Multimodal Aspect-Based Sentiment Analysis (MABSA) involves extracting aspect terms from text-image pairs and identifying their sentiments. Most existing tasks consider one fixed sentiment category with explicitly mentioned aspects. However, these tasks seldom consider expressive sentiment categories, implicit aspects, and explainability. To this end, we introduce a novel task of Open-domain Explainable Multimodal Aspect-Based Sentiment Reasoning (OX-MABSR). This task enables the prediction of open-vocabulary aspect-sentiment pairs, together with the generation of sentiment explanations and reasoning paths. To benchmark OX-MABSR task, we construct OX-MABSR-Bench, a dataset annotated with explicit and implicit aspects, expressive sentiment categories, as well as perceptual and cognitive two-level explanations. The explanations capture visual and textual cues, including aesthetics, facial expressions, scenes, and textual semantics, together with background and situational knowledge. In addition, we annotate the reasoning paths that trace how the sentiment evolves from surface cues to a deeper contextual understanding. To address OX-MABSR task, we propose MABSR-LLM. Extensive experimental results show our MABSR-LLM outperforms strong baselines. To the best of our knowledge, we are the first to provide a unified framework for open-domain and explainable MABSR.

Designing biologically plausible regulatory sequences, such as enhancers, is a key challenge in synthetic biology. Current approaches either classify active enhancers or generate synthetic sequences independently but often fail to address the need for cell-type-specific recruitment of multiple transcription factors (TFs) for effective transcription activation. This oversight leads to suboptimal designs. To overcome these limitations, we propose Leonine, a novel framework redefining enhancer sequence design as a multimodal question-answering task within a cellular context. Leonine utilizes large language models (LLMs) trained on DNA sequences and integrates multimodal cellular data-including promoter sequences, gene expression profiles, and cell type information-to optimize enhancer sequences for specific regulatory environments. A large-scale dataset of over 1.5 million cell-type-specific promoter-enhancer pairs and a robust evaluation benchmark were developed to support this effort. Extensive evaluations across seven cell types demonstrate that Leonine outperforms state-of-the-art LLMs, generating biologically and functionally coherent sequences. This work introduces a new paradigm for context-aware DNA sequence design, advancing research in synthetic biology and gene regulation.

The single-cell sequencing revolution enables simultaneous molecular profiling of various modalities across thousands of individual cells, allowing scientists to investigate the diverse functions of complex tissues. Among all the analysis steps, assigning individual cells to specific types is fundamental for understanding cellular heterogeneity. However, this process is labor-intensive and requires extensive expert knowledge. Recent advances in large language models (LLMs) have demonstrated their ability to automatically extract biological knowledge, such as marker genes, promoting efficient, and automated cell-type annotations. To evaluate the capability of modern LLMs in automating the cell-type identification process, we first introduce an automated cell-type annotation method with comprehensive benchmark: Single-cell Omics Arena). Specifically, we began by compiling 11 publicly available single-cell RNA sequencing (scRNA-seq) datasets and evaluating eight LLMs across 1226 cell-type annotation-related tasks. This effort established a foundation for automated cell-type annotation from scRNA-seq data using interpretable features such as gene names. Building upon this benchmark, we introduced domain-specific chain-of-thought prompting techniques to enhance the accuracy of cell-type annotation and facilitate the extraction of relevant biological insights. Finally, to accommodate non-interpretable features, we proposed to leverage a pretrained VAE-based cross-modality translation module to convert features such as epigenetic marks into interpretable representations, which enables the seamless extension of LLM-based cell-type annotation to non-RNA-based sequencing technologies. In summary, our benchmark provides key insights into automated cell-type annotation from scRNA-seq data and demonstrates the potential of cross-modality translation for handling non-interpretable features.

Spatial transcriptomics has transformed genomic research by measuring spatially resolved gene expressions, allowing us to investigate how cells adapt to their microenvironment via modulating their expressed genes. This essential process usually starts from cell-cell communication (CCC) via ligand-receptor (LR) interaction, leading to regulatory changes within the receiver cell. However, few methods were developed to connect them to provide biological insights into intercellular regulation. To fill this gap, we propose iMiracle, an iterative multi-view graph neural network that models each cell's intercellular regulation with three key features. Firstly, iMiracle integrates inter- and intra-cellular networks to jointly estimate cell-type- and micro-environment-driven gene expressions. Optionally, it allows prior knowledge of intra-cellular networks as pre-structured masks to maintain biological relevance. Secondly, iMiracle employs iterative learning to overcome the sparsity of spatial transcriptomic data and gradually fill in the missing edges in the CCC network. Thirdly, iMiracle infers a cell-specific ligand-gene regulatory score based on the contributions of different LR pairs to interpret inter-cellular regulation. We applied iMiracle to nine simulated and eight real datasets from three sequencing platforms and demonstrated that iMiracle consistently outperformed ten methods in gene expression imputation and four methods in regulatory score inference. Lastly, we developed iMiracle as an open-source software and anticipate that it can be a powerful tool in decoding the complexities of inter-cellular transcriptional regulation.

Single-cell sequencing technologies have revolutionized genomics by enabling the simultaneous profiling of various molecular modalities within individual cells. Their integration, especially cross-modality translation, offers deep insights into cellular regulatory mechanisms. Many methods have been developed for cross-modality translation, but their reliance on scarce high-quality co-assay data limits their applicability. Addressing this, we introduce scACT, a deep generative model designed to extract cross-modality biological insights from unpaired single-cell data. scACT tackles three major challenges: aligning unpaired multi-modal data via adversarial training, facilitating cross-modality translation without prior knowledge via cycle-consistent training, and enabling interpretable regulatory interconnections explorations via in-silico perturbations. To test its performance, we applied scACT on diverse single-cell datasets and found it outperformed existing methods in all three tasks. Finally, we have developed scACT as an individual open-source software package to advance single-cell omics data processing and analysis within the research community.

Dynamic compartmentalization of eukaryotic DNA into active and repressed states enables diverse transcriptional programs to arise from a single genetic blueprint, whereas its dysregulation can be strongly linked to a broad spectrum of diseases. While single-cell Hi-C experiments allow for chromosome conformation profiling across many cells, they are still expensive and not widely available for most labs. Here, we propose an alternate approach, scENCORE, to computationally reconstruct chromatin compartments from the more affordable and widely accessible single-cell epigenetic data. First, scENCORE constructs a long-range epigenetic correlation graph to mimic chromatin interaction frequencies, where nodes and edges represent genome bins and their correlations. Then, it learns the node embeddings to cluster genome regions into A/B compartments and aligns different graphs to quantify chromatin conformation changes across conditions. Benchmarking using cell-type-matched Hi-C experiments demonstrates that scENCORE can robustly reconstruct A/B compartments in a cell-type-specific manner. Furthermore, our chromatin confirmation switching studies highlight substantial compartment-switching events that may introduce substantial regulatory and transcriptional changes in psychiatric disease. In summary, scENCORE allows accurate and cost-effective A/B compartment reconstruction to delineate higher-order chromatin structure heterogeneity in complex tissues.