Distinct myofibre domains of the human myotendinous junction revealed by single nucleus RNA-seq

The myotendinous junction (MTJ) is a specialized domain of the multinucleated myofibre, faced with the challenge of maintaining robust cell-matrix contact with the tendon under high mechanical stress and strain. Here, we profiled 24,161 nuclei in semitendinosus muscle-tendon samples from 3 healthy males by single nucleus RNA-sequencing (snRNA-seq), alongside spatial transcriptomics, to gain insight into the genes characterizing this specialization in humans. We identified a cluster of MTJ myonuclei, represented by 47 enriched transcripts, of which the presence of ABI3BP, ABLIM1, ADAMTSL1, BICD1, CPM, FHOD3, FRAS1 and FREM2 was confirmed at the MTJ at the protein level by immunofluorescence. Four distinct subclusters of MTJ myonuclei were apparent and segregated into two COL22A1-expressing subclusters and two lacking COL22A1 but with a clear fibre type profile expressing MYH7 or MYH1/2. Our findings reveal distinct myonuclei profiles of the human MTJ, a weak link in the musculoskeletal system, which is selectively affected in pathological conditions, from muscle strains to muscular dystrophies.

Comparison of the Illumina NextSeq 2000 and GeneMind Genolab M sequencing platforms for spatial transcriptomics

BACKGROUND

The Illumina sequencing systems demonstrate high efficiency and power and remain the most popular platforms. Platforms with similar throughput and quality profiles but lower costs are under intensive development. In this study, we compared two platforms Illumina NextSeq 2000 and GeneMind Genolab M for 10x Genomics Visium spatial transcriptomics.

RESULTS

The performed comparison demonstrates that GeneMind Genolab M sequencing platform produces highly consistent with Illumina NextSeq 2000 sequencing results. Both platforms have similar performance in terms of sequencing quality and detection of UMI, spatial barcode, and probe sequence. Raw read mapping and following read counting produced highly comparable results that is confirmed by quality control metrics and strong correlation between expression profiles in the same tissue spots. Downstream analysis including dimension reduction and clustering demonstrated similar results, and differential gene expression analysis predominantly detected the same genes for both platforms.

CONCLUSIONS

GeneMind Genolab M instrument is similar to Illumina sequencing efficacy and is suitable for 10x Genomics Visium spatial transcriptomics.

scMAGS: Marker gene selection from scRNA-seq data for spatial transcriptomics studies

Single-Cell RNA sequencing (scRNA-seq) has provided unprecedented opportunities for exploring gene expression and thus uncovering regulatory relationships between genes at the single-cell level. However, scRNA-seq relies on isolating cells from tissues. Therefore, the spatial context of the regulatory processes is lost. A recent technological innovation, spatial transcriptomics, allows for the measurement of gene expression while preserving spatial information. An initial step in the spatial transcriptomic analysis is to identify the cell type, which requires a careful selection of cell-specific marker genes. For this purpose, currently, scRNA-seq data is used to select a limited number of marker genes from among all genes that distinguish cell types from each other. This study proposes scMAGS (single-cell MArker Gene Selection), a novel method for marker gene selection from scRNA-seq data for spatial transcriptomics studies. scMAGS uses a filtering step in which the candidate genes are identified before the marker gene selection step. For the selection of marker genes, cluster validity indices, the Silhouette index, or the Calinski-Harabasz index (for large datasets) are utilized. Experimental results showed that, in comparison to the existing methods, scMAGS is scalable, fast, and accurate. Even for large datasets with millions of cells, scMAGS could find the required number of marker genes in a reasonable amount of time with fewer memory requirements. scMAGS is made freely available at https://github.com/doganlab/scmags and can be downloaded from the Python Package Directory (PyPI) software repository with the command pip install scmags.

Single-cell spatial explorer: easy exploration of spatial and multimodal transcriptomics

BACKGROUND

The development of single-cell technologies yields large datasets of information as diverse and multimodal as transcriptomes, immunophenotypes, and spatial position from tissue sections in the so-called 'spatial transcriptomics'. Currently however, user-friendly, powerful, and free algorithmic tools for straightforward analysis of spatial transcriptomic datasets are scarce.

RESULTS

Here, we introduce Single-Cell Spatial Explorer, an open-source software for multimodal exploration of spatial transcriptomics, examplified with 9 human and murine tissues datasets from 4 different technologies.

CONCLUSIONS

Single-Cell Spatial Explorer is a very powerful, versatile, and interoperable tool for spatial transcriptomics analysis.

Analysis of Spatial Molecular Data

Digital analysis of pathology whole-slide images has been recently gaining interest in the context of cancer diagnosis and treatment. In particular, deep learning methods have demonstrated significant potential in supporting pathology analysis, recently detecting molecular traits never before recognized in pathology H&E whole-slide images (WSIs). Alongside these advancements in the digital analysis of WSIs, it is becoming increasingly evident that both spatial and overall tumor heterogeneity may be significant determinants of cancer prognosis and treatment outcome. In this chapter, we describe methods that aim to support these two elements. We describe both an end-to-end deep learning pipeline for producing limited spatial transcriptomics from WSIs with associated bulk gene expression data, as well as an algorithm for quantifying spatial tumor heterogeneity based on the results of this pipeline.

[Applications of single-cell analysis in blood research]

Single-cell analysis encompasses analyses at the single-cell level. Specifically, DNA sequences, RNA and protein expression levels, and epigenome modifications are currently analyzed at the single-cell level. In recent years, single-cell sequencing technologies have been incorporated into numerous blood studies. This paper introduces an overview of single-cell technologies and further explains achievements in blood research using single-cell analysis.

Profiling Cellular Ecosystems at Single-Cell Resolution and at Scale with EcoTyper

Tissues are composed of diverse cell types and cellular states that organize into distinct ecosystems with specialized functions. EcoTyper is a collection of machine learning tools for the large-scale delineation of cellular ecosystems and their constituent cell states from bulk, single-cell, and spatially resolved gene expression data. In this chapter, we provide a primer on EcoTyper and demonstrate its use for the discovery and recovery of cell states and ecosystems from healthy and diseased tissue specimens.

Spatial Transcriptomics in Kidney Tissue

Unlike bulk and single-cell/single-nuclei RNA sequencing methods, spatial transcriptome sequencing (ST-seq) resolves transcriptome expression within the spatial context of intact tissue. This is achieved by integrating histology with RNA sequencing. These methodologies are completed sequentially on the same tissue section placed on a glass slide with printed oligo-dT spots, termed ST-spots. Transcriptomes within the tissue section are captured by the underlying ST-spots and receive a spatial barcode in the process. The sequenced ST-spot transcriptomes are subsequently aligned with the hematoxylin and eosin (H&E) image, giving morphological context to the gene expression signatures within intact tissue. We have successfully employed ST-seq to characterize mouse and human kidney tissue. Here, we describe in detail the application of Visium Spatial Tissue Optimization (TO) and Visium Spatial Gene Expression (GEx) protocols for ST-seq in fresh frozen kidney tissue.

A Hands-On Guide to Generate Spatial Gene Expression Profiles by Integrating scRNA-seq and 3D-Reconstructed Microscope-Based Plant Structures

Transcriptome profiles of individual cells in the plant are strongly dependent on their relative position. Cell differentiation is associated with tissue-specific transcriptomic changes. For that reason, it is important to study gene expression changes in a spatial context, and therefore to link those to potential morphological changes over developmental time. Even though great experimental advances have been made in recording spatial gene expression profiles, those attempts are limited in the plant field. New computational approaches attempt to solve this problem by integrating spatial expression profiles of few marker genes with single-cell/single-nuclei RNA-seq (scRNA-seq) methodologies. In this chapter, we provide a practical guide on how to predict gene expression patterns in a 3D plant structure by combining scRNA-seq data and 3D microscope-based reconstructed expression profiles of a small set of reference genes. We also show how to visualize these results.

Genome-Wide RNA Tomography in the Mouse Whole Olfactory Mucosa

Spatial transcriptomics allows for the genome-wide profiling of topographic gene expression patterns within a tissue of interest. Here, we describe our methodology to generate high-quality RNA-seq libraries from cryosections from fresh frozen mouse whole olfactory mucosae. This methodology can be extended to virtually any vertebrate organ or tissue sample.

Multiple sclerosis: 2023 update

Multiple sclerosis (MS) is the most frequent inflammatory and demyelinating disease of the Central Nervous System (CNS). Significant progress has been made during recent years in preventing relapses by using systemic immunomodulatory or immunosuppressive therapies. However, the limited effectiveness of such therapies for controlling the progressive disease course indicates there is a continuous disease progression independent of relapse activity which may start very early during the disease course. Dissecting the underlying mechanisms and developing therapies for preventing or stopping this disease progression represent, currently, the biggest challenges in the field of MS. Here, we summarize publications of 2022 which provide insight into susceptibility to MS, the basis of disease progression and features of relatively recently recognized distinct forms of inflammatory/demyelinating disorders of the CNS, such as myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD).

Soluble TREM2 levels reflect the recruitment and expansion of TREM2+ macrophages that localize to fibrotic areas and limit NASH

BACKGROUND & AIMS

Previous single-cell RNA-sequencing analyses have shown that Trem2-expressing macrophages are present in the liver during obesity, non-alcoholic steatohepatitis (NASH) and cirrhosis. Herein, we aimed to functionally characterize the role of bone marrow-derived TREM2-expressing macrophage populations in NASH.

METHODS

We used bulk RNA sequencing to assess the hepatic molecular response to lipid-dependent dietary intervention in mice. Spatial mapping, bone marrow transplantation in two complementary murine models and single-cell sequencing were applied to functionally characterize the role of TREM2⁺ macrophage populations in NASH.

RESULTS

We found that the hepatic transcriptomic profile during steatohepatitis mirrors the dynamics of recruited bone marrow-derived monocytes that already acquire increased expression of Trem2 in the circulation. Increased Trem2 expression was reflected by elevated levels of systemic soluble TREM2 in mice and humans with NASH. In addition, soluble TREM2 levels were superior to traditionally used laboratory parameters for distinguishing between different fatty liver disease stages in two separate clinical cohorts. Spatial transcriptomics revealed that TREM2⁺ macrophages localize to sites of hepatocellular damage, inflammation and fibrosis in the steatotic liver. Finally, using multiple murine models and in vitro experiments, we demonstrate that hematopoietic Trem2 deficiency causes defective lipid handling and extracellular matrix remodeling, resulting in exacerbated steatohepatitis, cell death and fibrosis.

CONCLUSIONS

Our study highlights the functional properties of bone marrow-derived TREM2⁺ macrophages and implies the clinical relevance of systemic soluble TREM2 levels in the context of NASH.

LAY SUMMARY

Our study defines the origin and function of macrophages (a type of immune cell) that are present in the liver and express a specific protein called TREM2. We find that these cells have an important role in protecting against non-alcoholic steatohepatitis (a progressive form of fatty liver disease). We also show that the levels of soluble TREM2 in the blood could serve as a circulating marker of non-alcoholic fatty liver disease.

Unravelling the landscape of skin cancer through single-cell transcriptomics

The human skin is a complex organ that forms the first line of defense against pathogens and external injury. It is composed of a wide variety of cells that work together to maintain homeostasis and prevent disease, such as skin cancer. The exponentially rising incidence of skin malignancies poses a growing public health challenge, particularly when the disease course is complicated by metastasis and therapeutic resistance. Recent advances in single-cell transcriptomics have provided a high-resolution view of gene expression heterogeneity that can be applied to skin cancers to define cell types and states, understand disease evolution, and develop new therapeutic concepts. This approach has been particularly valuable in characterizing the contribution of immune cells in skin cancer, an area of great clinical importance given the increasing use of immunotherapy in this setting. In this review, we highlight recent skin cancer studies utilizing bulk RNA sequencing, introduce various single-cell transcriptomics approaches, and summarize key findings obtained by applying single-cell transcriptomics to skin cancer.

Evaluation of cell-cell interaction methods by integrating single-cell RNA sequencing data with spatial information

BACKGROUND

Cell-cell interactions are important for information exchange between different cells, which are the fundamental basis of many biological processes. Recent advances in single-cell RNA sequencing (scRNA-seq) enable the characterization of cell-cell interactions using computational methods. However, it is hard to evaluate these methods since no ground truth is provided. Spatial transcriptomics (ST) data profiles the relative position of different cells. We propose that the spatial distance suggests the interaction tendency of different cell types, thus could be used for evaluating cell-cell interaction tools.

RESULTS

We benchmark 16 cell-cell interaction methods by integrating scRNA-seq with ST data. We characterize cell-cell interactions into short-range and long-range interactions using spatial distance distributions between ligands and receptors. Based on this classification, we define the distance enrichment score and apply an evaluation workflow to 16 cell-cell interaction tools using 15 simulated and 5 real scRNA-seq and ST datasets. We also compare the consistency of the results from single tools with the commonly identified interactions. Our results suggest that the interactions predicted by different tools are highly dynamic, and the statistical-based methods show overall better performance than network-based methods and ST-based methods.

CONCLUSIONS

Our study presents a comprehensive evaluation of cell-cell interaction tools for scRNA-seq. CellChat, CellPhoneDB, NicheNet, and ICELLNET show overall better performance than other tools in terms of consistency with spatial tendency and software scalability. We recommend using results from at least two methods to ensure the accuracy of identified interactions. We have packaged the benchmark workflow with detailed documentation at GitHub ( https://github.com/wanglabtongji/CCI ).

Spatial transcriptomics tools allow for regional exploration of heterogeneous muscle pathology in the pre-clinical rabbit model of rotator cuff tear

Background

Conditions affecting skeletal muscle, such as chronic rotator cuff tears, low back pain, dystrophies, and many others, often share changes in muscle phenotype: intramuscular adipose and fibrotic tissue increase while contractile tissue is lost. The underlying changes in cell populations and cell ratios observed with these phenotypic changes complicate the interpretation of tissue-level transcriptional data. Novel single-cell transcriptomics has limited capacity to address this problem because muscle fibers are too long to be engulfed in single-cell droplets and single nuclei transcriptomics are complicated by muscle fibers’ multinucleation. Therefore, the goal of this project was to evaluate the potential and challenges of a spatial transcriptomics technology to add dimensionality to transcriptional data in an attempt to better understand regional cellular activity in heterogeneous skeletal muscle tissue.

Methods

The 3′ Visium spatial transcriptomics technology was applied to muscle tissue of a rabbit model of rotator cuff tear. Healthy control and tissue collected at 2 and 16 weeks after tenotomy was utilized and freshly snap frozen tissue was compared with tissue stored for over 6 years to evaluate whether this technology is retrospectively useful in previously acquired tissues. Transcriptional information was overlayed with standard hematoxylin and eosin (H&E) stains of the exact same histological sections.

Results

Sequencing saturation and number of genes detected was not affected by sample storage duration. Unbiased clustering matched the underlying tissue type-based on H&E assessment. Connective-tissue-rich areas presented with lower unique molecular identifier counts are compared with muscle fibers even though tissue permeabilization was standardized across the section. A qualitative analysis of resulting datasets revealed heterogeneous fiber degeneration–regeneration after tenotomy based on (neonatal) myosin heavy chain 8 detection and associated differentially expressed gene analysis.

Conclusions

This protocol can be used in skeletal muscle to explore spatial transcriptional patterns and confidently relate them to the underlying histology, even for tissues that have been stored for up to 6 years. Using this protocol, there is potential for novel transcriptional pathway discovery in longitudinal studies since the transcriptional information is unbiased by muscle composition and cell type changes.

TIST: Transcriptome and Histopathological Image Integrative Analysis for Spatial Transcriptomics

Sequencing-based spatial transcriptomics (ST) is an emerging technology to study in situ gene expression patterns at the whole-genome scale. Currently, ST data analysis is still complicated by high technical noises and low resolution. In addition to the transcriptomic data, matched histopathological images are usually generated for the same tissue sample along the ST experiment. The matched high-resolution histopathological images provide complementary cellular phenotypical information, providing an opportunity to mitigate the noises in ST data. We present a novel ST data analysis method called transcriptome and histopathological image integrative analysis for ST (TIST), which enables the identification of spatial clusters (SCs) and the enhancement of spatial gene expression patterns by integrative analysis of matched transcriptomic data and images. TIST devises a histopathological feature extraction method based on Markov random field (MRF) to learn the cellular features from histopathological images, and integrates them with the transcriptomic data and location information as a network, termed TIST-net. Based on TIST-net, SCs are identified by a random walk-based strategy, and gene expression patterns are enhanced by neighborhood smoothing. We benchmark TIST on both simulated datasets and 32 real samples against several state-of-the-art methods. Results show that TIST is robust to technical noises on multiple analysis tasks for sequencing-based ST data and can find interesting microstructures in different biological scenarios. TIST is available at http://lifeome.net/software/tist/ and https://ngdc.cncb.ac.cn/biocode/tools/BT007317.

Proper acquisition of cell class identity in organoids allows definition of fate specification programs of the human cerebral cortex

Realizing the full utility of brain organoids to study human development requires understanding whether organoids precisely replicate endogenous cellular and molecular events, particularly since acquisition of cell identity in organoids can be impaired by abnormal metabolic states. We present a comprehensive single-cell transcriptomic, epigenetic, and spatial atlas of human cortical organoid development, comprising over 610,000 cells, from generation of neural progenitors through production of differentiated neuronal and glial subtypes. We show that processes of cellular diversification correlate closely to endogenous ones, irrespective of metabolic state, empowering the use of this atlas to study human fate specification. We define longitudinal molecular trajectories of cortical cell types during organoid development, identify genes with predicted human-specific roles in lineage establishment, and uncover early transcriptional diversity of human callosal neurons. The findings validate this comprehensive atlas of human corticogenesis in vitro as a resource to prime investigation into the mechanisms of human cortical development.

Intratumor heterogeneity and T cell exhaustion in primary CNS lymphoma

BACKGROUND

Primary central nervous system lymphoma (PCNSL) is a rare lymphoma of the central nervous system, usually of diffuse large B cell phenotype. Stereotactic biopsy followed by histopathology is the diagnostic standard. However, limited material is available from CNS biopsies, thus impeding an in-depth characterization of PCNSL.

METHODS

We performed flow cytometry, single-cell RNA sequencing, and B cell receptor sequencing of PCNSL cells released from biopsy material, blood, and cerebrospinal fluid (CSF), and spatial transcriptomics of biopsy samples.

RESULTS

PCNSL-released cells were predominantly activated CD19+CD20+CD38+CD27+ B cells. In single-cell RNA sequencing, PCNSL cells were transcriptionally heterogeneous, forming multiple malignant B cell clusters. Hyperexpanded B cell clones were shared between biopsy- and CSF- but not blood-derived cells. T cells in the tumor microenvironment upregulated immune checkpoint molecules, thereby recognizing immune evasion signals from PCNSL cells. Spatial transcriptomics revealed heterogeneous spatial organization of malignant B cell clusters, mirroring their transcriptional heterogeneity across patients, and pronounced expression of T cell exhaustion markers, co-localizing with a highly malignant B cell cluster.

CONCLUSIONS

Malignant B cells in PCNSL show transcriptional and spatial intratumor heterogeneity. T cell exhaustion is frequent in the PCNSL microenvironment, co-localizes with malignant cells, and highlights the potential of personalized treatments.

Computational solutions for spatial transcriptomics

Transcriptome level expression data connected to the spatial organization of the cells and molecules would allow a comprehensive understanding of how gene expression is connected to the structure and function in the biological systems. The spatial transcriptomics platforms may soon provide such information. However, the current platforms still lack spatial resolution, capture only a fraction of the transcriptome heterogeneity, or lack the throughput for large scale studies. The strengths and weaknesses in current ST platforms and computational solutions need to be taken into account when planning spatial transcriptomics studies. The basis of the computational ST analysis is the solutions developed for single-cell RNA-sequencing data, with advancements taking into account the spatial connectedness of the transcriptomes. The scRNA-seq tools are modified for spatial transcriptomics or new solutions like deep learning-based joint analysis of expression, spatial, and image data are developed to extract biological information in the spatially resolved transcriptomes. The computational ST analysis can reveal remarkable biological insights into spatial patterns of gene expression, cell signaling, and cell type variations in connection with cell type-specific signaling and organization in complex tissues. This review covers the topics that help choosing the platform and computational solutions for spatial transcriptomics research. We focus on the currently available ST methods and platforms and their strengths and limitations. Of the computational solutions, we provide an overview of the analysis steps and tools used in the ST data analysis. The compatibility with the data types and the tools provided by the current ST analysis frameworks are summarized.

Zebrafish Danio rerio myotomal muscle structure and growth from a spatial transcriptomics perspective

Fish exhibit different muscle structures and growth characteristics compared with mammals. We used a spatial transcriptomics approach and examined myotomal muscle sections from zebrafish. Adult muscles were divided into eight regions according to spatial gene expression characteristics. Slow muscle was located in the wedge-shaped region near the lateral line and at the base of the dorsal fin, intermediate muscle was located in a ribbon-shaped region adjacent to slow muscle, and fast muscle was located in the deep region of the trunk, surrounded by intermediate muscle; the interior of fast muscle was further divided into 6 parts by their transcriptomic features. Combined analysis of adult and larval data revealed that adult muscles contain specific regions similar to larval muscles. These regions showed active myogenesis and a high expression of genes associated with muscle hyperplasia. This is the first study to apply spatial transcriptomics to fish myotomal muscle structure and growth.

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

Spatially resolved transcriptomics provides a new way to define spatial contexts and understand the pathogenesis of complex human diseases. Although some computational frameworks can characterize spatial context via various clustering methods, the detailed spatial architectures and functional zonation often cannot be revealed and localized due to the limited capacities of associating spatial information. We present RESEPT, a deep-learning framework for characterizing and visualizing tissue architecture from spatially resolved transcriptomics. Given inputs such as gene expression or RNA velocity, RESEPT learns a three-dimensional embedding with a spatial retained graph neural network from spatial transcriptomics. The embedding is then visualized by mapping into color channels in an RGB image and segmented with a supervised convolutional neural network model. Based on a benchmark of 10x Genomics Visium spatial transcriptomics datasets on the human and mouse cortex, RESEPT infers and visualizes the tissue architecture accurately. It is noteworthy that, for the in-house AD samples, RESEPT can localize cortex layers and cell types based on pre-defined region- or cell-type-enriched genes and furthermore provide critical insights into the identification of amyloid-beta plaques in Alzheimer's disease. Interestingly, in a glioblastoma sample analysis, RESEPT distinguishes tumor-enriched, non-tumor, and regions of neuropil with infiltrating tumor cells in support of clinical and prognostic cancer applications.

Sample-multiplexing approaches for single-cell sequencing

Single-cell sequencing is widely used in biological and medical studies. However, its application with multiple samples is hindered by inefficient sample processing, high experimental costs, ambiguous identification of true single cells, and technical batch effects. Here, we introduce sample-multiplexing approaches for single-cell sequencing in transcriptomics, epigenomics, genomics, and multiomics. In single-cell transcriptomics, sample multiplexing uses variants of native or artificial features as sample markers, enabling sample pooling and decoding. Such features include: (1) natural genetic variation, (2) nucleotide-barcode anchoring on cellular or nuclear membranes, (3) nucleotide-barcode internalization to the cytoplasm or nucleus, (4) vector-based barcode expression in cells, and (5) nucleotide-barcode incorporation during library construction. Other single-cell omics methods are based on similar concepts, particularly single-cell combinatorial indexing. These methods overcome current challenges, while enabling super-loading of single cells. Finally, selection guidelines are presented that can accelerate technological application.

BASS: multi-scale and multi-sample analysis enables accurate cell type clustering and spatial domain detection in spatial transcriptomic studies

Spatial transcriptomic studies are reaching single-cell spatial resolution, with data often collected from multiple tissue sections. Here, we present a computational method, BASS, that enables multi-scale and multi-sample analysis for single-cell resolution spatial transcriptomics. BASS performs cell type clustering at the single-cell scale and spatial domain detection at the tissue regional scale, with the two tasks carried out simultaneously within a Bayesian hierarchical modeling framework. We illustrate the benefits of BASS through comprehensive simulations and applications to three datasets. The substantial power gain brought by BASS allows us to reveal accurate transcriptomic and cellular landscape in both cortex and hypothalamus.

Spatiotemporal Dynamics of the Molecular Expression Pattern and Intercellular Interactions in the Glial Scar Response to Spinal Cord Injury

Nerve regeneration in adult mammalian spinal cord is poor because of the lack of intrinsic regeneration of neurons and extrinsic factors - the glial scar is triggered by injury and inhibits or promotes regeneration. Recent technological advances in spatial transcriptomics (ST) provide a unique opportunity to decipher most genes systematically throughout scar formation, which remains poorly understood. Here, we first constructed the tissue-wide gene expression patterns of mouse spinal cords over the course of scar formation using ST after spinal cord injury from 32 samples. Locally, we profiled gene expression gradients from the leading edge to the core of the scar areas to further understand the scar microenvironment, such as neurotransmitter disorders, activation of the pro-inflammatory response, neurotoxic saturated lipids, angiogenesis, obstructed axon extension, and extracellular structure re-organization. In addition, we described 21 cell transcriptional states during scar formation and delineated the origins, functional diversity, and possible trajectories of subpopulations of fibroblasts, glia, and immune cells. Specifically, we found some regulators in special cell types, such as Thbs1 and Col1a2 in macrophages, CD36 and Postn in fibroblasts, Plxnb2 and Nxpe3 in microglia, Clu in astrocytes, and CD74 in oligodendrocytes. Furthermore, salvianolic acid B, a blood-brain barrier permeation and CD36 inhibitor, was administered after surgery and found to remedy fibrosis. Subsequently, we described the extent of the scar boundary and profiled the bidirectional ligand-receptor interactions at the neighboring cluster boundary, contributing to maintain scar architecture during gliosis and fibrosis, and found that GPR37L1_PSAP, and GPR37_PSAP were the most significant gene-pairs among microglia, fibroblasts, and astrocytes. Last, we quantified the fraction of scar-resident cells and proposed four possible phases of scar formation: macrophage infiltration, proliferation and differentiation of scar-resident cells, scar emergence, and scar stationary. Together, these profiles delineated the spatial heterogeneity of the scar, confirmed the previous concepts about scar architecture, provided some new clues for scar formation, and served as a valuable resource for the treatment of central nervous system injury.

Integrating spatial transcriptomics with single-cell transcriptomics reveals a spatiotemporal gene landscape of the human developing kidney

BACKGROUND

Research on spatiotemporal gene landscape can provide insights into the spatial characteristics of human kidney development and facilitate kidney organoid cultivation. Here, we profiled the spatiotemporal gene programs of the human embryonic kidneys at 9 and 18 post-conception weeks (PCW) by integrating the application of microarray-based spatial transcriptomics and single-cell transcriptomics.

RESULTS

We mapped transcriptomic signatures of scRNA-seq cell types upon the 9 and 18 PCW kidney sections based on cell-type deconvolution and multimodal intersection analyses, depicting a spatial landscape of developing cell subpopulations. We established the gene characteristics in the medullary regions and revealed a strong mitochondrial oxidative phosphorylation and glycolysis activity in the deeper medullary region. We also built a regulatory network centered on GDNF-ETV4 for nephrogenic niche development based on the weighted gene co-expression network analysis and highlighted the key roles of Wnt, FGF, and JAG1-Notch2 signaling in maintaining renal branching morphogenesis.

CONCLUSIONS

Our findings obtained by this spatiotemporal gene program are expected to improve the current understanding of kidney development.