Evidence map›Paper›PMID 39181830›Full record

ArticlePlacenta2025

Flow cytometric analysis of the murine placenta to evaluate nanoparticle platforms during pregnancy.

Kelsey L Swingle, Alex G Hamilton, Michael J Mitchell

Abstract read
In one paragraph

Article in Placenta, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
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

3 authors.

Kelsey L SwingleDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States.
Alex G HamiltonDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States.
Michael J MitchellDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States; Penn Institute for RNA Innovation, Perelman School of Medicine, Philadelphia, PA, United States; Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Cardiovascular Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Institute for Regenerative Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Center for Cellular Immunotherapies, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States; Center for Precision Engineering for Health, University of Pennsylvania, Philadelphia, PA, United States. Electronic address: mjmitch@seas.upenn.edu.

Funding

In utero gene editing to cure a metabolic liver diseaseR01DK123049 · NIDDK · CHILDREN'S HOSP OF PHILADELPHIA · PI PERANTEAU, WILLIAM H. · 2020 to 2024
$3.7M
Targeting stem-like cells and their niche in pancreatic cancerR37CA244911 · NCI · SLOAN-KETTERING INST CAN RESEARCH · PI Tuomas Tammela · 2020 to 2026
$3.4M
A data-driven drug delivery (4D) platform for probing and treating the chemoresistant bone marrow microenvironmentDP2TR002776 · NCATS · UNIVERSITY OF PENNSYLVANIA · PI MITCHELL, MICHAEL J · 2018 to 2018
$2.4M
Modular approach for the delivery of antibodies into the cytoplasm of cellsR01CA241661 · NCI · UNIVERSITY OF PENNSYLVANIA · PI TSOURKAS, ANDREW · 2019 to 2023
$1.8M
mRNA lipid nanoparticles for pre-eclampsiaR01HD115877 · NICHD · UNIVERSITY OF PENNSYLVANIA · PI Michael J Mitchell · 2024 to 2026
$1.0M
NCATS NIH HHS DP2 TR002776NCI NIH HHS R01 CA241661NCI NIH HHS R37 CA244911NICHD NIH HHS R01 HD115877NIDDK NIH HHS R01 DK123049
6 · The paper itself

Abstract

Clinically approved therapeutics for obstetric conditions are extremely limited, with over 80% of drugs lacking appropriate labeling information for pregnant individuals. The pathology for many of these obstetric conditions can be linked to the placenta, necessitating the development of therapeutic platforms for selective drug delivery to the placenta. When evaluating therapeutics for placental delivery, literature has focused on ex vivo delivery to human placental cells and tissue, which can be difficult to source for non-clinical researchers. Evaluating in vivo drug delivery to the placenta using small animal models can be more accessible than using human tissue, but robust, quantitative methods to characterize delivery remain poorly established. Here, we report a flow cytometric method to evaluate in vivo drug delivery to the murine placenta. Specifically, we describe techniques to identify key cell types in the murine placenta - trophoblasts, endothelial cells, and immune cells - via flow cytometric analysis. While we have employed this method to detect lipid nanoparticle-mediated nucleic acid delivery, this approach can extend to a variety of drug carriers (e.g., liposomes, exosomes, polymeric and metallic nanoparticles) and payloads (e.g., small molecules, proteins, other nucleic acids). Similarly, we describe the application of this method toward immunophenotypic analysis to assess changes in the placental immune environment during disease or in response to a therapeutic. Together, the techniques reported herein aim to broaden the accessibility of placental research in an effort to encourage collaboration between physician-scientists, engineers, placental biologists, and clinicians for developing novel therapeutics to treat placental conditions during pregnancy.

Indexed as

Drug Delivery SystemsFlow CytometryNanoparticlesPlacentaAnimalsFemaleMicePregnancyTrophoblastsDrug deliveryFlow cytometryPlacentaPregnancyReproductive health

Identifiers

PMID39181830
PMCPMC11822046

What Socratic 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 Socratic graph.