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  1. Home
  3. Space-seq Integrates Spatial Transcriptomics And Lineage Tracing In Native Tissues.
  1. Home
  3. Space-seq Integrates Spatial Transcriptomics And Lineage Tracing In Native Tissues.

Related Concept Videos

RNA-seq03:21

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 microarray-based...

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Related Experiment Video

Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

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

Published on: October 31, 2025

SPACE-seq integrates spatial transcriptomics and lineage tracing in native tissues.

Yuemeng Jia1, Dawei Sun2, Jackson A Weir3

  • 1Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA.

Cell Stem Cell
|June 9, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces SPACE-seq, a new method combining lineage tracing and spatial transcriptomics. It reveals how cells change, interact, and form tissues, offering insights into cancer and development.

Keywords:
brain developmentcellular barcodinglineage tracingliver cancerliver developmentspatial transcriptomics

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Mining Spatial Transcriptomics Datasets using DeepSpaceDB
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Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

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Comprehensive Spatial Profiling of Species-agnostic Transcriptomes via Stereo-seq
10:22

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

Published on: October 31, 2025

Mining Spatial Transcriptomics Datasets using DeepSpaceDB
10:16

Mining Spatial Transcriptomics Datasets using DeepSpaceDB

Published on: September 5, 2025

Area of Science:

  • Cell Biology
  • Genomics
  • Developmental Biology

Background:

  • Understanding cellular dynamics in tissues requires methods that track cell lineage and spatial organization.
  • Current techniques often lack the resolution or ability to integrate lineage, cell state, and spatial information simultaneously.

Purpose of the Study:

  • To develop and validate SPACE-seq, a platform for in situ spatial lineage tracing and transcriptomics.
  • To investigate cellular diversification within tumors and developmental tissue patterning.

Main Methods:

  • SPACE-seq integrates CRISPR-based lineage recording with Slide-seq spatial transcriptomics.
  • This allows simultaneous resolution of cell lineage, cell state, and tissue architecture at near-cellular resolution.
  • The platform was applied to study tumor microenvironments and liver development.

Main Results:

  • SPACE-seq uncovered intratumor transcriptional diversification in clonally related cells.
  • It identified reciprocal tumor-stroma crosstalk influencing both malignant and stromal cell behaviors.
  • The study revealed hepatoblast dispersion patterns contributing to liver lobar architecture during development.

Conclusions:

  • SPACE-seq is a versatile platform for uncovering principles of cellular organization, lineage dynamics, and tissue patterning.
  • It provides unprecedented insights into complex biological processes in both disease and development.
  • The findings open new avenues for studying in situ cellular behavior and tissue architecture.