A pool of sgRNAs in the CROP-seq vector pMK1334 targeting 100 genes was transduced into Day 0 CRISPRa NGN2-neurons. Transcriptomes of ~38,000 Day 10 neurons were obtained by 10X Chromium (v3.1). sgRNA-containing transcripts were additionally amplified to facilitate sgRNA assignment.
The mean reads per cell was ~36,000 and the median number of genes detected per cell was ~3,100. Single sgRNAs were assigned to ~21,000 cells. For each gene perturbation, differentially expressed genes were determined by comparing cells containing the perturbation to cells containing non-targeting control sgRNAs.
A pool of sgRNAs in the CROP-seq vector pMK1334 targeting 27 genes was transduced into Day 0 CRISPRi iPSCs. These cells were differentiated into neurons by doxycycline-induced Ngn2 expression. Transcriptomes of ~20,000 Day 7 neurons were obtained by 10X Chromium (v2). sgRNA-containing transcripts were additionally amplified to facilitate sgRNA assignment.
The mean reads per cell was **~ 91,000** and the median number of genes detected per cell was **~ 4,600**. Single sgRNAs were assigned to ~8,400 cells. For each gene perturbation, differentially expressed genes were determined by comparing cells containing the perturbation to cells containing non-targeting control sgRNAs.
A pool of sgRNAs in the CROP-seq vector pMK1334 targeting 184 genes was transduced into Day 0 CRISPRi NGN2-neurons. Transcriptomes of ~58,000 Day 10 neurons were obtained by 10X Chromium (v3.1). sgRNA-containing transcripts were additionally amplified to facilitate sgRNA assignment.
The mean reads per cell was **~ 48,000** and the median number of genes detected per cell was **~ 4,300**. Single sgRNAs were assigned to ~35,000 cells. For each gene perturbation, differentially expressed genes were determined by comparing cells containing the perturbation to cells containing non-targeting control sgRNAs.
A pool of sgRNAs in the CROP-seq vector pMK1334 targeting 27 genes was transduced into Day 0 CRISPRi iPSCs. Transcriptomes of ~20,000 iPSCs were obtained by 10X Chromium (v2). sgRNA-containing transcripts were additionally amplified to facilitate sgRNA assignment.
The mean reads per cell was **~ 84,000** and the median number of genes detected per cell was **~ 5,000**. Single sgRNAs were assigned to ~15,000 cells. For each gene perturbation, differentially expressed genes were determined by comparing cells containing the perturbation to cells containing non-targeting control sgRNAs.
Maybe - CRISPRi FACS screen for reactive oxygen species in human iPSC-derived glutamatergic neurons.
WT CRISPRi-iPSCs were transduced with genome-wide CRISPRi-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSC) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in standard Neuronal Medium.
Day 10 neurons were dissociated using Papain (Worthington; Code: PAP2; Cat. No.LK003178) and stained with 2.5 μM CellRox Green (Invitrogen/Thermo Fisher Scientific; Cat.No.C10444) for 30 mins at 37 °C.
The cells were then sorted into high and low signal populations corresponding to the top 40% and bottom 40% of the staining signal distribution, followed by sample preparation for next-generation sequencing.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
Twelve 10-cm Matrigel-coated dishes were each seeded with 4*106 CRISPRi-iPSCs infected with virus for the H1 CRISPRi-v2 sgRNA library in N2 Pre-Differentiation Medium (day -3) and differentiated by doxycyclin-induced Ngn2 expression.
On Day 14, dead (floating cells) were removed and live (adherent) cells from two 10-cm dishes were harvested per replicate. Since neuronal death occurred over time, the estimated library representation for these time points was ~410 cells/library element on Day 14. Adherent cells were released by papain, and pelleted cells were snap frozen for downstream sample preparation.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
Twelve 10-cm Matrigel-coated dishes were each seeded with 4*106 CRISPRi-iPSCs infected with virus for the H1 CRISPRi-v2 sgRNA library in N2 Pre-Differentiation Medium (day -3) and differentiated by doxycyclin-induced Ngn2 expression.
On Day 21, dead (floating cells) were removed and live (adherent) cells from two 10-cm dishes were harvested per replicate. Since neuronal death occurred over time, the estimated library representation for these time points was ~380 cells/library element on Day 21. Adherent cells were released by papain, and pelleted cells were snap frozen for downstream sample preparation.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
Twelve 10-cm Matrigel-coated dishes were each seeded with 4*106 CRISPRi-iPSCs infected with virus for the H1 CRISPRi-v2 sgRNA library in N2 Pre-Differentiation Medium (day -3) and differentiated by doxycyclin-induced Ngn2 expression.
On Day 28, dead (floating cells) were removed and live (adherent) cells from two 10-cm dishes were harvested per replicate. Since neuronal death occurred over time, the estimated library representation for these time points was ~330 cells/library element on Day 28. Adherent cells were released by papain, and pelleted cells were snap frozen for downstream sample preparation.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs. The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’. A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%. Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
WT CRISPRi-iPSCs were transduced with a custom sgRNA library containing 2,190 sgRNAs targeting 730 genes. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in standard Neuronal Medium.
Day 10 neurons were dissociated using Papain (Worthington; Code: PAP2; Cat.No.LK003178) and stained with 5 μM FeRhoNox-1 (Goryo Chemical; Cat.No.GC901) for 60 mins at 37 °C.
The cells were then sorted into high and low signal populations corresponding to the top 40% and bottom 40% of the staining signal distribution, followed by sample preparation for next-generation sequencing.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
WT CRISPRi-iPSCs were transduced with genome-wide CRISPRi-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in the Neuronal Medium without antioxidants (no AO).
Day 10 neurons were dissociated using Papain (Worthington; Code: PAP2; Cat.No.LK003178) and stained with 5 μM Liperfluo (Dojindo Molecular Technologies, Inc.;Cat.No.L248-10) for 30 mins at 37 °C.
The cells were then sorted into high and low signal populations corresponding to the top 40% and bottom 40% of the staining signal distribution, followed by sample preparation for next-generation sequencing.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
WT CRISPRi-iPSCs were transduced with a custom sgRNA library containing 2,190 sgRNAs targeting 730 genes. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in standard Neuronal Medium.
Day 10 neurons were dissociated using Papain (Worthington; Code: PAP2; Cat.No.LK003178) and stained with 50 nM Lysotracker Green (Cell Signaling Technology; Cat.No.8783S) for 5 mins at 37 °C.
The cells were then sorted into high and low signal populations corresponding to the top 40% and bottom 40% of the staining signal distribution, followed by sample preparation for next-generation sequencing.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
WT CRISPRa-iPSCs were transduced with genome-wide CRISPRa-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in the Neuronal Medium without antioxidants (no AO). TMP was added to Day 0 neurons to induce CRISPRa activity. Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which overexpression inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
WT CRISPRi-iPSCs were transduced with genome-wide CRISPRi-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in the Neuronal Medium without antioxidants (no AO).
Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which knockdown inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
CRISPRa survival screen in human PSAP KO iPSC-derived glutamatergic neurons (medium lacking antioxidants).
PSAP-KO CRISPRa-iPSCs were transduced with genome-wide CRISPRi-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in the Neuronal Medium without antioxidants (no AO). TMP was added to Day0 neurons to induce CRISPRa activity.
Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which overexpression inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
CRISPRi survival screen in human PSAP KO iPSC-derived glutamatergic neurons (medium lacking antioxidants).
PSAP-KO CRISPRi-iPSCs were transduced with genome-wide CRISPRi-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in the Neuronal Medium without antioxidants (no AO).
Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which knockdown inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
PSAP-KO CRISPRa-iPSCs were transduced with genome-wide CRISPRa-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in standard Neuronal Medium. TMP was added to Day0 neurons to induce CRISPRa activity.
Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which overexpression inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
PSAP-KO CRISPRi-iPSCs were transduced with genome-wide CRISPRi-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in standard Neuronal Medium.
Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which knockdown inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
WT CRISPRa-iPSCs were transduced with genome-wide CRISPRa-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in standard Neuronal Medium. TMP was added to Day0 neurons to induce CRISPRa activity.
Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which overexpression inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
WT CRISPRi-iPSCs were transduced with genome-wide CRISPRi-v2 sgRNA libraries. Two days after infection, the cells were selected for lentiviral integration using puromycin (1 μg/mL) for 3 days as the cultures were expanded for the screens.
After selection and expansion, a fraction of the cells (Day -3 iPSCs) were harvested and subjected to sample preparation for next-generation sequencing. Another fraction of Day -3 iPSCs, with a cell count corresponding to 1000x coverage per library element, were differentiated into neurons and cultured in standard Neuronal Medium.
Day 10 neurons were harvested and subjected to sample preparation for next-generation sequencing.
Based on the depletion or enrichment of sgRNAs targeting specific genes at Day 10 compared to Day -3, genes for which knockdown inhibits or promotes neuronal survival were identified.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
The genome-scale screen of 13,243 gene targets was divided into five pools of plasmids, each encoding 13,000 gRNAs with the positive control gRNA AHR_8 spiked in. The protocol below is per pool of 13,000 gRNAs.
Cord blood CD34+ cells were thawed, washed with 1× HBSS and resuspended in complete culture media (StemSpan SFEM (Stemcell Technologies) with 50 ng/ml each SCF, TPO, FLT3 ligand, IL-6 and 1× antibiotic–antimycotic) with 750 nM SR1.
Two days post-thaw, 3.9 × 106 cells were pelleted, washed with PBS, resuspended in complete culture media and transduced with the lentiviral pool equaling ~300 cells per gRNA.
Two days post-transduction, the transduced cells (~15 × 106) were electroporated with nontargeting nt_A RNP using P3 Primary Cell 4D-Nucleofector X Kit S (Lonza V4XP-3032), program code CM-137, at a density of 1 × 106 cells per 25-µl electroporation volume (20 µl of cells resuspended in supplemented P3 plus 5 µl of nt_A non-targeting RNP).
After electroporation, the cells were seeded in complete culture media and cultured for 10–11 d, with the culture expanded as needed to keep the cell density less than 1 × 106 cells/ml.
After 10–11 d of culture, the cells were pelleted, resuspended in staining media with 1:100 anti-CD34–APC (BD Biosciences; 555824) and incubated at room temperature for 1 h.
Cells were then pelleted and resuspended in staining media with DAPI, and live RFP+CD34+ and RFP+CD34– cells were sorted on a FACSAria cell sorter (Becton Dickinson).Purity was confirmed by post-sort purity check. Genomic DNA was isolated with the DNeasy Blood and Tissue Kit (Qiagen; 69504) according to the manufacturer’s protocol. Sequencing libraries were generated by PCR amplification of the lentiviral vector backbone sequence from genomic DNA.
For each sample (RFP+CD34+ and RFP+CD34–), a total of 15 2-µg PCR reactions were performed using Q5 polymerase (NEB). The PCR products were purified and sequenced as above with a HiSeq 1000 (Illumina).
Original data from Ting et al., 2018 was re-analyzed using the MAGeCK-iNC pipeline. Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
Two T175 Matrigel-coated flasks were each seeded with 1107 iPSCs infected with virus for the H1 sgRNA library in 20mL Essential 8 medium with ROCK inhibitor (time point t0), corresponding to a library representation of ~1,200 cells per library element. Approxi- mately 2 107 t0 cells were also snap frozen in liquid nitrogen for downstream sample preparation to represent the Day 0 sample, corresponding to a library representation of ~1,200 cells per library element. Media was replaced on day two (t2), omitting ROCK inhibitor. Cells were released on day three (t3), and each replicate was seeded into two new T175 Matrigel-coated flasks with 1107 cells each in 20mL Essential 8 medium with ROCK inhibitor. Media was replaced on day five (t5), omitting ROCK inhibitor. Cells were released on day six (t6), cells within the same replicate were mixed across flasks, and each replicate was seeded into two new T175 Matrigel-coated flasks with 1107 cells each in 20mL Essential 8 medium with ROCK inhibitor. Media was replaced on days eight (t8) and nine (t9), omitting ROCK inhibitor. Cells were released on day ten (t10), cells within the same replicate were mixed across flasks, and 4*107 cells from each replicate were snap frozen for downstream sample preparation, corresponding to a library rep- resentation of ~2,500 cells per library element. Raw Phenotype scores and significance Pvalues were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs. The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’. A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%. Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively. .
PBMCs from multiple healthy human donors were isolated from TRIMA residuals. After CD8+ T cells isolation (Day 0), cells were stimulated with plate-bound anti-human CD3/CD28 and IL-2 at 50U/mL.
The following day, 24 hours after stimulation (Day 1), cells were transduced with concentrated lentivirus encoding the pooled sgRNA library. 24 hours after transduction (Day 2), cells were electroporated with Cas9 protein. Cells were then cultured in media with IL-2 at 50U/mL and split every two days, keeping a density of 1e6 cells/mL.
On day 14, cells were CFSE stained and then restimulated with ImmunoCult Human CD3/CD28/CD2 T Cell Activator (STEMCELL, Cat #10970). ImmunoCult was used at 1/16 of the manufacturer’s recommended dose of 25μL/1e6 cells.
Four days later cells were FACS sorted based on CFSE level. Specifically, we defined the non-proliferating cells as those with the highest CFSE peak, and the highly proliferative cells as in the 3rd highest CFSE peak and below.
Original data from Shifrut et al., 2018 was re-analyzed using the MAGeCK-iNC pipeline. Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs.
The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A ‘Gene score’ was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 5%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
A pooled sgRNA library consisting of 2 sgRNAs per targeted gene and 4 non-targeting control sgRNAs was designed to target 38 genes which were selected hit genes from iTF-Microglia survival and FACS-based screens (Supplemental Table). Briefly, top and bottom strands of sgRNA oligos were synthesized (Integrated DNA Technologies) and annealed in an arrayed format, pooled in equal amounts, and ligated into our optimized CROP-seq vector, as previously described (Tian et al., 2019).
Inducible CRISPRi-iTF-iPSCs were infected with the pooled sgRNA library at <0.15 MOI and then selected for lentiviral integration. Next, iTF-iPSCs were differentiated into iTF-microglia and cultured with the addition of TMP. Day 8 iTF-Microglia were washed 3X with DPBS, dissociated with TrypLE, and resuspended in nuclease-free water before loading onto four wells of the 10x Chromium Controller (10x Genomics, v3.1) according to the manufacturer’s protocol, with 35,000 cells recovered per sample as the target. Sample preparation was performed using the Chromium Next GEM Single Cell 3′ Reagent Kits version 3.1 (10x Genomics, cat. no. PN-1000121) according to the manufacturer’s protocol, reserving 10-30 ng full-length cDNA to facilitate sgRNA assignment by amplifying sgRNA-containing transcripts using hemi-nested PCR reactions adapted from a previously published approach (Hill et al., 2018; Tian et al., 2019). cDNA fragment analysis was performed using the 4200 TapeStation System and sgRNA enrichment libraries were separately indexed and sequenced as spike-ins alongside the whole-transcriptome scRNA-seq libraries using a NovaSeq 6000 using the following configuration: Read 1: 28; i7 index: 8; i5 index: 0; Read 2: 91.
Alignment and gene expression quantification was performed on scRNAseq libraries and sgRNA-enriched libraries using Cell Ranger (version 5.0.1, 10X Genomics) with default parameters and reference genome GRCh38-3.0.0. Cellranger aggr was used to aggregate counts belonging to the same sample across different GEM wells. The resulting gene vs. cell barcode matrix contained 58,302 cells which had on average 41,827 reads per cell, and a median of 3,346 genes per cell. sgRNA unique molecular identifier (UMI) counts for each cell barcode were obtained using a previously described mapping workflow (Hill et al., 2018). To facilitate sgRNA identity assignment, a combination of demuxEM (Gaublomme et al., 2019) and a z-score cut-off method we previously described (Tian et al., 2019) were used such that only cells with a single sgRNA as determined by both methods were carried forward in the analysis.
The raw gene vs. barcode matrix outputted by Cell Ranger was converted into a SingleCellExperiment (SCE) object using the read10xCounts function from the DropletUtils package version 1.10.3 (Lun et al., 2019) in R (v 4.0.3). sgRNA assignments were appended to the SCE metadata and filtered to only include cells with a single sgRNA, resulting in 28,905 cells. The SCE was converted into a Seurat object using Seurat::as.Seurat version 4.0.1 (Hao et al., 2020). The data was normalized and highly variable genes were identified using Seurat::SCTransform (Hafemeister and Satija, 2019). For initial data exploration, principal-component analysis was performed using Seurat::RunPCA to determine the number of principal components to retain. UMAP dimensional reduction using Seurat::RunUMAP and clustering using Seurat::FindNeighbors and Seurat::FindClusters were performed on the retained principal components with resolution = 0.7.
Initial data exploration revealed clusters that were not of interest due to a high proportion of mitochondrial-encoding genes or disrupted microglia differentiation. These clusters were removed from the downstream analysis and the remaining “microglia cluster” population was normalized, clustered, and visualized using UMAP, as described above with resolution = 0.25.
To determine the differentially expressed genes between UMAP clusters, Seurat::FindAllMarkers was used. Single-cell heatmaps, ridge plots, rank plots, and UMAPs were made using Seurat::DoHeatmap, Seurat::RidgePlot, Seurat::VizDimLoadings, and Seurat::DimPlot or Seurat::FeaturePlot, respectively.
The relative proportion of cells with a given sgRNA in a given cluster compared to cells with non-targeting control (NTC) sgRNAs in the given cluster was calculated and visualized using Complex Heatmap version 2.6.2 (Gu et al., 2016).
For each CRISPRi target gene, the population of cells with the strongest knockdown (cells with expression of target gene less than the median expression of the target gene) was carried forward to perform differential gene expression analysis using Seurat::FindMarkers with parameters test.use = ‘t’ (student’s t-test), assay = “SCT”, slot = “scale.data”, to compare the Pearson residuals of cells (Hafemeister and Satija, 2019) with knockdown sgRNAs versus non-targeting control cells. Genes with an adjusted p-value < 0.1 were deemed significant. The top 20 DEGs for each target gene which had >50 cells comprised the set of genes used to visualize convergent pathways using Complex Heatmap version 2.6.2 (Gu et al., 2016).
# iTF-Microglia-Survival/Proliferation-CRISPRi
Inducible CRISPRi iTF-iPSCs were infected with pooled CRISPRi sgRNA libraries targeting the druggable genome (Horlbeck et al., 2016) and selected with 2 µg/ml Puromycin (Gibco; Cat. No. A11138-03) for 2 - 4 days and recovered 2 - 4 days until MOI >0.9 as determined by flow cytometry of sgRNA-BFP fluorescence. Day 0 iTF-iPSCs, with a cell count corresponding to an average 1000x coverage per library element, were differentiated into iTF-Microglia with constant TMP supplementation for dCas9 stabilization.
Day 0 iPSCs and Day 15 iTF-Microglia were lifted with Accutase or TryplE Express, respectively. Lifted cells were harvested and subjected to sample preparation for next-generation sequencing as previously described (Tian et al., 2019). Briefly, for each screen sample, genomic DNA was isolated using a Macherey-Nagel Blood L kit (Macherey-Nagel; Cat. No. 740954.20). sgRNA-encoding regions were amplified and sequenced on an Illumina HiSeq-4000.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs. The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A 'Gene score' was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 10%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively. # iTF-Microglia-ImmuneActivation-CRISPRi
CRISPRi FACS screen for CD38 surface protein levels to readout immune activation in human iPSC-derived iTF-microglia.
Inducible CRISPRi iTF-iPSCs were infected with pooled CRISPRi sgRNA libraries targeting the druggable genome (Horlbeck et al., 2016) and selected with 2 µg/ml Puromycin (Gibco; Cat. No. A11138-03) for 2 - 4 days and recovered 2 - 4 days until MOI >0.9 as determined by flow cytometry of sgRNA-BFP fluorescence. Day 0 iTF-iPSCs, with a cell count corresponding to an average 1000x coverage per library element, were differentiated into iTF-Microglia with constant TMP supplementation for dCas9 stabilization.
Day 8 iTF-Microglia were dissociated with TrypleE and then blocked and stained with anti-PE-CD38. Cells were sorted into high and low signal population corresponding to the top 30% and the bottom 30% of the CD38-PE signal distribution.
Cells were subjected to sample preparation for next-generation sequencing as previously described (Tian et al., 2019). Briefly, for each screen sample, genomic DNA was isolated using a Macherey-Nagel Blood L kit (Macherey-Nagel; Cat. No. 740954.20). sgRNA-encoding regions were amplified and sequenced on an Illumina HiSeq-4000.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs. The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A 'Gene score' was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 10%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
# iTF-Microglia-Phagocytosis-CRISPRi
CRISPRi FACS screen for phagocytosis of pHRodo-labeled rat synaptosomes in human iPSC-derived iTF-microglia.
Inducible CRISPRi iTF-iPSCs were infected with pooled CRISPRi sgRNA libraries targeting the druggable genome (Horlbeck et al., 2016) and selected with 2 µg/ml Puromycin (Gibco; Cat. No. A11138-03) for 2 - 4 days and recovered 2 - 4 days until MOI >0.9 as determined by flow cytometry of sgRNA-BFP fluorescence. Day 0 iTF-iPSCs, with a cell count corresponding to an average 1000x coverage per library element, were differentiated into iTF-Microglia with constant TMP supplementation for dCas9 stabilization.
iTF-Microglia were incubated with PhRodo-Red synaptosomes (1 mg/ml) for 1.5 h. Cells were then dissociated with TryplE and sorted into high and low signal population corresponding to the top 30% and the bottom 30% of the PhRodoRed signal distribution.
Cells were subjected to sample preparation for next-generation sequencing as previously described (Tian et al., 2019). Briefly, for each screen sample, genomic DNA was isolated using a Macherey-Nagel Blood L kit (Macherey-Nagel; Cat. No. 740954.20). sgRNA-encoding regions were amplified and sequenced on an Illumina HiSeq-4000.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs. The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A 'Gene score' was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 10%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
# iTF-Microglia-Phagocytosis-CRISPRa
CRISPRa FACS screen for phagocytosis of pHRodo-labeled rat synaptosomes in human iPSC-derived iTF-microglia.
Inducible CRISPRa iTF-iPSCs were infected with pooled CRISPRa sgRNA libraries targeting the druggable genome (Horlbeck et al., 2016) and selected with 2 µg/ml Puromycin (Gibco; Cat. No. A11138-03) for 2 - 4 days and recovered 2 - 4 days until MOI >0.9 as determined by flow cytometry of sgRNA-BFP fluorescence. Day 0 iTF-iPSCs, with a cell count corresponding to an average 1000x coverage per library element, were differentiated into iTF-Microglia with constant TMP supplementation for dCas9 stabilization.
iTF-Microglia were incubated with PhRodo-Red synaptosomes (1 mg/ml) for 1.5 h. Cells were then dissociated with TryplE and sorted into high and low signal population corresponding to the top 30% and the bottom 30% of the PhRodoRed signal distribution.
Cells were subjected to sample preparation for next-generation sequencing as previously described (Tian et al., 2019). Briefly, for each screen sample, genomic DNA was isolated using a Macherey-Nagel Blood L kit (Macherey-Nagel; Cat. No. 740954.20). sgRNA-encoding regions were amplified and sequenced on an Illumina HiSeq-4000.
Raw Phenotype scores and significance P values were calculated using MAGeCK-iNC for target genes, as well as for ‘negative-control-quasi-genes’ that were generated by random sampling of 5 with replacement from non-targeting control sgRNAs. The final Phenotype score for each gene was calculated by subtracting the raw Phenotype score by the median of ‘negative-control-quasi-genes’ and then dividing it by the standard deviation of ‘negative-control-quasi-genes’.
A 'Gene score' was defined as the product of Phenotype score and –log10(Pvalue). Hit genes were determined based on Gene score cutoff corresponding to an empirical false discovery rate (FDR) of 10%.
Hit class values of 1, -1 or 0 were assigned to hit genes with positive Phenotype scores, hit genes with negative Phenotype scores or non-hits, respectively.
CRISPRi FACS screen for cell-surface LAMP1 (marker of lysosome exocytosis) in human iPSC-derived astrocytes (iAstrocytes) treated with IL1a+TNF+C1q.
To screen against the druggable genome or genes involved in vesicular trafficking, we used the H1 and H4 sub-libraries from Horlbeck et al. [PMID: 27661255]. The H1 and H4 sublibraries were packaged into lentivirus as previously described [PMID: 31422865]. For each experimental replicate, ~10 million iAstrocytes were plated onto 4 Matrigel-coated 15-cm dishes, transduced with the lentiviral transcription factor or H1 sgRNA library at high multiplicity of infection so that >70% of cells were transduced, treated with vehicle control or IL-1α+TNF+C1q for 24 hours, and then incubated with AF488-conjugated LAMP1 antibody. iAstrocytes were sorted into LAMP1 high vs. low samples (top and bottom 30% of cells) using a BD FACSAria Fusion cell sorter at 5,000-10,000 events per second, and then pelleted for genomic DNA extraction. sgRNA abundances were then measured using next-generation sequencing as previously described [PMID: 31422865]. The screens were performed with one experimental replicate per condition.
We analyzed the data from the pooled CRISPRi screens using the MAGeCK-iNC bioinformatic pipeline previously described in Tian et al. [PMID: 31422865].
CRISPRi FACS screen for cell-surface LAMP1 (marker of lysosome exocytosis) in human iPSC-derived astrocytes (iAstrocytes) treated with vehicle control.
To screen against the druggable genome or genes involved in vesicular trafficking, we used the H1 and H4 sub-libraries from Horlbeck et al. [PMID: 27661255]. The H1 and H4 sublibraries were packaged into lentivirus as previously described [PMID: 31422865]. For each experimental replicate, ~10 million iAstrocytes were plated onto 4 Matrigel-coated 15-cm dishes, transduced with the lentiviral transcription factor or H1 sgRNA library at high multiplicity of infection so that >70% of cells were transduced, treated with vehicle control or IL-1α+TNF+C1q for 24 hours, and then incubated with AF488-conjugated LAMP1 antibody. iAstrocytes were sorted into LAMP1 high vs. low samples (top and bottom 30% of cells) using a BD FACSAria Fusion cell sorter at 5,000-10,000 events per second, and then pelleted for genomic DNA extraction. sgRNA abundances were then measured using next-generation sequencing as previously described [PMID: 31422865]. The screens were performed with one experimental replicate per condition.
We analyzed the data from the pooled CRISPRi screens using the MAGeCK-iNC bioinformatic pipeline previously described in Tian et al. [PMID: 31422865].
CRISPRi FACS screen for LysoTracker staining in human iPSC-derived astrocytes (iAstrocytes) treated with IL1a+TNF+C1q.
To identify transcriptional regulators of inflammatory reactivity, we created a custom sgRNA library targeting the human transcription factors [PMID: 29425488], using 5 sgRNAs with the highest predicted activity scores from Horlbeck et al. [PMID: 27661255] per gene. The library was created by cloning a pool of sgRNA-containing oligonucleotides custom-synthesized by Agilent Technologies into our optimized sgRNA expression vector as previously described [PMID: 25307932]. To screen against the druggable genome, we used the H1 sub-library from Horlbeck et al. [PMID: 27661255]. The transcription factor and druggable genome libraries were packaged into lentivirus as previously described [PMID: 31422865]. For each experimental replicate, ~10 million iAstrocytes were plated onto 4 Matrigel-coated 15-cm dishes, transduced with the lentiviral transcription factor or H1 sgRNA library at high multiplicity of infection so that >70% of cells were transduced, treated with vehicle control or IL-1α+TNF+C1q for 24 hours, and then stained for cell-surface VCAM1. iAstrocytes were sorted into VCAM1 high vs. low samples (top and bottom 30% of cells) using a BD FACSAria Fusion cell sorter at 5,000-10,000 events per second, and then pelleted for genomic DNA extraction. sgRNA abundances were then measured using next-generation sequencing as previously described [PMID: 31422865]. The screens were performed with two experimental replicates per condition.
We analyzed the data from the pooled CRISPRi screens using the MAGeCK-iNC bioinformatic pipeline previously described in Tian et al. [PMID: 31422865].
CRISPRi FACS screen for LysoTracker staining in human iPSC-derived astrocytes (iAstrocytes) treated with vehicle control.
To identify transcriptional regulators of lysosome remodeling induced by IL-1α+TNF+C1q, we created a custom sgRNA library targeting the human transcription factors [PMID: 29425488], using 5 sgRNAs with the highest predicted activity scores from Horlbeck et al. [PMID: 27661255] per gene. The library was created by cloning a pool of sgRNA-containing oligonucleotides custom-synthesized by Agilent Technologies into our optimized sgRNA expression vector as previously described [PMID: 25307932]. To screen against the druggable genome, we used the H1 sub-library from Horlbeck et al. [PMID: 27661255]. The transcription factor and druggable genome libraries were packaged into lentivirus as previously described [PMID: 31422865]. For each experimental replicate, ~10 million iAstrocytes were plated onto 4 Matrigel-coated 15-cm dishes, transduced with the lentiviral transcription factor or H1 sgRNA library at high multiplicity of infection so that >70% of cells were transduced, treated with vehicle control or IL-1α+TNF+C1q for 24 hours, and then loaded with 50 nM LysoTracker Green. iAstrocytes were sorted into LysoTracker high vs. low samples (top and bottom 30% of cells) using a BD FACSAria Fusion cell sorter at 5,000-10,000 events per second, and then pelleted for genomic DNA extraction. sgRNA abundances were then measured using next-generation sequencing as previously described [PMID: 31422865]. The screens were performed with two experimental replicates per condition.
We analyzed the data from the pooled CRISPRi screens using the MAGeCK-iNC bioinformatic pipeline previously described in Tian et al. [PMID: 31422865].
CRISPRi FACS screen for phagocytosis of pHRodo-labeled rat synaptosomes in human iPSC-derived astrocytes (iAstrocytes) treated with IL1a+TNF+C1q.
To identify transcriptional regulators of inflammatory reactivity, we created a custom sgRNA library targeting the human transcription factors [PMID: 29425488], using 5 sgRNAs with the highest predicted activity scores from Horlbeck et al. [PMID: 27661255] per gene. The library was created by cloning a pool of sgRNA-containing oligonucleotides custom-synthesized by Agilent Technologies into our optimized sgRNA expression vector as previously described [PMID: 25307932]. To screen against the druggable genome, we used the H1 sub-library from Horlbeck et al. [PMID: 27661255]. The transcription factor and druggable genome libraries were packaged into lentivirus as previously described [PMID: 31422865]. For each experimental replicate, ~10 million iAstrocytes were plated onto 4 Matrigel-coated 15-cm dishes, transduced with the lentiviral transcription factor or H1 sgRNA library at high multiplicity of infection so that >70% of cells were transduced, treated with vehicle control or IL-1α+TNF+C1q for 24 hours, and then incubated with pHrodo-labeled rat synaptosomes. iAstrocytes were sorted into pHrodo high vs. low samples (top and bottom 30% of cells) using a BD FACSAria Fusion cell sorter at 5,000-10,000 events per second, and then pelleted for genomic DNA extraction. sgRNA abundances were then measured using next-generation sequencing as previously described [PMID: 31422865]. The screens were performed with two experimental replicates per condition.
We analyzed the data from the pooled CRISPRi screens using the MAGeCK-iNC bioinformatic pipeline previously described in Tian et al. [PMID: 31422865].
CRISPRi FACS screen for phagocytosis of pHRodo-labeled rat synaptosomes in human iPSC-derived astrocytes (iAstrocytes) treated with vehicle control.
To identify transcriptional regulators of inflammatory reactivity, we created a custom sgRNA library targeting the human transcription factors [PMID: 29425488], using 5 sgRNAs with the highest predicted activity scores from Horlbeck et al. [PMID: 27661255] per gene. The library was created by cloning a pool of sgRNA-containing oligonucleotides custom-synthesized by Agilent Technologies into our optimized sgRNA expression vector as previously described [PMID: 25307932]. To screen against the druggable genome, we used the H1 sub-library from Horlbeck et al. [PMID: 27661255]. The transcription factor and druggable genome libraries were packaged into lentivirus as previously described [PMID: 31422865]. For each experimental replicate, ~10 million iAstrocytes were plated onto 4 Matrigel-coated 15-cm dishes, transduced with the lentiviral transcription factor or H1 sgRNA library at high multiplicity of infection so that >70% of cells were transduced, treated with vehicle control or IL-1α+TNF+C1q for 24 hours, and then incubated with pHrodo-labeled rat synaptosomes. iAstrocytes were sorted into pHrodo high vs. low samples (top and bottom 30% of cells) using a BD FACSAria Fusion cell sorter at 5,000-10,000 events per second, and then pelleted for genomic DNA extraction. sgRNA abundances were then measured using next-generation sequencing as previously described [PMID: 31422865]. The screens were performed with two experimental replicates per condition.
We analyzed the data from the pooled CRISPRi screens using the MAGeCK-iNC bioinformatic pipeline previously described in Tian et al. [PMID: 31422865].
CRISPRi FACS screen for cell-surface VCAM1 (a marker of inflammatory activation) in human iPSC-derived astrocytes (iAstrocytes) treated with IL1a+TNF+C1q.
To identify transcriptional regulators of inflammatory reactivity, we created a custom sgRNA library targeting the human transcription factors [PMID: 29425488], using 5 sgRNAs with the highest predicted activity scores from Horlbeck et al. [PMID: 27661255] per gene. The library was created by cloning a pool of sgRNA-containing oligonucleotides custom-synthesized by Agilent Technologies into our optimized sgRNA expression vector as previously described [PMID: 25307932]. To screen against the druggable genome, we used the H1 sub-library from Horlbeck et al. [PMID: 27661255]. The transcription factor and druggable genome libraries were packaged into lentivirus as previously described [PMID: 31422865]. For each experimental replicate, ~10 million iAstrocytes were plated onto 4 Matrigel-coated 15-cm dishes, transduced with the lentiviral transcription factor or H1 sgRNA library at high multiplicity of infection so that >70% of cells were transduced, treated with vehicle control or IL-1α+TNF+C1q for 24 hours, and then stained for cell-surface VCAM1. iAstrocytes were sorted into VCAM1 high vs. low samples (top and bottom 30% of cells) using a BD FACSAria Fusion cell sorter at 5,000-10,000 events per second, and then pelleted for genomic DNA extraction. sgRNA abundances were then measured using next-generation sequencing as previously described [PMID: 31422865]. The screens were performed with two experimental replicates per condition.
We analyzed the data from the pooled CRISPRi screens using the MAGeCK-iNC bioinformatic pipeline previously described in Tian et al. [PMID: 31422865].