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SummaryIn the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations.Availability and ImplementationThe Python package SEAGAL can be installed using pip:https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at:https://github.com/linhuawang/SEAGAL.Contactlinhuaw@bcm.edu

MotivationWith the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity.ResultsHere, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurementL. The graph-based correlation method assigns each cell a localLindex, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson’s r in ruling out confounding regulatory relationships that are not of research interests.Availabilityhttps://github.com/ChaozhongLiu/scGREAT

Sequencing-based spatial transcriptomics (ST) techniques have been groundbreaking in dissecting cell-cell communications within tissues by profiling positional gene expression. However, the most widely used ST technique, Visium Spatial Gene Expression by 10x Genomics (Visium), does not provide single-cell resolution, making it difficult to profile cell type-level information. Many reference-based deconvolution methods have been developed to increase its resolution, but the platform and batch effects between the reference and ST data compromise their accuracy. Here, we propose a new approach,Region-based cellSorting (ReSort), that generates a pseudo-internal-reference to reduce these platform effects. By simulating ST datasets under various scenarios, we demonstrate that ReSort significantly improves the accuracy of six state-of-the-art reference-based deconvolution methods. Moreover, applying ReSort to a mouse breast cancer tumor bearing both epithelial and mesenchymal clones identifies the spatial differences of immune cells between the clones, providing important insights for understanding the relationship between epithelial-mesenchymal transition and immune infiltration in breast cancer.

With the growing availability of Alzheimer’s disease (AD) transcriptomic data, several studies have nominated new therapeutic targets. However, a major challenge is accounting for latent (hidden) factors which affect the discovery of therapeutic targets. Using unsupervised machine learning, we identified a latent factor in brain tissue, and we validated the factor in AD and normal samples, across multiple studies, and different brain tissues. Moreover, significant metabolic differences were observed due to the latent factor. The latent factor was found to reflect cell-type heterogeneity in the brain and after adjusting for it, we were able to identify new biological pathways. The changes observed at both transcriptomic and metabolomic levels support the importance of identifying any latent factors before pursuing downstream analysis to accurately identify biomarkers.

Background Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. Results By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. Conclusions MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Recent studies reported that METTL4 regulates DNA 6mA in vivo and therefore is a candidate DNA m6A methyltransfease. However, the enzymatic activity of METTL4 in vitro has not been demonstrated in part due to the difficulties of obtaining well-folded proteins. Here we show that mettl4 is a major methyltransfase responsible for m6A methylation of U2 snRNA both in vitro and in vivo in fly, and identify adenosine at 29th position as the site of m6A methylation. This study answered a long-standing question regarding the enzymatic activity of METTL4, and thus paved the way for further investigating the functions of METTL4 in different biological settings.

The interplay between cells and materials is a fundamental topic in biomaterial-based tissue regeneration. One of the principles for biomaterial development in tendon regeneration is to stimulate tenogenic differentiation of stem cells. To this end, efforts have been made to optimize the physicochemical and bio-mechanical properties of biomaterials for tendon tissue engineering. However, recent progress indicated that innate immune cells, especially macrophages, can also respond to the material cues and undergo phenotypical changes, which will either facilitate or hinder tissue regeneration. This process has been, to some extent, neglected by traditional strategies and may partially explain the unsatisfactory outcomes of previous studies; thus, more researchers have turned their focus on developing and designing immunoregenerative biomaterials to enhance tendon regeneration. In this review, we will first summarize the effects of material cues on tenogenic differentiation and paracrine secretion of stem cells. A brief introduction will also be made on how material cues can be manipulated for the regeneration of tendon-to-bone interface. Then, we will discuss the characteristics and influences of macrophages on the repair process of tendon healing and how they respond to different materials cues. These principles may benefit the development of novel biomaterials provided with combinative bioactive cues to activate tenogenic differentiation of stem cells and pro-resolving macrophage phenotype. Statement of Significance The progress achieved with the rapid development of biomaterial-based strategies for tendon regeneration has not yielded broad benefits to clinical patients. In addition to the interplay between stem cells and biomaterials, the innate immune response to biomaterials also plays a determinant role in tissue regeneration. Here, we propose that fine-tuning of stem cell behaviors and alternative activation of macrophages through material cues may lead to effective tendon/ligament regeneration. We first review the characteristics of key material cues that have been manipulated to promote tenogenic differentiation and paracrine secretion of stem cells in tendon regeneration. Then, we discuss the potentiality of corresponding material cues in activating macrophages toward a pro-resolving phenotype to promote tissue repair.

Estrogen receptor-positive (ER

Estrogen receptor-positive (ER+) breast cancer exhibits a strong bone tropism in metastasis. How the bone microenvironment (BME) impacts ER signaling and endocrine therapy remains poorly understood. Here, we discover that the osteogenic niche transiently and reversibly reduces ER expression and activities specifically in bone micrometastases (BMMs), leading to endocrine resistance. As BMMs progress, the ER reduction and endocrine resistance may partially recover in cancer cells away from the osteogenic niche, creating phenotypic heterogeneity in macrometastases. Using multiple approaches, including an evolving barcoding strategy, we demonstrated that this process is independent of clonal selection, and represents an EZH2-mediated epigenomic reprogramming. EZH2 drives ER+ BMMs toward a basal and stem-like state. EZH2 inhibition reverses endocrine resistance. These data exemplify how epigenomic adaptation to BME promotes phenotypic plasticity of metastatic seeds, fosters intra-metastatic heterogeneity, and alters therapeutic responses. Our study provides insights into the clinical enigma of ER+ metastatic recurrences despite endocrine therapies.

A functionalized high-density polyethylene (HDPE) with maleic anhydride (MAH) was prepared using a reactive extruding method. This copolymer was used as a compatibilizer of blends of polyamide 6 (PA6) and ultrahigh molecular weight polyethylene (UHMWPE). Morphologies were examined by a scanning electron microscope. It was found that the dimension of UHMWPE and HDPE domains in the PA6 matrix decreased dramatically, compared with that of the uncompatibilized blending system. The size of the UHMWPE domains was reduced from 35 mu m (PA6/UHMWPE, 80/20) to less than 4 mu m (PA6/UHMWPE/HDPE-g-MAH, 80/20/20). The tensile strength and Izod impact strength of PA6/UHMWPE/HDPE-g-MAH (80/20/20) were 1.5 and 1.6 times as high as those of PA6/UHMWPE: (80/20), respectively. This behavior could be attributed to chemical reactions between the anhydride groups of HDPE-g-MAH and the terminal amino groups of PA6 in PA6/UHMWPE/HDPE-g-MAH blends. Thermal analysis was performed to confirm that the above chemical reactions took place during the blending process. (C) 2000 John Wiley & Sons, Inc.