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.

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.

Life hinges on the precise interplay between gene regulation and metabolism, a dynamic balance that unfolds in specific tissues and underlies both normal physiology and disease. This thesis follows a unifying red thread, advancing spatial omics for tissue-specific metabolic insights and translational applications by combining cutting-edge spatial transcriptomics and spatial epigenomics to illuminate how local regulatory mechanisms shape metabolic function.

In the initial segment of the thesis, we establish the conceptual groundwork, exploring how chromatin accessibility and transcriptional programs orchestrate cellular metabolism. We then apply spatial transcriptomics to two distinct yet metabolically active tissues. Paper I maps subcutaneous white adipose tissue (WAT) and discovers multiple adipocyte subtypes with divergent insulin responses, underscoring the critical role of tissue architecture in metabolic homeostasis. Paper II extends these methods to the human placenta, revealing region-specific gene expression patterns that help explain the metabolic dysregulation observed in preeclampsia. Given the placenta’s pivotal role in maternal-foetal nutrient exchange, these findings offer novel insights into how morphological compartments become disrupted in disease.

Building on these insights, Paper III introduces spatial ATAC-seq, a novel technique for profiling open chromatin within intact tissues, linking regulatory elements to their spatial context. Paper IV refines this protocol for broader adoption, integrating it with commercial platforms and enabling seamless multi-omic workflows. Building on these technological advances, Paper V returns to adipose tissue in a clinically relevant setting, employing a multi-omic approach to chart the long-term remodelling of WAT after bariatric surgery. By capturing transcriptional shifts in adipocytes and immune–stromal interactions, we highlight the tissue-level transformations that underpin sustained metabolic improvements.

Collectively, these studies showcase how spatial omics can deepen our understanding of tissue-specific metabolism, bridging foundational biology and translational research. They also underscore the power of integrated multi-omic approaches in revealing how chromatin states, gene expression, and metabolic function intersect in situ. By decoding the spatial architecture of gene regulation, we not only unravel the cellular intricacies of adipose and placental tissues but also pave the way for targeted therapeutic interventions in metabolic diseases, offering a powerful lens through which to view, and ultimately shape human health.

Livet vilar på det precisa samspelet mellan genreglering och metabolism, en dynamisk balans som utspelar sig i specifika vävnader och ligger till grund för både normal fysiologi och sjukdom. Denna avhandling beskriver utvecklingen av spatiala omik-metoder som ger insikter i vävnadsspecifik metabolism och möjliggör translationella tillämpningar. Genom att kombinera banbrytande spatiala transkriptomik och spatiala epigenomik belyser arbetet hur lokala regulatoriska mekanismer formar den metabola funktionen. I avhandlingens inledande del lägger vi den konceptuella grunden och utforskar hur kromatintillgänglighet och transkriptionsprogram styr cellens metabolism. Därefter tillämpar vi spatial transkriptomik på två metabolt aktiva vävnader. Paper I kartlägger subkutant vitt fett (WAT) och identifierar flera adipocyt-subtyper med olika insulinsvar, vilket understryker hur vävnadsarkitekturen är avgörande för metabol homeostas. Paper II överför dessa metoder till den mänskliga placentan och avslöjar region-specifika genuttrycksmönster som förklarar de metabola störningar som uppträder vid preeklampsi. Med tanke på placentans centrala roll i näringsutbytet mellan moder och foster ger dessa resultat nya insikter i hur morfologiska avgränsningar kan rubbas vid sjukdom. Utifrån dessa fynd presenterar Paper III spatial ATAC-seq, en ny teknik för att profilera öppet kromatin i intakta vävnader, vilket knyter samman regulatoriska element med deras spatiala kontext. Paper IV förfinar denna metod för bredare användning genom att integrera den med kommersiella plattformar och möjliggöra smidiga multi-omiska arbetsflöden. Slutligen återvänder Paper V till fettvävnaden i ett kliniskt relevant sammanhang och använder en multi-omisk strategi för att kartlägga långsiktig ombyggnad av WAT efter gastrisk bypass-operation. Genom att fånga upp transkriptionella förändringar i adipocyter och immunceller-stromaceller belyser vi de förändringar på vävnadsnivå som ligger bakom varaktiga metabola förbättringar. Sammantaget visar dessa studier hur spatiala omikmetoder kan fördjupa vår förståelse av vävnadsspecifik metabolism och överbrygga avståndet mellan grundläggande biologi och translationell forskning. De framhäver också styrkan i integrerade multi-omiska angreppssätt för att synliggöra hur kromatintillstånd, genuttryck och metabol funktion samverkar in situ. Genom att bevara den spatiala arkitekturen hos genreglering kan vi inte bara blottlägga de cellulära komplexiteterna i fettvävnad och placenta, utan även bana väg för riktade terapeutiska behandlingar vid metabola sjukdomar, och därigenom erbjuda ett kraftfullt verktyg för hur vi kan förstå och i förlängningen påverka människans hälsa

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.

While triple-negative breast cancer (TNBC) is known to be heterogeneous at the genomic and transcriptomic levels, spatial information on tumor organization and cell composition is still lacking. Here, we investigate TNBC tumor architecture including its microenvironment using spatial transcriptomics on a series of 92 patients. We perform an in-depth characterization of tumor and stroma organization and composition using an integrative approach combining histomorphological and spatial transcriptomics. Furthermore, a detailed molecular characterization of tertiary lymphoid structures leads to identify a gene signature strongly associated to disease outcome and response to immunotherapy in several tumor types beyond TNBC. A stepwise clustering analysis identifies nine TNBC spatial archetypes, further validated in external datasets. Several spatial archetypes are associated with disease outcome and characterized by potentially actionable features. In this work, we provide a comprehensive insight into the complexity of TNBC ecosystem with potential clinical relevance, opening avenues for treatment tailoring including immunotherapy.

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.

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.

Spatial transcriptomics (ST) maps RNA level patterns within a tissue. This technology has not been previously applied to human placental tissue. We demonstrate analysis of human placental samples with ST. Unsupervised clustering revealed that distinct RNA patterns were found corresponding to different morphological structures. Additionally, when focusing upon terminal villi and hemoglobin associated structures, RNA levels differed between placentas from full term healthy pregnancies and those complicated by preeclampsia. The results from this study can provide a benchmark for future ST studies in placenta.