Article

Pooled CRISPR screening of high-content cellular
phenotypes using ghost cytometry
Graphical abstract

Authors
Asako Tsubouchi, Yuri An,
Yoko Kawamura, ..., Hiroyuki Aburatani,
Nozomu Yachie, Sadao Ota

Correspondencesadaota@solab.rcast.u-tokyo.ac.jp

In brief
Tsubouchi et al. present multimodal
ghost cytometry for pooled CRISPR
screening. Their approach, combining
both fluorescence and label-freephenotyping, allows rapid, large-scale
gene perturbation analysis. This method
successfully identifies genes involved inkinase signaling and macrophage
polarization and significantly expands thepossibilities for designing perturbation
screens.

HighlightsdHigh-content phenotyping of large-scale pooled CRISPR
screens with ghost cytometrydPlatform enables both label-free and fluorescence-based cell
phenotypingdKinase-targeting CRISPR screening demonstrated withfluorescence modedLabel-free screening identifies key genes in macrophage
polarization

Tsubouchi et al., 2024, Cell Reports Methods 4, 100737
March 25, 2024 ª 2024 The Authors.
https://doi.org/10.1016/j.crmeth.2024.100737

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OPEN ACCESS

ArticlePooled CRISPR screening of high-content
cellular phenotypes using ghost cytometry
Asako Tsubouchi,1,4 Yuri An,1,4 Yoko Kawamura,1,4 Yuichi Yanagihashi,1 Hirofumi Nakayama,1 Yuri Murata,1Kazuki Teranishi,1 Soh Ishiguro,2 Hiroyuki Aburatani,3 Nozomu Yachie,2,3 and Sadao Ota1,3,5,*
1ThinkCyte Inc., Tokyo 113-8654, Japan
2School of Biomedical Engineering, Faculty of Medicine and Faculty of Applied Science, University of British Columbia, Vancouver, BC V6T
1Z3, Canada
3Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan
4These authors contributed equally
5Lead contact*Correspondence: sadaota@solab.rcast.u-tokyo.ac.jp
https://doi.org/10.1016/j.crmeth.2024.100737

MOTIVATION Pooled CRISPR screening based on image information has great potential to map complex
genotype-phenotype associations in mammalian cells. However, barriers to efficient cell enrichment basedon diverse morphological information remain, limiting large-scale gene perturbation screening acrossdiverse cell phenotypes. Further complications arise when appropriate biomarkers and staining techniques
are not available, making phenotypic evaluation under label-free conditions a challenge. To address thesechallenges, this study aims to increase the feasibility and reach of pooled CRISPR screening by analyzing
and sorting both fluorescence and label-free high-content cell phenotypes using a machine vision-based
approach.

SUMMARY

Recent advancements in image-based pooled CRISPR screening have facilitated the mapping of diverse
genotype-phenotype associations within mammalian cells. However, the rapid enrichment of cells basedon morphological information continues to pose a challenge, constraining the capacity for large-scale
gene perturbation screening across diverse high-content cellular phenotypes. In this study, we demonstrate
the applicability of multimodal ghost cytometry-based cell sorting, including both fluorescent and label-freehigh-content phenotypes, for rapid pooled CRISPR screening within vast cell populations. Using the highcontent cell sorter operating in fluorescence mode, we successfully executed kinase-specific CRISPR
screening targeting genes influencing the nuclear translocation of RelA. Furthermore, using the multiparametric, label-free mode, we performed large-scale screening to identify genes involved in macrophage
polarization. Notably, the label-free platform can enrich target phenotypes without requiring invasive staining, preserving untouched cells for downstream assays and expanding the potential for screening cellular
phenotypes even when suitable markers are absent.
INTRODUCTIONCRISPR-based pooled screening offers several advantages,
including increased throughput, reduced cost, and reduced
well-to-well batch effects, over conventional array-based approaches for perturbation screening.1,2 In pooled phenotypic
screening, cells and intracellular molecules have been labeledwith fluorescent dyes, reporters, or immunofluorescent antibodies. Cell phenotyping typically requires quantifying explicitly defined features; thus, fluorescence-based labeling provides distinct advantages owing to its high specificity andsensitivity to the molecule of interest.3 For example, represen-tative values such as total fluorescence are measured from
temporal signals obtained in fluorescence-activated cell sorting (FACS), or more detailed features, such as molecular
localization and morphologic parameters, are evaluated fromoptical microscopic images.4–9 However, when suitablebiomarkers or staining methods are unavailable, and cell phenotypes can only be assessed without labeling, image
analysis based on human-recognizable features can becomechallenging. To address this challenge, machine learning(ML)-based analysis of label-free high-content cell phenotypes emerges as a promising alternative.10,11 In this study,
we present a versatile approach for large-scale pooled

Cell Reports Methods 4, 100737, March 25, 2024 ª 2024 The Authors.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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A
flGMI

Estimate classificationand sorting performance

Flow

dGMI:
Diffractive GMI(label-free)bfGMI:
Bright field GMI(label-free)Phenotype X

ML
Model

DefinedGMI・Surface marker
・Cell preparation
・Cell function
・Cell differentiation

bfGMI

Confusion matrix

True

pos

pos

neg

Ground truth label

bsGMI

neg

SVM score [a.u.]neg
posPredicted

PR curve

ROC curve
True positive [%]bsGMI:
Backscattering GMI(label-free)

Histogram

Develop a classifier
for multiple GMI
waveforms

fsGMI

Cell counts [cells]

Acquirewaveforms
in parallel

fsGMI:
Forward scattering GMI(label-free)

Precision [%]Structured illumination

flGMI:
Fluorescence GMI

Recall [%]B

False positive [%]Screening Validation
Gene perturbation

Pooled CRISPR lentivirus
sgRNAs library

+

ML
Model

Cell culture+
Stimulation(if needed)

AcquireAmplicon PCR+ NGSSingle-cell
analysis

FACS

ELISA

Gene
analysis

Predict
Proteinassay

Mixture of phenotypes

Ghost cytometry-based
cell sortingCas9-expressing cells

CultureGrowth

Cell-based
assay

Figure 1. The ghost cytometry (GC)-based cell sorter employs multimodal GC enabled for fast-pooled CRISPR screening of high-content
fluorescent and label-free cell phenotypes(A) Schematic illustration of the simultaneous acquisition of various cellular morphological and structural information as different ghost motion imaging (GMI)
waveforms, such as flGMI, fsGMI, bsGMI, dGMI, and bfGMI, for each cell. These datasets are used to develop machine learning (ML)-based classifiers for highcontent cellular phenotypes. GMI waveforms are analogs to microscopic images, enabling subcellular-resolution cell phenotyping.
(B) The workflow of pooled high-content CRISPR screening by the GC-based cell sorter. A gene knockout cell library prepared by using the CRISPR-Cas9 system
is treated with compounds or reagents to induce phenotypic changes. The GC-based cell sorter, equipped with a pre-trained ML model, selectively enriches cells
displaying the target high-content phenotype for downstream analysis. For gene analysis, the sgRNA regions within the isolated genomic DNAs are amplified andsequenced to identify enriched or depleted genes in response to the treatment. When live cells are used, transcriptomic analysis and cell-based functional assays
are widely applicable.

CRISPR screening, including both fluorescent and labelfree high-content cell phenotypes, utilizing a cell sorter
based on fluorescence and label-free ghost cytometry (GC)technologies.11,12
Central to GC is an ML-based direct and integrative analysis
of cellular morphological information without image reconstruction. In GC, as depicted in Figure 1A (left), fluorescenceand stain-free high-content information from cells is compressively converted into temporal signals, which we call ghost
motion imaging (GMI) waveforms. This conversion occurs as
cells pass through static structured light illumination in a microchannel, and optical interactions are measured using single-pixel detectors. With configurations to observe the opticalinteractions through different angles and paths, we simultaneously detect multiple different temporal GMI waveforms using multiple detectors. Specifically, fluorescence GMI (flGMI)
waveforms excited by a 488-nm laser, as well as forward
scattering GMI (fsGMI), backscattering GMI (bsGMI), diffractive GMI (dGMI), and bright-field GMI (bfGMI) waveforms
generated by a 405-nm laser, are measured as analogs totheir corresponding microscopic images (Figure 1A, left;
STAR Methods). To develop a classifier model based on a
support vector machine (SVM), we first used a training cell

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Cell Reports Methods 4, 100737, March 25, 2024

sample to measure multiple GMI waveforms along withground truth labels created using molecular staining, including
surface markers and functional evaluations. Using this training
dataset, comprising pairs of waveforms and true labels,
we defined the target high-content phenotypes and trained
the SVM model (Figure 1A, center). After training, the classifier can predict the labels directly from the multiple
GMI waveforms using SVM-based scoring. These SVM
scores enable users to estimate sorting performance throughmetrics such as the precision-recall (PR) curve and thearea under the receiver operating characteristic-area under
the curve (ROC-AUC) score before conducting any sorting experiments (Figure 1A, right; STAR Methods). During cellsorting, the trained classifier was implemented on a fieldprogrammable gate array (FPGA), which can judge cells inreal time using only the generated GMI waveforms, facilitating subsequent cell sorting based on this assessment.
This direct analysis of GMI waveforms, devoid of computationally intensive image reconstruction, enhances the speed
and accuracy of high-content cell sorting during large-scale
screening.
Figure 1B illustrates the workflow of CRISPR-based pooled
screening employing the trained classifier in the GC. Initially,

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Figure 2. High-content cell phenotypes
distinguishable by fluorescence GC

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cells expressing the Cas9 protein were transduced withpooled CRISPR lentiviral libraries for loss-of-function genesets and selected for stable viral integration. Subsequently,
the pooled knockout cell library was treated with compounds
or reagents, manifesting diverse phenotypes. Additional assays, such as immunostaining, could be conducted if necessary. In the GC-based cell sorter, a pre-trained ML model
was then applied to selectively enrich cells exhibiting thedesired high-content phenotypes. Finally, the sorted cells
can be subject to various biological assays, including gene
analyses such as genome sequencing, protein assays, andcell-based functional analyses. In the case of standard
CRISPR perturbation screening, genomic DNA is extracted
from sorted cells, and regions of single guide RNAs (sgRNAs)

P(A–P) Fluorescence GC effectively distinguishes(A)–(D) nuclear translocation of RelA proteins in
THP-1 cell suspension, (E)–(H) lysosomes (Lamp1)
and mitochondria (COX III) in adherent HEK293
cells, (I)–(L) autophagosome translocation of LC3GFP proteins in adherent HeLa cells, and (M)–(P)mitochondrial morphological changes in activatedand non-activated human primary T cells.
(A), (E), (I), and (M) Representative cell images obtained using Amnis Image Stream. Scale bars,
10 mm.
(B), (F), (J), and (N) Conventional FACS scatterplots
depicting the fluorescence intensity of labeling forcellular phenotyping versus that of a ground-truth
marker.
(B) Fluorescence intensity of Alexa 488-labeled RelA
versus that of fixable far-red dye, which exclusively
labeled LPS-unstimulated cells as the ground truth.
(F) Fluorescence intensity of Alexa 488-labeled
Lamp1 (lysosome) and COX III (mitochondria) proteins versus that of fixable far-red dye, whichlabeled only cells with Lamp1 proteins stained as
ground truth.
(J) Fluorescence intensity of LC3-GFP proteinsversus that of fixable far-red dye, which labeled only
untreated cells as ground truth.
(N) Fluorescence intensity of MitoTracker Greenversus that of CD25/CD69 double-positive cells as
the ground truth.
(C), (G), (K), and (O) Representative fluorescence
GMI (flGMI) waveforms of 20 randomly selected
cells for each condition.
(D), (H), (L), and (P) Classification of fluorescent cell
phenotypes. The ML-based classification performance is displayed as histograms of SVM scores,
where red and blue colors correspond to groundtruth labels and area under the ROC curve (AUC)
scores, respectively.

are amplified by polymerase chain reaction (PCR), pooled, and read using
commercially available next-generation
sequencing (NGS) platforms to identify
genes inducing the target phenotype.
When live cells are sorted, transcriptomics analyses using single-cell RNA sequencing and cell-based
functional assays are widely applicable.
RESULTS
Pooled fluorescence high-content cellular phenotyping
We first investigated the capability of GC to evaluate a wide rangeof fluorescent high-content cellular phenotypes (Figure 2).
Figures 2A–2D illustrate sample images of an intracellular phenotype, specifically the nuclear translocation of RelA protein in THP-1
cells, and outlines the procedure for developing and assessing thisclassifier in the GC. To train the classifier for nuclear translocation,a cell mixture consisting of lipopolysaccharide (LPS)-stimulated
and unstimulated THP-1 cells was prepared. These cells were

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stained with a combination of anti-RelA primary antibody andAlexa Fluor 488-conjugated secondary antibody. Notably, only
the unstimulated cells were stained with a ground-truth marker
dye before mixing.13 Note that the total fluorescence intensity for
RelA staining in the training sample was similar between LPS-stimulated and unstimulated cells, rendering them challenging todistinguish using conventional FACS (Figure 2B). Consequently,
an SVM-based classifier for flGMI waveforms representing
nuclear translocation phenotypes was trained, using 1,250 cells
for each ground-truth label. When applied to a test dataset
comprising 1,000 waveforms, the histogram of returned scores
exhibited bimodal peaks colored according to ground-truth labels,
demonstrating robust and high performance, as indicated by an
AUC score of 0.98 (Figures 2C, 2D, and S1A–S1C). Similarly, we
assessed the capacity of GC to distinguish various high-content
fluorescence cellular phenotypes. One such instance involves distinguishing between lysosomes and mitochondria in adherent
HEK293 cells (Figures 2E–2H and S1D–S1F). Individual cells
were immunostained with anti-Lamp-1 primary antibody forlysosomes and anti-complex III-core1 primary antibody for mitochondria. Subsequently, they were stained with Alexa Fluor
488-conjugated secondary antibody, with only the anti-Lamp1stained cells being additionally marked with a ground-truth marker
dye before their mixing. In all cases, the models exhibited excellent
performance, achieving AUC scores of 0.96.
Additionally, we investigated LC3-GFP dynamics in adherent
HeLa cells in response to autophagy induction (Figures 2I–2Land S1G–S1I). Under normal conditions, LC3 protein is evenly
distributed in the cell cytoplasm and nuclei, but during autophagy
induction, it aggregates into autophagosomes, forming distinct
foci in the cell cytoplasm.14 Treatment with 10 mM chloroquine
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Cell Reports Methods 4, 100737, March 25, 2024

Figure 3. High-throughput pooled CRISPR
screening of fluorescent high-content phenotypes(A) Genes downstream of the TLR4 signaling pathway
were targeted for knockout using a small-scale library.
(B) Volcano plot visualization of statistical significance(y axis) and magnitude of the change (x axis) before
and after cell sorting, with p values calculated using
the Mann-Whitney U test. Dashed lines represent thecutoff for hit genes (false discovery rate [FDR] = 0.01).
(C) Fluorescent images display RelA (green) co-localized with nuclei (magenta) inside MYD88, MAP3K7,
IRAK4, and TNFRSF CRISPR knockout cells and LPS(!) and LPS (+) cells as controls. Scale bars, 10 mm.
(D) The correlation coefficient between SVM-based
prediction probabilities in GC and similarity scoresobtained using Amnis Image Stream was 0.914. N = 3
biological replicates.
See also Table S4.

prevents autophagosome-lysosome fusionand degradation, leading to the accumulation of autophagosomes in the cell cytoplasm.15 To classify LC3-GFP localization,
both induced and uninduced HeLa cells expressing LC3-GFP were individually prepared, and autophagosome accumulationin the uninduced cells was stained with CellTracker DeepRed
dye as a ground-truth label. For classification, an equal concentration of each cell type was mixed, and 2,000 cells were randomly
selected (without overlap), evenly distributed between induced
and uninduced cells, and used as training data. The models
consistently achieved high performance with AUC scores of 0.96.
Furthermore, we examined whether GC could differentiate between mitochondrial morphological changes in activated andnon-activated human primary T cells, given the establishedconnection between T cell fate and mitochondrial dynamics.16,17
Activated and non-activated T cells were separately preparedand stained with MitoTracker Green and the T cell activation
markers CD25 and CD69. To classify activated and non-activatedcells, each cell type was mixed at an equal concentration. CD25/
CD69 double-positive cells represented activated cells, whereas
CD25/CD69 double-negative cells represented non-activatedT cell populations. For training, 2,000 cells were randomly
selected (without overlap) with an equal number of induced anduninduced cells. Consistently, the models demonstrated robust
performance, achieving AUC scores of 0.96 (Figures 2M–2P and
S1J–S1L). These results confirm the ability of GC to classify adiverse range of high-content fluorescence phenotypes.
Pooled CRISPR screening of fluorescence high-content
phenotypes
Subsequently, we focused on nuclear translocation as the target
fluorescence high-content phenotype for pooled CRISPR
screening. RelA translocates to the nucleus downstream of the
Toll-like receptor 4 (TLR4) pathway upon activation by LPS stimulation (Figure 3A).13 In the initial evaluation, the trained classifier in
the GC-based cell sorter was applied to a small-scale pooled cell

Articlelibrary, where 60 sgRNAs, including those downstream of the
TLR4 pathway, were perturbed using the CRISPR-based system.
Specifically, 40 sgRNAs targeted 10 genes downstream of the
TLR4 signaling pathway as positive controls, whereas 20 nontargeting sgRNAs outside of the pathway served as negative
controls (Table S1). Cells exhibiting suppressed RelA nuclear
translocation were sorted and subjected to deep sequencing analysis (coverage, 1,166). The results indicated an enrichment of cells
containing sgRNAs downstream of the TLR4 pathway in the sorted
samples compared with the input (Figures S2).
Subsequently, we performed large-scale screening with 7,290
sgRNAs targeting 729 kinase genes (approximately 82.3
coverage), where we did not apply any staining as ground-truthlabeling to the cells. This comprehensive screening analyzed
6,000,000 cells and successfully sorted target cells within 2 h(Figures S3). Analysis of the deep sequencing data revealed
that the enriched cells contained sgRNAs targeting genes,
including MAP3K7, IRAK4, IKBKB, and IKBKG, that are downstream of the TLR4 pathway (Figures 2B and S4). Therefore, we
demonstrated the large-scale applicability of GC-based pooled
CRISPR screening to fluorescent high-content phenotypes.
Furthermore, we assessed whether the degree of the cellular
phenotype could be quantified using fluorescent GC to thesame extent as image analysis performed with a commercial image flow cytometer (Amnis Image Stream). Specifically, we
compared SVM-based analysis of GMI waveforms and nuclear
translocation scores obtained from the analysis of images
captured using the Amnis Image Stream. Specifically, we examined CRISPR knockout cell lines targeting MYD88, MAP3K7,
IRAK4, and TNFRSF individually. SVM-based prediction probabilities were calculated as values ranging from 0–1 using thetrained SVM classifier (STAR Methods), and nuclear translocation scores were determined as the degree of overlap between
two images (RelA and nuclear images) using analysis softwareequipped with the Amnis Image Stream.18,19 The correlation coefficient between these scores was notably high (R = 0.914), indicating that GC-based phenotypic screening was quantitatively
comparable with that of high-content microscopy image analysis(Figures 3C, 3D, and S5).
Pooled label-free high-content cellular phenotyping
We investigated the capacity of GC to assess a range of labelfree high-content cellular phenotypes, including cell polarization,differentiation, and exhaustion (Figure 4). Figures 4A and 4B present microscopy images and scattering properties associatedwith label-free morphological phenotypes linked to THP-1 cell
polarization from M0 to M1 macrophages. Figures 4C and 4D depict the process for developing and evaluating morphologicalclassifiers within GC. To train a classifier, we separately prepared M0 and M1 macrophages, subsequently combining
them as the training sample (Figure S6A–S6D).20 In this study,
we employed a combination of forward scatter (FSC), back scatter (BSC), and label-free fsGMI and bfGMI waveforms to train
SVM-based classifiers, utilizing 1,000 cells for each ground-truthlabel. Upon applying the trained model to a test dataset of 1,000
waveforms, the histogram of returned scores exhibited bimodal
peaks color coded based on validation labels, resulting in highperformance, as indicated by an AUC score of 0.89. This facili-llOPEN ACCESS

tated the classification of M0 and M1 macrophage high-content
phenotypes even in the absence of surface markers. While distinguishing M0 from M1 polarized macrophages appears challenging through bright-field microscopy or FACS (Figures 4A
and 4B), these results underscore the robust and accurate classification capabilities of GC.
Similarly, we evaluated the capability of classifying other labelfree high-content phenotypes, such as THP-1 monocytes and
THP-1-derived macrophages (cell differentiation) (Figures 4E–
4G and S6E–S6G) as well as exhausted (LAG3/PD-1 doublepositive) and non-exhausted (LAG3/PD-1 double-negative)
human primary T cells (Figures 4H–4J and S6H–S6J).20,21 In
each case, the models exhibited high performance with AUC
scores of 0.94 and 0.92, respectively (Figures 4G and 4J). These
results suggest that GC can classify various types of label-freehigh-content phenotypes.
Pooled CRISPR screening of label-free high-content
phenotypes
We subsequently focused on morphological changes related topolarization, specifically from inactivated macrophages (M0macrophages) to classical pro-inflammatory macrophages (M1macrophages), serving as the target label-free phenotype forpooled CRISPR screening to identify associated genes. Macrophages are pivotal in the innate immune system, involved in critical processes such as tissue repair, inflammation, and cancer.
These cells exhibit polarization into various subtypes, eachwith distinct functions, including cytokine secretion andresponse to injury or pathogenic threats.22,23 Defining their biological functions using only a few surface markers is often challenging, rendering the isolation of live cell populations based on
their functions challenging. Thus, we hypothesized that macrophage polarization correlates with their morphology, prompting
us to assess whether our system could identify relevant genes
based on changes in label-free high-content cell morphologicalinformation without relying on surface markers. Accordingly,
we employed the kinase CRISPR library, including various
signaling pathways associated with macrophage polarization,
particularly within the downstream pathway containing kinase
genes.24 To train a classifier in the GC, we separately preparedM0 and M1 macrophages before merging them as the training
sample. Only M0 macrophages were stained as the ground-truthlabel before mixing with the M1 macrophages (Figures S6A–
S6D). Using the trained classifier and kinase library and the cells
without ground truth labeling, we conducted a large-scalepooled CRISPR screening (approximately 68.6 coverage),sorting cells that exhibited suppression of the M1 polarization phenotype and subjecting them to deep sequencing(Figures 4A–4D, S7, and S8).
Analyzing sgRNA enrichment after sorting revealed several
genes with the potential to induce macrophage polarization(Figures 5A and S9; Tables S2 and S3). Notably, interferon g(IFNg), a primary cytokine associated with M1 activation, wasprominent. JAK2, a major mediator of IFNg-induced signaling,
was among the hit genes.25 Other candidate genes linked tomacrophage polarization were also identified. For example, it
is reported that DLG2 is altered in response to inflammationand can activate the formation of NLRP3 inflammasomes.26

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Figure 4. High-content cell phenotypes distinguishable using label-free GC(A–J) Label-free GC classified (A)–(D) THP-1-derived M0 and M1 macrophages, (E)–(G) THP-1 monocytes and THP-1-derived macrophages, and (H)–(J)exhausted (LAG3/PD-1 double-positive) and non-exhausted (LAG3/PD-1 double-negative) human primary T cells.
(A), (E), and (H) Representative bright-field cell images on a dish were obtained using a microscope and Amnis Image Stream. Scale bars, 30 mm and 10 mm,
respectively.
(B) Conventional fluorescence-activated cell sorting (FACS) scatterplots of forward scatter (FSC) and side scatter (SSC).
(C), (F), and (I) Representative label-free GMI waveforms of 20 randomly selected cells for each condition.
(D), (G), and (J) Classification results for label-free cell phenotypes. The ML-based classification performance is displayed as histograms of SVM scores, where
red and blue colors indicate ground-truth labels and AUC scores.

Considering the function of cellular membrane trafficking, STK16
localizes to the Golgi complex and is involved in transporting
secretory vesicles, which may indirectly contribute to cytokine
production for pro-inflammatory responses.27 The top-hit
gene, BRD2, is reportedly essential for pro-inflammatory cytokine production in macrophages.20,28 Concordantly, reduced
expression of the M1 marker and secretion of pro-inflammatory
cytokines, such as IFNg and tumor necrosis factor alpha (TNF-a),in BRD2 CRISPR knockout M1 cells validated the hypothesisthat BRD2 plays a pivotal role as a modulator of the M1 inducer
gene (Figures 5B, 5C, and S10).

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Collectively, we demonstrated that large-scale GC-based
pooled CRISPR screening is applicable for label-free high-content phenotypes, which can be challenging to distinguish withconventional image-feature-based analyses using standard
FACS and possibly even conventional microscopes.
Unsupervised representation of GMI waveforms
ML models trained within the GC framework exhibit versatility inaddressing various high-content cell phenotypes, depending on
the characteristics of the cell population and the classification
objective. In this study, we systematically trained models forll

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Figure 5. High-throughput pooled CRISPR screening of label-free high-content phenotypes(A) Volcano plot displaying statistical significance (y axis) and magnitude of the change (x axis) before and after cell sorting, with p values calculated using theMann-Whitney U test. Dashed lines represent the cutoff for hit genes (FDR = 0.01).
(B) Expression of macrophage surface markers in control M0, control M1, and BRD2 CRISPR knockout (KO) M1 cells, using CD11b as a pan-macrophage marker
and CD38 as an M1-specific marker. N = 3 biological replicates.
(C) Cytokine (IFNg and TNF-a) release profiling of control M0, control M1, and BRD2 CRISPR KO M1 cells. Supernatants were collected from three independent
experiments. Data are presented as mean ± SD; Welch’s t test: *p < 0.05, ***p < 0.001. N = 3 biological replicates.
See also Table S4.

each pair of cellular phenotypes, specifically defined throughground truth markers, thereby establishing the robust performance of these classification models. This methodologicalapproach can be extended to scenarios where only one phenotype can be defined during the screening process. It can beachieved by leveraging anomaly detection techniques, such as
the one-class SVM, which can be readily implemented within
the existing GC framework. However, practical situations often
arise where suitable molecular markers or staining methods
are unavailable for defining a particular cell phenotype. Moreover, the cells of interest within a pooled population may exhibit
morphological heterogeneity. In such instances, defining andsorting the desired phenotype solely based on the GMI waveforms becomes imperative.
To explore the potential capabilities of GC in this context,
we employed uniform manifold approximation and projection(UMAP) to visualize the GMI waveforms, as depicted in
Figures 2C and 4C. Dimensionality reduction through principalcomponent analysis29 preceded the UMAP analysis (Figure 6).
In Figure 6A, the UMAP of flGMI waveforms, obtained from a
mixture of LPS-stimulated and unstimulated cells labeled withfluorescently tagged RelA molecules, revealed two distinct clusters. These clusters were subsequently validated as representing LPS-stimulated and unstimulated populations, respectively.
The clear separation of these clusters suggests the potential totrain classification models based on populations delineatedwithin the UMAP space, similar to those defined using molecularmarkers.
Furthermore, Figure 6B illustrates that cells distinguished within
the UMAP space exhibit differential SVM scores, as quantified in
Figure 2D. These results indicate that the morphological differences identified through supervised learning algorithms can also
be captured via unsupervised learning algorithms, including
UMAP and other dimensionality reduction methodologies.

In Figure 6C, similar to the fluorescence scenario, we projected label-free GMI waveforms derived from a mixture of
THP-1-derived M0 and M1 macrophages onto the UMAP space.
Colors were assigned based on ground-truth labels. While M0
and M1 macrophages did not present as distinct clusters within
the UMAP, Figure 6D demonstrates that their distributions correlated with the SVM scores obtained from the classification modelin Figure 4D. These findings strongly suggest that GMI waveforms alone hold the potential to define target high-content
cellular phenotypes, provided that discernible morphologicaldifferences exist in an unsupervised context.
DISCUSSION
An important technical advantage of CRISPR screening using
the GC-based cell sorter, compared with conventional FACSbased screening, lies in its ability to analyze and integrate intricate cellular information, leading to more precise cell selection.
In cases involving fluorescence-based cell phenotyping, thisapproach has demonstrated its superiority, especially when
distinguishing between cell phenotypes, such as variations insubcellular protein localization or alterations in organelles morphologies, with similar total fluorescence intensity (Figure 2).
The advantage of utilizing high-dimensional GMI waveforms forlabel-free cellular phenotyping is also evident. This is highlighted
by the fact that conventional FSCs and side scatters (SSCs) (Figure 4B) were insufficient in distinguishing the polarized state ofmacrophages, even when utilizing the SVM method (Figure S6D).
In addition, the results presented in Figure S6D show that the
combination of average characteristics from FSC and SSC,
along with detailed GMI waveforms, can synergistically enhanceclassification accuracy.
We anticipate the potential benefits of combining surface
marker-based cell definition with label-free high-content cellular

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Figure 6. Uniform manifold approximation and projection (UMAP) of GMI waveforms(A) UMAP of flGMI waveforms obtained for LPS-stimulated (orange) and unstimulated (blue) THP-1 cells.
(B) The same UMAP plot as presented in (A) is colored according to SVM scores obtained in the classification of RelA flGMI waveforms for the LPS-treated cells(stimulated versus unstimulated).
(C) UMAP of label-free GMI waveforms obtained for THP-1-derived M0 (blue) and M1 (orange) macrophages.
(D) The UMAP plot presented in (C) is colored according to SVM scores obtained in the classification of label-free multimodal GMI waveforms for the cells at
different polarization states (M0 versus M1).

phenotyping within the GC. This combination is particularly valuable for exploring subtle cell differences among immune cell
subtypes and various cell states, including activation, exhaustion, and differentiation. Moreover, the unique capability of GC
to discern both fluorescent and label-free high-content cellular
phenotypes holds potential for discovering novel targets,
including gene perturbations and compounds. The fluorescencemode proves effective, particularly when the molecules or subcellular features of interest are pre-defined, enabling screening
based on changes in their spatial distribution. Examples include
the aggregation or degradation of proteins and alterations inintracellular organelles, serving as indicators of the underlying
mechanisms of action.30,31 The label-free GMI waveforms
uniquely capture complex morphological changes as a phenotypic response within each entire cell. In future implementations,
simultaneous use of fluorescence and label-free GMI waveforms
holds the potential for cell-based screening through the combinatorial analysis of changes in fluorescently labeled target proteins and detailed holistic high-content cellular phenotypes.32–34
From the viewpoint of applying ML methods in GC, we envision the following cases for supervised and unsupervised
methods, respectively. Supervised learning approaches will be
powerful when a labeled dataset is available for training classification models. This includes instances where the presence orabsence of specific phenotypes can be defined by surface
marker expression or scenarios when separate cell preparations
with and without the desired phenotypes can be prepared. When
accessible, leveraging class label information in supervised
learning tends to achieve superior separation of phenotypes

8

Cell Reports Methods 4, 100737, March 25, 2024

compared with unsupervised methods. On the other hand, unsupervised learning methods stand out for their potential in exploratory scenarios, especially in the absence of a labeled training
dataset. These include experiments where the expected phenotypes are uncertain or when the target phenotypes cannot be
defined only by surface markers. Such situations can typicallyarise in primary samples during disease progression, infection,or differentiation processes.
Image-based pooled CRISPR screening has witnessed rapid
advancements. A noteworthy strategy involves fluorescence microscopy for both fluorescence image-based cellular phenotyping and subsequent reading of DNA barcodes assigned to eachcell.6 The use of fluorescence markers is essential to delineate
target image features and phenotypes. However, analyzing
explicitly discernible image features becomes intricate whensuitable biomarkers are unavailable, and cell phenotypes mustbe evaluated without labeling. Nevertheless, optical microscopy
offers the unique advantage of characterizing cellular phenotypes in adherent states, coupled with the potential for high-resolution time-lapse observations, albeit at the cost of throughput.
Another recently introduced screening method relies on
the enrichment of fluorescence image phenotypes using afluorescence image-activated cell sorter (ICS),7 which has
demonstrated even higher throughput than that of the current
GC-based cell sorter. In ICS, a distinct set of image features isquantified by analyzing the reconstructed images, whereas the
GC approach analyzes GMI modalities that are beyond visualcomprehension. We acknowledge that the absence of images
during GC-based sorting may be a limitation for those who

llArticleprioritize visual verification. Nonetheless, in an era where machines frequently outperform human capabilities, we believe inthe potential of utilizing modalities that are machine suitable.
We have also demonstrated that GC can perform phenotyping
quantitatively, resembling a 2D image-based analysis of image
features (Figures 3C and 3D).
Moreover, in the context of label-free cell phenotyping,whether a limited set of image features computed from non-fluorescent images can sufficiently support optimal analysis remains
uncertain. In contrast, GC classifies multimodal GMI waveforms,
which include holistic, multiparametric morphological data ofcells without requiring image reconstruction or feature extraction. Consequently, our ML-driven, less biased approach has
showcased its advantages in detecting both simple molecular
phenotypes (e.g., nuclear translocation of fluorescently labeled
RelA) and more intricate, holistic phenotypes (e.g., macrophage
polarization without labels).
Last, we discuss the similarities and differences in the screening
results when compared with recent reports. In one report,7 the authors used HeLa cells and stimulated them with TNF-a to induce
nuclear translocation of RelA. They performed a CRISPR screen
using their custom nuclear factor kB (NF-kB) pathway-focused library targeting 1,068 genes and obtained MAP3K7, IKBKB,IKBKG, MAP3K7, IRAK4, IKBKB, and IKBKG, which are genes
relevant to NF-kB signaling and were also hit in our screens, as
statistically predominant hits. In another report,6 the authors
used HeLa cells and stimulated with interleukin-1b (IL-1b) andTNF-a for induction of RelA nuclear translocation. They performeda screen using their custom library targeting 963 genes and obtained MAP3K7, IKBKB, IKBKG, MAP3K7, IRAK1, IRAK4,IKBKB, and IKBKG, which are genes relevant to NF-kB signaling
and were also hit in our screens, as statistically predominant hits.
In this study, we used THP-1 cells and stimulated them with LPS
to induce RelA nuclear translocation. We first used a small-scale
library targeting pathways containing NF-kB signaling and obtained TRAF6, MAP3K7, MYD88, IKBKB, IRAK1, IRAK4, and
TIRAP as statistically predominant hits. We then used kinaselibrary targeting 729 kinase genes and detected five genes,
RelA, MAP3K7, IRAK4, IKBKB, and IKBKG, as statistically predominant hits relevant to NF-kB signaling. The large-scale library
we used targeted only kinase genes and thus did not include nonkinase genes such as MYD88, which we included in the smallscale library. TAB1 and NFKB1A genes were hit in a report by
Feldman et al.6 but not in another by Schraivogel et al.7 or in our
screen. As the cell lines and stimulation conditions were different,
and the genes in each library were not completely the same, it isdifficult to compare the sensitivity directly. Nevertheless, manyof the hit genes overlap with the previous two reports.
Collectively, we successfully developed a high-throughput andlarge-scale pooled CRISPR screening method, facilitated by rapid
and selective cell sorting based on machine vision of high-content
cellular phenotypes. We anticipate widespread utilization of GCbased cell sorters for screening critical cellular phenotypes byintegrating various available ML methods and existing biomarkers
with high-content analysis capabilities. When combined with single-cell sequencing techniques,35–38 this method seamlesslyaligns with the pooled screening of various DNA-tagged perturbations, including antibodies,39 compounds,40 short hairpin RNAs

OPEN ACCESS(shRNAs),41 and peptides.42 Notably, as it is not confined to fluorescence-based cellular phenotypes, our ML -based, label-free,high-content cell analysis enables the enrichment of target phenotypes without invasive staining, preserving ‘‘untouched’’ cells fordownstream functional assays, thereby expanding its applicability
across a wide spectrum of biological studies.
Limitations of the study
To perform CRISPR screening using GC-based cell sorting, it isnecessary to determine what to screen as the target positive
phenotype and train the machine classifier using training samples. The design and preparation of positive and negative phenotypes in the step of training classifiers are the most importanttasks for successful screening of genes that induce target image
cell phenotypes.
STAR+METHODS
Detailed methods are provided in the online version of this paper
and include the following:d KEY RESOURCES TABLE
d RESOURCE AVAILABILITY

B Lead contact
B Materials availability
B Data and code availabilityd EXPERIMENTAL MODEL AND STUDY PARTICIPANT DE-

TAILS
B Cell cultured METHOD DETAILSB Ghost cytometry (GC)-based cell sorter
B Construction of small-scale CRISPR sgRNA expression plasmids
B Amplicon sequencing
B Lentiviral production and transduction
B Immunostaining
B Flow cytometry
B ELISAB Experimental conditions for fluorescence and labelfree GC
B Classification of lysosomes and mitochondria
d CLASSIFICATION OF AUTOPHAGOSOME LOCALIZATION OF LC3-GFP PROTEINS IN HeLA CELLSB Classification of mitochondrial morphological changes
in activated and non-activated human primary T cells
B Classification of THP-1-derived monocytes and macrophages
B Classification of exhausted and non-exhausted cells
B Classification and sorting of CRISPR library using fluorescence GC
B Classification and sorting of the CRISPR kinase libraryusing label-free GC
d QUANTIFICATION AND STATISTICAL ANALYSIS
SUPPLEMENTAL INFORMATION
Supplemental information can be found online at https://doi.org/10.1016/j.
crmeth.2024.100737.

Cell Reports Methods 4, 100737, March 25, 2024

9ll
OPEN ACCESS

ArticleACKNOWLEDGMENTS

granule-regulatory factors. Nat. Methods 17, 636–642. https://doi.org/
10.1038/s41592-020-0826-8.

We thank all members of ThinkCyte Inc. for extending experimental support
and their discussions, with special recognition to Keisuke Toda, Yasuhiro Kajiwara, Hikari Morita, and Keiji Nakagawa for their roles in instrument development. We also acknowledge Hiroaki Adachi for his support during data analysis, Hiroaki Suita and Kaoru Komoriya for their assistance with biologicalexperiments, and Keisuke Wagatsuma for their insightful scientific discussions. We would like to express our appreciation to Greg Schneider, Andy
Wu, and Willem Westra for reviewing the manuscript review and to Kaori Shiinafrom The University of Tokyo for her assistance with sequencing data analysis.
This work was supported by the Product Commercialization Alliance (PCA)
program funded by the New Energy and Industrial Technology Development
Organization (NEDO; 20001038-0).

6. Feldman, D., Singh, A., Schmid-Burgk, J.L., Carlson, R.J., Mezger, A.,
Garrity, A.J., Zhang, F., and Blainey, P.C. (2019). Optical Pooled Screensin Human Cells. Cell 179, 787–799.e17. https://doi.org/10.1016/j.cell.
2019.09.016.

AUTHOR CONTRIBUTIONS

9. Wang, C., Lu, T., Emanuel, G., Babcock, H.P., and Zhuang, X. (2019). Imaging-based pooled CRISPR screening reveals regulators of lncRNA
localization. Proc. Natl. Acad. Sci. USA 116, 10842–10851. https://doi.
org/10.1073/pnas.1903808116.

A.T. and S.O. conceived the study, designed the experiments, interpreted theresults, and wrote the manuscript. S.O. supervised the study. A.T. developed
the experimental tools and performed most of the experiments, including construction of the CRISPR library, flow cytometry analysis, cell culture, cell staining, GC data analysis, and NGS library preparation. Y.A. performed the experiments and analyzed the data using GC and NGS. Y.K. designed andfabricated the microfluidic sorting chips, integrated the GC-based cell sorter,and performed all GC-based cell sorting experiments. Y.M. performed all GCbased cell sorting experiments. H.N. wrote the manuscript. Y.Y. performed thecomputational analysis of the NGS data. K.T. performed the GC analysis. S.I.,
H.A., and N.Y. provided conceptual input for the pooled CRISPR screening,
CRISPR library design, library cloning strategy, and technical inputs. All authors contributed to manuscript revision.
DECLARATION OF INTERESTS
S.O. is a co-founder and a board member of ThinkCyte Inc., a company
engaged in the development of GC-based cell sorting. A.T., Y.A., Y.K., Y.Y.,
H.N., Y.M., and K.T. are employees of ThinkCyte, Inc. A.T., Y.A., Y.K., Y.M.,
Y.Y., H.N., and K.T. have shares of stock options from ThinkCyte, Inc. S.O.,
A.T., Y.A., Y.K., Y.M., Y.Y., H.N., and T.K. have filed patent applications forthis study.
Received: May 17, 2023
Revised: October 30, 2023
Accepted: February 27, 2024
Published: March 25, 2024
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ArticleOPEN ACCESS

STAR+METHODSKEY RESOURCES TABLEREAGENT or RESOURCESOURCEIDENTIFIER

Antibodies
anti-UQCRC1 antibody

Invitrogen459140, RRID: AB_2532227

anti-Lamp1 antibody

CST

9091, RRID: AB_2687579

anti-RelA p65 antibody

CST

8242, RRID: AB_10859369

Alexa Fluor 488-conjugated secondary antibody

ThermoFisher

A32723, RRID: AB_2633275

Alexa Fluor 488-conjugated secondary antibody

ThermoFisher

A11008, RRID: AB_143165

PE anti-human LAG3 (11C3C65)

BioLegend

369305, RRID: AB_2629591

APC anti-human PD-1 (EH12.2H7)

BioLegend

329908, RRID: AB_950475

PE-anti-human CD11b (ICRF4)

BioLegend

301306, RRID: AB_314158

Alexa Fluor 488 anti-human CD38 (HIT2)

BioLegend

303512, RRID: AB_493088

APC anti-human CD25 (BC96)

BioLegend

302610, RRID: AB_314280

BV421 anti-human CD69 (FN50)

BioLegend

310930, RRID: AB_2561909

LentiBrite GFP-LC3 lentiviral biosensor

MERCK

17–10193

One Shot Stbl3 Chemically Competent E. coli

ThermoFisher

C737303

Chloroquine

Sigma-Aldrich

C6628-25G

Phorbol 12-myristate 13-acetate (PMA)

Sigma-Aldrich

P8139-1MG

LPS

Sigma-Aldrich

L4391-1MG

Bacterial and virus strains

Chemicals, peptides, and recombinant proteinsIFNg

R&D Systems

300–02rhIL-2

Pepro Tech

200-02-50ug

Arigo Biolaboratories Corp

ARG82862

Critical commercial assays
Human Pro-inflammatory
Cytokine Multiplex ELISA Kit
KAPA library quantification kit Illumina platform

Roche

KK4824

QIAamp DNA FFPE Tissue Kit

QIAGEN

56404

Gel/PCR Extraction Kit

Nippon Genesis

FG-91012

CellTiter-Glo Luminescent Cell Viability Assay

Promega

G7571

TruSeq rapid SBS kit (2 x 151 bp paired-end)

Illumina

FC-402-4023

MiSeq v2 kit (2 x 301 bp paired-end)

Illumina

MS-102-2002

Raw and analyzed data - Zenodo

This paper

https://doi.org/10.5281/zenodo.7701145,
https://doi.org/10.5281/zenodo.7703670,
https://doi.org/10.5281/zenodo.7709846
https://doi.org/10.5281/zenodo.10472989

Sequencing data

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DDBJ submission DRA017748

Deposited data

Experimental models: Cell lines
HEK293T cells

Applied Biological Materials

T3327

HeLa cells

Japanese Collection of
Research Bioresources

JCRB9004

THP-1 cells

ATCC

TIB-202

pLentiGuide-Puro

Addgene52963

pLenti-Cas9-Blast

Addgene52962

pMD2.G

Addgene12259

Recombinant DNA(Continued on next page)e1

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Continued
REAGENT or RESOURCESOURCEIDENTIFIER

psPAX2Addgene12260

Python scripts

This paper

https://doi.org/10.5281/zenodo.10472989

FlowJoBD Biosciences

Version 10.9.0

IDEAS

Luminex

Version 6.2

pythonpython.org

3.7.12

scikit-learn

scikit-learn.org

1.0.2

pandaspandas.pydata.org

1.3.5

numpy

numpy.org

1.21.6

optunaoptuna.org

2.10.1

matplotlib

matplotlib.org

3.5.3

GCApp

ThinkCyte

v1.2.9.82

Software and algorithms

Other
Dulbecco’s Modified Eagle’s Medium (DMEM)

FUJIFILM Wako

044–29765

RPMI-1640

FUJIFILM Wako

189–02025

X-VIVO 15

Lonza

BEBP04-744Q

Penicillin-streptomycin solution

FUJIFILM Wako

168–23191

Fetal bovine serum (FBS)

HyCloneSH30396.03

0.25w/v% Trypsin-1mmol/L EDTA･4Na
Solution with Phenol Red

FUJIFILM Wako

209–16941

Earle’s Balanced Salt Solution (EBSS)

ThermoFisher

14155063

StemSure 2-mercaptoethanol solution

FUJIFILM Wako

198–15781

Glutamax supplement

ThermoFisher

35050061

CD3/CD28 Dynabeads

ThermoFisher

11132DRESOURCE AVAILABILITY
Lead contact
Requests for further information and resources relating to this paper should be directed to Sadao Ota (sadaota@solab.rcast.u-tokyo.
ac.jp).
Materials availability
Materials used in this study are commercially available.
Data and code availabilityd All data are reported in the paper or deposited at Zenodo: https://doi.org/10.5281/zenodo.7701145, https://doi.org/10.5281/
zenodo.7703670, https://doi.org/10.5281/zenodo.7709846, https://doi.org/10.5281/zenodo.10472989.
d NGS fastq files of NFkB screening (replicate 1&2 of pilot screening, related to Figures S2A and S2K) are available in Zenodo:
https://doi.org/10.5281/zenodo.7701145.
d NGS fastq files of NFkB screening (replicate 1&2 kinase library screening, related to Figures 3B, and S4A–S4C) are available
in Zenodo: https://doi.org/10.5281/zenodo.7701145.
d NGS fastq files of macrophage label-free screening (replicate 2&3, related Figures S9D–S9G; Table S3) are available in
Zenodo: https://doi.org/10.5281/zenodo.7701145.
d Fcs files of conventional flow cytometer data (Figures 4B and 5B, S1J–S1L, S6H–S6J, and S7) are available in Zenodo:
https://doi.org/10.5281/zenodo.7701145.
d Text files of similarity score taken by Amnis image flow cytometer (Figure 3D) are available in Zenodo: https://doi.org/10.
5281/zenodo.7701145.
d NGS fastq files of macrophage label-free screening (replicate 1, related to Figure 5A) are available in Zenodo: https://doi.org/
10.5281/zenodo.7703670.
d Csv files of ML classifier training and test data (NFkB nuclear translocation, related to Figures 2D and 6) are available in
Zenodo: https://doi.org/10.5281/zenodo.7709846.

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Articled Csv files of ML classifier training and test data (mitochondria/lysosome, related to Figure 2H) are available in Zenodo:

https://doi.org/10.5281/zenodo.7709846.
d Csv files of ML classifier training and test data (LC3 aggregation, related to Figure 2L) are available in Zenodo: https://doi.org/

10.5281/zenodo.7709846.
d Csv files of ML classifier training and test data (T cell mitochondria, related to Figure 2P) are available in Zenodo: https://doi.

org/10.5281/zenodo.7709846.
d Csv files of ML classifier training and test data (Macrophage M0/M1, related to Figure 4D) are available in Zenodo https://doi.

org/10.5281/zenodo.7709846.
d Csv files of ML classifier training and test data (monocyte/macrophage, related to Figure 4G) are available in Zenodo:

https://doi.org/10.5281/zenodo.7709846.
d Csv files of ML classifier training and test data (T cell exhaustion, related to Figure 4J) are available in Zeonodo: https://doi.

org/10.5281/zenodo.7709846.
d Text files of SVM-based prediction probabilities in GC (Figures 3D and S5) are available in Zenodo: https://doi.org/10.5281/

zenodo.7709846.
d NGS sequencing raw data (fastq files) were also deposited at DNA DataBank of Japan (DDBJ)
d Submission: DDBJ: DRA017748 (sadaotalab1-0001_Submission)d BioProject: DDBJ: PRJDB17362 (PSUB022209)
d BioSample: DDBJ: SAMD00732055-SAMD00732060 (SSUB028198)
d Experiment: DDBJ: DRX509723-DRX509728 (sadaotalab1-0001_Experiment_0001–0024)
d Run: DDBJ: DRR525841-DRR525847 (sadaotalab1-0001_Run_0001–0020)
d Data analysis was performed using code written in Python, which is available in Zenodo. To analyze the data, csv files areimported and run Python code.
d Python code file (svm.py) for ML classification and drawing confusion matrix, SVM histogram, and PR curve, and calculationof probabilities from SVM score (Figures 3D, S1L, and S6J) is available in Zenodo: https://doi.org/10.5281/zenodo.
10472989.
d Python code file (umap_plot.py) for UMAP plot (Figure 6) is available in Zenodo: https://doi.org/10.5281/zenodo.10472989.
d Instruction file (how_to_use.txt) is also available in Zenodo: https://doi.org/10.5281/zenodo.10472989.
d Any additional information needed to re-analyze the data reported in this paper are available from the lead contact uponrequest.
EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS
Cell culture
HEK293T cells (gender: female) were purchased from Applied Biological Materials (abm) and cultured in Dulbecco’s Modified Eagle’s
Medium (DMEM) (FUJIFILM Wako), supplemented with 10% fetal bovine serum (FBS) (HyClone) and 1% penicillin-streptomycin solution (FUJIFILM Wako) at 37" C with 5% CO2. Routine testing for mycoplasma contamination was conducted using nested PCR with
culture medium as the template.
HeLa cells were obtained from the Japanese Collection of Research Bioresources (JCRB) Cell Bank and cultured in DMEM
(FUJIFILM Wako), supplemented with 10% FBS (HyClone) and 1% penicillin-streptomycin solution (FUJIFILM Wako) at 37" C with
5% CO2.
HeLa cells (gender: female) expressing LC3-GFP were transduced by adding LentiBrite GFP-LC3 Lentiviral Biosensor (Millipore) ata multiplicity of infection (MOI) of 20 for 24 h, following the manufacturer’s instructions. After 48 h post-infection, cells were passaged
into complete culture medium. Routine testing for mycoplasma contamination was conducted using nested PCR with culture medium as the template. Cells were either maintained in complete media or incubated for 4 h in Earle’s Balanced Salt Solution(EBSS) (ThermoFisher) containing 20 mM chloroquine (Sigma-Aldrich) to induce autophagosome accumulation.
THP-1 human monocytic leukemia cells (gender: male) were obtained from ATCC and cultured in RPMI-1640 (FUJIFILM Wako),supplemented with 10% FBS (HyClone), 1% penicillin-streptomycin (FUJIFILM Wako), and 1% StemSure 2-mercaptoethanol solution (FUJIFILM Wako) at 37" C with 5% CO2. Routine testing for mycoplasma contamination was conducted using nested PCR with
culture medium as the template. To activate TLR4 signaling, THP-1 cells were stimulated with lipopolysaccharide (LPS) (SigmaAldrich) at a concentration of 300 ng/mL for 1 h at 37" C with 5% CO2. For macrophage differentiation and polarization from
THP-1 cells, THP-1 monocytes were differentiated into resting macrophages (M0) using 100 nM phorbol 12-myristate 13-acetate(PMA) (Sigma-Aldrich) for 72 h, followed by 24 h in PMA-free medium. For M1 polarization, M0 macrophages were further culturedin M1-polarization medium containing 100 ng/mL LPS (Sigma-Aldrich) and 20 ng/mL IFNg (R&D Systems) for 24 h, starting on thethird day of PMA treatment [20].
For primary T cells, Pan-T cells were acquired from Precision for Medicine and cultured in X-VIVO 15 (Lonza), supplemented with
10% FBS (HyClone), 1% Glutamax supplement (Thermo Fisher), 1% penicillin-streptomycin (FUJIFILM Wako), and 1% StemSure
2-mercaptoethanol solution (FUJIFILM Wako) at 37" C with 5% CO2. For activation, Pan-T cells were cultured in the presence of
CD3/CD28 Dynabeads (Thermo Fisher) and 25 U/mL rhIL-2 (Pepro Tech) for 4 days. For non-activated cells, Pan-T cells weree3

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cultured with only 25 U/mL rhIL-2 for 4 days. To mimic transient stimulation for the T cell exhaustion experiment, cells were cultured
with CD3/CD28 Dynabeads and 25 U/mL rhIL-2 for 3 days, followed by incubation in the presence of only rhIL-2 for the remaining
11 days of culture [21].
METHOD DETAILS
Ghost cytometry (GC)-based cell sorterIn the optical setup of the GC-based cell sorter prototype, diffractive optical elements (DOEs) are employed to generate structured
illumination of light for acquiring ghost motion imaging (GMI) waveforms. The structured light pattern generated using a 488 laser isutilized to measure fluorescence GMI (flGMI) waveforms, whereas that produced using a 405 laser is employed for measuring labelfree GMI (fsGMI, bsGMI, dGMI, and bfGMI) waveforms. In the fluidic setup, a sample volume is controlled using a syringe pump, and asheath flow is driven by a pressure pump. Within the microfluidic device, cells are initially hydrodynamically focused to form atight stream. Subsequently, they undergo structured illumination by light and are then sorted using a piezo-based microfluidic
sorting device, based on the analysis using a support vector machine (SVM) model implemented i.n a field programmable gate array(FPGA), similar to a method reported previously.12 During large-scale CRISPR screenings, cells flowed at an input rate of approximately 1,000 cells/s. This prototype allowed us to flow cells, whose size ranged from 4 to 40 mm, at a flow rate up to 40 mL/min,at a maximum rate of 3,000 events/s, with an optical resolution of approximately 0.7 mm, which was estimated by the single spot
size of the structured illumination. The updated information on machines is available on Thinkcyte.43
Construction of small-scale CRISPR sgRNA expression plasmids
gRNA spacer inserts were prepared through a single-pot reaction to phosphorylate and anneal ssDNA pairs. To generate eachspacer fragment (see Table S1), a T4 polynucleotide kinase reaction sample was prepared with two ssDNAs in accordance withthe manufacturer’s protocol (Takara) and subjected to the following thermal cycling conditions: 37" C for 30 min; 95" C for 5 min;70 cycles of 12 s starting at 95" C and decreasing by 1" C per cycle, followed by incubation at 25" C. The annealed spacer inserts
were subsequently ligated into a pLentiGuide-Puro vector (Addgene) via Golden Gate Assembly using BsmBI (NEB) and T4 DNA
ligase (NEB). The assembly process followed these thermal cycling conditions: 15 cycles of 37" C for 5 min and 20" C for 5 min,55" C for 30 min, and then maintenance at 4" C.44 pLenti-Cas9-Blast plasmids were obtained from Addgene. Plasmid sequenceswere validated using Sanger sequencing.
Amplicon sequencing
Genomic DNA from fixed cells was extracted using the QIAamp DNA FFPE Tissue Kit (QIAGEN) or the hotshot method.37 For eachsample, target regions were amplified using the extracted genomic DNA as the PCR template with the corresponding first HTS primer
pair (see Table S1). The PCR was conducted following the previously established protocol [43]. The PCR product was purified using
the FastGene Gel/PCR Extraction Kit (Nippon Genetics) and re-amplified with custom Illumina index primers (see Table S1). Eachindexed library was electrophoresed in a 2% agarose gel, and the expected band was purified using the FastGene Gel/PCR Extraction Kit (Nippon Genetics). The sequencing libraries were quantified using qPCR with the KAPA Library Quantification Kit Illumina(KAPA Biosystems) for multiplexing. The multiplexed libraries were quantified using the same qPCR protocol and sequenced with
30–40% PhiX control using Illumina HiSeq2500 (HiSeq Rapid SBS kit; 2 x 151 bp paired-end) or MiSeq (MiSeq v2 kit; 2 x 301 bp
paired-end).
Lentiviral production and transduction
HEK293T cells were seeded onto a 10-cm plate at a density of 100,000 cells/cm2. After 20 h, cells were transfected with pMD2.G(0.75 mg) (Addgene), psPAX2 (2.25 mg) (Addgene), and a lentiviral transfer plasmid (3.6 mg) using polyethylenimine (PEI) MAX(Cosmobio).44 The media was changed after 24 h. Viral supernatant was harvested 48 h post-transfection and filtered through0.45 mm cellulose acetate filters. Virus titer was determined using CellTiter-Glo (Promega), following the manufacturer’s protocol.
THP-1 cells expressing Cas9 protein were transduced in bulk with the library at a low MOI (0.15 viral particles per cell) by adding viralsupernatant supplemented with polybrene (8 mg/mL) and centrifuging at 510 g for 1 h at 37" C. After 24 h post-infection, cells werepassaged into media containing selection antibiotics, 0.5 mg/mL puromycin (Sigma-Aldrich) and 10 mg/mL blasticidin (Sigma-Aldrich).
Immunostaining
HEK293T cells were washed once with PBS and treated with 0.25 w/v% Trypsin-EDTA solution (FUJIFILM Wako) for 5 min at 37" C todetach them from the culture dish. Cells were fixed with 4% formaldehyde in PBS (Thermo Fisher) for 20 min at room temperature. To
visualize mitochondria, cells in PBS were incubated at 95" C for 5 min, blocked with 5% FBS and 0.3% Triton X-100 (Sigma-Aldrich,#T8787-100 mL) in PBS solution for 30 min at room temperature, and incubated with a 1/200 diluted anti-UQCRC1 antibody for Complex III-core1 (Invitrogen, #459140) in the blocking buffer overnight at 4" C. After washing with wash buffer (0.5% FBS in PBS), cells
were incubated with Alexa Fluor 488-conjugated secondary antibody (Thermo Fisher, #A32723) for 1 h at room temperature. To
visualize lysosomes, cells were permeabilized with 0.2% Triton X-100 in PBS for 3 min at room temperature after fixation. Afterwashing with 0.5% BSA in PBS, cells were blocked with 5% BSA in PBS for 1 h at room temperature and incubated with a 1/200

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Articlediluted anti-Lamp1 antibody (CST, #9091) in 1% BSA in PBS overnight at 4" C. After washing with 0.5% BSA in PBS, cells were incubated with Alexa Fluor 488-conjugated secondary antibody (Thermo Fisher, #A11008) for 1 h at room temperature.
THP-1 cells were fixed with 4% formaldehyde in PBS (Thermo Fisher, #R37814) for 20 min at room temperature. After washing with
PBS once, cells were incubated with ice-cold methanol for 5 min at !20" C. After washing with 0.5% BSA in PBS, cells were blockedwith 5% BSA in PBS for 1 h at room temperature and incubated with anti-RelA p65 antibody (CST, #8242) in 1% BSA in PBS overnightat 4" C. After washing with 0.5% BSA in PBS, cells were incubated with Alexa Fluor 488-conjugated secondary antibody (Thermo
Fisher, #A11008) for 1 h at room temperature.
Flow cytometry
For flow cytometry and ghost cytometry, the following monoclonal antibodies were employed: PE anti-human LAG3 (11C3C65), APC
anti-human PD-1 (EH12.2H7), PE anti-human CD11b (ICRF4), Alexa Fluor 488 anti-human CD38 (HIT2), APC anti-human CD25
(BC96), and BV421 anti-human CD69 (FN50), along with their corresponding isotype controls (all from BioLegend). Cell viability
was assessed using 7-AAD (BD Biosciences) or Zombie NIR staining (BioLegend). Nuclear staining was performed using Hoechst
33342 (Thermo Fisher, #H3570). Cells were suspended in a staining buffer (2% FSB with 1 mM EDTA in PBS) containing humanFcR blocking reagent (Miltenyi Biotec, #130-059-901) and incubated with monoclonal antibodies for 30 min at 4" C. Flow cytometry
data were acquired using the Attune Flow Cytometer (Thermo Fisher) or JSAN (Bay Bioscience). Fluorescence was compared to corresponding isotype-stained controls. FlowJo software (Tree Star Inc.) was used for data analysis. Cell images in Amnis Image Stream
were captured using the FlowSight system, and black or gray areas were added to the surroundings to ensure uniformly sized images
for improved visualization in figures. Similarity scores for RelA and Hoechst were obtained using IDEAS 6.2 (Luminex).
ELISA
THP-1 cells were polarized as described earlier. The supernatant was collected from three independent experiments, pooled, andsubjected to the Human Pro-inflammatory Cytokine Multiplex ELISA Kit (Arigo Biolaboratories Corp) following the manufacturer’s
protocol. Optical density (OD) was read with a microplate reader (Speark, Tecan) at 450 nm, and average absorbance values
were calculated for each set of standards and samples. The experiments were performed as three independent replicates.
Experimental conditions for fluorescence and label-free GC
Electronic settings were detailed previously.11,12 Briefly, all photomultiplier tubes (PMTs) were purchased from Hamamatsu
Photonics Inc. PMTs with a frequency of 10 MHz and built-in amplifiers were used for detecting the flGMI, fsGMI, bsGMI, anddGMI, whereas PMTs with frequencies of 200 kHz, 1 MHz, or 10 MHz detected fluorescence and BSC signals. FSC signals wereobtained using either a photodetector or a 200 kHz PMT. Multi-pixel photon counters from Hamamatsu Photonics Inc. were used
for detecting bfGMI. DC signals for flGMI, fsGMI, bsGMI, bfGMI, dGMI, and FSC were filtered using an electronic high-pass filter.
PMT signals were recorded with electronic filters using a digitizer or an FPGA development board with a custom analog-to-digitalconverter. The digitizer and/or FPGA continually collected fixed-length signal segments from each color channel simultaneously,with a fixed trigger condition applied to the FSC signals.
Cells were passed through either a quartz flow cell (Hamamatsu) or a polydimethylsiloxane (PDMS)-based microfluidic device
using a custom pressure pump and/or syringe pump (KD Scientific). The quartz flow cell had a channel cross-section dimensionof 150 3 150 mm2 at the measurement position, with the sheath fluid (IsoFlow, Beckman Coulter) driven at a pressure of 305 kPa
for cell analysis. The sample fluid was driven at a flow rate between 10 and 40 mL/min. For cell sorting, custom handmade sorting
chips were used. The PDMS device had a channel with a cross-section dimension of 32 3 80 mm2 at the measurement position.
The sheath flow was driven at a pressure of about 180–270 kPa, and the sample fluid was driven at a flow rate of 20 mm/min during
cell sorting. For sorting, a piezoelectric (PZT) actuator implemented on the sorting chip was driven by an input voltage, displacing thefluid containing target cells toward a collection channel.
Machine learning details were described previously.11,12 For binary cell classification, a support vector machine (SVM) algorithmwith a radial basis function (rbf) kernel from the scikit-learn library was used. All model training and validations were performed using
equal sample amounts for each class label. We avoided the leakage interference of the signals from ground truth markers with
the imaging signal by carefully choosing dyes and designing the configrations of optical illuminations. The number of cells for training
and testing SVM is described in subsequent sections. After training, probability or decision functions were computed using thepredict_proba method or the decision_function method from the scikit-learn library, serving as SVM scores. Score distributions
for each sample were visualized as histograms using the matplotlib library. We evaluated trained machine learning models using
accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC or ROC-AUC). ROC curves wereplotted using the matplotlib library after calculating the true positive rate (tpr) and false positive rate (fpr) with the scikit-learn library.
Precision-recall pairs for different SVM score thresholds were computed with the scikit-learn library, and precision-recall (PR) curves
were plotted using the matplotlib library. Hyperparameters (regularization and kernel parameters in SVM) were optimized through3-fold cross-validation of the AUC score.

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ArticleOPEN ACCESSClassification of lysosomes and mitochondria
HEK293T cells were immunostained with anti-Lamp1 and anti-UQCRC1 antibodies as described above. As a ground truth label, antiLamp1 antibody-stained cells were incubated with the LIVE/DEAD Fixable Far-Red (FFR) Dead Cell staining kit (Thermo Fisher
Scientific). To classify the lysosomal and mitochondrial organelle fluorescence distribution patterns, each cell type was mixed at
an equal concentration. The mixed suspension was allowed to flow through the fluorescence GC system. Cells were gated using
FSC/SSC scatterplots to remove doublets and debris from the training samples. flGMI waveforms were used as the classification
modality. Within the data, 2,500 and 1,000 cells were randomly selected (without overlap), with an equal number of anti-Lamp1and anti-UQCRC1-stained cells used as training and testing data, respectively.
CLASSIFICATION OF AUTOPHAGOSOME LOCALIZATION OF LC3-GFP PROTEINS IN HeLa CELLS
Autophagosome-induced and -uninduced HeLa cells expressing LC3-GFP proteins were prepared individually, as described earlier.
After washing once with PBS, cells were incubated with 0.25 w/v% Trypsin-EDTA solution for 5 min at 37" C to detach them gently
from the culture dish. As a ground truth label, the accumulation of autophagosome-uninduced cells was stained with CellTracker
DeepRed Dye (Thermo Fisher Scientific). To classify LC3-GFP autophagosome localization, each cell line was mixed at equal concentrations. The mixed suspension was allowed to flow through the fluorescence GC system. Cells were gated using FSC/SSC scatterplots to remove doublets and debris from the training samples. flGMI waveforms were used as the classification modality. Within
the data, 2,000 and 1,000 randomly selected cells (without overlap) with equal numbers of induced and uninduced cells were used astraining and testing data, respectively.
Classification of mitochondrial morphological changes in activated and non-activated human primary T cells
Activated and non-activated T cells cultured for 4 days were individually stained with MitoTracker Green according to the manufacturer’s instructions. Following PBS washing, cells were further labeled with cell surface markers and the viability dye
mentioned previously. To classify activated and non-activated cells, the cell populations were mixed equally and passed
through the fluorescence GC system. FSC/SSC scatterplot gating was applied to remove doublets and debris, and live cells
were selected. CD25/CD69 double-positive cells were designated as activated, while CD25/CD69 double-negative cells
were identified as non-activated T cell populations. Classification was based on flGMI waveforms. In the dataset, 2,000 and
1,000 cells from each category (activated and non-activated, respectively) were randomly selected without overlap for training
and testing.
Classification of THP-1-derived monocytes and macrophages
THP-1-derived monocytes and macrophages were stained with cell surface markers and the viability dye described earlier. To classify these cells, FSC/SSC scatterplot gating was used to eliminate doublets and debris. After gating for live cells, CD11b-positive
cells were identified as macrophages, whereas CD11b-negative cells were categorized as monocytes. Classification relied on fsGMI,
bsGMI, and dGMI waveforms. In the dataset, 1,000 and 500 cells from each category (monocytes and macrophages) were randomly
selected without overlap for training and testing, respectively.
Classification of exhausted and non-exhausted cells
Transiently-stimulated T cells were stained with cell surface markers and the viability dye mentioned earlier. To classify exhausted and non-exhausted cells, FSC/SSC scatterplot gating was used to remove doublets and debris, and live cells wereselected. LAG3/PD-1 double-positive cells were classified as exhausted, whereas LAG3/PD-1 double-negative cells wereconsidered non-exhausted T cell populations. Classification relied on fsGMI, bsGMI, and dGMI waveforms. In the dataset,
400 and 400 cells from each category (exhausted and non-exhausted) were randomly selected without overlap for training
and testing, respectively.
Classification and sorting of CRISPR library using fluorescence GC
THP-1 cells were immunostained with the previously described anti-RelA antibody. Cells unstimulated with LPS were labeled with the
LIVE/DEAD FFR dead cell staining kit (Thermo Fisher Scientific) as a reference label. Classification of LPS-stimulated and -unstimulated cell populations was performed with equal concentrations of each cell type passing through the fluorescence GC system. FSC/
SSC scatterplot gating was applied to exclude doublets and debris from the training dataset. Classification was based on flGMI
waveforms. In the dataset, 1,250 and 1,000 cells from each category (LPS-stimulated and unstimulated) were randomly selectedwithout overlap for training and testing, respectively.
The pooled CRISPR library cells were stimulated with LPS and stained with anti-RelA antibody described earlier. The trained classifier was implemented on an FPGA, allowing real-time cell judgment using GMI signals, enabling subsequent cell sorting. The timefor judgment of a single cell with the FPGA was 6.0 ms. For small-scale library sorting, 100,000 cells were processed, with 70,000 cells
predicted as positive and sorted using the trained model (coverage 1,166). The coverage is the cell number of after sorting divided by
the number of gRNAs. The experiments were performed as two independent replicates. The cells expressing 40 out of 60 pooledsgRNAs were labeled with a ground truth marker to validate the sorting. Sorting purity was validated, with 90.8% of cells in the sorted

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Articlesample labeled with the ground truth marker (Figure S2C). The result closely matched the predicted sorting purity of 92.6% and arecovery of 96.3% (Figure S2B). Reproducibility of the trained model was assessed, with an average AUC score of 0.98 ± 0.02
(n = 7) and a high Zʹ-factor value of 0.83 (Figure S2D).45 For sorting of the kinase library, 6,000,000 cells (approximately 82.3 coverage)
were processed, with approximately 600,000 cells predicted as positive and sorted using the trained model within 2 h (Figure S2J).
The experiments were performed as four independent replicates.
Classification and sorting of the CRISPR kinase library using label-free GC
THP-1-derived M0 and M1 macrophages were individually prepared as previously described. Following a single PBS wash, the cells
were incubated at 37" C with a 0.25% w/v Trypsin-EDTA solution (FUJIFILM Wako) for 5 min and gently scraped using a Cell Lifter(Corning) to detach them from the culture dish. As a ground truth label, M0 macrophages were stained with CellTracker GreenCMFDA Dye (Thermo Fisher). For the classification of M0 and M1 macrophages, an equal concentration of each cell type was mixed,
and this mixture was passed through the fluorescence GC system. FSC/SSC scatterplot gating was employed to eliminate doubletsand debris from the training dataset. Classification was based on fsGMI and bfGMI waveforms. In this dataset, 1,000 cells from eachcategory (M0 and M1 macrophages) were randomly selected without overlap for training, with an additional 1,000 cells for testing.
The reproducibility of creating a trained model was evaluated (the average AUC score was 0.85 ± 0.06, n = 7), and a high Zʹ-factor
value from the SVM score (0.75, n = 7) was obtained (Figure S7C).45 For validation of differentiation and polarization, M0 and M1
macrophages in the training sample were stained with known M0 and M1 CD markers individually (Figures S7D–S7G).20
The pooled CRISPR kinase library cells were differentiated and incubated with an M1-polarization medium. For sorting the kinaselibrary, 6,000,000 cells (approximately 68.6 coverage) were processed by the sorter, and approximately 500,000 cells predicted as
positive were sorted within 2 h using a classifier trained and implemented on an FPGA. To enhance purity, a threshold of SVM scores>1 was set for cell sorting. In cases where the percentage of target cells in the pre-sorting sample was 8.3%, the predicted purity
was 37.5%, and the predicted recovery was 16.7% in the sorted sample (Figures S7A and S7B). These experiments were performed
as three independent replicates.
QUANTIFICATION AND STATISTICAL ANALYSIS
Adapter trimming and demultipexing were performed using Ultraplex.46 The large-scale screening data were analyzed according to
the published bioinformatics pipeline (kampmannlab.ucsf.edu/mageck-inc).47,48 Briefly, raw sequencing reads from next-generation
sequencing were cropped and aligned to the reference using Bowtie2 to determine sgRNA counts in each sample. Subsequently,
counts files from two samples for comparison were input into MAGeCK, where log2 fold changes (LFCs) and p values were calculated
for each sgRNA. Gene-level knockout phenotypic scores were determined by averaging LFCs from the top 3 sgRNAs targeting thegene with the most significant p values.
Statistical significance for each gene was determined by comparing the set of p values for sgRNAs targeting it with the set ofp values for non-targeting control sgRNAs, employing the Mann–Whitney U test. A cutoff value was selected based on the distributionof all products to ensure a false discovery rate (FDR) of < 0.01.

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Supplemental information

Pooled CRISPR screening of high-content
cellular phenotypes using ghost cytometry
Asako Tsubouchi, Yuri An, Yoko Kawamura, Yuichi Yanagihashi, Hirofumi
Nakayama, Yuri Murata, Kazuki Teranishi, Soh Ishiguro, Hiroyuki Aburatani, Nozomu
Yachie, and Sadao OtaSubmitted Manuscript: Confidential

1

Supplemental Figures2

1

Submitted Manuscript: Confidential

3
4

Figure S1. Development and performance of a machine learning-based model that classifies thehigh-content phenotypes of fluorescently labeled proteins and organelles, related to Figure 2.

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6
7

A-C: Fluorescently labeled RelA in suspension THP-1 cells based on fluorescence ghost motion
imaging (GMI) waveforms. (A) Initial cell gating. (B) Confusion matrix of the classification result. (C)Receiver operating characteristic (ROC) curve. LPS, lipopolysaccharide.

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D-F. Fluorescently-labeled lysosome (Lamp1) and mitochondria (COX III) in adherent HEK293 cells
based on fluorescence GMI waveforms. (D) Gating schemes; cell gate, singlet gate, and size gate. (E)Confusion matrix of the classification result. (F) ROC curve.

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G-I. Autophagosome LC3-GFP protein translocalization in adherent HeLa cells based on
fluorescence GMI waveforms. (G) Gating schemes; cell, singlet, and size gates. (H) Confusion matrix ofthe classification result. (I) ROC curve.

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J-L. Mitochondrial morphological changes in activated and non-activated human primary T cells
based on fluorescence GMI waveforms. (J) Gating schemes; cell gate, singlet gate, size, live, andCD25/CD69 gate. (K) Confusion matrix of the classification result. (L) ROC curve.

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Figure S2. Data obtained from the pooled CRISPR screening of a small-scale (60 sgRNAs) libraryusing a GC-based cell sorter (GC) in the fluorescence mode, related to Figure 2 and Figure 3.

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A-D: Replicate 1: a sample used in Figures 2A-2D and Figure 3B. (A) Enrichment of gRNAs after GCbased enrichment based on nuclear translocation of RelA. The genes downstream of the Toll-like receptor
4 (TLR4) signaling pathway are shown as blue bars. (B) Precision-Recall (PR) curve. The yellow circle
indicates the threshold < 0 of the support vector machine (SVM) score. (C) Validation of sorting purity.
Target cells exhibiting the RelA inhibition phenotype were enriched from 67% in the pre-sorting sample to91% in the sorted sample. (D) Evaluation of the reproducibility of training classifiers. The average of thearea under curve (AUC) scores was 0.98 ± 0.02 (N = 7). Mean ± SD values of the SVM scores arepresented, and the Zʹ-factor value was obtained as 0.83 (N = 7).

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E-K. Replicate 2: a sample different from replicate 1 but analyzed with the same methods. (E) Gating
was first performed by the cell gate (left) and then by the fluorescent intensity of the RelA staining and aground truth marker (Fixable Far-Red dye), which only labeled LPS-unstimulated cells (right). (F) Randomly
selected GMI waveforms for 20 cells from each condition. (G) SVM score histogram and AUC score for theclassification of LPS (-) and LPS (+) cells. (H) Confusion matrix of the classification result. (I) ROC curve.
(J) PR curve. The yellow circle indicates the threshold =0 of the SVM score. (K) Enrichment of sgRNAs inthe small-scale pooled library. Genes downstream of the TLR4 signaling pathway are presented as blue
bars.

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Figure S3. Data obtained in the pooled CRISPR screening of a kinase library (7,290 sgRNAs) usinga GC-based cell sorter in the fluorescence mode, related to Figure 3.

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A-F: Replicate 1: a sample used in Figure 3C. (A) Gating was first performed using the cell gate (left)
and subsequently using fluorescence intensity of the RelA staining and a ground truth marker (Fixable FarRed dye), which only labeled LPS-unstimulated cells (right). (B) Randomly selected GMI waveforms of 20
cells from each condition. (C) SVM score histogram and AUC score for the classification of LPS (-) andLPS (+) cells. (D) Confusion matrix of the classification result. (E) ROC curve. (F) Percentage of cells
predicted as positive (0 < SVM score) during sorting. Batch 1: 1–1,000,000 cells, batch 2: 1,000,0012,000,000 cells, batch 3: 2,000,001–3,000,000 cells, batch 4: 3,000,001–4,000,000 cells, batch 5:
4,000,001–5,000,000 cells, and batch 6: 5,000,001–6,000,000 cells.

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G-K. Replicate 2: a sample different from replicate 1 but analyzed with the same method. (G) Gating
was first performed by the cell gate (left) and then by fluorescence intensity of the RelA staining and aground truth marker (Fixable Far-Red dye), which only labeled LPS-unstimulated cells (right). (H) Randomly
selected GMI waveforms of 20 cells from each condition. (I) SVM score histogram and AUC score for theclassification of LPS (-) and LPS (+) cells. (J) Confusion matrix of the classification result. (K) ROC curve.

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Figure S4. Results of pooled CRISPR screening using a GC-based cell sorter in the fluorescencemode, related to Figure 3.

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A-C: Replicate 1: a sample used in Figure 3C. (A) Roust Ranking Aggregation (RRA) scores of top 10
hit genes. (B) p-values of top 10 hit genes. (C) Read counts of sgRNAs for representative hit genes before
sorting and their subsequent enrichment in the collected sorted cells.

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D-G. Replicate 2: a sample different from replicate 1 but analyzed with the same methods). (D)Volcano plot visualization of statistical significance (y-axis) and magnitude of the change (x-axis) comparing
before and after cell sorting, wherein the p-values were calculated with the Mann–Whitney U test. Dashedlines: cutoff for hit genes (false discovery rate; FDR = 0.01). (E) RRA scores of the top 10 hit genes. (F) pvalues of top hit genes. (G) Read counts of sgRNAs for representative hit genes before sorting and theirsubsequent enrichment in the collected sorted cells.

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Figure S5. SVM histograms, related to Figure 3.

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The three columns of histograms were obtained for three series of independent experiments, respectively.
N = 3 biological replicates (N1, N2, and N3). In the top row, the SVM scores were obtained when wedeveloped models for classifying the nuclear translocation using the training samples. From the second toeleventh row, yellow histograms overlaid on top of those from the first row represent the SVM scoresobtained when the trained model was applied to cells of each condition: MAP3K7-1, MAP3K-2, MYD88-1,
MYD88-2, IRAK4-1, IRAK4-2, TNFRSF-1, TNFRSF-2, and non-targeting sgRNA CRISPR knockout cells.

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Figure S6. Development and performance of a machine learning-based model that classifies thehigh-content phenotypes, related to Figure 4.

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A-D: M0 and M1 macrophages based on label-free GMI waveforms. (A) Gating schemes: cell gate,
singlet gate, size gate, live gate, and FSC/Ground truth marker plot of the training sample. M0 macrophages
were labeled with a ground truth marker (CellTracker Green dye). (B) Confusion matrix for the classificationof M0 and M1 polarized macrophages. (C) ROC curve. (D) Combination analysis of the label-free GMI
waveforms for classification of M0 and M1 macrophages.

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E-G. THP-1 monocytes and THP-1-derived macrophages based on label-free GMI waveforms. (E)Gating schemes; cell, singlet, size, live, and CD11b/FSC gates. THP-1-derived CD11b-positive and CD11bnegative cells were identified as positive and negative, respectively, for a training data set. (F) Confusion
matrix of the classification result. (G) ROC curve.

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H-J. Exhausted (LAG3/PD-1 double-positive) and non-exhausted (LAG3/PD-1 double-negative)
human primary T cells based on label-free GMI waveforms. (H) Gating schemes; cell, singlet, size, live,and LAG3/PD-1 gates. LAG3/PD-1 double-positive cells were identified as positive and LAG3/PD-1 doublenegative cells were identified as negative for a training data set. (I) Confusion matrix of the classification
result. (J) ROC curve.

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Figure S7. Data obtained in the pooled CRISPR screening using a GC-based cell sorter in the labelfree mode (replicate 1: a sample used in Figure 5A), related to Figure 5.

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A-C: Assuming the percentage of target cells in the sample was 3 %, (A) purity in the sorted sample was
expected to be 37.5 %. The yellow circle indicates the threshold of 1 < SVM score. (B) The predicted
recovery in the sorted sample was 16.7 %. The yellow circle indicates the threshold of 1 < SVM score. (C)Evaluation of the reproducibility in training models. The average of the AUC score was 0.85 ± 0.07 (N = 7).
Mean ± SD values of the SVM scores are presented, and the Zʹ-factor value was obtained as 0.75 (N = 7).

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D-G: Validation of the training sample using cluster of differentiation (CD) markers: (D) CD marker-stained
M0 cells, (E) CD marker-stained M1 cells, (F) unstained M0 cells, and (G) unstained M1 cells.

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Figure S8. Data obtained in the pooled CRISPR screening using a GC-based cell sorting the labelfree mode (Replicate 2: a sample different from replicate 1 but analyzed with the same), Related
Figure 5.

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(A) Gating schemes: cell gate, singlet gate, size gate, live gate, and FSC/Ground truth marker plot of the
training sample. M0 macrophages were labeled with a ground truth marker (CellTracker Green dye). (B)Randomly selected fsGMI and bfGMI waveforms of 20 cells from each population. (C) SVM score histogram
for the classification of M0 and M1 macrophages. (D) Confusion matrix for the classification of M0 and M1
macrophages. (E) ROC curve. (F-G) Assuming the percentage of target cells in the pre-sorting sample was
3 %, (F) purity in the sorted sample was expected to be 16.7 %. Yellow circle indicates the threshold of 1 <SVM score. (G) Predicted recovery in the sorted sample was 16.7 %. Yellow circle indicates the thresholdof 1 < SVM score. (H-K) Validation of the training sample using CD markers: (H) CD marker-stained M0
cells, (I) CD marker-stained M1 cells, (J) unstained M0 cells, and (K) unstained M1 cells.

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Figure S9. Results of pooled CRISPR screening using a GC-based cell sorter in the label-free mode,
related to Figure 5.

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A-C: Replicate 1: a sample used in Figure 5A. (A) RRA scores of the top 10 hit genes. (B) p-values of
the top 10 hit genes. (C) Read counts of sgRNAs for the representative hit genes before sorting and theirsubsequent enrichment in the collected sorted cells.

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D-G. Replicate 2: a sample different from replicate 1 but analyzed with the same methods. (D)Volcano plot visualization of statistical significance (y-axis) and magnitude of the change (x-axis) comparing
before and after the cell sorting, wherein the p-values were calculated with the Mann–Whitney U test.
Dashed lines: cutoff for hit genes (FDR = 0.01). (E) RRA scores of the top 10 hit genes. (F) p-values of top
10 hit genes. (G) Read counts of sgRNAs for the representative hit genes before sorting and theirsubsequent enrichment in the collected sorted cells.

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Figure S10. Macrophage surface marker expression of control M0, control M1, and BRD2 CRISPR
KO M1 cells, Related Figure 5.

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Samples were different from those used in Figure 5B but analyzed using the same methods (A: N2, B: N3).
CD11b and CD38 were used as pan-macrophage and M1-specific markers, respectively.

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Supplemental Tables

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Table S5. Correspondence table between replicates and figure number, related to Figure 2A-D, 3B,
4A-D, and 5A.

NFkB small scale (pilot)

NFkB large scale (kinase)

Macrophage (kinase)

Replicate 1

Fig 2A-D, S1A-C, S2A-D

Replicate 2

S2E-K

Replicate 1

Fig 3B, S3A-F, S4A-C

Replicate 2

S3G-K, S4D-G

Replicate 1

Fig 4A-D, Fig 5A, S6A-D, S7,
S9A-C

Replicate 2

S8, S9D-G

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