Measurement of Platelet Aggregation in Ageing Samples and After

in-Vitro Activation

Christian Klenk

1 a

, David Elias Fresacher

1,2 b

, Stefan R

ohrl

2 c

, Dominik Heim

1 d

Manuel Lengl

2 e

, Simon Schumann

2 f

, Martin Knopp

1 g

, Klaus Diepold

2 h

Stefan Holdenrieder

3 i

and Oliver Hayden

1 j

Heinz Nixdorf Chair for Biomedical Engineering, Technical University Munich, Arcisstr. 21, Munich, Germany

Chair for Data Processing, Technical University Munich, Arcisstr. 21, Munich, Germany

Institute for Laboratory Medicine, German Heart Centre Munich, Lazarettstr. 36, Munich, Germany

Keywords:

Quantitative Phase Imaging, Microﬂuidics, Haematology, Machine Learning, Thrombocytes, Haemostasis,

Digital Holographic Microscopy, Blood Cells, Flow Cytometry.

Abstract:

Blood cell aggregates are gaining importance as a possible biomarker for various diseases. However, due

to technical limitations of common analysers, mostly only interactions between leukocytes and platelets are

measured directly as aggregates. Interactions between platelets are usually only measured indirectly after using

an activation assay or by analysing surface proteins. Here, an imaging ﬂow cytometer is used to measure and

characterize platelet-aggregates directly in whole blood samples. Inﬂuences of sample ageing and in-vitro

activation with adenosine diphosphate (ADP) was investigated for blood anticoagulated with either EDTA,

citrate, heparin or hirudin. Here, the number of platelet-aggregates and their composition was measured.

Blood anticoagulated with hirudin and EDTA showed a stable number of aggregates within a timeframe of 240

minutes. While no aggregate concentration changes were observed in EDTA blood after activation with ADP,

a clear increase in aggregates was seen in hirudin, citrate and heparin blood. This effect is also observable

when looking at the composition of the clots. However, after an initial spike a large number of aggregates

disintegrate within a time frame of nine minutes. This effect is particularly prominent for large aggregates

containing six or more platelets.

1 INTRODUCTION

The analysis of processes in haemostasis is a cru-

cial ﬁeld for medical diagnostics and the monitoring

of interventions. By understanding the mechanisms

behind haemostasis, speciﬁc dysfunctions can be de-

tected and traced back to possible causes.

Haemostasis describes the process of regulating

the formation of blood clots. This can result in the de-

velopment of blood clots, to limit the extent of bleed-

ings. In the opposite scenario, excessive thrombus

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formation must be counteracted to prevent vascular

occlusion. An important role in these processes in-

volves blood platelets (thrombocytes), which are usu-

ally found as single cells and in an inactivated dis-

coid shape. External factors can lead to an activa-

tion of platelets, which leads to a change in their mor-

phology. This process triggers a number of reactions

resulting in the formation of platelet-aggregates, in

other words the cohesion of the platelets by ﬁbrino-

gen (Kamath et al., 2001). If these cell clusters are

not stabilised by ﬁbrin strands, this usually results in

unstable aggregates, which in turn often disintegrate

within a few minutes (Michelson et al., 2001).

Various diseases can inﬂuence haemostasis in

such a way that the formation of blood clots is ei-

ther increased or inhibited. Prominent examples are

the hyperactivity of platelets in blood of patients suf-

fering from COVID-19 (Rampotas and Pavord, 2021)

and cardiovascular diseases (Allen et al., 2019) as

well as a reduced haemostasis in haemophilia (Riedl

et al., 2017). In addition, the observation and tar-

geted alteration of blood coagulation through drug

Klenk, C., Fresacher, D., Röhrl, S., Heim, D., Lengl, M., Schumann, S., Knopp, M., Diepold, K., Holdenrieder, S. and Hayden, O.

Measurement of Platelet Aggregation in Ageing Samples and After in-Vitro Activation.

DOI: 10.5220/0011634200003414

In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 2: BIOIMAGING, pages 57-65

ISBN: 978-989-758-631-6; ISSN: 2184-4305

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

interventions also play a major role in the treatment

of patients. Thus, anticoagulant therapies are used

to speciﬁcally inhibit blood coagulation in patients

with various clinical conditions in order to counter-

act complications such as the formation of a thrombus

(Lazaridis et al., 2021; Lazo-Langner et al., 2007).

Nevertheless, it must be noted that an excessive ad-

ministration of these pharmaceutics can lead to in-

creased bleeding events (C¸ ankaya et al., 2021). For

this reason, actions must be considered carefully and

regularly controlled for every patient. A balance in

the treatment with anticoagulants between the risks

of thrombosis and bleeding can only be achieved with

an in-depth knowledge of the patients state of health

(Al-Samkari and Connors, 2019).

As a diagnostic tool of choice, coagulation is usu-

ally determined indirectly with the help of in-vitro

analysers. Those devices determine dynamics in co-

agulation with the help of so-called activation assays.

Here, an activating substance is added to the blood

sample, which starts the process of blood clotting.

The time and dynamics are measured until a certain

sample volume is partially or completely clumped.

The underlying technology for measuring this kind of

assays can be determined both mechanically (Whit-

ing and DiNardo, 2014) and by impedance measure-

ment (Sibbing et al., 2008). Both methods have a

wide range of possible applications but have the dis-

advantage that large sample volumes are usually re-

quired, and partial aspects of coagulation can only be

measured after the use of an activation assay. The

direct measurement of blood cell aggregates is an al-

ternative approach, which generally does not suffer

from these disadvantages. Aggregates can be mea-

sured using many different methods. One prominent

example is the use of a so-called blood smear anal-

ysis (Rampotas and Pavord, 2021). Here, the cells

are presented on a slide, stained and examined under

a microscope. This method provides a high contrast,

but requires trained personnel, time-consuming sam-

ple preparation steps and measures only a statistically

small number of cells. Fluorescent ﬂow cytometry

combines ﬂuorescent staining of blood cells with a

microﬂuidic system and an optical readout (Michel-

son et al., 2001). This results in a rapid image ac-

quisition and therefore in high numbers of measured

cells in a short time frame. The highly speciﬁc bind-

ing of the ﬂuorescent dye labelled antibodies allows

accurate cell identiﬁcation (Herzenberg et al., 2000).

However, a major disadvantage is the low spatial res-

olution and the cost and time intensive staining. Es-

pecially the low spatial resolution makes a detection

of small platelet-aggregates difﬁcult.

Here, we present a method that combines the high

spatial resolution of an imaging method like the blood

smear analysis with the level of automation in ﬂuo-

rescent ﬂow cytometry by combining a quantitative

phase contrast microscope with a microﬂuidic sys-

tem and an adapted image analysis. This not only al-

lows the measurement of aggregates in high through-

put and without staining, but also allows a quantita-

tive characterisation of their single components. In

the work presented here, the method is applied for

two purposes. First, to measure the inﬂuence of the

ageing of blood samples on platelets aggregates (P-

aggregates) while storing them in different blood col-

lection tubes. And second, to measure dynamics in

P-aggregate formation and decay when using a com-

monly used activation assay.

2 MATERIAL AND METHODS

The applied protocols and the methodology of the

image-based ﬂow cytometer used for the analysis of

dynamics in cell aggregation are described in this

section in more detail. The presented ﬂow cytome-

ter is composed of a digital holographic microscope

(DHM), a microﬂuidic channel and a customized im-

age analysis (see Figure 1). Previous to this work,

this technique was already utilized to analyse changes

in leukocyte morphology due to sample preparation

(Klenk et al., 2019) and to distinguish between differ-

ent types of leukaemia (Ugele et al., 2018).

2.1 Digital Holographic Microscopy

For the recording of the image data a transmission

digital holographic microscope (Ovizio Imaging Sys-

tem) was used. Interferograms on the GS3-U3-

32S4M camera (Teledyne FLIR LLC) are generated

by off-axis holography combined with a double shear-

ing interferometric approach. A 528 nm SLED (Os-

ram) along with a Koehler illumination unit serves as

a light source. Light passes through the sample to-

wards the objective (CFI LWD, Nikon). The objec-

tive has a 40x magniﬁcation and a numeric aperture

of 0.55 providing moderate resolution in both axial

and lateral direction. A grating is used to generate

diffraction maxima, which together with a spatial ﬁl-

ter are essential for the in-line approach. Interference

of the single beams is generated on the camera with a

speciﬁc phase angle between them (off-axis) and re-

sults in the creation of an interferogram. Images are

generated with a frame rate of 105 frames per second

at an exposure time of 5 µs using the Software Os-

One Version 5.12.12 (Ovizio Imaging Systems). Each

image contains on average 5 - 20 cells, resulting in

BIOIMAGING 2023 - 10th International Conference on Bioimaging

Figure 1: Hardware components of the optical ﬂow cytome-

ter containing a digital holographic microscope, a microﬂu-

idic channel and a syringe pump system.

525 - 2100 cells per second. This leads to approxi-

mately 50,000 measured blood cells per capture.

2.2 Microﬂuidics

A microﬂuidic system was implemented to achieve

a high throughput of samples with a precise blood

cell alignment in a sub monolayer. For this, a poly-

methylmethacrylate (PMMA) channel with a width of

500 µm, a height of 50 µm and a length of 5000 µm

was used. Viscoelastic and hydrodynamic focussing

methods were combined to ensure a high precision.

To enable viscoelastic focussing, a portion of the

polymer polyethylene oxide (PEO) was added to the

measurement solution. Hydrodynamic focussing was

implemented, using four sheath ﬂows surrounding the

sample from bottom, top and both sides. Additionally

to the horizontal focussing this also allows a vertical

alignment. The total ﬂow rate was ﬁxed to 1.6 µl/s re-

sulting in a ﬂow velocity of 6.4 cm/s and a Reynolds

number in a single digit range (Re ≈ 6). Due to this

low ﬂow Reynolds number, experiments were carried

out in the laminar ﬂow regime.

2.3 Image Analysis

The analysis of the quantitative phase images was

done in three distinct steps: preprocessing of the im-

ages, Mask R-CNN based segmentation, and analysis

of detected objects.

• Preprocessing of the data included a background

subtraction, cell detection, masking and normal-

Figure 2: Block diagram of image analysis processes

needed to get from the raw holographic images towards in-

terpretable data.

ization. Background subtraction is needed to

eliminate background noise due to channel walls

and other phase inﬂuencing elements. By using

threshold segmentation, binary images of the sin-

gle elements were be obtained. Contours of ob-

jects were extracted based on the thresholding re-

sults and the algorithm of Suzuki et al. (Suzuki

et al., 1985). By choosing a suitable size ﬁlter on

the contours of detected objects, background arte-

facts from measurement media or debris could be

removed. As a ﬁnal preprocessing step, normal-

isation steps were carried out which are serving

two purposes. First, limiting the value range of

the phase images and then normalizing them by

a min-max normalisation to transform the image

values into the range 0-1 which is suitable for neu-

ral networks.

• As a second level of segmentation a Mask R-CNN

(He et al., 2017) approach was used for both a

reﬁnement of the segmentation contour and an

instance segmentation of cell aggregates. Mask

R-CNN performed both object detection and ob-

ject mask computation at the same time and is

based on the Faster R-CNN (Ren et al., 2015).

This region-based convolutional neural network

(CNN) operates in four stages. First, a CNN pro-

vides a convolutional feature map based on the in-

put patches. Secondly, a region proposal network

(RPN) provides regions of interest (RoI) by slid-

ing a small network over the convolutional fea-

Measurement of Platelet Aggregation in Ageing Samples and After in-Vitro Activation

ture map. Then, a RoI align layer utilizes bi-

linear interpolation to provide feature maps with

the same size as the RoI. These are used in the

fourth stage for classiﬁcation and bounding box

regression (BBR). In parallel, a small fully convo-

lutional network (FCN) was applied to each RoI to

predict the individual object masks at pixel reso-

lution. For the analysis of aggregates in this work,

a ResNet50 (He et al., 2016) was used as a convo-

lutional network. For training, two datasets were

used. One with single deﬁned blood cells as well

as a dataset with synthetically created aggregates.

These were made by composing several single cell

pictures together. Blood cells were classiﬁed in

three different classes: red blood cells (erythro-

cytes), white blood cells (leukocytes) and platelets

(thrombocytes). The network was trained on a to-

tal of 200,000 cell patches.

• Analysis of detected objects is the last step of the

imaging analysis pipeline. Based on the segmen-

tation and classiﬁcation results, image patches are

categorized as single cells or cell aggregates. This

allows an analysis of the number of cells form-

ing an aggregate and how this clot is structured.

An example of a single platelet and platelet-

aggregates with the corresponding results of the

segmentation and classiﬁcation is shown in Figure

3. Here, the highlighted green lines represent the

borders of the segmentation. Green in this case

represents a classiﬁed platelet.

Figure 3: Example images showing single platelets and

platelet-aggregates of different sizes. The top row shows

the quantitative phase contrast images in false colour. The

bottom row shows the same cells, with applied segmenta-

tion and classiﬁcation by the image analysis algorithm. The

highlighted edges represent the edges of the segmentation,

with the colour illustrating the classiﬁcation. Green in this

case represents platelets. The scale bars correspond to 5 µm.

2.4 Sample Preparation

Two different protocols were used for the experi-

ments presented in this work. One for measuring the

ageing of blood samples and the other for in-vitro

activation of blood cells. In both cases, dilution was

performed by mixing the sample with a measurement

solution containing 99.95 % phosphate buffered

saline (PBS) and 0.05 % polyethylene oxide (PEO,

molecular weight MW = 4 × 10

Da, Sigma Aldrich)

Table 1: Overview of the used anticoagulants and their con-

centrations speciﬁed by the supplier.

Name Anticoagulant Concentra-

tion

Citrate Trisodium citrate 0.129

mmol/ml

EDTA K3 Ethylenediamine-

tetraacetic acid

1.6 mg/ml

Heparin Lithium heparin 16 IU/ml

Hirudin Hirudin 525 ATU

Hirudin / ml

2.4.1 Ageing Experiment

Blood from a total of four independent donors

was

collected in the blood collection tubes mentioned in

Table 1. Directly before the measurement, blood was

diluted 1:100 in the measurement solution to reduce

cell density.

2.4.2 In-vitro Activation Experiment

Figure 4: Schematic of sample preparation steps for the ac-

tivation protocol.

For the in-vitro activation experiment, blood of three

independent donors was drawn and collected in the

same blood collection tubes mentioned in Table 1.

After a subsequent dilution step (1:2 with PBS) and

mixing for three minutes at 37°C, adenosine diphos-

All human samples were collected with informed con-

sent and procedures approved by application 620/21 S-KK

of the ethic committee of the Technical University Hospital

of Munich

BIOIMAGING 2023 - 10th International Conference on Bioimaging

phate (ADP) with a ﬁnal concentration of 0.33 µM,

1.61 µM or 6.45 µM was added as an activator. This

step was followed by an additional dilution step (1:50

with measurement solution). Platelet-aggregates were

measured continuously for twelve minutes. Since

there were three to four minutes between each mea-

surement, a total number of four measurements were

carried out. Note, that the ﬁrst measurement was per-

formed shortly before the activation with ADP and

that the ﬁrst three preanalytical steps (see top row of

Figure 4) are adapted from existing activation pro-

tocols to allow for comparability of results (Sibbing

et al., 2008).

3 RESULTS

The ﬁndings of this study focus on the inﬂuence of

sample ageing on aggregates as well as the change in

blood after in-vitro activation by ADP. The measured

target variables include the number and composition

of P-aggregates. The number of P-aggregates is al-

ways given percentage to the total amount of platelets

in the blood sample. The composition is described by

the amount of the involved platelets. For this, the total

of all P-aggregates is always normalized to 100 %.

3.1 Effect of Sample Ageing on

Measured P-Aggregates

Blood of all donors was anticoagulated in citrate,

EDTA, heparin and hirudin tubes. The storage and

measurement of the sample was then performed at

room temperature over a period of two hours. Fig-

ure 5a shows such a progression for one exemplary

donor. After 0, 30, 60, 120 and 240 minutes, the

P-aggregates were measured. The different antico-

agulants are shown in different colours with citrate,

EDTA, heparin and hirudin represented in black, blue,

orange and green, respectively. All measurements

within the 240 minutes of EDTA, citrate and hirudin

range between 0.5 - 3 % of P-aggregates. For donor

1, there are only two outliers, namely in the heparin

tube after 30 min (5.71 %) and 60 min (8.94 %). A

similar picture can be seen when observing Figure 5b.

Here, the aggregates of the ageing measurement are

compared with the respective 0 min measurement for

all patients and the difference is plotted on the Y-axis.

Each point represents one measurement. In addition,

the normal distribution ﬁtted over all points was dis-

played. For EDTA and hirudin blood, an expected

distribution around the origin can be observed. As

for donor 1, heparin shows an increased aggregation

compared to the zero measurement. Citrate, on the

other hand, shows a reduced aggregate number over

time.

Figure 5: Ageing effects on the measured number of

platelet-aggregates in different blood collection tubes. (a)

Analysis of platelet-aggregate behaviour for different anti-

coagulants over a period of 240 minutes for one donor. (b)

Difference in platelet-aggregate for all donors of the 30-,

60-, 120- and 240-minute measurement compared to their

respective 0-minutes reference.

3.2 In-vitro Activation of Thrombocytes

In this section, the effects of activation assays on

P-aggregates are investigated. For this, the ADP

concentration was varied and different anticoagulants

were used. The in-vitro activation protocol presented

in chapter 2.4.2 was applied.

3.2.1 Impact of ADP Concentration

In a ﬁrst step, the concentration of the added ADP

for activation was varied to test whether this has an

Measurement of Platelet Aggregation in Ageing Samples and After in-Vitro Activation

Figure 6: Change in the number (a) and composition (b) of platelet-aggregates over time after activation with different

concentrations of ADP. In all three cases, blood was collected in a hirudin blood collection tube. For the characterization of

the P-aggregate composition (b) all results are presented for the 6.45 µM experiment.

impact on the measured number and composition of

P-aggregates. For this purpose, the starting concen-

tration of ADP with 6.45 µM was diluted to 1.61 µM

and 0.33 µM and added to hirudin blood samples.

Measurements were taken shortly before activation

(0 min) and after activation (3, 7 and 12 min). As

shown in Figure 6a, the activation step results in

an increase in aggregates after three minutes for all

ADP concentrations. However, at 0.33 µM (brown

squares) the value only slightly increases from 1.56 %

to 4.13 %, whereas at 1.61 µM (gray circles) and

6.45 µM (green triangles) the values increase signiﬁ-

cantly more (to 24.28 % and 25.15 %). After reaching

this maximum value of aggregation at three minutes,

in all three cases the values decrease over time. A

similar course can also be observed for the aggregate

composition. Figure 6b shows the size distribution of

the aggregates for the case of activation with 6.45 µM

ADP. For the ﬁrst measurement, most of the aggre-

gates consist of two or three platelets. Only 4.1 %

of the aggregates consist of more than three platelets.

Immediately after activation, this proportion increases

to 21.1 %. Particularly noteworthy is, clots with 10 or

more platelets account for 2.2 % of all aggregates. For

the seven-minute measurement not only a large share

of aggregates disintegrated but especially the majority

of large aggregates. At this point, aggregates with 10

or more platelets could no longer be observed.

3.2.2 Impact of Used Anticoagulants

The choice of anticoagulant inﬂuences the in-vitro ac-

tivatability of blood cells. Thus, they intervene at

different points in the coagulation cascade, prevent-

ing uncontrolled formation of large amounts of blood

clots. To test whether these differences can also be

observed when measuring P-aggregates, differently

anticoagulated blood was activated with a total con-

centration of 6.45 µM ADP each. Figure 7 shows the

difference between the single measurement points and

their associated zero measurement before activation.

Thus, differences due to activation can be observed

directly for each point in time. When looking at the

numbers of the EDTA measurement (blue circles) no

signiﬁcant increase in P-aggregates after activation

can be observed. For all three measurement points

the increase ranges between 0.16 % and 0.66 %. In

contrast to this, a sharp increase can be observed for

blood samples with citrate (black squares), heparin

(orange triangles) and hirudin (green triangles). Di-

rectly after activation, the three values of the anticoag-

ulants lie close to each other with values ranges from

21.68 % to 23.59 %. This rise is then followed by a

drop at the time points 7 min and 12 min, whereby

this decrease is lower for heparinized blood than for

the other two anticoagulants.

BIOIMAGING 2023 - 10th International Conference on Bioimaging

Figure 7: Inﬂuence of in-vitro activation on measured P-

aggregates in blood samples with different anticoagulants.

Shown is the difference in platelet-aggregate for all donors

3-, 7- and 12-minutes after activation compared to their re-

spective 0-minutes value.

3.2.3 Decay of P-Aggregates by Size over Time

As shown in the previous chapters, P-aggregates tend

to decay after an initial formation in the in-vitro ac-

tivation experiments. The dynamics of this process

are visible for citrate, heparin and hirudin as anti-

coagulant. In Figure 8, this decay was examined

in term of the size of the decaying aggregates. For

this purpose, all citrate, heparin and hirudin measure-

ments were pooled. Aggregates were analysed at 3

and 12 minutes, by this the number of disintegrating

clots within these nine minutes was determined. For

this analysis aggregates were considered from a size

of two platelets up to a size of ten platelets. Within the

nine minutes of analysis, the majority of P-aggregates

decayed. This effect could especially be observed in

larger aggregate structures. While the number of re-

maining clots with two, three, four or ﬁve platelets

were still between 41.03 % and 45.59 %, large ag-

gregates tend to decay faster. In clots of six to nine

platelets a nearly linear increase from 77.40 % to

100 % disintegrated P-aggregates could be seen. A

slight decrease is then observed for structures with

10 PLTs (95.58 %), which might be due to the small

amount of such large structures after 12 minutes and

the associated measurement inaccuracy.

4 DISCUSSION

As shown in Figure 5 and 6, a stable measurement

of blood was achieved. Measured aggregate values

directly after blood draw laid between 0.50 % and

3.00 %, which is in a similar range as published

Figure 8: In depth analysis of P-aggregate decay by size

between minute 3 and 12 of the activation experiments.

For this purpose all measurements of citrate, heparin and

hirudin were pooled and then analysed together.

data. Leytin et al. analysed the number of activated

platelets by measuring P-selectin, a protein which is

presented on the surface of the thrombocytes when

activated (Leytin et al., 2000). Here, an amount of

1.02 ± 0.49 % of activated platelets could be observed

for healthy blood samples. The slight difference in

range to the here presented data could be explained

by the use of different analysing techniques and the

measurement of different biomarkers. Although the

number of activated platelets and P-aggregates are di-

rectly related, it does not allow direct comparability of

the values.

For samples without ageing effect, the measure-

ments for EDTA and hirudin showed a normal distri-

bution around the respective zero measurement (Fig-

ure 5b). The measured P-aggregate values tended to

vary between ±2 % during the two hours of moni-

toring, allowing stable measurement conditions. For

heparinised blood on the other hand, an increased ag-

gregate formation due to ageing effects could be ob-

served. Since this is mostly observed shortly after

blood collection (Figure 5a), thus after 30 and 60 min,

these effects could be due to the blood collection and

subsequent lower anticoagulation with heparin. For

citrate blood a decrease of values over time, hence a

disintegration of aggregates could be observed. Both

effects, for citrate and heparin, show a clear trend for

the four measured blood samples, but need to be vali-

dated by a higher sample collective in the future.

In addition to measuring ageing effects, the

method was also applied to measure highly dynamic

processes in the formation and decay of blood cell ag-

gregates. Figure 6 - 8 show the rate of this dynamics.

Depending on the anticoagulant, between 42.79 % -

Measurement of Platelet Aggregation in Ageing Samples and After in-Vitro Activation

79.98 % of all P-aggregates disintegrated within nine

minutes. Michelson et al. performed a comparable

study in-vivo. Here, they activated baboon platelet

concentrates, infused it into the animals and mea-

sured the number of leukocyte-platelet-aggregates in

the blood stream. For monocyte-platelet-aggregates

approximately 72.73 % disintegrated within a time

frame of ten minutes (Michelson et al., 2001). Al-

though this experimental protocol and the measured

aggregates cannot be compared directly to the P-

aggregates, it still shows a similar physiological dy-

namic. The fast changes in values underline the need

of a measurement technique which is not dependent

on time consuming sample preparation steps or long

acquisition times. Increased aggregate values can still

be detected several minutes after activation, but on a

reduced scale. Furthermore, it cannot be ruled out that

the sample is activated due to sample preparation and

handling (Ramstack et al., 1979; Jesty et al., 2003).

When observing P-aggregate formation, results in

Figure 7 suggest that there was a difference in acti-

vation of blood, depending on the used anticoagulant.

There were anticoagulants that allowed an in-vitro ac-

tivation of blood with ADP, namely citrate, heparin

and hirudin, and then there was EDTA, that prevented

any activation. These results are in line with ﬁndings,

showing that EDTA prevents an interaction between

ﬁbrinogen and the exposed receptors on the platelet

membranes resulting in strongly inhibited formation

of aggregates (Peerschke and Zucker, 1981). Cit-

rate, heparin and hirudin on the other hand are com-

monly used for in-vitro activation assays and allow a

subsequent blood coagulation (Wall

en et al., 1997).

Besides the chosen anticoagulant also the amount

of added ADP had an impact on platelet activation.

Thus, only a marginal increase in aggregates could

be registered after activation with 0.33 µM ADP. The

effects were signiﬁcantly higher for 1.61 µM and

6.45 µM with only a small difference between these

two concentrations. This suggests that saturation is

reached and further concentration increase does not

lead to a signiﬁcant increase in P-aggregates.

Lastly, when looking at the composition of the P-

aggregates it can be seen, that after activating blood

with ADP not only the number of aggregates but also

the number of the involved platelets changes. While

the aggregates for the measurements before activation

(Figure 6) mostly consisted of two platelets, the num-

ber of larger aggregates changed considerably after

activation. The ADP promoted cohesion of platelets

by ﬁbrinogen, thus supports not only the formation

of small but also large blood clots. When observing

their decay (Figure 8), it can be seen that the propor-

tion of large aggregates with more than six platelets

tended to dissolve faster than smaller aggregates. This

effect may be explained by a homogeneous disinte-

gration of all aggregates but a decay of big clots into

smaller ones. Thus, nine minutes after activation, al-

most no aggregates with nine or more platelets could

be recorded.

5 CONCLUSION

The work presented here, shows a method that

is capable of high throughput measurement of P-

aggregates without any staining or other lengthy sam-

ple preparations. Through combination of quantita-

tive phase microscopy, microﬂuidics and image anal-

ysis a quantitative and qualitative analysis of these

aggregates was achieved. This was done in such a

way that even fast dynamics in aggregation formation

and decay could be observed. When looking at the

analysed anticoagulants, two reagents show promis-

ing results for long time storage of blood, regarding

the measured P-aggregates. EDTA and hirudin fulﬁl

this goal, whereby only the latter allows an in-vitro

activation. The application of the presented method

for ageing and activation measurements is a ﬁrst step

to characterise the measurement system as well as the

biomarker. The long-term goal, however, is to mea-

sure increased platelet activity in blood of patients

with various diseases. An increased platelet activ-

ity was already observed for patients with COVID-

19, cancer and cardiovascular diseases. Although fur-

ther studies are needed to validate the biomarker in

these medical ﬁelds, the presented method could be a

promising tool to reach this goal.

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Measurement of Platelet Aggregation in Ageing Samples and After in-Vitro Activation