GEO series

Disruption of Immune Responses By Type I Diabetes Exacerbates SARS-CoV-2 Mediated Lung Injury [bulk RNA-seq]

GSE276775 Mus musculus (mouse) Single-cell RNA-seq 11 samples Submitted 2025/01/08 Platform GPL13112

At a glance

Single-cell RNA-seq · mouse · 11 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Mus musculus (mouse)
Samples: 11 samples: 11 samples, each with a distinct title — no replicated groupsGroups 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: Disruption of immune responses by type 1 diabetes exacerbates SARS-CoV-2 mediated lung injuryAmerican journal of physiology. Lung cellular and molecular physiology · 2024

Summary (written by the submitters)

COVID-19 commonly presents as pneumonia, with those most severely affected progressing to respiratory failure. Patient responses to SARS-CoV-2 infection are varied, with comorbidities acting as major contributors to varied outcomes. Focusing on one such major comorbidity, we assessed whether pharmacological induction of Type I Diabetes Mellitus (T1DM) would increase the severity of lung injury in a murine model of COVID-19 pneumonia utilizing wild type mice infected with mouse-adapted SARS-CoV-2.

Read the full summary ↓

Hyperglycemic mice exhibited increased weight loss and reduced blood oxygen saturation in comparison to their euglycemic counterparts, suggesting that these animals indeed experienced more severe lung injury. Transcriptomic analysis revealed a significant impairment of the adaptive immune response in the lungs of diabetic mice compared to those of control. In order to expand the limited options available for tissue analysis due to biosafety restrictions, we also employed a new technique to digest highly fixed tissue into a single cell suspension, originally designed for scRNA-Seq, which we then adapted for flow cytometric analysis. Flow immunophenotyping and scRNA-Seq confirmed impaired recruitment of T cells into the lungs of T1DM animals. Additionally, scRNA-Seq revealed a distinct, highly inflammatory macrophage profile in the diabetic cohort that correlates with the more severe infection these mice experienced clinically, allowing insight into a possible mechanism for this phenomenon. Recognizing the near certainty that respiratory viruses will continue to present significant public health concerns for the foreseeable future, our study provides key insights into how T1DM results in a much more severe infection and identifies possible targets to ameliorate comorbidity-associated severe disease.

Published in

Disruption of immune responses by type 1 diabetes exacerbates SARS-CoV-2 mediated lung injury

Kass-Gergi S, Zhao G, Wong J et al. · American journal of physiology. Lung cellular and molecular physiology 2024 · PMID 39320093 · doi:10.1152/ajplung.00250.2024

This dataset

Open GSE276775 in finder → Find more mouse RNA-seq datasets →

Download · which file do I need?

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.

1 · Just have a look

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.

Series matrix ↓

2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE276775_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

3 · Start from the raw reads

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.

Raw reads on ENA ↗

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 PRJNA1158835 and SRA study SRP531661. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39320093) ↗

Study design

11 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 11 sample titles: 11 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.

Samples in this study

GSM8505584 827, lung, +Streptozocin, +MA-SARS-CoV-2
GSM8505585 829, lung, +Streptozocin, +MA-SARS-CoV-2
GSM8505586 830, lung, +Streptozocin, +MA-SARS-CoV-2
GSM8505587 831, lung, +Streptozocin, -MA-SARS-CoV-2
GSM8505588 832, lung, +Streptozocin, -MA-SARS-CoV-2
GSM8505589 838, lung, -Streptozocin, -MA-SARS-CoV-2
GSM8505590 839, lung, -Streptozocin, -MA-SARS-CoV-2
GSM8505591 840, lung, -Streptozocin, -MA-SARS-CoV-2
GSM8505592 841, lung, -Streptozocin, +MA-SARS-CoV-2
GSM8505593 842, lung, -Streptozocin, +MA-SARS-CoV-2
GSM8505594 843, lung, -Streptozocin, +MA-SARS-CoV-2

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