RNA Imaging with Multiplexed Error-Robust Fluorescence In Situ Hybridization (MERFISH)

Single-cell and spatial omics: exploring hypothalamic heterogeneity

Elucidating the complex dynamic cellular organization in the hypothalamus is critical for understanding its role in coordinating fundamental body functions. Over the past decade, single-cell and spatial omics technologies have significantly evolved, overcoming initial technical challenges in capturing and analyzing individual cells. These high-throughput omics technologies now offer a remarkable opportunity to comprehend the complex spatiotemporal patterns of transcriptional diversity and cell-type characteristics across the entire hypothalamus. Current single-cell and single-nucleus RNA sequencing methods comprehensively quantify gene expression by exploring distinct phenotypes across various subregions of the hypothalamus. However, single-cell/single-nucleus RNA sequencing requires isolating the cell/nuclei from the tissue, potentially resulting in the loss of spatial information concerning neuronal networks. Spatial transcriptomics methods, by bypassing the cell dissociation, can elucidate the intricate spatial organization of neural networks through their imaging and sequencing technologies. In this review, we highlight the applicative value of single-cell and spatial transcriptomics in exploring the complex molecular-genetic diversity of hypothalamic cell types, driven by recent high-throughput achievements.

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs

Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.

The unveiled mosaic of intra-tumor heterogeneity in ovarian cancer through spatial transcriptomic technologies: A systematic review

Epithelial ovarian cancer is a significant global health issue among women. Diagnosis and treatment pose challenges due to difficulties in predicting patient responses to therapy, primarily stemming from gaps in understanding tumor chemoresistance mechanisms. Recent advancements in transcriptomic technologies like single-cell RNA sequencing and spatial transcriptomics have greatly improved our understanding of ovarian cancer intratumor heterogeneity and tumor microenvironment composition. Spatial transcriptomics, in particular, comprises a plethora of technologies that enable the detection of hundreds of transcriptomes and their spatial distribution within a histological section, facilitating the study of cell types, states, and interactions within the tumor and its microenvironment. Studies investigating the spatial distribution of gene expression in ovarian cancer masses have identified specific features that impact prognosis and therapy outcomes. Emerging evidence suggests that specific spatial patterns of tumor cells and their immune and non-immune microenvironment significantly influence therapy response, as well as the behavior and progression of primary tumors and metastatic sites. The importance of spatially contextualizing ovarian cancer transcriptomes is underscored by these findings, which will advance our understanding and therapeutic approaches for this complex disease.

Mapping spatial organization and genetic cell-state regulators to target immune evasion in ovarian cancer

The drivers of immune evasion are not entirely clear, limiting the success of cancer immunotherapies. Here we applied single-cell spatial and perturbational transcriptomics to delineate immune evasion in high-grade serous tubo-ovarian cancer. To this end, we first mapped the spatial organization of high-grade serous tubo-ovarian cancer by profiling more than 2.5 million cells in situ in 130 tumors from 94 patients. This revealed a malignant cell state that reflects tumor genetics and is predictive of T cell and natural killer cell infiltration levels and response to immune checkpoint blockade. We then performed Perturb-seq screens and identified genetic perturbations-including knockout of PTPN1 and ACTR8-that trigger this malignant cell state. Finally, we show that these perturbations, as well as a PTPN1/PTPN2 inhibitor, sensitize ovarian cancer cells to T cell and natural killer cell cytotoxicity, as predicted. This study thus identifies ways to study and target immune evasion by linking genetic variation, cell-state regulators and spatial biology.

The burgeoning spatial multi-omics in human gastrointestinal cancers

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Spatial mapping of mobile genetic elements and their bacterial hosts in complex microbiomes

The exchange of mobile genetic elements (MGEs) facilitates the spread of functional traits including antimicrobial resistance within bacterial communities. Tools to spatially map MGEs and identify their bacterial hosts in complex microbial communities are currently lacking, limiting our understanding of this process. Here we combined single-molecule DNA fluorescence in situ hybridization (FISH) with multiplexed ribosomal RNA-FISH to enable simultaneous visualization of both MGEs and bacterial taxa. We spatially mapped bacteriophage and antimicrobial resistance (AMR) plasmids and identified their host taxa in human oral biofilms. This revealed distinct clusters of AMR plasmids and prophage, coinciding with densely packed regions of host bacteria. Our data suggest spatial heterogeneity in bacterial taxa results in heterogeneous MGE distribution within the community, with MGE clusters resulting from horizontal gene transfer hotspots or expansion of MGE-carrying strains. Our approach can help advance the study of AMR and phage ecology in biofilms.

Imaging the Architecture of Granulomas Induced by Mycobacterium tuberculosis Infection with Single-molecule Fluorescence In Situ Hybridization

Granulomas are an important hallmark of Mycobacterium tuberculosis infection. They are organized and dynamic structures created when immune cells assemble around the sites of infection in the lungs that locally restrict M. tuberculosis growth and the host's inflammatory responses. The cellular architecture of granulomas is traditionally studied by immunofluorescence labeling of surface markers on the host cells. However, very few Abs are available for model animals used in tuberculosis research, such as nonhuman primates and rabbits, and secreted immunological markers such as cytokines cannot be imaged in situ using Abs. Furthermore, traditional phenotypic surface markers do not provide sufficient resolution for the detection of the many subtypes and differentiation states of immune cells. Using single-molecule fluorescence in situ hybridization (smFISH) and its derivatives, amplified smFISH and iterative smFISH, we developed a platform for imaging mRNAs encoding immune markers in rabbit and macaque tuberculosis granulomas. Multiplexed imaging for several mRNA and protein markers was followed by quantitative measurement of the expression of these markers in single cells. An analysis of the combinatorial expressions of these markers allowed us to classify the cells into several subtypes, and to chart their densities within granulomas. For one mRNA target, hypoxia-inducible factor-1α, we imaged its mRNA and protein in the same cells, demonstrating the specificity of the probes. This method paves the way for defining granular differentiation states and cell subtypes from transcriptomic data, identifying key mRNA markers for these cell subtypes, and then locating the cells in the spatial context of granulomas.

ST-SCSR: identifying spatial domains in spatial transcriptomics data via structure correlation and self-representation

Recent advances in spatial transcriptomics (ST) enable measurements of transcriptome within intact biological tissues by preserving spatial information, offering biologists unprecedented opportunities to comprehensively understand tissue micro-environment, where spatial domains are basic units of tissues. Although great efforts are devoted to this issue, they still have many shortcomings, such as ignoring local information and relations of spatial domains, requiring alternatives to solve these problems. Here, a novel algorithm for spatial domain identification in Spatial Transcriptomics data with Structure Correlation and Self-Representation (ST-SCSR), which integrates local information, global information, and similarity of spatial domains. Specifically, ST-SCSR utilzes matrix tri-factorization to simultaneously decompose expression profiles and spatial network of spots, where expressional and spatial features of spots are fused via the shared factor matrix that interpreted as similarity of spatial domains. Furthermore, ST-SCSR learns affinity graph of spots by manipulating expressional and spatial features, where local preservation and sparse constraints are employed, thereby enhancing the quality of graph. The experimental results demonstrate that ST-SCSR not only outperforms state-of-the-art algorithms in terms of accuracy, but also identifies many potential interesting patterns.

Role of long noncoding RNAs in diabetes-associated peripheral arterial disease

Diabetes mellitus (DM) is a metabolic disease that heightens the risks of many vascular complications, including peripheral arterial disease (PAD). Various types of cells, including but not limited to endothelial cells (ECs), vascular smooth muscle cells (VSMCs), and macrophages (MΦs), play crucial roles in the pathogenesis of DM-PAD. Long non-coding RNAs (lncRNAs) are epigenetic regulators that play important roles in cellular function, and their dysregulation in DM can contribute to PAD. This review focuses on the developing field of lncRNAs and their emerging roles in linking DM and PAD. We review the studies investigating the role of lncRNAs in crucial cellular processes contributing to DM-PAD, including those in ECs, VSMCs, and MΦ. By examining the intricate molecular landscape governed by lncRNAs in these relevant cell types, we hope to shed light on the roles of lncRNAs in EC dysfunction, inflammatory responses, and vascular remodeling contributing to DM-PAD. Additionally, we provide an overview of the research approach and methodologies, from identifying disease-relevant lncRNAs to characterizing their molecular and cellular functions in the context of DM-PAD. We also discuss the potential of leveraging lncRNAs in the diagnosis and therapeutics for DM-PAD. Collectively, this review provides a summary of lncRNA-regulated cell functions contributing to DM-PAD and highlights the translational potential of leveraging lncRNA biology to tackle this increasingly prevalent and complex disease.

Spatiotemporal transcriptomic profiling and modeling of mouse brain at single-cell resolution reveals cell proximity effects of aging and rejuvenation

Old age is associated with a decline in cognitive function and an increase in neurodegenerative disease risk1. Brain aging is complex and accompanied by many cellular changes2-20. However, the influence that aged cells have on neighboring cells and how this contributes to tissue decline is unknown. More generally, the tools to systematically address this question in aging tissues have not yet been developed. Here, we generate spatiotemporal data at single-cell resolution for the mouse brain across lifespan, and we develop the first machine learning models based on spatial transcriptomics ('spatial aging clocks') to reveal cell proximity effects during brain aging and rejuvenation. We collect a single-cell spatial transcriptomics brain atlas of 4.2 million cells from 20 distinct ages and across two rejuvenating interventions-exercise and partial reprogramming. We identify spatial and cell type-specific transcriptomic fingerprints of aging, rejuvenation, and disease, including for rare cell types. Using spatial aging clocks and deep learning models, we find that T cells, which infiltrate the brain with age, have a striking pro-aging proximity effect on neighboring cells. Surprisingly, neural stem cells have a strong pro-rejuvenating effect on neighboring cells. By developing computational tools to identify mediators of these proximity effects, we find that pro-aging T cells trigger a local inflammatory response likely via interferon-γ whereas pro-rejuvenating neural stem cells impact the metabolism of neighboring cells possibly via growth factors (e.g. vascular endothelial growth factor) and extracellular vesicles, and we experimentally validate some of these predictions. These results suggest that rare cells can have a drastic influence on their neighbors and could be targeted to counter tissue aging. We anticipate that these spatial aging clocks will not only allow scalable assessment of the efficacy of interventions for aging and disease but also represent a new tool for studying cell-cell interactions in many spatial contexts.

A point cloud segmentation framework for image-based spatial transcriptomics

Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https://github.com/fish-quant/ComSeg .

Technology to the rescue: how to uncover the role of transposable elements in preimplantation development

Transposable elements (TEs) are highly expressed in preimplantation development. Preimplantation development is the phase when the cells of the early embryo undergo the first cell fate choice and change from being totipotent to pluripotent. A range of studies have advanced our understanding of TEs in preimplantation, as well as their epigenetic regulation and functional roles. However, many questions remain about the implications of TE expression during early development. Challenges originate first due to the abundance of TEs in the genome, and second because of the limited cell numbers in preimplantation. Here we review the most recent technological advancements promising to shed light onto the role of TEs in preimplantation development. We explore novel avenues to identify genomic TE insertions and improve our understanding of the regulatory mechanisms and roles of TEs and their RNA and protein products during early development.

Chromosome segregation during spermatogenesis occurs through a unique center-kinetic mechanism in holocentric moth species

Precise regulation of chromosome dynamics in the germline is essential for reproductive success across species. Yet, the mechanisms underlying meiotic chromosomal events such as homolog pairing and chromosome segregation are not fully understood in many species. Here, we employ Oligopaint DNA FISH to investigate mechanisms of meiotic homolog pairing and chromosome segregation in the holocentric pantry moth, Plodia interpunctella, and compare our findings to new and previous studies in the silkworm moth, Bombyx mori, which diverged from P. interpunctella over 100 million years ago. We find that pairing in both Bombyx and Plodia spermatogenesis is initiated at gene-rich chromosome ends. Additionally, both species form rod shaped cruciform-like bivalents at metaphase I. However, unlike the telomere-oriented chromosome segregation mechanism observed in Bombyx, Plodia can orient bivalents in multiple different ways at metaphase I. Surprisingly, in both species we find that kinetochores consistently assemble at non-telomeric loci toward the center of chromosomes regardless of where chromosome centers are located in the bivalent. Additionally, sister kinetochores do not seem to be paired in these species. Instead, four distinct kinetochores are easily observed at metaphase I. Despite this, we find clear end-on microtubule attachments and not lateral microtubule attachments co-orienting these separated kinetochores. These findings challenge the classical view of segregation where paired, poleward-facing kinetochores are required for accurate homolog separation in meiosis I. Our studies here highlight the importance of exploring fundamental processes in non-model systems, as employing novel organisms can lead to the discovery of novel biology.

MNMST: topology of cell networks leverages identification of spatial domains from spatial transcriptomics data

Advances in spatial transcriptomics provide an unprecedented opportunity to reveal the structure and function of biology systems. However, current algorithms fail to address the heterogeneity and interpretability of spatial transcriptomics data. Here, we present a multi-layer network model for identifying spatial domains in spatial transcriptomics data with joint learning. We demonstrate that spatial domains can be precisely characterized and discriminated by the topological structure of cell networks, facilitating identification and interpretability of spatial domains, which outperforms state-of-the-art baselines. Furthermore, we prove that network model offers an effective and efficient strategy for integrative analysis of spatial transcriptomics data from various platforms.

Multiplex DNA fluorescence in situ hybridization to analyze maternal vs. paternal C. elegans chromosomes

Recent advances in microscopy have enabled studying chromosome organization at the single-molecule level, yet little is known about inherited chromosome organization. Here we adapt single-molecule chromosome tracing to distinguish two C. elegans strains (N2 and HI) and find that while their organization is similar, the N2 chromosome influences the folding parameters of the HI chromosome, in particular the step size, across generations. Furthermore, homologous chromosomes overlap frequently, but alignment between homologous regions is rare, suggesting that transvection is unlikely. We present a powerful tool to investigate chromosome architecture and to track the parent of origin.

Fluorescent In Situ Hybridization for the Detection of Intracellular Bacteria in Companion Animals

FISH techniques have been applied for the visualization and identification of intracellular bacteria in companion animal species. Most frequently, these techniques have focused on the identification of adhesive-invasive Escherichia coli in gastrointestinal disease, although various other organisms have been identified in inflammatory or neoplastic gastrointestinal disease. Previous studies have investigated a potential role of Helicobacter spp. in inflammatory gastrointestinal and hepatic conditions. Other studies evaluating the role of infectious organisms in hepatopathies have received some attention with mixed results. FISH techniques using both eubacterial and species-specific probes have been applied in inflammatory cardiovascular, urinary, and cutaneous diseases to screen for intracellular bacteria. This review summarizes the results of these studies.

Spatially coordinated heterochromatinization of long synaptic genes in fragile X syndrome

Short tandem repeat (STR) instability causes transcriptional silencing in several repeat expansion disorders. In fragile X syndrome (FXS), mutation-length expansion of a CGG STR represses FMR1 via local DNA methylation. Here, we find megabase-scale H3K9me3 domains on autosomes and encompassing FMR1 on the X chromosome in FXS patient-derived iPSCs, iPSC-derived neural progenitors, EBV-transformed lymphoblasts, and brain tissue with mutation-length CGG expansion. H3K9me3 domains connect via inter-chromosomal interactions and demarcate severe misfolding of TADs and loops. They harbor long synaptic genes replicating at the end of S phase, replication-stress-induced double-strand breaks, and STRs prone to stepwise somatic instability. CRISPR engineering of the mutation-length CGG to premutation length reverses H3K9me3 on the X chromosome and multiple autosomes, refolds TADs, and restores gene expression. H3K9me3 domains can also arise in normal-length iPSCs created with perturbations linked to genome instability, suggesting their relevance beyond FXS. Our results reveal Mb-scale heterochromatinization and trans interactions among loci susceptible to instability.

The Role of Protocadherin γ in Adult Sensory Neurons and Skin Reinnervation

The clustered protocadherins (cPcdhs) play a critical role in the patterning of several CNS axon and dendritic arbors, through regulation of homophilic self and neighboring interactions. While not explored, primary peripheral sensory afferents that innervate the epidermis may require similar constraints to convey spatial signals with appropriate fidelity. Here, we show that members of the γ-Pcdh (Pcdhγ) family are expressed in both adult sensory neuron axons and in neighboring keratinocytes that have close interactions during skin reinnervation. Adult mice of both sexes were studied. Pcdhγ knock-down either through small interfering RNA (siRNA) transduction or AAV-Cre recombinase transfection of adult mouse primary sensory neurons from floxed Pcdhγ mice was associated with a remarkable rise in neurite outgrowth and branching. Rises in outgrowth were abrogated by Rac1 inhibition. Moreover, AAV-Cre knock-down in Pcdhγ floxed neurons generated a rise in neurite self-intersections, and a robust rise in neighbor intersections or tiling, suggesting a role in sensory axon repulsion. Interestingly, preconditioned (3-d axotomy) neurons with enhanced growth had temporary declines in Pcdhγ and lessened outgrowth from Pcdhγ siRNA. In vivo, mice with local hindpaw skin Pcdhγ knock-down by siRNA had accelerated reinnervation by new epidermal axons with greater terminal branching and reduced intra-axonal spacing. Pcdhγ knock-down also had reciprocal impacts on keratinocyte density and nuclear size. Taken together, this work provides evidence for a role of Pcdhγ in attenuating outgrowth of sensory axons and their interactions, with implications in how new reinnervating axons following injury fare amid skin keratinocytes that also express Pcdhγ.SIGNIFICANCE STATEMENT The molecular mechanisms and potential constraints that govern skin reinnervation and patterning by sensory axons are largely unexplored. Here, we show that γ-protocadherins (Pcdhγ) may help to dictate interaction not only among axons but also between axons and keratinocytes as the former re-enter the skin during reinnervation. Pcdhγ neuronal knock-down enhances outgrowth in peripheral sensory neurons, involving the growth cone protein Rac1 whereas skin Pcdhγ knock-down generates rises in terminal epidermal axon growth and branching during re-innervation. Manipulation of sensory axon regrowth within the epidermis offers an opportunity to influence regenerative outcomes following nerve injury.

Gene transcription regulation by ER at the single cell and allele level

In this short review we discuss the current view of how the estrogen receptor (ER), a pivotal member of the nuclear receptor superfamily of transcription factors, regulates gene transcription at the single cell and allele level, focusing on in vitro cell line models. We discuss central topics and new trends in molecular biology including phenotypic heterogeneity, single cell sequencing, nuclear phase separated condensates, single cell imaging, and image analysis methods, with particular focus on the methodologies and results that have been reported in the last few years using microscopy-based techniques. These observations augment the results from biochemical assays that lead to a much more complex and dynamic view of how ER, and arguably most transcription factors, act to regulate gene transcription.

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism

MOTIVATION

There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration.

RESULTS

In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance.

AVAILABILITY AND IMPLEMENTATION

The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

Cochlear transcriptome analysis of an outbred mouse population (CFW)

Age-related hearing loss (ARHL) is the most common cause of hearing loss and one of the most prevalent conditions affecting the elderly worldwide. Despite evidence from our lab and others about its polygenic nature, little is known about the specific genes, cell types, and pathways involved in ARHL, impeding the development of therapeutic interventions. In this manuscript, we describe, for the first time, the complete cell-type specific transcriptome of the aging mouse cochlea using snRNA-seq in an outbred mouse model in relation to auditory threshold variation. Cochlear cell types were identified using unsupervised clustering and annotated via a three-tiered approach-first by linking to expression of known marker genes, then using the NSForest algorithm to select minimum cluster-specific marker genes and reduce dimensional feature space for statistical comparison of our clusters with existing publicly-available data sets on the gEAR website, and finally, by validating and refining the annotations using Multiplexed Error Robust Fluorescence In Situ Hybridization (MERFISH) and the cluster-specific marker genes as probes. We report on 60 unique cell-types expanding the number of defined cochlear cell types by more than two times. Importantly, we show significant specific cell type increases and decreases associated with loss of hearing acuity implicating specific subsets of hair cell subtypes, ganglion cell subtypes, and cell subtypes within the stria vascularis in this model of ARHL. These results provide a view into the cellular and molecular mechanisms responsible for age-related hearing loss and pathways for therapeutic targeting.

Spatial transcriptomics reveals the distinct organization of mouse prefrontal cortex and neuronal subtypes regulating chronic pain

The prefrontal cortex (PFC) is a complex brain region that regulates diverse functions ranging from cognition, emotion and executive action to even pain processing. To decode the cellular and circuit organization of such diverse functions, we employed spatially resolved single-cell transcriptome profiling of the adult mouse PFC. Results revealed that PFC has distinct cell-type composition and gene-expression patterns relative to neighboring cortical areas-with neuronal excitability-regulating genes differently expressed. These cellular and molecular features are further segregated within PFC subregions, alluding to the subregion-specificity of several PFC functions. PFC projects to major subcortical targets through combinations of neuronal subtypes, which emerge in a target-intrinsic fashion. Finally, based on these features, we identified distinct cell types and circuits in PFC underlying chronic pain, an escalating healthcare challenge with limited molecular understanding. Collectively, this comprehensive map will facilitate decoding of discrete molecular, cellular and circuit mechanisms underlying specific PFC functions in health and disease.

Spatial Transcriptomics of Aging Rat Ovaries Reveals Unexplored Cell Subpopulations with Reduced Antioxidative Defense

INTRODUCTION

Ovarian aging is characterized by a gradual decline in quantity and quality of oocytes and lower chance of fertility. Better understanding the genetic modulation during ovarian aging can further address available treatment options for aging-related ovarian diseases and fertility preservation.

METHODS

A novel technique spatial transcriptomics (ST) was used to investigate the spatial transcriptome features of rat ovaries. Transcriptomes from ST spots in the young and aged ovaries were clustered using differentially expressed genes. These data were analyzed to determine the spatial organization of age-induced heterogeneity and potential mechanisms underlying ovarian aging.

RESULTS

In this study, ST technology was applied to profile the comprehensive spatial imaging in young and aged rat ovary. Fifteen ovarian cell clusters with distinct gene-expression signatures were identified. The gene expression dynamics of granulosa cell clusters revealed three sub-types with sequential developmental stages. Aged ovary showed a significant decrease in the number of granulosa cells from the antral follicle. Besides, a remarkable rearrangement of interstitial gland cells was detected in aging ovary. Further analysis of aging-associated transcriptional changes revealed that the disturbance of oxidative pathway was a crucial factor in ovarian aging.

CONCLUSIONS

This study firstly described an aging-related spatial transcriptome changes in ovary and identified the potential targets for prevention of ovarian aging. These data may provide the basis for further investigations of the diagnosis and treatment of aging-related ovarian disorders.

Exploring the Role of Enhancer-Mediated Transcriptional Regulation in Precision Biology

The emergence of precision biology has been driven by the development of advanced technologies and techniques in high-resolution biological research systems. Enhancer-mediated transcriptional regulation, a complex network of gene expression and regulation in eukaryotes, has attracted significant attention as a promising avenue for investigating the underlying mechanisms of biological processes and diseases. To address biological problems with precision, large amounts of data, functional information, and research on the mechanisms of action of biological molecules is required to address biological problems with precision. Enhancers, including typical enhancers and super enhancers, play a crucial role in gene expression and regulation within this network. The identification and targeting of disease-associated enhancers hold the potential to advance precision medicine. In this review, we present the concepts, progress, importance, and challenges in precision biology, transcription regulation, and enhancers. Furthermore, we propose a model of transcriptional regulation for multi-enhancers and provide examples of their mechanisms in mammalian cells, thereby enhancing our understanding of how enhancers achieve precise regulation of gene expression in life processes. Precision biology holds promise in providing new tools and platforms for discovering insights into gene expression and disease occurrence, ultimately benefiting individuals and society as a whole.

Probabilistic establishment of speckle-associated inter-chromosomal interactions

Inter-chromosomal interactions play a crucial role in genome organization, yet the organizational principles remain elusive. Here, we introduce a novel computational method to systematically characterize inter-chromosomal interactions using in situ Hi-C results from various cell types. Our method successfully identifies two apparently hub-like inter-chromosomal contacts associated with nuclear speckles and nucleoli, respectively. Interestingly, we discover that nuclear speckle-associated inter-chromosomal interactions are highly cell-type invariant with a marked enrichment of cell-type common super-enhancers (CSEs). Validation using DNA Oligopaint fluorescence in situ hybridization (FISH) shows a strong but probabilistic interaction behavior between nuclear speckles and CSE-harboring genomic regions. Strikingly, we find that the likelihood of speckle-CSE associations can accurately predict two experimentally measured inter-chromosomal contacts from Hi-C and Oligopaint DNA FISH. Our probabilistic establishment model well describes the hub-like structure observed at the population level as a cumulative effect of summing individual stochastic chromatin-speckle interactions. Lastly, we observe that CSEs are highly co-occupied by MAZ binding and MAZ depletion leads to significant disorganization of speckle-associated inter-chromosomal contacts. Taken together, our results propose a simple organizational principle of inter-chromosomal interactions mediated by MAZ-occupied CSEs.

ADEPT: Autoencoder with differentially expressed genes and imputation for robust spatial transcriptomics clustering

Advancements in spatial transcriptomics (ST) have enabled an in-depth understanding of complex tissues by quantifying gene expression at spatially localized spots. Several notable clustering methods have been introduced to utilize both spatial and transcriptional information in the analysis of ST datasets. However, data quality across different ST sequencing techniques and types of datasets influence the performance of different methods and benchmarks. To harness spatial context and transcriptional profile in ST data, we developed a graph-based, multi-stage framework for robust clustering, called ADEPT. To control and stabilize data quality, ADEPT relies on a graph autoencoder backbone and performs an iterative clustering on imputed, differentially expressed genes-based matrices to minimize the variance of clustering results. ADEPT outperformed other popular methods on ST data generated by different platforms across analyses such as spatial domain identification, visualization, spatial trajectory inference, and data denoising.

Reference-based cell type matching of in situ image-based spatial transcriptomics data on primary visual cortex of mouse brain

With the advent of multiplex fluorescence in situ hybridization (FISH) and in situ RNA sequencing technologies, spatial transcriptomics analysis is advancing rapidly, providing spatial location and gene expression information about cells in tissue sections at single cell resolution. Cell type classification of these spatially-resolved cells can be inferred by matching the spatial transcriptomics data to reference atlases derived from single cell RNA-sequencing (scRNA-seq) in which cell types are defined by differences in their gene expression profiles. However, robust cell type matching of the spatially-resolved cells to reference scRNA-seq atlases is challenging due to the intrinsic differences in resolution between the spatial and scRNA-seq data. In this study, we systematically evaluated six computational algorithms for cell type matching across four image-based spatial transcriptomics experimental protocols (MERFISH, smFISH, BaristaSeq, and ExSeq) conducted on the same mouse primary visual cortex (VISp) brain region. We find that many cells are assigned as the same type by multiple cell type matching algorithms and are present in spatial patterns previously reported from scRNA-seq studies in VISp. Furthermore, by combining the results of individual matching strategies into consensus cell type assignments, we see even greater alignment with biological expectations. We present two ensemble meta-analysis strategies used in this study and share the consensus cell type matching results in the Cytosplore Viewer ( https://viewer.cytosplore.org ) for interactive visualization and data exploration. The consensus matching can also guide spatial data analysis using SSAM, allowing segmentation-free cell type assignment.

Spatial Mapping of Mobile Genetic Elements and their Cognate Hosts in Complex Microbiomes

The frequent exchange of mobile genetic elements (MGEs) between bacteria accelerates the spread of functional traits, including antimicrobial resistance, within the human microbiome. Yet, progress in understanding these intricate processes has been hindered by the lack of tools to map the spatial spread of MGEs in complex microbial communities, and to associate MGEs to their bacterial hosts. To overcome this challenge, we present an imaging approach that pairs single molecule DNA Fluorescence In Situ Hybridization (FISH) with multiplexed ribosomal RNA FISH, thereby enabling the simultaneous visualization of both MGEs and host bacterial taxa. We used this methodology to spatially map bacteriophage and antimicrobial resistance (AMR) plasmids in human oral biofilms, and we studied the heterogeneity in their spatial distributions and demonstrated the ability to identify their host taxa. Our data revealed distinct clusters of both AMR plasmids and prophage, coinciding with densely packed regions of host bacteria in the biofilm. These results suggest the existence of specialized niches that maintain MGEs within the community, possibly acting as local hotspots for horizontal gene transfer. The methods introduced here can help advance the study of MGE ecology and address pressing questions regarding antimicrobial resistance and phage therapy.

Imaging and Molecular Annotation of Xenographs and Tumours (IMAXT): High throughput data and analysis infrastructure

With the aim of producing a 3D representation of tumors, imaging and molecular annotation of xenografts and tumors (IMAXT) uses a large variety of modalities in order to acquire tumor samples and produce a map of every cell in the tumor and its host environment. With the large volume and variety of data produced in the project, we developed automatic data workflows and analysis pipelines. We introduce a research methodology where scientists connect to a cloud environment to perform analysis close to where data are located, instead of bringing data to their local computers. Here, we present the data and analysis infrastructure, discuss the unique computational challenges and describe the analysis chains developed and deployed to generate molecularly annotated tumor models. Registration is achieved by use of a novel technique involving spherical fiducial marks that are visible in all imaging modalities used within IMAXT. The automatic pipelines are highly optimized and allow to obtain processed datasets several times quicker than current solutions narrowing the gap between data acquisition and scientific exploitation.

Lung development and regeneration: newly defined cell types and progenitor status

The lung is the most critical organ of the respiratory system supporting gas exchange. Constant interaction with the external environment makes the lung vulnerable to injury. Thus, a deeper understanding of cellular and molecular processes underlying lung development programs and evaluation of progenitor status within the lung is an essential part of lung regenerative medicine. In this review, we aim to discuss the current understanding of lung development process and regenerative capability. We highlight the advances brought by multi-omics approaches, single-cell transcriptome, in particular, that can help us further dissect the cellular player and molecular signaling underlying those processes.

Deletion of histone demethylase Lsd1 (Kdm1a) during retinal development leads to defects in retinal function and structure

Purpose

The purpose of this study was to investigate the role of Lysine specific demethylase 1 (Lsd1) in murine retinal development. LSD1 is a histone demethylase that can demethylate mono- and di-methyl groups on H3K4 and H3K9. Using Chx10-Cre and Rho-iCre75 driver lines, we generated novel transgenic mouse lines to delete Lsd1 in most retinal progenitor cells or specifically in rod photoreceptors. We hypothesize that Lsd1 deletion will cause global morphological and functional defects due to its importance in neuronal development.

Methods

We tested the retinal function of young adult mice by electroretinogram (ERG) and assessed retinal morphology by in vivo imaging by fundus photography and SD-OCT. Afterward, eyes were enucleated, fixed, and sectioned for subsequent hematoxylin and eosin (H&E) or immunofluorescence staining. Other eyes were plastic fixed and sectioned for electron microscopy.

Results

In adult Chx10-Cre Lsd1fl/fl mice, we observed a marked reduction in a-, b-, and c-wave amplitudes in scotopic conditions compared to age-matched control mice. Photopic and flicker ERG waveforms were even more sharply reduced. Modest reductions in total retinal thickness and outer nuclear layer (ONL) thickness were observed in SD-OCT and H&E images. Lastly, electron microscopy revealed significantly shorter inner and outer segments and immunofluorescence showed modest reductions in specific cell type populations. We did not observe any obvious functional or morphological defects in the adult Rho-iCre75 Lsd1fl/fl animals.

Conclusion

Lsd1 is necessary for neuronal development in the retina. Adult Chx10-Cre Lsd1fl/fl mice show impaired retinal function and morphology. These effects were fully manifested in young adults (P30), suggesting that Lsd1 affects early retinal development in mice.

Scalable in situ single-cell profiling by electrophoretic capture of mRNA using EEL FISH

Methods to spatially profile the transcriptome are dominated by a trade-off between resolution and throughput. Here we develop a method named Enhanced ELectric Fluorescence in situ Hybridization (EEL FISH) that can rapidly process large tissue samples without compromising spatial resolution. By electrophoretically transferring RNA from a tissue section onto a capture surface, EEL speeds up data acquisition by reducing the amount of imaging needed, while ensuring that RNA molecules move straight down toward the surface, preserving single-cell resolution. We apply EEL on eight entire sagittal sections of the mouse brain and measure the expression patterns of up to 440 genes to reveal complex tissue organization. Moreover, EEL can be used to study challenging human samples by removing autofluorescent lipofuscin, enabling the spatial transcriptome of the human visual cortex to be visualized. We provide full hardware specifications, all protocols and complete software for instrument control, image processing, data analysis and visualization.

Spatially resolved transcriptomics: advances and applications

Spatial transcriptomics, which is capable of both measuring all gene activity in a tissue sample and mapping where this activity occurs, is vastly improving our understanding of biological processes and disease. The field has expanded rapidly in recent years, and the development of several new technologies has resulted in spatially resolved transcriptomics (SRT) becoming highly multiplexed, high-resolution, and high-throughput. Here, we summarize and compare the major methods of SRT, including imaging-based methods, sequencing-based methods, and in situ sequencing methods. We also highlight some typical applications of SRT in neuroscience, cancer biology, developmental biology, and hematology. Finally, we discuss future possibilities for improving spatially resolved transcriptomic methods and the expected applications of such methods, especially in the adult bone marrow, anticipating that new developments will unlock the full potential of spatially resolved multi-omics in both biological research and the clinic.

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.

Single-cell RNA sequencing for traumatic spinal cord injury

Traumatic spinal cord injury (tSCI) is a severe injury of the central nervous system (CNS) with complicated pathological microenvironment that results in hemorrhage, inflammation, and scar formation. The microenvironment of the injured spinal cord comprises heterogeneous neurons, glial cells, inflammatory cells, and stroma-related cells. Increasing evidence has indicated that the altered cellular and molecular microenvironment following tSCI is a key factor impeding functional recovery. Single-cell RNA sequencing (scRNA-seq) has provided deep insights into the dynamic cellular and molecular changes in the microenvironment by comprehensively characterizing the diversity of spinal cord cell types. Specifically, scRNA-seq enables the exploration of the molecular mechanisms underlying tSCI by elucidating intercellular communication in spinal cord samples between normal and injury conditions at a single-cell resolution. Here, we first described the pathological and physiological processes after tSCI and gave a brief introduction of the scRNA-seq technology. We then focused on the recent scRNA-seq researches in tSCI, which characterized diverse cell-type populations and specific cell-cell interactions in tSCI. In addition, we also highlighted some potential directions for the research of scRNA-seq in tSCI in the future.

An open source 16-channel fluidics system for automating sequential fluorescent in situ hybridization (FISH)-based imaging

Fluorescent in situ hybridization (FISH) can provide spatial information about DNA/RNA targets in fixed cells and tissues. However, the workflows of multiplexed FISH-based imaging that use sequential rounds of hybridization quickly become laborious as the number of rounds increases because of liquid handling demands. Here, we present an open-source and low-cost fluidics system that is purpose built for automating the workflows of sequential FISH-based imaging. Our system features a fluidics module with 16 addressable channels in which flow is positive pressure-driven and switched on/off by solenoid valves in order to transfer FISH reagents to the sample. Our system also includes a controller with a main printed circuit board that can control up to 120 solenoid valves and allows users to control the fluidics module via serial communication. We demonstrate the automatic and robust fluid exchange with this system by targeting the alpha satellite repeat in HeLa cell with 14 rounds of sequential hybridization and imaging. We anticipate that this simple and flexible system will be of utility to researchers performing multiplexed in situ assays in a range of experimental systems.

Technologies Enabling Single-Molecule Super-Resolution Imaging of mRNA

The transient nature of RNA has rendered it one of the more difficult biological targets for imaging. This difficulty stems both from the physical properties of RNA as well as the temporal constraints associated therewith. These concerns are further complicated by the difficulty in imaging endogenous RNA within a cell that has been transfected with a target sequence. These concerns, combined with traditional concerns associated with super-resolution light microscopy has made the imaging of this critical target difficult. Recent advances have provided researchers the tools to image endogenous RNA in live cells at both the cellular and single-molecule level. Here, we review techniques used for labeling and imaging RNA with special emphases on various labeling methods and a virtual 3D super-resolution imaging technique.

Efficient DNA fluorescence labeling via base excision trapping

Fluorescence labeling of DNAs is broadly useful, but methods for labeling are expensive and labor-intensive. Here we describe a general method for fluorescence labeling of oligonucleotides readily and cost-efficiently via base excision trapping (BETr), employing deaminated DNA bases to mark label positions, which are excised by base excision repair enzymes generating AP sites. Specially designed aminooxy-substituted rotor dyes trap the AP sites, yielding high emission intensities. BETr is orthogonal to DNA synthesis by polymerases, enabling multi-uracil incorporation into an amplicon and in situ BETr labeling without washing. BETr also enables labeling of dsDNA such as genomic DNA at a high labeling density in a single tube by use of nick translation. Use of two different deaminated bases facilitates two-color site-specific labeling. Use of a multi-labeled DNA construct as a bright fluorescence tag is demonstrated through the conjugation to an antibody for imaging proteins. Finally, double-strand selectivity of a repair enzyme is harnessed in sensitive reporting on the presence of a target DNA or RNA in a mixture with isothermal turnover and single nucleotide specificity. Overall, the results document a convenient and versatile method for general fluorescence labeling of DNAs.

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.

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes1-6, but it is not known whether these subtypes have correspondingly diverse patterns of activity in the living brain. Here we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, which are organized by a single factor: position along the main axis of transcriptomic variation. We combined in vivo two-photon calcium imaging of mouse V1 with a transcriptomic method to identify mRNA for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1-3 into a three-level hierarchy of 5 subclasses, 11 types and 35 subtypes using previously defined transcriptomic clusters3. Responses to visual stimuli differed significantly only between subclasses, with cells in the Sncg subclass uniformly suppressed, and cells in the other subclasses predominantly excited. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory subtypes that fired more in resting, oscillatory brain states had a smaller fraction of their axonal projections in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro7, and expressed more inhibitory cholinergic receptors. Subtypes that fired more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 subtypes shape state-dependent cortical processing.

Single-cell multi-omics sequencing and its applications in studying the nervous system

Single-cell sequencing has become one of the most powerful and popular techniques in dissecting molecular heterogeneity and modeling the cellular architecture of a biological system. During the past twenty years, the throughput of single-cell sequencing has increased from hundreds of cells to over tens of thousands of cells in parallel. Moreover, this technology has been developed from sequencing transcriptome to measure different omics such as DNA methylome, chromatin accessibility, and so on. Currently, multi-omics which can analyze different omics in the same cell is rapidly advancing. This work advances the study of many biosystems, including the nervous system. Here, we review current single-cell multi-omics sequencing techniques and describe how they improve our understanding of the nervous system. Finally, we discuss the open scientific questions in neural research that may be answered through further improvement of single-cell multi-omics sequencing technology.

TCF-1 promotes chromatin interactions across topologically associating domains in T cell progenitors

The high mobility group (HMG) transcription factor TCF-1 is essential for early T cell development. Although in vitro biochemical assays suggest that HMG proteins can serve as architectural elements in the assembly of higher-order nuclear organization, the contribution of TCF-1 on the control of three-dimensional (3D) genome structures during T cell development remains unknown. Here, we investigated the role of TCF-1 in 3D genome reconfiguration. Using gain- and loss-of-function experiments, we discovered that the co-occupancy of TCF-1 and the architectural protein CTCF altered the structure of topologically associating domains in T cell progenitors, leading to interactions between previously insulated regulatory elements and target genes at late stages of T cell development. The TCF-1-dependent gain in long-range interactions was linked to deposition of active enhancer mark H3K27ac and recruitment of the cohesin-loading factor NIPBL at active enhancers. These data indicate that TCF-1 has a role in controlling global genome organization during T cell development.

Morphological pseudotime ordering and fate mapping reveal diversification of cerebellar inhibitory interneurons

Understanding how diverse neurons are assembled into circuits requires a framework for describing cell types and their developmental trajectories. Here we combine genetic fate-mapping, pseudotemporal profiling of morphogenesis, and dual morphology and RNA labeling to resolve the diversification of mouse cerebellar inhibitory interneurons. Molecular layer interneurons (MLIs) derive from a common progenitor population but comprise diverse dendritic-, somatic-, and axon initial segment-targeting interneurons. Using quantitative morphology from 79 mature MLIs, we identify two discrete morphological types and presence of extensive within-class heterogeneity. Pseudotime trajectory inference using 732 developmental morphologies indicate the emergence of distinct MLI types during migration, before reaching their final positions. By comparing MLI identities from morphological and transcriptomic signatures, we demonstrate the dissociation between these modalities and that subtype divergence can be resolved from axonal morphogenesis prior to marker gene expression. Our study illustrates the utility of applying single-cell methods to quantify morphology for defining neuronal diversification.

Cell clustering for spatial transcriptomics data with graph neural networks

Spatial transcriptomics data can provide high-throughput gene expression profiling and the spatial structure of tissues simultaneously. Most studies have relied on only the gene expression information but cannot utilize the spatial information efficiently. Taking advantage of spatial transcriptomics and graph neural networks, we introduce cell clustering for spatial transcriptomics data with graph neural networks, an unsupervised cell clustering method based on graph convolutional networks to improve ab initio cell clustering and discovery of cell subtypes based on curated cell category annotation. On the basis of its application to five in vitro and in vivo spatial datasets, we show that cell clustering for spatial transcriptomics outperforms other spatial clustering approaches on spatial transcriptomics datasets and can clearly identify all four cell cycle phases from multiplexed error-robust fluorescence in situ hybridization data of cultured cells. From enhanced sequential fluorescence in situ hybridization data of brain, cell clustering for spatial transcriptomics finds functional cell subtypes with different micro-environments, which are all validated experimentally, inspiring biological hypotheses about the underlying interactions among the cell state, cell type and micro-environment.

FISH-quant v2: a scalable and modular tool for smFISH image analysis

Regulation of RNA abundance and localization is a key step in gene expression control. Single-molecule RNA fluorescence in situ hybridization (smFISH) is a widely used single-cell-single-molecule imaging technique enabling quantitative studies of gene expression and its regulatory mechanisms. Today, these methods are applicable at a large scale, which in turn come with a need for adequate tools for data analysis and exploration. Here, we present FISH-quant v2, a highly modular tool accessible for both experts and non-experts. Our user-friendly package allows the user to segment nuclei and cells, detect isolated RNAs, decompose dense RNA clusters, quantify RNA localization patterns and visualize these results both at the single-cell level and variations within the cell population. This tool was validated and applied on large-scale smFISH image data sets, revealing diverse subcellular RNA localization patterns and a surprisingly high degree of cell-to-cell heterogeneity.

Engineering the multiscale complexity of vascular networks

The survival of vertebrate organisms depends on highly regulated delivery of oxygen and nutrients through vascular networks that pervade nearly all tissues in the body. Dysregulation of these vascular networks is implicated in many common human diseases such as hypertension, coronary artery disease, diabetes and cancer. Therefore, engineers have sought to create vascular networks within engineered tissues for applications such as regenerative therapies, human disease modelling and pharmacological testing. Yet engineering vascular networks has historically remained difficult, owing to both incomplete understanding of vascular structure and technical limitations for vascular fabrication. This Review highlights the materials advances that have enabled transformative progress in vascular engineering by ushering in new tools for both visualizing and building vasculature. New methods such as bioprinting, organoids and microfluidic systems are discussed, which have enabled the fabrication of 3D vascular topologies at a cellular scale with lumen perfusion. These approaches to vascular engineering are categorized into technology-driven and nature-driven approaches. Finally, the remaining knowledge gaps, emerging frontiers and opportunities for this field are highlighted, including the steps required to replicate the multiscale complexity of vascular networks found in nature.

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/ .

A guide for single-particle chromatin tracking in live cell nuclei

The emergence of labeling strategies and live cell imaging methods enables the imaging of chromatin in living cells at single digit nanometer resolution as well as milliseconds temporal resolution. These technical breakthroughs revolutionize our understanding of chromatin structure, dynamics and functions. Single molecule tracking algorithms are usually preferred to quantify the movement of these intranucleus elements to interpret the spatiotemporal evolution of the chromatin. In this review, we will first summarize the fluorescent labeling strategy of chromatin in live cells which will be followed by a sys-tematic comparison of live cell imaging instrumentation. With the proper microscope, we will discuss the image analysis pipelines to extract the biophysical properties of the chromatin. Finally, we expect to give practical suggestions to broad biologists on how to select methods and link to the model properly according to different investigation pur-poses. This article is protected by copyright. All rights reserved.

Emerging Technologies to Study the Glomerular Filtration Barrier

Kidney disease is characterized by loss of glomerular function with clinical manifestation of proteinuria. Identifying the cellular and molecular changes that lead to loss of protein in the urine is challenging due to the complexity of the filtration barrier, constituted by podocytes, glomerular endothelial cells, and glomerular basement membrane. In this review, we will discuss how technologies like single cell RNA sequencing and bioinformatics-based spatial transcriptomics, as well as in vitro systems like kidney organoids and the glomerulus-on-a-chip, have contributed to our understanding of glomerular pathophysiology. Knowledge gained from these studies will contribute toward the development of personalized therapeutic approaches for patients affected by proteinuric diseases.

Prediction of RNA subcellular localization: Learning from heterogeneous data sources

RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.