: The big data era in biology is underway, but the study of organismal form has been slow to capitalize on advances in imaging and computation. Imaging approaches can digitize whole organisms, but low throughput has limited the effort to document morphological diversity. Here, within the open science initiative 'Antscan', we applied high-throughput synchrotron X-ray microtomography to capture phenotypes across a diverse and ecologically dominant insect group: ants. At https://www.antscan.info , we provide 2,193 whole-body three-dimensional ant datasets from 212 genera and 792 species to broadly cover the ant phylogeny with a global scope, also pairing phenomic data with genome sequencing projects. Scans acquired with standardized parameters facilitate automated analysis, and free access to data can broaden the audience and incentivize methods development. Antscan presents a scalable approach to create libraries of diverse anatomies, heralding an era of studies on the evolution, structure and function of organismal phenotypes.

High-throughput phenomics of global ant biodiversity / Katzke, J.; Hita Garcia, F.; Lösel, P. D.; Azuma, F.; Faragó, T.; Aibekova, L.; Casadei-Ferreira, A.; Gautam, S.; Richter, A.; Toulkeridou, E.; Bremer, S.; Hamann, E.; Hein, J.; Odar, J.; Sarkar, C.; Zuber, M.; Boomsma, J. J.; Feitosa, R. M.; Schrader, L.; Zhang, G.; Csősz, S.; Dong, M.; Evangelista, O.; Fischer, G.; Fisher, B. L.; Florez-Fernandez, J. A.; Wagner, H. C.; Villalta, I.; Tsuji, K.; Tartally, A.; Schiøtt, M.; Rehner, S.; Petitclerc, F.; Pedersen, J. S.; Orivel, J.; Nash, D.; Markó, B.; Larabee, F.; Ješovnik, A.; Helanterä, H.; De Greef, S.; Giannetti, D.; Gadau, J.; Frank, E. T.; Foitzik, S.; Feldhaar, H.; Cremer, S.; Boulay, R.; Bollazzi, M.; Bernadou, A.; Aron, S.; García, F.; Gómez, K.; Grasso, D. A.; Guénard, B.; Hawkes, P. G.; Johnson, R. A.; Keller, R. A.; Larsen, R. S.; Linksvayer, T. A.; Liu, C.; Matte, A.; Ogasawara, M.; Ran, H.; Rodriguez, J.; Schifani, E.; Schultz, T. R.; Shik, J. Z.; Sosa-Calvo, J.; Tong, C.; Tozetto, L.; Yoon, S.; Yoshimura, M.; Zhao, J.; Baumbach, T.; Economo, E. P.; Van De Kamp, T.. - In: NATURE METHODS. - ISSN 1548-7091. - 23:3(2026), pp. 663-672. [10.1038/s41592-026-03005-0]

High-throughput phenomics of global ant biodiversity

Giannetti D.;Grasso D. A.;Schifani E.;
2026-01-01

Abstract

: The big data era in biology is underway, but the study of organismal form has been slow to capitalize on advances in imaging and computation. Imaging approaches can digitize whole organisms, but low throughput has limited the effort to document morphological diversity. Here, within the open science initiative 'Antscan', we applied high-throughput synchrotron X-ray microtomography to capture phenotypes across a diverse and ecologically dominant insect group: ants. At https://www.antscan.info , we provide 2,193 whole-body three-dimensional ant datasets from 212 genera and 792 species to broadly cover the ant phylogeny with a global scope, also pairing phenomic data with genome sequencing projects. Scans acquired with standardized parameters facilitate automated analysis, and free access to data can broaden the audience and incentivize methods development. Antscan presents a scalable approach to create libraries of diverse anatomies, heralding an era of studies on the evolution, structure and function of organismal phenotypes.
2026
High-throughput phenomics of global ant biodiversity / Katzke, J.; Hita Garcia, F.; Lösel, P. D.; Azuma, F.; Faragó, T.; Aibekova, L.; Casadei-Ferreira, A.; Gautam, S.; Richter, A.; Toulkeridou, E.; Bremer, S.; Hamann, E.; Hein, J.; Odar, J.; Sarkar, C.; Zuber, M.; Boomsma, J. J.; Feitosa, R. M.; Schrader, L.; Zhang, G.; Csősz, S.; Dong, M.; Evangelista, O.; Fischer, G.; Fisher, B. L.; Florez-Fernandez, J. A.; Wagner, H. C.; Villalta, I.; Tsuji, K.; Tartally, A.; Schiøtt, M.; Rehner, S.; Petitclerc, F.; Pedersen, J. S.; Orivel, J.; Nash, D.; Markó, B.; Larabee, F.; Ješovnik, A.; Helanterä, H.; De Greef, S.; Giannetti, D.; Gadau, J.; Frank, E. T.; Foitzik, S.; Feldhaar, H.; Cremer, S.; Boulay, R.; Bollazzi, M.; Bernadou, A.; Aron, S.; García, F.; Gómez, K.; Grasso, D. A.; Guénard, B.; Hawkes, P. G.; Johnson, R. A.; Keller, R. A.; Larsen, R. S.; Linksvayer, T. A.; Liu, C.; Matte, A.; Ogasawara, M.; Ran, H.; Rodriguez, J.; Schifani, E.; Schultz, T. R.; Shik, J. Z.; Sosa-Calvo, J.; Tong, C.; Tozetto, L.; Yoon, S.; Yoshimura, M.; Zhao, J.; Baumbach, T.; Economo, E. P.; Van De Kamp, T.. - In: NATURE METHODS. - ISSN 1548-7091. - 23:3(2026), pp. 663-672. [10.1038/s41592-026-03005-0]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11381/3058575
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