Single-cell spatial transcriptomic analysis reveals common and divergent features of developing postnatal granule cerebellar cells and medulloblastoma

A Bibliometric Analysis of the Spatial Transcriptomics Literature from 2006 to 2023

In recent years, spatial transcriptomics (ST) research has become a popular field of study and has shown great potential in medicine. However, there are few bibliometric analyses in this field. Thus, in this study, we aimed to find and analyze the frontiers and trends of this medical research field based on the available literature. A computerized search was applied to the WoSCC (Web of Science Core Collection) Database for literature published from 2006 to 2023. Complete records of all literature and cited references were extracted and screened. The bibliometric analysis and visualization were performed using CiteSpace, VOSviewer, Bibliometrix R Package software, and Scimago Graphica. A total of 1467 papers and reviews were included. The analysis revealed that the ST publication and citation results have shown a rapid upward trend over the last 3 years. Nature Communications and Nature were the most productive and most co-cited journals, respectively. In the comprehensive global collaborative network, the United States is the country with the most organizations and publications, followed closely by China and the United Kingdom. The author Joakim Lundeberg published the most cited paper, while Patrik L. Ståhl ranked first among co-cited authors. The hot topics in ST are tissue recognition, cancer, heterogeneity, immunotherapy, differentiation, and models. ST technologies have greatly contributed to in-depth research in medical fields such as oncology and neuroscience, opening up new possibilities for the diagnosis and treatment of diseases. Moreover, artificial intelligence and big data drive additional development in ST fields.

Differential gene expression analysis of spatial transcriptomic experiments using spatial mixed models

Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).

A Prognostic Methylation-Driven Two-Gene Signature in Medulloblastoma

Medulloblastoma (MB) is one of the most common pediatric brain tumors and it is estimated that one-third of patients will not achieve long-term survival. Conventional prognostic parameters have limited and unreliable correlations with MB outcome, presenting a major challenge for patients' clinical improvement. Acknowledging this issue, our aim was to build a gene signature and evaluate its potential as a new prognostic model for patients with the disease. In this study, we used six datasets totaling 1679 samples including RNA gene expression and DNA methylation data from primary MB as well as control samples from healthy cerebellum. We identified methylation-driven genes (MDGs) in MB, genes whose expression is correlated with their methylation. We employed LASSO regression, incorporating the MDGs as a parameter to develop the prognostic model. Through this approach, we derived a two-gene signature (GS-2) of candidate prognostic biomarkers for MB (CEMIP and NCBP3). Using a risk score model, we confirmed the GS-2 impact on overall survival (OS) with Kaplan-Meier analysis. We evaluated its robustness and accuracy with receiver operating characteristic curves predicting OS at 1, 3, and 5 years in multiple independent datasets. The GS-2 showed highly significant results as an independent prognostic biomarker compared to traditional MB markers. The methylation-regulated GS-2 risk score model can effectively classify patients with MB into high and low-risk, reinforcing the importance of this epigenetic modification in the disease. Such genes stand out as promising prognostic biomarkers with potential application for MB treatment.

Heterogeneity and tumoral origin of medulloblastoma in the single-cell era

Medulloblastoma is one of the most common malignant pediatric brain tumors derived from posterior fossa. The current treatment includes maximal safe surgical resection, radiotherapy, whole cranio-spinal radiation and adjuvant with chemotherapy. However, it can only limitedly prolong the survival time with severe side effects and relapse. Defining the intratumoral heterogeneity, cellular origin and identifying the interaction network within tumor microenvironment are helpful for understanding the mechanisms of medulloblastoma tumorigenesis and relapse. Due to technological limitations, the mechanisms of cellular heterogeneity and tumor origin have not been fully understood. Recently, the emergence of single-cell technology has provided a powerful tool for achieving the goal of understanding the mechanisms of tumorigenesis. Several studies have demonstrated the intratumoral heterogeneity and tumor origin for each subtype of medulloblastoma utilizing the single-cell RNA-seq, which has not been uncovered before using conventional technologies. In this review, we present an overview of the current progress in understanding of cellular heterogeneity and tumor origin of medulloblastoma and discuss novel findings in the age of single-cell technologies.

Single-cell multi-omics analysis of lineage development and spatial organization in the human fetal cerebellum

Human cerebellum encompasses numerous neurons, exhibiting a distinct developmental paradigm from cerebrum. Here we conducted scRNA-seq, scATAC-seq and spatial transcriptomic analyses of fetal samples from gestational week (GW) 13 to 18 to explore the emergence of cellular diversity and developmental programs in the developing human cerebellum. We identified transitory granule cell progenitors that are conserved across species. Special patterns in both granule cells and Purkinje cells were dissected multidimensionally. Species-specific gene expression patterns of cerebellar lobes were characterized and we found that PARM1 exhibited inconsistent distribution in human and mouse granule cells. A novel cluster of potential neuroepithelium at the rhombic lip was identified. We also resolved various subtypes of Purkinje cells and unipolar brush cells and revealed gene regulatory networks controlling their diversification. Therefore, our study offers a valuable multi-omics landscape of human fetal cerebellum and advances our understanding of development and spatial organization of human cerebellum.

Meningeal macrophages inhibit chemokine signaling in pre-tumor cells to suppress mouse medulloblastoma initiation

The microenvironment profoundly influences tumor initiation across numerous tissues but remains understudied in brain tumors. In the cerebellum, canonical Wnt signaling controlled by Norrin/Frizzled4 (Fzd4) activation in meningeal endothelial cells is a potent inhibitor of preneoplasia and tumor progression in mouse models of Sonic hedgehog medulloblastoma (Shh-MB). Single-cell transcriptome profiling and phenotyping of the meninges indicate that Norrin/Frizzled4 sustains the activation of meningeal macrophages (mMΦs), characterized by Lyve1 and CXCL4 expression, during the critical preneoplastic period. Depleting mMΦs during this period enhances preneoplasia and tumorigenesis, phenocopying the effects of Norrin loss. The anti-tumorigenic function of mMΦs is derived from the expression of CXCL4, which counters CXCL12/CXCR4 signaling in pre-tumor cells, thereby inhibiting cell-cycle progression and promoting migration away from the pre-tumor niche. These findings identify a pivotal role for mMΦs as key mediators in chemokine-regulated anti-cancer crosstalk between the stroma and pre-tumor cells in the control of MB initiation.

Deciphering pathological behavior of pediatric medullary thyroid cancer from single-cell perspective

Background

Pediatric medullary thyroid cancer (MTC) is one of the rare pediatric endocrine neoplasms. Derived from C cells of thyroid glands, MTC is more aggressive and more prompt to metastasis than other types of pediatric thyroid cancer. The mechanism remains unclear.

Methods

We performed single-cell transcriptome sequencing on the samples of the primary tumor and metastases lymph nodes from one patient diagnosed with MTC, and it is the first single-cell transcriptome sequencing data of pediatric MTC. In addition, whole exome sequencing was performed and peripheral blood was regarded as a normal reference. All cells that passed quality control were merged and analyzed in R to discover the association between tumor cells and their microenvironment as well as tumor pathogenesis.

Results

We first described the landscape of the single-cell atlas of MTC and studied the interaction between the tumor cell and its microenvironment. C cells, identified as tumor cells, and T cells, as the dominant participant in the tumor microenvironment, were particularly discussed in their development and interactions. In addition, the WES signature of tumor cells and their microenvironment were also described. Actively immune interactions were found, indicating B cells, T cells and myeloid cells were all actively participating in immune reaction in MTC. T cells, as the major components of the tumor microenvironment, proliferated in MTC and could be divided into clusters that expressed proliferation, immune effectiveness, and naive markers separately.

Regulation of early cerebellar development

The study of cerebellar development has been at the forefront of neuroscience since the pioneering work of Wilhelm His Sr., Santiago Ramón y Cajal and many others since the 19th century. They laid the foundation to identify the circuitry of the cerebellum, already revealing its stereotypic three-layered cortex and discerning several of its neuronal components. Their work was fundamental in the acceptance of the neuron doctrine, which acknowledges the key role of individual neurons in forming the basic units of the nervous system. Increasing evidence shows that the cerebellum performs a variety of homeostatic and higher order neuronal functions beyond the mere control of motor behaviour. Over the last three decades, many studies have revealed the molecular machinery that regulates distinct aspects of cerebellar development, from the establishment of a cerebellar anlage in the posterior brain to the identification of cerebellar neuron diversity at the single cell level. In this review, we focus on summarizing our current knowledge on early cerebellar development with a particular emphasis on the molecular determinants that secure neuron specification and contribute to the diversity of cerebellar neurons.

Spatial RNA sequencing methods show high resolution of single cell in cancer metastasis and the formation of tumor microenvironment

Cancer metastasis often leads to death and therapeutic resistance. This process involves the participation of a variety of cell components, especially cellular and intercellular communications in the tumor microenvironment (TME). Using genetic sequencing technology to comprehensively characterize the tumor and TME is therefore key to understanding metastasis and therapeutic resistance. The use of spatial transcriptome sequencing enables the localization of gene expressions and cell activities in tissue sections. By examining the localization change as well as gene expression of these cells, it is possible to characterize the progress of tumor metastasis and TME formation. With improvements of this technology, spatial transcriptome sequencing technology has been extended from local regions to whole tissues, and from single sequencing technology to multimodal analysis combined with a variety of datasets. This has enabled the detection of every single cell in tissue slides, with high resolution, to provide more accurate predictive information for tumor treatments. In this review, we summarize the results of recent studies dealing with new multimodal methods and spatial transcriptome sequencing methods in tumors to illustrate recent developments in the imaging resolution of micro-tissues.

A Primer on Preprocessing, Visualization, Clustering, and Phenotyping of Barcode-Based Spatial Transcriptomics Data

Recent developments in spatially resolved transcriptomics (ST) have resulted in a large number of studies characterizing the architecture of tissues, the spatial distribution of cell types, and their interactions. Furthermore, ST promises to enable the discovery of more accurate drug targets while also providing a better understanding of the etiology and evolution of complex diseases. The analysis of ST brings similar challenges as seen in other gene expression assays such as scRNA-seq; however, there is the additional spatial information that warrants the development of suitable algorithms for the quality control, preprocessing, visualization, and other discovery-enabling approaches (e.g., clustering, cell phenotyping). In this chapter, we review some of the existing algorithms to perform these analytical tasks and highlight some of the unmet analytical challenges in the analysis of ST data. Given the diversity of available ST technologies, we focus this chapter on the analysis of barcode-based RNA quantitation techniques.

Pathological implications of metabolic reprogramming and its therapeutic potential in medulloblastoma

Tumor-specific alterations in metabolism have been recognized to sustain the production of ATP and macromolecules needed for cell growth, division and survival in many cancer types. However, metabolic heterogeneity poses a challenge for the establishment of effective anticancer therapies that exploit metabolic vulnerabilities. Medulloblastoma (MB) is one of the most heterogeneous malignant pediatric brain tumors, divided into four molecular subgroups (Wingless, Sonic Hedgehog, Group 3 and Group 4). Recent progresses in genomics, single-cell sequencing, and novel tumor models have updated the classification and stratification of MB, highlighting the complex intratumoral cellular diversity of this cancer. In this review, we emphasize the mechanisms through which MB cells rewire their metabolism and energy production networks to support and empower rapid growth, survival under stressful conditions, invasion, metastasis, and resistance to therapy. Additionally, we discuss the potential clinical benefits of currently available drugs that could target energy metabolism to suppress MB progression and increase the efficacy of the current MB therapies.

Integration of single-cell RNA sequencing and spatial transcriptomics to reveal the glioblastoma heterogeneity

Glioblastoma (GBM), a deadly brain tumor, is still one of a few lasting challenges of contemporary oncology. Current therapies fail to significantly improve patient survival due to GBM tremendous genetic, transcriptomic, immunological, and sex-dependent heterogeneity. Over the years, clinical differences between males and females were characterized. For instance, higher incidence of GBM in males or distinct responses to cancer chemotherapy and immunotherapy between males and females have been noted. Despite the introduction of single-cell RNA sequencing and spatial transcriptomics, these differences were not further investigated as studies were focused only on revealing the general picture of GBM heterogeneity. Hence, in this mini-review, we summarized the current state of knowledge on GBM heterogeneity revealed by single-cell RNA sequencing and spatial transcriptomics with regard to genetics, immunology, and sex-dependent differences. Additionally, we highlighted future research directions which would fill the gap of knowledge on the impact of patient's sex on the disease outcome.

Integration of single-cell RNA sequencing and spatial transcriptomics to reveal the glioblastoma heterogeneity

Glioblastoma (GBM), a deadly brain tumor, is still one of the few lasting challenges of contemporary oncology. Current therapies fail to significantly improve patient survival due to GBM's tremendous genetic, transcriptomic, immunological, and sex-dependent heterogeneity. Over the years, clinical differences between males and females were characterized. For instance, higher incidence of GBM in males or distinct responses to cancer chemotherapy and immunotherapy between males and females have been noted. However, despite the introduction of single-cell RNA sequencing and spatial transcriptomics, these differences were not further investigated as studies were focused only on exposing the general picture of GBM heterogeneity. Hence, in this study, we summarized the current state of knowledge on GBM heterogeneity exposed by single-cell RNA sequencing and spatial transcriptomics with regard to genetics, immunology, and sex-dependent differences. Additionally, we highlighted future research directions which would fill the gap of knowledge on the impact of patient's sex on the disease outcome.

The Tumor Microenvironment of Medulloblastoma: An Intricate Multicellular Network with Therapeutic Potential

Simple Summary

The current treatment options for medulloblastoma, the most common malignant childhood brain cancer, are associated with many negative side effects and toxicities. Therefore, novel treatment options are needed that target the tumor without affecting the healthy tissue. Medulloblastoma tumors consist of a wide variety of cell types and extracellular components that make up the microenvironment of the tumor. This tumor microenvironment influences the development, progression, and relapse of medulloblastoma through different cell–cell and cell–extracellular matrix interactions. Obtaining insights into these interactions will help with gaining a better understanding of this malignancy. Additionally, it could support the search for new targets of treatments directed at components of the tumor microenvironment.

Abstract

Medulloblastoma (MB) is a heterogeneous disease in which survival is highly affected by the underlying subgroup-specific characteristics. Although the current treatment modalities have increased the overall survival rates of MB up to 70–80%, MB remains a major cause of cancer-related mortality among children. This indicates that novel therapeutic approaches against MB are needed. New promising treatment options comprise the targeting of cells and components of the tumor microenvironment (TME). The TME of MB consists of an intricate multicellular network of tumor cells, progenitor cells, astrocytes, neurons, supporting stromal cells, microglia, immune cells, extracellular matrix components, and vasculature systems. In this review, we will discuss all the different components of the MB TME and their role in MB initiation, progression, metastasis, and relapse. Additionally, we briefly introduce the effect that age plays on the TME of brain malignancies and discuss the MB subgroup-specific differences in TME components and how all of these variations could affect the progression of MB. Finally, we highlight the TME-directed treatments, in which we will focus on therapies that are being evaluated in clinical trials.

Human Cerebellar Development and Transcriptomics: Implications for Neurodevelopmental Disorders

Developmental abnormalities of the cerebellum are among the most recognized structural brain malformations in human prenatal imaging. Yet reliable information regarding their cause in humans is sparse, and few outcome studies are available to inform prognosis. We know very little about human cerebellar development, in stark contrast to the wealth of knowledge from decades of research on cerebellar developmental biology of model organisms, especially mice. Recent studies show that multiple aspects of human cerebellar development significantly differ from mice and even rhesus macaques, a nonhuman primate. These discoveries challenge many current mouse-centric models of normal human cerebellar development and models regarding the pathogenesis of several neurodevelopmental phenotypes affecting the cerebellum, including Dandy-Walker malformation and medulloblastoma. Since we cannot model what we do not know, additional normative and pathological human developmental data are essential, and new models are needed.

Spatial-CUT&Tag: Spatially resolved chromatin modification profiling at the cellular level

Spatial omics emerged as a new frontier of biological and biomedical research. Here, we present spatial-CUT&Tag for spatially resolved genome-wide profiling of histone modifications by combining in situ CUT&Tag chemistry, microfluidic deterministic barcoding, and next-generation sequencing. Spatially resolved chromatin states in mouse embryos revealed tissue-type-specific epigenetic regulations in concordance with ENCODE references and provide spatial information at tissue scale. Spatial-CUT&Tag revealed epigenetic control of the cortical layer development and spatial patterning of cell types determined by histone modification in mouse brain. Single-cell epigenomes can be derived in situ by identifying 20-micrometer pixels containing only one nucleus using immunofluorescence imaging. Spatial chromatin modification profiling in tissue may offer new opportunities to study epigenetic regulation, cell function, and fate decision in normal physiology and pathogenesis.

Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research

In recent years, the diversity of cancer cells in tumor tissues as a result of intratumor heterogeneity has attracted attention. In particular, the development of single-cell analysis technology has made a significant contribution to the field; technologies that are centered on single-cell RNA sequencing (scRNA-seq) have been reported to analyze cancer constituent cells, identify cell groups responsible for therapeutic resistance, and analyze gene signatures of resistant cell groups. However, although single-cell analysis is a powerful tool, various issues have been reported, including batch effects and transcriptional noise due to gene expression variation and mRNA degradation. To overcome these issues, machine learning techniques are currently being introduced for single-cell analysis, and promising results are being reported. In addition, machine learning has also been used in various ways for single-cell analysis, such as single-cell assay of transposase accessible chromatin sequencing (ATAC-seq), chromatin immunoprecipitation sequencing (ChIP-seq) analysis, and multi-omics analysis; thus, it contributes to a deeper understanding of the characteristics of human diseases, especially cancer, and supports clinical applications. In this review, we present a comprehensive introduction to the implementation of machine learning techniques in medical research for single-cell analysis, and discuss their usefulness and future potential.