Fly cell atlas: a single-nucleus transcriptomic atlas of the adult fruit fly

Li, Hongjie, Janssens, Jasper, De Waegeneer, Maxime, Kolluru, Sai Saroja, Davie, Kristofer, Gardeux, Vincent, Saelens, Wouter, David, Fabrice P. A., Brbi, Maria, Spanier, Katina, Leskovec, Jure, McLaughlin, Colleen N., Xie, Qijing, Jones, Robert C., Brueckner, Katja, Shim, Jiwon, Tattikota, Sudhir Gopal, Schnorrer, Frank, Rust, Katja, Nystul, Todd G., Carvalho-Santos, Zita, Ribeiro, Carlos, Pal, Soumitra, Mahadevaraju, Sharvani, Przytycka, Teresa M., Allen, Aaron M., Goodwin, Stephen F., Berry, Cameron W., Fuller, Margaret T., White-Cooper, Helen ORCID: https://orcid.org/0000-0002-3373-8023, Matunis, Erika L., DiNardo, Stephen, Galenza, Anthony, O?Brien, Lucy Erin, Dow, Julian A. T., Jasper, Heinrich, Oliver, Brian, Perrimon, Norbert, Deplancke, Bart, Quake, Stephen R., Luo, Liqun, Aerts, Stein, Agarwal, Devika, Ahmed-Braimah, Yasir, Arbeitman, Michelle, Ariss, Majd M., Augsburger, Jordan, Ayush, Kumar, Baker, Catherine C., Banisch, Torsten, Birker, Katja, Bodmer, Rolf, Bolival, Benjamin, Brantley, Susanna E., Brill, Julie A., Brown, Nora C., Buehner, Norene A., Cai, Xiaoyu Tracy, Cardoso-Figueiredo, Rita, Casares, Fernando, Chang, Amy, Clandinin, Thomas R., Crasta, Sheela, Desplan, Claude, Detweiler, Angela M., Dhakan, Darshan B., Donà, Erika, Engert, Stefanie, Floc?hlay, Swann, George, Nancy, González-Segarra, Amanda J., Groves, Andrew K., Gumbin, Samantha, Guo, Yanmeng, Harris, Devon E., Heifetz, Yael, Holtz, Stephen L., Horns, Felix, Hudry, Bruno, Hung, Ruei-Jiun, Jan, Yuh Nung, Jaszczak, Jacob S., Jefferis, Gregory S. X. E., Karkanias, Jim, Karr, Timothy L., Katheder, Nadja Sandra, Kezos, James, Kim, Anna A., Kim, Seung K., Kockel, Lutz, Konstantinides, Nikolaos, Kornberg, Thomas B., Krause, Henry M., Labott, Andrew Thomas, Laturney, Meghan, Lehmann, Ruth, Leinwand, Sarah, Li, Jiefu, Li, Joshua Shing Shun, Li, Kai, Li, Ke, Li, Liying, Li, Tun, Litovchenko, Maria, Liu, Han-Hsuan, Liu, Yifang, Lu, Tzu-Chiao, Manning, Jonathan, Mase, Anjeli, Matera-Vatnick, Mikaela, Matias, Neuza Reis, McDonough-Goldstein, Caitlin E., McGeever, Aaron, McLachlan, Alex D., Moreno-Roman, Paola, Neff, Norma, Neville, Megan, Ngo, Sang, Nielsen, Tanja, O?Brien, Caitlin E., Osumi-Sutherland, David, Özel, Mehmet Neset, Papatheodorou, Irene, Petkovic, Maja, Pilgrim, Clare, Pisco, Angela Oliveira, Reisenman, Carolina, Sanders, Erin Nicole, dos Santos, Gilberto, Scott, Kristin, Sherlekar, Aparna, Shiu, Philip, Sims, David, Sit, Rene V., Slaidina, Maija, Smith, Harold E., Sterne, Gabriella, Su, Yu-Han, Sutton, Daniel, Tamayo, Marco, Tan, Michelle, Tastekin, Ibrahim, Treiber, Christoph, Vacek, David, Vogler, Georg, Waddell, Scott, Wang, Wanpeng, Wilson, Rachel I., Wolfner, Mariana F., Wong, Yiu-Cheung E., Xie, Anthony, Xu, Jun, Yamamoto, Shinya, Yan, Jia, Yao, Zepeng, Yoda, Kazuki, Zhu, Ruijun and Zinzen, Robert P. 2022. Fly cell atlas: a single-nucleus transcriptomic atlas of the adult fruit fly. Science 375 (6584) , 2432. 10.1126/science.abk2432

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Abstract

INTRODUCTION Drosophila melanogaster has had a fruitful history in biological research because it has contributed to many key discoveries in genetics, development, and neurobiology. The fruit fly genome contains ~14,000 protein-coding genes, ~63% of which have human orthologs. Single-cell RNA-sequencing has recently been applied to multiple Drosophila tissues and developmental stages. However, these data have been generated by different laboratories on different genetic backgrounds with different dissociation protocols and sequencing platforms, which has hindered the systematic comparison of gene expression across cells and tissues. RATIONALE We aimed to establish a cell atlas for the entire adult Drosophila with the same genetic background, dissociation protocol, and sequencing platform to (i) obtain a comprehensive categorization of cell types, (ii) integrate single-cell transcriptome data with existing knowledge about gene expression and cell types, (iii) systematically compare gene expression across the entire organism and between males and females, and (iv) identify cell type–specific markers across the entire organism. We chose single-nucleus RNA-sequencing (snRNA-seq) to circumvent the difficulties of dissociating cells that are embedded in the cuticle (e.g., sensory neurons) or that are multinucleated (e.g., muscle cells). We took two complementary strategies: sequencing nuclei from dissected tissues to know the identity of the tissue source and sequencing nuclei from the entire head and body to ensure that all cells are sampled. Experts from 40 laboratories participated in crowd annotation to assign transcriptomic cell types with the best knowledge available. RESULTS We sequenced 570,000 cells using droplet-based 10x Genomics from 15 dissected tissues as well as whole heads and bodies, separately in females and males. We also sequenced 10,000 cells from dissected tissues using the plate-based Smart-seq2 platform, providing deeper coverage per cell. We developed reproducible analysis pipelines using NextFlow and implemented a distributed cell-type annotation system with controlled vocabularies in SCope. Crowd-based annotations of transcriptomes from dissected tissues identified 17 main cell categories and 251 detailed cell types linked to FlyBase ontologies. Many of these cell types are characterized for the first time, either because they emerged only after increasing cell coverage or because they reside in tissues that had not been previously subjected to scRNA-seq. The excellent correspondence of transcriptomic clusters from whole body and dissected tissues allowed us to transfer annotations and identify a few cuticular cell types not detected in individual tissues. Cross-tissue analysis revealed location-specific subdivisions of muscle cells and heterogeneity within blood cells. We then determined cell type–specific marker genes and transcription factors with different specificity levels, enabling the construction of gene regulatory networks. Finally, we explored sexual dimorphism, finding a link between sex-biased expression and the presence of doublesex, and investigated tissue dynamics through trajectory analyses. CONCLUSION Our Fly Cell Atlas (FCA) constitutes a valuable resource for the Drosophila community as a reference for studies of gene function at single-cell resolution. All the FCA data are freely available for further analysis through multiple portals and can be downloaded for custom analyses using other single-cell tools. The ability to annotate cell types by sequencing the entire head and body will facilitate the use of Drosophila in the study of biological processes and in modeling human diseases at a whole-organism level with cell-type resolution. All data with annotations can be accessed from www.flycellatlas.org, which provides links to SCope, ASAP, and cellxgene portals.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences
Publisher:	American Association for the Advancement of Science
ISSN:	0036-8075
Date of First Compliant Deposit:	15 March 2022
Date of Acceptance:	19 January 2022
Last Modified:	01 Dec 2024 17:15
URI:	https://orca.cardiff.ac.uk/id/eprint/148381

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