A survey of human brain transcriptome diversity at the single cell level

By S. Darmanis, S. Sloan, Ye Zhang, M. Enge, Christine Caneda, L. Shuer, M. Hayden Gephart, B. Barres, Stephen R. Quake. Proceedings of the National Academy of Sciences of the United States of America, 2015

1,332 citations recorded by the source corpus.

Significance The brain comprises an immense number of cells and cellular connections. We describe the first, to our knowledge, single cell whole transcriptome analysis of human adult cortical samples. We have established an experimental and analytical framework with which the complexity of the human brain can be dissected on the single cell level. Using this approach, we were able to identify all major cell types of the brain and characterize subtypes of neuronal cells. We observed changes in neurons from early developmental to late differentiated stages in the adult. We found a subset of adult neurons which express major histocompatibility complex class I genes and thus are not immune privileged. The human brain is a tissue of vast complexity in terms of the cell types it comprises. Conventional approaches to classifying cell types in the human brain at single cell resolution have been limited to exploring relatively few markers and therefore have provided a limited molecular characterization of any given cell type. We used single cell RNA sequencing on 466 cells to capture the cellular complexity of the adult and fetal human brain at a whole transcriptome level. Healthy adult temporal lobe tissue was obtained during surgical procedures where otherwise normal tissue was removed to gain access to deeper hippocampal pathology in patients with medical refractory seizures. We were able to classify individual cells into all of the major neuronal, glial, and vascular cell types in the brain. We were able to divide neurons into individual communities and show that these communities preserve the categorization of interneuron subtypes that is typically observed with the use of classic interneuron markers. We then used single cell RNA sequencing on fetal human cortical neurons to identify genes that are differentially expressed between fetal and adult neurons and those genes that display an expression gradient that reflects the transition between replicating and quiescent fetal neuronal populations. Finally, we observed the expression of major histocompatibility complex type I genes in a subset of adult neurons, but not fetal neurons. The work presented here demonstrates the applicability of single cell RNA sequencing on the study of the adult human brain and constitutes a first step toward a comprehensive cellular atlas of the human brain.

Subfields: Immunology, Molecular Biology, Neurology

View Paper: https://www.semanticscholar.org/paper/7298ce16d07293954600a1e993deed6724a309ef

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Immunology

In Molecular Biology

In Neurology

Citations in the corpus

Integrative functional genomic analysis of human brain development and neuropsychiatric risks (2018, positive): “Confirming the validity of our prenatal scRNA-seq and adult snRNA-seq data, alignment of our prenatal data with cells from a previously published dataset (9) consisting of mid-fetal and adult human neocortical cells yielded similar relationships between prenatal and adult cell types (fig.”
Using neural networks for reducing the dimensions of single-cell RNA-Seq data (2017, background): “An important and exciting application of single cell sequencing is the ability to identify and characterize new cell types and cell states (4,5).”
In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus (2016, positive): “In comparison, single cell RNA-seq measurements are 5–20% efficient based on spike-in controls and in situ sequencing is less than 1% efficient (Darmanis et al., 2015; Klein et al., 2015; Lee et al., 2014; Macosko et al., 2015; Tasic et al., 2016; Zeisel et al., 2015; Ståhl et al., 2016).”
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data (2022, methodology): “The RNA-seq datasets used in the benchmarking originated from various tissues, including human liver 10 , pancreas 24 , peripheral blood mononuclear cells (PBMCs) 25 , brain 26 , as well as mouse lung 27 and retina samples 20 .”
Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain (2017, positive): “For comparison with single-cell RNA-seq data from the human temporal lobe 28 , gene count data was obtained from GEO (GSE67835), normalized using Seurat as mentioned above using a minimum cutoff of 1000 genes detected.”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “A number of scRNA-seq studies normalize for sequencing depth within a cell by calculating TPM [14, 15, 23, 32, 33] or RPKM/FPKM [34 – 37].”
An integrative model of cellular states, plasticity and genetics for glioblastoma (2019, positive): “To this end, we obtained scRNA-seq data for non-malignant brain cells from multiple sources (Darmanis et al., 2015; Nowakowski et al., 2017; Pollen et al., 2015; Tirosh et al., 2016b).”
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex (2020, background): “…increased identification of molecularly-defined cell types in the human brain and implicated unique cell classes in risk for specific brain disorders (Darmanis et al., 2015; Hodge et al., 2019; Lake et al., 2016, 2018; Mathys et al., 2019; Nowakowski et al., 2017; Velmeshev et al., 2019).”
Comprehensive functional genomic resource and integrative model for the human brain (2018, methodology): “First, we used standard pipelines to uniformly process single-cell RNA-seq data from PsychENCODE, in conjunction with other singlecell studies on the brain (14, 16, 20).”