Evidence map›Paper›PMID 38012306›Full record

ArticleNature biomedical engineering2024

Identification of druggable regulators of cell secretion via a kinome-wide screen and high-throughput immunomagnetic cell sorting.

Mahmoud Labib, Zongjie Wang, Yunhye Kim, Sichun Lin, Abdalla Abdrabou, Hanie Yousefi, Pei-Ying Lo, Stéphane Angers, Edward H Sargent, Shana O Kelley

Abstract read
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In one paragraph

Article in Nature biomedical engineering, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.1field-weighted citation impact, top 22% 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

5 citing papers in PubMed, 7 citations in OpenAlex.

  1. Review
  2. Article
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  5. 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

10 authors at 2 institutions in 4 countries.

Mahmoud Labib *Peninsula Medical School, Faculty of Health, University of Plymouth, Plymouth, UK.ORCID http://orcid.org/0000-0003-4565-2056
Zongjie Wang *Department of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0000-0001-9900-7197
Yunhye KimDepartment of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada.
Sichun LinDepartment of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada.
Abdalla AbdrabouRobert H. Laurie Comprehensive Cancer Center, Northwestern University, Chicago, IL, USA.
Hanie YousefiDepartment of Chemistry, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0000-0003-0127-7776
Pei-Ying LoDepartment of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.
Stéphane AngersDepartment of Pharmaceutical Sciences, University of Toronto, Toronto, Ontario, Canada.ORCID http://orcid.org/0000-0001-7241-9044
Edward H SargentDepartment of Biomedical Engineering, McCormick School of Engineering, Northwestern University, Evanston, IL, USA.ORCID http://orcid.org/0000-0003-0396-6495
Shana O KelleyDepartment of Chemistry, Northwestern University, Evanston, IL, USA. shana.kelley@northwestern.edu.ORCID http://orcid.org/0000-0003-3360-5359
Northwestern University · USUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The identification of genetic regulators of cell secretions is challenging because it requires the sorting of a large number of cells according to their secretion patterns. Here we report the development and applicability of a high-throughput microfluidic method for the analysis of the secretion levels of large populations of immune cells. The method is linked with a kinome-wide loss-of-function CRISPR screen, immunomagnetically sorting the cells according to their secretion levels, and the sequencing of their genomes to identify key genetic modifiers of cell secretion. We used the method, which we validated against flow cytometry for cytokines secreted from primary mouse CD4

Indexed as

Protein Kinase InhibitorsAnimalsMiceRNA InterferenceProtein Kinase Inhibitors

Identifiers

PMID38012306
OpenAlexW4389058615

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.