Impaired signaling pathways on Berardinelli–Seip congenital lipodystrophy macrophages during Leishmania infantum infection
RNA-seq (gene expression) · human · 36 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
- 36 samples: 10 conditions, mostly in triplicateGroups 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
- Impaired signaling pathways on Berardinelli-Seip congenital lipodystrophy macrophages during Leishmania infantum infectionScientific reports · 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 GSE210555_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 PRJNA866148 and SRA study SRP389980. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 36 samples.
How we read this: 10 replicated groups read from 36 sample titles; they account for 36 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.
- GSM6432399 RNA-seq: Infected AGPAT2mut 1 Infected AGPAT2mut
- GSM6432400 RNA-seq: Non-Infected AGPAT2mut 1 Non-Infected AGPAT2mut
- GSM6432401 RNA-seq: Infected AGPAT2mut 2 Infected AGPAT2mut
- GSM6432402 RNA-seq: Non-Infected AGPAT2mut 2 Non-Infected AGPAT2mut
- GSM6432403 RNA-seq: Infected AGPAT2mut 3 Infected AGPAT2mut
- GSM6432404 RNA-seq: Non-Infected AGPAT2mut 3 Non-Infected AGPAT2mut
- GSM6432405 RNA-seq: Infected BSCL2mut 4 Infected BSCL2mut
- GSM6432406 RNA-seq: Non-Infected BSCL2mut 4 Non-Infected BSCL2mut
- GSM6432407 RNA-seq: Infected BSCL2mut 5 Infected BSCL2mut
- GSM6432408 RNA-seq: Non-Infected BSCL2mut 5 Non-Infected BSCL2mut
- GSM6432409 RNA-seq: Infected BSCL2mut 6 Infected BSCL2mut
- GSM6432410 RNA-seq: Non-Infected BSCL2mut 6 Non-Infected BSCL2mut
- GSM6432411 RNA-seq: Infected BSCL2het 7 Infected BSCL2het
- GSM6432412 RNA-seq: Non-Infected BSCL2het 7 Non-Infected BSCL2het
- GSM6432413 RNA-seq: Infected BSCL2het 8 Infected BSCL2het
- GSM6432414 RNA-seq: Non-Infected BSCL2het 8 Non-Infected BSCL2het
- GSM6432415 RNA-seq: Infected BSCL2het 9 Infected BSCL2het
- GSM6432416 RNA-seq: Non-Infected BSCL2het 9 Non-Infected BSCL2het
- GSM6432417 RNA-seq: Infected BSCL2het 10 Infected BSCL2het
- GSM6432418 RNA-seq: Infected BSCL2het 11 Infected BSCL2het
- GSM6432419 RNA-seq: Infected WT 12 Infected WT
- GSM6432420 RNA-seq: Non-Infected WT 12 Non-Infected WT
- GSM6432421 RNA-seq: Infected WT 13 Infected WT
- GSM6432422 RNA-seq: Non-Infected WT 13 Non-Infected WT
- GSM6432423 RNA-seq: Infected WT 14 Infected WT
- GSM6432424 RNA-seq: Non-Infected WT 14 Non-Infected WT
- GSM6432425 RNA-seq: Infected WT 15 Infected WT
- GSM6432426 RNA-seq: Non-Infected WT 15 Non-Infected WT
- GSM6432427 RNA-seq: Infected WT LV-rec 16 Infected WT LV-rec
- GSM6432428 RNA-seq: Non-Infected WT LV-rec 16 Non-Infected WT LV-rec
- GSM6432429 RNA-seq: Infected WT LV-rec 17 Infected WT LV-rec
- GSM6432430 RNA-seq: Non-Infected WT LV-rec 17 Non-Infected WT LV-rec
- GSM6432431 RNA-seq: Infected WT LV-rec 18 Infected WT LV-rec
- GSM6432432 RNA-seq: Non-Infected WT LV-rec 18 Non-Infected WT LV-rec
- GSM6432433 RNA-seq: Infected WT LV-rec 19 Infected WT LV-rec
- GSM6432434 RNA-seq: Non-Infected WT LV-rec 19 Non-Infected WT LV-rec
- GSE313992 Comparative Immunotoxicity and Transcriptomic Profiling of Seven PFAS in THP-1 Macrophages 252 samples shares macrophages
- GSE281471 Targeting CD206+ macrophages disrupts the establishment of a key anti-tumor immune axis [Bulk RNA-seq] 243 samples shares macrophages
- GSE269009 RNA-seq of monocyte derived macrophages and dendritic cells in a Peruvian Tuberculosis cohort 227 samples shares macrophages
- GSE330843 The CRAC channel inhibitor Auxora interrupts inflammatory circuits between alveolar macrophages and T cells in patients with viral pneumonia 88 samples shares macrophages
- GSE268175 Research on Differences and Immune Infiltration-Related Macrophages Between Non-Small Cell Lung Cancer and Adjacent Tissues 67 samples shares macrophages
- GSE307755 Direct contact between iPSC-derived macrophages and hepatocytes drives reciprocal acquisition of Kupffer cell identity and hepatocyte maturation 53 samples shares macrophages
- GSE323984 MEF2C controls lysosomal and lipid clearance programs linked to Alzheimer’s disease risk in macrophages [bulkRNAseq_WTC11_iMGL] 48 samples shares macrophages
- GSE222412 Genome-wide gene expression profiling of macrophages treated with novel 1,2,4-oxadiazole scaffold-based compounds 48 samples shares macrophages
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.