Cortex Velmeshev Cortex Diagnosis Track Settings
Cerebral cortex RNA binned by ASD/control diagnosis from Velmeshev et al 2019

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

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Data last updated at UCSC: 2021-01-09 23:45:41


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

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.


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.


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.


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