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Sample GSM5836875

Query DataSets for GSM5836875

Status

Public on Dec 13, 2022

Title

AMBLA1 snRNA-seq

Sample type

SRA

Source name

Amygdala

Organism

Mus musculus

Characteristics

age: Adult
tissue: Amygdala
molecule: nuclear RNA

Treatment protocol

Without any treatment

Growth protocol

Human samples were from postmortem donors with very short postmortem interval (within 6 hours), and macaque, mouse and chicken amygdala were freshly dissected.All the fresh dissected sample were quick-frozen by liquied nitrogen and stored for nuclei isolation

Extracted molecule

total RNA

Extraction protocol

Frozen tumor tissue was homogenized into small cell pellets using a glass tissue grinder in ice-cold EZ buffer and incubated on ice for 5 min. Then nuclei were centrifuged at 500 × g for 5 min at 4 °C. Repeat the wash and centrifuge step again. After that, nuclei were resuspended with PBS, and Debris Removal Solution (MACS) was added to effectively remove cell debris. Isolated nuclei were suspended in Nuclei Suspension Buffer (NSB; consisting of 1× PBS, 0.01% BSA and 0.1% RNase inhibitor. A final nuclei suspension was filtered through a 35-μm cell strainer (Corning, Cat #352235) and used for single-cell libraries preparation.
Single nuclei were processed using Chromium Single V3 Chemistry Library Kits (10× Genomics) and according to the manufacturer’s instructions.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Description

10X genomic snRNA-seq

Data processing

All snRNA-seq raw fastq data were processed through Cell Ranger Single-Cell Software Suit (v3.1.0), which performed sequencing reads alignment and gene expression quantification. The sequencing reads from human were aligned to GRCh38 human reference genome, while mouse, monkey (macaque mulatta) and chicken sequencing reads were aligned to mm10-3.0.0, Macaca_mulatta.Mmul_10.102, GRCg6a species reference genome respectively.
We used CellRanger aggr pipeline to merge cells from the same species under different samples, with parameter “normalize=none”.
We computed the highest density interval of total UMI count (total_counts), detected genes number () and the percentage of mitochondrial gene expressed, by using hdi() function from HDInterval R packages with parameter “credMass=0.98”, and filtered cells outside of the confidence interval
Then, we removed potential doublets reported by Scrublet, with default parameter. With strict quality control procedure, we retained 91,699 nuclei with a median of 9417 unique molecular identifiers (UMIs) and 3617 genes in human datasets, 45,626 nuclei with a median of 4631 UMIs and 2373 genes in monkey datasets, 54,186 nuclei with a median of 5020 UMIs and 2316 genes in mouse datasets, while 12,460 nuclei with a median of 3660 UMIs and 1716 genes in chicken
We performed normalize_total and log1p function in SCANPY to normalize (library-size correct) and logarithmize the raw count matrix (filtered after quality control). Next, we detected highly variable genes (HVGs) using scanpy.pp.highly_varible_genes function with parameter “min_mean=0.0125, max_mean=3, min_disp=0.5” and selected all the HVGs for downstream analysis. Finally, we performed scanpy.pp.regress_out function to regress out effects of total counts and mitochondrial genes expression percentage.
Genome_build: GRCh38;Macaca_mulatta.Mmul_10.102;GRCg6a;mm10-3.0.0,
Supplementary_files_format_and_content: metadata.csv.gz file was metadata for each datasets. And normalized_count.csv was normalized gene expression file.

Submission date

Jan 26, 2022

Last update date

Dec 13, 2022

Contact name

qian qian zhang

E-mail(s)

zhangqianqian171@ibp.ac.cn

Phone

15611792311

Organization name

Institute of Biophysics