The Tabula Muris Consortium has created a comprehensive single-cell transcriptomic dataset from 20 mouse organs, comprising over 100,000 cells. This resource provides insights into gene expression in previously under-characterized cell populations and enables direct comparisons of gene expression across shared cell types in different tissues. Two methods were used: microfluidic droplet-based 3'-end counting for low coverage and FACS-based full-length transcript analysis for high sensitivity. The data enable the creation of an atlas of transcriptomic cell biology, revealing new information on cell types and their gene expression profiles. The study analyzed multiple organs from the same mouse, controlling for age, environment, and epigenetic effects, allowing for direct comparisons of cell type composition between organs. The dataset includes 100,605 cells from 20 organs, representing 3 female and 4 male, 3-month-old C57BL/6JN mice, analogous to 20-year-old humans. The data are publicly available for analysis, including gene counts, metadata, and raw data from GEO and code from GitHub. The study defines organ-specific cell types using principal component analysis and clustering, assigning annotations based on known markers and gene expression. The data reveal new insights into cell types, including unexpected roles for genes in the adult pancreas, transcriptional heterogeneity of brain endothelial cells, and the expression of MHC class II genes in adult mouse T cells. The study also compares two single-cell RNA-sequencing methods, FACS and microfluidic droplet-based, showing differences in the number of cells analyzed, reads per cell, and genes per cell. The data show that FACS captures a larger diversity of cell types compared to microfluidic droplets, but both methods accurately recapitulate average gene expression profiles for shared cell populations. The study also investigates global clustering across organs, revealing relationships between cells from different organs and identifying clusters with similar gene expression profiles. The data show that cell type identity is more strongly influenced by cell type than by batch or dissociation protocol. The study also analyzes transcription factors (TFs) and their role in defining cell types, showing that TFs are more informative for defining cell types than other molecular classes. The data provide a framework for understanding the regulatory networks underlying cell identity and may inform the design of reprogramming protocols. The study also addresses challenges in single-cell studies, including dissociation-related gene expression changes and the choice of multiple technologies. The data provide a rich resource for studying mouse organs and may inform future studies of mouse disease models. The study concludes that the Tabula Muris dataset provides a valuable resource for understanding cellular diversity in mice and may serve as a reference for healthy young adult organs in future studies.The Tabula Muris Consortium has created a comprehensive single-cell transcriptomic dataset from 20 mouse organs, comprising over 100,000 cells. This resource provides insights into gene expression in previously under-characterized cell populations and enables direct comparisons of gene expression across shared cell types in different tissues. Two methods were used: microfluidic droplet-based 3'-end counting for low coverage and FACS-based full-length transcript analysis for high sensitivity. The data enable the creation of an atlas of transcriptomic cell biology, revealing new information on cell types and their gene expression profiles. The study analyzed multiple organs from the same mouse, controlling for age, environment, and epigenetic effects, allowing for direct comparisons of cell type composition between organs. The dataset includes 100,605 cells from 20 organs, representing 3 female and 4 male, 3-month-old C57BL/6JN mice, analogous to 20-year-old humans. The data are publicly available for analysis, including gene counts, metadata, and raw data from GEO and code from GitHub. The study defines organ-specific cell types using principal component analysis and clustering, assigning annotations based on known markers and gene expression. The data reveal new insights into cell types, including unexpected roles for genes in the adult pancreas, transcriptional heterogeneity of brain endothelial cells, and the expression of MHC class II genes in adult mouse T cells. The study also compares two single-cell RNA-sequencing methods, FACS and microfluidic droplet-based, showing differences in the number of cells analyzed, reads per cell, and genes per cell. The data show that FACS captures a larger diversity of cell types compared to microfluidic droplets, but both methods accurately recapitulate average gene expression profiles for shared cell populations. The study also investigates global clustering across organs, revealing relationships between cells from different organs and identifying clusters with similar gene expression profiles. The data show that cell type identity is more strongly influenced by cell type than by batch or dissociation protocol. The study also analyzes transcription factors (TFs) and their role in defining cell types, showing that TFs are more informative for defining cell types than other molecular classes. The data provide a framework for understanding the regulatory networks underlying cell identity and may inform the design of reprogramming protocols. The study also addresses challenges in single-cell studies, including dissociation-related gene expression changes and the choice of multiple technologies. The data provide a rich resource for studying mouse organs and may inform future studies of mouse disease models. The study concludes that the Tabula Muris dataset provides a valuable resource for understanding cellular diversity in mice and may serve as a reference for healthy young adult organs in future studies.