The immune system safeguards the body from various pathogens, and B cells are key players within this complex system: they are mainly responsible for producing antibodies against pathogens and toxins, and contribute to the development of immune memory. To gain deeper insights into the underlying mechanisms governing B cell behavior, researchers have employed a technique known as pseudotime analysis. This approach enables the reconstruction of the cellular differentiation process along a continuous trajectory, unveiling the progression of B cells from their naive states, where they have yet to encounter any pathogens, to memory states, where they respond specifically to particular antigens. Moreover, through an examination of the gene expression profiles of these cells, namely the conversion of genetic information within genes into functional proteins, researchers have uncovered that the expression of genes can change as a function of the cell’s state trajectory. This thesis is centered around the concept of continuous cellular progression. It specifically focuses on how gene expression can be mathematically described as a function of a variable called pseudotime, which represents the position of cells along the trajectory of different cellular states, and on whether the shape of these gene expression functions is genetically regulated. Using single-cell RNA sequencing data derived from the OneK1K cohort, the study employs positive constrained cubic splines to model gene expression across the cell state trajectory. The application of advanced techniques such as Functional Principal Component Analysis (FPCA) and functional clustering to the obtained curves aids in identifying distinct patterns of gene expression behaviors. In addition to these contributions, this work assesses the influence of genetic variants on gene expression dynamics. This is achieved through an analysis known as expression Quantitative Trait Loci (eQTLs) analysis, and provides evidence that the shape of these gene expression functions is genetically controlled. Throughout the research process, challenges with data sparsity and high dimensionality were encountered and addressed using imputation and gene selection methods, which improved the quality and usability of the data. This thesis offers an enriched comprehension of gene expression dynamics throughout B cell differentiation and of genetic influences on gene expression by maintaining the continuity of gene expression functions across pseudotime. Furthermore, these findings offer avenues for exploring potential connections between genetic variants associated with gene expression dynamics and diseases, thus contributing to the development of treatment strategies.
Il sistema immunitario protegge l’organismo da vari agenti patogeni e i linfociti B sono tra i protagonisti di questo sistema, in quanto sono responsabili della produzione di anticorpi contro agenti patogeni e tossine e contribuiscono allo sviluppo della memoria immunitaria. Per approfondire i meccanismi che regolano il comportamento delle cellule B, i ricercatori hanno utilizzato una tecnica nota come analisi pseudotemporale. Questo approccio consente di ricostruire il processo di differenziazione cellulare lungo una traiettoria continua, descrivendo la progressione delle cellule B dallo stato naive, in cui non hanno ancora incontrato alcun agente patogeno, allo stato di memoria, in cui rispondono in modo specifico a particolari antigeni. Inoltre, attraverso l’esame dei profili di espressione genica di queste cellule, ovvero la conversione dell’informazione genetica all’interno dei geni in proteine funzionali, diversi studi hanno scoperto che l’espressione dei geni può cambiare in funzione della traiettoria di stato della cellula. Questa tesi è incentrata sul concetto di progressione cellulare continua, concentrandosi in particolare su come l’espressione genica possa essere descritta matematicamente come una funzione di una variabile chiamata pseudotempo, che rappresenta la posizione delle cellule lungo la traiettoria dei diversi stati cellulari, e sulla possibilità che la forma di queste funzioni di espressione genica sia regolata geneticamente. Utilizzando dati di sequenziamento dell’RNA di singole cellule derivati dalla coorte OneK1K, lo studio impiega spline cubiche a vincolo positivo per modellare l’espressione genica lungo la traiettoria dello stato cellulare. L’applicazione di tecniche avanzate come l’analisi funzionale delle componenti principali e il clustering funzionale delle curve ottenute aiuta a identificare gruppi distinti di comportamento dell’espressione genica. Oltre a questi contributi, questo lavoro valuta l’influenza delle varianti genetiche sulle dinamiche di espressione genica. Questo obiettivo è stato raggiunto attraverso un’analisi nota come analisi dei tratti quantitativi di espressione (eQTLs), e fornisce la prova che la forma di queste funzioni di espressione genica è geneticamente controllata. Nel corso del processo di ricerca, sono state incontrate sfide legate alla sparsità dei dati e all’alta dimensionalità. Sono state affrontate utilizzando metodi di imputazione e di selezione dei geni, che hanno migliorato la qualità e la fruibilità dei dati. Questa tesi offre una migliore comprensione delle dinamiche di espressione genica durante il differenziamento delle cellule B e delle influenze genetiche sull’espressione genica, mantenendo la continuità delle funzioni di espressione genica attraverso lo pseudotempo. Inoltre, questi risultati offrono la possibilità di esplorare le potenziali connessioni tra le varianti genetiche associate alle dinamiche di espressione genica e le malattie, contribuendo così allo sviluppo di strategie terapeutiche.
Modeling genetic effects on gene expression dynamics in B cells: a functional analysis approach
Zanotti, Daniela
2022/2023
Abstract
The immune system safeguards the body from various pathogens, and B cells are key players within this complex system: they are mainly responsible for producing antibodies against pathogens and toxins, and contribute to the development of immune memory. To gain deeper insights into the underlying mechanisms governing B cell behavior, researchers have employed a technique known as pseudotime analysis. This approach enables the reconstruction of the cellular differentiation process along a continuous trajectory, unveiling the progression of B cells from their naive states, where they have yet to encounter any pathogens, to memory states, where they respond specifically to particular antigens. Moreover, through an examination of the gene expression profiles of these cells, namely the conversion of genetic information within genes into functional proteins, researchers have uncovered that the expression of genes can change as a function of the cell’s state trajectory. This thesis is centered around the concept of continuous cellular progression. It specifically focuses on how gene expression can be mathematically described as a function of a variable called pseudotime, which represents the position of cells along the trajectory of different cellular states, and on whether the shape of these gene expression functions is genetically regulated. Using single-cell RNA sequencing data derived from the OneK1K cohort, the study employs positive constrained cubic splines to model gene expression across the cell state trajectory. The application of advanced techniques such as Functional Principal Component Analysis (FPCA) and functional clustering to the obtained curves aids in identifying distinct patterns of gene expression behaviors. In addition to these contributions, this work assesses the influence of genetic variants on gene expression dynamics. This is achieved through an analysis known as expression Quantitative Trait Loci (eQTLs) analysis, and provides evidence that the shape of these gene expression functions is genetically controlled. Throughout the research process, challenges with data sparsity and high dimensionality were encountered and addressed using imputation and gene selection methods, which improved the quality and usability of the data. This thesis offers an enriched comprehension of gene expression dynamics throughout B cell differentiation and of genetic influences on gene expression by maintaining the continuity of gene expression functions across pseudotime. Furthermore, these findings offer avenues for exploring potential connections between genetic variants associated with gene expression dynamics and diseases, thus contributing to the development of treatment strategies.File | Dimensione | Formato | |
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https://hdl.handle.net/10589/210146