A novel method for characterizing cell-cell interactions at single-cell resolution reveals unique signatures in blood T cell-monocyte complexes during infection
Single-cell RNA-seq · human · 67 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
- Organism
- Homo sapiens (human)
- Samples
- 67 samples: 63 distinct titles, 4 of them repeatedGroups 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
- A novel method for characterizing cell-cell interactions at single-cell resolution reveals unique signatures in blood T cell-monocyte complexes during infectionbioRxiv : the preprint server for biology · 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 GSE273019_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1139790. Searching any of these in the dataset finder brings you back here.
How we read this: Read from 67 sample titles: 63 distinct titles with little repetition. 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.
- GSM8417002 PBMC, TT0003, Diagnosis, Doublet, scRNA-seq
- GSM8417003 PBMC, TT0003, End-treatment, Doublet, scRNA-seq
- GSM8417004 PBMC, TT0027, Diagnosis, Doublet, scRNA-seq
- GSM8417005 PBMC, TT0027, End-treatment, Singlet, scRNA-seq
- GSM8417006 PBMC, TT0119, Diagnosis, Singlet, scRNA-seq
- GSM8417007 PBMC, TT0119, End-treatment, Singlet, scRNA-seq
- GSM8417008 PBMC, TT0003, Diagnosis, Singlet, scRNA-seq
- GSM8417009 PBMC, TT0003, End-treatment, Singlet, scRNA-seq
- GSM8417010 PBMC, TT0027, Diagnosis, Singlet, scRNA-seq
- GSM8417011 PBMC, TT0027, End-treatment, Doublet, scRNA-seq
- GSM8417012 PBMC, TT0119, Diagnosis, Doublet, scRNA-seq
- GSM8417013 PBMC, TT0119, End-treatment, Doublet, scRNA-seq
- GSM8417014 PBMC, TT0027, Diagnosis, Other doublets, scRNA-seq
- GSM8417015 PBMC, TT0033, Diagnosis, Doublet, scRNA-seq
- GSM8417016 PBMC, TT0033, End-treatment, Doublet, scRNA-seq
- GSM8417017 PBMC, TT0069, Diagnosis, Doublet, scRNA-seq
- GSM8417018 PBMC, TT0069, End-treatment, Singlet, scRNA-seq
- GSM8417019 PBMC, TT0124, Diagnosis, Singlet, scRNA-seq
- GSM8417020 PBMC, TT0124, End-treatment, Singlet, scRNA-seq
- GSM8417021 PBMC, TT0033, Diagnosis, Singlet, scRNA-seq
- GSM8417022 PBMC, TT0033, End-treatment, Singlet, scRNA-seq
- GSM8417023 PBMC, TT0069, Diagnosis, Singlet, scRNA-seq
- GSM8417024 PBMC, TT0069, End-treatment, Doublet, scRNA-seq
- GSM8417025 PBMC, TT0124, End-treatment, Doublet, scRNA-seq
- GSM8417026 PBMC, TT0106, Diagnosis, Doublet, scRNA-seq
- GSM8417027 PBMC, TT0105, End-treatment, Doublet, scRNA-seq
- GSM8417028 PBMC, TT0109, Diagnosis, Doublet, scRNA-seq
- GSM8417029 PBMC, TT0109, End-treatment, Singlet, scRNA-seq
- GSM8417030 PBMC, TT0108, Diagnosis, Singlet, scRNA-seq
- GSM8417031 PBMC, TT0108, End-treatment, Singlet, scRNA-seq
- GSM8417032 PBMC, TT0106, Diagnosis, Singlet, scRNA-seq
- GSM8417033 PBMC, TT0105, End-treatment, Singlet, scRNA-seq
- GSM8417034 PBMC, TT0109, Diagnosis, Singlet, scRNA-seq
- GSM8417035 PBMC, TT0109, End-treatment, Doublet, scRNA-seq
- GSM8417036 PBMC, TT0108, Diagnosis, Doublet, scRNA-seq
- GSM8417037 PBMC, TT0108, End-treatment, Doublet, scRNA-seq
- GSM8417038 PBMC, TT0109, Diagnosis, Other doublets, scRNA-seq
- GSM8417039 PBMC, HaHi03_Hu_M1, scTCR-seq: TT0003, TT0027, TT0119
- GSM8417040 PBMC, HaHi03_Hu_M2, scTCR-seq: TT0003, TT0027, TT0119
- GSM8417041 PBMC, HaHi04_Hu_M1, scTCR-seq: TT0033, TT0069, TT0124
+ 27 more — browse all 67 samples with per-sample file links →
- GSE309834 Massively multiplex multi-modal chemical screens at single-cell resolution with DOGMA-plex 140 samples shares single-cell, resolution
- GSE233321 Single-cell map of the healthy human immune system across the lifespan reveals unique infant immune signatures 118 samples shares single-cell, signatures
- GSE277165 Single-cell transcriptomics and epigenomics point to CD58/CD59-CD2 interactions in controlling primary melanoma growth and immunity 20 samples shares interactions, single-cell
- GSE279392 Single-cell RNA and TCR sequencing from skin, blood and cerebral spinal fluid (CSF) samples from Degos disease patients 116 samples shares single-cell
- GSE196198 Longitudinal single-cell multiomics data for different severity of COVID-19 100 samples shares single-cell
- GSE254176 Dynamic changes of immune cells in systemic lupus erythematosus with nephritis using longitudinal peripheral blood single-cell RNA-seq 57 samples shares single-cell
- GSE332623 Immunological Differences in Atopic Dermatitis Across Age Groups: Insights from Single-Cell Multi-Omics 54 samples shares single-cell
- GSE320042 High-resolution and noninvasive profiling of the tumor microenvironment with spatial ecotypes 38 samples shares resolution
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.