Challenges and Opportunities for Immunoprofiling Using a Spatial High-Plex Technology: The NanoString GeoMx® Digital Spatial Profiler

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.

Spatially resolved single-cell transcriptome analysis of mycosis fungoides reveals distinct biomarkers GNLY and FYB1 compared to psoriasis and chronic spongiotic dermatitis

Early mycosis fungoides (MF) and inflammatory dermatoses including psoriasis and chronic spongiotic dermatitis are often difficult to differentiate. We explored diagnostic markers differentiating MF from psoriasis and chronic spongiotic dermatitis via spatially resolved single-cell transcriptome analysis. Single-cell transcriptomics of intraepidermal T cells of MF patches, psoriasis and chronic spongiotic dermatitis were analyzed using CosMxTM spatial molecular imager utilizing surface markers, including CD3 and CD4. An immunohistochemical study with potential markers was performed to verify clinical utility. Compared to psoriasis and chronic spongiotic dermatitis, 41 upregulated differentially expressed genes (DEGs) in MF were associated with T-cell receptor (TCR) signaling pathway and apoptosis regulation. Protein-protein interaction network analysis of these DEGs revealed a main cluster associated with TCR signaling. Pathway enrichment analysis showed that apoptosis, Th17 cell differentiation, and TCR signaling pathways were enriched in MF. GNLY and FYB1, DEGs with the highest fold change values, were selected as potential diagnostic biomarkers for MF. For immunohistochemistry, biopsy specimens from 150 patients diagnosed with patch MF with CD4+ immunophenotype (n = 56), psoriasis (n = 48), and chronic eczema (n = 46) were included. The sensitivity and specificity of GNLY for distinguishing MF and psoriasis/chronic spongiotic dermatitis were 67.9% and 93.6%, respectively. For FYB1, those values were 73.2% and 69.2%, respectively. The AUC values of GNLY and FYB1 were 0.86 and 0.79, respectively. In conclusion, GNLY and FYB1 can be promising diagnostic biomarkers for differentiating early-stage MF from psoriasis and chronic spongiotic dermatitis.

The effect of Proteinase K treatment on GeoMx digital spatial profiling data quality from formalin-fixed, paraffin-embedded tissue

The emergence of spatial profiling technologies in recent years has accelerated opportunities to profile in detail the molecular attributes of a wide range of tissue pathologies using archival specimens. However, tissue treatment for fixation and storage does not always support generation of high-quality genomic data. The purpose of this study was to investigate the impacts of Proteinase K (ProtK) treatment, as a way to increase target transcript exposure, on downstream sequencing data quality metrics for spatial transcriptomic data using formalin-fixed, paraffin-embedded samples. In a series of four independent assessments using different tissue types (nasal mucosa, tonsil, pancreas), varying concentrations of ProtK (ranging from 0.1 to 1 μg/mL) were used as part of the sample processing workflow to generate transcriptomic data using the Nanostring GeoMx DSP and Illumina NextSeq 2000 platforms. Use of higher concentrations of ProtK was generally found to increase total reads (2-4-fold). However, negative probe counts also tended to be increased (2-12-fold), resulting in reductions in the signal-to-noise ratio (10-70% lower) and the number of genes detected above background (50-80% lower). These effects were not seen in all tissues and impacts of tissue handling and processing, beyond ProtK treatment, on data quality metrics, also require consideration. Regardless, these observations highlight the need for careful consideration of a range of sample processing factors and benefits that may be achieved through the optimisation of sample processing workflows for specific tissues as a way to maximise the generation of quality data using spatial transcriptomic approaches.

Spatial Transcriptomics Unravel the Tissue Complexity of Oral Pathogenesis

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

Using GeoMx DSP Spatial Proteomics to Investigate Immune Infiltration of NOD Mouse Islet and Exocrine Compartments

PURPOSE

Type 1 Diabetes (T1D) pathogenesis involves immune cells infiltrating pancreatic Islets of Langerhans, leading to T cell activation, beta cell destruction, and impaired insulin production. However, infiltration has a heterogenic nature that isn't described in detail, as not all islets are infiltrated. The aim of this study was to investigate if the observed heterogeneity is coupled to differences in immune and/or dysfunctional status of islets or exocrine cells, and if specific markers could elucidate mechanistic details of T1D pathogenesis.

PROCEDURES

The GeoMx platform was used to spatially quantify protein levels in pancreatic islets and exocrine tissue in Non-Obese Diabetic (NOD) mice. The protein panel included 17 immune activity markers and nine dysfunction markers. Immunohistochemical (IHC) staining and digital image analysis was used to analyze select marker proteins.

RESULTS

Use of the GeoMx platform to investigate T1D was shown to be possible, as Granzyme B protein levels were found to be lower in distal islet areas when compared to proximal areas. Smooth Muscle Actin protein levels were higher in exocrine areas proximal to immune-infiltrated islets, when compared to distally located exocrine areas. Findings from GeoMx were however not observed in IHC-stained sections.

CONCLUSIONS

This study demonstrates that investigating T1D is possible with spatial proteomics, as the assays revealed presence of heterogenic islet areas in NOD mice, which may play a role in T1D progression and escape from immune recognition. This study highlights the potential of spatial technologies for elucidating T1D pathogenesis and future treatment strategies.

Towards generative digital twins in biomedical research

Digital twins in biomedical research, i.e. virtual replicas of biological entities such as cells, organs, or entire organisms, hold great potential to advance personalized healthcare. As all biological processes happen in space, there is a growing interest in modeling biological entities within their native context. Leveraging generative artificial intelligence (AI) and high-volume biomedical data profiled with spatial technologies, researchers can recreate spatially-resolved digital representations of a physical entity with high fidelity. In application to biomedical fields such as computational pathology, oncology, and cardiology, these generative digital twins (GDT) thus enable compelling in silico modeling for simulated interventions, facilitating the exploration of 'what if' causal scenarios for clinical diagnostics and treatments tailored to individual patients. Here, we outline recent advancements in this novel field and discuss the challenges and future research directions.

Hepatic and pulmonary macrophage activity in a mucosal challenge model of Ebola virus disease

Background

The inflammatory macrophage response contributes to severe Ebola virus disease, with liver and lung injury in humans.

Objective

We sought to further define the activation status of hepatic and pulmonary macrophage populations in Ebola virus disease.

Methods

We compared liver and lung tissue from terminal Ebola virus (EBOV)-infected and uninfected control cynomolgus macaques challenged via the conjunctival route. Gene and protein expression was quantified using the nCounter and GeoMx Digital Spatial Profiling platforms. Macrophage phenotypes were further quantified by digital pathology analysis.

Results

Hepatic macrophages in the EBOV-infected group demonstrated a mixed inflammatory/non-inflammatory profile, with upregulation of CD163 protein expression, associated with macrophage activation syndrome. Hepatic macrophages also showed differential expression of gene sets related to monocyte/macrophage differentiation, antigen presentation, and T cell activation, which were associated with decreased MHC-II allele expression. Moreover, hepatic macrophages had enriched expression of genes and proteins targetable with known immunomodulatory therapeutics, including S100A9, IDO1, and CTLA-4. No statistically significant differences in M1/M2 gene expression were observed in hepatic macrophages compared to controls. The significant changes that occurred in both the liver and lung were more pronounced in the liver.

Conclusion

These data demonstrate that hepatic macrophages in terminal conjunctivally challenged cynomolgus macaques may express a unique inflammatory profile compared to other macaque models and that macrophage-related pharmacologically druggable targets are expressed in both the liver and the lung in Ebola virus disease.

A New Probiotic Formulation Promotes Resolution of Inflammation in a Crohn's Disease Mouse Model by Inducing Apoptosis in Mucosal Innate Immune Cells

The interaction between gut-residing microorganisms plays a critical role in the pathogenesis of Crohn's disease (CD), where microbiome dysregulation can alter immune responses, leading to unresolved local inflammation. The aim of this study is to analyze the immunomodulatory properties of a recently developed probiotic + amylase blend in the SAMP1/YitFc (SAMP) mouse model of CD-like ileitis. Four groups of SAMP mice were gavaged for 56 days with the following treatments: 1) probiotic strains + amylase (0.25 mg/100 µL PBS); 2) only probiotics; 3) only amylase; PBS-treated controls. Ilea were collected for GeoMx Digital Spatial Profiler (DSP) analysis and histological evaluation. Histology assessment for inflammation indicated a significantly reduced level of ileitis in mice administered the probiotics + amylase blend. DSP analysis showed decreased abundance of neutrophils and increased abundance of dendritic cells, regulatory T cells, and macrophages, with a significant enrichment of five intracellular pathways related to apoptosis, in probiotics + amylase-treated mice. Increased apoptosis occurrence was confirmed by (TdT)- deoxyuridine triphosphate (dUTP)-biotin nick end labeling assay. Our data demonstrate a beneficial role of the probiotic and amylase blend, highlighting an increased apoptosis of innate immunity-associated cell subsets, thus promoting the resolution of inflammation. Hence, we suggest that the developed probiotic enzyme blend may be a therapeutic tool to manage CD and therefore is a candidate formulation to be tested in clinical trials.

Digital spatial profiling for pathologists

The advent of "omics" technologies for high-depth tumor profiling has provided new information regarding cancer heterogeneity. However, a bulk omics profile can only partially reproduce tumor complexity, and it does not meet the preferences of pathologists used to perform an in situ assessment of marker expression, for instance, with immunohistochemistry. The NanoString GeoMx® Digital Spatial Profiler (DSP) is a platform for morphology-guided multiplex profiling of tissue slides, which allows the digital quantification of target analytes in different neoplastic settings. To illustrate the feasibility and opportunities offered by DSP from a pathologist's perspective, we applied DSP in three different representative neoplastic settings: breast carcinoma, thyroid anaplastic carcinoma, and biphasic mesothelioma. Because of the perfect overlap between the hematoxylin-eosin-stained slide and the GeoMx areas of interest, in breast carcinoma, two different antibodies allowed the distinction of the tumor cells from the surrounding tumor microenvironment. In biphasic mesothelioma, we could distinguish the epithelioid from the sarcomatoid neoplastic component, and in the thyroid, we easily separated the anaplastic areas from the well-differentiated carcinoma. DSP is a promising tool that combines traditional histological evaluation, allowing spatial assessment of a tumor and its surroundings, and innovative in situ digital profiling. Pathologists should not miss the opportunity to combine morphological and genomic analyses and be at the forefront of investigating the progression of dysplasia/neoplasia, low-grade or high-grade, epithelial/mesenchymal, and, more in general, overcoming the concept of in situ vs. bulk genomic methods.

Deciphering the Tumor Microenvironment in Prostate Cancer: A Focus on the Stromal Component

Prostate cancer progression is significantly affected by its tumor microenvironment, in which mesenchymal cells play a crucial role. Stromal cells are modified by cancer mutations, response to androgens, and lineage plasticity, and in turn, engage with epithelial tumor cells via a complex array of signaling pathways and ligand-receptor interactions, ultimately affecting tumor growth, immune interaction, and response to therapy. The metabolic rewiring and interplay in the microenvironment play an additional role in affecting the growth and progression of prostate cancer. Finally, therapeutic strategies and novel clinical trials with agents that target the stromal microenvironment or disrupt the interaction between cellular compartments are described. This review underscores cancer-associated fibroblasts as essential contributors to prostate cancer biology, emphasizing their potential as prognostic indicators and therapeutic targets.

Spatial-transcriptomic profiling: a new lens for understanding myelofibrosis pathophysiology

Myelofibrosis (MF) is a complex myeloproliferative neoplasm characterized by abnormal hematopoietic stem cell proliferation and subsequent bone marrow (BM) fibrosis. First documented in the late 19th century, MF has since been extensively studied to unravel its pathophysiology, clinical phenotypes, and therapeutic interventions. MF can be classified into primary and secondary forms, both driven by mutations in genes such as JAK2, CALR, and MPL, which activate the JAK-STAT signaling pathway. These driver mutations are frequently accompanied by additional non-driver mutations in genes like TET2, SRSF2, and TP53, contributing to disease complexity. The BM microenvironment, consisting of stromal cells, extracellular matrix, and cytokines such as TGF-β and TNF-α, plays a critical role in fibrosis and aberrant hematopoiesis. Clinically, MF manifests with symptoms ranging from anemia, splenomegaly, and fatigue to severe complications such as leukemic transformation. Splenomegaly, caused by extramedullary hematopoiesis, leads to abdominal discomfort and early satiety. Current therapeutic strategies include JAK inhibitors like Ruxolitinib, which target the JAK-STAT pathway, alongside supportive treatments such as blood transfusions, erythropoiesis-stimulating agents and developing combinatorial approaches. Allogeneic hematopoietic stem cell transplantation remains the only curative option, though it is limited to younger, high-risk patients. Recently approved JAK inhibitors, including Fedratinib, Pacritinib, and Momelotinib, have expanded the therapeutic landscape. Spatially Resolved Transcriptomics (SRT) has revolutionized the study of gene expression within the spatial context of tissues, providing unprecedented insights into cellular heterogeneity, spatial gene regulation, and microenvironmental interactions, including stromal-hematopoietic dynamics. SRT enables high-resolution mapping of gene expression in the BM and spleen, revealing molecular signatures, spatial heterogeneity, and pathological niches that drive disease progression. These technologies elucidate the role of the spleen in MF, highlighting its transformation into a site of abnormal hematopoietic activity, fibrotic changes, and immune cell infiltration, functioning as a "tumor surrogate." By profiling diverse cell populations and molecular alterations within the BM and spleen, SRT facilitates a deeper understanding of MF pathophysiology, helping identify novel therapeutic targets and biomarkers. Ultimately, integrating spatial transcriptomics into MF research promises to enhance diagnostic precision and therapeutic innovation, addressing the multifaceted challenges of this disease.

Spatial Profiling of Ovarian Clear Cell Carcinoma Reveals Immune-Hot Features

Ovarian clear cell carcinoma (OCCC) has a high incidence in Asia, with a frequent occurrence at an early stage, but without sufficient data on molecular stratification for high-risk patients. Recently, immune-hot features have been proposed as indicators of poor prognosis in early stage OCCC. Specific patterns of intratumoral heterogeneity associated with immune-hot features must be defined. NanoString Digital Spatial Profiling technology (Cold spring biotech corp.) was used to decipher the spatial distribution of the 18-plex protein panel. Regions of interest (ROIs) were collected based on the reference hematoxylin and eosin-stained morphology. Areas of illumination (AOIs) were defined according to the ROI segmentation using the fluorescence signals of the visualization markers pan-cytokeratin (PanCK), CD45, or DNA. Unsupervised hierarchical clustering of 595 AOIs from 407 ROIs showed that the PanCK segments expressed different combinations of immune markers, suggestive of immune mimicry. The following 3 immune-hot clusters were identified: granzyme B-high, immune signal-high , and immune-like cells; the following 2 immune-cold clusters were identified: fibronectin-high and immune checkpoint-high cells. In tumor samples at the International Federation of Gynecology and Obstetrics stage IC1/2 experiencing recurrence, there was an increased occurrence of PanCK+ AOIs with immune signal-high and immune-like cell groups in the papillary morphology surrounded by macrophage lineage tumor-infiltrating immune cells (TIIs). In contrast, for tumor samples at the International Federation of Gynecology and Obstetrics stage IC3/II with recurrence, PanCK+ AOIs were prevalent in the fibronectin-high group, particularly in those with a tubulocystic morphology surrounded by lymphoid lineage non-TIIs. Our study on the spatial profiling of early stage OCCC tumors revealed that the immune mimicry of tumor cells, presence of TIIs, and morphologic patterns were associated with recurrence, which switched during tumor progression.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

The burgeoning spatial multi-omics in human gastrointestinal cancers

The development and progression of diseases in multicellular organisms unfold within the intricate three-dimensional body environment. Thus, to comprehensively understand the molecular mechanisms governing individual development and disease progression, precise acquisition of biological data, including genome, transcriptome, proteome, metabolome, and epigenome, with single-cell resolution and spatial information within the body's three-dimensional context, is essential. This foundational information serves as the basis for deciphering cellular and molecular mechanisms. Although single-cell multi-omics technology can provide biological information such as genome, transcriptome, proteome, metabolome, and epigenome with single-cell resolution, the sample preparation process leads to the loss of spatial information. Spatial multi-omics technology, however, facilitates the characterization of biological data, such as genome, transcriptome, proteome, metabolome, and epigenome in tissue samples, while retaining their spatial context. Consequently, these techniques significantly enhance our understanding of individual development and disease pathology. Currently, spatial multi-omics technology has played a vital role in elucidating various processes in tumor biology, including tumor occurrence, development, and metastasis, particularly in the realms of tumor immunity and the heterogeneity of the tumor microenvironment. Therefore, this article provides a comprehensive overview of spatial transcriptomics, spatial proteomics, and spatial metabolomics-related technologies and their application in research concerning esophageal cancer, gastric cancer, and colorectal cancer. The objective is to foster the research and implementation of spatial multi-omics technology in digestive tumor diseases. This review will provide new technical insights for molecular biology researchers.

Tumor and α-SMA-expressing stromal cells in pancreatic neuroendocrine tumors have a distinct RNA profile depending on tumor grade

Alpha-smooth muscle actin (α-SMA) expression in the stroma is linked to the presence of cancer-associated fibroblasts and is known to correlate with worse outcomes in various tumors. In this study, using a GeoMx digital spatial profiling approach, we characterized the gene expression of the tumor and α-SMA-expressing stromal cell compartments in pancreatic neuroendocrine tumors (PanNETs). The profiling was performed on tissues from eight retrospective cases (three grade 1, four grade 2, and one grade 3). Selected regions of interest were segmented geometrically based on tissue morphology and fluorescent signals from synaptophysin and α-SMA markers. The α-SMA-expressing stromal-cell-associated genes were involved in pathways of extracellular matrix modification, whereas, in tumor cells, the gene expression profiles were associated with pathways involved in cell proliferation. The comparison of gene expression profiles across all three PanNET grades revealed that the differences between grades are not only present at the level of the tumor but also in the α-SMA-expressing stromal cells. Furthermore, the tumor cells from regions with a rich presence of adjacent α-SMA-expressing stromal cells revealed an upregulation of matrix metalloproteinase-9 (MMP9) expression in grade 3 tumors. This study provides an in-depth characterization of gene expression profiles in α-SMA-expressing stromal and tumor cells, and outlines potential crosstalk mechanisms.

Mapping the Spatial Dynamics of the CD4+ T Cell Spectrum in Classical Hodgkin Lymphoma

As around 25% to 30% of classical Hodgkin lymphoma (cHL) patients with advanced stages do not respond to standard therapies, the tumor microenvironment of cHL is one avenue that may be explored with the aim of improving risk stratification. CD4+ T cells are thought to be one of the main cell types in the tumor microenvironment. However, few immune signatures have been studied, and many of these lack related spatial data. Thus, our aim is to spatially resolve the CD4+ T cell subtypes that influence cHL outcome, depicting new immune signatures or transcriptional patterns that are in crosstalk with the tumor cells. This study was conducted using the NanoString GeoMx digital spatial profiling technology, based on the selection of distinct functional areas of patients' tissues followed by gene-expression profiling. The goals were to assess the differences in CD4+ T cell populations between tumor-rich and immune-predominant areas defined by different CD30 and PD-L1 expression levels and seek correlations with clinical metadata. Our results depict a complex map of CD4+ T cells with different functions and differentiation states that are enriched at distinct locations, the flux of cytokines and chemokines that could be related to these, and the specific relationships with the clinical outcome.

Spatial multi-omics: deciphering technological landscape of integration of multi-omics and its applications

The emergence of spatial multi-omics has helped address the limitations of single-cell sequencing, which often leads to the loss of spatial context among cell populations. Integrated analysis of the genome, transcriptome, proteome, metabolome, and epigenome has enhanced our understanding of cell biology and the molecular basis of human diseases. Moreover, this approach offers profound insights into the interactions between intracellular and intercellular molecular mechanisms involved in the development, physiology, and pathogenesis of human diseases. In this comprehensive review, we examine current advancements in multi-omics technologies, focusing on their evolution and refinement over the past decade, including improvements in throughput and resolution, modality integration, and accuracy. We also discuss the pivotal contributions of spatial multi-omics in revealing spatial heterogeneity, constructing detailed spatial atlases, deciphering spatial crosstalk in tumor immunology, and advancing translational research and cancer therapy through precise spatial mapping.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.

Tissue gene expression profiles and communication networks inform candidate blood biomarker identification in psoriasis and atopic dermatitis

Overlapping clinical and pathomechanistic features can complicate the diagnosis and treatment of inflammatory skin diseases, including psoriasis and atopic dermatitis (AD). Spatial transcriptomics allows the identification of disease- and cell-specific molecular signatures that may advance biomarker development and future treatments. This study identified transcriptional signatures in keratinocytes and sub-basal CD4+ and CD8+ T lymphocytes from patients with psoriasis and AD. In silico prediction of ligand:receptor interactions delivered key signalling pathways (interferon, effector T cells, stroma cell and matrix biology, neuronal development, etc.). Targeted validation of selected transcripts, including CCL22, RELB, and JUND, in peripheral blood T cells suggests the chosen approach as a promising tool also in other inflammatory diseases. Psoriasis and AD are characterized by transcriptional dysregulation in T cells and keratinocytes that may be targeted therapeutically. Spatial transcriptomics is a valuable tool in the search for molecular signatures that can be used as biomarkers and/or therapeutic targets.

Biomarker development for PD-(L)1 axis inhibition: a consensus view from the SITC Biomarkers Committee

Therapies targeting the programmed cell death protein-1/programmed death-ligand 1 (PD-L1) (abbreviated as PD-(L)1) axis are a significant advancement in the treatment of many tumor types. However, many patients receiving these agents fail to respond or have an initial response followed by cancer progression. For these patients, while subsequent immunotherapies that either target a different axis of immune biology or non-immune combination therapies are reasonable treatment options, the lack of predictive biomarkers to follow-on agents is impeding progress in the field. This review summarizes the current knowledge of mechanisms driving resistance to PD-(L)1 therapies, the state of biomarker development along this axis, and inherent challenges in future biomarker development for these immunotherapies. Innovation in the development and application of novel biomarkers and patient selection strategies for PD-(L)1 agents is required to accelerate the delivery of effective treatments to the patients most likely to respond.

Clonal hematopoiesis and its impact on the aging osteo-hematopoietic niche

Clonal hematopoiesis (CH) defines a premalignant state predominantly found in older persons that increases the risk of developing hematologic malignancies and age-related inflammatory diseases. However, the risk for malignant transformation or non-malignant disorders is variable and difficult to predict, and defining the clinical relevance of specific candidate driver mutations in individual carriers has proved to be challenging. In addition to the cell-intrinsic mechanisms, mutant cells rely on and alter cell-extrinsic factors from the bone marrow (BM) niche, which complicates the prediction of a mutant cell's fate in a shifting pre-malignant microenvironment. Therefore, identifying the insidious and potentially broad impact of driver mutations on supportive niches and immune function in CH aims to understand the subtle differences that enable driver mutations to yield different clinical outcomes. Here, we review the changes in the aging BM niche and the emerging evidence supporting the concept that CH can progressively alter components of the local BM microenvironment. These alterations may have profound implications for the functionality of the osteo-hematopoietic niche and overall bone health, consequently fostering a conducive environment for the continued development and progression of CH. We also provide an overview of the latest technology developments to study the spatiotemporal dependencies in the CH BM niche, ideally in the context of longitudinal studies following CH over time. Finally, we discuss aspects of CH carrier management in clinical practice, based on work from our group and others.

Single-cell, single-nucleus, and spatial transcriptomics characterization of the immunological landscape in the healthy and PSC human liver

BACKGROUND & AIMS

Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease for which there is an unmet need to understand the cellular composition of the affected liver and how it underlies disease pathogenesis. We aimed to generate a comprehensive atlas of the PSC liver using multi-omic modalities and protein-based functional validation.

METHODS

We employed single-cell and single-nucleus RNA sequencing (47,156 cells and 23,000 nuclei) and spatial transcriptomics (one sample by 10x Visium and five samples with Nanostring GeoMx DSP) to profile the cellular ecosystem in 10 PSC livers. Transcriptomic profiles were compared to 24 neurologically deceased donor livers (107,542 cells) and spatial transcriptomics controls, as well as 18,240 cells and 20,202 nuclei from three PBC livers. Flow cytometry was performed to validate PSC-specific differences in immune cell phenotype and function.

RESULTS

PSC explants with parenchymal cirrhosis and prominent periductal fibrosis contained a population of cholangiocyte-like hepatocytes that were surrounded by diverse immune cell populations. PSC-associated biliary, mesenchymal, and endothelial populations expressed chemokine and cytokine transcripts involved in immune cell recruitment. Additionally, expanded CD4+ T cells and recruited myeloid populations in the PSC liver expressed the corresponding receptors to these chemokines and cytokines, suggesting potential recruitment. Tissue-resident macrophages, by contrast, were reduced in number and exhibited a dysfunctional and downregulated inflammatory response to lipopolysaccharide and interferon-γ stimulation.

CONCLUSIONS

We present a comprehensive atlas of the PSC liver and demonstrate an exhaustion-like phenotype of myeloid cells and markers of chronic cytokine expression in late-stage PSC lesions. This atlas expands our understanding of the cellular complexity of PSC and has potential to guide the development of novel treatments.

IMPACT AND IMPLICATIONS

Primary sclerosing cholangitis (PSC) is a rare liver disease characterized by chronic inflammation and irreparable damage to the bile ducts, which eventually results in liver failure. Due to a limited understanding of the underlying pathogenesis of disease, treatment options are limited. To address this, we sequenced healthy and diseased livers to compare the activity, interactions, and localization of immune and non-immune cells. This revealed that hepatocytes lining PSC scar regions co-express cholangiocyte markers, whereas immune cells infiltrate the scar lesions. Of these cells, macrophages, which typically contribute to tissue repair, were enriched in immunoregulatory genes and demonstrated a lack of responsiveness to stimulation. These cells may be involved in maintaining hepatic inflammation and could be a target for novel therapies.

Quantitative Assessment of Intracellular Effectors and Cellular Response in RAGE Activation

The review delves into the methods for the quantitative assessment of intracellular effectors and cellular response of Receptor for Advanced Glycation End products (RAGE), a vital transmembrane receptor involved in a range of physiological and pathological processes. RAGE bind to Advanced Glycation End products (AGEs) and other ligands, which in turn activate diverse downstream signaling pathways that impact cellular responses such as inflammation, oxidative stress, and immune reactions. The review article discusses the intracellular signaling pathways activated by RAGE followed by differential activation of RAGE signaling across various diseases. This will ultimately guide researchers in developing targeted and effective interventions for diseases associated with RAGE activation. Further, we have discussed how PCR, western blotting, and microscopic examination of various molecules involved in downstream signaling can be leveraged to monitor, diagnose, and explore diseases involving proteins with unique post-translational modifications. This review article underscores the pressing need for advancements in molecular approaches for disease detection and management involving RAGE.

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.

Translational Aspects in Metaplastic Breast Carcinoma

Breast cancer is the most common cancer among women. Metaplastic breast carcinoma (MpBC) is a rare, heterogeneous group of invasive breast carcinomas, which are classified as predominantly triple-negative breast carcinomas (TNBCs; HR-negative/HER2-negative). Histologically, MpBC is classified into six subtypes. Two of these are considered low-grade and the others are high-grade. MpBCs seem to be more aggressive, less responsive to neoadjuvant chemotherapy, and have higher rates of chemoresistance than other TNBCs. MpBCs have a lower survival rate than expected for TNBCs. MpBC treatment represents a challenge, leading to a thorough exploration of the tumor immune microenvironment, which has recently opened the possibility of new therapeutic strategies. The epithelial-mesenchymal transition in MpBC is characterized by the loss of intercellular adhesion, downregulation of epithelial markers, underexpression of genes with biological epithelial functions, upregulation of mesenchymal markers, overexpression of genes with biological mesenchymal functions, acquisition of fibroblast-like (spindle) morphology, cytoskeleton reorganization, increased motility, invasiveness, and metastatic capabilities. This article reviews and summarizes the current knowledge and translational aspects of MpBC.

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.

Assessing Longitudinal Treatment Efficacies and Alterations in Molecular Markers Associated with Glutamatergic Signaling and Immune Checkpoint Inhibitors in a Spontaneous Melanoma Mouse Model

Previous work done by our laboratory described the use of an immunocompetent spontaneous melanoma-prone mouse model, TGS (TG-3/SKH-1), to evaluate treatment outcomes using inhibitors of glutamatergic signaling and immune checkpoint for 18 weeks. We showed a significant therapeutic efficacy with a notable sex-biased response in male mice. In this follow-up 18-week study, the dose of the glutamatergic signaling inhibitor was increased (from 1.7 mg/kg to 25 mg/kg), which resulted in improved responses in female mice but not male mice. The greatest reduction in tumor progression was observed in male mice treated with single-agent troriluzole and anti-PD-1. Furthermore, a randomly selected group of mice was removed from treatment after 18 weeks and maintained for up to an additional 48 weeks demonstrating the utility of the TGS mouse model to perform a ≥1-year preclinical therapeutic study in a physiologically relevant tumor-host environment. Digital spatial imaging analyses were performed in tumors and tumor microenvironments across treatment modalities using antibody panels for immune cell types and immune cell activation. The results suggest that immune cell populations and cytotoxic activities of T cells play critical roles in treatment responses in these mice. Examination of a group of molecular protein markers based on the proposed mechanisms of action of inhibitors of glutamatergic signaling and immune checkpoint showed that alterations in expression levels of xCT, γ-H2AX, EAAT2, PD-L1, and PD-1 are likely associated with the loss of treatment responses. These results suggest the importance of tracking changes in molecular markers associated with the mechanism of action of therapeutics over the course of a longitudinal preclinical therapeutic study in spatial and temporal manners.

Non-electrophysiological techniques targeting transient receptor potential (TRP) gene of gastrointestinal tract

Transient receptor potential (TRP) channels are cation channels related to a wide range of physical and chemical stimuli, they are expressed all along the gastrointestinal system, and a myriad of diseases are often associated with aberrant expression or mutation of the TRP gene, suggesting that TRPs are promising targets for drug therapy. Therefore, a better understanding of the information of TRPs in health and disease could facilitate the development of effective drugs for the treatment of gastrointestinal diseases like IBD. But there are very few generalizations about the experimental techniques studied in this field. In view of the promise of TRP as a therapeutic target, we discuss experimental methods that can be used for TRPs including their distribution, function and interaction with other proteins, as well as some promising emerging technologies to provide experimental methods for future studies.

Lymph node metastasis regulation by peritumoral tonsillar tissue mitochondria-related pathway activation in oropharyngeal cancer

Immune-related gene expression profiles of peritumoral tonsillar tissues are modified by oropharyngeal cancer (OPC) nodal status. This study explored immunometabolism and immune cell count alterations in peritumoral tonsillar tissue according to OPC nodal status. Microarray data analysis of 27 peritumoral tonsillar tissue samples, using a newly generated mitochondrial metabolism-related gene set comprised of 948 genes, detected 228 differentially expressed genes (DEGs) (206 up- and 22 downregulated) in metastasis-negative cases compared to metastasis-positive ones. REACTOME pathway analysis of the 206 upregulated genes revealed the Toll-like receptor 4 cascade were most enriched. Immune cell proportion analysis using the CIBERSORTx algorithm revealed a significantly higher rate of naïve B cells, but lower rates of regulatory T cells and resting natural killer cells in metastasis-negative cases. Digital spatial profiling of the 6 OPC tissues detected 9 DEGs in the lymphoid regions, in contrast, no DEGs were identified in tumor regions according to nodal status. Cancer cell nests and pair matched normal epithelia mitochondrial DNA (mtDNA) from 5 OPC tissues were analyzed by next generation sequencing for variant detection. However, no significant mtDNA variation was found. This study identified mitochondria-related immune cell transcriptional programs and immune cell profiles associated with OPC lymphatic spread in peritumoral tonsil tissue, further evaluation of which will elucidate targetable immune mechanisms associated with OPC lymphatic dissemination.

Spatially Resolved Microglia/Macrophages in Recurrent Glioblastomas Overexpress Fatty Acid Metabolism and Phagocytic Genes

BACKGROUND

Glioblastoma (GBM) tumors are rich in tumor-associated microglia/macrophages. Changes associated with treatment in this specific cell population are poorly understood. Therefore, we studied changes in gene expression of tumor-associated microglia/macrophages (Iba1+) cells in de novo versus recurrent GBMs.

METHODS

NanoString GeoMx® Digital Spatial Transcriptomic Profiling of microglia/macrophages (Iba1+) and glial cells (Gfap+) cells identified on tumor sections was performed on paired de novo and recurrent samples obtained from three IDH-wildtype GBM patients. The impact of differentially expressed genes on patient survival was evaluated using publicly available data.

RESULTS

Unsupervised analyses of the NanoString GeoMx® Digital Spatial Profiling data revealed clustering based on the transcriptomic data from Iba1+ and Gfap+ cells. As expected, conventional differential gene expression and enrichment analyses revealed upregulation of immune-function-related genes in Iba1+ cells compared to Gfap+ cells. A focused differential gene expression analysis revealed upregulation of phagocytosis and fatty acid/lipid metabolism genes in Iba1+ cells in recurrent GBM samples compared to de novo GBM samples. Importantly, of these genes, the lipid metabolism gene PLD3 consistently correlated with survival in multiple different publicly available datasets.

CONCLUSION

Tumor-associated microglia/macrophages in recurrent GBM overexpress genes involved in fatty acid/lipid metabolism. Further investigation is needed to fully delineate the role of PLD phospholipases in GBM progression.

Mapping cancer biology in space: applications and perspectives on spatial omics for oncology

Technologies to decipher cellular biology, such as bulk sequencing technologies and single-cell sequencing technologies, have greatly assisted novel findings in tumor biology. Recent findings in tumor biology suggest that tumors construct architectures that influence the underlying cancerous mechanisms. Increasing research has reported novel techniques to map the tissue in a spatial context or targeted sampling-based characterization and has introduced such technologies to solve oncology regarding tumor heterogeneity, tumor microenvironment, and spatially located biomarkers. In this study, we address spatial technologies that can delineate the omics profile in a spatial context, novel findings discovered via spatial technologies in oncology, and suggest perspectives regarding therapeutic approaches and further technological developments.

Enhanced antitumour immunity following neoadjuvant chemoradiotherapy mediates a favourable prognosis in women with resected pancreatic cancer

BACKGROUND

This study investigates sex disparities in clinical outcomes and tumour immune profiles in patients with pancreatic ductal adenocarcinoma (PDAC) who underwent upfront resection or resection preceded by gemcitabine-based neoadjuvant chemoradiotherapy (nCRT).

METHODS

Patients originated from the PREOPANC randomised controlled trial. Upfront surgery was performed in 82 patients, and 66 received nCRT before resection. The impact of sex on overall survival (OS) was investigated using Cox proportional hazards models. The immunological landscape within the tumour microenvironment (TME) was mapped using transcriptomic and spatial proteomic profiling.

RESULTS

The 5-year OS rate differed between the sexes following resection preceded by nCRT, with 43% for women compared with 22% for men. In multivariate analysis, the female sex was a favourable independent prognostic factor for OS only in the nCRT group (HR 0.19; 95% CI 0.07 to 0.52). Multivariate heterogeneous treatment effects analysis revealed a significant interaction between sex and treatment, implying increased nCRT efficacy among women with resected PDAC. The TME of women contained fewer protumoural CD163+MRC1+M2 macrophages than that of men after nCRT, as indicated by transcriptomic and validated using spatial proteomic profiling.

CONCLUSION

PDAC tumours of women are more sensitive to gemcitabine-based nCRT, resulting in longer OS after resection compared with men. This may be due to enhanced immunity impeding the infiltration of protumoral M2 macrophages into the TME. Our findings highlight the importance of considering sex disparities and mitigating immunosuppressive macrophage polarisation for personalised PDAC treatment.

New perspectives on biology, disease progression, and therapy response of head and neck cancer gained from single cell RNA sequencing and spatial transcriptomics

Head and neck squamous cell carcinoma (HNSCC) is one of the most frequent cancers worldwide. The main risk factors are consumption of tobacco products and alcohol, as well as infection with human papilloma virus. Approved therapeutic options comprise surgery, radiation, chemotherapy, targeted therapy through epidermal growth factor receptor inhibition, and immunotherapy, but outcome has remained unsatisfactory due to recurrence rates of ~50% and the frequent occurrence of second primaries. The availability of the human genome sequence at the beginning of the millennium heralded the omics era, in which rapid technological progress has advanced our knowledge of the molecular biology of malignant diseases, including HNSCC, at an unprecedented pace. Initially, microarray-based methods, followed by approaches based on next-generation sequencing, were applied to study the genetics, epigenetics, and gene expression patterns of bulk tumors. More recently, the advent of single-cell RNA sequencing (scRNAseq) and spatial transcriptomics methods has facilitated the investigation of the heterogeneity between and within different cell populations in the tumor microenvironment (e.g., cancer cells, fibroblasts, immune cells, endothelial cells), led to the discovery of novel cell types, and advanced the discovery of cell-cell communication within tumors. This review provides an overview of scRNAseq, spatial transcriptomics, and the associated bioinformatics methods, and summarizes how their application has promoted our understanding of the emergence, composition, progression, and therapy responsiveness of, and intercellular signaling within, HNSCC.

Concepts of multi-level dynamical modelling: understanding mechanisms of squamous cell carcinoma development in Fanconi anemia

Fanconi anemia (FA) is a rare disease (incidence of 1:300,000) primarily based on the inheritance of pathogenic variants in genes of the FA/BRCA (breast cancer) pathway. These variants ultimately reduce the functionality of different proteins involved in the repair of DNA interstrand crosslinks and DNA double-strand breaks. At birth, individuals with FA might present with typical malformations, particularly radial axis and renal malformations, as well as other physical abnormalities like skin pigmentation anomalies. During the first decade of life, FA mostly causes bone marrow failure due to reduced capacity and loss of the hematopoietic stem and progenitor cells. This often makes hematopoietic stem cell transplantation necessary, but this therapy increases the already intrinsic risk of developing squamous cell carcinoma (SCC) in early adult age. Due to the underlying genetic defect in FA, classical chemo-radiation-based treatment protocols cannot be applied. Therefore, detecting and treating the multi-step tumorigenesis process of SCC in an early stage, or even its progenitors, is the best option for prolonging the life of adult FA individuals. However, the small number of FA individuals makes classical evidence-based medicine approaches based on results from randomized clinical trials impossible. As an alternative, we introduce here the concept of multi-level dynamical modelling using large, longitudinally collected genome, proteome- and transcriptome-wide data sets from a small number of FA individuals. This mechanistic modelling approach is based on the "hallmarks of cancer in FA", which we derive from our unique database of the clinical history of over 750 FA individuals. Multi-omic data from healthy and diseased tissue samples of FA individuals are to be used for training constituent models of a multi-level tumorigenesis model, which will then be used to make experimentally testable predictions. In this way, mechanistic models facilitate not only a descriptive but also a functional understanding of SCC in FA. This approach will provide the basis for detecting signatures of SCCs at early stages and their precursors so they can be efficiently treated or even prevented, leading to a better prognosis and quality of life for the FA individual.

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

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

The Overlooked Role of Specimen Preparation in Bolstering Deep Learning-Enhanced Spatial Transcriptomics Workflows

The application of deep learning methods to spatial transcriptomics has shown promise in unraveling the complex relationships between gene expression patterns and tissue architecture as they pertain to various pathological conditions. Deep learning methods that can infer gene expression patterns directly from tissue histomorphology can expand the capability to discern spatial molecular markers within tissue slides. However, current methods utilizing these techniques are plagued by substantial variability in tissue preparation and characteristics, which can hinder the broader adoption of these tools. Furthermore, training deep learning models using spatial transcriptomics on small study cohorts remains a costly endeavor. Necessitating novel tissue preparation processes enhance assay reliability, resolution, and scalability. This study investigated the impact of an enhanced specimen processing workflow for facilitating a deep learning-based spatial transcriptomics assessment. The enhanced workflow leveraged the flexibility of the Visium CytAssist assay to permit automated H&E staining (e.g., Leica Bond) of tissue slides, whole-slide imaging at 40x-resolution, and multiplexing of tissue sections from multiple patients within individual capture areas for spatial transcriptomics profiling. Using a cohort of thirteen pT3 stage colorectal cancer (CRC) patients, we compared the efficacy of deep learning models trained on slide prepared using an enhanced workflow as compared to the traditional workflow which leverages manual tissue staining and standard imaging of tissue slides. Leveraging Inceptionv3 neural networks, we aimed to predict gene expression patterns across matched serial tissue sections, each stemming from a distinct workflow but aligned based on persistent histological structures. Findings indicate that the enhanced workflow considerably outperformed the traditional spatial transcriptomics workflow. Gene expression profiles predicted from enhanced tissue slides also yielded expression patterns more topologically consistent with the ground truth. This led to enhanced statistical precision in pinpointing biomarkers associated with distinct spatial structures. These insights can potentially elevate diagnostic and prognostic biomarker detection by broadening the range of spatial molecular markers linked to metastasis and recurrence. Future endeavors will further explore these findings to enrich our comprehension of various diseases and uncover molecular pathways with greater nuance. Combining deep learning with spatial transcriptomics provides a compelling avenue to enrich our understanding of tumor biology and improve clinical outcomes. For results of the highest fidelity, however, effective specimen processing is crucial, and fostering collaboration between histotechnicians, pathologists, and genomics specialists is essential to herald this new era in spatial transcriptomics-driven cancer research.

Advancing Understanding of Non-Small Cell Lung Cancer with Multiplexed Antibody-Based Spatial Imaging Technologies

Non-small cell lung cancer (NSCLC) remains a cause of significant morbidity and mortality, despite significant advances made in its treatment using immune checkpoint inhibitors (ICIs) over the last decade; while a minority experience prolonged responses with ICIs, benefit is limited for most patients. The development of multiplexed antibody-based (MAB) spatial tissue imaging technologies has revolutionised analysis of the tumour microenvironment (TME), enabling identification of a wide range of cell types and subtypes, and analysis of the spatial relationships and interactions between them. Such study has the potential to translate into a greater understanding of treatment susceptibility and resistance, factors influencing prognosis and recurrence risk, and identification of novel therapeutic approaches and rational treatment combinations to improve patient outcomes in the clinic. Herein we review studies that have leveraged MAB technologies to deliver novel insights into the TME of NSCLC.

Single-cell profiling of murine bladder cancer identifies sex-specific transcriptional signatures with prognostic relevance

Bladder cancer (BLCA) is more common in men but more aggressive in women. Sex-based differences in cancer biology are commonly studied using a murine model with BLCA generated by N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN). While tumors in the BBN model have been profiled, these profiles provide limited information on the tumor microenvironment. Here, we applied single-cell RNA sequencing to characterize cell-type specific transcriptional differences between male and female BBN-induced tumors. We found proportional and gene expression differences in epithelial and non-epithelial subpopulations between male and female tumors. Expression of several genes predicted sex-specific survival in several human BLCA datasets. We identified novel and clinically relevant sex-specific transcriptional signatures including immune cells in the tumor microenvironment and it validated the relevance of the BBN model for studying sex differences in human BLCA. This work highlights the importance of considering sex as a biological variable in the development of new and accurate cancer markers.

Unique miRNome and transcriptome profiles underlie microvascular heterogeneity in mouse kidney

Endothelial cells in blood vessels in the kidney exert different functions depending on the (micro)vascular bed they are located in. The present study aimed to investigate microRNA and mRNA transcription patterns that underlie these differences. We zoomed in on microvascular compartments in the mouse renal cortex by laser microdissecting the microvessels prior to small RNA- and RNA-sequencing analyses. By these means, we characterized microRNA and mRNA transcription profiles of arterioles, glomeruli, peritubular capillaries, and postcapillary venules. Quantitative RT-PCR, in situ hybridization, and immunohistochemistry were used to validate sequencing results. Unique microRNA and mRNA transcription profiles were found in all microvascular compartments, with dedicated marker microRNAs and mRNAs showing enriched transcription in a single microvascular compartment. In situ hybridization validated the localization of microRNAs mmu-miR-140-3p in arterioles, mmu-miR-322-3p in glomeruli, and mmu-miR-451a in postcapillary venules. Immunohistochemical staining showed that von Willebrand factor protein was mainly expressed in arterioles and postcapillary venules, whereas GABRB1 expression was enriched in glomeruli, and IGF1 was enriched in postcapillary venules. More than 550 compartment-specific microRNA-mRNA interaction pairs were identified that carry functional implications for microvascular behavior. In conclusion, our study identified unique microRNA and mRNA transcription patterns in microvascular compartments of the mouse kidney cortex that underlie microvascular heterogeneity. These patterns provide important molecular information for future studies into differential microvascular engagement in health and disease.NEW & NOTEWORTHY Renal endothelial cells display a high level of heterogeneity depending on the (micro)vascular bed they reside in. The molecular basis contributing to these differences is poorly understood yet of high importance to increase understanding of microvascular engagement in the kidney in health and disease. This report describes m(icro)RNA expression profiles of microvascular beds in the mouse renal cortex and uncovers microvascular compartment-specific m(icro)RNAs and miRNA-mRNA pairs, thereby revealing important molecular mechanisms underlying renal microvascular heterogeneity.

[Advances of spatial omics in the individualized diagnosis and treatment of head and neck cancer]

Spatialomics is another research hotspot of biotechnology after single-cell sequencing technology, which can make up for the defect that single-cell sequencing technology can not obtain cell spatial distribution information. Spatialomics mainly studies the relative position of cells in tissue samples to reveal the effect of cell spatial distribution on diseases. In recent years, spatialomics has made new progress in the pathogenesis, target exploration, drug development and many other aspects of head and neck tumors. This paper summarizes the latest progress of spatialomics in the diagnosis and treatment of head and neck cancer.

From Chaos to Opportunity: Decoding Cancer Heterogeneity for Enhanced Treatment Strategies

Cancer manifests as a multifaceted disease, characterized by aberrant cellular proliferation, survival, migration, and invasion. Tumors exhibit variances across diverse dimensions, encompassing genetic, epigenetic, and transcriptional realms. This heterogeneity poses significant challenges in prognosis and treatment, affording tumors advantages through an increased propensity to accumulate mutations linked to immune system evasion and drug resistance. In this review, we offer insights into tumor heterogeneity as a crucial characteristic of cancer, exploring the difficulties associated with measuring and quantifying such heterogeneity from clinical and biological perspectives. By emphasizing the critical nature of understanding tumor heterogeneity, this work contributes to raising awareness about the importance of developing effective cancer therapies that target this distinct and elusive trait of cancer.

Macrophage and neutrophil heterogeneity at single-cell spatial resolution in human inflammatory bowel disease

Ulcerative colitis and Crohn's disease are chronic inflammatory intestinal diseases with perplexing heterogeneity in disease manifestation and response to treatment. While the molecular basis for this heterogeneity remains uncharacterized, single-cell technologies allow us to explore the transcriptional states within tissues at an unprecedented resolution which could further understanding of these complex diseases. Here, we apply single-cell RNA-sequencing to human inflamed intestine and show that the largest differences among patients are present within the myeloid compartment including macrophages and neutrophils. Using spatial transcriptomics in human tissue at single-cell resolution (CosMx Spatial Molecular Imaging) we spatially localize each of the macrophage and neutrophil subsets identified by single-cell RNA-sequencing and unravel further macrophage diversity based on their tissue localization. Finally, single-cell RNA-sequencing combined with single-cell spatial analysis reveals a strong communication network involving macrophages and inflammatory fibroblasts. Our data sheds light on the cellular complexity of these diseases and points towards the myeloid and stromal compartments as important cellular subsets for understanding patient-to-patient heterogeneity.

Multiplex Immunohistochemistry and Immunofluorescence: A Practical Update for Pathologists

Our understanding of the biology and management of human disease has undergone a remarkable evolution in recent decades. Improved understanding of the roles of complex immune populations in the tumor microenvironment has advanced our knowledge of antitumor immunity, and immunotherapy has radically improved outcomes for many advanced cancers. Digital pathology has unlocked new possibilities for the assessment and discovery of the tumor microenvironment, such as quantitative and spatial image analysis. Despite these advances, tissue-based evaluations for diagnosis and prognosis continue to rely on traditional practices, such as hematoxylin and eosin staining, supplemented by the assessment of single biomarkers largely using chromogenic immunohistochemistry (IHC). Such approaches are poorly suited to complex quantitative analyses and the simultaneous evaluation of multiple biomarkers. Thus, multiplex staining techniques have significant potential to improve diagnostic practice and immuno-oncology research. The different approaches to achieve multiplexed IHC and immunofluorescence are described in this study. Alternatives to multiplex immunofluorescence/IHC include epitope-based tissue mass spectrometry and digital spatial profiling (DSP), which require specialized platforms not available to most clinical laboratories. Virtual multiplexing, which involves digitally coregistering singleplex IHC stains performed on serial sections, is another alternative to multiplex staining. Regardless of the approach, analysis of multiplexed stains sequentially or simultaneously will benefit from standardized protocols and digital pathology workflows. Although this is a complex and rapidly advancing field, multiplex staining is now technically feasible for most clinical laboratories and may soon be leveraged for routine diagnostic use. This review provides an update on the current state of the art for tissue multiplexing, including the capabilities and limitations of different techniques, with an emphasis on potential relevance to clinical diagnostic practice.

Y chromosome loss in cancer drives growth by evasion of adaptive immunity

Loss of the Y chromosome (LOY) is observed in multiple cancer types, including 10-40% of bladder cancers1-6, but its clinical and biological significance is unknown. Here, using genomic and transcriptomic studies, we report that LOY correlates with poor prognoses in patients with bladder cancer. We performed in-depth studies of naturally occurring LOY mutant bladder cancer cells as well as those with targeted deletion of Y chromosome by CRISPR-Cas9. Y-positive (Y+) and Y-negative (Y-) tumours grew similarly in vitro, whereas Y- tumours were more aggressive than Y+ tumours in immune-competent hosts in a T cell-dependent manner. High-dimensional flow cytometric analyses demonstrated that Y- tumours promote striking dysfunction or exhaustion of CD8+ T cells in the tumour microenvironment. These findings were validated using single-nuclei RNA sequencing and spatial proteomic evaluation of human bladder cancers. Of note, compared with Y+ tumours, Y- tumours exhibited an increased response to anti-PD-1 immune checkpoint blockade therapy in both mice and patients with cancer. Together, these results demonstrate that cancer cells with LOY mutations alter T cell function, promoting T cell exhaustion and sensitizing them to PD-1-targeted immunotherapy. This work provides insights into the basic biology of LOY mutation and potential biomarkers for improving cancer immunotherapy.

Spatial mapping of the DNA adducts in cancer

DNA adducts and strand breaks are induced by various exogenous and endogenous agents. Accumulation of DNA damage is implicated in many disease processes, including cancer, aging, and neurodegeneration. The continuous acquisition of DNA damage from exogenous and endogenous stressors coupled with defects in DNA repair pathways contribute to the accumulation of DNA damage within the genome and genomic instability. While mutational burden offers some insight into the level of DNA damage a cell may have experienced and subsequently repaired, it does not quantify DNA adducts and strand breaks. Mutational burden also infers the identity of the DNA damage. With advances in DNA adduct detection and quantification methods, there is an opportunity to identify DNA adducts driving mutagenesis and correlate with a known exposome. However, most DNA adduct detection methods require isolation or separation of the DNA and its adducts from the context of the nuclei. Mass spectrometry, comet assays, and other techniques precisely quantify lesion types but lose the nuclear context and even tissue context of the DNA damage. The growth in spatial analysis technologies offers a novel opportunity to leverage DNA damage detection with nuclear and tissue context. However, we lack a wealth of techniques capable of detecting DNA damage in situ. Here, we review the limited existing in situ DNA damage detection methods and examine their potential to offer spatial analysis of DNA adducts in tumors or other tissues. We also offer a perspective on the need for spatial analysis of DNA damage in situ and highlight Repair Assisted Damage Detection (RADD) as an in situ DNA adduct technique with the potential to integrate with spatial analysis and the challenges to be addressed.

Single-Cell Analysis in the Omics Era: Technologies and Applications in Cancer

Cancer molecular profiling obtained with conventional bulk sequencing describes average alterations obtained from the entire cellular population analyzed. In the era of precision medicine, this approach is unable to track tumor heterogeneity and cannot be exploited to unravel the biological processes behind clonal evolution. In the last few years, functional single-cell omics has improved our understanding of cancer heterogeneity. This approach requires isolation and identification of single cells starting from an entire population. A cell suspension obtained by tumor tissue dissociation or hematological material can be manipulated using different techniques to separate individual cells, employed for single-cell downstream analysis. Single-cell data can then be used to analyze cell-cell diversity, thus mapping evolving cancer biological processes. Despite its unquestionable advantages, single-cell analysis produces massive amounts of data with several potential biases, stemming from cell manipulation and pre-amplification steps. To overcome these limitations, several bioinformatic approaches have been developed and explored. In this work, we provide an overview of this entire process while discussing the most recent advances in the field of functional omics at single-cell resolution.

Spatial Distribution of Immune Cells in Primary and Recurrent Glioblastoma: A Small Case Study

Only a minority of patients with glioblastoma (GBM) respond to immunotherapy, and always only partially. There is a lack of knowledge on immune distribution in GBM and in its tumor microenvironment (TME). To address the question, we used paired primary and recurrent tumors from GBM patients to study the composition and the evolution of the immune landscape upon treatment. We studied the expression of a handful of immune markers (CD3, CD8, CD68, PD-L1 and PD-1) in GBM tissues in 15 paired primary and recurrent GBM. In five selected patients, we used Nanostring Digital Spatial Profiling (DSP) to obtain simultaneous assessments of multiple biomarkers both within the tumor and the microenvironment in paired primary and recurrent GBM. Our results suggest that the evolution of the immune landscape between paired primary and recurrent GBM tumors is highly heterogeneous. However, our study identifies B3-H7 and HLA-DR as potential targets in primary and recurrent GBM. Spatial profiling of immune markers from matched primary and recurrent GBM shows a nonlinear complex evolution during the progression of cancer. Nonetheless, our study demonstrated a global increase in macrophages, and revealed differential localization of some markers, such as B7-H3 and HLA-DR, between GBM and its TME.

Immune-related gene expression profile at peri-tumoral tonsillar tissue is modified by oropharyngeal cancer nodal status

Secondary lymphoid organs (SLOs), such as lymph nodes and tonsils, serve as an interface between the immune system and tumor cells as an initial antigen presentation site, critical in anti-tumor immune response and disease progression. For oropharyngeal cancers (OPCs) originating from palatine tonsils, we hypothesized that characterizing the immunological process occurring at the peri-tumoral tonsillar tissue will elucidate immune mechanisms for the lymphatic spread of the disease. A total of 33 patients were enrolled and subdivided into two cohorts. For cohort 1 (6 cases), gene expression profiles at the peri-tumoral lymphoid regions and tumor regions were analyzed using the GeoMx whole transcriptome atlas. In the peri-tumoral lymphoid regions, 237 genes were upregulated in metastasis-negative cases compared with metastasis-positive ones, but only one gene in tumor regions. For cohort 2 (27 cases), microarray analysis of peri-tumoral tonsillar tissues revealed 192 upregulated genes. Gene Ontology (GO) analyses revealed the significantly enriched GO terms associated with T cell activation and detected 10 hub genes according to the degree rank (PTPRC, TLR4, CD80, CD40, STAT3, CD28, CD40LG, CD44, CCR7, and IL7R). Gene set enrichment analysis combined with principal component analysis effectively sorted patients with or without lymph node metastases. These findings suggest peri-tumoral tonsils as a potential target to investigate the immune mechanisms associated with the lymphatic spread of the disease in OPCs.

Exploring Spatial Heterogeneity of Immune Cells in Nasopharyngeal Cancer

Nasopharyngeal cancer (NPC) is a malignant tumor. In a recent publication, we described the presence and distribution of CD8+ T cells in NPC and used the information to identify 'inflamed', 'immune-excluded', and 'desert' immune phenotypes, where 'inflamed' and 'immune-excluded' NPCs were correlated with CD8 T cell infiltration and survival. Arguably, more detailed and, in particular, spatially resolved data are required for patient stratification and for the identification of new treatment targets. In this study, we investigate the phenotype of CD45+ leukocytes in the previously analyzed NPC samples by applying multiplexed tissue analysis to assess the spatial distribution of cell types and to quantify selected biomarkers. A total of 47 specified regions-of-interest (ROIs) were generated based on CD45, CD8, and PanCK morphological staining. Using the GeoMx® Digital Spatial Profiler (DSP), 49 target proteins were digitally quantified from the selected ROIs of a tissue microarray consisting of 30 unique NPC biopsies. Protein targets associated with B cells (CD20), NK cells (CD56), macrophages (CD68), and regulatory T cells (PD-1, FOXP3) were most differentially expressed in CD45+ segments within 'immune-rich cancer cell islet' regions of the tumor (cf. 'surrounding stromal leukocyte' regions). In contrast, markers associated with suppressive populations of myeloid cells (CD163, B7-H3, VISTA) and T cells (CD4, LAG3, Tim-3) were expressed at a higher level in CD45+ segments in the 'surrounding stromal leukocyte' regions (cf. 'immune-rich cancer cell islet' regions). When comparing the three phenotypes, the 'inflamed' profile (cf. 'immune-excluded' and 'desert') exhibited higher expression of markers associated with B cells, NK cells, macrophages, and myeloid cells. Myeloid markers were highly expressed in the 'immune-excluded' phenotype. Granulocyte markers and immune-regulatory markers were higher in the 'desert' profile (cf. 'inflamed' and 'immune-excluded'). In conclusion, this study describes the spatial heterogeneity of the immune microenvironment in NPC and highlights immune-related biomarkers in immune phenotypes, which may aid in the stratification of patients for therapeutic purposes.