Additional file 1 of Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens
Creators: name: Alda-Catalinas, Celia
name: Ibarra-Soria, Ximena
name: Flouri, Christina
name: Gordillo, Jorge Esparza
name: Cousminer, Diana
name: Hutchinson, Anna
name: Sun, Bin
name: Pembroke, William
name: Ullrich, Sebastian
name: Krejci, Adam
name: Cortes, Adrian
name: Acevedo, Alison
name: Malla, Sunir
name: Fishwick, Carl
name: Drewes, Gerard
name: Rapiteanu, Radu
Publisher: name: figshare
Year: 2024
Subjects: subject: Genetics
subject: FOS: Biological sciences

description: Additional file 1: Fig. S1. A) Schematic of dCas9-KRAB lentiviral constructs tested. B) Bar plot showing the percentage of cells retaining protein expression for different target genes (CD4, CD81, BST2, ATP1B3) 10 days after TSS-targeting gRNA transduction into primary CD4+ T cells expressing different dCas9-repressor constructs, analysed by flow cytometry and normalised to the corresponding non-targeting gRNA control sample. gRNA #1 and #2 refer to two different gRNA designs for a given TSS. C) Bar plots for the same experiment as B), showing flow cytometry data for days 6, 10 and 16 post-gRNA transduction. D) Normalised expression levels of ATP1B3, measured by 10X Genomics 3’ scRNA-seq 11 days after the corresponding targeting (red) or non-targeting (grey) gRNAs were transduced into primary CD4+ T cells expressing a CBh-ZIM3-dCas9 repressor construct. Dashed line indicates median expression level in cells with non-targeting controls. E) Percentage of cells showing protein downregulation of the target gene after CRISPRi by flow cytometry (x-axis) versus significance (-log10 FDR) of downregulation of the target gene (mRNA) by 3’ scRNA-seq analysis (y-axis), normalised to the corresponding NT control. Pearson r2 = 0.98. Fig. S2. A) Distributions of the total UMIs per cell, number of detected genes per cell, and fraction of reads mapping to mitochondrial genes, used for quality control of the scRNA-seq data. Each violin corresponds to a technical replicate (channel in a 10X chip); colours indicate different 10X chips. The dotted lines indicate the thresholds used for each replicate to exclude poor-quality cells. B) Same as Fig. 2C but split per technical replicate. C) Barplot showing the number of cells where the same gRNA is the most abundant in both the cDNA and gRNA libraries (concordant, blue); the most abundant gRNA is different between libraries (discordant, orange); or no gRNA information was recovered from the cDNA library (yellow) D) Scatter plot of the relative abundance of each gRNA in the plasmid library (x-axis, assessed by DNA sequencing of the library) versus the number of cells positive for each gRNA (y-axis, assessed from the scRNA-seq data). Fig. S3. A) Barplots of the fraction of significant differentially expressed genes (DEGs) from positive control perturbations that are supported by different numbers of gRNAs (raw gRNA-level p-value &lt; 0.05). The expected genes are shown separately from all other DEGs. B) QQ plot of the expected vs observed p-values reported by MAST when testing for expression changes from non-targeting gRNAs, which should not induce any significant changes in expression. Deviation from the diagonal indicates inflated p-values. C) Same as B) but for results from SCEPTRE, limma-voom or a Wilcoxon rank sum test. D) Upset plot indicating the number of DEGs for positive control targets that are identified by any of the four methods. The height of the bar indicates the number of DEGs, split by target class (indicated by different colours). Under each bar, the methods that called the gene as significant are indicated. The expected genes are shown separately from all other DEGs. E) Same as D) but with the colours indicating the number of gRNAs supporting each DEG (raw gRNA-level p-value &lt; 0.05).