Evidence map›Paper›PMID 40715743›Full record

ArticleNature protocols2025

Manufacturing of CRISPR-edited primary mouse CAR T cells for cancer immunotherapy.

Puneeth Guruprasad, Ranjani Ramasubramanian, Siena Nason, Alberto Carturan, Shan Liu, Luca Paruzzo, Vladlena Hornet, Jacqueline Plesset, Ruchi P Patel, Vijay Bhoj and 2 more

Abstract read
PubMed Publisher
In one paragraph

Article in Nature protocols, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

12 authors.

Puneeth GuruprasadDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Ranjani RamasubramanianDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Siena NasonCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Alberto CarturanCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Shan LiuCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Luca ParuzzoCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.ORCID 0000-0002-6505-0194
Vladlena HornetDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.ORCID 0009-0002-1712-4969
Jacqueline PlessetCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Ruchi P PatelCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Vijay BhojCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Gregory L BeattyCenter for Cellular Immunotherapies, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Marco RuellaDepartment of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA. mruella@upenn.edu.ORCID 0000-0003-4301-5811

Funding

MODULATION OF CD5 SIGNALING TO ENHANCE ADOPTIVE T-CELL THERAPIES FOR CANCERR37CA262362 · NCI · UNIVERSITY OF PENNSYLVANIA · PI Marco Ruella · 2022 to 2026
$2.6M
Resistance To Targeted Immunotherapies:CART19 as a ParadigmR00CA212302 · NCI · UNIVERSITY OF PENNSYLVANIA · PI RUELLA, MARCO · 2018 to 2020
$608k
NCI NIH HHS R37 CA262362U.S. Department of Health & Human Services | National Institutes of Health (NIH) R00CA212302U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01-37-CA262362-03viTToria Biotherapeutics, AbClon and Oxford Nanoimaging
6 · The paper itself

Abstract

Editing chimeric antigen receptor (CAR) T cells by using CRISPR-Cas9 has become a routine strategy to improve their antitumor function or safety profile. Xenograft tumor models in immunodeficient mice are often used to evaluate the function of CRISPR-edited human CAR T cells. These models, however, lack functional immune systems and thus fail to recapitulate barriers such as the immunosuppressive tumor microenvironment (TME) that CAR T cells will encounter in patients. Thus, genetically modifying mouse CAR T cells for use in immune-intact models is an attractive approach to explore the impact of a given gene deletion on CAR T cells within a natural TME. Here, we describe a protocol to perform CRISPR-Cas9 editing in primary mouse T cells, thereby enabling studies of gene-edited CAR T within the TME and in the presence of a functional immune system. This protocol is integrated into a standard mouse CAR T manufacturing workflow, a process that typically spans ~5-6 days. The first stage of this protocol involves isolating mouse T cells, electroporating them with a ribonucleoprotein complex and activating them by using magnetic bead stimulation. The second stage involves transducing the CAR gene and expanding these cells, and the third stage focuses on validating knockout efficiency and the functionality of gene-edited mouse CAR T cells. This procedure requires a proficiency in aseptic cell culture techniques and a basic understanding of T cell biology. We anticipate that efficient and reliable genetic modification of mouse T cells will have wide-ranging applications for cancer immunotherapies and related fields.

Indexed as

CRISPR-Cas SystemsGene EditingImmunotherapy, AdoptiveNeoplasmsReceptors, Chimeric AntigenT-LymphocytesAnimalsHumansMiceTumor MicroenvironmentReceptors, Chimeric Antigen

Identifiers

What OpenQuestion holds

Textmetadata
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.