Decoding the immune checkpoint signatures in human atherosclerosis: implications of type 2 diabetes and lipid-lowering intervention
RNA-seq (gene expression) · human · 49 samples
- What it is
- RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
- Organism
- Homo sapiens (human)
- Samples
- 49 samples: 12 × CEA_scRNAseq vs 12 × PBMC_scRNAseq_NT vs 12 × PBMC_scRNAseq_LL vs 3 × LAD_scRNAseqGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Immune checkpoint landscape of human atherosclerosis and influence of cardiometabolic factorsNature cardiovascular research · 2024
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE246318_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.
Also filed as BioProject PRJNA1032539 and SRA study SRP468579. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 39 samples.
How we read this: 4 replicated groups read from 49 sample titles; they account for 39 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM7866650 4h_RNA
- GSM7866651 4h_ADT
- GSM7866652 4h_HTO
- GSM7866653 24h_RNA
- GSM7866654 24h_ADT
- GSM7866655 24h_HTO
- GSM8393425 CEA_scRNAseq_1 CEA_scRNAseq
- GSM8393426 CEA_scRNAseq_2 CEA_scRNAseq
- GSM8393427 CEA_scRNAseq_3 CEA_scRNAseq
- GSM8393428 CEA_scRNAseq_4 CEA_scRNAseq
- GSM8393429 CEA_scRNAseq_5 CEA_scRNAseq
- GSM8393430 CEA_scRNAseq_6 CEA_scRNAseq
- GSM8393431 CEA_scRNAseq_7 CEA_scRNAseq
- GSM8393432 CEA_scRNAseq_8 CEA_scRNAseq
- GSM8393433 CEA_scRNAseq_9 CEA_scRNAseq
- GSM8393434 CEA_scRNAseq_10 CEA_scRNAseq
- GSM8393435 CEA_scRNAseq_11 CEA_scRNAseq
- GSM8393436 CEA_scRNAseq_12 CEA_scRNAseq
- GSM8395208 LAD_scRNAseq_1 LAD_scRNAseq
- GSM8395209 LAD_scRNAseq_2 LAD_scRNAseq
- GSM8395210 LAD_scRNAseq_4 LAD_scRNAseq
- GSM8395214 MousePlaque_LL_male, mRNA
- GSM8395215 MousePlaque_LL_male, HTO
- GSM8395216 MousePlaque_LL_female, mRNA
- GSM8395217 MousePlaque_LL_female, HTO
- GSM8397940 PBMC_scRNAseq_NT_1 PBMC_scRNAseq_NT
- GSM8397941 PBMC_scRNAseq_LL_1 PBMC_scRNAseq_LL
- GSM8397942 PBMC_scRNAseq_NT_2 PBMC_scRNAseq_NT
- GSM8397943 PBMC_scRNAseq_LL_2 PBMC_scRNAseq_LL
- GSM8397944 PBMC_scRNAseq_NT_3 PBMC_scRNAseq_NT
- GSM8397945 PBMC_scRNAseq_LL_3 PBMC_scRNAseq_LL
- GSM8397946 PBMC_scRNAseq_NT_4 PBMC_scRNAseq_NT
- GSM8397947 PBMC_scRNAseq_LL_4 PBMC_scRNAseq_LL
- GSM8397948 PBMC_scRNAseq_NT_5 PBMC_scRNAseq_NT
- GSM8397949 PBMC_scRNAseq_LL_5 PBMC_scRNAseq_LL
- GSM8397950 PBMC_scRNAseq_NT_6 PBMC_scRNAseq_NT
- GSM8397951 PBMC_scRNAseq_LL_6 PBMC_scRNAseq_LL
- GSM8397952 PBMC_scRNAseq_NT_7 PBMC_scRNAseq_NT
- GSM8397953 PBMC_scRNAseq_LL_7 PBMC_scRNAseq_LL
- GSM8397954 PBMC_scRNAseq_NT_8 PBMC_scRNAseq_NT
+ 9 more — browse all 49 samples with per-sample file links →
- GSE287736 Sex-specific KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes 138 samples shares atherosclerosis
- GSE287688 Sex-specific KDM6A-HNF4A-CREBH network controls lipoprotein cholesterol metabolism and atherosclerosis via epigenetic reprograming of hepatocytes [RNA-seq] 68 samples shares atherosclerosis
- GSE326269 Hepatic HuR protects against atherosclerosis through bile acid production 38 samples shares atherosclerosis
- GSE309462 Endothelial BMP6 Drives Hemodynamic-Dependent VSMCs Calcification in Carotid Atherosclerosis 14 samples shares atherosclerosis
- GSE292019 Phosphorylation of CSDE1 drives ribosome interactions with implications for cancer malignancy 4 samples shares implications
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.