Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Resource
  • Published:

Genome-wide CRISPRi/a screens in human neurons link lysosomal failure to ferroptosis

Nature Neuroscience volume 24, pages 1020–1034 (2021)Cite this article

Subjects

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Genome-wide CRISPRi and CRISPRa screens in human iPSC-derived neurons identify regulators of neuronal survival.
Fig. 2: Genome-wide CRISPRi and CRISPRa screens in human iPSC-derived neurons identify regulators of oxidative stress survival and redox homeostasis.
Fig. 3: Loss of prosaposin induces ROS and lipid peroxidation in neurons and causes neuronal ferroptosis in the absence of antioxidants.
Fig. 4: Loss of prosaposin disrupts glycosphingolipid degradation specifically in neurons and leads to cholesterol accumulation.
Fig. 5: Impaired lipid degradation in PSAP KO neurons leads to lipofuscin formation, iron accumulation and impaired autophagy.
Fig. 6: CROP-seq reveals transcriptomic responses to perturbations of neurodegenerative disease-associated genes in human iPSC-derived neurons.
Fig. 7: Overexpression of NQO1 induces unexpected transcriptome changes in human iPSC-derived neurons that provide hypotheses for its toxicity.
Fig. 8: CRISPRbrain, a data commons for functional genomics screens in differentiated human cell types.

Similar content being viewed by others

Data availability

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.

Code availability

All data analyses were performed using published computational pipelines and standard Python/R packages as described in the Methods. Our pipeline for analysis of the CROP-seq data is available at https://kampmannlab.ucsf.edu/crop-seq.

References

  1. Tian, R. et al. CRISPR interference-based platform for multimodal genetic screens in human iPSC-derived neurons. Neuron 104, 239–255.e12 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kampmann, M. CRISPR-based functional genomics for neurological disease. Nat. Rev. Neurol. 16, 465–480 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Weltner, J. et al. Human pluripotent reprogramming with CRISPR activators. Nat. Commun. 9, 2643 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Wang, C. et al. Scalable production of iPSC-derived human neurons to identify tau-lowering compounds by high-content screening. Stem Cell Rep. 9, 1221–1233 (2017).

    Article  CAS  Google Scholar 

  6. Horlbeck, M. A. et al. Compact and highly active next-generation libraries for CRISPR-mediated gene repression and activation. eLife 5, e19760 (2016).

  7. Yilmaz, A., Peretz, M., Aharony, A., Sagi, I. & Benvenisty, N. Defining essential genes for human pluripotent stem cells by CRISPR–Cas9 screening in haploid cells. Nat. Cell Biol. 20, 610–619 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Hart, T. et al. Evaluation and design of genome-wide CRISPR/SpCas9 knockout screens. G3 (Bethesda) 7, 2719–2727 (2017).

    Article  CAS  Google Scholar 

  9. Colacurcio, D. J. & Nixon, R. A. Disorders of lysosomal acidification—the emerging role of v-ATPase in aging and neurodegenerative disease. Ageing Res. Rev. 32, 75–88 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Dandage, R. & Landry, C. R. Paralog dependency indirectly affects the robustness of human cells. Mol. Syst. Biol. 15, e8871 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kügler, S. et al. The X-linked inhibitor of apoptosis (XIAP) prevents cell death in axotomized CNS neurons in vivo. Cell Death Differ. 7, 815–824 (2000).

    Article  PubMed  CAS  Google Scholar 

  12. Schulz, A. et al. The stress-responsive gene GDPGP1/mcp-1 regulates neuronal glycogen metabolism and survival. J. Cell Biol. 219, e201807127 (2020).

  13. Chen, X. et al. Antiapoptotic and trophic effects of dominant-negative forms of dual leucine zipper kinase in dopamine neurons of the substantia nigra in vivo. J. Neurosci. 28, 672–680 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Smith, C. D. et al. Excess brain protein oxidation and enzyme dysfunction in normal aging and in Alzheimer disease. Proc. Natl Acad. Sci. USA 88, 10540–10543 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Hensley, K. et al. Brain regional correspondence between Alzheimer’s disease histopathology and biomarkers of protein oxidation. J. Neurochem. 65, 2146–2156 (1995).

    Article  CAS  PubMed  Google Scholar 

  16. Yao, Y. et al. Enhanced brain levels of 8,12-iso-iPF2α-VI differentiate AD from frontotemporal dementia. Neurology 61, 475–478 (2003).

    Article  CAS  PubMed  Google Scholar 

  17. Dalfó, E. et al. Evidence of oxidative stress in the neocortex in incidental Lewy body disease. J. Neuropathol. Exp. Neurol. 64, 816–830 (2005).

    Article  PubMed  Google Scholar 

  18. Subbarao, K. V., Richardson, J. S. & Ang, L. C. Autopsy samples of Alzheimer’s cortex show increased peroxidation in vitro. J. Neurochem. 55, 342–345 (1990).

    Article  CAS  PubMed  Google Scholar 

  19. Yang, W. S. et al. Regulation of ferroptotic cancer cell death by GPX4. Cell 156, 317–331 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Wang, C. et al. Elevated p62/SQSTM1 determines the fate of autophagy-deficient neural stem cells by increasing superoxide. J. Cell Biol. 212, 545–560 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Taguchi, K. et al. Keap1 degradation by autophagy for the maintenance of redox homeostasis. Proc. Natl Acad. Sci. USA 109, 13561–13566 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Reho, J. J., Guo, D.-F. & Rahmouni, K. Mechanistic target of rapamycin complex 1 signaling modulates vascular endothelial function through reactive oxygen species. J. Am. Heart Assoc. 8, e010662 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Delgado-Camprubi, M., Esteras, N., Soutar, M. P., Plun-Favreau, H. & Abramov, A. Y. Deficiency of Parkinson’s disease-related gene Fbxo7 is associated with impaired mitochondrial metabolism by PARP activation. Cell Death Differ. 24, 120–131 (2017).

    Article  CAS  PubMed  Google Scholar 

  24. Doll, S. et al. ACSL4 dictates ferroptosis sensitivity by shaping cellular lipid composition. Nat. Chem. Biol. 13, 91–98 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Yuan, H., Li, X., Zhang, X., Kang, R. & Tang, D. Identification of ACSL4 as a biomarker and contributor of ferroptosis. Biochem. Biophys. Res. Commun. 478, 1338–1343 (2016).

    Article  CAS  PubMed  Google Scholar 

  26. Ishimoto, T. et al. CD44 variant regulates redox status in cancer cells by stabilizing the xCT subunit of system xc− and thereby promotes tumor growth. Cancer Cell 19, 387–400 (2011).

    Article  CAS  PubMed  Google Scholar 

  27. Weber, R. A. et al. Maintaining iron homeostasis is the key role of lysosomal acidity for cell proliferation. Mol. Cell 77, 645–655 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Haack, T. B. et al. Exome sequencing reveals de novo WDR45 mutations causing a phenotypically distinct, X-linked dominant form of NBIA. Am. J. Hum. Genet. 91, 1144–1149 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Diaz-Font, A. et al. A mutation within the saposin D domain in a Gaucher disease patient with normal glucocerebrosidase activity. Hum. Genet. 117, 275–277 (2005).

    Article  PubMed  Google Scholar 

  30. Radha Rama Devi, A. et al. Acute Gaucher disease-like condition in an Indian infant with a novel biallelic mutation in the prosaposin gene. J. Pediatr. Genet. 8, 81–85 (2019).

    Article  PubMed  Google Scholar 

  31. Spiegel, R. et al. A mutation in the saposin A coding region of the prosaposin gene in an infant presenting as Krabbe disease: first report of saposin A deficiency in humans. Mol. Genet. Metab. 84, 160–166 (2005).

    Article  CAS  PubMed  Google Scholar 

  32. Oji, Y. et al. Variants in saposin D domain of prosaposin gene linked to Parkinson’s disease. Brain 143, 1190–1205 (2020).

    Article  PubMed  Google Scholar 

  33. Jain, I. H. et al. Genetic screen for cell fitness in high or low oxygen highlights mitochondrial and lipid metabolism. Cell 181, 716–727(2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Bartz, F. et al. Identification of cholesterol-regulating genes by targeted RNAi screening. Cell Metab. 10, 63–75 (2009).

    Article  CAS  PubMed  Google Scholar 

  35. Puri, V. et al. Sphingolipid storage induces accumulation of intracellular cholesterol by stimulating SREBP-1 cleavage. J. Biol. Chem. 278, 20961–20970 (2003).

    Article  CAS  PubMed  Google Scholar 

  36. Glaros, E. N. et al. Glycosphingolipid accumulation inhibits cholesterol efflux via the ABCA1/apolipoprotein A-I pathway: 1-phenyl-2-decanoylamino-3-morpholino-1-propanol is a novel cholesterol efflux accelerator. J. Biol. Chem. 280, 24515–24523 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Brunk, U. T., Jones, C. B. & Sohal, R. S. A novel hypothesis of lipofuscinogenesis and cellular aging based on interactions between oxidative stress and autophagocytosis. Mutat. Res. 275, 395–403 (1992).

    Article  CAS  PubMed  Google Scholar 

  38. Ross, D. & Siegel, D. Functions of NQO1 in cellular protection and Coq10 metabolism and its potential role as a redox sensitive molecular switch. Front. Physiol. 8, 595 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Asher, G., Lotem, J., Kama, R., Sachs, L. & Shaul, Y. NQO1 stabilizes p53 through a distinct pathway. Proc. Natl Acad. Sci. USA 99, 3099–3104 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Ramsey, C. P. et al. Expression of Nrf2 in neurodegenerative diseases. J. Neuropathol. Exp. Neurol. 66, 75–85 (2007).

    Article  CAS  PubMed  Google Scholar 

  41. Imaizumi, Y. et al. Mitochondrial dysfunction associated with increased oxidative stress and α-synuclein accumulation in PARK2 iPSC-derived neurons and postmortem brain tissue. Mol. Brain 5, 35 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Tsherniak, A. et al. Defining a cancer dependency map. Cell 170, 564–576 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Oughtred, R. et al. The BioGRID database: a comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 30, 187–200 (2021).

    Article  CAS  PubMed  Google Scholar 

  44. Cui, Y. et al. CRISP-view: a database of functional genetic screens spanning multiple phenotypes. Nucleic Acids Res. 49, D848–D854 (2021).

    Article  CAS  PubMed  Google Scholar 

  45. Guo, X. et al. Mitochondrial stress is relayed to the cytosol by an OMA1-DELE1-HRI pathway. Nature 579, 427–432 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Topol, A., Tran, N. N. & Brennand, K. J. A guide to generating and using hiPSC derived NPCs for the study of neurological diseases. J. Vis. Exp. e52495 (2015).

  47. Cheng, C., Fass, D. M., Folz-Donahue, K., MacDonald, M. E. & Haggarty, S. J. Highly expandable human iPS cell-derived neural progenitor cells (NPC) and neurons for central nervous system disease modeling and high-throughput screening. Curr. Protoc. Hum. Genet 92, 21.8.1–21.8.21 (2017).

    CAS  Google Scholar 

  48. Tcw, J. et al. An efficient platform for astrocyte differentiation from human induced pluripotent stem cells. Stem Cell Rep. 9, 600–614 (2017).

    Article  CAS  Google Scholar 

  49. Brownjohn, P. W. et al. Functional studies of missense TREM2 mutations in human stem cell-derived microglia. Stem Cell Rep. 10, 1294–1307 (2018).

    Article  CAS  Google Scholar 

  50. Piñero, J. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxf.) 2015, bav028 (2015).

    Article  CAS  Google Scholar 

  51. Bersuker, K. et al. The CoQ oxidoreductase FSP1 acts parallel to GPX4 to inhibit ferroptosis. Nature 575, 688–692 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Komarov, P. G. et al. A chemical inhibitor of p53 that protects mice from the side effects of cancer therapy. Science 285, 1733–1737 (1999).

    Article  CAS  PubMed  Google Scholar 

  53. Zhao, C. et al. Dual regulatory switch through interactions of Tcf7l2/Tcf4 with stage-specific partners propels oligodendroglial maturation. Nat. Commun. 7, 10883 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Hauser, M. et al. The spectrin-actin-based periodic cytoskeleton as a conserved nanoscale scaffold and ruler of the neural stem cell lineage. Cell Rep. 24, 1512–1522 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wojcik, M., Hauser, M., Li, W., Moon, S. & Xu, K. Graphene-enabled electron microscopy and correlated super-resolution microscopy of wet cells. Nat. Commun. 6, 7384 (2015).

    Article  CAS  PubMed  Google Scholar 

  56. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    PubMed  PubMed Central  Google Scholar 

  57. Hill, A. J. et al. On the design of CRISPR-based single-cell molecular screens. Nat. Methods 15, 271–274 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  59. Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Soneson, C., Love, M. I. & Robinson, M. D. Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences. F1000Res. 4, 1521 (2015).

    Article  PubMed  CAS  Google Scholar 

  63. Pang, X. P., Hershman, J. M., Chung, M. & Pekary, A. E. Characterization of tumor necrosis factor-α receptors in human and rat thyroid cells and regulation of the receptors by thyrotropin. Endocrinology 125, 1783–1788 (1989).

    Article  CAS  PubMed  Google Scholar 

  64. Liao, Y., Wang, J., Jaehnig, E. J., Shi, Z. & Zhang, B. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic Acids Res. 47, W199–W205 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

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.

Author information

Author notes

  1. Ruilin Tian

    Present address: School of Medicine, Southern University of Science and Technology, Shenzhen, China

Authors and Affiliations

  1. Institute for Neurodegenerative Diseases, Department of Biochemistry and Biophysics, University of California, San Francisco, San Francisco, CA, USA

    Ruilin Tian, Anthony Abarientos, Jason Hong, Nina Dräger, Kun Leng & Martin Kampmann

  2. Biophysics Graduate Program, University of California, San Francisco, San Francisco, CA, USA

    Ruilin Tian

  3. Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Sayed Hadi Hashemi & Faraz Faghri

  4. Department of Chemistry, University of California, Berkeley, Berkeley, CA, USA

    Rui Yan & Ke Xu

  5. Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA

    Mike A. Nalls, Andrew B. Singleton & Faraz Faghri

  6. Data Tecnica International, LLC, Glen Echo, MD, USA

    Mike A. Nalls & Faraz Faghri

  7. Chan Zuckerberg Biohub, San Francisco, CA, USA

    Martin Kampmann

Authors
  1. Ruilin Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Anthony Abarientos
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jason Hong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Sayed Hadi Hashemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nina Dräger
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Kun Leng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mike A. Nalls
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Andrew B. Singleton
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Ke Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Faraz Faghri
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Martin Kampmann
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.T. and M.K. conceived this study and wrote the manuscript with input from the other authors. R.T. designed and conducted experiments with help from A.A. and J.H. and guidance from M.K. R.T. performed data analyses. R.Y. performed STORM imaging with guidance from K.X. N.D. generated iPSC-derived microglia, and K.L. generated iPSC-derived astrocytes. S.H.H., M.A.N. and F.F. developed the CRISPRbrain data commons with critical input from R.T. and M.K. and feedback from A.B.S. All authors reviewed and approved the final manuscript.

Corresponding authors

Correspondence to Ruilin Tian or Martin Kampmann.

Ethics declarations

Competing interests

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.

Additional information

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

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.

Extended Data Fig. 2 Comparing CRISPRa survival screens in +AO and -AO conditions.

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.

Extended Data Fig. 3 Characterization of PSAP KO in other cell types.

(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 information

Supplementary Information

Supplementary Fig. 1 and Supplementary Note.

Reporting Summary

Supplementary Table 1

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.

Supplementary Table 2

Hit class info for Fig. 2g. This table shows the hit class info for genes in each screen in Fig. 2g. 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.

Supplementary Table 3

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.

Supplementary Table 4

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.

Supplementary Table 5

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.

Source data

Source Data Fig. 3

Unprocessed western blots for Fig. 3c.

Source Data Fig. 5

Unprocessed western blots for Fig. 5l.

Source Data Fig. 7

Unprocessed western blots for Fig. 7e.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-021-00862-0

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research