Dataset Information

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Dataset Information	Value
Dataset ID	GSE130955_GPL16791_raw
Title	Global skin gene expression analysis of early diffuse cutaneous systemic sclerosis shows a prominent innate and adaptive inflammatory profile
Summary	RNA from skin biopsies from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by nextGen RNA sequencing
Overall Design	3_ or 4_mm diameter punch biopsies were obtained from the forearm skin and immersed in RNAlater solution (Qiagen). RNA was extracted using miRNeasy Mini kits (Qiagen). cDNA libraries were prepared using the Illumina TruSeq stranded Total RNA Library Prep Gold kit, loaded on cBot (Illumina) at a final concentration of 10 pM to perform cluster generation, followed by 2 x 76 bp paired_end sequencing on HiSeq 2500 (Illumina), generating on average around 50 million reads per sample.
Number of samples	91
Publication Link	Link
Abstract	Objectives: Determine global skin transcriptome patterns of early diffuse systemic sclerosis (SSc) and how they differ from later disease. Methods: Skin biopsy RNA from 48 patients in the Prospective Registry for Early Systemic Sclerosis (PRESS) cohort (mean disease duration 1.3 years) and 33 matched healthy controls was examined by next_generation RNA sequencing. Data were analysed for cell type_specific signatures and compared with similarly obtained data from 55 previously biopsied patients in Genetics versus Environment in Scleroderma Outcomes Study cohort with longer disease duration (mean 7.4 years) and their matched controls. Correlations with histological features and clinical course were also evaluated. Results: SSc patients in PRESS had a high prevalence of M2 (96%) and M1 (94%) macrophage and CD8 T cell (65%), CD4 T cell (60%) and B cell (69%) signatures. Immunohistochemical staining of immune cell markers correlated with the gene expression_based immune cell signatures. The prevalence of immune cell signatures in early diffuse SSc patients was higher than in patients with longer disease duration. In the multivariable model, adaptive immune cell signatures were significantly associated with shorter disease duration, while fibroblast and macrophage cell type signatures were associated with higher modified Rodnan Skin Score (mRSS). Immune cell signatures also correlated with skin thickness progression rate prior to biopsy, but did not predict subsequent mRSS progression. Conclusions: Skin in early diffuse SSc has prominent innate and adaptive immune cell signatures. As a prominently affected end organ, these signatures reflect the preceding rate of disease progression. These findings could have implications in understanding SSc pathogenesis and clinical trial design.
Disease	Systemic Sclerosis [Scleroderma], Normal
Tissue	Skin
Drug	None
Cell Lines	None
Cell Type	None
Organism	Homo Sapiens
Custom Curation	experiment_type, kw_curated_disease, treat_group, donor_identifier, patient_medication

Processing Information

The section provides processing details for the data coming from source.

Data Processing	The data was processed at the user level, deviating from our standard pipeline procedures

QUALITY ASSURANCE CONTENT

Metadata Information
Feature Identifier
Data Matrix
Histogram for Expression Distribution
Boxplot for samplewise expression distribution
Barplot for number of genes expressing

1. Metadata Information

Metadata information	Value
Polly curated metadata fields are present at dataset level ℹ	Pass
Polly curated metadata fields are present at sample level ℹ	Pass
Polly curated metadata fields are present in gct file ℹ	Pass
Publication Link is provided ℹ	Pass
Publication Link is valid ℹ	Pass
Dataset-Level vs. Sample-Level Metadata: concordance check ℹ	Pass
Custom fields are present and valid ℹ	Pass

2. Feature Identifier

Feature Identifier Check	Value
Ensembl Gene IDs present ℹ	Pass
Ensembl Gene IDs are valid ℹ	Pass
Gene Symbol present ℹ	Pass
Gene Symbol are valid ℹ	Pass

3. Data Matrix

Data Matrix	Value
Data Matrix Values Valid ℹ	Pass
Data Matrix Range ℹ	0.00 to 1248732.25

4. Histogram for Expression Distribution

Figure 1: Histogram showing frequency and distribution of TPM normalised expression values across all samples.

The histogram displays data distribution from counts matrix. The Raw count values are TPM normalized and log2(x+1) transformed for clarity.

5. Sample wise distribution of expression values using a boxplot.

Figure 2: Boxplot showing TPM expression values across all samples.

The boxplot displays sample-wise distribution of counts matrix. The Raw count values are TPM normalized and log2(x+1) transformed for clarity.

6. Sample Wise Distribution of Number of Genes Expressing Using a Barplot

Figure 3: Barplot showing the distribution of number of genes with expresion value equal to 0 per sample.

This barplot helps identify if there are any samples with significantly number of genes which are lowly expressed which may indicate low mapping of reads to the genome.

DATA EXPLORATION CONTENT

Polly's Curated Metadata Field Distribution
Source Metadata Field Distribution

1. Polly's Curated Metadata Field Distribution

Figure 1: The umap plot(s) represent different samples in a reduced dimensional space, with colors indicating the Polly standard and custom curated fields.

The plot(s) aid in understanding the biological differences between different samples as described by different metadata fields. Note: Umap plot for the raw counts will not be a reflective of correct distribution as the data requires normalisation

Figure 2: The sunburst plot(s) represent counts of different samples, with colors representing values from the Polly standard and custom curated fields.

The plot(s) aid in understanding the distribution of different samples as per the categorical metadata variables of Polly standard curated fields.

2. Source Metadata Field Distribution

Figure 3: The umap plot(s) represent different samples in a reduced dimensional space, with colors indicating the source metadata fields.

The plot(s) aid in understanding the biological differences between different samples as described by different metadata fields. Note: Umap plot for the raw counts will not be a reflective of correct distribution as the data requires normalisation

Figure 4: The sunburst plot represent counts of different samples, with colors representing values from the source.

The plot(s) aid in understanding the distribution of different samples as per the categorical metadata variables of source fields

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