RESUMEN
Transcription factors regulate the molecular, morphological, and physiological characteristics of neurons and generate their impressive cell-type diversity. To gain insight into the general principles that govern how transcription factors regulate cell-type diversity, we used large-scale single-cell RNA sequencing to characterize the extensive cellular diversity in the Drosophila optic lobes. We sequenced 55,000 single cells and assigned them to 52 clusters. We validated and annotated many clusters using RNA sequencing of FACS-sorted single-cell types and cluster-specific genes. To identify transcription factors responsible for inducing specific terminal differentiation features, we generated a "random forest" model, and we showed that the transcription factors Apterous and Traffic-jam are required in many but not all cholinergic and glutamatergic neurons, respectively. In fact, the same terminal characters often can be regulated by different transcription factors in different cell types, arguing for extensive phenotypic convergence. Our data provide a deep understanding of the developmental and functional specification of a complex brain structure.
Asunto(s)
Drosophila melanogaster/embriología , Regulación del Desarrollo de la Expresión Génica/fisiología , Neurogénesis/fisiología , Animales , Diferenciación Celular , Neuronas Colinérgicas/fisiología , Análisis por Conglomerados , Simulación por Computador , Proteínas de Drosophila/genética , Proteínas de Drosophila/metabolismo , Drosophila melanogaster/genética , Drosophila melanogaster/metabolismo , Perfilación de la Expresión Génica/métodos , Proteínas de Homeodominio , Proteínas con Homeodominio LIM/metabolismo , Factores de Transcripción Maf de Gran Tamaño/metabolismo , Neuroglía/fisiología , Neuronas/fisiología , Neurotransmisores/genética , Neurotransmisores/fisiología , Lóbulo Óptico de Animales no Mamíferos/fisiología , Fenotipo , Proteínas Proto-Oncogénicas/metabolismo , Análisis de Secuencia de ARN/métodos , Análisis de la Célula Individual/métodos , Factores de Transcripción/genética , Factores de Transcripción/metabolismo , Factores de Transcripción/fisiologíaRESUMEN
An amendment to this paper has been published and can be accessed via a link at the top of the paper.
RESUMEN
Inferring the full genealogical history of a set of DNA sequences is a core problem in evolutionary biology, because this history encodes information about the events and forces that have influenced a species. However, current methods are limited, and the most accurate techniques are able to process no more than a hundred samples. As datasets that consist of millions of genomes are now being collected, there is a need for scalable and efficient inference methods to fully utilize these resources. Here we introduce an algorithm that is able to not only infer whole-genome histories with comparable accuracy to the state-of-the-art but also process four orders of magnitude more sequences. The approach also provides an 'evolutionary encoding' of the data, enabling efficient calculation of relevant statistics. We apply the method to human data from the 1000 Genomes Project, Simons Genome Diversity Project and UK Biobank, showing that the inferred genealogies are rich in biological signal and efficient to process.