Article in Development (Cambridge, England), 2026. 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.
Ying PangGZMB, Department of Evolutionary Developmental Genetics, Georg-August-University Göttingen, 37077 Göttingen, Germany.ORCID 0009-0002-5277-3032
Clayton PavlichDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA.ORCID 0009-0003-1359-9155
Beke LohmannDepartment of Biology and Psychology, Georg-August-University Göttingen, 37077 Göttingen, Germany.ORCID 0009-0006-0076-0159
Maria Lorenza RuscianoDepartment of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, 80121 Napoli, Italy.ORCID 0009-0000-6428-2674
Mattia StrangesEuropean Research Institute for the Biology of Ageing, University of Groningen, 9713AV Groningen, The Netherlands.ORCID 0009-0008-8512-7970
Marie ZillioxInstitut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, CNRS and UCBL Lyon 1, 69007 Lyon, France.ORCID 0009-0003-0143-9621
Kirill UstyantsevEuropean Research Institute for the Biology of Ageing, University of Groningen, 9713AV Groningen, The Netherlands.ORCID 0000-0003-4346-3868
Béryl Laplace-BuilhéInstitut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, CNRS and UCBL Lyon 1, 69007 Lyon, France.ORCID 0000-0002-1437-587X
Manon KoenigInstitut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, CNRS and UCBL Lyon 1, 69007 Lyon, France.ORCID 0009-0009-7537-5134
Jeffrey J ColgrenMichael Sars Centre, University of Bergen, 5006 Bergen, Norway.ORCID 0000-0003-3912-0926
Eugene BerezikovEuropean Research Institute for the Biology of Ageing, University of Groningen, 9713AV Groningen, The Netherlands.ORCID 0000-0002-1145-2884
Gregor BucherGZMB, Department of Evolutionary Developmental Genetics, Georg-August-University Göttingen, 37077 Göttingen, Germany.ORCID 0000-0002-4615-6401
Pawel BurkhardtMichael Sars Centre, University of Bergen, 5006 Bergen, Norway.ORCID 0000-0001-9826-057X
Daniel J DickinsonDepartment of Molecular Biosciences, University of Texas at Austin, Austin, TX 78712, USA.ORCID 0000-0003-2651-2584
Jan HuiskenDepartment of Biology and Psychology, Georg-August-University Göttingen, 37077 Göttingen, Germany.ORCID 0000-0001-7250-3756
Maria Ina ArnoneDepartment of Biology and Evolution of Marine Organisms, Stazione Zoologica Anton Dohrn, 80121 Napoli, Italy.ORCID 0000-0002-9012-7624
Evelyn HoulistonCentre National de la Recherche Scientifique (CNRS), France.ORCID 0000-0001-9264-2585
Michalis AverofInstitut de Génomique Fonctionnelle de Lyon (IGFL), École Normale Supérieure de Lyon, CNRS and UCBL Lyon 1, 69007 Lyon, France.ORCID 0000-0002-6803-7251
Funding
Understanding developmental control of cell polarity using single-cell in vivo biochemistryR01GM138443 · NIGMS · UNIVERSITY OF TEXAS AT AUSTIN · PI DICKINSON, DANIEL J · 2020 to 2025
$2.4M
Agence Nationale de la Recherche ANR-19-CE13-0003Agence Nationale de la Recherche ANR-21-CE13-0044-01Agence Nationale de la Recherche ANR-22-CE92-0027Agence Nationale de la Recherche ANR-24-CE13-5353-01Alexander von Humboldt-StiftungAustrian Science Fund P36080Austrian Science Fund PAT 2395824Centre national de la recherche scientifique Diversity of Biological MechanismsChina Scholarship Council 202104910052Deutsche Forschungsgemeinschaft DFG 1443/17-1European Research Council 101044989Marie Skłodowska-Curie Actions 101073238NIGMS NIH HHS R01 GM138443NIH HHS R01-GM138443Stazione Zoologica Anton Dohrn
6 · The paper itself
Abstract
Transgenic markers and tools have revolutionised how we study cells and developing organisms. Some elements needed to construct those tools are universally applicable (e.g. fluorescent proteins), while others are species specific (e.g. cis-regulatory elements driving transcription). Membrane-localising signals that target proteins to the plasma membrane have been identified in some organisms, but their efficacy varies across species. To address this problem, we generated a toolkit of 11 membrane-localising tags that can be screened rapidly in diverse organisms. The toolkit includes tags targeting the plasma membrane through different mechanisms, including signal peptides, lipid attachments or fusion with lipid-binding domains. Each tag was fused to the fluorescent protein mScarlet3 and placed downstream of a T7 promoter, to produce mRNA that can be delivered in a wide range of embryos and cells. Through a collaborative effort, we tested this toolkit in ten animals spanning diverse phyla, including chordates, echinoderms, arthropods, nematodes, annelids, flatworms and cnidarians. We identify robust membrane-localising tags in each of these animals, and in the animals' closest relatives, the choanoflagellates. Three tags (KRas, GAP43 and Src64B) work in all the species we tested.
Indexed as
Cell MembraneAnimalsComparative cell biologyEvo devoNon-conventional model organismsPlasma membrane targeting
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.
Targeting the cell membrane in established and emerging model organisms. · full record | Socratic