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      MALDI MS Guided Liquid Microjunction Extraction for Capillary Electrophoresis–Electrospray Ionization MS Analysis of Single Pancreatic Islet Cells

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          Abstract

          The ability to characterize chemical heterogeneity in biological structures is essential to understanding cellular-level function in both healthy and diseased states, but these variations remain difficult to assess using a single analytical technique. While mass spectrometry (MS) provides sufficient sensitivity to measure many analytes from volume-limited samples, each type of mass spectrometric analysis uncovers only a portion of the complete chemical profile of a single cell. Matrix-assisted laser desorption/ionization (MALDI) MS and capillary electrophoresis electrospray ionization (CE–ESI)-MS are complementary analytical platforms frequently utilized for single-cell analysis. Optically guided MALDI MS provides a high-throughput assessment of lipid and peptide content for large populations of cells, but is typically nonquantitative and fails to detect many low-mass metabolites because of MALDI matrix interferences. CE–ESI-MS allows quantitative measurements of cellular metabolites and increased analyte coverage, but has lower throughput because the electrophoretic separation is relatively slow. In this work, the figures of merit for each technique are combined via an off-line method that interfaces the two MS systems with a custom liquid microjunction surface sampling probe. The probe is mounted on an xyz translational stage, providing 90.6 ± 0.6% analyte removal efficiency with a spatial targeting accuracy of 42.8 ± 2.3 μm. The analyte extraction footprint is an elliptical area with a major diameter of 422 ± 21 μm and minor diameter of 335 ± 27 μm. To validate the approach, single rat pancreatic islet cells were rapidly analyzed with optically guided MALDI MS to classify each cell into established cell types by their peptide content. After MALDI MS analysis, a majority of the analyte remains for follow-up measurements to extend the overall chemical coverage. Optically guided MALDI MS was used to identify individual pancreatic islet α and β cells, which were then targeted for liquid microjunction extraction. Extracts from single α and β cells were analyzed with CE–ESI-MS to obtain qualitative information on metabolites, including amino acids. Matching the molecular masses and relative migration times of the extracted analytes and related standards allowed identification of several amino acids. Interestingly, dopamine was consistently detected in both cell types. The results demonstrate the successful interface of optical microscopy-guided MALDI MS and CE–ESI-MS for sequential chemical profiling of individual, mammalian cells.

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          Single Cell Profiling of Circulating Tumor Cells: Transcriptional Heterogeneity and Diversity from Breast Cancer Cell Lines

          Background To improve cancer therapy, it is critical to target metastasizing cells. Circulating tumor cells (CTCs) are rare cells found in the blood of patients with solid tumors and may play a key role in cancer dissemination. Uncovering CTC phenotypes offers a potential avenue to inform treatment. However, CTC transcriptional profiling is limited by leukocyte contamination; an approach to surmount this problem is single cell analysis. Here we demonstrate feasibility of performing high dimensional single CTC profiling, providing early insight into CTC heterogeneity and allowing comparisons to breast cancer cell lines widely used for drug discovery. Methodology/Principal Findings We purified CTCs using the MagSweeper, an immunomagnetic enrichment device that isolates live tumor cells from unfractionated blood. CTCs that met stringent criteria for further analysis were obtained from 70% (14/20) of primary and 70% (21/30) of metastatic breast cancer patients; none were captured from patients with non-epithelial cancer (n = 20) or healthy subjects (n = 25). Microfluidic-based single cell transcriptional profiling of 87 cancer-associated and reference genes showed heterogeneity among individual CTCs, separating them into two major subgroups, based on 31 highly expressed genes. In contrast, single cells from seven breast cancer cell lines were tightly clustered together by sample ID and ER status. CTC profiles were distinct from those of cancer cell lines, questioning the suitability of such lines for drug discovery efforts for late stage cancer therapy. Conclusions/Significance For the first time, we directly measured high dimensional gene expression in individual CTCs without the common practice of pooling such cells. Elevated transcript levels of genes associated with metastasis NPTN, S100A4, S100A9, and with epithelial mesenchymal transition: VIM, TGFß1, ZEB2, FOXC1, CXCR4, were striking compared to cell lines. Our findings demonstrate that profiling CTCs on a cell-by-cell basis is possible and may facilitate the application of ‘liquid biopsies’ to better model drug discovery.
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            Human islets contain four distinct subtypes of β cells

            Human pancreatic islets of Langerhans contain five distinct endocrine cell types, each producing a characteristic hormone. The dysfunction or loss of the insulin-producing β cells causes diabetes mellitus, a disease that harms millions. Until now, β cells were generally regarded as a single, homogenous cell population. Here we identify four antigenically distinct subtypes of human β cells, which we refer to as β1–4, and which are distinguished by differential expression of ST8SIA1 and CD9. These subpopulations are always present in normal adult islets and have diverse gene expression profiles and distinct basal and glucose-stimulated insulin secretion. Importantly, the β cell subtype distribution is profoundly altered in type 2 diabetes. These data suggest that this antigenically defined β cell heterogeneity is functionally and likely medically relevant.
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              Profiling metabolites and peptides in single cells.

              The intracellular levels and spatial localizations of metabolites and peptides reflect the state of a cell and its relationship to its surrounding environment. Moreover, the amounts and dynamics of metabolites and peptides are indicative of normal or pathological cellular conditions. Here we highlight established and evolving strategies for characterizing the metabolome and peptidome of single cells. Focused studies of the chemical composition of individual cells and functionally defined groups of cells promise to provide a greater understanding of cell fate, function and homeostatic balance. Single-cell bioanalytical microanalysis has also become increasingly valuable for examining cellular heterogeneity, particularly in the fields of neuroscience, stem cell biology and developmental biology.
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                Author and article information

                Journal
                Anal Chem
                Anal. Chem
                ac
                ancham
                Analytical Chemistry
                American Chemical Society
                0003-2700
                1520-6882
                21 June 2017
                18 July 2017
                : 89
                : 14
                : 7765-7772
                Affiliations
                [1] Department of Chemistry and the Beckman Institute, and Department of Molecular and Integrative Physiology, University of Illinois , Urbana−Champaign, Illinois 61801, United States
                Author notes
                [* ]E-mail: jsweedle@ 123456illinois.edu . Phone: 217-244-7359.
                Article
                10.1021/acs.analchem.7b01782
                5518278
                28636327
                28cfb130-cb66-4887-8bf6-7315dc3db4d8
                Copyright © 2017 American Chemical Society

                This is an open access article published under an ACS AuthorChoice License, which permits copying and redistribution of the article or any adaptations for non-commercial purposes.

                History
                : 11 May 2017
                : 21 June 2017
                Categories
                Article
                Custom metadata
                ac7b01782
                ac-2017-017825

                Analytical chemistry
                Analytical chemistry

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