, U. Bahr and F. Hillenkamp, Int. J. Mass Spectrom. Ion Processes, 1987, 78, 53–68 CrossRef CAS.
  279. F. Hillenkamp, M. Karas, R. C. Beavis and B. T. Chait, Anal. Chem., 1991, 63(24), 1193A–1203A CrossRef CAS PubMed.
  280. F. Hillenkamp and M. Karas, Methods Enzymol., 1990, 193, 280–295 CAS.
  281. C. Eckerskorn, K. Strupat, M. Karas, F. Hillenkamp and F. Lottspeich, Electrophoresis, 1992, 13(9–10), 664–665 CrossRef CAS PubMed.
  282. E. J. Lanni, S. J. B. Dunham, P. Nemes, S. S. Rubakhin and J. V. Sweedler, J. Am. Soc. Mass Spectrom., 2014, 25, 1897–1907 CrossRef CAS PubMed.
  283. J. Krismer, R. F. Steinhoff and R. Zenobi, Chimia, 2016, 70, 236–239 CrossRef CAS PubMed.
  284. J. Krismer, J. Sobek, R. F. Steinhoff, S. R. Fagerer, M. Pabst and R. Zenobi, Appl. Environ. Microbiol., 2015, 81, 5546–5551 CrossRef CAS PubMed.
  285. A. Korenaga, F. M. Chen, H. F. Li, K. Uchiyama and J. M. Lin, Talanta, 2017, 162, 474–478 CrossRef CAS PubMed.
  286. M. A. Hossen, Y. Nagata, M. Waki, Y. Ide, S. Takei, H. Fukano, G. A. Romero-Perez, S. Tajima, I. Yao, K. Ohnishi and M. Setou, Anal. Bioanal. Chem., 2015, 407, 5273–5280 CrossRef CAS PubMed.
  287. S. Holst, B. Heijs, N. de Haan, R. J. M. van Zeijl, I. H. Briaire-de Bruijn, G. W. van Pelt, A. S. Mehta, P. M. Angel, W. E. Mesker, R. A. Tollenaar, R. R. Drake, J. V. M. G. Bovée, L. A. McDonnell and M. Wuhrer, Anal. Chem., 2016, 88, 5904–5913 CrossRef CAS PubMed.
  288. S. Guo, Y. M. Wang, D. Zhou and Z. L. Li, Anal. Chem., 2015, 87, 5860–5865 CrossRef CAS PubMed.
  289. R. W. Garden, L. L. Moroz, T. P. Moroz, S. A. Shippy and J. V. Sweedler, J. Mass Spectrom., 1996, 31, 1126–1130 CrossRef CAS.
  290. L. Li, R. W. Garden, E. V. Romanova and J. V. Sweedler, Anal. Chem., 1999, 71, 5451–5458 CrossRef CAS PubMed.
  291. C. R. Jimenez and A. L. Burlingame, Exp. Nephrol., 1998, 6, 421–428 CrossRef CAS PubMed.
  292. M. E. Duenas, A. T. Klein, L. E. Alexander, M. D. Yandeau-Nelson, B. J. Nikolau and Y. J. Lee, Plant J., 2017, 89, 825–838 CrossRef CAS PubMed.
  293. O. Guillaume-Gentil, T. Rey, P. Kiefer, A. J. Ibáñez, R. Steinhoff, R. Bronnimann, L. Dorwling-Carter, T. Zambelli, R. Zenobi and J. A. Vorholt, Anal. Chem., 2017, 89, 5017–5023 CrossRef CAS PubMed.
  294. S. Neupert, S. S. Rubakhin and J. V. Sweedler, Chem. Biol., 2012, 19, 1010–1019 CrossRef CAS PubMed.
  295. A. D. Feenstra, M. E. Duenas and Y. J. Lee, J. Am. Soc. Mass Spectrom., 2017, 28, 434–442 CrossRef CAS PubMed.
  296. A. J. Ibáñez, S. R. Fagerer, A. M. Schmidt, P. L. Urban, K. Jefimovs, P. Geiger, R. Dechant, M. Heinemann and R. Zenobi, Proc. Natl. Acad. Sci. U. S. A., 2013, 110, 8790–8794 CrossRef PubMed.
  297. T. J. Comi, M. A. Makurath, M. C. Philip, S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2017, 89, 7765–7772 CrossRef CAS PubMed.
  298. M. E. Duenas, J. J. Essner and Y. J. Lee, Sci. Rep., 2017, 7(1), 14946 CrossRef PubMed.
  299. O. Guillaume-Gentil, T. Rey, P. Kiefer, A. J. Ibáñez, R. Steinhoff, R. Bronnimann, L. Dorwling-Carter, T. Zambelli, R. Zenobi and J. A. Vorholt, Anal. Chem., 2017, 89, 5017–5023 CrossRef CAS PubMed.
  300. M. Giampa, M. B. Lissel, T. Patschkowski, J. Fuchser, V. H. Hans, O. Gembruch, H. Bednarz and K. Niehaus, Chem. Commun., 2016, 52, 9801–9804 RSC.
  301. M. M. Gessel, J. L. Norris and R. M. Caprioli, J. Proteomics, 2014, 107, 71–82 CrossRef CAS PubMed.
  302. C. L. de Laorden, A. Beloqui, L. Yate, J. Calvo, M. Puigivila, J. Llop and N. C. Reichardt, Anal. Chem., 2015, 87, 431–440 CrossRef PubMed.
  303. F. Cattina, S. Bernardi, V. Mantovani, E. Toffoletti, A. Santoro, D. Pastore, B. Martino, G. Console, G. Martinelli and M. Malagola, Hematol. Rep., 2017, 9, 91–97 Search PubMed.
  304. A. Buck and A. Walch, Bioanalysis, 2014, 6, 1241–1253 CrossRef CAS PubMed.
  305. A. Buck, B. Balluff, A. Voss, R. Langer, H. Zitzelsberger, M. Aichler and A. Walch, Anal. Chem., 2016, 88, 5281–5289 CrossRef CAS PubMed.
  306. A. Bondarenko, Y. Zhu, L. Qiao, F. C. Salazar, H. Pick and H. H. Girault, Analyst, 2016, 141, 3403–3410 RSC.
  307. P. L. Urban, A. M. Schmidt, S. R. Fagerer, A. Amantonico, A. Ibáñez, K. Jefimovs, M. Heinemann and R. Zenobi, Mol. BioSyst., 2011, 7, 2837–2840 RSC.
  308. Y. Fan, C. Y. Lee, S. S. Rubakhin and J. V. Sweedler, Analyst, 2013, 138, 6337–6346 RSC.
  309. T. D. Do, J. F. Ellis, E. K. Neumann, T. J. Comi, E. G. Tillmaand, A. E. Lenhart, S. S. Rubakhin and J. V. Sweedler, ChemPhysChem, 2018, 19(10), 1180–1191 CrossRef CAS PubMed.
  310. A. Zavalin, J. Yang, K. Hayden, M. Vestal and R. M. Caprioli, Anal. Bioanal. Chem., 2015, 407, 2337–2342 CrossRef CAS PubMed.
  311. J. Niziol, K. Ossolinski, T. Ossolinski, A. Ossolinska, V. Bonifay, J. Sekula, Z. Dobrowolski, J. Sunner, I. Beech and T. Ruman, Anal. Chem., 2016, 88, 7365–7371 CrossRef CAS PubMed.
  312. S. Shimma, H. O. Kumada, H. Taniguchi, A. Konno, I. Yao, K. Furuta, T. Matsuda and S. Ito, Anal. Bioanal. Chem., 2016, 408, 7607–7615 CrossRef CAS PubMed.
  313. D. Sturtevant, Y. J. Lee and K. D. Chapman, Curr. Opin. Biotechnol., 2016, 37, 53–60 CrossRef CAS PubMed.
  314. Q. Wu, T. J. Comi, B. Li, S. S. Rubakhin and J. V. Sweedler, Anal. Chem., 2016, 88, 5988–5995 CrossRef CAS PubMed.
  315. R. Bradshaw, N. Denison and S. Francese, Analyst, 2017, 142, 1581–1590 RSC.
  316. D. Zhou, S. Guo, M. Zhang, Y. J. Liu, T. J. Chen and Z. L. Li, Anal. Chim. Acta, 2017, 962, 52–59 CrossRef CAS PubMed.
  317. W. T. Andrews, S. B. Skube and A. B. Hummon, Analyst, 2018, 143, 133–140 RSC.
  318. N. E. Mascini, J. Teunissen, R. Noorlag, S. M. Willems and R. M. A. Heeren, Methods, 2018, 13(6), 612–632 Search PubMed.
  319. A. D. Feenstra, R. L. Hansen and Y. J. Lee, Analyst, 2015, 140, 7293–7304 RSC.
  320. A. R. Korte, M. D. Yandeau-Nelson, B. J. Nikolau and Y. J. Lee, Anal. Bioanal. Chem., 2015, 407, 2301–2309 CrossRef CAS PubMed.
  321. M. Kompauer, S. Heiles and B. Spengler, Nat. Methods, 2017, 14, 90–96 CrossRef CAS PubMed.
  322. T. J. Comi, E. K. Neumann, T. D. Do and J. V. Sweedler, J. Am. Soc. Mass Spectrom., 2017, 28, 1919–1928 CrossRef CAS PubMed.
  323. R. L. Hansen and Y. J. Lee, Chem. Rec., 2018, 18, 65–77 CrossRef CAS PubMed.
  324. S. Saalfeld, A. Cardona, V. Hartenstein and P. Tomancak, Bioinformatics, 2010, 26, i57–i63 CrossRef CAS PubMed.
  325. A. Cardona, S. Saalfeld, J. Schindelin, I. Arganda-Carreras, S. Preibisch, M. Longair, P. Tomancak, V. Hartenstein and R. J. Douglas, PLoS One, 2012, 7 CAS.
  326. K. W. Eliceiri, M. R. Berthold, I. G. Goldberg, L. Ibáñez, B. S. Manjunath, M. E. Martone, R. F. Murphy, H. C. Peng, A. L. Plant, B. Roysam, N. Stuurmann, J. R. Swedlow, P. Tomancak and A. E. Carpenter, Nat. Methods, 2012, 9, 697–710 CrossRef CAS PubMed.
  327. E. J. Lanni, S. S. Rubakhin and J. V. Sweedler, J. Proteomics, 2012, 75, 5036–5051 CrossRef CAS PubMed.
  328. N. J. Popczun, L. Breuer, A. Wucher and N. Winograd, J. Am. Soc. Mass Spectrom., 2017, 28, 1182–1191 CrossRef CAS PubMed.
  329. Z. Wang, Y. Zhang, B. Liu, K. Wu, S. Thevuthasan, D. R. Baer, Z. Zhu, X. Y. Yu and F. Wang, Anal. Chem., 2017, 89, 960–965 CrossRef CAS PubMed.
  330. K. Wu, F. Jia, W. Zheng, Q. Luo, Y. Zhao and F. Wang, J. Biol. Inorg. Chem., 2017, 22, 653–661 CrossRef CAS PubMed.
  331. U. N. Gries, H. Schraknepper, K. Skaja, F. Gunkel, S. Hoffmann-Eifert, R. Waser and R. A. De Souza, Phys. Chem. Chem. Phys., 2018, 20, 989–996 RSC.
  332. D. Touboul and A. Brunelle, Bioanalysis, 2016, 8, 367–369 CrossRef CAS PubMed.
  333. P. Sjovall, M. Rossmeisl, J. Hanrieder, O. Kuda, J. Kopecky and M. Bryhn, Anal. Bioanal. Chem., 2015, 407, 5101–5111 CrossRef PubMed.
  334. S. J. B. Dunham, J. F. Ellis, N. F. Baig, N. Morales-Soto, T. Cao, J. D. Shrout, P. W. Bohn and J. V. Sweedler, Anal. Chem., 2018, 90(9), 5654–5663 CrossRef CAS PubMed.
  335. S. Aoyagi, T. Shimanouchi, T. Kawashima and H. Iwai, Anal. Bioanal. Chem., 2015, 407, 2859–2863 CrossRef CAS PubMed.
  336. M. Jasieniak, B. R. Coad and H. J. Griesser, Biointerphases, 2015, 10, 04A310 CrossRef PubMed.
  337. A. J. Taylor, D. J. Graham and D. G. Castner, Analyst, 2015, 140, 6005–6014 RSC.
  338. Z. E. Voras, K. deGhetaldi, M. B. Wiggins, B. Buckley, B. Baade, J. L. Mass and T. P. Beebe Jr., Appl. Phys. A, 2015, 121, 1015–1030 CrossRef CAS PubMed.
  339. D. Touboul and A. Brunelle, Methods Mol. Biol., 2015, 1203, 21–27 CrossRef CAS PubMed.
  340. T. B. Angerer, Y. Magnusson, G. Landberg and J. S. Fletcher, Anal. Chem., 2016, 88, 11946–11954 CrossRef CAS PubMed.
  341. K. J. Adams, J. D. DeBord and F. Fernandez-Lima, J. Vac. Sci. Technol., B: Nanotechnol. Microelectron.: Mater., Process., Meas., Phenom., 2016, 34, 051804 Search PubMed.
  342. W. Wu, H. Xiang, W. Fan, J. Wang, H. Wang, X. Hua, Z. Wang, Y. Long, H. Tian and W. H. Zhu, ACS Appl. Mater. Interfaces, 2017, 9, 16081–16090 CrossRef CAS PubMed.
  343. L. J. White, A. J. Taylor, D. M. Faulk, T. J. Keane, L. T. Saldin, J. E. Reing, I. T. Swinehart, N. J. Turner, B. D. Ratner and S. F. Badylak, Acta Biomater., 2017, 50, 207–219 CrossRef CAS PubMed.
  344. N. G. Welch, R. M. T. Madiona, T. B. Payten, C. D. Easton, L. Pontes-Braz, N. Brack, J. A. Scoble, B. W. Muir and P. J. Pigram, Acta Biomater., 2017, 55, 172–182 CrossRef CAS PubMed.
  345. W. Wei, Y. Zhang, R. Komorek, A. Plymale, R. Yu, B. Wang, Z. Zhu, F. Liu and X. Y. Yu, Biointerphases, 2017, 12, 05G601 CrossRef PubMed.
  346. Y. W. You, H. Y. Chang, W. C. Lin, C. H. Kuo, S. H. Lee, W. L. Kao, G. J. Yen, C. J. Chang, C. P. Liu, C. C. Huang, H. Y. Liao and J. J. Shyue, Rapid Commun. Mass Spectrom., 2011, 25, 2897–2904 CrossRef CAS PubMed.
  347. H. Tian, A. Wucher and N. Winograd, J. Am. Soc. Mass Spectrom., 2016, 27, 2014–2024 CrossRef CAS PubMed.
  348. G. Legent, A. Delaune, V. Norris, A. Delcorte, D. Gibouin, F. Lefebvre, G. Misevic, M. Thellier and C. Ripoll, J. Phys. Chem. B, 2008, 112, 5534–5546 CrossRef CAS PubMed.
  349. J. L. Guerquin-Kern, T. D. Wu, C. Quintana and A. Croisy, Biochim. Biophys. Acta, 2005, 1724, 228–238 CrossRef CAS PubMed.
  350. D. L. Woerdeman, W. Ou, J. L. Lenhart and R. S. Parnas, J. Colloid Interface Sci., 2003, 262, 594–602 CrossRef CAS PubMed.
  351. I. Gay, D. R. Lorey 2nd, R. F. Schinazi, G. H. Morrison and S. Chandra, Anticancer Res., 2001, 21, 2369–2375 CAS.
  352. S. Chandra, D. R. Smith and G. H. Morrison, Anal. Chem., 2000, 72, 104A–114A CrossRef CAS PubMed.
  353. J. M. Candy, A. E. Oakley, S. A. Mountfort, G. A. Taylor, C. M. Morris, H. E. Bishop and J. A. Edwardson, Biol. Cell, 1992, 74, 109–118 CrossRef CAS PubMed.
  354. C. Derue, D. Gibouin, M. Demarty, M. C. Verdus, F. Lefebvre, M. Thellier and C. Ripoll, Microsc. Res. Tech., 2006, 69, 53–63 CrossRef CAS PubMed.
  355. R. N. Sodhi, Analyst, 2004, 129, 483–487 RSC.
  356. C. E. Chini, G. L. Fisher, B. Johnson, M. M. Tamkun and M. L. Kraft, Biointerphases, 2018, 13, 03B409 CrossRef PubMed.
  357. V. Mirshafiee, B. A. C. Harley and M. L. Kraft, Tissue Eng., Part C, 2018, 24(6), 322–330 CrossRef CAS PubMed.
  358. J. Park, H. K. Na, H. K. Shon, H. Y. Son, Y. M. Huh, S. W. Lee and T. G. Lee, Biointerphases, 2018, 13, 03B404 CrossRef PubMed.
  359. H. Jungnickel, P. Laux and A. Luch, Toxics, 2016, 4(1), 5 CrossRef PubMed.
  360. N. Winograd and A. Bloom, Methods Mol. Biol., 2015, 1203, 9–19 CrossRef CAS PubMed.
  361. M. A. Robinson, D. J. Graham, F. Morrish, D. Hockenbery and L. J. Gamble, Biointerphases, 2015, 11, 02A303 CrossRef PubMed.
  362. Y. Yokoyama, S. Aoyagi, T. Shimanouchi, M. Iwamura and H. Iwai, Biointerphases, 2016, 11, 02A314 CrossRef PubMed.
  363. C. F. Newman, R. Havelund, M. K. Passarelli, P. S. Marshall, I. Francis, A. West, M. R. Alexander, I. S. Gilmore and C. T. Dollery, Anal. Chem., 2017, 89, 11944–11953 CrossRef CAS PubMed.
  364. D. Aoki and K. Fukushima, Methods Mol. Biol., 2017, 1544, 249–256 CrossRef PubMed.
  365. K. Schaepe, J. Kokesch-Himmelreich, M. Rohnke, A. S. Wagner, T. Schaaf, A. Henss, S. Wenisch and J. Janek, Biointerphases, 2016, 11, 02A313 CrossRef PubMed.
  366. G. Barreto, A. Soininen, T. Sillat, Y. T. Konttinen and E. Kaivosoja, Methods Mol. Biol., 2014, 1142, 177–188 CrossRef CAS PubMed.
  367. I. Lanekoff, P. Sjovall and A. G. Ewing, Anal. Chem., 2011, 83, 5337–5343 CrossRef CAS PubMed.
  368. M. Lefevre, J. P. Dodelet and P. Bertrand, J. Phys. Chem. B, 2005, 109, 16718–16724 CrossRef CAS PubMed.
  369. T. L. Colliver, C. L. Brummel, M. L. Pacholski, F. D. Swanek, A. G. Ewing and N. Winograd, Anal. Chem., 1997, 69, 2225–2231 CrossRef CAS PubMed.
  370. C. Quintana, T. D. Wu, B. Delatour, M. Dhenain, J. L. Guerquin-Kern and A. Croisy, Microsc. Res. Tech., 2007, 70, 281–295 CrossRef CAS PubMed.
  371. P. L. Clode, R. A. Stern and A. T. Marshall, Microsc. Res. Tech., 2007, 70, 220–229 CrossRef CAS PubMed.
  372. D. Gao, X. Huang and Y. Tao, Crit. Rev. Biotechnol., 2016, 36, 884–890 CrossRef CAS PubMed.
  373. J. Lovric, P. Malmberg, B. R. Johansson, J. S. Fletcher and A. G. Ewing, Anal. Chem., 2016, 88, 8841–8848 CrossRef CAS PubMed.
  374. N. Musat, F. Musat, P. K. Weber and J. Pett-Ridge, Curr. Opin. Biotechnol., 2016, 41, 114–121 CrossRef CAS PubMed.
  375. J. Nunez, R. Renslow, J. B. Cliff 3rd and C. R. Anderton, Biointerphases, 2017, 13, 03B301 CrossRef PubMed.
  376. R. F. S. Lee, T. Riedel, S. Escrig, C. Maclachlan, G. W. Knott, C. A. Davey, K. Johnsson, A. Meibom and P. J. Dyson, Metallomics, 2017, 9, 1413–1420 RSC.
  377. C. He, L. G. Fong, S. G. Young and H. Jiang, J. Invest. Med., 2017, 65, 669–672 CrossRef PubMed.
  378. Y. V. Chew, A. J. Holmes and J. B. Cliff, Methods Mol. Biol., 2014, 1096, 133–146 CrossRef CAS PubMed.
  379. S. G. Boxer, M. L. Kraft and P. K. Weber, Annu. Rev. Biophys., 2009, 38, 53–74 CrossRef CAS PubMed.
  380. A. Henss, S. K. Otto, K. Schaepe, L. Pauksch, K. S. Lips and M. Rohnke, Biointerphases, 2018, 13, 03B410 CrossRef PubMed.
  381. X. Hua, H. W. Li and Y. T. Long, Anal. Chem., 2018, 90, 1072–1076 CrossRef CAS PubMed.
  382. L. Huang, Y. Chen, L. T. Weng, M. Leung, X. Xing, Z. Fan and H. Wu, Anal. Chem., 2016, 88, 12196–12203 CrossRef CAS PubMed.
  383. M. L. Kraft and H. A. Klitzing, Biochim. Biophys. Acta, 2014, 1841, 1108–1119 CrossRef CAS PubMed.
  384. H. Jiang, M. R. Kilburn, J. Decelle and N. Musat, Curr. Opin. Biotechnol., 2016, 41, 130–135 CrossRef CAS PubMed.
  385. F. Penen, J. Malherbe, M. P. Isaure, D. Dobritzsch, I. Bertalan, E. Gontier, P. Le Coustumer and D. Schaumloffel, J. Trace Elem. Med. Biol., 2016, 37, 62–68 CrossRef CAS PubMed.
  386. A. Biesemeier, O. Eibl, S. Eswara, J. N. Audinot, T. Wirtz, G. Pezzoli, F. A. Zucca, L. Zecca and U. Schraermeyer, J. Neurochem., 2016, 138, 339–353 CrossRef CAS PubMed.
  387. J. Malherbe, F. Penen, M. P. Isaure, J. Frank, G. Hause, D. Dobritzsch, E. Gontier, F. Horreard, F. Hillion and D. Schaumloffel, Anal. Chem., 2016, 88, 7130–7136 CrossRef CAS PubMed.
  388. A. E. Dekas, S. A. Connon, G. L. Chadwick, E. Trembath-Reichert and V. J. Orphan, ISME J., 2016, 10, 678–692 CrossRef CAS PubMed.
  389. M. Speich, A. Pineau and F. Ballereau, Clin. Chim. Acta, 2001, 312, 1–11 CrossRef CAS.
  390. H. Haraguchi, Metallomics, 2017, 9, 1001–1013 RSC.
  391. Y. Q. Lin, R. Trouillon, G. Safina and A. G. Ewing, Anal. Chem., 2011, 83, 4369–4392 CrossRef CAS PubMed.
  392. F. M. Li, D. W. Armstrong and R. S. Houk, Anal. Chem., 2005, 77, 1407–1413 CrossRef CAS PubMed.
  393. S. C. Bendall, G. P. Nolan, M. Roederer and P. K. Chattopadhyay, Trends Immunol., 2012, 33, 323–332 CrossRef CAS PubMed.
  394. X. Zhang, B. B. Chen, M. He, Y. Zhang, L. Peng and B. Hu, Analyst, 2016, 141, 1286–1293 RSC.
  395. H. Wang, Z. K. Wu, B. B. Chen, M. He and B. Hu, Analyst, 2015, 140, 5619–5626 RSC.
  396. M. Grotti, F. Soggia and J. L. Todoli, Analyst, 2008, 133, 1388–1394 RSC.
  397. G. Cerchiaro, T. M. Manieri and F. R. Bertuchi, Metallomics, 2013, 5, 1336–1345 RSC.
  398. C. N. Tsang, K. S. Ho, H. Z. Sun and W. T. Chan, J. Am. Chem. Soc., 2011, 133, 7355–7357 CrossRef CAS PubMed.
  399. A. S. Groombridge, S. Miyashita, S. Fujii, K. Nagasawa, T. Okahashi, M. Ohata, T. Umemura, A. Takatsu, K. Inagaki and K. Chiba, Anal. Sci., 2013, 29, 597–603 CrossRef CAS PubMed.
  400. D. R. Bandura, V. I. Baranov, O. I. Ornatsky, A. Antonov, R. Kinach, X. D. Lou, S. Pavlov, S. Vorobiev, J. E. Dick and S. D. Tanner, Anal. Chem., 2009, 81, 6813–6822 CrossRef CAS PubMed.
  401. S. C. Bendall, E. F. Simonds, P. Qiu, E. A. D. Amir, P. O. Krutzik, R. Finck, R. V. Bruggner, R. Melamed, A. Trejo, O. I. Ornatsky, R. S. Balderas, S. K. Plevritis, K. Sachs, D. Pe'er, S. D. Tanner and G. P. Nolan, Science, 2011, 332, 687–696 CrossRef CAS PubMed.
  402. K. Eyer, P. Kuhn, S. Stratz and P. S. Dittrich, J. Visualized Exp., 2013, 80, e50618 Search PubMed.
  403. R. Trouillon, M. K. Passarelli, J. Wang, M. E. Kurczy and A. G. Ewing, Anal. Chem., 2013, 85, 522–542 CrossRef CAS PubMed.
  404. S. Miyashita, A. S. Groombridge, S. Fujii, A. Takatsu, K. Chiba and K. Inagaki, Anal. Sci., 2014, 30, 219–224 CrossRef CAS PubMed.
  405. H. L. Wang, B. Wang, M. Wang, L. N. Zheng, H. Q. Chen, Z. F. Chai, Y. L. Zhao and W. Y. Feng, Analyst, 2015, 140, 523–531 RSC.
  406. H. Wang, B. Chen, M. He and B. Hu, Anal. Chem., 2017, 89, 4931–4938 CrossRef CAS PubMed.
  407. J. S. Hamilton, E. L. Gorishek, P. M. Mach, D. Sturtevant, M. L. Ladage, N. Suzuki, P. A. Padilla, R. Mittler, K. D. Chapman and G. F. Verbeck, J. Anal. At. Spectrom., 2016, 31, 1030–1033 RSC.
  408. M. Wang, L. N. Zheng, B. Wang, H. Q. Chen, Y. L. Zhao, Z. F. Chai, H. J. Reid, B. L. Sharp and W. Y. Feng, Anal. Chem., 2014, 86, 10252–10256 CrossRef CAS PubMed.
  409. A. J. Managh, S. L. Edwards, A. Bushell, K. J. Wood, E. K. Geissler, J. A. Hutchinson, R. W. Hutchinson, H. J. Reid and B. L. Sharp, Anal. Chem., 2013, 85, 10627–10634 CrossRef CAS PubMed.
  410. C. Giesen, L. Waentig, T. Mairinger, D. Drescher, J. Kneipp, P. H. Roos, U. Panne and N. Jakubowski, J. Anal. At. Spectrom., 2011, 26, 2160–2165 RSC.
  411. J. W. Grate, J. J. Gonzalez, M. J. O'Hara, C. M. Kellogg, S. S. Morrison, D. W. Koppenaal, G. C. Y. Chan, X. L. Mao, V. Zorba and R. E. Russo, Analyst, 2017, 142, 3333–3340 RSC.
  412. S. G. Elci, B. Yan, S. T. Kim, K. Saha, Y. Jiang, G. A. Klemmer, D. F. Moyano, G. Y. Tonga, V. M. Rotello and R. W. Vachet, Analyst, 2016, 141, 2418–2425 RSC.
  413. O. Syta, L. Kepa, A. Mistewicz, C. Wesolowska and B. Wagner, Microchem. J., 2018, 137, 37–44 CrossRef CAS.
  414. D. P. Bishop, M. Grossgarten, D. Dietrich, A. Vennemann, N. Cole, M. Sperling, M. Wiemann, P. A. Doble and U. Karst, Anal. Methods, 2018, 10, 926–926 RSC.
  415. X. Y. Zheng, B. L. Beard and C. M. Johnson, J. Anal. At. Spectrom., 2018, 33, 68–83 RSC.
  416. M. Sala, V. S. Selih and J. T. van Elteren, Analyst, 2017, 142, 3356–3359 RSC.
  417. A. J. Herrmann, S. Techritz, N. Jakubowski, A. Haase, A. Luch, U. Panne and L. Mueller, Analyst, 2017, 142, 1703–1710 RSC.
  418. S. Kanje, A. J. Herrmann, S. Hober and L. Mueller, Analyst, 2016, 141, 6374–6380 RSC.
  419. M. Bonta, H. Lohninger, M. Marchetti-Deschmann and A. Limbeck, Analyst, 2014, 139, 1521–1531 RSC.
  420. R. M. Baxter, D. S. Kong, J. E. Garcia-Perez, W. E. O'Gorman and E. W. Y. Hsieh, J. Visualized Exp., 2018, 136, e57780 Search PubMed.
  421. P. Cheung, F. Vallania, M. Dvorak, S. E. Chang, S. Schaffert, M. Donato, A. M. Rao, R. Mao, P. J. Utz, P. Khatri and A. J. Kuo, Clin. Immunol., 2018 DOI:10.1016/j.clim.2018.06.009.
  422. H. Winkels, E. Ehinger, M. Vassallo, K. Buscher, H. Q. Dinh, K. Kobiyama, A. A. J. Hamers, C. Cochain, E. Vafadarnejad, A. E. Saliba, A. Zernecke, A. B. Pramod, A. K. Ghosh, N. Anto Michel, N. Hoppe, I. Hilgendorf, A. Zirlik, C. C. Hedrick, K. Ley and D. Wolf, Circ. Res., 2018, 122, 1675–1688 CrossRef CAS PubMed.
  423. B. Ajami, N. Samusik, P. Wieghofer, P. P. Ho, A. Crotti, Z. Bjornson, M. Prinz, W. J. Fantl, G. P. Nolan and L. Steinman, Nat. Neurosci., 2018, 21, 541–551 CrossRef CAS PubMed.
  424. E. Porpiglia, N. Samusik, A. T. V. Ho, B. D. Cosgrove, T. Mai, K. L. Davis, A. Jager, G. P. Nolan, S. C. Bendall, W. J. Fantl and H. M. Blau, Nat. Cell Biol., 2018, 20, 990 CrossRef CAS PubMed.
  425. V. D. Gonzalez, N. Samusik, T. J. Chen, E. S. Savig, N. Aghaeepour, D. A. Quigley, Y. W. Huang, V. Giangarra, A. D. Borowsky, N. E. Hubbard, S. Y. Chen, G. Han, A. Ashworth, T. J. Kipps, J. S. Berek, G. P. Nolan and W. J. Fantl, Cell Rep., 2018, 22, 1875–1888 CrossRef CAS PubMed.
  426. M. A. Kunicki, L. C. Amaya Hernandez, K. L. Davis, R. Bacchetta and M. G. Roncarolo, J. Immunol., 2018, 200, 336–346 CrossRef CAS PubMed.
  427. M. Orecchioni, D. Bedognetti, L. Newman, C. Fuoco, F. Spada, W. Hendrickx, F. M. Marincola, F. Sgarrella, A. F. Rodrigues, C. Menard-Moyon, G. Cesareni, K. Kostarelos, A. Bianco and L. G. Delogu, Nat. Commun., 2017, 8, 1109 CrossRef PubMed.
  428. Y. S. Yang, P. U. Atukorale, K. D. Moynihan, A. Bekdemir, K. Rakhra, L. Tang, F. Stellacci and D. J. Irvine, Nat. Commun., 2017, 8, 15343 CrossRef PubMed.
  429. Y. Guo, S. Baumgart, H. J. Stark, H. Harms and S. Muller, Front. Microbiol., 2017, 8, 1326 CrossRef PubMed.
  430. T. M. Brodie and V. Tosevski, Curr. Protoc. Immunol., 2017, 118, 51111–151125 Search PubMed.
  431. Z. Zeng, M. Konopleva and M. Andreeff, Methods Mol. Biol., 2017, 1633, 75–86 CrossRef CAS PubMed.
  432. S. Bandyopadhyay, D. A. C. Fisher, O. Malkova and S. T. Oh, Methods Mol. Biol., 2017, 1636, 371–392 CrossRef CAS PubMed.
  433. N. Leelatian, D. B. Doxie, A. R. Greenplate, B. C. Mobley, J. M. Lehman, J. Sinnaeve, R. M. Kauffman, J. A. Werkhaven, A. M. Mistry, K. D. Weaver, R. C. Thompson, P. P. Massion, M. A. Hooks, M. C. Kelley, L. B. Chambless, R. A. Ihrie and J. M. Irish, Cytometry, Part B, 2017, 92, 68–78 CrossRef CAS PubMed.
  434. A. Ivask, A. J. Mitchell, C. M. Hope, S. C. Barry, E. Lombi and N. H. Voelcker, Anal. Chem., 2017, 89, 8228–8232 CrossRef CAS PubMed.
  435. S. E. Gullaksen, J. Skavland, S. Gavasso, V. Tosevski, K. Warzocha, C. Dumrese, A. Ferrant, T. Gedde-Dahl, A. Hellmann, J. Janssen, B. Labar, A. Lang, W. Majeed, G. Mihaylov, J. Stentoft, L. Stenke, J. Thaler, N. Thielen, G. Verhoef, J. Voglova, G. Ossenkoppele, A. Hochhaus, H. Hjorth-Hansen, S. Mustjoki, S. Sopper, F. Giles, K. Porkka, D. Wolf and B. T. Gjertsen, Haematologica, 2017, 102, 1361–1367 CrossRef CAS PubMed.
  436. E. Porpiglia, N. Samusik, A. T. V. Ho, B. D. Cosgrove, T. Mai, K. L. Davis, A. Jager, G. P. Nolan, S. C. Bendall, W. J. Fantl and H. M. Blau, Nat. Cell Biol., 2017, 19, 558–567 CrossRef CAS PubMed.
  437. T. R. Matos, H. Liu and J. Ritz, J. Invest. Dermatol., 2017, 137, e31–e38 CrossRef CAS PubMed.
  438. E. Kadic, R. J. Moniz, Y. Huo, A. Chi and I. Kariv, BMC Immunol., 2017, 18, 6 CrossRef PubMed.
  439. Y. S. Yang, P. U. Atukorale, K. D. Moynihan, A. Bekdemir, K. Rakhra, L. Tang, F. Stellacci and D. J. Irvine, Nat. Commun., 2017, 8, 14069 CrossRef CAS PubMed.

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