A Molecular Atlas of Human Granulopoiesis
This dataset contains transcriptome (mRNA), proteome, and microRNA (miRNA) profiles across seven defined stages of human neutrophil development:
Samples: 4 healthy donors + 1 biological replicate
8,432 mRNA transcripts quantified across all maturation stages
Gene-level read counts quantified from RNA-seq. Ready for differential expression analysis with DESeq2 or edgeR.
CSV format
Download mrn_counts_raw.csvLog2-transformed count data with pseudocount. Useful for visualization and fold-change calculations.
CSV format
Download mrn_counts_log2.csvVariance-stabilized count values. Optimal for statistical analysis and clustering across samples.
CSV format
Download mrn_counts_vsn.csvTranscripts Per Million normalization. Best for comparing expression between genes within a sample.
CSV format
Download mrn_tpm_raw.csvLog2-transformed TPM values. Useful for visualizing expression patterns and calculating fold changes.
CSV format
Download mrn_tpm_lg2.csvVariance-stabilized TPM values. Best for multi-sample comparisons and integrative analysis.
CSV format
Download mrn_tpm_vsn.csv3,156 proteins quantified across all maturation stages
Label-free quantification (LFQ) intensities from MaxQuant. Primary protein abundance values before normalization.
CSV format
Download pro_raw.csvLog2-transformed LFQ values. Standard format for protein differential expression analysis.
CSV format
Download pro_log2.csvVariance-stabilized protein intensities. Optimal for multi-sample comparisons and clustering.
CSV format
Download pro_vsn.csv283 mature miRNA transcripts quantified across all maturation stages
Mature miRNA read counts from small RNA-seq. Starting point for miRNA analysis pipelines.
CSV format
Download mic_raw.csvLog2-transformed miRNA counts. Suitable for differential expression and target prediction analysis.
CSV format
Download mic_log2.csvVariance-stabilized miRNA expression. Best for integrative analysis with mRNA targets.
CSV format
Download mic_vsn.csvGene/protein-level quantification from standard pipelines (STAR/featureCounts for RNA-seq, MaxQuant for proteomics). These are not raw sequencing files but processed count matrices.
Normalizes for gene length and sequencing depth. Best for comparing gene expression levels within a sample.
Transforms data to stabilize variance across the entire range of expression values. Optimal for downstream statistical analysis and clustering.
Logarithmic transformation (base 2) with pseudocount addition. Makes data more normally distributed and facilitates fold-change calculations.
Accession: GSE294330
Contains raw RNA-seq and miRNA-seq data files
View on GEO
Accession: PXD063208
Contains raw mass spectrometry proteomics data
View on PRIDEIf you use this data in your research, please cite:
Hesse S, Mao J, Hadziahmetovic A, et al. (2025) A molecular atlas of human granulopoiesis. Nature Communications (in submission)
Data availability:
• Transcriptome & miRNA: GEO GSE294330
• Proteome: PRIDE PXD063208
• Interactive platform: www.granulopoiesis.com