ZipSeq: barcoding for real-time mapping of single cell transcriptomes

Image-seq: spatially resolved single-cell sequencing guided by in situ and in vivo imaging

Tissue function depends on cellular organization. While the properties of individual cells are increasingly being deciphered using powerful single-cell sequencing technologies, understanding their spatial organization and temporal evolution remains a major challenge. Here, we present Image-seq, a technology that provides single-cell transcriptional data on cells that are isolated from specific spatial locations under image guidance, thus preserving the spatial information of the target cells. It is compatible with in situ and in vivo imaging and can document the temporal and dynamic history of the cells being analyzed. Cell samples are isolated from intact tissue and processed with state-of-the-art library preparation protocols. The technique therefore combines spatial information with highly sensitive RNA sequencing readouts from individual, intact cells. We have used both high-throughput, droplet-based sequencing as well as SMARTseq-v4 library preparation to demonstrate its application to bone marrow and leukemia biology. We discovered that DPP4 is a highly upregulated gene during early progression of acute myeloid leukemia and that it marks a more proliferative subpopulation that is confined to specific bone marrow microenvironments. Furthermore, the ability of Image-seq to isolate viable, intact cells should make it compatible with a range of downstream single-cell analysis tools including multi-omics protocols.

Spatial multi-omics analyses of the tumor immune microenvironment

In the past decade, single-cell technologies have revealed the heterogeneity of the tumor-immune microenvironment at the genomic, transcriptomic, and proteomic levels and have furthered our understanding of the mechanisms of tumor development. Single-cell technologies have also been used to identify potential biomarkers. However, spatial information about the tumor-immune microenvironment such as cell locations and cell-cell interactomes is lost in these approaches. Recently, spatial multi-omics technologies have been used to study transcriptomes, proteomes, and metabolomes of tumor-immune microenvironments in several types of cancer, and the data obtained from these methods has been combined with immunohistochemistry and multiparameter analysis to yield markers of cancer progression. Here, we review numerous cutting-edge spatial 'omics techniques, their application to study of the tumor-immune microenvironment, and remaining technical challenges.

Light-Seq: light-directed in situ barcoding of biomolecules in fixed cells and tissues for spatially indexed sequencing

We present Light-Seq, an approach for multiplexed spatial indexing of intact biological samples using light-directed DNA barcoding in fixed cells and tissues followed by ex situ sequencing. Light-Seq combines spatially targeted, rapid photocrosslinking of DNA barcodes onto complementary DNAs in situ with a one-step DNA stitching reaction to create pooled, spatially indexed sequencing libraries. This light-directed barcoding enables in situ selection of multiple cell populations in intact fixed tissue samples for full-transcriptome sequencing based on location, morphology or protein stains, without cellular dissociation. Applying Light-Seq to mouse retinal sections, we recovered thousands of differentially enriched transcripts from three cellular layers and discovered biomarkers for a very rare neuronal subtype, dopaminergic amacrine cells, from only four to eight individual cells per section. Light-Seq provides an accessible workflow to combine in situ imaging and protein staining with next generation sequencing of the same cells, leaving the sample intact for further analysis post-sequencing.

Spatial transcriptomics technology in cancer research

In recent years, spatial transcriptomics (ST) technologies have developed rapidly and have been widely used in constructing spatial tissue atlases and characterizing spatiotemporal heterogeneity of cancers. Currently, ST has been used to profile spatial heterogeneity in multiple cancer types. Besides, ST is a benefit for identifying and comprehensively understanding special spatial areas such as tumor interface and tertiary lymphoid structures (TLSs), which exhibit unique tumor microenvironments (TMEs). Therefore, ST has also shown great potential to improve pathological diagnosis and identify novel prognostic factors in cancer. This review presents recent advances and prospects of applications on cancer research based on ST technologies as well as the challenges.

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.

Advances in application of single-cell RNA sequencing in cardiovascular research

Single-cell RNA sequencing (scRNA-seq) provides high-resolution information on transcriptomic changes at the single-cell level, which is of great significance for distinguishing cell subtypes, identifying stem cell differentiation processes, and identifying targets for disease treatment. In recent years, emerging single-cell RNA sequencing technologies have been used to make breakthroughs regarding decoding developmental trajectories, phenotypic transitions, and cellular interactions in the cardiovascular system, providing new insights into cardiovascular disease. This paper reviews the technical processes of single-cell RNA sequencing and the latest progress based on single-cell RNA sequencing in the field of cardiovascular system research, compares single-cell RNA sequencing with other single-cell technologies, and summarizes the extended applications and advantages and disadvantages of single-cell RNA sequencing. Finally, the prospects for applying single-cell RNA sequencing in the field of cardiovascular research are discussed.

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.

Spatiotemporal co-dependency between macrophages and exhausted CD8+ T cells in cancer

T cell exhaustion is a major impediment to antitumor immunity. However, it remains elusive how other immune cells in the tumor microenvironment (TME) contribute to this dysfunctional state. Here, we show that the biology of tumor-associated macrophages (TAMs) and exhausted T cells (Tex) in the TME is extensively linked. We demonstrate that in vivo depletion of TAMs reduces exhaustion programs in tumor-infiltrating CD8+ T cells and reinvigorates their effector potential. Reciprocally, transcriptional and epigenetic profiling reveals that Tex express factors that actively recruit monocytes to the TME and shape their differentiation. Using lattice light sheet microscopy, we show that TAM and CD8+ T cells engage in unique, long-lasting, antigen-specific synaptic interactions that fail to activate T cells but prime them for exhaustion, which is then accelerated in hypoxic conditions. Spatially resolved sequencing supports a spatiotemporal self-enforcing positive feedback circuit that is aligned to protect rather than destroy a tumor.

Spatially resolved transcriptomics provide a new method for cancer research

A major feature of cancer is the heterogeneity, both intratumoral and intertumoral. Traditional single-cell techniques have given us a comprehensive understanding of the biological characteristics of individual tumor cells, but the lack of spatial context of the transcriptome has limited the study of cell-to-cell interaction patterns and hindered further exploration of tumor heterogeneity. In recent years, the advent of spatially resolved transcriptomics (SRT) technology has made possible the multidimensional analysis of the tumor microenvironment in the context of intact tissues. Different SRT methods are applicable to different working ranges due to different working principles. In this paper, we review the advantages and disadvantages of various current SRT methods and the overall idea of applying these techniques to oncology studies, hoping to help researchers find breakthroughs. Finally, we discussed the future direction of SRT technology, and deeper investigation into the complex mechanisms of tumor development from different perspectives through multi-omics fusion, paving the way for precisely targeted tumor therapy.

Visualizing Spatial and Stoichiometric Barriers to Bispecific T-cell Engager Efficacy

Bispecific T-cell engager (BiTE) molecules are biologic T cell-directing immunotherapies. Blinatumomab is approved for treatment of B-cell malignancies, but BiTE molecule development in solid tumors has been more challenging. Here, we employed intravital imaging to characterize exposure and pharmacodynamic response of an anti-muCD3/anti-huEGFRvIII mouse surrogate BiTE molecule in epidermal growth factor receptor variant III (EGFRvIII)-positive breast tumors implanted within immunocompetent mice. Our study revealed heterogeneous temporal and spatial dynamics of BiTE molecule extravasation into solid tumors, highlighting physical barriers to BiTE molecule function. We also discovered that high, homogeneous EGFRvIII expression on cancer cells was necessary for a BiTE molecule to efficiently clear tumors. Additionally, we found that resident tumor-infiltrating lymphocytes (TILs) were sufficient for optimal tumor killing only at high BiTE molecule dosage, whereas inclusion of peripheral T-cell recruitment was synergistic at moderate to low dosages. We report that deletion of stimulatory conventional type I DCs (cDC1) diminished BiTE molecule-induced T-cell activation and tumor clearance, suggesting that in situ antigen-presenting cell (APC) engagements modulate the extent of BiTE molecule efficacy. In summary, our work identified multiple requirements for optimal BiTE molecule efficacy in solid tumors, providing insights that could be harnessed for solid cancer immunotherapy development.

Hypoxia Is a Dominant Remodeler of the Effector T Cell Surface Proteome Relative to Activation and Regulatory T Cell Suppression

Immunosuppressive factors in the tumor microenvironment (TME) impair T cell function and limit the antitumor immune response. T cell surface receptors and surface proteins that influence interactions and function in the TME are proven targets for cancer immunotherapy. However, how the entire surface proteome remodels in primary human T cells in response to specific suppressive factors in the TME remains to be broadly and systematically characterized. Here, using a reductionist cell culture approach with primary human T cells and stable isotopic labeling with amino acids in cell culture-based quantitative cell surface capture glycoproteomics, we examined how two immunosuppressive TME factors, regulatory T cells (Tregs) and hypoxia, globally affect the activated CD8+ surface proteome (surfaceome). Surprisingly, coculturing primary CD8+ T cells with Tregs only modestly affected the CD8+ surfaceome but did partially reverse activation-induced surfaceomic changes. In contrast, hypoxia drastically altered the CD8+ surfaceome in a manner consistent with both metabolic reprogramming and induction of an immunosuppressed state. The CD4+ T cell surfaceome similarly responded to hypoxia, revealing a common hypoxia-induced surface receptor program. Our surfaceomics findings suggest that hypoxic environments create a challenge for T cell activation. These studies provide global insight into how Tregs and hypoxia remodel the T cell surfaceome and we believe represent a valuable resource to inform future therapeutic efforts to enhance T cell function.

Antigen-Specific T Cell Detection via Photocatalytic Proximity Cell Labeling (PhoXCELL)

Quantitative detection and characterization of antigen-specific T cells are crucial to our understanding of immune responses as well as the development of new immunotherapies. Herein, we report a spatiotemporally resolved method for the detection and quantification of cell-cell interactions via Photocatalytic proXimity CELl Labeling (PhoXCELL). The biocompatible photosensitizer dibromofluorescein (DBF) was leveraged and optimized as a nongenetic alternative of enzymatic approaches for efficient generation of singlet oxygen upon photoirradiation (520 nm) on the cell surface, which allowed the subsequent labeling of nearby oxidized proteins with primary aliphatic amine-based probes. We demonstrated that DBF-functionalized dendritic cells (DCs) could spatiotemporally label interacting T cells in immune synapses via rapid photoirradiation with quantitatively discriminated interaction strength, which revealed distinct gene signatures for T cells that strongly interact with antigen-pulsed DCs. Furthermore, we employed PhoXCELL to simultaneously detect tumor antigen-specific CD8⁺ as well as CD4⁺ T cells from tumor-infiltrating lymphocytes and draining lymph nodes in murine tumor models, enabling PhoXCELL as a powerful platform to identify antigen-specific T cells in T cell receptor (TCR)-relevant personal immunotherapy.

Museum of spatial transcriptomics

The function of many biological systems, such as embryos, liver lobules, intestinal villi, and tumors, depends on the spatial organization of their cells. In the past decade, high-throughput technologies have been developed to quantify gene expression in space, and computational methods have been developed that leverage spatial gene expression data to identify genes with spatial patterns and to delineate neighborhoods within tissues. To comprehensively document spatial gene expression technologies and data-analysis methods, we present a curated review of literature on spatial transcriptomics dating back to 1987, along with a thorough analysis of trends in the field, such as usage of experimental techniques, species, tissues studied, and computational approaches used. Our Review places current methods in a historical context, and we derive insights about the field that can guide current research strategies. A companion supplement offers a more detailed look at the technologies and methods analyzed: https://pachterlab.github.io/LP_2021/ .

Systems-based approaches to study immunometabolism

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.

ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction

MOTIVATION

Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells.

RESULTS

We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies.

AVAILABILITY AND IMPLEMENTATION

Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Discovering dominant tumor immune archetypes in a pan-cancer census

Cancers display significant heterogeneity with respect to tissue of origin, driver mutations, and other features of the surrounding tissue. It is likely that individual tumors engage common patterns of the immune system-here "archetypes"-creating prototypical non-destructive tumor immune microenvironments (TMEs) and modulating tumor-targeting. To discover the dominant immune system archetypes, the University of California, San Francisco (UCSF) Immunoprofiler Initiative (IPI) processed 364 individual tumors across 12 cancer types using standardized protocols. Computational clustering of flow cytometry and transcriptomic data obtained from cell sub-compartments uncovered dominant patterns of immune composition across cancers. These archetypes were profound insofar as they also differentiated tumors based upon unique immune and tumor gene-expression patterns. They also partitioned well-established classifications of tumor biology. The IPI resource provides a template for understanding cancer immunity as a collection of dominant patterns of immune organization and provides a rational path forward to learn how to modulate these to improve therapy.

Deciphering the Immune-Tumor Interplay During Early-Stage Lung Cancer Development via Single-Cell Technology

Lung cancer is the leading cause of cancer-related death worldwide. Cancer immunotherapy has shown great success in treating advanced-stage lung cancer but has yet been used to treat early-stage lung cancer, mostly due to lack of understanding of the tumor immune microenvironment in early-stage lung cancer. The immune system could both constrain and promote tumorigenesis in a process termed immune editing that can be divided into three phases, namely, elimination, equilibrium, and escape. Current understanding of the immune response toward tumor is mainly on the "escape" phase when the tumor is clinically detectable. The detailed mechanism by which tumor progenitor lesions was modulated by the immune system during early stage of lung cancer development remains elusive. The advent of single-cell sequencing technology enables tumor immunologists to address those fundamental questions. In this perspective, we will summarize our current understanding and big gaps about the immune response during early lung tumorigenesis. We will then present the state of the art of single-cell technology and then envision how single-cell technology could be used to address those questions. Advances in the understanding of the immune response and its dynamics during malignant transformation of pre-malignant lesion will shed light on how malignant cells interact with the immune system and evolve under immune selection. Such knowledge could then contribute to the development of precision and early intervention strategies toward lung malignancy.

Optical Cell Tagging for Spatially Resolved Single-Cell RNA Sequencing

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for profiling gene expression of distinct cell populations at the single-cell level. However, the information of the positions of cells within the multicellular samples is missing in scRNA-seq datasets. To overcome this limitation, we herein develop OpTAG (optical cell tagging) as a new chemical platform for attaching functional tags onto cell surfaces in a spatially resolved manner. With OpTAG, we establish OpTAG-seq, which enables spatially resolved scRNA-seq. We apply OpTAG-seq to investigate the spatially defined transcriptional program in migrating cancer cells and identified a list of genes that are potential regulators for cancer cell migration and invasion. OpTAG-seq provides a convenient method for mapping cellular heterogeneity with spatial information within multicellular biological systems.

From imaging a single cell to implementing precision medicine: an exciting new era

In the age of high-throughput, single-cell biology, single-cell imaging has evolved not only in terms of technological advancements but also in its translational applications. The synchronous advancements of imaging and computational biology have produced opportunities of merging the two, providing the scientific community with tools towards observing, understanding, and predicting cellular and tissue phenotypes and behaviors. Furthermore, multiplexed single-cell imaging and machine learning algorithms now enable patient stratification and predictive diagnostics of clinical specimens. Here, we provide an overall summary of the advances in single-cell imaging, with a focus on high-throughput microscopy phenomics and multiplexed proteomic spatial imaging platforms. We also review various computational tools that have been developed in recent years for image processing and downstream applications used in biomedical sciences. Finally, we discuss how harnessing systems biology approaches and data integration across disciplines can further strengthen the exciting applications and future implementation of single-cell imaging on precision medicine.

Spatial transcriptomics at subspot resolution with BayesSpace

Recent spatial gene expression technologies enable comprehensive measurement of transcriptomic profiles while retaining spatial context. However, existing analysis methods do not address the limited resolution of the technology or use the spatial information efficiently. Here, we introduce BayesSpace, a fully Bayesian statistical method that uses the information from spatial neighborhoods for resolution enhancement of spatial transcriptomic data and for clustering analysis. We benchmark BayesSpace against current methods for spatial and non-spatial clustering and show that it improves identification of distinct intra-tissue transcriptional profiles from samples of the brain, melanoma, invasive ductal carcinoma and ovarian adenocarcinoma. Using immunohistochemistry and an in silico dataset constructed from scRNA-seq data, we show that BayesSpace resolves tissue structure that is not detectable at the original resolution and identifies transcriptional heterogeneity inaccessible to histological analysis. Our results illustrate BayesSpace's utility in facilitating the discovery of biological insights from spatial transcriptomic datasets.

Mapping Gene Expression in the Spatial Dimension

The main function and biological processes of tissues are determined by the combination of gene expression and spatial organization of their cells. RNA sequencing technologies have primarily interrogated gene expression without preserving the native spatial context of cells. However, the emergence of various spatially-resolved transcriptome analysis methods now makes it possible to map the gene expression to specific coordinates within tissues, enabling transcriptional heterogeneity between different regions, and for the localization of specific transcripts and novel spatial markers to be revealed. Hence, spatially-resolved transcriptome analysis technologies have broad utility in research into human disease and developmental biology. Here, recent advances in spatially-resolved transcriptome analysis methods are summarized, including experimental technologies and computational methods. Strengths, challenges, and potential applications of those methods are highlighted, and perspectives in this field are provided.

Single-cell analysis of regions of interest (SCARI) using a photosensitive tag

The functional activity and differentiation potential of cells are determined by their interactions with surrounding cells. Approaches that allow unbiased characterization of cell states while at the same time providing spatial information are of major value to assess this environmental influence. However, most current techniques are hampered by a tradeoff between spatial resolution and cell profiling depth. Here, we develop a photocage-based technology that allows isolation and in-depth analysis of live cells from regions of interest in complex ex vivo systems, including primary human tissues. The use of a highly sensitive 4-nitrophenyl(benzofuran) cage coupled to a set of nanobodies allows high-resolution photo-uncaging of different cell types in areas of interest. Single-cell RNA-sequencing of spatially defined CD8+ T cells is used to exemplify the feasibility of identifying location-dependent cell states. The technology described here provides a valuable tool for the analysis of spatially defined cells in diverse biological systems, including clinical samples.

Archetypes of checkpoint-responsive immunity

Responsiveness to immune checkpoint blockade (ICB) therapy in cancer is currently predicted by disparate individual measures - with varying degrees of accuracy - including tumor mutation burden, tumor-infiltrating T cell densities, dendritic cell frequencies, and the expression of checkpoint ligands. We propose that many of these individual parameters are linked, forming two distinct 'reactive' immune archetypes - collections of cells and gene expression - in ICB-responsive patients. We hypothesize that these are 'seeds' of antitumor immunity and are supported by specific elements of the tumor microenvironment (TME) and by actions of the microbiome. Although removing 'immunosuppressive' factors in the TME is important, understanding and parsing reactive immunity is crucial for optimal prognosis and for engaging this biology with candidate therapies to increase tumor cure rates.

Advances in spatial transcriptomic data analysis

Spatial transcriptomics is a rapidly growing field that promises to comprehensively characterize tissue organization and architecture at the single-cell or subcellular resolution. Such information provides a solid foundation for mechanistic understanding of many biological processes in both health and disease that cannot be obtained by using traditional technologies. The development of computational methods plays important roles in extracting biological signals from raw data. Various approaches have been developed to overcome technology-specific limitations such as spatial resolution, gene coverage, sensitivity, and technical biases. Downstream analysis tools formulate spatial organization and cell-cell communications as quantifiable properties, and provide algorithms to derive such properties. Integrative pipelines further assemble multiple tools in one package, allowing biologists to conveniently analyze data from beginning to end. In this review, we summarize the state of the art of spatial transcriptomic data analysis methods and pipelines, and discuss how they operate on different technological platforms.

Integrating single-cell and spatial transcriptomics to elucidate intercellular tissue dynamics

Single-cell RNA sequencing (scRNA-seq) identifies cell subpopulations within tissue but does not capture their spatial distribution nor reveal local networks of intercellular communication acting in situ. A suite of recently developed techniques that localize RNA within tissue, including multiplexed in situ hybridization and in situ sequencing (here defined as high-plex RNA imaging) and spatial barcoding, can help address this issue. However, no method currently provides as complete a scope of the transcriptome as does scRNA-seq, underscoring the need for approaches to integrate single-cell and spatial data. Here, we review efforts to integrate scRNA-seq with spatial transcriptomics, including emerging integrative computational methods, and propose ways to effectively combine current methodologies.

Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence

Immunotherapies play critical roles in cancer treatment. However, given that only a few patients respond to immune checkpoint blockades and other immunotherapeutic strategies, more novel technologies are needed to decipher the complicated interplay between tumor cells and the components of the tumor immune microenvironment (TIME). Tumor immunomics refers to the integrated study of the TIME using immunogenomics, immunoproteomics, immune-bioinformatics, and other multi-omics data reflecting the immune states of tumors, which has relied on the rapid development of next-generation sequencing. High-throughput genomic and transcriptomic data may be utilized for calculating the abundance of immune cells and predicting tumor antigens, referring to immunogenomics. However, as bulk sequencing represents the average characteristics of a heterogeneous cell population, it fails to distinguish distinct cell subtypes. Single-cell-based technologies enable better dissection of the TIME through precise immune cell subpopulation and spatial architecture investigations. In addition, radiomics and digital pathology-based deep learning models largely contribute to research on cancer immunity. These artificial intelligence technologies have performed well in predicting response to immunotherapy, with profound significance in cancer therapy. In this review, we briefly summarize conventional and state-of-the-art technologies in the field of immunogenomics, single-cell and artificial intelligence, and present prospects for future research.

Live cell tagging tracking and isolation for spatial transcriptomics using photoactivatable cell dyes

A cell's phenotype and function are influenced by dynamic interactions with its microenvironment. To examine cellular spatiotemporal activity, we developed SPACECAT-Spatially PhotoActivatable Color Encoded Cell Address Tags-to annotate, track, and isolate cells while preserving viability. In SPACECAT, samples are stained with photocaged fluorescent molecules, and cells are labeled by uncaging those molecules with user-patterned near-UV light. SPACECAT offers single-cell precision and temporal stability across diverse cell and tissue types. Illustratively, we target crypt-like regions in patient-derived intestinal organoids to enrich for stem-like and actively mitotic cells, matching literature expectations. Moreover, we apply SPACECAT to ex vivo tissue sections from four healthy organs and an autochthonous lung tumor model. Lastly, we provide a computational framework to identify spatially-biased transcriptome patterns and enriched phenotypes. This minimally perturbative and broadly applicable method links cellular spatiotemporal and/or behavioral phenotypes with diverse downstream assays, enabling insights into the connections between tissue microenvironments and (dys)function.

Spatial omics and multiplexed imaging to explore cancer biology

Understanding intratumoral heterogeneity-the molecular variation among cells within a tumor-promises to address outstanding questions in cancer biology and improve the diagnosis and treatment of specific cancer subtypes. Single-cell analyses, especially RNA sequencing and other genomics modalities, have been transformative in revealing novel biomarkers and molecular regulators associated with tumor growth, metastasis and drug resistance. However, these approaches fail to provide a complete picture of tumor biology, as information on cellular location within the tumor microenvironment is lost. New technologies leveraging multiplexed fluorescence, DNA, RNA and isotope labeling enable the detection of tens to thousands of cancer subclones or molecular biomarkers within their native spatial context. The expeditious growth in these techniques, along with methods for multiomics data integration, promises to yield a more comprehensive understanding of cell-to-cell variation within and between individual tumors. Here we provide the current state and future perspectives on the spatial technologies expected to drive the next generation of research and diagnostic and therapeutic strategies for cancer.

Spatial architecture of the immune microenvironment orchestrates tumor immunity and therapeutic response

Tumors are not only aggregates of malignant cells but also well-organized complex ecosystems. The immunological components within tumors, termed the tumor immune microenvironment (TIME), have long been shown to be strongly related to tumor development, recurrence and metastasis. However, conventional studies that underestimate the potential value of the spatial architecture of the TIME are unable to completely elucidate its complexity. As innovative high-flux and high-dimensional technologies emerge, researchers can more feasibly and accurately detect and depict the spatial architecture of the TIME. These findings have improved our understanding of the complexity and role of the TIME in tumor biology. In this review, we first epitomized some representative emerging technologies in the study of the spatial architecture of the TIME and categorized the description methods used to characterize these structures. Then, we determined the functions of the spatial architecture of the TIME in tumor biology and the effects of the gradient of extracellular nonspecific chemicals (ENSCs) on the TIME. We also discussed the potential clinical value of our understanding of the spatial architectures of the TIME, as well as current limitations and future prospects in this novel field. This review will bring spatial architectures of the TIME, an emerging dimension of tumor ecosystem research, to the attention of more researchers and promote its application in tumor research and clinical practice.

Quantifying and visualising the nuances of cellular dynamics in vivo using intravital imaging

Intravital imaging is a powerful technology used to quantify and track dynamic changes in live cells and tissues within an intact environment. The ability to watch cell biology in real-time 'as it happens' has provided novel insight into tissue homeostasis, as well as disease initiation, progression and response to treatment. In this minireview, we highlight recent advances in the field of intravital microscopy, touching upon advances in awake versus anaesthesia-based approaches, as well as the integration of biosensors into intravital imaging. We also discuss current challenges that, in our opinion, need to be overcome to further advance the field of intravital imaging at the single-cell, subcellular and molecular resolution to reveal nuances of cell behaviour that can be targeted in complex disease settings.

Bridging scales: From cell biology to physiology using in situ single-cell technologies

Biological organization crosses multiple spatial scales: from molecular, cellular, to tissues and organs. The proliferation of molecular profiling technologies enables increasingly detailed cataloging of the components at each scale. However, the scarcity of spatial profiling has made it challenging to bridge across these scales. Emerging technologies based on highly multiplexed in situ profiling are paving the way to study the spatial organization of cells and tissues in greater detail. These new technologies provide the data needed to cross the scale from cell biology to physiology and identify the fundamental principles that govern tissue organization. Here, we provide an overview of these key technologies and discuss the current and future insights these powerful techniques enable.

Functionalization of Cellular Membranes with DNA Nanotechnology

Due to its versatility and programmability, DNA nanotechnology has greatly expanded the experimental toolbox for biomedical research. Recent advances allow reliable and efficient functionalization of cellular plasma membranes with a variety of synthetic DNA constructs, ranging from single strands to complex 3D DNA origami. The scope for applications, which probe biophysical parameters or equip cells with novel functions, is rapidly increasing. These applications extend from programmed cellular connectivity and tissue engineering to molecular force measurements, controlled receptor-ligand interactions, membrane-anchored biosensors, and artificial transmembrane structures. Here, we give guidance on different strategies to functionalize cellular membranes with DNA nanotechnology and summarize current trends employing membrane-anchored DNA as a tool in biophysics, cell biology, and synthetic biology.

Spatially resolved transcriptomics and its applications in cancer

Spatially resolved transcriptomics (SRT) offers the promise of understanding cells and their modes of dysfunction in the context of intact tissues. Technologies for SRT have advanced rapidly with a large number being published in recent years. Diverse methods for SRT produce data at widely varying depth, throughput, accessibility and cost. Many published SRT methods have been demonstrated only in their labs of origin, while others have matured to the point of commercialization and widespread availability. Here we review technologies for SRT, and their application in studies of tumor heterogeneity.

Single-Cell RNA Sequencing to Disentangle the Blood System

The blood system is often represented as a tree-like structure with stem cells that give rise to mature blood cell types through a series of demarcated steps. Although this representation has served as a model of hierarchical tissue organization for decades, single-cell technologies are shedding new light on the abundance of cell type intermediates and the molecular mechanisms that ensure balanced replenishment of differentiated cells. In this Brief Review, we exemplify new insights into blood cell differentiation generated by single-cell RNA sequencing, summarize considerations for the application of this technology, and highlight innovations that are leading the way to understand hematopoiesis at the resolution of single cells. Graphic Abstract: A graphic abstract is available for this article.

Measuring and Modeling Single-Cell Heterogeneity and Fate Decision in Mouse Embryos

Cellular heterogeneity is a property of any living system; however, its relationship with cellular fate decision remains an open question. Recent technological advances have enabled valuable insights, especially in complex systems such as the mouse embryo. In this review, we discuss recent studies that characterize cellular heterogeneity at different levels during mouse development, from the two-cell stage up to gastrulation. In addition to key experimental findings, we review mathematical modeling approaches that help researchers interpret these findings. Disentangling the role of heterogeneity in cell fate decision will likely rely on the refined integration of experiments, large-scale omics data, and mathematical modeling, complemented by the use of synthetic embryos and gastruloids as promising in vitro models.