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Single-cell technologies enable researchers to investigate cell functions at an individual cell level and study cellular processes with higher resolution. Several multi-omics single-cell sequencing techniques have been developed to explore various aspects of cellular behavior. Using NEAT-seq as an example, this method simultaneously obtains three kinds of omics data for each cell: gene expression, chromatin accessibility, and protein expression of transcription factors (TFs). Consequently, NEAT-seq offers a more comprehensive understanding of cellular activities in multiple modalities. However, there is a lack of tools available for effectively integrating the three types of omics data. To address this gap, we propose a novel pipeline called MultiSC for the analysis of MULTIomic Single-Cell data. Our pipeline leverages a multimodal constraint autoencoder (single-cell hierarchical constraint autoencoder) to integrate the multi-omics data during the clustering process and a matrix factorization–based model (scMF) to predict target genes regulated by a TF. Moreover, we utilize multivariate linear regression models to predict gene regulatory networks from the multi-omics data. Additional functionalities, including differential expression, mediation analysis, and causal inference, are also incorporated into the MultiSC pipeline. Extensive experiments were conducted to evaluate the performance of MultiSC. The results demonstrate that our pipeline enables researchers to gain a comprehensive view of cell activities and gene regulatory networks by fully leveraging the potential of multiomics single-cell data. By employing MultiSC, researchers can effectively integrate and analyze diverse omics data types, enhancing their understanding of cellular processes.

 

The recent development of spatially resolved transcriptomics (SRT) technologies has facilitated research on gene expression in the spatial context. Annotating cell types is one crucial step for downstream analysis. However, many existing algorithms use an unsupervised strategy to assign cell types for SRT data. They first conduct clustering analysis and then aggregate cluster-level expression based on the clustering results. This workflow fails to leverage the marker gene information efficiently. On the other hand, other cell annotation methods designed for single-cell RNA-seq data utilize the cell-type marker genes information but fail to use spatial information in SRT data.

We introduce a statistical spatial transcriptomics cell assignment model, SPAN, to annotate clusters of cells or spots into known types in SRT data with prior knowledge of predefined marker genes and spatial information. The SPAN model annotates cells or spots from SRT data using predefined overexpressed marker genes and combines a mixture model with a hidden Markov random field to model the spatial dependency between neighboring spots. We demonstrate the effectiveness of SPAN against spatial and nonspatial clustering algorithms through extensive simulation and real data experiments.

https://github.com/ChengZ352/SPAN.

 

Spatially resolved scRNA-seq (sp-scRNA-seq) technologies provide the potential to comprehensively profile gene expression patterns in tissue context. However, the development of computational methods lags behind the advances in these technologies, which limits the fulfillment of their potential. In this study, we develop a deep learning approach for clustering sp-scRNA-seq data, named Deep Spatially constrained Single-cell Clustering (DSSC). In this model, we integrate the spatial information of cells into the clustering process in two steps: (1) the spatial information is encoded by using a graphical neural network model, and (2) cell-to-cell constraints are built based on the spatial expression pattern of the marker genes and added in the model to guide the clustering process. Then, a deep embedding clustering is performed on the bottleneck layer of autoencoder by Kullback–Leibler (KL) divergence along with the learning of feature representation. DSSC is the first model that can use information from both spatial coordinates and marker genes to guide cell/spot clustering. Extensive experiments on both simulated and real data sets show that DSSC boosts clustering performance significantly compared with the state-of-the-art methods. It has robust performance across different data sets with various cell type/tissue organization and/or cell type/tissue spatial dependency. We conclude that DSSC is a promising tool for clustering sp-scRNA-seq data. 

ABSTRACT Although Wnt signaling is clearly important for the intestinal epithelial homeostasis, the relevance of various sources of Wnt ligands themselves remains incompletely understood. Blocking the release of Wnt in distinct stromal cell types suggests obligatory functions of several stromal cell sources and yields different observations. The physiological contribution of epithelial Wnt to tissue homeostasis remains unclear. We show here that blocking epithelial Wnts affects colonic Reg4+ epithelial cell differentiation and impairs colonic epithelial regeneration after injury in mice. Single-cell RNA analysis of intestinal stroma showed that the majority of Wnt-producing cells were contained in transgelin (Tagln+) and smooth muscle actin α2 (Acta2+) expressing populations. We genetically attenuated Wnt production from these stromal cells using Tagln-Cre and Acta2-CreER drivers, and found that blockage of Wnt release from either epithelium or Tagln+ and Acta2+ stromal cells impaired colonic epithelial healing after chemical-induced injury. Aggregated blockage of Wnt release from both epithelium and Tagln+ or Acta2+ stromal cells drastically diminished epithelial repair, increasing morbidity and mortality. These results from two uncharacterized stromal populations suggested that colonic recovery from colitis-like injury depends on multiple Wnt-producing sources. 

Boron displays many unusual structural and bonding properties due to its electron deficiency. Here we show that a boron atom in a boron monoxide cluster (B 9 O − ) exhibits transition-metal-like properties. Temperature-dependent photoelectron spectroscopy provided evidence of the existence of two isomers for B 9 O − : the main isomer has an adiabatic detachment energy (ADE) of 4.19 eV and a higher energy isomer with an ADE of 3.59 eV. The global minimum of B 9 O − is found surprisingly to be an umbrella-like structure ( C 6v , 1 A 1 ) and its simulated spectrum agrees well with that of the main isomer observed. A low-lying isomer ( C s , 1 A′) consisting of a BO unit bonded to a disk-like B 8 cluster agrees well with the 3.59 eV ADE species. The unexpected umbrella-like global minimum of B 9 O − can be viewed as a central boron atom coordinated by a η 7 -B 7 ligand on one side and a BO ligand on the other side, [(η 7 -B 7 )-B-BO] − . The central B atom is found to share its valence electrons with the B 7 unit to fulfill double aromaticity, similar to that in half-sandwich [(η 7 -B 7 )-Zn-CO] − or [(η 7 -B 7 )-Fe(CO) 3 ] − transition-metal complexes. The ability of boron to form a half-sandwich complex with an aromatic ligand, a prototypical property of transition metals, brings out new metallomimetic properties of boron. 

RAB11 small GTPases and associated recycling endosome have been localized to mitotic spindles and implicated in regulating mitosis. However, the physiological significance of such regulation has not been observed in mammalian tissues. We have used newly engineered mouse models to investigate intestinal epithelial renewal in the absence of single or double isoforms of RAB11 family members: Rab11a and Rab11b. Comparing with single knockouts, mice with compound ablation demonstrate a defective cell cycle entry and robust mitotic arrest followed by apoptosis, leading to a total penetrance of lethality within 3 days of gene ablation. UponRab11deletionex vivo, enteroids show abnormal mitotic spindle formation and cell death. Untargeted proteomic profiling of Rab11a and Rab11b immunoprecipitates has uncovered a shared interactome containing mitotic spindle microtubule regulators. Disrupting Rab11 alters kinesin motor KIF11 function and impairs bipolar spindle formation and cell division. These data demonstrate that RAB11A and RAB11B redundantly control mitotic spindle function and intestinal progenitor cell division, a mechanism that may be utilized to govern the homeostasis and renewal of other mammalian tissues.

 

Paneth cells are the primary source of C-type lysozyme, a b-1,4-N-acetylmuramoylhydrolase that enzymatically processes bacterial cell walls. Paneth cells are normally present in human cecum and ascending colon, but are rarely found in descending colon and rectum; Paneth cell metaplasia in this region and aberrant lysozyme production are hallmarks of inflammatory bowel disease (IBD) pathology. Here, we examined the impact of aberrant lysozyme production in colonic inflammation. Targeted disruption of Paneth cell lysozyme (Lyz1) protected mice from experimental colitis. Lyz1-deficiency diminished intestinal immune responses to bacterial molecular patterns and resulted in the expansion of lysozyme-sensitive mucolytic bacteria, including Ruminococcus gnavus, a Crohn’s disease-associated pathobiont. Ectopic lysozyme production in colonic epithelium suppressed lysozyme-sensitive bacteria and exacerbated colitis. Transfer of R. gnavus into Lyz1
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hosts elicited a type 2 immune response, causing epithelial reprograming and enhanced anti-colitogenic capacity. In contrast, in lysozyme-intact hosts, processed R. gnavus drove pro-inflammatory responses. Thus, Paneth cell lysozyme balances intestinal anti- and pro-inflammatory responses, with implications for IBD. 

Paneth cells (PCs), a specialized secretory cell type in the small intestine, are increasingly recognized as having an essential role in host responses to microbiome and environmental stresses. Whether and how commensal and pathogenic microbes modify PC composition to modulate inflammation remain unclear. Using newly developed PC‐reporter mice under conventional and gnotobiotic conditions, we determined PC transcriptomic heterogeneity in response to commensal and invasive microbes at single cell level. Infection expands the pool of CD74⁺PCs, whose number correlates with auto or allogeneic inflammatory disease progressions in mice. Similar correlation was found in human inflammatory disease tissues. Infection‐stimulated cytokines increase production of reactive oxygen species (ROS) and expression of a PC‐specific mucosal pentraxin (Mptx2) in activated PCs. A PC‐specific ablation ofMyD88reduced CD74⁺PC population, thus ameliorating pathogen‐induced systemic disease. A similar phenotype was also observed in mice lacking Mptx2. Thus, infection stimulates expansion of a PC subset that influences disease progression.