<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article PUBLIC "-//NLM//DTD JATS (Z39.96) Journal Archiving and Interchange DTD with MathML3 v1.3 20210610//EN"  "JATS-archivearticle1-3-mathml3.dtd"><article xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" article-type="research-article" dtd-version="1.3"><front><journal-meta><journal-id journal-id-type="nlm-ta">elife</journal-id><journal-id journal-id-type="publisher-id">eLife</journal-id><journal-title-group><journal-title>eLife</journal-title></journal-title-group><issn publication-format="electronic" pub-type="epub">2050-084X</issn><publisher><publisher-name>eLife Sciences Publications, Ltd</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">74989</article-id><article-id pub-id-type="doi">10.7554/eLife.74989</article-id><article-version article-version-type="publication-state">version of record</article-version><article-categories><subj-group subj-group-type="display-channel"><subject>Research Article</subject></subj-group><subj-group subj-group-type="heading"><subject>Cell Biology</subject></subj-group><subj-group subj-group-type="heading"><subject>Structural Biology and Molecular Biophysics</subject></subj-group></article-categories><title-group><article-title>Insulator-based dielectrophoresis-assisted separation of insulin secretory vesicles</article-title></title-group><contrib-group><contrib contrib-type="author" equal-contrib="yes"><name><surname>Barekatain</surname><given-names>Mahta</given-names></name><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="equal-contrib1">†</xref><xref ref-type="fn" rid="con1"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" equal-contrib="yes"><name><surname>Liu</surname><given-names>Yameng</given-names></name><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="fn" rid="equal-contrib1">†</xref><xref ref-type="fn" rid="con2"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author"><name><surname>Archambeau</surname><given-names>Ashley</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-9933-1787</contrib-id><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="other" rid="fund2"/><xref ref-type="fn" rid="con3"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author"><name><surname>Cherezov</surname><given-names>Vadim</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0002-5265-3914</contrib-id><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="fn" rid="con4"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author"><name><surname>Fraser</surname><given-names>Scott</given-names></name><xref ref-type="aff" rid="aff3">3</xref><xref ref-type="fn" rid="con5"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author" corresp="yes"><name><surname>White</surname><given-names>Kate L</given-names></name><contrib-id authenticated="true" contrib-id-type="orcid">https://orcid.org/0000-0001-8894-9621</contrib-id><email>katewhit@usc.edu</email><xref ref-type="aff" rid="aff1">1</xref><xref ref-type="other" rid="fund3"/><xref ref-type="fn" rid="con6"/><xref ref-type="fn" rid="conf1"/></contrib><contrib contrib-type="author"><name><surname>Hayes</surname><given-names>Mark A</given-names></name><xref ref-type="aff" rid="aff2">2</xref><xref ref-type="other" rid="fund1"/><xref ref-type="fn" rid="con7"/><xref ref-type="fn" rid="conf2"/></contrib><aff id="aff1"><label>1</label><institution-wrap><institution-id institution-id-type="ror">https://ror.org/03taz7m60</institution-id><institution>Department of Chemistry, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California</institution></institution-wrap><addr-line><named-content content-type="city">Los Angeles</named-content></addr-line><country>United States</country></aff><aff id="aff2"><label>2</label><institution-wrap><institution-id institution-id-type="ror">https://ror.org/03efmqc40</institution-id><institution>School of Molecular Sciences, Arizona State University</institution></institution-wrap><addr-line><named-content content-type="city">Tempe</named-content></addr-line><country>United States</country></aff><aff id="aff3"><label>3</label><institution-wrap><institution-id institution-id-type="ror">https://ror.org/03taz7m60</institution-id><institution>Department of Biological Sciences, Bridge Institute, USC Michelson Center for Convergent Bioscience, University of Southern California</institution></institution-wrap><addr-line><named-content content-type="city">Los Angeles</named-content></addr-line><country>United States</country></aff></contrib-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Ailion</surname><given-names>Michael</given-names></name><role>Reviewing Editor</role><aff><institution-wrap><institution-id institution-id-type="ror">https://ror.org/00cvxb145</institution-id><institution>University of Washington</institution></institution-wrap><country>United States</country></aff></contrib><contrib contrib-type="senior_editor"><name><surname>Malhotra</surname><given-names>Vivek</given-names></name><role>Senior Editor</role><aff><institution-wrap><institution-id institution-id-type="ror">https://ror.org/03kpps236</institution-id><institution>Barcelona Institute for Science and Technology</institution></institution-wrap><country>Spain</country></aff></contrib></contrib-group><author-notes><fn fn-type="con" id="equal-contrib1"><label>†</label><p>These authors contributed equally to this work</p></fn></author-notes><pub-date publication-format="electronic" date-type="publication"><day>27</day><month>08</month><year>2024</year></pub-date><volume>13</volume><elocation-id>e74989</elocation-id><history><date date-type="received" iso-8601-date="2021-10-25"><day>25</day><month>10</month><year>2021</year></date><date date-type="accepted" iso-8601-date="2024-07-24"><day>24</day><month>07</month><year>2024</year></date></history><pub-history><event><event-desc>This manuscript was published as a preprint at bioRxiv.</event-desc><date date-type="preprint" iso-8601-date="2021-12-01"><day>01</day><month>12</month><year>2021</year></date><self-uri content-type="preprint" xlink:href="https://doi.org/10.1101/2021.12.01.470798"/></event></pub-history><permissions><copyright-statement>© 2024, Barekatain, Liu et al</copyright-statement><copyright-year>2024</copyright-year><copyright-holder>Barekatain, Liu et al</copyright-holder><ali:free_to_read/><license xlink:href="http://creativecommons.org/licenses/by/4.0/"><ali:license_ref>http://creativecommons.org/licenses/by/4.0/</ali:license_ref><license-p>This article is distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License</ext-link>, which permits unrestricted use and redistribution provided that the original author and source are credited.</license-p></license></permissions><self-uri content-type="pdf" xlink:href="elife-74989-v1.pdf"/><self-uri content-type="figures-pdf" xlink:href="elife-74989-figures-v1.pdf"/><abstract><p>Organelle heterogeneity and inter-organelle contacts within a single cell contribute to the limited sensitivity of current organelle separation techniques, thus hindering organelle subpopulation characterization. Here, we use direct current insulator-based dielectrophoresis (DC-iDEP) as an unbiased separation method and demonstrate its capability by identifying distinct distribution patterns of insulin vesicles from INS-1E insulinoma cells. A multiple voltage DC-iDEP strategy with increased range and sensitivity has been applied, and a differentiation factor (ratio of electrokinetic to dielectrophoretic mobility) has been used to characterize features of insulin vesicle distribution patterns. We observed a significant difference in the distribution pattern of insulin vesicles isolated from glucose-stimulated cells relative to unstimulated cells, in accordance with maturation of vesicles upon glucose stimulation. We interpret the difference in distribution pattern to be indicative of high-resolution separation of vesicle subpopulations. DC-iDEP provides a path for future characterization of subtle biochemical differences of organelle subpopulations within any biological system.</p></abstract><kwd-group kwd-group-type="author-keywords"><kwd>vesicle isolation</kwd><kwd>insulin vesicle</kwd><kwd>dielectrophoresis</kwd></kwd-group><kwd-group kwd-group-type="research-organism"><title>Research organism</title><kwd>Rat</kwd></kwd-group><funding-group><award-group id="fund1"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000002</institution-id><institution>National Institutes of Health</institution></institution-wrap></funding-source><award-id>5R03AI133397-02</award-id><principal-award-recipient><name><surname>Hayes</surname><given-names>Mark A</given-names></name></principal-award-recipient></award-group><award-group id="fund2"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/501100008982</institution-id><institution>National Science Foundation</institution></institution-wrap></funding-source><award-id>DGE-1842487</award-id><principal-award-recipient><name><surname>Archambeau</surname><given-names>Ashley</given-names></name></principal-award-recipient></award-group><award-group id="fund3"><funding-source><institution-wrap><institution-id institution-id-type="FundRef">http://dx.doi.org/10.13039/100000002</institution-id><institution>National Institutes of Health</institution></institution-wrap></funding-source><award-id>R35GM154893</award-id><principal-award-recipient><name><surname>White</surname><given-names>Kate L</given-names></name></principal-award-recipient></award-group><funding-statement>The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.</funding-statement></funding-group><custom-meta-group><custom-meta specific-use="meta-only"><meta-name>Author impact statement</meta-name><meta-value>An unbiased separation method reveals distinct subpopulations of insulin secretory vesicles that undergo dynamic remodeling upon glucose stimulation.</meta-value></custom-meta></custom-meta-group></article-meta></front><body><sec id="s1" sec-type="intro"><title>Introduction</title><p>Cell-to-cell heterogeneity of function within a given cell type arises from differences in genomic, epigenomic, transcriptomic, and proteomic (<xref ref-type="bibr" rid="bib15">Goldman et al., 2019</xref>) components and their subcellular localizations. Some examples are subpopulations of organelles as identified for synaptic vesicles (<xref ref-type="bibr" rid="bib8">Crawford and Kavalali, 2015</xref>), mitochondria (<xref ref-type="bibr" rid="bib2">Aryaman et al., 2018</xref>), and insulin secretory vesicles (<xref ref-type="bibr" rid="bib57">Suckale and Solimena, 2010</xref>), among others. The biochemical composition of each of the different subpopulations of organelles must vary, reflecting differences in age, maturation, or specific biological role. To better understand cell function, the organelle subpopulations must be identified so their distinct functional roles may be determined. This fundamental aspect of cell biology, quantification of organelle subpopulations, is limited by lack of appropriate experimental methods. To address this gap in technology, we have developed a new approach for organelle subpopulation identification and quantification.</p><p>The new approach is centered on high-resolution separations technology built on conventional separation methods which are useful for numerous biological analyses. A traditional workflow consists of iterative centrifugation steps (<xref ref-type="bibr" rid="bib20">Harford and Bonifacino, 2011</xref>) to isolate populations of targeted organelles for follow-up analysis with mass spectrometric or other omics-scale assays (<xref ref-type="bibr" rid="bib28">Huber et al., 2003</xref>). Gradient columns have successfully isolated clathrin-coated vesicles (<xref ref-type="bibr" rid="bib13">Girard et al., 2005</xref>), lipid droplets (<xref ref-type="bibr" rid="bib5">Brasaemle and Wolins, 2016</xref>), mitochondria, and endoplasmic reticulum (ER) populations (<xref ref-type="bibr" rid="bib4">Bozidis et al., 2007</xref>), from other organelles, based on differences in organelle densities. However, subpopulations of a given organelle with similar buoyant densities are often hard to differentiate and separate in these columns. Immunoisolation of organelles, although yielding pure populations, is limited by the need for specific organelle markers (<xref ref-type="bibr" rid="bib34">Lange et al., 2000</xref>). Electrophoretic approaches, such as free-flow electrophoresis, have been adopted for use in microfluidic separation of organelles (<xref ref-type="bibr" rid="bib42">Lu et al., 2004</xref>), enabling downstream analyses of the enriched fractions in the absence of major contaminants. However, these separations are limited to differentiation based on charge and size of the components. Although powerful, each of these traditional isolation approaches lacks sufficient sensitivity and robustness to isolate subpopulations of an organelle for biochemical characterizations such as proteomic analysis or imaging, except for smooth versus rough ER (<xref ref-type="bibr" rid="bib36">Lee et al., 2015</xref>). Thus, new approaches which are sensitive to complex features beyond differences in density, size-to-charge or epitope recognition are needed.</p><p>One attractive strategy is to exploit higher order electric field effects which directly probe the nuanced physical properties of small complex bioparticles—differentiating on radius, zeta potential, permittivity, interfacial polarizability, charge distribution, deformability, and conductivity of the particles, to name but a few (<xref ref-type="bibr" rid="bib7">Chen et al., 2009</xref>; <xref ref-type="bibr" rid="bib24">Hilton et al., 2020</xref>; <xref ref-type="bibr" rid="bib53">Pethig, 2019</xref>; <xref ref-type="bibr" rid="bib44">Matyushov, 2019</xref>; <xref ref-type="bibr" rid="bib21">Hayes, 2020</xref>). Direct current insulator-based dielectrophoresis (DC-iDEP) utilizes these features for separating subpopulations of bioparticles such as viruses, bacteria, organelles, and proteins (<xref ref-type="bibr" rid="bib21">Hayes, 2020</xref>; <xref ref-type="bibr" rid="bib11">Ding et al., 2016</xref>; <xref ref-type="bibr" rid="bib31">Jones et al., 2015</xref>; <xref ref-type="bibr" rid="bib40">Liu and Hayes, 2021</xref>). This approach offers a wide dynamic range, as it has been specifically used for separations ranging from neural progenitors and stem cells (<xref ref-type="bibr" rid="bib38">Liu et al., 2019</xref>) to resistant versus susceptible strains of cellular pathogens (<xref ref-type="bibr" rid="bib24">Hilton et al., 2020</xref>; <xref ref-type="bibr" rid="bib31">Jones et al., 2015</xref>). In DC-iDEP, subtle biophysical differences can be distinguished by the differences in dielectrophoretic (DEP) and electrokinetic (EK) forces that result from a rich set of distinguishing factors such that all constituents of the bioparticle influence the potential for separation. Unlike epitope recognition strategies, DC-iDEP is well suited for assaying subpopulations of organelles, where differentiating factors are not known. DC-iDEP can be used as a discovery-based approach to interrogate a broader spectrum of organelle subpopulations (<xref ref-type="bibr" rid="bib38">Liu et al., 2019</xref>) because the bioparticle separations occur quickly and require small sample volume (<xref ref-type="bibr" rid="bib32">Kim et al., 2019</xref>; <xref ref-type="bibr" rid="bib35">Lapizco‐Encinas and Rito‐Palomares, 2007</xref>). Here, we demonstrate the power of DC-iDEP in organelle separation, by using it to investigate subtle differences in the subpopulations of insulin vesicles upon differential stimulation in the INS-1E insulinoma model of the pancreatic β-cells (<xref ref-type="bibr" rid="bib45">Merglen et al., 2004</xref>).</p><p>Pancreatic β-cells are responsible for secreting insulin in a tightly regulated process that is key to maintaining glucose homeostasis. Insulin vesicles undergo a complex functional maturation process that is required for proper secretion of insulin and this process is dysregulated in diabetes (<xref ref-type="bibr" rid="bib57">Suckale and Solimena, 2010</xref>). Immature insulin vesicles act as a sorting compartment (<xref ref-type="bibr" rid="bib12">Feng and Arvan, 2003</xref>; <xref ref-type="bibr" rid="bib27">Huang and Arvan, 1994</xref>) and mature into two distinct pools of functional vesicles within the cell: the readily releasable pool and the reserve pool (<xref ref-type="bibr" rid="bib3">Boland et al., 2017</xref>; <xref ref-type="bibr" rid="bib51">Orci, 1985</xref>; <xref ref-type="bibr" rid="bib52">Orci et al., 1986</xref>; <xref ref-type="bibr" rid="bib10">Dean, 1973</xref>; <xref ref-type="fig" rid="fig1">Figure 1A</xref>). The heterogeneity among subpopulations of insulin vesicles (<xref ref-type="bibr" rid="bib26">Hou et al., 2009</xref>; <xref ref-type="bibr" rid="bib46">Michael et al., 2006</xref>; <xref ref-type="bibr" rid="bib61">Zhang et al., 2020</xref>) likely arises from the varying stages of the maturation process. This accounts for modifications of insulin vesicle membrane proteins, and variances in their age, mobility, and localization within the cell (<xref ref-type="bibr" rid="bib57">Suckale and Solimena, 2010</xref>; <xref ref-type="bibr" rid="bib19">Hao et al., 2005</xref>; <xref ref-type="bibr" rid="bib18">Halban, 1982</xref>; <xref ref-type="bibr" rid="bib14">Gold et al., 1982</xref>). However, the associated biochemical constituents of insulin vesicles have remained elusive in the absence of sensitive isolation approaches. The importance of insulin vesicles in glucose homeostasis has led several groups to attempt to isolate and characterize the insulin vesicles (<xref ref-type="bibr" rid="bib58">Thurmond, 2007</xref>; <xref ref-type="bibr" rid="bib29">Hutton et al., 1982</xref>). While these studies and follow-up proteomics analysis of the isolated vesicles <xref ref-type="bibr" rid="bib54">Schvartz et al., 2012</xref>; <xref ref-type="bibr" rid="bib37">Li et al., 2018</xref>; <xref ref-type="bibr" rid="bib6">Brunner et al., 2007</xref>; <xref ref-type="bibr" rid="bib22">Hickey et al., 2009</xref> have provided insights into the biochemistry of these vesicles, there’s very little overlap in protein IDs associated with these organelles from different studies (<xref ref-type="fig" rid="fig1">Figure 1B</xref>). Thus, there is a need for more robust isolation methods that can reproducibly differentiate between the heterogeneous subpopulations of insulin vesicles, among other organelles, and allow for their downstream characterization.</p><fig-group><fig id="fig1" position="float"><label>Figure 1.</label><caption><title>Schematic diagram for the formation of heterogenous insulin vesicles in INS-1E cells, graphical summation of disparate vesicle protein identifications, and processing of insulin vesicles including direct current insulator-based dielectrophoresis (DC-iDEP) device.</title><p>(<bold>A</bold>) Insulin vesicle formation and maturation in a pancreatic β-cell. Newly synthesized insulin is packed inside secretory vesicles which mature to store crystalline insulin in vesicles until secretion is stimulated through different signaling pathways. (<bold>B</bold>) Four published insulin vesicle proteomics studies (<xref ref-type="bibr" rid="bib54">Schvartz et al., 2012</xref>; <xref ref-type="bibr" rid="bib37">Li et al., 2018</xref>; <xref ref-type="bibr" rid="bib6">Brunner et al., 2007</xref>; <xref ref-type="bibr" rid="bib22">Hickey et al., 2009</xref>) aimed to identify the proteome of the heterogenous populations of secretory vesicles in INS-1E cells with only five proteins identified consistently. (<bold>C</bold>) Separation of insulin vesicles using a DC-iDEP device. Differential and density gradient centrifugation were used to enrich each sample for insulin vesicle populations. Samples were then immunolabeled and introduced into DC-iDEP device for high-resolution separation. Fluorescently labeled particles trapped near various gates in the channel are biophysically different subpopulations with varied EKMr values. The gates were constricted by increasing sizes of paired triangles, forming channel widths of 73 μm to 25 μm from inlet to the outlet. The different gates created <inline-formula><mml:math id="inf1"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:mstyle></mml:math></inline-formula> and <inline-formula><mml:math id="inf2"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mi>▽</mml:mi><mml:mo fence="false" stretchy="false">‖</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo fence="false" stretchy="false">‖</mml:mo></mml:mrow></mml:mstyle></mml:math></inline-formula> distributions for EKMr values.</p></caption><graphic mimetype="image" mime-subtype="tiff" xlink:href="elife-74989-fig1-v1.tif"/></fig><fig id="fig1s1" position="float" specific-use="child-fig"><label>Figure 1—figure supplement 1.</label><caption><title>Glucose sensitivity of INS-1E insulinoma cells was tested by stimulation at increasing concentrations of glucose and measurement of insulin secretion by enzyme linked immunosorbent assay (ELISA).</title></caption><graphic mimetype="image" mime-subtype="tiff" xlink:href="elife-74989-fig1-figsupp1-v1.tif"/></fig><fig id="fig1s2" position="float" specific-use="child-fig"><label>Figure 1—figure supplement 2.</label><caption><title>Selection of fractions for use in separation experiments.</title><p>(<bold>A</bold>) Fractions of the density column were screened for insulin content in the enzyme linked immunosorbent assay (ELISA) assay. (<bold>B</bold>) Western blotting of the density column fractions revealed high concentrations of insulin vesicle marker synaptotagmin IX in fractions with high insulin content as well as the presence of endoplasmic reticulum (ER) and mitochondria contaminants as indicated by organelle markers SEC61 and cytochrome <italic>c</italic>, respectively, in the same fractions. Raw blot in <xref ref-type="supplementary-material" rid="fig1s2sdata1 fig1s2sdata2">Figure 1—figure supplement 2—source data 1 and 2</xref>. (<bold>C</bold>) Dynamic light scattering (DLS) was performed on fractions of interest to validate the presence of particles of 150–200 nm in radius, corresponding to radii of insulin vesicles.</p><p><supplementary-material id="fig1s2sdata1"><label>Figure 1—figure supplement 2—source data 1.</label><caption><title>Full image of western blotting (WB) with labels indicating synaptotagmin IX, SEC16 B, and cytochrome <italic>c</italic>.</title></caption><media mimetype="application" mime-subtype="pdf" xlink:href="elife-74989-fig1-figsupp2-data1-v1.pdf"/></supplementary-material></p><p><supplementary-material id="fig1s2sdata2"><label>Figure 1—figure supplement 2—source data 2.</label><caption><title>Raw image of western blotting (WB).</title></caption><media mimetype="application" mime-subtype="pdf" xlink:href="elife-74989-fig1-figsupp2-data2-v1.pdf"/></supplementary-material></p></caption><graphic mimetype="image" mime-subtype="tiff" xlink:href="elife-74989-fig1-figsupp2-v1.tif"/></fig><fig id="fig1s3" position="float" specific-use="child-fig"><label>Figure 1—figure supplement 3.</label><caption><title>The identity of insulin vesicles was confirmed via several microscopy methods: confocal microscopy, transmission electron microscopy, and cryo-electron microscopy.</title><p>(<bold>A</bold>) Fluorescence confocal microscopy maximum projection image of enriched insulin vesicles. (<bold>B</bold>) Transmission electron microscopy (TEM) image of enriched insulin vesicles negatively stained with Nano-W. The diameters of these particles are characteristic of insulin vesicles. (<bold>C</bold>) Cryo-electron microscopy (cryo-EM) image of enriched insulin vesicles. Scale bars, 10 µm (<bold>A</bold>), 500 nm (<bold>B</bold>), 50 nm (<bold>C</bold>).</p></caption><graphic mimetype="image" mime-subtype="tiff" xlink:href="elife-74989-fig1-figsupp3-v1.tif"/></fig></fig-group><p>In this study, a new scanning voltage DC-iDEP separation strategy has been applied to immunolabeled insulin vesicles of the INS-1E insulinoma cells and has been shown to separate the full range of insulin vesicle subpopulations with improved resolution within multiple ranges of biophysical parameters (<xref ref-type="fig" rid="fig1">Figure 1C</xref>). We demonstrate that glucose treatment, which has been shown to influence the maturity or molecular content (<xref ref-type="bibr" rid="bib59">White et al., 2020</xref>; <xref ref-type="bibr" rid="bib41">Loconte et al., 2022</xref>), affects the biophysical characteristics associated with the vesicle subpopulations captured within our DC-iDEP device. This method allows discovery of subpopulations with distinct biophysical properties among insulin vesicles from untreated cells (n-insulin vesicles) and 25 mM glucose-treated cells (g-insulin vesicles). Our observations are consistent with previous studies where a pronounced shift in the molecular density of the insulin vesicles was noted under the two conditions (<xref ref-type="bibr" rid="bib59">White et al., 2020</xref>; <xref ref-type="bibr" rid="bib41">Loconte et al., 2022</xref>). This study substantiates the sensitivity of DC-iDEP separation technique in resolving subpopulations of insulin vesicles, among other organelles, and opens the avenue for numerous studies of the biochemical constituents where complex and heterogeneous populations of organelles are of interest.</p></sec><sec id="s2" sec-type="results"><title>Results</title><sec id="s2-1"><title>Analysis of enriched insulin vesicle samples post fractionation</title><p>The INS-1E cells used in these studies were capable of insulin secretion in response to increasing concentrations of glucose (<xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>). Isolated membrane fractions from differential and density gradient centrifugations were analyzed through enzyme linked immunosorbent assay (ELISA), western blotting (WB), dynamic light scattering (DLS), confocal microscopy, and electron microscopy (EM). ELISAs identified that lower fractions of the density column (9–12) contained the highest insulin content (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2A</xref>), the same fractions shown to be enriched in the insulin vesicle marker, synaptotagmin IX, by WB (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2B</xref>). DLS indicated that these fractions contained particles of 150–200 nm radius, which corresponds to the known range of insulin vesicles radii (<xref ref-type="bibr" rid="bib50">Olofsson et al., 2002</xref>; <xref ref-type="bibr" rid="bib16">Greider et al., 1969</xref>). Although the WB revealed that the final enriched vesicle sample included some contaminants from unwanted organelles, such as the ER (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2B</xref>), this will not confound the analysis of insulin vesicles in the iDEP device as the fluorescent labeling was targeted only at insulin vesicles.</p><p>To verify that the insulin vesicles were intact prior to DC-iDEP, we imaged a modified INS-1E cell line that contains a human insulin and green fluorescent protein-tagged C peptide (hPro-CpepSfGFP) (<xref ref-type="bibr" rid="bib17">Haataja et al., 2013</xref>). This GFP tag allowed for quick visual verification of intact vesicles using fluorescence confocal microscopy. We observed distinct puncta rather than a diffuse GFP signal which indicated that the vesicles were intact and not ruptured. Further analysis of isolated vesicles was done using EM. We observed intact vesicles with the expected size and shape using both transmission electron microscopy (TEM) and cryo-electron microscopy (cryo-EM) (<xref ref-type="fig" rid="fig1s3">Figure 1—figure supplement 3</xref>).</p></sec><sec id="s2-2"><title>Introduction of DC-iDEP as a discovery and quantification tool for insulin vesicle subpopulations</title><p>Enriched vesicle samples from INS-1E cells were subjected to analysis using DC-iDEP after labeling with insulin vesicle marker synaptotagmin IX (<xref ref-type="bibr" rid="bib54">Schvartz et al., 2012</xref>; <xref ref-type="bibr" rid="bib6">Brunner et al., 2007</xref>), which was confirmed to colocalize with insulin in fluorescence microscopy imaging (<xref ref-type="fig" rid="fig2">Figure 2</xref>). The separation system was operated in a discovery or scanning mode by first applying a high voltage (2100 V, empirically determined, <xref ref-type="fig" rid="fig3">Figure 3</xref>) such that all particles were prevented from entering the first gate, because DEP forces exceed EK forces. The voltage was then lowered incrementally (300 V for each step) allowing various subpopulations to enter the separation zone and be sorted along the device according to their specific EKMr values (<xref ref-type="fig" rid="fig3">Figure 3</xref>). A bolus forms at a gate, corresponding to an EKMr value that is a result of a balance between DEP and EK forces on each particle and reflects a complex set of biological, chemical, and biophysical properties of the vesicles (<xref ref-type="bibr" rid="bib31">Jones et al., 2015</xref>; <xref ref-type="bibr" rid="bib23">Hilton and Hayes, 2019</xref>; <xref ref-type="bibr" rid="bib9">Crowther et al., 2019</xref>; <xref ref-type="bibr" rid="bib39">Liu and Hayes, 2020</xref>). The fluorescence intensity was captured for each gate over a full range of voltages (1800–600 V), such that the largest EKMr values are probed with the higher applied voltage (<xref ref-type="fig" rid="fig4">Figure 4</xref>).</p><fig id="fig2" position="float"><label>Figure 2.</label><caption><title>Colocalization of insulin vesicle marker used in this study (synaptotagmin IX) with insulin.</title><p>(<bold>A, E</bold>) Nuclei of INS-1E insulinoma cells stained with NucBlue. (<bold>B, F</bold>) Synaptotagmin IX labeled with rabbit anti-synaptotagmin IX and goat anti-rabbit IgG (H+L), Alexa 647. (<bold>C, G</bold>) Insulin hormone labeled with mouse anti-insulin and goat anti-mouse IgG (H+L), Alexa 488. (<bold>D, H</bold>) Localization of synaptotagmin IX to insulin vesicles as apparent from the merged intensities of panels (<bold>B</bold>) and (<bold>C</bold>) or (<bold>F</bold>) and (<bold>G</bold>). A strong colocalization was observed between insulin and synaptotagmin IX. Pearson’s r value, 0.66 (<bold>D</bold>) and 0.64 (<bold>H</bold>). Microscopy was performed with a Leica Mica using a 63×/1.2NA water immersion objective on cells mounted in ProLong Glass Antifade Mountant. Scale bars, 5 µm.</p></caption><graphic mimetype="image" mime-subtype="tiff" xlink:href="elife-74989-fig2-v1.tif"/></fig><fig id="fig3" position="float"><label>Figure 3.</label><caption><title>Schematic diagram of direct current insulator-based dielectrophoresis (DC-iDEP) system operated in a discovery or scanning mode.</title><p>As a function of the design of the sawtooth channel with different gate sizes along the channel, the applied voltage defines dielectrophoretic (DEP) and electrokinetic (EK) forces at each gate, and capture occurs when the EK force of the particle is equal to or smaller than the DEP force. At high voltages, only particles with high EKMr values can enter the channel; the highest applied voltage of 2100 V prevents all particles of the sample from entering the inlet of the device due to the induced dielectrophoretic forces (no fluorescent signal detected anywhere along the channel). Sequentially lower voltages allow the various subpopulations to enter and be separated throughout the channel. When a subpopulation’s EKMr value surpasses the channel’s DEP force limit, it travels freely and leaves the channel at the outlet. The right panel indicates fluorescent intensities of the captured particles are recorded along the channel at each voltage. Tracking these intensities allows the discovery and quantification of unknown subpopulations according to their biophysical properties.</p></caption><graphic mimetype="image" mime-subtype="tiff" xlink:href="elife-74989-fig3-v1.tif"/></fig><fig id="fig4" position="float"><label>Figure 4.</label><caption><title>Comparison of distributions for the biophysical properties as reflected in EKMr values of n-insulin vesicles (blue circles) and g-insulin vesicles (yellow squares) with varied voltages applied.</title><p>Fluorescent intensities of captured n- and g-insulin vesicles with different EKMr values were recorded at each gate. Each data point reflects fluorescent intensities recorded at three subsequent gates with the same EKMr values, averaged out over biologically replicated experiments and normalized over all signals recorded at a given voltage. (<bold>A</bold>) Full profile of the sample’s biophysical distribution was recorded at 1800 V. (<bold>B–E</bold>) Insulin vesicle subpopulations were separated at subsequent applied voltages of 1500, 1200, 900, and 600 V. Values are mean ± SEM (n=3 for n-vesicles or 4 for g-vesicles for biologically independent experiments) (*p&lt;0.05 using ANOVA with Bonferroni post hoc multiple comparison correction). Raw data in <xref ref-type="supplementary-material" rid="fig4sdata1">Figure 4—source data 1</xref>.</p><p><supplementary-material id="fig4sdata1"><label>Figure 4—source data 1.</label><caption><title>The raw data depicted in <xref ref-type="fig" rid="fig4">Figure 4</xref>.</title></caption><media mimetype="application" mime-subtype="xlsx" xlink:href="elife-74989-fig4-data1-v1.xlsx"/></supplementary-material></p></caption><graphic mimetype="image" mime-subtype="tiff" xlink:href="elife-74989-fig4-v1.tif"/></fig></sec><sec id="s2-3"><title>Biophysical subpopulations of vesicles from untreated INS-1E cells</title><p>The distribution of fluorescently labeled n-insulin vesicles captured at each gate formed a characteristic arc (<xref ref-type="fig" rid="fig1">Figure 1C</xref>), indicating a well-operating and consistent system. Higher voltages provide a broader dynamic range of EKMr values for capturing a wider range of particles, while lower voltages provide detailed distribution of particles based on their associated EKMr values. At an applied voltage of 1800 V, particles were sensed at EKMr values below 1.5×10<sup>10</sup> V/m<sup>2</sup> in patterns of overlapping subpopulations (<xref ref-type="fig" rid="fig4">Figure 4A</xref>, blue circles). These overlapping features begin to spread out with an applied voltage of 1500 V. At incrementally lower settings of applied voltages (1200, 900, and 600 V), distinctive patterns become identifiable (<xref ref-type="fig" rid="fig4">Figure 4B–E</xref>). Notable and discernible features of bioparticle distribution are apparent around 1.2×10<sup>10</sup> and 1.8×10<sup>10</sup> V/m<sup>2</sup> at an applied voltage of 1200 V (<xref ref-type="fig" rid="fig4">Figure 4C</xref>). Lowering the voltage to 900 V, and redistribution of bioparticles based on adapted properties of the channel, reveals a similar but attenuated feature of the distribution around 1.2×10<sup>10</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4D</xref>), whereas particles with EKMr values greater than 1.5×10<sup>10</sup> V/m<sup>2</sup> leave the channel at this voltage. At this voltage, redistribution of particles, previously retained in overlapping patterns at 1200 V, forms a distinct peak around 5–6×10<sup>9</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4D</xref>). Further lowering the voltage to 600 V shows similar patterns of particle distribution around 5–6×10<sup>9</sup> V/m<sup>2</sup>, as well as distinctive patterns around 3–4×10<sup>9</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4E</xref>), while leaving out populations with EKMr values higher than 1.0×10<sup>10</sup> V/m<sup>2</sup>.</p></sec><sec id="s2-4"><title>Biophysical subpopulations of vesicles from glucose-stimulated INS-1E cells</title><p>Insulin vesicles obtained from 25 mM glucose-treated INS-1E cells (g-insulin vesicles) were studied with the same method used for the untreated cells (<xref ref-type="fig" rid="fig4">Figure 4</xref>, yellow squares). Consistent with the vesicles from untreated cells, patterns of primarily overlapping subpopulations were detectable at a voltage of 1800 V. Distribution of particles was observed at values up to 2.3×10<sup>10</sup> V/m<sup>2</sup> (compared to a maximum value of 1.5×10<sup>10</sup> V/m<sup>2</sup> for the untreated populations) (<xref ref-type="fig" rid="fig4">Figure 4A</xref>), which suggests that these vesicles have a broader range of properties than the population from the untreated cells. The first evidence of a distinct distribution feature was captured around 7–8×10<sup>9</sup> V/m<sup>2</sup> when the voltage was lowered to 1500 V (<xref ref-type="fig" rid="fig4">Figure 4B</xref>). Decreasing the voltage to 1200 V and subsequent particle redistribution in the channel revealed a distribution pattern with discernible features around 7–8×10<sup>9</sup> V/m<sup>2</sup>, like those observed at 1500 V, as well as a distinct peak around 1.1×10<sup>10</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4C</xref>). Further lowering the voltage to 900 V resulted in a unique distribution pattern with discernible features around 1.1×10<sup>10</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4D</xref>), similar to 1200 V, and 8×10<sup>9</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4D</xref>), previously observed at 1500 and 1200 V (<xref ref-type="fig" rid="fig4">Figure 4B and C</xref>). Another feature of this distribution pattern was a peak around 4×10<sup>9</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4D</xref>). Ultimately, decreasing the voltage to 600 V revealed a distribution pattern with features around 4×10<sup>9</sup> and 8×10<sup>9</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4E</xref>), like those observed at higher voltages, leaving out patterns that were observed at EKMr values higher than 1.0×10<sup>10</sup> V/m<sup>2</sup>. This distinct distribution pattern, although consistent with distribution patterns at higher voltages, featured stretched-out peaks, consistent with a smaller and more refined range of EKMr values assigned throughout the channel.</p></sec><sec id="s2-5"><title>Comparison of n- and g-insulin vesicle separation patterns within the iDEP device</title><p>Visual inspection of the collected data revealed generally similar patterns of vesicles collected at specific EKMr values (<xref ref-type="fig" rid="fig4">Figure 4</xref>). However, at 1200 V we achieved adequate separation of vesicle populations to discern unique populations of vesicles from cells treated with glucose compared to no treatment. Using a two-way ANOVA, we found a statistically significant interaction between the effect of treatment on vesicles collected at each EKMr value for data collected only at 1200 V (F(8, 45)=3.61, p=0.003). A Bonferroni post hoc test revealed a significant difference in the intensity or quantity of vesicles collected between treated and untreated samples at 1.10×10<sup>9</sup> V/m<sup>2</sup> (p=0.0249), 5.35×10<sup>9</sup> V/m<sup>2</sup> (p=0.0469), 7.45×10<sup>9</sup> V/m<sup>2</sup> (p=0.0369). These differences reflect a shift in the populations of insulin vesicles upon glucose stimulation.</p></sec></sec><sec id="s3" sec-type="discussion"><title>Discussion</title><p>Using the DC-iDEP separation method, we identified distinct populations of insulin vesicles with varying biophysical properties. Furthermore, we observed different distribution patterns for insulin vesicles isolated from glucose-stimulated versus unstimulated cells, which suggests glucose stimulation alters the insulin vesicle subpopulations. This is consistent with findings from single-cell soft X-ray tomography, which revealed heterogeneities in the composition of insulin vesicles and their molecular densities upon glucose stimulation due to vesicle maturation (<xref ref-type="bibr" rid="bib59">White et al., 2020</xref>; <xref ref-type="bibr" rid="bib41">Loconte et al., 2022</xref>). Other studies have also observed enrichment of certain subpopulations of vesicles in response to glucose stimulation (<xref ref-type="bibr" rid="bib56">Straub et al., 2004</xref>). Our approach addresses an important need to identify and separate distinct subpopulations of insulin vesicles which can allow for investigating their apparent heterogeneity.</p><p>The intensity peaks we observed at specific EKMr values likely correspond to some of the previously described insulin vesicle subpopulations (<xref ref-type="bibr" rid="bib61">Zhang et al., 2020</xref>; <xref ref-type="bibr" rid="bib49">Norris et al., 2021</xref>; <xref ref-type="bibr" rid="bib48">Neukam et al., 2020</xref>; <xref ref-type="bibr" rid="bib60">Yau et al., 2020</xref>; <xref ref-type="bibr" rid="bib33">Kreutzberger et al., 2020</xref>). Larger particles are expected to have a smaller EKMr value compared to smaller particles (<xref ref-type="bibr" rid="bib24">Hilton et al., 2020</xref>). Subpopulations containing larger insulin vesicles, such as a mature pool (<xref ref-type="bibr" rid="bib61">Zhang et al., 2020</xref>; <xref ref-type="bibr" rid="bib49">Norris et al., 2021</xref>), synaptotagmin IX-positive vesicles (<xref ref-type="bibr" rid="bib33">Kreutzberger et al., 2020</xref>), or docked vesicles near the plasma membrane (<xref ref-type="bibr" rid="bib61">Zhang et al., 2020</xref>) may have lower EKMr values than smaller immature vesicles. Additionally, phosphatidylcholine lipids increase the zeta potential of tristearoylglycerol crystals (<xref ref-type="bibr" rid="bib1">Arts et al., 1994</xref>). This effect may extend to insulin vesicle subpopulations containing more phosphatidylcholine, such as young insulin vesicles (<xref ref-type="bibr" rid="bib48">Neukam et al., 2020</xref>) which could lead to higher EKMr values. Taken together, these two properties may be used to predict the EKMr values of known insulin vesicle subpopulations. For example, insulin vesicles with EKMr values of 1–2×10<sup>9</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4C</xref>) may represent a synaptotagmin IX-positive subpopulation due to their larger radii and depletion under glucose stimulation. Additionally, young insulin vesicles may have EKMr values between 5 and 7.5×10<sup>9</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4C</xref>) due to higher amounts of phosphatidylcholine present in this subpopulation (<xref ref-type="bibr" rid="bib48">Neukam et al., 2020</xref>). In this EKMr range, we observed a higher intensity for glucose-treated cells which may suggest biosynthesis of new vesicles. Immature insulin vesicles are likely to have higher EKMr values due to their smaller size (<xref ref-type="bibr" rid="bib61">Zhang et al., 2020</xref>), such as an EKMr value between 1.5 and 1.6×10<sup>10</sup> V/m<sup>2</sup> (<xref ref-type="fig" rid="fig4">Figure 4C</xref>). Here, we demonstrated the capabilities of DC-iDEP to separate insulin vesicle subpopulations in an unbiased manner. Future experiments using chemical probes to label subpopulations will be useful to accurately define the EKMr values associated with specific subpopulations.</p><p>The intensity at each EKMr value is influenced by individual insulin vesicles. The distribution of this pattern reflects differences in physical properties of the vesicles such as physical changes to the vesicles as they mature. The specific changes to the individual insulin vesicles which result in varied EKMr measurements can be associated with any alteration of the biochemical makeup of the bioparticle. There is an ongoing evolution of the theoretical underpinnings of electric field gradient techniques, where past physical descriptors were limited to conductivity and permittivity of the particle (<xref ref-type="bibr" rid="bib53">Pethig, 2019</xref>). It is becoming better understood that the overall structure and subtle details of the bioparticle, including the particle-solvent cross-polarizations, will influence these forces (<xref ref-type="bibr" rid="bib53">Pethig, 2019</xref>; <xref ref-type="bibr" rid="bib44">Matyushov, 2019</xref>; <xref ref-type="bibr" rid="bib21">Hayes, 2020</xref>; <xref ref-type="bibr" rid="bib25">Hölzel and Pethig, 2021</xref>; <xref ref-type="bibr" rid="bib43">Martin and Matyushov, 2012</xref>). This new view of the forces imparted on the insulin vesicles aligns with a rather simple proposition that the makeup of the particle has changed: something has been added, subtracted, or altered which changed the force on the particle in a measurable and quantifiable way. This is consistent with findings that glucose can affect the maturation of existing vesicles, increasing their molecular density or the concentration of biomolecules within the vesicle lumen (<xref ref-type="bibr" rid="bib59">White et al., 2020</xref>). Additionally, the subpopulations we observe could have been altered by a change in surface protein expression or enrichment of unsaturated lipids (<xref ref-type="bibr" rid="bib47">Moore et al., 2019</xref>). This is also consistent with findings that glucose enhances vesicle-mitochondria association which is hypothesized to contribute to insulin vesicle maturation (<xref ref-type="bibr" rid="bib59">White et al., 2020</xref>). Identical particles always have the same EKMr in the same manner that identical proteins always have the same molecular weight (<xref ref-type="bibr" rid="bib62">Zhu et al., 2019</xref>). Thus, we can use EKMr values to evaluate the presence of specific insulin vesicle subpopulations and explore how specific chemical probes may influence vesicle identity.</p><p>There are some subtleties in the presented data which accentuate features, and some limitations, of the DC-iDEP separations and the imaging system. The data for 1800 V, for instance, shows no discernable local maxima above 1.5×10<sup>10</sup> V/m<sup>2</sup> for the n-vesicles, despite having identifiable populations with EKMr values higher than 1.5×10<sup>10</sup> V/m<sup>2</sup> at lower voltages. Another feature that is quite apparent is a lack of distinct ‘peaks’ or identifiable patterns appearing at consistent EKMr values within the datasets from differing applied voltage values. While the applied voltage does not affect the properties of bioparticles, it defines the forces that oppose bioparticle movements across the channel at different gates. Accordingly, in an overlapping pattern of subpopulations, those with higher EKMr values overcome the weaker opposing forces at a given gate once the voltage is lowered (<xref ref-type="bibr" rid="bib40">Liu and Hayes, 2021</xref>; <xref ref-type="bibr" rid="bib30">Jones and Hayes, 2015</xref>). Hence, the signal which was previously averaged with signals from the other overlapping subpopulations is now discernible at a higher EKMr value. Considering the high resolution of the technique, homogeneous subpopulations of vesicles undoubtably consist of a very narrow range of EKMr values. Each data point shown in <xref ref-type="fig" rid="fig4">Figure 4</xref> represents a homogenous subpopulation captured at the gate. While this feature is unsatisfying to classic separation scientists, it still allows for quantitative comparison of paired samples, as has been shown here. To further optimize the separation, one could modify the channels including the gate size and periodicity, as well as scan even more refined ranges of voltages to induce varying spectra of EKMr values along the device (<xref ref-type="bibr" rid="bib11">Ding et al., 2016</xref>; <xref ref-type="bibr" rid="bib31">Jones et al., 2015</xref>). Additionally, developing the capability to port the collected individual boluses will enable downstream analyses such as mass spectrometry or EM, transiting the technique from analytical to preparative.</p><p>In essence, the current work introduces DC-iDEP in a scanning mode as a powerful tool to interrogate complex organelle subpopulations and study their distinctive distribution patterns under different treatment conditions with the goal of organelle subpopulation discovery and quantification. Further, the putatively subtle differences in subpopulations between stimulated and unstimulated INS-1E insulinoma cells are quantified more extensively than previously possible. Finally, this method serves as a stepping stone toward isolation and concentration of fractions which show the largest difference between the two population patterns for further bioanalysis (imaging, proteomics, lipidomics, etc.) that otherwise would not be possible given the low-abundance components of these subpopulations. This approach can be broadly applied to any cell type and organelle beyond the scope of our model system of insulin vesicles and INS-1E insulinoma cells.</p></sec><sec id="s4" sec-type="materials|methods"><title>Materials and methods</title><p>No statistical methods were used to predetermine sample size. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment.</p><sec id="s4-1"><title>Cell culture</title><p>INS-1E cells (Addex Bio C0018009; RRID: <ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:CVCL_0351">CVCL_0351</ext-link>) were cultured according to the supplier’s protocol. Briefly, cells were seeded in RPMI 1640 media (modified to contain 2 mM L-glutamine; 10 mM HEPES pH 7.2, 1 mM sodium pyruvate, 2 g/L glucose, and 1.5 g/L sodium bicarbonate, 50 µM 2-mercaptoethanol, 100 U/mL penicillin, and 100 µg/mL streptomycin; sterile filtered through 0.22 µm filter) supplemented with 10% fetal bovine serum (FBS) and grown to 80% confluency. Cells were plated at a density of 10<sup>5</sup> cells/cm<sup>2</sup> in a 24-well plate for the glucose sensitivity assay and incubated at 37°C with 5% CO<sub>2</sub> in growth media for 4–5 days. Cells were pretreated at 60–80% confluency with Krebs-Ringer bicarbonate HEPES (KRBH) buffer (135 mM NaCl, 3.6 mM KCl, 5 mM NaHCO<sub>3</sub>, 0.5 mM NaH<sub>2</sub>PO<sub>4</sub>, 0.5 mM MgCl<sub>2</sub>, 1.5 mM CaCl<sub>2</sub>, 10 mM HEPES, pH 7.4, and 0.1% bovine serum albumin [BSA]; made fresh within 7 days of use) free of glucose and incubated at 37°C with 5% CO<sub>2</sub> for 30 min. They were then stimulated for 30 min at 37°C using KRBH buffers containing 1.1, 5.6, 8.4, 11.1, 16.7, and 25 mM glucose, in the presence of house-made protease inhibitor (PI) cocktail (0.5 M AEBSF, 1 mM E-64, 1.13 mM leupeptin, and 151.36 μM aprotinin). KRBH buffer was removed and saved from cells for downstream analysis. Insulin secretion in response to increasing concentrations of glucose was confirmed in an ELISA (Mercodia 10-1250-01) following the manufacturer’s manual (<xref ref-type="fig" rid="fig1s1">Figure 1—figure supplement 1</xref>). For each biological replica, cells were plated at a density of 4×10<sup>4</sup> cells/cm<sup>2</sup> in a five-layer cell chamber (VWR 76045-402) to yield enough material for completing the experiment. All cell stacks were at least 95% viable after the harvest with 0.05% trypsin. For glucose treatment, near 80% confluent cells were gently rinsed with dialyzed phosphate-buffered saline (PBS) twice and starved in a KRBH buffer with no glucose for 30 min, followed by a 30 min stimulation of insulin release by KRBH buffer supplemented with 25 mM glucose. Cells were then harvested by mild trypsinization.</p></sec><sec id="s4-2"><title>Colocalization of synaptotagmin IX and insulin vesicles</title><p>Cells were grown on ibidi 8 well high ibiTreat slides (80806-96), precoated with poly-L-lysine. Cells were fixed with 4% ice-cold PFA for 10 min, and then stained with antibody cocktail (Mouse anti-insulin antibody [Cell Signaling Technology #8138], 1:100; Rabbit anti-synaptotagmin IX [Thermo Fisher Scientific PA5-44987; RRID: <ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:AB_2610517">AB_2610517</ext-link>], 1:100) in 0.5% BSA, 0.2% saponin, 1% FBS of PBS buffer for 2 hr at room temperature (RT). After three washes with PBST for 10 min, cells were incubated for 1 hr with secondary antibody cocktail (Goat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 488 [Invitrogen A-11034] 1:100; Goat anti-Mouse IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor 647 [Invitrogen A-21236], 1:100). Cells were then mounted in ProLong Glass Antifade Mountant with NucBlue Stain (Thermo Fisher Scientific P36981) and cured for 24 hr before imaging.</p><p>A Leica Mica microhub was used for confocal imaging. Image acquisition was performed using 63×/1.2NA water immersion objective, using 888×742 format, with 0.04 μm pixel size, and pinhole diameter automatically set. The signal was collected with 359, 499, and 650 nm excitations and 461, 520, 668 nm emissions for nuclei, insulin, and synaptotagmin IX by HyD FS detector, respectively. The images were deconvoluted by LAS X, lightning module. The refraction index was set to 1.52 for processing. 20–25 planes were collected with a Z-step size of 0.16 µm, and a maximum intensity projection was produced.</p></sec><sec id="s4-3"><title>Insulin secretory vesicle enrichment</title><p>Following trypsinization, all the steps were performed at 4°C. Cells were gently washed twice with PBS, followed by Dounce homogenization of the cells with 20 strokes in homogenization buffer (HB) (0.3 M sucrose, 10 mM MES, 1 mM EGTA, 1 mM MgSO<sub>4</sub>, pH 6.3) supplemented with house-made PI cocktail (0.5 M AEBSF, 1 mM E-64, 1.13 mM leupeptin, and 151.36 μM aprotinin). Cell debris was collected by centrifugation at 600 × <italic>g</italic> for 10 min and re-homogenized as described above to lyse the remaining intact cells, followed by a second spin at 600 × <italic>g</italic>. Supernatants were pooled and centrifuged at 5400 × <italic>g</italic> for 15 min to remove mitochondria, ER, and other subcellular compartments of similar density. The pellet was discarded, and the supernatant was centrifuged at 35,000 × <italic>g</italic> for 30 min to sediment insulin vesicles, among other contaminants, yielding up to ~5 µg of dry material per every million cells. This pellet was resuspended in ~450 μL HB and loaded on a density gradient column formed by layering decreasing densities of OptiPrep density media (Sigma-Aldrich D1556) and HB in 0.9 mL fractions of 40%, 35%, 30%, 25%, and 20% OptiPrep in an open-top thin-wall polypropylene tube (Beckman 326819). The density column was then spun in an SW55i Beckman rotor of an ultracentrifuge at 160,000 × <italic>g</italic> for 8 hr to fractionate the insulin vesicle-containing population. Insulin vesicle subpopulations were isolated in fractions of 400 µL, and ELISA and WB were used to identify the fractions most enriched in insulin. A similar dilution was applied to all the fractions. The manufacturer’s manual was followed for ELISA (Mercodia 10-1250-01) (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2A</xref>). For WB, fraction samples were mixed with 4X NuPAGE LDS sample buffer (Invitrogen NP0007), loaded on a 15-well NuPAGE 4–12% bis-tris gel (Invitrogen NP0323PK2) and run in a mini-gel tank (Life Technologies A25977) at 200 V for 30 min (Bio-Rad 1645050). Protein was then transferred to a PVDF membrane using iBlot 2 Transfer Stacks (Invitrogen IB24002) in iBlot 2 dry blotting device (Invitrogen IB21001). The membrane was blocked using 5% BSA, then cut according to marker protein size and incubated with antibodies against marker proteins for mitochondria (Cytochrome c Antibody; Novus Biologicals NB100-56503), ER (SEC61B Polyclonal Antibody; Life Technologies PA3015), and insulin vesicles (synaptotagmin IX; Thermo Fisher Scientific PA5-44987; RRID: <ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:AB_2610517">AB_2610517</ext-link>) at RT for 2–5 hr. Membranes were then washed with 0.1% Tween supplemented PBS (PBST) twice and incubated with the secondary antibody (anti-rabbit IgG, anti-mouse IgG) at RT for 1 hr. Membranes were washed with PBST two to three times and bands were visualized upon addition of SigmaFast BCIP/NBT tablets (Sigma B5655) (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2B</xref>). Fractions containing the highest insulin levels and high concentration of the vesicle marker synaptotagmin IX were regarded as insulin vesicle samples and were further tested for DLS using Wyatt Technology’s Mobius to confirm the size distribution of particles corresponding to the insulin vesicle diameter, reportedly 200–500 nm. Data was analyzed in DYNAMICS and manually corrected against the control (HB) (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2C</xref>). Insulin vesicle fractions confirmed to have the expected insulin vesicle diameter by DLS were then pooled together and spun down at 35,000 × <italic>g</italic> for 15 min to sediment the insulin vesicles. The pellet obtained from this step was used for immunolabeling of insulin vesicles.</p></sec><sec id="s4-4"><title>Verification of intact isolated insulin vesicles</title><p>Fluorescent confocal microscopy was used to verify that enriched insulin vesicles were intact. Suspended insulin vesicles were imaged using a 63× water objective on 0.6-mm-thick coverslips on a Mica (Leica Microsystems). The signal was collected with an excitation wavelength of 488 nm and emission wavelength of 509 nm. Images were deconvoluted by LAS X, lightning module. The refractive index was set to 1.33 for processing.</p><p>Negative stain EM was used to evaluate if the isolated insulin vesicles were intact prior to analysis with DC-iDEP. In this case, we used a modified INS-1 cell line that expresses a human insulin and green fluorescent protein-tagged C peptide (hPro-CpepSfGFP) (<xref ref-type="bibr" rid="bib17">Haataja et al., 2013</xref>) (GRINCH cells) which allowed for quick visual verification of isolated insulin-containing vesicles. Insulin vesicles isolated from GRINCH cells (RRID:<ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:CVCL_WH61">CVCL_WH61</ext-link>) using an EDTA-supplemented enrichment strategy were deposited directly on EM grids (Formvar/Carbon 200 mesh, Copper, Ted Pella). Insulin vesicles were allowed to settle on the grid for 10 min at 20°C before blotting. A tungsten-based negative stain (Nano-W, Ted Pella) was spotted onto the grid, incubated at 20°C for 30 s, then excess stain was blotted. The negative staining was repeated twice more, then the grid was allowed to dry at 37°C for 1 hr. Prepared grids were imaged at RT on a Talos F200C G2 Transmission Electron Microscope (Thermo Fisher Scientific) running at 80 kV acceleration voltage.</p><p>To further validate the integrity of the insulin vesicles isolated, they were applied onto EM grids (Ted Pella Lacey Carbon, 200 mesh, TH, Gold) at 4°C and 100% humidity for cryo-EM imaging. Excess buffer was blotted for 2 s before grids were plunge-frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific). Grids were imaged under cryogenic conditions using a 200 kV Glacios Cryo TEM equipped with a Falcon 4 detector (Thermo Fisher Scientific) or a 300 kV Krios G3i equipped with a Gatan K3 direct detection camera (Thermo Fisher Scientific).</p></sec><sec id="s4-5"><title>Immunolabeling of insulin vesicles</title><p>The pellet was resuspended in HB and incubated with 5–10 μg of anti-synaptotagmin IX (Thermo Fisher Scientific PA5-44987; RRID:<ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:AB_2610517">AB_2610517</ext-link>; 1:50–1:100) overnight to tag an insulin vesicle marker. This primary antibody was then fluorescently labeled by 2–4 hr incubation with 5–10 μg of Alexa 568-conjugated secondary antibody (Invitrogen A-11011; RRID:<ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:AB_143157">AB_143157</ext-link>; 1:50–1:100) (<xref ref-type="fig" rid="fig1">Figure 1</xref>). Insulin vesicles were washed with HB three times to remove the excess antibody and were finally resuspended in a low conductivity buffer (LCHB; 0.3 M sucrose, 5 mM MES, pH 6.3), which is compatible with dielectrophoresis studies.</p></sec><sec id="s4-6"><title>Device fabrication</title><p>The design and fabrication methods of the separation device were described in prior publications (<xref ref-type="bibr" rid="bib16">Greider et al., 1969</xref>). The device contains a 27-gate sawtooth channel with a depth of approximately 20 μm and a length of 3.5 cm from inlet to outlet (<xref ref-type="fig" rid="fig1">Figure 1C</xref>). The distance between two paired triangle tips (gates) decreases from 73 to 25 μm in the channel. The gate size decreases approximately 5 µm after every three repeats. Direct current was applied to the device between the inlet and outlet. The potentials were between 0 and 1800 V for testing.</p><p>The microfluidic devices were fabricated by standard soft lithographic technique as described previously (<xref ref-type="bibr" rid="bib55">Staton et al., 2010</xref>). The design of the channel was created by AutoCAD (Autodesk, Inc, San Rafael, CA, USA) and was used for fabricating a photomask. The channel was created by exposing AZ P4620-positive photoresist (AZ Electronic Materials, Branchburg, NJ, USA) on Si wafer CEM388SS (Shin-Etsu MicroSi, Inc, Phoenix, AZ, USA) by contact lithography. Extra materials were removed from the Si wafer. A weight of 22 g of polydimethylsiloxane (PDMS, Sylgard 184, Dow/Corning, Midland, MI, USA) was used to fabricate four channels simultaneously. The PDMS mixture was placed on the Si wafer template and left to stand for 30 min to allow bubbles to dissipate, and then it was baked for 1 hr at a temperature of 80°C. Holes 2.5 mm in diameter were punched for inlet and outlet reservoirs. Each channel was capped with a glass microscope slide to fabricate the enclosed channels after cleaning and activation by plasma cleaner (Harrick Plasma, Ithaca, NY, USA) with a voltage of 50 kV.</p></sec><sec id="s4-7"><title>Electric field simulations</title><p>Finite element modeling (COMSOL, Inc, Burlington, MA, USA) of the distribution of the electric field in the microchannel was performed as previously detailed (<xref ref-type="bibr" rid="bib55">Staton et al., 2010</xref>). The <italic>AC/DC module</italic> was used to interrogate the <inline-formula><mml:math id="inf3"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:mstyle></mml:math></inline-formula>, <inline-formula><mml:math id="inf4"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msup><mml:mrow><mml:mi mathvariant="normal">∇</mml:mi><mml:mrow><mml:mo>|</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo>|</mml:mo></mml:mrow></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:mstyle></mml:math></inline-formula>, and <inline-formula><mml:math id="inf5"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mfrac><mml:mrow><mml:mi mathvariant="normal">∇</mml:mi><mml:mrow><mml:mo>|</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo>|</mml:mo></mml:mrow></mml:mrow><mml:msup><mml:mi>E</mml:mi><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msup></mml:mfrac><mml:mo>⋅</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:mstyle></mml:math></inline-formula> in an accurately scaled 2D model of the microchannel.</p></sec><sec id="s4-8"><title>DC-iDEP</title><p>The separation channel was treated with 5% (wt/vol) BSA for 15 min followed by a wash with LCHB. A volume of 15 μL of the insulin vesicle sample was introduced to the device from the inlet. This sample fraction contains particles, ~75% of which have radii characteristic of insulin vesicle as apparent from DLS experiments (<xref ref-type="fig" rid="fig1s2">Figure 1—figure supplement 2C</xref>). The volume in each reservoir was maintained with LCHB to prevent pressure-induced flow. Direct current was applied at 600, 900, 1200, 1500, and 1800 V between the inlet and outlet and particles were driven through the channel by the EK force experienced.</p></sec><sec id="s4-9"><title>Imaging of vesicles during separation</title><p>Images and recordings were acquired using an Olympus IX70 inverted microscope with 4×, NA 0.16, and 20×, NA 0.40, objectives. The 20× objective was used to inspect the channel to confirm that the device was properly formed. The 4× objective was used in recording the intensity of the insulin vesicle signal shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>. A mercury short arc lamp (H30102 w/2, OSRAM) and a triple band pass cube (Olympus, Center Valley, PA, USA) were used for sample illumination and detection (excitation: 400/15-495/15-570/25 nm; dichroic: 410-510-590 nm; emission: 460/20-530/30-625/50 nm). Fluorescent intensities of immunolabeled insulin vesicles were recorded using the 4× objective by a LightWise Allegro camera (LW-AL-CMV12000, USB3, 0059-0737-B, Imaging Solutions Group) after the voltage had been applied 90 s. Images were recorded from three to four biological replicates at each gate (27 total gates) for any given voltage. Images were further processed in ImageJ (NIH, freeware). The intensity at each gate was recorded along with the intensity of a nearby open area of the channel (image intensity background), which was subtracted from the intensity at each gate to adjust for any variation in illumination intensity. The data for each applied voltage value was normalized to the highest intensity within that dataset.</p></sec><sec id="s4-10"><title>Theory</title><p>The forces exerted on bioparticles in the microfluidic device in the presence of direct current include DEP and EK forces. Separation of subpopulations is achieved based on the different magnitude of the forces each bioparticle experiences, related to the properties of the particles, including their radius, conductivity, and zeta potential.</p><p>The EK mobility, <inline-formula><mml:math id="inf6"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula>, and velocity, <inline-formula><mml:math id="inf7"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mrow><mml:mover><mml:mi>υ</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>E</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula>, are described as:<disp-formula id="equ1"><mml:math id="m1"><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub><mml:mo>+</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>O</mml:mi><mml:mi>F</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:math></disp-formula><disp-formula id="equ2"><mml:math id="m2"><mml:mrow><mml:msub><mml:mi>υ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:math></disp-formula></p><p>where  <inline-formula><mml:math id="inf8"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> is the electrophoretic (EP) mobility and  <inline-formula><mml:math id="inf9"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>O</mml:mi><mml:mi>F</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> is the electro-osmotic flow mobility. The DEP mobility, <italic>μ<sub>DEP</sub></italic>, and velocity,  <inline-formula><mml:math id="inf10"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:msub><mml:mrow><mml:mover><mml:mi>ν</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula>, can be expressed as:<disp-formula id="equ3"><mml:math id="m3"><mml:mrow><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:mfrac><mml:mrow><mml:msub><mml:mi>ε</mml:mi><mml:mi>m</mml:mi></mml:msub><mml:msup><mml:mi>r</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:msub><mml:mi>f</mml:mi><mml:mrow><mml:mi>C</mml:mi><mml:mi>M</mml:mi></mml:mrow></mml:msub></mml:mrow><mml:mrow><mml:mn>3</mml:mn><mml:mi>η</mml:mi></mml:mrow></mml:mfrac></mml:mrow></mml:math></disp-formula><disp-formula id="equ4"><mml:math id="m4"><mml:mrow><mml:msub><mml:mrow><mml:mover><mml:mi>ν</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub><mml:mo>=</mml:mo><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub><mml:mi>▽</mml:mi><mml:msup><mml:mrow><mml:mo>|</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo>|</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow></mml:math></disp-formula></p><p>where <italic>r</italic> is the radius of the particle, <italic>f<sub>CM</sub></italic> is the Clausius-Mossotti factor, <italic>ε<sub>m</sub></italic> is the dielectric constant of the solution, and <italic>η</italic> is the viscosity.</p><p>A combination of biophysical properties of the particles, such as insulin vesicles, determines the location where they will be captured in a microfluidic device. Capture occurs when the EK velocity of the particle is equal to that of DEP. The condition is:<disp-formula id="equ5"><mml:math id="m5"><mml:mrow><mml:mrow><mml:mover><mml:mi>j</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo>⋅</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo>=</mml:mo><mml:mn>0</mml:mn></mml:mrow></mml:math></disp-formula><disp-formula id="equ6"><mml:math id="m6"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>▽</mml:mi><mml:msup><mml:mrow><mml:mo>|</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo>|</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:msup><mml:mi>E</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mfrac><mml:mo>⋅</mml:mo><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mo>≥</mml:mo><mml:mfrac><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>E</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mi>μ</mml:mi><mml:mrow><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:mfrac></mml:mrow></mml:math></disp-formula></p><p>where <inline-formula><mml:math id="inf11"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:mstyle></mml:math></inline-formula>  is the electric field intensity, <inline-formula><mml:math id="inf12"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mrow><mml:mover><mml:mi>j</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow></mml:mrow></mml:mstyle></mml:math></inline-formula> is the particle flux, and <inline-formula><mml:math id="inf13"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mi mathvariant="normal">∇</mml:mi><mml:mrow><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mrow><mml:mo stretchy="false">|</mml:mo></mml:mrow></mml:mrow></mml:mstyle></mml:math></inline-formula> is the gradient of the electric field. The ratio of EK to DEP mobilities (EKMr, <inline-formula><mml:math id="inf14"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mfrac><mml:msub><mml:mtext>µ</mml:mtext><mml:mrow><mml:mi>E</mml:mi><mml:mi>K</mml:mi></mml:mrow></mml:msub><mml:msub><mml:mtext>µ</mml:mtext><mml:mrow><mml:mi>D</mml:mi><mml:mi>E</mml:mi><mml:mi>P</mml:mi></mml:mrow></mml:msub></mml:mfrac></mml:mrow></mml:mstyle></mml:math></inline-formula>) can be used to characterize the biophysical properties of different subpopulations (<xref ref-type="bibr" rid="bib38">Liu et al., 2019</xref>). The EKMr of an insulin vesicle is larger than <inline-formula><mml:math id="inf15"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>▽</mml:mi><mml:mrow><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:msup><mml:mrow><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:msup><mml:mi>E</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mfrac><mml:mo>⋅</mml:mo><mml:msub><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mn>1</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> of the gates which it has passed through and is smaller/equal to <inline-formula><mml:math id="inf16"><mml:mstyle displaystyle="true" scriptlevel="0"><mml:mrow><mml:mfrac><mml:mrow><mml:mi>▽</mml:mi><mml:mrow><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:msup><mml:mrow><mml:mo stretchy="false">|</mml:mo></mml:mrow><mml:mn>2</mml:mn></mml:msup></mml:mrow><mml:msup><mml:mi>E</mml:mi><mml:mn>2</mml:mn></mml:msup></mml:mfrac><mml:mo>⋅</mml:mo><mml:msub><mml:mrow><mml:mover><mml:mi>E</mml:mi><mml:mo stretchy="false">→</mml:mo></mml:mover></mml:mrow><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:mstyle></mml:math></inline-formula> of the gates where it is captured. In this way, insulin vesicles are separated in the microfluidic channel, thus measuring the EKMr values for the insulin vesicles.</p></sec></sec></body><back><sec sec-type="additional-information" id="s5"><title>Additional information</title><fn-group content-type="competing-interest"><title>Competing interests</title><fn fn-type="COI-statement" id="conf1"><p>No competing interests declared</p></fn><fn fn-type="COI-statement" id="conf2"><p>serves as the CSO of CBio, and COB, CEO &amp; CSO of Hayes Diagnostics, Inc</p></fn></fn-group><fn-group content-type="author-contribution"><title>Author contributions</title><fn fn-type="con" id="con1"><p>Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review and editing</p></fn><fn fn-type="con" id="con2"><p>Formal analysis, Investigation, Writing – original draft, Writing – review and editing</p></fn><fn fn-type="con" id="con3"><p>Formal analysis, Investigation, Writing – original draft, Writing – review and editing</p></fn><fn fn-type="con" id="con4"><p>Formal analysis, Supervision, Investigation, Writing – original draft, Writing – review and editing</p></fn><fn fn-type="con" id="con5"><p>Conceptualization, Supervision, Investigation, Writing – original draft, Writing – review and editing</p></fn><fn fn-type="con" id="con6"><p>Conceptualization, Formal analysis, Supervision, Writing – original draft, Project administration, Writing – review and editing</p></fn><fn fn-type="con" id="con7"><p>Conceptualization, Formal analysis, Supervision, Funding acquisition, Methodology, Writing – original draft, Project administration, Writing – review and editing</p></fn></fn-group></sec><sec sec-type="supplementary-material" id="s6"><title>Additional files</title><supplementary-material id="mdar"><label>MDAR checklist</label><media xlink:href="elife-74989-mdarchecklist1-v1.docx" mimetype="application" mime-subtype="docx"/></supplementary-material></sec><sec sec-type="data-availability" id="s7"><title>Data availability</title><p>All data generated or analysed during this study are included in the manuscript and supporting file; Source Data files have been provided for Figures S2 and 4.</p></sec><ack id="ack"><title>Acknowledgements</title><p>The authors would like to thank Raymond Stevens and the Bridge Institute for supporting our project and members of the Pancreatic β-Cell Consortium for their inspiring discussions and feedback. We additionally thank Chris Hanson for assisting with the cell culture, Yekaterina Kadyshevskaya for helping with illustrations, Claire Cato for feedback on the manuscript, Brett Barbaro for assisting with analysis of the existing proteomics data, the USC NanoBiophysics Core Facility for facilitating the DLS experiments, and the USC Center of Excellence in Nano Imaging for facilitating the EM. This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. DGE-1842487. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 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pub-id-type="doi">10.7554/eLife.74989.sa0</article-id><title-group><article-title>Editor's evaluation</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Ailion</surname><given-names>Michael</given-names></name><role specific-use="editor">Reviewing Editor</role><aff><institution-wrap><institution-id institution-id-type="ror">https://ror.org/00cvxb145</institution-id><institution>University of Washington</institution></institution-wrap><country>United States</country></aff></contrib></contrib-group><related-object id="sa0ro1" object-id-type="id" object-id="10.1101/2021.12.01.470798" link-type="continued-by" xlink:href="https://sciety.org/articles/activity/10.1101/2021.12.01.470798"/></front-stub><body><p>This important study presents an exciting new method for separating organelles in an unbiased way and applies this method to the separation of distinct subpopulations of insulin vesicles. Solid evidence is presented that this method is capable of separating distinct subpopulations of insulin vesicles, but the identification of these subpopulations is incomplete and the biological significance of the proposed changes in vesicle populations remains unclear. This work will be of interest to cell biologists studying a variety of organelles.</p></body></sub-article><sub-article article-type="decision-letter" id="sa1"><front-stub><article-id pub-id-type="doi">10.7554/eLife.74989.sa1</article-id><title-group><article-title>Decision letter</article-title></title-group><contrib-group content-type="section"><contrib contrib-type="editor"><name><surname>Ailion</surname><given-names>Michael</given-names></name><role>Reviewing Editor</role><aff><institution-wrap><institution-id institution-id-type="ror">https://ror.org/00cvxb145</institution-id><institution>University of Washington</institution></institution-wrap><country>United States</country></aff></contrib></contrib-group><contrib-group><contrib contrib-type="reviewer"><name><surname>Ailion</surname><given-names>Michael</given-names></name><role>Reviewer</role><aff><institution-wrap><institution-id institution-id-type="ror">https://ror.org/00cvxb145</institution-id><institution>University of Washington</institution></institution-wrap><country>United States</country></aff></contrib><contrib contrib-type="reviewer"><name><surname>Kreutzberger</surname><given-names>Alex JB</given-names></name><role>Reviewer</role><aff><institution>Harvard Medical School</institution><country>United States</country></aff></contrib></contrib-group></front-stub><body><boxed-text id="sa2-box1"><p>Our editorial process produces two outputs: i) <ext-link ext-link-type="uri" xlink:href="https://sciety.org/articles/activity/10.1101/2021.12.01.470798">public reviews</ext-link> designed to be posted alongside <ext-link ext-link-type="uri" xlink:href="https://www.biorxiv.org/content/10.1101/2021.12.01.470798v1">the preprint</ext-link> for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.</p></boxed-text><p><bold>Decision letter after peer review:</bold></p><p>Thank you for submitting your article &quot;iDEP-assisted isolation of insulin secretory vesicles&quot; for consideration by <italic>eLife</italic>. Your article has been reviewed by 4 peer reviewers, including Michael Ailion as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Nancy Carrasco as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Alex J B Kreutzberger (Reviewer #2).</p><p>The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.</p><p>All of the reviewers are excited about the new technique for separating insulin vesicles, but there was also a concern that the manuscript falls short in definitively demonstrating that the vesicles being measured are indeed insulin vesicles, with the biological significance of the proposed changes in vesicle populations remaining unclear. It is also unclear so far whether the technique permits further biochemical characterization of these vesicles. To be published in <italic>eLife</italic>, these issues must be resolved. Essential revisions are listed below, followed by a general evaluation summary, and the individual reviews of each of the reviewers.</p><p>Essential revisions (for the authors):</p><p>1. What are the vesicles being measured? Are these insulin containing vesicles? There is concern given that these vesicles are identified by a single marker and that the data show poor overlap of this marker with insulin by immunostaining. It needs to be resolved which specific synaptotagmin is labeled and further evidence that these vesicles are insulin vesicles needs to be provided. This could possibly be addressed by the use of additional markers and some type of composition or visual (EM) identification.</p><p>2. What do the changes in vesicle populations mean? Are the differences biologically meaningful? Without knowing what the various populations of vesicles are, it is difficult to understand what the changes in response to glucose mean. Given that β cells only secrete 5-10% of their content with glucose stimulation, it is hard to imagine that whole pools of vesicles change. If they do, this would be fascinating and a significant finding, but parameters need to be defined that could account for the magnitude of the observed changes. For example, how much remodeling of proteins or lipids would be required to account for the observed differences? Is there a way to provide some sort of standardization or comparison for such changes?</p><p>3. One of the major advances of the new technology presented is the claimed ability to isolate populations of vesicles for further characterization. However, the feasibility of this is unclear and it is not demonstrated in the paper. Evidence needs to be provided that this method will yield enough sample material to enable further biochemical characterization. This could be as simple as taking two of the different granule populations separated using iDEP and running a Western blot for a few markers of insulin vesicles. That would demonstrate that the method is sorting insulin vesicles and that after sorting they can be characterized to some degree.</p><p><italic>Reviewer #1 (Recommendations for the authors):</italic></p><p>1. In multiple places, the paper refers to performing experiments on β cells (e.g. lines 17, 258, 275, 366, 371). INS-1E cells are not true β cells and should not be referred to as such.</p><p>2. There should be more detailed methods describing the immunolabeling of the vesicles. What pellet is referred to in line 166? How much primary and secondary antibody were used and how long was staining performed?</p><p>3. t tests are used to compare specific data points between low and high glucose conditions (Figure 4), but such comparisons require a statistical test that corrects for multiple comparisons.</p><p>4. The Results sections describing the data in Figure 4 were difficult to read. Rather than using phrases like &quot;pattern with discernible features,&quot; it would help to be specific about what features are being referred to.</p><p>5. line 193: what is meant by &quot;Behavior comparison?&quot; This is an unintuitive heading.</p><p>6. Line 175 should refer to Figure 1C (not Figure 2C).</p><p>7. The transparent reporting form does not point to lines in the manuscript that specify how the sample size was determined, and I could not find that information in the manuscript. Other lines referred to in the transparent reporting form are off and appear to have shifted.</p><p>8. According to <italic>eLife</italic> instructions, authors should avoid acronyms in the title:</p><p>Titles of <italic>eLife</italic> research papers should avoid unfamiliar abbreviations or acronyms, or authors should spell out in full or provide a brief explanation for any acronyms. Please revise your title with this advice in mind.</p><p>9. For <italic>eLife</italic> papers, the biological system should be indicated in the title and/or abstract:</p><p>The title and/or abstract should provide a clear indication of the biological system under investigation (i.e., species name or broader taxonomic group, if appropriate). For this paper, the biological system would be INS-1E insulinoma cells. Please revise your title and/or abstract with this advice in mind.</p><p><italic>Reviewer #2 (Recommendations for the authors):</italic></p><p>1. Synaptotagmin V was chosen as a marker for insulin granules before sorting subpopulations. Synaptotagmin isoforms are well known to differentially sort into sub-populations (Zhang et al. 2011 Mol. Biol Cell, Rao et al. 2014 Mol Biol Cell, Rao et al. 2017 JGP, Kreutzberger et al. 2020 <italic>eLife</italic>). Could you be missing populations because Synaptotagmin V is only on a subset of insulin granules (ie are you sorting a sub-population of a sub-population)? Synaptotagmin VII is well characterized to control a distinct pool of insulin granules – would granules within this population be missed in your isolation procedure?</p><p>2. Insulin granules were found to have different biophysical properties when cells were treated with glucose. Why is this a significant finding?</p><p>This glucose stimulation will trigger secretion and release of insulin and other secretory properties from some fraction of the insulin granules. Would you not expect the loss of these secretory contents to create a difference in dielectrophoretic and electrokinetic properties of the granules by no longer having the released secretory content?</p><p>3. If there was more of a biochemical characterization of what compositional differences in lipids and proteins are present within isolated populations of granules – the impact of this work would be greatly increased.</p><p><italic>Reviewer #3 (Recommendations for the authors):</italic></p><p>1) Studies with additional stimuli or cell treatments may reveal some clues regarding the identities of the major populations being observed that are changing.</p><p>2) Control experiments modifying vesicle parameters would be really helpful to understand the implications. This could be done chemically or perhaps knocking down certain vesicle components targeting features of trafficking or maturation.</p><p>3) Perhaps examining maturation markers, or age markers, or at least various insulin vesicle markers would be informative and or further confirmation of the changes in response to glucose (i.e. are general to the insulin population and not specific to SytV-positive vesicles).</p><p>4) Examination of insulin vesicles from primary cells (rather than an insulinoma cell line), although challenging due to the limited numbers of cells, would greatly improve the impact of this study. Alternatively, data from other insulinoma cell lines may be a useful comparison to determine if the changes are universal or specific to the INS-1E cell line.</p></body></sub-article><sub-article article-type="reply" id="sa2"><front-stub><article-id pub-id-type="doi">10.7554/eLife.74989.sa2</article-id><title-group><article-title>Author response</article-title></title-group></front-stub><body><disp-quote content-type="editor-comment"><p>Essential revisions (for the authors):</p><p>Reviewer #1 (Recommendations for the authors):</p><p>1. In multiple places, the paper refers to performing experiments on β cells (e.g. lines 17, 258, 275, 366, 371). INS-1E cells are not true β cells and should not be referred to as such.</p></disp-quote><p>We thank the reviewer for this comment. The incorrect labeling of INS-1E cells as β cells has been fixed to refer to them as either “INS-1E” or “INS-1E insulinoma” cells.</p><disp-quote content-type="editor-comment"><p>2. There should be more detailed methods describing the immunolabeling of the vesicles. What pellet is referred to in line 166? How much primary and secondary antibody were used and how long was staining performed?</p></disp-quote><p>We have modified the methods section to clarify which pellet is being referred to.</p><p>“The pellet obtained from this step was used for immunolabeling of insulin vesicles.” page 12, lines 309-310.</p><p>We have also added the amounts of antibody used in each step.</p><p>“The pellet was resuspended in HB and incubated with 5-10 μg of anti-synaptotagmin IX (Thermo Fisher Scientific PA5-44987; RRID: <ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:AB_2610517">AB_2610517</ext-link>; 1:50-1:100) overnight to tag an insulin vesicle marker. This primary antibody was then fluorescently labeled by 2-4 hours incubation with 5-10 μg of Alexa 568-conjugated secondary antibody (Invitrogen A-11011; RRID: <ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:AB_143157">AB_143157</ext-link>; 1:50-1:100) (Figure 1). Insulin vesicles were washed with HB three times to remove the excess antibody and were finally resuspended in a low conductivity buffer (LCHB; 0.3 M sucrose, 5 mM MES, pH 6.3), which is compatible with dielectrophoresis studies.” page 13, line 334-339.</p><disp-quote content-type="editor-comment"><p>3. t tests are used to compare specific data points between low and high glucose conditions (Figure 4), but such comparisons require a statistical test that corrects for multiple comparisons.</p></disp-quote><p>We thank the reviewer for pointing this out. We have performed new statistical analysis using an ANOVA test with the Bonferroni post hoc correction. We have also rewritten this section to explain the data and our interpretation in a more straightforward manner as requested by major comment # 3 (page 7, lines 158-165 of the manuscript).</p><disp-quote content-type="editor-comment"><p>4. The Results sections describing the data in Figure 4 were difficult to read. Rather than using phrases like &quot;pattern with discernible features,&quot; it would help to be specific about what features are being referred to.</p></disp-quote><p>We thank the reviewer for the helpful feedback and have rewritten this section as addressed in major comment #3. We also made minor edits to all Results sections which describe figure 4 to improve clarity.</p><disp-quote content-type="editor-comment"><p>5. line 193: what is meant by &quot;Behavior comparison?&quot; This is an unintuitive heading.</p></disp-quote><p>We appreciate this concern and have changed it to the heading below:</p><p>“Comparison of n- and g-insulin vesicle separation patterns within the iDEP device” page 6, line 157</p><disp-quote content-type="editor-comment"><p>6. Line 175 should refer to Figure 1C (not Figure 2C).</p></disp-quote><p>We thank the reviewer and have fixed the error.</p><disp-quote content-type="editor-comment"><p>7. The transparent reporting form does not point to lines in the manuscript that specify how the sample size was determined, and I could not find that information in the manuscript. Other lines referred to in the transparent reporting form are off and appear to have shifted.</p></disp-quote><p>We thank the reviewer for bringing this to our attention. We have added a section addressing sample size determination:</p><p>“No statistical methods were used to predetermine sample size. The experiments were not randomized, and investigators were not blinded to allocation during experiments and outcome assessment.” Page 10, lines 237-238.</p><disp-quote content-type="editor-comment"><p>8. According to eLife instructions, authors should avoid acronyms in the title:</p><p>Titles of eLife research papers should avoid unfamiliar abbreviations or acronyms, or authors should spell out in full or provide a brief explanation for any acronyms. Please revise your title with this advice in mind.</p></disp-quote><p>We thank the reviewer for pointing this out and have made the correction below.</p><p>“Insulator-based dielectrophoresis-assisted separation of insulin secretory vesicles”</p><disp-quote content-type="editor-comment"><p>9. For eLife papers, the biological system should be indicated in the title and/or abstract:</p><p>The title and/or abstract should provide a clear indication of the biological system under investigation (i.e., species name or broader taxonomic group, if appropriate). For this paper, the biological system would be INS-1E insulinoma cells. Please revise your title and/or abstract with this advice in mind.</p></disp-quote><p>We thank the reviewer for this comment and have made the appropriate changes to our abstract.</p><p>“Organelle heterogeneity and inter-organelle contacts within a single cell contribute to the limited sensitivity of current organelle separation techniques, thus hindering organelle subpopulation characterization. Here we use direct current insulator-based dielectrophoresis (DC-iDEP) as an unbiased separation method and demonstrate its capability by identifying distinct distribution patterns of insulin vesicles from INS-1E insulinoma cells. A multiple voltage DC-iDEP strategy with increased range and sensitivity has been applied, and a differentiation factor (ratio of electrokinetic to dielectrophoretic mobility) has been used to characterize features of insulin vesicle distribution patterns. We observed a significant difference in the distribution pattern of insulin vesicles isolated from glucose-stimulated cells relative to unstimulated cells, in accordance with maturation of vesicles upon glucose stimulation. We interpret the difference in distribution pattern to be indicative of high-resolution separation of vesicle subpopulations. DC-iDEP provides a path for future characterization of subtle biochemical differences of organelle subpopulations within any biological system.” Page 1, line 17</p><disp-quote content-type="editor-comment"><p>Reviewer #2 (Recommendations for the authors):</p><p>1. Synaptotagmin V was chosen as a marker for insulin granules before sorting subpopulations. Synaptotagmin isoforms are well known to differentially sort into sub-populations (Zhang et al. 2011 Mol. Biol Cell, Rao et al. 2014 Mol Biol Cell, Rao et al. 2017 JGP, Kreutzberger et al. 2020 eLife). Could you be missing populations because Synaptotagmin V is only on a subset of insulin granules (ie are you sorting a sub-population of a sub-population)? Synaptotagmin VII is well characterized to control a distinct pool of insulin granules – would granules within this population be missed in your isolation procedure?</p></disp-quote><p>We thank the reviewer for their insight. As the antibody we used to label insulin vesicles is likely targeting only a subset of insulin vesicles (Synaptotagmin IX-positive insulin vesicles), our visualization and analysis are limited to this subset and do not include Synaptotagmin VII-positive insulin vesicles. Nonetheless, our results demonstrated the potential for iDEP to reveal heterogeneity in supposedly similar particles. We have begun experiments using a modified INS-1 cell line with a GFP-tagged C-peptide (hPro-CpepSfGFP, GRINCH cells RRID:<ext-link ext-link-type="uri" xlink:href="https://identifiers.org/RRID/RRID:CVCL_WH61">CVCL_WH61</ext-link>). This cell line would allow for the detection of a more complete insulin vesicle population with iDEP.</p><disp-quote content-type="editor-comment"><p>2. Insulin granules were found to have different biophysical properties when cells were treated with glucose. Why is this a significant finding?</p><p>This glucose stimulation will trigger secretion and release of insulin and other secretory properties from some fraction of the insulin granules. Would you not expect the loss of these secretory contents to create a difference in dielectrophoretic and electrokinetic properties of the granules by no longer having the released secretory content?</p></disp-quote><p>We appreciate this comment. The observed differences between n- and g- glucose are not a novel finding but rather the first demonstration of the detection of these changes with the iDEP device. There is likely a balance of some subpopulations being absent due to secretion and new populations emerging due to biochemical maturation of insulin and vesicles. This method has the potential to be further developed for isolation of these subpopulations for more thorough characterization. As mentioned for Review #1 question #1, we have expanded our discussion to include possible explanations of the observed changes in the subpopulations between treatment conditions (Page 7-8, lines 176-191).</p><disp-quote content-type="editor-comment"><p>3. If there was more of a biochemical characterization of what compositional differences in lipids and proteins are present within isolated populations of granules – the impact of this work would be greatly increased.</p></disp-quote><p>We agree with the reviewer that further biochemical characterization of these subpopulations would be useful. However, it is important to note that the capability of this method to isolate the separated and concentrated subpopulations is still under development. Additional method refinement is necessary and unfortunately, completing it in a timely manner for resubmission of this paper will not be feasible.</p><disp-quote content-type="editor-comment"><p>Reviewer #3 (Recommendations for the authors):</p><p>1) Studies with additional stimuli or cell treatments may reveal some clues regarding the identities of the major populations being observed that are changing.</p></disp-quote><p>We thank the reviewer for this suggestion. We are planning experiments to further probe these subpopulations in our future studies. We expect to see some subpopulations that have been described in the literature, some combinations of these subpopulations, and potentially some subpopulations that have not been previously reported. We also plan to stimulate cells to gain a better understanding of how these subpopulations behave under different conditions. We view our current work as a method development proof-of-concept and plan to apply it for more detailed biological exploration in future studies.</p><disp-quote content-type="editor-comment"><p>2) Control experiments modifying vesicle parameters would be really helpful to understand the implications. This could be done chemically or perhaps knocking down certain vesicle components targeting features of trafficking or maturation.</p></disp-quote><p>We agree this would be a useful approach for future studies to dissect the biological roles for different subpopulations. However, we believe this is out of scope for our current manuscript.</p><disp-quote content-type="editor-comment"><p>3) Perhaps examining maturation markers, or age markers, or at least various insulin vesicle markers would be informative and or further confirmation of the changes in response to glucose (i.e. are general to the insulin population and not specific to SytV-positive vesicles).</p></disp-quote><p>We agree with the reviewer that these experiments would be informative on the biology, but we believe these experiments are out of scope for our current study. We plan to conduct experiments with the GRINCH cell line to better visualize these changes with a more complete set of insulin vesicles. We also plan to explore the age and maturation of each subpopulation and how those factors fluctuate under different conditions. These significant biology efforts are more suitable for separate publications.</p><disp-quote content-type="editor-comment"><p>4) Examination of insulin vesicles from primary cells (rather than an insulinoma cell line), although challenging due to the limited numbers of cells, would greatly improve the impact of this study. Alternatively, data from other insulinoma cell lines may be a useful comparison to determine if the changes are universal or specific to the INS-1E cell line.</p></disp-quote><p>We agree that studying insulin vesicles from primary cells would yield valuable data, and we plan to use primary cells to compare results in future studies. We have observed similar results from insulin vesicles isolated from the GRINCH cell line, and plan to extend our experiments to other cell lines which include genetic knockouts of proteins involved in insulin maturation.</p></body></sub-article></article>