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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Related Experiment Video

Updated: May 5, 2026

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM

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Scalable image-based visualization and alignment of spatial transcriptomics datasets.

Stephan Preibisch¹, Michael Innerberger¹, Daniel León-Periñán²

¹Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA.

|April 23, 2025

Summary

This summary is machine-generated.

We introduce the spatial transcriptomics imaging framework (STIM) for visualizing and aligning spatial sequencing data. STIM achieves human-level accuracy in aligning complex biological datasets.

Keywords:

ImgLib2 alignment computer vision imaging registration spatial transcriptomics visualization

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Last Updated: May 5, 2026

Lineage Tracing and Clonal Analysis in Developing Cerebral Cortex Using Mosaic Analysis with Double Markers MADM

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Published on: July 6, 2022

Area of Science:

Computational biology
Bioinformatics
Imaging science

Background:

Spatial transcriptomics generates high-throughput data with irregular spacing and resolution.
Existing computational tools struggle to effectively visualize and align these complex datasets.
There is a need for advanced imaging-based frameworks to handle spatial sequencing data.

Purpose of the Study:

To present the spatial transcriptomics imaging framework (STIM).
To enable visualization, alignment, and analysis of high-throughput spatial sequencing data.
To leverage computer vision techniques for spatial transcriptomics.

Main Methods:

STIM is an imaging-based computational framework built on ImgLib2 and BigDataViewer (BDV).
It supports novel development and transfer of computer vision techniques.
Methods include interactive visualization, 3D rendering, automatic registration, and segmentation.

Main Results:

STIM successfully processed and visualized spatial sequencing data from mouse brain and human lymph node tissues.
The framework demonstrated capabilities in representing, registering, and segmenting serial section data.
The simplest alignment mode of STIM achieved human-level accuracy.

Conclusions:

STIM provides a powerful and scalable solution for spatial transcriptomics data analysis.
The framework facilitates advanced visualization and alignment of complex biological imaging data.
STIM opens new avenues for applying computer vision to spatial sequencing technologies.