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For the characterization of the metabolic heterogeneity of cell populations, high-throughput single-cell analysis platforms are needed. In this study, we utilized mass spectrometry (MS) enhanced with ion mobility separation (IMS) and coupled with an automated sampling platform, fiber-based laser ablation electrospray ionization (f-LAESI), for in situ high-throughput single-cell metabolomics in soybean ( Glycine max ) root nodules. By fully automating the in situ sampling platform, an overall sampling rate of 804 cells/h was achieved for high numbers (>500) of tissue-embedded plant cells. This is an improvement by a factor of 13 compared to the previous f-LAESI-MS configuration. By introducing IMS, the molecular coverage improved, and structural isomers were separated on a millisecond time scale. The enhanced f-LAESI-IMS-MS platform produced 259 sample-related peaks/cell, almost twice as much as the 131 sample-related peaks/cell produced by f-LAESI-MS without IMS. Using the upgraded system, two types of metabolic heterogeneity characterization methods became possible. For unimodal metabolite abundance distributions, the metabolic noise reported on the metabolite level variations within the cell population. For bimodal distributions, the presence of metabolically distinct subpopulations was established. Discovering these latent cellular phenotypes could be linked to the presence of different cell states, e.g., proliferating bacteria in partially occupied plant cells and quiescent bacteroids in fully occupied cells in biological nitrogen fixation, or spatial heterogeneity due to altered local environments.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) is a powerful analytical technique that provides spatially preserved detection and quantification of analytes in tissue specimens. However, clinical translation still requires improved throughput, precision, and accuracy. To accomplish this, we created "Chemical QuantArray", a gelatin tissue microarray (TMA) mold filled with serial dilutions of isotopically labeled endogenous metabolite standards. The mold is then cryo-sectioned onto a tissue homogenate to produce calibration curves. To improve precision and accuracy, we automatically remove pixels outside of each TMA well and investigated several intensity normalizations, including the utilization of a second stable isotope internal standard (IS). Chemical QuantArray enables the quantification of several endogenous metabolites over a wide dynamic range and significantly improve over current approaches. The technique reduces the space needed on the MALDI slides for calibration standards by approximately 80%. Furthermore, removal of empty pixels and normalization to an internal standard or matrix peak provided precision (<20% RSD) and accuracy (<20% DEV). Finally, we demonstrate the applicability of Chemical QuantArray by quantifying multiple purine metabolites in 14 clinical tumor specimens using a single MALDI slide. Chemical QuantArray improves the analytical characteristics and practical feasibility of MALDI-MSI metabolite quantification in clinical and translational applications.

In biological tissues, cell-to-cell variations stem from the stochastic and modulated expression of genes and the varying abundances of corresponding proteins. These variations are then propagated to downstream metabolite products and result in cellular heterogeneity. Mass spectrometry imaging (MSI) is a promising tool to simultaneously provide spatial distributions for hundreds of biomolecules without the need for labels or stains. Technological advances in MSI instrumentation for the direct analysis of tissue-embedded single cells are dominated by improvements in sensitivity, sample pretreatment, and increased spatial resolution but are limited by low throughput. Herein, we introduce a bimodal microscopy imaging system combined with fiber-based laser ablation electrospray ionization (f-LAESI) MSI with improved throughput ambient analysis of tissue-embedded single cells ( n > 1000) to provide insight into cellular heterogeneity. Based on automated image analysis, accurate single-cell sampling is achieved by f-LAESI leading to the discovery of cellular phenotypes characterized by differing metabolite levels.

Therapeutic development of histone deacetylase inhibitors (HDACi) has been hampered by a number of barriers to drug delivery, including poor solubility and inadequate tissue penetration. Nanoparticle encapsulation could be one approach to improve the delivery of HDACi to target tissues; however, effective and generalizable loading of HDACi within nanoparticle systems remains a long-term challenge. We hypothesized that the common terminally ionizable moiety on many HDACi molecules could be capitalized upon for loading in polymeric nanoparticles. Here, we describe the simple, efficient formulation of a novel library of β-cyclodextrin-poly (β-amino ester) networks (CDN) to achieve this goal. We observed that network architecture was a critical determinant of CDN encapsulation of candidate molecules, with a more hydrophobic core enabling effective self-assembly and a PEGylated surface enabling high loading (up to ∼30% w/w), effective self-assembly of the nanoparticle, and slow release of drug into aqueous media (up to 24 days) for the model HDACi panobinostat. We next constructed a library of CDNs to encapsulate various small, hydrophobic, terminally ionizable molecules (panobinostat, quisinostat, dacinostat, givinostat, bortezomib, camptothecin, nile red, and cytarabine), which yielded important insights into the structural requirements for effective drug loading and CDN self-assembly. Optimized CDN nanoparticles were taken up by GL261 cells in culture and a released panobinostat was confirmed to be bioactive. Panobinostat-loaded CDNs were next administered by convection-enhanced delivery (CED) to mice bearing intracranial GL261 tumors. These studies confirm that CDN encapsulation enables a higher deliverable dose of drug to effectively slow tumor growth. Matrix-assisted laser desorption/ionization (MALDI) analysis on tissue sections confirms higher exposure of tumor to drug, which likely accounts for the therapeutic effects. Taken in sum, these studies present a novel nanocarrier platform for encapsulation of HDACi via both ionic and hydrophobic interactions, which is an important step toward better treatment of disease via HDACi therapy.

Characterization of the metabolic heterogeneity in cell populations requires the analysis of single cells. Most current methods in single-cell analysis rely on cell manipulation, potentially altering the abundance of metabolites in individual cells. A small sample volume and the chemical diversity of metabolites are additional challenges in single-cell metabolomics. Here, we describe the combination of fiber-based laser ablation electrospray ionization (f-LAESI) with 21 T Fourier transform ion cyclotron resonance mass spectrometry (21TFTICR-MS) for in situ single-cell metabolic profiling in plant tissue. Single plant cells infected by bacteria were selected and sampled directly from the tissue without cell manipulation through mid-infrared ablation with a fine optical fiber tip for ionization by f-LAESI. Ultrahigh performance 21T-FTICR-MS enabled the simultaneous capture of isotopic fine structures (IFSs) for 47 known and 11 unknown compounds, thus elucidating their elemental compositions from single cells and providing information on metabolic heterogeneity in the cell population.

Mass spectrometry (MS) is an indispensable analytical tool to capture the array of metabolites within complex biological systems. However, conventional MS-based metabolomic workflows require extensive sample processing and separation resulting in limited throughput and potential alteration of the native molecular states in these systems. Ambient ionization methods, capable of sampling directly from tissues, circumvent some of these issues but require high-performance MS to resolve the molecular complexity within these samples. Here, we demonstrate a unique combination of laser ablation electrospray ionization (LAESI) coupled with a 21 tesla Fourier transform ion cyclotron resonance (21T-FTICR) for direct MS analysis and imaging applications. This analytical platform provides isotopic fine structure information directly from biological tissues, enabling the rapid assignment of molecular formulas and delivering a higher degree of confidence for molecular identification.

The unique challenges presented by metabolomics have driven the development of new mass spectrometry (MS)-based techniques for small molecule analysis. We have previously demonstrated silicon nanopost arrays (NAPA) to be an effective substrate for laser desorption ionization (LDI) of small molecules for MS. However, the utility of NAPA-LDI-MS for a wide range of metabolite classes has not been investigated. Here we apply NAPA-LDI-MS to the large-scale acquisition of high-resolution mass spectra and tandem mass spectra from a collection of metabolite standards covering a range of compound classes including amino acids, nucleotides, carbohydrates, xenobiotics, lipids, and other classes. In untargeted analysis of metabolite standard mixtures, detection was achieved for 374 compounds and useful MS/MS spectra were obtained for 287 compounds, without individual optimization of ionization or fragmentation conditions. Metabolite detection was evaluated in the context of 31 metabolic pathways, and NAPA-LDI-MS was found to provide detection for 63% of investigated pathway metabolites. Individual, targeted analysis of the 20 common amino acids provided detection of 100% of the investigated compounds, demonstrating that improved coverage is possible through optimization and targeting of individual analytes or analyte classes. In direct analysis of aqueous and organic extracts from human serum samples, spectral features were assigned to a total of 108 small metabolites and lipids. Glucose and amino acids were quantitated within their physiological concentration ranges. The broad coverage demonstrated by this large-scale screening experiment opens the door for use of NAPA-LDI-MS in numerous metabolite analysis applications.