Biology subjects

Levine, D.

Publications and source records attributed to Levine, D..

5 recordsLinked to original sources

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

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↗

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↗

bioRxiv2019

Cryptochromes-mediated Inhibition of the CRL4Cop1-Complex Assembly Defines an Evolutionary Conserved Signaling Mechanism

In plants, Cryptochromes are photoreceptors that negatively regulate CRL4Cop1(Cul4-RING ubiquitin ligase and its substrate receptor Cop1). In mammals, Cryptochromes are core components of the circadian clock and repressors of the glucocorticoid receptor (GR). Moreover, mammalian Cryptochromes lost their ability to interact with Cop1, suggesting that they are unable to inhibit CRL4Cop1. Contrary to this assumption, we found that mammalian Cryptochromes are also negative regulators of CRL4Cop1and through this mechanism they repress the GR transcriptional network both in cultured cells and in mouse liver. Mechanistically, Cryptochromes inactivate Cop1 by interacting with Det1, a specific subunit of the mammalian CRL4Cop1complex. Through this interaction, the ability of Cop1 to join the CRL4 complex is inhibited; therefore, its substrates accumulate. Thus, the interaction between Cryptochromes and Det1 in mammals mirrors the interaction between Cryptochromes and Cop1 in planta, pointing to a common ancestor in which the cryptochrome-Cop1 axis originated.

molecular biology↗