MANUAL VS MACHINERY SMALL RNA EXTRACTION BY USING A QIACUBE® MACHINE: Two methods. Two volumes.

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MANUAL

VS

MACHINERY

SMALL RNA EXTRACTION BY

USING A QIACUBE® MACHINE

Two methods. Two volumes.

Bachelor Degree Project in Bioscience

G2E level, 30 ECTS

Spring term 2020

Khatoon Aldosaky

a17khaal@student.his.se

Supervisor:

Anna-karin Penestig

anna-karin.penestig @his.se

Examiner:

Magnus Fagerlind

magnus.fagerlind@his.se

School of Bioscience

University of Skövde

Box 408

541 28 Skövde

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Abstract

Sepsis is a serious condition caused by a dysregulated immune response of the host triggered by an infection that can potentially lead to malfunction of various organs or even death in severe cases. Some studies have shown that the use of biomarkers could aid in early diagnosis as well as early treatment of sepsis patients. Furthermore, various studies have investigated the idea of using extracellular microRNAs as biomarkers for sepsis diagnosis. This study aimed to see if there were any differences in the quantity and purity of small RNA -which includes microRNA- by performing two different RNA extraction methods (manual and machinery by using a QIAcube) as well as two different volumes by using the ExoRNeasy Serum/Plasma Midi Kit. Blood samples were collected solely from the same self-assessed healthy donor. The plasma samples were frozen and then thawed before the RNA extraction, whether manually or machinery by the QIAcube. The extracted small RNA was then measured for quantity and purity. The quantitative results were analysed by ANOVA followed by post-hoc Tukey test to show the statistically significant difference in the concentration of small RNA. The QIAcube showed higher concentration values compared to the manual method as well as larger initial plasma volume in comparison to the lower initial plasma volume. Meanwhile, the Kruskal-Wallis test showed no statistically significant difference in the purity values among the different methods and volumes. In conclusion, based on this study, the QIAcube could do what human hands do.

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List of abbreviations

CRP C-reactive protein

DNA Deoxyribonucleic acid

EDTA Ethylenediaminetetraacetate

NGS

Next Generation Sequencing

PCT Procalcitonin

RNA Ribonucleic acid

RT-qPCR Real Time quantitative polymerase chain reaction

SIRS Systemic Inflammatory Response Syndrome

SOFA Sequential Organ Failure Assessment

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Introduction

Sepsis is a serious condition caused by a dysregulated immune response of the host triggered by an infection that can potentially lead to malfunction of various organs or even death in severe cases (Sartelli et al., 2018). Historically, sepsis has been a condition that was difficult to identify and diagnose (Martin, 2012). In the fourth century BC, the Hippocrates introduced the term “σήψις” (sepsis), by which for the term to mean decay or decomposition of organic matter (Marshall, 2008). Terms like “septicemia” and “blood poisoning” were also used for sepsis while referring to microorganism or their toxins in blood, those terms were gradually interrupted (Angus & van der Poll, 2013). “It was classically described by the eminent American physician William Osler (1849–1919) in his seminal observation that the patient appears to die from the body’s response to an infection rather than from the infection itself” (Martin, 2012).

Sepsis is estimated to affect more than 30 million people worldwide every year, with potential deaths of 6 million cases (World Health Organization, 2018). However, the incidence of sepsis is somehow patient-specific; it is believed that sepsis could mainly develop in patients with weaker immune systems (Danai, Moss, Mannino & Martin, 2006; Iskander et al., 2013). Babies and seniors as well as patients with comorbid medical conditions such as HIV, Diabetes and cancer, each of which weakens the immune system (Danai, Moss, Mannino & Martin, 2006; Iskander et al., 2013). Furthermore, those conditions could result in higher risks of developing sepsis, as well as increase the risk of nosocomial sepsis due to their frequent interactions with the healthcare system (Martin, Mannino, Eaton & Moss, 2003). Some studies introduced respiratory infections and urinary infections as the most common causes of sepsis (Arshad, Ayaz, Haroon, Jamil & Hussain, 2020; Danai, Sinha, Moss, Haber & Martin, 2007; Esper et al., 2006; Martin, Mannino, Eaton & Moss, 2003). Sepsis could be developed from any bacterial, fungal or viral attack (Martin, 2012). Before the Sepsis-3 Conference in 2016, sepsis was catagorised into three stages: sepsis, severe sepsis and septic shock. However, in the Sepsis-3 Conference in 2016, new definitions were given to sepsis and septic shock with removing the term “severe sepsis” (Singer et al., 2016). The Sepsis-3 Conference defined sepsis as a “life-threatening organ dysfunction caused by a dysregulated host response to infection” (Singer et al., 2016). Furthermore, septic shock as a “subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality” (Singer et al., 2016).

Sepsis symptoms begin with fever and chills, confusion, rapid breathing, high heart rate and discomfort (Centers for Disease Control and Prevention, 2019). If sepsis is not diagnosed early enough, it can cause severe organ dysfunction and multiple organ failure followed by the septic shock; which is the severe stage of sepsis and is characterised by low blood pressure and death in severe cases (Annane, Bellissant & Cavaillon, 2005; Mahapatra & Heffner, 2019).

Sepsis diagnosis

Sepsis remains the primary cause of morbidity and mortality in severely ill patients (Kaukonen, Bailey, Suzuki, Pilcher & Bellomo, 2014). In sepsis, the host’s immune system fails to fight the infection correctly and leads to involved complications. However, in 1992 consensus conference under the name Sepsis-1, mentioned that sepsis would include the use of the Systemic inflammatory response syndrome (SIRS) term and criteria (Bone, Sibbald & Sprung, 1992). SIRS was defined as a complex pathophysiological response to conditions such as infection, burns, trauma, pancreatitis, or a variety of other injuries (Bone et al., 1992). However, the SIRS term was used when there was a widespread inflammation in patients with various disorders (Bone, Sibbald & Sprung, 1992). The four SIRS criteria were: tachypnea (respiratory rate >20 breaths/min), tachycardia (heart rate >90 beats/min), fever or hypothermia (temperature >38 or

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Page: 2 <36 °C) and leukocytosis, leukopenia, or bandemia (white blood cells >1,200/mm3, <4,000/mm3 or bandemia ≥10%) (Dellinger et al., 2013). A patient fulfils the criteria for sepsis when more than two of the four criteria were present in the patient (Bone, Sibbald & Sprung, 1992).

Another consensus panel was held in 2001, under the name Sepsis-2. The panel supported the previous definitions but with a warning of not all systemic inflammatory responses result in sepsis; some would result in infectious or noninfectious insults, such as elevated white-cell count or tachycardia, thus, making it harder to distinguish sepsis from other conditions (Levy et al., 2003). Moreover, the Sequential Organ Failure Assessment (SOFA)/ qSOFA scores were also evaluated and were shown to be a highly specific and preferred method for diagnosis of sepsis (Levy et al., 2003). The qSOFA system evaluates; altered mentation, respiratory rate of 22/min or higher systolic blood pressure of 100 mm Hg (Levy et al., 2003). In Sepsis-3 conference in 2016, the use of SIRS criteria was considered to be unhelpful (Singer et al., 2016).

Today, the broad-range spectrum application is no longer used to detect sepsis; one of the reasons is; it could increase the resistance to antibiotics (Pradipta et al., 2013). Therefore, it is essential to know the origin of the pathogen based on the infection site and microbial sensitivity to reduce the use of broad-spectrum antibiotic and improper antibiotic use (Pradipta et al., 2013). Instead, blood cultures are used. Blood culture is regarded as the golden standards for the detection of viable fungal or bacterial organisms in the blood. Approximately 40 ml of blood is withdrawn from suspected sepsis patients (Laukemann et al., 2015). The whole blood will then be cultured in aerobic and anaerobic media (Dellinger et al., 2013). The blood cultures would allow the growth of any microorganism present in the blood if any (Marlowe, Gibson, Hogan, Kaplan & Bruckner, 2003). Later, to determine the susceptibility to various antibiotics, disks of different antibiotics are put on the cultures. Then the diameter of bacterial inhibition around the antibiotic is measured and compared with disk diffusion interpretive criteria updated annually (Syal et al., 2017). However, there are still some limitations to the methods used. The microorganisms need to grow in sufficient numbers until detected. There is also the risk of false-positive results due to previous use of antibiotics (Vincent, Mira & Antonelli, 2016). The improper handling of culture bottles may lead to blood contaminations, and eventually, in false results from the cultures (Patel, 2016). Furthermore, the time until the identification of the causative microorganism is ≈ 48 hours (Dellinger et al., 2013). Moreover, the lack of relevant results calls for the development of novel methods for early sepsis identification (Wolk & Johnson, 2019).

Sepsis and biomarkers

Sepsis could be lethal, especially when not diagnosed and treated ahead. Early diagnosis could increase the life expectancy of sepsis patients (Daviaud et al., 2015). Moreover, a study by Mancini et al. in 2010, investigated in the non-culture-based methods for sepsis diagnosis, which was the use of biomarkers in sepsis diagnosis. Biomarkers could aid in the early diagnosis as well as early treatment for sepsis patients (Ljungström et al., 2017). Numerous definitions explain what biomarkers are, many of which overlap but each discusses and explains the purpose of specific biomarker (Califf, 2018). A biomarker is described as “defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention” (Califf, 2018). However, for a macromolecule to be considered as a biomarker, it must fulfil specific properties or criteria. Irrespective of the area of use, the biomarker should be accessible, affordable and have a quick method to obtain (Dumache et al., 2015). Another important aspect is how specific the macromolecule is to a particular tissue or specific injury it is (Dumache et al., 2015). However, for many years the white blood cells (WBC) count have been used to diagnose numerous infections in the body (Blumenreich, 1990). A study by Aminzadeh & Parsa in 2011 showed that the WBC count increases as a response to infections and especially in sepsis, the increase is very tense. However, the medical staff cannot rely solely on the WBC count

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Page: 3 to predict sepsis (Seigel, Shapiro, Howell & Donnino, 2007). Moreover, Procalcitonin (PCT) the precursor of the hormone calcitonin, is one of the most abundant biomarkers that are currently used to detect sepsis alongside C- reactive protein (CRP). PCT is elevated in patients with an invasive bacterial infection, and not only produced by the infected area but also by the other tissues and cells in the body (Fan, Miller, Lee & Remick, 2016). Where CRP occurs in the early stages of sepsis -during the first 24h- and is characterised by its high sensitivity (Faix, 2013). Unlike PCT, WBC and CRP are not as sufficiently specific for bacterial infections (Hildenwall et al., 2016). Moreover, according to Ha in 2011, microRNAs could be used as promising biomarkers in various cancer types. Moreover, microRNAs have been firmly linked in numerous human infectious diseases and hence serve as possible biomarkers in the diagnosis of sepsis (Correia et al., 2017). A study by Vasilescu et al. in 2017, showed that the alteration in microRNA network could have a significant outcome in septic patients.

MicroRNAs are small non-coding RNA molecules consist of 20-24 nucleotides, quite stable and work as negative regulators by controlling the gene expression when pairing with their target messenger RNAs (Fan, Miller, Lee & Remick, 2016; Ha, 2011). MicroRNAs are also found in body fluids, including plasma; the microRNAs are released into the extracellular space incorporated in exosomes, microvesicles or even bound to high-density lipoproteins (Liang et al., 2013).

Various studies have investigated the idea of using extracellular microRNAs as biomarkers for sepsis by profiling the dysregulation of microRNAs in the blood plasma of sepsis patients (Essandoh & Fan, 2014). A study by Cochetti et al. in 2016, confirmed that the serum microRNA is a reliable candidate for developing minimal invasive biomarkers for sepsis diagnosis. So far, miR-146a, miR-223 and miR-150 have been identified to have promising prognostic and diagnostic value to sepsis (Essandoh & Fan, 2014).

The extracellular microRNA could be considered as a biomarker for sepsis detection (Essandoh & Fan, 2014). However, not alone but accompanied by other biomarkers, such as Procalcitonin (PCT), lactate, CRP, Cytokines, D-Dimers and a few others (Fan, Miller, Lee & Remick, 2016). Concerning the selectivity, specificity and high stability of exosomal microRNAs, they could be ideal biomarkers in various fields (Roderburg et al., 2013). The exosomal microRNA are delivered from viable cells and can be taken up by the recipient cells and modulate the expression of genes (Sohel et al., 2013). Unlike the non-exosomal microRNA, which is the result of nonspecific and passive discharge from the cells and might not be the better choice for the biomarker study (Sohel, 2018).

However, microRNA is found in low concentrations in biological fluids as well as microRNA being tiny and exhibiting a high degree of homology makes it challenging to extract (Binderup et al., 2018; Kreth, Hübner & Hinske, 2018). It is necessary to find assuring methods and protocols to extract microRNA as well as the input material’s size and type could play a significant role in the microRNA measurements results, for it to be later used in clinical practices (El-Khoury, Pierson, Kaoma, Bernardin & Berchem, 2016).

Furthermore, a study by Samraj, Zingarelli & Wong in 2013 pointed out that monitored levels of various biomarkers solely or in combinations could be considered promising evidence in sepsis diagnosis. Research in sepsis was directed in developing a unique approach and a standard working system to verify multi-level biomarkers during sepsis diagnostics (Dave et al., 2018). The primary purpose of future sepsis diagnostics is developing a multi-marker panel by combining all the vital parameters for an accurate and early diagnosis of the suspected septic patient (Ljungström et al., 2017; Nolan, O’Leary, Bos & Martin-Loeches, 2017).

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QIAcube® (QIAGEN)

The use of automated machines could offer laboratories to save money, process more tests as well as give reliable results with fewer contaminations (Ledeboer & Dallas, 2014). Also saving clinical laboratory scientists from performing repetitive, mundane tasks and focus on tests of higher complexity (Ledeboer & Dallas, 2014).

Lately, a robotic workstation has been constructed to reduce the work done by human hands in the laboratory. QIAcube is a hand-free robot that can automatically purify nucleic acid and protein (McGraw et al., 2014). “Qiacube consists of a centrifuge, a robotic arm that contains a micro-pipettor and a gripper to transfer spin columns as well as a micro-tube incubator and shaker” (McGraw et al., 2014). “QIAcube also accepts up to 12 micro-tube samples and up to 9 different purification solutions to automatically pipette, mix, incubate, transfer and spin in order to purify nucleic acid or protein” (McGraw et al., 2014).

This project

This thesis study aimed to see if there were any variations in the concentration and purity of the extracted small RNA between two different RNA extraction methods, manual extraction and the machinery extraction by using the QIAcube®_{(QIAGEN), from using healthy blood plasma. It was}

also aimed to see if there were any differences in the concentration and purity of the extracted small RNA between two different initial plasma volumes 100 µl and 275 µl within each method used. This project’s objective was to use the ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) for both extraction methods, manual and machinery by using the QIAcube®_{(QIAGEN) machine.}

This thesis work is a continuation of the “Future diagnostics of sepsis” study, which is ongoing research at the University of Skövde. The research aims in finding innovative ways to detect sepsis for early and accurate diagnosis. Moreover, the study aims to develop a multi-marker panel to enable early recognition of sepsis, where data mining techniques are utilised to select an optimal combination of biomarkers and clinical data. Moreover, the study is also focusing on validating the miRSepsTM_{and establishing the output.}

In general, this study extracted total RNA from self-assessed healthy blood donors in order to see if QIAcube®_{(QIAGEN) machine could replace human handwork by providing reliable and concise}

concentration and purity results as well as time efficiency. Moreover, the study might aid in a quicker and earlier detection in suspected sepsis patients by focusing on extracting as abundant small RNA -may include microRNA which is a possible biomarker- from as little initial plasma volume and as quick as possible.

Materials and methods

This thesis work took place in the Bioscience Department at the University of Skövde, Sweden from February to June 2020.

Ethical consideration

This sepsis work used healthy adult blood donated by self-assessed healthy donors. The blood was taken from persons who willingly wanted to contribute with their blood for the research. No ethical approval was required in this case.

Sample collection and plasma preparation

Whole blood samples were collected from self-assessed healthy blood donors at the University of Skövde campus. The donated whole blood was put in 6 ml EDTA treated tubes (Greiner Bio-One), to prevent blood coagulation (Banfi, Salvagno & Lippi, 2007). The 6 ml EDTA tubes (Greiner

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Bio-Page: 5 One) were centrifuged at 4 °C with 2000 x g for 15 minutes in Scan Speed 1580R (LABOGENE) (Henry, 1979; Thavasu et al., 1992). Later, plasma from the centrifuged EDTA tubes (Greiner Bio-One) was randomly transferred to 45 Eppendorf tubes of 1.5 ml in capacity. The plasma samples were then put in -80 °C freezer until further use.

Throughout the experiment, every plasma sample was thawed before use. The thawing procedure went as follow; the plasma samples were transferred from -80 °C freezer and put on ice for 10-12 minutes, then the samples were moved again to a tube rack at room temperature for 12-15 minutes until the plasma was completely thawed. After the plasma had melted entirely, the plasma samples were then centrifuged at 16,000 x g for 15 minutes at 4 °C. This extra centrifugation step was recommended for small plasma volumes such as 100 µl by the ExoRNeasy Midi/Maxi Handbook (QIAGEN, 2019), to eliminate residual cellular material from the plasma samples. However, in this project, all the plasma volumes went through this additional centrifugation step.

Manual extraction

Twenty samples were extracted manually, ten samples with 100 µl of initial plasma volume and the other ten with 275 µl of initial plasma volume. ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) was the kit used for the manual small RNA extraction by following the ExoRNeasy Midi/Maxi Handbook (QIAGEN, 2019). A few modifications were made to the protocol to increase the kit’s performance according to A-K. Pernestig (personal communication, February 9, 2020). The centrifugation steps were calculated to be one minute in “step three” at 500 x g and five minutes in step four and six at 5000 x g. However, the centrifugation times on every step, three, four and six were increased by two minutes, because the centrifuge takes time to reach the desired speed according to A-K. Pernestig (personal communication, February 9, 2020). Moreover, the g force on steps four and six, instead of 5000 x g, the centrifuge was run at 3500 x g, and this was because the Scan Speed 1580R centrifuge with swinging rotor bucket could not go as high as 5000 x g. However, the temperature in the Scan Speed 1580R centrifuge was set at 24 °C in all the steps performed in this particular centrifuge.

Moreover, on “step 17” from the ExoRNeasy Midi/Maxi Handbook (QIAGEN, 2019), the centrifugation time was also increased to eight minutes instead of five. The samples after centrifugation were incubated on ice for three more minutes for ethanol to evaporate completely. This step was performed after noticing the purity values from the 275 µl initial plasma volume were lower than 1.8 at A260/280. However, this modification was done only on the last three manual

samples of 100 µl of initial plasma volume.

Machinery extraction

For machinery small RNA extraction, QIAcube®_{(QIAGEN) machine was used. In the machinery}

method, another twenty plasma samples were extracted using QIAcube®_{(QIAGEN). Ten samples}

with 100 µl and ten with 275 µl of initial plasma volume. According to the QIAcube®_{Protocol Sheet}

2015, the machine starts the extraction at “step 12” of the ExoRNeasy Midi/Maxi Handbook (QIAGEN, 2019). All the steps prior to “step 12” were performed the same as they were performed for the manual extraction samples, as mentioned above according to the ExoRNeasy Midi/Maxi Handbook (QIAGEN, 2019). Moreover, on “step 11”, before transforming the samples to the QIAcube®_{(QIAGEN) machine, 350 µl of the upper, colourless aqueous phase containing RNA was}

transferred to a 2 ml Eppendorf tube. Then, the QIAcube®_{(QIAGEN) continued the addition of}

ethanol and all the other steps following, according to ExoRNeasy serum/plasma kit (QIAGEN) QIAcube®_protocol.

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Methods of time estimation

The hands-on time estimation and turnaround-time estimation were limited on two samples for each method used in this study. A stopwatch measured the hands-on time estimations for each time hands handled the extraction, whereas the turnaround-time estimation was measured by setting another stopwatch from the beginning until the end of each method.

Quantity and purity measurements

Throughout the experiment, extracted small RNA concentration values were measured by using Qubit® _{4.0 Fluorometer (Thermo Fisher Scientific) with the Qubit}® _{microRNA Assay kit (Thermo}

Fisher Scientific) which measures the small RNA concentrations that also include microRNA (Thermo Fisher Scientific, 2015). Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific) was used to measure the purity of the extracted small RNA samples at the A260/280 and A260/230

absorbances.

Statistical analysis

In the IBM SPSS Statistics 25 software, all the statistical test for this study were performed. Shapiro-Wilk tests were used to test the normality of data. Descriptive statistics were presented in mean and standard deviation for the detection of biological variation between the methods and the different volumes. Later was a one-way analysis of variance (ANOVA) followed by post-hoc Tukey performed for the concentration values, at the same time, Kruskal-Wallis test was used to determine if there were any variations in purity measurements among the four data groups. A p-value < 0.05 was decided to reject the null hypothesis.

Hypothesis tested

The null hypothesis (H0) for the difference between the methods stated; there is no statistically significant difference in small RNA yield between performing manual or machinery method. Meanwhile, the null hypothesis (H0) for the different volumes within each method stated; there is no statistically significant difference in small RNA yield between the small and large volumes of plasma. Furthermore, the null hypothesis (H0) for the purity measurements among the four data groups stated; there are no statistically significant variations in RNA purity measurements among the four data groups performed.

Results

In order to identify any possible differences between the manual and machinery QIAcube®

(QIAGEN) methods as well as between the two different initial plasma volumes, 40-reactions of total RNA extraction were performed, solely from the same donor (Table 1).

Table 1. Number of RNA extraction reactions performed in this study with different initial plasma volumes.

100 µl volume 275 µl volume Manual extraction n=10 n=10 Machinery extraction n=10 n=10

Concentration and purity

The concentrations of the small RNA for the 100 µl and 275 µl extractions of manual and machinery QIAcube® _{(QIAGEN) methods are presented in Appendix 1a and 1b.}

The purity of the 100 µl extractions for the two methods as well as the 275 µl extractions are presented in Appendix 2a and 2b.

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Data analysis

Extracted small RNA concentration

The four data groups, M100; manual 100 µl samples, M275; Manual 275 µl samples, Q100; Machinery QIAcube®_{(QIAGEN) 100 µl samples and Q275; Machinery QIAcube}®_{(QIAGEN) 275 µl}

samples were tested for normality in their data sets. Shapiro-Wilks test for normality was performed. The p-value from Shapiro-Wilks test was > 0.05 for M100 (p= 0.191), M275 (p= 0.468), Q100 (p=0.097) and Q275 (p= 0.262). Therefore, assuming the four data groups had normal distribution within their data sets.

To see if there were any possible biological variation among the data group performed reliant on their initial plasma volume used or the specific method used, the mean and the standard deviation for each of the four data groups were measured (Table 2).

Table 2. The mean and standard deviation of extracted small RNA concentration values for all four groups.

Data group Concentration ng/µl mean (±SD)

M100 (n=10) 0.678 (± 0.078)

M275 (n=10) 0.860 (± 0.149)

Q100 (n=10) 0.852 (± 0.108)

Q275 (n=10) 0.998 (±0.074)

M100; manual 100 µl samples, M275; Manual 275 µl samples, Q100; Machinery QIAcube®_{(QIAGEN) 100 µl} samples and Q275; Machinery QIAcube®_{(QIAGEN) 275 µl samples.}

In order to see if there were any possible differences in the small RNA yield among the four data groups, One-way variance (ANOVA) followed by post-hoc Tukey test was chosen to be performed. However, the data groups should meet two assumptions in order for the ANOVA test to be performed. First, the data groups must be normally distributed; second, the data groups must have equal variance.

Levene’s test for homogeneity of variance was conducted, and the mean in all four groups had equal variance. Furthermore, the two assumptions to perform one-way variance (ANOVA) were met. ANOVA followed by post-hoc Tukey test was applied (Figure 1). The p-value for the M100 versus the Q100 was < 0.05, as well as the p-value for the M275 versus the Q275. Therefore, the null hypothesis stating that there was no statistically significant difference in the small RNA yield between performing manual or machinery method was rejected.

Whereas, the p-value between the two different volumes performed in each method -manual or machinery- was also < 0.05, determining the rejection of the null hypothesis which stated; that there was no statistically significant difference in small RNA yield between the small and large volumes of plasma.

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Figure 1. The mean of the extracted small RNA concentration values in two methods with two different starting volumes, Manual 100 µl and 275 µl and also machinery with 100 µl and 275 µl. Error bars represent the mean value ± 1 SD. Statistical significance was determined by one-way ANOVA (F3,26 =23.122, P=0.00), followed by post-hoc Tukey test (n= 10 in each group).

Purity of the extracted small RNA

The purity measures at A260/280 and A260/230 for the four data groups were tested to see if there

were any possible variations in the purity between the two methods used as well as between the two different volumes used in each method. The purity measures at A260/280 and A260/230 for both

methods and their different volumes -100 µl and 275 µl- were comparatively low (A260/280 < 2.0).

At the A260/280, The Q275 purity data set was not normally distributed. At the A260/230, the Q100

purity data set was not normally distributed; hence, led to the use of nonparametric tests.

A Kruskal-Wallis test was run to determine if there were any differences in the purity measurements of A260/280 among the four data groups performed. The test showed no statistically

significant difference between the two methods nor the two different volumes used, χ2_{3 = 3.074,}

p = 0.380. Each data group had a sample size of ten subjects (results not shown).

Another Kruskal-Wallis test was run to determine if there were any differences in the purity measurements of A260/230 among the four data groups performed. The test showed no statistically

significant difference between the two methods nor the two different volumes used, χ2_{3 = 1.657,}

p = 0.647. Each data group had a sample size of ten subjects (results not shown).

Time estimation results

The hands-on time and turn-around time estimations for the manual, and the machinery method using QIAcube®_{(QIAGEN) was recorded when performing two extractions at a time for each}

method used (Table 3). However, the turn-around time included 37-40 minutes of plasma thawing and the recommended centrifugation step before starting the procedure of extraction according to the protocol.

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Table 3. Recorded hands-on time and turn-around time estimations (n=2 in each method).

Hands-on time estimation

Hours: minutes: seconds turn-around time estimations Hours: minutes: seconds

Manual method 00:16:11 02:06:00

Machinery method using QIAcube®_(QIAGEN)

00:12:00 02:05:00

Discussion

In sepsis field, studies are concentrating on measuring the exosomal microRNA and its types to see how they are affected in sepsis patients in comparison to healthy people. However, the tiny amount of microRNA in body fluids and most specifically plasma, makes it so tricky to extract and detect. Furthermore, this study is concentrating on finding better and faster ways to extract small RNA, which also includes microRNA. Later, microRNA could be detected by other methods, for example, downstream applications such as real-time quantitative polymerase chain reaction (RT-qPCR), next-generation sequencing (NGS) and Northern blotting (Baker, 2010; Dong et al., 2013). A comparison between human handwork (the manual method) and QIAcube®_{(QIAGEN) (the}

machinery method) was made by using the ExoRNeasy serum/plasma midi kit (QIAGEN), to extract RNA from the initial plasma volume of 100 µl and 275 µl. The reason these two volumes were used was that the ExoRNeasy serum/plasma midi kit (QIAGEN) could perform extraction on initial volumes starting as low as 100 µl and up to 1 ml of plasma only. In this study, the classical QIAcube® _{(QIAGEN) machine was used to examine if QIAcube}®_{(QIAGEN) could do what human}

hand does and to see if it could be used in clinical laboratories.

Results from this study discussed

In this study, the qubit® 4.0 Fluorometer (Thermo Fisher Scientific) was used to measure the

concentrations of the extracted small RNA. Because the qubit® _{platform is the most suitable for}

small RNA quantification as it offers a low detection range and high specificity for small RNA molecules compared to other platforms (El-Khoury, Pierson, Kaoma, Bernardin & Berchem, 2016). The Qubit® _{microRNA Assay kit (Thermo Fisher Scientific, 2015) used, allows an easy and}

accurate quantification of small RNA. The assay detects all types of small RNA, including microRNA and siRNA, both single-stranded and double-stranded (Thermo Fisher Scientific, 2015). Moreover, the nanodrop 1000 spectrophotometer (Thermo Fisher Scientific) was used to measure the purity of the extracted small RNA samples.

Small RNA concentrations between the methods

Two methods were performed in this study, the manual and machinery using QIAcube®_(QIAGEN).

Each of which had two different initial plasma volumes to extract, settling in four data groups to analyse (Table 1).

The concentration of the M100 was compared with the Q100 (Table 2) because both data sets had the same initial plasma volume while each was performed in a different method. As expected, it was noticed that the Q100 concentrations had higher mean compared with the M100 concentrations, leading to the assumption that higher concentration value results were obtained when performing the machinery method rather than when the manual method was performed. Nevertheless, the standard deviation of Q100 was higher than the M100 (Table 2), which was not expected and led to the conclusion that the data from Q100 was spread on a broader range than the data from M100 was.

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Page: 10 Theoretically, the higher standard deviation which represents the biological variation present in Q100 showed that the results from the machinery method could not be concise enough to be later reliable and eventually be used in the clinical laboratory. However, it is worth mentioning that in this project, during machinery method extractions, the Q100 extractions were performed before the Q275 extractions. Considering the Q100 extractions were performed without broader knowledge in how the QIAcube®_{(QIAGEN) machine works, which could be one of the reasons why}

the concentrations in Q100 had a higher standard deviation than the M100. Moreover, the sample number of ten samples per method could be a small number to judge a method.

The small RNA concentrations of M275 were compared with Q275 concentrations (Table 2). As expected, the results showed that Q275 had higher mean but lower standard deviation leading to the conclusion that the RNA extraction by QIAcube®_{(QIAGEN) resulted in a higher yield of small}

RNA which there was a possibility of it having a higher yield of microRNA as well. Besides, the machinery method in larger volumes had more concise data than manual method did. The reason could be due to the QIAcube®_{(QIAGEN) using a robotic arm instead of human hands, which could}

be one of the reasons to have less biological variation in the QIAcube®_{(QIAGEN) machine results.}

However, the ANOVA test followed by post-hoc Tukey test was used to see if there were any statistical differences between the data groups, method-wise (Figure 1). There was a significant statistical difference in the small RNA concentration between the M100 and Q100 data groups and between the M275 and Q275 data groups, resulting in the rejection of the null hypothesis which stated that there was no statistically significant difference in the small RNA yield between performing manual or machinery method.

The concentration values from the QIAcube®_{(QIAGEN) machine in this study were compared with}

a study by Bhattacharya, Das, Pandey, Harishankar & Chandy in 2016. The study used three different protocols and two different methods to extract fungal DNA with QIAamp DNA mini kit (Bhattacharya, Das, Pandey, Harishankar & Chandy, 2016). One of the methods used was QIAcube® (QIAGEN), and it was compared with the other method, which was fully human-hand

work. However, the results from the same study showed a quite poor DNA yield from the QIAcube®_{(QIAGEN) method compared to the other two protocols (Bhattacharya, Das, Pandey,}

Harishankar & Chandy, 2016).

In this study, the QIAcube®_{(QIAGEN) machine showed promising concentration results compared}

to the manual method performed, unlike the study by Bhattacharya, Das, Pandey, Harishankar & Chandy in 2016. However, comparing these two studies might not do justice to the QIAcube®

(QIAGEN) machine, because both studies used different nucleic acid to extract, as well as from different organisms. In addition to that, the QIAcube® (QIAGEN) in the by Bhattacharya, Das,

Pandey, Harishankar & Chandy in 2016, did not use the same protocol as the manual method did, while in this study both methods used the same protocol and under the same conditions. Nevertheless, the only study that reviewed QIAcube®_{(QIAGEN) was the study by Bhattacharya,}

Das, Pandey, Harishankar & Chandy in 2016.

Even though the QIAcube®_{(QIAGEN) resulted in a higher concentration of small RNA, it was not}

definite that these extractions had higher microRNA concentration. Due to the low concentrations of small RNA in this study, the best method to detect the microRNA would be by using NGS or by performing RT-qPCR to detect the amounts of a specific microRNA. Furthermore, after the quantity of the specific microRNA is detected, then it would be definite to say how much microRNA concentrations there is in the samples performed. Another alternative would be the use of fragment analyser, which could be of good help in detecting the approximate concentrations of specific microRNAs present in the samples performed. With a fragment analyser, it is possible to

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Page: 11 identify particular sizes of microRNA, rather than to measure the concentration of all small RNAs as it is in the qubit.

Small RNA yield between the 100 µl and 275 µl initial plasma volumes

Within every method, two different initial plasma volumes were used. Based on the mean and standard deviation from M100 and M275 (Table 2), it was noticed that the concentration of the small RNA from M275 was higher than M100. This result was expected because the higher initial plasma volume could contain a higher percentage of small RNA concentration. However, the M275 had higher standard deviation leading to the assumptions that the data in M275 had higher biological variation. This result was not expected, due to the plasma being collected solely from the same individual. Theoretically, the results within every data set -M100, M275, Q100 and Q275- must have similar concentrations of small RNA with minimal if any biological variation. Not to forget mentioning that higher concentration values should show more concise results due to the abundance of material present in them. However, between Q100 and Q275, as expected, it was noticed that Q275 had higher mean value but lower standard deviation, leading to the conclusion that the data from Q275 had more concise data distribution than the Q100 as well as higher small RNA yield.

The one-way ANOVA, followed by post-hoc Tukey test, was performed on the data groups (Figure 1). There was a significant statistical difference in the concentration of the small RNA between the M100 and M275 data groups and between the Q100 and Q275 data groups, resulting in the rejection of the null hypothesis which stated that there was no statistically significant difference in the small RNA yield between the small and large initial plasma volumes. Hence, based on this study, the larger the initial plasma volume used, the higher the small RNA yield, which later on with the help of other methods it might result in higher microRNA as well.

Purity measurements

The purity of the extracted small RNA was measured to see if it was affected by performing two different methods -manual and machinery- or using different initial plasma volumes -100 µl and 275 µl-.

All the recorded purity values in this study did not exceed the 1.75 at A260/280 (Appendix 2a & b).

A pure RNA ratio, according to Desjardins & Conklin in 2010, is ~1.8-2.0 with neutral pH at 260/280. However, Different purity values would indicate the presence of protein or contaminants that absorb strongly at or near 280 nm (Desjardins & Conklin, 2010). One possible reason for the low ratios at A260/280 could be the deficient nucleic acid concentration < 10 ng/μl

(Matlock, 2015). The A260/230 recorded ratios were on the lower range (Appendix 2a & b). One

possible reason for the low A260/230 could be the presence of guanidine thiocyanate, which is an

organic compound found in the QIAzol lysis reagent (QIAGEN) used in the ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) (Matlock, 2015).

When processing RNA extractions, important factors could affect the purity and concentration of the isolated small RNA most specifically microRNA; haemolysed plasma samples could significantly change the commonly used referenced microRNA, while the microRNA profile could also be altered due to rupturing of cellular components of the blood (Kirschner et al., 2011 McDonald, Milosevic, Reddi, Grebe & Algeciras-Schimnich, 2011; Mitchell et al., 2016; Page et al., 2013)

The purity measurements of the four data groups were tested for any possible statistically significant differences among them. The purity values did not show any statistically significant difference among the four data groups at both absorbances A260/280 and A260/230, leading to the

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Page: 12 However, another study that used the ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) to isolate small RNA from serum instead of plasma did show a similar range of purity within the absorbance at A260/280 (Xu et al., 2018).

The ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) used in this study, have also been used by Ding et al. in 2018. However, the concentration results from the kit were insufficient according to Illumina sequencing via synthesis (SBS) technology and were excluded from the commercial exosome isolation kits comparison (Ding et al., 2018). The reason of the previous statement was to see that the ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) used in this study already yields low concentrations of total RNA but based on this study with high-purity measurements.

Time estimations

The hands-on time was measured to see if less product-touching could change the purity of the extracted small RNA, as well as the turn-around time was measured to see which of the methods used -Manual or machinery- had a quicker process-time.

The recorded turn-around time estimations for both methods did not show vast differences between the two methods performed (Table 3). However, the hands-on time estimation was lower in the machinery method, and that was because the QIAcube® (QIAGEN) used a robotic arm to

perform the RNA extraction after “step 12” according to QIAcube® Protocol Sheet 2015. In general,

it could be said that the less hands-on time, the cleaner the product. Because every time a hand touches a product, it increases the risk of contamination for the product (Ledeboer & Dallas, 2014). Now, with fewer hands touching the product that risk is lowered.

Ethical considerations and impact of the society

In this study, no written consent was taken from healthy self-assessed donors. Because they willingly contributed with their blood to carry out this study, all the self-assessed donors were either students or staff from the University of Skövde who had previous knowledge of the studies taking places. However, if other studies are to be done in this area, especially with infectious blood, consent must be signed by donors before any action is taken. In addition to that, the ethical consideration in the ongoing “Future diagnostics of sepsis” study is approved by the Regional Ethics Committee in Gothenburg (no. 376-11) and all patients who contributed with their blood have signed consent. All the samples are stored in a biobank (Biobank Sverige).

Regarding the impact on the society, this study could aid in performing future studies that express the same idea of trying to extract as much RNA as possible with as little starting material, for it later to detect microRNA by using downstream applications and help in an earlier diagnosis in sepsis patients. Generally, the use of multi-marker panel could help in saving time, money and most importantly, saving patients’ lives.

Sepsis studies

In general, the use of multi-maker panel could make a revolutionary shift in sepsis diagnostics if the studies covering it shows promising and more reliable results than the blood culturing does, which is the current golden standard for sepsis diagnosis. The blood cultures are to detect if there are any organisms in the blood, and this process takes ≈ 48 hours (Dellinger et al., 2013). Later, more tests are to be done as well as the examinations of the symptoms, until a patient is clinically diagnosed with sepsis. During the time all these tests are happening, there are consequences also taking place, such as every hour of antibiotic administration delay increases the mortality of septic shock by 7.6 % (Kumar et al., 2006). Another question rises “why not treat patients with broad-spectrum antibiotics?” see, the use of broad-broad-spectrum antibiotics could delay treatment of the

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Page: 13 underlying systemic inflammation and provides the development of antibiotic resistance (Wenzel & Edmond, 2000).

However, the multi-marker panel offers more tests, by measuring the different biomarkers such as CRP, PCT, WBC, lactate, D-dimers (Fan, Miller, Lee & Remick, 2016). Maybe even microRNAs in the future, in approximately 30 minutes to 4 hours of processing time according to A-K. Pernestig (personal communication, May 25, 2020). Each biomarker indicates different results when measured. PCT could predict bacteraemia (Hoenigl et al., 2013). Elevated CRP levels show if a body is defending against a pathogen invasion or an inflammation (Wu, Potempa, El Kebir & Filep, 2015). However, until now, no study has yet mentioned the complete replacement of blood culture but rather an assist in preselecting patients for immediate molecular testing besides blood culture (Loonen et al., 2014).

Future suggestions

This study made a comparison between two methods -manual and machinery by using QIAcube®

(QIAGEN)- aiming to see if QIAcube®_{(QIAGEN) could result in a high purity and abundant quantity}

RNA. This study used only one extracting kit the ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) to perform the comparison. For future inspired studies in the field of utilising QIAcube®_(QIAGEN)

in extracting nucleic acid or proteins in clinical laboratories. It would be helpful to use a larger sample size and compare the two methods -manual and machinery- with again healthy blood but with different isolation kits. To confirm if the QIAcube® _{(QIAGEN) is an applicable method and}

have results similar or even better than manual methods performed. However, it is of great interest to use this extracted small RNA in downstream applications to see if the RT-qPCR can detect the candidate microRNA.

Pros and cons of the study

Comparing the manual and machinery methods was the aim of this study, which made it hard to state the pros and cons of the methods used. However, the pros and cons of the whole study would be presented instead. It would just be fair to state the pros of this study by stating; the study made a fair comparison between the two methods by using the same isolation kit, same conditions and plasma solely from the same donor. When performing extraction in the QIAcube®_{(QIAGEN), there}

was plenty -based on the number of samples in the machine- of time to perform other work in the laboratory. However, the cons of the study included that the ExoRNeasy Serum/ Plasma Midi Kit (QIAGEN) used does not yield high concentrations of RNA. The QIAcube®_{(QIAGEN) machine being}

used for the first time had some consequences due to not having enough knowledge about the machine nor how to use it properly. Finally, small sample size, twenty samples per method, each ten with different volume, was not enough to judge a machine.

Conclusion

In this study, the QIAcube®_{(QIAGEN) machine showed better results in the quantity of the}

extracted small RNA than the manual method did, as well as in the larger initial plasma volumes used. However, it is not definite that there is higher microRNA concentration in the higher RNA concentration samples unless the samples undergo NGS and the results show a precise concentration of microRNA. The samples’ purity was not affected by the different methods nor the different volumes performed.

Moreover, this study could aid in performing more vital extractions in the future with the help of the QIAcube®_{(QIAGEN) machine. Even when low initial volumes are used, higher yields could}

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Page: 14

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