Statistical methods for the analysis of high-content organotypic cancer cell culture imaging data

Organotypic cancer cell cultures combined with modern imaging technology have greatly expanded the possibilities of in vitro cancer research and drug development. In fact, imaging and subsequent image analyses have become a main component for high content screening in early stage drug discovery. The scale of such screening campaigns is rapidly growing, while at the same time, cell cultures become increasingly complex and now also include multicellular organoids in three-dimensional cultures. As a result of these imaging experiments, large amounts of image data are generated, posing ever-increasing demands to the related analysis methodology. In this doctoral thesis, novel and efficient statistical methods are introduced to meet these demands, spanning a variety of research topics in both statistics and machine learning. As a starting point, the preprocessing and segmentation of the image data are described, leading to the statistical analysis of treatment effects through descriptive features of the multicellular structures. A novel flexible finite mixture regression model is introduced in this context to account for the intra-tumor heterogeneity in the cultures. To gain a more direct interpretation for the treatment effects, an unsupervised analysis sequence is proposed leading to the phenotypic grouping of the cell structures. This is achieved by using a selected set of feature principal components as inputs for clustering algorithms. Finally, the problem of global level novelty detection is formulated and tackled with permutation tests. While the feature analysis and clustering approaches deal with very specific applications, the flexible FMR and global level novelty detection methods represent more abstract problems that are inspired by the challenges in image analysis but are not directly motivated by them. The application of all methods is demonstrated with a real cancer culture dataset in the introductory part of this thesis

Optimization of Invasion-Specific Effects of Betulin Derivatives on Prostate Cancer Cells through Lead Development

The anti-invasive and anti-proliferative effects of betulins and abietane derivatives was systematically tested using an organotypic model system of advanced, castration-resistant prostate cancers. A preliminary screen of the initial set of 93 compounds was performed in two-dimensional (2D) growth conditions using non-transformed prostate epithelial cells (EP156T), an androgen-sensitive prostate cancer cell line (LNCaP), and the castration-resistant, highly invasive cell line PC-3. The 25 most promising compounds were all betulin derivatives. These were selected for a focused secondary screen in three-dimensional (3D) growth conditions, with the goal to identify the most effective and specific anti-invasive compounds. Additional sensitivity and cytotoxicity tests were then performed using an extended cell line panel. The effects of these compounds on cell cycle progression, mitosis, proliferation and unspecific cytotoxicity, versus their ability to specifically interfere with cell motility and tumor cell invasion was addressed. To identify potential mechanisms of action and likely compound targets, multiplex profiling of compound effects on a panel of 43 human protein kinases was performed. These target de-convolution studies, combined with the phenotypic analyses of multicellular organoids in 3D models, revealed specific inhibition of AKT signaling linked to effects on the organization of the actin cytoskeleton as the most likely driver of altered cell morphology and motility.Peer reviewe

Increased HSF1 expression predicts shorter disease-specific survival of prostate cancer patients following radical prostatectomy

Prostate cancer is a highly heterogeneous disease and the clinical outcome is varying. While current prognostic tools are regarded insufficient, there is a critical need for markers that would aid prognostication and patient risk-stratification. Heat shock transcription factor 1 (HSF1) is crucial for cellular homeostasis, but also a driver of oncogenesis. The clinical relevance of HSF1 in prostate cancer is, however, unknown. Here, we identified HSF1 as a potential biomarker in mRNA expression datasets on prostate cancer. Clinical validation was performed on tissue microarrays from independent cohorts: one constructed from radical prostatectomies from 478 patients with long term follow-up, and another comprising of regionally advanced to distant metastatic samples. Associations with clinical variables and disease outcomes were investigated. Increased nuclear HSF1 expression correlated with disease advancement and aggressiveness and was, independently from established clinicopathological variables, predictive of both early initiation of secondary therapy and poor disease-specific survival. In a joint model with the clinical Cancer of the Prostate Risk Assessment post-Surgical (CAPRA-S) score, nuclear HSF1 remained a predictive factor of shortened disease-specific survival. The results suggest that nuclear HSF1 expression could serve as a novel prognostic marker for patient risk-stratification on disease progression and survival after radical prostatectomy.</p

Internet of Things : Wireless Technologies in Home Automation Solutions

Esineiden internet on kasvava ala ja kotiautomaatioratkaisut ovat todennäköi-sesti kuluttajien ensimmäisiä kosketuksia tämänlaiseen teknologiaan. Radiotaa-juusteknologiat ovat olennainen osa sensoriverkkoa ja tällä hetkellä moni tek-nologia pyrkii olemaan standardi kotiautomaatiossa. Kotiautomaatio liiketoi-mintana tulee kasvamaan voimakkaasti tulevina vuosina ja kehittyneillä senso-riverkoilla pelkästään kotona mahdollisuudet ovat suuret. Tässä tutkielmassa esitellään neljä teknologiaa: Wi-Fi, ZigBee, Z-wave ja Bluetooth. Teknologioiden pääpiirteet on selitetty ja esitetty olemassa olevien tuotteiden kautta. Olen käyttänyt SWOT–analyysiä tuotteisiin ja niitä tuottaviin yrityksiin saadakseni selville, millä tekniikoista on etu muihin nähden.Internet of things is a growing phenomenon and home automation solutions are most likely to be customers’ first introduction to this kind of technology. Radio frequency technologies are significant part of sensor network and at this point there are many technologies trying to be standard of home automation. Home automation as a business will grow rapidly in the coming years and with wire-less sensors networks the possibilities just in home is enormous. In this thesis there will be an introduction to four technologies: Wi-Fi, ZigBee, Z-wave and Bluetooth. Technologies’ main aspects are explained and afterwards reviewed with available products. These products and the compa-nies producing them are analysed with SWOT analysis and later on discussed which of these technologies have an advance upon each other

Perceived usefulness of business intelligence system in decision making process

Liiketoimintatiedon järjestelmät ovat kasvattaneet suosiotaan. Tämän tutkielman tavoitteena on selvittää edesauttavatko nämä järjestelmät päätöksentekoprosessia. Tutkielmassa esitellään päätöksenteon tukijärjestelmien synty, historia ja tällaisten järjestelmien erilaiset tavoitteet. Järjestelmien teknologiset taustat ja päätöksentekoprosessi esitellään. Tutkielman aineisto on kerätty elektronisella kyselylomakkeella eri organisaatioiden työntekijöiltä. Kyselylomake koostui seitsemästä taustakysymyksestä ja 24 väittämästä mitaten kahdeksaa eri muuttujaa. Muuttujat käsittävät yleisiä tietojärjestelmiä, liiketoimintatiedon järjestelmiä sekä päätöksentekoprosessia. Aineiston data on analysoitu tilastollisilla menetelmillä ja visualisoitu. Tutkimustuloksina saatiin käyttäjien kokevan myynnin olevan tärkein liiketoiminnan ala kyseisille järjestelmille, ne koetaan hyödyllisiksi sekä auttavan päätöksentekoprosessia. Vaikean käytettävyyden koetaan olevan suurin käyttöä haittaava tekijä. Kyselytutkimus oli luotettava ja onnistunut, mutta vastaajien määrä rajoittaa tutkimustulosten yleistettävyyttä. Tulokset kuitenkin inspiroivat jatkotutkimuksiin ja liiketoimintatiedon järjestelmät kasvattavat merkitystään organisaatioissa.Business intelligence systems are getting more popular in organizations. This thesis is investigating if current day users perceive usefulness of business intelligence systems in decision making. Research is clarifying the origins of decision support systems past and present state, with clarifying various systems and their goals. Technological foundations of the systems and how decision making occurs are explained. Empirical material was gathered using electronic survey distributed to various organizations. The survey consists seven background questions and 24 claims measuring eight variables. The variables included common information systems, business intelligence systems and decision making process. Results are interpreted with statistical models and data is visualized. The main contributions of this research are the following: sales is the most used business area in business intelligence systems, they are seen as useful software and decisions are driven from them. Problems in usability is the biggest restricting issue for users. The survey conducted was determined reliable and successful, but the number of respondents is limiting issue for further generalization. The results are encouraging for further studies and business intelligence systems significance in organizations is increasing

MODELLING OF CONDITIONAL VARIANCE AND UNCERTAINTY USING INDUSTRIAL PROCESS DATA

Academic dissertation to be presented, with the assent o

Suomalaisen eläin- ja kasvitieteellisen seuran Vanamon julkaisuja osa 15

Kielet saksa ja suomi

Enhancing Bioaccessibility of Plant Protein Using Probiotics: An In Vitro Study

As plant-based diets become more popular, there is an interest in developing innovations to improve the bioaccessibility of plant protein. In this study, seven probiotic strains (Bifidobacterium animalis subsp. lactis B420, B. lactis Bl-04, Lactobacillus acidophilus NCFM, Lacticaseibacillus rhamnosus HN001, Lacticaseibacillus paracasei subsp. paracasei Lpc-37, Lactiplantibacillus plantarum Lp-115, and Lactococcus lactis subsp. lactis Ll-23) were evaluated for their capacity to hydrolyze soy and pea protein ingredients in an in vitro digestion model of the upper gastrointestinal tract (UGIT). Compared to the control digestion of protein without a probiotic, all the studied strains were able to increase the digestion of soy or pea protein, as evidenced by an increase in free α-amino nitrogen (FAN) and/or free amino acid concentration. The increase in FAN varied between 13 and 33% depending on the protein substrate and probiotic strain. The survival of probiotic bacteria after exposure to digestive fluids was strain-dependent and may have affected the strain’s capacity to function and aid in protein digestion in the gastrointestinal environment. Overall, our results from the standardized in vitro digestion model provide an approach to explore probiotics for improved plant protein digestion and bioaccessibility of amino acids; however, human clinical research is needed to evaluate the efficacy of probiotics on amino acid absorption and bioavailability in vivo

Quantification of dynamic morphological drug responses in 3D organotypic cell cultures by automated image analysis

Glandular epithelial cells differentiate into complex multicellular or acinar structures, when embedded in three-dimensional (3D) extracellular matrix. The spectrum of different multicellular morphologies formed in 3D is a sensitive indicator for the differentiation potential of normal, non-transformed cells compared to different stages of malignant progression. In addition, single cells or cell aggregates may actively invade the matrix, utilizing epithelial, mesenchymal or mixed modes of motility. Dynamic phenotypic changes involved in 3D tumor cell invasion are sensitive to specific small-molecule inhibitors that target the actin cytoskeleton. We have used a panel of inhibitors to demonstrate the power of automated image analysis as a phenotypic or morphometric readout in cell-based assays. We introduce a streamlined stand-alone software solution that supports large-scale high-content screens, based on complex and organotypic cultures. AMIDA (Automated Morphometric Image Data Analysis) allows quantitative measurements of large numbers of images and structures, with a multitude of different spheroid shapes, sizes, and textures. AMIDA supports an automated workflow, and can be combined with quality control and statistical tools for data interpretation and visualization. We have used a representative panel of 12 prostate and breast cancer lines that display a broad spectrum of different spheroid morphologies and modes of invasion, challenged by a library of 19 direct or indirect modulators of the actin cytoskeleton which induce systematic changes in spheroid morphology and differentiation versus invasion. These results were independently validated by 2D proliferation, apoptosis and cell motility assays. We identified three drugs that primarily attenuated the invasion and formation of invasive processes in 3D, without affecting proliferation or apoptosis. Two of these compounds block Rac signalling, one affects cellular cAMP/cGMP accumulation. Our approach supports the growing needs for user-friendly, straightforward solutions that facilitate large-scale, cell-based 3D assays in basic research, drug discovery, and target validation