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Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView

Nature Biotechnology volume 43, pages 1091–1100 (2025)Cite this article

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An Author Correction to this article was published on 26 February 2025

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Abstract

Optical pooled screening (OPS) is a scalable method for linking image-based phenotypes with cellular perturbations. However, it has thus far been restricted to relatively low-plex phenotypic readouts in cancer cell lines in culture due to limitations associated with in situ sequencing of perturbation barcodes. Here, we develop PerturbView, an OPS technology that leverages in vitro transcription to amplify barcodes before in situ sequencing, enabling screens with highly multiplexed phenotypic readouts across diverse systems, including primary cells and tissues. We demonstrate PerturbView in induced pluripotent stem cell-derived neurons, primary immune cells and tumor tissue sections from animal models. In a screen of immune signaling pathways in primary bone marrow-derived macrophages, PerturbView uncovered both known and novel regulators of NF-κB signaling. Furthermore, we combine PerturbView with spatial transcriptomics in tissue sections from a mouse xenograft model, paving the way to in situ screens with rich optical and transcriptomic phenotypes. PerturbView broadens the scope of OPS to a wide range of models and applications.

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Fig. 1: A chimeric U6 and T7 promoter enables robust editing and in situ sgRNA detection with PerturbView.
Fig. 2: PerturbView screens with multiplex molecular readouts in primary immune cells reveal shared and context-specific mechanisms of NF-κB signaling.
Fig. 3: PerturbView enables simultaneous sgRNA detection and spatial transcriptomics in a xenograft model.

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Data availability

Screening and tissue images will be available on Image Data Resource (https://idr.openmicroscopy.org/). Additional data are available from the corresponding author upon request.

Code availability

Tissue analysis code is available at https://github.com/Genentech/PerturbView. Additional information required to reanalyze the data reported in this paper is available from the corresponding author on request.

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Acknowledgements

We thank L. Gaffney and A. Hupalowska for their help in figure making. Schematic figures were created with BioRender.com.

Author information

Author notes

  1. Shuangyi Cai

    Present address: Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, USA

  2. Cherry Sakura Lu

    Present address: Faculty of Environment and Information Studies, Keio University, Tokyo, Japan

  3. These authors jointly supervised this work: Aviv Regev, Eric Lubeck.

Authors and Affiliations

  1. Genentech Research and Early Development, Genentech, Inc., South San Francisco, CA, USA

    Takamasa Kudo, Ana M. Meireles, Reuben Moncada, Yushu Chen, Ping Wu, Joshua Gould, Xiaoyu Hu, Opher Kornfeld, Rajiv Jesudason, Conrad Foo, Burkhard Höckendorf, Hector Corrada Bravo, Jason P. Town, Runmin Wei, Antonio Rios, Vineethkrishna Chandrasekar, Melanie Heinlein, Amy S. Chuong, Shuangyi Cai, Cherry Sakura Lu, Paula Coelho, Monika Mis, Cemre Celen, Noelyn Kljavin, Jian Jiang, David Richmond, Pratiksha Thakore, Elia Benito-Gutiérrez, Kathryn Geiger-Schuller, Jose Sergio Hleap, Nobuhiko Kayagaki, Felipe de Sousa e Melo, Lisa McGinnis, Bo Li, Avtar Singh, Levi Garraway, Orit Rozenblatt-Rosen, Aviv Regev & Eric Lubeck

  2. Bioinformatics Department, ProCogia, Toronto, Ontario, Canada

    Jose Sergio Hleap

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Contributions

T.K., E.L. and A.R. conceived the study. T.K. and E.L. designed the study, experiments and analysis and interpreted data. T.K., R.M. and Y.C. performed ISS in tumors. A.M.M. and P.C. advised on and supported macrophage screens. P.C., V.C. and N. Kayagaki advised on the interpretation of screens. M.H., N. Kljavin, J.J., F.d.S.M. and L.M. advised on and performed animal experiments. P.W. performed MCF7 experiments. P.W., A.S.C. and A.S. supported microscopy. X.H., O.K., N. Kayagaki, V.C., C.C., M.M., P.C., P.T. and K.G.-S. advised on and provided cell lines. C.S.L. and S.C. supported optimization of multiplexed imaging. T.K., R.J., C.F., H.C.B., J.P.T., R.W. and E.L. analyzed tissue data. T.K., B.H. and A.R. performed multimodal screen analysis. J.G., J.S.H., B.L., E.B.-G., D.R. and E.L. advised on and supported analysis. T.K., E.L., A.S. and A.R. wrote the paper, and E.L., L.G., O.R.-R. and A.R. supervised the work.

Corresponding authors

Correspondence to Aviv Regev or Eric Lubeck.

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Competing interests

Genentech has submitted a provisional patent application that is based on the technology described in this manuscript. All authors are or were employed by Genentech, Inc., South San Francisco, California, at the time of their contribution to this work. A.R. is a cofounder and equity holder of Celsius Therapeutics and an equity holder in Immunitas and, until 31 July 2020, was a scientific advisory board member of Thermo Fisher Scientific, Syros Pharmaceuticals, Neogene Therapeutics and Asimov. T.K. is a shareholder of Genomelink, Inc. E.L. is an equity holder in insitro, Inc. A.M.M., R.M., Y.C., P.W., J.G., X.H., O.K., R.J., C.F., B.H., H.C.B., J.P.T., R.W., A.S.C., M.M., C.C., N. Kljavin, J.J., D.R., P.T., E.B.-G., K.G.-S., J.S.H., N. Kayagaki, F.d.S.M., L.M., B.L., A.S., L.G., O.R.-R., A.R. and E.L. are equity holders in Roche.

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Nature Biotechnology thanks Howard Chang, Prisca Liberali and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Establishment of PerturbView.

a, Varied activity of CROP-Seq vector by HCR FISH in primary cells. Left, HCR FISH of CROP-Seq expressed transcript (green; detected with FISH probes against puromycin and U6 promoter regions; Supplementary Table 7) across different mouse cell lines (scale bar, 200 μm). Right, CROP-Seq transcript density in A549 (left) and mouse BMDM (right) cells (scale bar, 50 μm). All cells were transduced at low MOI and puromycin-selected. b, T7 promoter position dramatically affects IVT-enhanced ISS signal. Distribution of median single cell nuclear intensity following IVT in the original U6 promoter (top; gray, n = 62,321 cells pooled from three well replicates), T7 promoter in place of the U6 promoter (orange, n = 48,123), and T7 promoter upstream of U6 promoter (blue, n = 53,777). Bottom, representative cellular images from each construct (scale bar, 25 μm). c, Example gating strategy for CD81 flow cytometry assays. d, The U6/T7 promoter32 has lower CRISPR editing activity than the unmodified U6 promoter. The distribution of CD81 expression by flow cytometry for each construct. The fraction of knocked-out cells (thresholded by the vertical line) is noted on the left. k = 34,950 cells, at least 11,247 cells per condition. e, Editing efficiency of the PerturbView vector is identical to wild-type U6 promoter in A549 cells, immortalized macrophages and fibroblasts. Distribution of target gene expression of each construct in A549 cells (top; CD81, n = 3 independent infection replicates combined; 876,505 cells, at least 61,250 cells per replicate and condition), primary IMR90 fibroblasts (bottom; CD81, 17,497 cells, at least 5,524 cells per condition) and immortalized macrophages (right; CD44, sg1 and sg2: separate guide replicates; 126,093 cells, at least 20,844 cells per condition). The fraction of knocked-out cells (thresholded by the vertical line) is noted on the left. f, g, Decrosslinking and IVT duration impact sensitivity and precision of barcode detection. Bottom, mean sensitivity (purple) and precision (blue)at different durations of decrosslinking (f, n = 4 well replicates (dots), at least 9,393 cells per replicate) or IVT (g, n = 4 well replicates (dots), at least 9,387 cells per replicate). Error bars: SD. Top, representative cellular images from each condition (scale bar, 25 μm).

Extended Data Fig. 2 Optimization of PerturbView.

a, PerturbView with methanol fixation. Mean sensitivity (blue) and precision (orange) with (65 °C) or without (25 °C) decrosslinking in methanol-fixed cells (left) or PFA-fixed cells (right) (n = 2 independent replicates). b, PerturbView enables in situ sequencing at 4x magnification. Left, Whole well (top; scale bar, 1 mm) or zoomed image (bottom; scale bar, 50 μm) representative image acquired at 4X magnification with either conventional ISS (left) or PerturbView (right). All images are contrasted identically. Heat decrosslinking prior to PerturbView results in increased nuclear DAPI intensity. c,d, DTT rescues IVT efficiency at low T7 polymerase concentration in MCF7 cells. c, representative cell images from each condition (scale bar, 25 μm). d, Mean maximum normalized intensity of in situ sequencing spots (y axis) at different T7 polymerase concentrations (x axis) with (orange, n = 3 technical replicates) or without (grey, n = 2 technical replicates) DTT.

Extended Data Fig. 3 Context and phenotype specific perturbation effects in PerturbView screen in primary immune cells.

a, p65 staining based assay. Representative images cells stained with anti-p65 antibody (top) and their segmented nuclei and cytoplasm (bottom, green and magenta, respectively). Scale bar, 25 µm. b, Agreement in sgRNA detection in PerturbView and NGS. PerturbView ISS cell counts (y axis) and NGS read counts of the cell library (x axis) for each perturbation barcode (dot). Top left: Pearson’s r. c–f, Shared and stimulation-specific perturbation effects. c, Intersection of hit genes between TNFα, IL1β, and LPS PerturbView screens (circles) of NFΚB translocation in primary BMDMs. d–f, Cumulative distribution functions (CDFs) of robust z-scores for p65 nuclear intensity in response to LPS (left), TNFα (middle), or IL1β (right) for each of four guides (colored curves; n = cell number per guide) targeting the genes Map3k7 (d), Prkar1a (e), and Tnfrsf1a (f), compared to cells with non-targeting or non-essential controls (Methods; gray line (combined); shading standard deviation of robust z-scores).

Extended Data Fig. 4 Multimodal PerturbView screen in primary immune cells.

a, PerturbView enables efficient sgRNA recovery after different multiplexed imaging techniques. Left, BMDMs were stained with F4/80 repeatedly using 4i, IBEX and cycIF (Scale Bar, 50 μm). Middle, Representative images of in situ sequencing results after 6 rounds of staining (Scale Bar, 50 μm). Right, Mean percentage of cells positive for sgRNA signal (y axis, ≥1 sgRNA read per cell, mean of well replicates) after each staining step (x axis). Error bar, SD. n = 2 independent replicates. b, Agreement in phenotypic profiles of different guides targeting the same gene. PHATE embeddings of RNA/protein joint profiles (dots) for each sgRNAs in the TNFα (left) or IL1β (right) screen targeting a gene (color) or controls (grey). Gene names are shown for each guide whose 5 nearest neighbors contain another guide targeting the same gene. c, Distribution of cosine similarity of the phenotypic profiles derived from joint RNA/protein profiling for random pairs of guides (“random”, blue) and either guides targeting the same gene across all genes (orange; “same gene”, top) or targeting Olfr genes (orange “same gene”, bottom). d, e, Contribution of different molecular phenotypes to perturbation impact. d, Impact score (FDR based on one-sided tests adjusted with Benjamini-Hochberg method, color bar) for each perturbed gene (row) in the TNFα (top) or IL1β (bottom) screen, when assessed only based on one imaging feature (single-channel, left) or all other features (dropping a channel, right). Rows and columns are clustered by hierarchical clustering. e, Top, CDFs of phospho-rpS6 intensity (x axis) in response to TNFα for four guides (colored curves), compared to cells with non-targeting or non-essential controls (gray line (mean); shading standard deviation). FDRs were computed for the genes listed (in the rows) in d. Bottom, representative phospho-rpS6 images under perturbations. Two cells were sampled from each of a 10-percentile group (according to the mean cellular phospho-rpS6 level) and arranged from low (left) to high (right).

Extended Data Fig. 5 PerturbView in tissue.

a–d, High barcoding decoding accuracy in tissue sections with PerturbView. a, Barcode mapping rate (y axis, fraction of assigned reads at a Hamming distance ≤1 to the pre-defined sgRNA lookup table) for conventional ISS and PerturbView (x axis) in FF and FFPE tumor tissue (n = 4). b, Mean percentage of cells with detectable sgRNA (y axis) captured by conventional ISS or by PerturbView (x axis) in FFPE (light green) or fresh frozen (FF) (dark green) subcutaneous tumors. n = 2 independent experiments, Error bars: SD. c, Representative image of immunofluorescence stain with anti-human PRKDC in DLD-1 xenograft tissue section. Mouse cells (outer rim) or necrotic regions (central region) show low PRKDC staining. Scale bar, 1 mm. d, Frequency of each sgRNA (dots; normalized counts) estimated by NGS of the plasmid library (x axis) or PerturbView (y axis) of FF (dark green) or FFPE (light green) samples. n = 2 for each FF and FFPE treatment. Pearson’s r is noted. e, Spatial transcriptome and barcode detection in UMAP and spatial domains. UMAP embedding on transcriptome profiles (dots) colored and numbered by Leiden clusters (far left); and tissue section colored by the same Leiden clusters (second from left), total transcript counts in each cell (second from right) and minimum barcode hamming distance (up to 2, right); and distribution of total transcript counts per cell (x axis) for each Leiden cluster (y axis). Interior box plots indicate the median (white lines), interquartile range (hinges) and 1.5 times of the interquartile range (whiskers). (n = 2 consecutive sections per tumor). f, g, Expression changes associated with clonal diversity. f, Tumor sections profiled (scale bar, 1 mm), colored by Shannon diversity (top) or corresponding diversity groups (bottom) for all analyzed sections aside from the one shown in Fig. 3g. g, Genes (columns) that are differentially expressed (log fold-change > 0.5) between regions with high and low Shannon diversity (rows) for each tumor section (n = 2 consecutive sections per tumor).

Supplementary information

Supplementary Information

PerturbView protocols.

Reporting Summary

Supplementary Tables 1–7

Supplementary Table 1. Sequences of U6 promoter variants. Supplementary Table 2. sgRNA library information. Supplementary Table 3. Summary of p65 regulator screens. For each stimulation, perturbed gene (gene_name), cell counts (count), median p65 intensity (nuclei_median), P values computed with the two-sided empirical CDF method (pval) and FDR (pvals_corrected) are listed. Supplementary Table 4. Summary of joint RNA and protein screens. For each gene perturbation (gene_symbol), a type of stimulation (stimulation), Leiden cluster number (cluster), P values (pval), FDR (pval_bh) and test results (Is_significant) are listed. Supplementary Table 5. PHATE map embedding locations of each gene perturbations. Supplementary Table 6. Primers and sgRNA sequences. Supplementary Table 7. HCR FISH probe sequences.

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Kudo, T., Meireles, A.M., Moncada, R. et al. Multiplexed, image-based pooled screens in primary cells and tissues with PerturbView. Nat Biotechnol 43, 1091–1100 (2025). https://doi.org/10.1038/s41587-024-02391-0

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