Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

The simultaneous measurement of multiple modalities, known as multimodal analysis, represents an exciting frontier for single-cell genomics and necessitates new computational methods that can define cellular states based on multiple data types. Here, we introduce "weighted-nearest neighbor analysis", an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of hundreds of thousands of human white blood cells alongside a panel of 228 antibodies to construct a multimodal reference atlas of the circulating immune system. We demonstrate that integrative analysis substantially improves our ability to resolve cell states and validate the presence of previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets, and to interpret immune responses to vaccination and COVID-19. Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets, including paired measurements of RNA and chromatin state, and to look beyond the transcriptome towards a unified and multimodal definition of cellular identity. Availability: Installation instructions, documentation, tutorials, and CITE-seq datasets are available at http://www.satijalab.org/seurat

Context Medroxyprogesterone acetate (MPA) is a widely used progestin in feminizing hormone therapy. However, the side effects and hormonal changes elicited by this drug have never been investigated in the transgender population. Objective We evaluated the incidence of self-reported effects among transwomen using MPA and this drug’s impact on hormonal and metabolic parameters. Design, Setting, and Participants We retrospectively collected data from 290 follow-up visits (FUVs) of transwomen treated at Rhode Island Hospital from January 2011 to July 2018 (mean duration of therapy 3.4 ± 1.7 years). FUVs followed regimens of estradiol (E) and spironolactone, with MPA (n = 102) or without MPA (n = 188). Main Outcome Measures We assessed the incidence of self-reported effects after MPA treatment. We also compared blood levels of E, testosterone, and various laboratory parameters between MPA and non-MPA groups. Results Mean weighted E level was 211 ± 57 pg/mL after MPA treatment and 210 ± 31 pg/mL otherwise; this difference was nonsignificant [t(274) = 0.143, P = 0.886]. Mean weighted testosterone level was 79 ± 18 ng/dL after MPA treatment and 215 ± 29 ng/dL otherwise; testosterone levels were significantly lower in the MPA group [t(122) = 32.4, P < 0.001]. There were minimal changes in other laboratory parameters. Of 39 patients receiving MPA, 26 reported improved breast development and 11 reported decreased facial hair. Five patients experienced mood swings on MPA. Conclusions In our cohort of transwomen, we found minimal side effects, unchanged E levels, and a decline in testosterone associated with MPA, outcomes consistent with feminization. Prospective studies are needed to confirm our findings.