Macrophage-derived KIF13B acts as a potential therapeutic target in atherosclerosis by enhancing MERTK-mediated efferocytosis [RNA-Seq]
RNA-seq (gene expression) · mouse · 12 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
- Mus musculus (mouse)
- Samples
- 12 samples: 3 × WT vs 3 × KO vs 3 × WA vs 3 × KAGroups 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.
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 GSE298492_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 PRJNA1269541 and SRA study SRP588624. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 12 samples.
How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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.
- GSM9015436 WT_1 WT
- GSM9015437 WT_2 WT
- GSM9015438 WT_3 WT
- GSM9015439 KO_1 KO
- GSM9015440 KO_2 KO
- GSM9015441 KO_3 KO
- GSM9015442 WA_1 WA
- GSM9015443 WA_2 WA
- GSM9015444 WA_3 WA
- GSM9015445 KA_1 KA
- GSM9015446 KA_2 KA
- GSM9015447 KA_3 KA
- GSE269426 Integrated multi-omic high-throughput strategies across-species identified potential key diagnostic, prognostic, and therapeutic targets for atherosclerosis under high glucose conditions 16 samples shares atherosclerosis, therapeutic, potential
- GSE319375 Trained immunity of alveolar macrophages promotes injury resolution by enhancing KLF4-MERTK-mediated efferocytosis [2] 18 samples shares mertk-mediated, efferocytosis, enhancing
- GSE297831 Imidazole propionate is an early biomarker and therapeutic target for atherosclerosis [bulk RNA-seq] 27 samples shares atherosclerosis, therapeutic
- GSE255321 Therapeutic effects of FGF21 mimetic bFKB1 on MASH and atherosclerosis in Ldlr-/-.Leiden mice 24 samples shares atherosclerosis, therapeutic
- GSE239602 Single-cell and spatial transcriptomics uncovers the role of senescent vascular cells in pathological arterial remodeling during murine atherosclerosis 82 samples shares atherosclerosis
- GSE254396 Single-cell and bulk RNA sequencing of mouse atherosclerosis disease stage course [TRAP-seq] 71 samples shares atherosclerosis
- GSE254395 Single-cell and bulk RNA sequencing of mouse atherosclerosis disease stage course [adipose_liver_bulkRNA] 48 samples shares atherosclerosis
- GSE322757 IL-17RC signaling connects intestinal microbiota and neuroimmune interactions in atherosclerosis 42 samples shares atherosclerosis
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.