Glucose dissociates DDX21 dimers to regulate mRNA splicing and tissue differentiation.

Glucose is a universal bioenergy source; however, its role in controlling protein interactions is unappreciated, as are its actions during differentiation-associated intracellular glucose elevation. Azido-glucose click chemistry identified glucose binding to a variety of RNA binding proteins (RBPs), including the DDX21 RNA helicase, which was found to be essential for epidermal differentiation. Glucose bound the ATP-binding domain of DDX21, altering protein conformation, inhibiting helicase activity, and dissociating DDX21 dimers. Glucose elevation during differentiation was associated with DDX21 re-localization from the nucleolus to the nucleoplasm where DDX21 assembled into larger protein complexes containing RNA splicing factors. DDX21 localized to specific SCUGSDGC motif in mRNA introns in a glucose-dependent manner and promoted the splicing of key pro-differentiation genes, including GRHL3, KLF4, OVOL1, and RBPJ. These findings uncover a biochemical mechanism of action for glucose in modulating the dimerization and function of an RNA helicase essential for tissue differentiation.

Multimodal Analysis of Composition and Spatial Architecture in Human Squamous Cell Carcinoma.

To define the cellular composition and architecture of cutaneous squamous cell carcinoma (cSCC), we combined single-cell RNA sequencing with spatial transcriptomics and multiplexed ion beam imaging from a series of human cSCCs and matched normal skin. cSCC exhibited four tumor subpopulations, three recapitulating normal epidermal states, and a tumor-specific keratinocyte (TSK) population unique to cancer, which localized to a fibrovascular niche. Integration of single-cell and spatial data mapped ligand-receptor networks to specific cell types, revealing TSK cells as a hub for intercellular communication. Multiple features of potential immunosuppression were observed, including T regulatory cell (Treg) co-localization with CD8 T cells in compartmentalized tumor stroma. Finally, single-cell characterization of human tumor xenografts and in vivo CRISPR screens identified essential roles for specific tumor subpopulation-enriched gene networks in tumorigenesis. These data define cSCC tumor and stromal cell subpopulations, the spatial niches where they interact, and the communicating gene networks that they engage in cancer.

Combined protein and nucleic acid imaging reveals virus-dependent B cell and macrophage immunosuppression of tissue microenvironments.

Understanding the mechanisms of HIV tissue persistence necessitates the ability to visualize tissue microenvironments where infected cells reside; however, technological barriers limit our ability to dissect the cellular components of these HIV reservoirs. Here, we developed protein and nucleic acid in situ imaging (PANINI) to simultaneously quantify DNA, RNA, and protein levels within these tissue compartments. By coupling PANINI with multiplexed ion beam imaging (MIBI), we measured over 30 parameters simultaneously across archival lymphoid tissues from healthy or simian immunodeficiency virus (SIV)-infected nonhuman primates. PANINI enabled the spatial dissection of cellular phenotypes, functional markers, and viral events resulting from infection. SIV infection induced IL-10 expression in lymphoid B cells, which correlated with local macrophage M2 polarization. This highlights a potential viral mechanism for conditioning an immunosuppressive tissue environment for virion production. The spatial multimodal framework here can be extended to decipher tissue responses in other infectious diseases and tumor biology.

Robust single-cell matching and multimodal analysis using shared and distinct features.

The ability to align individual cellular information from multiple experimental sources is fundamental for a systems-level understanding of biological processes. However, currently available tools are mainly designed for single-cell transcriptomics matching and integration, and generally rely on a large number of shared features across datasets for cell matching. This approach underperforms when applied to single-cell proteomic datasets due to the limited number of parameters simultaneously accessed and lack of shared markers across these experiments. Here, we introduce a cell-matching algorithm, matching with partial overlap (MARIO) that accounts for both shared and distinct features, while consisting of vital filtering steps to avoid suboptimal matching. MARIO accurately matches and integrates data from different single-cell proteomic and multimodal methods, including spatial techniques and has cross-species capabilities. MARIO robustly matched tissue macrophages identified from COVID-19 lung autopsies via codetection by indexing imaging to macrophages recovered from COVID-19 bronchoalveolar lavage fluid by cellular indexing of transcriptomes and epitopes by sequencing, revealing unique immune responses within the lung microenvironment of patients with COVID.

Stable Persistent Room-Temperature Phosphorescent Hydrogels Based on Ionically Crosslinked Nonaromatic Carboxylate Polymers.

Developing pure organic room-temperature phosphorescent (RTP) hydrogels is important for expanding the practical applications of phosphorescent materials. However, most of the reported RTP hydrogels containing aromatic phosphors suffer from short phosphorescent lifetimes, unstable underwater RTP emissions, and complex preparation processes. Herein, novel nonaromatic RTP hydrogels are prepared by using two types of non-traditional luminescent polymers, sodium alginate and a polymeric carboxylate, which are not RTP emissive or very weakly emissive in aqueous environments. The prepared hydrogels exhibit the following features: I) color-tunable RTP emissions with ultra-long lifetimes up to 451.1 ms, II) excellent anti-swelling properties and stable persistent RTP emission even after being immersed in deionized water for months, III) efficient and large-scale preparation of hydrogel fibers by wet spinning technique. Experiment results and theoretical calculations show that the stable and long-lifetime RTP emissions of the hydrogels originate from the introduction of more nonconventional chromophores which are strongly crosslinked with ionic bonding between carboxylate groups and calcium ions and enhanced through-space interactions between them. This work provides a reliable strategy for designing nonaromatic hydrogels with stable and persistent RTP.

Pyrazine as a More Efficient Luminophore than Benzene for Producing Red-Shifted and Enhanced Photoluminescence.

The development of organic photoluminescent (PL) materials with red-shifted and enhanced emissions is beneficial to promoting their applications. Luminescent materials based on aromatic heterocycles (e.g., pyrazine) usually have red-shifted and enhanced photoluminescence compared with phenyl-based luminescent materials. In this work, the photoluminescence behaviors of pyrazine and its derivatives (o-dichloro-, o-dicyano-, and dichlorodicyano-substituted) are compared with those of benzene and its derivatives. All compounds exhibit fluorescence emissions ranging from blue to yellow, and the fluorescence emissions of pyrazinyl compounds are more red-shifted than those of phenyl compounds. Except for the o-dicyano-substituted compound, pyrazinyl compounds exhibit stronger fluorescence emissions than corresponding phenyl compounds in both pure substances and ethanol solutions. In addition, both 5,6-dichloro-2,3-dicyanopyrazine (P4) and 4,5-dichloro-1,2-dicyanobenzene (B4) exhibit room temperature phosphorescence, and the maximum delayed emission wavelength is red-shifted from 575 nm of B4 to 637 nm of P4. The energy gaps between the highest occupied molecular orbital and the lowest unoccupied molecular orbital of the monomers of pyrazinyl compounds are reduced by 0.07-1.37 eV compared with the monomers of phenyl compounds, which is the fundamental reason for the red-shifted emissions of the pyrazinyl compounds. Moreover, compared to B4, the smaller molecular spacing in the P4 crystal structure facilitates interlayer electron transfer and hence the formation of more extended through-space conjugation, resulting in the red-shifted emission of P4. This work proves that pyrazine is a more efficient luminophore than benzene for constructing PL compounds with longer emission wavelengths and higher quantum yields, which are important in guiding the design and preparation of organic PL materials.

Hydrogen-Bonding-Driven Nontraditional Photoluminescence of a ß-Enamino Ester.

Nontraditional luminogens (NTLs) do not contain any conventional chromophores (large π-conjugated structures), but they do show intrinsic photoluminescence. To achieve photoluminescence from NTLs, it is necessary to increase the extent of through-space conjugation (TSC) and suppress nonradiative decay. Incorporating strong physical interactions such as hydrogen bonding is an effective strategy to achieve this. In this work, we carried out comparative studies on the photoluminescence behaviors of two ß-enamino esters with similar chemical structures, namely methyl 3-aminocrotonate (MAC) and methyl (E)-3-(1-pyrrolidinyl)-2-butenoate (MPB). MAC crystal emits blue fluorescence under UV irradiation. The critical cluster concentration of MAC in ethanol solutions was determined by studying the relationship between the photoluminescence intensity (UV-visible absorbance) and concentration. Furthermore, MAC exhibits solvatochromism, and its emission wavelength redshifts as the solvent polarity increases. On the contrary, MPB is non-emissive in both solid state and solutions. Crystal structures and theoretical calculation prove that strong inter- and intramolecular hydrogen bonds lead to the formation of large amounts of TSC of MAC molecules in aggregated states. No hydrogen bonds and thus no effective TSC can be formed between or within MPB molecules, and this is the reason for its non-emissive nature. This work provides a deeper understanding of how hydrogen bonding contributes to the luminescence of NTLs.

Effects of Through-Bond and Through-Space Conjugations on the Photoluminescence of Small Aromatic and Aliphatic Aldimines.

Through-bond conjugation (TBC) and/or through-space conjugation (TSC) determine the photophysical properties of organic luminescent compounds. No systematic studies have been carried out to understand the transition from aromatic TBC to non-aromatic TSC on the photoluminescence of organic luminescent compounds. In this work, a series of small aromatic and aliphatic aldimines were synthesized. For the aromatic imines, surprisingly, N,1-diphenylmethanimine with the highest TBC is non-emissive, while N-benzyl-1-phenylmethanimine and N-cyclohexyl-1-phenylmethanimine emit bright fluorescence in aggregate states. The aliphatic imines are all emissive, and their maximum emission wavelength decreases while the quantum yield increases with a decrease in steric hindrance. The imines show concentration-dependent and excitation-dependent emissions. Theoretical calculations show that the TBC extents in the aromatic imines are not strong enough to induce photoluminescence in a single molecule state, while the intermolecular TSC becomes dominant for the fluorescence emissions of both aromatic and aliphatic imines in aggregate states, and the configurations and spatial conformations of the molecules in aggregate states play a key role in the formation of effective TSC. This study provides an understanding of how chemical and spatial structures affect the formation of TBC and TSC and their functions on the photoluminescence of organic luminescent materials.

Bottom-Up Strategy To Prepare Nanoparticles with a Single DNA Strand.

We describe the preparation of cross-linked, polymeric organic nanoparticles (ONPs) with a single, covalently linked DNA strand. The structure and functionalities of the ONPs are controlled by the synthesis of their parent linear block copolymers that provide monovalency, fluorescence and narrow size distribution. The ONP can also guide the deposition of chloroaurate ions allowing gold nanoparticles (AuNPs) to be prepared using the ONPs as templates. The DNA strand on AuNPs is shown to preserve its functions.

Supramolecular Chemistry of Cucurbiturils: Tuning Cooperativity with Multiple Noncovalent Interactions from Positive to Negative.

Rational control of the cooperativity of multiple noncovalent interactions often plays an important role in the design and construction of supramolecular self-assemblies and materials, especially in precision supramolecular engineering. However, it still remains a challenge to control the cooperativity of multiple noncovalent interactions through tuning the hydrophobic effect. In this work, we demonstrate that the binding cooperativity of cucurbit[8]uril(CB[8])-mediated homoternary complexes is strongly influenced by the amphiphilicity of guest molecule side groups on account of an interplay between both classical (entropy-driven) and nonclassical (enthalpy-driven) hydrophobic effects. To this end, we rationally designed and prepared a series of guest molecules bearing a benzyl group as the CB[8] homoternary binding motif with various hydrophilic and hydrophobic side groups for cooperative control. By gradually tuning side groups of the guest molecules from hydrophilic to hydrophobic, we are able to control the binding from positive to negative cooperativity. An advanced molecular recognition process and self-assembling system can be developed by adjusting the positive and negative cooperativity. The ability to regulate and control the binding cooperativity will enrich the field of supramolecular chemistry, and employing cooperativity-controlled multiple noncovalent interactions in precision supramolecular engineering is highly anticipated.

Prediction of protein subcellular localization in single cells.

The subcellular localization of a protein is important for its function and interaction with other molecules, and its mislocalization is linked to numerous diseases. While atlas-scale efforts have been made to profile protein localization across various cell lines, existing datasets only contain limited pairs of proteins and cell lines which do not cover all human proteins. We present a method that uses both protein sequences and cellular landmark images to perform Predictions of Unseen Proteins' Subcellular localization (PUPS), which can generalize to both proteins and cell lines not used for model training. PUPS combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images for protein localization prediction. The protein sequence input enables generalization to unseen proteins and the cellular image input enables cell type specific prediction that captures single-cell variability. PUPS' ability to generalize to unseen proteins and cell lines enables us to assess the variability in protein localization across cell lines as well as across single cells within a cell line and to identify the biological processes associated with the proteins that have variable localization. Experimental validation shows that PUPS can be used to predict protein localization in newly performed experiments outside of the Human Protein Atlas used for training. Collectively, PUPS utilizes both protein sequences and cellular images to predict protein localization in unseen proteins and cell lines with the ability to capture single-cell variability.

Masked autoencoders with handcrafted feature predictions: Transformer for weakly supervised esophageal cancer classification.

BACKGROUND AND OBJECTIVE: Esophageal cancer is a serious disease with a high prevalence in Eastern Asia. Histopathology tissue analysis stands as the gold standard in diagnosing esophageal cancer. In recent years, there has been a shift towards digitizing histopathological images into whole slide images (WSIs), progressively integrating them into cancer diagnostics. However, the gigapixel sizes of WSIs present significant storage and processing challenges, and they often lack localized annotations. To address this issue, multi-instance learning (MIL) has been introduced for WSI classification, utilizing weakly supervised learning for diagnosis analysis. By applying the principles of MIL to WSI analysis, it is possible to reduce the workload of pathologists by facilitating the generation of localized annotations. Nevertheless, the approach's effectiveness is hindered by the traditional simple aggregation operation and the domain shift resulting from the prevalent use of convolutional feature extractors pretrained on ImageNet. METHODS: We propose a MIL-based framework for WSI analysis and cancer classification. Concurrently, we introduce employing self-supervised learning, which obviates the need for manual annotation and demonstrates versatility in various tasks, to pretrain feature extractors. This method enhances the extraction of representative features from esophageal WSI for MIL, ensuring more robust and accurate performance. RESULTS: We build a comprehensive dataset of whole esophageal slide images and conduct extensive experiments utilizing this dataset. The performance on our dataset demonstrates the efficiency of our proposed MIL framework and the pretraining process, with our framework outperforming existing methods, achieving an accuracy of 93.07% and AUC (area under the curve) of 95.31%. CONCLUSION: This work proposes an effective MIL method to classify WSI of esophageal cancer. The promising results indicate that our cancer classification framework holds great potential in promoting the automatic whole esophageal slide image analysis.

3D reconstruction of the uterus and automatic segmentation of the uterine cavity on 3D magnetic resonance imaging: A preliminary study.

Purpose: To determine the differences in 3D shape features between septate uterus (SU) and normal uterus and to train a network to automatically delineate uterine cavity on 3D magnetic resonance imaging (MRI). Methods: A total of 43 patients (22 cases of partial septate uterus and 21 cases of complete septate uterus) were included in the experimental group. Nine volunteers were recruited as a control group. The uterine cavity (UC), myometrium (UM), and cervical canal of the uterus were segmented manually using ITK-SNAP software. The three-dimensional shape features of the UC and UM were extracted by using PyRadiomics. The recurrent saliency transformation network (RSTN) method was used to segment the UC. Results: The values of four 3D shape features were significantly lower in the control group than in the partial septate group and the complete septate group, while the values of two features were significantly higher (p < 0.05). The UCs of the three groups were significantly different in terms of flatness and sphericity. The values of six features were significantly lower in the UMs of the control group than in those of the partial septate group and the complete septate group (p < 0.05). After the deep learning networks were trained, the Dice similarity coefficient (DSC) scores of the four folds for different thresholds were all over 80 %. The average volume ratio between predictions and manual segmentation was 101.2 %. Conclusions: Based on 3D reconstruction, 3D shape features can be used to comprehensively evaluate septate uterus and provide a reference for subsequent research. The UC can be automatically segmented on 3D MRI using the RSTN method.

A Hitchhiker's guide to high-dimensional tissue imaging with multiplexed ion beam imaging.

Advancements in multiplexed tissue imaging technologies are vital in shaping our understanding of tissue microenvironmental influences in disease contexts. These technologies now allow us to relate the phenotype of individual cells to their higher-order roles in tissue organization and function. Multiplexed Ion Beam Imaging (MIBI) is one of such technologies, which uses metal isotope-labeled antibodies and secondary ion mass spectrometry (SIMS) to image more than 40 protein markers simultaneously within a single tissue section. Here, we describe an optimized MIBI workflow for high-plex analysis of Formalin-Fixed Paraffin-Embedded (FFPE) tissues following antigen retrieval, metal isotope-conjugated antibody staining, imaging using the MIBI instrument, and subsequent data processing and analysis. While this workflow is focused on imaging human FFPE samples using the MIBI, this workflow can be easily extended to model systems, biological questions, and multiplexed imaging modalities.

MAPS: pathologist-level cell type annotation from tissue images through machine learning.

Highly multiplexed protein imaging is emerging as a potent technique for analyzing protein distribution within cells and tissues in their native context. However, existing cell annotation methods utilizing high-plex spatial proteomics data are resource intensive and necessitate iterative expert input, thereby constraining their scalability and practicality for extensive datasets. We introduce MAPS (Machine learning for Analysis of Proteomics in Spatial biology), a machine learning approach facilitating rapid and precise cell type identification with human-level accuracy from spatial proteomics data. Validated on multiple in-house and publicly available MIBI and CODEX datasets, MAPS outperforms current annotation techniques in terms of speed and accuracy, achieving pathologist-level precision even for typically challenging cell types, including tumor cells of immune origin. By democratizing rapidly deployable and scalable machine learning annotation, MAPS holds significant potential to expedite advances in tissue biology and disease comprehension.

A Spatial Multi-Modal Dissection of Host-Microbiome Interactions within the Colitis Tissue Microenvironment.

The intricate and dynamic interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host features and its microbiome across multiple spatial modalities. We demonstrate MicroCart by comprehensively investigating the alterations in both gut host and microbiome components in a murine model of colitis by coupling MicroCart with spatial proteomics, transcriptomics, and glycomics platforms. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, and bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multiomics.

Cross-domain information fusion for enhanced cell population delineation in single-cell spatial-omics data.

Cell population delineation and identification is an essential step in single-cell and spatial-omics studies. Spatial-omics technologies can simultaneously measure information from three complementary domains related to this task: expression levels of a panel of molecular biomarkers at single-cell resolution, relative positions of cells, and images of tissue sections, but existing computational methods for performing this task on single-cell spatial-omics datasets often relinquish information from one or more domains. The additional reliance on the availability of "atlas" training or reference datasets limits cell type discovery to well-defined but limited cell population labels, thus posing major challenges for using these methods in practice. Successful integration of all three domains presents an opportunity for uncovering cell populations that are functionally stratified by their spatial contexts at cellular and tissue levels: the key motivation for employing spatial-omics technologies in the first place. In this work, we introduce Cell Spatio- and Neighborhood-informed Annotation and Patterning (CellSNAP), a self-supervised computational method that learns a representation vector for each cell in tissue samples measured by spatial-omics technologies at the single-cell or finer resolution. The learned representation vector fuses information about the corresponding cell across all three aforementioned domains. By applying CellSNAP to datasets spanning both spatial proteomic and spatial transcriptomic modalities, and across different tissue types and disease settings, we show that CellSNAP markedly enhances de novo discovery of biologically relevant cell populations at fine granularity, beyond current approaches, by fully integrating cells' molecular profiles with cellular neighborhood and tissue image information.

Epstein-Barr Virus Orchestrates Spatial Reorganization and Immunomodulation within the Classic Hodgkin Lymphoma Tumor Microenvironment.

Classic Hodgkin Lymphoma (cHL) is a tumor composed of rare malignant Hodgkin and Reed-Sternberg (HRS) cells nested within a T-cell rich inflammatory immune infiltrate. cHL is associated with Epstein-Barr Virus (EBV) in 25% of cases. The specific contributions of EBV to the pathogenesis of cHL remain largely unknown, in part due to technical barriers in dissecting the tumor microenvironment (TME) in high detail. Herein, we applied multiplexed ion beam imaging (MIBI) spatial pro-teomics on 6 EBV-positive and 14 EBV-negative cHL samples. We identify key TME features that distinguish between EBV-positive and EBV-negative cHL, including the relative predominance of memory CD8 T cells and increased T-cell dysfunction as a function of spatial proximity to HRS cells. Building upon a larger multi-institutional cohort of 22 EBV-positive and 24 EBV-negative cHL samples, we orthogonally validated our findings through a spatial multi-omics approach, coupling whole transcriptome capture with antibody-defined cell types for tu-mor and T-cell populations within the cHL TME. We delineate contrasting transcriptomic immunological signatures between EBV-positive and EBV-negative cases that differently impact HRS cell proliferation, tumor-immune interactions, and mecha-nisms of T-cell dysregulation and dysfunction. Our multi-modal framework enabled a comprehensive dissection of EBV-linked reorganization and immune evasion within the cHL TME, and highlighted the need to elucidate the cellular and molecular fac-tors of virus-associated tumors, with potential for targeted therapeutic strategies.

A spatial cell atlas of neuroblastoma reveals developmental, epigenetic and spatial axis of tumor heterogeneity.

Neuroblastoma is a pediatric cancer arising from the developing sympathoadrenal lineage with complex inter- and intra-tumoral heterogeneity. To chart this complexity, we generated a comprehensive cell atlas of 55 neuroblastoma patient tumors, collected from two pediatric cancer institutions, spanning a range of clinical, genetic, and histologic features. Our atlas combines single-cell/nucleus RNA-seq (sc/scRNA-seq), bulk RNA-seq, whole exome sequencing, DNA methylation profiling, spatial transcriptomics, and two spatial proteomic methods. Sc/snRNA-seq revealed three malignant cell states with features of sympathoadrenal lineage development. All of the neuroblastomas had malignant cells that resembled sympathoblasts and the more differentiated adrenergic cells. A subset of tumors had malignant cells in a mesenchymal cell state with molecular features of Schwann cell precursors. DNA methylation profiles defined four groupings of patients, which differ in the degree of malignant cell heterogeneity and clinical outcomes. Using spatial proteomics, we found that neuroblastomas are spatially compartmentalized, with malignant tumor cells sequestered away from immune cells. Finally, we identify spatially restricted signaling patterns in immune cells from spatial transcriptomics. To facilitate the visualization and analysis of our atlas as a resource for further research in neuroblastoma, single cell, and spatial-omics, all data are shared through the Human Tumor Atlas Network Data Commons at www.humantumoratlas.org.