Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain.... Show moreCharacterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain. Show less
The primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals(1). Here, using high-throughput transcriptomic and epigenomic profiling of... Show moreThe primary motor cortex (M1) is essential for voluntary fine-motor control and is functionally conserved across mammals(1). Here, using high-throughput transcriptomic and epigenomic profiling of more than 450,000 single nuclei in humans, marmoset monkeys and mice, we demonstrate a broadly conserved cellular makeup of this region, with similarities that mirror evolutionary distance and are consistent between the transcriptome and epigenome. The core conserved molecular identities of neuronal and non-neuronal cell types allow us to generate a cross-species consensus classification of cell types, and to infer conserved properties of cell types across species. Despite the overall conservation, however, many species-dependent specializations are apparent, including differences in cell-type proportions, gene expression, DNA methylation and chromatin state. Few cell-type marker genes are conserved across species, revealing a short list of candidate genes and regulatory mechanisms that are responsible for conserved features of homologous cell types, such as the GABAergic chandelier cells. This consensus transcriptomic classification allows us to use patch-seq (a combination of whole-cell patch-clamp recordings, RNA sequencing and morphological characterization) to identify corticospinal Betz cells from layer 5 in non-human primates and humans, and to characterize their highly specialized physiology and anatomy. These findings highlight the robust molecular underpinnings of cell-type diversity in M1 across mammals, and point to the genes and regulatory pathways responsible for the functional identity of cell types and their species-specific adaptations. Show less
Ballantyne, K.N.; Goedbloed, M.; Fang, R.X.; Schaap, O.; Lao, O.; Wollstein, A.; ... ; Kayser, M. 2010
Nonrecombining Y-chromosomal microsatellites (Y-STRs) are widely used to infer population histories, discover genealogical relationships, and identify males for criminal justice purposes. Although... Show moreNonrecombining Y-chromosomal microsatellites (Y-STRs) are widely used to infer population histories, discover genealogical relationships, and identify males for criminal justice purposes. Although a key requirement for their application is reliable mutability knowledge, empirical data are only available for a small number of Y-STRs thus far. To rectify this, we analyzed a large number of 186 Y-STR markers in nearly 2000 DNA-confirmed father-son pairs, covering an overall number of 352,999 meiotic transfers. Following confirmation by DNA sequence analysis, the retrieved mutation data were modeled via a Bayesian approach, resulting in mutation rates from 3.78 x 10(-4) (95% credible interval [CI], 1.38 x 10(-5) - 2.02 x 10(-3)) to 7.44 x 10(-2) (95% Cl, 6.51 x 10(-2) - 9.09 x 10(-2)) per marker per generation. With the 924 mutations at 120 Y-STR markers, a nonsignificant excess of repeat losses versus gains (1.16:1), as well as a strong and significant excess of single-repeat versus multirepeat changes (25.23:1), was observed. Although the total repeat number influenced Y-STR locus mutability most strongly, repeat complexity, the length in base pairs of the repeated motif, and the father's age also contributed to Y-STR mutability. To exemplify how to practically utilize this knowledge, we analyzed the 13 most mutable Y-STRs in an independent sample set and empirically proved their suitability for distinguishing close and distantly related males. This finding is expected to revolutionize Y-chromosomal applications in forensic biology, from previous male lineage differentiation toward future male individual identification. Show less
