Massively multiplex chemical transcriptomics at single-cell resolution

Abstract

High-throughput chemical screens typically use coarse assays such as cell survival, limiting what can be learned about mechanisms of action, off-target effects, and heterogeneous responses. Here, we introduce "sci-Plex," which uses "nuclear hashing" to quantify global transcriptional responses to thousands of independent perturbations at single-cell resolution. As a proof of concept, we applied sci-Plex to screen three cancer cell lines exposed to 188 compounds. In total, we profiled ~650,000 single-cell transcriptomes across ~5000 independent samples in one experiment. Our results reveal substantial intercellular heterogeneity in response to specific compounds, commonalities in response to families of compounds, and insight into differential properties within families. In particular, our results with histone deacetylase inhibitors support the view that chromatin acts as an important reservoir of acetate in cancer cells.

Fig. 1.. sci-Plex uses polyadenylated single-stranded oligonucleotides to label nuclei, enabling cell hashing and doublet detection.

(A) Fluorescent images of permeabilized nuclei after incubation with DAPI (top) and an Alexa Fluor-647–conjugated single-stranded oligonucleotide (bottom). (B) Overview of sci-Plex. Cells corresponding to different perturbations are lysed in-well, their nuclei labeled with well-specific “hash” oligos, followed by fixation, pooling, and sci-RNA-seq. (C) Scatter plot depicting the number of UMIs from single-cell transcriptomes derived from a mixture of hashed human HEK293T cells and murine NIH3T3 cells. Points are colored on the basis of hash oligo assignment. (D) Boxplot depicting the number of mRNA UMIs recovered per cell for fresh versus frozen human and mouse cell lines. (E) Scatter plot of overloading experiment; axes are as in (C). Identified hash oligo collisions (red) identify cellular collisions with high sensitivity.

Fig. 2.. sci-Plex enables multiplex chemical transcriptomics at single-cell resolution.

(A) Diagram depicting compounds and corresponding targets assayed within the pilot sci-Plex experiment. A549 lung adenocarcinoma cells were treated with either vehicle [dimethylsulfoxide (DMSO) or ethanol] or one of four compounds (BMS345541, dexamethasone, nutlin-3a, or SAHA). (B) UMAP embedding of chemically perturbed A549 cells colored by drug treatment. (C) UMAP embedding of chemically perturbed A549 cells faceted by treatment with cells colored by dose. (D and E) Expression of a canonical (D) glucocorticoid receptor activated (ANGPTL4) and repressed (GDF15) target genes as a function of dexamethasone dose or (E) p53 target genes as a function of nutlin-3a dose. y-axes indicate the percentage of cells with at least one read corresponding to the transcript. (F) Dose–response viability estimates for BMS345541-, dexamethasone-, nutlin-3a-, and SAHA-treated A549 cells on the basis of the relative number of cells recovered at each dose.

Fig. 3.. sci-Plex enables global transcriptional profiling of thousands of chemical perturbations in a single experiment.

(A) Schematic of the large-scale sci-Plex experiment (sci-RNA-seq3). A total of 188 small molecules were tested for their effects on A549, K562, and MCF7 human cell lines, each at four doses and in biological replicate, after 24 hours of treatment. The plate positions of doses and drugs were varied between replicates, and a median of 100 to 200 cells were recovered per condition. Colors demarcate cell line, compound pathway, and dose. (B) UMAP embeddings of A549, K562, and MCF7 cells in our screen with each cell colored by the pathway targeted by the compound to which a given cell was exposed. To facilitate visualization of significant molecular phenotypes, we added transparency to cells treated with compound or dose combinations that did not appreciably alter the corresponding cells’ distribution in UMAP space compared with vehicle controls (Fisher’s exact test, FDR < 1%). (C) Viability estimates obtained from hash-based counts of nuclei at each dose of selected compounds (bosutinib is highlighted in red text). Rows represent compound doses increasing from top to bottom, and columns represent individual compounds. Annotation bar at top depicts the broad cellular activity targeted by each compound. (D) UMAP embeddings highlighted by treatment with the MEK inhibitor trametinib (red), an HSP90 inhibitor (purple), or vehicle control (gray). (E) HSP90AA1 expression levels in cells exposed to increasing doses of trametinib. y-axes indicate the percentage of cells with at least one read corresponding to the transcript.

Fig. 4.. HDAC inhibitor trajectory captures cellular heterogeneity in drug response and biochemical affinity.

(A) MNN alignment and UMAP embedding of transcriptional profiles of cells treated with one of 17 HDAC inhibitors. Pseudodose root is displayed as a red dot. (B) Ridge plots displaying the distribution of cells along pseudodose by dose shown for three HDAC inhibitors with varying biochemical affinities. (C) Relationship between TC₅₀ and average log₁₀(IC₅₀) from in vitro measurements. Asterisks indicate compounds with a solubility <200 μM (in DMSO) that were not included in the fit.

Fig. 5.. HDAC inhibitors shared transcriptional response indicative of acetyl-CoA deprivation.

(A) Heatmap of row-centered and z-scaled gene expression depicting the up-regulation of pseudodose-dependent genes involved in cellular carbon metabolism. (B) Diagram of the roles of genes from (A) in cytoplasmic acetyl-CoA regulation. Red circles indicate acetyl groups. Enzymes are shown in gray. Transporters are shown in green (FA, fatty acid; Ac-CoA, acetyl-CoA; C, citrate).