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Spatial transcriptomics and in situ immune cell profiling of the host ectocervical landscape of HIV infected Kenyan sex working women

GSE290350 Homo sapiens (human) Spatial transcriptomics 10 samples Submitted 2025/02/26 Platform GPL24676
At a glance

Spatial transcriptomics · human · 10 samples

What it is
Spatial transcriptomicsGene activity measured with its position in a tissue section.
Organism
Homo sapiens (human)
Samples
10 samples: 10 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
Spatial transcriptomics and in situ immune cell profiling of the host ectocervical landscape of HIV infected Kenyan sex working womenFrontiers in immunology · 2024
Summary (written by the submitters)
Introduction: Chronic immune activation is a hallmark of human immunodeficiency virus (HIV) infection that significantly impacts disease pathogenesis. However, in-depth studies characterizing the immunological landscape of the ectocervix during chronic HIV infection remain scarce despite the importance of this tissue site for HIV transmission.
Read the full summary ↓
Methods: Ectocervical tissue samples were obtained from antiretroviral-naïve HIV-seropositive and -seronegative Kenyan female sex workers. Sampeling aimed for the follicular phase of the menstrual cycle. These samples were assessed by spatial transcriptomics and Gene Set Enrichment Analysis. We further performed multi-epitope ligand cartography (MELC) using an in situ staining panel that included 17 markers of primarily T cell–mediated immune responses. Results: Spatial transcriptomics revealed tissue-wide immune activation encompassing immune responses associated with chronic HIV infection. First, both the epithelial and submucosal compartments showed diverse but significant upregulation of humoral immune responses, as indicated by the expression of several antibody-related genes. Second, an antiviral state–associated cellular immunity was also observed in the HIV-seropositive group, characterized by upregulation of genes involved in interferon signaling across the mucosal tissue and a more spatially restricted mucosal expression of genes related to T cell activity and effector functions relative to the HIV-seronegative group. Additionally, HIV associated structural alterations were evident within both compartments. Downregulated genes across the epithelium were mainly linked to epithelial integrity, with the outer layer involved in terminal differentiation and the inner layer associated with epithelial structure. MELC analysis further revealed a significantly increased ectocervical leukocyte population in HIV-seropositive participants, primarily driven by an increase in CD8+ T cells while the CD4+ T cell population remained stable. Consistent with our spatial transcriptomics data, T cells from HIV-seropositive participants showed an increased effector phenotype, defined by elevated expression of various granzymes. Conclusion: By combining spatial transcriptomics and MELC, we identified significant HIV-associated cervical immune activity driven by induction of both T and B cell activity, together with a general antiviral state characterized by sustained interferon induction. These findings underscore that chronic HIV infection is associated with an altered ectocervical mucosal immune landscape years after primary infection. This sheds light on HIV pathogenesis at distant local sites and complements current knowledge on HIV-associated systemic immune activation.
Published in
Spatial transcriptomics and in situ immune cell profiling of the host ectocervical landscape of HIV infected Kenyan sex working women
Franzén Boger M, Kaldhusdal V, Pascual-Reguant A et al. · Frontiers in immunology 2024 · PMID 39687623 · doi:10.3389/fimmu.2024.1483346
This dataset
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.

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 GSE290350_RAW.tar (it can run to tens of gigabytes).

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.

What the files usually look like: Spatial studies usually ship Space Ranger style folders (count matrix plus tissue images). Look for them among the supplementary files.

Also filed as BioProject PRJNA1227522. Searching any of these in the dataset finder brings you back here.

Study design
10 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 10 sample titles: 10 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
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