MIBI-TOF: A multiplexed imaging platform relates cellular phenotypes and tissue structure

Imaging and spatially resolved mass spectrometry applications in nephrology

The application of spatially resolved mass spectrometry (MS) and MS imaging approaches for studying biomolecular processes in the kidney is rapidly growing. These powerful methods, which enable label-free and multiplexed detection of many molecular classes across omics domains (including metabolites, drugs, proteins and protein post-translational modifications), are beginning to reveal new molecular insights related to kidney health and disease. The complexity of the kidney often necessitates multiple scales of analysis for interrogating biofluids, whole organs, functional tissue units, single cells and subcellular compartments. Various MS methods can generate omics data across these spatial domains and facilitate both basic science and pathological assessment of the kidney. Optimal processes related to sample preparation and handling for different MS applications are rapidly evolving. Emerging technology and methods, improvement of spatial resolution, broader molecular characterization, multimodal and multiomics approaches and the use of machine learning and artificial intelligence approaches promise to make these applications even more valuable in the field of nephology. Overall, spatially resolved MS and MS imaging methods have the potential to fill much of the omics gap in systems biology analysis of the kidney and provide functional outputs that cannot be obtained using genomics and transcriptomic methods.

Advancing tissue analysis: Integrating mass tags with mass spectrometry imaging and immunohistochemistry

In biological and biomedical research, it's a crucial task to detect or quantify proteins or proteomes accurately across multiple samples. Immunohistochemistry (IHC) and spatial proteomics based on mass spectrometry imaging (MSI) are used to detect proteins in tissue samples. IHC can detect precisely but has a limited throughput, whereas MSI can simultaneously visualize thousands of specific chemical components but hindered by detailed protein annotation. Thereby, the introduction of mass tags may be adopted to expand the potential for integrating MSI and IHC. By enriching optical information for IHC and enhancing MS signals, mass tags can boost the accuracy of qualitative, localization, and quantitative detection of specific proteins in tissue sections, thereby widening the scope of protein detection and annotation results. Consequently, more comprehensive information regarding biological processes and disease states can be obtained, which aids in understanding complex biological processes and disease mechanisms and provides additional perspectives for clinical diagnosis and treatment. In the current review, we aim to discuss the role of different mass tags (e.g., mass tags based on inorganic molecules and organic molecules) in the combined application of MSI and IHC.

Comprehensive High-Spatial-Resolution Imaging Metabolomics Workflow for Heterogeneous Tissues

Mass spectrometry imaging is a developing technique that maps the molecular composition of samples in a label-free manner. However, highly heterogeneous samples, including bones, usually cannot be easily analyzed due to challenging sample preparation, particularly in minimizing cracks and maintaining flatness. To comprehensively address these issues, we developed a sample preparation method for the fresh frozen heterogeneous samples such as knee joint and skull of murine, which includes complex structures and tissue types, such as neuronal tissue, peripheral nerve, muscle, tracks, connective tissue, cartilage, mineralized bone, and bone marrow. By controlling the sample thickness and employing an optimized drying method, we achieved minimal cracking. We found that a combination of lyophilization and 5 μm section thickness, when attached to a cryofilm, was readily achievable and significantly reduced cracking in bone tissue. Additionally, we implemented a contactless spin-flattening technique to ensure surface uniformity. Centrifuging the section at 7000g improved surface flatness, bringing the height variation within the range typically observed in soft tissues while also removing excess mounting medium and bubbles. This approach enhances the sample quality and reliability without requiring complex manual skills, making it more practical and reproducible for routine analysis. High molecular coverage was achieved, including small metabolites, metals, and lipids, by using the N-(1-naphthyl) ethylenediamine dihydrochloride (NEDC) matrix. To further explore the potential of our workflow, high-resolution MSI was performed on rat tibial growth plates at different growth stages. Numbers of N-acetyl disaccharide sulfate and PE (34:1) are found to be complementary expressed in growth plate cartilage and have different intensities at different growth stages. Our findings suggested the potential involvement of those metabolites in bone development. By addressing the challenges of sample preparation, including surface flatness, bubbles, and severe cracking, our approach significantly improves the quality of the MS imaging. Additionally, this method offers a broad detection range that encompasses both metal ions and metabolites. This advancement enables detailed and accurate molecular characterization of rigid biological samples, enhancing the potential for applications in biomedical research.

Spatial proteomics of the tumor microenvironment in melanoma: current insights and future directions

Over the past years, cancer research has transitioned from a 'cancer cell-centered' focus to a more integrative view of tumors as dynamic ecosystems. This paradigm shift emphasizes the tumor microenvironment (TME) as a complex network of interacting cellular and acellular components, where tumor cells orchestrate a supportive environment that facilitates progression, metastasis, and immune evasion. Understanding the spatial organization of these components within the TME is crucial, as the positioning and interactions between cancerous and non-cancerous cells significantly influence tumor behavior and therapy response. Spatial proteomics has emerged as a powerful tool for TME analysis, enabling the detection and quantification of proteins within intact tissue architecture at subcellular resolution. This approach provides insights into cellular interactions, signaling pathways, and functional states, facilitating the discovery of novel biomarkers and therapeutic targets linked to specific tissue regions and cellular contexts. Translating spatial proteomics into clinical practice requires overcoming challenges related to technology refinement, standardization of workflows, and adaptation to routine pathology settings. Melanoma is an aggressive, highly immunogenic malignancy with variable response rates to existing immunotherapies. Given that over half of patients treated with immune checkpoint inhibitors (ICIs) fail to respond or experience disease progression, the identification of novel biomarkers and therapeutic targets to enhance current therapies is urgently required. Spatial imaging technologies are increasingly being utilized to dissect the complex interplay between stroma, melanoma, and immune cell types within the TME to address this need. This review examines key spatial proteomics methods, their applications in melanoma biology, and associated image analysis pipelines. We highlight the current limitations, and future directions, emphasizing the potential for clinical translation to guide personalized treatment strategies, inform prognosis, and predict therapeutic response.

Mature tertiary lymphoid structures evoke intra-tumoral T and B cell responses via progenitor exhausted CD4+ T cells in head and neck cancer

Tumor tertiary lymphoid structures (TLS), especially mature TLS (mTLS), have been associated with better prognosis and improved responses to immune checkpoint blockade (ICB), but the underlying mechanisms remain incompletely understood. Here, by performing single-cell RNA, antigen receptor sequencing and spatial transcriptomics on tumor tissue from head and neck squamous cell carcinoma (HNSCC) patients with different statuses of TLS, we observe that mTLS are enriched with stem-like T cells, and B cells at various maturation stages. Notably, progenitor exhausted CD4+ T cells, with features resembling follicular helper T cells, support these responses, by activating B cells to produce plasma cells in the germinal center, and interacting with DC-LAMP+ dendritic cells to support CD8+ T cell activation. Conversely, non-mTLS tumors do not promote local anti-tumor immunity which is abundant of immunosuppressive cells or a lack of stem-like B and T cells. Furthermore, patients with mTLS manifest improved overall survival and response to ICB compared to those with non-mTLS. Overall, our study provides insights into mechanisms underlying mTLS-mediated intra-tumoral immunity events against cancer.

Unravelling tumour spatiotemporal heterogeneity using spatial multimodal data

Analysing the genome, epigenome, transcriptome, proteome, and metabolome within the spatial context of cells has transformed our understanding of tumour spatiotemporal heterogeneity. Advances in spatial multi-omics technologies now reveal complex molecular interactions shaping cellular behaviour and tissue dynamics. This review highlights key technologies and computational methods that have advanced spatial domain identification and their pseudo-relations, as well as inference of intra- and inter-cellular molecular networks that drive disease progression. We also discuss strategies to address major challenges, including data sparsity, high-dimensionality, scalability, and heterogeneity. Furthermore, we outline how spatial multi-omics enables novel insights into disease mechanisms, advancing precision medicine and informing targeted therapies. KEY POINTS: Advancements in spatial multi-omics facilitate our understanding of tumour spatiotemporal heterogeneity. AI-driven multimodal models uncover complex molecular interactions that underlie cellular behaviours and tissue dynamics. Combining multi-omics technologies and AI-enabled bioinformatics tools helps predict critical disease stages, such as pre-cancer, advancing precision medicine, and informing targeted therapeutic strategies.

Targeting the intracellular immune checkpoint CISH with CRISPR-Cas9-edited T cells in patients with metastatic colorectal cancer: a first-in-human, single-centre, phase 1 trial

BACKGROUND

Over the past decade, immunotherapeutic strategies-mainly targeting the PD-1-PD-L1 immune checkpoint axis-have altered cancer treatment for many solid tumours, but few patients with gastrointestinal forms of cancer have benefited to date. There remains an urgent need to extend immunotherapy efficacy to more patients while addressing resistance to current immune checkpoint inhibitors. The aim of this study was to determine the safety and anti-tumour activity of knockout of CISH, which encodes cytokine-inducible SH2-containing protein, a novel intracellular immune checkpoint target and a founding member of the SOCS family of E3-ligases, using tumour infiltrating lymphocyte (TILs) genetically edited with CRISPR-Cas9 in patients with metastatic gastrointestinal epithelial cancers.

METHODS

For this first-in-human, single-centre, phase 1 trial, patients aged 18-70 years with a diagnosis of metastatic gastrointestinal epithelial cancer with progressive disease following at least one first line standard therapy, measurable disease with at least one lesion identified as resectable for TIL generation and at least one other lesion meeting RECIST criteria as measurable to serve as an indicator of disease response, and an ECOG performance status of 0 or 1 were screened and enrolled if meeting these and all other eligibility criteria. TILs procured from tumour biopsies were expanded on the basis of neoantigen reactivity, subjected to CRISPR-Cas9-mediated CISH knockout, and infused intravenously into 12 patients after non-myeloablative lymphocyte depleting chemotherapy (cyclophosphamide 60 mg/kg per dose on study days -6 and -5, and fludarabine 25 mg/m2 per dose on days -7 to -3) followed by high-dose IL-2 (aldesleukin; 720 000 IU/kg per dose). The primary endpoint was safety of administration of neoantigen-reactive TILs with knockout of the CISH gene, and a key secondary endpoint was anti-tumour activity measured as objective radiographic response and progression-free and overall survival. This study is registered with ClinicalTrials.gov, NCT04426669, and is complete.

FINDINGS

Between May 12, 2020, and Sept 16, 2022, 22 participants were enrolled in the trial (one patient was enrolled twice owing to lack of TIL outgrowth on the first attempt); ten patients were female, and 11 were male (self-defined). One patient was Asian, the remainder were White (self-defined). We successfully manufactured CISH knockout TIL products for 19 (86%) of the patients, of whom 12 (63%) received autologous CISH knockout TIL infusion. The median follow-up time for the study was 129 days (IQR 15-283). All 12 (100%) patients had treatment-related severe adverse events. The most common grade 3-4 adverse events included haematological events (12 patients [100%]) attributable to the preparative lymphodepleting chemotherapy regimen or expected effects of IL-2, fatigue (four patients [33%]), and anorexia (three patients [25%]). Deaths of any cause for patients on study were attributed to the underlying disease under study (metastatic gastrointestinal cancer) and related complications (10 patients) or infection (grade 5 septicaemia in one patient). There were no severe (≥grade 3) cytokine release or neurotoxicity events. Six (50%) of 12 patients had stable disease by day 28, and four (33%) had stable disease ongoing at 56 days. One young adult patient with microsatellite-instability-high colorectal cancer refractory to anti-PD1/CTLA-4 therapies had a complete and ongoing response (>21 months).

INTERPRETATION

These results support the safety and potential antitumour activity of inhibiting the immune checkpoint CISH through the administration of neoantigen-reactive CISH-knockout TILs, with implications for patients with advanced metastatic cancers refractory to checkpoint inhibitor immunotherapies, and provide the first evidence that a novel intracellular checkpoint can be targeted with therapeutic effect.

FUNDING

Intima Bioscience.

Application of metal stable isotopes labeling and elemental mass spectrometry for biomacromolecule profiling

Biomacromolecules including proteins and nucleic acids are widely recognized for their pivotal and irreplaceable role in maintaining the normal functions of biological systems. By combining metal stable isotope labeling with elemental mass spectrometry, researchers can quantify the amount and track the spatial distribution of specific biomacromolecules in complex biological systems. In this review, the probes classification and metal stable isotope labeling strategies are initially summarized. Secondly, the technical characteristics and working principle of the elemental mass spectrometry techniques including inductively coupled plasma mass spectrometry and secondary ion mass spectrometry are introduced to achieve highly sensitive detection of multiple biomacromolecules at molecular, cellular and tissue levels. Lastly, we underline the advantages and limitations of elemental mass spectrometry combined with metal stable isotope labeling strategies, and propose the perspectives for future developments.

Learning tissue representation by identification of persistent local patterns in spatial omics data

Spatial omics data provide rich molecular and structural information on tissues. Their analysis provides insights into local heterogeneity of tissues and holds promise to improve patient stratification by associating clinical observations with refined tissue representations. We introduce Kasumi, a method for identifying spatially localized neighborhood patterns of intra- and intercellular relationships that are persistent across samples and conditions. The tissue representation based on these patterns can facilitate translational tasks, as we show for stratification of cancer patients for disease progression and response to treatment using data from different experimental platforms. On these tasks, Kasumi outperforms related approaches and offers explanations of spatial coordination and relationships at the cell-type or marker level. We show that persistent patterns comprise regions of different sizes, and that non-abundant, localized relationships in the tissue are strongly associated with unfavorable outcomes.

Engineered Proteins and Chemical Tools to Probe the Cell Surface Proteome

The cell surface proteome, or surfaceome, is the hub for cells to interact and communicate with the outside world. Many disease-associated changes are hard-wired within the surfaceome, yet approved drugs target less than 50 cell surface proteins. In the past decade, the proteomics community has made significant strides in developing new technologies tailored for studying the surfaceome in all its complexity. In this review, we first dive into the unique characteristics and functions of the surfaceome, emphasizing the necessity for specialized labeling, enrichment, and proteomic approaches. An overview of surfaceomics methods is provided, detailing techniques to measure changes in protein expression and how this leads to novel target discovery. Next, we highlight advances in proximity labeling proteomics (PLP), showcasing how various enzymatic and photoaffinity proximity labeling techniques can map protein-protein interactions and membrane protein complexes on the cell surface. We then review the role of extracellular post-translational modifications, focusing on cell surface glycosylation, proteolytic remodeling, and the secretome. Finally, we discuss methods for identifying tumor-specific peptide MHC complexes and how they have shaped therapeutic development. This emerging field of neo-protein epitopes is constantly evolving, where targets are identified at the proteome level and encompass defined disease-associated PTMs, complexes, and dysregulated cellular and tissue locations. Given the functional importance of the surfaceome for biology and therapy, we view surfaceomics as a critical piece of this quest for neo-epitope target discovery.

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.

From surfing to diving into the tumor microenvironment through multiparametric imaging mass cytometry

The tumor microenvironment (TME) is a complex ecosystem where malignant and non-malignant cells cooperate and interact determining cancer progression. Cell abundance, phenotype and localization within the TME vary over tumor development and in response to therapeutic interventions. Therefore, increasing our knowledge of the spatiotemporal changes in the tumor ecosystem architecture is of importance to better understand the etiologic development of the neoplastic diseases. Imaging Mass Cytometry (IMC) represents the elective multiplexed imaging technology enabling the in-situ analysis of up to 43 different protein markers for in-depth phenotypic and spatial investigation of cells in their preserved microenvironment. IMC is currently applied in cancer research to define the composition of the cellular landscape and to identify biomarkers of predictive and prognostic significance with relevance in mechanisms of drug resistance. Herein, we describe the general principles and experimental workflow of IMC raising the informative potential in preclinical and clinical cancer research.

Multi-omic landscape of human gliomas from diagnosis to treatment and recurrence

Gliomas are among the most lethal cancers, with limited treatment options. To uncover hallmarks of therapeutic escape and tumor microenvironment (TME) evolution, we applied spatial proteomics, transcriptomics, and glycomics to 670 lesions from 310 adult and pediatric patients. Single-cell analysis shows high B7H3+ tumor cell prevalence in glioblastoma (GBM) and pleomorphic xanthoastrocytoma (PXA), while most gliomas, including pediatric cases, express targetable tumor antigens in less than 50% of tumor cells, potentially explaining trial failures. Longitudinal samples of isocitrate dehydrogenase (IDH)-mutant gliomas reveal recurrence driven by tumor-immune spatial reorganization, shifting from T-cell and vasculature-associated myeloid cell-enriched niches to microglia and CD206+ macrophage-dominated tumors. Multi-omic integration identified N-glycosylation as the best classifier of grade, while the immune transcriptome best predicted GBM survival. Provided as a community resource, this study opens new avenues for glioma targeting, classification, outcome prediction, and a baseline of TME composition across all stages.

The role of immunotherapy in targeting tumor microenvironment in genitourinary cancers

Genitourinary (GU) cancers, including renal cell carcinoma, prostate cancer, bladder cancer, and testicular cancer, represent a significant health burden and are among the leading causes of cancer-related mortality worldwide. Despite advancements in traditional treatment modalities such as chemotherapy, radiotherapy, and surgery, the complex interplay within the tumor microenvironment (TME) poses substantial hurdles to achieving durable remission and cure. The TME, characterized by its dynamic and multifaceted nature, comprises various cell types, signaling molecules, and the extracellular matrix, all of which are instrumental in cancer progression, metastasis, and therapy resistance. Recent breakthroughs in immunotherapy (IO) have opened a new era in the management of GU cancers, offering renewed hope by leveraging the body's immune system to combat cancer more selectively and effectively. This approach, distinct from conventional therapies, aims to disrupt cancer's ability to evade immune detection through mechanisms such as checkpoint inhibition, therapeutic vaccines, and adoptive cell transfer therapies. These strategies highlight the shift towards personalized medicine, emphasizing the importance of understanding the intricate dynamics within the TME for the development of targeted treatments. This article provides an in-depth overview of the current landscape of treatment strategies for GU cancers, with a focus on IO targeting the specific cell types of TME. By exploring the roles of various cell types within the TME and their impact on cancer progression, this review aims to underscore the transformative potential of IO strategies in TME targeting, offering more effective and personalized treatment options for patients with GU cancers, thereby improving outcomes and quality of life.

End-User Confidence in Artificial Intelligence-Based Predictions Applied to Biomedical Data

Applications of Artificial Intelligence (AI) are revolutionizing biomedical research and healthcare by offering data-driven predictions that assist in diagnoses. Supervised learning systems are trained on large datasets to predict outcomes for new test cases. However, they typically do not provide an indication of the reliability of these predictions, even though error estimates are integral to model development. Here, we introduce a novel method to identify regions in the feature space that diverge from training data, where an AI model may perform poorly. We utilize a compact precompiled structure that allows for fast and direct access to confidence scores in real time at the point of use without requiring access to the training data or model algorithms. As a result, users can determine when to trust the AI model's outputs, while developers can identify where the model's applicability is limited. We validate our approach using simulated data and several biomedical case studies, demonstrating that our approach provides fast confidence estimates ([Formula: see text] milliseconds per case), with high concordance to previously developed methods (f-[Formula: see text]). These estimates can be easily added to real-world AI applications. We argue that providing confidence estimates should be a standard practice for all AI applications in public use.

SOAPy: a Python package to dissect spatial architecture, dynamics, and communication

Advances in spatial omics enable deeper insights into tissue microenvironments while posing computational challenges. Therefore, we developed SOAPy, a comprehensive tool for analyzing spatial omics data, which offers methods for spatial domain identification, spatial expression tendency, spatiotemporal expression pattern, cellular co-localization, multi-cellular niches, cell-cell communication, and so on. SOAPy can be applied to diverse spatial omics technologies and multiple areas in physiological and pathological contexts, such as tumor biology and developmental biology. Its versatility and robust performance make it a universal platform for spatial omics analysis, providing diverse insights into the dynamics and architecture of tissue microenvironments.

High-dimensional imaging using combinatorial channel multiplexing and deep learning

Understanding tissue structure and function requires tools that quantify the expression of multiple proteins at single-cell resolution while preserving spatial information. Current imaging technologies use a separate channel for each protein, limiting throughput and scalability. Here, we present combinatorial multiplexing (CombPlex), a combinatorial staining platform coupled with an algorithmic framework to exponentially increase the number of measured proteins. Every protein can be imaged in several channels and every channel contains agglomerated images of several proteins. These combinatorically compressed images are then decompressed to individual protein images using deep learning. We achieve accurate reconstruction when compressing the stains of 22 proteins to five imaging channels. We demonstrate the approach both in fluorescence microscopy and in mass-based imaging and show successful application across multiple tissues and cancer types. CombPlex can escalate the number of proteins measured by any imaging modality, without the need for specialized instrumentation.

Integrated workflow for analysis of immune enriched spatial proteomic data with IMmuneCite

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.

Bi-level graph learning unveils prognosis-relevant tumor microenvironment patterns in breast multiplexed digital pathology

The tumor microenvironment (TME) is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. Increasing efforts have been dedicated to characterizing it, including its analysis with modern deep learning. However, identifying generalizable biomarkers has been limited by the uninterpretable nature of their predictions. We introduce a data-driven yet interpretable approach for identifying cellular patterns in the TME associated with patient prognoses. Our method relies on constructing a bi-level graph model: a cellular graph, which models the TME, and a population graph, capturing inter-patient similarities given their respective cellular graphs. We demonstrate our approach in breast cancer, showing that the identified patterns provide a risk-stratification system with new complementary information to standard clinical subtypes, and these results are validated in two independent cohorts. Our methodology could be applied to other cancer types more generally, providing insights into the spatial cellular patterns associated with patient outcomes.

Tribus: semi-automated discovery of cell identities and phenotypes from multiplexed imaging and proteomic data

MOTIVATION

Multiplexed imaging and single-cell analysis are increasingly applied to investigate the tissue spatial ecosystems in cancer and other complex diseases. Accurate single-cell phenotyping based on marker combinations is a critical but challenging task due to (i) low reproducibility across experiments with manual thresholding, and, (ii) labor-intensive ground-truth expert annotation required for learning-based methods.

RESULTS

We developed Tribus, an interactive knowledge-based classifier for multiplexed images and proteomic datasets that avoids hard-set thresholds and manual labeling. We demonstrated that Tribus recovers fine-grained cell types, matching the gold standard annotations by human experts. Additionally, Tribus can target ambiguous populations and discover phenotypically distinct cell subtypes. Through benchmarking against three similar methods in four public datasets with ground truth labels, we show that Tribus outperforms other methods in accuracy and computational efficiency, reducing runtime by an order of magnitude. Finally, we demonstrate the performance of Tribus in rapid and precise cell phenotyping with two large in-house whole-slide imaging datasets.

AVAILABILITY AND IMPLEMENTATION

Tribus is available at https://github.com/farkkilab/tribus as an open-source Python package.

The immunometabolic topography of tuberculosis granulomas governs cellular organization and bacterial control

Despite being heavily infiltrated by immune cells, tuberculosis (TB) granulomas often subvert the host response to Mycobacterium tuberculosis (Mtb) infection and support bacterial persistence. We previously discovered that human TB granulomas are enriched for immunosuppressive factors typically associated with tumor-immune evasion, raising the intriguing possibility that they promote tolerance to infection. In this study, our goal was to identify the prime drivers for establishing this tolerogenic niche and to determine if the magnitude of this response correlates with bacterial persistence. To do this, we conducted a multimodal spatial analysis of 52 granulomas from 16 non-human primates (NHP) who were infected with low dose Mtb for 9-12 weeks. Notably, each granuloma's bacterial burden was individually quantified allowing us to directly ask how granuloma spatial structure and function relate to infection control. We found that a universal feature of TB granulomas was partitioning of the myeloid core into two distinct metabolic environments, one of which is hypoxic. This hypoxic environment associated with pathologic immune cell states, dysfunctional cellular organization of the granuloma, and a near-complete blockade of lymphocyte infiltration that would be required for a successful host response. The extent of these hypoxia-associated features correlated with worsened bacterial burden. We conclude that hypoxia governs immune cell state and organization within granulomas and is a potent driver of subverted immunity during TB.

Identification and characterization of cell niches in tissue from spatial omics data at single-cell resolution

Deciphering the features, structure, and functions of the cell niche in tissues remains a major challenge. Here, we present scNiche, a computational framework to identify and characterize cell niches from spatial omics data at single-cell resolution. We benchmark scNiche with both simulated and biological datasets, and demonstrate that scNiche can effectively and robustly identify cell niches while outperforming other existing methods. In spatial proteomics data from human triple-negative breast cancer, scNiche reveals the influence of the microenvironment on cellular phenotypes, and further dissects patient-specific niches with distinct cellular compositions or phenotypic characteristics. By analyzing mouse liver spatial transcriptomics data across normal and early-onset liver failure donors, scNiche uncovers disease-specific liver injury niches, and further delineates the niche remodeling from normal liver to liver failure. Overall, scNiche enables decoding the cellular microenvironment in tissues from single-cell spatial omics data.

Building Spatiotemporal Understanding of Mycobacterium tuberculosis-Host Interactions

Heterogeneity during Mycobacterium tuberculosis (Mtb) infection greatly impacts disease outcome and complicates treatment. This heterogeneity encompasses many facets, spanning local differences in the host immune response to Mtb and the environment experienced by the bacterium, to nonuniformity in Mtb replication state. All of these facets are interlinked and each can affect Mtb susceptibility to antibiotic treatment. In-depth spatiotemporal understanding of Mtb-host interactions is thus critical to both fundamental comprehension of Mtb infection biology and for the development of effective therapeutic regimens. Such spatiotemporal understanding dictates the need for analysis at the single bacterium/cell level in the context of intact tissue architecture, which has been a significant technical challenge. Excitingly, innovations in spatial single cell methodology have opened the door to such studies, beginning to illuminate aspects ranging from intergranuloma differences in cellular composition and phenotype, to sublocation differences in Mtb physiology and replication state. In this perspective, we discuss recent studies that demonstrate the potential of these methodological advancements to reveal critical spatiotemporal insight into Mtb-host interactions, and highlight future avenues of research made possible by these advances.

Spatial Patterning Analysis of Cellular Ensembles (SPACE) finds complex spatial organization at the cell and tissue levels

Spatial patterns of cells and other biological elements drive physiologic and pathologic processes within tissues. While many imaging and transcriptomic methods document tissue organization, discerning these patterns is challenging, especially when they involve multiple elements in complex arrangements. To address this challenge, we present Spatial Patterning Analysis of Cellular Ensembles (SPACE), an R package for analysis of high-plex spatial data. SPACE is compatible with any data collection modality that records values (i.e., categorical cell/structure types or quantitative expression levels) at fixed spatial coordinates (i.e., 2d pixels or 3d voxels). SPACE detects not only broad patterns of co-occurrence but also context-dependent associations, quantitative gradients and orientations, and other organizational complexities. Via a robust information theoretic framework, SPACE explores all possible ensembles of tissue elements-single elements, pairs, triplets, and so on-and ranks the most strongly patterned ensembles. For single images, rankings reflect differences from random assortment. For sets of images, rankings reflect differences across sample groups (e.g., genotypes, treatments, timepoints, etc.). Further tools then characterize the nature of each pattern for intuitive interpretation. We validate SPACE and demonstrate its advantages using murine lymph node images for which ground truth has been defined. We then detect new patterns across varied datasets, including tumors and tuberculosis granulomas.

CelloType: a unified model for segmentation and classification of tissue images

Cell segmentation and classification are critical tasks in spatial omics data analysis. Here we introduce CelloType, an end-to-end model designed for cell segmentation and classification for image-based spatial omics data. Unlike the traditional two-stage approach of segmentation followed by classification, CelloType adopts a multitask learning strategy that integrates these tasks, simultaneously enhancing the performance of both. CelloType leverages transformer-based deep learning techniques for improved accuracy in object detection, segmentation and classification. It outperforms existing segmentation methods on a variety of multiplexed fluorescence and spatial transcriptomic images. In terms of cell type classification, CelloType surpasses a model composed of state-of-the-art methods for individual tasks and a high-performance instance segmentation model. Using multiplexed tissue images, we further demonstrate the utility of CelloType for multiscale segmentation and classification of both cellular and noncellular elements in a tissue. The enhanced accuracy and multitask learning ability of CelloType facilitate automated annotation of rapidly growing spatial omics data.

Spatial Architecture of Single-Cell and Vasculature in Tumor Microenvironment Predicts Clinical Outcomes in Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer with limited treatment options, which warrants the identification of novel therapeutic targets. Deciphering nuances in the tumor microenvironment (TME) may unveil insightful links between antitumor immunity and clinical outcomes; however, such connections remain underexplored. Here, we employed a data set derived from imaging mass cytometry of 71 TNBC patient specimens at single-cell resolution and performed in-depth quantifications with a suite of multiscale computational algorithms. The TNBC TME reflected a heterogeneous ecosystem with high spatial and compositional heterogeneity. Spatial analysis identified 10 recurrent cellular neighborhoods-a collection of local TME characteristics with unique cell components. The prevalence of cellular neighborhoods enriched with B cells, fibroblasts, and tumor cells, in conjunction with vascular density and perivasculature immune profiles, could significantly enrich long-term survivors. Furthermore, relative spatial colocalization of SMAhi fibroblasts and tumor cells compared with B cells correlated significantly with favorable clinical outcomes. Using a deep learning model trained on engineered spatial data, we can predict with high accuracy (mean area under the receiver operating characteristic curve of 5-fold cross-validation = 0.71) how a separate cohort of patients in the NeoTRIP clinical trial will respond to treatment based on baseline TME features. These data reinforce that the TME architecture is structured in cellular compositions, spatial organizations, vasculature biology, and molecular profiles and suggest novel imaging-based biomarkers for the treatment development in the context of TNBC.

A multi-omics spatial framework for host-microbiome dissection within the intestinal tissue microenvironment

The intricate interactions between the host immune system and its microbiome constituents undergo dynamic shifts in response to perturbations to the intestinal tissue environment. Our ability to study these events on the systems level is significantly limited by in situ approaches capable of generating simultaneous insights from both host and microbial communities. Here, we introduce Microbiome Cartography (MicroCart), a framework for simultaneous in situ probing of host and microbiome across multiple spatial modalities. We demonstrate MicroCart by investigating gut host and microbiome changes in a murine colitis model, using spatial proteomics, transcriptomics, and glycomics. Our findings reveal a global but systematic transformation in tissue immune responses, encompassing tissue-level remodeling in response to host immune and epithelial cell state perturbations, bacterial population shifts, localized inflammatory responses, and metabolic process alterations during colitis. MicroCart enables a deep investigation of the intricate interplay between the host tissue and its microbiome with spatial multi-omics.

Temporal and spatial composition of the tumor microenvironment predicts response to immune checkpoint inhibition

Immune checkpoint inhibition (ICI) has fundamentally changed cancer treatment. However, only a minority of patients with metastatic triple negative breast cancer (TNBC) benefit from ICI, and the determinants of response remain largely unknown. To better understand the factors influencing patient outcome, we assembled a longitudinal cohort with tissue from multiple timepoints, including primary tumor, pre-treatment metastatic tumor, and on-treatment metastatic tumor from 117 patients treated with ICI (nivolumab) in the phase II TONIC trial. We used highly multiplexed imaging to quantify the subcellular localization of 37 proteins in each tumor. To extract meaningful information from the imaging data, we developed SpaceCat, a computational pipeline that quantifies features from imaging data such as cell density, cell diversity, spatial structure, and functional marker expression. We applied SpaceCat to 678 images from 294 tumors, generating more than 800 distinct features per tumor. Spatial features were more predictive of patient outcome, including features like the degree of mixing between cancer and immune cells, the diversity of the neighboring immune cells surrounding cancer cells, and the degree of T cell infiltration at the tumor border. Non-spatial features, including the ratio between T cell subsets and cancer cells and PD-L1 levels on myeloid cells, were also associated with patient outcome. Surprisingly, we did not identify robust predictors of response in the primary tumors. In contrast, the metastatic tumors had numerous features which predicted response. Some of these features, such as the cellular diversity at the tumor border, were shared across timepoints, but many of the features, such as T cell infiltration at the tumor border, were predictive of response at only a single timepoint. We trained multivariate models on all of the features in the dataset, finding that we could accurately predict patient outcome from the pre-treatment metastatic tumors, with improved performance using the on-treatment tumors. We validated our findings in matched bulk RNA-seq data, finding the most informative features from the on-treatment samples. Our study highlights the importance of profiling sequential tumor biopsies to understand the evolution of the tumor microenvironment, elucidating the temporal and spatial dynamics underlying patient responses and underscoring the need for further research on the prognostic role of metastatic tissue and its utility in stratifying patients for ICI.

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.

SpatialRef: a reference of spatial omics with known spot annotation

Spatial omics technologies have enabled the creation of intricate spatial maps that capture molecular features and tissue morphology, providing valuable insights into the spatial associations and functional organization of tissues. Accurate annotation of spot or domain types is essential for downstream spatial omics analyses, but this remains challenging. Therefore, this study aimed to develop a manually curated spatial omics database (SpatialRef, https://bio.liclab.net/spatialref/), to provide comprehensive and high-quality spatial omics data with known spot labels across multiple species. The current version of SpatialRef aggregates >9 million manually annotated spots across 17 Human, Mouse and Drosophila tissue types through extensive review and strict quality control, covering multiple spatial sequencing technologies and >400 spot/domain types from original studies. Furthermore, SpatialRef supports various spatial omics analyses about known spot types, including differentially expressed genes, spatially variable genes, Gene Ontology (GO)/KEGG annotation, spatial communication and spatial trajectories. With a user-friendly interface, SpatialRef facilitates querying, browsing and visualizing, thereby aiding in elucidating the functional relevance of spatial domains within the tissue and uncovering potential biological effects.

Exploring Multiplex Immunohistochemistry (mIHC) Techniques and Histopathology Image Analysis: Current Practice and Potential for Clinical Incorporation

BACKGROUND

By simultaneously staining multiple immunomarkers on a single tissue section, multiplexed immunohistochemistry (mIHC) enhances the amount of information that can be observed in a single tissue section and thus can be a powerful tool to visualise cellular interactions directly in the tumour microenvironment. Performing mIHC remains technically and practically challenging, and this technique has many limitations if not properly validated. However, with proper validation, heterogeneity between histopathological images can be avoided.

AIMS

This review aimed to summarize the currently used methods and to propose a standardised method for effective mIHC.

MATERIALS AND METHODS

An extensive literature review was conducted to identify different methods currently in use for mIHC.

RESULTS

Guidelines for antibody selection, panel design, antibody validation and analytical strategies are given. The advantages and disadvantages of each method are discussed.

CONCLUSION

This review summarizes widely used pathology imaging software and discusses the potential for automation of pathology image analysis so that mIHC technology can be a truly powerful tool for research as well as clinical use.

Spatial-Omics Methods and Applications

Traditional tissue profiling approaches have evolved from bulk studies to single-cell analysis over the last decade; however, the spatial context in tissues and microenvironments has always been lost. Over the last 5 years, spatial technologies have emerged that enabled researchers to investigate tissues in situ for proteins and transcripts without losing anatomy and histology. The field of spatial-omics enables highly multiplexed analysis of biomolecules like RNAs and proteins in their native spatial context-and has matured from initial proof-of-concept studies to a thriving field with widespread applications from basic research to translational and clinical studies. While there has been wide adoption of spatial technologies, there remain challenges with the standardization of methodologies, sample compatibility, throughput, resolution, and ease of use. In this chapter, we discuss the current state of the field and highlight technological advances and limitations.

The spatial biology of HIV infection

HIV infection implicates a spectrum of tissues in the human body starting with viral transmission in the anogenital tract and subsequently persisting in lymphoid tissues and brain. Though studies using isolated cells have contributed significantly towards our understanding of HIV infection, the tissue microenvironment is characterised by a complex interplay of a range of factors, all of which can influence the course of infection but are otherwise missed in ex vivo studies. To address this knowledge gap, it is necessary to investigate the dynamics of infection and the host immune response in situ using imaging-based approaches. Over the last decade, emerging imaging techniques have continually redefined the limits of detection, both in terms of the scope and the scale of the targets. In doing so, this has opened up new questions that can be answered by in situ studies. This review discusses the high-dimensional imaging modalities that are now available and their application towards understanding the spatial biology of HIV infection.

Multiplexed and Multiomic Mass Spectrometry Imaging of MALDI-IHC Photocleavable Mass Tag Tissue Probes, N-Glycomics, and the Extracellular Matrisome from FFPE Tissue Sections

Recent work in single-cell imaging has allowed unprecedented insight into single-cell interactions that control disease progression. However, approaches to understanding the combined extracellular and cellular microenvironment are limited. In the current protocol, we describe an approach that allows single-cell type imaging using matrix-assisted laser desorption/ionization immunohistochemistry (MALDI-IHC) of UV (ultraviolet) photocleavable mass tags combined with N-glycomic and ECM-targeted proteomic imaging from the same formalin-fixed paraffin-embedded tissue section. These approaches use the same imaging mass spectrometry platform to provide a comprehensive view of both the cellular and extracellular tissue microenvironment.

Flow-on-repellent biofabrication of fibrous decellularized breast tumor-stroma models

On-the-fly biofabrication of reproducible 3D tumor models at a pre-clinical level is highly desirable to level-up their applicability and predictive potential. Incorporating ECM biomolecular cues and its complex 3D bioarchitecture in the design stages of such in vitro platforms is essential to better recapitulate the native tumor microenvironment. To materialize these needs, herein we describe an innovative flow-on-repellent (FLORE) 3D extrusion bioprinting technique that leverages expedited and automatized bioink deposition onto a customized superhydrophobic printing bed. We demonstrate that this approach enables the rapid generation of quasi-spherical breast cancer-stroma hybrid models in a mode governed by surface wettability rather than bioink rheological features. For this purpose, an ECM-mimetic bioink comprising breast tissue-specific decellularized matrix in the form of microfiber bundles (dECM-μF) and photocrosslinkable hyaluronan (HAMA), was formulated to generate triple negative breast tumor-stroma models. Leveraging on the FLORE bioprinting approach, a rapid, automated, and reproducible fabrication of physiomimetic breast cancer hydrogel beads was successfully demonstrated. Hydrogel beads size with and without dECM-μF was easily tailored by modelling droplet deposition time on the superhydrophobic bed. Interestingly, in heterotypic breast cancer-stroma beads a self-arrangement of different cellular populations was observed, independent of dECM-μF inclusion, with CAFs clustering overtime within the fabricated models. Drug screening assays showed that the inclusion of CAFs and dECM-μF also impacted the overall response of these living constructs when incubated with gemcitabine chemotherapeutics, with dECM-μF integration promoting a trend for higher resistance in ECM-enriched models. Overall, we developed a rapid fabrication approach leveraging on extrusion bioprinting and superhydrophobic surfaces to process photocrosslinkable dECM bioinks and to generate increasingly physiomimetic tumor-stroma-matrix platforms for drug screening.

Multiparameter quantitative analyses of diagnostic cells in brain tissues from tuberous sclerosis complex

The advent of high-dimensional imaging offers new opportunities to molecularly characterize diagnostic cells in disorders that have previously relied on histopathological definitions. One example case is found in tuberous sclerosis complex (TSC), a developmental disorder characterized by systemic growth of benign tumors. Within resected brain tissues from patients with TSC, detection of abnormally enlarged balloon cells (BCs) is pathognomonic for this disorder. Though BCs can be identified by an expert neuropathologist, little is known about the specificity and broad applicability of protein markers for these cells, complicating classification of proposed BCs identified in experimental models of this disorder. Here, we report the development of a customized machine learning pipeline (BAlloon IDENtifier; BAIDEN) that was trained to prospectively identify BCs in tissue sections using a histological stain compatible with high-dimensional cytometry. This approach was coupled to a custom 36-antibody panel and imaging mass cytometry (IMC) to explore the expression of multiple previously proposed BC marker proteins and develop a descriptor of BC features conserved across multiple tissue samples from patients with TSC. Here, we present a modular workflow encompassing BAIDEN, a custom antibody panel, a control sample microarray, and analysis pipelines-both open-source and in-house-and apply this workflow to understand the abundance, structure, and signaling activity of BCs as an example case of how high-dimensional imaging can be applied within human tissues.

Highly Multiplexed Immunofluorescence PhenoCycler Panel for Murine Formalin-Fixed Paraffin-Embedded Tissues Yields Insight Into Tumor Microenvironment Immunoengineering

Spatial proteomics profiling is an emerging set of technologies that has the potential to elucidate the cell types, interactions, and molecular signatures that make up complex tissue microenvironments, with applications in the study of cancer, immunity, and much more. An emerging technique in the field is CoDetection by indEXing, recently renamed as the PhenoCycler system. This is a highly multiplexed immunofluorescence imaging technology that relies on oligonucleotide-barcoded antibodies and cyclic immunofluorescence to visualize many antibody markers in a single specimen while preserving tissue architecture. Existing PhenoCycler panels are primarily designed for fresh frozen tissues. Formalin-fixed paraffin-embedded blocks offer several advantages in preclinical research, but few antibody clones have been identified in this setting for PhenoCycler imaging. Here, we present a novel PhenoCycler panel of 28 validated antibodies for murine formalin-fixed paraffin-embedded tissues. We describe our workflow for selecting and validating clones, barcoding antibodies, designing our panel, and performing multiplex imaging. We further detail our analysis pipeline for comparing marker expressions, clustering and phenotyping single-cell proteomics data, and quantifying spatial relationships. We then apply our panel and analysis protocol to profile the effects of 3 gene delivery nanoparticle formulations, in combination with systemic anti-PD1, on the murine melanoma tumor immune microenvironment. Intralesional delivery of genes expressing the costimulatory molecule 4-1BBL and the cytokine IL-12 led to a shift toward intratumoral M1 macrophage polarization and promoted closer associations between intratumoral CD8 T cells and macrophages. Delivery of interferon gamma, in addition to 4-1BBL and IL-12, not only further increased markers of antigen presentation on tumor cells and intratumoral antigen-presenting cells but also promoted greater expression of checkpoint marker PD-L1 and closer associations between intratumoral CD8 T cells and PD-L1-expressing tumor cells. These findings help explain the benefits of 4-1BBL and IL-12 delivery while offering additional mechanistic insights into the limitations of interferon gamma therapeutic efficacy.

Statistical analysis supports pervasive RNA subcellular localization and alternative 3' UTR regulation

Targeted low-throughput studies have previously identified subcellular RNA localization as necessary for cellular functions including polarization, and translocation. Furthermore, these studies link localization to RNA isoform expression, especially 3' Untranslated Region (UTR) regulation. The recent introduction of genome-wide spatial transcriptomics techniques enables the potential to test if subcellular localization is regulated in situ pervasively. In order to do this, robust statistical measures of subcellular localization and alternative poly-adenylation (APA) at single-cell resolution are needed. Developing a new statistical framework called SPRAWL, we detect extensive cell-type specific subcellular RNA localization regulation in the mouse brain and to a lesser extent mouse liver. We integrated SPRAWL with a new approach to measure cell-type specific regulation of alternative 3' UTR processing and detected examples of significant correlations between 3' UTR length and subcellular localization. Included examples, Timp3, Slc32a1, Cxcl14, and Nxph1 have subcellular localization in the mouse brain highly correlated with regulated 3' UTR processing that includes the use of unannotated, but highly conserved, 3' ends. Together, SPRAWL provides a statistical framework to integrate multi-omic single-cell resolved measurements of gene-isoform pairs to prioritize an otherwise impossibly large list of candidate functional 3' UTRs for functional prediction and study. In these studies of data from mice, SPRAWL predicts that 3' UTR regulation of subcellular localization may be more pervasive than currently known.

A comprehensive review of approaches for spatial domain recognition of spatial transcriptomes

In current bioinformatics research, spatial transcriptomics (ST) as a rapidly evolving technology is gradually receiving widespread attention from researchers. Spatial domains are regions where gene expression and histology are consistent in space, and detecting spatial domains can better understand the organization and functional distribution of tissues. Spatial domain recognition is a fundamental step in the process of ST data interpretation, which is also a major challenge in ST analysis. Therefore, developing more accurate, efficient, and general spatial domain recognition methods has become an important and urgent research direction. This article aims to review the current status and progress of spatial domain recognition research, explore the advantages and limitations of existing methods, and provide suggestions and directions for future tool development.

Current state and future prospects of spatial biology in colorectal cancer

Over the past century, colorectal cancer (CRC) has become one of the most devastating cancers impacting the human population. To gain a deeper understanding of the molecular mechanisms driving this solid tumor, researchers have increasingly turned their attention to the tumor microenvironment (TME). Spatial transcriptomics and proteomics have emerged as a particularly powerful technology for deciphering the complexity of CRC tumors, given that the TME and its spatial organization are critical determinants of disease progression and treatment response. Spatial transcriptomics enables high-resolution mapping of the whole transcriptome. While spatial proteomics maps protein expression and function across tissue sections. Together, they provide a detailed view of the molecular landscape and cellular interactions within the TME. In this review, we delve into recent advances in spatial biology technologies applied to CRC research, highlighting both the methodologies and the challenges associated with their use, such as the substantial tissue heterogeneity characteristic of CRC. We also discuss the limitations of current approaches and the need for novel computational tools to manage and interpret these complex datasets. To conclude, we emphasize the importance of further developing and integrating spatial transcriptomics into CRC precision medicine strategies to enhance therapeutic targeting and improve patient outcomes.

Spatial analysis by current multiplexed imaging technologies for the molecular characterisation of cancer tissues

Tumours are composed of tumour cells and the surrounding tumour microenvironment (TME), and the molecular characterisation of the various elements of the TME and their interactions is essential for elucidating the mechanisms of tumour progression and developing better therapeutic strategies. Multiplex imaging is a technique that can quantify the expression of multiple protein markers on the same tissue section while maintaining spatial positioning, and this method has been rapidly developed in cancer research in recent years. Many multiplex imaging technologies and spatial analysis methods are emerging, and the elucidation of their principles and features is essential. In this review, we provide an overview of the latest multiplex imaging techniques by type of imaging and staining method and an introduction to image analysis methods, primarily focusing on spatial cellular properties, providing deeper insight into tumour organisation and spatial molecular biology in the TME.

PENGUIN: A rapid and efficient image preprocessing tool for multiplexed spatial proteomics

Multiplex spatial proteomic methodologies can provide a unique perspective on the molecular and cellular composition of complex biological systems. Several challenges are associated to the analysis of imaging data, specifically in regard to the normalization of signal-to-noise ratios across images and subtracting background noise. However, there is a lack of user-friendly solutions for denoising multiplex imaging data that can be applied to large datasets. We have developed PENGUIN -Percentile Normalization GUI Image deNoising: a straightforward image preprocessing tool for multiplexed spatial proteomics data. Compared to existing approaches, PENGUIN distinguishes itself by eliminating the need for manual annotation or machine learning models. It effectively preserves signal intensity differences while reducing noise, improving downstream tasks such as cell segmentation and phenotyping. PENGUIN's simplicity, speed, and intuitive interface, available as both a script and a Jupyter notebook, make it easy to adjust image processing parameters, providing a user-friendly experience. We further demonstrate the effectiveness of PENGUIN by comparing it to conventional image processing techniques and solutions tailored for multiplex imaging data.

Approaches to investigate tissue-resident innate lymphocytes metabolism at the single-cell level

Tissue-resident innate immune cells have important functions in both homeostasis and pathological states. Despite advances in the field, analyzing the metabolism of tissue-resident innate lymphocytes is still challenging. The small number of tissue-resident innate lymphocytes such as ILC, NK, iNKT and γδ T cells poses additional obstacles in their metabolic studies. In this review, we summarize the current understanding of innate lymphocyte metabolism and discuss potential pitfalls associated with the current methodology relying predominantly on in vitro cultured cells or bulk-level comparison. Meanwhile, we also summarize and advocate for the development and adoption of single-cell metabolic assays to accurately profile the metabolism of tissue-resident immune cells directly ex vivo.

Multi-sample non-negative spatial factorization

Analyzing multi-sample spatial transcriptomics data requires accounting for biological variation. We present multi-sample non-negative spatial factorization (mNSF), an alignment-free framework extending single-sample spatial factorization (NSF) to multi-sample datasets. mNSF incorporates sample-specific spatial correlation modeling and extracts low-dimensional data representations. Through simulations and real data analysis, we demonstrate mNSF's efficacy in identifying true factors, shared anatomical regions, and region-specific biological functions. mNSF's performance is comparable to alignment-based methods when alignment is feasible, while enabling analysis in scenarios where spatial alignment is unfeasible. mNSF shows promise as a robust method for analyzing spatially resolved transcriptomics data across multiple samples.

Generalized cell phenotyping for spatial proteomics with language-informed vision models

We present a novel approach to cell phenotyping for spatial proteomics that addresses the challenge of generalization across diverse datasets with varying marker panels. Our approach utilizes a transformer with channel-wise attention to create a language-informed vision model; this model's semantic understanding of the underlying marker panel enables it to learn from and adapt to heterogeneous datasets. Leveraging a curated, diverse dataset with cell type labels spanning the literature and the NIH Human BioMolecular Atlas Program (HuBMAP) consortium, our model demonstrates robust performance across various cell types, tissues, and imaging modalities. Comprehensive benchmarking shows superior accuracy and generalizability of our method compared to existing methods. This work significantly advances automated spatial proteomics analysis, offering a generalizable and scalable solution for cell phenotyping that meets the demands of multiplexed imaging data.

Improvements in Fast Mass Microscopy for Large-Area Samples

Mass spectrometry imaging (MSI) is a technique that analyzes the chemical information and spatial distribution of surface analytes. Most MSI studies are conducted in microprobe mode, in which a mass spectrum is collected for each pixel to create a mass image. Thus, the spatial resolution, sample imaging area, and imaging speed are linked. In this mode, halving the pixel size quadruples the analytical time, which presents a practical limit on the high spatial resolution MSI throughput. Fast mass microscopy (FMM) is, in contrast, a microscope-mode MSI technique that decouples spatial resolution and imaging speed. FMM circumvents the linear-quadratic relationship of pixel size and analytical time, which enables increased imaging size area and the analytical speed achievable. In this study, we implement instrument modifications to the FMM system, including the addition of linear encoders that enable roughly 8.5× faster imaging than was previously achieved, allowing a 42.5 × 26 mm2 sample area to be imaged at a 1 μm pixel size in <4.5 min. Linear encoders also enable the alignment of multipass images that increase image homogeneity and signal intensity. The applicability of FMM to large area samples has made it important to define the tolerance to height variations of the technique, which was determined to be at least 218 ± 0.03 (n = 3) μm.

Multimodal Mass Spectrometry Imaging in Atlas Building: A Review

In the era of precision medicine, scientists are creating atlases of the human body to map cells at the molecular level, providing insight into what fundamentally makes each cell different. In these atlas efforts, multimodal imaging techniques that include mass spectrometry imaging (MSI) have revolutionized the way biomolecules, such as lipids, peptides, proteins, and small metabolites, are visualized in the native spatial context of biological tissue. As such, MSI has become a fundamental arm of major cell atlasing efforts, as it can analyze the spatial distribution of hundreds of molecules in diverse sample types. These rich molecular data are then correlated with orthogonal assays, including histologic staining, proteomics, and transcriptomics, to analyze molecular classes that are not traditionally detected by MSI. Additional computational methods enable further examination of the correlations between biomolecular classes and creation of visualizations that serve as a powerful resource for researchers and clinicians trying to understand human health and disease. In this review, we examine modern multimodal imaging methods and how they contribute to precision medicine and the understanding of fundamental disease mechanisms.

Spatial Metabolomics and Lipidomics in Kidney Disease

Kidney disease is a global health issue that affects over 850 million people, and early detection is key to preventing severe disease and complications. Kidney diseases are associated with complex and dysregulation of lipid metabolism. Spatial metabolomics through mass spectrometry imaging (MSI) enables spatial mapping of the lipids in tissue and includes a variety of techniques that can be used to image lipids. In the kidney, MSI studies often seek to resolve individual functional tissue units such as glomeruli and proximal tubules. Several different MSI techniques, such as matrix-assisted laser desorption/ionization (MALDI) and desorption electrospray ionization (DESI), have been used to characterize lipids and small molecules in chronic kidney disease, acute kidney injury, genetic kidney disease, and cancer. In this review we provide several examples of how spatial metabolomics data can provide critical information concerning the localization of changes in various disease states. Additionally, when combined with pathology, transcriptomics, or proteomics, the metabolomic changes can illuminate underlying mechanisms and provide new clinical insights into disease mechanisms.

SPOT: spatial proteomics through on-site tissue-protein-labeling

BACKGROUND

Spatial proteomics seeks to understand the spatial organization of proteins in tissues or at different subcellular localization in their native environment. However, capturing the spatial organization of proteins is challenging. Here, we present an innovative approach termed Spatial Proteomics through On-site Tissue-protein-labeling (SPOT), which combines the direct labeling of tissue proteins in situ on a slide and quantitative mass spectrometry for the profiling of spatially-resolved proteomics.

MATERIALS AND METHODS

Efficacy of direct TMT labeling was investigated using seven types of sagittal mouse brain slides, including frozen tissues without staining, formalin-fixed paraffin-embedded (FFPE) tissues without staining, deparaffinized FFPE tissues, deparaffinized and decrosslinked FFPE tissues, and tissues with hematoxylin & eosin (H&E) staining, hematoxylin (H) staining, eosin (E) staining. The ability of SPOT to profile proteomes at a spatial resolution was further evaluated on a horizontal mouse brain slide with direct TMT labeling at eight different mouse brain regions. Finally, SPOT was applied to human prostate cancer tissues as well as a tissue microarray (TMA), where TMT tags were meticulously applied to confined regions based on the pathological annotations. After on-site direct tissue-protein-labeling, tissues were scraped off the slides and subject to standard TMT-based quantitative proteomics analysis.

RESULTS

Tissue proteins on different types of mouse brain slides could be directly labeled with TMT tags. Moreover, the versatility of our direct-labeling approach extended to discerning specific mouse brain regions based on quantitative outcomes. The SPOT was further applied on both frozen tissues on slides and FFPE tissues on TMAs from prostate cancer tissues, where a distinct proteomic profile was observed among the regions with different Gleason scores.

CONCLUSIONS

SPOT is a robust and versatile technique that allows comprehensive profiling of spatially-resolved proteomics across diverse types of tissue slides to advance our understanding of intricate molecular landscapes.

Bi-level Graph Learning Unveils Prognosis-Relevant Tumor Microenvironment Patterns in Breast Multiplexed Digital Pathology

The tumor microenvironment is widely recognized for its central role in driving cancer progression and influencing prognostic outcomes. There have been increasing efforts dedicated to characterizing this complex and heterogeneous environment, including developing potential prognostic tools by leveraging modern deep learning methods. However, the identification of generalizable data-driven biomarkers has been limited, in part due to the inability to interpret the complex, black-box predictions made by these models. In this study, we introduce a data-driven yet interpretable approach for identifying patterns of cell organizations in the tumor microenvironment that are associated with patient prognoses. Our methodology relies on the construction of a bi-level graph model: (i) a cellular graph, which models the intricate tumor microenvironment, and (ii) a population graph that captures inter-patient similarities, given their respective cellular graphs, by means of a soft Weisfeiler-Lehman subtree kernel. This systematic integration of information across different scales enables us to identify patient subgroups exhibiting unique prognoses while unveiling tumor microenvironment patterns that characterize them. We demonstrate our approach in a cohort of breast cancer patients and show that the identified tumor microenvironment patterns result in a risk stratification system that provides new complementary information with respect to standard stratification systems. Our results, which are validated in two independent cohorts, allow for new insights into the prognostic implications of the breast tumor microenvironment. This methodology could be applied to other cancer types more generally, providing insights into the cellular patterns of organization associated with different outcomes.