Evidence map›Paper›PMID 37639143›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2024

Tackling Tissue Macrophage Heterogeneity by SplitCre Transgenesis.

Sigalit Boura-Halfon, Rebecca Haffner-Krausz, Shifra Ben-Dor, Jung-Seok Kim, Steffen Jung

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 21% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Review
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 1 institution in 1 country.

Sigalit Boura-Halfon *Department of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel. s.boura-halfon@weizmann.ac.il.
Rebecca Haffner-Krausz *Department of Veterinary Resources, Weizmann Institute of Science, Rehovot, Israel.
Shifra Ben-Dor *Department of Life Sciences Core Facilities, Weizmann Institute of Science, Rehovot, Israel.
Jung-Seok KimDepartment of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel.
Steffen JungDepartment of Immunology and Regenerative Biology, Weizmann Institute of Science, Rehovot, Israel. s.jung@weizmann.ac.il.
Weizmann Institute of Science · IL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Macrophages represent a broad spectrum of distinct, but closely related tissue-resident immune cells. This presents a major challenge for the study of functional aspects of these cells using classical Cre recombinase-mediated conditional mutagenesis in mice, since single promoter-driven Cre transgenic models often display limited specificity toward their intended target. The advent of CRISPR/Cas9 technology has now provided a time- and cost-effective method to explore the full potential of binary transgenic, intersectional genetics. Specifically, the use of two promoters driving inactive Cre fragments that, when co-expressed, dimerize and only then gain recombinase activity allows the characterization and manipulation of genetically defined tissue macrophage subpopulations. Here, we will elaborate on the use of this protocol to capitalize on these recent technological advances in mouse genetics and discuss their strengths and pitfalls to improve the study of tissue macrophage subpopulations in physiology and pathophysiology.

Indexed as

Gene Transfer TechniquesMacrophagesAnimalsAnimals, Genetically ModifiedDimerizationMiceMutagenesisCre loxPCRISPR-Cas9intersectional geneticssplitCretissue macrophages

Identifiers

PMID37639143
OpenAlexW4386209373

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.