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Systematic high-content genome-wide RNAi screens of endothelial cell migration and morphology

Identifieur interne : 008E02 ( Ncbi/Merge ); précédent : 008E01; suivant : 008E03

Systematic high-content genome-wide RNAi screens of endothelial cell migration and morphology

Auteurs : Steven P. Williams [Australie] ; Cathryn M. Gould [Australie] ; Cameron J. Nowell [Australie] ; Tara Karnezis [Australie] ; Marc G. Achen [Australie] ; Kaylene J. Simpson [Australie] ; Steven A. Stacker [Australie]

Source :

RBID : PMC:5332011

Abstract

Many cell types undergo migration during embryogenesis and disease. Endothelial cells line blood vessels and lymphatics, which migrate during development as part of angiogenesis, lymphangiogenesis and other types of vessel remodelling. These processes are also important in wound healing, cancer metastasis and cardiovascular conditions. However, the molecular control of endothelial cell migration is poorly understood. Here, we present a dataset containing siRNA screens that identify known and novel components of signalling pathways regulating migration of lymphatic endothelial cells. These components are compared to signalling in blood vascular endothelial cells. Further, using high-content microscopy, we captured a dataset of images of migrating cells following transfection with a genome-wide siRNA library. These datasets are suitable for the identification and analysis of genes involved in endothelial cell migration and morphology, and for computational approaches to identify signalling networks controlling the migratory response and integration of cell morphology, gene function and cell signaling. This may facilitate identification of protein targets for therapeutically modulating angiogenesis and lymphangiogenesis in the context of human disease.


Url:
DOI: 10.1038/sdata.2017.9
PubMed: 28248931
PubMed Central: 5332011

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PMC:5332011

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<p>Many cell types undergo migration during embryogenesis and disease. Endothelial cells line blood vessels and lymphatics, which migrate during development as part of angiogenesis, lymphangiogenesis and other types of vessel remodelling. These processes are also important in wound healing, cancer metastasis and cardiovascular conditions. However, the molecular control of endothelial cell migration is poorly understood. Here, we present a dataset containing siRNA screens that identify known and novel components of signalling pathways regulating migration of lymphatic endothelial cells. These components are compared to signalling in blood vascular endothelial cells. Further, using high-content microscopy, we captured a dataset of images of migrating cells following transfection with a genome-wide siRNA library. These datasets are suitable for the identification and analysis of genes involved in endothelial cell migration and morphology, and for computational approaches to identify signalling networks controlling the migratory response and integration of cell morphology, gene function and cell signaling. This may facilitate identification of protein targets for therapeutically modulating angiogenesis and lymphangiogenesis in the context of human disease.</p>
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<name>
<surname>Williams</surname>
<given-names>Steven P.</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a2">2</xref>
<xref ref-type="author-notes" rid="n1">*</xref>
<contrib-id contrib-id-type="orcid">http://orcid.org/0000-0002-9382-4337</contrib-id>
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<surname>Gould</surname>
<given-names>Cathryn M.</given-names>
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<xref ref-type="aff" rid="a3">3</xref>
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<given-names>Cameron J.</given-names>
</name>
<xref ref-type="aff" rid="a2">2</xref>
<xref ref-type="aff" rid="a4">4</xref>
<xref ref-type="author-notes" rid="n3"></xref>
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<name>
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<given-names>Tara</given-names>
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<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a2">2</xref>
<xref ref-type="aff" rid="a5">5</xref>
<xref ref-type="author-notes" rid="n4">§</xref>
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<name>
<surname>Achen</surname>
<given-names>Marc G.</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a2">2</xref>
<xref ref-type="aff" rid="a5">5</xref>
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<name>
<surname>Simpson</surname>
<given-names>Kaylene J.</given-names>
</name>
<xref ref-type="aff" rid="a3">3</xref>
<xref ref-type="aff" rid="a5">5</xref>
<xref ref-type="aff" rid="a6">6</xref>
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<name>
<surname>Stacker</surname>
<given-names>Steven A.</given-names>
</name>
<xref ref-type="corresp" rid="c1">a</xref>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a2">2</xref>
<xref ref-type="aff" rid="a5">5</xref>
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<aff id="a1">
<label>1</label>
<institution>Tumour Angiogenesis Program, Peter MacCallum Cancer Centre, 305 Grattan St, Melbourne, Victoria 3000, Australia</institution>
</aff>
<aff id="a2">
<label>2</label>
<institution>Ludwig Institute for Cancer Research, Royal Melbourne Hospital</institution>
, Parkville, Victoria 3050,
<country>Australia</country>
</aff>
<aff id="a3">
<label>3</label>
<institution>Victorian Centre for Functional Genomics, Peter MacCallum Cancer Centre</institution>
, 305 Grattan St, Melbourne, Victoria 3000,
<country>Australia</country>
</aff>
<aff id="a4">
<label>4</label>
<institution>The Walter and Eliza Hall Institute of Medical Research</institution>
, Parkville, Victoria 3052,
<country>Australia</country>
</aff>
<aff id="a5">
<label>5</label>
<institution>Sir Peter MacCallum Department of Oncology, University of Melbourne, Parkville, Victoria 3010, Australia</institution>
</aff>
<aff id="a6">
<label>6</label>
<institution>Department of Pathology, University of Melbourne</institution>
, Parkville, Victoria 3010,
<country>Australia</country>
</aff>
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<author-notes>
<corresp id="c1">
<label>a</label>
S.A.S. (email:
<email>steven.stacker@petermac.org</email>
).</corresp>
<fn id="n1">
<label>*</label>
<p>Present address: Cancer Functional Genomics, Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK</p>
</fn>
<fn id="n2">
<label></label>
<p>Present address: Genomics and Epigenetics Division, Garvan Institute for Medical Research, Darlinghurst, New South Wales 2010, Australia</p>
</fn>
<fn id="n3">
<label></label>
<p>Present address: Faculty of Pharmacy and Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia</p>
</fn>
<fn id="n4">
<label>§</label>
<p>Present address: St Vincent’s Institute of Medical Research, Fitzroy, Victoria 3065, Australia</p>
</fn>
<fn id="con">
<label></label>
<p>S.P.W. designed and performed experiments, conducted analyses, prepared figures and wrote the Data Descriptor. C.M.G. wrote code and conducted analyses. C.J.N. wrote code and provided intellectual input. T.K. assisted in experimental design and provided intellectual input. M.G.A. assisted in project direction and provided intellectual input. K.J.S. assisted in experimental design and provided intellectual input. S.A.S. provided project direction, assisted in experimental design and had intellectual input.</p>
</fn>
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<pub-date pub-type="epub">
<day>01</day>
<month>03</month>
<year>2017</year>
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<year>2017</year>
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<volume>4</volume>
<elocation-id>170009</elocation-id>
<history>
<date date-type="received">
<day>18</day>
<month>07</month>
<year>2016</year>
</date>
<date date-type="accepted">
<day>30</day>
<month>11</month>
<year>2016</year>
</date>
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<permissions>
<copyright-statement>Copyright © 2017, The Author(s)</copyright-statement>
<copyright-year>2017</copyright-year>
<copyright-holder>The Author(s)</copyright-holder>
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<pmc-comment>author-paid</pmc-comment>
<license-p>This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0">http://creativecommons.org/licenses/by/4.0</ext-link>
Metadata associated with this Data Descriptor is available at
<ext-link ext-link-type="uri" xlink:href="http://www.nature.com/sdata/">http://www.nature.com/sdata/</ext-link>
and is released under the CC0 waiver to maximize reuse. </license-p>
</license>
</permissions>
<related-article ext-link-type="doi" related-article-type="data-paper-referrer" id="d32e22" xlink:href="10.1038/ncb1762"></related-article>
<abstract>
<p>Many cell types undergo migration during embryogenesis and disease. Endothelial cells line blood vessels and lymphatics, which migrate during development as part of angiogenesis, lymphangiogenesis and other types of vessel remodelling. These processes are also important in wound healing, cancer metastasis and cardiovascular conditions. However, the molecular control of endothelial cell migration is poorly understood. Here, we present a dataset containing siRNA screens that identify known and novel components of signalling pathways regulating migration of lymphatic endothelial cells. These components are compared to signalling in blood vascular endothelial cells. Further, using high-content microscopy, we captured a dataset of images of migrating cells following transfection with a genome-wide siRNA library. These datasets are suitable for the identification and analysis of genes involved in endothelial cell migration and morphology, and for computational approaches to identify signalling networks controlling the migratory response and integration of cell morphology, gene function and cell signaling. This may facilitate identification of protein targets for therapeutically modulating angiogenesis and lymphangiogenesis in the context of human disease.</p>
</abstract>
<kwd-group kwd-group-type="npg.subject">
<title>Subject terms</title>
<kwd>Collective cell migration</kwd>
<kwd>Metastasis</kwd>
<kwd>High-throughput screening</kwd>
<kwd>Inhibitory RNA techniques</kwd>
</kwd-group>
</article-meta>
</front>
<floats-group>
<fig id="f1">
<label>Figure 1</label>
<caption>
<title>Overview of the successive siRNA migration screens.</title>
<p>(
<bold>a</bold>
) Experimental scheme for the genome-wide siRNA screen to identify genes required for cell migration. HDLEC cells were reverse transfected with siRNA SMARTpools 48 h prior to assessment for migratory capacity in the monolayer scratch assay. Image based analysis was used to calculate a migration score and a cell density score. The primary screen assessed siRNA SMARTpools targeting 18,120 protein-coding genes (see Data Record 1). (
<bold>b</bold>
) Automated image analysis was used firstly for cell nuclei count; samples that were identified as ‘Low Cell Density’ were excluded from migration analysis. Image analysis was then used to measure cell migration (Data Record 4). Results with migration |robust z score|>2 were considered as candidate hits. (
<bold>c</bold>
) The deconvolution screen confirmed the phenotypes of 154 genes as regulators of LEC migration (see Data Record 2). (
<bold>d</bold>
) A tertiary screen was performed to compare and contrast the role of confirmed migration regulators in two dermal endothelial cell types: HDLECs and HMBECs (Data Record 3). Morphological analysis of gene knockdown phenotypes was also performed in order to identify genes with similar functional roles (Data Records 5 and 6).</p>
</caption>
<graphic xlink:href="sdata20179-f1"></graphic>
</fig>
<fig id="f2">
<label>Figure 2</label>
<caption>
<title>Distribution of primary screen metrics.</title>
<p>(
<bold>a</bold>
) Distribution of Pearson R values for correlation between technical replicate plates. (
<bold>b</bold>
) Distribution of Strictly Standardised Mean Difference (SSMD) scores, a measure of control siRNA performance and plate QC. (
<bold>c</bold>
) Distribution of cell nuclei count for siRNA treated cells. A threshold of 3300 nuclei was used for binning as ‘Low Cell Count’ (coloured in orange). (
<bold>d</bold>
) Distribution of siRNA migration z-scores, with hits |robust z score |>2 coloured in orange.</p>
</caption>
<graphic xlink:href="sdata20179-f2"></graphic>
</fig>
<fig id="f3">
<label>Figure 3</label>
<caption>
<title>Examples of Mock-transfected migration, and siRNAs that were scored as impaired HDLEC migration from the primary siRNA screen.</title>
<p>The cells were stained with Celltracker Green CMFDA for the image captured at 0 h, and then fixed and stained with Phalloidin CF488 for the image captured at 24 h.</p>
</caption>
<graphic xlink:href="sdata20179-f3"></graphic>
</fig>
<fig id="f4">
<label>Figure 4</label>
<caption>
<title>Morphology phenotype correlations between HDLEC and HMBEC.</title>
<p>The distribution of Pearson correlation values for the morphology signatures of HDLEC and HMBEC treated with siRNAs in the Tertiary screen is shown.</p>
</caption>
<graphic xlink:href="sdata20179-f4"></graphic>
</fig>
<table-wrap position="float" id="t1">
<label>Table 1</label>
<caption>
<title>Binning results of secondary siRNA screen.</title>
</caption>
<table frame="hsides" rules="groups" border="1">
<colgroup>
<col align="left"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
<col align="center"></col>
</colgroup>
<thead valign="bottom">
<tr>
<th rowspan="2" align="left" valign="top" charoff="50"> </th>
<th colspan="7" align="center" valign="top" charoff="50">
<bold>Binning</bold>
<hr></hr>
</th>
</tr>
<tr>
<th align="center" valign="top" charoff="50">
<bold>Toxic</bold>
</th>
<th align="center" valign="top" charoff="50">
<bold>0/4</bold>
</th>
<th align="center" valign="top" charoff="50">
<bold>1/4</bold>
</th>
<th align="center" valign="top" charoff="50">
<bold>2/4</bold>
</th>
<th align="center" valign="top" charoff="50">
<bold>3/4</bold>
</th>
<th align="center" valign="top" charoff="50">
<bold>4/4</bold>
</th>
<th align="center" valign="top" charoff="50">
<bold>Total</bold>
</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left" valign="top" charoff="50">
<bold>Impaired</bold>
</td>
<td align="center" valign="top" charoff="50">21</td>
<td align="center" valign="top" charoff="50">79</td>
<td align="center" valign="top" charoff="50">147</td>
<td align="center" valign="top" charoff="50">113</td>
<td align="center" valign="top" charoff="50">38</td>
<td align="center" valign="top" charoff="50">3</td>
<td align="center" valign="top" charoff="50">401</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50">
<bold>Accelerated</bold>
</td>
<td align="center" valign="top" charoff="50">1</td>
<td align="center" valign="top" charoff="50">92</td>
<td align="center" valign="top" charoff="50">6</td>
<td align="center" valign="top" charoff="50">0</td>
<td align="center" valign="top" charoff="50">0</td>
<td align="center" valign="top" charoff="50">0</td>
<td align="center" valign="top" charoff="50">99</td>
</tr>
<tr>
<td align="left" valign="top" charoff="50">
<bold>Percentage</bold>
</td>
<td align="center" valign="top" charoff="50">4.4%</td>
<td align="center" valign="top" charoff="50">34.2%</td>
<td align="center" valign="top" charoff="50">30.6%</td>
<td align="center" valign="top" charoff="50">22.6%</td>
<td align="center" valign="top" charoff="50">7.6%</td>
<td align="center" valign="top" charoff="50">0.6%</td>
<td align="center" valign="top" charoff="50">100.0%</td>
</tr>
</tbody>
</table>
</table-wrap>
</floats-group>
</pmc>
<affiliations>
<list>
<country>
<li>Australie</li>
</country>
</list>
<tree>
<country name="Australie">
<noRegion>
<name sortKey="Williams, Steven P" sort="Williams, Steven P" uniqKey="Williams S" first="Steven P." last="Williams">Steven P. Williams</name>
</noRegion>
<name sortKey="Achen, Marc G" sort="Achen, Marc G" uniqKey="Achen M" first="Marc G." last="Achen">Marc G. Achen</name>
<name sortKey="Gould, Cathryn M" sort="Gould, Cathryn M" uniqKey="Gould C" first="Cathryn M." last="Gould">Cathryn M. Gould</name>
<name sortKey="Karnezis, Tara" sort="Karnezis, Tara" uniqKey="Karnezis T" first="Tara" last="Karnezis">Tara Karnezis</name>
<name sortKey="Nowell, Cameron J" sort="Nowell, Cameron J" uniqKey="Nowell C" first="Cameron J." last="Nowell">Cameron J. Nowell</name>
<name sortKey="Nowell, Cameron J" sort="Nowell, Cameron J" uniqKey="Nowell C" first="Cameron J." last="Nowell">Cameron J. Nowell</name>
<name sortKey="Simpson, Kaylene J" sort="Simpson, Kaylene J" uniqKey="Simpson K" first="Kaylene J." last="Simpson">Kaylene J. Simpson</name>
<name sortKey="Simpson, Kaylene J" sort="Simpson, Kaylene J" uniqKey="Simpson K" first="Kaylene J." last="Simpson">Kaylene J. Simpson</name>
<name sortKey="Stacker, Steven A" sort="Stacker, Steven A" uniqKey="Stacker S" first="Steven A." last="Stacker">Steven A. Stacker</name>
</country>
</tree>
</affiliations>
</record>

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