Spatial transcriptomics in development and disease

Comprehensive biomarker profiles in hematological malignancies: improving diagnosis, prognosis, and treatment

Hematological malignancies present substantial challenges in clinical practice due to their heterogeneity and complex biological profiles. In these diseases, biomarkers - measurable indicators of biological states - are indispensable for diagnosis, prognosis, and therapeutic decision-making. Emerging biomarkers are significantly improving outcomes in hematological cancers by enhancing early detection, refining prognostic assessments, enabling personalized treatment approaches, and optimizing overall patient management. This progress translates into better clinical outcomes and more effective strategies to treat and manage malignancies. The field of biomarker discovery has developed from basic morphological and cytogenetic markers to advanced molecular techniques, including polymerase chain reaction (PCR) and next-generation sequencing (NGS), which have significantly enhanced diagnostic accuracy and led to the development of targeted therapies. Additionally, the recent advent of technologies like mass spectrometry and single-cell RNA sequencing enables comprehensive molecular profiling and reveals novel biomarkers that were previously undetectable. Our aim in this manuscript is to provide a comprehensive overview of recent and novel immunohematological biomarkers, their diagnostic and therapeutic applications, and the future directions of this field.

Single-cell genomics and spatial transcriptomics in islet transplantation for diabetes treatment: advancing towards personalized therapies

Diabetes mellitus (DM) is a global health crisis affecting millions, with islet transplantation emerging as a promising treatment strategy to restore insulin production. This review synthesizes the current research on single-cell and spatial transcriptomics in the context of islet transplantation, highlighting their potential to revolutionize DM management. Single-cell RNA sequencing, offers a detailed look into the diversity and functionality within islet grafts, identifying specific cell types and states that influence graft acceptance and function. Spatial transcriptomics complements this by mapping gene expression within the tissue's spatial context, crucial for understanding the microenvironment surrounding transplanted islets and their interactions with host tissues. The integration of these technologies offers a comprehensive view of cellular interactions and microenvironments, elucidating mechanisms underlying islet function, survival, and rejection. This understanding is instrumental in developing targeted therapies to enhance graft performance and patient outcomes. The review emphasizes the significance of these research avenues in informing clinical practices and improving outcomes for patients with DM through more effective islet transplantation strategies. Future research directions include the application of these technologies in personalized medicine, developmental biology, and regenerative medicine, with the potential to predict disease progression and treatment responses. Addressing ethical and technical challenges will be crucial for the successful implementation of these integrated approaches in research and clinical practice, ultimately enhancing our ability to manage DM and improve patient quality of life.

STMiner: Gene-centric spatial transcriptomics for deciphering tumor tissues

Analyzing spatial transcriptomics data from tumor tissues poses several challenges beyond those of healthy samples, including unclear boundaries between different regions, uneven cell densities, and relatively higher cellular heterogeneity. Collectively, these bias the background against which spatially variable genes are identified, which can result in misidentification of spatial structures and hinder potential insight into complex pathologies. To overcome this problem, STMiner leverages 2D Gaussian mixture models and optimal transport theory to directly characterize the spatial distribution of genes rather than the capture locations of the cells expressing them (spots). By effectively mitigating the impacts of both background bias and data sparsity, STMiner reveals key gene sets and spatial structures overlooked by spot-based analytic tools, facilitating novel biological discoveries. The core concept of directly analyzing overall gene expression patterns also allows for a broader application beyond spatial transcriptomics, positioning STMiner for continuous expansion as spatial omics technologies evolve.

Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. The lowering cost of SRT data generation presents an unprecedented opportunity to create large-scale spatial atlases and enable population-level investigation, integrating SRT data across multiple tissues, individuals, species, or phenotypes. Here, unique challenges are described in the SRT data integration, where the analytic impact of varying spatial and biological resolutions is characterized and explored. A succinct review of spatially-aware integration methods and computational strategies is provided. Exciting opportunities to advance computational algorithms amenable to atlas-scale datasets along with standardized preprocessing methods, leading to improved sensitivity and reproducibility in the future are further highlighted.

Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challenges

Cancer stem cells (CSCs) often exhibit stem-like attributes that depend on an intricate stemness-promoting cellular ecosystem within their niche. The interplay between CSCs and their niche has been implicated in tumor heterogeneity and therapeutic resistance. Normal stem cells (NSCs) and CSCs share stemness features and common microenvironmental components, displaying significant phenotypic and functional plasticity. Investigating these properties across diverse organs during normal development and tumorigenesis is of paramount research interest and translational potential. Advancements in next-generation sequencing (NGS), single-cell transcriptomics, and spatial transcriptomics have ushered in a new era in cancer research, providing high-resolution and comprehensive molecular maps of diseased tissues. Various spatial technologies, with their unique ability to measure the location and molecular profile of a cell within tissue, have enabled studies on intratumoral architecture and cellular cross-talk within the specific niches. Moreover, delineation of spatial patterns for niche-specific properties such as hypoxia, glucose deprivation, and other microenvironmental remodeling are revealed through multilevel spatial sequencing. This tremendous progress in technology has also been paired with the advent of computational tools to mitigate technology-specific bottlenecks. Here we discuss how different spatial technologies are used to identify NSCs and CSCs, as well as their associated niches. Additionally, by exploring related public data sets, we review the current challenges in characterizing such niches, which are often hindered by technological limitations, and the computational solutions used to address them.

Advancements in pathology: Digital transformation, precision medicine, and beyond

Pathology, a cornerstone of medical diagnostics and research, is undergoing a revolutionary transformation fueled by digital technology, molecular biology advancements, and big data analytics. Digital pathology converts conventional glass slides into high-resolution digital images, enhancing collaboration and efficiency among pathologists worldwide. Integrating artificial intelligence (AI) and machine learning (ML) algorithms with digital pathology improves diagnostic accuracy, particularly in complex diseases like cancer. Molecular pathology, facilitated by next-generation sequencing (NGS), provides comprehensive genomic, transcriptomic, and proteomic insights into disease mechanisms, guiding personalized therapies. Immunohistochemistry (IHC) plays a pivotal role in biomarker discovery, refining disease classification and prognostication. Precision medicine integrates pathology's molecular findings with individual genetic, environmental, and lifestyle factors to customize treatment strategies, optimizing patient outcomes. Telepathology extends diagnostic services to underserved areas through remote digital pathology. Pathomics leverages big data analytics to extract meaningful insights from pathology images, advancing our understanding of disease pathology and therapeutic targets. Virtual autopsies employ non-invasive imaging technologies to revolutionize forensic pathology. These innovations promise earlier diagnoses, tailored treatments, and enhanced patient care. Collaboration across disciplines is essential to fully realize the transformative potential of these advancements in medical practice and research.

Advancements in the Application of scRNA-Seq in Breast Research: A Review

Single-cell sequencing technology provides apparent advantages in cell population heterogeneity, allowing individuals to better comprehend tissues and organs. Sequencing technology is currently moving beyond the standard transcriptome to the single-cell level, which is likely to bring new insights into the function of breast cells. In this study, we examine the primary cell types involved in breast development, as well as achievements in the study of scRNA-seq in the microenvironment, stressing the finding of novel cell subsets using single-cell approaches and analyzing the problems and solutions to scRNA-seq. Furthermore, we are excited about the field's promising future.

Single-cell multi-omics in the study of digestive system cancers

Digestive system cancers are prevalent diseases with a high mortality rate, posing a significant threat to public health and economic burden. The diagnosis and treatment of digestive system cancer confront conventional cancer problems, such as tumor heterogeneity and drug resistance. Single-cell sequencing (SCS) emerged at times required and has developed from single-cell RNA-seq (scRNA-seq) to the single-cell multi-omics era represented by single-cell spatial transcriptomics (ST). This article comprehensively reviews the advances of single-cell omics technology in the study of digestive system tumors. While analyzing and summarizing the research cases, vital details on the sequencing platform, sample information, sampling method, and key findings are provided. Meanwhile, we summarize the commonly used SCS platforms and their features, as well as the advantages of multi-omics technologies in combination. Finally, the development trends and prospects of the application of single-cell multi-omics technology in digestive system cancer research are prospected.

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis

Spatial transcriptomics (ST) is a cutting-edge methodology that enables the simultaneous profiling of global gene expression and spatial information within histological tissue sections. Traditional transcriptomic methods lack the spatial resolution required to sufficiently examine the complex interrelationships between cellular regions in diseased and healthy tissue states. We review the general workflows for ST, from specimen processing to ST data analysis and interpretations of the ST dataset using visualizations and cell deconvolution approaches. We show how recent studies used ST to explore the development or pathogenesis of specific craniofacial regions, including the cranium, palate, salivary glands, tongue, floor of mouth, oropharynx, and periodontium. Analyses of cranial suture patency and palatal fusion during development using ST identified spatial patterns of bone morphogenetic protein in sutures and osteogenic differentiation pathways in the palate, in addition to the discovery of several genes expressed at critical locations during craniofacial development. ST of salivary glands from patients with Sjögren's disease revealed co-localization of autoimmune antigens with ductal cells and a subpopulation of acinar cells that was specifically depleted by the dysregulated autoimmune response. ST of head and neck lesions, such as premalignant leukoplakia progressing to established oral squamous cell carcinomas, oral cancers with perineural invasions, and oropharyngeal lesions associated with HPV infection spatially profiled the complex tumor microenvironment, showing functionally important gene signatures of tumor cell differentiation, invasion, and nontumor cell dysregulation within patient biopsies. ST also enabled the localization of periodontal disease-associated gene expression signatures within gingival tissues, including genes involved in inflammation, and the discovery of a fibroblast subtype mediating the transition between innate and adaptive immune responses in periodontitis. The increased use of ST, especially in conjunction with single-cell analyses, promises to improve our understandings of craniofacial development and pathogenesis at unprecedented tissue-level resolution in both space and time.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

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.

ST-GEARS: Advancing 3D downstream research through accurate spatial information recovery

Three-dimensional Spatial Transcriptomics has revolutionized our understanding of tissue regionalization, organogenesis, and development. However, existing approaches overlook either spatial information or experiment-induced distortions, leading to significant discrepancies between reconstruction results and in vivo cell locations, causing unreliable downstream analysis. To address these challenges, we propose ST-GEARS (Spatial Transcriptomics GEospatial profile recovery system through AnchoRS). By employing innovative Distributive Constraints into the Optimization scheme, ST-GEARS retrieves anchors with exceeding precision that connect closest spots across sections in vivo. Guided by the anchors, it first rigidly aligns sections, next solves and denoises Elastic Fields to counteract distortions. Through mathematically proved Bi-sectional Fields Application, it eventually recovers the original spatial profile. Studying ST-GEARS across number of sections, sectional distances and sequencing platforms, we observed its outstanding performance on tissue, cell, and gene levels. ST-GEARS provides precise and well-explainable 'gears' between in vivo situations and in vitro analysis, powerfully fueling potential of biological discoveries.

Scoping Review: Methods and Applications of Spatial Transcriptomics in Tumor Research

Spatial transcriptomics (ST) examines gene expression within its spatial context on tissue, linking morphology and function. Advances in ST resolution and throughput have led to an increase in scientific interest, notably in cancer research. This scoping study reviews the challenges and practical applications of ST, summarizing current methods, trends, and data analysis techniques for ST in neoplasm research. We analyzed 41 articles published by the end of 2023 alongside public data repositories. The findings indicate cancer biology is an important focus of ST research, with a rising number of studies each year. Visium (10x Genomics, Pleasanton, CA, USA) is the leading ST platform, and SCTransform from Seurat R library is the preferred method for data normalization and integration. Many studies incorporate additional data types like single-cell sequencing and immunohistochemistry. Common ST applications include discovering the composition and function of tumor tissues in the context of their heterogeneity, characterizing the tumor microenvironment, or identifying interactions between cells, including spatial patterns of expression and co-occurrence. However, nearly half of the studies lacked comprehensive data processing protocols, hindering their reproducibility. By recommending greater transparency in sharing analysis methods and adapting single-cell analysis techniques with caution, this review aims to improve the reproducibility and reliability of future studies in cancer research.

Emerging measurements for tumor-infiltrating lymphocytes in breast cancer

Tumor-infiltrating lymphocytes are a general term for lymphocytes or immune cells infiltrating the tumor microenvironment. Numerous studies have demonstrated tumor-infiltrating lymphocytes to be robust prognostic and predictive biomarkers in breast cancer. Recently, immune checkpoint inhibitors, which directly target tumor-infiltrating lymphocytes, have become part of standard of care treatment for triple-negative breast cancer. Surprisingly, tumor-infiltrating lymphocytes quantified by conventional methods do not predict response to immune checkpoint inhibitors, which highlights the heterogeneity of tumor-infiltrating lymphocytes and the complexity of the immune network in the tumor microenvironment. Tumor-infiltrating lymphocytes are composed of diverse immune cell populations, including cytotoxic CD8-positive T lymphocytes, B cells and myeloid cells. Traditionally, tumor-infiltrating lymphocytes in tumor stroma have been evaluated by histology. However, the standardization of this approach is limited, necessitating the use of various novel technologies to elucidate the heterogeneity in the tumor microenvironment. This review outlines the evaluation methods for tumor-infiltrating lymphocytes from conventional pathological approaches that evaluate intratumoral and stromal tumor-infiltrating lymphocytes such as immunohistochemistry, to the more recent advancements in computer tissue imaging using artificial intelligence, flow cytometry sorting and multi-omics analyses using high-throughput assays to estimate tumor-infiltrating lymphocytes from bulk tumor using immune signatures or deconvolution tools. We also discuss higher resolution technologies that enable the analysis of tumor-infiltrating lymphocytes heterogeneity such as single-cell analysis and spatial transcriptomics. As we approach the era of personalized medicine, it is important for clinicians to understand these technologies.

Research progress in isolation and identification of rumen probiotics

With the increasing research on the exploitation of rumen microbial resources, rumen probiotics have attracted much attention for their positive contributions in promoting nutrient digestion, inhibiting pathogenic bacteria, and improving production performance. In the past two decades, macrogenomics has provided a rich source of new-generation probiotic candidates, but most of these "dark substances" have not been successfully cultured due to the restrictive growth conditions. However, fueled by high-throughput culture and sorting technologies, it is expected that the potential probiotics in the rumen can be exploited on a large scale, and their potential applications in medicine and agriculture can be explored. In this paper, we review and summarize the classical techniques for isolation and identification of rumen probiotics, introduce the development of droplet-based high-throughput cell culture and single-cell sequencing for microbial culture and identification, and finally introduce promising cultureomics techniques. The aim is to provide technical references for the development of related technologies and microbiological research to promote the further development of the field of rumen microbiology research.

scBSP: A fast and accurate tool for identifying spatially variable genes from spatial transcriptomic data

Spatially resolved transcriptomics have enabled the inference of gene expression patterns within two and three-dimensional space, while introducing computational challenges due to growing spatial resolutions and sparse expressions. Here, we introduce scBSP, an open-source, versatile, and user-friendly package designed for identifying spatially variable genes in large-scale spatial transcriptomics. scBSP implements sparse matrix operation to significantly increase the computational efficiency in both computational time and memory usage, processing the high-definition spatial transcriptomics data for 19,950 genes on 181,367 spots within 10 seconds. Applied to diverse sequencing data and simulations, scBSP efficiently identifies spatially variable genes, demonstrating fast computational speed and consistency across various sequencing techniques and spatial resolutions for both two and three-dimensional data with up to millions of cells. On a sample with hundreds of thousands of sports, scBSP identifies SVGs accurately in seconds to on a typical desktop computer.

Cellular diversity through space and time: adding new dimensions to GBM therapeutic development

The current median survival for glioblastoma (GBM) patients is only about 16 months, with many patients succumbing to the disease in just a matter of months, making it the most common and aggressive primary brain cancer in adults. This poor outcome is, in part, due to the lack of new treatment options with only one FDA-approved treatment in the last decade. Advances in sequencing techniques and transcriptomic analyses have revealed a vast degree of heterogeneity in GBM, from inter-patient diversity to intra-tumoral cellular variability. These cutting-edge approaches are providing new molecular insights highlighting a critical role for the tumor microenvironment (TME) as a driver of cellular plasticity and phenotypic heterogeneity. With this expanded molecular toolbox, the influence of TME factors, including endogenous (e.g., oxygen and nutrient availability and interactions with non-malignant cells) and iatrogenically induced (e.g., post-therapeutic intervention) stimuli, on tumor cell states can be explored to a greater depth. There exists a critical need for interrogating the temporal and spatial aspects of patient tumors at a high, cell-level resolution to identify therapeutically targetable states, interactions and mechanisms. In this review, we discuss advancements in our understanding of spatiotemporal diversity in GBM with an emphasis on the influence of hypoxia and immune cell interactions on tumor cell heterogeneity. Additionally, we describe specific high-resolution spatially resolved methodologies and their potential to expand the impact of pre-clinical GBM studies. Finally, we highlight clinical attempts at targeting hypoxia- and immune-related mechanisms of malignancy and the potential therapeutic opportunities afforded by single-cell and spatial exploration of GBM patient specimens.

A comprehensive review: synergizing stem cell and embryonic development knowledge in mouse and human integrated stem cell-based embryo models

Mammalian stem cell-based embryo models have emerged as innovative tools for investigating early embryogenesis in both mice and primates. They not only reduce the need for sacrificing mice but also overcome ethical limitations associated with human embryo research. Furthermore, they provide a platform to address scientific questions that are otherwise challenging to explore in vivo. The usefulness of a stem cell-based embryo model depends on its fidelity in replicating development, efficiency and reproducibility; all essential for addressing biological queries in a quantitative manner, enabling statistical analysis. Achieving such fidelity and efficiency requires robust systems that demand extensive optimization efforts. A profound understanding of pre- and post-implantation development, cellular plasticity, lineage specification, and existing models is imperative for making informed decisions in constructing these models. This review aims to highlight essential differences in embryo development and stem cell biology between mice and humans, assess how these variances influence the formation of partially and fully integrated stem cell models, and identify critical challenges in the field.