PDAC heterogeneity resolved spatially at the single-cell level: new biological answers, new questions on optimal translation†.

Pancreatic cancer is a highly aggressive disease. Developing new strategies and using powerful methodologies for its early detection, coupled with in-depth comprehension of the mechanisms governing subtype evolution, will not only help to stratify PDAC patients' prognosis but also prevent unfavourable subtype plasticity upon treatment with chemotherapy. Michiels et al have developed a new approach to better capture PDAC heterogeneity at the single tumour duct spatial resolution level, leveraging detection of transcripts for mutant KRAS and multiple subtype markers. Their study sheds light on the association of mutant KRAS and PDAC phenotypic heterogeneity. The findings support functional cooperation of plastic tumour cells and opens new challenges towards PDAC patient stratification and therapeutic intervention. Pathology-based tools will be of prime importance to address these issues in a clinically meaningful manner. © 2024 The Pathological Society of Great Britain and Ireland.

A high-throughput technique to map cell images to cell positions using a 3D imaging flow cytometer.

We develop a high-throughput technique to relate positions of individual cells to their three-dimensional (3D) imaging features with single-cell resolution. The technique is particularly suitable for nonadherent cells where existing spatial biology methodologies relating cell properties to their positions in a solid tissue do not apply. Our design consists of two parts, as follows: recording 3D cell images at high throughput (500 to 1,000 cells/s) using a custom 3D imaging flow cytometer (3D-IFC) and dispensing cells in a first-in-first-out (FIFO) manner using a robotic cell placement platform (CPP). To prevent errors due to violations of the FIFO principle, we invented a method that uses marker beads and DNA sequencing software to detect errors. Experiments with human cancer cell lines demonstrate the feasibility of mapping 3D side scattering and fluorescent images, as well as two-dimensional (2D) transmission images of cells to their locations on the membrane filter for around 100,000 cells in less than 10 min. While the current work uses our specially designed 3D imaging flow cytometer to produce 3D cell images, our methodology can support other imaging modalities. The technology and method form a bridge between single-cell image analysis and single-cell molecular analysis.

Metabolomics at the tumor microenvironment interface: Decoding cellular conversations.

Cancer heterogeneity remains a significant challenge for effective cancer treatments. Altered energetics is one of the hallmarks of cancer and influences tumor growth and drug resistance. Studies have shown that heterogeneity exists within the metabolic profile of tumors, and personalized-combination therapy with relevant metabolic interventions could improve patient response. Metabolomic studies are identifying novel biomarkers and therapeutic targets that have improved treatment response. The spatial location of elements in the tumor microenvironment are becoming increasingly important for understanding disease progression. The evolution of spatial metabolomics analysis now allows scientists to deeply understand how metabolite distribution contributes to cancer biology. Recently, these techniques have spatially resolved metabolite distribution to a subcellular level. It has been proposed that metabolite mapping could improve patient outcomes by improving precision medicine, enabling earlier diagnosis and intraoperatively identifying tumor margins. This review will discuss how altered metabolic pathways contribute to cancer progression and drug resistance and will explore the current capabilities of spatial metabolomics technologies and how these could be integrated into clinical practice to improve patient outcomes.

Parsing 20 Years of Public Data by AI Maps Trends in Proteomics and Forecasts Technology.

The trends of the last 20 years in biotechnology were revealed using artificial intelligence and natural language processing (NLP) of publicly available data. Implementing this "science-of-science" approach, we capture convergent trends in the field of proteomics in both technology development and application across the phylogenetic tree of life. With major gaps in our knowledge about protein composition, structure, and location over time, we report trends in persistent, popular approaches and emerging technologies across 94 ideas from a corpus of 29 journals in PubMed over two decades. New metrics for clusters of these ideas reveal the progression and popularity of emerging approaches like single-cell, spatial, compositional, and chemical proteomics designed to better capture protein-level chemistry and biology. This analysis of the proteomics literature with advanced analytic tools quantifies the Rate of Rise for a next generation of technologies to better define, quantify, and visualize the multiple dimensions of the proteome that will transform our ability to measure and understand proteins in the coming decade.

Spatial proteomics of human diabetic kidney disease, from health to class III.

AIMS/HYPOTHESIS: Diabetic kidney disease (DKD) is the leading cause of chronic and end-stage kidney disease in the USA and worldwide. Animal models have taught us much about DKD mechanisms, but translation of this knowledge into treatments for human disease has been slowed by the lag in our molecular understanding of human DKD. METHODS: Using our Spatial TissuE Proteomics (STEP) pipeline (comprising curated human kidney tissues, multiplexed immunofluorescence and powerful analysis tools), we imaged and analysed the expression of 21 proteins in 23 tissue sections from individuals with diabetes and healthy kidneys (n=5), compared to those with DKDIIA, IIA-B and IIB (n=2 each) and DKDIII (n=1). RESULTS: These analyses revealed the existence of 11 cellular clusters (kidney compartments/cell types): podocytes, glomerular endothelial cells, proximal tubules, distal nephron, peritubular capillaries, blood vessels (endothelial cells and vascular smooth muscle cells), macrophages, myeloid cells, other CD45+ inflammatory cells, basement membrane and the interstitium. DKD progression was associated with co-localised increases in inflammatory cells and collagen IV deposition, with concomitant loss of native proteins of each nephron segment. Cell-type frequency and neighbourhood analyses highlighted a significant increase in inflammatory cells and their adjacency to tubular and αSMA+ (α-smooth muscle actin-positive) cells in DKD. Finally, DKD progression showed marked regional variability within single tissue sections, as well as inter-individual variability within each DKD class. CONCLUSIONS/INTERPRETATION: Using the STEP pipeline, we found alterations in protein expression, cellular phenotypic composition and microenvironment structure with DKD progression, demonstrating the power of this pipeline to reveal the pathophysiology of human DKD.

Lipid imaging of Alzheimer's disease pathology.

Alzheimer's disease (AD) affects one in eight individuals over 65 and poses an immense societal challenge. AD pathology is characterized by the formation of beta-amyloid plaques and Tau tangles in the brain. While some disease-modifying treatments targeting beta-amyloid are emerging, the exact chain of events underlying the pathogenesis of this disease remains unclear. Brain lipids have long been implicated in AD pathology, though their role in AD pathogenesis remains not fully resolved. Significant advancements in mass spectrometry imaging (MSI) allow to detail spatial lipid regulations in biological tissues at the low um scale. In this issue, Huang et al. resolve spatial lipid patterns in human AD brain and genetic mouse models using desorption electrospray ionization (DESI)-based MSI integrated with other spatial techniques such as imaging mass cytometry of correlative protein signatures. Those spatial multiomics experiments identify plaque-associated lipid regulations that are dependent on progressing plaque pathology in both mouse models and the human brain. Of those lipid species, particularly pro-inflammatory lysophospholipids have been implicated in AD pathology through their interaction with both aggregating Aß and microglial activation through lipid sensing surface receptors. Together, this study provides further insight into how brain lipid homeostasis is linked to progressing AD pathology, and thereby highlights the potential of MSI-based spatial lipidomics as an emerging spatial biology technology for biomedical research.

OMIP-103: A 35-marker imaging mass cytometry panel for the co-detection of HIV and immune cell populations in human formalin fixed paraffin embedded intestinal tissue.

We introduce a 35-marker imaging mass cytometry (IMC) panel for a detailed examination of immune cell populations and HIV RNA in formalin fixed paraffin embedded (FFPE) human intestinal tissue. The panel has broad cell type coverage and particularly excels in delineating subsets of mononuclear phagocytes and T cells. Markers for key tissue structures are included, enabling identification of epithelium, blood vessels, lymphatics, and musculature. The described method for HIV RNA detection can be generalized to other low abundance RNA targets, whether endogenous or pathogen derived. As such, the panel presented here is useful for high parameter spatial mapping of intestinal immune cells and their interactions with pathogens such as HIV.

The seductive allure of spatial biology: accelerating new discoveries in the life sciences.

Spatial biology is a rapidly developing field which enables the visualization of protein and transcriptomic data while preserving tissue context and architecture. Initially used in discovery, there is growing promise for translational and diagnostic assay developments. Immediate applications are in precision medicine, such as being able to match patients to optimal therapies through better understanding the tumor microenvironment. However, it also has ramifications for many other disciplines (e.g. immunology, cancer, infectious disease and digital pathology). With increasingly massive data sets being generated, data storage, curation, analysis and sharing require more computational approaches and artificial intelligence-powered tools to fully utilize spatial tools. Here, we discuss spatial biology as an important convergent science approach to tackling complex global challenges in areas such as health.

Phosphorylation, compartmentalization, and cardiac function.

Protein phosphorylation is a fundamental element of cell signaling. First discovered as a biochemical switch in glycogen metabolism, we now know that this posttranslational modification permeates all aspects of cellular behavior. In humans, over 540 protein kinases attach phosphate to acceptor amino acids, whereas around 160 phosphoprotein phosphatases remove phosphate to terminate signaling. Aberrant phosphorylation underlies disease, and kinase inhibitor drugs are increasingly used clinically as targeted therapies. Specificity in protein phosphorylation is achieved in part because kinases and phosphatases are spatially organized inside cells. A prototypic example is compartmentalization of the cyclic adenosine 3',5'-monophosphate (cAMP)-dependent protein kinase A through association with A-kinase anchoring proteins. This configuration creates autonomous signaling islands where the anchored kinase is constrained in proximity to activators, effectors, and selected substates. This article primarily focuses on A kinase anchoring protein (AKAP) signaling in the heart with an emphasis on anchoring proteins that spatiotemporally coordinate excitation-contraction coupling and hypertrophic responses.

A novel process for H&E, immunofluorescence, and imaging mass cytometry on a single slide with a concise analytics pipeline.

Imaging mass cytometry (IMC) is a powerful spatial technology that utilizes cytometry time of flight to acquire multiplexed image datasets with up to 40 markers, via metal-tagged antibodies. Recent advances in IMC have led to the inclusion of RNAScope probes and multiple new analysis pipelines have led to faster analyses and better results. However, IMC still suffers from lower resolution (1 µm2 pixels) and relatively small regions of interest (ROIs) (<2 mm2 ) compared to other, light-based microscope technologies. Capturing higher-resolution images on serial sections causes great difficulty when attempting to align cells and structures across serial sections, especially when observing smaller cell types and structures. Therefore, we demonstrate the combination of H&E and multiplex immunofluorescence imaging, for much higher resolution of the structural and cellular compartments found throughout the entire tissue section, with the high-dimensionality of IMC for specific ROIs on a single slide. Additionally, we demonstrate a simple and effective open-source cell segmentation and IMC analysis pipeline with previously published and freely available software.

Novel Chromogens for Immunohistochemistry in Spatial Biology.

Spatial relations between tumor cells and host-infiltrating cells are increasingly important in both basic science and clinical research. In this study, we have tested the feasibility of using standard methods of immunohistochemistry (IHC) in a multiplex staining system using a newly developed set of chromogenic substrates for the peroxidase and alkaline phosphatase enzymes. Using this approach, we have developed a set of chromogens characterized by (1) providing fine cellular detail, (2) non-overlapping spectral profiles, (3) an absence of interactions between chromogens, (4) stability when stored, and (5) compatibility with current standard immunohistochemistry practices. When viewed microscopically under brightfield illumination, the chromogens yielded the following colors: red, black, blue, yellow, brown, and green. By selecting compatible color combinations, we have shown feasibility for four-color multiplex staining. Depending on the particular type of analysis being performed, visual analysis, without the aid of computer-assisted image analysis, was sufficient to differentiate up to four different markers.

Leveraging spatial omics for the development of precision sarcoma treatments.

Sarcomas are rare and heterogeneous cancers that arise from bone or soft tissue, and are the second most prevalent solid cancer in children and adolescents. Owing to the complex nature of pediatric sarcomas, the development of therapeutics for pediatric sarcoma has seen little progress in the past decades. Existing treatments are largely limited to chemotherapy, radiation, and surgery. Limited knowledge of the sarcoma tumor microenvironment (TME) and of well-defined target antigens in the different subtypes necessitates an alternative investigative approach to improve treatments. Recent advances in spatial omics technologies have enabled a more comprehensive study of the TME in multiple cancers. In this opinion article we discuss advances in our understanding of the TME of some cancers enabled by spatial omics technologies, and we explore how these technologies might advance the development of precision treatments for sarcoma, especially pediatric sarcoma.

Redefining clinical practice through spatial profiling: a revolution in tissue analysis.

Spatial biology, which combines molecular biology and advanced imaging, enhances our understanding of tissue cellular organisation. Despite its potential, spatial omics encounters challenges related to data complexity, computational requirements and standardisation of analysis. In clinical applications, spatial omics has the potential to revolutionise biomarker discovery, disease stratification and personalised treatments. It can identify disease-specific cell patterns, and could help risk stratify patients for clinical trials and disease-appropriate therapies. Although there are challenges in adopting it in clinical practice, spatial omics has the potential to significantly enhance patient outcomes. In this paper, we discuss the recent evolution of spatial biology, and its potential for improving our tissue level understanding and treatment of disease, to help advance precision and effectiveness in healthcare interventions.

IMmuneCite: an integrated workflow for analysis of immune enriched spatial proteomic data.

Spatial proteomics enable detailed analysis of tissue at single cell resolution. However, creating reliable segmentation masks and assigning accurate cell phenotypes to discrete cellular phenotypes can be challenging. We introduce IMmuneCite, a computational framework for comprehensive image pre-processing and single-cell dataset creation, focused on defining complex immune landscapes when using spatial proteomics platforms. We demonstrate that IMmuneCite facilitates the identification of 32 discrete immune cell phenotypes using data from human liver samples while substantially reducing nonbiological cell clusters arising from co-localization of markers for different cell lineages. We established its versatility and ability to accommodate any antibody panel and different species by applying IMmuneCite to data from murine liver tissue. This approach enabled deep characterization of different functional states in each immune compartment, uncovering key features of the immune microenvironment in clinical liver transplantation and murine hepatocellular carcinoma. In conclusion, we demonstrated that IMmuneCite is a user-friendly, integrated computational platform that facilitates investigation of the immune microenvironment across species, while ensuring the creation of an immune focused, spatially resolved single-cell proteomic dataset to provide high fidelity, biologically relevant analyses.

Digital spatial profiling identifies the tumor center as a topological niche in prostate cancer characterized by an upregulation of BAD.

Prostate cancer is characterized by a high degree of intratumoral heterogeneity. However, little is known about the spatial distribution of cancer cells with respect to specific functional characteristics and the formation of spatial niches. Here, we used digital spatial profiling (DSP) to investigate differences in protein expression in the tumor center versus the tumor periphery. Thirty-seven regions of interest were analyzed for the expression of 47 proteins, which included components of the PI3K-AKT, MAPK, and cell death signaling pathways as well as immune cell markers. A total of 1739 data points were collected from five patients. DSP identified the BCL-2 associated agonist of cell death (BAD) protein as the most significantly upregulated protein in the tumor center. BAD upregulation was confirmed by conventional immunohistochemistry, which furthermore showed a phosphorylation of BAD at serine 112 indicating its inactivation. Knockdown of BAD in prostate cancer cells in vitro led to decreased cell viability and colony growth. Clinically, high BAD expression was associated with a shorter time to biochemical recurrence in 158 mostly high-risk prostate cancer patients. Collectively, our results suggest that the tumor center is a topological niche with high BAD expression that may drive prostate cancer progression.

Multiplex Digital Spatial Profiling in Breast Cancer Research: State-of-the-Art Technologies and Applications across the Translational Science Spectrum.

While RNA sequencing and multi-omic approaches have significantly advanced cancer diagnosis and treatment, their limitation in preserving critical spatial information has been a notable drawback. This spatial context is essential for understanding cellular interactions and tissue dynamics. Multiplex digital spatial profiling (MDSP) technologies overcome this limitation by enabling the simultaneous analysis of transcriptome and proteome data within the intact spatial architecture of tissues. In breast cancer research, MDSP has emerged as a promising tool, revealing complex biological questions related to disease evolution, identifying biomarkers, and discovering drug targets. This review highlights the potential of MDSP to revolutionize clinical applications, ranging from risk assessment and diagnostics to prognostics, patient monitoring, and the customization of treatment strategies, including clinical trial guidance. We discuss the major MDSP techniques, their applications in breast cancer research, and their integration in clinical practice, addressing both their potential and current limitations. Emphasizing the strategic use of MDSP in risk stratification for women with benign breast disease, we also highlight its transformative potential in reshaping the landscape of breast cancer research and treatment.

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.

Spatial detection of mitochondrial DNA and RNA in tissues.

Background: Mitochondrial health has gained attention in a number of diseases, both as an indicator of disease state and as a potential therapeutic target. The quality and amount of mitochondrial DNA (mtDNA) and RNA (mtRNA) can be important indicators of mitochondrial and cell health, but are difficult to measure in complex tissues. Methods: mtDNA and mtRNA in zebrafish retina samples were fluorescently labeled using RNAscope™ in situ hybridization, then mitochondria were stained using immunohistochemistry. Pretreatment with RNase was used for validation. Confocal images were collected and analyzed, and relative amounts of mtDNA and mtRNA were reported. Findings regarding mtDNA were confirmed using qPCR. Results: Signals from probes detecting mtDNA and mtRNA were localized to mitochondria, and were differentially sensitive to RNase. This labeling strategy allows for quantification of relative mtDNA and mtRNA levels in individual cells. As a demonstration of the method in a complex tissue, single photoreceptors in zebrafish retina were analyzed for mtDNA and mtRNA content. An increase in mtRNA but not mtDNA coincides with proliferation of mitochondria at night in cones. A similar trend was measured in rods. Discussion: Mitochondrial gene expression is an important component of cell adaptations to disease, stress, or aging. This method enables the study of mtDNA and mtRNA in single cells of an intact, complex tissue. The protocol presented here uses commercially-available tools, and is adaptable to a range of species and tissue types.

Predicting lung cancer's metastats' locations using bioclinical model.

Background: Lung cancer is a global leading cause of cancer-related deaths, and metastasis profoundly influences treatment outcomes. The limitations of conventional imaging in detecting small metastases highlight the crucial need for advanced diagnostic approaches. Methods: This study developed a bioclinical model using three-dimensional CT scans to predict the spatial spread of lung cancer metastasis. Utilizing a three-layer biological model, we identified regions with a high probability of metastasis colonization and validated the model on real-world data from 10 patients. Findings: The validated bioclinical model demonstrated a promising 74% accuracy in predicting metastasis locations, showcasing the potential of integrating biophysical and machine learning models. These findings underscore the significance of a more comprehensive approach to lung cancer diagnosis and treatment. Interpretation: This study's integration of biophysical and machine learning models contributes to advancing lung cancer diagnosis and treatment, providing nuanced insights for informed decision-making.

spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis.

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.