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| Status |
Public on Oct 01, 2023 |
| Title |
Sheep_NeuN2 |
| Sample type |
SRA |
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| Source name |
Retina
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| Organism |
Ovis aries |
| Characteristics |
tissue: Retina
cell type: Retinal neurons
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| Treatment protocol |
None
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| Extracted molecule |
nuclear RNA |
| Extraction protocol |
Frozen retinal tissues were homogenized in a Dounce homogenizer in 1ml Tris-based lysis buffer with 0.1% NP-40. Nuclei were stained with NEUN/RBFOX3 and/or CHX10/VSX2, washed once, pelleted at 500g for 5min, resuspended in PBS/BSA and stained with DAPI. NEUN+ and/or CHX10+ DAPI+ single nuclei were collected using a flow cytometer. The sorted nuclei were pelleted again, resuspended in PBS/BSA solution, and adjusted to a concentration of 1000 nuclei/µL. ~12µL of the nuclei suspension was loaded into a 10X Chromium Single Cell Chip (10X Genomics, Pleasanton, CA) with a targeted recovery of 8000 nuclei.
Single nuclei libraries were generated using the Chromium 3’ V3.1 platform (10X Genomics, Pleasanton, CA) according to the manufacturer’s protocol. Briefly, single nuclei were partitioned into Gel-beads-in-EMulsion (GEMs) where nuclear lysis and barcoded reverse transcription of RNA would take place to yield full-length cDNA; this was followed by amplification, enzymatic fragmentation, and 5’ adaptor and sample index attachment to yield the final libraries.
Single nucleus RNA sequecning (10X Genomics)
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
Sheep_count_mat.csv
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| Data processing |
We used cellranger (v7.0, 10X Genomics) to align the sc- and snRNA-seq datasets, following manufacturer’s instructions. For each species, sequencing reads were demultiplexed into distinct samples and the .fastq.gz files corresponding to each sample were aligned to reference transcriptomes to obtain binary alignment map (.bam) files.
Assembly: Oar_rambouillet_v1.0
Supplementary files format and content: To include both exonic and intronic reads in the quantification of gene expression for each sample, regardless of cellular or nuclear origin, we applied velocyto to the corresponding .bam files. This generated two separate gene expression matrices (GEMs; genes x cells) for each sample, corresponding to “spliced” and “unspliced” reads. The two GEMs were summed element by element to obtain “total” GEM for each sample.
Supplementary files format and content: GEMs from different sample runs were combined (column-wise concatenated) to yield a full species’ GEM.
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| Submission date |
Jul 12, 2023 |
| Last update date |
Oct 01, 2023 |
| Contact name |
Joshua William Hahn |
| E-mail(s) |
[email protected]
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| Organization name |
UC Berkeley
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| Department |
Chemical and Biomolecular Engineering
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| Lab |
Karthik Shekhar
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| Street address |
Stanley Hall
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| City |
Berkeley |
| State/province |
CA |
| ZIP/Postal code |
94720 |
| Country |
USA |
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| Platform ID |
GPL27721 |
| Series (2) |
| GSE237211 |
Evolution of neuronal cell classes and types in the vertebrate retina [Sheep] |
| GSE237215 |
Evolution of neuronal cell classes and types in the vertebrate retina |
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| Relations |
| BioSample |
SAMN36418946 |
| SRA |
SRX20999211 |
| Supplementary data files not provided |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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