Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors

Abstract

Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and are currently impossible to screen with pooled methods. Here we combine pooled CRISPR–Cas9 screening with microraft array technology and high-content imaging to screen image-based phenotypes (CRaft-ID; CRISPR-based microRaft followed by guide RNA identification). By isolating microrafts that contain genetic clones harboring individual guide RNAs (gRNA), we identify RNA-binding proteins (RBPs) that influence the formation of stress granules, the punctate protein–RNA assemblies that form during stress. To automate hit identification, we developed a machine-learning model trained on nuclear morphology to remove unhealthy cells or imaging artifacts. In doing so, we identified and validated previously uncharacterized RBPs that modulate stress granule abundance, highlighting the applicability of our approach to facilitate image-based pooled CRISPR screens.

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Fig. 1: Microraft arrays enable the culture and stress granule quantification of thousands of clonal cells.
Fig. 2: Image analysis with CRaft-ID software identifies candidate colonies.
Fig. 3: sgRNA target identification and validation reveal new stress granule modifiers.

Data availability

Sequencing data available under GEO accession GSE139815. RBP CRISPR plasmid library is available on Addgene (141438). Protein–protein interaction data used in this study are curated from Mentha (v.2018-01-08) (https://mentha.uniroma2.it/doDownload.php?file=2018-01-08.zip) and BioPlex v.2.0 (https://bioplex.hms.harvard.edu/data/BioPlex_interactionList_v2.tsv). Any additional data that support the findings of this study are available from the corresponding author upon reasonable request.

Code availability

CRaft-ID software available at https://github.com/YeoLab/CRaftID.

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Acknowledgements

We thank S. Gebhart and N. Trotta from Cell Microsystems for extensive consultation and troubleshooting support on this project. We thank Yeo laboratory members S. Markmiller for the HEK293T-G3BP1-GFP cell line and F. Tan for the PiggyBAC shuttle vectors. We acknowledge Yeo laboratory members S. Markmiller, M. Perelis, J. Nussbacher, A. Smargon, M. Corley and E. Boyle for critical reading of the manuscript. We thank the members of the Nikon Imaging Center at UC San Diego for help with imaging experiments. E.C.W. and A.Q.V were supported by the National Science Foundation Graduate Research Fellowship. E.C.W. and N.A. were supported in part by a Ruth L. Kirschstein Institutional National Research Award from the National Institute for General Medical Sciences, T32 GM008666. J.M.E. is supported by the Ruth L. Kirschstein F31 National Research Service Award (F31 CA217173) and Cancer Systems Biology Training Program (P50 GM085764 and U54 CA209891). M.D. is supported by the Ruth L. Kirschstein F31 National Research Service Award (F31 CA206233). E.L.V. is supported by the National Human Genome Research Institute (K99HG009530). This work is partially supported by NIH grants HG004659 and NS103172 to G.W.Y and NIH grant EY024556 to N.L.A.

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Authors

Contributions

E.C.W., A.Q.V. and G.W.Y conceptualized the project. E.L.V. designed the CRISPR library. J.M.E. cloned the CRISPR library and performed viral infections. A.Q.V. optimized cell plating on microraft arrays. E.C.W. wrote analysis software and performed targeted library preparation. M.D. assisted with confocal imaging and fabricated microraft arrays. A.A.S. and E.L.V. designed the bulk CRISPR library preparation method. N.A. and A.Q.V. implemented neural network analysis. W.J. performed PPI analyses. A.Q.V. and E.C.W. performed validation experiments. E.C.W., A.Q.V. and G.W.Y. wrote the manuscript. N.L.A. and G.W.Y supervised the project.

Corresponding author

Correspondence to Gene W. Yeo.

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Competing interests

G.W.Y. is co-founder, member of the Board of Directors, on the SAB, equity holder and paid consultant for Locana and Eclipse BioInnovations. G.W.Y is a visiting professor at the National University of Singapore. E.L.V. is co-founder, member of the Board of Directors, on the SAB, equity holder and paid consultant for Eclipse BioInnovations. The interests of G.W.Y. and E.L.V. have been reviewed and approved by the University of California, San Diego in accordance with its conflict of interest policies. N.L.A. is a co-founder, on the SAB, equity holder and paid consultant for Altis Biosystems and a co-founder and equity holder in Cell Microsystems. The interests of N.L.A. have been reviewed and approved by the University of North Carolina, Chapel Hill through 1 November 2019 and by University of Washington, Seattle as of 1 November 2019 in accordance with their conflict of interest policies. The authors declare no other competing interests.

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Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Optimization of microraft arrays for stress granule quantification.

a, Uncropped Western blots measuring EIF2AK1 protein expression in cells infected with sg-NTC (nontargeting control), sg-EIF2AK1, or uninfected control cells (293 T). (n = 1) b, Scatterplot of mCherry and mCitrine area measured in the nuclei of all colonies detected on a microraft array. Colonies that contain fluorescent signal from both channels in more than 10% of the total nuclei area are determined as doublets (gray). c, Time-course analysis of stress granule formation in HEK293T cells under multiple sodium arsenite concentrations measured in 30-minute intervals. Stress granule area is quantified as G3BP1(+) cytoplasmic puncta across n = 1 image. d, Top, schematic of microraft array without glass-back support. Orthogonal view of autofluorescence (green) in microrafts across PDMS array after imaging with high laser power. Bottom, diagram of microraft array with 1 mm glass support with orthogonal view of autofluorescent microrafts after imaging with high laser power (green). e, Random sampling to estimate plating frequency of sgRNAs on rafts in this screen. Given the relative abundances of sgRNAs on day 7 and the total number of colonies plated (~120,000), random sampling was used to estimate the number of rafts that contain each sgRNA (x-axis), binned in counts of 5. Bars are the average of n = 10 random samplings with error bars displaying standard deviation.

Extended Data Fig. 2 Performance of classifiers in image filtering model.

a, Learning curves for each binary classifier for 10,000 epochs of training. b, Top, confusion matrix for 365 test images comparing the overall model’s predicted classifications for each image with its ground-truth. Bottom, average precision rate, recall rate (true positive rate), F1-scores (harmonic mean of precision and recall), and number of images (n) for each binary classifier.

Extended Data Fig. 3 Library preparation scheme to sequence sgRNA infected in colonies.

a, Schematic of PCR barcoding design targeting common regions flanking the sgRNA insert. b, Agarose gel of PCR products with increasing cycle numbers to determine the minimum number of PCR cycles required to amplify a product for sequencing. Input material from bulk sample is used as a positive control. All rafts sequenced in this study were amplified with 22 cycles for PCR1 and 10 cycles for PCR2 (n = 213 total, 173 successful). c, TapeStation results of PCR products for one representative library containing four pooled microrafts. d, Agarose gel of PCR2 product for three representative libraries, each containing four pooled microrafts. Gel extraction was used to isolate the product of interest (red box) from a total of n = 32 libraries. e, Summary of the number of sgRNAs identified from each isolated cell colony. f, Bar chart of the total number of rafts picked, sequenced, and confirmed by siRNA depletion.

Extended Data Fig. 4 Uncropped Western blots for siRNA knock-down experiments.

a, Samples with knock-down of each protein (labeled above) compared to nontargeting control and untransfected sample. 1 - si-KD targeting, 2 - si-Nontargeting control, 3 - Untransfected HEK293T cells. b, GAPDH blots for each respective sample tested in panel a using antibodies of the opposite species on the same membrane.

Extended Data Fig. 5 Depletion of stress-granule regulatory proteins also reduces UBAP2L puncta formation.

a, siRNA depletion of target RBPs. UBAP2L(+) granule/nuclei area was normalized to the nontargeting control (NTC) for each experiment. RBPs are ordered in order of appearance in Fig. 3c. *RBPs that had significant reduction (P < 0.05, unpaired two-tailed t test, d.f. = 4, 95% confidence interval) of UBAP2L (+) granule area relative to NTC in at least 3 of the 4 biological replicates. Data are mean ± s.d. across n = 3 wells/condition (4 images/well). b, G3BP1(+) granule/nuclei area from respective wells measured in panel a. Values are normalized to nontargeting control (NTC) for each experiment. *RBPs that had significant reduction (P < 0.05, unpaired two-tailed t test, d.f. = 4, 95% confidence interval) of UBAP2L (+) granule area relative to NTC in at least 3 of the 4 biological replicates. Data are mean ± s.d. across n = 3 wells/condition (4 images/well).

Supplementary information

Supplementary Information

Step-by-step experimental protocol required to perform a screen with CRaft-ID.

Reporting Summary

Supplementary Tables

Excel workbook with Supplementary Tables 1–3.

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Wheeler, E.C., Vu, A.Q., Einstein, J.M. et al. Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors. Nat Methods 17, 636–642 (2020). https://doi.org/10.1038/s41592-020-0826-8

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