# Open-source pipeline for integrating spatial transcriptomics and proteomics in 3D microscopy

**URL:** <https://discourse.scverse.org/t/open-source-pipeline-for-integrating-spatial-transcriptomics-and-proteomics-in-3d-microscopy/4046>\
**Category:** General\
**Tags:** integration\
**Created:** [September 4, 2026, 9:33pm UTC](https://discourse.scverse.org/t/open-source-pipeline-for-integrating-spatial-transcriptomics-and-proteomics-in-3d-microscopy/4046 "2026-09-04T21:33:24Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![yumin-lian](https://yyz1.discourse-cdn.com/flex035/user_avatar/discourse.scverse.org/yumin-lian/32/1659_2.png) [@yumin-lian](https://discourse.scverse.org/u/yumin-lian)\
**Post date:** [September 4, 2026, 9:33pm UTC](https://discourse.scverse.org/t/open-source-pipeline-for-integrating-spatial-transcriptomics-and-proteomics-in-3d-microscopy/4046/1 "2026-09-04T21:33:24Z")

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We developed a machine-learning-aided analysis pipeline for highly multiplexed spatial transcriptomics and proteomics data from mouse brain tissue. The pipeline integrates image processing, 3D registration, cell/nucleus segmentation, RNA spot assignment, protein quantification, and downstream machine-learning analysis to investigate molecular organization at the sub-nuclear level.

The complete analysis pipeline is openly available on GitHub:

> **[GitHub - liulabspatial/Omics-cycleHCR-QuAC: Analysis pipelines for paper Machine...](https://github.com/liulabspatial/Omics-cycleHCR-QuAC)**
>
> Analysis pipelines for paper Machine learning-guided spatial omics for tissue-scale discovery of cell-type-specific architectures

We’d be interested in feedback from the community.