GEO series

NEST: Spatially-mapped cell-cell communication patterns using a deep learning-based attention mechanism

GSE262245 Homo sapiens (human) Other 2 samples Submitted 2025/03/16 Platform GPL24676

At a glance

human · 2 samples

Organism: Homo sapiens (human)
Samples: 2 samples
Data you can get: A processed table (series matrix) and supplementary files from NCBI.

Summary (written by the submitters)

Dysregulation of communication between cells mediates complex diseases such as cancer and diabetes. However, detecting cell-cell communication (CCC) at scale remains one of the greatest challenges in transcriptomics. While gene expression remains one of the greatest challenges in transcriptomics.

Read the full summary ↓

While gene expression measured with single-cell RNA sequencing and spatial transcriptomics reinvigorated computational approaches to detecting CCC, most existing methods exhibit high false positive rates, do not integrate spatial proximity of ligand-receptor interactions, and cannot detect CCC between individual cells. We overcome these challenges by presenting NEST (NEural network on Spatial Transcriptomics), which uses a graph attention network paired with an unsupervised contrastive learning approach to decipher patterns of communication while retaining the strength of each signal. We introduce new synthetic benchmarking experiments which demonstrate how NEST outperforms existing tools and detects biologically-relevant CCC along with directionality and confidence across spot- and cell-based technologies measuring several different tissues and diseases. In our applications, NEST identifies T-cell homing signals in human lymph nodes, aggressive cancer CCC in lung adenocarcinoma, and discovers new patterns of communication in pancreatic cancer. Beyond two-dimensional data, we also highlight NEST's ability to detect CCC in three-dimensional spatial transcriptomic data.

This dataset

Open GSE262245 in finder → Find more human datasets →

Download · which file do I need?

Direct links to NCBI, 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.

All supplementary files ↗

3 · The original raw files

Whole study · RAW.tar

Everything the authors deposited as one archive (GSE262245_RAW.tar), including the raw array or instrument files. Can be large; the picker in step 2 takes just the samples you need.

Whole study · RAW.tar ↓

Also filed as BioProject PRJNA1090897. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40481363) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 2 more — browse all 2 samples with per-sample file links →

Datasets on a similar topic

GSE247369 Pathogenic TSC2 variants alter developmental trajectories of human cortical organoids and perturb cell-cell communication networks 92 samples shares communication, cell-cell
GSE247367 Pathogenic TSC2 variants alter developmental trajectories of human cortical organoids and perturb cell-cell communication networks [bulk RNA-seq] 80 samples shares communication, cell-cell
GSE291867 Gene expression profile Predictor on chemical Structures (GPS): Deep Learning-based platform to screen and design novel therapeutics 27 samples shares learning-based, deep
GSE242346 Human stem cells with in vivo high plasticity generated by cell-cell communication 22 samples shares communication, cell-cell
GSE243002 Comprehensive Profiling of Peripheral Blood Mononuclear Cells Reveals Monocyte-CD8+ T cell-B cell Communication as Central Cell-cell Interaction and pathogenesis in IgA Vasculitis 21 samples shares communication, cell-cell
GSE247368 Pathogenic TSC2 variants alter developmental trajectories of human cortical organoids and perturb cell-cell communication networks [scRNA-seq] 12 samples shares communication, cell-cell
GSE335667 Predicting relevant microglia-associated cell-cell communication pathways in Alzheimer’s disease: a role for SPP1 10 samples shares communication, cell-cell
GSE269390 A single-cell RNA sequencing atlas of the COPD distal lung to predict cell-cell communication 6 samples shares communication, cell-cell

Search all human datasets in GEO →

Share this dataset

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.