Spatial Transcriptomics Technologies Research Articles

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

BackgroundCerebellar neurogenesis involves the generation of large numbers of cerebellar granule neurons (GNs) throughout development of the cerebellum, a process that involves tight regulation of proliferation and differentiation of granule neuron progenitors (GNPs). A number of transcriptional regulators, including Math1, and the signaling molecules Wnt and Shh have been shown to have important roles in GNP proliferation and differentiation, and deregulation of granule cell development has been reported to be associated with the pathogenesis of medulloblastoma. While the progenitor/differentiation states of cerebellar granule cells have been broadly investigated, a more detailed association between developmental differentiation programs and spatial gene expression patterns, and how these lead to differential generation of distinct types of medulloblastoma remains poorly understood. Here, we provide a comparative single-cell spatial transcriptomics analysis to better understand the similarities and differences between developing granule and medulloblastoma cells.ResultsTo acquire an enhanced understanding of the precise cellular states of developing cerebellar granule cells, we performed single-cell RNA sequencing of 24,919 murine cerebellar cells from granule neuron-specific reporter mice (Math1-GFP; Dcx-DsRed mice). Our single-cell analysis revealed that there are four major states of developing cerebellar granule cells, including two subsets of granule progenitors and two subsets of differentiating/differentiated granule neurons. Further spatial transcriptomics technology enabled visualization of their spatial locations in cerebellum. In addition, we performed single-cell RNA sequencing of 18,372 cells from Patched+/− mutant mice and found that the transformed granule cells in medulloblastoma closely resembled developing granule neurons of varying differentiation states. However, transformed granule neuron progenitors in medulloblastoma exhibit noticeably less tendency to differentiate compared with cells in normal development.ConclusionIn sum, our study revealed the cellular and spatial organization of the detailed states of cerebellar granule cells and provided direct evidence for the similarities and discrepancies between normal cerebellar development and tumorigenesis.

SpatialDWLS: accurate deconvolution of spatial transcriptomic data

Recent development of spatial transcriptomic technologies has made it possible to characterize cellular heterogeneity with spatial information. However, the technology often does not have sufficient resolution to distinguish neighboring cell types. Here, we present spatialDWLS, to quantitatively estimate the cell-type composition at each spatial location. We benchmark the performance of spatialDWLS by comparing it with a number of existing deconvolution methods and find that spatialDWLS outperforms the other methods in terms of accuracy and speed. By applying spatialDWLS to a human developmental heart dataset, we observe striking spatial temporal changes of cell-type composition during development.

Spatial Transcriptomics of Supraoptic Nucleus and Paraventricular Nucleus of the Hypothalamus

Magnocellular neurosecretory cells (MNCs) in the supraoptic nucleus (SON) and paraventricular nucleus (PVN) of the hypothalamus release oxytocin (OXY) and arginine vasopressin (AVP) into the peripheral circulation, playing a key role in the regulation of fluid and electrolyte homeostasis. Several groups have investigated transcriptomics in these regions; however, the data lack spatial context important for better understanding physiological functions of genes. Our study leverages spatially-resolved transcriptomics to visualize the relationship between cells, their gene expression profiles, and their relative locations within a tissue sample. Here, we use a novel spatial transcriptomics technology, Visium Spatial Gene Expression, manufactured by 10x Genomics to obtain spatially-resolved gene expression data for the SON and PVN of an adult male Sprague-Dawley rat. Briefly, a fresh frozen brain was sectioned at 10μm thickness to collect four sections (~4x4mm) containing SON and PVN regions. The tissue was then mounted and fixed on the capture areas of a Visium slide containing probes that bind mRNA. Next, hematoxylin and eosin (H&E) staining and high-resolution imaging of the slide was performed prior to tissue permeabilization. The permeabilization releases mRNA from cells that then binds to capture probes on the slide beneath the tissue. cDNA is then synthesized from captured mRNA from which sequencing libraries are prepared. Sequence data is then visualized, using 10x Genomics’ Space Ranger and Loupe Browser applications, to overlay spatial gene expression data with our H&E stained images. Foremost, our results show that gene cluster analysis successfully differentiated myelinated fiber tracts from nuclei and identified several distinct neuronal populations. Gene clusters overlaying fiber tracts contain glia-specific markers (e.g., Mbp and Gfap) and those overlaying SON and PVN regions contain neuron-specific markers (e.g., Syn1 and Nrgn). These data also have the resolution to resolve OXY- and AVP-specific clusters in the SON via targeted gene analysis, with anatomical distribution consistent with immunohistochemistry results for the regions. Additionally, our results show clustering of SON with magnocellular PVN, while parvocellular PVN was a different cluster. Differential expression analysis of these two clusters revealed relevant genes for the distinct regions and neuronal subpopulations in the top 20 globally distinguishing genes (e.g., Avp and Oxt for SON and magnocellular PVN cluster and Trh for parvocellular PVN cluster). This spatially-resolved transcriptomic data allows us to obtain whole transcriptomic data without sacrificing morphological context (e.g., SON and PVN regions). Future spatial transcriptomic studies can be used to investigate changes in MNC gene expression in terms of sex differences and disease models that can potentially inform cellular mechanisms involved in pathophysiology.

Robust decomposition of cell type mixtures in spatial transcriptomics.

A limitation of spatial transcriptomics technologies is that individual measurements may contain contributions from multiple cells, hindering the discovery of cell-type-specific spatial patterns of localization and expression. Here, we develop robust cell type decomposition (RCTD), a computational method that leverages cell type profiles learned from single-cell RNA-seq to decompose cell type mixtures while correcting for differences across sequencing technologies. We demonstrate the ability of RCTD to detect mixtures and identify cell types on simulated datasets. Furthermore, RCTD accurately reproduces known cell type and subtype localization patterns in Slide-seq and Visium datasets of the mouse brain. Finally, we show how RCTD's recovery of cell type localization enables the discovery of genes within a cell type whose expression depends on spatial environment. Spatial mapping of cell types with RCTD enables the spatial components of cellular identity to be defined, uncovering new principles of cellular organization in biological tissue. RCTD is publicly available as an open-source R package at https://github.com/dmcable/RCTD .

Characterizing Tissue Composition Through Combined Analysis of Morphologies and Transcriptional States

Advances in spatial transcriptomics technologies enable simultaneous profiling of morphological and transcriptional modalities from the same cells or regions within tissues. Here, we present multi-modal structured embedding (MUSE), an approach to deeply characterize tissue heterogeneity through analysis of combined image and transcriptional measurements. We demonstrate that MUSE can discover tissue subpopulations missed by either modality as well as compensate for modality-specific noise. We apply MUSE to diverse datasets containing spatial transcriptomics (seqFISH+, STARmap or ST) and imaging (H&E or fluorescence microscopy) modalities. MUSE identified biologically meaningful tissue subpopulations and stereotyped spatial patterning within heterogeneous mouse cortex, human cancer tissue and mouse brain sections with Alzheimer’s disease. MUSE provides a framework for combining multi-modal data to reveal deeper insights into the states, functions and organizations of cells in complex biological tissues.

The spatial RNA integrity number assay for in situ evaluation of transcriptome quality

The RNA integrity number (RIN) is a frequently used quality metric to assess the completeness of rRNA, as a proxy for the corresponding mRNA in a tissue. Current methods operate at bulk resolution and provide a single average estimate for the whole sample. Spatial transcriptomics technologies have emerged and shown their value by placing gene expression into a tissue context, resulting in transcriptional information from all tissue regions. Thus, the ability to estimate RNA quality in situ has become of utmost importance to overcome the limitation with a bulk rRNA measurement. Here we show a new tool, the spatial RNA integrity number (sRIN) assay, to assess the rRNA completeness in a tissue wide manner at cellular resolution. We demonstrate the use of sRIN to identify spatial variation in tissue quality prior to more comprehensive spatial transcriptomics workflows.

Heterogeneity of Neuroinflammatory Responses in Amyotrophic Lateral Sclerosis: A Challenge or an Opportunity?

Amyotrophic Lateral Sclerosis (ALS) is a complex pathology: (i) the neurodegeneration is chronic and progressive; it starts focally in specific central nervous system (CNS) areas and spreads to different districts; (ii) multiple cell types further than motor neurons (i.e., glial/immune system cells) are actively involved in the disease; (iii) both neurosupportive and neurotoxic neuroinflammatory responses were identified. Microglia cells (a key player of neuroinflammation in the CNS) attracted great interest as potential target cell population that could be modulated to counteract disease progression, at least in preclinical ALS models. However, the heterogeneous/multifaceted microglia cell responses occurring in different CNS districts during the disease represent a hurdle for clinical translation of single-drug therapies. To address this issue, over the past ten years, several studies attempted to dissect the complexity of microglia responses in ALS. In this review, we shall summarize these results highlighting how the heterogeneous signature displayed by ALS microglia reflects not only the extent of neuronal demise in different regions of the CNS, but also variable engagement in the attempts to cope with the neuronal damage. We shall discuss novel avenues opened by the advent of single-cell and spatial transcriptomics technologies, underlining the potential for discovery of novel therapeutic targets, as well as more specific diagnostic/prognostic not-invasive markers of neuroinflammation.

Mapping Cellular Coordinates through Advances in Spatial Transcriptomics Technology.

Complex cell-to-cell communication underlies the basic processes essential for homeostasis in the given tissue architecture. Obtaining quantitative gene-expression of cells in their native context has significantly advanced through single-cell RNA sequencing technologies along with mechanical and enzymatic tissue manipulation. This approach, however, is largely reliant on the physical dissociation of individual cells from the tissue, thus, resulting in a library with unaccounted positional information. To overcome this, positional information can be obtained by integrating imaging and positional barcoding. Collectively, spatial transcriptomics strategies provide tissue architecture-dependent as well as position-dependent cellular functions. This review discusses the current technologies for spatial transcriptomics ranging from the methods combining mechanical dissociation and single-cell RNA sequencing to computational spatial re-mapping.

A Hidden Markov Random Field Model for Detecting Domain Organizations from Spatial Transcriptomic Data.

Cells in complex tissues are organized by distinct microenvironments and anatomical structures. This spatial environment of cells is thought to be important for division of labor and other specialized functions of tissues. Recently developed spatial transcriptomic technologies enable the quantification of expression of hundreds of genes while accounting for cells' spatial coordinates, providing an opportunity to study spatially organized structures. Here, we describe a computational pipeline for detecting the spatial organization of cells based on a hidden Markov random field model. We illustrate this pipeline with data generated from multiplexed smFISH from the adult mouse visual cortex.

Barcoded solid-phase RNA capture for Spatial Transcriptomics profiling in mammalian tissue sections.

Spatial resolution of gene expression enables gene expression events to be pinpointed to a specific location in biological tissue. Spatially resolved gene expression in tissue sections is traditionally analyzed using immunohistochemistry (IHC) or in situ hybridization (ISH). These technologies are invaluable tools for pathologists and molecular biologists; however, their throughput is limited to the analysis of only a few genes at a time. Recent advances in RNA sequencing (RNA-seq) have made it possible to obtain unbiased high-throughput gene expression data in bulk. Spatial Transcriptomics combines the benefits of traditional spatially resolved technologies with the massive throughput of RNA-seq. Here, we present a protocol describing how to apply the Spatial Transcriptomics technology to mammalian tissue. This protocol combines histological staining and spatially resolved RNA-seq data from intact tissue sections. Once suitable tissue-specific conditions have been established, library construction and sequencing can be completed in ~5-6 d. Data processing takes a few hours, with the exact timing dependent on the sequencing depth. Our method requires no special instruments and can be performed in any laboratory with access to a cryostat, microscope and next-generation sequencing.

Unilateral leukonychia and hair depigmentation in multifocal motor neuropathy

Neurophysiology studies in a 50-year-old man with slowly progressive weakness of the left upper limb revealed conduction block in the ulnar nerve above the elbow. His weakness remained stable with regular subcutaneous immunoglobulin, but he noted gradual hemibody hair depigmentation. Examination also revealed unilateral left hand leukonychia (figure). MRI of the brain and cervical spine was normal.