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Biology subjects

Ugur, E.

Publications and source records attributed to Ugur, E..

7 recordsLinked to original sources

Aggregate Deep Visual Proteomics reveals selective organization of α-synuclein inclusions

-Synuclein inclusions, the lesions typical of Parkinson's disease, have been interpreted as protective deposits, toxic species or failed degradation products, each predicting a different composition and correlating that enrichment with neuron dysfunction and eventual degeneration. Distinguishing them requires measuring what an inclusion concentrates relative to the cell that built it. Here we developed Aggregate Deep Visual Proteomics, which uses ultrasensitive mass spectrometry to profile laser-microdissected inclusions against the remainder of the same cell or a matched host. In HEK293 cells, iPSC-derived dopaminergic neurons and post-mortem patient brain, each inclusion concentrated a selective subset of its cell's proteome, independent of abundance and dominated by membrane, in neurons largely presynaptic. Inclusions took up whole assemblies, with subunits moving together in 421 of 438 complexes, and the V-ATPase alone was enriched in all three systems. Degradation cargo stayed with the receptors that route it, consistent with sequestration and failed degradation acting together. Neuritic and somatic inclusions differed, the former rich in UBQLN2 and the latter in intermediate filaments. In the cell line, phosphorylated -synuclein was tenfold higher in the inclusion than in the rest of the cell, with about one third of the rise already present before an inclusion was visible. Measured against its host, the composition of an inclusion becomes a readout of how it formed and a source of candidate markers and therapeutic targets.

biochemistry↗

In the same cell, the proteome defines cellular state and the transcriptome marks transitions

Bulk transcriptome and proteome correlate only modestly, but this has not been investigated in the same cell or across cell-state changes. Here we introduce a scalable technology that quantifies thousands of proteins and transcripts in the same cell, separating RNA from protein by tip-based C18 capture and pairing full-length RNA sequencing with latest-generation mass spectrometry. In HeLa cells, transcript and protein abundances agree on the broad ranking within a cell (r = 0.45), but do not co-vary across the population (r = 0.038). In pluripotency transitions, only a third of matched transcripts and proteins change synchronously, yet the transcription factors defining each state stay tightly co-regulated. Transcript variance is several-fold larger than protein variance, reflecting transcriptional bursting and mRNA sampling noise. The proteome is thus the stable, low-noise definition of cell state, while the transcriptome marks cellular transitions; consequently, the proteome defines cell-state from far fewer cells.

systems biology↗

Deep Learning-Driven Fragment Ion Selection for Improved Quantification in MS based Proteomics

Quantitative proteomics relies on accurate selection of fragment ions for quantification, yet most current algorithms apply simple strategies such as median intensity or single quality filters. Modern data-independent acquisition (DIA) searches generate rich features such as fragment ion correlations, retention time and many others that could be leveraged to assess fragment quality. We introduce QuantSelect, a novel strategy to select optimal fragments by systematically integrating these features via self-supervised deep learning. QuantSelect uses a regularized, weighted-variance loss on intensity traces normalized via our directLFQ algorithm. This allows learning a fragment quality score without ground truth labels, enabling on-the-fly training on label-free DIA datasets. Integrated within our alphaDIA pipeline, QuantSelect significantly improves quantitative accuracy and in some cases substantially corrects protein intensity estimation. Sensitivity in differential expression improved by 68% in a mixed-species benchmarking dataset and by 18% in single-cell data. QuantSelect provides a practical framework for data-driven fragment selection that improves accuracy, precision and downstream inference in DIA proteomics.

bioinformatics↗

Mechanistic basis of gene regulation by SRCAP and H2A.Z

Discriminating regulatory functions of chromatin composition from those of chromatin-modifying complexes is a central problem in gene regulation. This question remains unexplored in the context of histone variants and their dedicated chromatin remodelers. Here we dissect the distinct and cell cycle-dependent functions of Snf2 Related CREBBP Activator Protein (SRCAP) and H2A.Z in gene regulation of pluripotent stem cells. We uncover dynamic changes of H2A.Z occupancy and continuous requirement of SRCAP over the cell cycle. We discover that SRCAP exhibits essential H2A.Z-independent functions in inhibiting DNA binding of dozens of pioneer transcription factors at enhancers by steric hindrance. In contrast, H2A.Z acts mainly as a transcriptional repressor gatekeeping the expression of lineage-specific genes. Our study establishes the catalytic-independent role of a chromatin remodeler in broadly regulating transcription factor binding, and demonstrates how a chromatin remodeler-histone variant pair orchestrates transcription to maintain self-renewal and plasticity of pluripotent stem cells.

molecular biology↗

Capture of Human Neuromesodermal and Posterior Neural Tube Axial Stem Cells

The spinal cord, nerves, and skeletal muscles arise from neuromesodermal progenitors (NMPs). We have developed a growth-factor screening strategy, utilizing ES and iPS cells, facilitating the indefinite self-renewal of two types of human axial stem cells (AxSCs), closely resembling mouse NMPs (NM-AxSCs) and posterior neural tube progenitors (N-AxSCs). Under specific regimens-- Wnt/CHIR99021, FGF2, and TGF-{beta} inhibitor SB431542 (CFS) and excluding FGF2 (CS), respectively--these AxSCs self-renew and sustain telomeres. Single cell transcriptomics and proteomics have revealed expression of posterior growth-zone and dorsoventral neural tube markers in NM-AxSCs, and correspondingly, differentiation to a wide spectrum of neural tube neurons and myocytes. N-AxSCs rapidly matured into dorsal sensory subsets and neural crest. Crucially, neither AxSC type produces teratomas, and analogous mouse NM-AxSCs integrated successfully into the neural tube and somites. Capturing of AxSCs from patient and GMP ES / iPS cells without transgenesis unveils ontogeny and promises modeling and therapy in neuropathies.

developmental biology↗

Non-canonical functions of UHRF1 maintain DNA methylation homeostasis in cancer cells

DNA methylation is an essential epigenetic chromatin modification, and its maintenance in mammals requires the protein UHRF1. It is yet unclear if UHRF1 functions solely by stimulating DNA methylation maintenance by DNMT1, or if it has important additional functions. Using degron alleles, we show that UHRF1 depletion causes a much greater loss of DNA methylation than DNMT1 depletion. This is not caused by passive demethylation as UHRF1-depleted cells proliferate more slowly than DNMT1-depleted cells. Instead, bioinformatics, proteomics and genetics experiments establish that UHRF1, besides activating DNMT1, interacts with DNMT3A and DNMT3B and promotes their activity. In addition, we show that UHRF1 antagonizes active DNA demethylation by TET2. Therefore, UHRF1 has non-canonical roles that contribute importantly to DNA methylation homeostasis; these findings have practical implications for epigenetics in health and disease.

molecular biology↗

Comprehensive chromatin proteomics resolves functional phases of pluripotency

The establishment of cellular identity is driven by transcriptional and epigenetic regulation exerted by the components of the chromatin proteome - the chromatome. However, chromatome composition and its dynamics in functional phases of pluripotency have not been comprehensively analyzed thus limiting our understanding of these processes. To address this problem, we developed an accurate mass spectrometry (MS)-based proteomic method called Chromatin Aggregation Capture (ChAC) followed by Data-Independent Acquisition (DIA) to analyze chromatome reorganizations during the transition from ground to formative and primed pluripotency states. This allowed us to generate a comprehensive atlas of proteomes, chromatomes, and chromatin affinities for the three pluripotency phases, revealing the specific binding and rearrangement of regulatory complexes. The technical advances, the comprehensive chromatome atlas, and the extensive analysis reported here provide a foundation for an in-depth understanding of mechanisms that govern the phased progression of pluripotency and changes of cellular identities in development and disease.

biochemistry↗