Reproducible Gating for High-Resolution Flow Cytometric

Characterization of Extracellular Vesicles in Next-Generation Biomarker

Studies

Ishwor Thapa

1 a

, Yohan Kim

2 b

, Fabrice Lucien

2 c

and Hesham Ali

1 d

College of Information Science and Technology, University of Nebraska at Omaha, Omaha, U.S.A.

Department of Urology, Mayo Clinic, Rochester, U.S.A.

Keywords:

Extracellular Vesicles, High Resolution Flow Cytometry, Automated Gating, Reproducibility and Robustness,

Biological Signals, FCS.

Abstract:

With the continuous advancements of biomedical technologies, we have access to instruments capable of

producing new types of biological data or generating traditional data with higher degrees of quality. With

the support of such data, researchers and practitioners continue to explore the possibilities of developing new

approaches to obtain valuable data-driven signatures or biosignals to be used for diagnosis, classiﬁcation, or

assessment of treatments. However, with the emergence of new types of data, it is often the case that they

are available in raw formats that are not suitable for extracting the needed biomarkers. Hence, much work

is needed to process the raw data sets obtained from new medical instruments and transform the signals into

products capable of capturing the desired knowledge. Next-generation biomarkers such as “liquid biopsies” are

emerging tools to improve cancer diagnostics, disease stratiﬁcation, and treatment monitoring. As potential

cancer biomarkers, circulating Extracellular Vesicles (EV) levels may early-predict disease recurrence and

resistance to treatment. High-resolution ﬂow cytometry (hrFC) is a sensitive and high-throughput method for

quantifying circulating levels of EVs with minimal sample processing. One of the beneﬁts of using hrFC

is that there is no need to isolate or purify the molecules of interest from the biological samples prior to

running the ﬂow. However, signals in hrFC data currently depend on manual and subjective approaches to

gating the positive events. Such approaches are often time-consuming, error-prone, and lack the levels of

robustness and reproducibility needed to trust the obtained information. This study proposes an automated

quantitative technique to process ﬂow cytometry data for EVs with a high degree of accuracy consistency. A

publicly available Shiny web application is presented that performs quality check of ﬂow cytometry ﬁles and

automated gating of biosignals, viz. subpopulations of EVs that are of interest to next generation biomarker

studies.

1 INTRODUCTION

According to the International Society for Extracel-

lular Vesicles (ISEV), extracellular vesicle (EV) is

a generic term for particles naturally released from

the cell that are delimited by a lipid bilayer and can-

not replicate, i.e. do not contain a functional nu-

cleus (Th

ery et al., 2018). EVs are heterogenous in

size (<100nm to >1µm) and their distribution fol-

lows a power-law function, meaning that the large

https://orcid.org/0000-0002-3594-1631

https://orcid.org/0000-0002-2378-0068

https://orcid.org/0000-0001-6149-345X

https://orcid.org/0000-0002-8016-6144

number of EVs of small size are observed and the

concentration of EVs decreases with increasing size.

EVs are released by normal and malignant cells in

body ﬂuids such as blood and urine. Various stud-

ies have shown that circulating EVs may be the next-

generation of biomarkers for the management of mul-

tiple diseases including cancers, neurodegenerative

diseases and chronic liver diseases (Lucien et al.,

2022), (Samuel et al., 2018), (Pan et al., 2023), (El-

lison et al., 2023), (Ohmichi et al., 2019), (Aharon

et al., 2020), (Newman et al., 2022).

EVs can be extracted from bioﬂuids like plasma

and urine in a wet-lab setting. This is of great

signiﬁcance in a clinical setting because with this

technology, EVs can directly be characterized from

1020

Thapa, I., Kim, Y., Lucien, F. and Ali, H.

Reproducible Gating for High-Resolution Flow Cytometric Characterization of Extracellular Vesicles in Next-Generation Biomarker Studies.

DOI: 10.5220/0013318900003911

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2025) - Volume 1, pages 1020-1027

ISBN: 978-989-758-731-3; ISSN: 2184-4305

the bioﬂuids. The isolation of EVs are often com-

plemented with antibody labeling of known mark-

ers such as Mammaglobin-A in breast cancer plasma

samples and PSMA in prostate cancer plasma or

urine samples to identify cancer marker enriched

EVs (Salmond et al., 2021), (Lucien et al., 2022).

The diluted EV fractions are then run through High

Resolution Flow Cytometry (hrFC) allowing high-

throughput detection and immunophenotyping of EVs

at the single-particle level (Kim et al., 2022). Us-

ing hrFC, it was observed that the levels of circu-

lating prostate cancer-speciﬁc extracellular vesicles

(PCEVs) were found to be the highest in metastatic

castration-resistant prostate cancer (mCRPC) patients

and were the lowest in localized prostate cancer (Lu-

cien et al., 2022). To address variability in EV exper-

iments, two initiatives viz. a) calibration of ﬂow cy-

tometers in standard units and standardization of de-

tected EV concentration (van der Pol et al., 2018), and

b) transparent reporting (Welsh et al., 2020b) have

been established. The MIFLowCyt-EV framework

requires standardization of reporting the steps under-

taken, including experiment design, sample prepara-

tion, assay control, instrument calibration and data ac-

quisition (Welsh et al., 2020b). The implementation

of this framework is adapted into a public repository

for EV ﬂow cytometry data (Arce et al., 2023). Addi-

tionally, Welsh et al. have developed FCMPass soft-

ware that provides a new method to standardize light

scatter utilizing the instrument speciﬁc sensitivity pa-

rameters (Welsh et al., 2020a).

Figure 1: Representative ﬁgure to show scatter plots of two

different ﬂuorescence channels versus the large angle side

scatter channel.

For each captured EV particle, the forward-

scattered light, side-scattered light and dye-speciﬁc

ﬂuorescence signals are captured and represented nu-

merically in a standard data ﬁle, called Flow Cytome-

try Standard (FCS) ﬁles. The FCS ﬁles can be visual-

ized in scatter plots with different channels on X and

Y axes. For example, in Figure 1, two different ﬂuo-

rescence channels are plotted against the large angle

side scatter channel (LALS).

An objective method of quantiﬁcation of EV lev-

els is critical in establishing the circulating EVs as

the next-generation of biomarkers. The current chal-

lenge in analyzing the ﬂow cytometry data of EVs is

the manual gating (ﬁltering) step where the popula-

tion of EVs that are potential markers for a biolog-

ical condition are separated from background noise.

Manual gating is not only time-consuming and user-

dependent but also error-prone, which ultimately can

result in irreproducible data. Our research goal is to

develop an automated quantitative technique to facil-

itate the manual gating process, which is key to next-

generation biomarker studies. We propose two auto-

mated gating techniques, viz. a) rectangular gating

and b) ﬁve-sided gating. In rectangular gating, the

algorithm is straight-forward and is primarily based

on minimum size of the particle and its ﬂuorescence

level. However, this approach still captures some EVs

whose ﬂuorescence doesn’t increase linearly with the

size. To reduce false positives, an additional point is

added in the gate to ﬁlter out EVs, which are larger

in size but not as ﬂuorescent. Furthermore, we have

developed a web application based on Shiny R pack-

age to allow biomedical researchers to upload a FCS

ﬁle from hrFC and obtain number of positive events

in a sample using automated gating. Our tool also

summarizes the number of events for every second

over the duration of acquisition time in Flow Cytom-

etry. This provides a quality checking step in the

processing of hrFC and is a critical feature that can

identify ﬂow runs that are prone with errors. The

results of gating obtained using the proposed auto-

mated gating pipeline show that it accurately identi-

ﬁes the disease speciﬁc EV sub-population in an unbi-

ased manner and produces results comparable to that

of manual gating from experienced users. Addition-

ally, the method can be extended to identify unique

sub-population of EVs that can serve as signiﬁcant

features in machine learning-based classiﬁcation.

2 DATASET AND METHODS

2.1 EV Labeling and Quantiﬁcation

In our previous work, we presented a strategy for

standardization of acquisition parameters for side-

scatter detection of extracellular vesicles (EVs) and

other particles from cell-depleted plasma and urine

(Kim et al., 2022). In this process, optimal acquisi-

tion settings such as illumination wavelength power,

side scatter triggering threshold, and ﬂow rate for

cytometer (Apogee A60-Micro Plus) have been de-

rived to improve the sensitivity of EV detection.

PSMA and STEAP1 antibodies were labeled with two

Reproducible Gating for High-Resolution Flow Cytometric Characterization of Extracellular Vesicles in Next-Generation Biomarker Studies

1021

Alexa Fluor (AF647 and AF488) antibody labeling

kits. With desired concentration of urine and anti-

body mixture, the antibody-bioﬂuid mixture can be

incubated to label prostate speciﬁc EVs with the an-

tibodies. Next, three technical replicates from urine

samples of thirty cancer patients were run on Apogee

A60-Micro Plus (A60MP, Apogee Flow Systems Inc.,

Northwood, UK) cytometer for 60 seconds as de-

scribed in our previous work (Kim et al., 2022). For

each run, the resulting data from cytometer are saved

in a FCS ﬁle.

2.2 FCS Files

The Flow Cytometry Standard (FCS) ﬁle provides

speciﬁcations and experimental data that includes the

light scatter and ﬂuorescence measurements (Spidlen

et al., 2021), (Spidlen et al., 2010). The information

stored in FCS ﬁles can be read in a programming lan-

guage like R using packages such as ﬂowCore (Hahne

et al., 2009). The ﬂowCore package allows creation of

a ﬂowFrame, a container storing all the metadata and

events captured in the Flow cytometry experiments

(Hahne et al., 2009). The experiment data is stored

in a matrix format that can be accessed from the exprs

slot of the ﬂowFrame object. The rows of this matrix

represent the events and columns represent different

measurement channels including light scatter and ﬂu-

orescence.

2.3 Quality Check Reporting

In the data section of information stored in FCS ﬁle,

the TIME variable is also stored that depicts the time

point of the event being recorded. The number of

events for every second is computed and is visualized

as a plot with additional mean and standard deviation

values (see Figure 2). It can also show how the num-

ber of events changes over time and can be used to as-

sess ﬂow rate stability and artifacts (e.g. bubbles) that

could occur during acquisition and inﬂuence quantita-

tive analysis. In addition, boxplots are generated for

each channel to check any aberrant pattern in scatter

and ﬂuorescence acquisition (see Figure 5).

2.4 Automated Gating for Enumeration

of EVs

Scatter plots such as one in Figure 1 show more than

49,000 events captured, among which majority of the

events belong to background noise and a small num-

ber of events (about 0.14% left and 0.33% right) are

positive events. Positive events are the EVs labeled

with a ﬂuorescent antibody against a speciﬁc marker

0 3 6 9 12 16 20 24 28 32 36 40 44 48 52 56 60

No. of events over time

TIME

count of events

0 100 200 300 400 500 600

one standard dev.

mean

Figure 2: Total events captured per second.

Figure 3: Rectangular gate for identiﬁcation of sub-

population of events.

and are characterized by linear relationship between

these channel readings.

One of the major limitations in the reproducibility

of EV studies is the manual gating of EVs . The gat-

ing process involves manual creation of boundaries

on the scatter plot using tools such as FlowJo to count

the events inside the boundaries. This manual step

is error-prone and can vary for different individuals

who gate these events. In order to overcome these

challenges, two automated gating strategies have been

developed to enumerate the positive EVs. The au-

tomated gating mechanisms involve identifying the

starting position for gate and ﬁnding the rest of gating

coordinates. In the ﬁrst strategy, we describe a rect-

angular gating scheme based on minimum size of the

particle and its ﬂuorescence. Since bigger EVs will

have greater surface area to accommodate binding of

more number of surface markers, the dye-speciﬁc ﬂu-

orescence signal is expected to be higher. Hence, we

describe ﬁve-sided gating based on size exclusion cri-

BIOSIGNALS 2025 - 18th International Conference on Bio-inspired Systems and Signal Processing

1022

teria of EVs and linear scaling of ﬂuorescence with

the size. We then compare the results of the auto-

mated gating to that of manual gating by two inde-

pendent reviewers.

Algorithm 1: Algorithm for rectangular automatic gating of

EVs. The points x[1-4] and y[1-4] refers to the points of

gates as shown in Figure 3.

Data: X1:= lower limit of the EV size,

inputMatrix is matrix of measurements

with ‘n’ events as rows and ‘m’

columns as channels

Result: (x1,y1,x2,y2,x3,y3,x4,y4)

1 trans f ormed := arcsinh(inputMatrix);

2 X := trans f ormed[1 : n,‘LALS

′

];

3 Y := trans f ormed[1 : n,‘RedChannel

′

];

4 x1 := arcsinh(X1);

5 densityY := density(Y );

6 poi := pointO f In f lection(densityY );

7 y1 := abscissa(poi) +

distance(peak(densityY ), poi);

8 x2 := max(X);

9 y2 := y1;

10 x3 := max(X);

11 y3 := max(Y );

12 x4 := x1;

13 y4 := max(Y );

14 return

(polygonGate(x1,y1,x2,y2, x3,y3,x4,y4));

2.4.1 Rectangular Gating

The gating involves plotting the EV particle size on

X-axis and ﬂuorescence channel measurement on Y-

axis. A side scatter triggering threshold set at 2,300

arbitrary units (a.u.) on X-axis will be set as x1. This

marks the minimum size of the EVs. For identify-

ing the ﬁrst point on Y-axis (y1), peak density of the

ﬂuorescence values is identiﬁed and point of inﬂec-

tion in the downward slope of the density plot after

the peak is determined. The value of y1 is set to the

sum of abscissa of point of inﬂection and distance be-

tween point of inﬂection and peak. This ensures that

the background noise region in ﬂuorescence axis is

avoided to begin the gating (see Figure 3). This is

a critical step in capturing the signal. Hence, it is

described independently as rectangular gating and all

the steps in this approach are listed in Algorithm 1.

2.4.2 Five-Sided Gating

The rectangular gating has few shortcomings. Among

the larger EVs, those that do not exhibit elevated ﬂu-

orescence may be misclassiﬁed as true positives be-

Figure 4: Five-sided gate for identiﬁcation of sub-

population of events.

Algorithm 2: Algorithm for ﬁve-sided automatic gating of

EVs. The points x[1-5] and y[1-5] refers to the points of

gates as shown in Figure 4.

Data: X1:= lower limit of the EV size, X2 :=

higher limit of the EV size, inputMatrix is

matrix of measurements with ‘n’ events as

rows and ‘m’ columns as channels

Result: (x1,y1,x2,y2,x3,y3,x4,y4,x5,y5)

1 trans f ormed := arcsinh(inputMatrix);

2 X := trans f ormed[1 : n,‘LALS

′

];

3 Y := trans f ormed[1 : n,‘RedChannel

′

];

4 x1 := arcsinh(X1);

5 densityY := density(Y );

6 poi := pointO f In f lection(densityY );

7 y1 := abscissa(poi) +

distance(peak(densityY ), poi);

8 x2 := arcsinh(X2);

9 y2 := y1;

10 x4 := max(X);

11 y4 := max(Y);

12 x3 := ⌊x4⌋;

13 y3 := y2 +(y4 −y2)/2;

14 x5 := x1;

15 y5 := y4;

16 return

(polygonGate(x1, y1, x2, y2,x3,y3,x4,y4,x5,y5));

cause larger EVs typically have increased ﬂuores-

cence due to their size. Incorporating this information

as a criteria to ﬁlter noise is critical in accurately iden-

tifying positive EVs. Based on the input from hrFC

experts in the team, rectangular gating is ﬁne-tuned to

add a threshold of maximum size of EV. Algorithm 1

is updated to limit the size of EV to a speciﬁed value

(123,000 a.u.). In addition, one more polygon point

is added in between the minimum and maximum ﬂu-

orescence. This point (x3, y3) as shown in Figure

4 will aid in ﬁltering the points whose ﬂuorescence

doesn’t scale linearly with EV size.

Reproducible Gating for High-Resolution Flow Cytometric Characterization of Extracellular Vesicles in Next-Generation Biomarker Studies

1023

Figure 5: A snapshot of the Shiny web application.

Mathematical formulation of ﬁve-sided gating is

listed in an algorithm (see Algorithm 2). A R pro-

gram is written to implement this reﬁned gating algo-

rithm in order to count the positive events within these

gates. The ﬁlter() function from ﬂowCore R pack-

age is utilized for enumerating the sub-population of

events in the gate (Hahne et al., 2009). Additionally,

the ﬂowViz R package that is compatible with data

structures deﬁned in the ﬂowCore package is utilized

for plotting the ﬁgures with the gates (Sarkar et al.,

2008).

2.5 Shiny R App for Automated Gating

An open-source R package called, Shiny is utilized

to create a web application for the quality assess-

ment and automated gating. The automated gating

in this app utilizes the ﬁve-sided gating strategy de-

scribed in Algorithm 2. The Shiny app is available

at the following URL: https://ishworthapa.shinyapps.

io/gatingﬂow/. In this web application, users can up-

load a FCS ﬁle for which the quality check and the

automated gating procedure will be carried out. A

snapshot of the user-interface is shown in Figure 5.

3 RESULTS

Next, we apply the quality checking step and auto-

mated gating algorithm on a real dataset from patient

samples using the Shiny app. The quality check fea-

ture provides a quick and effective way to identify

samples that are erroneous. Additionally, automated

gating algorithm performs sample speciﬁc gating that

accurately identiﬁes the sub-population of events.

BIOSIGNALS 2025 - 18th International Conference on Bio-inspired Systems and Signal Processing

1024

3.1 Quality Check Identiﬁes Run with

Erroneous Data

By plotting the values from different channels and

TIME variable, the web application provided easy

mechanism to identify samples with errors. For an

instance, samples that saved time information incor-

rectly were easily captured (see Figure 6). In this sam-

ple, the timeline represented by X-axis is incorrect

and should span from 0 to 60. In sample(s) that passed

the quality checking step, like the one shown in Fig-

ure 2, the plots provided assurance that the acquisi-

tion process remained consistent and did not show any

signs of deterioration over the observed time period.

Figure 6: An example sample with erroneous data in the

‘TIME’ column of the FCS ﬁle.

3.2 Automated Gating

Antibody labeling was performed with two markers

in the urine samples of cancer patients. A total of

90 FCS ﬁles (30 samples with 3 replicates each) were

analyzed using the automated gating algorithm within

the Shiny application. Between different marker pro-

teins, the ﬁve-sided automated gating shows appro-

priate selection of boundary between the noise and

the signal (see Figure 7). Additionally, to standard-

ize the counts from different experiments, the number

of positive events identiﬁed in automated gating was

translated into a scaled number with appropriate dilu-

tion factor obtained from wet-lab protocol.

3.3 Comparison of Automated and

Manual Gating Results

The ﬁnal enumeration of positive EVs were then com-

pared between both automated gating strategies (rect-

angular and ﬁve-sided) and manual gating performed

by two independent reviewers. The comparison of

the standardized number of events (in log10 scale)

from both methods are shown in Figure 8. For PSMA

Figure 7: Automated (ﬁve-sided) gating results for two dif-

ferent markers showing accurate predictions of the gating

boundaries.

marker positive EVs, the counts from automated gat-

ing are slightly less than those obtained from man-

ual gating process. For STEAP1 marker positive EVs,

the counts from the automated and manual gating re-

sults ﬂuctuate between samples as shown in Figure 8.

For both markers, the ﬁve-sided automated gating re-

sulted in less positive events than the rectangular gat-

ing by avoiding the points on the far right side of the

rectangular gating. Overall, the results show that the

automated gating is comparable to the manual gating,

especially for PSMA marker positive EVs.

Next, we investigated the correlation between the

gating results. As shown in Table 1, the Spearman’s

rank correlation value between manual gating from

two independent reviewers was 0.90 and 0.59 for

PSMA and STEAP1 markers, respectively. Interest-

ingly, correlation between manual gating from second

user and the automated gating approaches for PSMA

marker was the highest (ρ = 0.87). The correlation

values for STEAP1 marker are less than that for PSMA

marker.

In addition, intraclass correlation coefﬁcient

Reproducible Gating for High-Resolution Flow Cytometric Characterization of Extracellular Vesicles in Next-Generation Biomarker Studies

1025

0 5 10 15 20 25 30

0 2 4 6 8

PSMA marker

samples

log10(standardized count)

Rectangular−Automated

Five−Sided−Automated

Manual 1

Manual 2

0 5 10 15 20 25 30

0 2 4 6 8

STEAP1 marker

samples

log10(standardized count)

Rectangular−Automated

Five−Sided−Automated

Manual 1

Manual 2

Figure 8: Manual versus automated gating results for PSMA

and STEAP1 markers (in log10 scale).

(ICC) was computed to measure index of inter-rater

reliability of quantitative data using ‘irr’ R package.

When comparing automated gating results with the

manual gating results from ﬁrst reviewer, the intra-

class correlation value for PSMA and STEAP1 mark-

ers were observed to be 0.73 and 0.51, respectively.

Similarly, when comparing automated gating results

with the manual gating results from second reviewer,

ICC for PSMA and STEAP1 markers were 0.751 and

0.9, respectively. Together, these results show that the

automated gating enumeration is comparable to that

of manual gating performed by experienced person-

nel.

Table 1: Spearman’s rank correlation between gating results

for each marker(P − value < 0.01).

Correlation comparison

groups

PSMA STEAP1

Manual Reviewer1-vs-

Manual Reviewer2

0.90 0.59

Automated: Rectangular-vs-

Manual Reviewer1

0.85 0.74

Automated: Rectangular-vs-

Manual Reviewer2

0.87 0.85

Automated: Five-sided-vs-

Manual Reviewer1

0.86 0.73

Automated: Five-sided-vs-

Manual Reviewer2

0.87 0.73

4 CONCLUSIONS

This study proposed a novel computational platform

for processing of high-resolution Flow Cytometry

(hrFC) data from EV studies. We provide an auto-

mated method for checking the quality of cytome-

try data and algorithms to perform quantiﬁcation of

positive events in hrFC. In addition, we developed

a web application that performs the quality check-

ing step and automatically identiﬁes the gate for sub-

population of EVs that were otherwise enumerated by

creating the boundaries manually. We showed that

the manual gating step can vary between the individ-

uals who perform the gating and is time consuming

and not reproducible. However, our proposed algo-

rithm is robust, deterministic, and always produces

consistent gating highlighting the reproducible fea-

ture of our method. Currently, our web application

is at the prototype stage to highlight the relevance of

our pipeline and is compatible with data generated by

Apogee A60-Micro Plus ﬂow cytometer. Additional

validation studies with larger number of samples are

required to ensure the reliability and accuracy of our

results, involving a larger pool of human reviewers

for manual gating. In the future, we plan to enhance

the app to allow multiple FCS ﬁles and accommodate

other instruments. A study like ours is critical to fa-

cilitate EV-based biomarker studies for the develop-

ment of innovative, next-generation molecular diag-

nostics in cancer. Accurate quantiﬁcation and char-

acterization of EVs in modern oncology will play a

pivotal role in identifying prognostic, diagnostic and

predictive markers in everyday medical practice. Be-

fore reaching the clinic, limitations such as imprecise

EV clustering and traditional manual gating need to

be replaced.

BIOSIGNALS 2025 - 18th International Conference on Bio-inspired Systems and Signal Processing

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ACKNOWLEDGEMENTS

The authors would like to acknowledge Daniel Quest,

PhD from Mayo Clinic in constructive feedback dur-

ing our meetings.

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