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Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling

GSE287770 Homo sapiens (human) Non-coding RNA profiling 74 samples Submitted 2026/02/16 Platform GPL34284Platform GPL34290
At a glance

Non-coding RNA profiling · human · 74 samples

What it is
Non-coding RNA profilingLevels of RNAs that do not code for proteins, such as miRNA or lncRNA.
Organism
Homo sapiens (human)
Samples
74 samples: 24 conditions, mostly in triplicateGroups 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
Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labelingNature communications · 2026
Summary (written by the submitters)
Phenotyping cells at transcriptomic and proteomic levels is an essential step to understanding cellular contributions to development, aging, injury, and disease. Since proteome and transcrip-tome level abundances modestly correlate, complementary profiling of both is needed. We report a method called simultaneous protein and RNA -omics (SPARO) to capture the cell type-specific transcriptome and proteome simultaneously in vitro and in vivo.
Read the full summary ↓
SPARO leverages TurboID to biotinylate RNA-interacting cytosolic proteins, enabling enrichment of proteins for proteomics and protein-associated RNA for transcriptomics. We validate SPARO first using well-controlled in vitro systems to verify that the proteomes and transcriptomes obtained reflect the global proteo-mes and transcriptomes. The effect of neuroinflammatory activation by lipopolysaccharide is al-so faithfully captured. We apply SPARO to obtain native-state proteomes and transcriptomes from astrocytes and neurons, thereby validating the approach in vivo. We interrogate mRNA-protein concordance and discordance, providing insights into molecular processes that exhibit uniform or cell type-specific patterns.
Published in
Simultaneous profiling of native-state proteomes and transcriptomes of neural cell types using proximity labeling
Ramelow CC, Dammer EB, Xiao H et al. · Nature communications 2026 · PMID 41917013 · doi:10.1038/s41467-026-71098-4
This dataset
Open GSE287770 in finder → Find more 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 GSE287770_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 ↗

Also filed as BioProject PRJNA1214674 and SRA study SRP559362. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41917013) ↗
Study design
24 conditions, mostly in triplicate
Astrocyte-TurboID LPS bulk ×5 Astrocyte-TurboID LPS pulldown ×5 BV2 bulk ×3 BV2 LPS bulk ×3 BV2-TurboID bulk ×3 BV2-TurboID LPS bulk ×3 BV2-TurboID pulldown ×3 BV2-TurboID LPS pulldown ×3 +19 more

Supports a between-group comparison across 71 samples.

How we read this: 24 replicated groups read from 74 sample titles; they account for 71 of them. 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

+ 34 more — browse all 74 samples with per-sample file links →

Datasets on a similar topic

Search all RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.