Cortex Velmeshev Cortex Sample Track Settings
 
Cerebral cortex RNA binned by biosample from Velmeshev et al 2019

Track collection: Cerebral cortex single cell data from Velmeshev et al 2019

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+  All tracks in this collection (5)

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Label: Name or ID of item, ideally both human readable and unique    Alternative name for item   

Log10(x+1) transform:    View limits maximum: UMI/cell (range 0-10000)

Categories:  
 1823 BA24
 4341 BA24
 4341 BA46
 4849 BA24
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 5144 PFC
 5163 BA24
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 5278 PFC
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 5387 BA9
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 5419 PFC
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 5538 PFC Nova
 5554 BA24
 5565 BA24
 5565 BA9
 5577 BA9
 5841 BA9
 5864 BA9
 5879 PFC Nova
 5893 BA24
 5893 PFC
 5936 PFC Nova
 5939 BA24
 5939 BA9
 5945 PFC
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 6032 BA24
 6033 BA24
 6033 BA9
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Data last updated at UCSC: 2021-01-09 23:45:45

Description

This track displays data from Single-cell genomics identifies cell type-specific molecular changes in autism. Single-nucleus RNA sequencing (snRNA-seq) was performed on post-mortem cortical tissue samples from patients with autism spectrum disorder (ASD) as well as control donors. A total of 17 cell clusters were identified using known cell type markers found in Velmeshev et al., 2019.

This track collection contains five bar chart tracks of RNA expression in the human cerebral cortex where cells are grouped by cell type (Cortex Cells), diagnosis (Cortex Diagnosis), donor (Cortex Donor), sample (Cortex Sample), and sex (Cortex Sex). The default track displayed is Cortex Cells.

Display Conventions

The cell types are colored by which class they belong to according to the following table.

Color Cell classification
neural
immune
endothelial
glia

Cells that fall into multiple classes will be colored by blending the colors associated with those classes. The colors will be purest in the Cortex Cells subtrack, where the bars represent relatively pure cell types. They can give an overview of the cell composition within other categories in other subtracks as well.

Method

Healthy cortical samples were taken from 16 controls (ages 4-22) without neurological disorders and 15 ASD patients (ages 7-21). A total of 41 post-mortem tissue samples were obtained from both the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). When present, subcortical white matter was removed prior to collection from cortical samples containing all layers of cortical grey matter. ASD and control samples were matched for sex and age and processed together to minimize batch effects. Nuclei were isolated from brain tissue using a glass dounce homogenizer in lysis buffer and then filtered twice through a 30 µm cell strainer. Next, samples were processed using 10x Genomics 3' library kit and the resulting single-nucleus libraries were pooled together and sequenced on an Illumina NovaSeq 6000. This process generated 104,559 single-nuclei gene expression profiles in total.

The cell/gene matrix and cell-level metadata was downloaded from the UCSC Cell Browser. The UCSC command line utility matrixClusterColumns, matrixToBarChart, and bedToBigBed were used to transform these into a bar chart format bigBed file that can be visualized. The coloring was done by defining colors for the broad level cell classes and then using another UCSC utility, hcaColorCells, to interpolate the colors across all cell types. The UCSC utilities can be found on our download server.

Data Access

The raw bar chart data can be explored interactively with the Table Browser, or the Data Integrator. For automated analysis, the data may be queried from our REST API. Please refer to our mailing list archives for questions, or our Data Access FAQ for more information.

Credit

Thanks to Dmitry Velmeshev and to the many authors who worked on producing and publishing this data set. The data were integrated into the UCSC Genome Browser by Jim Kent and Brittney Wick then reviewed by by Daniel Schmelter. The UCSC work was paid for by the Chan Zuckerberg Initiative.

References

Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, Bhaduri A, Goyal N, Rowitch DH, Kriegstein AR. Single-cell genomics identifies cell type-specific molecular changes in autism. Science. 2019 May 17;364(6441):685-689. PMID: 31097668; PMC: PMC7678724