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Single cell and clonal analysis of AL amyloidosis plasma cells and their bone marrow microenvironment

GSE292189 Homo sapiens (human) Single-cell RNA-seq 52 samples Submitted 2025/03/18 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 52 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Samples
52 samples: 52 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
Single-cell and clonal analysis of AL amyloidosis plasma cells and their bone marrow microenvironmentBlood · 2025
Summary (written by the submitters)
AL amyloidosis is a disorder characterized by expansion of clonal plasma cells in the bone marrow and distant end organ damage mediated by misfolded immunoglobulin free light chains. There are currently limited data regarding the functional characteristics of AL amyloidosis plasma cells and their surrounding bone marrow microenvironment.
Read the full summary ↓
We performed 5’ single cell RNA sequencing on 9 newly diagnosed, treatment naive AL amyloidosis patients and 8 healthy subjects. We identified generalized suppression of normal bone marrow hematopoiesis with distinct expansion of CD16 monocytes and subsets of CD4+ T cells in AL amyloidosis patients. We detected significant transcriptional changes broadly occurring among immune cells with increased interferon α and γ response and decreased TNF-α signaling. T and B cell receptor profiling revealed no overt clonal expansion of B or T cells in AL amyloidosis patients. However, we noted a disproportionate expansion of a distinct population of non-malignant plasma cells in AL amyloidosis patients. Finally, clonal AL amyloidosis plasma cells were identified based on their unique VDJ rearrangement and they showed increased expression of genes involved in proteostasis and antigen processing when compared to autologous, polyclonal plasma cells. Inter-patient transcriptional heterogeneity was evident, with transcriptional states reflective of common genomic translocations easily identifiable. Overall, this study defines the transcriptional characteristics of AL amyloidosis plasma cells and their surrounding bone marrow microenvironment, identifying transcriptional signatures that serve as candidates in early diagnosis in larger studies, and potential molecular targets for therapy.
Published in
Single-cell and clonal analysis of AL amyloidosis plasma cells and their bone marrow microenvironment
Gort-Freitas NA, Moscvin M, Da Vià MC et al. · Blood 2025 · PMID 40493887 · doi:10.1182/blood.2024024719
This dataset
Open GSE292189 in finder → Find more human 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 GSE292189_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 PRJNA1237356. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40493887) ↗
Study design
52 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 52 sample titles: 52 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

+ 12 more — browse all 52 samples with per-sample file links →

Datasets on a similar topic

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