Biology subjects

Ohler, U.

Publications and source records attributed to Ohler, U..

At least 19 recordsLinked to original sources

bioRxiv2025

GeneSys: Generative Modeling of Developmental System

Temporal single-cell transcriptomics enables the reconstruction of dynamic gene expression changes during development. Yet, its analytical power is often limited by data sparsity, technical noise, and imbalanced representation of cell types across time points. To overcome these challenges, we present GeneSys (Generative Modeling of Developmental System), a generative deep learning model that simulates single-cell transcriptomic landscapes under developmental constraints, which is informed by prior biological knowledge or user-defined hypotheses. GeneSys integrates a temporal variational autoencoder with a cell-type classifier, requiring a lineage blueprint as input, which enables it to model the temporal transitions of transcriptional states with cell-type specificity. Leveraging data from Arabidopsis thaliana roots and mouse embryos, we show that GeneSys learns robust developmental trajectories, generates imputed and representative transcriptomes, and enhances gene prioritization accuracy compared to unregularized scRNA-seq data. By applying gene masking and augmentation, GeneSys reveals interpretable gene expression programs (GEPs) and serves as an in silico platform to test the impact of specific genes or gene sets on user-defined developmental outcomes. Additionally, GeneSys computes linear interaction matrices (LIMAs) to infer dynamic gene networks and prioritize transcription factors with spatiotemporal resolution. These features enable GeneSys to nominate key genes governing state transitions in a developmental system, supporting both mechanistic insight and hypothesis generation. Together, GeneSys provides a flexible and extensible framework to denoise single-cell data and simulate transcriptomic developmental landscape guided by known or hypothesized developmental constraints, empowering the discovery of regulatory mechanisms from high-dimensional single-cell datasets.

systems biology↗

bioRxiv2025

Metrics Matter: Why We Need to Stop Using Silhouette in Single-Cell Benchmarking

Current-day single-cell studies comprise complex data sets affected by nested batch effects caused by technical and biological factors, relying on advanced integration methods. Silhouette is an established metric for assessing clustering results, comparing within-cluster cohesion to between-cluster separation, and adaptations of it have emerged as the dominant choice to evaluate the success of these integration methods. However, silhouettes assumptions are often violated in single-cell data integration scenarios. We demonstrate that silhouette-based metrics can neither reliably assess batch effect removal nor biological signal conservation and are thus inherently unsuitable for data with (nested) batch effects. We propose alternative, robust evaluation strategies that enable accurate integration method assessment and call to update benchmarking practices.

bioinformatics↗

bioRxiv2024

Cell-type specific prediction of RNA stability from RNA-protein interactions

RNA-binding proteins (RBPs) are important contributors to post-transcriptional regulatory processes. The combinatorial action of expressed RBPs and non-coding factors bound to the same transcript determines post-transcriptional properties of the mRNA in a context-dependent manner. To gain a better understanding of RNA stability and translational activity across different conditions, we have compiled and analyzed a set of ribosome profiling datasets for four human cell lines and used existing and newly generated metabolic labeling data to determine matching RNA degradation rates. We then used machine learning methods to predict RNA degradation rate and translation level from RBP binding information, which comprised existing in vivo binding datasets and computationally predicted binding sites. Utilizing this new RNA stability resource, we predicted RNA degradation rate and translation level from RBP binding alone. In vivo binding sites had higher importance for prediction compared to computationally predicted binding sites, likely due to confounding effects. We further explored the feature importance of different RBPs for stability prediction in the context of differential stability conferred by 3UTR isoforms. Taken together, an RNA stability machine learning model trained on one context successfully generalizes but is impacted by the availability and reliability of current data.

bioinformatics↗

bioRxiv2024

Sensitive dissection of a genomic regulatory landscape using bulk and targeted single-cell activation

Transcriptional enhancers are non-coding DNA elements that regulate gene transcription in a temporal and tissue-specific manner. Despite advances in computational and experimental methods, identifying enhancers and their target genes essential for specific biological processes remains challenging. Determining target genes for enhancers is also complex and often relies on indirect, low-resolution, and/or assumptive methodologies. To identify and functionally perturb enhancers at their endogenous sites without altering their sequence, we performed a pooled tiling CRISPR activation (CRISPRa) screen surrounding PHOX2B, a master regulator of neuronal cell fate and a key player in neuroblastoma development. This screen allowed the de novo identification of CRISPRa responsive elements (CaREs) that alter cellular growth within the 2 Mb genomic region. To determine CaRE target genes, we developed TESLA-seq (TargEted SingLe cell Activation), which combines CRISPRa screening with targeted single-cell RNA-sequencing and enables the parallel readout of the effect of hundreds of enhancers on all genes in the locus. While most TESLA-revealed CaRE-gene relationships involved neuroblastoma-related regulatory elements already active in the system, we found many CaREs and target connections normally active only in other tissue types or with no previous evidence and induced out of context by CRISPRa. This highlights the power of TESLA-seq to reveal gene regulatory networks, including edges active outside of a given experimental system. HighlightsO_LISystematically perturbed regulatory landscape in a 2 Mb genomic region surrounding PHOX2B to identify hundreds of CRISPRa-responsive elements that affect cellular growth C_LIO_LIDeveloped TESLA-seq as a principled molecular approach to find gene targets of dozens of candidate regulatory elements C_LIO_LIValidated interactions between identified regulatory elements and target genes and characterized their genomic features C_LIO_LIIntegrated a compendium of epigenomic datasets to identify regulatory relationships induced out of context or with no previous evidence C_LI

genomics↗

bioRxiv2024

High-quality peptide evidence for annotating non-canonical open reading frames as human proteins

A major scientific drive is to characterize the protein-coding genome as it provides the primary basis for the study of human health. But the fundamental question remains: what has been missed in prior genomic analyses? Over the past decade, the translation of non-canonical open reading frames (ncORFs) has been observed across human cell types and disease states, with major implications for proteomics, genomics, and clinical science. However, the impact of ncORFs has been limited by the absence of a large-scale understanding of their contribution to the human proteome. Here, we report the collaborative efforts of stakeholders in proteomics, immunopeptidomics, Ribo-seq ORF discovery, and gene annotation, to produce a consensus landscape of protein-level evidence for ncORFs. We show that at least 25% of a set of 7,264 ncORFs give rise to translated gene products, yielding over 3,000 peptides in a pan-proteome analysis encompassing 3.8 billion mass spectra from 95,520 experiments. With these data, we developed an annotation framework for ncORFs and created public tools for researchers through GENCODE and PeptideAtlas. This work will provide a platform to advance ncORF-derived proteins in biomedical discovery and, beyond humans, diverse animals and plants where ncORFs are similarly observed.

molecular biology↗

bioRxiv2023

MatK impacts Differential Chloroplast Translation of Ribosomal and Photosynthetic genes by limiting spliced tRNA-K(UUU) abundance

The protein levels of chloroplast photosynthetic genes and genes related to the chloroplast genetic apparatus vary to adapt to different conditions. However, the underlying mechanisms governing these variations remain unclear. The chloroplast intron Maturase K is encoded within the trnK intron and has been suggested to be required for splicing several group IIA introns, including the trnK intron. In this study, we employed RNA immunoprecipitation followed by high-throughput sequencing (RIP-Seq) to identify MatKs preference for binding to group IIA intron domains I and VI within target transcripts. Importantly, these domains are crucial for branch point selection, and we discovered alternative branch points in three MatK target introns, the first observed instances of alternative splicing in chloroplasts. The alternative trnK lariat structure showed increased accumulation during heat acclimation. The cognate codon of tRNA-K(UUU) is highly enriched in mRNAs encoding ribosomal proteins and ribosome profiling in a trnK-matK over-expressor exhibited elevated levels of the spliced tRNA-K(UUU). Our analysis revealed a significant up-shift in the translation of ribosomal proteins compared to photosynthetic genes. Our findings suggest the existence of a novel regulatory mechanism linked to the abundance of tRNA-K(UUU), enabling the differential expression of functional chloroplast gene groups.

plant biology↗

bioRxiv2023

Explaining Deep Neural Networks for the Prediction of Translation Rates

A recent convolutional neural network model accurately quantifies the relationship between massively parallel synthetic 5 untranslated regions (5UTRs) and translation levels, but the underlying sequence determinants remain elusive. Applying model interpretation, we extract representations of regulatory logic, revealing a complex interplay of regulatory sequence elements. Guided by insights from model interpretation, we adapt the model by human reporter data to obtain superior performance, which will promote applications in synthetic biology and precision medicine.

bioinformatics↗

bioRxiv2022

Liam tackles complex multimodal single-cell data integration challenges

Multi-omics characterization of single cells holds outstanding potential for profiling gene regulatory states of thousands of cells and their dynamics and relations. How to integrate multimodal data is an open problem, especially when aiming to combine data from multiple sources or conditions containing biological and technical variation. We introduce liam, a flexible model for the simultaneous horizontal and vertical integration of paired single-cell multimodal data. Liam learns a joint low-dimensional representation of two concurrently measured modalities, which proves beneficial when the information content or quality of the modalities differ. Its integration accounts for complex batch effects using a tuneable combination of conditional and adversarial training and can be optimized using replicate information while retaining selected biological variation. We demonstrate liams superior performance on multiple multimodal data sets, including Multiome and CITE-seq data. Detailed benchmarking experiments illustrate the complexities and challenges remaining for integration and the meaningful assessment of its success.

bioinformatics↗

bioRxiv2022

Brassinosteroid gene regulatory networks at cellular resolution

Brassinosteroids (BRs) are plant steroid hormones that regulate diverse processes such as cell division and cell elongation. BRs control thousands of genes through gene regulatory networks that vary in space and time. By using time-series single-cell RNA-sequencing to identify BR-responsive gene expression specific to different cell types and developmental stages of the Arabidopsis root, we uncovered the elongating cortex as a site where BRs trigger a shift from proliferation to elongation associated with increased expression of cell wall-related genes. Our analysis revealed HAT7 and GTL1 as BR-responsive transcription factors that regulate cortex cell elongation. These results establish the cortex as an important site for BR-mediated growth and unveil a BR signaling network regulating the transition from proliferation to elongation, illuminating new aspects of spatiotemporal hormone response.

plant biology↗

bioRxiv2022

Copy-number dosage regulates telomere maintenance and disease-associated pathways in neuroblastoma

Telomere maintenance in neuroblastoma is linked to poor outcome and caused by either TERT activation or through alternative lengthening of telomeres (ALT). In contrast to TERT activation, commonly caused by genomic rearrangements or MYCN amplification, ALT is less well understood. Alterations at the ATRX locus are key drivers of ALT but only present in [~]50% of ALT tumors. To identify potential new pathways to telomere maintenance, we investigate allele-specific gene dosage effects from whole genomes and transcriptomes in 115 primary neuroblastomas. We show that copy-number dosage deregulates telomere maintenance, genomic stability, and neuronal pathways and identify upregulation of variants of histone H3 and H2A as a potential alternative pathway to ALT. We investigate the interplay between TERT activation, overexpression and copy-number dosage and reveal loss of imprinting at the RTL1 gene associated with poor clinical outcome. These results highlight the importance of gene dosage in key oncogenic mechanisms in neuroblastoma.

genomics↗

bioRxiv2022

Single-cell transcriptomics of the Arabidopsis floral abscission zone

Abscission is the programmed separation of plant organs. It is widespread in the plant kingdom with important functions in development and environmental response. In Arabidopsis, abscission of floral organs (sepals, petals, and stamens) is controlled by two receptor-like protein kinases HAESA (HAE) and HAESA LIKE-2 (HSL2), which orchestrate the programmed dissolution of the abscission zone connecting floral organs to the developing fruit. In this work, we use single-cell RNA-Sequencing to characterize the core HAE/HSL2 abscission gene expression program. We identify the MAP KINASE PHOSPHATASE-1/MKP1 gene as a negative regulator of this pathway. MKP1 acts prior to activation of HAE/HSL2 signaling to establish a signaling threshold required for the initiation of abscission. By analogy to electrical circuit control, we liken MKP1 to a molecular grounding mechanism that dissipates errant pathway activation absent HAE/HSL2 signaling. Furthermore, we use single-cell data to identify genes expressed in two sub-populations of abscission zone cells: those proximal and those distal to the plane of separation. We identify INFLORESCENCE DEFICIENT IN ABSCISSION/IDA, encoding the activating ligand of HAE/HSL2, as one of the mRNAs most highly enriched in distal abscission zone cells at the base of the abscising organs. We show how this expression pattern forms a biophysical circuit breaker whereby, when the organ is shed, the source of the IDA peptide is removed, leading to cessation of HAE/HSL2 signaling. Overall, this work provides insight into the multiple control mechanisms acting on the abscission-signaling pathway.

plant biology↗

bioRxiv2022

Genome-wide analysis of dendritic cell differentiation

Dendritic cells (DCs) are immune sentinel cells that comprise antigen-presenting conventional DCs (cDCs) and cytokine-producing plasmacytoid DCs (pDCs). Cytokine Flt3 ligand (Flt3L) supports the proliferation of hematopoietic progenitors, and is also necessary and sufficient for DC differentiation. Here we characterized the spontaneous differentiation of a Flt3L-dependent murine progenitor cell line into pDCs and "myeloid" cDCs (cDC2s), and interrogated it using a genome-wide CRISPR/Cas9 dropout screen. The screen revealed multiple regulators of DC differentiation including the glycosylphosphatidylinositol transamidase complex, the Nieman-Pick type C cholesterol transporter and arginine methyltransferase Carm1; the role of Carm1 in pDC and cDC2 differentiation was confirmed by conditional targeting in vivo. We also found that negative regulators of mTOR signaling, including the subunits of TSC and GATOR1 complexes, restricted progenitor growth but enabled DC differentiation. The results provide a comprehensive forward genetic analysis of DC differentiation, and help explain how the opposing processes of proliferation and differentiation could be driven by the same cytokine.

immunology↗

bioRxiv2022

Multimodal single cell data integration challenge: results and lessons learned

Biology has become a data-intensive science. Recent technological advances in single-cell genomics have enabled the measurement of multiple facets of cellular state, producing datasets with millions of single-cell observations. While these data hold great promise for understanding molecular mechanisms in health and disease, analysis challenges arising from sparsity, technical and biological variability, and high dimensionality of the data hinder the derivation of such mechanistic insights. To promote the innovation of algorithms for analysis of multimodal single-cell data, we organized a competition at NeurIPS 2021 applying the Common Task Framework to multimodal single-cell data integration. For this competition we generated the first multimodal benchmarking dataset for single-cell biology and defined three tasks in this domain: prediction of missing modalities, aligning modalities, and learning a joint representation across modalities. We further specified evaluation metrics and developed a cloud-based algorithm evaluation pipeline. Using this setup, 280 competitors submitted over 2600 proposed solutions within a 3 month period, showcasing substantial innovation especially in the modality alignment task. Here, we present the results, describe trends of well performing approaches, and discuss challenges associated with running the competition.

bioinformatics↗

bioRxiv2021

Computational Mapping of the Human-SARS-CoV-2 Protein-RNA Interactome

Strong evidence suggests that human human RNA-binding proteins (RBPs) are critical factors for viral infection, yet there is no feasible experimental approach to map exact binding sites of RBPs across the SARS-CoV-2 genome systematically at a large scale. We investigated the role of RBPs in the context of SARS-CoV-2 by constructing the first in silico map of human RBP / viral RNA interactions at nucleotide-resolution using two deep learning methods (pysster and DeepRiPe) trained on data from CLIP-seq experiments. We evaluated conservation of RBP binding between 6 other human pathogenic coronaviruses and identified sites of conserved and differential binding in the UTRs of SARS-CoV-1, SARS-CoV-2 and MERS. We scored the impact of variants from 11 viral strains on protein-RNA interaction, identifying a set of gain-and loss of binding events. Lastly, we linked RBPs to functional data and OMICs from other studies, and identified MBNL1, FTO and FXR2 as potential clinical biomarkers. Our results contribute towards a deeper understanding of how viruses hijack host cellular pathways and are available through a comprehensive online resource (https://sc2rbpmap.helmholtz-muenchen.de).

bioinformatics↗

bioRxiv2021

Integrative annotation of genomic features reveal functional elements and epigenetic landscapes in the developing zebrafish

Zebrafish, a popular model for embryonic development and for modelling human diseases, has so far lacked a systematic functional annotation programme akin to those in other animal models. To address this, we formed the international DANIO-CODE consortium and created the first central repository to store and process zebrafish developmental functional genomic data. Our Data Coordination Center (https://danio-code.zfin.org) combines a total of 1,802 sets of unpublished and reanalysed published genomics data, which we used to improve existing annotations and show its utility in experimental design. We identified over 140,000 cis-regulatory elements in development, including novel classes with distinct features dependent on their activity in time and space. We delineated the distinction between regulatory elements active during zygotic genome activation and those active during organogenesis, identifying new aspects of how they relate to each other. Finally, we matched regulatory elements and epigenomic landscapes between zebrafish and mouse and predict functional relationships between them beyond sequence similarity, extending the utility of zebrafish developmental genomics to mammals.

developmental biology↗

bioRxiv2021

Timed global reorganization of protein synthesis during neocortex neurogenesis at codon resolution

Translation modulates the timing and amplification of gene expression after transcription. Brain development requires uniquely complex gene expression patterns, but large-scale measurements of translation directly in the prenatal brain are lacking. We measure the reactants, synthesis, and products of translation spanning mouse neocortex neurogenesis, and discover a transient window of dynamic regulation at mid-gestation. Timed translation upregulation of chromatin binding proteins like Satb2, which is essential for neuronal subtype differentiation, restricts protein expression in neuronal lineages despite broad transcriptional priming in progenitors. In contrast, translation downregulation of ribosomal proteins sharply decreases ribosome number, coinciding with a major shift in protein synthesis dynamics at mid-gestation. Changing levels of eIF4EBP1, a direct inhibitor of ribosomal protein translation, are concurrent with ribosome downregulation and controls Satb2 fate acquisition during neuronal differentiation. Thus, the refinement of transcriptional programs by translation is central to the molecular logic of brain development. Modeling of the developmental neocortex translatome is provided as an open-source searchable resource: https://shiny.mdc-berlin.de/cortexomics/.

neuroscience↗

bioRxiv2021

A community-driven roadmap to advance research on translated open reading frames detected by Ribo-seq

Ribosome profiling (Ribo-seq) has catalyzed a paradigm shift in our understanding of the translational vocabulary of the human genome, discovering thousands of translated open reading frames (ORFs) within long non-coding RNAs and presumed untranslated regions of protein-coding genes. However, reference gene annotation projects have been circumspect in their incorporation of these ORFs due to uncertainties about their experimental reproducibility and physiological roles. Yet, it is indisputable that certain Ribo-seq ORFs make stable proteins, others mediate gene regulation, and many have medical implications. Ultimately, the absence of standardized ORF annotation has created a circular problem: while Ribo-seq ORFs remain unannotated by reference biological databases, this lack of characterisation will thwart research efforts examining their roles. Here, we outline the initial stages of a community-led effort supported by GENCODE / Ensembl, HGNC and UniProt to produce a consolidated catalog of human Ribo-seq ORFs.

genomics↗

bioRxiv2021

Identifying interpretable gene-biomarker associations with functionally informed kernel-based tests in 190,000 exomes

Here we present an exome-wide rare genetic variant association study for 30 blood biomarkers in 191,971 individuals in the UK Biobank. We compare gene-based association tests for separate functional variant categories to increase interpretability and identify 193 significant gene-biomarker associations. Genes associated with biomarkers were ~ 4.5-fold enriched for conferring Mendelian disorders. In addition to performing weighted gene-based variant collapsing tests, we design and apply variant-category-specific kernel-based tests that integrate quantitative functional variant effect predictions for missense variants, splicing and the binding of RNA-binding proteins. For these tests, we present a statistically powerful and computationally efficient combination of the likelihood-ratio and score tests that found 36% more associations than the score test alone. Kernel-based tests identified 13% more associations than their gene-based collapsing counterparts and had advantages in the presence of gain of function missense variants. We introduce local collapsing by amino acid position for missense variants and use it to interpret associations and identify potential novel gain of function variants in PIEZO1. Our results show the benefits of investigating different functional mechanisms when performing rare-variant association tests, and demonstrate pervasive rare-variant contribution to biomarker variability.

genetics↗