Ghost Cytometry
for Functional Genomics Screening

1288-A

Asako Tsubouchi1, Gregory Schneider2, Yuri An1, Yoko Kawamura1, Yuichi Yanagihashi1, Yuri Murata1, Kazuki Teranishi2, Soh Ishiguro3, Nozomu Yachie3, Sadao Ota1, 4
1 ThinkCyte. K.K, Tokyo, Japan, 2 ThinkCyte. Inc., San Carlos, USA, 3 University of British Columbia, BC, Canada,4 The University of Tokyo, Tokyo, Japan

Abstract

Results

Functional genomics requires a rapid and unbiased screening tool to determine cells that havethe proper functional outcome after genetic manipulation. Ghost Cytometry (GC) offers such atool that can rapidly screen cells for signaling events such as nuclear translocation. Standard
FACS with cell sorting requires staining the cells with fluorescent NFkB labels may render thecells unsuitable for downstream culture. Thus, the need for a label-free method of isolating
genetically redirected cells is needed to discriminate those cells that are functionally desirable
while not having any additional labels that can interfere with downstream applications. We
show here that GC corresponds well to NFkB translocation to the nucleus afterLipopolysaccharide (LPS) stimulation using standard fluorescent staining techniques. GhostCytometry with its label free and AI technology offers a method to ensure the sorting ofgenetically modified cells that have the correct phenotype and suitable for downstream
application.

Supervised Learning to Train Ghost Cytometry Model

Introduction
Conventional methods of high – content screening can take up to 10,000 plate and takebetween 6-12 months. Ghost Cytometry simplifies the process with pooled phenotypicscreening. The cells are altered and pooled and assayed by GC for their phenotypic expression
with the cells of interest being sorted for downstream analysis. In the present study, we use alentivirus RNA library and select for those cells that block NFkB translocation to the nucleus inresponse to LPS stimulation.

LPS (-)AUC : 0.987 *GC imaging signal
Training
model

LPS (+)(+) Phenotype

LPS (-)

GC
imagebased
sorting

LPS (+)LPS (-)LPS (+)LPS (+)(-) Phenotype* Mean = 0.982±0.0229 (N=7)
Z’ = 0.828 (N=7)

Figure 3: Supervised Learning for NFkB Translocation for Ghost Cytometry Provide a Reliable, Label-free Assay to Assaya Pooled Mixture of Gene Knockout Cells. Ghost Cytometry was trained using LPS treated THP1 cells resulting in thetranslocation of labelled NFkB into the cell nucleus.

After the Ghost Cytometry Model is Created –Cells Carrying Genes that Inhibit LPS-induced nuclear translocation of NF-kBMethods
Functional genomics requires a rapid and unbiased screening tool to determine cells that havethe proper functional outcome after genetic manipulation. Ghost Cytometry (GC) offers such atool that can rapidly screen cells for signaling events such as nuclear translocation. Standard
FACS with cell sorting requires staining the cells with fluorescent NFkB labels may render thecells unsuitable for downstream culture. Thus, the need for a label-free method of isolating
genetically redirected cells is needed to discriminate those cells that are functionally desirable
while not having any additional labels that can interfere with downstream applications. We
show here that GC corresponds well to NFkB translocation to the nucleus after
Lipopolysaccharide (LPS) stimulation using standard fluorescent staining techniques. GhostCytometry with its label free and AI technology offers a method to ensure the sorting ofgenetically modified cells that have the correct phenotype and suitable for downstream
application.

Intensity
LPS (-)

Training sample

Test sample

Virus infection

Pooled lentiviralguide RNA library
Pooled library: 60 gRNAs(4 gRNAs/gene)

GC image-based
sorting(+) Phenotype

Total # of cells: 3*10ˆ7 cells
Throughput: 1000 cps(-) Phenotype

LPS stimulation

Pooled cell library(Cas-9 expressing
THP1 cells)

NGS

Ghost Cytometry Sorted Cells Containing Knockouts for TLR4 Signaling Pathway Genes

POWERED BY

Ghost CytometryTime

dGMI: diffractive GMI(analogous to phasecontrast images)
Light Intensity

bfGMI: bright field GMI(analogous to brightfield images)
Light Intensity

Light Intensity

Light Intensity

Label free Ghost Motion Imaging Signals

TimeA B
D10 µm

0.57

LPS +
LPS +
MYD88 gRNA IKBKB gRNA

0.88

1.50

LPS + (Neg)

Target:LPS-induced activationof TLR4 signaling pathway

1.97* Images/nuclear translocation scores weretaken/calculated by FlowSight system (Luminex)

TimeTimeFigure 1: ThinkCyte® ImageSort™ – Combining Ghost Cytometry with Traditional Fluorescence Cytometry.
ImageSort™ utilizes standard, fluorescent flow cytometry with ThinkCyte® Ghost Cytometry. Ghost Cytometry
utilizes structured illumination where the laser lite is diffused through a pinhole structure to reduce the laser
intensity and increase the signal to noise ratio. The signals are read by four modalities: forward scatter, backscatter, bright field and diffractive Ghost Motion Imaging (GMI) to create waveforms for each mode. The artificial
intelligence interprets the waveforms and uses specific peaks that are predictive of cell populations. Two generalmodes can be used in Ghost Cytometry: Supervised learning where cells of interest are defined by existing
fluorescently labeled biomarkers for Ground Truth Labels

e.g. Lentivirus library(gRNA, shRNA, peptide)gRNA fold enrichment (sorted/before)

LPS - (Pos)bsGMI: backscattering GMI(analogous toback-scatteringdark field images)

Identify genes
knocked out

Figure 4: Ghost Cytometry – Based Pooled CRISPR Screening. Once the AI model was created, cells containing the RNA librarywere assayed and used to determine which gene constructs interrupted LPS induced NFkB translocation to the nucleus.

VisionSort

fsGMI: forwardscattering GMI(analogous toforward-scatteringdark field images)

Readout
gRNA

TLR4 signal pathway genes
Non-TLR4 signal pathway genes / Non-targeting

Target genes

Figure 5: Image-based Pooled CRISPR Screening Shows the Utility of Ghost Cytometry. The pooled library of 60 gRNAs (4gRNAs/gene) were assay in pooled cells that were isolated by GC for those cells which indicated interrupted NFkB translocation. Thecells were enriched, collected and the cells assayed for gene knockouts.

Conclusions
Incubation/assayCEAC

Imagephenotype

BED

GC Imagebased CellSorting

1. Ghost Cytometry can detect and create a model NFkB localization in the nucleus
2. VisionSort™ GC was able to create a label – free AI model that was used to screen genomic constructs
3. Cells with knockouts in the TLR4 pathway were enriched with the GC AI Model

Cells of interest(THP1 cells)

Pooled functional genomic screening

Figure 2: Protocol for Image-based Phenotypic Screening in Flow Format

NGS-based
identification

4. Ghost Cytometry allows for genomic screening with out the need for single cell plating – significantly
increasing throughput and reducing time to results