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Spatially organizing million callus cells identify the core-network enable tomato shoot regeneration [RNA-Seq]

GSE292970 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2026/03/27 Platform GPL30209
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 18 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.
Summary (written by the submitters)
Plants can regenerate entire organs from somatic cells, offering a powerful system to uncover fundamental principles of cellular reprogramming and self-organization. Unlike animals, depending on pre-existing stem cell niches, plants re-establish pluripotency de novo. Although current understanding emphasizes hormonal control and external environmental cues, a unifying framework that explains how millions of cells coordinate fate decisions across space and time remains elusive.
Read the full summary ↓
Here, we construct a million-cell spatial transcriptomic atlas of plant regeneration, achieving mesoscale resolution observation for all stages of tomato callus development. This atlas reveals a tripartite stem cell niche architecture: an outer signaling layer, intermediate plastic zone, and a quiescent core, features reminiscent of animal regenerative niches. We uncover a fundamental Inducer-Maintainer-Trigger (IMT) triad model, wherein PI-2 (Inducer) establishes a stem cell activating microenvironment, DCL2/22-nt siRNAs (Maintainer) safeguard pluripotency through post-transcriptional regulation, and EPFL8 (Trigger) initiates stem cell niche establishment. This triad model unifies niche signaling, small RNA-mediated plasticity, and metabolic-state-dependent activation, drawing parallels between plant and animal regeneration. Beyond resolving key questions in plant biology, our findings highlight deep evolutionary convergence in regenerative logic across multicellular life, offering new blueprints for engineering regeneration in synthetic biology, crop science, and tissue engineering.
This dataset
Open GSE292970 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 GSE292970_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 PRJNA1240863 and SRA study SRP572856. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 conditions, mostly in triplicate
HCT116, IL10, 3h, mRNA-seq, ×3 HCT116, IL10, 6h, mRNA-seq, ×3 HCT116, IL6, 3h, mRNA-seq, ×3 HCT116, IL6, 6h, mRNA-seq, ×3 HCT116, NC, mRNA-seq, ×3 HCT116, TNF-a, 3h, mRNA-seq, ×3 HCT116, TNF-a, 6h, mRNA-seq, ×3 HCT116, Torin1, 3h, mRNA-seq, ×3 +10 more

Supports a between-group comparison across 54 samples.

How we read this: 18 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

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