Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis

Comprehensive multi-omics analysis of the prognostic value and immune signature of NCF2 in pan-cancer and its relationship with acute myeloid leukemia

BACKGROUND

The neutrophil cytoplasmic factor 2 (NCF2) gene encodes the p67phox protein, which has been implicated in the pathogenesis of several diseases. However, its specific role in tumorigenesis remains ambiguous. This study seeks to clarify the prognostic implications, immune profile, and therapeutic responses associated with NCF2 across different cancer types.

METHODS

We conducted a comprehensive analysis using multi-omics data to investigate tissue-specific and single-cell specific expression, pan-cancer expression patterns, epigenetic modifications, the immune microenvironment, and therapeutic responses. Our study specifically examined NCF2-associated immune signatures, molecular mechanisms, and potential therapeutic targets in acute myeloid leukemia (AML). Additionally, we performed in vitro experiments to assess how NCF2 knockdown influences cell proliferation, apoptosis, and cell cycle dynamics in AML cell lines U937 and KG-1.

RESULTS

NCF2 is dysregulated in more than two-thirds of cancer types, with elevated expression strongly correlating with poor prognosis in various cancers, including leukemia. Multifactorial Cox analysis has identified NCF2 as an independent prognostic factor for leukemia. Immunological studies have highlighted NCF2's impact on the tumor microenvironment, particularly affecting monocytes and macrophages. Furthermore, NCF2 expression closely correlates with responses to immunotherapy and chemotherapy. In vitro experiments demonstrate that NCF2 knockdown alters proliferation, apoptosis, and cell cycle dynamics of U937 cells and KG-1 cells. Notably, NCF2 is involved in regulating the differentiation of monocytes into macrophages.

CONCLUSIONS

These findings highlight NCF2 as a promising pan-cancer biomarker that significantly impacts tumor microenvironment, therapeutic response, and is critically associated with cell cycle regulation, apoptosis and macrophage transformation in AML.

Spatial oncology: Translating contextual biology to the clinic

Microscopic examination of cells in their tissue context has been the driving force behind diagnostic histopathology over the past two centuries. Recently, the rise of advanced molecular biomarkers identified through single cell profiling has increased our understanding of cellular heterogeneity in cancer but have yet to significantly impact clinical care. Spatial technologies integrating molecular profiling with microenvironmental features are poised to bridge this translational gap by providing critical in situ context for understanding cellular interactions and organization. Here, we review how spatial tools have been used to study tumor ecosystems and their clinical applications. We detail findings in cell-cell interactions, microenvironment composition, and tissue remodeling for immune evasion and therapeutic resistance. Additionally, we highlight the emerging role of multi-omic spatial profiling for characterizing clinically relevant features including perineural invasion, tertiary lymphoid structures, and the tumor-stroma interface. Finally, we explore strategies for clinical integration and their augmentation of therapeutic and diagnostic approaches.

Spatial transcriptomics: a new frontier in accurate localization of breast cancer diagnosis and treatment

Breast cancer is one of the most prevalent cancers in women globally. Its treatment and prognosis are significantly influenced by the tumor microenvironment and tumor heterogeneity. Precision therapy enhances treatment efficacy, reduces unwanted side effects, and maximizes patients' survival duration while improving their quality of life. Spatial transcriptomics is of significant importance for the precise treatment of breast cancer, playing a critical role in revealing the internal structural differences of tumors and the composition of the tumor microenvironment. It offers a novel perspective in studying the spatial structure and cell interactions within tumors, facilitating more effective personalized treatments for breast cancer. This article will summarize the latest findings in the diagnosis and treatment of breast cancer from the perspective of spatial transcriptomics, focusing on the revelation of the tumor microenvironment, identification of new therapeutic targets, enhancement of disease diagnostic accuracy, comprehension of tumor progression and metastasis, assessment of drug responses, creation of high-resolution maps of tumor cells, representation of tumor heterogeneity, and support for clinical decision-making, particularly in elucidating the tumor microenvironment, tumor heterogeneity, immunotherapy and their correlation with clinical outcomes.

Single-cell and spatial omics unravel the spatiotemporal biology of tumour border invasion and haematogenous metastasis

Solid tumours exhibit a well-defined architecture, comprising a differentiated core and a dynamic border that interfaces with the surrounding tissue. This border, characterised by distinct cellular morphology and molecular composition, serves as a critical determinant of the tumour's invasive behaviour. Notably, the invasive border of the primary tumour represents the principal site for intravasation of metastatic cells. These cells, known as circulating tumour cells (CTCs), function as 'seeds' for distant dissemination and display remarkable heterogeneity. Advancements in spatial sequencing technology are progressively unveiling the spatial biological features of tumours. However, systematic investigations specifically targeting the characteristics of the tumour border remain scarce. In this comprehensive review, we illuminate key biological insights along the tumour body-border-haematogenous metastasis axis over the past five years. We delineate the distinctive landscape of tumour invasion boundaries and delve into the intricate heterogeneity and phenotype of CTCs, which orchestrate haematogenous metastasis. These insights have the potential to explain the basis of tumour invasion and distant metastasis, offering new perspectives for the development of more complex and precise clinical interventions and treatments.

Biointerface-Engineered Hybrid Nanovesicles for Targeted Reprogramming of Tumor Microenvironment

The tumor microenvironment (TME) of typical tumor types such as triple-negative breast cancer is featured by hypoxia and immunosuppression with abundant tumor-associated macrophages (TAMs), which also emerge as potential therapeutic targets for antitumor therapy. M1-like macrophage-derived exosomes (M1-Exos) have emerged as a promising tumor therapeutic candidate for their tumor-targeting and macrophage-polarization capabilities. However, the limited drug-loading efficiency and stability of M1-Exos have hindered their effectiveness in antitumor applications. Here, a hybrid nanovesicle is developed by integrating M1-Exos with AS1411 aptamer-conjugated liposomes (AApt-Lips), termed M1E/AALs. The obtained M1E/AALs are loaded with perfluorotributylamine (PFTBA) and IR780, as P-I, to construct P-I@M1E/AALs for reprogramming TME by alleviating tumor hypoxia and engineering TAMs. P-I@M1E/AAL-mediated tumor therapy enhances the in situ generation of reactive oxygen species, repolarizes TAMs toward an antitumor phenotype, and promotes the infiltration of T lymphocytes. The synergistic antitumor therapy based on P-I@M1E/AALs significantly suppresses tumor growth and prolongs the survival of 4T1-tumor-bearing mice. By integrating multiple treatment modalities, P-I@M1E/AAL nanoplatform demonstrates a promising therapeutic approach for overcoming hypoxic and immunosuppressive TME by targeted TAM reprogramming and enhanced tumor photodynamic immunotherapy. This study highlights an innovative TAM-engineering hybrid nanovesicle platform for the treatment of tumors characterized by hypoxic and immunosuppressive TME.

A ratiometric SERS strategy for the prediction of cancer cell proportion and guidance of glioma surgical resection

Rapid and accurate identification of tumor boundaries is critical for the cure of glioma, but it is difficult due to the invasive nature of glioma cells. This paper aimed to explore a rapid diagnostic strategy based on a label-free surface-enhanced Raman scattering (SERS) technique for the quantitative detection of glioma cell proportion intraoperatively. With silver nanoparticles as substrate, an in-depth SERS analysis was performed on simulated clinical samples containing normal brain tissue and different concentrations of patient-derived glioma cells. The results revealed two universal characteristic peaks of 655 and 717 cm-1, which strongly correlated with glioma cell proportion regardless of individual differences. Based on the intensity ratio of the two peaks, a ratiometric SERS strategy for the quantification of glioma cells was established by employing an artificial neuron network model and a polynomial regression model. Such a strategy accurately estimated the proportion of glioma cells in simulated clinical samples (R2 = 0.98) and frozen samples (R2 = 0.85). More importantly, it accurately facilitated the delineation of tumor margins in freshly obtained samples. Taken together, this SERS-based method ensured a rapid and more detailed identification of tumor margins during surgical resection, which could be beneficial for intraoperative decision-making and pathological evaluation.

SpaTopic: A statistical learning framework for exploring tumor spatial architecture from spatially resolved transcriptomic data

Tumor tissues exhibit a complex spatial architecture within the tumor microenvironment (TME). Spatially resolved transcriptomics (SRT) is promising for unveiling the spatial structures of the TME at both cellular and molecular levels, but identifying pathology-relevant spatial domains remains challenging. Here, we introduce SpaTopic, a statistical learning framework that harmonizes spot clustering and cell-type deconvolution by integrating single-cell transcriptomics and SRT data. Through topic modeling, SpaTopic stratifies the TME into spatial domains with coherent cellular organization, facilitating refined annotation of the spatial architecture with improved performance. We assess SpaTopic across various tumor types and show accurate prediction of tertiary lymphoid structures and tumor boundaries. Moreover, marker genes derived from SpaTopic are transferrable and can be applied to mark spatial domains in other datasets. In addition, SpaTopic enables quantitative comparison and functional characterization of spatial domains across SRT datasets. Overall, SpaTopic presents an innovative analytical framework for exploring, comparing, and interpreting tumor SRT data.

SELF-Former: multi-scale gene filtration transformer for single-cell spatial reconstruction

The spatial reconstruction of single-cell RNA sequencing (scRNA-seq) data into spatial transcriptomics (ST) is a rapidly evolving field that addresses the significant challenge of aligning gene expression profiles to their spatial origins within tissues. This task is complicated by the inherent batch effects and the need for precise gene expression characterization to accurately reflect spatial information. To address these challenges, we developed SELF-Former, a transformer-based framework that utilizes multi-scale structures to learn gene representations, while designing spatial correlation constraints for the reconstruction of corresponding ST data. SELF-Former excels in recovering the spatial information of ST data and effectively mitigates batch effects between scRNA-seq and ST data. A novel aspect of SELF-Former is the introduction of a gene filtration module, which significantly enhances the spatial reconstruction task by selecting genes that are crucial for accurate spatial positioning and reconstruction. The superior performance and effectiveness of SELF-Former's modules have been validated across four benchmark datasets, establishing it as a robust and effective method for spatial reconstruction tasks. SELF-Former demonstrates its capability to extract meaningful gene expression information from scRNA-seq data and accurately map it to the spatial context of real ST data. Our method represents a significant advancement in the field, offering a reliable approach for spatial reconstruction.

The spatial multi-omics revolution in cancer therapy: Precision redefined

Spatially resolved multi-omics revolutionizes cancer therapy by decoding the cellular and molecular heterogeneity of the tumor microenvironment through spatial coordinates. This commentary discusses the roles of spatial multi-omics in identifying precise therapeutic targets and predicting treatment responses while also highlighting the challenges that impede its integration into precision medicine.

Identification of Hypoxia-ALCAMhigh Macrophage- Exhausted T Cell Axis in Tumor Microenvironment Remodeling for Immunotherapy Resistance

Although hypoxia is known to be associated with immune resistance, the adaptability to hypoxia by different cell populations in the tumor microenvironment and the underlying mechanisms remain elusive. This knowledge gap has hindered the development of therapeutic strategies to overcome tumor immune resistance induced by hypoxia. Here, bulk, single-cell, and spatial transcriptomics are integrated to characterize hypoxia associated with immune escape during carcinogenesis and reveal a hypoxia-based intercellular communication hub consisting of malignant cells, ALCAMhigh macrophages, and exhausted CD8+ T cells around the tumor boundary. A hypoxic microenvironment promotes binding of HIF-1α complex is demonstrated to the ALCAM promoter therefore increasing its expression in macrophages, and the ALCAMhigh macrophages co-localize with exhausted CD8+ T cells in the tumor spatial microenvironment and promote T cell exhaustion. Preclinically, HIF-1ɑ inhibition reduces ALCAM expression in macrophages and exhausted CD8+ T cells and potentiates T cell antitumor function to enhance immunotherapy efficacy. This study reveals the systematic landscape of hypoxia at single-cell resolution and spatial architecture and highlights the effect of hypoxia on immunotherapy resistance through the ALCAMhigh macrophage-exhausted T cell axis, providing a novel immunotherapeutic strategy to overcome hypoxia-induced resistance in cancers.

DAB2 + macrophages support FAP + fibroblasts in shaping tumor barrier and inducing poor clinical outcomes in liver cancer

Background: Cancer-associated fibroblasts (CAFs) are the key components of the immune barrier in liver cancer. Therefore, gaining a deeper understanding of the heterogeneity and intercellular communication of CAFs holds utmost importance in boosting immunotherapy effectiveness and improving clinical outcomes. Methods: A comprehensive analysis by combing single-cell, bulk, and spatial transcriptome profiling with multiplexed immunofluorescence was conducted to unravel the complexities of CAFs in liver cancer. Results: Through an integrated approach involving 235 liver cancer scRNA-seq samples encompassing over 1.2 million cells, we found that CAFs were particularly increased in hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC). FAP + fibroblasts were identified as the dominant subtype of CAFs, and which were mainly involved in extracellular matrix organization and angiogenesis. These CAFs were enriched in the tumor boundary of HCC, but diffusely scattered within ICC. The DAB2 + and SPP1 + tumor-associated macrophages (TAMs) reinforce the function of FAP + CAFs through signals such as TGF-β, PDGF, and ADM. Notably, the interaction between DAB2 + TAMs and FAP + CAFs promoted the formation of immune barrier and correlated with poorer patient survival, non-response to immunotherapy in HCC. High FAP and DAB2 immunohistochemical scores predicted shorter survival and higher serum AFP concentration in a local clinical cohort of 90 HCC patients. Furthermore, this communication pattern might be applicable to other solid malignancies as well. Conclusions: The interaction between DAB2 + TAMs and FAP + CAFs appears crucial in shaping the immune barrier. Strategies aimed at disrupting this communication or inhibiting the functions of FAP + CAFs could potentially enhance immunotherapy effectiveness and improve clinical outcomes.

Heterogeneity in Liver Cancer Immune Microenvironment: Emerging Single-Cell and Spatial Perspectives

Primary liver cancer is a solid malignancy with a high mortality rate. The success of immunotherapy has shown great promise in improving patient care and highlights a crucial need to understand the complexity of the liver tumor immune microenvironment (TIME). Recent advances in single-cell and spatial omics technologies, coupled with the development of systems biology approaches, are rapidly transforming the landscape of tumor immunology. Here we review the cellular landscape of liver TIME from single-cell and spatial perspectives. We also discuss the cellular interaction networks within the tumor cell community in regulating immune responses. We further highlight the challenges and opportunities with implications for biomarker discovery, patient stratification, and combination immunotherapies.

Single-cell transcriptomics reveal distinct immune-infiltrating phenotypes and macrophage-tumor interaction axes among different lineages of pituitary neuroendocrine tumors

BACKGROUND

Pituitary neuroendocrine tumors (PitNETs) are common gland neoplasms demonstrating distinctive transcription factors. Although the role of immune cells in PitNETs has been widely recognized, the precise immunological environment and its control over tumor cells are poorly understood.

METHODS

The heterogeneity, spatial distribution, and clinical significance of macrophages in PitNETs were analyzed using single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, spatial transcriptomics, immunohistochemistry, and multiplexed quantitative immunofluorescence (QIF). Cell viability, cell apoptosis assays, and in vivo subcutaneous xenograft experiments have confirmed that INHBA-ACVR1B influences the process of tumor cell apoptosis.

RESULTS

The present study evaluated scRNA-seq data from 23 PitNET samples categorized into 3 primary lineages. The objective was to explore the diversity of tumors and the composition of immune cells across these lineages. Analyzed data from scRNA-seq and 365 bulk RNA sequencing samples conducted in-house revealed the presence of three unique subtypes of tumor immune microenvironment (TIME) in PitNETs. These subtypes were characterized by varying levels of immune infiltration, ranging from low to intermediate to high. In addition, the NR5A1 lineage is primarily associated with the subtype characterized by limited infiltration of immune cells. Tumor-associated macrophages (TAMs) expressing CX3CR1+, C1Q+, and GPNMB+ showed enhanced contact with tumor cells expressing NR5A1 + , TBX19+, and POU1F1+, respectively. This emphasizes the distinct interaction axes between TAMs and tumor cells based on their lineage. Moreover, the connection between CX3CR1+ macrophages and tumor cells via INHBA-ACVR1B regulates tumor cell apoptosis.

CONCLUSIONS

In summary, the different subtypes of TIME and the interaction between TAM and tumor cells offer valuable insights into the control of TIME that affects the development of PitNET. These findings can be utilized as prospective targets for therapeutic interventions.

The Web-Based Portal SpatialTME Integrates Histological Images with Single-Cell and Spatial Transcriptomics to Explore the Tumor Microenvironment

The tumor microenvironment (TME) represents a complex network in which tumor cells communicate not only with each other but also with stromal and immune cells. The intercellular interactions in the TME contribute to tumor initiation, progression, metastasis, and treatment outcome. Recent advances in spatial transcriptomics (ST) have revolutionized the molecular understanding of the TME at the spatial level. A comprehensive interactive analysis resource specifically designed for characterizing the spatial TME could facilitate further advances using ST. In this study, we collected 296 ST slides covering 19 cancer types and developed a computational pipeline to delineate the spatial structure along the malignant-boundary-nonmalignant axis. The pipeline identified differentially expressed genes and their functional enrichment, deconvoluted the cellular composition of the TME, reconstructed cell type-specific gene expression profiles at the sub-spot level, and performed cell-cell interaction analysis. Finally, the user-friendly database SpatialTME (http://www.spatialtme.yelab.site/) was constructed to provide search, visualization, and downloadable results. These detailed analyses are able to reveal the heterogeneous regulatory network of the spatial microenvironment and elucidate associations between spatial features and tumor development or response to therapy, offering a valuable resource to study the complex TME.

SIGNIFICANCE

SpatialTME provides spatial structure, cellular composition, expression, function, and cell-cell interaction information to enable investigations into the tumor microenvironment at the spatial level to advance understanding of cancer development and treatment.

Domain generalization enables general cancer cell annotation in single-cell and spatial transcriptomics

Single-cell and spatial transcriptome sequencing, two recently optimized transcriptome sequencing methods, are increasingly used to study cancer and related diseases. Cell annotation, particularly for malignant cell annotation, is essential and crucial for in-depth analyses in these studies. However, current algorithms lack accuracy and generalization, making it difficult to consistently and rapidly infer malignant cells from pan-cancer data. To address this issue, we present Cancer-Finder, a domain generalization-based deep-learning algorithm that can rapidly identify malignant cells in single-cell data with an average accuracy of 95.16%. More importantly, by replacing the single-cell training data with spatial transcriptomic datasets, Cancer-Finder can accurately identify malignant spots on spatial slides. Applying Cancer-Finder to 5 clear cell renal cell carcinoma spatial transcriptomic samples, Cancer-Finder demonstrates a good ability to identify malignant spots and identifies a gene signature consisting of 10 genes that are significantly co-localized and enriched at the tumor-normal interface and have a strong correlation with the prognosis of clear cell renal cell carcinoma patients. In conclusion, Cancer-Finder is an efficient and extensible tool for malignant cell annotation.

Rebuilding the microenvironment of primary tumors in humans: a focus on stroma

Conventional tumor models have critical shortcomings in that they lack the complexity of the human stroma. The heterogeneous stroma is a central compartment of the tumor microenvironment (TME) that must be addressed in cancer research and precision medicine. To fully model the human tumor stroma, the deconstruction and reconstruction of tumor tissues have been suggested as new approaches for in vitro tumor modeling. In this review, we summarize the heterogeneity of tumor-associated stromal cells and general deconstruction approaches used to isolate patient-specific stromal cells from tumor tissue; we also address the effect of the deconstruction procedure on the characteristics of primary cells. Finally, perspectives on the future of reconstructed tumor models are discussed, with an emphasis on the essential prerequisites for developing authentic humanized tumor models.

scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution

SUMMARY

Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME.

AVAILABILITY AND IMPLEMENTATION

The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.

Integration of Pan-Cancer Single-Cell and Spatial Transcriptomics Reveals Stromal Cell Features and Therapeutic Targets in Tumor Microenvironment

Stromal cells are physiologically essential components of the tumor microenvironment (TME) that mediates tumor development and therapeutic resistance. Development of a logical and unified system for stromal cell type identification and characterization of corresponding functional properties could help design antitumor strategies that target stromal cells. Here, we performed a pan-cancer analysis of 214,972 nonimmune stromal cells using single-cell RNA sequencing from 258 patients across 16 cancer types and analyzed spatial transcriptomics from 16 patients across seven cancer types, including six patients receiving anti-PD-1 treatment. This analysis uncovered distinct features of 39 stromal subsets across cancer types, including various functional modules, spatial locations, and clinical and therapeutic relevance. Tumor-associated PGF+ endothelial tip cells with elevated epithelial-mesenchymal transition features were enriched in immune-depleted TME and associated with poor prognosis. Fibrogenic and vascular pericytes (PC) derived from FABP4+ progenitors were two distinct tumor-associated PC subpopulations that strongly interacted with PGF+ tips, resulting in excess extracellular matrix (ECM) abundance and dysfunctional vasculature. Importantly, ECM-related cancer-associated fibroblasts enriched at the tumor boundary acted as a barrier to exclude immune cells, interacted with malignant cells to promote tumor progression, and regulated exhausted CD8+ T cells via immune checkpoint ligand-receptors (e.g., LGALS9/TIM-3) to promote immune escape. In addition, an interactive web-based tool (http://www.scpanstroma.yelab.site/) was developed for accessing, visualizing, and analyzing stromal data. Taken together, this study provides a systematic view of the highly heterogeneous stromal populations across cancer types and suggests future avenues for designing therapies to overcome the tumor-promoting functions of stromal cells.

SIGNIFICANCE

Comprehensive characterization of tumor-associated nonimmune stromal cells provides a robust resource for dissecting tumor microenvironment complexity and guiding stroma-targeted therapy development across multiple human cancer types.

Deciphering the Immune Microenvironment at the Forefront of Tumor Aggressiveness by Constructing a Regulatory Network with Single-Cell and Spatial Transcriptomic Data

The heterogeneity and intricate cellular architecture of complex cellular ecosystems play a crucial role in the progression and therapeutic response of cancer. Understanding the regulatory relationships of malignant cells at the invasive front of the tumor microenvironment (TME) is important to explore the heterogeneity of the TME and its role in disease progression. In this study, we inferred malignant cells at the invasion front by analyzing single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data of ER-positive (ER+) breast cancer patients. In addition, we developed a software pipeline for constructing intercellular gene regulatory networks (IGRNs), which help to reduce errors generated by single-cell communication analysis and increase the confidence of selected cell communication signals. Based on the constructed IGRN between malignant cells at the invasive front of the TME and the immune cells of ER+ breast cancer patients, we found that a high expression of the transcription factors FOXA1 and EZH2 played a key role in driving tumor progression. Meanwhile, elevated levels of their downstream target genes (ESR1 and CDKN1A) were associated with poor prognosis of breast cancer patients. This study demonstrates a bioinformatics workflow of combining scRNA-seq and ST data; in addition, the study provides the software pipelines for constructing IGRNs automatically (cIGRN). This strategy will help decipher cancer progression by revealing bidirectional signaling between invasive frontline malignant tumor cells and immune cells, and the selected signaling molecules in the regulatory network may serve as biomarkers for mechanism studies or therapeutic targets.

Spatial modelling of the tumor microenvironment from multiplex immunofluorescence images: methods and applications

Spatial modelling methods have gained prominence with developments in high throughput imaging platforms. Multiplex immunofluorescence (mIF) provides the scope to examine interactions between tumor and immune compartment at single cell resolution using a panel of antibodies that can be chosen based on the cancer type or the clinical interest of the study. The markers can be used to identify the phenotypes and to examine cellular interactions at global and local scales. Several translational studies rely on key understanding of the tumor microenvironment (TME) to identify drivers of immune response in immunotherapy based clinical trials. To improve the success of ongoing trials, a number of retrospective approaches can be adopted to understand differences in response, recurrence and progression by examining the patient's TME from tissue samples obtained at baseline and at various time points along the treatment. The multiplex immunofluorescence (mIF) technique provides insight on patient specific cell populations and their relative spatial distribution as qualitative measures of a favorable treatment outcome. Spatial analysis of these images provides an understanding of the intratumoral heterogeneity and clustering among cell populations in the TME. A number of mathematical models, which establish clustering as a measure of deviation from complete spatial randomness, can be applied to the mIF images represented as spatial point patterns. These mathematical models, developed for landscape ecology and geographic information studies, can be applied to the TME after careful consideration of the tumor type (cold vs. hot) and the tumor immune landscape. The spatial modelling of mIF images can show observable engagement of T cells expressing immune checkpoint molecules and this can then be correlated with single-cell RNA sequencing data.

Systems immunology spanning tumors, lymph nodes, and periphery

The immune system defines a complex network of tissues and cell types that orchestrate responses across the body in a dynamic manner. The local and systemic interactions between immune and cancer cells contribute to disease progression. Lymphocytes are activated in lymph nodes, traffic through the periphery, and impact cancer progression through their interactions with tumor cells. As a result, therapeutic response and resistance are mediated across tissues, and a comprehensive understanding of lymphocyte dynamics requires a systems-level approach. In this review, we highlight experimental and computational methods that can leverage the study of leukocyte trafficking through an immunomics lens and reveal how adaptive immunity shapes cancer.

Spatial transcriptomics in development and disease

The proper functioning of diverse biological systems depends on the spatial organization of their cells, a critical factor for biological processes like shaping intricate tissue functions and precisely determining cell fate. Nonetheless, conventional bulk or single-cell RNA sequencing methods were incapable of simultaneously capturing both gene expression profiles and the spatial locations of cells. Hence, a multitude of spatially resolved technologies have emerged, offering a novel dimension for investigating regional gene expression, spatial domains, and interactions between cells. Spatial transcriptomics (ST) is a method that maps gene expression in tissue while preserving spatial information. It can reveal cellular heterogeneity, spatial organization and functional interactions in complex biological systems. ST can also complement and integrate with other omics methods to provide a more comprehensive and holistic view of biological systems at multiple levels of resolution. Since the advent of ST, new methods offering higher throughput and resolution have become available, holding significant potential to expedite fresh insights into comprehending biological complexity. Consequently, a rapid increase in associated research has occurred, using these technologies to unravel the spatial complexity during developmental processes or disease conditions. In this review, we summarize the recent advancement of ST in historical, technical, and application contexts. We compare different types of ST methods based on their principles and workflows, and present the bioinformatics tools for analyzing and integrating ST data with other modalities. We also highlight the applications of ST in various domains of biomedical research, especially development and diseases. Finally, we discuss the current limitations and challenges in the field, and propose the future directions of ST.

Harnessing computational spatial omics to explore the spatial biology intricacies

Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.

Spatially Resolved Transcriptomics Technology Facilitates Cancer Research

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

Response of cells and tissues to shear stress

Shear stress is essential for normal physiology and malignancy. Common physiological processes - such as blood flow, particle flow in the gut, or contact between migratory cell clusters and their substrate - produce shear stress that can have an impact on the behavior of different tissues. In addition, shear stress has roles in processes of biomedical interest, such as wound healing, cancer and fibrosis induced by soft implants. Thus, understanding how cells react and adapt to shear stress is important. In this Review, we discuss in vivo and in vitro data obtained from vascular and epithelial models; highlight the insights these have afforded regarding the general mechanisms through which cells sense, transduce and respond to shear stress at the cellular levels; and outline how the changes cells experience in response to shear stress impact tissue organization. Finally, we discuss the role of shear stress in collective cell migration, which is only starting to be appreciated. We review our current understanding of the effects of shear stress in the context of embryo development, cancer and fibrosis, and invite the scientific community to further investigate the role of shear stress in these scenarios.

Role of the bone marrow microenvironment in multiple myeloma treatment using CAR-T therapy

INTRODUCTION

Multiple myeloma (MM) is a malignant tumor caused by abnormal proliferation of bone marrow (BM) plasma cells and is the second most common hematologic malignancy. A variety of CAR-T cells targeting multiple myeloma-specific markers have shown good efficacy in clinical trials. However, CAR-T therapy still limits the insufficient duration of efficacy and recurrence of the disease.

AREAS COVERED

This article reviews the cell populations in the bone marrow of MM, and discusses the potential way to improve the efficiency of CAR-T cells in the treatment of MM by targeting the bone marrow microenvironment.

EXPERT OPINION

The limits of CAR-T therapy in MM may related to the impairment of T cell activity in the bone marrow microenvironment. This article reviews the cell populations of the immune microenvironment and nonimmune microenvironment in the bone marrow of multiple myeloma, and discusses the potential way to improve the efficiency of CAR-T cells in the treatment of MM by targeting the bone marrow. This may provides a new idea for the CAR-T therapy of multiple myeloma.

Spatial landscape of the tumor immune microenvironment

Two recent studies published in Nature by Karimi et al. and Sorin et al. applied multiplexed imaging mass cytometry (IMC) to characterize the single-cell tumor immune landscapes covering millions of cells in brain tumors and lung adenocarcinomas (LUAD). They identified specific cell subtypes and cell-cell interactions that correlate with distinct clinical outcomes, thus providing valuable insights into tumor biology and prognostic prediction based on spatial architecture.