While it is well established that the cellular composition of white adipose tissue (WAT) varies between depots, the functional relevance of this heterogeneity remains unclear. By combining spatial and single-nucleus RNA sequencing, we provide a comprehensive map of subcutaneous and visceral (omental, mesenteric, mesocolic, and epiploic) WAT in both men and women. Our analyses reveal shared features, such as the spatial organization of adipogenesis, alongside depot-specific characteristics, including distinct cell-type enrichments and unique cell-cell communication routes. Epiploic WAT stands out by harboring high proportions of serum amyloid A expressing fat cells (encoded by SAA1 / SAA2 ) and several leukocyte populations. Through mechanistic studies, we demonstrate that adipocyte SAA1 / SAA2 expression is induced by inflammatory signals, including lipopolysaccharide, and that SAA1 activates immune responses in adipose-resident myeloid cells. Collectively, our findings suggest that visceral WAT exhibits distinct cytoarchitectural properties, with those located near the colon adapting by developing specialized adipocytes and immune cell populations.

Recent developments in spatially resolved -omics have enabled the joint study of gene expression, metabolite levels and tissue morphology, offering greater insights into biological pathways. Integrating these modalities from matched tissue sections to probe spatially-coordinated processes, however, remains challenging. Here we introduce MAGPIE, a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We show MAGPIE’s generalisability and scalability on spatial multi-omics data from multiple tissues, combining Visium with MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to new multi-modal datasets generated with a specialised sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis and to link small-molecule co-detection with endogenous lung responses. MAGPIE demonstrates the refined resolution and enhanced interpretability that spatial multi-modal analyses provide for studying tissue injury especially in pharmacological contexts, and delivers a modular, accessible workflow for data integration.

We developed the Adipose Tissue Knowledge Portal by centralizing previously dispersed datasets, integrating clinical and experimental results with transcriptomic and proteomic data from >6,000 women and men. The platform includes multiple adipose depots, resident cell types, and adipocyte perturbation studies. By providing streamlined data access, the portal enables integrative analyses and serves as a powerful tool to interrogate various dimensions of adipose biology down to the single-cell level.

Spatial transcriptomics technologies aim to spatially map gene expression in tissues and typically use oligonucleotide array surfaces that have undergone spatial indexing. These arrays are used to capture nucleic acids diffusing from adjacently placed tissues, allowing subsequent sequencing to reveal both gene and position. Slide-tags is a recently developed method by Russell et al. that inverts this principle. Instead of capturing molecules released from the tissue, probes are detached from a pre-decoded bead array and diffused into tissues, tagging nuclei with spatial barcodes. In this work we reanalyze this data and discover a latent, spatially informative cell-bead network formed incidentally from barcode diffusion and the biophysical properties of the tissue. This allows us to treat Slide-tags as a network-based imaging-by-sequencing approach. By optimizing spatial constraints encoded in the cell-bead network structure, we can achieve unassisted tissue reconstruction, a fundamental shift from classical spatial technologies based on pre-indexed arrays.

Physiologically relevant human skin models that include key skin cell types can be used forin vitrodrug testing, skin pathology studies, or clinical applications such as skin grafts. However, there is still no golden standard for such a model. We investigated the potential of a recombinant functionalized spider silk protein, FN-silk, for the construction of a dermal, an epidermal, and a bilayered skin equivalent (BSE). Specifically, two formats of FN-silk (i.e. 3D network and nanomembrane) were evaluated. The 3D network was used as an elastic ECM-like support for the dermis, and the thin, permeable nanomembrane was used as a basement membrane to support the epidermal epithelium. Immunofluorescence microscopy and spatially resolved transcriptomics analysis demonstrated the secretion of key ECM components and the formation of microvascular-like structures. Furthermore, the epidermal layer exhibited clear stratification and the formation of a cornified layer, resulting in a tight physiologic epithelial barrier. Our findings indicate that the presented FN-silk-based skin models can be proposed as physiologically relevant standalone epidermal or dermal models, as well as a combined BSE.

A tool that tags individual cells in a tissue with a unique barcode means that the gene-expression profile of each cell can be plotted in its original location. This allows spatial information to be captured at single-cell resolution.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with poor prognosis and limited treatment options. Efforts to identify effective treatments are thwarted by limited understanding of IPF pathogenesis and poor translatability of available preclinical models. Here we generated spatially resolved transcriptome maps of human IPF (n = 4) and bleomycin-induced mouse pulmonary fibrosis (n = 6) to address these limitations. We uncovered distinct fibrotic niches in the IPF lung, characterized by aberrant alveolar epithelial cells in a microenvironment dominated by transforming growth factor beta signaling alongside predicted regulators, such as TP53 and APOE. We also identified a clear divergence between the arrested alveolar regeneration in the IPF fibrotic niches and the active tissue repair in the acutely fibrotic mouse lung. Our study offers in-depth insights into the IPF transcriptional landscape and proposes alveolar regeneration as a promising therapeutic strategy for IPF.

Idiopathic pulmonary fibrosis (IPF), a deadly lung disease of unclear etiology, lacks sufficient therapeutic options. We extensively mapped the spatial transcriptomes of patient lungs and made translational comparisons with a mouse model of lung fibrosis, providing insights into disease mechanisms and the utility of the animal model for drug discovery.

Single-cell studies of human white adipose tissue (WAT) provide insights into the specialized cell types in the tissue. Here the authors combine publicly available and newly generated high-resolution and bulk transcriptomic results from multiple human datasets to provide a comprehensive cellular map of white adipose tissue. To date, single-cell studies of human white adipose tissue (WAT) have been based on small cohort sizes and no cellular consensus nomenclature exists. Herein, we performed a comprehensive meta-analysis of publicly available and newly generated single-cell, single-nucleus, and spatial transcriptomic results from human subcutaneous, omental, and perivascular WAT. Our high-resolution map is built on data from ten studies and allowed us to robustly identify >60 subpopulations of adipocytes, fibroblast and adipogenic progenitors, vascular, and immune cells. Using these results, we deconvolved spatial and bulk transcriptomic data from nine additional cohorts to provide spatial and clinical dimensions to the map. This identified cell-cell interactions as well as relationships between specific cell subtypes and insulin resistance, dyslipidemia, adipocyte volume, and lipolysis upon long-term weight changes. Altogether, our meta-map provides a rich resource defining the cellular and microarchitectural landscape of human WAT and describes the associations between specific cell types and metabolic states.

BackgroundBasecalling long DNA sequences is a crucial step in nanopore-based DNA sequencing protocols. In recent years, the CTC-RNN model has become the leading basecalling model, supplanting preceding hidden Markov models (HMMs) that relied on pre-segmenting ion current measurements. However, the CTC-RNN model operates independently of prior biological and physical insights.ResultsWe present a novel basecaller named Lokatt: explicit duration Markov model and residual-LSTM network. It leverages an explicit duration HMM (EDHMM) designed to model the nanopore sequencing processes. Trained on a newly generated library with methylation-free Ecoli samples and MinION R9.4.1 chemistry, the Lokatt basecaller achieves basecalling performances with a median single read identity score of 0.930, a genome coverage ratio of 99.750%, on par with existing state-of-the-art structure when trained on the same datasets.ConclusionOur research underlines the potential of incorporating prior knowledge into the basecalling processes, particularly through integrating HMMs and recurrent neural networks. The Lokatt basecaller showcases the efficacy of a hybrid approach, emphasizing its capacity to achieve high-quality basecalling performance while accommodating the nuances of nanopore sequencing. These outcomes pave the way for advanced basecalling methodologies, with potential implications for enhancing the accuracy and efficiency of nanopore-based DNA sequencing protocols.