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Beyond amyloid-β and tau: A cell-based multivariate blood test (iCAP) for early Alzheimer’s detection

GSE312139 Homo sapiens (human) RNA-seq (gene expression) 625 samples Submitted 2025/12/02 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 625 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
625 samples: 343 distinct titles, 2 of them repeatedGroups 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.
Summary (written by the submitters)
Abstract: Background: With amyloid-β (Aβ) therapies for Alzheimer’s disease (AD) under active debate, there is a need for diagnostic tools that reflect disease biology beyond Aβ and tau for early detection and for patient stratification. The indicator cell assay platform (iCAP) is a tool for blood-based diagnostics that uses standardized cells as biosensors to transduce complex circulating signals into gene-expression readouts, which are then used to train multivariate disease classifiers for precision medicine.
Read the full summary ↓
We developed an iCAP for early detection of Alzheimer’s disease (AD-iCAP). Methods: In a retrospective study, AD-iCAP was developed by incubating banked plasma samples from patients with early-stage AD (mild cognitive impairment or mild dementia) and age-matched controls with standardized neurons; whole-transcriptome responses were measured and used train disease classifiers by machine learning. The assay was optimized and analytically validated. Patient AD-iCAP data were separated into a training set, external validation set and an independent test set and used to parameterize and test models. To minimize bias, modeling features were selected from a predefined (a priori) 84-gene panel derived from an independent AD-iCAP dataset generated using plasma from 5xFAD mice. Results: We developed AD-iCAP using 191 banked plasma samples across three cohorts. Performance was assessed in two held-out sets: an external validation set (n=82; AUC 0.64, 95% CI 0.51–0.78) and an independent test set (n=23, AUC 0.77, 95% CI 0.57–0.96). Systems biology analyses of differential response profiles showed concordance with postmortem AD brain transcriptomes and enrichment of AD-relevant pathways beyond amyloid, including cholesterol biosynthesis, synaptic structure/neurotransmission and NGF/TrkA signaling (FDR < 0.05). The final model’s features included AD-linked genes AKT3, GPR50, PALLD and RGMA, related to neuronal signaling and cytoskeletal/axon-guidance processes. Conclusions: AD-iCAP is a blood-based diagnostic for early-stage detection of AD. It outputs cell-based transcriptional profiles with disease-relevance that are induced by circulating factors. In retrospective, multi-cohort testing, it showed modest-to-good discrimination, supporting prospective confirmation in larger cohorts. Because its readout captures biology beyond amyloid and tau, it may provide complementary information for combined testing. The multivariate readout supports further development for patient stratification as Aβ-targeted therapies and alternatives evolve.
This dataset
Open GSE312139 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 GSE312139_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1372253 and SRA study SRP650466. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
343 distinct titles, 2 of them repeated
reference_001, reference, ×50 CellsOnly_001, CellsOnly, ×9

How we read this: Read from the first 400 of 625 sample titles: 343 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

+ 585 more — browse all 625 samples with per-sample file links →

Datasets on a similar topic

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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.