Elucidata | Polly Verification Report Bulk RNA-seq

Dataset Information

This report has been verified by Polly as per framework version 1.0 Learn More

Dataset Information	Value
Dataset ID	GSE85531_GPL15456_raw
Title	Partial exhaustion of CD8 T cells and clinical response to teplizumab in new-onset type 1 diabetes.
Summary	Biologic agents active in other autoimmune settings have had variable effectiveness in newly diagnosed type 1 diabetes (T1D) where treatment across therapeutic targets is accompanied by transient stabilization of c-peptide levels in some patients, followed by progression at the same rate as in control groups. Why disparate treatments lead to similar clinical courses is currently unknown. Here, we use integrated systems biology and flow cytometry approaches to elucidate immunologic mechanisms associated with c-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody, teplizumab. This work is part of the Immune Tolerance Network ABATE study (Autoimmunity-Blocking Antibody for Tolerance in Recently Diagnosed Type 1 Diabetes); Data are also available through the ITN TrialShare portal: ITN027AIDB Study Data.
Overall Design	We performed bulk RNA-seq on 190 whole blood samples from visit months 0 (R: 14, NR: 16, C: 15, total: 45), 6 (R: 16, NR: 17, C: 15, total: 48), 12 (R: 13, NR: 14, C: 16, total: 43), and 24 (R: 17, NR: 20, C: 17, total: 54).
Number of samples	88
Publication Link	Link
Abstract	Biologic treatment of T1D typically results in transient stabilization of C-peptide levels (a surrogate for endogenous insulin secretion) in some patients, followed by progression at the same rate as in untreated control groups. Here, we used integrated systems biology and flow cytometry approaches with clinical trial blood samples to elucidate pathways associated with C-peptide stabilization in T1D subjects treated with the anti-CD3 monoclonal antibody teplizumab. We identified a population of CD8 T cells that accumulated in subjects with the best response to treatment (responders) and showed that these cells phenotypically resembled exhausted T cells by expressing high levels of the transcription factor EOMES, effector molecules, and multiple inhibitory receptors (IRs), including TIGIT and KLRG1. These cells expanded after treatment, with levels peaking after 3–6 months. To functionally characterize these exhausted-like T cells, we isolated memory CD8 TIGIT+KLRG1+ T cells from responders and showed that they exhibited expanded TCR clonotypes, indicative of prior in vivo expansion; recognized a broad-based spectrum expressed of environmental and auto-antigens; and were hypo-proliferative during polyclonal stimulation, increasing expression of IR genes and decreasing cell cycle genes. Triggering these cells with a recombinant ligand for TIGIT during polyclonal stimulation further downregulated their activation, demonstrating their exhausted phenotype was not terminal. These findings identify and functionally characterize a partially exhausted cell type associated with response to teplizumab therapy and suggest that pathways regulating T cell exhaustion may play a role in successful immune interventions for T1D.
Curated Disease	Diabetes Mellitus, Type 1
Curated Tissue	Blood
Curated Drug	Teplizumab
Curated Cell Lines	None
Curated Cell Type	None
Curated Organism	Homo Sapiens
Custom Curation	curated_disease_snomedct, curated_disease_icd10

Processing Information

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

Data Processing

SRA files are converted to fastq files using fasterq dump, then QC'ed using FastQC with short read threshold of 20. MinION adapter search with adapter threshold 2 is performed on Fastq file(s) and skewer quality trimming is done, with min. read length (18), and phred quality threshold (10). Kallisto quantification with fragment length (100) and standard deviation (20) is used to get read counts. These parameters ensure robust analysis and reliable interpretation of bulk RNA-seq data.

QUALITY ASSURANCE CONTENT

Metadata Information
Feature Identifier
Data Matrix
Histogram for Expression Distribution
Sample Wise Distribution of Expression Values Using a Boxplot
Sample Wise Distribution of Number of Genes Expressing Using a Barplot

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
Data Source Link is provided ℹ	Pass
Data Source Link is valid ℹ	Pass
Dataset-Level vs. Sample-Level Metadata: concordance check ℹ	Pass
Custom fields are present and valid ℹ	Pass
Schema compliance ℹ	kw_curated_genetic_mod_type should be stringified

2. Feature Identifier

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

3. Data Matrix

Data Matrix	Value
Data Matrix Values Valid ℹ	Pass

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.

This visualizatThe 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 normalisationion aids in understanding the heterogeneity within the dataset and can hint at different cellular states or subtypes within a cell type.

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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