Spatial transcriptome profiling by MERFISH reveals fetal liver hematopoietic stem cell niche architecture

The hematopoietic stem cell expansion niche in fetal liver: Current state of the art and the way forward

Hematopoietic development goes through a number of embryonic sites that host hematopoietic progenitor and stem cells with function required at specific developmental stages. Among embryonic sites, the fetal liver (FL) hosts definitive hematopoietic stem cells (HSCs) capable of engrafting adult hematopoietic system and supports their rapid expansion. Hence, this site provides an excellent model to understand the cellular and molecular components of the machinery involved in HSC-proliferative events, leading to their overall expansion. It has been unequivocally established that extrinsic regulators orchestrate events that maintain HSC function. Although most studies on extrinsic regulation of HSC function are targeted at adult bone marrow (BM) hematopoiesis, little is known about how FL HSC function is regulated by their microniche. This review provides the current state of our understanding on molecular and cellular niche factors that support FL hematopoiesis.

Multiplexed spatial transcriptomics methods and the application of expansion microscopy

While spatial transcriptomics has undeniably revolutionized our ability to study cellular organization, it has driven the development of a great number of innovative transcriptomics methods, which can be classified into in situ sequencing (ISS) methods, in situ hybridization (ISH) techniques, and next-generation sequencing (NGS)-based sequencing with region capture. These technologies not only refine our understanding of cellular processes, but also open up new possibilities for breakthroughs in various research domains. One challenge of spatial transcriptomics experiments is the limitation of RNA detection due to optical crowding of RNA in the cells. Expansion microscopy (ExM), characterized by the controlled enlargement of biological specimens, offers a means to achieve super-resolution imaging, overcoming the diffraction limit inherent in conventional microscopy and enabling precise visualization of RNA in spatial transcriptomics methods. In this review, we elaborate on ISS, ISH and NGS-based spatial transcriptomic protocols and on how performance of these techniques can be extended by the combination of these protocols with ExM. Moving beyond the techniques and procedures, we highlight the broader implications of transcriptomics in biology and medicine. These include valuable insight into the spatial organization of gene expression in cells within tissues, aid in the identification and the distinction of cell types and subpopulations and understanding of molecular mechanisms and intercellular changes driving disease development.

Characterization of Multicellular Niches Supporting Hematopoietic Stem Cells Within Distinct Zones

Previous studies of hematopoietic stem cells (HSCs) primarily focused on single cell-based niche models, yielding fruitful but conflicting findings 1-5 . Here we report our investigation on the fetal liver (FL) as the primary fetal hematopoietic site using spatial transcriptomics. Our study reveals two distinct niches: the portal-vessel (PV) niche and the sinusoidal niche. The PV niche, composing N-cadherin (N-cad) Hi Pdgfrα + mesenchymal stromal cells (MSCs), endothelial cells (ECs), and N-cad Lo Albumin + hepatoblasts, maintains quiescent and multipotential FL-HSCs. Conversely, the sinusoidal niche, comprising ECs, hepatoblasts and hepatocytes, as well as potential macrophages and megakaryocytes, supports proliferative FL-HSCs biased towards myeloid lineages. Unlike prior reports on the role of Cxcl12, with its depletion from vessel-associated stromal cells leading to 80% of HSCs' reduction in the adult bone marrow (BM) 6,7 , depletion of Cxcl12 via Cdh2 CreERT (encoding N-cad) induces altered localization of HSCs from the PV to the sinusoidal niches, resulting in an increase of HSC number but with myeloid-bias. Similarly, we discovered that adult BM encompasses two niches within different zones, each composed of multi-cellular components: trabecular bone area (TBA, or metaphysis) supporting deep-quiescent HSCs, and central marrow (CM, or diaphysis) fostering heterogenous proliferative HSCs. This study transforms our understanding of niches by shifting from single cell-based to multicellular components within distinct zones, illuminating the intricate regulation of HSCs tailored to their different cycling states.

Bone marrow niches for hematopoietic stem cells: life span dynamics and adaptation to acute stress

ABSTRACT

Hematopoietic stem cells (HSCs) are instrumental for organismal survival because they are responsible for lifelong production of mature blood lineages in homeostasis and response to external stress. To fulfill their function, HSCs rely on reciprocal interactions with specialized tissue microenvironments, termed HSC niches. From embryonic development to advanced aging, HSCs transition through several hematopoietic organs in which they are supported by distinct extrinsic cues. Here, we describe recent discoveries on how HSC niches collectively adapt to ensure robust hematopoietic function during biological aging and after exposure to acute stress. We also discuss the latest strategies leveraging niche-derived signals to revert aging-associated phenotypes and enhance hematopoietic recovery after myeloablation.

The hematopoietic microenvironment of the fetal liver and transient abnormal myelopoiesis associated with Down syndrome: A review

Transient abnormal myelopoiesis (TAM) in neonates with Down syndrome is a distinct form of leukemia or preleukemia that mirrors the hematological features of acute megakaryoblastic leukemia. However, it typically resolves spontaneously in the early stages. TAM originates from fetal liver (FL) hematopoietic precursor cells and emerges due to somatic mutations in GATA1 in utero. In TAM, progenitor cells proliferate and differentiate into mature megakaryocytes and granulocytes. This process occurs both in vitro, aided by hematopoietic growth factors (HGFs) produced in the FL, and in vivo, particularly in specific anatomical sites like the FL and blood vessels. The FL's hematopoietic microenvironment plays a crucial role in TAM's pathogenesis and may contribute to its spontaneous regression. This review presents an overview of current knowledge regarding the unique features of TAM in relation to the FL hematopoietic microenvironment, focusing on the functions of HGFs and the pathological features of TAM.

Emerging Roles of Spatial Transcriptomics in Liver Research

Spatial transcriptomics, leveraging sequencing- and imaging-based techniques, has emerged as a groundbreaking technology for mapping gene expression within the complex architectures of tissues. This approach provides an in-depth understanding of cellular and molecular dynamics across various states of healthy and diseased livers. Through the integration of sophisticated bioinformatics strategies, it enables detailed exploration of cellular heterogeneity, transitions in cell states, and intricate cell-cell interactions with remarkable precision. In liver research, spatial transcriptomics has been particularly revelatory, identifying distinct zonated functions of hepatocytes that are crucial for understanding the metabolic and detoxification processes of the liver. Moreover, this technology has unveiled new insights into the pathogenesis of liver diseases, such as the role of lipid-associated macrophages in steatosis and endothelial cell signals in liver regeneration and repair. In the domain of liver cancer, spatial transcriptomics has proven instrumental in delineating intratumor heterogeneity, identifying supportive microenvironmental niches and revealing the complex interplay between tumor cells and the immune system as well as susceptibility to immune checkpoint inhibitors. In conclusion, spatial transcriptomics represents a significant advance in hepatology, promising to enhance our understanding and treatment of liver diseases.

scGIST: gene panel design for spatial transcriptomics with prioritized gene sets

A critical challenge of single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, they are typically limited to panels of about a thousand genes. This constrains researchers to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or those in specific pathways. We propose scGIST, a constrained feature selection tool that designs sc-ST panels prioritizing user-specified genes without compromising cell type detection accuracy. We demonstrate scGIST's efficacy in diverse use cases, highlighting it as a valuable addition to sc-ST's algorithmic toolbox.

High resolution spatial profiling of kidney injury and repair using RNA hybridization-based in situ sequencing

Emerging spatially resolved transcriptomics technologies allow for the measurement of gene expression in situ at cellular resolution. We apply direct RNA hybridization-based in situ sequencing (dRNA HybISS, Cartana part of 10xGenomics) to compare male and female healthy mouse kidneys and the male kidney injury and repair timecourse. A pre-selected panel of 200 genes is used to identify cell state dynamics patterns during injury and repair. We develop a new computational pipeline, CellScopes, for the rapid analysis, multi-omic integration and visualization of spatially resolved transcriptomic datasets. The resulting dataset allows us to resolve 13 kidney cell types within distinct kidney niches, dynamic alterations in cell state over the course of injury and repair and cell-cell interactions between leukocytes and kidney parenchyma. At late timepoints after injury, C3+ leukocytes are enriched near pro-inflammatory, failed-repair proximal tubule cells. Integration of snRNA-seq dataset from the same injury and repair samples also allows us to impute the spatial localization of genes not directly measured by dRNA HybISS.

PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics

Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying spatially variable genes (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated with depicting spatial domains, they are technically dissociated in most methods. Here, we present a framework (PROST) for the quantitative recognition of spatial transcriptomic patterns, consisting of (i) quantitatively characterizing spatial variations in gene expression patterns through the PROST Index; and (ii) unsupervised clustering of spatial domains via a self-attention mechanism. We demonstrate that PROST performs superior SVG identification and domain segmentation with various spatial resolutions, from multicellular to cellular levels. Importantly, PROST Index can be applied to prioritize spatial expression variations, facilitating the exploration of biological insights. Together, our study provides a flexible and robust framework for analyzing diverse spatial transcriptomic data.

Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep

MOTIVATION

Spatial clustering is essential and challenging for spatial transcriptomics' data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation.

RESULTS

In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering.

AVAILABILITY AND IMPLEMENTATION

The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.

Secretory GFP reconstitution labeling of neighboring cells interrogates cell-cell interactions in metastatic niches

Cancer cells inevitably interact with neighboring host tissue-resident cells during the process of metastatic colonization, establishing a metastatic niche to fuel their survival, growth, and invasion. However, the underlying mechanisms in the metastatic niche are yet to be fully elucidated owing to the lack of methodologies for comprehensively studying the mechanisms of cell-cell interactions in the niche. Here, we improve a split green fluorescent protein (GFP)-based genetically encoded system to develop secretory glycosylphosphatidylinositol-anchored reconstitution-activated proteins to highlight intercellular connections (sGRAPHIC) for efficient fluorescent labeling of tissue-resident cells that neighbor on and putatively interact with cancer cells in deep tissues. The sGRAPHIC system enables the isolation of metastatic niche-associated tissue-resident cells for their characterization using a single-cell RNA sequencing platform. We use this sGRAPHIC-leveraged transcriptomic platform to uncover gene expression patterns in metastatic niche-associated hepatocytes in a murine model of liver metastasis. Among the marker genes of metastatic niche-associated hepatocytes, we identify Lgals3, encoding galectin-3, as a potential pro-metastatic factor that accelerates metastatic growth and invasion.

Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling

In this Perspective, we discuss the current status and advances in spatial transcriptomics technologies, which allow high-resolution mapping of gene expression in intact cell and tissue samples. Spatial transcriptomics enables the creation of high-resolution maps of gene expression patterns within their native spatial context, adding an extra layer of information to the bulk sequencing data. Spatial transcriptomics has expanded significantly in recent years and is making a notable impact on a range of fields, including tissue architecture, developmental biology, cancer, and neurodegenerative and infectious diseases. The latest advancements in spatial transcriptomics have resulted in the development of highly multiplexed methods, transcriptomic-wide analysis, and single-cell resolution utilizing diverse technological approaches. In this Perspective, we provide a detailed analysis of the molecular foundations behind the main spatial transcriptomics technologies, including methods based on microdissection, in situ sequencing, single-molecule FISH, spatial capturing, selection of regions of interest, and single-cell or nuclei dissociation. We contextualize the detection and capturing efficiency, strengths, limitations, tissue compatibility, and applications of these techniques as well as provide information on data analysis. In addition, this Perspective discusses future directions and potential applications of spatial transcriptomics, highlighting the importance of the continued development to promote widespread adoption of these techniques within the research community.

Harnessing computational spatial omics to explore the spatial biology intricacies

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

Fetal liver development and implications for liver disease pathogenesis

The metabolic, digestive and homeostatic roles of the liver are dependent on proper crosstalk and organization of hepatic cell lineages. These hepatic cell lineages are derived from their respective progenitors early in organogenesis in a spatiotemporally controlled manner, contributing to the liver's specialized and diverse microarchitecture. Advances in genomics, lineage tracing and microscopy have led to seminal discoveries in the past decade that have elucidated liver cell lineage hierarchies. In particular, single-cell genomics has enabled researchers to explore diversity within the liver, especially early in development when the application of bulk genomics was previously constrained due to the organ's small scale, resulting in low cell numbers. These discoveries have substantially advanced our understanding of cell differentiation trajectories, cell fate decisions, cell lineage plasticity and the signalling microenvironment underlying the formation of the liver. In addition, they have provided insights into the pathogenesis of liver disease and cancer, in which developmental processes participate in disease emergence and regeneration. Future work will focus on the translation of this knowledge to optimize in vitro models of liver development and fine-tune regenerative medicine strategies to treat liver disease. In this Review, we discuss the emergence of hepatic parenchymal and non-parenchymal cells, advances that have been made in in vitro modelling of liver development and draw parallels between developmental and pathological processes.

Spatial transcriptomics: Technologies, applications and experimental considerations

The diverse cell types of an organ have a highly structured organization to enable their efficient and correct function. To fully appreciate gene functions in a given cell type, one needs to understand how much, when and where the gene is expressed. Classic bulk RNA sequencing and popular single cell sequencing destroy cell structural organization and fail to provide spatial information. However, the spatial location of gene expression or of the cell in a complex tissue provides key clues to comprehend how the neighboring genes or cells cross talk, transduce signals and work together as a team to complete the job. The functional requirement for the spatial content has been a driving force for rapid development of the spatial transcriptomics technologies in the past few years. Here, we present an overview of current spatial technologies with a special focus on the commercially available or currently being commercialized technologies, highlight their applications by category and discuss experimental considerations for a first spatial experiment.

Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection

Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.

Spatial organization of the mouse retina at single cell resolution by MERFISH

The visual signal processing in the retina requires the precise organization of diverse neuronal types working in concert. While single-cell omics studies have identified more than 120 different neuronal subtypes in the mouse retina, little is known about their spatial organization. Here, we generated the single-cell spatial atlas of the mouse retina using multiplexed error-robust fluorescence in situ hybridization (MERFISH). We profiled over 390,000 cells and identified all major cell types and nearly all subtypes through the integration with reference single-cell RNA sequencing (scRNA-seq) data. Our spatial atlas allowed simultaneous examination of nearly all cell subtypes in the retina, revealing 8 previously unknown displaced amacrine cell subtypes and establishing the connection between the molecular classification of many cell subtypes and their spatial arrangement. Furthermore, we identified spatially dependent differential gene expression between subtypes, suggesting the possibility of functional tuning of neuronal types based on location.

A genome-wide single-cell 3D genome atlas of lung cancer progression

Alterations in three-dimensional (3D) genome structures are associated with cancer1-5. However, how genome folding evolves and diversifies during subclonal cancer progression in the native tissue environment remains unknown. Here, we leveraged a genome-wide chromatin tracing technology to directly visualize 3D genome folding in situ in a faithful Kras-driven mouse model of lung adenocarcinoma (LUAD)6, generating the first single-cell 3D genome atlas of any cancer. We discovered stereotypical 3D genome alterations during cancer development, including a striking structural bottleneck in preinvasive adenomas prior to progression to LUAD, indicating a stringent selection on the 3D genome early in cancer progression. We further showed that the 3D genome precisely encodes cancer states in single cells, despite considerable cell-to-cell heterogeneity. Finally, evolutionary changes in 3D genome compartmentalization - partially regulated by polycomb group protein Rnf2 through its ubiquitin ligase-independent activity - reveal novel genetic drivers and suppressors of LUAD progression. Our results demonstrate the importance of mapping the single-cell cancer 3D genome and the potential to identify new diagnostic and therapeutic biomarkers from 3D genomic architectures.

The Utility of Spatial Transcriptomics for Solid Organ Transplantation

Spatial transcriptomics (ST) measures and maps transcripts within intact tissue sections, allowing the visualization of gene activity within the spatial organization of complex biological systems. This review outlines advances in genomic sequencing technologies focusing on in situ sequencing-based ST, including applications in transplant and relevant nontransplant settings. We describe the experimental and analytical pipelines that underpin the current generation of spatial technologies. This context is important for understanding the potential role ST may play in expanding our knowledge, including in organ transplantation, and the important caveats/limitations when interpreting the vast data output generated by such methodological platforms.

Spatial Single-Cell Technologies for Exploring Gastrointestinal Tissue Transcriptome

In the gastrointestinal (GI) system, like in other organ systems, the histological structure is a key determinant of physiological function. Tissues form multiple layers in the GI tract to perform their specialized functions in secretion, absorption, and motility. Even at the single layer, the heterogeneous cell population performs a diverse range of digestive or regulatory functions. Although many details of such functions at the histological and cell biological levels were revealed by traditional methods such as cell sorting, isolation, and culture, as well as histological methods such as immunostaining and RNA in situ hybridization, recent advances in spatial single-cell technologies could further contribute to our understanding of the molecular makeup of GI histological structures by providing a genome-wide overview of how different genes are expressed across individual cells and tissue layers. The current minireview summarizes recent advances in the spatial transcriptomics field and discusses how such technologies can promote our understanding of GI physiology. © 2023 American Physiological Society. Compr Physiol 13:4709-4718, 2023.

The tissue-specific transcriptional landscape underlines the involvement of endothelial cells in health and disease

Endothelial cells (ECs) that line vascular and lymphatic vessels are being increasingly recognized as important to organ function in health and disease. ECs participate not only in the trafficking of gases, metabolites, and cells between the bloodstream and tissues but also in the angiocrine-based induction of heterogeneous parenchymal cells, which are unique to their specific tissue functions. The molecular mechanisms regulating EC heterogeneity between and within different tissues are modeled during embryogenesis and become fully established in adults. Any changes in adult tissue homeostasis induced by aging, stress conditions, and various noxae may reshape EC heterogeneity and induce specific transcriptional features that condition a functional phenotype. Heterogeneity is sustained via specific genetic programs organized through the combinatory effects of a discrete number of transcription factors (TFs) that, at the single tissue-level, constitute dynamic networks that are post-transcriptionally and epigenetically regulated. This review is focused on outlining the TF-based networks involved in EC specialization and physiological and pathological stressors thought to modify their architecture.

Modeling intercellular communication in tissues using spatial graphs of cells

Models of intercellular communication in tissues are based on molecular profiles of dissociated cells, are limited to receptor-ligand signaling and ignore spatial proximity in situ. We present node-centric expression modeling, a method based on graph neural networks that estimates the effects of niche composition on gene expression in an unbiased manner from spatial molecular profiling data. We recover signatures of molecular processes known to underlie cell communication.

Application of spatial transcriptome technologies to neurological diseases

Spatial transcriptome technology acquires gene expression profiles while retaining spatial location information, it displays the gene expression properties of cells in situ. Through the investigation of cell heterogeneity, microenvironment, function, and cellular interactions, spatial transcriptome technology can deeply explore the pathogenic mechanisms of cell-type-specific responses and spatial localization in neurological diseases. The present article overviews spatial transcriptome technologies based on microdissection, in situ hybridization, in situ sequencing, in situ capture, and live cell labeling. Each technology is described along with its methods, detection throughput, spatial resolution, benefits, and drawbacks. Furthermore, their applications in neurodegenerative disease, neuropsychiatric illness, stroke and epilepsy are outlined. This information can be used to understand disease mechanisms, pick therapeutic targets, and establish biomarkers.

Immuno-localization of definitive hematopoietic stem cells in the vascular niche of mouse fetal liver

Understanding the murine fetal liver (FL) hematopoietic microenvironment, which promotes HSC proliferation, warrants identifying innate relationships between stem cells and the niche. An inclusive study of these cell associations remains elusive. Here, we optimized a protocol to immunolabel HSCs alongside the FL vasculature, a promising niche component. We provide a comprehensive plan from tissue processing, immunohistochemistry, and confocal microscopy, to three-dimensional distance analyses between HSCs and vasculature. This technique can be adapted for achieving congruous outcomes for other cell types. For complete details on the use and execution of this protocol, please refer to Biswas et al. (2020).

Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing

Spatial transcriptomics extends single-cell RNA sequencing (scRNA-seq) by providing spatial context for cell type identification and analysis. Imaging-based spatial technologies such as multiplexed error-robust fluorescence in situ hybridization (MERFISH) can achieve single-cell resolution, directly mapping single-cell identities to spatial positions. MERFISH produces a different data type than scRNA-seq, and a technical comparison between the two modalities is necessary to ascertain how to best integrate them. We performed MERFISH on the mouse liver and kidney and compared the resulting bulk and single-cell RNA statistics with those from the Tabula Muris Senis cell atlas and from two Visium datasets. MERFISH quantitatively reproduced the bulk RNA-seq and scRNA-seq results with improvements in overall dropout rates and sensitivity. Finally, we found that MERFISH independently resolved distinct cell types and spatial structure in both the liver and kidney. Computational integration with the Tabula Muris Senis atlas did not enhance these results. We conclude that MERFISH provides a quantitatively comparable method for single-cell gene expression and can identify cell types without the need for computational integration with scRNA-seq atlases.

Techniques for Profiling the Cellular Immune Response and Their Implications for Interventional Oncology

In recent years there has been increased interest in using the immune contexture of the primary tumors to predict the patient's prognosis. The tumor microenvironment of patients with cancers consists of different types of lymphocytes, tumor-infiltrating leukocytes, dendritic cells, and others. Different technologies can be used for the evaluation of the tumor microenvironment, all of which require a tissue or cell sample. Image-guided tissue sampling is a cornerstone in the diagnosis, stratification, and longitudinal evaluation of therapeutic efficacy for cancer patients receiving immunotherapies. Therefore, interventional radiologists (IRs) play an essential role in the evaluation of patients treated with systemically administered immunotherapies. This review provides a detailed description of different technologies used for immune assessment and analysis of the data collected from the use of these technologies. The detailed approach provided herein is intended to provide the reader with the knowledge necessary to not only interpret studies containing such data but also design and apply these tools for clinical practice and future research studies.

The emerging landscape of spatial profiling technologies

Improved scale, multiplexing and resolution are establishing spatial nucleic acid and protein profiling methods as a major pillar for cellular atlas building of complex samples, from tissues to full organisms. Emerging methods yield omics measurements at resolutions covering the nano- to microscale, enabling the charting of cellular heterogeneity, complex tissue architectures and dynamic changes during development and disease. We present an overview of the developing landscape of in situ spatial genome, transcriptome and proteome technologies, exemplify their impact on cell biology and translational research, and discuss current challenges for their community-wide adoption. Among many transformative applications, we envision that spatial methods will map entire organs and enable next-generation pathology.

Single-cell RNA Sequencing Analysis Reveals New Immune Disorder Complexities in Hypersplenism

Hypersplenism (HS) is a concomitant symptom of liver or blood disease. Not only does the treatment of HS face challenges, but the transcriptome of individual cells is also unknown. Here, the transcriptional profiles of 43,037 cells from four HS tissues and one control tissue were generated by the single-cell RNA sequencing and nine major cell types, including T-cells, B-cells, NK cells, hematopoietic stem cells, neutrophil cells, mast cells, endothelial cells, erythrocytes, and dendritic cells were identified. Strikingly, the main features were the lack of CCL5+ B-cells in HS and the presence of SESN1+ B cells in HS with hepatocellular carcinoma (HS-HCC). In cell-cell interaction analysis, CD74-COPA and CD94-HLA-E in HS were found to be up-regulated. We further explored HS-specifically enriched genes (such as FKBP5, ADAR, and RPS4Y1) and found that FKBP5 was highly expressed in HCC-HS, leading to immunosuppression. Taken together, this research provides new insights into the genetic characteristics of HS via comprehensive single-cell transcriptome analysis.

Home Away From Home: Bioengineering Advancements to Mimic the Developmental and Adult Stem Cell Niche

The inherent self-organizing capacity of pluripotent and adult stem cell populations has advanced our fundamental understanding of processes that drive human development, homeostasis, regeneration, and disease progression. Translating these principles into in vitro model systems has been achieved with the advent of organoid technology, driving innovation to harness patient-specific, cell-laden regenerative constructs that can be engineered to augment or replace diseased tissue. While developmental organization and regenerative adult stem cell niches are tightly regulated in vivo, in vitro analogs lack defined architecture and presentation of physicochemical cues, leading to the unhindered arrangement of mini-tissues that lack complete physiological mimicry. This review aims to highlight the recent integrative engineering approaches that elicit spatio-temporal control of the extracellular niche to direct the structural and functional maturation of pluripotent and adult stem cell derivatives. While the advances presented here leverage multi-pronged strategies ranging from synthetic biology to microfabrication technologies, the methods converge on recreating the biochemical and biophysical milieu of the native tissue to be modeled or regenerated.

Single-cell RNA sequencing to track novel perspectives in HSC heterogeneity

As the importance of cell heterogeneity has begun to be emphasized, single-cell sequencing approaches are rapidly adopted to study cell heterogeneity and cellular evolutionary relationships of various cells, including stem cell populations. The hematopoietic stem and progenitor cell (HSPC) compartment contains HSC hematopoietic stem cells (HSCs) and distinct hematopoietic cells with different abilities to self-renew. These cells perform their own functions to maintain different hematopoietic lineages. Undeniably, single-cell sequencing approaches, including single-cell RNA sequencing (scRNA-seq) technologies, empower more opportunities to study the heterogeneity of normal and pathological HSCs. In this review, we discuss how these scRNA-seq technologies contribute to tracing origin and lineage commitment of HSCs, profiling the bone marrow microenvironment and providing high-resolution dissection of malignant hematopoiesis, leading to exciting new findings in HSC biology.

Articulating the "stem cell niche" paradigm through the lens of non-model aquatic invertebrates

Stem cells (SCs) in vertebrates typically reside in "stem cell niches" (SCNs), morphologically restricted tissue microenvironments that are important for SC survival and proliferation. SCNs are broadly defined by properties including physical location, but in contrast to vertebrates and other "model" organisms, aquatic invertebrate SCs do not have clearly documented niche outlines or properties. Life strategies such as regeneration or asexual reproduction may have conditioned the niche architectural variability in aquatic or marine animal groups. By both establishing the invertebrates SCNs as independent types, yet allowing inclusiveness among them, the comparative analysis will allow the future functional characterization of SCNs.

Single-Cell Approaches to Deconvolute the Development of HSCs

Hematopoietic stem cells (HSCs) play a core role in blood development. The ability to efficiently produce HSCs from various pluripotent stem cell sources is the Holy Grail in the hematology field. However, in vitro or in vivo HSC production remains low, which may be attributable to the lack of understanding of hematopoiesis. Here, we review the recent progress in this area and introduce advanced technologies, such as single-cell RNA-seq, spatial transcriptomics, and molecular barcoding, which may help to acquire missing information about HSC generation. We finally discuss unresolved questions, the answers to which may be conducive to HSC production, providing a promising path toward HSC-based immunotherapies.