Single-cell transcriptomics provide a systematic map of gene expression in different human cell types. The next challenge is to systematically understand cell-type-specific gene function. The integration of CRISPR-based functional genomics and stem cell technology enables the scalable interrogation of gene function in differentiated human cells. Here we present the first genome-wide CRISPR interference and CRISPR activation screens in human neurons. We uncover pathways controlling neuronal response to chronic oxidative stress, which is implicated in neurodegenerative diseases. Unexpectedly, knockdown of the lysosomal protein prosaposin strongly sensitizes neurons, but not other cell types, to oxidative stress by triggering the formation of lipofuscin, a hallmark of aging, which traps iron, generating reactive oxygen species and triggering ferroptosis. We also determine transcriptomic changes in neurons after perturbation of genes linked to neurodegenerative diseases. To enable the systematic comparison of gene function across different human cell types, we establish a data commons named CRISPRbrain.
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All screen datasets are publicly available on the CRISPRbrain website (https://crisprbrain.org/) (associated with Figs. 1 and 2 and Extended Data Figs. 1 and 2). The accession number for the RNA-seq datasets reported in this paper is GSE152988, and mapping of sgRNAs to single cells is available at https://kampmannlab.ucsf.edu/crop-seq (associated with Figs. 4, 6 and 7 and Extended Data Fig. 4). The DisGeNET database is available at https://www.disgenet.org/ (associated with Fig. 6). There are no restrictions on data availability. Source data are provided with this paper.
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We thank L. Gan and B. Conklin for support and advice; P. Kennedy and A. Nummy at Cayman Chemical for untargeted lipidomics; E. Chow (UCSF), D. Bogdanoff (UCSF), A. Detweiler (CZI Biohub), N. Neff (CZI Biohub) and M. Tan (CZI Biohub) for next-generation sequencing; A. Samelson and X. Guo for comments on this manuscript; and E. Li and J. Olzmann for discussions. We thank the staff at the University of California, Berkeley Electron Microscope Laboratory for advice and assistance in electron microscopy sample preparation and data collection. This research was supported by the Intramural Research Program of the NIH/NINDS, an NIH Director’s New Innovator Award (NIH/ NIGMS DP2 GM119139 to M.K.), NIH/NIA grants (R01 AG062359 and R56 AG057528 to M.K. and F30 AG066418 to K.L.), the NINDS Tau Center Without Walls (NIH/NINDS U54 NS100717 to M.K.), an Allen Distinguished Investigator Award (Paul G. Allen Family Foundation) to M.K., a Chan Zuckerberg Biohub Investigator Award (to M.K.) and a Tau Consortium Investigator Award (Rainwater Charitable Foundation) to M.K. K.X. is a Chan Zuckerberg Biohub investigator and acknowledges support from the National Institute of General Medical Sciences of the National Institutes of Health (DP2GM132681). The participation of A.B.S. was supported, in part, by the Intramural Research Program of the National Institute on Aging, National Institutes of Health, part of the Department of Health and Human Services (project number ZIA AG000957-16). CRISPRbrain development was supported, in part, by a collaboration among the Kampmann Lab, UCSF and Data Tecnica International, LLC.
M.K. has filed a patent application related to CRISPRi and CRISPRa screening (PCT/US15/40449) and serves on the Scientific Advisory Boards of Engine Biosciences, Casma Therapeutics and Cajal Neuroscience. The remaining authors declare no competing financial interests.
Peer review information Nature Neuroscience thanks Kristen Brennand and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended Data Fig. 1 Karyotyping of the monoclonal CRISPRa-iPSC line, and analysis of CRISPRi and CRISPRa hits.
(a) A normal karyotype was confirmed for the monoclonal CRISPRa-iPSC line. (b,c) Comparison of CRISPRi (b) and CRISPRa (c) efficacy in iPSCs and iPSC-derived neurons. The relative mRNA level of each targeted gene was calculated as the ratio of its expression in cells expressing a targeting sgRNA as compared to a non-targeting control sgRNA measured by qPCR (mean +/s sd, n = 3 technical replicates). The housekeeping gene ACTB was used for normalization. (d,e) Top, heatmaps showing phenotype scores (Log2-fold change) of all 5 sgRNAs (x-axis) targeting each hit gene (y-axis) from the primary CRISPRi (left) and CRISPRa (right) survival screens. The five sgRNAs targeting a given gene are ranked by the significance of their P values and are shown from left to right. Bottom, bar graphs summarizing the percentage of hit genes that have a certain number of sgRNAs (x-axis) showing a significant phenotype (false discovery rate (FDR) < 0.1; P values were calculated by α-RRA in the MAGeCK pipeline and FDR values were calculated using the Benjamini-Hochberg method to adjust for multiple comparisons) in CRISPRi (left) and CRISPRa (right) survival screens. (f,g) Scatter plots showing the relationship between Gene Score and gene coding sequence (CDS) length (left) or gene length (right) for genome-wide CRISPRi (f) and CRISPRa (g) survival screens. (h, i) Top: Venn diagrams comparing CRISPRi (h) and CRISPRa (i) screen results for neuronal survival from this paper with other published survival screens for different human cell types. For CRISPRi, hit genes with toxic phenotypes for the survival of neurons were compared with those for cancer cells (‘gold-standard’ essential genes 14) and pluripotent stem cells 11–13 (genes that were identified as essential in more than one studies were retained for comparison). Protective hits for the survival of neurons were compared with those for human pluripotent stem cells 11,12 (genes that were identified as essential in both studies were retained for comparison). For CRISPRa, hits were compared with our published survival screen in K562 cells 10 reanalyzed using our MAGeCK-iNC pipeline. Bottom: Gene Ontology (GO) term enrichment analysis was conducted for hits resulting in increased survival (red) or decreased survival (blue); terms are shown up to an FDR of 0.05. (j) Neuronal expression levels of neuron-specific hit genes and other hit genes from CRISPRi (top) and CRISPRa (bottom) screens are shown, binned by order of magnitude.
Each dot represents one gene, and its Gene Score in the +AO screen was plotted on the x-axis and Gene Score in the -AO screen on the y-axis. The Pearson correlation coefficient is shown.
(a) qPCR validation of PSAP knockdown in neural progenitor cells (left), astrocytes (middle) and microglia (right) diffentiated from CRISPRi iPSCs expression a PSAP sgRNA as compared to a non-targeting control sgRNA (mean +/s sd, n = 3 technical replicates). The housekeeping gene ACTB was used for normalization. (b) Representative fluorescence microscopy images for neural progenitor cells (left), astrocytes (middle) and microglia (right) diffentiated from CRISPRi iPSCs expression a non-targeting sgRNA or a PSAP sgRNA, stained with LAMP2 and LC3B antibodies from 3 independent experiments. DRAQ5 was used for nuclear staining. Scale bar, 10 μm.
Extended Data Fig. 4 Examples of the CROP-seq classification method, and shared transcriptomic signatures of VPS54, PAXIP1, and PON2 knockdown in human iPSC-derived neurons.
(a,b) CROP-seq examples showing the application of the outlier detection-based classification method in cases where two sgRNAs targeting the same gene had heterogeneous efficacy (a, SOX5 in CRISPRa) or the expression level of the target gene was too low to quantify knockdown level (b, ZNF592 in CRISPRi). (c) Transcriptomic changes induced by knockdown of VPS54 (left), PAXIP1 (middle), and PON2 (right) in neurons. For each perturbation, the top 200 upregulated and downregulated genes compared to control (that is unperturbed cells) are shown in red and blue, respectively. Within this set, shared genes among all three perturbations are highlighted in green.
Supplementary Fig. 1 and Supplementary Note.
Screen results for primary and pooled validation screens. Screens were analyzed using the MAGeCK-iNC pipeline (see Methods for details). 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. P values were calculated using the Mann–Whitney U test in the MAGeCK-iNC pipeline. P values were not corrected for multiple hypothesis testing; instead, an empirical FDR was determined as described in the Methods. Each screen is provided in a separate tab.
Untargeted lipidomics data for WT and PSAP KO neurons. Untargeted lipidomics data for WT and PSAP KO neurons. P values were calculated using two-sided Student’s t-test and were corrected for multiple testing using the Benjamini–Hochberg method.
sgRNA sequences for pooled validation and CROP-seq libraries. These tables show the protospacer sequences for sgRNAs in the pooled validation and CROP-seq libraries. Each library is provided in a separate tab. sgRNA information for the genome-wide libraries was previously published10.
sgRNA cell counts for CROP-seq screens. These tables summarize the number of cells for sgRNAs in the CRISPRi and CRISPRa CROP-seq screens. First tab: CRISPRi; second tab: CRISPRa.
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Tian, R., Abarientos, A., Hong, J. et al. Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis. Nat Neurosci 24, 1020–1034 (2021). https://doi.org/10.1038/s41593-021-00862-0
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