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The skeletal muscle T-tubule is a specialized membrane domain essential for coordinated muscle contraction. However, in the absence of genetically tractable systems the mechanisms involved in T-tubule formation are unknown. Here, we use the optically transparent and genetically tractable zebrafish system to probe T-tubule development in vivo. By combining live imaging of transgenic markers with three-dimensional electron microscopy, we derive a four-dimensional quantitative model for T-tubule formation. To elucidate the mechanisms involved in T-tubule formation in vivo, we develop a quantitative screen for proteins that associate with and modulate early T-tubule formation, including an overexpression screen of the entire zebrafish Rab protein family. We propose an endocytic capture model involving firstly, formation of dynamic endocytic tubules at transient nucleation sites on the sarcolemma, secondly, stabilization by myofibrils/sarcoplasmic reticulum and finally, delivery of membrane from the recycling endosome and Golgi complex. It is unclear how the T-tubule structure of skeletal muscle, which regulates coordinated muscle contraction, forms. Here, the authors develop a four-dimensional quantitative model for T-tubule formation in zebrafish, based on live imaging, proposing a dynamic endocytic capture model.

Protein interaction networks are crucial for complex cellular processes. However, the elucidation of protein interactions occurring within highly specialised cells and tissues is challenging. Here, we describe the development, and application, of a new method for proximity-dependent biotin labelling in whole zebrafish. Using a conditionally stabilised GFP-binding nanobody to target a biotin ligase to GFP-labelled proteins of interest, we show tissue-specific proteomic profiling using existing GFP-tagged transgenic zebrafish lines. We demonstrate the applicability of this approach, termed BLITZ (Biotin Labelling In Tagged Zebrafish), in diverse cell types such as neurons and vascular endothelial cells. We applied this methodology to identify interactors of caveolar coat protein, cavins, in skeletal muscle. Using this system, we defined specific interaction networks within in vivo muscle cells for the closely related but functionally distinct Cavin4 and Cavin1 proteins.

Alveolar macrophages (AM) must perform three seemingly opposing roles including homeostasis, driving inflammation, and facilitating tissue repair. Whilst there is now consensus (supported by a large body of human single cell RNA sequencing (scRNA-seq) data) that the cell subsets that perform these tasks can readily be found based on their transcriptome, their ontogeny has remained unclear. Moreover, there is agreement that in all types of pulmonary fibrosis (PF) there is an expanded population of profibrotic AM that may aberrantly drive PF. From a therapeutic viewpoint, there is great appeal in the notion that the transcriptional program in different AM subsets is not fixed but remains plastic and amenable to pharmacological reprogramming. Accordingly, this study addresses this question by performing scRNA-seq on human AM following treatment with drugs or perturbagens including pioglitazone, trametinib, nintedanib, lipopolysaccharide and the natural compound endiandrin A. Each treatment induced a unique global transcriptional change, driving the cells towards distinct subsets, further supported by trajectory analysis, confirming a high level of plasticity. Confirmatory experiments using qPCR demonstrated that single exposure to a compound induced a relatively stable transcriptome, whereas serial exposure to a different compound allowed the cells to be reprogrammed yet again to a different phenotype. These findings add new insight into the biology of AM and support the development of novel therapies to treat PF.

The tumor microenvironment (TME) is composed of a diverse and dynamic spectrum of cell types, cellular activities, and cell–cell interactions (CCI). Understanding the complex CCI within the TME is critical for advancing cancer treatment strategies, including modulating or predicting drug responses. Recent advances in omics technologies, including spatial transcriptomics and proteomics, have allowed improved mapping of CCI within the TME. The integration of omics insights from different platforms may facilitate the identification of novel biomarkers and therapeutic targets. This review discusses the latest computational methods for inferring CCIs from different omics data and various CCI and drug databases, emphasizing their applications in predicting drug responses. We also comprehensively summarize recent patents, clinical trials, and publications that leverage these cellular interactions to refine cancer treatment approaches. We believe that the integration of these CCI-focused technologies can improve personalized therapy for cancer patients, thereby optimizing treatment outcomes and paving the way for next-generation precision oncology.

The mechanisms that modulate the kinetics of muscle relaxation are critically important for muscle function. A prime example of the impact of impaired relaxation kinetics is nemaline myopathy caused by mutations in KBTBD13 (NEM6). In addition to weakness, NEM6 patients have slow muscle relaxation, compromising contractility and daily life activities. The role of KBTBD13 in muscle is unknown, and the pathomechanism underlying NEM6 is undetermined. A combination of transcranial magnetic stimulation-induced muscle relaxation, muscle fiber- and sarcomere-contractility assays, low-angle x-ray diffraction, and superresolution microscopy revealed that the impaired muscle-relaxation kinetics in NEM6 patients are caused by structural changes in the thin filament, a sarcomeric microstructure. Using homology modeling and binding and contractility assays with recombinant KBTBD13, Kbtbd13-knockout and Kbtbd13R408C-knockin mouse models, and a GFP-labeled Kbtbd13-transgenic zebrafish model, we discovered that KBTBD13 binds to actin - a major constituent of the thin filament - and that mutations in KBTBD13 cause structural changes impairing muscle-relaxation kinetics. We propose that this actin-based impaired relaxation is central to NEM6 pathology.

Cell-cell interaction (CCI) analyses are an indispensable tool for harnessing the detail and depth of spatial and single-cell transcriptomics datasets by inferring inter-cellular communications, but no methods to integrate CCI results across samples exist currently. To address this, we have developed a computational pipeline, Multimodal CCI (MMCCI), to statistically integrate and analyze CCI results from existing popular CCI tools. We benchmarked MMCCI’s integration on single-cell spatial datasets and found it to be highly accurate compared to simpler methods. We utilized MMCCI’s integration and downstream biological analyses to uncover global and differential interaction patterns in multimodal aging brain and melanoma spatial datasets.

Cutaneous squamous cell carcinoma (cSCC), basal cell carcinoma (BCC), and melanoma, the three major types of skin cancer, account for over 70% of all cancer cases. Despite their prevalence, the skin cancer microenvironment remains poorly characterized, both in the outer skin layer where the cancer originates and at the deeper junctional and dermal layers into which it progresses. To address this, we integrated 12 complementary spatial single-cell technologies to construct orthogonally-validated cell signatures, spatial maps, and interactomes for cSCC, BCC, and melanoma. We comprehensively compared and integrated these spatial methods and provided practical guidelines on experimental design. Integrating four spatial transcriptomics platforms, we found keratinocyte cancer signatures, including six consistently validated gene markers. Spatial integration of transcriptomics, proteomics, and glycomics uncovered cancer communities enriched in melanocyte-fibroblast-T-cell colocalization with altered tyrosine and pyrimidine metabolism. Ligand-receptor analysis across >700 cell-type combinations and >1.5 million interactions highlighted key roles for CD44, integrins, and collagens, with CD44-FGF2 emerging as a potential therapeutic target. We consistently found differential interactions of melanocytes with fibroblasts and T-cells. We validated these interactions using Opal Polaris, RNAScope, and Proximal Ligation Assay. To integrate population-scale data, genetic association mapping in >500,000 individuals suggested SNPs enriched for spatial domains containing melanocytes, dysplastic keratinocytes, and fibroblasts, shedding light on functional mechanisms linking genetic heritability to cells within cancer tissue. This publicly available multiomics resource offers insights into the initiation and progression of the most lethal skin cancer (melanoma) and the most common forms (cSCC and BCC) and can be explored interactively at https://skincanceratlas.com.

Cell-cell interaction (CCI) analyses are becoming an indispensable discovery tool for cutting-edge single cell and spatial omics technologies, identifying ligand-receptor (LR) targets in intercellular communications at the molecular, cellular, and microenvironment levels. Different transcriptional-based modalities can add complementary information and provide independent validation of a CCI, but so far no robust methods exist to integrate CCI results together. To address this, we have developed a statistical and computational pipeline, Multimodal CCI (MMCCI), implemented in an open-source Python package, which integrates, analyzes, and visualizes multiple LR-cell-type CCI networks between multiple samples of a single transcriptomic-based modality as well as between multiple modalities. MMCCI implements new and in-depth downstream analyses, including comparison between biological conditions, network and interaction clustering, sender-receiver interaction querying, and pathway analysis. We applied MMCCI to integrate CCIs in our spatial transcriptomics datasets of aging mouse brains (from 10X Visium and BGI STOmics) and melanoma (10X Visium, 10X Xenium and NanoString CosMx) and identified biologically meaningful interactions. Using simulated data, we applied MMCCI integration on four popular CCI algorithms, stLearn, CellChat, Squidpy, and NATMI, and detected highly confident interactions while reducing false interaction discoveries through the integration of multiple samples. With MMCCI, the community will have access to a valuable tool for harnessing the power of multimodal single cell and spatial transcriptomics. MMCCI source code and documentation are available at: https://github.com/BiomedicalMachineLearning/MultimodalCCI.Competing Interest StatementThe authors have declared no competing interest.Footnotes* https://github.com/BiomedicalMachineLearning/MultimodalCCI