TIVelo: RNA velocity estimation leveraging cluster-level trajectory inference.

RNA velocity inference is a valuable tool for understanding cell development, differentiation, and disease progression. However, existing RNA velocity inference methods typically rely on explicit assumptions of ordinary differential equations (ODE), which prohibits them from capturing complex transcriptomic expression patterns. In this study, we introduce TIVelo, a RNA velocity estimation approach that first determines the velocity direction at the cell cluster level based on trajectory inference, before estimating velocity for individual cells.

Protective Role of Quercetin in Chronic Atrophic Gastritis: Modulation of Gastric Mucosal Integrity via the Transforming Growth Factor-beta1/Smads Signaling Pathway.

Objective: This study aimed to elucidate the protective effects of quercetin (Que) on the gastric mucosa in a rat model of chronic atrophic gastritis (CAG), with emphasis on the regulation of the transforming growth factor-beta1 (TGF-β1)/Smads signaling pathway.

Methods: Wistar rats were randomly divided into five groups: control, model, low-dose Que (Que-L), and high-dose Que (Que-H). After 30 days of oral gavage, the rats were euthanized for further analysis.

Precise gene expression deconvolution in spatial transcriptomics with STged.

Spatially resolved transcriptomics (SRT) has transformed tissue biology by linking gene expression profiles with spatial information. However, sequencing-based SRT methods aggregate signals from multiple cell types within capture locations ("spots"), masking cell-type-specific gene expression patterns. Traditional cell-type deconvolution methods estimate cell compositions within spots but fail to resolve cell-type-specific gene expression, limiting their ability to uncover critical biological processes such as cellular interactions and microenvironmental dynamics.

Crosstalk between Lipid Metabolism and Bone Homeostasis: Exploring Intricate Signaling Relationships.

Bone is a dynamic tissue reshaped by constant bone formation and bone resorption to maintain its function. The skeletal system accounts for approximately 70% of the total volume of the body, and continuous bone remodeling requires quantities of energy and material consumption. Adipose tissue is the main energy storehouse of the body and has a strong adaptive capacity to participate in the regulation of various physiological processes.

scParser: sparse representation learning for scalable single-cell RNA sequencing data analysis.

The rapid rise in the availability and scale of scRNA-seq data needs scalable methods for integrative analysis. Though many methods for data integration have been developed, few focus on understanding the heterogeneous effects of biological conditions across different cell populations in integrative analysis. Our proposed scalable approach, scParser, models the heterogeneous effects from biological conditions, which unveils the key mechanisms by which gene expression contributes to phenotypes.

Arachidonic acid in aging: New roles for old players.

Background: Arachidonic acid (AA), one of the most ubiquitous polyunsaturated fatty acids (PUFAs), provides fluidity to mammalian cell membranes. It is derived from linoleic acid (LA) and can be transformed into various bioactive metabolites, including prostaglandins (PGs), thromboxanes (TXs), lipoxins (LXs), hydroxy-eicosatetraenoic acids (HETEs), leukotrienes (LTs), and epoxyeicosatrienoic acids (EETs), by different pathways. All these processes are involved in AA metabolism.

ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas.

The diagnosis of solid pseudopapillary neoplasm of the pancreas (SPN) can be challenging due to potential confusion with other pancreatic neoplasms, particularly pancreatic neuroendocrine tumors (NETs), using current pathological diagnostic markers. We conducted a comprehensive analysis of bulk RNA sequencing data from SPNs, NETs, and normal pancreas, followed by experimental validation. This analysis revealed an increased accumulation of peroxisomes in SPNs.

INSIDER: Interpretable sparse matrix decomposition for RNA expression data analysis.

RNA sequencing (RNA-Seq) is widely used to capture transcriptome dynamics across tissues, biological entities, and conditions. Currently, few or no methods can handle multiple biological variables (e.g.

SGCAST: symmetric graph convolutional auto-encoder for scalable and accurate study of spatial transcriptomics.

Recent advances in spatial transcriptomics (ST) have enabled comprehensive profiling of gene expression with spatial information in the context of the tissue microenvironment. However, with the improvements in the resolution and scale of ST data, deciphering spatial domains precisely while ensuring efficiency and scalability is still challenging. Here, we develop SGCAST, an efficient auto-encoder framework to identify spatial domains.

Therapeutic Potential of Adipose-Derived Stem Cell-Conditioned Medium and Extracellular Vesicles in an In Vitro Radiation-Induced Skin Injury Model.

Radiotherapy (RT) is one of three major treatments for malignant tumors, and one of its most common side effects is skin and soft tissue injury. However, the treatment of these remains challenging. Several studies have shown that mesenchymal stem cell (MSC) treatment enhances skin wound healing.

Integrating spatial and single-cell transcriptomics data using deep generative models with SpatialScope.

The rapid emergence of spatial transcriptomics (ST) technologies is revolutionizing our understanding of tissue spatial architecture and biology. Although current ST methods, whether based on next-generation sequencing (seq-based approaches) or fluorescence in situ hybridization (image-based approaches), offer valuable insights, they face limitations either in cellular resolution or transcriptome-wide profiling. To address these limitations, we present SpatialScope, a unified approach integrating scRNA-seq reference data and ST data using deep generative models.

stVAE deconvolves cell-type composition in large-scale cellular resolution spatial transcriptomics.

Motivation: Recent rapid developments in spatial transcriptomic techniques at cellular resolution have gained increasing attention. However, the unique characteristics of large-scale cellular resolution spatial transcriptomic datasets, such as the limited number of transcripts captured per spot and the vast number of spots, pose significant challenges to current cell-type deconvolution methods.

Results: In this study, we introduce stVAE, a method based on the variational autoencoder framework to deconvolve the cell-type composition of cellular resolution spatial transcriptomic datasets.

scICML: Information-Theoretic Co-Clustering-Based Multi-View Learning for the Integrative Analysis of Single-Cell Multi-Omics Data.

Modern high-throughput sequencing technologies have enabled us to profile multiple molecular modalities from the same single cell, providing unprecedented opportunities to assay cellular heterogeneity from multiple biological layers. However, the datasets generated from these technologies tend to have high level of noise and are highly sparse, bringing challenges to data analysis. In this paper, we develop a novel information-theoretic co-clustering-based multi-view learning (scICML) method for multi-omics single-cell data integration.

iPoLNG-An unsupervised model for the integrative analysis of single-cell multiomics data.

Single-cell multiomics technologies, where the transcriptomic and epigenomic profiles are simultaneously measured in the same set of single cells, pose significant challenges for effective integrative analysis. Here, we propose an unsupervised generative model, iPoLNG, for the effective and scalable integration of single-cell multiomics data. iPoLNG reconstructs low-dimensional representations of the cells and features using computationally efficient stochastic variational inference by modelling the discrete counts in single-cell multiomics data with latent factors.

RefTM: reference-guided topic modeling of single-cell chromatin accessibility data.

Single-cell analysis is a valuable approach for dissecting the cellular heterogeneity, and single-cell chromatin accessibility sequencing (scCAS) can profile the epigenetic landscapes for thousands of individual cells. It is challenging to analyze scCAS data, because of its high dimensionality and a higher degree of sparsity compared with scRNA-seq data. Topic modeling in single-cell data analysis can lead to robust identification of the cell types and it can provide insight into the regulatory mechanisms.

scAWMV: an adaptively weighted multi-view learning framework for the integrative analysis of parallel scRNA-seq and scATAC-seq data.

Motivation: Technological advances have enabled us to profile single-cell multi-omics data from the same cells, providing us with an unprecedented opportunity to understand the cellular phenotype and links to its genotype. The available protocols and multi-omics datasets [including parallel single-cell RNA sequencing (scRNA-seq) and single-cell ATAC sequencing (scATAC-seq) data profiled from the same cell] are growing increasingly. However, such data are highly sparse and tend to have high level of noise, making data analysis challenging.

Mendelian randomization for causal inference accounting for pleiotropy and sample structure using genome-wide summary statistics.

Mendelian randomization (MR) is a valuable tool for inferring causal relationships among a wide range of traits using summary statistics from genome-wide association studies (GWASs). Existing summary-level MR methods often rely on strong assumptions, resulting in many false-positive findings. To relax MR assumptions, ongoing research has been primarily focused on accounting for confounding due to pleiotropy.

FIRM: Flexible integration of single-cell RNA-sequencing data for large-scale multi-tissue cell atlas datasets.

Single-cell RNA-sequencing (scRNA-seq) is being used extensively to measure the mRNA expression of individual cells from deconstructed tissues, organs and even entire organisms to generate cell atlas references, leading to discoveries of novel cell types and deeper insight into biological trajectories. These massive datasets are usually collected from many samples using different scRNA-seq technology platforms, including the popular SMART-Seq2 (SS2) and 10X platforms. Inherent heterogeneities between platforms, tissues and other batch effects make scRNA-seq data difficult to compare and integrate, especially in large-scale cell atlas efforts; yet, accurate integration is essential for gaining deeper insights into cell biology.

Adversarial domain translation networks for integrating large-scale atlas-level single-cell datasets.

The rapid emergence of large-scale atlas-level single-cell RNA-seq datasets presents remarkable opportunities for broad and deep biological investigations through integrative analyses. However, harmonizing such datasets requires integration approaches to be not only computationally scalable, but also capable of preserving a wide range of fine-grained cell populations. We have created Portal, a unified framework of adversarial domain translation to learn harmonized representations of datasets.

JSNMF enables effective and accurate integrative analysis of single-cell multiomics data.

The single-cell multiomics technologies provide an unprecedented opportunity to study the cellular heterogeneity from different layers of transcriptional regulation. However, the datasets generated from these technologies tend to have high levels of noise, making data analysis challenging. Here, we propose jointly semi-orthogonal nonnegative matrix factorization (JSNMF), which is a versatile toolkit for the integrative analysis of transcriptomic and epigenomic data profiled from the same cell.

scAMACE: model-based approach to the joint analysis of single-cell data on chromatin accessibility, gene expression and methylation.

Motivation: The advancement in technologies and the growth of available single-cell datasets motivate integrative analysis of multiple single-cell genomic datasets. Integrative analysis of multimodal single-cell datasets combines complementary information offered by single-omic datasets and can offer deeper insights on complex biological process. Clustering methods that identify the unknown cell types are among the first few steps in the analysis of single-cell datasets, and they are important for downstream analysis built upon the identified cell types.

coupleCoC+: An information-theoretic co-clustering-based transfer learning framework for the integrative analysis of single-cell genomic data.

Technological advances have enabled us to profile multiple molecular layers at unprecedented single-cell resolution and the available datasets from multiple samples or domains are growing. These datasets, including scRNA-seq data, scATAC-seq data and sc-methylation data, usually have different powers in identifying the unknown cell types through clustering. So, methods that integrate multiple datasets can potentially lead to a better clustering performance.

RA3 is a reference-guided approach for epigenetic characterization of single cells.

The recent advancements in single-cell technologies, including single-cell chromatin accessibility sequencing (scCAS), have enabled profiling the epigenetic landscapes for thousands of individual cells. However, the characteristics of scCAS data, including high dimensionality, high degree of sparsity and high technical variation, make the computational analysis challenging. Reference-guided approaches, which utilize the information in existing datasets, may facilitate the analysis of scCAS data.