Here we present STModule, a Bayesian method developed to identify tissue modules from spatially resolved transcriptomics that reveal spatial components and essential characteristics of tissues. STModule uncovers diverse expression signals in transcriptomic landscapes such as cancer, intraepithelial neoplasia, immune infiltration, outcome-related molecular features and various cell types, which facilitate downstream analysis and provide insights into tumor microenvironments, disease mechanisms, treatment development, and histological organization of tissues. STModule captures a broader spectrum of biological signals compared to other methods and detects novel spatial components. The tissue modules characterized by gene sets demonstrate greater robustness and transferability across different biopsies. STModule: https://github.com/rwang-z/STModule.git .

We recently reported non-coding variants in a cis-regulatory element of the beta-cell disallowed gene hexokinase 1 (HK1) as a novel cause of congenital hyperinsulinism. These variants lead to a loss of repression of HK1 in pancreatic beta-cells, causing insulin secretion during hypoglycaemia. In this study, we aimed to determine the prevalence, genetics, and phenotype of HK1-hyperinsulinism by screening a large international cohort of patients living with the condition.

Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial genetic influence. While genome-wide association studies (GWAS) have identified numerous risk loci for late-onset AD (LOAD), the functional mechanisms underlying most of these associations remain unresolved. Large genomic rearrangements, known as structural variants (SVs), represent a promising avenue for elucidating such mechanisms within some of these loci.

Non-tuberculous mycobacteria (NTM) are a diverse group of environmental bacteria that are increasingly associated with human infections and difficult to treat. Plasmids, which might carry resistance and virulence factors, remain largely unexplored in NTM.

Ineffective medication is a major healthcare problem causing significant patient suffering and economic costs. This issue stems from the complex nature of diseases, which involve altered interactions among thousands of genes across multiple cell types and organs. Disease progression can vary between patients and over time, influenced by genetic and environmental factors. To address this challenge, digital twins have emerged as a promising approach, which have led to international initiatives aiming at clinical implementations. Digital twins are virtual representations of health and disease processes that can integrate real-time data and simulations to predict, prevent, and personalize treatments. Early clinical applications of DTs have shown potential in areas like artificial organs, cancer, cardiology, and hospital workflow optimization. However, widespread implementation faces several challenges: (1) characterizing dynamic molecular changes across multiple biological scales; (2) developing computational methods to integrate data into DTs; (3) prioritizing disease mechanisms and therapeutic targets; (4) creating interoperable DT systems that can learn from each other; (5) designing user-friendly interfaces for patients and clinicians; (6) scaling DT technology globally for equitable healthcare access; (7) addressing ethical, regulatory, and financial considerations. Overcoming these hurdles could pave the way for more predictive, preventive, and personalized medicine, potentially transforming healthcare delivery and improving patient outcomes.

Deciphering variants of uncertain significance (VUS) represents a major diagnostic challenge, partially due to the lack of easy-to-use and versatile cellular readouts that aid the interpretation of pathogenicity and pathophysiology. To address this challenge, we propose a high-throughput screening of cellular functionality through an imaging flow cytometry (IFC)-based platform.

Helicobacter pylori (H. pylori) infection is a significant risk factor for gastric cancer (GC) development. A growing body of evidence suggests a causal link between infection with H. pylori and increased DNA breakage in the host cells. While several mechanisms have been proposed for this damage, their relative impact on the overall bacterial genotoxicity is unknown. Moreover, the link between the formation of DNA damage following infection and the emergence of cancerous structural variants (SV) in the genome of infected cells remained unexplored.

Profiling circulating cell-free DNA (cfDNA) has become a fundamental practice in cancer medicine, but the effectiveness of cfDNA at elucidating tumor-derived molecular features has not been systematically compared to standard single-lesion tumor biopsies in prospective cohorts of patients. The use of plasma instead of tissue to guide therapy is particularly attractive for patients with small cell lung cancer (SCLC), due to the aggressive clinical course of this cancer, which makes obtaining tumor biopsies exceedingly challenging.

Spatial transcriptomics (ST) enables the study of gene expression in spatial context, but many ST technologies face challenges due to limited resolution, leading to cell mixtures at each spot. We present LETSmix to deconvolve cell types by integrating spatial correlations through a tailored LETS filter, which leverages layer annotations, expression similarities, image texture features, and spatial coordinates to refine ST data. Additionally, LETSmix employs a mixup-augmented domain adaptation strategy to address discrepancies between ST and reference single-cell RNA sequencing data. Comprehensive evaluations across diverse ST platforms and tissue types demonstrate its high accuracy in estimating cell-type proportions and spatial patterns, surpassing existing methods (URL: https://github.com/ZhanYangen/LETSmix ).

Exercise rehabilitation therapy has garnered widespread recognition for its beneficial effects on the restoration of locomotor function in individuals with spinal cord injury (SCI). Notably, resistance exercise has demonstrated significant improvements in muscle strength, coordination, and overall functional recovery. However, to optimize clinical management and mimic exercise-like effects, it is imperative to obtain a comprehensive understanding of the molecular alterations that underlie these positive effects.

Germline heterozygous pathogenic variants (PVs) in TP53 cause Li-Fraumeni syndrome (LFS), a condition associated with increased risk of multiple tumor types. As the associated cancer risks were refined over time, clinical criteria also evolved to optimize diagnostic yield. The implementation of multi-gene panel germline testing in different clinical settings has led to the identification of TP53 PV carriers outside the classic LFS-associated cancer phenotypes, leading to a broader cancer phenotypic redefinition and to the renaming of the condition as "heritable TP53-related cancer syndrome" (hTP53rc). Germline TP53 variant interpretation is challenging due to the diverse nature of TP53 PVs, variable penetrance of the syndrome, possible occurrence of TP53 somatic mosaicism, and TP53 involvement in clonal hematopoiesis of indeterminate potential (CHIP). Here we aim to assess the relevance and impact of these issues on the diagnostic routine, and to evaluate the sensitivity of the different LFS clinical criteria to identify hTP53rc.

A subset of developmental disorders (DD) is characterized by disease-specific genome-wide methylation changes. These episignatures inform on the underlying pathogenic mechanisms and can be used to assess the pathogenicity of genomic variants as well as confirm clinical diagnoses. Currently, the detection of these episignature requires the use of indirect methylation profiling methodologies. We hypothesized that long-read whole genome sequencing would not only enable the detection of single nucleotide variants and structural variants but also episignatures.

Familial Meniere's disease (FMD) is a rare polygenic disorder of the inner ear. Mutations in the connexin gene family, which encodes gap junction proteins, can also cause hearing loss, but their role in FMD is largely unknown.

Senescence classification is an acknowledged challenge within the field, as markers are cell-type and context dependent. Currently, multiple morphological and immunofluorescence markers are required. However, emerging scRNA-seq datasets have enabled an increased understanding of senescent cell heterogeneity.

Despite extensive analysis, the dynamic changes in prostate epithelial cell states during tissue homeostasis as well as tumor initiation and progression have been poorly characterized. However, recent advances in single-cell RNA-sequencing (scRNA-seq) technology have greatly facilitated studies of cell states and plasticity in tissue maintenance and cancer, including in the prostate.

Large-scale pharmacogenomic resources, such as the Connectivity Map (CMap), have greatly assisted computational drug discovery. However, despite their widespread use, CMap-based methods have thus far been agnostic to the biological activity of drugs as well as to the genomic effects of drugs in multiple disease contexts. Here, we present a network-based statistical approach, Pathopticon, that uses CMap to build cell type-specific gene-drug perturbation networks and integrates these networks with cheminformatic data and diverse disease phenotypes to prioritize drugs in a cell type-dependent manner.

Central nervous system tumors are among the most lethal types of cancer. A critical factor for tailored neurosurgical resection strategies depends on specific tumor types. However, it is uncommon to have a preoperative tumor diagnosis, and intraoperative morphology-based diagnosis remains challenging. Despite recent advances in intraoperative methylation classifications of brain tumors, accuracy may be compromised by low tumor purity. Copy number variations (CNVs), which are almost ubiquitous in cancer, offer highly sensitive molecular biomarkers for diagnosis. These quantitative genomic alterations provide insight into dysregulated oncogenic pathways and can reveal potential targets for molecular therapies.

Mixed infection with multiple strains of the same pathogen in a single host can present clinical and analytical challenges. Whole genome sequence (WGS) data can identify signals of multiple strains in samples, though the precision of previous methods can be improved. Here, we present MixInfect2, a new tool to accurately detect mixed samples from Mycobacterium tuberculosis short-read WGS data. We then evaluate three approaches for reconstructing the underlying mixed constituent strain sequences. This allows these samples to be included in downstream analysis to gain insights into the epidemiology and transmission of mixed infections.

Klebsiella pneumoniae is one of the most prevalent pathogens responsible for multiple infections in healthcare settings and the community. K. pneumoniae CG147, primarily including ST147 (the founder ST), ST273, and ST392, is one of the most globally successful MDR clone linked to various carbapenemases.

In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed standardized variant curation guidelines for Mendelian disorders. Although these guidelines have been widely adopted, they are not gene- or disease-specific. To mitigate classification discrepancies, the Clinical Genome Resource FBN1 variant curation expert panel (VCEP) was established in 2018 to develop adaptations to the ACMG/AMP criteria for FBN1 in association with Marfan syndrome.