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Updated: Jun 23, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Zero-shot reconstruction of mutant spatial transcriptomes.

Yasushi Okochi¹, Takaaki Matsui^2,3,4, Shunta Sakaguchi¹

¹Laboratory for Data-driven Biology, Nagoya University Graduate School of Medicine, Nagoya, Aichi 466-8550, Japan.

Patterns (New York, N.Y.)

|June 22, 2026

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This summary is machine-generated.

ZENomix, a novel zero-shot learning framework, predicts mutant spatial transcriptomes without prior data. This approach aids biological research by analyzing complex tissue phenotypes from single-cell RNA sequencing data.

Area of Science:

Genomics
Computational Biology
Developmental Biology

Background:

Spatial transcriptomics is crucial for understanding tissue phenotypes in biological and pathological research.
High costs and technical hurdles limit spatial transcriptome analysis in large mutant studies.
Predicting spatial transcriptomes from single-cell RNA sequencing (scRNA-seq) is challenging due to the lack of specific training data for most mutants.

Purpose of the Study:

To introduce ZENomix, a zero-shot learning framework for predicting mutant spatial transcriptomes.
To enable the analysis of mutant spatial transcriptomes without requiring specific teaching data (e.g., mutant spatial atlases).
To leverage existing scRNA-seq data for predicting spatial gene expression patterns in mutants.

Main Methods:

Keywords:

single-cell RNA sequencing spatial transcriptome zero-shot learning

Related Experiment Videos

Last Updated: Jun 23, 2026

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq

Published on: October 31, 2025

Developed ZENomix, a zero-shot learning framework utilizing machine learning principles.
Applied ZENomix to predict spatial transcriptomes in various biological models.
Validated the framework's accuracy in predicting gene expression patterns.

Main Results:

ZENomix accurately predicted spatial transcriptomes in Alzheimer's model mice and human brains.
The framework successfully predicted spatial transcriptomes in Nodal-signaling-deficient mutant zebrafish embryos.
A ZENomix-based screening approach identified Nodal-downregulated genes in zebrafish.

Conclusions:

ZENomix provides a powerful tool for predicting mutant spatial transcriptomes, overcoming data limitations.
The framework facilitates phenotypic insights by analyzing large scRNA-seq datasets from mutants and diseases.
ZENomix is expected to significantly advance biological and pathological research by enabling large-scale mutant analysis.