Cell type identification, differential expression analysis and trajectory inference in single-cell transcriptomics

Single-cell RNA-sequencing (scRNA-seq) is a cutting-edge technology that enables to quantify the transcriptome, the set of expressed RNA transcripts, of a group of cells at the single-cell level. It represents a significant upgrade from bulk RNA-seq, which measures the combined signal of thousands of cells. Measuring gene expression by bulk RNA-seq is an invaluable tool for biomedical researchers who want to understand how cells alter their gene expression due to an illness, differentiation, ternal stimulus, or other events. Similarly, scRNA-seq has become an essential method for biomedical researchers, and it has brought several new applications previously unavailable with bulk RNA-seq. scRNA-seq has the same applications as bulk RNA-seq. However, the single-cell resolution also enables cell annotation based on gene markers of clusters, that is, cell populations that have been identified based on machine learning to be, on average, dissimilar at the transcriptomic level. Researchers can use the cell clusters to detect cell-type-specific gene expression changes between conditions such as case and control groups. Clustering can sometimes even discover entirely new cell types. Besides the cluster-level representation, the single-cell resolution also enables to model cells as a trajectory, representing how the cells are related at the cell level and what is the dynamic differentiation process that the cells undergo in a tissue. This thesis introduces new computational methods for cell type identification and trajectory inference from scRNA-seq data. A new cell type identification method (ILoReg) was proposed, which enables high-resolution clustering of cells into populations with subtle transcriptomic differences. In addition, two new trajectory inference methods were developed: scShaper, which is an accurate and robust method for inferring linear trajectories; and Totem, which is a user-friendly and flexible method for inferring tree-shaped trajectories. In addition, one of the works benchmarked methods for detecting cell-type-specific differential states from scRNA-seq data with multiple subjects per comparison group, requiring tailored methods to confront false discoveries. KEYWORDS: Single-cell RNA sequencing, transcriptome, cell type identification, trajectory inference, differential expressionYksisoluinen RNA-sekvensointi on huipputeknologia, joka mahdollistaa transkriptomin eli ilmentyneiden RNA-transkriptien laskennallisen määrittämisen joukolle soluja yhden solun tarkkuudella, ja sen kehittäminen oli merkittävä askel eteenpäin perinteisestä bulkki-RNA-sekvensoinnista, joka mittaa tuhansien solujen yhteistä signaalia. Bulkki-RNA-sekvensointi on tärkeä työväline biolääketieteen tutkijoille, jotka haluavat ymmärtää miten solut muuttavat geenien ilmentymistä sairauden, erilaistumisen, ulkoisen ärsykkeen tai muun tapahtuman seurauksena. Yksisoluisesta RNA-sekvensoinnista on vastaavasti kehittynyt tärkeä työväline tutkijoille, ja se on tuonut useita uusia sovelluksia. Yksisoluisella RNA-sekvensoinnilla on samat sovellukset kuin bulkki-RNA-sekvensoinnilla, mutta sen lisäksi se mahdollistaa solujen tunnistamisen geenimarkkerien perusteella. Geenimarkkerit etsitään tilastollisin menetelmin solupopulaatioille, joiden on tunnistettu koneoppimisen menetelmin muodostavan transkriptomitasolla keskenään erilaisia joukkoja eli klustereita. Tutkijat voivat hyödyntää soluklustereita tutkimaan geeniekspressioeroja solutyyppien sisällä esimerkiksi sairaiden ja terveiden välillä, ja joskus klusterointi voi jopa tunnistaa uusia solutyyppejä. Yksisolutason mittaukset mahdollistavat myös solujen mallintamisen trajektorina, joka esittää kuinka solut kehittyvät dynaamisesti toisistaan geenien ilmentymistä vaativien prosessien aikana. Tämä väitöskirja esittelee uusia laskennallisia menetelmiä solutyyppien ja trajektorien tunnistamiseen yksisoluisesta RNA-sekvensointidatasta. Väitöskirja esittelee uuden solutyyppitunnistusmenetelmän (ILoReg), joka mahdollistaa hienovaraisia geeniekspressioeroja sisältävien solutyyppien tunnistamisen. Sen lisäksi väitöskirjassa kehitettiin kaksi uutta trajektorin tunnistusmenetelmää: scShaper, joka on tarkka ja robusti menetelmä lineaaristen trajektorien tunnistamiseen, sekä Totem, joka on käyttäjäystävällinen ja joustava menetelmä puumallisten trajektorien tunnistamiseen. Lopuksi väitöskirjassa vertailtiin menetelmiä solutyyppien sisäisten geeniekspressioerojen tunnistamiseen ryhmien välillä, joissa on useita koehenkilöitä tai muita biologisia replikaatteja, mikä vaatii erityisiä menetelmiä väärien positiivisten löydösten vähentämiseen. ASIASANAT: yksisoluinen RNA-sekvensointi, klusterointi, trajektorin tunnistus, geeniekspressi

Role of adipose tissue in the pathogenesis and treatment of metabolic syndrome

© Springer International Publishing Switzerland 2014. Adipocytes are highly specialized cells that play a major role in energy homeostasis in vertebrate organisms. Excess adipocyte size or number is a hallmark of obesity, which is currently a global epidemic. Obesity is not only the primary disease of fat cells, but also a major risk factor for the development of Type 2 diabetes, cardiovascular disease, hypertension, and metabolic syndrome (MetS). Today, adipocytes and adipose tissue are no longer considered passive participants in metabolic pathways. In addition to storing lipid, adipocytes are highly insulin sensitive cells that have important endocrine functions. Altering any one of these functions of fat cells can result in a metabolic disease state and dysregulation of adipose tissue can profoundly contribute to MetS. For example, adiponectin is a fat specific hormone that has cardio-protective and anti-diabetic properties. Inhibition of adiponectin expression and secretion are associated with several risk factors for MetS. For this purpose, and several other reasons documented in this chapter, we propose that adipose tissue should be considered as a viable target for a variety of treatment approaches to combat MetS

Proceedings Of The 18th Annual Meeting Of The Asia Oceania Geosciences Society (Aogs 2021)

The 18th Annual Meeting of the Asia Oceania Geosciences Society (AOGS 2021) was held from 1st to 6th August 2021. This proceedings volume includes selected extended abstracts from a challenging array of presentations at this conference. The AOGS Annual Meeting is a leading venue for professional interaction among researchers and practitioners, covering diverse disciplines of geosciences

Advances in Cereal Crops Breeding

This Special Issue on ‘Advances in Cereal Crops Breeding’ comprises 10 papers covering a wide range of subjects, including the expression-level investigation of genes in terms of salinity stress adaptations and their relationships with proteomics in rice, the use of genetic analysis to assess the general combining ability (GCA) and specific combining ability (SCA) in promising hybrids of maize, the use of DNA markers based on PCR in rice, the identification of quantitative trait loci (QTLs) in wheat and simple sequence repeats (SSR) in rice, the use of single-nucleotide polymorphisms (SNP) in a genome-wide association study (GWAS) in cereals, and Nanopore direct RNA sequencing of related with LTR RNA retrotransposon in triticale prior to the genomic selection of heterotic maize hybrids

Interplay between astrocytic and neuronal networks during virtual navigation in the mouse hippocampus

Encoding of spatial information in hippocapal place cells is believed to contribute to spatial cognition during navigation. Whether the processing of spatial information is exclusively limited to neuronal cells or it involves other cell types, e.g. glial cells, in the brain is currently unknown. In this thesis work, I developed an analysis pipeline to tackle this question using statistical methods and Information Theory approaches. I applied these analytical tools to two experimental data sets in which neuronal place cells in the hippocampus were imaged using two-photon microscopy, while selectively manipulating astrocytic calcium dynamics with pharmacogenetics during virtual navigation. Using custom analytical methods, we observed that pharmacogenetic perturbation of astrocytic calcium dynamics, through clozapine-n-oxyde (CNO) injection, induced a significant increase in neuronal place field and response profile width compared to control conditions. The distributions of neuronal place field and response profile center were also significantly different upon perturbation of astrocytic calcium dynamics compared to control conditions. Moreover, we found contrasting effect of astrocytic calcium dynamics perturbation on neuronal content of spatial information in the two data sets. In the first data set, we found that CNO injection resulted in a significant increase in the average information content in all neurons. In the second data set, we instead found that mutual information values were not significantly different upon CNO application compared to controls. Although the presented results are still preliminary and more experiments and analyses are needed, these findings suggest that astrocytic calcium dynamics may actively control the way hippocampal neuronal networks encode spatial information during virtual navigation. These data thus suggest a complex and tight interplay between neuronal and astrocytic networks during higher cognitive functions

COVID-19 Booster Vaccine Acceptance in Ethnic Minority Individuals in the United Kingdom: a mixed-methods study using Protection Motivation Theory

Background: Uptake of the COVID-19 booster vaccine among ethnic minority individuals has been lower than in the general population. However, there is little research examining the psychosocial factors that contribute to COVID-19 booster vaccine hesitancy in this population.Aim: Our study aimed to determine which factors predicted COVID-19 vaccination intention in minority ethnic individuals in Middlesbrough, using Protection Motivation Theory (PMT) and COVID-19 conspiracy beliefs, in addition to demographic variables.Method: We used a mixed-methods approach. Quantitative data were collected using an online survey. Qualitative data were collected using semi-structured interviews. 64 minority ethnic individuals (33 females, 31 males; mage = 31.06, SD = 8.36) completed the survey assessing PMT constructs, COVID-19conspiracy beliefs and demographic factors. 42.2% had received the booster vaccine, 57.6% had not. 16 survey respondents were interviewed online to gain further insight into factors affecting booster vaccineacceptance.Results: Multiple regression analysis showed that perceived susceptibility to COVID-19 was a significant predictor of booster vaccination intention, with higher perceived susceptibility being associated with higher intention to get the booster. Additionally, COVID-19 conspiracy beliefs significantly predictedintention to get the booster vaccine, with higher conspiracy beliefs being associated with lower intention to get the booster dose. Thematic analysis of the interview data showed that barriers to COVID-19 booster vaccination included time constraints and a perceived lack of practical support in the event ofexperiencing side effects. Furthermore, there was a lack of confidence in the vaccine, with individuals seeing it as lacking sufficient research. Participants also spoke of medical mistrust due to historical events involving medical experimentation on minority ethnic individuals.Conclusion: PMT and conspiracy beliefs predict COVID-19 booster vaccination in minority ethnic individuals. To help increase vaccine uptake, community leaders need to be involved in addressing people’s concerns, misassumptions, and lack of confidence in COVID-19 vaccination