Insights to aging prediction with AI based epigenetic clocks.

Over the past century, human lifespan has increased remarkably, yet the inevitability of aging persists. The disparity between biological age, which reflects pathological deterioration and disease, and chronological age, indicative of normal aging, has driven prior research focused on identifying mechanisms that could inform interventions to reverse excessive age-related deterioration and reduce morbidity and mortality. DNA methylation has emerged as an important predictor of age, leading to the development of epigenetic clocks that quantify the extent of pathological deterioration beyond what is typically expected for a given age.

Integrative Co-Registration of Elemental Imaging and Histopathology for Enhanced Spatial Multimodal Analysis of Tissue Sections through TRACE.

Summary: Elemental imaging provides detailed profiling of metal bioaccumulation, offering more precision than bulk analysis by targeting specific tissue areas. However, accurately identifying comparable tissue regions from elemental maps is challenging, requiring the integration of hematoxylin and eosin (H&E) slides for effective comparison. Facilitating the streamlined co-registration of Whole Slide Images (WSI) and elemental maps, TRACE enhances the analysis of tissue regions and elemental abundance in various pathological conditions.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e.

Stepwise differentiation of follicular helper T cells reveals distinct developmental and functional states.

Follicular helper T (Tfh) cells are essential for the formation of high affinity antibodies after vaccination or infection. Although the signals responsible for initiating Tfh differentiation from naïve T cells have been studied, the signals controlling sequential developmental stages culminating in optimal effector function are not well understood. Here we use fate mapping strategies for the cytokine IL-21 to uncover sequential developmental stages of Tfh differentiation including a progenitor-like stage, a fully developed effector stage and a post-effector Tfh stage that maintains transcriptional and epigenetic features without IL-21 production.

Feasibility of Inferring Spatial Transcriptomics from Single-Cell Histological Patterns for Studying Colon Cancer Tumor Heterogeneity.

Background: Spatial transcriptomics involves studying the spatial organization of gene expression within tissues, offering insights into the molecular diversity of tumors. While spatial gene expression is commonly amalgamated from 1-10 cells across 50-micron spots, recent methods have demonstrated the capability to disaggregate this information at subspot resolution by leveraging both expression and histological patterns. However, elucidating such information from histology alone presents a significant challenge but if solved can better permit spatial molecular analysis at cellular resolution for instances where Visium data is not available, reducing study costs.

Spatial Omics Driven Crossmodal Pretraining Applied to Graph-based Deep Learning for Cancer Pathology Analysis.

Graph-based deep learning has shown great promise in cancer histopathology image analysis by contextualizing complex morphology and structure across whole slide images to make high quality downstream outcome predictions (ex: prognostication). These methods rely on informative representations (i.e.

Potential to Enhance Large Scale Molecular Assessments of Skin Photoaging through Virtual Inference of Spatial Transcriptomics from Routine Staining.

The advent of spatial transcriptomics technologies has heralded a renaissance in research to advance our understanding of the spatial cellular and transcriptional heterogeneity within tissues. Spatial transcriptomics allows investigation of the interplay between cells, molecular pathways and the surrounding tissue architecture and can help elucidate developmental trajectories, disease pathogenesis, and various niches in the tumor microenvironment. Photoaging is the histological and molecular skin damage resulting from chronic/acute sun exposure and is a major risk factor for skin cancer.

Follicular regulatory T cells control humoral and allergic immunity by restraining early B cell responses.

Follicular regulatory T (T) cells have specialized roles in modulating follicular helper T (T) cell activation of B cells. However, the precise role of T cells in controlling antibody responses to foreign antigens and autoantigens in vivo is still unclear due to a lack of specific tools. A T cell-deleter mouse was developed that selectively deletes T cells, facilitating temporal studies.

FoxP3 and Ezh2 regulate Tfr cell suppressive function and transcriptional program.

Follicular regulatory T (Tfr) cells are a regulatory T cell subset that controls antibody production by inhibiting T follicular helper (Tfh)-mediated help to B cells. Tfh and Tfr cells possess opposing functions suggesting unique programming. Here we elucidated the transcriptional program controlling Tfr suppressive function.

TRIM65 regulates microRNA activity by ubiquitination of TNRC6.

MicroRNAs (miRNAs) are small evolutionarily conserved regulatory RNAs that modulate mRNA stability and translation in a wide range of cell types. MiRNAs are involved in a broad array of biological processes, including cellular proliferation, differentiation, and apoptosis. To identify previously unidentified regulators of miRNA, we initiated a systematic discovery-type proteomic analysis of the miRNA pathway interactome in human cells.

Using networks to measure similarity between genes: association index selection.

Biological networks can be used to functionally annotate genes on the basis of interaction-profile similarities. Metrics known as association indices can be used to quantify interaction-profile similarity. We provide an overview of commonly used association indices, including the Jaccard index and the Pearson correlation coefficient, and compare their performance in different types of analyses of biological networks.

Extensive rewiring and complex evolutionary dynamics in a C. elegans multiparameter transcription factor network.

Gene duplication results in two identical paralogs that diverge through mutation, leading to loss or gain of interactions with other biomolecules. Here, we comprehensively characterize such network rewiring for C. elegans transcription factors (TFs) within and across four newly delineated molecular networks.

Enhanced yeast one-hybrid assays for high-throughput gene-centered regulatory network mapping.

A major challenge in systems biology is to understand the gene regulatory networks that drive development, physiology and pathology. Interactions between transcription factors and regulatory genomic regions provide the first level of gene control. Gateway-compatible yeast one-hybrid (Y1H) assays present a convenient method to identify and characterize the repertoire of transcription factors that can bind a DNA sequence of interest.