Last updated: 2022-06-14

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1. Understand STING-seq

a. Read the Morris paper

Morris et al. 2021: STING-Seq

Some key points:

Some key ideas:

  • STING-seq: Systematic Targeting and Inbition of Noncoding GWAS loci with scRNA-seq
  • prioritizes candidate cis-regulatory elements (cCREs, 1kb<distance to TSS<1Mb) using fine-mapped GWAS
  • selected 88 variants (in 56 loci) with enhancer activity
  • dual CRISPR inhibition: dCas9 as the GPS, MeCP2 and KRAB as the repressors
  • confirming dual CRISPRi efficacy: gRNAs target TSS of MRPS23, CTSB, FSCN1
  • CRIPSRi on the 88 variants: two gRNAs for each variant, both within 200bp of the variant
  • ECCITE-seq: captures gRNAs and epitopes

Some data processing steps and results:

  • QC: remove cells with low total reads or excessive mitochondrial reads, gRNA assignment UMI>5 (9,343 cells after QC)
  • Kallisto: counting
  • Seurat: QC and reference mapping? read more on the official website
  • SCEPTRE: gRNA_to_gene-expression pairwise test
  • non-targeting gRNA-gene pairs: not significant (negative ctrl)
  • TSS-targeting gRNA-gene pairs: expression significantly decreased (positive ctrl)
  • 37 of the 88 variants were significant
  • Trans-regulatory elements: I’ll come back later

Note:

2. Prelim QC for STING-seq data

Read a RNA-seq analysis pipeline review

Yalamanchili et al. 2017: RNA-seq analysis pipeline

Some key points:

Protocol-1 (differential expression of genes):

  • demuxed raw reads (FastQC)
  • trimming reads (awk)
  • aligning reads (TopHat2)
  • counting reads (HTSeq; may filter out genes with low counts before next step)
  • detect DE using counted reads (DEseq2)
  • more QC (PCA/correlation heatmap)

Protocol-2 (differential usage fo isoforms):

  • Protocol-1
  • counting isoforms (Kallisto)
  • detect DU using counted isoforms (Sleuth)
  • more QC (aslo PCA/correlation heatmap)

Protocol-3 (crypic splicing):

  • Protocol-1
  • detect differential junstions (CrypSplice)

b. Download data

rsync -a hchen131@midway2.rcc.uchicago.edu:/project2/xuanyao/data/Morris_2021 ~/Desktop

c. Perform FastQC on all fastq files

# install fastqc
sudo apt-get update && sudo apt-get install fastqc -y

# run fastqc in parallel
cd ~/rotation2/
[ ! -d ../Morris_2021/fastqc_results/ ] && mkdir ../Morris_2021/fastqc_results/
fastqc --threads 16 ../Morris_2021/STINGseq_Morris_2021_raw/*.fastq.gz --outdir=../Morris_2021/fastqc_results/

Sample SRR14141135:

Sample SRR14141136:

Sample SRR14141137:

Sample SRR14141138:

Sample SRR14141139:

Sample SRR14141140:

Sample SRR14141141:

Sample SRR14141142:

Sample SRR14141143:

Sample SRR14141144:

Sample SRR14141145:

Sample SRR14141146:

d. A brief summary

  • samples: 12
  • length: 26bp or 57bp (trimmed?)
  • depth: 30-35x
  • overall quality: good (within ~40 bp)

3. Analyze QC’ed STING-seq data

Note:

  • cDNA, HTO (Hashing antibody-oligos), GDO (gRNAs)
  • ECCITE-seq:

a. Install R packages

sudo apt-get update && sudo apt-get install libgeos-dev -y
sudo -i R
installe.packages("Seurat")
q()

b. Import QC’ed data

f_exp_matrix <- Seurat::ReadMtx(mtx = "../Morris_2021/GSM5225857_cDNA.mtx", 
                                features = "../Morris_2021/GSM5225857_cDNA.features.txt", 
                                cells = "../Morris_2021/GSM5225857_cDNA.barcodes.txt", 
                                cell.column=1, 
                                feature.column=1, 
                                mtx.transpose=T)
f_grna_matrix <- Seurat::ReadMtx(mtx = "../Morris_2021/GSM5225858_GDO.mtx", 
                                 features = "../Morris_2021/GSM5225858_GDO.features.txt", 
                                 cells = "../Morris_2021/GSM5225858_GDO.barcodes.txt", 
                                 cell.column=1, 
                                 feature.column=1, 
                                 mtx.transpose=T)

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.7.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.8.3     bslib_0.3.1      compiler_4.2.0   pillar_1.7.0    
 [5] later_1.3.0      git2r_0.30.1     jquerylib_0.1.4  tools_4.2.0     
 [9] getPass_0.2-2    digest_0.6.29    jsonlite_1.8.0   evaluate_0.15   
[13] tibble_3.1.7     lifecycle_1.0.1  pkgconfig_2.0.3  rlang_1.0.2     
[17] cli_3.3.0        rstudioapi_0.13  yaml_2.3.5       xfun_0.31       
[21] fastmap_1.1.0    httr_1.4.3       stringr_1.4.0    knitr_1.39      
[25] sass_0.4.1       fs_1.5.2         vctrs_0.4.1      rprojroot_2.0.3 
[29] glue_1.6.2       R6_2.5.1         processx_3.6.0   fansi_1.0.3     
[33] rmarkdown_2.14   callr_3.7.0      magrittr_2.0.3   whisker_0.4     
[37] ps_1.7.0         promises_1.2.0.1 htmltools_0.5.2  ellipsis_0.3.2  
[41] httpuv_1.6.5     utf8_1.2.2       stringi_1.7.6    crayon_1.5.1