STAG-seq

Here, we integrate variant editing with simultaneous measurements of genomic DNA and RNA to phenotype variants associated with inborn errors of immunity and autoimmune disease.

Sensitive Transcriptomics And Genotyping by sequencing (STAG-seq) enabled high-content variant functionalization in primary human immune cells while controlling for allele-dose effects and editing precision.

Collectively, we provide a framework to bridge the gap between genetic associations and functional mechanisms to advance our understanding of disease biology.

This repository contains the scripts and functions necessary to process, merge, and analyze this data, allowing for comprehensive insights into cellular heterogeneity.

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🧬 Workflow Overview

Fig 1. A brief illustration of the STAG-seq workflow.

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🧬 Data Processing

Raw STAG-seq RNA modality FASTQ data (.fastq.gz) was processed into cell-by-probe UMI count matrices (count_matrix_f100.h5ad) using a reproducible, containerized WDL pipeline executed on the Terra.bio platform.

The complete pipeline, including the WDL workflow, Docker environment, and detailed, step-by-step instructions for execution, is available in the scripts directory of this repository. Please refer to the README.md file within that folder for the full data processing guide.

The STAG-seq DNA modality was processed using the commercial MissionBio Tapestri pipeline (v3) on their web portal, which performs cell calling and GATK-based genotyping. All reference and amplicon panel files used for this analysis will be available on GEO upon publication.

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📊 Analysis Notebooks for Paper Figures

The notebooks/ directory contains the Jupyter and R notebooks used to generate the figures for our paper. Each subdirectory corresponds to a specific figure.

Figure 1: Sensitivity

• fig1_e-g.ipynb
• fig1_h-k.ipynb
• STAGseq_10X_Fig1.Rmd
• THP1_Jurkat_Fig1.Rmd
• count_tapestri_S7.csv
• count_tapestri_S8.csv

Figure 2: Matrix Screen

• preprocess_matrix_screen.ipynb
• matrix_screen_DE.ipynb
• make_quadrant_plot.ipynb

Figure 3: DS IFNγ

• figure3_preprocess.ipynb
• figure3b_umap.ipynb
• figure3c_boxplot.ipynb
• figure3d-i_ifng_score.ipynb
• figure3k.ipynb
• figure3k.R
• variants_config.ini

Figure 4: TNRC18

• preprocess_TNRC18.ipynb
• make_figures.ipynb
• figure4d.ipynb
• figure4fgh.ipynb
• figure4_preprocess.ipynb
• TNRC18.ini

Figure 5: TCF7

• fig5_c_efg.ipynb
• fig5_preprocess.ipynb
• TCF7.ini

Figure 6: RORC

• figure6_preprocess.ipynb
• figure6_preprocess2.ipynb
• figure6d-p.ipynb
• RORC.ini