High-speed fluorescence image-enabled cell sorting

Abstract

Fast and selective isolation of single cells with unique spatial and morphological traits remains a technical challenge. Here, we address this by establishing high-speed image-enabled cell sorting (ICS), which records multicolor fluorescence images and sorts cells based on measurements from image data at speeds up to 15,000 events per second. We show that ICS quantifies cell morphology and localization of labeled proteins and increases the resolution of cell cycle analyses by separating mitotic stages. We combine ICS with CRISPR-pooled screens to identify regulators of the nuclear factor κB (NF-κB) pathway, enabling the completion of genome-wide image-based screens in about 9 hours of run time. By assessing complex cellular phenotypes, ICS substantially expands the phenotypic space accessible to cell-sorting applications and pooled genetic screening.

Fig. 1. Functionality of the high speed image-enabled cell sorter (ICS).

(A) Schematic representation of the ICS optical and flow hardware components. Excitation beam path: The acousto-optic deflector (AOD) splits a single laser beam (λ = 488 nm) into an array of beamlets, each having different optical frequency and angle. A second AOD tunes the optical frequency of a reference beam, which is then overlapped with the array of beamlets. The overlapping beams intersect the flow cell (FC) of a cuvette sorter. Inset left side: The array of FIRE-beams (dark cyan) is shown overlapping with the reference beam (light cyan). Due to their differing optical frequencies, the overlapping beams exhibit a beating behavior, which causes each beamlet to carry a sinusoidal modulation at a distinct frequency f_1-n. Emission beam path: Images are generated from digitized signals on a per event basis and include light loss, forward scatter (FSC) and side scatter (SSC) images, and four different fluorescent channels. Example images: Hela cells expressing the Golgi marker GalNAcT2-GFP (green) were stained with cell surface marker CD147 PE-CF594 (orange) and DRAQ5 nuclear dye (red). FSC, SSC and light loss images are shown in grayscale. BS, beam splitter; M, mirror; Obj, objective; DP, deflection plates; OB, obscuration bar;P, pinhole; L, lens; BP, band pass; PMT, photomultiplier tube; PD, photodiode. Scale bar represents 20 μm. (B) Overview of the ICS low-latency data processing pipeline. Each photodetector produces a pulse with high-frequency modulations encoding the image (waveform). Fourier analysis is performed to reconstruct the image from the modulated pulse. An image processing pipeline produces a set of image features (image analysis), which are combined with features derived from a pulse processing pipeline (event packet). Real-time sort classification electronics then classify the particle based on image features, producing a sort decision that is used to selectively charge the droplets (dotted gray line in panel (A)). (C) ICS-based imaging of HeLa cells expressing GFP- or mNeonGreen-tagged fluorescent proteins or stained with organellespecific green fluorescent dyes. One representative image is shown per organelle, the full datasets containing 10,000 images each are shared as described in the data and materials availability section. The following dyes or protein fusions were used: cell membrane (Cellmask dye), cytoplasm (GFP fused to HIV Rev nuclear export sequence), mitochondria (Mitotracker dye), nucleus (H2B-mNeonGreen), Golgi apparatus (GalNAcT2-GFP), endoplasmic reticulum (ER, ERtracker dye), nucleolus (eGFP-Ki-67), nuclear envelope (LamB1-GFP), P-bodies (eGFP-DDX6), Cajal bodies (eGFP-COIL), and centrosomes (anti-pericentrin antibody). P-bodies and Cajal bodies were recorded from fixed cells, centrosomes from fixed and metaphase-stalled cells; fixation resulted in decreased contrast in the light loss (LL) image. Scale bar represents 20 μm.

Fig. 2. ICS measurements quantify spatial cellular processes and isolate phenotypes of interest.

(A) HeLa cells expressing eGFP-Ki-67 were gated for singlets and live cells, and the ICS size parameter of the eGFP-Ki-67 signal was used to distinguish between cells with single small nucleoli and those with multiple or large nucleoli. Size is defined by the number of pixels above a user-defined threshold. n, nucleolus. Scale bar represents 20 μm. (B) HeLa cells stained with the nuclear dye DyeCycle Green were gated for singlets and live cells, and the radial moment of DyeCycle Green was used to differentiate cells with single or multiple nuclei. Radial moment is the mean-square distance of the signal from the centroid. n., nucleus. Scale bar represents 20 μm. (C) HeLa cells were gated for singlets and live cells, and the eccentricity calculated from the side scatter image was used to distinguish round from elongated cells. Eccentricity is computed by first finding the magnitudes of the spread along the two principal components of the image, then taking their ratio. Scale bar represents 20 μm. (D) HeLa cells expressing the Golgi marker GalNAcT2-GFP were gated for singlets and live cells, and treated with brefeldin A (BFA) or left untreated. The maximum intensity of the GalNAcT2-GFP channel was used to distinguish treated from untreated cells, while the overall GFP intensity (y-axis) was largely unaffected by the treatment. Maximum intensity is the value of the brightest pixel. A, area. Scale bar represents 20 μm. (E) HeLa cells expressing RelA-mNeonGreen (mNG) were treated with TNFα or left untreated, and stained with the cell permeable nuclear dye DRAQ5. Cells were then gated for singlets and live cells, and the correlation between RelA-mNG and DRAQ5 was used to differentiate between the treated (nuclear RelA) and untreated (cytoplasmic RelA) conditions. Correlation is the Pearson’s correlation score between the intensities of the pixel values from two imaging channels. Scale bar represents 20 μm. (F) HeLa cells expressing H2B-mNeonGreen (mNG) were synchronized to increase the frequency of rare mitotic stages, and released into mitosis without chemical perturbation. Then, cells were fixed for labeling with an antibody recognizing phosphorylated serine 10 on histone H3 (pS10H3), and to allow microscopic validation after sorting. Samples were stained with DAPI for univariate cell cycle analysis. Representative images of individual cells within the G2/M population reveal captures of major mitotic stages. LL, light loss. Scale bar represents 20 μm. (G) A decision tree model was trained to distinguish the mitotic stages of manually classified datasets (n = 100 per stage, three replicate recordings and classifications). Shown are the results of a feature importance analysis of ICS measurements, that represents the summarized reduction in the loss function attributed to each feature at each split in the tree. A, area; RM, radial moment; ecc, eccentricity; MI, maximum intensity. (H) Feature values from panel (G) were standardized, and median values for cells and from three replicates of classified datasets are shown as a heatmap. Only features that vary between the mitotic stages are shown (variable importance greater than 0 in panel (G)). (I) Based on the identified features in panel (H), a hierarchical gating strategy was built that enriches for interphase, prometaphase, metaphase, anaphase, and telophase stages. A, area; RM, radial moment; MI, maximum intensity. (J) 5,000 cells were sorted for microscopic validation based on the gating strategy established in panel (I), and manual classification from confocal z-stacks of the sorted cells was performed. Shown are mean percentages of three independent replicates. Prometaphase cells were generated by two consecutive sorts (see Methods). interph., interphase. (K) Representative single-slice confocal fluorescence microscopy images from sorted cells from panel (J) with brightfield/H2B-mNG overlays as inlays. Scale bar represents 50 μm.

Fig. 3. ICS detects the effects of CRISPR perturbations and enables pooled genetic screens of protein localization.

(A) Effects of individual CRISPR perturbations on RelA nuclear translocation. HeLa cells with Tet-inducible Cas9 and stably expressing RelA-mNeonGreen (mNG) were transduced with guide RNAs (gRNA-1, -2, -3) targeting core NF-κB pathway proteins IKBKG, IKBKA and MAP3K6 or non-targeting (nt) control gRNAs. gRNA expression was induced with Doxycycline (Dox) or left uninduced. Correlation between RelA-mNG and DRAQ5 was quantified using ICS as a measurement for RelA nuclear translocation in the presence or absence of TNFα. (B) Overview of the pooled CRISPR screening setup and readout using ICS. Positive regulators of RelA nuclear translocation are enriched in the lower bin and depleted from the upper bin. Dox, doxycycline; Tet::Cas9, Tetracycline/Doxycycline-inducible Cas9. (C-E) Results of the ICS-based CRISPR screen using an NF-κB pathway-focused library (n = 1,068 genes). (C) The screen was performed at different library coverages, and reads from collected samples were combined in silico to a high-coverage (359 cells per gRNA per sorted bin) dataset. Hits were called using the software MAUDE (26). Genes are ranked by their statistical significance and selected positive/negative regulators are highlighted. The horizontal dashed lines indicate a false discovery rate (FDR) of 1%, while genes with FDR < 1% were marked in cyan and orange, respectively. (D) Comparison of phenotypes measured in individual perturbation experiments from panel (A) (x-axis) or the pooled screen (y-axis) using the same gRNAs. For the pooled screen, differences in gRNA abundance in the upper (top panel) and lower (bottom panel) sorted bins compared to the input sample were determined from the high-coverage dataset in panel (C). R-values represent Pearson correlation coefficients. FC, fold change. (E) Screen hits as determined at different library coverages (12 to 155 cells per gRNA per sorted bin) using between one and six gRNAs per gene were compared to a high coverage reference sample (359x, six gRNAs per gene) by precision-recall analysis. Heat map shows area under the precision-recall curve (AUPRC) values for different levels of library coverage and different numbers of gRNAs per gene. (F-J) Results of the ICS-based genome-wide screen (n = 18,408 genes). (F) Scatter plot of fold changes visualizing gRNA abundance changes in upper (x-axis) and lower (y-axis) sorted bins compared to the plasmid library. Blue and yellow dots indicate statistically significant positive and negative regulators (FDR < 1% according to MAUDE). (G) Genome-wide CRISPR screen identified core canonical NF-κB pathway components. Left panel: Schematic of the core canonical NF-κB signaling pathway. Right top panel: Distribution of the gRNA Z-score for the whole genome-wide library. Right panels: gRNA Z-score for individual gRNAs per gene, overlayed with a gradient (grayscale) depicting overall Z-score distribution. Right bar chart: Gene essentiality as determined by the log2 fold change (FC) of the gRNA abundance in the unsorted cell population compared to the plasmid library. (H) GO network of hits with FDR < 1%, colored by modules identified from protein–protein interactions using STRING-db (45). Gray lines connect associated GO terms, edges represent GO terms. Names of individual edges were omitted, clusters that were not associated with immune signaling or chromatin modification were collected in a third class called “others”. (I) Screen results for SAGA and INO80 protein complex components. Left panel: Schematic illustration of the SAGA and INO80 protein complexes. Right panels: as described in panel (G). (J) Selected hits from the genome-wide screen (1 gRNA per gene, we picked the gRNA that showed the strongest z-score in the pooled genetic screen) were validated using two orthologous methods (individual validation using ICS, individual validation using microscopy). The top row in the heatmap shows the phenotypes measured in the genome-wide screen (MAUDE Z-score). The phenotype in the second and third rows of the heatmap represents the standardized difference in signal medians between the knockout and control gRNA cell populations. Nuclear RelA abundance was quantified using microscopy by measuring the correlation between RelA-mNeonGreen and DRAQ5.