In dieser Dissertation haben wir das Verhalten menschlicher Zellen in Raum und Zeit untersucht. Hochwertige Datensätze subzellulärer Regionen in HEK293-Zellen wurden mit Hilfe der BirA* Proximity-Labelling-Aktivität erstellt, wobei die Lokalisierung auf zelluläre Regionen beschränkt wurde, die mit herkömmlichen Methoden nur schwer zu reinigen sind (d. h. die dem Zytosol zugewandten Seiten des ER, Mitochondrien und Plasma-membranen). Wir entwickelten daraufhin einen Ansatz zur Kartierung der Verteilung von Proteinen, die aktiv an RNA binden, und nannten ihn f-XRNAX. Wir stellten hintergrundkorrigierte Proteome für Zellkerne, Zytoplasma und Membranen von HEK293-Zellen her. Überraschenderweise wurden viele nicht-kanonische RBPs in der Membranfraktion identifiziert, und ihre Peptidprofile waren in Regionen mit hoher Dichte an intrinsisch ungeordneten Regionen angereichert, was auf eine möglicherweise schwache, durch diese nicht-strukturellen Motive vermittelte Interaktion mit RNA hinweist. Schließlich konnten wir die unterschiedliche Bindung desselben Proteins an RNA in verschiedenen HEK293-Kompartimenten nachweisen. Im zweiten Teil dieser Arbeit konzentrierten wir uns auf die Bestimmung und Quantifizierung von neu transkribierten RNAs auf Einzelzellebene. Die Kinetik der RNA-Transkription und -Degradation war bis vor kurzem auf Einzelzellebene nicht messbar. Daher haben wir einen neuen Ansatz (SLAM-Drop-seq genannt) entwickelt, indem wir die veröffentlichte SLAM-seq-Methode an Einzelzellen angepasst haben. Wir haben SLAM-Drop-seq verwendet, um die zeitabhängigen RNA-Kinetikraten der Transkription und des Umsatzes für Hunderte von oszillierenden Transkripten während des Zellzyklus von HEK293-Zellen zu schätzen. Wir fanden heraus, dass Gene ihre Expression mit unterschiedlichen Strategien regulieren und spezifische Modi zur Feinabstimmung ihrer kinetischen Raten entlang des Zellzyklus haben. / In this PhD dissertation we investigated the behaviour of human cells through space and time. High quality datasets of subcellular regions in HEK293 cells were generated using BirA* proximity labelling activity and restricting its localization at cellular regions difficult to purified with traditional methods (i.e., the cytosol-facing sides of the endoplasmic reticulum, mitochondria, and plasma membranes). We then developed an approach to map the distribution of proteins actively binding to RNA, and named it f-XRNAX. We recovered background-corrected proteomes for nuclei, cytoplasm and membranes of HEK293 cells. Surprisingly, many non-canonical RBPs were identified in the membrane fraction, and their peptide profiles were enriched in regions with high density of intrinsically disordered regions, indicating a possibly weak interaction with RNA mediated by these non-structural motives. Lastly, we provided evidence of the differential binding to RNA of the same protein in different HEK293 compartments. In the second part of this thesis, we focused on the determination and quantification of newly transcribed RNAs at the single-cell level. The kinetics of RNA transcription, processing and degradation were until recently not measurable at the single-cell level. Thus, we have developed a novel approach (called SLAM-Drop-seq ) by adapting the published SLAM-seq method to single cells. We used SLAM Drop-seq to estimate time-dependent RNA kinetics rates of transcription and turnover for hundreds of oscillating transcripts during the cell cycle of HEK293 cells. We found that genes regulate their expression with different strategies and have specific modes to fine-tune their kinetic rates along the cell cycle.

Since the discovery of the structure of DNA in the early 1953s and its double-chained complement of information hinting at its means of replication, biologists have recognized the strong connection between molecular structure and function. In the past two decades, there has been a surge of research on an ever-growing class of RNA molecules that are non-coding but whose various folded structures allow a diverse array of vital functions. From the well-known splicing and modification of ribosomal RNA, non-coding RNAs (ncRNAs) are now known to be intimately involved in possibly every stage of DNA translation and protein transcription, as well as RNA signalling and gene regulation processes. Despite the rapid development and declining cost of modern molecular methods, they typically can only describe ncRNA's structural conformations in vitro, which differ from their in vivo counterparts. Moreover, it is estimated that only a tiny fraction of known ncRNAs has been documented experimentally, often at a high cost. There is thus a growing realization that computational methods must play a central role in the analysis of ncRNAs. Not only do computational approaches hold the promise of rapidly characterizing many ncRNAs yet to be described, but there is also the hope that by understanding the rules that determine their structure, we will gain better insight into their function and design. Many studies revealed that the ncRNA functions are performed by high-level structures that often depend on their low-level structures, such as the secondary structure. This thesis studies the computational folding mechanism and inverse folding of ncRNAs at the secondary level. In this thesis, we describe the development of two bioinformatic tools that have the potential to improve our understanding of RNA secondary structure. These tools are as follows: (1) RAFFT for efficient prediction of pseudoknot-free RNA folding pathways using the fast Fourier transform (FFT)}; (2) aRNAque, an evolutionary algorithm inspired by Lévy flights for RNA inverse folding with or without pseudoknot (A secondary structure that often poses difficulties for bio-computational detection). The first tool, RAFFT, implements a novel heuristic to predict RNA secondary structure formation pathways that has two components: (i) a folding algorithm and (ii) a kinetic ansatz. When considering the best prediction in the ensemble of 50 secondary structures predicted by RAFFT, its performance matches the recent deep-learning-based structure prediction methods. RAFFT also acts as a folding kinetic ansatz, which we tested on two RNAs: the CFSE and a classic bi-stable sequence. In both test cases, fewer structures were required to reproduce the full kinetics, whereas known methods (such as Treekin) required a sample of 20,000 structures and more. The second tool, aRNAque, implements an evolutionary algorithm (EA) inspired by the Lévy flight, allowing both local global search and which supports pseudoknotted target structures. The number of point mutations at every step of aRNAque's EA is drawn from a Zipf distribution. Therefore, our proposed method increases the diversity of designed RNA sequences and reduces the average number of evaluations of the evolutionary algorithm. The overall performance showed improved empirical results compared to existing tools through intensive benchmarks on both pseudoknotted and pseudoknot-free datasets. In conclusion, we highlight some promising extensions of the versatile RAFFT method to RNA-RNA interaction studies. We also provide an outlook on both tools' implications in studying evolutionary dynamics.