Project-1: STING-seq

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Last updated: 2022-06-21

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1. Understand RNA-seq and STING-seq

a. Read about RNA-seq analysis

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 of isoforms):

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

Protocol-3 (crypic splicing):

• Protocol-1
• detect differential junstions (CrypSplice)

b. Read more about RNA-seq analysis

Luecken et al. 2019: RNA-seq analysis pipeline

Some key points:

c. 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 read more on the official website
• 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:

• Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq)
• Expanded CRISPR-compatible CITE-seq (ECCITE-Seq)
• cDNA, HTO (Hashtag oligos), GDO (gRNAs)
• ECCITE-seq:

2. Prelim QC for raw STING-seq data

a. Download all data

Code: download.sh

b. Perform FastQC on all fastq files

Code: fastqc.sh

SRR14141135:

• SRR14141135_1
  
• SRR14141135_2

SRR14141136:

• SRR14141136_1
  
• SRR14141136_2

SRR14141137:

• SRR14141137_1
  
• SRR14141137_2

SRR14141138:

• SRR14141138_1
  
• SRR14141138_2

SRR14141139:

• SRR14141139_1
  
• SRR14141139_2

SRR14141140:

• SRR14141140_1
  
• SRR14141140_2

SRR14141141:

• SRR14141141_1
  
• SRR14141141_2

SRR14141142:

• SRR14141142_1
  
• SRR14141142_2

SRR14141143:

• SRR14141143_1
  
• SRR14141143_2

SRR14141144:

• SRR14141144_1
  
• SRR14141144_2

SRR14141145:

• SRR14141145_1
  
• SRR14141145_2

SRR14141146:

• SRR14141146_1
  
• SRR14141146_2

A brief summary:

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

d. Kallisto | bustools pipeline

Code: pip3-kb.sh
Code: anaconda_kallisto.sh

3. Analyze QC’ed STING-seq data

Code: overview.R

a. Install packages

Code: seurat.sh

b. Data overview

Note: about sparse matrix

The [Expression] matrix has: 
- 35,606 rows/genes/targets 
- 686,612 columns/barcodes/cells 
- 24,447,506,872 values in total 
- 82,507,471 values that are non-zero 
- 50,421,358 values that are 1 
- 32,086,113 values that are bigger than 1 
- 3,370,699 values that are bigger than 10 
- 259,734 values that are bigger than 100 
- 2,515 values that are bigger than 1,000 
- 0 values that are bigger than 10,000 
- 0 values that are bigger than 100,000 
 
The [gRNA] matrix has: 
- 210 rows/genes/targets 
- 137,347 columns/barcodes/cells 
- 28,842,870 values in total 
- 2,506,474 values that are non-zero 
- 1,510,919 values that are 1 
- 995,555 values that are bigger than 1 
- 121,554 values that are bigger than 10 
- 41,071 values that are bigger than 100 
- 2,232 values that are bigger than 1,000 
- 20 values that are bigger than 10,000 
- 0 values that are bigger than 100,000 
 
The [Hashtag] matrix has: 
- 4 rows/genes/targets 
- 410,228 columns/barcodes/cells 
- 1,640,912 values in total 
- 739,820 values that are non-zero 
- 409,830 values that are 1 
- 329,990 values that are bigger than 1 
- 218,280 values that are bigger than 10 
- 8,155 values that are bigger than 100 
- 282 values that are bigger than 1,000 
- 46 values that are bigger than 10,000 
- 0 values that are bigger than 100,000 

c. Calculate means and sum non-zero UMI counts

Code: Expression_barcode_stats.R
Code: Expression_target_stats.R

Code: gRNA_barcode_stats.R
Code: gRNA_target_stats.R

Code: Hashtag_barcode_stats.R
Code: Hashtag_target_stats.R

Output:

Expression_matrix_barcodes_summary.csv (pending)
Expression_matrix_targets_summary.csv

gRNA_matrix_barcodes_summary.csv
gRNA_matrix_targets_summary.csv

Hashtag_matrix_barcodes_summary.csv
Hashtag_matrix_targets_summary.csv

d. Expression (cDNA) dataset

i) Expression barcodes

ii) Expression targets

e. gRNA dataset

i) gRNA barcodes

ii) gRNA targets

f. Hashtag dataset

i) Hashtag barcodes

i) Hashtag targets

Session information

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