Recent advances in spatially resolved transcriptomics: challenges and opportunities

Spatial visualization provides insight into immune modulation by an L-DBF vaccine formulation against Shigella

Shigellosis remains a global public health problem, especially in regions with poor sanitation measures. Our prior work has demonstrated the protective efficacy of a three-dose regimen of L-DBF, a recombinant fusion of IpaD and IpaB from Shigella flexneri with the LTA1 moiety of enterotoxigenic E. coli labile toxin. Here, we investigate how a two-dose regimen (one prime and one booster) of L-DBF, formulated in an oil-in-water emulsion called ME, modulates immune responses in the lung using a spatial transcriptomics approach. Our findings show significant changes in the lung immune landscape following the vaccination, including increased expression of B cell markers, antigen presentation genes, and T cell-associated markers. Our analysis also revealed significant reprogramming of fibroblasts and cardiomyocytes, showing that fibroblasts are shifted from extracellular matrix production to immune modulation, while cardiomyocytes enhanced the signaling for immune cell recruitment and vascular stability. The communication between alveolar type 2 (AT2) cells and cardiomyocytes also increased, reflecting coordinated support for immune readiness and maintaining tissue integrity. These findings underscore the potential of L-DBF/ME vaccination to enhance both humoral and cellular immunity, as well as to reshape lung immune architecture while enhancing immune readiness, thereby offering a promising approach for effective protection against Shigella infections.

Advancements in Nanomaterials and Molecular Probes for Spatial Omics

Spatial omics is emerging as a focus of life sciences because of its applications in investigating the molecular mechanisms of cancer, mapping cellular distributions, and revealing specific cellular ecological niches. Notably, the in-depth acquisition of spatial omics information relies on highly sensitive, high-resolution, and high-throughput biological analysis tools and techniques. However, conventional methods of omics data acquisition still suffer from some drawbacks such as limited-resolution and low-throughput and are difficult to adapt directly to the collection of high-quality spatial omics data. Recently, an increasing number of advanced nanomaterials and molecular probes are employed in spatial omics due to their excellent optoelectronic properties, biocompatibility, and multifunction. These well-designed innovative nanoscaffolds successfully enhance the key parameters of spatial omics and, thus, increase the spatial resolution, detection sensitivity, and detection throughput. This review summarizes the design and application of functional nanoscaffolds for spatial omics in recent years, with a particular emphasis on nanomaterials and molecular probes. We believe that the present review can inspire and motivate researchers in designing and selecting appropriate materials and probes for high-quality spatial omics, thus promoting the development of spatial omics and life sciences.

Mapping molecular landscapes in triple-negative breast cancer: insights from spatial transcriptomics

The tumor microenvironment (TME) of triple-negative breast cancer (TNBC) is a highly heterogeneous and very aggressive form of the disease that has few suitable treatment options; however, spatial transcriptomics (ST) is a powerful tool for elucidation of the TME in TNBC. Because of its spatial context preservation, ST has a unique capability to map tumor-stroma interactions, immune infiltration, and therapy resistance mechanisms (which are key to understanding TNBC progression), compared with conventional transcriptomics. This review shows the use of ST in TNBC, its utilization in spatial biomarker identification, intratumoral heterogeneity definition, molecular subtyping refinement, and prediction of immunotherapy responses. Recent insight from ST-driven insights has explained the key spatial patterns on immune evasion, chemotherapy resistance, racial disparities of TNBC, and aspects for patient stratification and therapeutic decision. With the integration of ST with the subjects of proteomics and imaging mass cytometry, this approach has been enlarged and is now applied in precision medicine and biomarker discovery. Recently, advancements in AI-based spatial analysis for tumor classification, identification of biomarkers, and creation of therapy prediction models have occurred. However, continued developments in ST technologies, computational tools, and partnerships amongst multiple centers to facilitate the integration of ST into clinical routine practice are needed to unlock novel therapeutic targets.

Detection of mRNA Transcript Variants

Most eukaryotic genes express more than one mature mRNA, defined as transcript variants. This complex phenomenon arises from various mechanisms, such as using alternative transcription start sites and alternative post-transcriptional processing events. The resulting transcript variants can lead to synthesizing proteins that possess distinct functional domains or may even generate noncoding RNAs, each with unique roles in cellular processes. The generation of these transcript variants is not merely a random occurrence; it is cell-type specific and varies with developmental stages, aging processes, or pathogenesis of diseases. This highlights the biological significance of transcript variants in regulating gene expression and their potential impact on cellular functionality. Despite the biological importance, investigating transcript variants has been hampered by challenges associated with detecting their expression. This review article addresses the advancements in molecular techniques in detecting transcript variants. Traditional methods such as RT-PCR and RT-qPCR can easily detect known transcript variants using primers that target unique exons associated with the variants. Other techniques like RACE-PCR and hybridization-based methods, including Northern blotting, RNase protection assays, and microarrays, have also been utilized to detect transcript variants. Nevertheless, RNA sequencing (RNA-Seq) has emerged as a powerful technique for identifying transcript variants, especially those with previously unknown sequences. The effectiveness of RNA sequencing in transcript variant detection depends on the specific sequencing approach and the precision of data analysis. By understanding the strengths and weaknesses of each laboratory technique, researchers can develop more effective strategies for detecting mRNA transcript variants. This ability will be crucial for our comprehensive understanding of gene regulation and the implications of transcript diversity in various biological contexts.

Identifying gene expression and cellular pathways involved in glomerular AL-amyloidosis and correlation with experimental data: seeking novel therapeutic interventions

The prognosis of myeloma is based on controlling the plasma cell burden and thus, management of the production of monoclonal light chains has improved considerably, expanding survival and quality of life. However, the effects of the monoclonal light chains in the various organs result in alterations that may lead to renal failure. There is a crucial need to ameliorate or abolish renal damage. Organ-based therapies must be developed. Glomerulopathic light chains interact with mesangial cells using the SORL1 receptor and downstream effects lead to divergent mesangial alterations. While the multi-step process occurring when amyloidogenic light chains interact with mesangial cells has been elucidated in the laboratory, gene expression profiles and activated cellular pathways in human glomeruli have not been probed. Mesangial cells from five renal biopsies at different stages of glomerular amyloidosis were interrogated using spatial transcriptomics and compared with those from normal biopsy controls to identify cellular pathways and gene expression changes. The two most significant statistically overexpressed genes (FDR <0.05) when comparing control, early vs late cases were heat shock protein 90AB1 and HSPB1, known to be involved in protein misfolding and aggregation. The overexpressed genes exercise function and regulation over cellular pathways promoting apoptosis, vesicular transport, metalloproteinase activation, collagen degradation, gap junction degradation, GTPase cycle activation, and organelle biogenesis. This data confirmed the results previously reached in the research laboratory. Spatial transcriptomics demonstrated uniquely activated genes and cellular pathways in mesangial cells involved in the initiation and progression of glomerular amyloidosis, uncovering novel genes and new therapeutic targets.

Advancements in single-cell RNA sequencing and spatial transcriptomics: transforming biomedical research

In recent years, significant advancements in biochemistry, materials science, engineering, and computer-aided testing have driven the development of high-throughput tools for profiling genetic information. Single-cell RNA sequencing (scRNA-seq) technologies have established themselves as key tools for dissecting genetic sequences at the level of single cells. These technologies reveal cellular diversity and allow for the exploration of cell states and transformations with exceptional resolution. Unlike bulk sequencing, which provides population-averaged data, scRNA-seq can detect cell subtypes or gene expression variations that would otherwise be overlooked. However, a key limitation of scRNA-seq is its inability to preserve spatial information about the RNA transcriptome, as the process requires tissue dissociation and cell isolation. Spatial transcriptomics is a pivotal advancement in medical biotechnology, facilitating the identification of molecules such as RNA in their original spatial context within tissue sections at the single-cell level. This capability offers a substantial advantage over traditional single-cell sequencing techniques. Spatial transcriptomics offers valuable insights into a wide range of biomedical fields, including neurology, embryology, cancer research, immunology, and histology. This review highlights single-cell sequencing approaches, recent technological developments, associated challenges, various techniques for expression data analysis, and their applications in disciplines such as cancer research, microbiology, neuroscience, reproductive biology, and immunology. It highlights the critical role of single-cell sequencing tools in characterizing the dynamic nature of individual cells.

Points to Consider From the ESTP Pathology 2.0 Working Group: Overview on Spatial Omics Technologies Supporting Drug Discovery and Development

Recent advances in bioanalytical and imaging technologies have revolutionized our ability to assess complex biological and pathological changes within tissue samples. Spatial omics, a rapidly evolving technology, enables the simultaneous detection of multiple biomolecules in tissue sections, allowing for high-dimensional molecular profiling within tissue microanatomical contexts. This offers a powerful opportunity for precise, multidimensional exploration of complex disease pathophysiology. The Pathology 2.0 working group within the European Society of Toxicologic Pathology (ESTP) includes a subgroup dedicated to spatial omics technologies. Their primary goal is to raise awareness about these emerging technologies and their potential applications in discovery and toxicologic pathology. This review provides an overview of commonly used, commercially available platforms for transcriptomic, proteomic, and multiomic analysis, discussing technical aspects and illustrative examples of their applications. To harness the power of spatial omics for translational drug discovery and human safety risk assessment, we emphasize the important role of pathologists at every stage of the workflow-from hypothesis generation to sample preparation, data analysis, and interpretation. Spatial omics technologies offer novel opportunities in target discovery, lead selection, preclinical assessment, and clinical development in compound development.

The Spatial Structure of the Tumor Immune Microenvironment Can Explain and Predict Patient Response in High-Grade Serous Carcinoma

Ovarian cancer is the deadliest gynecologic malignancy, and therapeutic options and mortality rates over the last three decades have largely not changed. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes. To improve spatial understanding of the TIME, we performed multiplexed ion beam imaging on 83 human high-grade serous carcinoma tumor samples, identifying approximately 160,000 cells across 23 cell types. From the 77 of these samples that met inclusion criteria, we generated composition features based on cell type proportions, spatial features based on the distances between cell types, and spatial network features representing cell interactions and cell clustering patterns, which we linked to traditional clinical and IHC variables and patient overall survival (OS) and progression-free survival (PFS) outcomes. Among these features, we found several significant univariate correlations, including B-cell contact with M1 macrophages (OS HR = 0.696; P = 0.011; PFS HR = 0.734; P = 0.039). We then used high-dimensional random forest models to evaluate out-of-sample predictive performance for OS and PFS outcomes and to derive relative feature importance scores for each feature. The top model for predicting low or high PFS used TIME composition and spatial features and achieved an average AUC score of 0.71. The results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

Characterization of an orthotopic mouse transplant model reveals early changes in the tumor microenvironment of lung cancer

To understand the cellular and molecular dynamics in the early stages of lung cancer, we explored a mouse model of orthotopic tumor transplant created from the Lewis Lung Carcinoma (LLC) cell line. Employing single-cell RNA sequencing, we analyzed the cellular landscape during tumor engraftment, focusing particularly on LLC cells harboring the Kras G12C mutation. This allowed us to identify LLC tumor cells via the detection of mutant Kras transcripts and observe elevated levels of Myc and mesenchymal gene expression. Moreover, our study revealed significant alterations in the lung microenvironment, including the activation of tissue remodeling genes in fibroblasts and the downregulation of MHC class II genes in myeloid subsets. Additionally, T/NK cell subsets displayed more regulatory phenotypes, coupled with reduced proliferation in CD8+ T cells. Collectively, these findings enhance our understanding of lung cancer progression, particularly in a tumor microenvironment with low immunogenicity. [BMB Reports 2024; 57(11): 484-489].

BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

Ultraplex microscopy: versatile highly-multiplexed molecular labeling and imaging across scale and resolution

The molecular organization of cells and tissue is challenging to study due to the inefficiency of multiplexed molecular labeling methods and the limited options for combining microscopy modalities in a single specimen, especially when high spatial resolution is needed. Here we describe ultraplex microscopy, which combines serial multiplexing, ultrathin sectioning, and reversible embedding to circumvent incompatibilities between labeling and imaging techniques, enhance resolution, and expand multiplexing capacity within and across modalities. Samples can be labeled with antibodies, RNA probes, and tissue stains for imaging by brightfield, epifluorescence, super-resolution, and electron microscopy without specialized reagents or materials. We demonstrate applications in brain tissue including molecular profiling of single cells and axonal boutons, high-resolution molecular colocalization, and correlative imaging of fluorescent proteins with confocal and ultraplex microscopy. The power and versatility of ultraplex microscopy will be valuable in addressing currently intractable experimental questions in many systems and contexts.

Decoding the spatiotemporal heterogeneity of tumor-associated macrophages

Tumor-associated macrophages (TAMs) are pivotal in cancer progression, influencing tumor growth, angiogenesis, and immune evasion. This review explores the spatial and temporal heterogeneity of TAMs within the tumor microenvironment (TME), highlighting their diverse subtypes, origins, and functions. Advanced technologies such as single-cell sequencing and spatial multi-omics have elucidated the intricate interactions between TAMs and other TME components, revealing the mechanisms behind their recruitment, polarization, and distribution. Key findings demonstrate that TAMs support tumor vascularization, promote epithelial-mesenchymal transition (EMT), and modulate extracellular matrix (ECM) remodeling, etc., thereby enhancing tumor invasiveness and metastasis. Understanding these complex dynamics offers new therapeutic targets for disrupting TAM-mediated pathways and overcoming drug resistance. This review underscores the potential of targeting TAMs to develop innovative cancer therapies, emphasizing the need for further research into their spatial characteristics and functional roles within the TME.

Joint Bayesian estimation of cell dependence and gene associations in spatially resolved transcriptomic data

Recent technologies such as spatial transcriptomics, enable the measurement of gene expressions at the single-cell level along with the spatial locations of these cells in the tissue. Spatial clustering of the cells provides valuable insights into the understanding of the functional organization of the tissue. However, most such clustering methods involve some dimension reduction that leads to a loss of the inherent dependency structure among genes at any spatial location in the tissue. This destroys valuable insights of gene co-expression patterns apart from possibly impacting spatial clustering performance. In spatial transcriptomics, the matrix-variate gene expression data, along with spatial coordinates of the single cells, provides information on both gene expression dependencies and cell spatial dependencies through its row and column covariances. In this work, we propose a joint Bayesian approach to simultaneously estimate these gene and spatial cell correlations. These estimates provide data summaries for downstream analyses. We illustrate our method with simulations and analysis of several real spatial transcriptomic datasets. Our work elucidates gene co-expression networks as well as clear spatial clustering patterns of the cells. Furthermore, our analysis reveals that downstream spatial-differential analysis may aid in the discovery of unknown cell types from known marker genes.

Spatial transcriptomics data and analytical methods: An updated perspective

Spatial transcriptomics (ST) is a newly emerging field that integrates high-resolution imaging and transcriptomic data to enable the high-throughput analysis of the spatial localization of transcripts in diverse biological systems. The rapid progress in this field necessitates the development of innovative computational methods to effectively tackle the distinct challenges posed by the analysis of ST data. These platforms, integrating AI techniques, offer a promising avenue for understanding disease mechanisms and expediting drug discovery. Despite significant advances in the development of ST data analysis techniques, there is an ongoing need to enhance these models for increased biological relevance. In this review, we briefly discuss the ST-related databases and current deep-learning-based models for spatial transcriptome data analyses and highlight their roles and future perspectives in biomedical applications.

Spatial transcriptomics and the anatomical pathologist: Molecular meets morphology

In recent years anatomical pathology has been revolutionised by the incorporation of molecular findings into routine diagnostic practice, and in some diseases the presence of specific molecular alterations are now essential for diagnosis. Spatial transcriptomics describes a group of technologies that provide up to transcriptome-wide expression profiling while preserving the spatial origin of the data, with many of these technologies able to provide these data using a single tissue section. Spatial transcriptomics allows expression profiling of highly specific areas within a tissue section potentially to subcellular resolution, and allows correlation of expression data with morphology, tissue type and location relative to other structures. While largely still research laboratory-based, several spatial transcriptomics methods have now achieved compatibility with formalin-fixed paraffin-embedded tissue (FFPE), allowing their use in diagnostic tissue samples, and with further development potentially leading to their incorporation in routine anatomical pathology practice. This mini review provides an overview of spatial transcriptomics methods, with an emphasis on platforms compatible with FFPE tissue, approaches to assess the data and potential applications in anatomical pathology practice.

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors - one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled - and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

Efficient 3D imaging and pathological analysis of the human lymphoma tumor microenvironment using light-sheet immunofluorescence microscopy

Rationale: The composition and spatial structure of the lymphoma tumor microenvironment (TME) provide key pathological insights for tumor survival and growth, invasion and metastasis, and resistance to immunotherapy. However, the 3D lymphoma TME has not been well studied owing to the limitations of current imaging techniques. In this work, we take full advantage of a series of new techniques to enable the first 3D TME study in intact lymphoma tissue. Methods: Diverse cell subtypes in lymphoma tissues were tagged using a multiplex immunofluorescence labeling technique. To optically clarify the entire tissue, immunolabeling-enabled three-dimensional imaging of solvent-cleared organs (iDISCO+), clear, unobstructed brain imaging cocktails and computational analysis (CUBIC) and stabilization to harsh conditions via intramolecular epoxide linkages to prevent degradation (SHIELD) were comprehensively compared with the ultimate dimensional imaging of solvent-cleared organs (uDISCO) approach selected for clearing lymphoma tissues. A Bessel-beam light-sheet fluorescence microscope (B-LSFM) was developed to three-dimensionally image the clarified tissues at high speed and high resolution. A customized MATLAB program was used to quantify the number and colocalization of the cell subtypes based on the acquired multichannel 3D images. By combining these cutting-edge methods, we successfully carried out high-efficiency 3D visualization and high-content cellular analyses of the lymphoma TME. Results: Several antibodies, including CD3, CD8, CD20, CD68, CD163, CD14, CD15, FOXP3 and Ki67, were screened for labeling the TME in lymphoma tumors. The 3D imaging results of the TME from three types of lymphoma, reactive lymphocytic hyperplasia (RLN), diffuse large B-cell lymphoma (DLBCL), and angioimmunoblastic T-cell lymphoma (AITL), were quantitatively analyzed, and their cell number, localization, and spatial correlation were comprehensively revealed. Conclusion: We present an advanced imaging-based method for efficient 3D visualization and high-content cellular analysis of the lymphoma TME, rendering it a valuable tool for tumor pathological diagnosis and other clinical research.

Genetic and Gene Expression Resources for Osteoporosis and Bone Biology Research

PURPOSE OF REVIEW

The integration of data from multiple genomic assays from humans and non-human model organisms is an effective approach to identify genes involved in skeletal fragility and fracture risk due to osteoporosis and other conditions. This review summarizes genome-wide genetic variation and gene expression data resources relevant to the discovery of genes contributing to skeletal fragility and fracture risk.

RECENT FINDINGS

Genome-wide association studies (GWAS) of osteoporosis-related traits are summarized, in addition to gene expression in bone tissues in humans and non-human organisms, with a focus on rodent models related to skeletal fragility and fracture risk. Gene discovery approaches using these genomic data resources are described. We also describe the Musculoskeletal Knowledge Portal (MSKKP) that integrates much of the available genomic data relevant to fracture risk. The available genomic resources provide a wealth of knowledge and can be analyzed to identify genes related to fracture risk. Genomic resources that would fill particular scientific gaps are discussed.

Fragment-sequencing unveils local tissue microenvironments at single-cell resolution

Cells collectively determine biological functions by communicating with each other-both through direct physical contact and secreted factors. Consequently, the local microenvironment of a cell influences its behavior, gene expression, and cellular crosstalk. Disruption of this microenvironment causes reciprocal changes in those features, which can lead to the development and progression of diseases. Hence, assessing the cellular transcriptome while simultaneously capturing the spatial relationships of cells within a tissue provides highly valuable insights into how cells communicate in health and disease. Yet, methods to probe the transcriptome often fail to preserve native spatial relationships, lack single-cell resolution, or are highly limited in throughput, i.e. lack the capacity to assess multiple environments simultaneously. Here, we introduce fragment-sequencing (fragment-seq), a method that enables the characterization of single-cell transcriptomes within multiple spatially distinct tissue microenvironments. We apply fragment-seq to a murine model of the metastatic liver to study liver zonation and the metastatic niche. This analysis reveals zonated genes and ligand-receptor interactions enriched in specific hepatic microenvironments. Finally, we apply fragment-seq to other tissues and species, demonstrating the adaptability of our method.

Spatial Transcriptomics: Emerging Technologies in Tissue Gene Expression Profiling

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

Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

A progression sequence for age-related macular degeneration (AMD) learned from optical coherence tomography (OCT)-based multimodal (MMI) clinical imaging could add prognostic value to laboratory findings. In this work, ex vivo OCT and MMI were applied to human donor eyes prior to retinal tissue sectioning. The eyes were recovered from non-diabetic white donors aged ≥80 years old, with a death-to-preservation time (DtoP) of ≤6 h. The globes were recovered on-site, scored with an 18 mm trephine to facilitate cornea removal, and immersed in buffered 4% paraformaldehyde. Color fundus images were acquired after anterior segment removal with a dissecting scope and an SLR camera using trans-, epi-, and flash illumination at three magnifications. The globes were placed in a buffer within a custom-designed chamber with a 60 diopter lens. They were imaged with spectral domain OCT (30° macula cube, 30 µm spacing, averaging = 25), near-infrared reflectance, 488 nm autofluorescence, and 787 nm autofluorescence. The AMD eyes showed a change in the retinal pigment epithelium (RPE), with drusen or subretinal drusenoid deposits (SDDs), with or without neovascularization, and without evidence of other causes. Between June 2016 and September 2017, 94 right eyes and 90 left eyes were recovered (DtoP: 3.9 ± 1.0 h). Of the 184 eyes, 40.2% had AMD, including early intermediate (22.8%), atrophic (7.6%), and neovascular (9.8%) AMD, and 39.7% had unremarkable maculas. Drusen, SDDs, hyper-reflective foci, atrophy, and fibrovascular scars were identified using OCT. Artifacts included tissue opacification, detachments (bacillary, retinal, RPE, choroidal), foveal cystic change, an undulating RPE, and mechanical damage. To guide the cryo-sectioning, OCT volumes were used to find the fovea and optic nerve head landmarks and specific pathologies. The ex vivo volumes were registered with the in vivo volumes by selecting the reference function for eye tracking. The ex vivo visibility of the pathology seen in vivo depends on the preservation quality. Within 16 months, 75 rapid DtoP donor eyes at all stages of AMD were recovered and staged using clinical MMI methods.

GeneViT: Gene Vision Transformer with Improved DeepInsight for cancer classification

Analysis of gene expression data is crucial for disease prognosis and diagnosis. Gene expression data has high redundancy and noise that brings challenges in extracting disease information. Over the past decade, several conventional machine learning and deep learning models have been developed for classification of diseases using gene expressions. In recent years, vision transformer networks have shown promising performance in many fields due to their powerful attention mechanism that provides a better insight into the data characteristics. However, these network models have not been explored for gene expression analysis. In this paper, a method for classifying cancerous gene expression is presented that uses a Vision transformer. The proposed method first performs dimensionality reduction using a stacked autoencoder followed by an Improved DeepInsight algorithm that converts the data into image format. The data is then fed to the vision transformer for building the classification model. Performance of the proposed classification model is evaluated on ten benchmark datasets having binary classes or multiple classes. Its performance is also compared with nine existing classification models. The experimental results demonstrate that the proposed model outperforms existing methods. The t-SNE plots demonstrate the distinctive feature learning property of the model.

Spatial Mapping of Genes Implicated in SARS-CoV-2 Neuroinvasion to Dorsolateral Prefrontal Cortex Gray Matter

Introduction: SARS-CoV-2 is the newest beta coronavirus family member to demonstrate neuroinvasive capability in severe cases of infection. Despite much research activity in the SARS-CoV-2/COVID-19 space, the gene-level biology of this phenomenon remains poorly understood. In the present analysis, we leveraged spatial transcriptomics methodologies to examine relevant gene heterogeneity in tissue retrieved from the human prefrontal cortex. Methods: Expression profiles of genes with established relations to the SARS-CoV-2 neuroinvasion process were spatially resolved in dorsolateral prefrontal cortex tissue (N = 4). Spotplots were generated with mapping to six (6) previously defined gray matter layers. Results: Docking gene BSG, processing gene CTSB, and viral defense gene LY6E demonstrated similar spatial enrichment. Docking gene ACE2 and transmembrane series proteases involved in spike protein processing were lowly expressed across DLPFC samples. Numerous other findings were obtained. Conclusion: Efforts to spatially represent expression levels of key SARS-CoV-2 brain infiltration genes remain paltry to date. Understanding the sobering history of beta coronavirus neuroinvasion represents a weak point in viral research. Here we provide the first efforts to characterize a motley of such genes in the dorsolateral prefrontal cortex.

What has single-cell transcriptomics taught us about long non-coding RNAs in the ventricular-subventricular zone?

Long non-coding RNA (lncRNA) function is mediated by the process of transcription or through transcript-dependent associations with proteins or nucleic acids to control gene regulatory networks. Many lncRNAs are transcribed in the ventricular-subventricular zone (V-SVZ), a postnatal neural stem cell niche. lncRNAs in the V-SVZ are implicated in neurodevelopmental disorders, cancer, and brain disease, but their functions are poorly understood. V-SVZ neurogenesis capacity declines with age due to stem cell depletion and resistance to neural stem cell activation. Here we analyzed V-SVZ transcriptomics by pooling current single-cell RNA-seq data. They showed consistent lncRNA expression during stem cell activation, lineage progression, and aging. In conjunction with epigenetic and genetic data, we predicted V-SVZ lncRNAs that regulate stem cell activation and differentiation. Some of the lncRNAs validate known epigenetic mechanisms, but most remain uninvestigated. Our analysis points to several lncRNAs that likely participate in key aspects of V-SVZ stem cell activation and neurogenesis in health and disease.

Amyotrophic Lateral Sclerosis, FUS and Protein Synthesis Defects

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that mainly affects the motor system. It is a very heterogeneous disorder, so far more than 40 genes have been described as responsible for ALS. The cause of motor neuron degeneration is not yet fully understood, but there is consensus in the literature that it is the result of a complex interplay of several pathogenic processes, which include alterations in nucleocytoplasmic transport, defects in transcription and splicing, altered formation and/or disassembly of stress granules and impaired proteostasis. These defects result in protein aggregation, impaired DNA repair, mitochondrial dysfunction and oxidative stress, neuroinflammation, impaired axonal transport, impaired vesicular transport, excitotoxicity, as well as impaired calcium influx. We argue here that all the above functions ultimately lead to defects in protein synthesis. Fused in Sarcoma (FUS) is one of the genes associated with ALS. It causes ALS type 6 when mutated and is found mislocalized to the cytoplasm in the motor neurons of sporadic ALS patients (without FUS mutations). In addition, FUS plays a role in all cellular functions that are impaired in degenerating motor neurons. Moreover, ALS patients with FUS mutations present the first symptoms significantly earlier than in other forms of the disease. Therefore, the aim of this review is to further discuss ALS6, detail the cellular functions of FUS, and suggest that the localization of FUS, as well as protein synthesis rates, could be hallmarks of the ALS phenotype and thus good therapeutic targets.