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.

SUMMARY: Spatially resolved transcriptomics technologies generate gene expression data with retained positional information from a tissue section, often accompanied by a corresponding histological image. Computational tools should make it effortless to incorporate spatial information into data analyses and present analysis results in their histological context. Here, we present semla, an R package for processing, analysis, and visualization of spatially resolved transcriptomics data generated by the Visium platform, that includes interactive web applications for data exploration and tissue annotation. AVAILABILITY AND IMPLEMENTATION: The R package semla is available on GitHub (https://github.com/ludvigla/semla), under the MIT License, and deposited on Zenodo (https://doi.org/10.5281/zenodo.8321645). Documentation and tutorials with detailed descriptions of usage can be found at https://ludvigla.github.io/semla/.

Current methods for epigenomic profiling are limited in their ability to obtain genome-wide information with spatial resolution. We introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathology.