Spatial proteomics in three-dimensional intact specimens

The RNA-binding protein LSM family regulating reproductive development via different RNA metabolism

The LSM (Like-Sm) protein family, characterized by highly conserved LSM domains, is integral to ribonucleic acid (RNA) metabolism. Ubiquitously present in both eukaryotes and select prokaryotes, these proteins bind to RNA molecules with high specificity through their LSM domains. They can also form ring-shaped complexes with other proteins, thereby facilitating various fundamental cellular processes such as mRNA degradation, splicing, and ribosome biogenesis. LSM proteins play crucial roles in gametogenesis, early embryonic development, sex determination, gonadal maturation, and reproductive system formation. In pathological conditions, the absence of LSM14B leads to arrest of oocytes at mid-meiosis, downregulation of LSM4 expression is associated with abnormal spermatogenesis, and aberrant expression of LSM1 protein is linked to the occurrence and progression of breast cancer. This review focuses on the recent advances in the functional research of LSM proteins in reproduction.

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.

Integration of MRI radiomics and clinical data for preoperative prediction of vascular invasion in breast cancer: A deep learning approach

BACKGROUND

Accurate preoperative prediction of vascular invasion in breast cancer is crucial for surgical planning and patient management. MRI radiomics has shown promise in enhancing diagnostic precision. This study aims to evaluate the effectiveness of integrating MRI radiomic features with clinical data using a deep learning approach to predict vascular invasion in breast cancer patients.

METHODS

A retrospective analysis was conducted on 102 patients with invasive breast cancer confirmed by surgical pathology. Using the MR750 3.0 T as the examination device, the subject underwent the examination in standard breast positions and sequences. Diffusion-weighted imaging (DWI) was performed with two selected b-values, specifically 0 and 1000 s/mm2. Following the injection of the contrast agent, dynamic scans were conducted across six phases, and delayed phase sagittal images were acquired using the VIBRANT sequence. Texture features were extracted from MRI images, and key radiomic and clinical features were selected using variance thresholding, correlation filtering, and logistic regression. A predictive model was developed combining these features, and its performance was evaluated through sensitivity, specificity, and area under the curve (AUC) metrics.

RESULTS

The univariate models based on individual MRI sequences or clinical data demonstrated variable diagnostic performance. In contrast, the multifactorial model that combined radiomic features with clinical data achieved significantly higher accuracy, with an AUC of 0.829, sensitivity of 76.9 %, and specificity of 83.3 %.

CONCLUSION

Integrating MRI radiomics and clinical data enhances the preoperative prediction of vascular invasion in breast cancer. This approach can improve diagnostic accuracy, providing valuable insights for clinical decision-making and personalized treatment strategies.

Mass Spectrometry-Based Proteomics for Next-Generation Precision Oncology

Cancer is the leading cause of death worldwide characterized by patient heterogeneity and complex tumor microenvironment. While the genomics-based testing has transformed modern medicine, the challenge of diverse clinical outcomes highlights unmet needs for precision oncology. As functional molecules regulating cellular processes, proteins hold great promise as biomarkers and drug targets. Mass spectrometry (MS)-based clinical proteomics has illuminated the molecular features of cancers and facilitated discovery of biomarkers or therapeutic targets, paving the way for innovative strategies that enhance the precision of personalized treatment. In this article, we introduced the tools and current achievements of MS-based proteomics, choice of discovery and targeted MS from discovery to validation phases, profiling sensitivity from bulk samples to single-cell level and tissue to liquid biopsy specimens, current regulatory landscape of MS-based protein laboratory-developed tests (LDTs). The challenges, success and future perspectives in translating research MS assay into clinical applications are also discussed. With well-designed validation studies to demonstrate clinical benefits and meet the regulatory requirements for both analytical and clinical performance, the future of MS-based assays is promising with numerous opportunities to improve cancer diagnosis, treatment, and monitoring.

Spatial omics strategies for investigating human carotid atherosclerotic disease

Atherosclerosis is a chronic inflammatory condition of the arteries, marked by the development of plaques within the arterial intima. The rupture of unstable plaques can lead to thrombosis, downstream vessel occlusion and serious clinical events. The composition of atherosclerotic plaques is complex and highly heterogeneous, posing challenges for their study. The current pathology and histological subtype classification of plaques may fail to fully encompass the microscopic molecular components in the tissue, the disease progress in various stages of atherosclerosis and the potential mechanism of plaque rupture. However, spatial mapping of the heterogeneity in plaque tissue components can enhance our understanding of these lesions. Despite the considerable progress made by traditional omics in the field of disease research, and its status as an indispensable technology, there remain inherent limitations in the investigation of minute molecular. In recent years, spatial omics techniques have advanced significantly, enabling the visualisation and analysis of specific components within plaques that may serve as causal targets associated with disease progression. The effective application of spatial omics in both research and clinical settings represents a promising area for further exploration. This review focuses on the recent advancements and findings related to spatial omics in the study of extracranial carotid atherosclerotic cerebrovascular disease. Spatial omics analysis of atherosclerotic plaques can facilitate the detection of biomarkers with diagnostic significance or potential relevance to disease, offering new methods and insights into the diagnosis of atherosclerosis and its complications.

Correlative multiscale 3D imaging of mouse primary and metastatic tumors by sequential light sheet and confocal fluorescence microscopy

Three-dimensional (3D) optical microscopy permits in situ interrogation of the tumor microenvironment (TME) in volumetric tumors for research while light sheet and confocal fluorescence microscopy are often used to achieve macroscopic and microscopic 3D images of tissues, respectively. Although each technique offers distinct fields of view (FOVs) and spatial resolution, the combination of the two to obtain correlative multiscale 3D images from the same tumor tissues has not yet been explored. We established a workflow that enables the tracking and 3D imaging of region of interests (ROIs) within tumor tissues through sequential light sheet and confocal fluorescence microscopy. This approach allowed for quantitative 3D spatial analysis of the immune response in the TME at multiple spatial scales and facilitated the direct localization of a metastatic lesion within a mouse brain. Our method offers an approach for correlative multiscale 3D optical microscopy with the potential to provide new insights into comprehensive research in disease mechanism or drug response.

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.

Nanocarrier imaging at single-cell resolution across entire mouse bodies with deep learning

Efficient and accurate nanocarrier development for targeted drug delivery is hindered by a lack of methods to analyze its cell-level biodistribution across whole organisms. Here we present Single Cell Precision Nanocarrier Identification (SCP-Nano), an integrated experimental and deep learning pipeline to comprehensively quantify the targeting of nanocarriers throughout the whole mouse body at single-cell resolution. SCP-Nano reveals the tissue distribution patterns of lipid nanoparticles (LNPs) after different injection routes at doses as low as 0.0005 mg kg-1-far below the detection limits of conventional whole body imaging techniques. We demonstrate that intramuscularly injected LNPs carrying SARS-CoV-2 spike mRNA reach heart tissue, leading to proteome changes, suggesting immune activation and blood vessel damage. SCP-Nano generalizes to various types of nanocarriers, including liposomes, polyplexes, DNA origami and adeno-associated viruses (AAVs), revealing that an AAV2 variant transduces adipocytes throughout the body. SCP-Nano enables comprehensive three-dimensional mapping of nanocarrier distribution throughout mouse bodies with high sensitivity and should accelerate the development of precise and safe nanocarrier-based therapeutics.

Unraveling the dual threat: sarcopenia and insufficient physical activity in diabetes risk

Purpose

This study aimed to investigate the alterations in diabetes risk associated with sarcopenia and insufficient physical activity, as well as the demographic shifts within the diabetic population.

Method

Utilizing pertinent data from the National Health and Nutrition Examination Survey (NHANES) database spanning 2011 to 2018, the criteria for sarcopenia were established by the Foundation for the National Institutes of Health. These criteria were calculated using lean body mass data in conjunction with body mass index data. Physical activity levels were assessed using the PAQ questionnaire from the NHANES database. The presence of diabetes was determined through the DIQ questionnaire and the laboratory examination within the NHANES database. The analysis was performed using multivariable logistic regression.

Result

The prevalence of both sarcopenia and insufficient physical activity in the diabetic population was 188% greater than in the non-diabetic population. Sarcopenia and insufficient physical activity were positively correlated with an increased risk of diabetes onset, demonstrating a 1.45-fold heightened risk when both conditions were present (OR=2.45,95%CI,1.35-4.44,P<0.05). This combined effect was significantly greater than the risk associated with sarcopenia alone (OR=1.84,95%CI,1.09-3.11,P<0.05) or insufficient physical activity alone (OR=1.55,95%CI,1.11-2.15,P<0.05).

Conclusion

A synergistic relationship exists between sarcopenia and insufficient physical activity, resulting in a markedly elevated risk of diabetes when both conditions are present concurrently. Therefore, comprehensive diabetes management strategies should prioritize populations exhibiting both sarcopenia and insufficient physical activity.

Towards routine proteome profiling of FFPE tissue: insights from a 1,220-case pan-cancer study

Proteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. In this study, we analyze 1220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4000 proteins and discovering that current software fails to process such large ion mobility-based online fractionated datasets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community, and can be explored via a web resource. It enables a range of analyses including quantitative comparisons of proteins between patients and cohorts, the discovery of protein fingerprints representing the tissue of origin or proteins enriched in certain cancer entities.

Persistence of spike protein at the skull-meninges-brain axis may contribute to the neurological sequelae of COVID-19

SARS-CoV-2 infection is associated with long-lasting neurological symptoms, although the underlying mechanisms remain unclear. Using optical clearing and imaging, we observed the accumulation of SARS-CoV-2 spike protein in the skull-meninges-brain axis of human COVID-19 patients, persisting long after viral clearance. Further, biomarkers of neurodegeneration were elevated in the cerebrospinal fluid from long COVID patients, and proteomic analysis of human skull, meninges, and brain samples revealed dysregulated inflammatory pathways and neurodegeneration-associated changes. Similar distribution patterns of the spike protein were observed in SARS-CoV-2-infected mice. Injection of spike protein alone was sufficient to induce neuroinflammation, proteome changes in the skull-meninges-brain axis, anxiety-like behavior, and exacerbated outcomes in mouse models of stroke and traumatic brain injury. Vaccination reduced but did not eliminate spike protein accumulation after infection in mice. Our findings suggest persistent spike protein at the brain borders may contribute to lasting neurological sequelae of COVID-19.

Berberine modulates microglial polarization by activating TYROBP in Alzheimer's disease

BACKGROUND

Characterized by β-amyloid (Aβ) plaques, neurofibrillary tangles, and aberrant neuroinflammation in the brain, Alzheimer's disease (AD) is the most common neurodegenerative disease. Microglial polarization is a subtle mechanism which maintains immunological homeostasis and has emerged as a putative therapeutic to combat AD. Berberine (BBR) is a natural alkaloid compound with multiple pharmacological effects, and has shown considerable therapeutic potential against inflammatory disorders. However, BBR functions and underlying mechanisms in neuroinflammation remain unclear.

PURPOSE

To examine BBR pharmacological effects and mechanisms in neuroinflammation with a view to treating AD.

METHODS

BBR effects on cognitive performance in 5 × FAD mice were assessed using open field, Y-maze, and Morris Water Maze (MWM) tests. Neuroinflammation-related markers and Aβ pathology were examined in brain sections from mice. Transcriptomic analyses of hippocampus tissues were also conducted. Microglial BV2 cells were also used to verify potential BBR mechanisms in neuroinflammation and microglial polarization.

RESULTS

BBR improved cognitive performance, reduced amyloid pathology, and alleviated aberrant neuroinflammation in an AD mouse model. BBR induced microglial polarization to an M2-like phenotype, which was manifested by lowered and elevated proinflammatory and anti-inflammatory cytokine production, respectively, improved microglial uptake and Aβ clearance. Mechanistically, BBR directly interacted with TYROBP and promoted its activation by stabilizing TYROBP oligomerization. TYROBP knockdown aggravated M1-like polarization and pro-inflammatory gene expression in microglial cells in the presence of lipopolysaccharide (LPS)+Aβ, while blocked microglial M2-like polarization benefited from BBR administration.

CONCLUSIONS

BBR modulated neuroinflammation by regulating microglial polarization via TYROBP activation. Our study provided new insight into BBR pharmacological actions in regulating microglial homeostasis and combating AD.

Beyond genomic studies of congenital heart defects through systematic modelling and phenotyping

Congenital heart defects (CHDs), the most common congenital anomalies, are considered to have a significant genetic component. However, despite considerable efforts to identify pathogenic genes in patients with CHDs, few gene variants have been proven as causal. The complexity of the genetic architecture underlying human CHDs likely contributes to this poor genetic discovery rate. However, several other factors are likely to contribute. For example, the level of patient phenotyping required for clinical care may be insufficient for research studies focused on mechanistic discovery. Although several hundred mouse gene knockouts have been described with CHDs, these are generally not phenotyped and described in the same way as CHDs in patients, and thus are not readily comparable. Moreover, most patients with CHDs carry variants of uncertain significance of crucial cardiac genes, further complicating comparisons between humans and mouse mutants. In spite of major advances in cardiac developmental biology over the past 25 years, these advances have not been well communicated to geneticists and cardiologists. As a consequence, the latest data from developmental biology are not always used in the design and interpretation of studies aimed at discovering the genetic causes of CHDs. In this Special Article, while considering other in vitro and in vivo models, we create a coherent framework for accurately modelling and phenotyping human CHDs in mice, thereby enhancing the translation of genetic and genomic studies into the causes of CHDs in patients.

Generation of Mammalian Astrocyte Functional Heterogeneity

Mammalian astrocytes have regional roles within the brain parenchyma. Indeed, the notion that astrocytes are molecularly heterogeneous could help explain how the central nervous system (CNS) retains embryonic positional information through development into specialized regions into adulthood. A growing body of evidence supports the concept of morphological and molecular differences between astrocytes in different brain regions, which might relate to their derivation from regionally patterned radial glia and/or local neuron inductive cues. Here, we review evidence for regionally encoded functions of astrocytes to provide an integrated concept on lineage origins and heterogeneity to understand regional brain organization, as well as emerging technologies to identify and further investigate novel roles for astrocytes.

Spatial proteomics of single cells and organelles on tissue slides using filter-aided expansion proteomics

Hydrogel-based tissue expansion combined with mass spectrometry (MS) offers an emerging spatial proteomics approach. Here, we present a filter-aided expansion proteomics (FAXP) strategy for spatial proteomics analysis of archived formalin-fixed paraffin-embedded (FFPE) specimens. Compared to our previous ProteomEx method, FAXP employed a customized tip device to enhance both the stability and throughput of sample preparation, thus guaranteeing the reproducibility and robustness of the workflow. FAXP achieved a 14.5-fold increase in volumetric resolution. It generated over 8 times higher peptide yield and a 255% rise in protein identifications while reducing sample preparation time by 50%. We also demonstrated the applicability of FAXP using human colorectal FFPE tissue samples. Furthermore, for the first time, we achieved bona fide single-subcellular proteomics under image guidance by integrating FAXP with laser capture microdissection.

Integrative proteomic and metabolomic elucidation of cardiomyopathy with in vivo and in vitro models and clinical samples

Cardiomyopathy is a prevalent cardiovascular disease that affects individuals of all ages and can lead to life-threatening heart failure. Despite its variety in types, each with distinct characteristics and causes, our understanding of cardiomyopathy at a systematic biology level remains incomplete. Mass spectrometry-based techniques have emerged as powerful tools, providing a comprehensive view of the molecular landscape and aiding in the discovery of biomarkers and elucidation of mechanisms. This review highlights the significant potential of integrating proteomic and metabolomic approaches with specialized databases to identify biomarkers and therapeutic targets across different types of cardiomyopathies. In vivo and in vitro models, such as genetically modified mice, patient-derived or induced pluripotent stem cells, and organ chips, are invaluable in exploring the pathophysiological complexities of this disease. By integrating omics approaches with these sophisticated modeling systems, our comprehension of the molecular underpinnings of cardiomyopathy can be greatly enhanced, facilitating the development of diagnostic markers and therapeutic strategies. Among the promising therapeutic targets are those involved in extracellular matrix remodeling, sarcomere damage, and metabolic remodeling. These targets hold the potential to advance precision therapy in cardiomyopathy, offering hope for more effective treatments tailored to the specific molecular profiles of patients.

Defining and modeling dynamic spatial heterogeneity within tumor microenvironments

Many solid tumors exhibit significant genetic, cellular, and biophysical heterogeneity which dynamically evolves during disease progression and after treatment. This constant flux in cell composition, phenotype, spatial relationships, and tissue properties poses significant challenges in accurately diagnosing and treating patients. Much of the complexity lies in unraveling the molecular changes in different tumor compartments, how they influence one another in space and time and where vulnerabilities exist that might be appropriate to target therapeutically. Recent advances in spatial profiling tools and technologies are enabling new insight into the underlying biology of complex tumors, creating a greater understanding of the intricate relationship between cell types, states, and the microenvironment. Here we reflect on some recent discoveries in this area, where the key knowledge and technology gaps lie, and the advancements in spatial measurements and in vitro models for the study of spatial intratumoral heterogeneity.

Spatial proteomics: unveiling the multidimensional landscape of protein localization in human diseases

Spatial proteomics is a multidimensional technique that studies the spatial distribution and function of proteins within cells or tissues across both spatial and temporal dimensions. This field multidimensionally reveals the complex structure of the human proteome, including the characteristics of protein spatial distribution, dynamic protein translocation, and protein interaction networks. Recently, as a crucial method for studying protein spatial localization, spatial proteomics has been applied in the clinical investigation of various diseases. This review summarizes the fundamental concepts and characteristics of tissue-level spatial proteomics, its research progress in common human diseases such as cancer, neurological disorders, cardiovascular diseases, autoimmune diseases, and anticipates its future development trends. The aim is to highlight the significant impact of spatial proteomics on understanding disease pathogenesis, advancing diagnostic methods, and developing potential therapeutic targets in clinical research.

Toxoplasma gondii Infection of Alzheimer's Disease Mice Reduces Brain Amyloid Density Globally and Regionally

BACKGROUND

Toxoplasma gondii infection of Alzheimer's disease model mice decreases amyloid β plaques. We aimed to determine if there is a brain regional difference in amyloid β reduction in the brains of T. gondii-infected compared to control mice.

METHOD

Three-month-old 5xFAD (AD model) mice were injected with T. gondii or with phosphate-buffered saline as a control. Intact brains were harvested at 6 weeks postinfection, optically cleared using iDISCO+, and brain-wide amyloid burden was visualized using volumetric light-sheet imaging. Amyloid signal was quantified across each brain and computationally mapped to the Allen Institute Brain Reference Atlas to determine amyloid density in each region.

RESULTS

A brain-wide analysis of amyloid in control and T. gondii-infected 5xFAD mice revealed that T. gondii infection decreased amyloid burden in the brain globally as well as in the cortex and hippocampus, and many daughter regions. Daughter regions that showed reduced amyloid burden included the prelimbic cortex, visual cortex, and retrosplenial cortex. The olfactory tubercle, a region known to have increased monocytes following T. gondii infection, also showed reduced amyloid after infection.

CONCLUSIONS

T. gondii infection of AD mice reduces amyloid burden in a brain region-specific manner that overlaps with known regions of T. gondii infection and peripheral immune cell infiltration.

Structural brain preservation: a potential bridge to future medical technologies

When faced with the prospect of death, some people would prefer a form of long-term preservation that may allow them to be restored to healthy life in the future, if technology ever develops to the point that this is feasible and humane. Some believe that we may have the capacity to perform this type of experimental preservation today-although it has never been proven-using contemporary methods to preserve the structure of the brain. The idea is that the morphomolecular organization of the brain encodes the information required for psychological properties such as personality and long-term memories. If these structures in the brain can be maintained intact over time, this could theoretically provide a bridge to access restorative technologies in the future. To consider this hypothesis, we first describe possible metrics that can be used to assess structural brain preservation quality. We next explore several possible methods to preserve structural information in the brain, including the traditional cryonics method of cryopreservation, as well as aldehyde-stabilized cryopreservation and fluid preservation. We focus in-depth on fluid preservation, which relies on aldehyde fixation to induce chemical gel formation in a wide set of biomolecules and appears to be a cost-effective method. We describe two theoretical recovery technologies, alongside several of the ethical and legal complexities of brain preservation, all of which will require a prudent approach. We believe contemporary structural brain preservation methods have a non-negligible chance of allowing successful restoration in the future and that this deserves serious research efforts by the scientific community.

Advances in mass spectrometry imaging for plant metabolomics-Expanding the analytical toolbox

Mass spectrometry imaging (MSI) has become increasingly popular in plant science due to its ability to characterize complex chemical, spatial, and temporal aspects of plant metabolism. Over the past decade, as the emerging and unique features of various MSI techniques have continued to support new discoveries in studies of plant metabolism closely associated with various aspects of plant function and physiology, spatial metabolomics based on MSI techniques has positioned it at the forefront of plant metabolic studies, providing the opportunity for far higher resolution than was previously available. Despite these efforts, profound challenges at the levels of spatial resolution, sensitivity, quantitative ability, chemical confidence, isomer discrimination, and spatial multi-omics integration, undoubtedly remain. In this Perspective, we provide a contemporary overview of the emergent MSI techniques widely used in the plant sciences, with particular emphasis on recent advances in methodological breakthroughs. Having established the detailed context of MSI, we outline both the golden opportunities and key challenges currently facing plant metabolomics, presenting our vision as to how the enormous potential of MSI technologies will contribute to progress in plant science in the coming years.

The impact and future of artificial intelligence in medical genetics and molecular medicine: an ongoing revolution

Artificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.

Recent Advancements in Subcellular Proteomics: Growing Impact of Organellar Protein Niches on the Understanding of Cell Biology

The mammalian cell is a complex entity, with membrane-bound and membrane-less organelles playing vital roles in regulating cellular homeostasis. Organellar protein niches drive discrete biological processes and cell functions, thus maintaining cell equilibrium. Cellular processes such as signaling, growth, proliferation, motility, and programmed cell death require dynamic protein movements between cell compartments. Aberrant protein localization is associated with a wide range of diseases. Therefore, analyzing the subcellular proteome of the cell can provide a comprehensive overview of cellular biology. With recent advancements in mass spectrometry, imaging technology, computational tools, and deep machine learning algorithms, studies pertaining to subcellular protein localization and their dynamic distributions are gaining momentum. These studies reveal changing interaction networks because of "moonlighting proteins" and serve as a discovery tool for disease network mechanisms. Consequently, this review aims to provide a comprehensive repository for recent advancements in subcellular proteomics subcontexting methods, challenges, and future perspectives for method developers. In summary, subcellular proteomics is crucial to the understanding of the fundamental cellular mechanisms and the associated diseases.

Proteomics of the heart

Mass spectrometry-based proteomics is a sophisticated identification tool specializing in portraying protein dynamics at a molecular level. Proteomics provides biologists with a snapshot of context-dependent protein and proteoform expression, structural conformations, dynamic turnover, and protein-protein interactions. Cardiac proteomics can offer a broader and deeper understanding of the molecular mechanisms that underscore cardiovascular disease, and it is foundational to the development of future therapeutic interventions. This review encapsulates the evolution, current technologies, and future perspectives of proteomic-based mass spectrometry as it applies to the study of the heart. Key technological advancements have allowed researchers to study proteomes at a single-cell level and employ robot-assisted automation systems for enhanced sample preparation techniques, and the increase in fidelity of the mass spectrometers has allowed for the unambiguous identification of numerous dynamic posttranslational modifications. Animal models of cardiovascular disease, ranging from early animal experiments to current sophisticated models of heart failure with preserved ejection fraction, have provided the tools to study a challenging organ in the laboratory. Further technological development will pave the way for the implementation of proteomics even closer within the clinical setting, allowing not only scientists but also patients to benefit from an understanding of protein interplay as it relates to cardiac disease physiology.

Deep 3D histology powered by tissue clearing, omics and AI

To comprehensively understand tissue and organism physiology and pathophysiology, it is essential to create complete three-dimensional (3D) cellular maps. These maps require structural data, such as the 3D configuration and positioning of tissues and cells, and molecular data on the constitution of each cell, spanning from the DNA sequence to protein expression. While single-cell transcriptomics is illuminating the cellular and molecular diversity across species and tissues, the 3D spatial context of these molecular data is often overlooked. Here, I discuss emerging 3D tissue histology techniques that add the missing third spatial dimension to biomedical research. Through innovations in tissue-clearing chemistry, labeling and volumetric imaging that enhance 3D reconstructions and their synergy with molecular techniques, these technologies will provide detailed blueprints of entire organs or organisms at the cellular level. Machine learning, especially deep learning, will be essential for extracting meaningful insights from the vast data. Further development of integrated structural, molecular and computational methods will unlock the full potential of next-generation 3D histology.

Virtual reality-empowered deep-learning analysis of brain cells

Automated detection of specific cells in three-dimensional datasets such as whole-brain light-sheet image stacks is challenging. Here, we present DELiVR, a virtual reality-trained deep-learning pipeline for detecting c-Fos+ cells as markers for neuronal activity in cleared mouse brains. Virtual reality annotation substantially accelerated training data generation, enabling DELiVR to outperform state-of-the-art cell-segmenting approaches. Our pipeline is available in a user-friendly Docker container that runs with a standalone Fiji plugin. DELiVR features a comprehensive toolkit for data visualization and can be customized to other cell types of interest, as we did here for microglia somata, using Fiji for dataset-specific training. We applied DELiVR to investigate cancer-related brain activity, unveiling an activation pattern that distinguishes weight-stable cancer from cancers associated with weight loss. Overall, DELiVR is a robust deep-learning tool that does not require advanced coding skills to analyze whole-brain imaging data in health and disease.

Understanding Cellular, Molecular, and Functional Specificity, Heterogeneity, and Diversity of the Endocannabinoid System

The endocannabinoid system (ECS) is a widely recognized lipid messenger system involved in many aspects of our health and diseases [...].

HIV-Associated Cardiovascular Disease Pathogenesis: An Emerging Understanding Through Imaging and Immunology

Cardiac abnormalities were identified early in the epidemic of AIDS, predating the isolation and characterization of the etiologic agent, HIV. Several decades later, the causation and pathogenesis of cardiovascular disease (CVD) linked to HIV infection continue to be the focus of intense speculation. Before the widespread use of antiretroviral therapy, HIV-associated CVD was primarily characterized by HIV-associated cardiomyopathy linked to profound immunodeficiency. With increasing antiretroviral therapy use, viral load suppression, and establishment of immune competency, the effects of HIV on the cardiovascular system are more subtle. Yet, people living with HIV still face an increased incidence of cardiovascular pathology. Advances in cardiac imaging modalities and immunology have deepened our understanding of the pathogenesis of HIV-associated CVD. This review provides an overview of the pathogenesis of HIV-associated CVD integrating data from imaging and immunologic studies with particular relevance to the HIV population originating from high-endemic regions, such as sub-Saharan Africa. The review highlights key evidence gaps in the field and suggests future directions for research to better understand the complex HIV-CVD interactions.

Correlative multiscale 3D imaging of mouse primary and metastatic tumors by sequential light sheet and confocal fluorescence microscopy

Three-dimensional (3D) optical microscopy, combined with advanced tissue clearing, permits in situ interrogation of the tumor microenvironment (TME) in large volumetric tumors for preclinical cancer research. Light sheet (also known as ultramicroscopy) and confocal fluorescence microscopy are often used to achieve macroscopic and microscopic 3D images of optically cleared tumor tissues, respectively. Although each technique offers distinct fields of view (FOVs) and spatial resolution, the combination of these two optical microscopy techniques to obtain correlative multiscale 3D images from the same tumor tissues has not yet been explored. To establish correlative multiscale 3D optical microscopy, we developed a method for optically marking defined regions of interest (ROIs) within a cleared mouse tumor by employing a UV light-activated visible dye and Z-axis position-selective UV irradiation in a light sheet microscope system. By integrating this method with subsequent tissue processing, including physical ROI marking, reversal of tissue clearing, tissue macrosectioning, and multiplex immunofluorescence, we established a workflow that enables the tracking and 3D imaging of ROIs within tumor tissues through sequential light sheet and confocal fluorescence microscopy. This approach allowed for quantitative 3D spatial analysis of the immune response in the TME of a mouse mammary tumor following cancer immunotherapy at multiple spatial scales. The workflow also facilitated the direct localization of a metastatic lesion within a whole mouse brain. These results demonstrate that our ROI tracking method and its associated workflow offer a novel approach for correlative multiscale 3D optical microscopy, with the potential to provide new insights into tumor heterogeneity, metastasis, and response to therapy at various spatial levels.

Explore & actuate: the future of personalized medicine in oncology through emerging technologies

PURPOSE OF REVIEW

The future of medicine is aimed to equip the physician with tools to assess the individual health of the patient for the uniqueness of the disease that separates it from the rest. The integration of omics technologies into clinical practice, reviewed here, would open new avenues for addressing the spatial and temporal heterogeneity of cancer. The rising cancer burden patiently awaits the advent of such an approach to personalized medicine for routine clinical settings.

RECENT FINDINGS

To weigh the translational potential, multiple technologies were categorized based on the extractable information from the different types of samples used, to the various omic-levels of molecular information that each technology has been able to advance over the last 2 years. This review uses a multifaceted classification that helps to assess translational potential in a meaningful way toward clinical adaptation.

SUMMARY

The importance of distinguishing technologies based on the flow of information from exploration to actuation puts forth a framework that allows the clinicians to better adapt a chosen technology or use them in combination to enhance their goals toward personalized medicine.

Decomprolute is a benchmarking platform designed for multiomics-based tumor deconvolution

Tumor deconvolution enables the identification of diverse cell types that comprise solid tumors. To date, however, both the algorithms developed to deconvolve tumor samples, and the gold-standard datasets used to assess the algorithms are geared toward the analysis of gene expression (e.g., RNA sequencing) rather than protein levels. Despite the popularity of gene expression datasets, protein levels often provide a more accurate view of rare cell types. To facilitate the use, development, and reproducibility of multiomic deconvolution algorithms, we introduce Decomprolute, a Common Workflow Language framework that leverages containerization to compare tumor deconvolution algorithms across multiomic datasets. Decomprolute incorporates the large-scale multiomic datasets produced by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), which include matched mRNA expression and proteomic data from thousands of tumors across multiple cancer types to build a fully open-source, containerized proteogenomic tumor deconvolution benchmarking platform. http://pnnl-compbio.github.io/decomprolute.

Tissue niches and immunopathology through the lens of spatial tissue profiling techniques

Spatial organization plays a fundamental role in biology, influencing the function of biological structures at various levels. The immune system, in particular, relies on the orchestrated interactions of immune cells with their microenvironment to mount protective or pathogenic immune responses. The COVID-19 pandemic has underscored the significance of studying immunity within target organs to understand disease progression and severity. To achieve this, multiplex histology and spatial transcriptomics have proven indispensable in providing a spatial context to protein and gene expression patterns. By combining these techniques, researchers gain a more comprehensive understanding of the complex interactions at the cellular and molecular level in distinct tissue niches, key functional units modulating health and disease. In this review, we discuss recent advances in spatial tissue profiling techniques, highlighting their advantages over traditional histopathology studies. The insights gained from these approaches have the potential to revolutionize the diagnosis and treatment of various diseases including cancer, autoimmune disorders, and infectious diseases. However, we also acknowledge their challenges and limitations. Despite these, spatial tissue profiling offers promising opportunities to improve our understanding of how tissue niches direct regional immunity, and their relevance in tissue immunopathology, as a basis for novel therapeutic strategies and personalized medicine.

The interaction between ageing and Alzheimer's disease: insights from the hallmarks of ageing

Ageing is a crucial risk factor for Alzheimer's disease (AD) and is characterised by systemic changes in both intracellular and extracellular microenvironments that affect the entire body instead of a single organ. Understanding the specific mechanisms underlying the role of ageing in disease development can facilitate the treatment of ageing-related diseases, such as AD. Signs of brain ageing have been observed in both AD patients and animal models. Alleviating the pathological changes caused by brain ageing can dramatically ameliorate the amyloid beta- and tau-induced neuropathological and memory impairments, indicating that ageing plays a crucial role in the pathophysiological process of AD. In this review, we summarize the impact of several age-related factors on AD and propose that preventing pathological changes caused by brain ageing is a promising strategy for improving cognitive health.

Organoids in COVID-19: can we break the glass ceiling?

COVID-19 emerged in September 2020 as a disease caused by the virus SARS-CoV-2. The disease presented as pneumonia at first but later was shown to cause multisystem infections and long-term complications. Many efforts have been put into discovering the exact pathogenesis of the disease. In this review, we aim to discuss an emerging tool in disease modeling, organoids, in the investigation of COVID-19. This review will introduce some methods and breakthroughs achieved by organoids and the limitations of this system.

Advances in cellular and tissue-based imaging techniques for sarcoid granulomas

Sarcoidosis embodies a complex inflammatory disorder spanning multiple systems, with its origin remaining elusive. It manifests as the infiltration of inflammatory cells that coalesce into distinctive noncaseous granulomas within afflicted organs. Unraveling this disease necessitates the utilization of cellular or tissue-based imaging methods to both visualize and characterize the biochemistry of these sarcoid granulomas. Although hematoxylin and eosin stain, standard in routine use alongside cytological stains have found utility in diagnosis within clinical contexts, special stains such as Masson's trichrome, reticulin, methenamine silver, and Ziehl-Neelsen provide additional varied perspectives of sarcoid granuloma imaging. Immunohistochemistry aids in pinpointing specific proteins and gene expressions further characterizing these granulomas. Finally, recent advances in spatial transcriptomics promise to divulge profound insights into their spatial orientation and three-dimensional (3-D) molecular mapping. This review focuses on a range of preexisting imaging methods employed for visualizing sarcoid granulomas at the cellular level while also exploring the potential of the latest cutting-edge approaches like spatial transcriptomics and matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI), with the overarching goal of shedding light on the trajectory of sarcoidosis research.

Programming human cell fate: overcoming challenges and unlocking potential through technological breakthroughs

In recent years, there have been notable advancements in the ability to programme human cell identity, enabling us to design and manipulate cell function in a Petri dish. However, current protocols for generating target cell types often lack efficiency and precision, resulting in engineered cells that do not fully replicate the desired identity or functional output. This applies to different methods of cell programming, which face similar challenges that hinder progress and delay the achievement of a more favourable outcome. However, recent technological and analytical breakthroughs have provided us with unprecedented opportunities to advance the way we programme cell fate. The Company of Biologists' 2023 workshop on 'Novel Technologies for Programming Human Cell Fate' brought together experts in human cell fate engineering and experts in single-cell genomics, manipulation and characterisation of cells on a single (sub)cellular level. Here, we summarise the main points that emerged during the workshop's themed discussions. Furthermore, we provide specific examples highlighting the current state of the field as well as its trajectory, offering insights into the potential outcomes resulting from the application of these breakthrough technologies in precisely engineering the identity and function of clinically valuable human cells.

Decoding the tumor microenvironment with spatial technologies

Visualization of the cellular heterogeneity and spatial architecture of the tumor microenvironment (TME) is becoming increasingly important to understand mechanisms of disease progression and therapeutic response. This is particularly relevant in the era of cancer immunotherapy, in which the contexture of immune cell positioning within the tumor landscape has been proven to affect efficacy. Although single-cell technologies have mostly replaced conventional approaches to analyze specific cellular subsets within tumors, those that integrate a spatial dimension are now on the rise. In this Review, we assess the strengths and limitations of emerging spatial technologies with a focus on their applications in tumor immunology, as well as forthcoming opportunities for artificial intelligence (AI) and the value of integrating multiomics datasets to achieve a holistic picture of the TME.

Genetically encoded barcodes for correlative volume electron microscopy

While genetically encoded reporters are common for fluorescence microscopy, equivalent multiplexable gene reporters for electron microscopy (EM) are still scarce. Here, by installing a variable number of fixation-stable metal-interacting moieties in the lumen of encapsulin nanocompartments of different sizes, we developed a suite of spherically symmetric and concentric barcodes (EMcapsulins) that are readable by standard EM techniques. Six classes of EMcapsulins could be automatically segmented and differentiated. The coding capacity was further increased by arranging several EMcapsulins into distinct patterns via a set of rigid spacers of variable length. Fluorescent EMcapsulins were expressed to monitor subcellular structures in light and EM. Neuronal expression in Drosophila and mouse brains enabled the automatic identification of genetically defined cells in EM. EMcapsulins are compatible with transmission EM, scanning EM and focused ion beam scanning EM. The expandable palette of genetically controlled EM-readable barcodes can augment anatomical EM images with multiplexed gene expression maps.

A region-resolved proteomic map of the human brain enabled by high-throughput proteomics

Substantial efforts are underway to deepen our understanding of human brain morphology, structure, and function using high-resolution imaging as well as high-content molecular profiling technologies. The current work adds to these approaches by providing a comprehensive and quantitative protein expression map of 13 anatomically distinct brain regions covering more than 11,000 proteins. This was enabled by the optimization, characterization, and implementation of a high-sensitivity and high-throughput microflow liquid chromatography timsTOF tandem mass spectrometry system (LC-MS/MS) capable of analyzing more than 2,000 consecutive samples prepared from formalin-fixed paraffin embedded (FFPE) material. Analysis of this proteomic resource highlighted brain region-enriched protein expression patterns and functional protein classes, protein localization differences between brain regions and individual markers for specific areas. To facilitate access to and ease further mining of the data by the scientific community, all data can be explored online in a purpose-built R Shiny app (https://brain-region-atlas.proteomics.ls.tum.de).

Applying proteomics in metabolic dysfunction-associated steatotic liver disease: From mechanism to biomarkers

Metabolic dysfunction-associated steatotic liver disease (MASLD), which represents the most common cause of liver disease, is emerging as a major health problem around the world. However, the molecular events that underline the pathogenesis and the progression of MASLD remain to be fully elucidated. Advanced stages of MASLD is strongly associated with liver-related outcomes and overall mortality. Despite this, highly accurate, sensitive, and non-invasive diagnostic tools are currently not aviailable, yet no FDA approved drugs for MASLD. The advance of proteomics has enable the study of protein expression, post-translational modifications (PTMs), subcellular distribution, and interactions. In this review, we discuss insights gained from the recent proteomics studies that shed new light on the pathogenesis, diagnosis and potential theraputic targets of MASLD.

Keep in touch: a perspective on the mitochondrial social network and its implication in health and disease

Mitochondria have been the focus of extensive research for decades since their dysfunction is linked to more than 150 distinct human disorders. Despite considerable efforts, researchers have only been able to skim the surface of the mitochondrial social complexity and the impact of inter-organelle and inter-organ communication alterations on human health. While some progress has been made in deciphering connections among mitochondria and other cytoplasmic organelles through direct (i.e., contact sites) or indirect (i.e., inter-organelle trafficking) crosstalk, most of these efforts have been restricted to a limited number of proteins involved in specific physiological pathways or disease states. This research bottleneck is further narrowed by our incomplete understanding of the cellular alteration timeline in a specific pathology, which prevents the distinction between a primary organelle dysfunction and the defects occurring due to the disruption of the organelle's interconnectivity. In this perspective, we will (i) summarize the current knowledge on the mitochondrial crosstalk within cell(s) or tissue(s) in health and disease, with a particular focus on neurodegenerative disorders, (ii) discuss how different large-scale and targeted approaches could be used to characterize the different levels of mitochondrial social complexity, and (iii) consider how investigating the different expression patterns of mitochondrial proteins in different cell types/tissues could represent an important step forward in depicting the distinctive architecture of inter-organelle communication.

Enablers and challenges of spatial omics, a melting pot of technologies

Spatial omics has emerged as a rapidly growing and fruitful field with hundreds of publications presenting novel methods for obtaining spatially resolved information for any omics data type on spatial scales ranging from subcellular to organismal. From a technology development perspective, spatial omics is a highly interdisciplinary field that integrates imaging and omics, spatial and molecular analyses, sequencing and mass spectrometry, and image analysis and bioinformatics. The emergence of this field has not only opened a window into spatial biology, but also created multiple novel opportunities, questions, and challenges for method developers. Here, we provide the perspective of technology developers on what makes the spatial omics field unique. After providing a brief overview of the state of the art, we discuss technological enablers and challenges and present our vision about the future applications and impact of this melting pot.

Multi-omic analyses and network biology in cardiovascular disease

Heart disease remains a leading cause of death in North America and worldwide. Despite advances in therapies, the chronic nature of cardiovascular diseases ultimately results in frequent hospitalizations and steady rates of mortality. Systems biology approaches have provided a new frontier toward unraveling the underlying mechanisms of cell, tissue, and organ dysfunction in disease. Mapping the complex networks of molecular functions across the genome, transcriptome, proteome, and metabolome has enormous potential to advance our understanding of cardiovascular disease, discover new disease biomarkers, and develop novel therapies. Computational workflows to interpret these data-intensive analyses as well as integration between different levels of interrogation remain important challenges in the advancement and application of systems biology-based analyses in cardiovascular research. This review will focus on summarizing the recent developments in network biology-level profiling in the heart, with particular emphasis on modeling of human heart failure. We will provide new perspectives on integration between different levels of large "omics" datasets, including integration of gene regulatory networks, protein-protein interactions, signaling networks, and metabolic networks in the heart.

Exploring the identity of individual plant cells in space and time

In recent years, single-cell genomics, coupled to imaging techniques, have become the state-of-the-art approach for characterising biological systems. In plant sciences, a variety of tissues and species have been profiled, providing an enormous quantity of data on cell identity at an unprecedented resolution, but what biological insights can be gained from such data sets? Using recently published studies in plant sciences, we will highlight how single-cell technologies have enabled a better comprehension of tissue organisation, cell fate dynamics in development or in response to various stimuli, as well as identifying key transcriptional regulators of cell identity. We discuss the limitations and technical hurdles to overcome, as well as future directions, and the promising use of single-cell omics to understand, predict, and manipulate plant development and physiology.

Distinct molecular profiles of skull bone marrow in health and neurological disorders

The bone marrow in the skull is important for shaping immune responses in the brain and meninges, but its molecular makeup among bones and relevance in human diseases remain unclear. Here, we show that the mouse skull has the most distinct transcriptomic profile compared with other bones in states of health and injury, characterized by a late-stage neutrophil phenotype. In humans, proteome analysis reveals that the skull marrow is the most distinct, with differentially expressed neutrophil-related pathways and a unique synaptic protein signature. 3D imaging demonstrates the structural and cellular details of human skull-meninges connections (SMCs) compared with veins. Last, using translocator protein positron emission tomography (TSPO-PET) imaging, we show that the skull bone marrow reflects inflammatory brain responses with a disease-specific spatial distribution in patients with various neurological disorders. The unique molecular profile and anatomical and functional connections of the skull show its potential as a site for diagnosing, monitoring, and treating brain diseases.

Large-Scale Multi-Omic Approaches and Atlas-Based Profiling for Cellular, Molecular, and Functional Specificity, Heterogeneity, and Diversity in the Endocannabinoid System

The endocannabinoid system (ECS) is a widely-recognized lipid messenger system involved in many aspects of our our lives in health and diseases [...].

A guidebook of spatial transcriptomic technologies, data resources and analysis approaches

Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.