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Goertler, F.

Publications and source records attributed to Goertler, F..

4 recordsLinked to original sources

HIDE-Deconv: A hierarchical deconvolution framework for multiscale characterization of cellular remodeling

Most deconvolution methods estimate cellular composition at a single level of cellular resolution despite biological processes often manifesting within fine-grained cellular subpopulations. We present HIDE-Deconv, a hierarchical deconvolution framework that jointly optimizes cellular compositions across multiple levels of a cell-type hierarchy while maintaining consistency between resolutions. In benchmark experiments, HIDE-Deconv achieved the highest overall predictive performance among evaluated methods. Analyses of lung adenocarcinoma, sepsis, COVID-19 and systemic lupus erythematosus revealed biologically relevant cellular remodeling that remained concealed at broader levels of cellular resolution. HIDE-Deconv is available as an open-source framework at https://github.com/dvoelkl/HIDE-deconv.

bioinformatics↗

DynaMiCs - Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions

Single-cell techniques facilitate the molecular analysis of individual cells, providing insights into cellular diversity, function, and the complexity of biological systems. However, their application is typically limited to small-scale studies involving individual or a few dozen samples, as a consequence of costs and experimental requirement. This complicates the inference of robust conclusions about populations. Bulk transcriptomics offers cost-efficient measurements with low experimental requirements. However, the cellular resolution is lost and only a complex linear combination of signals from multiple cells is observed. Thus, gene expression changes cannot be attributed to individual cells or cell populations. Cell-type deconvolution methods infer cellular compositions from bulk transcriptomics data. State-of-the-art approaches use single-cell data to build molecular reference profiles and identify powerful cell-type markers for improved deconvolution. In this context, we propose Dynamic cell-type deconvolution ensembles for Mapping in mixed Conditions (DynaMiCs) for the integration of single-cell and bulk transcriptomics data. Specifically, DynaMiCs dynamically extracts information from single-cell experiments to (1) provide more accurate estimates of cellular compositions, and (2) establish a mapping between bulk and single-cell data. Consequently, DynaMiCs enables the investigation of how cell populations change in both quantity and molecular characteristics between different phenotypes, informed by single-cell experiments.

bioinformatics↗

Deconvolution of omics data in Python with Deconomix -- cellular compositions, cell-type specific gene regulation, and background contributions

BackgroundGene expression profiles derived from heterogeneous bulk samples contain signals from various cell populations. Cell-type deconvolution approaches are computational tools to reverse engineer the composition of bulks in term of cell populations. Accurate estimates of cell compositions are crucial for identifying cell populations relevant for disease. Moreover, analyses, such as the identification of differentially expressed genes, can be confounded by cellular composition, as differences in gene expression may arise from both variations in cellular composition and gene regulation. ResultsWe present Deconvolution of omics data (Deconomix) - a comprehensive toolbox for the cell-type deconvolution of bulk transcriptomics data, available as a Python package and standalone graphical user interface. Deconomix stands apart from competing solutions with rich functionality and highly efficient implementations. It facilitates (A) the inference of cellular compositions from bulk transcriptomics data, (B) the machine learning-based optimization of gene weights to resolve small cell populations and to disentangle phenotypically related cells, (C) the inference of background contributions which otherwise would deteriorate cell-type deconvolution, and (D) population estimates of cell-type specific gene regulation. To showcase the application of Deconomix, we present a case study on breast cancer data from TCGA, highlighting subtype-specific cellular compositions and cell-type-specific gene-regulatory programs. ConclusionWe present Deconomix, a comprehensive Python package including a graphical user interface for the inference of cellular compositions, cell-type-specific gene regulation, and background contributions from bulk transcriptomics data. O_TEXTBOXKey PointsO_LIDeconomix optimizes gene weights to disentangle small cell populations and phenotypically related cells. C_LIO_LIDeconomix estimates cell compositions, unknown background contributions and cell-type specific gene regulation from bulk transcriptomics data. C_LIO_LIA Python package is available as code repository and installable via PyPI [1]. C_LIO_LIA standalone graphical user interface is available as code repository [2]. C_LIO_LIAn exemplary analysis of a breast cancer case study is provided as tutorial [3]. C_LI C_TEXTBOX

bioinformatics↗

Progressive protein aggregation in PRPF31 patient retinal pigment epithelium cells: the mechanism and its reversal through activation of autophagy

Mutations in pre-mRNA processing factor 31 (PRPF31), a core protein of the spliceosomal tri-snRNP complex, cause autosomal-dominant retinitis pigmentosa (adRP). It has remained an enigma why mutations in ubiquitously expressed tri-snRNP proteins result in retina-specific disorders, and so far, the underlying mechanism of splicing factors-related RP is poorly understood. Here, we used iPSC technology to generate retinal organoids and RPE models from three patients with severe and very severe PRPF31-adRP, normal individuals and a CRISPR/Cas9-corrected isogenic control. To fully assess the impacts of PRPF31 mutations, quantitative proteomics analyses of retinal organoids and RPE cells was carried out showing RNA splicing, autophagy and lysosome, unfolded protein response (UPR) and visual cycle-related pathways to be significantly affected. Strikingly, the patient-derived RPE and retinal cells were characterised by the presence of large amounts of cytoplasmic aggregates containing the mutant PRPF31 and misfolded, ubiquitin-conjugated proteins including key visual cycle proteins, which accumulated progressively with time. Mutant PRPF31 variant was not incorporated into splicing complexes, but reduction of PRPF31 wildtype levels led to tri-snRNP assembly defects in Cajal bodies of PRPF31 patient retinal cells with reduced U4/U6 snRNPs and accumulation of U5, smaller nuclear speckles and reduced formation of active spliceosomes giving rise to global splicing dysregulation. Moreover, the impaired waste disposal mechanisms further exacerbated aggregate formation, and targeting these by activating the autophagy pathway using Rapamycin resulted in reduction of cytoplasmic aggregates and improved cell survival. Our data demonstrate that it is the progressive aggregate accumulation that overburdens the waste disposal machinery rather than direct PRPF31-initiated mis-splicing, and thus relieving the RPE cells from insoluble cytoplasmic aggregates presents a novel therapeutic strategy that can be combined with gene therapy studies to fully restore RPE and retinal cell function in PRPF31-adRP patients. HighlightsO_LIPRPF31 RP mutations lead to formation of insoluble aggregates containing the mutant PRPF31 and misfolded, ubiquitin conjugated proteins including key visual cycle proteins (e.g. RLBP1) in RPE cells, which accumulate progressively with time and affect tight junctions and cell survival. C_LIO_LIMutant PRPF31 is predominantly localised in cytoplasmic aggregates of patient specific RPE and retinal cells and is not able to be incorporated into splicing complexes to cause direct mis-splicing. C_LIO_LIHigh-throughput quantitative proteomics identifies significantly altered RNA splicing, visual perception, retinoid metabolism, waste disposal and unfolded protein response pathways in patient RPE cells, and autophagy and lysosome, unfolded protein response (UPR) and visual cycle-related pathways in photoreceptor cells. C_LIO_LIAccumulation of PRPF31 mutant variant as cytoplasmic aggregates reduces wildtype PRPF31 in the nucleus leading to tri-snRNP assembly defects, characterised by accumulation of U5 and reduction of U4/U6 snRNPs in Cajal bodies, altered morphology of nuclear speckles and consequently downregulation of active spliceosomes (Bact and C complexes) in PRPF31 patient RPE and retinal cells. C_LIO_LIProteomic study of insoluble aggregates identifies other RP-linked splicing factors and multiple key retinal-specific proteins, whose variants are linked to retinitis pigmentosa, within the aggregates of patient RPE cells. C_LIO_LIPRPF31 patient RPE cells have impaired waste disposal and proteasome mediated degradation, which together with the impaired autophagy pathway, further exacerbate aggregate formation. C_LIO_LIPhagocytosis of photoreceptor outer segment fragments (POS) shed daily by RPE cells accelerates aggregation of key proteins indicating enhanced cytoplasmic aggregate formation under physiological conditions in patient RPE cells. C_LIO_LIActivation of autophagy via administration of rapamycin results in reduction of cytoplasmic aggregates in RPE cells, correct localisation of mislocated and misfolded proteins to the nucleus, thereby improving cell survival. C_LI

cell biology↗