Human and Non-Human Primate Cell Atlas: Publication Package Highlights

The recording of this webinar is now available: https://www.youtube.com/watch?v=BTepiBgdsls

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The human brain contains more than 100 billion cells, but exactly how these cells are organized into types and how these cell types differ between brain regions and species is an open question.

This webinar presents a new collection of studies focused on defining and characterizing the underlying gene expression, gene regulatory, and morphoelectric features of cell types in the human and non-human primate (NHP) brain. These studies employ a range of cellular resolution methods (most notably single cell genomics) for this purpose, marking the beginning of an exciting new era of high-resolution human and NHP analyses. The implications for understanding of human brain function, disease and disease modeling are profound.

More about the collection of studies: https://www.biccn.org/science/human-and-nhp-cell-atlas 

Speakers: 

Ed Lein, Senior Investigator at the Allen Institute for Brain Science: Introduction

Kimberly Siletti, University Medical Center Utrecht, Netherlands: Transcriptomic diversity of cell types in the adult human brain (read the paper)

• The cell types that enable the human brain’s unique capabilities are poorly understood, particularly outside the cortex. We used single-cell RNA sequencing to profile nuclei from 100 brain regions in three postmortem donors. Our multi-level analysis identified 461 clusters and 3313 subclusters, revealing regional diversity across glia and neurons that was particularly high outside the cortex.

Wei Tian, Salk Institute: Epigenomic complexity of the human brain revealed by single-cell DNA methylomes and 3D genome structures (read the paper)

• To unveil the brain cell types and their underlying gene regulatory programs, we profiled and analyzed DNA methylation and chromatin conformation in over 500,000 cells from 46 brain regions. Our study has identified 188 distinct cell types, revealed their unique molecular signatures and discovered brain cell regional heterogeneity in DNA methylation and chromatin conformation, providing profound insights into the complexity of gene regulation in the adult human brain. Additionally, we developed single-cell DNA methylation barcodes (scMCodes) for accurately predicting brain cell types based on methylation patterns at specific genomic sites.

Trygve Bakken, Allen Institute for Brain Science: Comparative transcriptomics reveals human-specific cortical features (read the paper)

• The molecular and cellular basis of our distinct cognitive abilities remains poorly understood. This study uses single nucleus RNA-seq to profile the cell types comprising a language-associated region of the neocortex in humans and four non-human primate species. While the composition of cell types was generally conserved across primates, potentially adaptive changes in synaptic gene expression have occurred on the human lineage.

Chang Kim, University of California, San Francisco: Spatiotemporal molecular dynamics of the developing human thalamus (read the paper)

• The developing human thalamus has yet to be extensively molecularly profiled resulting in a gap of how their nuclei are formed during development. We used recent scRNA-seq atlases as well as performing spatial transcriptomics to profile how the thalamus develops in the second trimester, identifying increased diversity of neurons then assumed.

Lijuan Liu, Southeast University, China: Whole Human-Brain Mapping of Single Cortical Neurons for Profiling Morphological Diversity and Stereotypy (read the paper)

• We collected 3-D images of 1746 human cortical neurons, of which 852 neurons were subsequently reconstructed to quantify their local dendritic morphology, and mapped to standard atlases both computationally and semantically. In our data, human neurons are more diverse across brain regions than by subject age or gender. The strong stereotypy within cohorts of brain regions allows generating a statistical tensor-field of neuron morphology to characterize 3-D anatomical modularity of a human brain.