Evidence map›Paper›PMID 32522714›Full record

ArticleActa biomaterialia2020

Biomimetic tolerogenic artificial antigen presenting cells for regulatory T cell induction.

Kelly R Rhodes, Randall A Meyer, Justin Wang, Stephany Y Tzeng, Jordan J Green

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Acta biomaterialia, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.5field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 26 citations in OpenAlex.

  1. Biomaterial-EnabledCell biomaterials · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Kelly R RhodesDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Institute for NanoBioTechnology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Randall A MeyerDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Institute for NanoBioTechnology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Justin WangDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Institute for NanoBioTechnology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Stephany Y TzengDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Institute for NanoBioTechnology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA.
Jordan J GreenDepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Translational Tissue Engineering Center, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Institute for NanoBioTechnology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Department of Chemical & Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21231, USA; Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21231, USA; Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA; Department of Oncology, Sidney Kimmel Comprehensive Cancer Center and the Bloomberg~Kimmel Institute for Cancer Immunotherapy, Johns Hopkins University School of Medicine, Baltimore, MD 21231, USA. Electronic address: green@jhu.edu.
Johns Hopkins University · USBloomberg (United States) · US

Funding

TR&D Project 3P41EB028239 · NIBIB · JOHNS HOPKINS UNIVERSITY · PI Jordan Green · 2019 to 2026
$11.5M
T32: Predoctoral and Postdoctoral Training Program in Nanotechnology for Cancer ResearchT32CA153952 · NCI · JOHNS HOPKINS UNIVERSITY · PI Denis Wirtz · 2015 to 2026
$3.5M
Nanoparticle Modified Human Fat Derived Mesenchymal Stem Cells for Brain Cancer (Change of Organization Application)R01CA195503 · NCI · MAYO CLINIC JACKSONVILLE · PI GREEN, JORDAN, QUINONES-HINOJOSA, ALFREDO · 2016 to 2020
$1.9M
T-32 NIH Postdoctoral Training Program in Nanotechnology for Cancer Medicine NTCMT32CA130840 · NCI · JOHNS HOPKINS UNIVERSITY · PI WIRTZ, DENIS · 2008 to 2012
$1.2M
Biomimetic artificial antigen presenting cells for melanoma immunotherapyF31CA214147 · NCI · JOHNS HOPKINS UNIVERSITY · PI MEYER, RANDALL A · 2016 to 2017
$88k
NCI NIH HHS F31 CA214147NCI NIH HHS R01 CA195503NCI NIH HHS T32 CA130840NCI NIH HHS T32 CA153952NIBIB NIH HHS P41 EB028239
6 · The paper itself

Abstract

Regulatory T cell (Treg)-based therapeutics are receiving increased attention for their potential to treat autoimmune disease and prevent transplant rejection. Adoptively transferred Tregs have shown promise in early clinical trials, but cell-based therapies are expensive and complex to implement, and "off-the-shelf" alternatives are needed. Here, we investigate the potential of artificial antigen presenting cells (aAPCs) fabricated from a blend of negatively charged biodegradable polymer (poly(lactic-co-glycolic acid), PLGA) and cationic biodegradable polymer (poly(beta-amino ester), PBAE) with incorporation of extracellular protein signals 1 and 2 and a soluble released signal 3 to convert naïve T cells to induced Foxp3+ Treg-like suppressor cells (iTregs) both in vitro and in vivo in a biomimetic manner. The addition of PBAE to the aAPC core increased the conjugation efficiency of signal proteins to the particle surface and resulted in enhanced ability to bind to naïve T cells and induce iTregs with potent suppressive function. Furthermore, PLGA/PBAE tolerogenic aAPCs (TolAPCs) supported the loading and sustained release of signal 3 cytokine TGF-β. A single dose of TolAPCs administered intravenously to C57BL/6 J mice resulted in an increased percentage of Foxp3+ cells in the lymph nodes. Thus, PLGA/PBAE TolAPCs show potential as an "off-the-shelf" biomimetic material for tolerance induction. STATEMENT OF SIGNIFICANCE: Regulatory T cells (Tregs) are promising for basic research and translational medicine as they can induce tolerance and have the potential to treat autoimmune diseases such as type 1 diabetes and multiple sclerosis. As cell-based therapies are expensive and difficult to manufacture and implement, non-cellular methods of engineering endogenous Tregs are needed. The research reported here describes a new type of biomimetic particle, tolerogenic artificial antigen presenting cells (TolAPCs) fabricated from a blend of negatively charged biodegradable polymer, poly(lactic-co-glycolic acid), and positively charged biodegradable polymer, poly(beta-amino ester), along with key biomolecular signals: extracellularly presented protein signals 1 and 2 and a soluble released signal 3. These TolAPCs bind to naïve T cells and induce Foxp3+ Treg-like suppressor cells with potent suppressive function. In both in vitro and in vivo studies, it is shown that this non-cellular approach is useful to induce tolerance.

Indexed as

BiomimeticsT-Lymphocytes, RegulatoryAnimalsAntigen-Presenting CellsCD8-Positive T-LymphocytesMiceMice, Inbred C57BLArtificial antigen presenting cellBioengineeringImmunoengineeringImmunotherapyRegulatory T cellTolerance

Identifiers

PMID32522714
PMCPMC7365762
OpenAlexW3034085326

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.