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Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition

Identifieur interne : 000095 ( Pmc/Checkpoint ); précédent : 000094; suivant : 000096

Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition

Auteurs : Kai-Chih Huang [États-Unis] ; Junjie Li [États-Unis] ; Chi Zhang [États-Unis] ; Yuying Tan [États-Unis] ; Ji-Xin Cheng [États-Unis]

Source :

RBID : PMC:7078382

Abstract

Summary

In situ measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.


Url:
DOI: 10.1016/j.isci.2020.100953
PubMed: 32179477
PubMed Central: 7078382


Affiliations:


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PMC:7078382

Le document en format XML

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measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.</p>
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</TEI>
<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">iScience</journal-id>
<journal-id journal-id-type="iso-abbrev">iScience</journal-id>
<journal-title-group>
<journal-title>iScience</journal-title>
</journal-title-group>
<issn pub-type="ppub">2589-0042</issn>
<issn pub-type="epub">2589-0042</issn>
<publisher>
<publisher-name>Elsevier</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">32179477</article-id>
<article-id pub-id-type="pmc">7078382</article-id>
<article-id pub-id-type="publisher-id">S2589-0042(20)30137-1</article-id>
<article-id pub-id-type="doi">10.1016/j.isci.2020.100953</article-id>
<article-id pub-id-type="publisher-id">100953</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Multiplex Stimulated Raman Scattering Imaging Cytometry Reveals Lipid-Rich Protrusions in Cancer Cells under Stress Condition</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author" id="au1">
<name>
<surname>Huang</surname>
<given-names>Kai-Chih</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="fn1" ref-type="fn">4</xref>
</contrib>
<contrib contrib-type="author" id="au2">
<name>
<surname>Li</surname>
<given-names>Junjie</given-names>
</name>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="fn1" ref-type="fn">4</xref>
</contrib>
<contrib contrib-type="author" id="au3">
<name>
<surname>Zhang</surname>
<given-names>Chi</given-names>
</name>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="fn1" ref-type="fn">4</xref>
</contrib>
<contrib contrib-type="author" id="au4">
<name>
<surname>Tan</surname>
<given-names>Yuying</given-names>
</name>
<xref rid="aff1" ref-type="aff">1</xref>
</contrib>
<contrib contrib-type="author" id="au5">
<name>
<surname>Cheng</surname>
<given-names>Ji-Xin</given-names>
</name>
<email>jxcheng@bu.edu</email>
<xref rid="aff1" ref-type="aff">1</xref>
<xref rid="aff2" ref-type="aff">2</xref>
<xref rid="aff3" ref-type="aff">3</xref>
<xref rid="fn2" ref-type="fn">5</xref>
<xref rid="cor1" ref-type="corresp"></xref>
</contrib>
</contrib-group>
<aff id="aff1">
<label>1</label>
Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA</aff>
<aff id="aff2">
<label>2</label>
Department of Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA</aff>
<aff id="aff3">
<label>3</label>
Photonics Center, Boston University, Boston, MA 02215, USA</aff>
<author-notes>
<corresp id="cor1">
<label></label>
Corresponding author
<email>jxcheng@bu.edu</email>
</corresp>
<fn id="fn1">
<label>4</label>
<p id="ntpara0010">These authors contributed equally</p>
</fn>
<fn id="fn2">
<label>5</label>
<p id="ntpara0015">Lead Contact</p>
</fn>
</author-notes>
<pub-date pub-type="pmc-release">
<day>29</day>
<month>2</month>
<year>2020</year>
</pub-date>
<pmc-comment> PMC Release delay is 0 months and 0 days and was based on .</pmc-comment>
<pub-date pub-type="collection">
<day>27</day>
<month>3</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>29</day>
<month>2</month>
<year>2020</year>
</pub-date>
<volume>23</volume>
<issue>3</issue>
<elocation-id>100953</elocation-id>
<history>
<date date-type="received">
<day>1</day>
<month>11</month>
<year>2019</year>
</date>
<date date-type="rev-recd">
<day>10</day>
<month>1</month>
<year>2020</year>
</date>
<date date-type="accepted">
<day>25</day>
<month>2</month>
<year>2020</year>
</date>
</history>
<permissions>
<copyright-statement>© 2020 The Author(s)</copyright-statement>
<copyright-year>2020</copyright-year>
<license license-type="CC BY-NC-ND" xlink:href="http://creativecommons.org/licenses/by-nc-nd/4.0/">
<license-p>This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).</license-p>
</license>
</permissions>
<abstract id="abs0010">
<title>Summary</title>
<p>
<italic>In situ</italic>
measurement of cellular metabolites is still a challenge in biology. Conventional methods, such as mass spectrometry or fluorescence microscopy, would either destroy the sample or introduce strong perturbations to target molecules. Here, we present multiplex stimulated Raman scattering (SRS) imaging cytometry as a label-free single-cell analysis platform with chemical specificity and high-throughput capabilities. Using SRS imaging cytometry, we studied the metabolic responses of human pancreatic cancer cells under stress by starvation and chemotherapeutic drug treatments. We unveiled protrusions containing lipid droplets as a metabolic marker for stress-resistant cancer cells. Furthermore, by spectroscopic SRS mapping, we unveiled that triglyceride in lipid droplets are used for local energy production through lipolysis, autophagy, and β-oxidation. Our findings demonstrate the potential of targeting lipid metabolism for selective treatment of stress-resistant cancers. Collectively, these results highlight SRS imaging cytometry as a powerful label-free tool for biological discoveries with a high-throughput, high-content capacity.</p>
</abstract>
<abstract abstract-type="graphical" id="abs0015">
<title>Graphical Abstract</title>
<fig id="undfig1" position="anchor">
<graphic xlink:href="fx1"></graphic>
</fig>
</abstract>
<abstract abstract-type="author-highlights" id="abs0020">
<title>Highlights</title>
<p>
<list list-type="simple" id="ulist0010">
<list-item id="u0010">
<label></label>
<p id="p0010">Multiplex SRS imaging cytometry is developed for single-cell metabolic analysis</p>
</list-item>
<list-item id="u0015">
<label></label>
<p id="p0015">Lipid-rich protrusions are unveiled as a marker for stress-resistant cancer cells</p>
</list-item>
<list-item id="u0020">
<label></label>
<p id="p0020">Lipids in protrusions are used for local energy production via β-oxidation</p>
</list-item>
<list-item id="u0025">
<label></label>
<p id="p0025">Blockage of β-oxidation impairs survival of stress-resistant cancer cells</p>
</list-item>
</list>
</p>
</abstract>
<abstract abstract-type="teaser" id="abs0025">
<p>Optical Imaging; Metabolomics; Cancer</p>
</abstract>
<kwd-group id="kwrds0010">
<title>Subject Areas</title>
<kwd>Optical Imaging</kwd>
<kwd>Metabolomics</kwd>
<kwd>Cancer</kwd>
</kwd-group>
</article-meta>
<notes>
<p id="misc0010">Published: March 27, 2020</p>
</notes>
</front>
</pmc>
<affiliations>
<list>
<country>
<li>États-Unis</li>
</country>
</list>
<tree>
<country name="États-Unis">
<noRegion>
<name sortKey="Huang, Kai Chih" sort="Huang, Kai Chih" uniqKey="Huang K" first="Kai-Chih" last="Huang">Kai-Chih Huang</name>
</noRegion>
<name sortKey="Cheng, Ji Xin" sort="Cheng, Ji Xin" uniqKey="Cheng J" first="Ji-Xin" last="Cheng">Ji-Xin Cheng</name>
<name sortKey="Cheng, Ji Xin" sort="Cheng, Ji Xin" uniqKey="Cheng J" first="Ji-Xin" last="Cheng">Ji-Xin Cheng</name>
<name sortKey="Cheng, Ji Xin" sort="Cheng, Ji Xin" uniqKey="Cheng J" first="Ji-Xin" last="Cheng">Ji-Xin Cheng</name>
<name sortKey="Li, Junjie" sort="Li, Junjie" uniqKey="Li J" first="Junjie" last="Li">Junjie Li</name>
<name sortKey="Tan, Yuying" sort="Tan, Yuying" uniqKey="Tan Y" first="Yuying" last="Tan">Yuying Tan</name>
<name sortKey="Zhang, Chi" sort="Zhang, Chi" uniqKey="Zhang C" first="Chi" last="Zhang">Chi Zhang</name>
</country>
</tree>
</affiliations>
</record>

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