Enhanced label-free discovery proteomics through improved data analysis and knowledge enrichment

Mass spectrometry (MS)-based proteomics has evolved into an important tool applied in fundamental biological research as well as biomedicine and medical research. The rapid developments of technology have required the establishment of data processing algorithms, protocols and workflows. The successful application of such software tools allows for the maturation of instrumental raw data into biological and medical knowledge. However, as the choice of algorithms is vast, the selection of suitable processing tools for various data types and research questions is not trivial. In this thesis, MS data processing related to the label-free technology is systematically considered. Essential questions, such as normalization, choice of preprocessing software, missing values and imputation, are reviewed in-depth. Considerations related to preprocessing of the raw data are complemented with exploration of methods for analyzing the processed data into practical knowledge. In particular, longitudinal differential expression is reviewed in detail, and a novel approach well-suited for noisy longitudinal high-througput data with missing values is suggested. Knowledge enrichment through integrated functional enrichment and network analysis is introduced for intuitive and information-rich delivery of the results. Effective visualization of such integrated networks enables the fast screening of results for the most promising candidates (e.g. clusters of co-expressing proteins with disease-related functions) for further validation and research. Finally, conclusions related to the prepreprocessing of the raw data are combined with considerations regarding longitudinal differential expression and integrated knowledge enrichment into guidelines for a potential label-free discovery proteomics workflow. Such proposed data processing workflow with practical suggestions for each distinct step, can act as a basis for transforming the label-free raw MS data into applicable knowledge.Massaspektrometriaan (MS) pohjautuva proteomiikka on kehittynyt tehokkaaksi työkaluksi, jota hyödynnetään niin biologisessa kuin lääketieteellisessäkin tutkimuksessa. Alan nopea kehitys on synnyttänyt erikoistuneita algoritmeja, protokollia ja ohjelmistoja datan käsittelyä varten. Näiden ohjelmistotyökalujen oikeaoppinen käyttö lopulta mahdollistaa datan tehokkaan esikäsittelyn, analysoinnin ja jatkojalostuksen biologiseksi tai lääketieteelliseksi ymmärrykseksi. Mahdollisten vaihtoehtojen suuresta määrästä johtuen sopivan ohjelmistotyökalun valinta ei usein kuitenkaan ole yksiselitteistä ja ongelmatonta. Tässä väitöskirjassa tarkastellaan leimaamattomaan proteomiikkaan liittyviä laskennallisia työkaluja. Väitöskirjassa käydään läpi keskeisiä kysymyksiä datan normalisoinnista sopivan esikäsittelyohjelmiston valintaan ja puuttuvien arvojen käsittelyyn. Datan esikäsittelyn lisäksi tarkastellaan datan tilastollista jatkoanalysointia sekä erityisesti erilaisen ekspression havaitsemista pitkittäistutkimuksissa. Väitöskirjassa esitellään uusi, kohinaiselle ja puuttuvia arvoja sisältävälle suurikapasiteetti-pitkittäismittausdatalle soveltuva menetelmä erilaisen ekspression havaitsemiseksi. Uuden tilastollisen menetelmän lisäksi väitöskirjassa tarkastellaan havaittujen tilastollisten löydösten rikastusta käytännön ymmärrykseksi integroitujen rikastumis- ja verkkoanalyysien kautta. Tällaisten funktionaalisten verkkojen tehokas visualisointi mahdollistaa keskeisten tulosten nopean tulkinnan ja kiinnostavimpien löydösten valinnan jatkotutkimuksia varten. Lopuksi datan esikäsittelyyn ja pitkittäistutkimusten tilastollisen jatkokäsittelyyn liittyvät johtopäätökset yhdistetään tiedollisen rikastamisen kanssa. Näihin pohdintoihin perustuen esitellään mahdollinen työnkulku leimaamattoman MS proteomiikkadatan käsittelylle raakadatasta hyödynnettäviksi löydöksiksi sekä edelleen käytännön biologiseksi ja lääketieteelliseksi ymmärrykseksi

A systematic evaluation of normalization methods in quantitative label-free proteomics

To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.</p

COVID-19-specific transcriptomic signature detectable in blood across multiple cohorts

The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is spreading across the world despite vast global vaccination efforts. Consequently, many studies have looked for potential human host factors and immune mechanisms associated with the disease. However, most studies have focused on comparing COVID-19 patients to healthy controls, while fewer have elucidated the specific host factors distinguishing COVID-19 from other infections. To discover genes specifically related to COVID-19, we reanalyzed transcriptome data from nine independent cohort studies, covering multiple infections, including COVID-19, influenza, seasonal coronaviruses, and bacterial pneumonia. The identified COVID-19-specific signature consisted of 149 genes, involving many signals previously associated with the disease, such as induction of a strong immunoglobulin response and hemostasis, as well as dysregulation of cell cycle-related processes. Additionally, potential new gene candidates related to COVID-19 were discovered. To facilitate exploration of the signature with respect to disease severity, disease progression, and different cell types, we also offer an online tool for easy visualization of the selected genes across multiple datasets at both bulk and single-cell levels

Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach

Quantitative proteomics has matured into an established tool and longitudinal proteomics experiments have begun to emerge. However, no effective, simple-to-use differential expression method for longitudinal proteomics data has been released. Typically, such data is noisy, contains missing values, and has only few time points and biological replicates. To address this need, we provide a comprehensive evaluation of several existing differential expression methods for high-throughput longitudinal omics data and introduce a Robust longitudinal Differential Expression (RolDE) approach. The methods are evaluated using over 3000 semi-simulated spike-in proteomics datasets and three large experimental datasets. In the comparisons, RolDE performs overall best; it is most tolerant to missing values, displays good reproducibility and is the top method in ranking the results in a biologically meaningful way. Furthermore, RolDE is suitable for different types of data with typically unknown patterns in longitudinal expression and can be applied by non-experienced users

CIP2A Constrains Th17 Differentiation by Modulating STAT3 Signaling

Cancerous Inhibitor of Protein Phosphatase 2A (CIP2A) is an oncogene and a potential cancer therapy target protein. Accordingly, a better understanding of the physiological function of CIP2A, especially in the context of immune cells, is a prerequisite for its exploitation in cancer therapy. Here, we report that CIP2A negatively regulates interleukin (IL)-17 production by Th17 cells in human and mouse. Interestingly, concomitant with increased IL-17 production, CIP2A-deficient Th17 cells had increased strength and duration of STAT3 phosphorylation. We analyzed the interactome of phosphorylated STAT3 in CIP2A-deficient and CIP2A-sufficient Th17 cells and indicated together with genome-wide gene expression profiling, a role of Acylglycerol Kinase (AGK) in the regulation of Th17 differentiation by CIP2A. We demonstrated that CIP2A regulates the strength of the interaction between AGK and STAT3, and thereby modulates STAT3 phosphorylation and expression of IL-17 in Th17 cells

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.publishedVersionPeer reviewe

Pancreas Whole Tissue Transcriptomics Highlights the Role of the Exocrine Pancreas in Patients With Recently Diagnosed Type 1 Diabetes

Although type 1 diabetes (T1D) is primarily a disease of the pancreatic beta-cells, understanding of the disease-associated alterations in the whole pancreas could be important for the improved treatment or the prevention of the disease. We have characterized the whole-pancreas gene expression of patients with recently diagnosed T1D from the Diabetes Virus Detection (DiViD) study and non-diabetic controls. Furthermore, another parallel dataset of the whole pancreas and an additional dataset from the laser-captured pancreatic islets of the DiViD patients and non-diabetic organ donors were analyzed together with the original dataset to confirm the results and to get further insights into the potential disease-associated differences between the exocrine and the endocrine pancreas. First, higher expression of the core acinar cell genes, encoding for digestive enzymes, was detected in the whole pancreas of the DiViD patients when compared to non-diabetic controls. Second, In the pancreatic islets, upregulation of immune and inflammation related genes was observed in the DiViD patients when compared to non-diabetic controls, in line with earlier publications, while an opposite trend was observed for several immune and inflammation related genes at the whole pancreas tissue level. Third, strong downregulation of the regenerating gene family (REG) genes, linked to pancreatic islet growth and regeneration, was observed in the exocrine acinar cell dominated whole-pancreas data of the DiViD patients when compared with the non-diabetic controls. Fourth, analysis of unique features in the transcriptomes of each DiViD patient compared with the other DiViD patients, revealed elevated expression of central antiviral immune response genes in the whole-pancreas samples, but not in the pancreatic islets, of one DiViD patient. This difference in the extent of antiviral gene expression suggests different statuses of infection in the pancreas at the time of sampling between the DiViD patients, who were all enterovirus VP1+ in the islets by immunohistochemistry based on earlier studies. The observed features, indicating differences in the function, status and interplay between the exocrine and the endocrine pancreas of recent onset T1D patients, highlight the importance of studying both compartments for better understanding of the molecular mechanisms of T1D.</p

Comparison of five popular normalization methods using a spike-in proteomics data set

Mass spectrometry (MS)-based proteomics has seen significant technical advances during the past two decades and mass spectrometry has become a central tool in many biosciences. Despite the popularity of MS-based methods, the handling of the systematic non-biological variation in the data remains a common problem. This biasing variation can result from several sources ranging from sample handling to differences caused by the instrumentation. Normalization is the procedure which aims to account for this biasing variation and make samples comparable. Many normalization methods commonly used in proteomics have been adapted from the DNA-microarray world. Studies comparing normalization methods with proteomics data sets using some variability measures exist. However, a more thorough comparison looking at the quantitative and qualitative differences of the performance of the different normalization methods and at their ability in preserving the true differential expression signal of proteins, is lacking. In this thesis, several popular and widely used normalization methods (the Linear regression normalization, Local regression normalization, Variance stabilizing normalization, Quantile-normalization, Median central tendency normalization and also variants of some of the forementioned methods), representing different strategies in normalization are being compared and evaluated with a benchmark spike-in proteomics data set. The normalization methods are evaluated in several ways. The performance of the normalization methods is evaluated qualitatively and quantitatively on a global scale and in pairwise comparisons of sample groups. In addition, it is investigated, whether performing the normalization globally on the whole data or pairwise for the comparison pairs examined, affects the performance of the normalization method in normalizing the data and preserving the true differential expression signal. In this thesis, both major and minor differences in the performance of the different normalization methods were found. Also, the way in which the normalization was performed (global normalization of the whole data or pairwise normalization of the comparison pair) affected the performance of some of the methods in pairwise comparisons. Differences among variants of the same methods were also observed.Siirretty Doriast

Turvallisen arjen käsikirja kauppakeskus Ison Omenan Palvelutorille

Tämän opinnäytetyön tarkoituksena oli kehittää sekä päivittää Turvallisen arjen käsikirjan sisältöä, jotta se vastaisi aiempaa tehokkaammin toimeksiantokohteen turvallisuustarpeisiin. Turvallisen arjen käsikirjan tarkoituksena on tukea toimeksiantokohteessa toimivien julkisen palvelun yksiköiden henkilöstöä yhteisiin turvallisuusaiheisiin perehtymisessä sekä niihin sitoutumisessa. Kehittämistehtäväksi muodostui Turvallisen arjen käsikirjan sisällön ajantasaistaminen ja termistön selkeyttäminen yleistajuisemmaksi sekä esitystavan päivittäminen visuaalisempaan muotoon. Alkuperäinen käsikirja oli vuodelta 2016, joten vuosien mittaan myös muutoksia niin säädösten kuin ohjeistuksienkin suhteen oli ehtinyt muodostumaan. Työn toimeksiantajana oli Espoon kaupunki ja toimeksiantokohteena kauppakeskus Ison Omenan palvelutori. Tämän toiminnallisen opinnäytetyön tiedonkeruumenetelmiksi valikoituivat kirjallisuuskatsaus sekä palvelutoriympäristön riskikartoitus. Palvelutorin henkilöstölle suoritettiin lisäksi lomakehaastattelu, jonka tarkoituksena oli selvittää henkilöstön omia kokemuksia toimintaympäristön turvallisuuskulttuurin nykytilasta sekä erityisesti kartoittaa kehitystoiveet koskien Turvallisen arjen käsikirjaa. Työn tietoperusta pohjautuu luotettaviin ja asianmukaisiin lähdeaineistoihin, kuten kansallisiin ja kansainvälisiin säädöksiin ja standardeihin sekä palvelutoriympäristön olemassa oleviin ohjeistuksiin. Opinnäytetyön analysointimenetelminä käytettiin aineistoanalyysia sekä vika- ja vaikutusanalyysia. Käsikirjaa kehitettiin huomioiden henkilöstön kehitysehdotukset sekä palvelutoriympäristön erityispiirteet. Käsikirja toteutettiin PowerPoint -esityksen muodossa ja aineisto sisältää havainnollistavia kuvia ja kuvioita sekä tekstiä. Käsikirjan formaatin ansiosta se on julkaistavissa niin sähköisesti kuin fyysisenä aineistonakin. Alkuperäisen käsikirjan koettiin olevan liian laaja sekä hankalasti omaksuttava, joten aineiston tiivistäminen sekä visualisointi osoittautuivat tärkeiksi prioriteeteiksi työn aikana. Turvallisen arjen käsikirjan sisältöä onnistuttiin tiivistämään noin puolella alkuperäiseen aineistoon verrattuna. Visualisoinnin ansiosta tärkeitä ohjeistuksia esitetään päivitetyssä käsikirjassa kuvioin, kun alkuperäisessä versiossa ohjeistus oli useamman tekstisivun laajuinen. Opinnäytetyön tuloksena syntynyt produkti on luokiteltu luottamukselliseksi, mutta yleisesti käsikirjan sisällön ja kehittämisen taustalla vaikuttava teoriatieto puolestaan on julkista

Musiikin käyttö sosiaalialan asiakastyössä

Kirjallisuuskatsauksen tavoitteena oli selvittää mitä hajanaista tietoa musiikin käytöstä sosiaalialan asiakastyössä löytyy ja koota se kaikki yhteen. Lisäksi tietoa koottiin siitä, miten musiikki vaikuttaa ihmisen kognitiivisiin kykyihin. Tietoperustana käytettiin aiempien tutkimuksia kyseisistä aiheista. Työllä ei ollut tilaajaa, vaan se tehtiin ryhmän omien mielenkiinnon kohteiden mukaan. Samasta aiheesta ei löytynyt aiempia kirjallisuuskatsauksia, joten uskomme tälle olevan tarvetta asiantuntijapiireissä. Yhteenvetona voidaan sanoa, että musiikki vaikuttaa ihmiseen hyvin laaja-alaisesti, ja pienissäkin määrin se voi saada aikaan positiivisia tuloksia niin ihmisen hyvinvointiin, kuin kognitiivisiin kykyihinkin. Lisäksi huomasimme, että musiikin käyttö sosiaalialan asiakastyössä on edelleen liian vähäistä, ja sitä tulisi positiivisten kokemusten ja tulosten myötä lisätä. Kirjallisuuskatsauksemme on hyödynnettävissä esimerkiksi sosiaalialan taideopetuksessa, jotta opiskelijat tai jo alalla olevat ymmärtäisivät musiikin merkityksen asiakastyössä. Kehitysehdotuksena pidimme sitä, että musiikin käytölle sosiaalialan asiakastyössä räätälöitäisiin jokin kansallinen ohje tai ohjelma, jotta työntekijöiden olisi helpompaa ottaa musiikki osaksi heidän jokapäiväistä arkeaan