Journal: Cell

Article Title: Perturb-Multimodal: a platform for pooled genetic screens with sequencing and imaging in intact mammalian tissue

doi: 10.1016/j.cell.2025.05.022

Figure Lengend Snippet: A. Liver lobule organization showing pericentral to periportal axis (BioRender). B. Integrated UMAP of RCA-MERFISH and Flex scRNA-seq data (Left: both; Top right: RCA-MERFISH; Bottom right: Flex) showing liver cell types, as determined by unsupervised clustering. C. Spatial distribution of periportal (left) and pericentral (right) scores in hepatocytes based on marker gene expression . D. Periportal vs pericentral gene expression scores across hepatocyte subtypes. E. Spatial organization of hepatocyte subtypes (left) and non-hepatocyte cells (right). F. Spatial map of hepatocyte zone marker expression radially organized around a central vein. Yellow-boxed region from (E) with cell types (top) and imputed gene expression are separately scaled for each gene (bottom). PP: Periportal; PC: Pericentral. G. Morphology panel showing 4 abundant RNA species and 14 proteins (top) with zoomed details of a subset of targets (bottom). H. Deep learning autoencoder diagram reducing protein morphologies to 512-dimensional embeddings using the VQ-VAE model with auxiliary tasks of discriminating cell types, cell states, or conditions. I. UMAP of subcellular morphology image embeddings colored by channel (target protein and abundant RNA) identity. J. Similarity of subcellular morphology channel embedding quantified by Kullback-Leibler (KL) divergence. K. Correlation heatmap of high-signal features across image embeddings, ordered by hierarchical clustering to reveal nine feature classes (see ). L. Cells displaying high weight scores from selected feature classes, including (ii) double nucleus, (iii) membrane enrichment, (iv) diffuse expression, and (viii) punctate patterns. M. Tissue-scale spatial organization of morphological embedding features. Left: Albumin mRNA feature 431; Right: Perilipin feature 203 N. Confusion matrix of hepatocyte subtype classification accuracy on held-out cells using MERFISH transcriptomic data of 209 genes. O. Confusion matrix of hepatocyte subtype classification accuracy on held-out cells using morphological feature embeddings from 14 proteins and 4 abundant RNAs. P. Heatmap of mutual information between hepatocyte subtypes Hep1 and Hep6 for individual morphological channels, quantified by quantified by KL divergence. Q. UMAP of anti-Perilipin morphological embeddings of single-cell images, colored by hepatocyte subtype. R. UMAP of anti-Perilipin morphological embeddings of single-cell images, colored by Leiden cluster. S. Sampling of hepatocytes from Perilipin embedding clusters 2 (Hep 6-enriched) and 6 (Hep 1-enriched). T. Diet experiment diagram. U. scRNA-seq UMAP from mice under ad lib, overnight fasting, or high-fat diet (HFD) conditions. V. Heatmap of mutual information between ad lib and fasted hepatocytes for individual morphological embedding features, quantified by quantified by KL divergence. W. Sampling of hepatocytes from anti-p-S6 RP embedding cluster 7 (from ad lib condition) and cluster 0 (from fasted condition). Cluster 7 is most enriched in the ad lib condition and cluster 0 is most enriched in the fasted condition. X. Same as (V), but for morphological channel embeddings between ad lib and HFD hepatocytes. Y. Same as (W) but for anti-perilipin embedding cluster 0 (from ad lib condition) and cluster 10 (from HFD condition). See also - and - .

Article Snippet: We then allowed the mice to grow to adulthood (>P30) and induced Cas9 through the retro-orbital injection of AAV8 with Cre driven by a hepatocyte promoter (Addgene 107787-AAV8; ~5 × 10 11 genome copies per animal).

Techniques: Marker, Gene Expression, Expressing, Membrane, Sampling

Journal: Cell

Article Title: Perturb-Multimodal: a platform for pooled genetic screens with sequencing and imaging in intact mammalian tissue

doi: 10.1016/j.cell.2025.05.022

Figure Lengend Snippet: A. Lentiviral CROP-seq vector for dual-mode mosaic screens with mU6-driven sgRNA expression and hepatocyte promoter driving expression of mTurquoise transcripts with perturbation-specific barcode in the 3’ UTR. B. CRISPR experiment workflow: LSL-Cas9 pups was injected with sgRNA library, followed by Cas9 activation in young adults via AAV8 TBG-CRE and perfusion-fixation of livers for RCA-MERFISH or Perturb-seq. C. Fluorescence micrograph of PFA-perfused, lentivirus- and AAV-transduced liver tissue showing Cas9-EGFP (green) and sgRNA-mTurquoise (purple) expression. D. Multimodal readout of 209 endogenous mRNAs and 456 perturbation barcodes via RCA-MERFISH and 14 proteins and 4 abundant RNAs via sequential imaging. E. Representative fluorescence micrograph showing the first three (of 21 total) bits of RCA-MERFISH perturbation imaging. F. Distribution of barcode calls per sgRNA-harboring cell: 85.3% with one barcode, 14.7% with two or more. Only single-barcode cells were analyzed. G. Fluorescence micrograph of a hepatocyte dissociated from fixed liver (Blue: DAPI ; Red: phalloidin). H. Flow cytometry of dissociated, PFA-perfused, lentivirus- and AAV-transduced liver tissue and mTurquoise+ and GFP+ cells are selected to enrich for cell containing sgRNA and active Cas9. I. Histogram of Alb_0 sgRNA counts per cell. J. Barcode calls per sgRNA-harboring cell in Perturb-seq: 85.7% with one barcode, 14.3% with two or more. K. Albumin mRNA expression histograms comparing cells receiving control vs. Albumin -targeting sgRNAs in Perturb-seq data. L. Fraction of sgRNAs causing significant Perturb-seq phenotypes: 109/406 targeting sgRNAs (27%) vs. 0/50 non-targeting sgRNAs (0%) by Holm-Šídák-corrected energy distance test (p<0.05). M. Histogram of Pearson correlations of pseudobulk Perturb-seq phenotypes between active sgRNA pairs targeting same gene, versus control sgRNA pairs. N. Knockouts ranked by energy distance between cells that received active targeting sgRNA vs cells that received control sgRNA. Energy distance is calculated using the top 20 PCs of Z-normalized Perturb-seq gene expression. O. Unbiased sampling of cells with control sgRNAs and sgRNAs targeting Albumin showing Albumin mRNA and polyA signals.. P. Histogram comparing Albumin mRNA signal between cells receiving control and Albumin -targeting sgRNAs, from the imaging data. Q. Histogram of Pearson correlations of pseudobulk imaging intensity phenotypes between active sgRNA pairs targeting same gene, versus control sgRNA pairs. R. Venn diagram of genes with significant knockout effects in imaging and sequencing phenotypes. Phenotype significance is measured by a Holm-Šídák-corrected energy distance permutation tests (p < 0.05). There is significant overlap in the two sets of genes (hypergeometric p < 10 −13 ). See also and - .

Article Snippet: We then allowed the mice to grow to adulthood (>P30) and induced Cas9 through the retro-orbital injection of AAV8 with Cre driven by a hepatocyte promoter (Addgene 107787-AAV8; ~5 × 10 11 genome copies per animal).

Techniques: Plasmid Preparation, Expressing, CRISPR, Injection, Activation Assay, Fluorescence, Imaging, Flow Cytometry, Control, Gene Expression, Sampling, Knock-Out, Sequencing

Journal: Cell

Article Title: Perturb-Multimodal: a platform for pooled genetic screens with sequencing and imaging in intact mammalian tissue

doi: 10.1016/j.cell.2025.05.022

Figure Lengend Snippet: A. Spatial distribution of sgRNAs in the imaging dataset showing proliferation of infected cells. Cells are colored by cell type (left; as in ) or by sgRNA barcode identity (right and zoom). B. A UMAP generated from transcriptome profiles of cells with sgRNAs targeting Hnf4a and from a random sub-sampling of cells with control sgRNAs, colored by sgRNA identity (left) or by Apoa1 expression (right). C. Heat map representation of pseudobulk transcriptional changes (log2-fold change measured by sequencing, left) and staining protein and RNA level changes (Z-normalized changes measured by imaging, right) associated with each sgRNA, relative to cells with control sgRNAs. The colormaps are clipped for visual emphasis. D. Perturbation-perturbation correlation of RNA and protein changes associated with active sgRNAs (left) and zoom-in of the color boxed regions (right). Colors in the heatmap represent Pearson correlation of perturbed gene-level pseudobulk phenotypes measured by sequencing (below diagonal) or imaging (above diagonal). Genetic perturbations are ordered by hierarchical clustering of joint sequencing and imaging phenotype vectors. E. Minimal distortion embedding. Each dot represents an mRNA expressed in hepatocytes. mRNAs that are co-varying in expression across the perturbations are placed in proximity. F. Heat map of the correlation between the expression levels of indicated proteins/RNAs across perturbations, in the imaging dataset. Imaging channels are ordered by hierarchical clustering.

Article Snippet: We then allowed the mice to grow to adulthood (>P30) and induced Cas9 through the retro-orbital injection of AAV8 with Cre driven by a hepatocyte promoter (Addgene 107787-AAV8; ~5 × 10 11 genome copies per animal).

Techniques: Imaging, Infection, Generated, Sampling, Control, Expressing, Sequencing, Staining

Journal: Cell

Article Title: Perturb-Multimodal: a platform for pooled genetic screens with sequencing and imaging in intact mammalian tissue

doi: 10.1016/j.cell.2025.05.022

Figure Lengend Snippet: A. Kernel density estimate plots showing the distribution of zonation gene expression in cells with control sgRNAs, sgRNAs targeting Ctnnb1 , and sgRNAs targeting APC . The single-cell zonation scores reflect the expression of periportal genes like Cyp2f2 and Hal and pericentral genes like Glul and Cyp2e1 . Periportal and pericentral genes contribute positively and negatively to zonation score, respectively. B. Ranking of perturbed genes by their average impact on zonal gene expression score. C. Heatmap summarizing categories of genes whose perturbation has a large impact on zonated gene expression. Here, the periportal and pericentral expression scores are shown separately. D. Perturbation-perturbation correlation heatmap showing Pearson coefficients of pseudobulk transcriptional changes between indicated sgRNA perturbations. E. Schematic of data-driven zonal segmentation. The proportion of each hepatocyte subtype is calculated in 50-μm x 50-μm bins. The bins are then grouped into two zones based on the local cell-type distribution and the enrichment of cells with each perturbation in the two zones is quantified. F. Cell types from RCA-MERFISH (left) and resulting periportal/pericentral zonal segmentation (right). G. Barplot of the fraction of cells in periportal and pericentral zones (as defined above), for the indicated perturbations. The white line represents the fraction of cells with control sgRNAs.

Article Snippet: We then allowed the mice to grow to adulthood (>P30) and induced Cas9 through the retro-orbital injection of AAV8 with Cre driven by a hepatocyte promoter (Addgene 107787-AAV8; ~5 × 10 11 genome copies per animal).

Techniques: Gene Expression, Control, Expressing