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Machine learning approach for discrimination of genotypes
based on bright-ﬁeld cellular images

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Godai Suzuki1, Yutaka Saito 1,2,3, Motoaki Seki 4, Daniel Evans-Yamamoto 4,5,6, Mikiko Negishi4, Kentaro Kakoi4, Hiroki Kawai7,
Christian R. Landry8,9,10,11,12, Nozomu Yachie 4,5,6,13 ✉ and Toutai Mitsuyama 1 ✉Morphological proﬁling is a combination of established optical microscopes and cutting-edge machine vision technologies, whichstacks up successful applications in high-throughput phenotyping. One major question is how much information can be extracted
from an image to identify genetic differences between cells. While ﬂuorescent microscopy images of speciﬁc organelles have beenbroadly used for single-cell proﬁling, the potential ability of bright-ﬁeld (BF) microscopy images of label-free cells remains to betested. Here, we examine whether single-gene perturbation can be discriminated based on BF images of label-free cells using amachine learning approach. We acquired hundreds of BF images of single-gene mutant cells, quantiﬁed single-cell proﬁlesconsisting of texture features of cellular regions, and constructed a machine learning model to discriminate mutant cells from wildtype cells. Interestingly, the mutants were successfully discriminated from the wild type (area under the receiver operating
characteristic curve = 0.773). The features that contributed to the discrimination were identiﬁed, and they included those related tothe morphology of structures that appeared within cellular regions. Furthermore, functionally close gene pairs showed similarfeature proﬁles of the mutant cells. Our study reveals that single-gene mutant cells can be discriminated from wild-type cells basedon BF images, suggesting the potential as a useful tool for mutant cell proﬁling.
npj Systems Biology and Applications (2021)7:31 ; https://doi.org/10.1038/s41540-021-00190-w

INTRODUCTION
Recent advancements in machine vision added more values tomicroscopy by making images machine readable, which is a
crucial factor for enabling a high-throughput image-based
screening at single-cell resolution1. Cell imaging with machine
vision automatically extracts hundreds of morphological featuresfrom a cell image and enables multivariate analyses to characterize various cell samples such as genetically perturbed cells,
differentiations of living cells, and drug-treated cells2–4. Thesephenotyping approaches are known as morphological proﬁling (orimage-based proﬁling), which shows successful results in variableapplications such as identifying phenotypes speciﬁc to diseases,
novel functions of genes, and modes of drug action5,6. Behindthese promising results, the details of morphological features are
not scrutinized. Moreover, the relationship between a morphological feature and a genotype is not fully understood.
In terms of imaging for morphological proﬁling, ﬂuorescent
microscopy images targeting speciﬁc organelles are broadly
used7–9, whereas bright-ﬁeld (BF) microscopy images of labelfree cells are used in few works. This is because BF images of labelfree cells show a lower contrast, and are relatively difﬁcult to be
used for characterizing single cells than phase-contrast, differential interference contrast (DIC), or ﬂuorescent imaging. However,
BF microscopy still has several advantages. First, BF microscopy
can image cells in a natural state in terms of sample preparation

because it does not require staining with dyes, which cansometimes be toxic. Second, BF microscopy conveys information
related to multiple organelles but not speciﬁc organelles. Third, BF
microscopy is less subject to artifacts such as shade-off and halo
which are found in phase-contrast imaging, and less expensive
than DIC imaging which requires special objective lenses andﬁltering systems. Thus, single-cell proﬁling based on BF microscopy will enable more efﬁcient and lower-cost phenotyping.
Previous studies demonstrated that BF image-based proﬁling
successfully discriminates different cell lines10, infected/noninfected macrophages, and live/dead cells11. Also, BF images
contained the information of localization or morphology oforganelles12–14. Another potential use of BF image-based proﬁling
would be to discriminate mutants from wild-type cells anddiscriminate among mutants in different genes. However, itremains to be tested whether the effects of single-gene
perturbation can be evaluated based on BF images.
In this study, we examine whether known morphologicalfeatures can identify genetically modiﬁed cells from the wild
type. CRISPR-Cas9 genome editing was performed to obtain
genetically modiﬁed cells and to acquire BF images of modiﬁed/
wild-type cells to interrogate. We targeted the ubiquitinproteasome complex, which is crucial for protein degeneration.
The complex consists of multiple pairs of paralogous genes (Table
1) that make the effect of single-gene silencing nonlethal andlimited. Possibly, they show zero or minimal morphological effects

1
Artiﬁcial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology (AIST), Tokyo 135-0064, Japan. 2AIST-Waseda University Computational
Bio Big-Data Open Innovation Laboratory (CBBD-OIL), Tokyo 169-8555, Japan. 3Graduate School of Frontier Sciences, The University of Tokyo, Chiba 277-8561, Japan. 4ResearchCenter for Advanced Science and Technology, The University of Tokyo, Tokyo 153-8904, Japan. 5Institute for Advanced Biosciences, Keio University, Tsuruoka 997-0035, Japan.
6
Systems Biology Program, Graduate School of Media and Governance, Keio University, Fujisawa 252-0882, Japan. 7Research and Development Department, LPIXEL Inc., Tokyo
100-0004, Japan. 8Institut de Biologie Intégrative et des Systémes, Université Laval, Québec, QC G1V 0A6, Canada. 9Département de Biochimie, Microbiologie et Bio-informatique,
Faculté de sciences et génie, Université Laval, Québec, QC G1V 0A6, Canada. 10PROTEO, le regroupement québécois de recherche sur la fonction, l’ingénierie et les applications
des protéines, Université Laval, Québec, QC G1V 0A6, Canada. 11Centre de Recherche en Données Massives (CRDM), Université Laval, Québec, QC G1V 0A6, Canada.
12Département de Biologie, Faculté des sciences et de Génie, Université Laval, Québec, QC G1V 0A6, Canada. 13School of Biomedical Engineering, The University of British
Columbia, Vancouver V6T1Z3, Canada. ✉email: yachie@synbiol.rcast.u-tokyo.ac.jp; mituyama-toutai@aist.go.jp

Published in partnership with the Systems Biology InstituteG. Suzuki et al.

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Table 1.

List of the target genes for creating mutant cells.

Target gene

Functional information

PSMA2

Subunit alpha-2 of the alpha ring of the proteasome core complex. Paralog of PSMA7.

PSMA7

Subunit alpha-4 of the alpha ring of the proteasome core complex. Paralog of PSMA2.

PSMB5Subunit beta-5 of the beta ring of the proteasome core complex. Paralog of PSMB6.

PSMB6Subunit beta-1 of the beta ring of the proteasome core complex. Paralog of PSMB5.

PSME1

Alpha subunit of the PA28 complex involved in the immunoproteasome. Paralog of PSME2.

PSME2UBQLN1

Beta subunit of the PA28 complex involved in the immunoproteasome. Paralog of PSME1.
Ubiqulin that shuttles a protein to the proteasome. Paralog of UBQLN2.

UBQLN2

Ubiqulin that shuttles a protein to the proteasome. Paralog of UBQLN1.

in a cellular image. With these conﬁgurations to have a minimal
effect of genetic perturbations and morphological changes, theacquired BF images were processed computationally to extract
hundreds of morphological features. By using these numericalfeatures and a machine learning method, we conducted aclassiﬁcation test to distinguish mutant cells from the wild type.
Interestingly, the mutants were successfully discriminated fromthe wild type (area under the receiver operating characteristic
curve (AUC) = 0.773). We investigated the morphological featuresthat contributed to the discrimination. Furthermore, it was foundthat the mutants of functionally close genes share similar feature
proﬁles.
RESULTS
BF image dataset of single-gene mutant cells and wild-type
cells
To examine whether single-gene mutant cells can be discriminatedfrom wild-type cells in BF images, we constructed a workﬂow toquantify the features of every single cell as shown in Fig. 1. We
prepared single-gene mutants using the CRISPR-Cas9 system (Fig.
1a). We selected eight nonlethal target genes involved in theubiquitin-proteasome system. These genes have functionally
redundant paralogs, which makes them nonessential (Table 1).
BF and ﬂuorescent images for every single cell with a stained
nucleus were acquired with a high-throughput image acquisition
system. Over 670 single cells were imaged for each mutant and thewild type (Fig. 1b). A bounding box surrounding a cell in the BF
images was determined using the position of spotted nucleus in
the ﬂuorescent images, and features corresponding to textures
were quantiﬁed by image processing (Fig. 1c). Finally, wequantiﬁed 296 texture features for every single cell (see “Methods”;
raw data are available as Supplementary Data 1–3).
Machine learning for discriminating genotypes based on BF
cellular images
We employed logistic regression as a machine learning model todiscriminate mutants from wild-type cells using 296 texture
features. To identify a subset of the features that contributed tothe discrimination, we used the logistic regression with feature
selection by L1 regularization (see “Methods”). As an example, theresults of the discrimination of PSMB5 mutant cells were shown in
Fig. 2a, b. The performance of the discrimination was evaluated by
AUC with tenfold cross-validation (the penalty parameter in L1
regularization was optimized by nested cross-validation; see“Methods”). The mean AUC was 0.773 (Fig. 2a; the evaluation
measures other than AUC are reported in Supplementary Table 1),
which indicated that the mutant cells can be discriminated fromwild-type cells compared to the random guessing baseline (i.e.,
AUC = 0.5). The distributions of linear predictors (see “Methods”)
were different between the mutant cells and wild-type cells while
overlapping partly (Fig. 2b). The discriminative models for the
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other mutant cell types were constructed in a similar way,achieving AUC larger than 0.59 (Fig. 2c). These results demonstratethat BF images contain information for discriminating mutant cells
from wild-type cells.
In addition to logistic regression with L1 regularization, we
tested other machine learning models including support vector
machine (SVM), random forest, and k-nearest neighbor with andwithout dimensionality reduction by principal component analysis(PCA) (see “Methods”). The all models showed comparable AUC
while logistic regression with L1 regularization achieved thehighest value (Supplementary Table 2). We also evaluated theeffects of different image preprocessing methods (blur, edge, andsharp) on the performance of the discriminative model (see“Methods”), and found that AUC was slightly decreased by thesepreprocessing methods (Supplementary Table 3).
Features of BF cellular images that contributed to thediscrimination
What kind of biological information was included in the features
which contributed to the discriminative models for mutant cells?
To address this question, we focused on the features that hadlarge weights in the discriminative models (i.e., features selectedby L1 regularization). We found that large-weight features
included those calculated from “clumps” observed in BF images,
which possibly represent organelles and other intracellular
structures (e.g., nucleus, nucleolus, and parts of nuclear envelop)
as suggested in a previous study13. For example, the three largeweight features15 in the discriminative model for PSMB5 mutant
cells quantify the number or morphology of darker clumps (Fig.
3a, e). First, regions with darker pixels were selected by thresholds.
Next, connected regions were identiﬁed as clumps, and measurements (e.g., number, size, and shape) were calculated from theidentiﬁed clumps. Finally, the sample mean was calculated fromthe measurements of different thresholds. The PSMB5 mutant cells
showed a larger number of darker clumps compared with wildtype cells (Fig. 3b). In addition, clumps with larger size (Fig. 3c) andmore irregular (noncircular) shape (Fig. 3d) were also observed in
the PSMB5 mutant cells than in wild-type cells. These results showthat the BF images captured the morphological changes in the
mutant cells. Supplementary Fig. 1 shows other examples offeatures contributed to the discrimination of PSMB5 mutant cells.
These features included the statistics computed from pixel
intensity distributions (Supplementary Fig. 1a) and various
shape-related measurements such as thinness, size, and perimeter
after binarization (Supplementary Fig. 1b–d).
On average, 53 features were selected for the discriminativemodel for each mutant by the L1 regularization in the logistic
regression (Fig. 4). This analysis indicated that there are multiple
traits by which mutant cells can be discriminated from wild-type
cells (number of features in Fig. 4). On the other hand, the proﬁlesof contributed features for the discriminative models were distinct
among the mutants (heatmap in Fig. 4).
Published in partnership with the Systems Biology InstituteG. Suzuki et al.

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Fig. 1 Workﬂow for the image analysis of single-gene mutant cells. a HEK293Ta was used as parental cells (wild type), and single-gene
knockout mutant cells were produced by CRISPR-Cas9 genome editing. b Cell imaging. Using an automated image acquisition system, the BF
image and the ﬂuorescent image (Hoechst33342) of over 670 cells were obtained for each mutant and the wild type. c Quantiﬁcation of
texture features for each single cell. Each single-cell region was identiﬁed based on the position of a nucleus, and texture features were
quantiﬁed from the BF images.

If morphological changes in mutant cells were caused by thefunctional deﬁciency of a target gene, mutants of functionallysimilar genes are expected to show similar morphological proﬁles.
To examine this hypothesis, we performed hierarchical clustering
based on the similarity of the feature proﬁles (i.e., regression
coefﬁcients in the discriminative model). As expected, paralog
pairs (PSME1 and PSME2; UBQLN1 and UBQLN2), which have
similar functions, were classiﬁed in the same clusters (Fig. 4).
PSME1 and PSME2, respectively, encode the alpha and beta
subunit of PA28, a component of the immunoproteasome16.
Therefore, the similarity of feature proﬁles between these mutants
may represent the common dysfunction of the immunoproteasome caused by the mutations. UBQLN1 and UBQLN2 both
encode a shuttling protein that brings ubiquitinated proteins to
the proteasome while their functional differences remain elusive17. The similarity of their feature proﬁles may suggest thisfunctional similarity. Interestingly, a nonparalog pair (PSMB5 andPSMA7) was classiﬁed in the same cluster (Fig. 4). We speculatethat this is associated with the proximity of encoded proteins in
the proteasome core complex. Speciﬁcally, PSMB5 and PSMA7,
Published in partnership with the Systems Biology Instituterespectively, encode the subunit beta-5 and alpha-4 that directly
contact in the proteasome core complex, while it is not the casefor proteins encoded by PSMA2 and PSMB618. In summary, theseresults suggest that the similarity of morphological changes in the
mutant cells represent not only the functional similarity ofmutated genes but also the physical interaction of their encoded
proteins.
We also explored the correlation between types of mutations
and features rather than individual mutations and features(Supplementary Table 4). The eight mutant cells were grouped
into four types based on their paralogous relationship (PSMA2/7,
PSMB5/6, PSME1/2, and UBQLN1/2). For each paralog pair, we
extracted speciﬁc features in the discriminative models (e.g.,features with positive regression coefﬁcients in the discriminativemodels of PSMA2 and PSMA7 mutant cells, but zero or negative
regression coefﬁcients in the discriminative models of the othermutant cells). The number of features detected in each type ofmutants was relatively small, suggesting that group-to-grouprelationships between mutants and features are not so strong.
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Fig. 2 Evaluation of the discriminative models for mutant cells. a Receiver operating characteristic (ROC) curves in the discrimination of
PSMB5 mutant cells from wild-type cells. Thin blue lines represent ROC curves calculated with tenfold cross-validation. Thick blue line andgray area represent the mean and the quartile of the ROC curves, respectively. b Relationship between posterior probabilities and linear
predictors of the discriminative model for PSMB5 mutant cells. Upper and middle plots show the distributions of linear predictors of wild-type
cells and mutant cells, respectively. Lower plot shows a sigmoid curve between posterior probabilities and linear predictors. c AUC of thediscriminative model for each mutant cell type. The error bar shows the standard deviation in tenfold cross-validation. Red dashed line
represents AUC = 0.59.

DISCUSSION
The objective of the present study was to investigate the potential
ability of BF microscopy cell images for classifying single-genemutants. We demonstrated that the BF images of the single-genemutants could be discriminated from those of wild types. In
addition, morphological features of structures detected within
cellular regions contributed to the discrimination, and gene pairs
sharing common functions showed similar feature proﬁles inmutant cells.
We showed that single-gene mutant cells could be discriminated from wild-type cells based on BF images. Only a few studies
performed the classiﬁcation of cells based on BF images before.
They classiﬁed multiple cell lines10 or discriminated between
infected and noninfected macrophages, and between living anddead macrophages11. The problems addressed in these previous
studies were the classiﬁcation of cells with largely different
characteristics: cell lines harboring different alleles in multiple locias in ref. 10, and different forms of macrophages harboringchanges in a cellular process involving multiple genes (infection
and cell death) as in ref. 11. In contrast, our study showed that BF
images can also be used for discriminating single-gene
perturbation.
We observed that the discriminative models of mutant cells
may be related with organelles. As shown in Fig. 3, features
calculated from darker clumps within cellular regions contributed
to the discrimination. In a previous study13, darker clumps of
npj Systems Biology and Applications (2021) 31

cellular regions were used as a morphological feature foranalyzing the dynamics of organelles. Other studies also showedthat BF images contain information on the morphologies oforganelles: cellular nuclei could be detected in BF images11, andthe localization and morphology of multiple organelles could be
predicted simultaneously from BF images by machine learning
trained with ﬂuorescent images as label data19. Therefore, we
considered that the discriminative models we constructed may
have learned the pattern of morphological changes of organelles
in mutant cells.
The similarity between feature proﬁles of mutants was
associated with the functional similarity of target genes (Fig. 4).
This suggests that feature proﬁles used in the present study may
reﬂect the effects of the dysfunction of target genes. A similar
result was observed in a previous study19 where the morphological analysis of nuclear core complex subunits was performed
using ﬂuorescent microscopy. Our study shows that the information of the dysfunction of target genes could also be extracted
from BF images.
We demonstrated that mutant cells could be analyzed based on
BF images using a simple machine learning model. In previous
studies, the morphological analysis of organelles based on BF
microscopy used a special high-resolution microscope equipment12 or 3D-stacked images19. In another previous study10, thediscrimination between different cell types based on BF microscopy was performed using convolutional neural network (CNN)
Published in partnership with the Systems Biology InstituteG. Suzuki et al.

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Fig. 3 Examples of the large-weight features in the discriminative model for PSMB5 mutant cells. a Schematics of the feature extraction
procedure from BF images. Tau indicates a threshold of pixel value to identify clumps within a cellular region as described previously15.
b Distribution of PSMB5 mutant cells and wild-type cells on the feature “sample mean of number of clumps”. Blue and red histograms
represent mutant and wild-type cells, respectively. P value of one-sided U test between mutant and wild-type cells, and the regression
coefﬁcient (RC) in the logistic regression model are shown. c Distribution of PSMB5 mutant cells and wild-type cells on the feature “sample
mean of average size of clump areas” shown in the same way as in b. d Distribution of PSMB5 mutant cells and wild-type cells on the feature“sample mean of average value of noncircularity” shown in the same way as in b. e Example of clump detection using an image of a wild-type
cell. The leftmost panel is an input cellular image, and the right panels are those processed with different thresholds. In the magniﬁed panel,each clump is shown in a different color.

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without labeling. In this regard, other types of nonlabel imaging
such as DIC and phase-contrast microscopy will be useful toprovide additional training data for improving the accuracy ofmachine learning prediction.
METHODS
Cells and culture

Fig. 4 Comparison of the mutants based on morphologicalproﬁles. Clustering of mutants (vertical) and features (horizontal)
using the regression coefﬁcients in the discriminative models. Red
and blue colors in the heatmap represent positive and negative
regression coefﬁcients, respectively. Features not selected by L1
regularization were colored in white. Numbers described on theright side of gene names show the number of selected features.

trained with over 8000 images for each cell type. In the present
study, we performed the discrimination of mutant cells using asimpler machine learning model (logistic regression) trained withsmaller training data (>670 nonstacked 2D images).
To investigate the inﬂuence of training data size, we performed
a computational experiment where the discriminative model wastrained using a subset of the whole training data (Supplementary
Fig. 2a). We found that the increase of AUC with the increase of
training data size was almost saturated except for the UBQLN1and UBQLN2 mutant cells, suggesting that the further increase of
training data size will not improve AUC largely. For UBQLN1 andUBQLN2 mutant cells, AUC may be improved by increasing
training data size. We note that the numbers of the whole training
data for UBQLN1 and UBQLN2 were larger than the other mutant
cells (Supplementary Fig. 2b).
We evaluated the reproducibility by utilizing the three datasetsthat differed in the numbers of cells initially seeded in a well:
C10000, C2000, and C400 (see “Methods”). Speciﬁcally, thediscriminative model was trained using the C10000 dataset, andits performance was evaluated using C2000 and C400 datasets as
independent fold out. The results are summarized in Supplementary Table 5. We found that AUC values on C2000 and C400 wereslightly lower than that with the cross-validation on C10000.
Nonetheless, they were higher than the random guessing baseline(AUC = 0.5) in the all mutant cells. These results suggest that ourdiscriminative models had a certain level of the applicability on
independent datasets, and thus the reproducibility of our study.
We acknowledge that the present study has limitations asfollows. First, there were possibilities of off-target mutations
caused by CRISPR-Cas9 genome editing. To deal with thisproblem, we pooled the datasets of two different mutant clones
for each target gene. However, to evaluate the effect of possible
off-target mutations, we need to perform whole genome
sequencing of mutant clones. Second, we only analyzed mutantsof genes involved in the ubiquitin-proteasome system. We needto analyze more mutants of genes related to different cellularpathways or functions.
The present study demonstrated that mutant cells can bediscriminated by BF images in combination with machinelearning. This ﬁnding has a broad impact on biomedical research
since it suggests that genetically altered cells can be detectednpj Systems Biology and Applications (2021) 31

We established Cas9-expressing 293Ta cells by transducing lentiCas9-blast
to human embryonic kidney (HEK) 293Ta cells (GeneCopoeia) andmaintained in Dulbecco’s modiﬁed Eagle’s medium (Sigma) supplemented
with 10% fetal bovine serum (Thermo Fisher Scientiﬁc), 5 µg/mL of
blasticidin S (Kaken Pharmaceutical), and penicillin–streptomycin (Wako) touse as a parental strain. Each of eight genes involved in the ubiquitinproteasome system was selected as a target gene and perturbed by thefollowing procedures. Plasmids encoding single-guide RNA targeting eachof the eight genes (Supplementary Table 6) were constructed by inserting
annealed oligonucleotides into a derivative of lentiGuide-Puro (Addgene
52963), named lentiTRACE-puro, which has a guide RNA expressing
module in 3′LTR, as previously reported20. To produce lentiviral particles,
500 ng of sgRNA-encoding lentiTRACE-puro, 375 ng of psPAX2 (Addgene
12260), and 125 ng of pMD2.G (Addgene 12259) were cotransfected to 2 ×
105 cells of HEK293Ta with 3 µL of 1 mg/mL polyethyleneimine MAX(Polysciences). On the next day of transfection, the culture media were
replaced with fresh media and the supernatant containing lentiviralparticles was harvested after 2 days of incubation. To transduce an sgRNA,
1 × 105 cells/well of Cas9-expressing 293Ta cells, which were seeded in a
12-well plate on the day before infection, were incubated in 500 µL of thelentiviral supernatant with 8 µg/mL of polybrene (Sigma). The infected cells
were selected by 2 µg/mL of puromycin (Thermo Fisher Scientiﬁc) from thenext day of transfection. After 20 days, cells were isolated by FACS Jazz cell
sorter (BD Biosciences) as a single cell/well in a 96-well plate. Following thepassage of the grown cells to three of 96-well plates, genomic DNA wasextracted by boiling in 50 µL of 50 mM sodium hydroxide at 95 °C for
15 min and neutralizing by adding 5 µL of 1 M Tris-HCl (pH 8.0). The
genotyping sequencing library was prepared by sequential PCRs with rowcolumn-plate-PCR (RCP-PCR) primer sets21. Brieﬂy, the ﬁrst PCR was
performed with gene-speciﬁc genotyping primers for every plate(Supplementary Table 6), followed by puriﬁcation by Agencourt AMPure
XP beads (Beckman Coulter) following the manufacturer’s instruction.
Puriﬁed PCR products were combined in a single 96-well plate withkeeping their well-position and used for the second PCR reaction with rowand column-tagged forward and reverse primers, respectively (Supplementary Table 6). The second PCR products were collected in a single tube
and puriﬁed by gel extraction with FastGene Gel/PCR Extraction Kit(Nippon Genetics) to be used for the third PCR to attach P5 and P7
adapters for Illumina sequencing on 5′ and 3′ ends of the fragments,
respectively (Supplementary Table 6). After gel extraction of the PCR
products, the genotyping libraries were quantiﬁed by KAPA Library
Quantiﬁcation Kit (KAPA Biosystems) and sequenced with paired-end
reads by Illumina MiSeq using MiSeq Reagent Kit v3 (Illumina). The
obtained sequence data were used to identify the genotype of isolated
clones. First, RCP-PCR indices and target regions were identiﬁed andextracted from each sequencing read for demultiplexing. Simultaneously,
the target region of the read was aligned to the wild-type sequence using
BLASTn version 2.4.022 with default parameters. Second, the alignment
results for demultiplexed reads were aggregated to compute the allele
frequency within each of the clone-derived sample. Any allele appearing
less than 10% within each demultiplexed sample was eliminated forfurther analysis to cancel out sequencing error. Finally, the remaining
count data of each allele were used to identify the genotype of the isolated
clones. Clones having frame-shift mutation on both alleles were used forfurther analysis (Supplementary Fig. 3). The scripts used for analyzing
genotype information from RCP-PCR data are available from the authors’
GitHub website (https://github.com/yachielab/RCP-PCR_CRISPR_KO).

Image acquisition
HEK293Ta single-gene knockout clones were seeded 400, 2000, and 10000
cells/well in Cellstar 96-well µ-clear plates (Greiner). One, two, and threedays after passage, cells were nuclear-stained by 5 µg/mL of Hoechst33342(Thermo Fisher Scientiﬁc) in a CO2 incubator for 15 min. Twenty-ﬁve ﬁeldsof cell images per well for BF and nuclear-staining images were obtainedby IN Cell Analyzer 6000 (GE Healthcare) with 20× lens and Bright FieldPublished in partnership with the Systems Biology InstituteG. Suzuki et al.

7
DsRed and UV-DAPI ﬁlter sets, respectively. We generated three datasetsthat differed in the numbers of cells initially seeded in a well: 10,000 cells(C10000), 2000 cells (C2000), and 400 cells (C400). Unless otherwise
described, the results reported in this paper were those on C10000 dataset.
C2000 and C400 datasets were used for evaluating the applicability of thediscriminative model on independent fold-out datasets (Supplementary
Table 5).

Hierarchical clustering of mutants based on morphologicalsimilarity

Single-cell feature quantiﬁcation

Reporting summary

For single-cell proﬁling, we quantiﬁed 296 texture features from BF images.
First, we detected nuclear regions in ﬂuorescent images of cells treatedwith Hoechst33342 by a machine learning method. In brief, the nuclei
detector was developed by using Faster R-CNN with ResNet-101 backbone23. We trained the nuclei detector with a part of the Hoechst33342
images in which each of the nuclei is annotated with a bounding box.
Then, the nuclei detector was applied to all of the Hoechst33342 images.
We cropped a cellular region of BF images based on the position of therectangle region enclosing the detected nucleus in the Hoechst33342
images. We quantiﬁed texture features in the segmented cellular area of BF
images using the LPX296 feature extractor (formerly the KBI feature
extractor24). The BF images used for feature quantiﬁcation are available as
Supplementary Data 1 and 2. The feature values for each BF image areavailable as Supplementary Data 3.

Further information on research design is available in the Nature Research
Reporting Summary linked to this article.

Data preprocessing

Received: 17 November 2020; Accepted: 1 July 2021;

Each of cellular texture features in BF images was standardized by themean and standard deviation calculated from the cell population of thewild-type strain. In each mutant cell population and in each of the texture
features, we identiﬁed outliers and removed them with the cutoff of3 standard deviations from the mean. After the normalization and removalof outliers, we pooled two datasets of different clones screened
independently for each target gene.

Discriminative model construction
To discriminate between mutant cells and wild-type cells, we built alogistic regression model using R package glmnet25. We employed theleast absolute shrinkage and selection operator method based on L1
regularization for feature selection. Brieﬂy, the logistic regression model isrepresented as follows:y ¼ σ βT x þ α þ ε(1)where y is the binary variable representing a cell is the mutant or the wild
type, σ is a sigmoidal function, x is a 296-dimensional feature vector
computed from the cellular image, and ε is an error term. Given a set oftraining data S ¼ fðy; x Þg, the model ﬁts regression coefﬁcients β (alsoreferred to as feature weights) and α by minimizing the error with the L1
regularization term:X2y  σ βT x þ α þ λkβk1
S(2)

In Fig. 2, the linear predictor and the posterior probability represent
βT x þ α and σ βT x þ α , respectively, computed from the ﬁtted model. To
measure a discrimination accuracy for each model, we calculated AUC
using tenfold cross-validation. To optimize the penalty parameter λ in L1
regularization, we performed nested tenfold cross-validation where thevalidation dataset in the inner loop was used for selecting λ, and the test
dataset in the outer loop was used for measuring the model accuracy.

Comparison of various machine learning models andpreprocessing methods
To compare various machine learning models with logistic regression, we
tested SVM, random forest, and k-nearest neighbor with and without PCA
on the PSMB5 dataset. These models were implemented with Python
package scikit-learn. The hyperparameters of each model were optimized
by nested tenfold cross-validation as in the case of logistic regression. To
evaluate the effect of various preprocessing methods, we used “BLUR”,“EDGE_ENHANCE,” and “SHARPEN” functions in Python PIL package.
Published in partnership with the Systems Biology InstituteThe similarity between mutants was evaluated by Pearson’s correlation
coefﬁcient of their morphological proﬁles. As the morphological proﬁle of amutant, we employed regression coefﬁcients (beta value) of features in thelogistic regression model. Complete linkage clustering was conductedusing hclust function in R.

DATA AVAILABILITY
All data generated in this study are provided in this paper and its Supplementary
Information ﬁles.

CODE AVAILABILITY
The codes used for analyzing genotype information from RCP-PCR data are available
fromtheauthors’
GitHubwebsite(https://github.com/yachielab/RCPPCR_CRISPR_KO). The codes used for the other computational analyses are available
upon request to the authors.

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ACKNOWLEDGEMENTS
This paper is based on results obtained from a project subsidized by the New Energy
and Industrial Technology Development Organization. Y.S. was supported by MEXT/JSPS KAKENHI (17H06410; 19K20409; 19K06502; 19K06077; 20H00315) and AMED
grant (JP19ak0101122; JP19am0401023). Computations were partially performed on
the NIG supercomputer at ROIS National Institute of Genetics.

AUTHOR CONTRIBUTIONS
G.S. and Y.S. performed the computational analysis of cellular image data with thesupport of H.K. and T.M. Authors M.S., D.E.Y., M.N., K.K., C.R.L., and N.Y. participated in
the acquisition of cellular image data. G.S. and Y.S. drafted the manuscript. N.Y. and T.
M. coordinated the project. All authors read and approved the ﬁnal manuscript.

npj Systems Biology and Applications (2021) 31

COMPETING INTERESTS
The authors declare no competing interests.

ADDITIONAL INFORMATION
Supplementary information The online version contains supplementary material
available at https://doi.org/10.1038/s41540-021-00190-w.
Correspondence and requests for materials should be addressed to N.Y. or T.M.
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Published in partnership with the Systems Biology Institute