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| Status |
Public on Nov 28, 2023 |
| Title |
fenster2020-GCGTAAGA |
| Sample type |
SRA |
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| Source name |
nucleus accumbens
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| Organism |
Macaca mulatta |
| Characteristics |
Sex: M
weight (kg): 7.2
treatment: morphine
tissue: nucleus accumbens
date of_sacrifice: 8/23/2019
date of_birth: 5/7/2023
age (year): 12.3
drug history: ~1500 mg/kg morphine (2018)
pair: A
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| Treatment protocol |
Each morphine-treated subject was trained to come to the front of the enclosure for twice daily intramuscular (IM) injections (<0.5 cc) of morphine sulfate (NIDA Drug Supply Program) dissolved in 0.9% saline. Injections were administered at 09:30 and 17:00 hours. A gradual dosing escalation method (0.5 log unit increase every 3 days) was used to achieve the terminal dosage of 9 mg/kg/day (i.e., 4.5 mg/kg, BID). This dosage was selected to produce moderate opioid dependence128,129. All subjects received approximately 1500 mg of morphine over the 5-6-month period of chronic dosing. On the last day and approximately 3 hrs following the last IM morphine injection, each subject received an IM injection of ketamine (10 mg/kg) followed by 5.0 ml IV of a pentobarbital-based euthanasia solution (Beuthanasia-D).
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| Extracted molecule |
total RNA |
| Extraction protocol |
After sacrifice, brains were rapidly dissected into slabs and frozen on metal plates in liquid nitrogen vapor. Time between animal sacrifice and tissue freezing was between 1-2 hours for all animals. Brains were kept stored at -80 C until punching, when they were punched on a microtome with guidance from a macaque anatomist (Dr. S. Haber). Punches were stored at -80 C until the day of nuclear isolation and encapsulation.
Nuclei were isolated as in previous studies130, with minor modifications. Punches were placed into buffer HB (0.25 M sucrose, 25 mM KCl, 5 mM MgCl2, 20 mM Tricine-KOH pH 7.8, 1 mM DTT, 0.15 mM spermine, 0.5mM spermidine, protease inhibitors) and placed in a Dounce homogenizer for 10 strokes each with loose and tight pestles. A 5% IGEPAL solution was added to a final concentration of 0.3% followed by five additional dounce strokes, then the lysate was filtered through a 40-μm strainer. Nuclei were mixed with an equal volume of 50% iodixanol and then layered on top of an iodixanol gradient of 40% and 30% layers in a 2 mL dolphin microcentrifuge tube. Nuclei were spun by centrifuging at 10,000 x g for 4 min at 4°C and then collected by aspiration at the interface of the 30% and 40% iodixanol layers. Nuclear concentration and prep quality were ascertained by loading on a hemocytometer and were diluted to a concentration of 80-100K and 15% iodixanol with Buffer HB prior to loading on InDrops platform. Single-nuclei suspensions were encapsulated into droplets, lysed, and the RNA within each droplet was reverse-transcribed using a unique nucleotide barcode as described previously131. Approximately 6,000 nuclei, in two batches of 3,000 nuclei each were processed per library and sequenced on Illumina NovaSeq 6000 chips (at a density of approximately 20,000 reads/nucleus).
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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 |
inDrops v3
S6_2
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| Data processing |
The demultiplexing was performed using a custom shell script. Reads were aligned to the rheMac10 genome and counted at cell and gene level with STARsolo using a reference annotation lifted from the human genome to the rheMac10 genome.
To combine cells together across samples, we normalize the UMI counts with the variance-stabilizing SCTransform and glmGamPoi on each sample 7,8 and jointly embed cells across samples with reciprocal PCA integration 6 as outlined in https://satijalab.org/seurat/articles/integration_rpca.html. In this joint embedding, we over-clustered the dataset with FindClusters(algorithm = 2, resolution = 1)and removed any cluster with more than 10% of cells flagged by miQC, scds, or DropletQC as low-quality biased-clusters in the data.
We annotated our cells from the human striatum to a recently published high-resolution snRNA-seq reference dataset of the non-human primate striatum 9 using Seurat v4. We downloaded the monkey snRNA-seq processed, annotated gene UMI counts for all cells and MSNs from GSE167920. He, Kleyman et al. had aligned the snRNA-seq reads to the rheMac10 genome using the GRCh38 gene annotation liftover to rheMac10, so the gene-wise labels represent the UMI counts on the rheMac10 genome most orthologous to human. For both full nuclei and MSN subset datasets, we re-processed the macaque cells with SCTransform , glmGamPoi, and reciprocal PCA with default parameters as above to enable label transfer using the most recent integration algorithms in Seurat.
To transfer cell annotations from the reference macaque striatum dataset to the human striatum cells, we perform two label transfers at increasing resolutions: one with all cells and another with just MSNs. As He, Kleyman et al. described, the differences between transcriptionally and anatomically distinct MSN subtypes are subtle, so we split the annotations into two steps to optimally annotate the cells. The first label transfers the cell classes (Oligodendrocytes, MSNs, Interneurons, etc.) from the macaque to the human dataset with the Seurat functions FindTransferAnchors(reduction = ‘rpca’) and TransferData. Next, we identified cells or cell clusters that were labeled as MSNs and transferred MSN subtype labels (D1.Striosome, D2.Striomsome, etc.) from the macaque to human datasets. We filtered out cells where the cell class or cell subtype labels have max prediction scores less than 0.5 as these tend to represent noisy predictions due to low quality cells from either datasets. We confirmed accurate label transfer at the cell class and cell subtype levels with published marker genes and similar proportions across subjects and samples.
Even with the robust cutoffs that we applied to this dataset to remove likely low quality or doublet cells, we find a residual subset of the data that contain these cells. Upon clustering, doublet cells tend to project into the UMAP space as long streaks between two well-defined cell types. Low-quality cell types would project into the UMAP space as amorphous cell types without clear boundaries. Using these embedding features, we selected these clusters with Seurat’s FindClusters(resolution = 1) function, confirmed that they have the indicative QC metrics, and removed them from analyses.
Assembly: rheMac10
Supplementary files format and content: Annotated seurat h5 object with raw, processed, and integrated gene counts by cell aggregating samples across the entire dataset
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| Submission date |
May 23, 2023 |
| Last update date |
Nov 28, 2023 |
| Contact name |
BaDoi Nguyen Phan |
| E-mail(s) |
badoi.phan@pitt.edu
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| Organization name |
Carnegie Mellon University
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| Department |
Computational Biology
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| Lab |
Pfenning Lab
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| Street address |
5000 Forbes Ave
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| City |
Pittsburgh |
| State/province |
PA |
| ZIP/Postal code |
15213 |
| Country |
USA |
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| Platform ID |
GPL27943 |
| Series (2) |
| GSE233278 |
Single nucleus RNA-seq of the rhesus macaque nucleus accumbens after chronic morphine exposure |
| GSE233279 |
Single nuclei transcriptomics of human and monkey striatum implicates DNA damage, neuroinflammation, and neurodegeneration signaling in opioid use disorder |
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| Relations |
| BioSample |
SAMN35341094 |
| SRA |
SRX20489404 |
| 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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