Spatial-omics: Novel approaches to probe cell heterogeneity and extracellular matrix biology

Cognitive frailty: A comprehensive clinical paradigm beyond cognitive decline

Cognitive frailty is an emerging concept in research and clinical practice that incorporates both physical frailty and mild cognitive impairment (MCI) or subjective cognitive decline (SCD). Unlike traditional approaches that separate physical frailty and dementia, cognitive frailty treats these domains as interrelated and coexisting, with significant implications for clinical outcomes and predicting cognitive decline. Despite growing recognition of this interrelationship, a dualistic view of physical and cognitive processes persists. The paradigm of cognitive frailty holds promise as a biomarker- like amyloid plaques or neurofibrillary tangles- but with the advantage of identifying risk at a prefrail stage, before clinical signs of MCI or dementia emerge. This review examines the pathophysiological and clinical dimensions of cognitive frailty and promotes for its integration into routine assessments in memory clinics.

Alternative splicing in autism spectrum disorder: Recent insights from mechanisms to therapy

Alternative splicing (AS) is a vital and highly dynamic RNA regulatory mechanism that allows a single gene to generate multiple mRNA and protein isoforms. Dysregulation of AS has been identified as a key contributor to the pathogenesis of autism spectrum disorders (ASD). A comprehensive understanding of aberrant splicing mechanisms and their functional consequences in ASD can help uncover the molecular basis of the disorder and facilitate the development of therapeutic strategies. This review focuses on the major aberrant splicing events and key splicing regulators associated with ASD, highlighting their roles in linking defective splicing to ASD pathogenesis. In addition, a discussion of how emerging technologies, such as long-read sequencing, single-cell sequencing, spatial transcriptomics and CRISPR-Cas systems are offering novel insights into the role and mechanisms of AS in ASD is presented. Finally, the RNA splicing-based therapeutic strategies are evaluated, emphasizing their potential to address unmet clinical needs in ASD treatment.

Integrated 3D imaging of FFPE lung tissue combining microCT, light and electron microscopy allows for contextualized ultrastructural and histological analysis

Classical histopathology of formalin fixed and paraffin embedded (FFPE) tissue using light microscopy (LM) remains the undisputed gold standard in biomedical microstructural lung tissue analysis. To extend this method, we developed an integrative imaging and processing pipeline which adds 3D context and screening capabilities by microCT (µCT) imaging of the entire paraffin block and adds ultrastructural information by correlative same-slide scanning electron microscopy (SEM). The different modalities are integrated by elastic registration to provide hybrid image datasets. Without compromising standard light microscopic readout, we overcome the limitations of conventional histology by combining and integrating several imaging modalities. The biochemical information contained in histological and immunohistological tissue staining is embedded into the 3D tissue configuration and is amplified by adding ultrastructural visualization of features of interest. By combining µCT and conventional histological processing, specimens can be screened, and specifically preselected areas of interest can be targeted in the subsequent sectioning process. While most of the µCT data shown in the manuscript was acquired at a Synchrotron, we further demonstrate that our workflow can also be applied using X-ray microscopy.

Spatial Insights in Cardiovascular Aging

Spatial omics provides unprecedented insights into how the cardiovascular system is spatially organized and how cellular phenotypes are distributed. Researchers have been able to clarify the complex spatial architecture of the cardiovascular system and how cellular phenotypes are distributed during the aging process by integrating data from spatial omics. In addition, this new technology has revealed previously hidden patterns of gene expression and cellular communication that were not detected using traditional bulk omics approaches. In this review, we explore the contribution of spatial omics in clarifying the molecular mechanisms that influence cardiovascular aging, highlighting the importance and application of spatial omics in unraveling the spatial heterogeneity within the aging cardiovascular system. This will help us understand the molecular mechanisms implicated in age-related cardiovascular diseases.

Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications

Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell-cell interactions within their native environments. Despite challenges in data complexity and integration, advancements in multi-omics pipelines and computational tools are enhancing data accuracy and biological interpretation. This review provides a comprehensive overview of key spatial omics technologies, their analytical methods, validation strategies, and clinical applications. By integrating spatially resolved molecular data with traditional omics, spatial omics is transforming precision medicine, biomarker discovery, and personalized therapy. Future research should focus on improving standardization, reproducibility, and multimodal data integration to fully realize the potential of spatial omics in clinical and translational research.

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.

High-Throughput Single-Cell Analysis of Local Nascent Protein Deposition in 3D Microenvironments via Extracellular Protein Identification Cytometry (EPIC)

Extracellular matrix (ECM) guides cell behavior and tissue fate. Cell populations are notoriously heterogeneous leading to large variations in cell behavior at the single-cell level. Although insights into population heterogeneity are valuable for fundamental biology, regenerative medicine, and drug testing, current ECM analysis techniques only provide either averaged population-level data or single-cell data from a limited number of cells. Here, extracellular protein identification cytometry (EPIC) is presented as a novel platform technology that enables high-throughput measurements of local nascent protein deposition at single-cell level. Specifically, human primary chondrocytes are microfluidically encapsulated in enzymatically crosslinked microgels of 16 picoliter at kHz rates, forming large libraries of discrete 3D single-cell microniches in which ECM can be deposited. ECM proteins are labeled using fluorescence immunostaining to allow for nondestructive analysis via flow cytometry. This approach reveals population heterogeneity in matrix deposition at unprecedented throughput, allowing for the identification and fluorescent activated cell sorting-mediated isolation of cellular subpopulations. Additionally, it is demonstrated that inclusion of a second cell into microgels allows for studying the effect of cell-cell contact on matrix deposition. In summary, EPIC enables high-throughput single-cell analysis of nascent proteins in 3D microenvironments, which is anticipated to advance fundamental knowledge and tissue engineering applications.

Applications of Nanotechnology for Spatial Omics: Biological Structures and Functions at Nanoscale Resolution

Spatial omics methods are extensions of traditional histological methods that can illuminate important biomedical mechanisms of physiology and disease by examining the distribution of biomolecules, including nucleic acids, proteins, lipids, and metabolites, at microscale resolution within tissues or individual cells. Since, for some applications, the desired resolution for spatial omics approaches the nanometer scale, classical tools have inherent limitations when applied to spatial omics analyses, and they can measure only a limited number of targets. Nanotechnology applications have been instrumental in overcoming these bottlenecks. When nanometer-level resolution is needed for spatial omics, super resolution microscopy or detection imaging techniques, such as mass spectrometer imaging, are required to generate precise spatial images of target expression. DNA nanostructures are widely used in spatial omics for purposes such as nucleic acid detection, signal amplification, and DNA barcoding for target molecule labeling, underscoring advances in spatial omics. Other properties of nanotechnologies include advanced spatial omics methods, such as microfluidic chips and DNA barcodes. In this review, we describe how nanotechnologies have been applied to the development of spatial transcriptomics, proteomics, metabolomics, epigenomics, and multiomics approaches. We focus on how nanotechnology supports improved resolution and throughput of spatial omics, surpassing traditional techniques. We also summarize future challenges and opportunities for the application of nanotechnology to spatial omics methods.

New perspective on central nervous system disorders: focus on mass spectrometry imaging

An abnormally organized brain spatial network is linked to the development of various central nervous system (CNS) disorders, including neurodegenerative diseases and neuropsychiatric disorders. However, the complicated molecular mechanisms of these diseases remain unresolved, making the development of treatment strategies difficult. A novel molecular imaging technique, called mass spectrometry imaging (MSI), captures molecular information on the surface of samples in situ. With MSI, multiple compounds can be simultaneously visualized in a single experiment. The high spatial resolution enables the simultaneous visualization of the spatial distribution and relative content of various compounds. The wide application of MSI in biomedicine has facilitated extensive studies on CNS disorders in recent years. This review provides a concise overview of the processes, applications, advantages, and disadvantages, as well as mechanisms of the main types of MSI. Meanwhile, this review summarizes the main applications of MSI in studying CNS diseases, including Alzheimer's disease (AD), CNS tumors, stroke, depression, Huntington's disease (HD), and Parkinson's disease (PD). Finally, this review comprehensively discusses the synergistic application of MSI with other advanced imaging modalities, its utilization in organoid models, its integration with spatial omics techniques, and provides an outlook on its future potential in single-cell analysis.

Matrisomics: Beyond the extracellular matrix for unveiling tumor microenvironment

The matrisome, a group of proteins constituting or interacting with the extracellular matrix (ECM), has garnered attention as a potent regulator of cancer progression. An increasing number of studies have focused on cancer matrisome utilizing diverse -omics approaches. Here, we present diverse patterns of matrisomal populations within cancer tissues, exploring recent -omics studies spanning different '-omics' levels (epigenomics, genomics, transcriptomics, and proteomics), as well as newly developed sequencing techniques such as single-cell RNA sequencing and spatial transcriptomics. Some matrisome genes showed uniform patterns of upregulated or downregulated expression across various cancers, while others displayed different expression patterns according to the cancer types. This matrisomal dysregulation in cancer was further examined according to their originating cell type and spatial location in the tumor tissue. Experimental studies were also collected to demonstrate the identified roles of matrisome genes during cancer progression. Interestingly, many studies on cancer matrisome have suggested matrisome genes as effective biomarkers in cancer research. Although the specific mechanisms and clinical applications of cancer matrisome have not yet been fully elucidated, recent techniques and analyses on cancer matrisomics have emphasized their biological importance in cancer progression and their clinical implications in deciding the efficacy of cancer treatment.

Disturbance of Immune Microenvironment in Androgenetic Alopecia through Spatial Transcriptomics

Androgenetic alopecia (AGA) is characterized by microinflammation and abnormal immune responses, particularly in the upper segment of hair follicles (HFs). However, the precise patterns of immune dysregulation remain unclear, partly due to limitations in current analysis techniques to preserve tissue architecture. The infundibulum, a major part of the upper segment of HFs, is associated with significant clusters of immune cells. In this study, we investigated immune cells around the infundibulum, referred to as peri-infundibular immune infiltration (PII). We employed spatial transcriptome profiling, a high-throughput analysis technology, to investigate the immunological disruptions within the PII region. Our comprehensive analysis included an evaluation of overall immune infiltrates, gene set enrichment analysis (GSEA), cellular deconvolution, differential expression analysis, over-representation analysis, protein-protein interaction (PPI) networks, and upstream regulator analysis to identify cell types and molecular dysregulation in immune cells. Our results demonstrated significant differences in immune signatures between the PII of AGA patients (PII-A) and the PII of control donors (PII-C). Specifically, PII-A exhibited an enrichment of CD4+ helper T cells, distinct immune response patterns, and a bias toward a T helper (Th) 2 response. Immunohistochemistry revealed disruptions in T cell subpopulations, with more CD4+ T cells displaying an elevated Th2 response and a reduced Th1-cytotoxic response compared to PII-C. These findings reveal the unique immune landscapes of PII-A and PII-C, suggesting potential for the development of innovative treatment approaches.

Fibroblast and myofibroblast activation in normal tissue repair and fibrosis

The term 'fibroblast' often serves as a catch-all for a diverse array of mesenchymal cells, including perivascular cells, stromal progenitor cells and bona fide fibroblasts. Although phenotypically similar, these subpopulations are functionally distinct, maintaining tissue integrity and serving as local progenitor reservoirs. In response to tissue injury, these cells undergo a dynamic fibroblast-myofibroblast transition, marked by extracellular matrix secretion and contraction of actomyosin-based stress fibres. Importantly, whereas transient activation into myofibroblasts aids in tissue repair, persistent activation triggers pathological fibrosis. In this Review, we discuss the roles of mechanical cues, such as tissue stiffness and strain, alongside cell signalling pathways and extracellular matrix ligands in modulating myofibroblast activation and survival. We also highlight the role of epigenetic modifications and myofibroblast memory in physiological and pathological processes. Finally, we discuss potential strategies for therapeutically interfering with these factors and the associated signal transduction pathways to improve the outcome of dysregulated healing.

Multiple aspects of matrix stiffness in cancer progression

The biophysical and biomechanical properties of the extracellular matrix (ECM) are crucial in the processes of cell differentiation and proliferation. However, it is unclear to what extent tumor cells are influenced by biomechanical and biophysical changes of the surrounding microenvironment and how this response varies between different tumor forms, and over the course of tumor progression. The entire ensemble of genes encoding the ECM associated proteins is called matrisome. In cancer, the ECM evolves to become highly dysregulated, rigid, and fibrotic, serving both pro-tumorigenic and anti-tumorigenic roles. Tumor desmoplasia is characterized by a dramatic increase of α-smooth muscle actin expressing fibroblast and the deposition of hard ECM containing collagen, fibronectin, proteoglycans, and hyaluronic acid and is common in many solid tumors. In this review, we described the role of inflammation and inflammatory cytokines, in desmoplastic matrix remodeling, tumor state transition driven by microenvironment forces and the signaling pathways in mechanotransduction as potential targeted therapies, focusing on the impact of qualitative and quantitative variations of the ECM on the regulation of tumor development, hypothesizing the presence of matrisome drivers, acting alongside the cell-intrinsic oncogenic drivers, in some stages of neoplastic progression and in some tumor contexts, such as pancreatic carcinoma, breast cancer, lung cancer and mesothelioma.

Local, Quantitative Morphometry of Fibroproliferative Lung Injury Using Laminin

Investigations into the mechanisms of injury and repair in fibroproliferative disease require consideration of the spatial heterogeneity inherent in the disease. Most scoring of fibrotic remodeling in preclinical animal models relies on the modified Ashcroft score, which is an ordinal rubric of macroscopic resolution. The obvious limitations of manual histopathologic scoring have generated an unmet need for unbiased, repeatable scoring of fibroproliferative burden in tissue. Using computer vision approaches on immunofluorescence imaging of the extracellular matrix component laminin, we generated a robust and repeatable quantitative remodeling scorer. In the bleomycin lung injury model, the quantitative remodeling scorer shows significant agreement with the modified Ashcroft scale. This antibody-based approach is easily integrated into larger multiplex immunofluorescence experiments, which we demonstrate by testing the spatial apposition of tertiary lymphoid structures to fibroproliferative tissue, a poorly characterized phenomenon observed in both human interstitial lung diseases and preclinical models of lung fibrosis. The tool reported in this article is available as a stand-alone application that is usable without programming knowledge.

Advances in Integrated Multi-omics Analysis for Drug-Target Identification

As an essential component of modern drug discovery, the role of drug-target identification is growing increasingly prominent. Additionally, single-omics technologies have been widely utilized in the process of discovering drug targets. However, it is difficult for any single-omics level to clearly expound the causal connection between drugs and how they give rise to the emergence of complex phenotypes. With the progress of large-scale sequencing and the development of high-throughput technologies, the tendency in drug-target identification has shifted towards integrated multi-omics techniques, gradually replacing traditional single-omics techniques. Herein, this review centers on the recent advancements in the domain of integrated multi-omics techniques for target identification, highlights the common multi-omics analysis strategies, briefly summarizes the selection of multi-omics analysis tools, and explores the challenges of existing multi-omics analyses, as well as the applications of multi-omics technology in drug-target identification.

Effects of mechanical ventilation on the interstitial extracellular matrix in healthy lungs and lungs affected by acute respiratory distress syndrome: a narrative review

BACKGROUND

Mechanical ventilation, a lifesaving intervention in critical care, can lead to damage in the extracellular matrix (ECM), triggering inflammation and ventilator-induced lung injury (VILI), particularly in conditions such as acute respiratory distress syndrome (ARDS). This review discusses the detailed structure of the ECM in healthy and ARDS-affected lungs under mechanical ventilation, aiming to bridge the gap between experimental insights and clinical practice by offering a thorough understanding of lung ECM organization and the dynamics of its alteration during mechanical ventilation.

MAIN TEXT

Focusing on the clinical implications, we explore the potential of precise interventions targeting the ECM and cellular signaling pathways to mitigate lung damage, reduce inflammation, and ultimately improve outcomes for critically ill patients. By analyzing a range of experimental studies and clinical papers, particular attention is paid to the roles of matrix metalloproteinases (MMPs), integrins, and other molecules in ECM damage and VILI. This synthesis not only sheds light on the structural changes induced by mechanical stress but also underscores the importance of cellular responses such as inflammation, fibrosis, and excessive activation of MMPs.

CONCLUSIONS

This review emphasizes the significance of mechanical cues transduced by integrins and their impact on cellular behavior during ventilation, offering insights into the complex interactions between mechanical ventilation, ECM damage, and cellular signaling. By understanding these mechanisms, healthcare professionals in critical care can anticipate the consequences of mechanical ventilation and use targeted strategies to prevent or minimize ECM damage, ultimately leading to better patient management and outcomes in critical care settings.

Modelling and targeting mechanical forces in organ fibrosis

Few efficacious therapies exist for the treatment of fibrotic diseases, such as skin scarring, liver cirrhosis and pulmonary fibrosis, which is related to our limited understanding of the fundamental causes and mechanisms of fibrosis. Mechanical forces from cell-matrix interactions, cell-cell contact, fluid flow and other physical stimuli may play a central role in the initiation and propagation of fibrosis. In this Review, we highlight the mechanotransduction mechanisms by which various sources of physical force drive fibrotic disease processes, with an emphasis on central pathways that may be therapeutically targeted to prevent and reverse fibrosis. We then discuss engineered models of mechanotransduction in fibrosis, as well as molecular and biomaterials-based therapeutic approaches for limiting fibrosis and promoting regenerative healing phenotypes in various organs. Finally, we discuss challenges within fibrosis research that remain to be addressed and that may greatly benefit from next-generation bioengineered model systems.

Molecular analysis of the extracellular microenvironment: from form to function

The extracellular matrix (ECM) proteome represents an important component of the tissue microenvironment that controls chemical flux and induces cell signaling through encoded structure. The analysis of the ECM represents an analytical challenge through high levels of post-translational modifications, protease-resistant structures, and crosslinked, insoluble proteins. This review provides a comprehensive overview of the analytical challenges involved in addressing the complexities of spatially profiling the extracellular matrix proteome. A synopsis of the process of synthesizing the ECM structure, detailing inherent chemical complexity, is included to present the scope of the analytical challenge. Current chromatographic and spatial techniques addressing these challenges are detailed. Capabilities for multimodal multiplexing with cellular populations are discussed with a perspective on developing a holistic view of disease processes that includes both the cellular and extracellular microenvironment.

The journey towards clinical adoption of MALDI-MS-based imaging proteomics: from current challenges to future expectations

Among the spatial omics techniques available, mass spectrometry imaging (MSI) represents one of the most promising owing to its capability to map the distribution of hundreds of peptides and proteins, as well as other classes of biomolecules, within a complex sample background in a multiplexed and relatively high-throughput manner. In particular, matrix-assisted laser desorption/ionisation (MALDI-MSI) has come to the fore and established itself as the most widely used technique in clinical research. However, the march of this technique towards clinical utility has been hindered by issues related to method reproducibility, appropriate biocomputational tools, and data storage. Notwithstanding these challenges, significant progress has been achieved in recent years regarding multiple facets of the technology and has rendered it more suitable for a possible clinical role. As such, there is now more robust and extensive evidence to suggest that the technology has the potential to support clinical decision-making processes under appropriate circumstances. In this review, we will discuss some of the recent developments that have facilitated this progress and outline some of the more promising clinical proteomics applications which have been developed with a clear goal towards implementation in mind.

Proteomic analysis of decellularized mice liver and kidney extracellular matrices

BACKGROUND

The extracellular matrix (ECM) is a three-dimensional network of proteins that encases and supports cells within a tissue and promotes physiological and pathological cellular differentiation and functionality. Understanding the complex composition of the ECM is essential to decrypt physiological processes as well as pathogenesis. In this context, the method of decellularization is a useful technique to eliminate cellular components from tissues while preserving the majority of the structural and functional integrity of the ECM.

RESULTS

In this study, we employed a bottom-up proteomic approach to elucidate the intricate network of proteins in the decellularized extracellular matrices of murine liver and kidney tissues. This approach involved the use of a novel, perfusion-based decellularization protocol to generate acellular whole organ scaffolds. Proteomic analysis of decellularized mice liver and kidney ECM scaffolds revealed tissue-specific differences in matrisome composition, while we found a predominantly stable composition of the core matrisome, consisting of collagens, glycoproteins, and proteoglycans. Liver matrisome analysis revealed unique proteins such as collagen type VI alpha-6, fibrillin-2 or biglycan. In the kidney, specific ECM-regulators such as cathepsin z were detected.

CONCLUSION

The identification of distinct proteomic signatures provides insights into how different matrisome compositions might influence the biological properties of distinct tissues. This experimental workflow will help to further elucidate the proteomic landscape of decellularized extracellular matrix scaffolds of mice in order to decipher complex cell-matrix interactions and their contribution to a tissue-specific microenvironment.

High-dimensional comparison of monocytes and T cells in post-COVID and idiopathic pulmonary fibrosis

Introduction

Up to 30% of hospitalized COVID-19 patients experience persistent sequelae, including pulmonary fibrosis (PF).

Methods

We examined COVID-19 survivors with impaired lung function and imaging worrisome for developing PF and found within six months, symptoms, restriction and PF improved in some (Early-Resolving COVID-PF), but persisted in others (Late-Resolving COVID-PF). To evaluate immune mechanisms associated with recovery versus persistent PF, we performed single-cell RNA-sequencing and multiplex immunostaining on peripheral blood mononuclear cells from patients with Early- and Late-Resolving COVID-PF and compared them to age-matched controls without respiratory disease.

Results and discussion

Our analysis showed circulating monocytes were significantly reduced in Late-Resolving COVID-PF patients compared to Early-Resolving COVID-PF and non-diseased controls. Monocyte abundance correlated with pulmonary function forced vital capacity and diffusion capacity. Differential expression analysis revealed MHC-II class molecules were upregulated on the CD8 T cells of Late-Resolving COVID-PF patients but downregulated in monocytes. To determine whether these immune signatures resembled other interstitial lung diseases, we analyzed samples from Idiopathic Pulmonary Fibrosis (IPF) patients. IPF patients had a similar marked decrease in monocyte HLA-DR protein expression compared to Late-Resolving COVID-PF patients. Our findings indicate decreased circulating monocytes are associated with decreased lung function and uniquely distinguish Late-Resolving COVID-PF from Early-Resolving COVID-PF, IPF, and non-diseased controls.

History of key regulatory peptide systems and perspectives for future research

Throughout the 20th Century, regulatory peptide discovery advanced from the identification of gut hormones to the extraction and characterization of hypothalamic hypophysiotropic factors, and to the isolation and cloning of multiple brain neuropeptides. These discoveries were followed by the discovery of G-protein-coupled and other membrane receptors for these peptides. Subsequently, the systems physiology associated with some of these multiple regulatory peptides and receptors has been comprehensively elucidated and has led to improved therapeutics and diagnostics and their approval by the US Food and Drug Administration. In light of this wealth of information and further potential, it is truly a time of renaissance for regulatory peptides. In this perspective, we review what we have learned from the pioneers in exemplified fields of gut peptides, such as cholecystokinin, enterochromaffin-like-cell peptides, and glucagon, from the trailblazing studies on the key stress hormone, corticotropin-releasing factor, as well as from more recently characterized relaxin-family peptides and receptors. The historical viewpoints are based on our understanding of these topics in light of the earliest phases of research and on subsequent studies and the evolution of knowledge, aiming to sharpen our vision of the current state-of-the-art and those studies that should be prioritized in the future.

Chemical tagging mass spectrometry: an approach for single-cell omics

Single-cell (SC) analysis offers new insights into the study of fundamental biological phenomena and cellular heterogeneity. The superior sensitivity, high throughput, and rich chemical information provided by mass spectrometry (MS) allow MS to emerge as a leading technology for molecular profiling of SC omics, including the SC metabolome, lipidome, and proteome. However, issues such as ionization suppression, low concentration, and huge span of dynamic concentrations of SC components lead to poor MS response for certain types of molecules. It is noted that chemical tagging/derivatization has been adopted in SCMS analysis, and this strategy has been proven an effective solution to circumvent these issues in SCMS analysis. Herein, we review the basic principle and general strategies of chemical tagging/derivatization in SCMS analysis, along with recent applications of chemical derivatization to single-cell metabolomics and multiplexed proteomics, as well as SCMS imaging. Furthermore, the challenges and opportunities for the improvement of chemical derivatization strategies in SCMS analysis are discussed.

Evaluation of antibody-based single cell type imaging techniques coupled to multiplexed imaging of N-glycans and collagen peptides by matrix-assisted laser desorption/ionization mass spectrometry imaging

The integration of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) with single cell spatial omics methods allows for a comprehensive investigation of single cell spatial information and matrisomal N-glycan and extracellular matrix protein imaging. Here, the performance of the antibody-directed single cell workflows coupled with MALDI-MSI are evaluated. Miralys™ photocleavable mass-tagged antibody probes (MALDI-IHC, AmberGen, Inc.), GeoMx DSP® (NanoString, Inc.), and Imaging Mass Cytometry (IMC, Standard BioTools Inc.) were used in series with MALDI-MSI of N-glycans and extracellular matrix peptides on formalin-fixed paraffin-embedded tissues. Single cell omics protocols were performed before and after MALDI-MSI. The data suggests that for each modality combination, there is an optimal order for performing both techniques on the same tissue section. An overall conclusion is that MALDI-MSI studies may be completed on the same tissue section as used for antibody-directed single cell modalities. This work increases access to combined cellular and extracellular information within the tissue microenvironment to enhance research on the pathological origins of disease.

Advanced Omics Techniques for Understanding Cochlear Genome, Epigenome, and Transcriptome in Health and Disease

Advanced genomics, transcriptomics, and epigenomics techniques are providing unprecedented insights into the understanding of the molecular underpinnings of the central nervous system, including the neuro-sensory cochlea of the inner ear. Here, we report for the first time a comprehensive and updated overview of the most advanced omics techniques for the study of nucleic acids and their applications in cochlear research. We describe the available in vitro and in vivo models for hearing research and the principles of genomics, transcriptomics, and epigenomics, alongside their most advanced technologies (like single-cell omics and spatial omics), which allow for the investigation of the molecular events that occur at a single-cell resolution while retaining the spatial information.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

Single cell RNA sequencing (scRNA-seq) provides a great convenience for studying tumor occurrence and development for its ability to study gene expression at the individual cell level. However, patient-derived tumor tissues are composed of multiple types of cells including tumor cells and adjacent non-malignant cells such as stromal cells and immune cells. The spatial locations of various cells in situ tissues plays a pivotal role in the occurrence and development of tumors, which cannot be elucidated by scRNA-seq alone. Spatially resolved transcriptomics (SRT) technology emerges timely to explore the unrecognized relationship between the spatial background of a particular cell and its functions, and is increasingly used in cancer research. This review provides a systematic overview of the SRT technologies that are developed, in particular the more widely used cutting-edge SRT technologies based on next-generation sequencing (NGS). In addition, the main achievements by SRT technologies in precisely unveiling the underappreciated spatial locations on gene expression and cell function with unprecedented high-resolution in cancer research are emphasized, with the aim of developing more effective clinical therapeutics oriented to a deeper understanding of the interaction between tumor cells and surrounding non-malignant cells.

Promise of spatially resolved omics for tumor research

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

Dissecting the brain with spatially resolved multi-omics

Recent studies have highlighted spatially resolved multi-omics technologies, including spatial genomics, transcriptomics, proteomics, and metabolomics, as powerful tools to decipher the spatial heterogeneity of the brain. Here, we focus on two major approaches in spatial transcriptomics (next-generation sequencing-based technologies and image-based technologies), and mass spectrometry imaging technologies used in spatial proteomics and spatial metabolomics. Furthermore, we discuss their applications in neuroscience, including building the brain atlas, uncovering gene expression patterns of neurons for special behaviors, deciphering the molecular basis of neuronal communication, and providing a more comprehensive explanation of the molecular mechanisms underlying central nervous system disorders. However, further efforts are still needed toward the integrative application of multi-omics technologies, including the real-time spatial multi-omics analysis in living cells, the detailed gene profile in a whole-brain view, and the combination of functional verification.

Single-cell and spatial transcriptomics: Advances in heart development and disease applications

Current transcriptomics technologies, including bulk RNA-seq, single-cell RNA sequencing (scRNA-seq), single-nucleus RNA-sequencing (snRNA-seq), and spatial transcriptomics (ST), provide novel insights into the spatial and temporal dynamics of gene expression during cardiac development and disease processes. Cardiac development is a highly sophisticated process involving the regulation of numerous key genes and signaling pathways at specific anatomical sites and developmental stages. Exploring the cell biological mechanisms involved in cardiogenesis also contributes to congenital heart disease research. Meanwhile, the severity of distinct heart diseases, such as coronary heart disease, valvular disease, cardiomyopathy, and heart failure, is associated with cellular transcriptional heterogeneity and phenotypic alteration. Integrating transcriptomic technologies in the clinical diagnosis and treatment of heart diseases will aid in advancing precision medicine. In this review, we summarize applications of scRNA-seq and ST in the cardiac field, including organogenesis and clinical diseases, and provide insights into the promise of single-cell and spatial transcriptomics in translational research and precision medicine.

10 years of extracellular matrix proteomics: Accomplishments, challenges, and future perspectives

The extracellular matrix (ECM) is a complex assembly of hundreds of proteins forming the architectural scaffold of multicellular organisms. In addition to its structural role, the ECM conveys signals orchestrating cellular phenotypes. Alterations of ECM composition, abundance, structure, or mechanics, have been linked to diseases and disorders affecting all physiological systems, including fibrosis and cancer. Deciphering the protein composition of the ECM and how it changes in pathophysiological contexts is thus the first step toward understanding the roles of the ECM in health and disease and toward the development of therapeutic strategies to correct disease-causing ECM alterations. Potentially, the ECM also represents a vast, yet untapped reservoir of disease biomarkers. ECM proteins are characterized by unique biochemical properties that have hindered their study: they are large, heavily and uniquely post-translationally modified, and highly insoluble. Overcoming these challenges, we and others have devised mass-spectrometry-based proteomic approaches to define the ECM composition, or "matrisome", of tissues. This review provides a historical overview of ECM proteomics research and presents the latest advances that now allow the profiling of the ECM of healthy and diseased tissues. The second part highlights recent examples illustrating how ECM proteomics has emerged as a powerful discovery pipeline to identify prognostic cancer biomarkers. The third part discusses remaining challenges limiting our ability to translate findings to clinical application and proposes approaches to overcome them. Last, the review introduces readers to resources available to facilitate the interpretation of ECM proteomics datasets. The ECM was once thought to be impenetrable. MS-based proteomics has proven to be a powerful tool to decode the ECM. In light of the progress made over the past decade, there are reasons to believe that the in-depth exploration of the matrisome is within reach and that we may soon witness the first translational application of ECM proteomics.

Identification of a physiologic vasculogenic fibroblast state to achieve tissue repair

Tissue injury to skin diminishes miR-200b in dermal fibroblasts. Fibroblasts are widely reported to directly reprogram into endothelial-like cells and we hypothesized that miR-200b inhibition may cause such changes. We transfected human dermal fibroblasts with anti-miR-200b oligonucleotide, then using single cell RNA sequencing, identified emergence of a vasculogenic subset with a distinct fibroblast transcriptome and demonstrated blood vessel forming function in vivo. Anti-miR-200b delivery to murine injury sites likewise enhanced tissue perfusion, wound closure, and vasculogenic fibroblast contribution to perfused vessels in a FLI1 dependent manner. Vasculogenic fibroblast subset emergence was blunted in delayed healing wounds of diabetic animals but, topical tissue nanotransfection of a single anti-miR-200b oligonucleotide was sufficient to restore FLI1 expression, vasculogenic fibroblast emergence, tissue perfusion, and wound healing. Augmenting a physiologic tissue injury adaptive response mechanism that produces a vasculogenic fibroblast state change opens new avenues for therapeutic tissue vascularization of ischemic wounds.

Mapping Spatiotemporal Heterogeneity in Tumor Profiles by Integrating High-Throughput Imaging and Omics Analysis

Intratumoral heterogeneity associates with more aggressive disease progression and worse patient outcomes. Understanding the reasons enabling the emergence of such heterogeneity remains incomplete, which restricts our ability to manage it from a therapeutic perspective. Technological advancements such as high-throughput molecular imaging, single-cell omics, and spatial transcriptomics allow recording of patterns of spatiotemporal heterogeneity in a longitudinal manner, thus offering insights into the multiscale dynamics of its evolution. Here, we review the latest technological trends and biological insights from molecular diagnostics as well as spatial transcriptomics, both of which have witnessed burgeoning growth in the recent past in terms of mapping heterogeneity within tumor cell types as well as the stromal constitution. We also discuss ongoing challenges, indicating possible ways to integrate insights across these methods to have a systems-level spatiotemporal map of heterogeneity in each tumor and a more systematic investigation of the implications of heterogeneity for patient outcomes.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

The Role of Myofibroblasts in Physiological and Pathological Tissue Repair

Myofibroblasts are the construction workers of wound healing and repair damaged tissues by producing and organizing collagen/extracellular matrix (ECM) into scar tissue. Scar tissue effectively and quickly restores the mechanical integrity of lost tissue architecture but comes at the price of lost tissue functionality. Fibrotic diseases caused by excessive or persistent myofibroblast activity can lead to organ failure. This review defines myofibroblast terminology, phenotypic characteristics, and functions. We will focus on the central role of the cell, ECM, and tissue mechanics in regulating tissue repair by controlling myofibroblast action. Additionally, we will discuss how therapies based on mechanical intervention potentially ameliorate wound healing outcomes. Although myofibroblast physiology and pathology affect all organs, we will emphasize cutaneous wound healing and hypertrophic scarring as paradigms for normal tissue repair versus fibrosis. A central message of this review is that myofibroblasts can be activated from multiple cell sources, varying with local environment and type of injury, to either restore tissue integrity and organ function or create an inappropriate mechanical environment.

Essential Role of Multi-Omics Approaches in the Study of Retinal Vascular Diseases

Retinal vascular disease is a highly prevalent vision-threatening ocular disease in the global population; however, its exact mechanism remains unclear. The expansion of omics technologies has revolutionized a new medical research methodology that combines multiple omics data derived from the same patients to generate multi-dimensional and multi-evidence-supported holistic inferences, providing unprecedented opportunities to elucidate the information flow of complex multi-factorial diseases. In this review, we summarize the applications of multi-omics technology to further elucidate the pathogenesis and complex molecular mechanisms underlying retinal vascular diseases. Moreover, we proposed multi-omics-based biomarker and therapeutic strategy discovery methodologies to optimize clinical and basic medicinal research approaches to retinal vascular diseases. Finally, the opportunities, current challenges, and future prospects of multi-omics analyses in retinal vascular disease studies are discussed in detail.

Spatial Multiomics of Lipids, N-Glycans, and Tryptic Peptides on a Single FFPE Tissue Section

Mass spectrometry imaging (MSI) is an emerging technology that is capable of mapping various biomolecules within their native spatial context, and performing spatial multiomics on formalin-fixed paraffin-embedded (FFPE) tissues may further increase the molecular characterization of pathological states. Here we present a novel workflow which enables the sequential MSI of lipids, N-glycans, and tryptic peptides on a single FFPE tissue section and highlight the enhanced molecular characterization that is offered by combining the multiple spatial omics data sets. In murine brain and clear cell renal cell carcinoma (ccRCC) tissue, the three molecular levels provided complementary information and characterized different histological regions. Moreover, when the spatial omics data was integrated, the different histopathological regions of the ccRCC tissue could be better discriminated with respect to the imaging data set of any single omics class. Taken together, these promising findings demonstrate the capability to more comprehensively map the molecular complexity within pathological tissue.

Single-cell sequencing: A cutting edge tool in molecular medical research

The rapid development of advanced high throughput technologies and introduction of high resolution "omics" data through analysis of biological molecules has revamped medical research. Single-cell sequencing in recent years, is in fact revolutionising the field by providing a deeper, spatio-temporal analyses of individual cells within tissues and their relevance to disease. Like conventional sequencing, the single-cell approach deciphers the sequence of nucleotides in a given Deoxyribose Nucleic Acid (DNA), Ribose Nucleic Acid (RNA), Micro Ribose Nucleic Acid (miRNA), epigenetically modified DNA or chromatin DNA; however, the unit of analyses is changed to single cells rather than the entire tissue. Further, a large number of single cells analysed from a single tissue generate a unique holistic perception capturing all kinds of perturbations across different cells in the tissue that increases the precision of data. Inherently, execution of the technique generates a large amount of data, which is required to be processed in a specific manner followed by customised bioinformatic analysis to produce meaningful results. The most crucial role of single-cell sequencing technique is in elucidating the inter-cell genetic, epigenetic, transcriptomic and proteomic heterogeneity in health and disease. The current review presents a brief overview of this cutting-edge technology and its applications in medical research.

Quantitative and analytical tools to analyze the spatiotemporal population dynamics of microbial consortia

Microorganisms occupy almost every niche on earth. They play critical roles in maintaining ecological balance, atmospheric C/N cycle, and human health. Microbes live in consortia with metabolite exchange or signal communication. Quantitative and analytical tools are becoming increasingly important to study microbial consortia dynamics. We argue that a combined reductionist and holistic approach will be important to understanding the assembly rules and spatiotemporal population dynamics of the microbial community (MICOM). Reductionism allows us to reconstruct complex MICOM from isolated or simple synthetic consortia. Holism allows us to understand microbes as a community with cooperation and competition. Here we review the recent development of quantitative and analytical tools to uncover the underlying principles in microbial communities that govern their spatiotemporal change and interaction dynamics. Mathematical models and analytical tools will continue to provide essential knowledge and expand our capability to manipulate and control microbial consortia.

New Views of Old Proteins: Clarifying the Enigmatic Proteome

All human diseases involve proteins, yet our current tools to characterize and quantify them are limited. To better elucidate proteins across space, time, and molecular composition, we provide a >10 years of projection for technologies to meet the challenges that protein biology presents. With a broad perspective, we discuss grand opportunities to transition the science of proteomics into a more propulsive enterprise. Extrapolating recent trends, we describe a next generation of approaches to define, quantify, and visualize the multiple dimensions of the proteome, thereby transforming our understanding and interactions with human disease in the coming decade.

Infrared Laser Ablation Microsampling for Small Volume Proteomics

Infrared (IR) laser ablation was used to remove localized tissue regions from which proteins were extracted and processed with a low volume sample preparation workflow for bottom-up proteomics by liquid chromatography tandem mass spectrometry (LC-MS/MS). A polytetrafluoroethylene (PTFE) coated glass slide with 2 mm diameter microwells was used to capture ablated rat brain tissue for in situ protein digestion with submicroliter solution volumes. The resulting peptides were analyzed with LC-MS/MS for protein identification and label-free quantification. The method was used to identify an average of 600, 1350, and 1900 proteins from ablation areas of 0.01, 0.04, and 0.1 mm², respectively, from a 50 μm thick rat brain tissue section. Differential proteomics of 0.01 mm² regions captured from cerebral cortex and corpus callosum was accomplished to demonstrate the capabilities of the approach.

Unravelling Prostate Cancer Heterogeneity Using Spatial Approaches to Lipidomics and Transcriptomics

Simple Summary

Prostate cancer is a heterogenous disease in terms of disease aggressiveness and therapy response, leading to dilemmas in treatment decisions. This heterogeneity reflects the multifocal nature of prostate cancer and its diversity in cellular and molecular composition, necessitating spatial molecular approaches. Here in view of the emerging importance of rewired lipid metabolism as a source of biomarkers and therapeutic targets for prostate cancer, we highlight recent advancements in technologies that enable the spatial mapping of lipids and related metabolic pathways associated with prostate cancer development and progression. We also evaluate their potential for future implementation in treatment decision-making in the clinical management of prostate cancer.

Abstract

Due to advances in the detection and management of prostate cancer over the past 20 years, most cases of localised disease are now potentially curable by surgery or radiotherapy, or amenable to active surveillance without treatment. However, this has given rise to a new dilemma for disease management; the inability to distinguish indolent from lethal, aggressive forms of prostate cancer, leading to substantial overtreatment of some patients and delayed intervention for others. Driving this uncertainty is the critical deficit of novel targets for systemic therapy and of validated biomarkers that can inform treatment decision-making and to select and monitor therapy. In part, this lack of progress reflects the inherent challenge of undertaking target and biomarker discovery in clinical prostate tumours, which are cellularly heterogeneous and multifocal, necessitating the use of spatial analytical approaches. In this review, the principles of mass spectrometry-based lipid imaging and complementary gene-based spatial omics technologies, their application to prostate cancer and recent advancements in these technologies are considered. We put in perspective studies that describe spatially-resolved lipid maps and metabolic genes that are associated with prostate tumours compared to benign tissue and increased risk of disease progression, with the aim of evaluating the future implementation of spatial lipidomics and complementary transcriptomics for prognostication, target identification and treatment decision-making for prostate cancer.

The interplay of fibroblasts, the extracellular matrix, and inflammation in scar formation

Various forms of fibrosis, comprising tissue thickening and scarring, are involved in 40% of deaths across the world. Since the discovery of scarless functional healing in fetuses prior to a certain stage of development, scientists have attempted to replicate scarless wound healing in adults with little success. While the extracellular matrix (ECM), fibroblasts, and inflammatory mediators have been historically investigated as separate branches of biology, it has become increasingly necessary to consider them as parts of a complex and tightly regulated system that becomes dysregulated in fibrosis. With this new paradigm, revisiting fetal scarless wound healing provides a unique opportunity to better understand how this highly regulated system operates mechanistically. In the following review, we navigate the four stages of wound healing (hemostasis, inflammation, repair, and remodeling) against the backdrop of adult versus fetal wound healing, while also exploring the relationships between the ECM, effector cells, and signaling molecules. We conclude by singling out recent findings that offer promising leads to alter the dynamics between the ECM, fibroblasts, and inflammation to promote scarless healing. One factor that promises to be significant is fibroblast heterogeneity and how certain fibroblast subpopulations might be predisposed to scarless healing. Altogether, reconsidering fetal wound healing by examining the interplay of the various factors contributing to fibrosis provides new research directions that will hopefully help us better understand and address fibroproliferative diseases, such as idiopathic pulmonary fibrosis, liver cirrhosis, systemic sclerosis, progressive kidney disease, and cardiovascular fibrosis.

Engineering mammalian living materials towards clinically relevant therapeutics

Engineered living materials represent a new generation of human-made biotherapeutics that are highly attractive for a myriad of medical applications. In essence, such cell-rich platforms provide encodable bioactivities with extended lifetimes and environmental multi-adaptability currently unattainable in conventional biomaterial platforms. Emerging cell bioengineering tools are herein discussed from the perspective of materializing living cells as cooperative building blocks that drive the assembly of multiscale living materials. Owing to their living character, pristine cellular units can also be imparted with additional therapeutically-relevant biofunctionalities. On this focus, the most recent advances on the engineering of mammalian living materials and their biomedical applications are herein outlined, alongside with a critical perspective on major roadblocks hindering their realistic clinical translation. All in all, transposing the concept of leveraging living materials as autologous tissue-building entities and/or self-regulated biotherapeutics opens new realms for improving precision and personalized medicine strategies in the foreseeable future.

Deep-ultraviolet laser ablation sampling for proteomic analysis of tissue

Deep-ultraviolet laser ablation with a pulsed 193 nm ArF excimer laser was used to remove localized regions from tissue sections from which proteins were extracted for spatially resolved proteomic analysis by liquid chromatography tandem mass spectrometry (LC-MS/MS). The ability to capture intact proteins by ablation at 193 nm wavelength was verified by matrix-assisted laser desorption ionization (MALDI) of the protein standard bovine serum albumin (BSA), which showed that BSA was ablated and captured without fragmentation. A Bradford assay of the ablated and captured proteins indicated 90% efficiency for transfer of the intact protein at a laser fluence of 3 kJ/m². Rat brain tissue sections mounted on quartz microscope slides and ablated in transmission mode yielded 2 μg protein per mm² as quantified by the Bradford assay. Tissue areas ranging from 0.06 mm² to 1 mm² were ablated and the ejected material was collected for proteomic analysis. Extracted proteins were digested and the resulting peptides were analyzed by LC-MS/MS. The proteins extracted from the ablated areas were identified and the average number of identified proteins ranged from 85 in the 0.06 mm² area to 2400 in the 1 mm² area of a 50 μm thick tissue. In comparison to infrared laser ablation of equivalent sampled areas, both the protein mass and number of proteins identified using DUV laser ablation sampling were approximately four times larger.

Mapping Gene Expression in the Spatial Dimension

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

Cell segmentation in imaging-based spatial transcriptomics

Single-molecule spatial transcriptomics protocols based on in situ sequencing or multiplexed RNA fluorescent hybridization can reveal detailed tissue organization. However, distinguishing the boundaries of individual cells in such data is challenging and can hamper downstream analysis. Current methods generally approximate cells positions using nuclei stains. We describe a segmentation method, Baysor, that optimizes two-dimensional (2D) or three-dimensional (3D) cell boundaries considering joint likelihood of transcriptional composition and cell morphology. While Baysor can take into account segmentation based on co-stains, it can also perform segmentation based on the detected transcripts alone. To evaluate performance, we extend multiplexed error-robust fluorescence in situ hybridization (MERFISH) to incorporate immunostaining of cell boundaries. Using this and other benchmarks, we show that Baysor segmentation can, in some cases, nearly double the number of cells compared to existing tools while reducing segmentation artifacts. We demonstrate that Baysor performs well on data acquired using five different protocols, making it a useful general tool for analysis of imaging-based spatial transcriptomics.

Engineering stromal heterogeneity in cancer

Based on our exponentially increasing knowledge of stromal heterogeneity from advances in single-cell technologies, the notion that stromal cell types exist as a spectrum of unique subpopulations that have specific functions and spatial distributions in the tumor microenvironment has significant impact on tumor modeling for drug development and personalized drug testing. In this Review, we discuss the importance of incorporating stromal heterogeneity and tumor architecture, and propose an overall approach to guide the reconstruction of stromal heterogeneity in vitro for tumor modeling. These next-generation tumor models may support the development of more precise drugs targeting specific stromal cell subpopulations, as well as enable improved recapitulation of patient tumors in vitro for personalized drug testing.