1. health and fitness
2. disease

Acoustophoretic sample preparation for
PCR in sepsis diagnostics
Master’s thesis
University of Turku | Lund University
Faculty of mathematics | Faculty of engineering,
and natural sciences | LTH
Department of Biochemistry | Department of Measurement Technology
and Food Chemistry | and Industrial Electrical Engineering
Biotechnology | Division of Electrical Measurements
February 2013
Matleena Punkkinen
Supervisors: Pelle Ohlsson, Maria Nordin, Saara Wittfooth, and Ari Lehmusvuori
Abstract
Sepsis is a serious clinical syndrome and one of the most common causes of death. It
is defined as a microbial invasion and excessive immune reaction. There is no
common cure for sepsis, and mortality in sepsis is high and rises quickly, so it is
important to start precise treatment quickly. Sepsis is, however, hard to diagnose, as
it has no symptoms specific to it alone, and thus new ways to diagnose sepsis and its
cause are needed.
This study was part of an effort to develop a polymerase chain reaction (PCR)-based
diagnostic system for detecting and identifying bacteria in blood in sepsis. The blood
sample is first prepared by separating the substances that inhibit PCR in blood with
acoustophoresis, using ultrasound to direct the particles. This study aimed at
determining how acoustophoresis and the presence of inhibiting agents or
contamination in samples affected PCR and how effectively acoustophoresis had to
clean a blood sample so that PCR could reliably detect bacteria in it.
Limit of detection for PCR was approximately 150 bacteria per sample due to high
bacterial background. At approximately 0.003–0.03% blood plasma had very little
effect on detection. PCR tolerated red blood cells up to approximately dilution 1:15
000, but whole blood reduced amount of detected bacteria by 50% even at very high
dilutions. Acoustophoresis could separate red blood cells and other from diluted
blood efficiently, although detection of bacteria was somewhat affected. A large part
of the bacteria were also lost during the acoustophoretic run. In spite of the
problems, separation and detection of the bacteria seemed to work relatively well for
an unoptimized system.
Keywords: sepsis, diagnostics, PCR, acoustophoresis, plasmapheresis, blood
Summary in Finnish
Sepsis eli verenmyrkytys on tila, jossa kehon immuunipuolustus reagoi liian
voimakkaasti siihen tunkeutuneisiin mikrobeihin. Se on vakava kliininen syndrooma
ja yksi kaikkein yleisimmistä kuolinsyistä, eikä siihen ole yleistä parannuskeinoa.
Koska kuolleisuus nousee verenmyrkytyksessä hyvin nopeasti, on tärkeää myös
aloittaa oikea hoito mahdollisimman pian. Verenmyrkytyksen diagnosointi on
kuitenkin vaikeaa oireiden yleisluontoisuuden takia, joten uusille menetelmille on
tarvetta.
Tutkimus
suoritettiin
osana
projektia,
jossa
pyritään
kehittämään
polymeraasiketjureaktioon (PCR) perustuva menetelmä, jonka avulla pystytään
tunnistamaan bakteerit veressä verenmyrkytyksessä. Verinäyte esikäsitellään
akustoforeesiksi kutsutulla tekniikalla, jolla kappaleita voidaan ohjailla nesteessä.
Tutkimuksen tarkoituksena oli selvittää millainen esikäsitellyn verinäytteen
koostumuksen tulisi olla jotta siitä pystytään PCR:llä löytämään bakteerit.
Korkean bakteeritaustan takia PCR:n detektioraja oli noin 150 bakteeria näytteessä.
Veriplasman pitoisuus PCR-reaktiossa sai olla korkeintaan 0.003–0.03% ilman että
se häiritsi määritystä. Punaverisolut täytyi laimentaa vähintään 1:15 000, mutta
kokoveri häiritsi määritystä niin paljon, että sen suurillakin laimennoksilla
havaittujen
bakteerien
määrä
oli
puolet
todellisesta.
Akustoforeesi erotti
punaverisolut muusta verestä tehokkaasti, vaikka havaittujen bakteerien määrä
pienenikin, mikä johtui osaksi bakteerien menetyksestä akustoforeesiajon aikana.
Ongelmista huolimatta verinäytteen käsittely akustoforeesilla ennen PCR:ää vaikutti
toimivan hyvin.
Asiasanat: verenmyrkytys, diagnostiikka, akustoforeesi, veriplasma, veri
Acknowledgements
Experimental part of this thesis was conducted in Division of Electrical
Measurements, Department of Measurement Technology and Industrial Electrical
Engineering, Faculty of Engineering, LTH, Lund University. This study was
conducted as a part of the ACUSEP collaboration project, funded by EU’s Seventh
Framework Programme.
I want to thank all my supervisors, Saara Wittfooth and Ari Lehmusvuori from
University of Turku, and Pelle Ohlsson and Maria Nordin from Lund University, for
providing me support and help in my experiments. I am also grateful for the whole
personnel of the department of measurement technology at Lund for providing a
good and warmly welcoming atmosphere for research during my stay in Lund.
Table of Contents
1 Background............................................................................................................................1
1.1 Sepsis .............................................................................................................................. 1
1.1.1 Pathology, immunopathology, and treatment of sepsis ............................................ 1
1.1.2 Factors complicating diagnosis of sepsis ................................................................... 3
1.1.3 Sepsis diagnostics ..................................................................................................... 5
1.2 PCR.................................................................................................................................. 8
1.2.1 PCR-reaction............................................................................................................. 8
1.2.2 Quantitative PCR .................................................................................................... 10
1.2.3 Methods for analyzing quantitative PCR data.......................................................... 12
1.2.4 Detection in PCR ..................................................................................................... 14
1.2.5 PCR in blood ...........................................................................................................16
1.3 Microchannel acoustophoresis ...................................................................................... 17
1.3.1 Acoustic standing waves and forces ........................................................................ 18
1.3.2 Acoustic resonators ................................................................................................ 20
1.3.3 Acoustophoretic microchannel systems .................................................................. 22
1.3.4 Microchannel acoustophoresis in applications ........................................................ 24
1.3.5 Acoustophoresis of blood ....................................................................................... 25
1.4 Goals of the study .......................................................................................................... 26
2 Materials and methods........................................................................................................ 28
2.1 PCR-protocol ................................................................................................................. 28
2.2 Bacterial cultures ........................................................................................................... 29
2.3 Bacterial DNA and whole bacterial cells in PCR .............................................................. 30
2.4 Inhibition of PCR by blood plasma.................................................................................. 30
2.5 Inhibition of PCR by red blood cells ................................................................................ 31
2.6 Inhibition of PCR by whole blood ................................................................................... 31
2.7 Acoustophoresis ............................................................................................................ 31
2.8 Acoustophoresis of bacteria .......................................................................................... 32
2.9 Acoustophoresis of blood .............................................................................................. 33
2.10 Acoustophoresis of blood spiked with bacteria ............................................................ 33
2.11 Data analysis................................................................................................................ 34
3 Results ................................................................................................................................. 36
3.1 Bacterial DNA and whole bacterial cells in PCR .............................................................. 36
3.2 Inhibition of PCR by blood plasma.................................................................................. 37
3.3 Inhibition of PCR by red blood cells ................................................................................ 39
3.4 Inhibition of PCR by whole blood ................................................................................... 39
3.5 Acoustophoresis of bacteria .......................................................................................... 41
3.6 Acoustophoresis of blood .............................................................................................. 41
3.7 Acoustophoresis of blood spiked with bacteria .............................................................. 42
4 Discussion ............................................................................................................................ 44
4.1 Bacterial DNA and whole bacterial cells in PCR .............................................................. 44
4.2 Inhibition of PCR by blood plasma.................................................................................. 45
4.3 Inhibition of PCR by red blood cells ................................................................................ 46
4.4 Inhibition of PCR by whole blood ................................................................................... 47
4.5 Acoustophoresis of bacteria .......................................................................................... 47
4.6 Acoustophoresis of blood .............................................................................................. 48
4.7 Acoustophoresis of blood spiked with bacteria .............................................................. 48
5 Conclusions.......................................................................................................................... 50
References..............................................................................................................................51
Abbreviations
BSA
bovine serum albumin
CFU
Colony forming unit
Ct
Threshold cycle
DNA
Deoxyribonucleic acid
dNTP
ribonucleotide
E. coli
Escherichia coli
EDTA
Ethylenediaminetetraacetic acid
HIV
human immunodeficiency virus
IL-6
interleukin 6
mRNA
messenger RNA
PCR
Polymerase chain reaction
PRF
primary axial radiation force
RBC
Red blood cell
qPCR
real-time PCR
RNA
Ribonucleic acid
rDNA
ribosomal DNA
s-ELAM-1
E-selectin 1
SIRS
Systemic inflammatory response syndrome
SRF
secondary radiation force
TNF
Tumor necrosis factor alpha
MIP-1
macrophage inflammatory protein alpha
WBC
White blood cell
1 Background
1.1 Sepsis
Sepsis is a complex and serious clinical syndrome caused by microbial invasion of
the body, a subsequent excessive host inflammatory response, and complications
following these (Cohen 2002). It is the most common cause of death in non-coronary
intensive care units and among the most common causes of death overall at
approximately 10% of the total count (Martin et al 2003; Angus et al 2001; Engel et
al 2007). Average patient counts are generally 0.05–0.3% of the population with
incidence rising lately at a rate of 1.5% per year (Shimaoka and Park 2008; Martin et
al 2003; Angus et al 2001). The rise in incidence at least in western countries has
been attributed partly to the aging population (Martin et al 2003). Mortality rate of
sepsis is approximately 20–30% even with proper care, rising to 40–70% in its later
stages (Angus et al 2001; Rangel-Frausto et al 1995; Klouche and Schröder 2008;
Rivers et al 2001; Russell 2006; Engel et al 2007). The mortality rates have declined
with more efficient treatment, but due to the increase in incidence, absolute number
of deaths is on the rise (Martin et al 2003). Sepsis is a major burden to health care
systems, amounting to an annual cost of $ 16.7 billion in USA alone in 2001 due to
these problematic aspects and the long hospitalization times required for treatment
(Angus et al 2001). The ultimate cause of death in sepsis tends to be multiple organ
failure, with typically single organ failure developing first and further deterioration
following quickly, which also means that the mortality rate escalates quickly (Cohen
2002).
1.1.1 Pathology, immunopathology, and treatment of sepsis
Sepsis is thought to develop partially from amplified, misregulated host
inflammatory response where overabundant inflammatory agents damaging body
tissues, but the precise mechanism of sepsis is complex, incompletely understood,
and includes later suppression of the immune system and crosstalk with misregulated
coagulation. Suppression of the immune system, which results in immunoparalysis,
may even be a more important cause for severe sepsis than its earlier amplification.
(Riedemann et al 2003a; Riedmann et al 2003b; Shimaoka and Park 2008; Ulloa et al
2009; Russell 2006; Meakins et al 1977.) Sepsis is defined as systemic inflammatory
1
response (SIRS) coupled with infection. SIRS is characterised by a large set of
symptoms, and a patient is seen to have SIRS when at least two of the symptoms are
present. (Bone et al 1992; Levy et al 2003.) Sepsis is categorized by degree of
severity as the disease advances, and further developed sepsis is called severe sepsis.
At this stage symptoms include high blood pressure or organ dysfunction usually
indicated by decreased blood flow through an organ or oxygen deficiency in tissue.
At its final stages sepsis is called septic shock, with the additional symptom of low
arterial blood pressure despite sufficient fluid intake. (Bone et al 1992.) Other
possible symptoms of advanced sepsis include increased metabolism, failure of
respiratory organs or kidneys, and abnormalities of coagulation (Cohen 2002). The
later-stage complications lead to system oxygen imbalance, which in turn leads to
global tissue hypoxia or shock, the preceding conditions to multiple organ failure
and death (Belal and Cerra 1994). An expanded staging system for sepsis, PIRO, has
also been developed to allow for easier categorization of stages and evaluation of
risks (Levy et al 2003; Riedemann et al 2003a). It divides the symptoms to general,
inflammatory, hemodynamic, organ dysfunction related, and tissue perfusion related
(Levy et al 2003; Carrigan et al 2004).
Sepsis is most commonly caused by bacteria (Riedemann et al 2003a). Recent
studies have set the approximate relative frequencies of causes to: gram-positive
bacteria 40–50%, gram-negative bacteria 40%, and fungi 5–20%, with 5–20% of
infections being mixed (Vincent et al 2006; Martin et al 2003). Gram-negative
bacteria were formerly in the majority, and so the rise of gram-positive infections has
been theorized to be one of the possible reasons for the rising incidence, as has been
the major rise in fungal infections during the same period (Martin et al 2003;
Carrigan et al 2004). Microbes can invade any tissue or fluid of the body and spread
from there, but the most common infection sites are the lungs, abdominal cavity,
urinary tract, and primary infections of the blood stream (Angus et al 2001; Vincent
et al 2006).
Since sepsis is a wide clinical syndrome with many possible causes, treating it is
difficult unless the precise cause is identified (Riedemann et al 2003a; Glück and
Opal 2004). Attempted therapies for sepsis have been directed against both the overamplification of the host response and the later immunosuppression (Riedmann et al
2003b). A few strategies for treating sepsis irrespective of the cause are available,
2
with insulin therapy and administration of low-dose corticosteroids being effective in
some studies (Cohen 2002). Activated protein C also seems to be somewhat efficient
in treatment of severe sepsis, reducing mortality by approximately 6%, which gives
hope for other treatments. But it has become increasingly clear that only high-risk
patients enjoy this benefit, and even protein C is not very effective, since mortality
remains high in spite of the treatment. Use of protein C also drastically increases the
risk of internal haemorrhage, which is a problem in sepsis due to the abnormalities of
coagulation. This allows only a small subset of patients to safely use protein C.
(Bernard et al 2001; Glück and Opal 2004; Shimaoka and Park 2008; Abraham et al
2005; Vincent et al 2005; Ulloa et al 2009.) The only recommended strategies for all
severe sepsis patients are fluid administration and possibly vasopressors for
restoration of organ perfusion, and source control by early administration of
antibiotics and maximized oxygen delivery with supplemental oxygen and red blood
cells (Dellinger et al 2004; Dellinger et al 2008; Rivers et al 2001).
It has been suggested that the definition of sepsis encloses too many clinical
immunological disorders, which leads to severe difficulties in comparing study
results and repetition of phase I clinical trials and reduces the possibility of finding
an all-encompassing cure for sepsis (Riedemann et al 2003a; Glück and Opal 2004;
Ulloa et al 2009). This has lead to a large number of failed clinical trials, although
one likely reason for some of the failures is the fact that functioning treatments in
animal models do not translate well to success in humans in sepsis, likely partially
due to major differences in regulatory pathways (Riedmann et al 2003a; Ulloa et al
2009). The supposition that the definition is too wide is supported by the fact that
there has been no remarkable improvement in clinical outcomes for sepsis despite
the process of sepsis and host-pathogen interactions becoming relatively well-known
(Cohen 2002; Ulloa et al 2009).
1.1.2 Factors complicating diagnosis of sepsis
In addition to being difficult to treat effectively and universally, sepsis is also
difficult to diagnose. These difficulties arise from the symptoms: SIRS can also be
caused by other states than just sepsis (Figure 1), and none of the symptoms are
specific to sepsis. (Levy et al 2003.) A confirming diagnosis, especially for bacteria,
is traditionally done by blood culture, but this method is unreliable as, depending on
3
the type of pathogen, 60–70% or even 90% of results are negative even if bacteria
are present in the sample (Vincent et al 2006; Rangel-Frausto et al 1995; Sachse et al
2009; Shang et al 2005; Klouche and Schröder 2008; Carrigan et al 2004). The
positive predictive value can be improved by using greater volumes of blood, which
is, however, often unfeasible especially for children and neonates (Klouche and
Schröder 2008). Blood culture is also a slow method, as the median time for a
positive diagnosis is 15 h even without a full identification of species. Although
many of the most common bacteria that cause sepsis can be identified much faster,
and more rapid methods for culture identification have been developed, the results
may still arrive too late for intervention. (Ng et al 1997; Klouche and Schröder 2008;
Ozenci et al 2007.) This makes blood culture an insufficient method for diagnosis of
sepsis.
Figure 1. Relations between and causes for infection, sepsis and SIRS (Bone et al
1992).
Lethality of sepsis is augmented by its quickly escalating mortality, so a rapid
diagnosis is essential in improving the clinical outcome (Bauer and Reinhart 2010).
Although general therapeutic intervention works better at the severe sepsis state,
mortality is difficult to reduce due to complications (Riedemann et al 2003a; Parrish
et al 2008; Riedmann et al 2003b; Rivers et al 2001; Levy et al 2003; Glück and
Opal 2004). Due to this the process from sepsis to severe sepsis, lasting
4
approximately the first 6 h, is especially important for diagnosis and treatment, since
probability of patient survival is considerably higher in early stages of sepsis and the
survival rate can be improved, as effective treatment for infections is readily
available (Rivers et al 2001; Levy et al 2003; Glück and Opal 2004; Riedmann et al
2003b).
As rapid diagnosis and treatment for sepsis is important for the clinical outcome,
antibiotic treatment is often started even without a precise diagnosis as a common
procedure based on biomarkers that have high sensitivity but low specificity. This
leads to inappropriate procedures for 20–30% of patients suffering from sepsis.
(Dellinger et al 2004; Dellinger et al 2008; Harbarth et al 2003.) Since the diagnosis
is unspecific, the administrated antibiotics tend to be broad-spectrum, which
increases risk of strains with antibiotic resistance due to the natural selection
pressure for resistance (Malhotra-Kumar et al 2007; Hawkey and Jones 2009).
Antibiotic resistance of the bacteria is also one of the possible causes for rising
incidence of sepsis besides aging population, rising incidence of gram-positive
bacteria and fungi, increasing invasive procedures, immunosuppressive drugs, and
the HIV epidemic (Martin et al 2003). This combined with the fact that proper
antimicrobial treatment has been shown to be the most important factor in reducing
mortality and morbidity in both bacterial and fungal infections shows that early and
correct diagnosis and treatment of sepsis is essential (Riedemann et al 2003; Angus
et al 2001; Glück and Opal 2004; Garnacho-Monerto et al 2008; Hotchkiss and Karl
2003).
Based on all of the presented criteria for proper treatment and diagnosis of sepsis, a
good diagnostic method would have to be rapid, capable of determining the cause of
infection both by type of microbe and species, capable of using tissue or blood as a
sample, and capable of diagnosis at low concentrations of microbes, since in sepsis
the amount of bacteria in blood is relatively low (Klouche and Schröder 2008).
1.1.3 Sepsis diagnostics
Since symptoms of SIRS are unsuitable for specific identification of a cause or a
pattern, and since the reaction of the immune system is also required for a diagnosis,
immunologic or biochemical data are anticipated to be more useful than clinical data
5
in diagnosing sepsis. These data could include observing levels of pro-inflammatory
mediators, adrenomedullin, soluble (s)CD14, s-ELAM-1, MIP-1 , extracellular
phospholipase A2, acute-phase proteins (e.g. C-reactive protein), and procalcitonin,
since both cellular and humoral defence mechanisms are overactive at the first stages
of sepsis. (Bauer and Reinhart 2010; Harbarth et al 2001; Ng et al 1997; Levy et al
2003; Riedmann et al 2003b.) Recent data show that different sets of symptoms may
even have different mediator profiles (Ulloa et al 2009). Procalcitonin might be
useful in determining the optimal duration of antibiotic treatment (Nobre et al 2008).
Gene expression profiling through marker panels would be another possible way to
diagnose sepsis, since multiple markers will likely be more useful in differentiating
sepsis from other causes of SIRS (Bauer and Reinhart 2010; Johnson et al 2007;
Prucha et al 2004).
One way of addressing the problem of detecting an invasion is the use of general
nucleic acid amplification to detect the presence of microbes. This could be used
both for easy-to-culture species and for more difficult targets, such as fungi. (Shang
et al 2005; Bauer and Reinhart 2010; Klouche and Schröder 2008.) Analysis from
blood would be convenient for nucleic acid amplification since blood samples are
readily available, and polymerase chain reaction (PCR) -based system would likely
be the best feasible option for obtaining diagnosis (Bauer and Reinhart 2010; Prucha
et al 2004). There are various PCR-based methods that can be used for detecting
microbes in blood. They allow species-specific detection of the most important
pathogens that cause sepsis through specific probes, amplification of bacterial DNA
generically through conserved regions, and multiplexed tests. (Shang et al 2005;
Klouche and Schröder 2008; Böttger 1989; Peters et al 2004.) Some species can be
identified very quickly from blood, and some methods give the result in a few hours
(Klouche and Schröder 2008; Peters et al 2004). Amplification of bacterial DNA
from whole blood has been more successful than blood culture, although many cases
still fail to be diagnosed (Bauer and Reinhart 2010). The fact that a wide variety of
microbes can cause sepsis makes the precise diagnosis of species by DNA
amplification more complex, but the 10 most common bacterial pathogens cause 82–
92% of all bacterial bloodstream infections (Biedenbach et al 2004).
Diagnosing sepsis by detecting DNA with PCR from blood also has its problems.
Broad-range amplification, which would be used for non-species-specific detection,
6
is especially vulnerable to samples and PCR-reagents that have been contaminated
due to the ubiquitous presence of bacterial and fungal DNA, and to other cells in
blood causing additional background. (Bauer and Reinhart 2010; Klouche and
Schröder 2008; Mühl et al 2010; Peters et al 2004.) Thus, false positive results due to
either non-sepsis associated bacteria in blood, bacteria from naturally colonized
surfaces, or dead bacteria, are a common difficulty in diagnosis (Klouche and
Schröder 2008; Peters et al 2004). There have been attempts at decontamination, but
these methods tend to be either time-consuming or largely ineffective (Corless et al
2000; Silkie et al 2008). As a result of the elevated background and limitations, PCR
has an increased limit of detection compared to blood culture, since in sepsis the
amount of microbes in blood is low, less than 500 colony forming units (CFU) per
millilitre (Klouche and Schröder 2008). Depending on the sample volume PCRbased methods usually require loads of approximately 5–500 CFU/ml, leading to too
low negative predictive values in sepsis (Klouche and Schröder 2008; Wilson 1997).
Sensitivity and time-to-results could be improved significantly with an enrichment
step before PCR. It is, however, commonly perceived as work-intensive and timeconsuming, and at least with some techniques it causes a rise in false-positive rates.
(Bauer and Reinhart 2010; Sachse et al 2009.) Diagnosis through PCR is also
complicated due to lack of other standards than blood culture, leading to difficulties
in confirming results that show positive in PCR but negative in blood culture (Bauer
and Reinhart 2010; Klouche and Schröder 2008; Peters et al 2004). Other problems
associated with PCR-based techniques include being technically challenging and as a
consequence requiring specialized personnel, and results of studies on the PCRbased methods being varying and difficult to interpret, although by now several
promising studies with multiplexed PCR have recognized the clinically most
important species accurately. Detection of resistances to antibiotics is vital if
effective treatment is to be found, especially as resistant microbes become more
common. It is, however, difficult to implement into PCR. (Klouche and Schröder
2008; Peters et al 2004.) This is a problem with all molecular techniques, since a test
for resistances would require testing for a large number of resistance markers (Peters
et al 2004).
7
1.2 PCR
PCR has, since its discovery in 1983, brought significant advances to molecular
biology, and it is the standard method across a range of scientific fields for
amplifying and detecting the nucleic acids, DNA and ribonucleic acid (RNA) (Saiki
et al 1985; Mackay et al 2002). This is due to its multiple advantages compared to
other available techniques: PCR can be used for detecting even small amounts of
nucleic acids since it amplifies the target exponentially, and it amplifies its target
very specifically. It is a flexible and scalable method, and relatively fast, with a run
usually taking an hour or two to complete. Finally, it is readily available due to the
relative cheapness of the required reagents compared to those needed for many other
methods. (Udvardi et al 2008; Schmittge et al 2000; Saiki et al 1988.) PCR can be
used for various purposes, commonly for DNA quantitation or gene expression
measurement (VanGuilder et al 2008).
1.2.1 PCR-reaction
PCR requires the target DNA intended for amplification, primers that frame the
amplified area, nucleotides from which the new DNA strands are constructed,
polymerase to extend the DNA-strands, and suitable buffer conditions (Mackay et al
2002; Chien et al 1976). The polymerase, catalysing the reaction DNAn + dNTP
DNAn+1 + PPi, is commonly derived from bacterium Thermus aquaticus (Taqpolymerase), and thermostable in order to endure high-temperature steps of the PCR
without ceasing to function. This allows for an automated process that is generally
done with a PCR-cycler. (Chien et al 1976; Pavlov et al 2004; Innis et al 1988; Saiki
et al 1988.) Different polymerases are available for various types of reactions,
depending on the required extension rate, processivity, fidelity, yield, and sensitivity
or tolerance to inhibitors (Pavlov et al 2004; Brownstein 2004). Polymerase requires
divalent cations, either Mg2+ or Mn2+, the latter of which causes a higher error rate
but allows use of RNA as template in some reactions (Pavlov et al 2004; Chien et al
1976). In other cases, if RNA is used as a template, a complementary DNA strand
has to be transcribed first and then used in PCR. This is known as reversetranscriptase PCR. (Brownstein 2004.) Monovalent cations, such as K+ or Na+, may
also enhance the reaction at low concentrations, as can certain other substances, such
as formamine (Chien et al 1976; Innis et al 1988; Sakar et al 1990). Thermostability
8
of the polymerase can be affected by the conditions of the reaction, such as its pH,
and so the effect should always be checked in a new reaction (Pavlov et al 2004).
Primers, short DNA-strands that function as the starting points for DNA-polymerase
reaction, are designed based on the complementary sequences in the ends of the
amplified area, melting temperatures, lengths, and C/G content. It is important to
ensure that the primers are not complementary to each other, since primers binding
to each other may disturb the PCR or cause problems in signal detection, especially
if a label that binds to double-stranded DNA is used. (Kubista et al 2006.)
A basic PCR-cycle is conducted as follows: The reaction is first heated to
approximately 90 °C to get the DNA-strands to separate (denaturate) (Brownstein
2004). Temperature is then lowered to approximately 50-75 °C, depending on the
melting temperatures of the primers, to allow attaching (annealing) of the primers
(Mackay et al 2002; Brownstein 2004). This is a temperature where the original
strands would also anneal, but due to the excess of the primers, this is very unlikely
(Brownstein 2004). Finally, temperature is increased, depending on polymerase and
primers, to approximately 72-78 °C. This allows the polymerase to start extending
the strands from the primers by adding sequential nucleotides, thus creating
complementary strands to each of the original strands. The cycle is then started again
(figure 2). In an ideal reaction the number of DNA-strands doubles during each
cycle. Machines specifically designed for PCR are used for automated cycling, and
parameters, such as time for each step, rate of temperature change, and number of
cycles can easily be optimized for each assay. (Mackay et al 2002.) Confirming that
the PCR-reaction functions properly includes the use of controls. These usually
consist of a negative control, which is a sample that contains no target but is
otherwise similar in composition to the samples and ensures that nonspecific
products were not amplified, and a positive control, which is a sample that contains
target and ensures that the PCR amplifies DNA properly. (Espy et al 2006.)
9
Figure 2. Mechanism of PCR. The complementary DNA-strands are denaturated,
and primers attach to the ends of the amplified area (in green). Polymerase extends
the strands, which are again denaturated, and all strands function as templates during
the next round. At first nonspecific products that are obtained when primers are
extended to the ends of the strands are prevalent, but after the 5th cycle (Brownstein
2004) the specific products start to predominate.
1.2.2 Quantitative PCR
One of the biggest challenges in life science and basic clinical research is the
quantification of gene-specific mRNA. PCR can be used for amplifying DNA and
RNA, but a regular endpoint PCR, where the amount of amplified DNA is detected
10
at the end of a run, can usually only be used for detection. (Schefe et al 2006.)
Quantitative detection can be done with endpoint-PCR by amplifying different
dilutions for the same number of cycles, but this requires several samples and is
tedious and time-consuming, aside from having relatively large variation and
nonlinear decay plots (Schmittge et al 2000). A method called real-time PCR (qPCR)
is generally used for quantitative detection of target in PCR. qPCR is an accurate,
widely used, and reproducible method with a wide dynamic range. (Schefe et al
2006; Heid et al 1996.) It combines PCR chemistry and fluorescent probe detection,
allowing both PCR and product detection generally in less than an hour, which is
less than the time required for conventional PCR (Espy et al 2006). It also has
sensitivity and specificity equal to most other methods for DNA analysis, such as
Southern blot combined with conventional PCR. One major advantage of qPCR is
the isolation of reaction: since qPCR is performed in a closed tube, risk of
contaminating either the environment with the amplified product or the sample with
outside DNA is limited to the preparing of the reaction, and a closed reaction allows
the assay to be monitored closely. qPCR also requires much less hands-on time, and
testing it is much simpler than in conventional PCR. (Espy et al 2006.) Largest
disadvantages of qPCR compared to conventional PCR are the inability to monitor
size of the amplified target without opening the system, initial expenses in
equipment, incompatibility of certain platforms with some fluorogenic chemistries,
and relatively restricted multiplexing capabilities. Most of these problems are not
inherent to the techniques, rather than limitations of the current level of technology.
(Mackay et al 2002.)
Typically PCR has four kinetic stages roughly corresponding to the bacterial growth
curve, as seen in figure 3: lag-phase where received signal stays at background level,
logarithmic phase where amplification is exponential, retardation phase where
accumulation of inhibiting factors and loss of enzyme and substrates slows down the
reaction, and finally stationary phase where product is not amplified any more.
(Schefe et al 2006.) In qPRC the amount of DNA is measured after each PCR-cycle
by measuring the signal produced by a reporter molecule, and the absolute or relative
amount of DNA or RNA originally in the sample is deduced based on the rise in
signal (Schefe et al 2006).
11
Figure 3. Signal curve of a typical q-PCR run. Specific signal stays low during the
lag-phase, until the logarithmic phase starts at approximately cycle 27. Amplification
slows down from exponential to linear at approximately round 30, and stops at the
end in the stationary phase.
1.2.3 Methods for analysing quantitative PCR data
The threshold cycle (Ct), which is the cycle where the signal to background -ratio
rises above a predetermined level and thus signal separates from background, is the
most commonly determined value for the purpose of analysing amounts of target in
PCR. The threshold value must be set in the exponential phase of the signal curve in
order to be representative, but presuming that the curves behave normally, and thus
are parallel in the growth phase, the exact level should not be otherwise critical. The
predetermined level can be chosen either arbitrarily or through a computer algorithm.
(Higuchi et al 1993; Tichopad et al 2003; Winer et al 1999; Kubista et al 2006.)
Amounts of DNA or RNA in the samples can be determined either relatively, which
is often used for determining relative expression levels of genes, or absolutely, which
gives an estimate of the actual copy number in relation to the used standard (Pfaffl
2001; Bustin 2000; Dhanasekaran et al 2010; Winer et al 1999). The former is done
by comparing the Cts of the samples to either a standard amplified with the samples
or a standard curve, and the latter utilizes a standard plot determined with standards
that contain known amounts of target sequence. Absolute determination is
considered to be more reliable, but in many cases relative determination is sufficient.
(Luu-The et al 2005; Dhanasekaran et al 2010.)
12
Efficiency of PCR, essential in quantification, tends to be a difficult factor due to
variation: empirically it is generally 65-90%, when ideally it would be 100%.
Variations between runs can cause significant errors in estimates of quantities of
DNA or RNA, when the quantities are determined relatively from Cts alone, which
assumes perfect efficiency, or standard curve, which assumes constant efficiency.
The use of standard also assumes that the amplification efficiencies of the standard
and the samples are the same. (Tichopad et al 2003; Pfaffl 2001; Bustin 2000; Winer
et al 1999; Dhanasekaran et al 2010.) Efficiency of PCR can, however, be calculated
with several different methods. One of the most commonly used methods utilizes
linear regression: efficiency is assessed by obtaining the Cts of a series of dilutions
and plotting them against either the logarithm of the copy number, sample
concentrations, or template numbers, and fitting in equation 1.
k log( N 0 ) Ct (1)
From this efficiency of the PCR is calculated according to equation 2,
E
10
1
k
1 (2)
and the y-intercept corresponds to a Ct of a sample containing a single target.
(Rutledge and Cote 2003; Schefe et al 2006.) Same type of plot can also be utilized
as a standard curve (Dhanasekaran et al 2010).
Suitable standards for PCR include amplified target sequences, plasmids containing
the target sequence, and commercially prepared DNA (Dhanasekaran et al 2010).
The use of a standard has to be monitored carefully, since the stability of stored
standards may degrade over time. PCR-products are especially vulnerable, although
plasmids are relatively stable when stored at -20 °C. (Dhanasekaran et al 2010;
Winer et al 1999.) When gene expression is measured through RNA, a gene with
constant levels of expression, also known as a housekeeping gene, can be used as a
standard for relative quantitation (Heid et al 1996). The standard curve method has
its problems: standard curve often covers only two orders of magnitude whereas an
adequate determination of efficiency requires at least 3-5 orders of magnitude for
valid anchoring of the trendline. This leads to vulnerability to pipetting errors and
inter-well variations. Standard curve method also consumes a lot of reagents and
wells during each PCR run. (Dhanasekaran et al 2010; Winer et al 1999.) Generally
the best option is to use a new calibration curve during each analysis, but since this
requires much effort, other options have been developed. Use of a single “master”
13
calibration curve obtained from different runs over the course of the study has been
reported to lead to only small changes in slope but significant changes in intercept
values. This is most likely due to small daily variations, so run-to-run variations tend
to be ignored when using single curve. The preferred method for making a standard
curve depends largely on the size of the study, but in large studies where unknown
samples are used as standards the only option is a master curve, which will lead to
increased uncertainty of intercept. Factors such as standard source, nucleotide base
composition, method for concentration determination, dilution preparation, and
storage conditions can affect the reliability of a standard curve. (Sivaganesan et al
2010.) Important in use of mean value of efficiency is the standard deviation of
determined effectiveness, but generally averaged efficiency works well since they
tend to be normally distributed, if standard deviation of efficiency is less than 0.02
(Schefe et al 2006).
Generally, qPCR should be tested in a so-called melting curve analysis to ensure that
only one product is amplified. In a melting curve analysis the temperature is
increased steadily after strands extended. This gives a dissociation peak, the point
where double-stranded DNA separates to two single strands, for each
oligonucleotide within 2°C of its melting temperature. This can be determined since
fluorescence decreases steadily due to the increasing thermal motion, but when
melting temperature is reached, it drops sharply. Since the melting temperature is
dependent on G/C content, length, and sequence of the DNA, each reaction should
produce a single peak if there is only a single product. (Kubista et al 2006; Nygren et
al 1998; Ririe et al 1997.) A melting curve analysis ensures accurate baseline and
threshold settings especially in assays utilizing DNA-intercalating dyes (Schefe et al
2006).
1.2.4 Detection in PCR
Methods for detecting DNA in endpoint PCR are varied and include radiolabelling,
agarose gel run with staining, and probing of the product with a labelled molecule,
but using fluorescent molecules as labels is the standard method in detection of
qPCR (Kubista et al 2006; Schmittge et al 2000). Fluorescent technologies include
probe sequences that fluoresce on hydrolysis (such as TaqMan) or hybridization
(Such as LightCycler), fluorescent hairpins, and intercalating dyes (such as SYBR
14
Green), all of which require less target, have wider dynamic range, and are more
resistant to non-specific amplification than many traditional labels (VanGuilder et al
2008; Mackay et al 2002; Wittwer et al 1997). Currently the most commonly used
methods of detection rely upon fluorescence resonance energy transfer, and involve
hybridization of a complementary probe to the amplified target strand (Mackay et al
2002). In multiplexed qPCR multiple amplified products can be detected in a single
reaction by using labels that give different signals. This tends to be limited by the
comparatively low number of different labels and the use of monochromatic lighting
sources. (Mackay et al 2002.)
SYBR Green I, [2-[N-(3-dimethylaminopropyl)-N-propylamino]-4-[2,3-dihydro-3methyl-(benzo-1,3-thiazol-2-yl)-methylidene]-1-phenyl-quinolinium]+ tends to give
a more linear decay plot and be more precise than some other commonly used
methods, such as TaqMan (Schmittge et al 2000; Zipper et al 2004). As an
asymmetric cyanide dye SYBR Green is an intercalating dye and does not fluoresce
unbound, since the energy it receives from excitation is released as heat. On binding
to double-stranded DNA the movement of the molecule becomes restricted and
energy is released as fluorescence. (VanGuilder et al 2008; Nygren et al 1998.)
Several SYBR Green dye molecules bind to one strand of DNA, which means that
measured signal is based on the mass of DNA, not number of multiplied strands
(VanGuilder et al 2008). The binding is also very slightly affected by nucleotide
sequence, but the label is valid when used with samples that are compared at the
same levels of fluorescence in the absence of interfering DNA (Kubista et al 2006).
Intercalating dyes tend to be inexpensive and nonspecific, so they can be used for
any reaction. However, this also means they do not discriminate between sequences,
so they cannot be used in multiplexed assays, and primer-dimer artifacts and
incorrect polymerization products cause increased background. (VanGuilder et al
2008.) Primer-dimer formation can, however, be countered by adding a short hightemperature incubation before detection (Dhanasekaran et al 2010; Mackay et al
2002). SYBR Green I can be successfully used in many assay types thanks to its
temperature stability, favourable photophysical properties, selectivity for doublestranded DNA, and high sensitivity. It is also easily optimizable and has a wide
dynamic linear range. (Zipper et al 2004.)
15
1.2.5 PCR in blood
DNA extraction is costly, time-consuming, easily cross-contaminated, samplespecific, unsuitable for automation, and can potentially lose target microorganism or
nucleic acids. This means that when for example bacteria need to be detected from
blood, conducting PCR directly from blood would be advantageous. (Al-Soud and
Rådström 2001; Bu et al 2008.) PCR can, however, be inhibited by several
conditions and substances, leading to false-negative or low-sensitivity reactions.
Even after purification some inhibitors tend to still remain probably due to
incomplete removal of inhibitors, as some inhibitors can copurify with DNA. In
addition, removing inhibitors tends to be difficult, time-consuming and laborintensive, and can lead to loss of DNA or risk of contamination. (Al-Soud and
Rådström 2001; Nishimura et al 2000; Zhang et al 2010.) Inhibition can be caused
by interference in DNA extraction by inhibition of lysis of cells, degradation or
capture of nucleic acids, polymerase stability interference, interaction with singlestranded DNA, or chelation of magnesium ions (Wilson 1997). Inhibitors in blood
include haemoglobin in red blood cells, lactoferrin in leukocytes, leukocyte DNA,
and anticoagulants such as EDTA, sodium citrate, and heparin, although heparin may
not inhibit the detection of unpurified DNA from whole blood (Chen et al 1990;
Burkhardt 1994; Al-Soud and Rådström 2001; Morata et al 1998; Al-Soud et al
2000). Plasma contains numerous PCR-inhibiting substances, most importantly IgG
(Al-Soud et al 2000). In previous studies all fractions of blood have inhibited PCR at
volumetric fractions larger than 0.002%, erythrocytes have inhibited at 0.4%,
leukocytes at 4%, thrombocytes at 0.8%, 0.004% of whole blood has been enough to
inhibit most of the common Taq-type polymerases, and 20% blood has stopped PCR
entirely (Al-Soud and Rådström 1998; Al-Soud and Rådström 2001). Also other
factors in reaction can inhibit PCR, as for example commonly used SYBR Green I
dye is inhibitory to Taq enzyme (Nath et al 2000).
Blood samples are used extensively in a variety of types of diagnosis, and attempts
have been made to enhance or otherwise improve performance of PCR when blood
samples are used. Often, though, these corrections only improve one aspect of the
problem (Zhang et al 2010). Changes in conditions of PCR that have seen some
success include optimizing salt balance or pH of the buffer. Increasing the pH causes
possibly interfering proteins to become negatively charged, so they reject the
16
negatively charged DNA. (Nishimura et al 2000; Burkhardt 1994; Bu et al 2008.)
Other reagent coctails have also been made for suppression of inhibition, but their
other possible effects on detection have not been studied very thoroughly (Bu et al
2008; Nishimura et al 2000). Some cocktails increase the resistance to plasma, and
BSA reduces the effect of inhibition (Al-Soud and Rådström 2000; Zhang et al
2010). Also some modified polymerases are more resistant to inhibition
(Kermekchiev et al 2009). Most treatments are complicated, have high requirements,
or are otherwise difficult (Nishimura et al 2000).
Use of pure blood components or otherwise handled blood samples instead of whole
blood offers several advantages also in other uses than just PCR, so several methods
exist for handling of blood samples (Al-Soud and Rådström 2001; Petersson et al
2007). Acquiring pure fractions of blood is, however, often difficult, timeconsuming and costly as the preparation methods have to be gentle to avoid
damaging cells and possible contamination. Commonly used methods include
centrifugation, chromatography, electrophoresis, and immunoisolation. These often
include time-consuming steps, non-continuous operations, low throughput, or risks
of damaging biological samples due to stress or electrostatic fields, so simple, lowcost, high-throughput, efficient, and gentle methods are of great importance.
(Petersson et al 2007.) A relatively new method that fulfils most of these
requirements is microchannel acoustophoresis.
1.3 Microchannel acoustophoresis
Manipulation of microsized objects in defined patterns is essential in many lab-onchip applications, and it can be done with various techniques. Traditionally these
include optical, magnetic and optoelectronic tweezers and dielectrophoresis, each of
which has its advantages and disadvantages. (Lin et al 2012.) A more recent option
for particle manipulation is acoustophoresis, defined as migration with sound, which
can be used for both macro- and microsized applications. Microchannel
acoustophoresis refers to a system where microsized particles that are suspended in
fluid are manipulated with acoustic radiation forces in a continuous flow
microchannel. The paths of the particles that flow with the suspending fluid are
affected through force stemming from local distortion in a resonant sound field,
which in turn is caused by scattering from the particles. (Augustsson 2011; Lin et al
17
2012.) Microchannel acoustophoresis is commonly used for particle enrichment,
separation, and trapping, and suspension clarifying. Applications use either particle
transfer between fluids, separation of particles based on their acoustophysical
properties, retention against the flow, or aggregation of particles in defined locations.
(Augustsson 2011; Petersson et al 2005b; Lenshof et al 2012b.) Acoustophoresis is a
technique of particular interest since it offers a non-contact mode of particle
handling, which allows manipulation of cells without harming them since
mechanical stress applied by acoustic forces is minimal (Laurell et al 2007). It can
also be used in a continuous mode, which allows handling of larger volumes than
many other preparation systems, such as centrifugation. This makes microchannel
acoustophoresis a suitable technique for preparation of biological samples prior to
bioanalysis. (Augustsson et al 2009.)
1.3.1 Acoustic standing waves and forces
Manipulation of particles in microchannel acoustophoresis requires resonance
patterns of sound, which are created in the fluid inside the microchannel with one or
multiple ultrasonic transducers (Augustsson 2011; Laurell et al 2007). The force
induced on particles in an acoustic standing wave field results from primary axial
radiation force (PRF) and secondary radiation force (SRF), which are caused by
standing waves and sound waves scattered by particles, respectively. In general PRF
has a strong effect on the particles, while SRF affects only over short distances. PRF
causes particles to move towards either the node or antinode of the standing wave,
and in a microsized system it can be simplified to equation 3,
FAx
P 2Vc
2
w
5
2
p
2
p
f
f
p
f
where P = acoustic pressure amplitude,
vector, Vc = particle volume,
sin( 2kx) (3)
= density,
= compressibility, k = wave
= acoustic wavelength, and x = distance from
pressure antinode in the wave propagation axis, and
p
and
f
denote particles and
carrier fluid, respectively. (Laurell et al 2007; Augustsson 2011; Lenshof et al 2012a;
Lenshof et al 2012b.) PRF is also dependent on voltage (Nam et al 2011). SRF tends
to be more important in aggregation and sedimentation, particularly after particles
focus due to PRF, enhancing aggregation according to equation 4 for force between
particles,
18
FB ( x )
4 R
6
(
p
f
) 2 (3 cos 2
6
f
d
4
1)
2
2
v ( x)
f
(
p
9d
where d = distance between particles, p = pressure, and
f
2
)2
p 2 ( x ) (4)
= angle between the centre
line of the particles and the direction of propagation of the incident acoustic wave.
(Laurell et al 2007.) The net acoustic force is the result of these force components,
with large particles moving faster and high frequencies generating high acoustic
forces (Lenshof 2009).
A particle in the acoustic field will be affected by a force in the direction of acoustic
pressure gradient. The particle will then move either to a point of minimal pressure
variations or to a pressure maximum, as described by PRF. Acoustic radiation forces
are present if the acoustic properties of the particle do not match those of the carrier
fluid, as this creates a local distortion in the field due to scattering from particle. The
distortion causes time averaged pressure differences on the surface of the particle,
directing its motion. In a half wave resonator, one of the most commonly used
resonator types, acoustic resonance agglomerates most particles along a central
pressure node line in the channel. In this configuration, frequency (f) of the
transducer should be chosen so that the channel width (w) is a multiple of half the
acoustic wavelength ( ). (Augustsson 2011.)
2w
, n = 1, 2, …
n
f
c0
c0 n
2w
The acoustophoretic velocity field varies also along the length of the channel. In
experimental situations resonance patterns are generally complex, although for
design purposes a 1D wave approximation is reasonable. The force on a particle is
counteracted by friction, which creates a size and shape dependency. (Lenshof
2009.)
A standing acoustic wave in a fluid also invariably causes the fluid to move in
circular patterns within the flow, known as acoustic streaming. Streaming is caused
by absorption of the acoustic energy by the fluid, as this causes inhomogenity in the
ultrasonic wave field, which in turn forms pressure gradients in medium. Streaming
occurs mostly due to the energy losses in the viscous boundary layers along the
walls, where flow velocity ultimately becomes zero. Streaming is usually
19
disadvantageous, outside of the cases where the purpose is mixing the particles, since
it disturbs the linearization of particle streams. Streaming starts to have a significant
effect on particles that are 1 µm or larger in diameter. (Lenshof 2009; Lenshof et al
2012a; Augustsson 2011.)
Currently there exists no experimentally verified integrated model for microchannel
acoustophoresis that combines the effect of sound on microchip and water-filled
chamber, so simplified models for sound travelling in chip and the resulting pressure
field inside the channel are used instead. The acoustic radiation force on a
compressible spherical particle can be derived based on the acoustic field, which
allows determining of particle trajectories. (Augustsson 2011.)
1.3.2 Acoustic resonators
Construction of an acoustic resonator is a complex task, if optimal coupling of
acoustic energy into fluid chamber is to be achieved. In a well-designed resonator
chamber particles will be sharply focused in single or multiple pressure nodes at the
fundamental resonance frequency, forming a confined streaming bands of particles.
(Laurell et al 2007.) There are three main types of acoustophoretic resonators:
layered, transversal, and surface acoustic wave (SAW) resonator. Layered resonator
is complex to build and requires careful control of layer thickness, but allows use of
acoustically non-optimal materials, whereas transversal resonator has the exact
opposite requirements. Resonance in SAW relies on waves propagating into a fluidic
compartment via a wave-guiding substrate, so fluid and the enclosing material
should be acoustically similar. (Lin et al 2012: Lenshof et al 2012b, Lenshof et al
2012a.) All three are often used as half wavelength resonators with a pressure node
located in the middle of the flow channel and antinodes in the channel walls
(Lenshof et al 2012a). The transversal resonator, where acoustic manipulation
happens in plane with the chip and sound is reflected between channel walls, is the
easiest to use for visual observation of the focusing, in addition to being simple to
integrate with other microchip-based fluidic functions (Lenshof et al 2012b).
An acoustophoretic microchip consists of channels etched on a microchip, inlets and
outlets for the fluids, and a lid bonded on top (figure 4) (Augustsson 2011).
Microfluidic channels, in contrast to generally cross-sectionally circular macrofluidic
20
channels, are most often trapezoid or semi-circular. The design of the parts where the
flow splits is important, as sharp features are likely to increase streaming and bubble
formation. For this reason side channels are often in a 45 angle from the resonator
channel. (Laurell et al 2007; Lenshof et al 2012b.) The lid is most commonly made
of glass since this allows the observation of the flow. Glass and silicon also form an
anodic bond and as such do not need any adhesive between them, which increases
reproducibility, and the strong bonding allows the layers to resonate as one body.
(Lenshof et al 2012a; Lenshof et al 2012b).
Figure 4. Acoustophoretic double-y chip (2Y). The fluids that flow in the chip are
injected and extracted through inlets and outlets (side outlets/inlets 3 and 5, and
central outlets/inlets 2 and 6), and while the fluids flow in the main channel (4),
acoustophoresis is induced with a transducer (1).
A transversal half-wave resonance acoustophoresis microchip is made of materials
that transmit sound waves at low losses and have good acoustic reflection properties
(Laurell et al 2007). A commonly used material is silicon, which allows fabrication
of very precise structures and has a good thermal conductivity (Laurell et al 2007;
Lenshof et al 2012a). Since silicon is, however, a relatively expensive material and
the masking process requires great precision, alternative materials have also been
looked into. One of these materials is glass, which has optimal transparency,
hydrophilic surface properties, electrical insulation, good chemical resistance, and
low acoustic attenuation. (Evander et al 2008.) In addition it allows several
microscopy techniques silicon is unsuitable for. Glass has been successfully used in
manufacturing chips, even if the performance has not been quite as good as for
silicon (Evander et al 2008; Lenshof et al 2012a). Another possible material for chip
fabrication is steel, which has high density, good sound propagating properties, and
good heat transport capacity, although the fabrication process is necessarily different
than the one used for silicon and glass. Polymers are cheap and easy to produce and
21
mold, but they have poor acoustic reflective properties and as such can mostly be
used in SAW with two resonators. (Lenshof et al 2012b.)
Ultrasonic standing waves can be generated in the chip either with two opposing
ultrasonic transducers, with a single transducer facing a sound reflector (Laurell et al
2007), or by using a transducer that excites the whole chip and forms resonance
patterns between the channel walls. (Evander et al 2008) Standing waves can be
formed between top and bottom or side walls, or as a combination of these two
modes. (Evander et al 2008) The ultrasonic transducer is typically a piezoelectric
element, usually a lead zirconate titanate transducer (Augustsson 2011, Lenshof
2009).
1.3.3 Acoustophoretic microchannel systems
When particles are transferred from one medium to another, volumetric flow rates
and acoustic energy density are adjusted so that the focused particles reach the centre
of the channel just before the channel forks at the end of the chip. If a particle has a
smaller radius than the focused particles, it remains in the original medium, since the
transverse velocity of a particle is proportional to the square of its radius.
(Augustsson 2011.)
Contaminants in the central outlet can usually be attributed to diffusion of the
streams, acoustically induced streaming in lateral cross-section of flow, non-specific
binding of contaminants to particles, hydrodynamic interaction between particles and
fluid, and perturbations in flow due to bubble formation or oscillations in external
fluids. When contamination is minimized all of these aspects should be considered.
This means using high flow and optimized acoustic energy so that particles are
focused as precisely as possible for minimization of acoustic streaming, although it
should also be noted that at higher flow speeds particles have less time to be in the
standing wave field, which may cause a decrease in separation efficiency.
(Augustsson 2011; Evander et al 2008; Laurell et al 2007.) Hydrodynamic
interactions result from perturbations in fluid velocities caused by particles that are
dragged through fluid, so when a particle is focused, it can drag contaminants with it.
The relation between the effect on the contaminant and the number of particles is
linear, and when it becomes substantial related to the particle velocity, particles can
22
be seen as hydrodynamically coupled. This is reached when concentration of 1 µm
beads exceeds 108/ml, with means that the interparticle distance is approximately 20
µm. At concentrations 1010/ml particles of this size are fully coupled and drag along
all of the intermediate fluid. (Augustsson 2011.) Cross-contamination between
channels can be minimized by balancing the exit flow rates so that diffusional
mixing zone in the clear medium is directed to waste outlet (Laurell et al 2007).
Contamination can also stem from minor disturbances in flow, usually originating
from syringes, syringe pumps, and bubbles (Petersson et al 2005b).
The trajectory of a particle in a microfluidic system is an intricate function of the
acoustic field, affected by the radiation force potential, acoustic streaming, and flow
profile. The initial position of the particle has a large effect on the trajectory.
Parameters that affect the exit position of the particle are particle size, particle and
fluid mass density and compressibility, fluid viscosity, flow rate, channel geometry,
initial position of particle, pressure field, and velocity field. Two particles can, in
theory, be separated if their acoustofluidic properties differ, although the separation
is likely to be more complicated. This is because the attributes will likely have
variation, such as size distribution, and acoustophoretic velocity of two different
particles can be same in a given suspending fluid, so separability cannot be
anticipated very easily. On the other hand, two subsets of a population may be
separable due to these variations. (Augustsson 2011.)
Since the initial position of the particle in the channel has an effect on the trajectory,
it often has to be controlled in order for the particles to focus as wanted. This can be
done by adjusting properties of the suspending fluid, directly adjusting the starting
position, or by running the samples several subsequent times through the system,
although the latter is a complicated method and will likely result in a major loss of
particles. If pre-alignment is used instead of fluid optimization, the focusing has to
be built into the chip. The greatest problem with pre-alignment is a decreasing in the
concentration capacity of the chip. The pre-alignment system has also not been
studied very much, so absolute measurements of acoustic energy density cannot yet
be done with it, although the presented method is still valid for system calibration.
(Augustsson 2011.)
23
1.3.4 Microchannel acoustophoresis in applications
Acoustophoresis is commonly used in macroscale particle manipulation. This
includes enhanced sedimentation in fluidized bed systems, which could also be used
in enhancing the performance of immunoassays (Lenshof et al 2012a; Evander et al
2008). Sedimentation due to gravity starts to dominate when particle diameter
exceeds 20 µm, so that is usually the maximum limit for microscale applications
(Lenshof et al 2012b). Scaling laws prefer downscaling since forces affecting
particles in acoustophoresis are directly proportional to particle volumes and used
frequencies, and when resonator dimensions are reduced, as they are in a microchip,
frequency increases (Lenshof et al 2012b; Laurell et al 2007; Evander et al 2008).
This results in better separation efficiency and particle focusing (Evander et al 2008).
Separation is therefore ideally used for particles that are 1–10 µm in diameter
(Laurell et al 2007). Particles of this size need increased frequencies for PRF to be
large enough to affect them, and other forces, such as viscous drag, may begin to
dominate. Increase in frequency may in turn require the use of multiple node
approach as the wavelength increases. (Lenshof et al 2012b.) The size of particles in
microchannel acoustophoresis is limited at the upper end also due to channel sizes
(Evander et al 2008).
When acoustophoresis is utilized in microscale, one great advantage is the
predominantly laminar flow, making the splitting of flow easy and efficient in
continuous flow particle separation. Integration to advanced microfluidic network
and downstream analytical components is also easy. (Evander et al 2008.)
Microchannel acoustophoresis is very advantageous in that the fundamental acoustic
forces can be governed with solely the geometric dimensions and materials used in
the chip, and as such are mostly independent of biologically important factors such
as pH, ionic strength or surface charge. (Lenshof et al 2012b.) In addition,
controlling the system tends to be much easier than with the alternatives, plus the
tech is relatively easily integrated into systems (Lin et al 2012). Acoustophoretic
systems have been shown to match well other available techniques in terms of
efficiency (Lenshof and Laurell 2010).
In microchannel acoustophoresis the type of application is important since it defines
the type of chip that is used. This means, aside from resonator structure, the structure
24
of the chip. Many different types of chips are available for different uses, with for
instance different numbers of forks in the channels depending on the number of
different particles that need to be separated. (Augustsson 2011.) Type of application
also defines the used mode; for example particle separation can be easily done with
free flow acoustophoresis, where a net acoustic force separates particles and directs
them to pressure nodes as they flow in fluid (Lenshof et al 2012a).
All types of particles can be affected by ultrasonic standing wave forces presuming
their acoustic properties differ from those of the suspending fluid. It has been shown
that ultrasonic manipulation of bioparticles is harmless to the particles themselves,
so acoustophoresis offers possibilities for new high-performance platforms for
manipulation and separation of cells. One of the concerns raised regarding
microchannel acoustophoresis is the limited throughput. This is problematic since
perhaps the most frequent uses for acoustophoresis would be in preparatory areas,
such as separating different cell types from blood, where handled volumes would be
relatively large. (Laurell et al 2007; Lenshof et al 2012a.) One way around this
problem is the use of parallel channels, which can in certain methods be done with
one transducer, which allows for very easy upscaling in terms of volume (Evander et
al 2008; Laurell et al 2007). Input power can also be increased for a stronger field,
although this may heat up the chip through thermal losses in transducer. The heating
can in turn be countered with Peltier elements or heat sinks. (Evander et al 2008.)
Cooling the chip can also be used when handling heat-sensitive samples (Laurell et
al 2007). In general volumetric flow rates of a few hundred microliters per minute
are sufficient for separation (Evander et al 2008).
1.3.5 Acoustophoresis of blood
Separating and concentrating different components of blood is required in many
areas, and blood handling by removal of excess lipid particles was one of the first
applications for acoustophoresis, which can also be used for separating other
components of blood efficiently (Laurell et al 2007; Petersson et al 2005a). The
greatest limitation of using acoustophoresis on blood tends to be that only
concentrations of approximately 5-10% by volume can be run for acceptable
separation efficiencies, and diluted plasma fraction is usually not representative or
useful for clinical evaluation. The same problem is also encountered in other
25
microfluidic devices, with clogging and overload being common. Systems that can
handle high concentrations of blood are available, but they often have very low
throughput (Lenshof et al 2009). There are, however, some acoustophoretic
microchips that can handle high concentrations of blood through use of sequential
outlets that prevent clogging of the main outlet (Lenshof et al 2012a). Washing
efficiency for a simple particle transfer in blood is in generally 95% for both cell
focusing and contaminant removal (Petersson et al 2005b).
Plasma is a common target for separation. It can be separated by using filtration or
plasma skimming, in which blood is run in a channel at low speeds without
ultrasound and plasma is gathered from the sides of channel. Centrifugation is the
most commonly used technique for obtaining plasma, but it has its drawbacks: it
cannot be integrated into a total analysis system, requires large volumes of blood,
and it can cause contamination of plasma, since cells may rupture due to the high
shear forces. (Lenshof et al 2009; Petersson et al 2005b.) Acoustic forces can also be
used for plasma separation, and unlike centrifugation, they can also be used in
continuous processe. (Lenshof et al 2009). There are many potential uses for
microchip-based plasmapheresis, such as biomarker analysis for diagnosis of cancers
or monitoring of disease progression, and detection of low abundance proteins
(Lenshof et al 2009).
1.4 Goals of the study
The study is a part of the ACUSEP project for developing a diagnostic blood sample
based assay for sepsis caused by bacteria. The intention in the assay is to first
separate bacteria and the blood cells with acoustophoresis (figure 5), then
concentrate the bacteria by acoustic trapping, and afterwards detect the bacteria by
using PCR. The 20 most common bacteria are meant to be detected indiscriminately
and the 5 most common specifically.
26
Figure 5. Removing red blood cells from blood that contains bacteria with a 2Y-type
chip. Clean buffer is run from the central inlet (2) and blood from the side inlet (1).
Red blood cells are directed to the central channel, leaving other particles in the
remaining plasma. Red blood cells are collected as waste from the central outlet (3)
and plasma with bacteria is collected for analysis from the side outlet (4).
This study aimed at determining the requirements for sample handling before PCR.
This meant determining the detection limit for bacteria in PCR, the inhibitory effects
of different components of blood in PCR, the effect of acoustophoresis on bacteria,
capacity of blood cell removal for acoustophoresis, and how being suspended in
blood affected the behaviour of bacteria in acoustophoresis. Most importantly, the
combined result was meant to tell how effectively acoustophoresis needs to remove
from blood the components that inhibit PCR so that PCR works reliably. The study
was also done in order to establish a reliable method for estimating the amount of
bacteria in a sample that contains traces of blood after it is run through
acoustophoresis, and in particular the relative amounts of bacteria in output samples.
The method was intended for later stages of the project for both observing the
capabilities of the PCR and how those relate to other methods of detecting bacteria.
Since the method was not intended to be used for the final assay, used primers were
for bacteria in general and the signal detection method did not yet use specific
probes.
27
2 Materials and methods
Evaluation of the inhibitory effect of blood fractions on PCR and the capability of
acoustophoresis for removing them required first ensuring the functionality of both
acoustophoresis and PCR. Inhibitory effects of blood components were tested
separately, and finally PCR and acoustophoresis of a spiked blood sample were
combined.
2.1 PCR-protocol
All PRC-reactions were run in a GenomEra real-time PCR machine (Abacus
diagnostica, Finland) using sterile PCR-chips for GenomEra, also manufactured by
Abacus. The volume of the PCR-reaction mixture added to each chip was 35 µl, of
which the volume of analyte was 1.13 µl and the rest consisted of reaction mix.
Reaction mix contained SYBR Green I mastermix (KAPA biosystems, USA),
diluted in sterile water according to the manufacturer’s instructions, and bacterial
16S-DNA specific primers (U4-primers, University of Turku), each primer at
concentration 0.2 µM. The PCR-protocol that was used is presented in table 1. Both
mastermix and primers were stored at -20 °C, while water was kept at room
temperature. All reagents were aliquoted in order to prevent contamination and avoid
repeated freeze-thaw cycles.
Table 1. The PCR protocol. The number of repeats for each cycle is indicated in the
protocol phase column. The peltier-step is not needed in PCR, but it is part of the
internal device controls.
protocol phase
denaturation
non-measuring
cycle
x 10
measuring
cycle
x 34
step
peltier
extension
denaturation
annealing
peltier
extension
measure
denaturation
annealing
time (s) temp ( C)
150
100
1.7
27
20
60
7.2
108
4.5
100
1.7
27
20
60
1
60
7.2
108
4.5
100
28
Label excitation and signal emission measurement were done at the wavelengths 494
and 525 nm, respectively.
GenomEra has a maximum limit of four PCR-chips per run, so each sample set
required several PCR-runs. Samples were run so that those samples that were
especially compared in each assay were run in the same run. Negative controls and
positive controls, same sterile water as was used in dilutions and pure diluted
bacterial culture, respectively, were included in each sample set. A minimum of two
chips per sample were run in each set of PCR-runs for a group of samples, and each
set of PCR-runs was done with at least two sets of samples collected separately in
order to ensure the reliability of the results. The GenomEra that was used in this
study was an experimental model, which means that for example temperatures may
have fluctuated somewhat during PCR.
2.2 Bacterial cultures
Escherichia coli (E. coli) strain DH5
(Invitrogen, Finland) was used in all
experiments that involved bacteria. The bacteria were cultured in liquid LB-medium
(tryptone 10 g/l, yeast extract 5 g/l, NaCl 10 g/l) with 100 µg/ml ampicillin, and on
solid LB plates (liquid LB with agar 15 g/l), which were both stored at +4 C.
Bacteria cultivated in liquid culture were used as samples, and plate cultures were
used for long-time storage of one month at maximum. Cultures were started by
inoculating bacteria from either a plate cultivation or liquid medium cultivation from
previous day to liquid medium. Bacteria were grown overnight without shaking at
+37 C and used on the following day. In addition to detection by PCR the bacteria
were counted under a microscope in suitable dilutions (1:10–1:1000) using a
Neubauer 1 x 1 x 0.02 mm counting chamber, and by plating when standard curves
for PCR were made. The concentrations of bacteria were estimated both by counting
and from standard curve, since although in all tests in this study the purpose was
mainly to estimate relative concentrations of the bacteria, comparing calculated and
observed counts of bacteria gives a better estimate on the validity of the standard
curve. To stop the bacteria from growing (Lichstein and Soule 1944), 0.05% NaN3
was added to the cultures that were to be used for PCR, acoustophoresis or cell
counting in the counting chamber.
29
2.3 Bacterial DNA and whole bacterial cells in PCR
Isolated E. coli DNA (stock 90.5 ng/ml, University of Turku) was run in PCR to
ensure that the PCR functioned properly and the PCR-protocol was optimal for the
used label. It was also used in determining levels of contamination in the areas where
the mastermix was prepared, where the samples were pipetted, and where the
acoustophoretic runs were conducted (molecular biology laboratory and cleanroom
at department of electrical measurements, Lund University), as the PCR is supposed
to detect all bacterial DNA, and bacterial DNA is found in almost everywhere. This
was done by pipetting background samples into reaction mixes at each of the areas.
Whole E. coli cells were similarly tested in PCR in order to compare behaviour of
whole cells and DNA in PCR. Standard curves were made for both whole cells and
DNA by running dilution sets of bacterial culture or DNA-solution that had
approximately 100 to 10 000 copies of DNA or bacteria per chip. The whole cell
standard curve was used in subsequent tests for estimating the amounts of bacteria in
samples. Average background contamination was calculated based on all negative
controls, and detection limit for bacteria was determined as background plus three
times the standard deviation of background.
2.4 Inhibition of PCR by blood plasma
All blood used in experiments was obtained from healthy donors at Lund university
hospital, unit of Haematology. Blood was stored in tubes containing citrate as
anticoagulant. The citrate was tested in PCR to ensure that it had no effect on the
signals. Blood was kept at room temperature and used within 10 h of obtaining it.
Blood plasma was tested in the PCR to evaluate the level of inhibition. Plasma was
obtained from blood by centrifuging it at 1500 g for 10 min and collecting plasma
from the top by pipetting. For storage plasma was kept at -20 C in aliquots, and
defrosted plasma was mixed by vortexing. Possibly remaining solid particles were
concentrated to the bottoms of the tubes by spinning, and plasma for testing was
collected from the top. Plasma dilutions were tested both for background and spiked
with bacteria, and the results were compared to corresponding PCR-runs which had
plasma replaced with sterile water. Plasma was tested in concentrations between
0.003% and 3.2% of the volume of PCR-reaction.
30
2.5 Inhibition of PCR by red blood cells
Red blood cells (RBC) were tested in PCR to evaluate the strength of inhibition.
Blood was processed like plasma, as described in chapter 2.4, except RBCs were
obtained from the RBC layer, situated below the layer of white blood cells, after the
removal of plasma. The volume of centrifuged RBC was estimated to be 50% of the
volume of whole blood by counting in the counting chamber, and dilutions were
adjusted accordingly so that the final dilutions were in relation to the concentration
in whole blood. Dilutions were done in sterile water and plasma with the following
method to simulate the effect of acoustophoresis: RBCs were first diluted in plasma,
then to the final dilution in water so that the concentration of plasma was 0.03 or
0.003%. Tested RBC dilutions were between 1:15 000 and 1:3 100 000, and they
were tested both for background and spiked with bacteria.
2.6 Inhibition of PCR by whole blood
Diluted whole blood was also tested in the PCR to estimate the effect of other
components of blood. The method of dilution was the same as with RBCs, again to
simulate the effect of acoustophoresis. Tested blood dilutions were between 1:310
and 1:3 100 000, and they were again tested both for background and spiked with
bacteria.
2.7 Acoustophoresis
All acoustophoretic runs were done in the same way: The acoustophoretic system
consisted of a double-y microchip (channel width 375 µm and depth 150 µm,
produced in Lund University) and an attached 2 MHz piezoelectric transducer, with
pump-actuated syringes attached to the inlets and 100 µl sample loops to the outlets,
all connected with ø 0.5 mm tubing. Bacterial and/or blood solution was injected
through side inlet while sterile water was injected through the central inlet. The
system was cleaned before and after each sample by flushing it with 0.05% sodium
hypochlorite, 70% ethanol and finally sterile water, 2 ml each. Sample loops were
flushed separately like the main system just before sample collection was started,
31
and the system was balanced by letting the sample and water flow through the
system before samples were collected.
All samples that were run in acoustophoresis were diluted in UV-sterilized, filtered
water, which was tested to ensure that it behaved in the PCR like the sterile water
used for other dilutions. Times for sample collection were tested for optimization:
less than 10 min was used for both system balancing and sample collection at first,
and later 25 min for each. Runs were made with ultrasound either on or off, and flow
speed was usually 20 µl/min for each inlet. Exceptions and corrections to other
parameters are stated in the descriptions of the tests. Two different chips were used:
the first at 1.83 MHz frequency, 4.8 V voltage and the second at 1.89 MHz
frequency, 2.2 V voltage. The slight differences in these values were due to the
individual variation in chip manufacturing. The first chip was used, due to the
breaking down of the second chip, for the combined runs of blood and bacteria, as
described in chapter 2.10, and the second chip for all other runs. These voltages and
frequencies were optimized for the chips by running 1:10 diluted blood through them
and adjusting the parameters so that RBCs concentrated to the central channels. The
differences in parameters likely result from the minor differences in the chips and
transducers. The flow was observed under a weakly magnifying microscope during
optimization, so the optimization was not necessarily perfect. The bacterial cultures
that were used were diluted to 1:100, so their concentrations were approximately
1000 copies/µl.
2.8 Acoustophoresis of bacteria
The effect of acoustophoresis was first tested with bacteria. Bacteria were run with
ultrasound on and off to see if the acoustic field that was optimized for red blood
cells affects bacteria, in different dilutions, both the normal 1:100 and the higher
1:10, to test the possible effect of a higher concentration on the flow of bacteria, and
with a faster flow speed of 50 µl/min per channel (voltage 7.6 V, optimized with
10% blood) in order to see the effects of flow speed. The conditions for the runs
were like described in chapter 2.7, apart from the parameter that was changed at a
time.
32
2.9 Acoustophoresis of blood
The amount of RBCs remaining in the plasma after acoustophoresis was tested with
1% blood using the settings described in chapter 2.7. The 1% blood diluted to the
final concentration of 0.03% in PCR-reaction. Samples were collected from side
channel, which should be clean of cells, and run in PCR as background or spiked
with bacteria, comparing to corresponding samples containing 0.03 or 0.003%
plasma or sterile water. RBCs were counted in the counting chamber from some of
the samples, both from central channel and side channel, in order to ensure that the
RBCs had concentrated to the middle.
2.10 Acoustophoresis of blood spiked with bacteria
To visualize the effect of acoustophoresis on bacteria and RBCs, a solution of 1%
blood and 10% concentrated, killed bacteria was run in acoustophoresis. The exact
concentration of bacteria was not evaluated, as the test was meant purely for
visualization. The bacteria were killed by incubation in 40% ethanol for 75 min at
RT, concentrated by centrifuging two times (15 min, 2500 g) and suspending each
time in saline solution, 9 mg/ml NaCl, and finally stained with a fluorescent label
(SYTO BC, Molecular probes, USA) according to the manufacturer’s instructions.
The run was observed with a microscope, RBCs with reflected light and bacteria
with fluorescence, but no PCR was run and no samples were collected. The
acoustophoresis was also tested at double flow speed, with voltage elevated to 6.8 V.
Excess label was this time removed from solution that contained the labelled bacteria
by centrifugation and resuspension.
A combined run of 1% blood and 1:100 dilution of bacteria, as described in chapters
2.8 and 2.9, was conducted to compare the loss of bacteria in acoustophoresis when
blood is either present or absent. Due to problems with balancing of the flows,
different settings were used for each set of runs, with flow speed of 50
µl/min/channel: 1st run: 1.732 MHz, 6.08 V; 2nd run: 2 MHz, 8.4 V. The following
samples were run: pure bacteria (samples from both outlets), pure blood (sample
from side outlet) and bacteria + blood (sample from side outlet). These and the
original diluted bacterial culture were run in PCR in eight different combinations, as
seen in table 2. PCR-samples 6 and 7 were run for better estimation of the loss of
33
bacteria when bacteria and blood were run together in acoustophoresis compared to
just bacteria run in acoustophoresis and the original culture, respectively, so that the
possible inhibition of the blood remaining after acostophoresis was taken into
account.
Table 2. Combinations of samples analyzed in PCR for bacteria and blood in
acoustophoresis.
run type
bacteria
outlet
center
side
blood
side
blood + bacteria side
(original bacterial culture)
Samples
1
2
+
+
-
3
+
-
4
+
-
5
+
6
+
+
-
7
+
+
8
-
2.11 Data analysis
Raw data obtained from PCR as fluorescence signal of each cycle was analysed in
Microsoft Office Excel 2003. Background signal for each run was calculated with
equation 5.
avg
x * std (5)
Where avg = average signal of cycles 10–15, stdev = standard deviation of signals of
cycles 10–15 and x = 5 if stdev/avg < 0.05 or 3 if stdev/avg >0.05.
Signal data and signal/background-ratio were plotted against cycle number.
Threshold cycles were obtained for signal/background-ratios 1.2 and 1.5 manually,
by estimating from the plotted curves at which cycle the ratios rise above the
background. That both of these levels were in the log-phase was tested by calculating
if the difference in threshold cycles for these two levels was constant, as it should be
for exponential growth (Kubista et al 2006). In final calculations the average value of
the two thresholds was used.
Standard curves, as described in chapter 2.3, were constructed by plotting the
threshold values of the standards against the log10 values of their bacterial counts.
Linear plot was calculated, with slope denoting the efficiency of PCR, as described
in chapter 1.2.3. Bacterial count for each subsequent sample was estimated by
calculating it from the line equation after the threshold cycles were first estimated.
34
Since the bacterial counts had daily variation due to the use of different cultures,
PCR-curves for spiked samples were adjusted to be more easily comparable, when
the inhibition of components of blood was tested: one water sample spiked with
bacteria was arbitrarily chosen as a fixed curve for concentration. Bacteria that were
from the same culture and thus had the same dilution were also presumed to have the
same concentration. The differences in threshold cycles between the fixed curve and
other dilutions were calculated from spiked water samples, after which the curves for
the other concentrations were shifted so that they all had their Cts at the Ct of the
fixed curve. This was done by moving each point of the other curves along the x-axis
towards the fixed curve by the amount of the difference in Cts. After this the curves
for the spiked diluted blood or RBC samples were shifted in the same direction and
by exactly the same amount as the corresponding water samples (i.e. the samples that
were made with the same culture and thus presumably had the same concentration).
The shift could be done due to the fact that when the difference in Cts between two
curves stays constant, so do their relative bacterial concentrations. This is easily
proven from the equation of the sample curve (6), another form of equation 1,
x 10
(
Ct c
)
k
(6)
where x = bacterial count, Ct = threshold cycle, and c and k are constants (xintercept and slope, respectively) for the standard curve that is plotted with threshold
cycle on the y-axis and log10 of bacterial count on the x-axis. If we then form this
into a ratio between two concentrations,
x
x
10
10
(
(
Ct
c
k
Ct
c
k
and , we get equation (7),
)
(7)
)
which can in turn be formed into equation (8),
x
x
10
(
Ct
Ct
k
)
(8)
which gives a constant ratio for the concentrations if the difference in threshold
cycles, Ct -Ct , is constant and the same standard curve is used. Thus if two curves
are moved in tandem, the ratio of their bacterial concentrations stays constant. The
modified curves can then be compared with each other, as a corresponding spiked
water sample and spiked blood sample are moved, but the comparison cannot be
made between a spiked sample and a background sample any more.
35
3 Results
The purpose of the study was to develop a PCR-based method for measuring the
amount of bacteria in samples obtained by separating bacteria from blood in
acoustophoresis, and, more importantly, determine the required purity of an
acoustophoretically handled sample so that it could be reliably run in PCR. This
included testing functionality of PCR with bacteria, determining levels of
interference in PCR from different components of blood, and finally testing samples
obtained from acoustophoretic runs with bacteria and/or blood.
3.1 Bacterial DNA and whole bacterial cells in PCR
Isolated E. coli DNA and whole E. coli cells were tested in PCR to ensure that the
PCR functions properly and copies E. coli DNA as it should. Levels of
contamination in different parts of the research laboratory were also looked into.
The overall bacterial background in all of the assays was determined from all nontemplate control water samples. This background was on average 53 bacteria/sample
with a standard deviation of 37 bacteria, so the detection limit in water was 165 cells.
The overall difference in threshold cycles for threshold limits 1.2 and 1.5
signal/background was determined to be approximately constant, with an average of
1.2 and standard deviation of 0.3. All calculations utilizing threshold values were
calculated using the average value of the threshold values of the threshold levels.
Standard curves were made for measuring the efficiency of PCR in both pure DNA
and whole bacteria. This was done by running known dilutions of bacteria or DNA,
measuring threshold cycles and plotting them against bacterial counts obtained by
counting samples in a counting chamber and on plates (figure 6). Comparing to
optimally functioning PCR where DNA doubles each turn and the slope in a
corresponding plot would be -3.322, Efficiency of PCR with whole bacteria was
99.8% and with DNA 90%. The same standard curve of whole cells was used for
estimating bacterial counts in other samples. Concentrations of bacteria were also
calculated manually in a counting chamber, and the obtained counts were compared
to counts calculated from standard curve. The counts from chamber did not correlate
36
entirely with the calculated counts, although the average counts were the same. The
variation was very large, but approximately even across all bacterial counts.
29
27
y = -3,3261x + 33,766
R² = 0,9856
25
Ct
23
21
19
y = -3,5809x + 33,143
R² = 0,9992
17
15
2,0
2,5
3,0
3,5
4,0
4,5
5,0
5,5
log bacterial count
Figure 6. Standard curve for efficiency of PCR and for estimating amounts of
bacteria in samples with DNA (in red) and whole bacteria (in blue). Threshold cycle
by log10 of bacterial count.
Background contamination levels in different parts of the laboratory were
approximately at the same level: Cleanroom, where the reaction mix was pipetted,
was the cleanest with approximately 22 copies of bacterial DNA per reaction. The
area where templates were pipetted and reactions were added on chips had an
average of 79 copies of bacterial DNA per reaction, and the main laboratory, where
acoustophoresis was run, had an average of 43 copies of bacterial DNA per reaction.
3.2 Inhibition of PCR by blood plasma
Blood plasma was tested in PCR for evaluation of its strength of inhibition. Plasma
dilutions were tested both for background and spiked with bacteria, and the results
were compared to corresponding PCR-runs where samples were diluted in sterile
water. The effect on background was compared at different concentrations of plasma
(Figure 7), as was the effect on bacterial counts (Figure 8). Variation in calculated
bacterial counts between replicate samples was relatively large, approximately 30%
across all plasma concentrations.
37
30
Signal/background
25
20
15
10
5
0
20
25
30
35
Cycle
40
45
Figure 7. Average effect of plasma on PCR amplification curve profile at
background level. Pure water as a standard in black, 0.003, 0.03, 0.3 and 3.2%
plasma in blue, green, orange, and purple, respectively.
100 000,0
observed bacterial count
10 000,0
1 000,0
100,0
10,0
1,0
0,1
0,0
10
100
1000
10000
100000
bacterial count in H2O
Figure 8. Average effect of plasma on bacterial counts that were determined from
samples wit PCR by using the standard curve, in both spiked samples and
background. Counts calculated from standard curve by actual counts (in water). H2O
as the standard level in black, 0.003, 0.03, 0.3 and 3.2% plasma in red, green, blue,
and purple, respectively.
38
3.3 Inhibition of PCR by red blood cells
Red blood cells were tested in PCR at different concentrations to evaluate the
strength of inhibition when plasma concentration was 0.003%. Here the signal
curves were for the first time shifted as described in chapter 2.11, since only relative
bacterial concentrations were observed. The effect of RBCs on PCR can be seen in
figure 9. Variation in bacterial counts between replicate samples was generally large,
approximately 40% on average, in both background and spiked samples.
35
30
Signal/background
25
20
15
10
5
0
15
20
25
30
Cycle
35
40
45
Figure 9. Average PCR amplification curves at different concentrations of red blood
cells. Spiked samples marked with solid lines, background samples marked with
dotted lines. Sterile water and RBC dilutions 1: 3 100 000, 1:460 000, 1:150 000 and
1:15 000 in black, blue, green, orange, and purple, respectively.
When RBC samples, both spiked and background, were compared to corresponding
samples without cells, their bacterial counts were somewhat reduced. They still were
approximately 70-90% of the counts in samples diluted to water for all except for the
smallest dilution. Variation in these samples was especially large, in some cases
higher than 50%.
3.4 Inhibition of PCR by whole blood
Diluted whole blood, prepared like RBCs, was also tested in PCR to estimate the
effect of other components of blood. The effect of whole blood on PCR can be seen
in figure 10, and the effect on relative bacterial counts in figure 11. Variation in
39
calculated bacterial counts between replicate samples was approximately at the same
level as in plasma.
35
30
Signal/background
25
20
15
10
5
0
15
20
25
30
Cycle
35
40
45
Figure 10. Average PCR amplification curves at different concentrations of blood.
Spiked samples marked with solid lines, background samples marked with dotted
lines. Sterile water and blood dilutions 1: 3 100 000, 1:460 000, 1:1500 and 1:310 in
bacteria in blood/bacteria in water
(%)
black, blue, green, orange, and purple, respectively.
60
50
40
30
20
10
0
1:3 100 000
1:310 000
1:1500
1:310
Blood dilution
Figure 11. Relative bacterial counts in blood samples compared to samples diluted in
water, as calculated from PCR standard curve. Background samples in blue, spiked
samples in red.
40
3.5 Acoustophoresis of bacteria
To first test the effect of acoustophoresis on bacteria, bacterial cultivation was run in
acoustophoresis and then analysed with PCR. Bacteria were run with ultrasound on
and off to test the effect of the ultrasound, at different flow speeds (high speed 50
ml/min/channel and low speed 20 µl/min/channel), and in different dilutions (1:10
and 1:100) to test the possible effect a higher concentration has on the flow of
bacteria (figure 12). The variation between samples was relatively large, especially
Portion of bacteria compared to original (%)
when higher bacterial concentrations were used.
48%
40%
56%
53%
59%
9%
90%
5%
46%
5%
1%
5%
52%
42%
40%
39%
7%
high flow
low flow
sound on
sound off
3%
high
low
concentration concentration
Figure 12. Relative amounts of bacteria in acoustophoretic samples, as determined
with PCR. Comparing high and low flow speeds, ultrasound on and ultrasound off,
and high and low bacterial concentration (dilution 1:10 and 1:100). Unless a specific
parameter was tested, flow speed was low, sound was on and concentration of
bacteria was low. Portion of bacteria lost during acoustophoresis in light yellow,
portion of bacteria going through central channel in light purple, and portion of
bacteria going through side channel in light blue.
3.6 Acoustophoresis of blood
The amount of RBCs remaining in blood after acoustophoresis was tested with
0.03% blood using the same settings as with bacteria. Samples were collected from
side channel and run in PCR as background or spiked with bacteria after
41
acoustophoretic processing, comparing to corresponding samples containing 0.003%
plasma or sterile water. RBCs were counted manually in a counting chamber from
some of the samples to ensure that the RBCs had concentrated to the middle, which
they had indeed done. Signals obtained from running diluted blood in
acoustophoresis were the same as with plasma of the same concentration.
3.7 Acoustophoresis of blood spiked with bacteria
Finally, the previous assay types were combined and blood samples spiked with
bacteria were processed in PCR to remove the RBCs. The flows of the bacteria
(figure 13) and RBCs (figure 14) were observed by taking pictures of the flow and
using a microscope.
Figure 13. Flow of fluorescently labelled bacteria in chip at the outlet end as seen in
fluorescent light, bacteria and label showing in green and clumps of bacteria in
brighter green. Direction of flow is from left to right.
42
Figure 14. Flow of RBCs in acoustophoretic chip at the outlet end. Ultrasound is off
in the rightmost picture so the RBCs can be seen flowing to the side channel, and on
in the leftmost picture so the RBCs can be seen focusing to the central channel. The
arrow points at the cells flowing in the channel. Direction of flow is from right to
left.
All three run types were also compared using PCR-analysis. Bacterial counts
obtained from blood and bacteria run together were 30–60% lower than if just
bacteria were run in acoustophoresis, so the total reduction from the original
bacterial counts, when the loss in a bacterial run was taken into account, was 70–
80%. This effect was likely larger than it would ideally have been, since the blood
remaining in the samples after acoustophoresis was run clearly affected the bacterial
counts unlike in earlier tests: A sample collected from blood run in acoustophoresis
reduced the counts obtained from bacteria run in acoustophoresis by 30%, although
the blood run sample did not reduce the bacterial count of the original culture. The
results could not be compared entirely reliably due to the use of 0.3% blood and
balancing issues in acoustophoresis.
43
4 Discussion
The aim of this study was to develop a possible tool for bacterial counting by realtime PCR when a solution of blood and bacteria has first been run in
acoustophoresis.
4.1 Bacterial DNA and whole bacterial cells in PCR
Since obtaining samples completely free of bacteria or their DNA is very difficult
even in strict conditions, getting no signal from background samples when the PCRprimers recognize a wide variety of bacteria is hard to achieve. The contamination
can originate from both environment and the PCR-reagents themselves, such as the
polymerase, which is usually produced in bacteria (Peters et al 2004), and usually
only the former can be controlled. In addition, since the platform is intended for
patient testing, the aim of analysing the background was not to get null background
from those samples but to examine the contamination levels in reasonably clean
conditions. In the tested samples the background was at a tolerable level and most
importantly very constant, when in other studies 10 gene copies of E. coli DNA has
been the detection limit at the cost of sensitivity (Klouche and Schröder 2008). A
much higher detection limit in this study was to be expected, since the laboratory the
study was done in was not specifically built for cleanliness.
The validity of the standard curve and the standards used with samples should be
good since the standards used in the curve were from the same source as the samples,
so they had the same amplification efficiency and contained exactly the same sample
matrix. In addition, no degradation of standard due to storage could happen, since it
was always used when it was fresh. And although one master curve was used for all
estimations, the results should still be quite reliable, since for the most part only
relative amounts of bacteria within a set of runs were estimated. Based on the
comparison of observed bacterial concentrations that compared bacteria counted in
counting chamber and calculated based on the standard curve, the standard curve
seems to indeed work rather well. The variance was large, but this can be easily
explained by errors in using counting chamber. More importantly, there seems to be
no clear trend in changes of observed concentration, so it can be presumed that the
44
PCR works with approximately constant efficiency at least with bacteria diluted in
water.
The PCR-protocol seemed to work almost ideally, with copying efficiency being
very near to 100% but still under it. This is somewhat suspicious since the efficiency
is unlikely to be this high, and might indicate that there is some interference that
affects the signal. When results from bacterial DNA only-run were compared to
whole bacterial run the correlation was large, as there was almost no difference in
efficiency of the PCR at the concentrations the PCR-runs were made. The actual
count of bacteria obtained using the bacterial standard curve, on the other hand, was
apparently approximately double of the amount of DNA in purified DNA run (if
estimated from the DNA standard curve), when there should be approximately three
gene copies per E. coli cell (Greisen et al 1994; Liu et al 2009). This could be
explained by several reasons: The counts of bacteria or DNA could have been
inaccurate, or whole cells could have had an effect on PCR by changing the sample
matrix.
4.2 Inhibition of PCR by blood plasma
The effect of blood plasma was tested in the PCR to evaluate inhibition. Even as low
concentrations of plasma as 0.003% showed some level of inhibition especially on
the maximum signal/background ratio, although the effect was not very large, and
only threshold cycles are used for bacterial quantification. The threshold cycles and
through them the bacterial counts were also affected, but the effect seemed to be
approximately linear and very small at low bacterial concentrations.
Using samples that have low concentrations of plasma, preferably 0.003%, would
seem to be ideal, as the inhibitory effect on PCR increases sharply at higher bacterial
concentrations. The stronger the inhibitory effect, the more bacteria are required for
them to be separable from background, and blood samples from patients with sepsis
are not likely to contain a high concentration of bacteria.
The effect of anticoagulant, citrate, was also tested with PCR. It had no effect despite
the fact that PCR is usually affected by anticoagulants and has to be optimized, but
this was possibly due to the used polymerase. This is difficult to know since the
45
KAPA mastermix is commercial, and thus the exact polymerase it contains is
unknown. This also raises the question of whether these results are a reliable
estimate of the effect of inhibition in the final assay, since presumably the effect is
very different for other polymerases.
4.3 Inhibition of PCR by red blood cells
As a part of the inhibition tests, red blood cells were tested to evaluate how much
they inhibit the PCR. The inhibitory effect seemingly did not affect the measured
threshold cycles very much, mostly due to high variation, which could also be seen
earlier in plasma. The variation may have been due to the possible remaining
heterogeneity of even diluted blood, or due to the bacteria somewhat clumping
together, which could be seen during the counting of the bacteria. The inhibition
could again be seen more clearly in reduced maximum signal/background ratios
resulting from decreased maximum signals, not increased background.
The bacterial counts in both spiked samples and background were not affected very
much by even high concentrations of RBCs, although the exact effect was difficult to
determine due to the high variation. Thus it would seem that red blood cells are not
very important inhibitors of this PCR-reaction, so by extension the acoustophoresis
does not need to remove red blood cells completely at least due to the inhibitory
effect, although the concentration of the red blood cells still cannot be very high
without it having some effect on PCR. The results were obtained by adjusting the
curves, but the method should be reliable since only the corresponding water
samples and samples containing RBCs were compared in relative concentrations of
bacteria, not spiked and background samples.
In this study blood was always diluted in pure, sterile water instead of saline buffer,
since saline buffer would have required making a new batch of buffer every day or
using a stock buffer. Both of these choices would have increased the risk of
contamination in comparison to using sterile water, and saline buffer might also have
interfered with PCR. Since the RBCs were run in acoustophoresis using sterile
water, their sizes were probably slightly affected, which in turn affected the tuning
frequencies, even if the runs were conducted as soon as possible after the dilutions
were made, but a more serious concern is the breaking of the cells. Based on
46
counting done in counting chamber the number of cells was not reduced, and in
samples taken from concentrating the RBC no cells were found in the side channel,
but the cells were often bloated. This likely means some of the content of the cells
had leaked, and so there was probably a higher concentration of inhibitors in PCR
than would have been in pure water.
4.4 Inhibition of PCR by whole blood
Inhibitory effect of whole blood was tested as a part of tests for inhibition in order to
evaluate the overall effect of blood components on PCR. While whole blood
behaved in a very similar way to plasma and RBCs earlier in that variation in
replicate samples was high, the inhibitory effect on PCR was more easily seen than
in red blood cells, stronger than with plasma (as even very large dilutions of blood
had a relatively strong effect), and clearly related to dilution. This might indicate
that, although plasma is clearly one of the components that inhibit PCR, for example
white blood cells may have a significant inhibitory effect.
4.5 Acoustophoresis of bacteria
Pure, diluted bacterial culture was run in acoustophoresis in order to test how the
bacteria behaved. The variation between samples was again relatively large,
especially when using higher bacterial concentrations, which would presumably be
due to clumping of the bacteria. The other possible effect of the clumping was seen
in running high bacterial concentrations in acoustophoresis: considerably larger
relative portions of the bacteria were affected by the acoustophoresis at higher
concentrations, as they exited the chip through the central channel. Reduces yield at
higher concentrations was to be expected, and other reasons for it may also have
been the demand for stronger acoustic force or the induced hydrodynamic drag
(Laurell et al 2007).
The relative portions of bacteria exiting through central and side channels were
approximately as expected (Laurell et al 2007), although the portion of the bacteria
that was lost during the acoustophoresis was somewhat larger than the generally
expected 25% (Augustsson et al 2009). The loss comes probably mostly from the
bacteria attaching to the tubes and chip, which can usually be diminished by system
47
optimization. Higher flow speed yields a higher portion of bacteria, presumably
since the flow is too strong for most of the bacteria to attach to the tubes, and also
possibly due to decreased acoustic streaming. As was expected, ultrasound optimized
to RBCs does not affect the bacteria significantly, since yield of bacteria with sound
on is almost as high as sound off.
4.6 Acoustophoresis of blood
The amount of RBCs remaining in blood after acoustophoresis was tested by running
diluted blood in acoustophoresis. Results were as expected, with acoustophoresis
removing the RBCs efficiently and yielding pure samples without RBCs from the
side channels as RBCs concentrated to the middle channel. The obtained samples
contained plasma from blood, as expected, which could be seen in the observed
bacterial signals of spiked samples. In addition it seems that processing samples with
acoustophoresis removes the components that cause the strong inhibition in whole
blood, or otherwise negates its effect.
4.7 Acoustophoresis of blood spiked with bacteria
The previous assay types were combined and acoustophoresis was run using diluted
blood spiked with bacteria. When comparing the results from this run to the
previously attempted runs, the observed bacterial count was reduced, although it was
almost at the same level as side sample obtained from pure blood run, which should
ideally contain no cells, spiked with the side sample from a pure bacterial run, which
should ideally have the original amount of bacteria. This might suggest that in this
assay focusing of the RBC to the central channel was not entirely successful or that
the possible leaking of substances from RBC to the surrounding fluid was stronger
than earlier, causing stronger inhibition of PCR. These are entirely possible
occasions due to the equipment breaking down and subsequent delay in
acoustophoresis.
In addition to the possible contamination by RBC, the reduced bacterial count when
bacteria and blood were run together was probably partially due to the time
constrains in optimizing the flows during the run. Since higher concentrations of
bacteria could not be run in acoustophoresis, the final samples could not be diluted to
48
0.003% plasma without the bacterial concentrations becoming unreasonably small,
which could mean that the interference was closer to that of the diluted whole blood.
This undoubtedly had some effect even with compensation, since corrections to
bacterial concentrations could not be made without a proper standard curve, as the
original standard curve was made with samples diluted in water. The data from the
interference of blood was neither consistent enough, nor was there enough of it, to
constitute making an applied standard curve, either. The runs were made with 0.03%
blood in order for them to be consistent with the previous runs, but probably the runs
should be attempted with 0.003% blood, although this would be inconvenient for the
actual removal of RBC in reasonable concentrations of blood in the developed assay.
Possible reasons for the smaller observed count when blood and bacteria were run
together in acoustophoresis are also the other effects of blood: either the blood cells
are still voluminous enough that they drag the fluid, and with it, the bacteria, or the
bacteria may attach to the surfaces of the RBCs with pili (Hultgren et al 1986).
Whatever the reason, more tests with the combined run should be made so that the
effect of the blood can be determined more precisely, although as it is, the separation
seems to work relatively well.
49
5 Conclusions
In conclusion,
the attempted
method of quantifying
bacteria
from an
acoustophoretically handled blood sample with PCR seems to work quite well. The
quantification was successful when the amounts of blood were sufficiently small,
and the effects of blood on the observed bacterial counts could be measured.
The greatest problem in the method is likely the large variation between samples.
One of the possible causes is the difficulty in homogenizing blood or plasma
samples, as is the influence of the anticoagulant, citrate, although it did not seem to
have much effect on PCR. Although these reasons are entirely viable, the most
probable cause is simply the variation in background: since the PCR is very sensitive
and recognizes a variety of common bacteria, even relatively small changes in the
pipetting conditions will likely have an effect. On the other hand, somewhat large
variations in SYBR Green I assays are also in some cases to be expected (Schmittgen
et al 2000). In addition, since the used GenomEra PCR-machine was experimental,
variations in conditions of PCR were likely.
For further testing of this method, more individual parts of blood, such as platelets,
which are the size of the bacteria, should be tested, since it is difficult to say if some
parts of the plasma will likely concentrate during either acoustophoresis or further
handling of the samples before PCR. Some parts, especially runs that combine blood
and bacteria, should also be tested further to ensure the repeatability of the results.
All of the further testing, however, depends on how the handling of samples after
acoustophoresis but before PCR-runs in the final assay affects the sample
composition and how efficient the acoustophoresis becomes. If the bacterial
concentrations delivered to PCR are going to be high, required sensitivity will be
much lower, especially since the purpose of this PCR-test is mostly to obtain a
qualitative result.
50
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