Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue

Advances in spatial transcriptomics and its applications in cancer research

Malignant tumors have increasing morbidity and high mortality, and their occurrence and development is a complicate process. The development of sequencing technologies enabled us to gain a better understanding of the underlying genetic and molecular mechanisms in tumors. In recent years, the spatial transcriptomics sequencing technologies have been developed rapidly and allow the quantification and illustration of gene expression in the spatial context of tissues. Compared with the traditional transcriptomics technologies, spatial transcriptomics technologies not only detect gene expression levels in cells, but also inform the spatial location of genes within tissues, cell composition of biological tissues, and interaction between cells. Here we summarize the development of spatial transcriptomics technologies, spatial transcriptomics tools and its application in cancer research. We also discuss the limitations and challenges of current spatial transcriptomics approaches, as well as future development and prospects.

A practical guide to spatial transcriptomics

Spatial transcriptomics is revolutionizing modern biology, offering researchers an unprecedented ability to unravel intricate gene expression patterns within tissues. From pioneering techniques to newly commercialized platforms, the field of spatial transcriptomics has evolved rapidly, ushering in a new era of understanding across various disciplines, from developmental biology to disease research. This dynamic expansion is reflected in the rapidly growing number of technologies and data analysis techniques developed and introduced. However, the expanding landscape presents a considerable challenge for researchers, especially newcomers to the field, as staying informed about these advancements becomes increasingly complex. To address this challenge, we have prepared an updated review with a particular focus on technologies that have reached commercialization and are, therefore, accessible to a broad spectrum of potential new users. In this review, we present the fundamental principles of spatial transcriptomic methods, discuss the challenges in data analysis, provide insights into experimental considerations, offer information about available resources for spatial transcriptomics, and conclude with a guide for method selection and a forward-looking perspective. Our aim is to serve as a guiding resource for both experienced users and newcomers navigating the complex realm of spatial transcriptomics in this era of rapid development. We intend to equip researchers with the necessary knowledge to make informed decisions and contribute to the cutting-edge research that spatial transcriptomics offers.

Next-Generation Sequencing-Based Spatial Transcriptomics: A Perspective from Barcoding Chemistry

Gene expression profiling of tissue cells with spatial context is in high demand to reveal cell types, locations, and intercellular or molecular interactions for physiological and pathological studies. With rapid advances in barcoding chemistry and sequencing chemistry, spatially resolved transcriptome (SRT) techniques have emerged to quantify spatial gene expression in tissue samples by correlating transcripts with their spatial locations using diverse strategies. These techniques provide both physical tissue structure and molecular characteristics and are poised to revolutionize many fields, such as developmental biology, neuroscience, oncology, and histopathology. In this context, this Perspective focuses on next-generation sequencing-based SRT methods, particularly highlighting spatial barcoding chemistry. It delves into optically manipulated spatial indexing methods and DNA array-barcoded spatial indexing methods by exploring current advances, challenges, and future development directions in this nascent field.

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Spatial genomics: mapping human steatotic liver disease

Metabolic dysfunction-associated steatotic liver disease (MASLD, formerly known as non-alcoholic fatty liver disease) is a leading cause of chronic liver disease worldwide. MASLD can progress to metabolic dysfunction-associated steatohepatitis (MASH, formerly known as non-alcoholic steatohepatitis) with subsequent liver cirrhosis and hepatocellular carcinoma formation. The advent of current technologies such as single-cell and single-nuclei RNA sequencing have transformed our understanding of the liver in homeostasis and disease. The next frontier is contextualizing this single-cell information in its native spatial orientation. This understanding will markedly accelerate discovery science in hepatology, resulting in a further step-change in our knowledge of liver biology and pathobiology. In this Review, we discuss up-to-date knowledge of MASLD development and progression and how the burgeoning field of spatial genomics is driving exciting new developments in our understanding of human liver disease pathogenesis and therapeutic target identification.

Leveraging cell-cell similarity for high-performance spatial and temporal cellular mappings from gene expression data

Single-cell trajectory mapping and spatial reconstruction are two important developments in life science and provide a unique means to decode heterogeneous tissue formation, cellular dynamics, and tissue developmental processes. The success of these techniques depends critically on the performance of analytical tools used for high-dimensional (HD) gene expression data processing. Existing methods discern the patterns of the data without explicitly considering the underlying biological characteristics of the system, often leading to suboptimal solutions. Here, we present a cell-cell similarity-driven framework of genomic data analysis for high-fidelity spatial and temporal cellular mappings. The approach exploits the similarity features of the cells to discover discriminative patterns of the data. We show that for a wide variety of datasets, the proposed approach drastically improves the accuracies of spatial and temporal mapping analyses compared with state-of-the-art techniques.

Spatial transcriptomics in development and disease

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.

Spatial transcriptomics: recent developments and insights in respiratory research

The respiratory system's complex cellular heterogeneity presents unique challenges to researchers in this field. Although bulk RNA sequencing and single-cell RNA sequencing (scRNA-seq) have provided insights into cell types and heterogeneity in the respiratory system, the relevant specific spatial localization and cellular interactions have not been clearly elucidated. Spatial transcriptomics (ST) has filled this gap and has been widely used in respiratory studies. This review focuses on the latest iterative technology of ST in recent years, summarizing how ST can be applied to the physiological and pathological processes of the respiratory system, with emphasis on the lungs. Finally, the current challenges and potential development directions are proposed, including high-throughput full-length transcriptome, integration of multi-omics, temporal and spatial omics, bioinformatics analysis, etc. These viewpoints are expected to advance the study of systematic mechanisms, including respiratory studies.

Genome annotation: From human genetics to biodiversity genomics

Within the next decade, the genomes of 1.8 million eukaryotic species will be sequenced. Identifying genes in these sequences is essential to understand the biology of the species. This is challenging due to the transcriptional complexity of eukaryotic genomes, which encode hundreds of thousands of transcripts of multiple types. Among these, a small set of protein-coding mRNAs play a disproportionately large role in defining phenotypes. Due to their sequence conservation, orthology can be established, making it possible to define the universal catalog of eukaryotic protein-coding genes. This catalog should substantially contribute to uncovering the genomic events underlying the emergence of eukaryotic phenotypes. This piece briefly reviews the basics of protein-coding gene prediction, discusses challenges in finalizing annotation of the human genome, and proposes strategies for producing annotations across the eukaryotic Tree of Life. This lays the groundwork for obtaining the catalog of all genes-the Earth's code of life.

The dawn of spatial omics

Spatial omics has been widely heralded as the new frontier in life sciences. This term encompasses a wide range of techniques that promise to transform many areas of biology and eventually revolutionize pathology by measuring physical tissue structure and molecular characteristics at the same time. Although the field came of age in the past 5 years, it still suffers from some growing pains: barriers to entry, robustness, unclear best practices for experimental design and analysis, and lack of standardization. In this Review, we present a systematic catalog of the different families of spatial omics technologies; highlight their principles, power, and limitations; and give some perspective and suggestions on the biggest challenges that lay ahead in this incredibly powerful-but still hard to navigate-landscape.

Promise of spatially resolved omics for tumor research

Tumors are spatially heterogeneous tissues that comprise numerous cell types with intricate structures. By interacting with the microenvironment, tumor cells undergo dynamic changes in gene expression and metabolism, resulting in spatiotemporal variations in their capacity for proliferation and metastasis. In recent years, the rapid development of histological techniques has enabled efficient and high-throughput biomolecule analysis. By preserving location information while obtaining a large number of gene and molecular data, spatially resolved metabolomics (SRM) and spatially resolved transcriptomics (SRT) approaches can offer new ideas and reliable tools for the in-depth study of tumors. This review provides a comprehensive introduction and summary of the fundamental principles and research methods used for SRM and SRT techniques, as well as a review of their applications in cancer-related fields.

Untangling Alzheimer's disease with spatial multi-omics: a brief review

Alzheimer's disease (AD) is the most common form of neurological dementia, specified by extracellular β-amyloid plaque deposition, neurofibrillary tangles, and cognitive impairment. AD-associated pathologies like cerebral amyloid angiopathy (CAA) are also affiliated with cognitive impairment and have overlapping molecular drivers, including amyloid buildup. Discerning the complexity of these neurological disorders remains a significant challenge, and the spatiomolecular relationships between pathogenic features of AD and AD-associated pathologies remain poorly understood. This review highlights recent developments in spatial omics, including profiling and molecular imaging methods, and how they are applied to AD. These emerging technologies aim to characterize the relationship between how specific cell types and tissue features are organized in combination with mapping molecular distributions to provide a systems biology view of the tissue microenvironment around these neuropathologies. As spatial omics methods achieve greater resolution and improved molecular coverage, they are enabling deeper characterization of the molecular drivers of AD, leading to new possibilities for the prediction, diagnosis, and mitigation of this debilitating disease.

New discoveries in the field of metabolism by applying single-cell and spatial omics

Single-cell multi-Omics (SCM-Omics) and spatial multi-Omics (SM-Omics) technologies provide state-of-the-art methods for exploring the composition and function of cell types in tissues/organs. Since its emergence in 2009, single-cell RNA sequencing (scRNA-seq) has yielded many groundbreaking new discoveries. The combination of this method with the emergence and development of SM-Omics techniques has been a pioneering strategy in neuroscience, developmental biology, and cancer research, especially for assessing tumor heterogeneity and T-cell infiltration. In recent years, the application of these methods in the study of metabolic diseases has also increased. The emerging SCM-Omics and SM-Omics approaches allow the molecular and spatial analysis of cells to explore regulatory states and determine cell fate, and thus provide promising tools for unraveling heterogeneous metabolic processes and making them amenable to intervention. Here, we review the evolution of SCM-Omics and SM-Omics technologies, and describe the progress in the application of SCM-Omics and SM-Omics in metabolism-related diseases, including obesity, diabetes, nonalcoholic fatty liver disease (NAFLD) and cardiovascular disease (CVD). We also conclude that the application of SCM-Omics and SM-Omics approaches can help resolve the molecular mechanisms underlying the pathogenesis of metabolic diseases in the body and facilitate therapeutic measures for metabolism-related diseases. This review concludes with an overview of the current status of this emerging field and the outlook for its future.

Advances in spatial transcriptomics and related data analysis strategies

Spatial transcriptomics technologies developed in recent years can provide various information including tissue heterogeneity, which is fundamental in biological and medical research, and have been making significant breakthroughs. Single-cell RNA sequencing (scRNA-seq) cannot provide spatial information, while spatial transcriptomics technologies allow gene expression information to be obtained from intact tissue sections in the original physiological context at a spatial resolution. Various biological insights can be generated into tissue architecture and further the elucidation of the interaction between cells and the microenvironment. Thus, we can gain a general understanding of histogenesis processes and disease pathogenesis, etc. Furthermore, in silico methods involving the widely distributed R and Python packages for data analysis play essential roles in deriving indispensable bioinformation and eliminating technological limitations. In this review, we summarize available technologies of spatial transcriptomics, probe into several applications, discuss the computational strategies and raise future perspectives, highlighting the developmental potential.

Photoselective sequencing: microscopically guided genomic measurements with subcellular resolution

In biological systems, spatial organization and function are interconnected. Here we present photoselective sequencing, a new method for genomic and epigenomic profiling within morphologically distinct regions. Starting with an intact biological specimen, photoselective sequencing uses targeted illumination to selectively unblock a photocaged fragment library, restricting the sequencing-based readout to microscopically identified spatial regions. We validate photoselective sequencing by measuring the chromatin accessibility profiles of fluorescently labeled cell types within the mouse brain and comparing with published data. Furthermore, by combining photoselective sequencing with a computational strategy for decomposing bulk accessibility profiles, we find that the oligodendrocyte-lineage-cell population is relatively enriched for oligodendrocyte-progenitor cells in the cortex versus the corpus callosum. Finally, we leverage photoselective sequencing at the subcellular scale to identify features of chromatin that are correlated with positioning at the nuclear periphery. These results collectively demonstrate that photoselective sequencing is a flexible and generalizable platform for exploring the interplay of spatial structures with genomic and epigenomic properties.

Femtosecond laser microdissection for isolation of regenerating C. elegans neurons for single-cell RNA sequencing

Our understanding of nerve regeneration can be enhanced by delineating its underlying molecular activities at single-neuron resolution in model organisms such as Caenorhabditis elegans. Existing cell isolation techniques cannot isolate neurons with specific regeneration phenotypes from C. elegans. We present femtosecond laser microdissection (fs-LM), a single-cell isolation method that dissects specific cells directly from living tissue by leveraging the micrometer-scale precision of fs-laser ablation. We show that fs-LM facilitates sensitive and specific gene expression profiling by single-cell RNA sequencing (scRNA-seq), while mitigating the stress-related transcriptional artifacts induced by tissue dissociation. scRNA-seq of fs-LM isolated regenerating neurons revealed transcriptional programs that are correlated with either successful or failed regeneration in wild-type and dlk-1 (0) animals, respectively. This method also allowed studying heterogeneity displayed by the same type of neuron and found gene modules with expression patterns correlated with axon regrowth rate. Our results establish fs-LM as a spatially resolved single-cell isolation method for phenotype-to-genotype mapping.

SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics

Spatially resolved transcriptomics (SRT)-specific computational methods are often developed, tested, validated, and evaluated in silico using simulated data. Unfortunately, existing simulated SRT data are often poorly documented, hard to reproduce, or unrealistic. Single-cell simulators are not directly applicable for SRT simulation as they cannot incorporate spatial information. We present SRTsim, an SRT-specific simulator for scalable, reproducible, and realistic SRT simulations. SRTsim not only maintains various expression characteristics of SRT data but also preserves spatial patterns. We illustrate the benefits of SRTsim in benchmarking methods for spatial clustering, spatial expression pattern detection, and cell-cell communication identification.

Subcellular omics: a new frontier pushing the limits of resolution, complexity and throughput

We argue that the study of single-cell subcellular organelle omics is needed to understand and regulate cell function. This requires and is being enabled by new technology development.

Advances and Challenges in Spatial Transcriptomics for Developmental Biology

Development from single cells to multicellular tissues and organs involves more than just the exact replication of cells, which is known as differentiation. The primary focus of research into the mechanism of differentiation has been differences in gene expression profiles between individual cells. However, it has predominantly been conducted at low throughput and bulk levels, challenging the efforts to understand molecular mechanisms of differentiation during the developmental process in animals and humans. During the last decades, rapid methodological advancements in genomics facilitated the ability to study developmental processes at a genome-wide level and finer resolution. Particularly, sequencing transcriptomes at single-cell resolution, enabled by single-cell RNA-sequencing (scRNA-seq), was a breath-taking innovation, allowing scientists to gain a better understanding of differentiation and cell lineage during the developmental process. However, single-cell isolation during scRNA-seq results in the loss of the spatial information of individual cells and consequently limits our understanding of the specific functions of the cells performed by different spatial regions of tissues or organs. This greatly encourages the emergence of the spatial transcriptomic discipline and tools. Here, we summarize the recent application of scRNA-seq and spatial transcriptomic tools for developmental biology. We also discuss the limitations of current spatial transcriptomic tools and approaches, as well as possible solutions and future prospects.

Perspectives on single-nucleus RNA sequencing in different cell types and tissues

Single-cell RNA sequencing has become a powerful and essential tool for delineating cellular diversity in normal tissues and alterations in disease states. For certain cell types and conditions, there are difficulties in isolating intact cells for transcriptome profiling due to their fragility, large size, tight interconnections, and other factors. Single-nucleus RNA sequencing (snRNA-seq) is an alternative or complementary approach for cells that are difficult to isolate. In this review, we will provide an overview of the experimental and analysis steps of snRNA-seq to understand the methods and characteristics of general and tissue-specific snRNA-seq data. Knowing the advantages and limitations of snRNA-seq will increase its use and improve the biological interpretation of the data generated using this technique.

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.

Correlated gene modules uncovered by high-precision single-cell transcriptomics

Correlations in gene expression are used to infer functional and regulatory relationships between genes. However, correlations are often calculated across different cell types or perturbations, causing genes with unrelated functions to be correlated. Here, we demonstrate that correlated modules can be better captured by measuring correlations of steady-state gene expression fluctuations in single cells. We report a high-precision single-cell RNA-seq method called MALBAC-DT to measure the correlation between any pair of genes in a homogenous cell population. Using this method, we were able to identify numerous cell-type specific and functionally enriched correlated gene modules. We confirmed through knockdown that a module enriched for p53 signaling predicted p53 regulatory targets more accurately than a consensus of ChIP-seq studies and that steady-state correlations were predictive of transcriptome-wide response patterns to perturbations. This approach provides a powerful way to advance our functional understanding of the genome.

Recent advances in high-throughput single-cell transcriptomics and spatial transcriptomics

Single-cell RNA sequencing (scRNA-seq) has been developed for characterizing the transcriptome of cells that are rare but of biological significance. With cell barcoding and microchip technologies, a suite of high-throughput scRNA-seq protocols enable transcriptome profiling in thousands of individual cells at single-cell resolution for classifying cell types, discovering novel cell populations, investigating cellular heterogeneity and elucidating lineage trajectories. Microchip technologies including microfluidics- and microwell-based platforms play a major role in high-throughput scRNA-seq. As the emerging technology, spatial transcriptomics integrates cellular transcriptomics with their spatial coordinates within tissues for spatially deciphering cellular composition, heterogeneity and cell-cell communications. Spatial transcriptomics has been increasingly recognized as one of the most powerful tools for discovering new biology and advancing precision medicine. Microfluidics as an enabling technology plays an increasingly important role in spatial transcriptomics. We review the technological spectrum and advances in high-throughput scRNA-seq and spatial transcriptomics, discuss their advantages and limitations, and pitch into new biology learned from these new tools.

Challenges and considerations for single-cell and spatially resolved transcriptomics sample collection during spaceflight

Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have experienced rapid development in recent years. The findings of spaceflight-based scRNA-seq and SRT investigations are likely to improve our understanding of life in space and our comprehension of gene expression in various cell systems and tissue dynamics. However, compared to their Earth-based counterparts, gene expression experiments conducted in spaceflight have not experienced the same pace of development. Out of the hundreds of spaceflight gene expression datasets available, only a few used scRNA-seq and SRT. In this perspective piece, we explore the growing importance of scRNA-seq and SRT in space biology and discuss the challenges and considerations relevant to robust experimental design to enable growth of these methods in the field.

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.

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.

Light-Seq uses light-directed DNA barcoding in fixed cells and tissues for multiplexed spatial indexing and subsequent next generation sequencing. This approach blends spatial and omics information to enable analysis of rare cell types in complex tissues.

The impact of single-cell genomics on the field of mycobacterial infection

Genome sequencing projects of humans and other organisms reinforced that the complexity of biological systems is largely attributed to the tight regulation of gene expression at the epigenome and RNA levels. As a consequence, plenty of technological developments arose to increase the sequencing resolution to the cell dimension creating the single-cell genomics research field. Single-cell RNA sequencing (scRNA-seq) is leading the advances in this topic and comprises a vast array of different methodologies. scRNA-seq and its variants are more and more used in life science and biomedical research since they provide unbiased transcriptomic sequencing of large populations of individual cells. These methods go beyond the previous “bulk” methodologies and sculpt the biological understanding of cellular heterogeneity and dynamic transcriptomic states of cellular populations in immunology, oncology, and developmental biology fields. Despite the large burden caused by mycobacterial infections, advances in this field obtained via single-cell genomics had been comparatively modest. Nonetheless, seminal research publications using single-cell transcriptomics to study host cells infected by mycobacteria have become recently available. Here, we review these works summarizing the most impactful findings and emphasizing the different and recent single-cell methodologies used, potential issues, and problems. In addition, we aim at providing insights into current research gaps and potential future developments related to the use of single-cell genomics to study mycobacterial infection.

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.

The role of single-cell genomics in human genetics

Single-cell sequencing is a powerful approach that can detect genetic alterations and their phenotypic consequences in the context of human development, with cellular resolution. Humans start out as single-cell zygotes and undergo fission and differentiation to develop into multicellular organisms. Before fertilisation and during development, the cellular genome acquires hundreds of mutations that propagate down the cell lineage. Whether germline or somatic in nature, some of these mutations may have significant genotypic impact and lead to diseased cellular phenotypes, either systemically or confined to a tissue. Single-cell sequencing enables the detection and monitoring of the genotype and the consequent molecular phenotypes at a cellular resolution. It offers powerful tools to compare the cellular lineage between 'normal' and 'diseased' conditions and to establish genotype-phenotype relationships. By preserving cellular heterogeneity, single-cell sequencing, unlike bulk-sequencing, allows the detection of even small, diseased subpopulations of cells within an otherwise normal tissue. Indeed, the characterisation of biopsies with cellular resolution can provide a mechanistic view of the disease. While single-cell approaches are currently used mainly in basic research, it can be expected that applications of these technologies in the clinic may aid the detection, diagnosis and eventually the treatment of rare genetic diseases as well as cancer. This review article provides an overview of the single-cell sequencing technologies in the context of human genetics, with an aim to empower clinicians to understand and interpret the single-cell sequencing data and analyses. We discuss the state-of-the-art experimental and analytical workflows and highlight current challenges/limitations. Notably, we focus on two prospective applications of the technology in human genetics, namely the annotation of the non-coding genome using single-cell functional genomics and the use of single-cell sequencing data for in silico variant prioritisation.

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.

Clinical and translational values of spatial transcriptomics

The combination of spatial transcriptomics (ST) and single cell RNA sequencing (scRNA-seq) acts as a pivotal component to bridge the pathological phenomes of human tissues with molecular alterations, defining in situ intercellular molecular communications and knowledge on spatiotemporal molecular medicine. The present article overviews the development of ST and aims to evaluate clinical and translational values for understanding molecular pathogenesis and uncovering disease-specific biomarkers. We compare the advantages and disadvantages of sequencing- and imaging-based technologies and highlight opportunities and challenges of ST. We also describe the bioinformatics tools necessary on dissecting spatial patterns of gene expression and cellular interactions and the potential applications of ST in human diseases for clinical practice as one of important issues in clinical and translational medicine, including neurology, embryo development, oncology, and inflammation. Thus, clear clinical objectives, designs, optimizations of sampling procedure and protocol, repeatability of ST, as well as simplifications of analysis and interpretation are the key to translate ST from bench to clinic.

Recent advances on single-cell sequencing technologies

Single-cell omics sequencing was first achieved for transcriptome in 2009, which was followed by fast development of technologies for profiling genome, DNA methylome, 3D genome architecture, chromatin accessibility, histone modifications and so on, in an individual cell. In this review we mainly focus on the recent progresses in four topics in single cell omics field- single-cell epigenome sequencing, single-cell genome sequencing for lineage tracing, spatially resolved single-cell transcriptomics, and third-generation sequencing platform-based single cell omics sequencing. We also discuss the potential applications and future directions of these single cell omics sequencing technologies for different biomedical systems, especially for the human stem cell field.

Single-cell sequencing and its applications in bladder cancer

Bladder cancer is the most common malignant tumour of the urinary system that is characterised by significant intra-tumoural heterogeneity. While large-scale sequencing projects have provided a preliminary understanding of tumour heterogeneity, these findings are based on the average signals obtained from the pooled populations of diverse cells. Recent advances in single-cell sequencing (SCS) technologies have been critical in this regard, opening up new ways of understanding the nuanced tumour biology by identifying distinct cellular subpopulations, dissecting the tumour microenvironment, and characterizing cellular genomic mutations. By integrating these novel insights, SCS technologies are expected to make powerful and meaningful changes to the current diagnosis and treatment of bladder cancer through the identification and usage of novel biomarkers as well as targeted therapeutics. SCS can discriminate complex heterogeneity in a large population of tumour cells and determine the key molecular properties that influence clinical outcomes. Here, we review the advances in single-cell technologies and discuss their applications in cancer research and clinical practice, with a specific focus on bladder cancer.

Live Cell Genomics: Cell-Specific Transcriptome Capture in Live Single Cells from Complex Tissues

Analysis of single-cell transcriptomes shows the single-cell heterogeneity between cells within a population which is vital to our understanding of normal function and disease development. To obtain single-cell transcriptome profiling, however, the poly-A RNA must be accurately isolated from the target cell. We developed a single-cell analysis procedure called transcriptome in vivo analysis (TIVA), which will allow accurate characterization of targeted cell-specific transcriptomes from live tissue. This is accomplished using a RNA capture molecule called TIVA tag that captures the transcriptome of selected cells in their natural microenvironment. An important aspect of the TIVA approach is that the tag is delivered into the cytoplasm of live cells using cell-penetrating peptides (CPPs). Once the TIVA tag is in the cellular cytoplasm, it binds to mRNA after photoactivation of the compound. Using CPPs in combination with photoactivation is the first noninvasive access method for accurately isolating single-cell mRNA from live single cells in tissues in their natural microenvironment.

Emerging landscape of cell-penetrating peptide-mediated nucleic acid delivery and their utility in imaging, gene-editing, and RNA-sequencing

The safety issues like immunogenicity and unacceptable cancer risk of viral vectors for DNA/mRNA vaccine delivery necessitate the development of non-viral vectors with no toxicity. Among the non-viral strategies, cell-penetrating peptides (CPPs) have been a topic of interest recently because of their ability to cross plasma membranes and facilitate nucleic acids delivery both in vivo and in vitro. In addition to the application in the field of gene vaccine and gene therapy, CPPs based nucleic acids delivery have been proved by its potential application like gene editing, RNA-sequencing, and imaging. Here, we focus on summarizing the recent applications and progress of CPPs-mediated nucleic acids delivery and discuss the current problems and solutions in this field.

Neuropsychiatric disorders: An immunological perspective

Neuropsychiatric diseases have traditionally been studied from brain, and mind-centric perspectives. However, mounting epidemiological and clinical evidence shows a strong correlation of neuropsychiatric manifestations with immune system activation, suggesting a likely mechanistic interaction between the immune and nervous systems in mediating neuropsychiatric disease. Indeed, immune mediators such as cytokines, antibodies, and complement proteins have been shown to affect various cellular members of the central nervous system in multitudinous ways, such as by modulating neuronal firing rates, inducing cellular apoptosis, or triggering synaptic pruning. These observations have in turn led to the exciting development of clinical therapies aiming to harness this neuro-immune interaction for the treatment of neuropsychiatric disease and symptoms. Besides the clinic, important theoretical fundamentals can be drawn from the immune system and applied to our understanding of the brain and neuropsychiatric disease. These new frameworks could lead to novel insights in the field and further potentiate the development of future therapies to treat neuropsychiatric disease.

Spatial mapping of cancer tissues by OMICS technologies

Spatial mapping of heterogeneity in gene expression in cancer tissues can improve our understanding of cancers and help in the rapid detection of cancers with high accuracy and reliability. Significant advancements have been made in recent years in OMICS technologies, which possess the strong potential to be applied in the spatial mapping of biopsy tissue samples and their molecular profiling to a single-cell level. The clinical application of OMICS technologies in spatial profiling of cancer tissues is also advancing. The current review presents recent advancements and prospects of applying OMICS technologies to the spatial mapping of various analytes in cancer tissues. We benchmark the current state of the art in the field to advance existing OMICS technologies for high throughput spatial profiling. The factors taken into consideration include spatial resolution, types of biomolecules, number of different biomolecules that can be detected from the same assay, labeled versus label-free approaches, and approximate time required for each assay. Further advancements are still needed for the widespread application of OMICs technologies in performing fast and high throughput spatial mapping of cancer tissues as well as their effective use in research and clinical applications.

Single-Neuron RNA Modification Analysis by Mass Spectrometry: Characterizing RNA Modification Patterns and Dynamics with Single-Cell Resolution

The entire collection of post-transcriptional modifications to RNA, known as the epitranscriptome, has been increasingly recognized as a critical regulatory layer in the cellular translation machinery. However, contemporary methods for the analysis of RNA modifications are limited to the detection of highly abundant modifications in bulk tissue samples, potentially obscuring unique epitranscriptomes of individual cells with population averages. We developed an approach, single-neuron RNA modification analysis by mass spectrometry (SNRMA-MS), that enables the detection and quantification of numerous post-transcriptionally modified nucleosides in single cells. When compared to a conventional RNA extraction approach that does not allow detection of RNA modifications in single cells, SNRMA-MS leverages an optimized sample preparation approach to detect up to 16 RNA modifications in individual neurons from the central nervous system of Aplysia californica. SNRMA-MS revealed that the RNA modification profiles of identified A. californica neurons with different physiological functions were mostly cell specific. However, functionally homologous neurons tended to demonstrate similar modification patterns. Stable isotope labeling with CD3-Met showed significant differences in RNA methylation rates that were dependent on the identity of the modification and the cell, with metacerebral cells (MCCs) displaying the fastest incorporation of CD3 groups into endogenous RNAs. Quantitative SNRMA-MS showed higher intracellular concentrations for 2'-O-methyladenosine and 2'-O-methylcytidine in homologous R2/LPl1 cell pairs than in MCCs. Overall, SNRMA-MS is the first analytical approach capable of simultaneously quantifying numerous RNA modifications in single neurons and revealing cell-specific modification profiles.

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.

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.

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.

Molecular tools for the characterization of seizure susceptibility in genetic rodent models of epilepsy

Epilepsy is a chronic neurological disorder characterized by abnormal neuronal activity that arises from imbalances between excitatory and inhibitory synapses, which are highly correlated to functional and structural changes in specific brain regions. The difference between the normal and the epileptic brain may harbor genetic alterations, gene expression changes, and/or protein alterations in the epileptogenic nucleus. It is becoming increasingly clear that such differences contribute to the development of distinct epilepsy phenotypes. The current major challenges in epilepsy research include understanding the disease progression and clarifying epilepsy classifications by searching for novel molecular biomarkers. Thus, the application of molecular techniques to carry out comprehensive studies at deoxyribonucleic acid, messenger ribonucleic acid, and protein levels is of utmost importance to elucidate molecular dysregulations in the epileptic brain. The present review focused on the great diversity of technical approaches available and new research methodology, which are already being used to study molecular alterations underlying epilepsy. We have grouped the different techniques according to each step in the flow of information from DNA to RNA to proteins, and illustrated with specific examples in animal models of epilepsy, some of which are our own. Separately and collectively, the genomic and proteomic techniques, each with its own strengths and limitations, provide valuable information on molecular mechanisms underlying seizure susceptibility and regulation of neuronal excitability. Determining the molecular differences between genetic rodent models of epilepsy and their wild-type counterparts might be a key in determining mechanisms of seizure susceptibility and epileptogenesis as well as the discovery and development of novel antiepileptic agents. This article is part of the Special Issue "NEWroscience 2018".

High-depth spatial transcriptome analysis by photo-isolation chemistry

In multicellular organisms, expression profiling in spatially defined regions is crucial to elucidate cell interactions and functions. Here, we establish a transcriptome profiling method coupled with photo-isolation chemistry (PIC) that allows the determination of expression profiles specifically from photo-irradiated regions of interest. PIC uses photo-caged oligodeoxynucleotides for in situ reverse transcription. PIC transcriptome analysis detects genes specifically expressed in small distinct areas of the mouse embryo. Photo-irradiation of single cells demonstrated that approximately 8,000 genes were detected with 7 × 10⁴ unique read counts. Furthermore, PIC transcriptome analysis is applicable to the subcellular and subnuclear microstructures (stress granules and nuclear speckles, respectively), where hundreds of genes can be detected as being specifically localised. The spatial density of the read counts is higher than 100 per square micrometre. Thus, PIC enables high-depth transcriptome profiles to be determined from limited regions up to subcellular and subnuclear resolutions.

Spatial analysis of RNAseq data is important. Here the authors report a method for transcriptome profiling combined with photo-isolation chemistry to allow determination of expression profiles specifically from photo-irradiated regions of interest which they use in mouse brains and embryonic tissues.

DNA Computing: NOT Logic Gates See the Light

DNA-based Boolean logic gates (for example, AND, OR, and NOT) can be assembled into complex computational circuits that generate an output signal in response to specific patterns of oligonucleotide inputs. However, the fundamental nature of NOT gates, which convert the absence of an input into an output, makes their implementation within DNA-based circuits difficult. Premature execution of a NOT gate before completion of its upstream computation introduces an irreversible error into the circuit. By utilizing photocaging groups, we developed a novel DNA gate design that prevents gate function until irradiation at a certain time point. Optical activation provides temporal control over circuit performance by preventing premature computation and is orthogonal to all other components of DNA computation devices. Using this approach, we designed NAND and NOR logic gates that respond to synthetic microRNA sequences. We further demonstrate the utility of the NOT gate within multilayer circuits in response to a specific pattern of four microRNAs.

Single-cell and spatial analyses of cancer cells: toward elucidating the molecular mechanisms of clonal evolution and drug resistance acquisition

Even within a single type of cancer, cells of various types exist and play interrelated roles. Each of the individual cells resides in a distinct microenvironment and behaves differently. Such heterogeneity is the most cumbersome nature of cancers, which is occasionally uncountable when effective prevention or total elimination of cancers is attempted. To understand the heterogeneous nature of each cell, the use of conventional methods for the analysis of “bulk” cells is insufficient. Although some methods are high-throughput and compressive regarding the genes being detected, the obtained data would be from the cell mass, and the average of a large number of the component cells would no longer be measured. Single-cell analysis, which has developed rapidly in recent years, is causing a drastic change. Genome, transcriptome, and epigenome analyses at single-cell resolution currently target cancer cells, cancer-associated fibroblasts, endothelial cells of vessels, and circulating and infiltrating immune cells. In fact, surprisingly diverse features of clonal evolution of cancer cells, during the development of cancer or acquisition of drug resistance, accompanied by corresponding gene expression changes in the circumstantial stromal cells, appeared in recent single-cell analyses. Based on the obtained novel insights, better optimal drug selection and new drug administration sequences were started. Even a remaining concern of the single cell analyses is being addressed. Until very recently, it was impossible to obtain positional information of cells in cancer via single-cell analysis because such information is lost during preparation of single-cell suspensions. A new method, collectively called spatial transcriptome (ST) analysis, has been developed and rapidly applied to various clinical specimens. In this review, we first outline the recent achievements of single-cell cancer analysis in analyzing the molecular basis underlying the acquisition of drug resistance, particularly focusing on the latest anti-epidermal growth factor receptor tyrosine kinase inhibitor, osimertinib. Further, we review the currently available ST analysis methods and introduce our recent attempts regarding the respective topics.

Cell-Penetrating Peptides and Transportan

In the most recent 25–30 years, multiple novel mechanisms and applications of cell-penetrating peptides (CPP) have been demonstrated, leading to novel drug delivery systems. In this review, I present a brief introduction to the CPP area with selected recent achievements. This is followed by a nostalgic journey into the research in my own laboratories, which lead to multiple CPPs, starting from transportan and paving a way to CPP-based therapeutic developments in the delivery of bio-functional materials, such as peptides, proteins, vaccines, oligonucleotides and small molecules, etc.

Spatial molecular profiling: platforms, applications and analysis tools

Molecular profiling technologies, such as genome sequencing and proteomics, have transformed biomedical research, but most such technologies require tissue dissociation, which leads to loss of tissue morphology and spatial information. Recent developments in spatial molecular profiling technologies have enabled the comprehensive molecular characterization of cells while keeping their spatial and morphological contexts intact. Molecular profiling data generate deep characterizations of the genetic, transcriptional and proteomic events of cells, while tissue images capture the spatial locations, organizations and interactions of the cells together with their morphology features. These data, together with cell and tissue imaging data, provide unprecedented opportunities to study tissue heterogeneity and cell spatial organization. This review aims to provide an overview of these recent developments in spatial molecular profiling technologies and the corresponding computational methods developed for analyzing such data.

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.