Whitehead Institute for Biomedical Research and Department of Biology, Massachusetts Institute of Technology, Howard Hughes Medical Institute, Cambridge, United States
Gabriela S Vida
Department of Cell and Developmental Biology, The Perelman School of Medicine and The Penn Institute for Regenerative Medicine, Philadelphia, United States
Whitehead Institute for Biomedical Research and Department of Biology, Massachusetts Institute of Technology, Howard Hughes Medical Institute, Cambridge, United States
Jennifer M Viveiros
Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, United States
Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, United States
Mara R Grace
Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, United States
Cameron W Berry
Department of Developmental Biology, Stanford University School of Medicine, Stanford, United States
Hongjie Li
Huffington Center on Aging and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, United States
Jasper Janssens
JVIB Center for Brain & Disease Research, and the Department of Human Genetics, KU Leuven, Leuven, Belgium
Wouter Saelens
Data Mining and Modeling for Biomedicine, VIB Center for Inflammation Research, and Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Ghent, Belgium
Zhantao Shao
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
Chun Hu
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada
Yukiko M Yamashita
Whitehead Institute for Biomedical Research and Department of Biology, Massachusetts Institute of Technology, Howard Hughes Medical Institute, Cambridge, United States
Teresa Przytycka
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, United States
Brian Oliver
National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, United States
Cell Biology Program, The Hospital for Sick Children, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada; Institute of Medical Science, University of Toronto, Toronto, Canada
Henry Krause
Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, Canada; Department of Molecular Genetics, University of Toronto, Toronto, Canada
Erika L Matunis
Department of Cell Biology, Johns Hopkins University School of Medicine, Baltimore, United States
Helen White-Cooper
School of Biosciences, Cardiff University, Cardiff, United Kingdom
Department of Cell and Developmental Biology, The Perelman School of Medicine and The Penn Institute for Regenerative Medicine, Philadelphia, United States
Department of Developmental Biology, Stanford University School of Medicine, Stanford, United States; Department of Genetics, Stanford University, Stanford, United States
Proper differentiation of sperm from germline stem cells, essential for production of the next generation, requires dramatic changes in gene expression that drive remodeling of almost all cellular components, from chromatin to organelles to cell shape itself. Here, we provide a single nucleus and single cell RNA-seq resource covering all of spermatogenesis in Drosophila starting from in-depth analysis of adult testis single nucleus RNA-seq (snRNA-seq) data from the Fly Cell Atlas (FCA) study. With over 44,000 nuclei and 6000 cells analyzed, the data provide identification of rare cell types, mapping of intermediate steps in differentiation, and the potential to identify new factors impacting fertility or controlling differentiation of germline and supporting somatic cells. We justify assignment of key germline and somatic cell types using combinations of known markers, in situ hybridization, and analysis of extant protein traps. Comparison of single cell and single nucleus datasets proved particularly revealing of dynamic developmental transitions in germline differentiation. To complement the web-based portals for data analysis hosted by the FCA, we provide datasets compatible with commonly used software such as Seurat and Monocle. The foundation provided here will enable communities studying spermatogenesis to interrogate the datasets to identify candidate genes to test for function in vivo.