Biology subjects

van Dijk, D.

Publications and source records attributed to van Dijk, D..

At least 19 recordsLinked to original sources

bioRxiv2026

CpG Atlas: A centralized multi-layer database and AI interface for DNA methylation research

DNA methylation research has vastly expanded over the past decade, producing a wealth of epigenome-wide association studies, biomarker algorithms such as epigenetic clocks, technical performance analyses, and functional annotations for CpG sites. However, these resources remain fragmented across dozens of databases and supplementary files within manuscripts, forcing researchers to spend time and effort on data cleaning and integration prior to meaningful analyses. No single resource currently unifies this information into a centralized, easy-to-query framework. Here, we present CpG Atlas, a curated relational database that integrates 18 distinct annotation layers encompassing over 1.2 million CpG sites across all four generations of Illumina methylation arrays (HM450K, EPIC v1, EPIC v2, and MSA). Built on a snowflake schema with a canonical probe identifier hub implemented in SQL, CpG Atlas consolidates over 800,000 CpG-trait associations, results from Mendelian randomization analyses, CpG membership across 81 epigenetic clocks, array manifest information, and probe reliability data. It further includes specialized layers such as solo-WCGW, CoRSIVs, PRC2 binding, transposon and retroelement annotations, tissue-specific differentially methylated positions across 17 tissues, and hallmarks of aging and cancer. To maximize utility and ease of use, the database is paired with an interactive web tool and a natural language-to-SQL query interface, enabling users to quickly perform complex multi-dimensional queries. Detailed documentation about every data source and table is also provided, facilitating the identification and interpretation of relevant studies. We demonstrate the utility of CpG Atlas through two case studies: a systematic enrichment analysis revealing distinct functional signatures across 16 epigenetic clocks, and an iterative biomarker discovery workflow for IBD that leverages cross-layer integration. Because it is readily scalable simply by adding or updating tables in the database, CpG Atlas provides a continuously evolving and extensible infrastructure for the epigenetics community that supports collaborative research, interpretable biomarker development, and integrative analyses across the growing landscape of epigenetic data.

bioinformatics↗

bioRxiv2025

Perturbational single-cell profiling of patient tumors defines lineage- and context-specific programs of innate immune resistance

AbstractDespite promise in preclinical models, most immuno-oncology drug candidates fail in clinical trials. These failures reflect limitations in our ability to directly model the response of human tumor and immune cells to immunotherapies. To address this gap and test the effect of innate immune agonists, we developed PERCEPT, an approach that uses ex vivo perturbational single-cell RNA sequencing to compare the response of immunomodulatory treatments with unstimulated controls directly in patient samples. Using PERCEPT, we tested cytokines and innate immune agonists in melanoma and Merkel cell carcinoma (MCC) and identified the dsRNA mimetic, RIG-I agonist, Stem Loop RNA (SLR) 14 as a powerful inducer of anti-viral states and enhancer of T cell activation. We compared transcriptional responder and non-responder patient samples and identified midkine (MDK), a multifunctional cytokine, as a potent repressor of IFN signaling in both tumor and immune cells. MDK expression dampened MHC-I presentation in human tumor cells and reduced activation of antigen-presenting cells, disrupting tumor immunity at multiple levels. In contrast to prior studies, we identified MDK as specifically enriched in neuroendocrine cancers such as MCC and small cell lung cancer compared with melanoma, suggesting the importance of lineage- and context-specific targeting. Our results demonstrate the utility of high-dimensional controlled perturbation of patient samples to identify mechanisms of innate immune response and resistance and demonstrate an actionable path towards clinical development of MDK-inhibiting therapies including FDA-approved ALK inhibitors in neuroendocrine cancers.

cancer biology↗

bioRxiv2025

Selective changes in cortical cholinergic signaling during learning

Cognition relies on the function of local and long-range neural circuits in the neocortex, which are dynamically regulated by neuromodulatory signals including acetylcholine (ACh). Recent work suggests that the role of acetylcholine varies across cortical areas and behavioral states. In addition, the precise role of cholinergic signaling during learning and plasticity remains unclear. We performed simultaneous, dual-color mesoscopic imaging of ACh and calcium signaling in the neocortex of awake mice across multiple stages of visual task learning to identify learning-related plasticity in neural activity and cholinergic signaling. Using a novel Pattern Reconstruction and Interpretation with a Structured Multimodal transformer (PRISMT) model combining masked autoencoding with multimodal causal attention, we identified key cortical regions that reorganize with learning. We found that cholinergic signaling exhibited spatiotemporally selective plasticity in frontal cortical subnetworks during visual perceptual learning. Overall, our findings demonstrate the utility of transformer models for generating biologically interpretable insight into large multimodal datasets and link long-term functional reorganization of cortical networks to enhanced task performance.

neuroscience↗

bioRxiv2025

Scaling Large Language Models for Next-Generation Single-Cell Analysis

AO_SCPLOWBSTRACTC_SCPLOWSingle-cell RNA sequencing has transformed our understanding of cellular diversity, yet current single-cell foundation models (scFMs) remain limited in their scalability, flexibility across diverse tasks, and ability to natively integrate textual information. In this work, we build upon the Cell2Sentence (C2S) framework, which represents scRNA-seq profiles as textual "cell sentences," to train Large Language Models (LLMs) on a corpus comprising over one billion tokens of transcriptomic data, biological text, and metadata. Scaling the model to 27 billion parameters yields consistent improvements in predictive and generative capabilities and supports advanced downstream tasks that require synthesis of information across multi-cellular contexts. Targeted fine-tuning with modern reinforcement learning techniques produces strong performance in perturbation response prediction, natural language interpretation, and complex biological reasoning. This predictive strength enabled a dual-context virtual screen that nominated the kinase inhibitor silmitasertib (CX-4945) as a candidate for context-selective upregulation of antigen presentation. Experimental assessment in human cell models unseen during training supported this prediction, demonstrating that C2S-Scale can effectively guide the discovery of context-conditioned biology. C2S-Scale unifies transcriptomic and textual data at unprecedented scales, surpassing both specialized single-cell models and general-purpose LLMs to provide a platform for next-generation single-cell analysis and the development of "virtual cells."

bioinformatics↗

bioRxiv2024

Cross-task implications: How hippocampal event boundary responses predict unrelated memory performance

Hippocampal responses at event boundaries have been shown to predict memory performance for these events. However, are these hippocampal event boundary responses specific to memory for those particular events, or can they also have predictive power across various memory tasks? We used data from the Cam-CAN project (fMRI data from continuous movie viewing and memory results from an unrelated Famous Faces Task, N = 630) to determine whether hippocampal responses at event boundaries during the continuous movie viewing were indicative of memory performance in the unrelated Famous Faces task using various machine learning algorithms. The results showed that memory performance in the Famous Faces Task could be predicted based on participants hippocampal event boundary responses in another task, which suggests that the hippocampal event boundary responses are indicative for general memory performance. This might indicate importance of these hippocampal event boundary responses in terms of general information processing of the human brain.

neuroscience↗

bioRxiv2024

AAnet resolves a continuum of spatially-localized cell states to unveil tumor complexity

Identifying functionally important cell states and structure within a heterogeneous tumor remains a significant biological and computational challenge. Moreover, current clustering or trajectory-based computational models are ill-equipped to address the notion that cancer cells reside along a phenotypic continuum. To address this, we present Archetypal Analysis network (AAnet), a neural network that learns key archetypal cell states within a phenotypic continuum of cell states in single-cell data. Applied to single-cell RNA sequencing data from pre-clinical models and a cohort of 34 clinical breast cancers, AAnet identifies archetypes that resolve distinct biological cell states and processes, including cell proliferation, hypoxia, metabolism and immune interactions. Notably, archetypes identified in primary tumors are recapitulated in matched liver, lung and lymph node metastases, demonstrating that a significant component of intratumoral heterogeneity is driven by cell intrinsic properties. Using spatial transcriptomics as orthogonal validation, AAnet-derived archetypes show discrete spatial organization within tumors, supporting their distinct archetypal biology. We further reveal that ligand:receptor cross-talk between cancer and adjacent stromal cells contributes to intra-archetypal biological mimicry. Finally, we use AAnet archetype identifiers to validate GLUT3 as a critical mediator of a hypoxic cell archetype harboring a cancer stem cell population, which we validate in human triple-negative breast cancer specimens. AAnet is a powerful tool to reveal functional cell states within complex samples from multimodal single-cell data.

bioinformatics↗

bioRxiv2023

Cell2Sentence: Teaching Large Language Models the Language of Biology

We introduce Cell2Sentence (C2S), a novel method to directly adapt large language models to a biological context, specifically single-cell transcriptomics. By transforming gene expression data into "cell sentences," C2S bridges the gap between natural language processing and biology. We demonstrate cell sentences enable the fine-tuning of language models for diverse tasks in biology, including cell generation, complex cell-type annotation, and direct data-driven text generation. Our experiments reveal that GPT-2, when fine-tuned with C2S, can generate biologically valid cells based on cell type inputs, and accurately predict cell types from cell sentences. This illustrates that language models, through C2S fine-tuning, can acquire a significant understanding of single-cell biology while maintaining robust text generation capabilities. C2S offers a flexible, accessible framework to integrate natural language processing with transcriptomics, utilizing existing models and libraries for a wide range of biological applications.

bioinformatics↗

bioRxiv2023

BrainLM: A foundation model for brain activity recordings

AO_SCPLOWBSTRACTC_SCPLOWWe introduce the Brain Language Model (BrainLM), a foundation model for brain activity dynamics trained on 6,700 hours of fMRI recordings. Utilizing self-supervised masked-prediction training, BrainLM demonstrates proficiency in both fine-tuning and zero-shot inference tasks. Fine-tuning allows for the accurate prediction of clinical variables like age, anxiety, and PTSD as well as forecasting of future brain states. Critically, the model generalizes well to entirely new external cohorts not seen during training. In zero-shot inference mode, BrainLM can identify intrinsic functional networks directly from raw fMRI data without any network-based supervision during training. The model also generates interpretable latent representations that reveal relationships between brain activity patterns and cognitive states. Overall, BrainLM offers a versatile and interpretable framework for elucidating the complex spatiotemporal dynamics of human brain activity. It serves as a powerful "lens" through which massive repositories of fMRI data can be analyzed in new ways, enabling more effective interpretation and utilization at scale. The work demonstrates the potential of foundation models to advance computational neuroscience research.

neuroscience↗

bioRxiv2022

Causal identification of single-cell experimental perturbation effects with CINEMA-OT

Recent advancements in single-cell technologies allow characterization of experimental perturbations at single-cell resolution. While methods have been developed to analyze such experiments, the application of a strict causal framework has not yet been explored for the inference of treatment effects at the single-cell level. In this work, we present a causal inference based approach to single-cell perturbation analysis, termed CINEMA-OT (Causal INdependent Effect Module Attribution + Optimal Transport). CINEMA-OT separates confounding sources of variation from perturbation effects to obtain an optimal transport matching that reflects counterfactual cell pairs. These cell pairs represent causal perturbation responses permitting a number of novel analyses, such as individual treatment effect analysis, response clustering, attribution analysis, and synergy analysis. We benchmark CINEMA-OT on an array of treatment effect estimation tasks for several simulated and real datasets and show that it outperforms other single-cell perturbation analysis methods. Finally, we perform CINEMA-OT analysis of two newly-generated datasets: (1) rhinovirus and cigarette smoke-exposed airway organoids, and (2) combinatorial cytokine stimulation of immune cells. In these experiments, CINEMA-OT reveals potential mechanisms by which cigarette smoke exposure dulls the airway antiviral response, as well as the logic that governs chemokine secretion and peripheral immune cell recruitment.

bioinformatics↗

bioRxiv2021

Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1

Neurodegeneration is a protracted process involving progressive changes in myriad cell types that ultimately result in neuronal death. Changes in vulnerable neuronal populations are highly influenced by concomitant changes in surrounding cells, complicating experimental approaches to interrogate the simultaneous events that underlie neurodegeneration. To dissect how individual cell types within a heterogeneous tissue contribute to the pathogenesis and progression of a neurodegenerative disorder, we performed longitudinal single-nucleus RNA sequencing of the mouse and human spinocerebellar ataxia type 1 (SCA1) cerebellum, establishing continuous dynamic trajectories of each population. Furthermore, we defined the precise transcriptional changes that precede loss of Purkinje cells and identified early oligodendroglial impairments that can profoundly impact cerebellar function. Finally, we applied a deep learning method to accurately predict disease state and identify drivers of disease. Together, this work uncovers new roles for diverse cerebellar cell types in SCA1 and provides a generalizable analysis framework for studying neurodegeneration.

neuroscience↗

bioRxiv2021

Single-Cell Transcriptional Profiling of the Adult Corticospinal Tract Reveals Forelimb and Hindlimb Molecular Specialization

The corticospinal tract (CST) is refractory to repair after CNS trauma, resulting in chronic debilitating functional motor deficits after spinal cord injury. While novel pro-axon growth activators have stimulated plasticity and regeneration of corticospinal neurons (CSNs) after injury, robust functional recovery remains elusive. These repair strategies are sub-optimal in part due to underexplored molecular heterogeneity within the developing and adult CST. In this study, we combine retrograde CST tracing with single-cell RNA sequencing to build a comprehensive atlas of CSN subtypes. By comparing CSNs to non-spinally projecting neurons in layer Vb, we identify pan-CSN markers including Wnt7b. By leveraging retrograde tracing, we are able to compare forelimb and hindlimb projecting CSNs, identifying subtype-specific markers, including Cacng7 and Slc16a2 respectively. These markers are expressed in embryonic and neonatal CSNs and can be used to study early postnatal patterning of the CST. Our results provide molecular insight into the differences between anatomically distinct CSN subtypes and provide a resource for future screening and exploitation of these subtypes to repair the damaged CST after injury and disease.

neuroscience↗

bioRxiv2021

Single-cell immune profiling reveals the impact of antiretroviral therapy on HIV-1-induced immune dysfunction, T cell clonal expansion, and HIV-1 persistence in vivo

Despite antiretroviral therapy (ART), HIV-1 persists in proliferating T cell clones that increase over time. To understand whether early ART affects HIV-1 persistence in vivo, we performed single-cell ECCITE-seq and profiled 89,279 CD4+ T cells in paired samples during viremia and after immediate versus delayed ART in six people in the randomized interventional Sabes study. We found that immediate ART partially reverted TNF responses while delayed ART did not. Antigen and TNF responses persisted despite immediate ART and shaped the transcriptional landscape of CD4+ T cells, HIV-1 RNA+ cells, and T cell clones harboring them (cloneHIV-1). Some HIV-1 RNA+ cells reside in the most clonally expanded cytotoxic T cell populations (GZMB and GZMK Th1 cells). CloneHIV-1+ were larger in clone size, persisted despite ART, and exhibited transcriptional signatures of antigen, cytotoxic effector, and cytokine responses. Using machine-learning algorithms, we identified markers for HIV-1 RNA+ cells and cloneHIV-1+ as potential therapeutic targets. Overall, by combining single-cell immune profiling and T cell expansion dynamics tracking, we identified drivers of HIV-1 persistence in vivo.

immunology↗

bioRxiv2020

The COVID-19 PHARMACOME: Rational Selection of Drug Repurposing Candidates from Multimodal Knowledge Harmonization

The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific communitys massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2 / COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

bioinformatics↗

bioRxiv2020

Generating hard-to-obtain information from easy-to-obtain information: applications in drug discovery and clinical inference

In many important contexts involving measurements of biological entities, there are distinct categories of information: some information is easy-to-obtain information (EI) and can be gathered on virtually every subject of interest, while other information is hard-to-obtain information (HI) and can only be gathered on some of the biological samples. For example, in the context of drug discovery, measurements like the chemical structure of a drug are EI, while measurements of the transcriptome of a cell population perturbed with the drug is HI. In the clinical context, basic health monitoring is EI because it is already being captured as part of other processes, while cellular measurements like flow cytometry or even ultimate patient outcome are HI. We propose building a model to make probabilistic predictions of HI from EI on the samples that have both kinds of measurements, which will allow us to generalize and predict the HI on a large set of samples from just the EI. To accomplish this, we present a conditional Generative Adversarial Network (cGAN) framework we call the Feature Mapping GAN (FMGAN). By using the EI as conditions to map to the HI, we demonstrate that FMGAN can accurately predict the HI, with heterogeneity in cases of distributions of HI from EI. We show that FMGAN is flexible in that it can learn rich and complex mappings from EI to HI, and can take into account manifold structure in the EI space where available. We demonstrate this in a variety of contexts including generating RNA sequencing results on cell lines subjected to drug perturbations using drug chemical structure, and generating clinical outcomes from patient lab measurements. Most notably, we are able to generate synthetic flow cytometry data from clinical variables on a cohort of COVID-19 patients--effectively describing their immune response in great detail, and showcasing the power of generating expensive FACS data from ubiquitously available patient monitoring data. Bigger PictureMany experiments face a trade-off between gathering easy-to-collect information on many samples or hard-to-collect information on a smaller number of small due to costs in terms of both money and time. We demonstrate that a mapping between the easy-to-collect and hard-to-collect information can be trained as a conditional GAN from a subset of samples with both measured. With our conditional GAN model known as Feature-Mapping GAN (FMGAN), the results of expensive experiments can be predicted, saving on the costs of actually performing the experiment. This can have major impact in many settinsg. We study two example settings. First, in the field of pharmaceutical drug discovery early phase pharmaceutical experiments require casting a wide net to find a few potential leads to follow. In the long term, development pipelines can be re-designed to specifically utilize FMGAN in an optimal way to accelerate the process of drug discovery. FMGAN can also have a major impact in clinical setting, where routinely measured variables like blood pressure or heart rate can be used to predict important health outcomes and therefore deciding the best course of treatment.

bioinformatics↗

bioRxiv2020

Neuroinvasive potential of SARS-CoV-2 revealed in a human brain organoid model

Although COVID-19 is considered to be primarily a respiratory disease, SARS-CoV-2 affects multiple organ systems including the central nervous system (CNS). Yet, there is no consensus whether the virus can infect the brain, or what the consequences of CNS infection are. Here, we used three independent approaches to probe the capacity of SARS-CoV-2 to infect the brain. First, using human brain organoids, we observed clear evidence of infection with accompanying metabolic changes in the infected and neighboring neurons. However, no evidence for the type I interferon responses was detected. We demonstrate that neuronal infection can be prevented either by blocking ACE2 with antibodies or by administering cerebrospinal fluid from a COVID-19 patient. Second, using mice overexpressing human ACE2, we demonstrate in vivo that SARS-CoV-2 neuroinvasion, but not respiratory infection, is associated with mortality. Finally, in brain autopsy from patients who died of COVID-19, we detect SARS-CoV-2 in the cortical neurons, and note pathologic features associated with infection with minimal immune cell infiltrates. These results provide evidence for the neuroinvasive capacity of SARS-CoV2, and an unexpected consequence of direct infection of neurons by SARS-CoV-2.

microbiology↗

bioRxiv2020

Genome-wide CRISPR screen reveals host genes that regulate SARS-CoV-2 infection

Identification of host genes essential for SARS-CoV-2 infection may reveal novel therapeutic targets and inform our understanding of COVID-19 pathogenesis. Here we performed a genome-wide CRISPR screen with SARS-CoV-2 and identified known SARS-CoV-2 host factors including the receptor ACE2 and protease Cathepsin L. We additionally discovered novel pro-viral genes and pathways including the SWI/SNF chromatin remodeling complex and key components of the TGF-{beta} signaling pathway. Small molecule inhibitors of these pathways prevented SARS-CoV-2-induced cell death. We also revealed that the alarmin HMGB1 is critical for SARS-CoV-2 replication. In contrast, loss of the histone H3.3 chaperone complex sensitized cells to virus-induced death. Together this study reveals potential therapeutic targets for SARS-CoV-2 and highlights host genes that may regulate COVID-19 pathogenesis.

genetics↗

bioRxiv2020

Single-cell longitudinal analysis of SARS-CoV-2 infection in human bronchial epithelial cells

SARS-CoV-2, the causative agent of COVID-19, has tragically burdened individuals and institutions around the world. There are currently no approved drugs or vaccines for the treatment or prevention of COVID-19. Enhanced understanding of SARS-CoV-2 infection and pathogenesis is critical for the development of therapeutics. To reveal insight into viral replication, cell tropism, and host-viral interactions of SARS-CoV-2 we performed single-cell RNA sequencing of experimentally infected human bronchial epithelial cells (HBECs) in air-liquid interface cultures over a time-course. This revealed novel polyadenylated viral transcripts and highlighted ciliated cells as a major target of infection, which we confirmed by electron microscopy. Over the course of infection, cell tropism of SARS-CoV-2 expands to other epithelial cell types including basal and club cells. Infection induces cell-intrinsic expression of type I and type III IFNs and IL6 but not IL1. This results in expression of interferon-stimulated genes in both infected and bystander cells. We observe similar gene expression changes from a COVID-19 patient ex vivo. In addition, we developed a new computational method termed CONditional DENSity Embedding (CONDENSE) to characterize and compare temporal gene dynamics in response to infection, which revealed genes relating to endothelin, angiogenesis, interferon, and inflammation-causing signaling pathways. In this study, we conducted an in-depth analysis of SARS-CoV-2 infection in HBECs and a COVID-19 patient and revealed genes, cell types, and cell state changes associated with infection.

microbiology↗

bioRxiv2020

A novel toxin-antitoxin module SlvT-SlvA governs megaplasmid stability and incites solvent tolerance in Pseudomonas putida S12

Pseudomonas putida S12 is highly tolerant towards organic solvents in saturating concentrations, rendering this microorganism suitable for the industrial production of various aromatic compounds. Previous studies reveal that P. putida S12 contains a single-copy 583 kbp megaplasmid pTTS12. This pTTS12 encodes several important operons and gene clusters facilitating P. putida S12 to survive and grow in the presence of toxic compounds or other environmental stresses. We wished to revisit and further scrutinize the role of pTTS12 in conferring solvent tolerance. To this end, we cured the megaplasmid from P. putida S12 and conclusively confirmed that the SrpABC efflux pump is the major contributor of solvent tolerance on the megaplasmid pTTS12. Importantly, we identified a novel toxin-antitoxin module (proposed gene names slvT and slvA respectively) encoded on pTTS12 which contributes to the solvent tolerant phenotype and is essential in conferring genetic stability to the megaplasmid. Chromosomal introduction of the srp operon in combination with slvAT gene pair created a solvent tolerance phenotype in non-solvent tolerant strains such as P. putida KT2440, E. coli TG1, and E. coli BL21(DE3). ImportanceSustainable alternatives for high-value chemicals can be achieved by using renewable feedstocks in bacterial biocatalysis. However, during bioproduction of such chemicals and biopolymers, aromatic compounds that function as products, substrates or intermediates in the production process may exert toxicity to microbial host cells and limit the production yield. Therefore, solvent-tolerance is a highly preferable trait for microbial hosts in the biobased production of aromatic chemicals and biopolymers. In this study, we revisit the essential role of megaplasmid pTTS12 from solvent-tolerant P. putida S12 for molecular adaptation to organic solvent. In addition to the RND efflux pump (SrpABC), we identified a novel toxin-antitoxin module (SlvAT) which contributes to tolerance in low solvent concentration as well as to genetic stability of pTTS12. These two gene clusters were successfully transferred to non-solvent tolerant strains of P. putida and to E. coli strains to confer and enhance solvent tolerance.

microbiology↗