Biology subjects

Vogel, I.

Publications and source records attributed to Vogel, I..

5 recordsLinked to original sources

bioRxiv2026

Predicting cell-state-specific gene regulation from DNA across species

DNA encodes the cell's phenotype, but the same DNA sequence is interpreted differently in every cell type, developmental stage, and activity state. Existing deep-learning models solve only half of this puzzle: sequence-to-function models read DNA but treat cell state as a fixed label, while single-cell foundation models represent cell state but not sequence. We developed Bolero, a sequence-to-function model whose parameters condition on atlas-scale cell-state embeddings and additional biological variables to predict cell-state-specific chromatin accessibility and transcript abundance from DNA. Trained on 10.7 million cells from 33 datasets across six mammals, Bolero matched or exceeded specialised models and resolved regulatory differences between closely related cell states, including cortical regional and developmental gradients. Extrapolating cell-state embeddings extended Bolero's predictions to cell states that are absent from training. Cell-state conditioning facilitated tracing of the effects of non-coding variants to the cell types in which they act. Using an AP-1 activity score as a further continuous conditioning axis, Bolero predicted activity-regulated chromatin changes without requiring actual stimulation data. Across human cell types, we reconstructed the cooperative grammar of motif identity and spacing underlying predicted AP-1 responses. Bolero further predicted cell-type-specific chromatin accessibility and AP-1 responses in species excluded from training, extending comparative genomics from sequence conservation to predicted functional conservation. Across 84 mammals, we found that predicted AP-1 responses were less conserved than chromatin accessibility at cis-regulatory elements, driven by the human-specific sequence variants on AP-1 and cooperating motifs. By combining reference genomes with reference cell atlases, Bolero allowed genome sequences, either native or mutated, to be queried across cell states, perturbations, and different species for their chromatin and expression signatures.

genomics↗

bioRxiv2026

Droplet single-cell CRISPR screens identify regulators of T cell-mediated target-cell killing

Cytotoxic CD8 T cells kill target cells through brief cell-cell encounters, but pooled genetic screens cannot readily link perturbations in individual T cells to the fate of the target cells they engage. We developed droplet single-cell CRISPR screening to pair individual primary human CD8 T cells with cancer cells, measure rapid target-cell death, and recover sgRNAs from phenotype-defined droplets. Applied across primary T cells from multiple donors, the platform recovered regulators of T cell receptor signaling, synapse formation, granule exocytosis and cytotoxic differentiation, and identified negative regulators of killing, including established inhibitory nodes such as PTEN, RASA2 and FOXO1, together with AFAP1L2 and components of the mTORC1 pathway. Validation across bispecific engager and TCR-engineered settings showed that selected hits modulate target-cell killing across recognition modalities and tumor models. Unexpectedly, perturbation of RPTOR or RHEB enhanced cytotoxic execution while reducing mTORC1 output, increasing AKT phosphorylation, and attenuating anabolic programs. Transient pharmacologic mTORC1 inhibition reproduced this rapid-killing state and improved antitumor activity after adoptive transfer. These results establish an interaction-resolved pooled genetic strategy for mapping cytotoxicity regulators and reveal that transient modulation of mTORC1 can shift T cells from anabolic growth toward rapid cytotoxic execution to enhance antitumor activity.

immunology↗

bioRxiv2019

Obesity Potentiates TH2 Immunopathology via Dysregulation of PPAR

How obesity affects immune function is not well understood. Clinically, obesity is strongly associated with severe TH2 immunopathology1-3, though the physiological, cellular, and molecular underpinnings of this association remain obscure. Here, we demonstrate that obese mice are susceptible to severe atopic dermatitis (AD), a major manifestation of TH2 immunopathology and disease burden in humans4,5. Mechanistically, we show that dysregulation of the nuclear hormone receptor (NHR) PPAR{gamma} (peroxisome proliferator-activated receptor gamma) in T cells is a causal link between obesity and the increased TH2 immunopathology. We find that PPAR{gamma} oversees a cellular metabolic transcriptional program that restrains nuclear gene expression of the chief TH2 priming and effector cytokine interleukin-4 (IL-4). Accordingly, thiazolidinediones (TZDs), potent PPAR{gamma} agonists, robustly protect obese mice from TH2 immunopathology. Collectively, these findings establish PPAR{gamma} as a molecular link between obesity and TH2 immune homeostasis and identify TZDs as novel therapeutic candidates for TH2 immunopathology. Fundamentally, these findings demonstrate that shifting physiologic metabolic states can shape the tone of adaptive immune responses to modulate differential disease susceptibility.

immunology↗

bioRxiv2019

Viral manipulation of functionally distinct neurons from mice to humans

Recent success in identifying gene regulatory elements in the context of recombinant adeno-associated virus vectors have enabled cell type-restricted gene expression. However, within the cerebral cortex these tools are presently limited to broad classes of neurons. To overcome this limitation, we developed a strategy that led to the identification of multiple novel enhancers to target functionally distinct neuronal subtypes. By investigating the regulatory landscape of the disease gene Scn1a, we identified enhancers that target the breadth of its expression, including two that are selective for parvalbumin and vasoactive intestinal peptide cortical interneurons. Demonstrating the functional utility of these elements, we found that the PV-specific enhancer allowed for the selective targeting and manipulation of these neurons across species, from mice to humans. Finally, we demonstrate that our selection method is generalizable to other genes and characterize four additional PV-specific enhancers with exquisite specificity for distinct regions of the brain. Altogether, these viral tools can be used for cell-type specific circuit manipulation and hold considerable promise for use in therapeutic interventions.

neuroscience↗

bioRxiv2018

SureTypeSC - A Random Forest and Gaussian Mixture predictor of high confidence genotypes in single cell data

MotivationAccurate genotyping of DNA from a single cell is required for applications such as de novo mutation detection, linkage analysis and lineage tracing. However, achieving high precision genotyping in the single cell environment is challenging due to the errors caused by whole genome amplification. Two factors make genotyping from single cells using single nucleotide polymorphism (SNP) arrays challenging. The lack of a comprehensive single cell dataset with a reference genotype and the absence of genotyping tools specifically designed to detect noise from the whole genome amplification step. Algorithms designed for bulk DNA genotyping cause significant data loss when used for single cell applications.\n\nResultsIn this study, we have created a resource of 28.7 million SNPs, typed at high confidence from whole genome amplified DNA from single cells using the Illumina SNP bead array technology. The resource is generated from 104 single cells from two cell lines that are available from the Coriell repository. We used mother-father-proband (trio) information from multiple technical replicates of bulk DNA to establish a high quality reference genotype for the two cell lines on the SNP array. This enabled us to develop SureTypeSC - a two-stage machine learning algorithm that filters a substantial part of the noise, thereby retaining the majority of the high quality SNPs. SureTypeSC also provides a simple statistical output to show the confidence of a particular single cell genotype using Bayesian statistics.\n\nContacteva@sund.ku.dk

bioinformatics↗