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<!DOCTYPE HTML>
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<title>SYLARAS: Systemic Lymphoid Architecture Response Assessment</title>
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<h2 id="colorlib-logo"><a href="index.html"><img src="images/sylarasassets/logo.png"
class="img-responsive"></a> <b>SY</b>stemic <b>L</b>ymphoid <b>A</b>rchitecture
<b>R</b>esponse <b>AS</b>sessment</h2>
<h5 align="right">A Platform for Systems Immunophenotyping</h5>
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<!-- <h1><a href="https://www.biorxiv.org/content/10.1101/555854v1"> <b>Read the preprint at BioRxiv.org, click here!</b></h1> -->
<h1>Multiplex Immunoprofiling</h1>
<h2>modular, extensible workflow.</h2>
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<!-- <h1><a href="https://www.biorxiv.org/content/10.1101/555854v1"> <b>Read the preprint at BioRxiv.org, click here!</b></h1> -->
<h1>Systematic Cell Subset ID</h1>
<h2>computer-assisted gating.</h2>
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<!-- <h1><a href="https://www.biorxiv.org/content/10.1101/555854v1"> <b>Read the preprint at BioRxiv.org, click here!</b></h1> -->
<h1>Programmatic Data Analysis</h1>
<h2>open-source software.</h2>
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<!-- <h1><a href="https://www.biorxiv.org/content/10.1101/555854v1"> <b>Read the preprint at BioRxiv.org, click here!</b></h1> -->
<h1>Intuitive Graphical Output</h1>
<h2>data-rich dashboard interface.</h2>
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<span class="heading-meta">Overview</span>
<p></a><b>SYLARAS</b> (<u><b>SY</b></u>stemic <u><b>L</b></u>ymphoid <u><b>A</b></u>rchitecture
<u><b>R</b></u>esponse <u><b>AS</b></u>sessment) is a preclinical research platform for the
interrogation of systemic immune response to disease and therapy. The approach
combines multiplex immunophenotyping with biological computation to transform
complex single-cell datasets into a visual compendium of the time and tissue-dependent
changes occurring in immune cell frequency and/or function in response to an arbitrary
immune stimulus (e.g. tumor model, infectious or autoimmune disease,
vaccine, immunotherapy, etc.).<p/>
<p>SYLARAS is deployed in three stages. In the first stage, longitudinal
immunophenotyping data are collected from mouse lymphoid organs of
test and control subjects in a high-throughput manner by <a><b>multiplex flow cytometry</b></a>.
In the second stage, raw FCS files are spectrally compensated and filtered for viable
cells before undergoing a systematic <a><b>immune cell subset identification</b></a> procedure.
In the final stage, the pre-processed data are computationally analyzed using an
<a><b>open-source data analysis tool</b></a> scripted in the Python programming language
and run at the command-line of a personal computer. This leads to the generation of a
set of data-rich graphical dashboards (1 per immune cell type) that together portray systemic
immune response to a given experimental perturbation.<p/>
<!-- <a href="https://github.com/gjbaker/sylaras.org/blob/master/protocol/protocol.docx"><b>experimental workflow</b></a> -->
<!-- <font size="2.5"><p><b>SYLARAS </b> is a modular and extensible experimental framework for the acquisition, statistical analysis, and tidy display of systems immunophenotyping data.
<br></p> -->
<!-- <p>The key publication for SYLARAS is <strong>Baker et al. (2019), Systemic Lymphoid Architecture Response
Assessment (SYLARAS): A Platform for Discovery-based Immunophenotyping.</strong> Please cite this resource
as Baker et al. (2019) </p></font> -->
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<a href="https://doi.org/10.1016/j.cels.2020.08.001" class="colorlib-icon"><i class="icon-pen2"></i></a>
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<h3>Read More</h3>
<p>Read about the platform's scientific underpinnings</p>
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<a href="https://github.com/gjbaker/sylaras" class="colorlib-icon"><i class="icon-code"></i></a>
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<h3>Source Code</h3>
<p>Access the GitHub repo</p>
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<a href="https://www.synapse.org/#!Synapse:syn21038562" class="colorlib-icon"><i class="icon-download"></i></a>
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<h3>Download</h3>
<p>Download datasets and protocols</p>
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<div id="details" class="colorlib-about">
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<div class="panel-group" id="accordion" role="tablist" aria-multiselectable="true">
<div class="panel panel-default">
<div class="panel-heading" role="tab" id="headingOne">
<h4 class="panel-title">
<a class="collapsed" data-toggle="collapse"
data-parent="#accordion" href="#collapseOne" aria-expanded="false"
aria-controls="collapseOne">Motivation
</a>
</h4>
</div>
<div id="collapseOne" class="panel-collapse collapse" role="tabpanel" aria-labelledby="headingOne">
<div class="panel-body">
<p>The ability to devise safe and effective medicines that elicit a targeted immune
response to cancer or infectious disease hinges on a more integrated
understanding of the cell and molecular mechanisms governing systemic immunology.
A more holistic perspective can be achieved through detailed studies of immune
structure and function using preclinical model systems that permit examination of
immune response in multiple immune organs.</p>
<p>Although state of the art technologies for single-cell data acquisition allow for the rapid
collection of expansive datasets pertinent to the study of systemic immune response,
a companion data analysis tool capable of fully automating statistical
analysis and data visualization is lacking. By leveraging a set of powerful Python-based
computational libraries against bulky single-cell datasets spanning time, tissue,
experimental perturbation, and biological replicate, SYLARAS overcomes the
labor-intensity of manual cell subset identification, statistical
analysis among subgroups, and visual display of single-cell data at scale.</p>
</div>
</div>
</div>
<br>
<div class="panel panel-default">
<div class="panel-heading" role="tab" id="headingTwo">
<h4 class="panel-title">
<a class="collapsed" data-toggle="collapse" data-parent="#accordion"
href="#collapseTwo" aria-expanded="false" aria-controls="collapseTwo">Principle
</a>
</h4>
</div>
<div id="collapseTwo" class="panel-collapse collapse" role="tabpanel" aria-labelledby="headingTwo">
<div class="panel-body">
<p>Immune response programs can induce proliferation, migration, and
differentiation of the biologically-specialized immune cell subsets
comprising lymphoid tissue architecture. Although the molecular
mechanisms underlying these changes may not always be clear, the resultant
redistribution in immune cell frequency and inter-cellular correlation structure can be readily quantified by
immunophenotyping: a technique for delineating the proportions of
cell types based on differential antigen expression. SYLARAS uses this concept
to infer changes in specific immune cell lineages and their network-level architectures
secondary to an immune stimulus. The ability of SYLARAS to generate
and display interpretable systemic immune response data
makes it a broadly useful preclinical research tool.</p>
</div>
</div>
</div>
<br>
<div class="panel panel-default">
<div class="panel-heading" role="tab" id="headingThree">
<h4 class="panel-title">
<a class="collapsed" data-toggle="collapse" data-parent="#accordion"
href="#collapseThree" aria-expanded="false" aria-controls="collapseThree">Cell Subset Identification
</a>
</h4>
</div>
<div id="collapseThree" class="panel-collapse collapse" role="tabpanel" aria-labelledby="headingThree">
<div class="panel-body">
<p>When immunoreactivity is reported in binary terms (e.g. CD<SUB>a</SUB><SUP>+</SUP>, CD<SUB>b</SUB><SUP>-</SUP>, CD<SUB>c</SUB><SUP>+</SUP>), the
number of possible immunophenotypes
specified by a panel of M antibodies is 2<sup>M</sup>. This exponential relationship
makes comprehensive cell subset identification through serial gating cumbersome,
time consuming, and, in many cases, non-commutative. SYLARAS addresses these drawbacks of manual gating
through computation by programmatically assigning an immunophenotype "bar code" to each cell in the analysis.</p>
<img src="images/sylarasassets/fig1.png" width="750" height="750" align="right" style="padding:25px;"
class="img-responsive"><p>In the SYLARAS procedure, the interface between background
and foreground signal is defined for each immunomarker by curating a set of
one dimensional gates (histogram gates).
The signal intensity distribution of unlabeled/isotype labeled cells is super-imposed on the
immunolabeled distributions to aid in objective gate placement by revealing how autofluorescence and/or
isotype control antibody binding compares to observed data (<strong> panel a </strong>).<p/>
<p>The data then undergo a linear data transformation to center the predetermined gate values at zero.
This causes background signal intensities to become negative valued and an M-dimensional Boolean immunophenotype
to be programmatically assigned to each cell in the dataset (<strong> panel b </strong>).
For example, a cell assigned the Boolean vector [1, 0, 1, 1, 0, 0, 0, 1, 1 ,0, 0] might
correspond to the immunophenotype:
CD<sub>a</sub><sup>+</sup>, CD<sub>b</sub><sup>-</sup>+, CD<sub>c</sub><sup>+</sup>,
CD<sub>d</sub><sup>+</sup>, CD<sub>e</sub><sup>-</sup>, CD<sub>f</sub><sup>-</sup>,
CD<sub>g</sub><sup>-</sup>, CD<sub>h</sub><sup>+</sup>, CD<sub>i</sub><sup>+</sup>,
CD<sub>j</sub><sup>-</sup>, CD<sub>k</sub><sup>-</sup><p/>
<p>In the case of a three immunomarker panel, the vectorization procedure
can be conceptualized geometrically as the binning of cells across the 2<sup>3</sup> (= 8)
octants of a 3-dimensional cube (<strong> panel c </strong>). Extrapolating the
principle into higher dimensions (i.e. more immunomarkers), the vectorization procedure
generalizes as the binning of cells across the 2<sup>M</sup> orthants of an
M-dimensional hypercube.</p>
</div>
</div>
</div>
<br>
<div class="panel panel-default">
<div class="panel-heading" role="tab" id="headingFour">
<h4 class="panel-title">
<a class="collapsed" data-toggle="collapse" data-parent="#accordion"
href="#collapseFour" aria-expanded="false" aria-controls="collapseFour">Workflow (experimental)
</a>
</h4>
</div>
<div id="collapseFour" class="panel-collapse collapse" role="tabpanel" aria-labelledby="headingFour">
<div class="panel-body">
<img src="images/sylarasassets/fig2.png" width="750" height="750"
align="right" style="padding:25px;" class="img-responsive">
<p><strong>(1)</strong> An immune stimulus (glioblastoma brain cancer shown here)
or control agent is administered to cohorts of age-matched mice.
<strong>(2)</strong> Lymphoid tissues are harvested from mice of each treatment
group at various time points after the onset of immune stimulation.
<strong>(3)</strong> Tissues are processed into single-cell suspensions and plated
in a 96 well V-bottom plate. <strong>(4)</strong> Cells are immunolabeled
with a cocktail of fluorophore-conjugated antibodies then stained with a fixable
viability dye (FVD). <strong>(5)</strong> Data are acquired by 12-color high-throughput
flow cytometry. <strong>(6)</strong> Raw data are spectrally
deconvolved and selected for viable singlets. <strong>(7)</strong> Pre-processed
data undergo a computer-assisted gating procedure (see the "Cell Subset
Identification" tab above for details) prior to computational analysis with SYLARAS software.
Click <a href="https://www.synapse.org/#!Synapse:syn21038562/wiki/597169">
<strong>here</strong></a> to download our experimental protocol.</p>
</div>
</div>
</div>
<br>
<div class="panel panel-default">
<div class="panel-heading" role="tab" id="headingFive">
<h4 class="panel-title">
<a class="collapsed" data-toggle="collapse" data-parent="#accordion"
href="#collapseFive" aria-expanded="false" aria-controls="collapseFive">Workflow (computational)
</a>
</h4>
</div>
<div id="collapseFive" class="panel-collapse collapse" role="tabpanel" aria-labelledby="headingFive">
<div class="panel-body">
<img src="images/sylarasassets/fig3.png" width="750" height="750" align="center" style="padding:25px;"
class="img-responsive">
</div>
</div>
</div>
<br>
<div class="panel panel-default">
<div class="panel-heading" role="tab" id="headingSix">
<h4 class="panel-title">
<a class="collapsed" data-toggle="collapse" data-parent="#accordion"
href="#collapseSix" aria-expanded="false" aria-controls="collapseSix">Results
</a>
</h4>
</div>
<div id="collapseSix" class="panel-collapse collapse" role="tabpanel" aria-labelledby="headingSix">
<div class="panel-body">
<img src="images/sylarasassets/fig4.png" width="750" height="750" align="right" style="padding:25px;"
class="img-responsive">
<p>SYLARAS portrays longitudinal, multi-organ immunophenotyping data on a per
cell type basis in a readily-interpretable dashboard layout. The example
of polymorphonuclear (PMN) immune cells shown here was programmatically generated during a
SYLARAS screen of the mouse immune response to syngneic, orthotopic glioblastoma(GBM) brain cancer.
<strong>(a)</strong> brief alias; <strong>(b)</strong> lineage; <strong>(c)</strong> immunomarker signature indicating whether the
immunophenotype corresponds to 1 of 14 major “landmark population”;
<strong>(d)</strong> distribution of cells across 5 lymphoid tissues color-coded as in (h, i and j);
<strong>(e)</strong> percentage of this cell type relative to all immune cells;
<strong>(f)</strong> forward and side scatter (FSC/SSC);
<strong>(g)</strong> Logicle-transformed, background-subtracted immunomarker signal intensity;
<strong>(h and i)</strong> time and tissue-specific difference in mean percentage and log<SUB>2</SUB> fold-change
between GBM-burdened and mock-engrafted animals (n=8 mice/group), asterisks denote one
of three levels of statistical significance; <strong>(j)</strong> contribution of this cell type (in percent)
in each tissue across the study’s 48 mice.</p>
</div>
</div>
</div>
<br>
<br>
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<h2>Publications</h2>
</div>
</div>
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<div class="col-md-12 col-md-offset-3 col-md-pull-3 animate-box" data-animate-effect="fadeInLeft">
<!-- <p class="colorlib-lead"> <strong> Please cite SYLARAS as reference (1).</strong> </p> -->
<p class="colorlib-lead"><strong>(1)</strong> Baker et al.
Cell Syst. 2020 Sep 23;11(3):272-285.e9. doi: 10.1016/j.cels.2020.08.001.
Epub 2020 Sep 7, SYLARAS: A Platform for the Statistical
Analysis and Visual Display of Systemic Immunoprofiling Data
and Its Application to Glioblastoma. <strong>Click</strong>
<a href="https://www.cell.com/cell-systems/fulltext/S2405-4712(20)30285-4" ><strong>here</strong></a>
<strong>to read the SYLARAS article.</strong></p>
<p class="colorlib-lead"><strong>(2)</strong> Baker et al. BioRxiv (2019)
doi: https://doi.org/10.1101/555854, Systemic immune response profiling with SYLARAS
implicates a role for CD45R/B220<SUP>+</SUP> CD8<SUP>+</SUP> T cells in glioblastoma immunology. <strong>Click</strong>
<a href="https://doi.org/10.1101/555854" ><strong>here</strong></a>
<strong>to read the SYLARAS preprint.</strong></p>
<p class="colorlib-lead"><strong>(3)</strong> Gregory J. Baker, Sucheendra K. Palaniappan,
Stephanie H. Davis, Jodene K. Moore and Peter K. Sorger. Systemic lymphoid architecture
response assessment (SYLARAS): Application to system-wide immunophenotyping of
glioblastoma; Cancer Res July 2018 (78) (13 Supplement) 5670;
DOI: 10.1158/1538-7445.AM2018-5670</p>
<p class="colorlib-lead"><strong>(4)</strong> Gregory J. Baker, P.S. Thiagarajan, Sucheendra
K. Palaniappan, Stephanie H. Davis, Jodene K. Moore and Peter K. Sorger.
A flow-based immunoprofiling strategy for interrogating system-wide leukocyte
composition in response to glioblastoma; Cancer Res July 1 2017 (77)
(13 Supplement) 1678; DOI: 10.1158/1538-7445.AM2017-1678</p>
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<p>The SYLARAS algorithm is a copyrighted, open-source software intended for
non-profit academic use.</p>
<p><em><strong>Copyright (c) 2019 - President and Fellows of Harvard College.
All rights reserved.</strong></em></p>
<p>Developed by: Gregory J. Baker</p>
<p>Harvard Program in Therapeutic Science (HiTS), Harvard Medical School
(<a href="http://hits.harvard.edu/"><em>http://hits.harvard.edu/</em></a>)</p>
<p>Harvard University case number HU 7716 - A computational tool for systems
immunophenotyping</p>
<p><a href="https://www.sylaras.org/"><em>https://www.sylaras.org/</em></a></p>
<p>Permission is hereby granted, free of charge, to any person obtaining a copy of this
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of conditions and the following disclaimers in the documentation and/or other materials
provided with the distribution.</p></li>
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School, Harvard University, the Harvard shield or logo, nor the names of its contributors may
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<p>THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
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<p class="colorlib-lead">The SYLARAS project was supported by
American Cancer Society Postdoctoral Fellowship PF-16-197-01-LIB
to G.J.B, and by NIH/NCI grants P50-GM107618 and U54-CA225088 to
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<p class="colorlib-lead">Cite the SYLARAS approach to systems
immunophenotyping and the use of its resources as: Baker et al.
Cell Syst. 2020 Sep 23;11(3):272-285.e9. doi: 10.1016/j.cels.2020.08.001.
Epub 2020 Sep 7. SYLARAS: A Platform for the Statistical
Analysis and Visual Display of Systemic Immunoprofiling Data
and Its Application to Glioblastoma.</p>
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