Max Frank

The genomic sequence of an organism is nearly identical in all its cells and over its lifetime. Epigenomic marks, however, such as DNA methylation and chromatin accessibility, are subject to drastic changes across different tissues and throughout organism development. Recent advancements, notably the development of multi-omics single-cell technologies, allow for simultaneous interrogation of DNA methylation, chromatin accessibility, and transcriptomes within individual cells. This offers unique opportunities to gain insight into mechanisms by which the epigenome shapes gene expression and influences cell fate. However, analyzing these datasets poses major challenges: Typically, smaller numbers of cells can be assayed per experiment than conventional single-cell RNAseq with lower coverage due to small amounts of input material. This means that classical statistical methods are underpowered to detect subtle changes in DNA methylation and chromatin accessibility. Furthermore, current tests can only detect differences between discrete and pre-defined cell populations, whereas single-cell approaches allow for studying continuous processes in organismal lineage development. To address this, I propose computational methods for decomposing single-cell epigenetic heterogeneity across developmental time and genomic loci. This thesis introduces new concepts, leveraging pseudotemporal ordering of cells to conduct statistical inferences upon epigenetic changes. At the core of these developments is GPmeth, a Gaussian process framework designed to model highly sparse single-cell methylation and accessibility information by enforcing smooth variation across pseudotime and genomic coordinates and thus effectively sharing information between cells and genomic positions. Importantly, this model does not rely on averaging methylation signals across fixed genomic windows but can identify differentially methylated/accessible regions in a data-driven way. Testing GPmeth against other models without dynamic aggregation of methylation data revealed increased sensitivity to detect even subtle epigenetic changes. Application of GPmeth to scNMT-seq data from mouse embryonic stem cells undergoing gastrulation revealed over 3000 enhancer elements that exhibited dynamic changes in chromatin accessibility or DNA methylation rates during germ layer formation. The detailed spatiotemporal model allowed for a precise definition of differentially methylated regions, validated by transcription factor binding motif analysis. Furthermore, the clustering of temporal epigenetic patterns identified lineage-specific enhancers in an unsupervised manner. I expect GPmeth to be a valuable tool for studying time-resolved epigenetic regulation in several emerging multimodal single-cell datasets.

To a large extent functional diversity in cells is achieved by the expansion of molecular complexity beyond that of the coding genome. Various processes create multiple distinct but related proteins per coding gene – so-called proteoforms – that expand the functional capacity of a cell. Evaluating proteoforms from classical bottom-up proteomics datasets, where peptides instead of intact proteoforms are measured, has remained difficult. Here we present COPF, a tool for COrrelation-based functional ProteoForm assessment in bottom-up proteomics data. It leverages the concept of peptide correlation analysis to systematically assign peptides to co-varying proteoform groups. We show applications of COPF to protein complex co-fractionation data as well as to more typical protein abundance vs. sample data matrices, demonstrating the systematic detection of assembly- and tissue-specific proteoform groups, respectively, in either dataset. We envision that the presented approach lays the foundation for a systematic assessment of proteoforms and their functional implications directly from bottom-up proteomic datasets.

Data-independent acquisition modes isolate and concurrently fragment populations of different precursors by cycling through segments of a predefined precursor m/z range. Although these selection windows collectively cover the entire m/z range, overall, only a few per cent of all incoming ions are isolated for mass analysis. Here, we make use of the correlation of molecular weight and ion mobility in a trapped ion mobility device (timsTOF Pro) to devise a scan mode that samples up to 100% of the peptide precursor ion current in m/z and mobility windows. We extend an established targeted data extraction workflow by inclusion of the ion mobility dimension for both signal extraction and scoring and thereby increase the specificity for precursor identification. Data acquired from whole proteome digests and mixed organism samples demonstrate deep proteome coverage and a high degree of reproducibility as well as quantitative accuracy, even from 10ng sample amounts.

Living systems integrate biochemical reactions that determine the functional state of each cell. Reactions are primarily mediated by proteins. In proteomic studies, these have been treated as independent entities, disregarding their higher-level organization into complexes that affects their activity and/or function and is thus of great interest for biological research. Here, we describe the implementation of an integrated technique to quantify cell-state-specific changes in the physical arrangement of protein complexes concurrently for thousands of proteins and hundreds of complexes. Applying this technique to a comparison of human cells in interphase and mitosis, we provide a systematic overview of mitotic proteome reorganization. The results recall key hallmarks of mitotic complex remodeling and suggest a model of nuclear pore complex disassembly, which we validate by orthogonal methods. To support the interpretation of quantitative SEC-SWATH-MS datasets, we extend the software CCprofiler and provide an interactive exploration tool, SECexplorer-cc.