GEO series

Generation of Alveolar Assembloids (AlvAssemb) with Functional Alveolar-like Macrophages

GSE269733 Homo sapiens (human) Single-cell RNA-seq 12 samples Submitted 2024/09/30 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 12 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: Homo sapiens (human)
Samples: 12 samples: 12 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: Generation of induced alveolar assembloids with functional alveolar-like macrophagesNature communications · 2025

Summary (written by the submitters)

Within the human lung, interplay between alveolar epithelial cells and resident macrophages shape lung development and function in both health and disease. To study these processes, we devised a strategy for co-culturing hPSC-derived alveolar epithelial organoids and induced macrophages to form functional environment, which we name alveolar assembloids (AlvAssemb).

Read the full summary ↓

By scRNAseq and functional analysis of AlvAssemb, we identified alveolar type 2-like cells that produced GM-CSF, a response associated with tissue adaptation of macrophages, and that resident alveolar macrophage-like cells secreted interleukin-1β and interleukin-6, a core immune response function, and expressed genes required for surfactant metabolism. Remarkably, in response to alveolar epithelial injury, we found that macrophage-like cells within AlvAssemb efficiently eliminated damaged cells and absorbed oxidized lipids. Moreover, in response to lipopolysaccharide exposure and Mycobacterium tuberculosis infection, we found that core features of the human respiratory defense response were replicated in AlvAssemb. These findings support that AlvAssemb could serve as a valuable platform to investigate human lung development and pathology.

Published in

Generation of induced alveolar assembloids with functional alveolar-like macrophages

Kang JS, Lee Y, Lee Y et al. · Nature communications 2025 · PMID 40199883 · doi:10.1038/s41467-025-58450-w

This dataset

Open GSE269733 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 GSE269733_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 PRJNA1123576 and SRA study SRP513673. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40199883) ↗

Study design

12 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 12 sample titles: 12 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

GSM8325879 iMAC,RNAseq [0_0_1]
GSM8325880 iMAC,RNAseq [0_0_2]
GSM8325881 iMAC infected M.tb (H37Rv) at MOIs of 10,RNAseq [0_10_1]
GSM8325882 iMAC infected M.tb (H37Rv) at MOIs of 10,RNAseq [0_10_2]
GSM8325883 iMAC infected M.tb (H37Rv) at MOIs of 5,RNAseq [0_5_1]
GSM8325884 iMAC infected M.tb (H37Rv) at MOIs of 5,RNAseq [0_5_2]
GSM8325885 iMAC with GM-CSF,RNAseq [GM_0_1]
GSM8325886 iMAC with GM-CSF infected ,RNAseq [GM_0_2]
GSM8325887 iMAC with GM-CSF infected M.tb (H37Rv) at MOIs of 10,RNAseq [GM_10_1]
GSM8325888 iMAC with GM-CSF infected M.tb (H37Rv) at MOIs of 10,RNAseq [GM_10_2]
GSM8325889 iMAC with GM-CSF infected M.tb (H37Rv) at MOIs of 5,RNAseq [GM_5_1]
GSM8325890 iMAC with GM-CSF infected M.tb (H37Rv) at MOIs of 5,RNAseq [GM_5_2]

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