GEO series

Artificial intelligence-assisted prediction of PANoptosis related molecular targets and precise screening of neuroprotective drugs for spinal cord injury

GSE313801 Mus musculus (mouse) Single-cell RNA-seq 9 samples Submitted 2026/05/01 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 9 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: 9 samples: 3 × Y-N vs 3 × Y-S3d vs 3 × Y-S21dGroups 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: Machine learning-assisted prediction of PANoptosis-related molecular targets and precise screening of neuroprotective drugs for spinal cord injuryExperimental neurology · 2026

Summary (written by the submitters)

Abstract: Spinal cord injury (SCI) is a highly disabling central nervous system disease with complex pathology, and targeted neuroprotective drugs remain clinically lacking. However, traditional molecular target screening and drug prediction methods are inefficient, costly, and poorly targeted, failing to meet clinical precision treatment needs.

Read the full summary ↓

To address this, we introduced artificial intelligence to construct a multi-dimensional data integration framework. First, we established normal, acute- and subacute-phase SCI mouse complete transection models, and RNA-seq combined with single-cell sequencing confirmed acute-phase extensive neuronal PANoptosis. Using WGCNA and MCC algorithms, 25 candidate genes for extensive neuronal PANoptosis in the acute phase were screened out. Then, we comprehensively applied machine learning algorithms including Random Forest, Support Vector Machine, and Elastic Net Model to predict and prioritize potential molecular targets, identifying 13 core genes for extensive neuronal PANoptosis, including Tacc3, Aurka, Mcm6, Mcm5, Ripk1, etc. With the help of the Connectivity Map, drug prediction was performed on these 13 genes, and the 8 candidate drugs with neuroprotective effects were screened out. Through protein domain screening, it was verified via proof-by-contradiction assays that the drug Xaliproden can establish robust interactions with the 7XMK, 7FCZ and 7FD0 domains of Ripk1, a core molecule of the PANoptosome, via a network of multiple hydrogen bonds. This finding provides a novel screening strategy for neuroprotective drugs for spinal cord injury and is of great significance for promoting the establishment of a precision treatment system for the acute phase of injury.

Published in

Machine learning-assisted prediction of PANoptosis-related molecular targets and precise screening of neuroprotective drugs for spinal cord injury

Wang D, Wang R, Liao H et al. · Experimental neurology 2026 · PMID 42309342 · doi:10.1016/j.expneurol.2026.115884

This dataset

Open GSE313801 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 GSE313801_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 PRJNA1381613 and SRA study SRP655280. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42309342) ↗

Study design

3 × Y-N vs 3 × Y-S3d vs 3 × Y-S21d

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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

GSM9375826 Y-N-1 Y-N
GSM9375827 Y-N-2 Y-N
GSM9375828 Y-N-3 Y-N
GSM9375829 Y-S3d-1 Y-S3d
GSM9375830 Y-S3d-2 Y-S3d
GSM9375831 Y-S3d-3 Y-S3d
GSM9375832 Y-S21d-1 Y-S21d
GSM9375833 Y-S21d-2 Y-S21d
GSM9375834 Y-S21d-3 Y-S21d

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