To understand the function of the brain and how its dysfunction leads to brain diseases, it is essential to uncover the cell type composition of the brain, how the cell types are connected with each other and what their roles are in circuit function. At the Allen Institute, we have built multiple technology platforms, including single-cell transcriptomics, spatial transcriptomics, single and multi-patching electrophysiology, 3D reconstruction of neuronal morphology, and brain-wide connectivity mapping, to characterize the molecular, anatomical, physiological, and connectional properties of brain cell types in a systematic manner, towards the creation of multi-modal cell atlases for the mouse and human brains¬.
We have now generated a comprehensive and high-resolution transcriptomic and spatial cell type atlas for the whole adult mouse brain, based on the combination of two single-cell-level, whole-brain-scale datasets by scRNA-seq and MERFISH. The atlas is hierarchically organized into four nested levels of classification: 34 classes, 338 subclasses, 1,201 supertypes and 5,322 clusters. We systematically analyzed the neuronal, non-neuronal, and immature neuronal cell types across the brain and identified a high degree of correspondence between transcriptomic identity and spatial specificity for each cell type. The study uncovered extensive heterogeneity in neurotransmitter and neuropeptide expression and co-expression patterns in different cell types, suggesting myriad modes of intercellular communications. We also found that transcription factors are major determinants of cell type classification in the adult mouse brain and identified a combinatorial transcription factor code that defines cell types across all parts of the brain. This study reveals extraordinary cellular diversity and underlying rules of brain organization. It establishes a benchmark reference atlas and a foundational resource for deep and integrative investigations of cellular and circuit function, development, and evolution of the mammalian brain.
Hongkui Zeng is Executive Vice President and Director of Allen Institute for Brain Science. Since joining the Allen Institute in 2006, she has led several efforts to develop and operate high-throughput pipelines to generate large-scale, open-access datasets and tools to accelerate neuroscience discovery. Her current research interests are in understanding neuronal diversity and connectivity in the mouse brain-wide circuits and how different cell types work together to process and transform information. Through her leadership of multiple scientific teams at the Allen Institute for Brain Science, she has built several research programs using transcriptomic, connectomic and multimodal approaches to characterize and classify the wide variety of cell types that constitute the mammalian brain, laying the foundation for unraveling the cell type basis of brain function. Her work has led to widely adopted community resources and standards, including transgenic mouse lines, Allen Mouse Brain Connectivity Atlas, the Common Coordinate Framework (CCF), and the brain-wide transcriptomic cell type taxonomy and atlas.
Zeng received her Ph.D. in molecular and cell biology from Brandeis University, where she studied the molecular mechanisms of the circadian clock in fruit flies. As a postdoctoral fellow at Massachusetts Institute of Technology, she studied the molecular and synaptic mechanisms underlying hippocampus-dependent plasticity and learning. She has received many honors, including the 2016 AWIS Award for Scientific Advancement, the 2018 Gill Transformative Investigator Award, and the 2023 Pradel Research Award from the National Academy of Sciences. She has served on multiple committees and advisory boards, including the Society for Neuroscience Program Committee, the Advisory Board of Cell and Neuron, and the National Advisory Mental Health Council. She is an elected member of the National Academy of Sciences and the National Academy of Medicine.
Google Scholar: https://scholar.google.com/citations?user=PclBFdMAAAAJ&hl=en