Spatial_epigenome-transcriptome_co-sequencing

Introduction

This repository aims to share the raw data processing and visualization codes used in Spatial_epigenome-transcriptome_co-sequencing project.

Data processing

Next Generation Sequencing (NGS) was performed using the Illumina NovaSeq 6000 sequencer (pair-end 150 bp mode).

Spatial_epigenome-transcriptome_co-sequencing chemistry (Zhang & Deng et al. 2023)

1.Raw Fastq data

• Read 1: contains the genome sequences
• Read 2: contains the spatial Barcode A, Barcode B and genome sequences

2. Reformat raw Fastq file to Cell Ranger ATAC format (10x Genomics)

Raw read 2 -> New Read 1 + New Read 2

• New Read 1: contains the genome sequences
• New Read 2: contains the spatial Barcode A and Barcode B

Raw read 1 -> New Read 3

Reformatting raw data was implemented by BC_process.py in the Data_preprocessing folder.

3. Sequence alignment and generation of fragments file

The reformated data was processed using Cell Ranger ATAC v1.2 with following references: Mouse reference (mm10):

curl -O https://cf.10xgenomics.com/supp/cell-atac/refdata-cellranger-atac-mm10-1.2.0.tar.gz

Human reference (GRCh38):

curl -O https://cf.10xgenomics.com/supp/cell-atac/refdata-cellranger-atac-GRCh38-1.2.0.tar.gz

A preprocessing pipeline we developed using Snakemake workflow management system is in the Data_preprocessing folder. To run the pipeline, use the command:

sbatch Snakemake.sh

Spatial_RNA-seq

1. Raw Fastq data processing using ST pipeline and generate expression matrix

The DBiT-seq Raw fastq file Read 1: Contains the cDNA sequence Read 2: Contains the spatial Barcode A, Barcode B and UMIs

2.Reformat Fastq Read 2 file To reformat the Raw data, run the fastq_process.py in Rawdata_processing folder and gzip the resulted fastq file to save space:

python fastq_process.py
gzip sample_R2_processed.fastq

The reformated data was processed following ST pipeline.

3、Run ST pipeline Run st_pipeline.sh to start the ST pipeline: The input is processed_R2.fastq.gz and Raw R1.fastq.gz. It also requires a "spatial_barcodes_index.txt" to decode the spatial location information. Genome references and annotatation files were aslo needed.

#!/bin/bash
# FASTQ reads
FW=$tmp/${sample}_R2_processed.fastq
RV=$tmp/${sample}_raw_qc_R1.fastq.gz
# References for mapping, annotation and nonRNA-filtering
MAP=/Dropseq_Alignment_References/mm10/
ANN=/Dropseq_Alignment_References/mm10/mm10.gtf 
CONT=/Spatial_omics_references/mouse/GRCm38_86/ncRNA/StarIndex/

# Barcodes settings
ID=/useful/spatial_barcodes.txt

# Output folder and experiment name
OUTPUT=../output/
mkdir -p $OUTPUT

TMP_ST=$OUTPUT/tmp
mkdir -p $TMP_ST

# Running the pipeline
st_pipeline_run.py \
  --output-folder $OUTPUT \
  --ids $ID \
  --ref-map $MAP \
  --ref-annotation $ANN \
  --expName $sample \
  --htseq-no-ambiguous \
  --verbose \
  --log-file $OUTPUT/${sample}_log.txt \
  --demultiplexing-kmer 5 \
  --threads 20 \
  --temp-folder $TMP_ST \
  --no-clean-up \
  --umi-start-position 16 \
  --umi-end-position 26 \
  --demultiplexing-overhang 0 \
  --min-length-qual-trimming 20 \
  $FW $RV

4.Convert Ensemble to Gene Names Then, Run converttoname.sh to annotate the resulting sample_stdata.tsv.

#!/bin/bash
tsv_E=$OUTPUT/${sample}_stdata.tsv
path_to_annotation_file=/Dropseq_Alignment_References/mm10/mm10.gtf

convertEnsemblToNames.py --annotation $path_to_annotation_file --output $OUTPUT/${sample}_stdata_names.tsv $tsv_E

Identify useful pixels (pixel on tissue) from microscope image using Matlab

link： https://github.com/edicliuyang/DBiT-seq_FFPE/tree/master/Figure_Processing

Data visualization

The data visualization were completed with R language. The package used extensively the functions in Signac V1.6， Seurat V4.0 and ArchR v1.0.2.