Spatial omics: Navigating to the golden era of cancer research

Selenide-modified hyaluronic acid hydrogel promotes scleral remodeling during the recovery phase of form-deprivation myopia by inhibiting HIF-1α-mediated inflammation

This study investigates the molecular mechanisms by which selenide-modified hyaluronic acid hydrogel (Se-HA gel) promotes scleral remodeling during the recovery phase of form-deprivation myopia (FDM). The Se-HA gel was synthesized and characterized, exhibiting an average hydrodynamic diameter of 191.72 nm, a polydispersity index (PDI) of 0.19, and a zeta potential of -7.96 mV, indicating a monodisperse state in PBS. Both in vitro experiments and the FDM mouse model confirmed its therapeutic efficacy. At a concentration of 250 μg/mL, Se-HA gel significantly promoted fibroblast proliferation, inhibited apoptosis, and prevented transdifferentiation. A 200 mg/kg subtenon injection improved key ocular biometric parameters in FDM mice. Single-cell and transcriptomic sequencing analyses revealed that Se-HA gel facilitated scleral remodeling by downregulating Hypoxia-Inducible Factor 1 Alpha (HIF-1α) expression and regulating inflammation-related gene expression. Notably, HIF-1α overexpression reversed the beneficial effects of Se-HA gel, reinforcing its pivotal role in mediating these therapeutic outcomes. This study introduces a novel biomaterial-based strategy and identifies new molecular targets for myopia treatment. Furthermore, it addresses a critical gap in understanding how Se-HA gel promotes scleral remodeling through HIF-1α-mediated signaling pathways, with important scientific and translational potential in the field of myopia management.

Delineation of Subcellular Molecular Heterogeneity in Single Cells via Ultralow-Flow-Rate Desorption Electrospray Ionization Mass Spectrometry Imaging

Single-cell analysis uncovers cellular heterogeneity and dynamic metabolism at the individual cell level, providing fundamental insights into the physiological and pathological mechanisms of life. Spatial molecular mapping of single cells requires advanced techniques that achieve high spatial resolution and comprehensive molecular coverage while preserving the cells' native state with minimal preparation. Here, we report an ultralow-flow-rate desorption electrospray ionization mass spectrometry imaging (u-DESI-MSI) platform for in situ biomolecule mapping of single cells with subcellular spatial resolution. The u-DESI-MSI single-cell platform utilizes a stable and ultralow-solvent-flow-rate (150 nL/min) system with optimized geometrical settings, avoiding complicated hardware modifications on the ESI emitter. The capability of u-DESI-MSI was demonstrated by spatial molecular mapping of human pancreatic cells from different lineages. An unprecedented spatial resolution was achieved for molecular mapping of the single cells under a rastering step size of 5 μm, even revealing lipid distribution in subcellular regions. Using this high-spatial-resolution u-DESI-MSI platform, we effectively visualized both intercellular and intracellular molecular heterogeneity in single cells. Notably, u-DESI-MSI provides a versatile tool for direct molecular imaging of single cells in their native states under ambient conditions, eliminating the need for extensive sample preparation. We anticipate that this platform will facilitate the exploration of single-cell heterogeneity, offering valuable insights into cellular variability and metabolism.

From Multi-Omics to Visualization and Beyond: Bridging Micro and Macro Insights in CAR-T Cell Therapy

Chimeric antigen receptor T (CAR-T) cell therapies, a cornerstone of immunotherapy, have demonstrated remarkable efficacy in treating hematological malignancies and have more recently expanded into applications for solid tumors and autoimmune diseases. Emerging multidimensional profiling technologies offer promising solutions for enhancing CAR-T efficacy, overcoming resistance, and facilitating the development of novel CAR-T constructs. The integration of genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics enables a comprehensive understanding of the intrinsic mechanisms underlying CAR-T therapy, while single-cell and spatial omics significantly improve data resolution and analytical depth. Coupled with advances in biomedical engineering, visualization technologies form the foundation for omics data generation by bridging microscopic and macroscopic scales and enabling dynamic, 3D in vivo monitoring of CAR-T behavior. Artificial intelligence (AI) further supports this framework by enabling the analysis of complex, high-dimensional datasets. This review highlights recent advances in the integration of multidimensional omics within CAR-T therapy and explores cutting-edge developments in visualization technologies and AI applications. The full convergence of multi-omics, visualization tools, and AI is poised to deliver transformative insights into the mechanisms governing CAR-T cell therapy.

StereoMM: a graph fusion model for integrating spatial transcriptomic data and pathological images

Spatial omics technologies, generating high-throughput and multimodal data, have necessitated the development of advanced data integration methods to facilitate comprehensive biological and clinical treatment discoveries. Based on the cross-attention concept, we developed an AI learning based toolchain called StereoMM, a graph based fusion model that can incorporate omics data such as gene expression, histological images, and spatial location. StereoMM uses an attention module for omics data interaction and a graph autoencoder to integrate spatial positions and omics data in a self-supervised manner. Applying StereoMM across various cancer types and platforms has demonstrated its robust capability. StereoMM outperforms competitors in identifying spatial regions reflecting tumour progression and shows promise in classifying colorectal cancer patients into deficient mismatch repair and proficient mismatch repair groups. The comprehensive inter-modal integration and efficiency of StereoMM enable researchers to construct spatial views of integrated multimodal features efficiently, advancing thorough tissue and patient characterization.

Spatial Omics in Clinical Research: A Comprehensive Review of Technologies and Guidelines for Applications

Spatial omics integrates molecular profiling with spatial tissue context, enabling high-resolution analysis of gene expression, protein interactions, and epigenetic modifications. This approach provides critical insights into disease mechanisms and therapeutic responses, with applications in cancer, neurology, and immunology. Spatial omics technologies, including spatial transcriptomics, proteomics, and epigenomics, facilitate the study of cellular heterogeneity, tissue organization, and cell-cell interactions within their native environments. Despite challenges in data complexity and integration, advancements in multi-omics pipelines and computational tools are enhancing data accuracy and biological interpretation. This review provides a comprehensive overview of key spatial omics technologies, their analytical methods, validation strategies, and clinical applications. By integrating spatially resolved molecular data with traditional omics, spatial omics is transforming precision medicine, biomarker discovery, and personalized therapy. Future research should focus on improving standardization, reproducibility, and multimodal data integration to fully realize the potential of spatial omics in clinical and translational research.

Advances in Microfluidic Single-Cell RNA Sequencing and Spatial Transcriptomics

The development of micro- and nano-fabrication technologies has greatly advanced single-cell and spatial omics technologies. With the advantages of integration and compartmentalization, microfluidic chips are capable of generating high-throughput parallel reaction systems for single-cell screening and analysis. As omics technologies improve, microfluidic chips can now integrate promising transcriptomics technologies, providing new insights from molecular characterization for tissue gene expression profiles and further revealing the static and even dynamic processes of tissues in homeostasis and disease. Here, we survey the current landscape of microfluidic methods in the field of single-cell and spatial multi-omics, as well as assessing their relative advantages and limitations. We highlight how microfluidics has been adapted and improved to provide new insights into multi-omics over the past decade. Last, we emphasize the contributions of microfluidic-based omics methods in development, neuroscience, and disease mechanisms, as well as further revealing some perspectives for technological advances in translational and clinical medicine.

Advancements in Nanomaterials and Molecular Probes for Spatial Omics

Spatial omics is emerging as a focus of life sciences because of its applications in investigating the molecular mechanisms of cancer, mapping cellular distributions, and revealing specific cellular ecological niches. Notably, the in-depth acquisition of spatial omics information relies on highly sensitive, high-resolution, and high-throughput biological analysis tools and techniques. However, conventional methods of omics data acquisition still suffer from some drawbacks such as limited-resolution and low-throughput and are difficult to adapt directly to the collection of high-quality spatial omics data. Recently, an increasing number of advanced nanomaterials and molecular probes are employed in spatial omics due to their excellent optoelectronic properties, biocompatibility, and multifunction. These well-designed innovative nanoscaffolds successfully enhance the key parameters of spatial omics and, thus, increase the spatial resolution, detection sensitivity, and detection throughput. This review summarizes the design and application of functional nanoscaffolds for spatial omics in recent years, with a particular emphasis on nanomaterials and molecular probes. We believe that the present review can inspire and motivate researchers in designing and selecting appropriate materials and probes for high-quality spatial omics, thus promoting the development of spatial omics and life sciences.

Spatial multi-omics analysis of tumor-stroma boundary cell features for predicting breast cancer progression and therapy response

Background

The tumor boundary of breast cancer represents a highly heterogeneous region. In this area, the interactions between malignant and non-malignant cells influence tumor progression, immune evasion, and drug resistance. However, the spatial transcriptional profile of the tumor boundary and its role in the prognosis and treatment response of breast cancer remain unclear.

Method

Utilizing the Cottrazm algorithm, we reconstructed the intricate boundaries and identified differentially expressed genes (DEGs) associated with these regions. Cell-cell co-positioning analysis was conducted using SpaCET, which revealed key interactions between tumor-associated macrophage (TAMs) and cancer-associated fibroblasts (CAFs). Additionally, Lasso regression analysis was employed to develop a malignant body signature (MBS), which was subsequently validated using the TCGA dataset for prognosis prediction and treatment response assessment.

Results

Our research indicates that the tumor boundary is characterized by a rich reconstruction of the extracellular matrix (ECM), immunomodulatory regulation, and the epithelial-to-mesenchymal transition (EMT), underscoring its significance in tumor progression. Spatial colocalization analysis reveals a significant interaction between CAFs and M2-like tumor-associated macrophage (TAM), which contributes to immune exclusion and drug resistance. The MBS score effectively stratifies patients into high-risk groups, with survival outcomes for patients exhibiting high MBS scores being significantly poorer. Furthermore, drug sensitivity analysis demonstrates that high-MB tumors had poor response to chemotherapy strategies, highlighting the role of the tumor boundary in modulating therapeutic efficacy.

Conclusion

Collectively, we investigate the spatial transcription group and bulk data to elucidate the characteristics of tumor boundary molecules in breast cancer. The CAF-M2 phenotype emerges as a critical determinant of immunosuppression and drug resistance, suggesting that targeting this interaction may improve treatment responses. Furthermore, the MBS serves as a novel prognostic tool and offers potential strategies for guiding personalized treatment approaches in breast cancer.

Bridging epigenomics and tumor immunometabolism: molecular mechanisms and therapeutic implications

Epigenomic modifications-such as DNA methylation, histone acetylation, and histone methylation-and their implications in tumorigenesis, progression, and treatment have emerged as a pivotal field in cancer research. Tumors undergo metabolic reprogramming to sustain proliferation and metastasis in nutrient-deficient conditions, while suppressing anti-tumor immunity in the tumor microenvironment (TME). Concurrently, immune cells within the immunosuppressive TME undergo metabolic adaptations, leading to alterations in their immune function. The complicated interplay between metabolites and epigenomic modulation has spotlighted the significance of epigenomic regulation in tumor immunometabolism. In this review, characteristics of the epigenomic modification associated with tumors are systematically summarized alongside with their regulatory roles in tumor metabolic reprogramming and immunometabolism. Classical and emerging approaches are delineated to broaden the boundaries of research on the crosstalk research on the crosstalk between tumor immunometabolism and epigenomics. Furthermore, we discuss potential therapeutic strategies that target tumor immunometabolism to modulate epigenomic modifications, highlighting the burgeoning synergy between metabolic therapies and immunotherapy as a promising avenue for cancer treatment.

Sophisticated roles of tumor microenvironment in resistance to immune checkpoint blockade therapy in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains a serious threat to global health, with rising incidence and mortality rates. Therapeutic options for advanced HCC are quite limited, and the overall prognosis remains poor. Recent advancements in immunotherapy, particularly immune-checkpoint blockade (ICB) targeting anti-PD1/PD-L1 and anti-CTLA4, have facilitated a paradigm shift in cancer treatment, demonstrating substantial survival benefits across various cancer types, including HCC. However, only a subset of HCC patients exhibit a favorable response to ICB therapy, and its efficacy is often hindered by the development of resistance. There are many studies to explore the underlying mechanisms of ICB response. In this review, we compiled the latest progression in immunotherapies for HCC and systematically summarized the sophisticated mechanisms by which components of the tumor microenvironment (TME) regulate resistance to ICB therapy. Additionally, we also outlined some scientific rationale strategies to boost antitumor immunity and enhance the efficacy of ICB in HCC. These insights may serve as a roadmap for future research and help improve outcomes for HCC patients.

Elucidating stearoyl metabolism and NCOA4-mediated ferroptosis in gastric cancer liver metastasis through multi-omics single-cell integrative mendelian analysis: advancing personalized immunotherapy strategies

BACKGROUND

The metabolism of stearoyl-GPE plays a key role in the liver metastasis of gastric cancer. This investigation delves into the mechanisms underlying the intricate tumor microenvironment (TME) heterogeneity triggered by stearoyl metabolism in gastric cancer with liver metastasis (LMGC), offering novel perspectives for LMGC.

OBJECTIVE

Utilizing Mendelian randomization, we determined that stearoyl metabolism significantly contributes to the progression of gastric cancer (GC). Following this, bulk transcriptome analyses and single-cell multiomics techniques to investigate the roles of stearoyl-GPE metabolism-related genes, particularly NCOA4, in regulating LMGC TME.

RESULTS

Our analysis highlights the crucial role of stearoyl metabolism in modulating the complex microenvironment of LMGC, particularly impacting monocyte cells. Through single-cell sequencing and spatial transcriptomics, we have identified key metabolic genes specific to stearoyl metabolism within the monocyte cell population, including NCOA4. Regarding the relationship between ferroptosis, stearoyl metabolism, and LMGC findings, it is plausible that stearoyl metabolism and LMGC pathways intersect with mechanisms involved in ferroptosis. Ferroptosis, characterized by iron-dependent lipid peroxidation, represents a regulated form of cell death. The activity of Stearoyl-CoA desaturase (SCD), a critical enzyme in stearoyl metabolism, has been associated with the modulation of lipid composition and susceptibility to ferroptosis. Furthermore, the LMGC is integral to cellular processes related to oxidative stress and lipid metabolism, both of which are significant factors in the context of ferroptosis.

CONCLUSION

This study enhances the understanding of the relationship between stearoyl metabolism and ferroptosis in promoting liver metastasis of gastric cancer and its role in the regulation of tumor heterogeneity. In addition, this study contributes to a deeper understanding of the dynamics of gastric cancer tumor microenvironment (TME) and provides a basis for the development of better interventions to combat cancer metastasis.

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.

Immunoproteomics Reveal Different Characteristics for the Prognostic Markers of Intratumoral-Infiltrating CD3+ T Lymphocytes and Immunoscore in Colorectal Cancer

Tumor-infiltrating lymphocytes (TILs) and immunoscoring based on densities of CD3+ and CD8+ TILs are both favorable prognostic markers in colorectal cancer (CRC). However, determination of the molecular features of TILs, particularly their immunoproteomic signatures would require the development of large scale in situ spatiotemporal technologies. Recently, a multiplex in situ digital spatial proteomic profiling (DSP) tool GeoMx DSP has been applied to identify biomarkers predictive of therapeutic responses and to understand disease mechanisms and progression. Taking advantage of this tool, we simultaneously characterized the spatial distribution and interactions of 42 immune proteins in tumor cells (TCs), CD3+ T stromal TILs (sTILs), and CD20+ B sTILs using tissue microarrays, and further studied their associations with CD3+ T TILs and immunoscores in CRC. First, our data showed that well-known immune checkpoints, such as PD-L1, PD-L2, and LAG3, were expressed at low levels, whereas some other immune proteins, such as CD11c, CD68, STING, and CD44, were highly expressed. Second, 8 spatial interactions were identified, including 5 interactions between TC and CD20+ B sTILs, 2 interactions between CD3+ T sTILs and CD20+ B sTILs, and 1 interaction among TC, CD3+ T sTILs, and CD20+ B sTILs. Third, the differential immune microlandscape in the spatial compartments was identified in tissues with positive CD3+ T intratumoral TILs and high immunoscores. Collectively, to our knowledge, our study is the first to provide in situ spatial immune characteristics at the proteomic level. Moreover, our findings provide direct evidence supporting the infiltration of CD3+ T sTILs from stoma to TC and shed important insights into better understanding and treating CRC patients related to different immune prognostic markers.

Computational methods for alignment and integration of spatially resolved transcriptomics data

Most of the complex biological regulatory activities occur in three dimensions (3D). To better analyze biological processes, it is essential not only to decipher the molecular information of numerous cells but also to understand how their spatial contexts influence their behavior. With the development of spatially resolved transcriptomics (SRT) technologies, SRT datasets are being generated to simultaneously characterize gene expression and spatial arrangement information within tissues, organs or organisms. To fully leverage spatial information, the focus extends beyond individual two-dimensional (2D) slices. Two tasks known as slices alignment and data integration have been introduced to establish correlations between multiple slices, enhancing the effectiveness of downstream tasks. Currently, numerous related methods have been developed. In this review, we first elucidate the details and principles behind several representative methods. Then we report the testing results of these methods on various SRT datasets, and assess their performance in representative downstream tasks. Insights into the strengths and weaknesses of each method and the reasons behind their performance are discussed. Finally, we provide an outlook on future developments. The codes and details of experiments are now publicly available at https://github.com/YangLabHKUST/SRT_alignment_and_integration.

Single-cell RNA sequencing reveals the landscape of the cellular ecosystem of primary hepatocellular carcinoma

Hepatocellular carcinoma (HCC) cells, along with multiple nonmalignant stromal cells, such as fibroblasts, endothelial cells and immune cells, comprise an intricate cellular ecosystem, undergo dynamic phenotypic changes and present complicated cellular interactions, thus synergistically facilitating HCC initiation and progression and leading to treatment resistance. Clarifying the heterogeneity, cell plasticity and complexity of the cellular ecosystem of HCC will be highly beneficial for understanding HCC development and identifying novel therapeutic targets. Single-cell RNA sequencing (scRNA-seq) refers to profiling the transcriptome at single-cell resolution, and the development of scRNA-seq technology and analysis algorithms has greatly promoted the analysis of cell composition, cell subpopulation heterogeneity, development trajectory and cell-to-cell interactions in cell populations. In this review, we systematically summarized and discussed scRNA-seq in treatment-naive primary HCC and revealed the global cell composition of HCC; the widespread molecular heterogeneity of HCC cells; the molecular subtypes of fibroblasts; the cell composition, functional states and development trajectory of immune cells; and the frequent interactions between different cell types to systematically draw the landscape of the cellular ecosystem of primary HCC.

Enhancing spatial domain detection in spatial transcriptomics with EnSDD

Advancements in spatial transcriptomics have transformed our understanding of organ function and tissue microenvironment. However, accurately identifying spatial domains to depict genome heterogeneity and cellular interactions remains a challenge. In this study, we propose EnSDD (Ensemble-learning for Spatial Domain Detection), a method that ingeniously integrates eight state-of-the-art spatial domain detection methods to automatically identify spatial domains. A key innovation of EnSDD is its dynamic weighting mechanism within the ensemble learning process, which optimizes the contribution of each base model and provides a performance evaluation metric without the need for ground truth data. By leveraging the spatial domains identified through EnSDD, we incorporate the detection of domain-specific spatially variable genes and the spatial distribution of cell types, thereby providing deeper insights into tissue heterogeneity. We validate EnSDD across diverse spatial transcriptomics datasets from various tissue organizational structures. Our results demonstrate that EnSDD significantly enhances spatial domain identification accuracy, identifies genes with spatial expression patterns, and reveals domain-specific cell type enrichment patterns, offering invaluable insights into tissue spatial heterogeneity and regionalization.

Isoporous Membrane Mediated Imprinting Mass Spectrometry Imaging for Spatially-Resolved Metabolomics and Rapid Histopathological Diagnosis

Surface-assisted laser desorption/ionization (SALDI) mass spectrometry imaging (MSI) holds great value in spatial metabolomics and tumor diagnosis. Tissue imprinting on the SALDI target can avoid laser-induced tissue ablation and simplifies the sample preparation. However, the tissue imprinting process always causes lateral diffusion of biomolecules, thereby losing the fidelity of metabolite distribution on tissue. Herein, a membrane-mediated imprinting mass spectrometry imaging (MMI-MSI) strategy is proposed using isoporous nuclepore track-etched membrane as a mediating imprinting layer to selectively transport metabolites through uniform and vertical pores onto silicon nanowires (SiNWs) array. Compared with conventional direct imprinting technique, MMI-MSI can not only exclude the adsorption of large biomolecules but also avoid the lateral diffusion of metabolites. The whole time for MMI-based sample preparation can be reduced to 2 min, and the lipid peak number can increase from 46 to 113 in kidney tissue detection. Meanwhile, higher resolution of MSI can be achieved due to the confinement effect of the pore channel in the diffusion of metabolites. Based on MMI-MSI, the tumor margins of liver cancer can be clearly discriminated and their different subtypes can be precisely classified. This work demonstrates MMI-MSI is a rapid, highly sensitive, robust and high-resolution technique for spatially-resolved metabolomics and pathological diagnosis.

Next-generation spatial transcriptomics: unleashing the power to gear up translational oncology

The growing advances in spatial transcriptomics (ST) stand as the new frontier bringing unprecedented influences in the realm of translational oncology. This has triggered systemic experimental design, analytical scope, and depth alongside with thorough bioinformatics approaches being constantly developed in the last few years. However, harnessing the power of spatial biology and streamlining an array of ST tools to achieve designated research goals are fundamental and require real-world experiences. We present a systemic review by updating the technical scope of ST across different principal basis in a timeline manner hinting on the generally adopted ST techniques used within the community. We also review the current progress of bioinformatic tools and propose in a pipelined workflow with a toolbox available for ST data exploration. With particular interests in tumor microenvironment where ST is being broadly utilized, we summarize the up-to-date progress made via ST-based technologies by narrating studies categorized into either mechanistic elucidation or biomarker profiling (translational oncology) across multiple cancer types and their ways of deploying the research through ST. This updated review offers as a guidance with forward-looking viewpoints endorsed by many high-resolution ST tools being utilized to disentangle biological questions that may lead to clinical significance in the future.

ZNF468-mediated epigenetic upregulation of VEGF-C facilitates lymphangiogenesis and lymphatic metastasis in ESCC via PI3K/Akt and ERK1/2 signaling pathways

PURPOSE

Dysfunctional lymphangiogenesis is pivotal for various pathological processes including tumor lymph node metastasis which is a crucial cause of therapeutic failure for ESCC. In this study, we aim to elucidate the molecular mechanisms and clinical relevance of Zinc-finger protein ZNF468 in lymphangiogenesis and lymphatic metastasis in ESCC.

METHODS

Immunohistochemistry, Western blot, Kaplan-Meier plotter analysis and Gene Set Enrichment Analysis were preformed to detect the association of ZNF468 with lymphangiogenesis and poor prognosis in ESCC patients. Foot-pads lymph node metastasis model, tube formation assay, 3D-culture assay and invasion assay were preformed to verify the effect of ZNF468 on lymphangiogenesis and lymph node metastasis. CUT&Tag analysis, immunoprecipitation and mass spectrometry analysis and ChIP-PCR assay were preformed to study the molecular mechanisms of ZNF468 in lymphangiogenesis.

RESULTS

We found that ectopic expression of ZNF468 was correlated with higher microlymphatic vessel density in ESCC tissues, leading to poorer prognosis of ESCC patients. ZNF468 enhanced the capacity of lymphangiogenesis and promoted lymphatic metastasis in ESCC both in vitro and in vivo. However, silencing ZNF468 reversed these phenotypes in ESCC. Mechanically, we demonstrated that ZNF468 recruits the histone modification factors (PRMT1/HAT1) to increase the levels of H4R2me2a and H3K9ac, which then leads to the recruitment of the transcription initiation complex on the VEGF-C promoter, ultimately promoting the upregulation of VEGF-C transcription. Strikingly, the promoting effect of lymphatic metastasis induced by ZNF468 in ESCC was abrogated by targeting PRMT1 using Arginine methyltransferase inhibitor-1 or silencing VEGF-C. Furthermore, we found that the activation of PI3K/AKT and ERK1/2 signaling is required for ZNF468-medicated lymphatic metastasis in ESCC. Importantly, the clinical relevance between ZNF468 and VEGF-C were confirmed not only in ESCC samples and but also in multiple cancer types.

CONCLUSION

Our results identified a precise mechanism underlying ZNF468-induced epigenetic upregulation of VEGF-C in facilitating lymphangiogenesis and lymph node metastasis of ESCC, which might provide a novel prognostic biomarker and potential therapeutic for ESCC patients.

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.

Application of multi-omics in the study of traditional Chinese medicine

Traditional Chinese medicine (TCM) is playing an increasingly important role in disease treatment due to the advantages of multi-target, multi-pathway mechanisms, low adverse reactions and cost-effectiveness. However, the complexity of TCM system poses challenges for research. In recent years, there has been a surge in the application of multi-omics integrated research to explore the active components and treatment mechanisms of TCM from various perspectives, which aids in advancing TCM's integration into clinical practice and holds immense importance in promoting modernization. In this review, we discuss the application of proteomics, metabolomics, and mass spectrometry imaging in the study of composition, quality evaluation, target identification, and mechanism of action of TCM based on existing literature. We focus on the workflows and applications of multi-omics based on mass spectrometry in the research of TCM. Additionally, potential research ideas for future exploration in TCM are outlined. Overall, we emphasize the advantages and prospects of multi-omics based on mass spectrometry in the study of the substance basis and mechanism of action of TCM. This synthesis of methodologies holds promise for enhancing our understanding of TCM and driving its further integration into contemporary medical practices.

FUSION: A web-based application for in-depth exploration of multi-omics data with brightfield histology

Spatial -OMICS technologies facilitate the interrogation of molecular profiles in the context of the underlying histopathology and tissue microenvironment. Paired analysis of histopathology and molecular data can provide pathologists with otherwise unobtainable insights into biological mechanisms. To connect the disparate molecular and histopathologic features into a single workspace, we developed FUSION (Functional Unit State IdentificatiON in WSIs [Whole Slide Images]), a web-based tool that provides users with a broad array of visualization and analytical tools including deep learning-based algorithms for in-depth interrogation of spatial -OMICS datasets and their associated high-resolution histology images. FUSION enables end-to-end analysis of functional tissue units (FTUs), automatically aggregating underlying molecular data to provide a histopathology-based medium for analyzing healthy and altered cell states and driving new discoveries using "pathomic" features. We demonstrate FUSION using 10x Visium spatial transcriptomics (ST) data from both formalin-fixed paraffin embedded (FFPE) and frozen prepared datasets consisting of healthy and diseased tissue. Through several use-cases, we demonstrate how users can identify spatial linkages between quantitative pathomics, qualitative image characteristics, and spatial --omics.

Spatially Resolved Single-Cell Omics: Methods, Challenges, and Future Perspectives

Overlaying omics data onto spatial biological dimensions has been a promising technology to provide high-resolution insights into the interactome and cellular heterogeneity relative to the organization of the molecular microenvironment of tissue samples in normal and disease states. Spatial omics can be categorized into three major modalities: (a) next-generation sequencing-based assays, (b) imaging-based spatially resolved transcriptomics approaches including in situ hybridization/in situ sequencing, and (c) imaging-based spatial proteomics. These modalities allow assessment of transcripts and proteins at a cellular level, generating large and computationally challenging datasets. The lack of standardized computational pipelines to analyze and integrate these nonuniform structured data has made it necessary to apply artificial intelligence and machine learning strategies to best visualize and translate their complexity. In this review, we summarize the currently available techniques and computational strategies, highlight their advantages and limitations, and discuss their future prospects in the scientific field.

The roles of tertiary lymphoid structures in genitourinary cancers: molecular mechanisms, therapeutic strategies, and clinical applications

The presence of tertiary lymphoid structures (TLSs) associated with distinct treatment efficacy and clinical prognosis has been identified in various cancer types. However, the mechanistic roles and clinical implications of TLSs in genitourinary (GU) cancers remain incompletely explored. Despite their potential role as predictive markers described in numerous studies, it is essential to comprehensively evaluate the characteristics of TLSs, including drivers of formation, structural foundation, cellular compositions, maturation stages, molecular features, and specific functionality to maximize their positive impacts on tumor-specific immunity. The unique contributions of these structures to cancer progression and biology have fueled interest in these structures as mediators of antitumor immunity. Emerging data are trying to explore the effects of therapeutic interventions targeting TLSs. Therefore, a better understanding of the molecular and phenotypic heterogeneity of TLSs may facilitate the development of TLSs-targeting therapeutic strategies to obtain optimal clinical benefits for GU cancers in the setting of immunotherapy. In this review, the authors focus on the phenotypic and functional heterogeneity of TLSs in cancer progression, current therapeutic interventions targeting TLSs and the clinical implications and therapeutic potential of TLSs in GU cancers.

The role of immune metabolism in skin cancers: implications for pathogenesis and therapy

The skin is a complex organ that serves as a critical barrier against external pathogens and environmental impact. Recent advances in immunometabolism have highlighted the intricate link between cellular metabolism and immune function, particularly in the context of skin cancers. This review aims to provide a comprehensive overview of the key metabolic pathways and adaptations that occur in immune cells during homeostasis and activation, and explore how metabolic reprogramming contributes to the pathogenesis of specific skin cancers. We discuss the complex interplay between tumor cells and infiltrating immune cells, which shapes the tumor microenvironment and influences disease outcomes. The review delves into the role of various metabolic pathways, such as glycolysis, oxidative phosphorylation, and lipid metabolism, in the regulation of immune cell function and their impact on the development and progression of skin cancers. Furthermore, we examine the potential of targeting metabolic pathways as a therapeutic strategy in skin cancers and discuss the challenges and future perspectives in this rapidly evolving field. By understanding the metabolic basis of skin immune responses, we can develop novel, personalized therapies for the treatment of skin cancers, ultimately improving patient outcomes and quality of life. The insights gained from this review will contribute to the growing body of knowledge in immunometabolism and its application in the management of skin cancers, paving the way for more effective and targeted interventions in the future.

SANTO: a coarse-to-fine alignment and stitching method for spatial omics

With the flourishing of spatial omics technologies, alignment and stitching of slices becomes indispensable to decipher a holistic view of 3D molecular profile. However, existing alignment and stitching methods are unpractical to process large-scale and image-based spatial omics dataset due to extreme time consumption and unsatisfactory accuracy. Here we propose SANTO, a coarse-to-fine method targeting alignment and stitching tasks for spatial omics. SANTO firstly rapidly supplies reasonable spatial positions of two slices and identifies the overlap region. Then, SANTO refines the positions of two slices by considering spatial and omics patterns. Comprehensive experiments demonstrate the superior performance of SANTO over existing methods. Specifically, SANTO stitches cross-platform slices for breast cancer samples, enabling integration of complementary features to synergistically explore tumor microenvironment. SANTO is then applied to 3D-to-3D spatiotemporal alignment to study development of mouse embryo. Furthermore, SANTO enables cross-modality alignment of spatial transcriptomic and epigenomic data to understand complementary interactions.

Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

MOTIVATION

Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.

RESULTS

To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.

AVAILABILITY AND IMPLEMENTATION

The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.

Multi-OMICS approaches in cancer biology: New era in cancer therapy

Innovative multi-omics frameworks integrate diverse datasets from the same patients to enhance our understanding of the molecular and clinical aspects of cancers. Advanced omics and multi-view clustering algorithms present unprecedented opportunities for classifying cancers into subtypes, refining survival predictions and treatment outcomes, and unravelling key pathophysiological processes across various molecular layers. However, with the increasing availability of cost-effective high-throughput technologies (HTT) that generate vast amounts of data, analyzing single layers often falls short of establishing causal relations. Integrating multi-omics data spanning genomes, epigenomes, transcriptomes, proteomes, metabolomes, and microbiomes offers unique prospects to comprehend the underlying biology of complex diseases like cancer. This discussion explores algorithmic frameworks designed to uncover cancer subtypes, disease mechanisms, and methods for identifying pivotal genomic alterations. It also underscores the significance of multi-omics in tumor classifications, diagnostics, and prognostications. Despite its unparalleled advantages, the integration of multi-omics data has been slow to find its way into everyday clinics. A major hurdle is the uneven maturity of different omics approaches and the widening gap between the generation of large datasets and the capacity to process this data. Initiatives promoting the standardization of sample processing and analytical pipelines, as well as multidisciplinary training for experts in data analysis and interpretation, are crucial for translating theoretical findings into practical applications.

Omics-based molecular classifications empowering in precision oncology

BACKGROUND

In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine.

CONCLUSION

This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Advances in high throughput cell culture technologies for therapeutic screening and biological discovery applications

Cellular phenotypes and functional responses are modulated by the signals present in their microenvironment, including extracellular matrix (ECM) proteins, tissue mechanical properties, soluble signals and nutrients, and cell-cell interactions. To better recapitulate and analyze these complex signals within the framework of more physiologically relevant culture models, high throughput culture platforms can be transformative. High throughput methodologies enable scientists to extract increasingly robust and broad datasets from individual experiments, screen large numbers of conditions for potential hits, better qualify and predict responses for preclinical applications, and reduce reliance on animal studies. High throughput cell culture systems require uniformity, assay miniaturization, specific target identification, and process simplification. In this review, we detail the various techniques that researchers have used to face these challenges and explore cellular responses in a high throughput manner. We highlight several common approaches including two-dimensional multiwell microplates, microarrays, and microfluidic cell culture systems as well as unencapsulated and encapsulated three-dimensional high throughput cell culture systems, featuring multiwell microplates, micromolds, microwells, microarrays, granular hydrogels, and cell-encapsulated microgels. We also discuss current applications of these high throughput technologies, namely stem cell sourcing, drug discovery and predictive toxicology, and personalized medicine, along with emerging opportunities and future impact areas.

Finding new analgesics: Computational pharmacology faces drug discovery challenges

Despite the worldwide prevalence and huge burden of pain, pain is an undertreated phenomenon. Currently used analgesics have several limitations regarding their efficacy and safety. The discovery of analgesics possessing a novel mechanism of action has faced multiple challenges, including a limited understanding of biological processes underpinning pain and analgesia and poor animal-to-human translation. Computational pharmacology is currently employed to face these challenges. In this review, we discuss the theory, methods, and applications of computational pharmacology in pain research. Computational pharmacology encompasses a wide variety of theoretical concepts and practical methodological approaches, with the overall aim of gaining biological insight through data acquisition and analysis. Data are acquired from patients or animal models with pain or analgesic treatment, at different levels of biological organization (molecular, cellular, physiological, and behavioral). Distinct methodological algorithms can then be used to analyze and integrate data. This helps to facilitate the identification of biological molecules and processes associated with pain phenotype, build quantitative models of pain signaling, and extract translatable features between humans and animals. However, computational pharmacology has several limitations, and its predictions can provide false positive and negative findings. Therefore, computational predictions are required to be validated experimentally before drawing solid conclusions. In this review, we discuss several case study examples of combining and integrating computational tools with experimental pain research tools to meet drug discovery challenges.

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.

Applicability of Spatial Technology in Cancer Research

This review explores spatial mapping technologies in cancer research, highlighting their crucial role in understanding the complexities of the tumor microenvironment (TME). The TME, which is an intricate ecosystem of diverse cell types, has a significant impact on tumor dynamics and treatment outcomes. This review closely examines cutting-edge spatial mapping technologies, categorizing them into capture-, imaging-, and antibody-based approaches. Each technology was scrutinized for its advantages and disadvantages, factoring in aspects such as spatial profiling area, multiplexing capabilities, and resolution. Additionally, we draw attention to the nuanced choices researchers face, with capture-based methods lending themselves to hypothesis generation, and imaging/antibody-based methods that fit neatly into hypothesis testing. Looking ahead, we anticipate a scenario in which multi-omics data are seamlessly integrated, artificial intelligence enhances data analysis, and spatiotemporal profiling opens up new dimensions.

Mapping cutaneous field carcinogenesis of nonmelanoma skin cancer using mesoscopic imaging of pro-inflammation cues

Nonmelanoma skin cancers remain the most widely diagnosed types of cancers globally. Thus, for optimal patient management, it has become imperative that we focus our efforts on the detection and monitoring of cutaneous field carcinogenesis. The concept of field cancerization (or field carcinogenesis), introduced by Slaughter in 1953 in the context of oral cancer, suggests that invasive cancer may emerge from a molecularly and genetically altered field affecting a substantial area of underlying tissue including the skin. A carcinogenic field alteration, present in precancerous tissue over a relatively large area, is not easily detected by routine visualization. Conventional dermoscopy and microscopy imaging are often limited in assessing the entire carcinogenic landscape. Recent efforts have suggested the use of noninvasive mesoscopic (between microscopic and macroscopic) optical imaging methods that can detect chronic inflammatory features to identify pre-cancerous and cancerous angiogenic changes in tissue microenvironments. This concise review covers major types of mesoscopic optical imaging modalities capable of assessing pro-inflammatory cues by quantifying blood haemoglobin parameters and hemodynamics. Importantly, these imaging modalities demonstrate the ability to detect angiogenesis and inflammation associated with actinically damaged skin. Representative experimental preclinical and human clinical studies using these imaging methods provide biological and clinical relevance to cutaneous field carcinogenesis in altered tissue microenvironments in the apparently normal epidermis and dermis. Overall, mesoscopic optical imaging modalities assessing chronic inflammatory hyperemia can enhance the understanding of cutaneous field carcinogenesis, offer a window of intervention and monitoring for actinic keratoses and nonmelanoma skin cancers and maximise currently available treatment options.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Molecular, Metabolic, and Subcellular Mapping of the Tumor Immune Microenvironment via 3D Targeted and Non-Targeted Multiplex Multi-Omics Analyses

Most platforms used for the molecular reconstruction of the tumor-immune microenvironment (TIME) of a solid tumor fail to explore the spatial context of the three-dimensional (3D) space of the tumor at a single-cell resolution, and thus lack information about cell-cell or cell-extracellular matrix (ECM) interactions. To address this issue, a pipeline which integrated multiplex spatially resolved multi-omics platforms was developed to identify crosstalk signaling networks among various cell types and the ECM in the 3D TIME of two FFPE (formalin-fixed paraffin embedded) gynecologic tumor samples. These platforms include non-targeted mass spectrometry imaging (glycans, metabolites, and peptides) and Stereo-seq (spatial transcriptomics) and targeted seqIF (IHC proteomics). The spatially resolved imaging data in a two- and three-dimensional space demonstrated various cellular neighborhoods in both samples. The collection of spatially resolved analytes in a voxel (3D pixel) across serial sections of the tissue was also demonstrated. Data collected from this analytical pipeline were used to construct spatial 3D maps with single-cell resolution, which revealed cell identity, activation, and energized status. These maps will provide not only insights into the molecular basis of spatial cell heterogeneity in the TIME, but also novel predictive biomarkers and therapeutic targets, which can improve patient survival rates.

Multiplex Single-Cell Bioprinting for Engineering of Heterogeneous Tissue Constructs with Subcellular Spatial Resolution

Tissue development, function, and disease are largely driven by the spatial organization of individual cells and their cell-cell interactions. Precision engineered tissues with single-cell spatial resolution, therefore, have tremendous potential for next generation disease models, drug discovery, and regenerative therapeutics. Despite significant advancements in biofabrication approaches to improve feature resolution, strategies to fabricate tissues with the exact same organization of individual cells in their native cellular microenvironment have remained virtually non-existent to date. Here we report a method to spatially pattern single cells with up to eight cell phenotypes and subcellular spatial precision. As proof-of-concept we first demonstrate the ability to systematically assess the influence of cellular microenvironments on cell behavior by controllably altering the spatial arrangement of cell types in bioprinted precision cell-cell interaction arrays. We then demonstrate, for the first time, the ability to produce high-fidelity replicas of a patient's annotated cancer biopsy with subcellular resolution. The ability to replicate native cellular microenvironments marks a significant advancement for precision biofabricated in-vitro models, where heterogenous tissues can be engineered with single-cell spatial precision to advance our understanding of complex biological systems in a controlled and systematic manner.

Breaks for Precision Medicine in Cancer: Development and Prospects of Spatiotemporal Transcriptomics

With the development of the social economy and the deepening understanding of cancer, cancer has become a significant cause of death, threatening human health. Although researchers have made rapid progress in cancer treatment strategies in recent years, the overall survival of cancer patients is still not optimistic. Therefore, it is essential to reveal the spatial pattern of gene expression, spatial heterogeneity of cell populations, microenvironment interactions, and other aspects of cancer. Spatiotemporal transcriptomics can help analyze the mechanism of cancer occurrence and development, greatly help precise cancer treatment, and improve clinical prognosis. Here, we review the integration strategies of single-cell RNA sequencing and spatial transcriptomics data, summarize the recent advances in spatiotemporal transcriptomics in cancer studies, and discuss the combined application of spatial multiomics, which provides new directions and strategies for the precise treatment and clinical prognosis of cancer.

20 years of polybrominated diphenyl ethers on toxicity assessments

Polybrominated diphenyl ethers (PBDEs) serve as brominated flame retardants which continue to receive considerable attention because of their persistence, bioaccumulation, and potential toxicity. Although PBDEs have been restricted and phased out, large amounts of commercial products containing PBDEs are still in use and discarded annually. Consequently, PBDEs added to products can be released into our surrounding environments, particularly in aquatic systems, thus posing great risks to human health. Many studies and reviews have described the possible toxic effects of PBDEs, while few studies have comprehensively summarized and analyzed the global trends of their toxicity assessment. Therefore, this study utilizes bibliometrics to evaluate the worldwide scientific output of PBDE toxicity and analyze the hotspots and future trends of this field. Firstly, the basic information including the most contributing countries/institutions, journals, co-citations, influential authors, and keywords involved in PBDE toxicity assessment will be visualized. Subsequently, the potential toxicity of PBDE exposure to diverse systems, such as endocrine, reproductive, neural, and gastrointestinal tract systems, and related toxic mechanisms will be discussed. Finally, we conclude this review by outlining the current challenges and future perspectives in environmentally relevant PBDE exposure, potential carriers for PBDE transport, the fate of PBDEs in the environment and human bodies, advanced stem cell-derived organoid models for toxicity assessment, and promising omics technologies for obtaining toxic mechanisms. This review is expected to offer systematical insights into PBDE toxicity assessments and facilitate the development of PBDE-based research.

Decoding leukemia at the single-cell level: clonal architecture, classification, microenvironment, and drug resistance

Leukemias are refractory hematological malignancies, characterized by marked intrinsic heterogeneity which poses significant obstacles to effective treatment. However, traditional bulk sequencing techniques have not been able to effectively unravel the heterogeneity among individual tumor cells. With the emergence of single-cell sequencing technology, it has bestowed upon us an unprecedented resolution to comprehend the mechanisms underlying leukemogenesis and drug resistance across various levels, including the genome, epigenome, transcriptome and proteome. Here, we provide an overview of the currently prevalent single-cell sequencing technologies and a detailed summary of single-cell studies conducted on leukemia, with a specific focus on four key aspects: (1) leukemia's clonal architecture, (2) frameworks to determine leukemia subtypes, (3) tumor microenvironment (TME) and (4) the drug-resistant mechanisms of leukemia. This review provides a comprehensive summary of current single-cell studies on leukemia and highlights the markers and mechanisms that show promising clinical implications for the diagnosis and treatment of leukemia.

Retention time prediction and MRM validation reinforce the biomarker identification of LC-MS based phospholipidomics

Dysfunctional lipid metabolism plays a crucial role in the development and progression of various diseases. Accurate measurement of lipidomes can help uncover the complex interactions between genes, proteins, and lipids in health and diseases. The prediction of retention time (RT) has become increasingly important in both targeted and untargeted metabolomics. However, the potential impact of RT prediction on targeted LC-MS based lipidomics is still not fully understood. Herein, we propose a simplified workflow for predicting RT in phospholipidomics. Our approach involves utilizing the fatty acyl chain length or carbon-carbon double bond (DB) number in combination with multiple reaction monitoring (MRM) validation. We found that our model's predictive capacity for RT was comparable to that of a publicly accessible program (QSRR Automator). Additionally, MRM validation helped in further mitigating the interference in signal recognition. Using this developed workflow, we conducted phospholipidomics of sorafenib resistant hepatocellular carcinoma (HCC) cell lines, namely MHCC97H and Hep3B. Our findings revealed an abundance of monounsaturated fatty acyl (MUFA) or polyunsaturated fatty acyl (PUFA) phospholipids in these cell lines after developing drug resistance. In both cell lines, a total of 29 lipids were found to be co-upregulated and 5 lipids were co-downregulated. Further validation was conducted on seven of the upregulated lipids using an independent dataset, which demonstrates the potential for translation of the established workflow or the lipid biomarkers.

StereoSiTE: a framework to spatially and quantitatively profile the cellular neighborhood organized iTME

BACKGROUND

Spatial transcriptome (ST) technologies are emerging as powerful tools for studying tumor biology. However, existing tools for analyzing ST data are limited, as they mainly rely on algorithms developed for single-cell RNA sequencing data and do not fully utilize the spatial information. While some algorithms have been developed for ST data, they are often designed for specific tasks, lacking a comprehensive analytical framework for leveraging spatial information.

RESULTS

In this study, we present StereoSiTE, an analytical framework that combines open-source bioinformatics tools with custom algorithms to accurately infer the functional spatial cell interaction intensity (SCII) within the cellular neighborhood (CN) of interest. We applied StereoSiTE to decode ST datasets from xenograft models and found that the CN efficiently distinguished different cellular contexts, while the SCII analysis provided more precise insights into intercellular interactions by incorporating spatial information. By applying StereoSiTE to multiple samples, we successfully identified a CN region dominated by neutrophils, suggesting their potential role in remodeling the immune tumor microenvironment (iTME) after treatment. Moreover, the SCII analysis within the CN region revealed neutrophil-mediated communication, supported by pathway enrichment, transcription factor regulon activities, and protein-protein interactions.

CONCLUSIONS

StereoSiTE represents a promising framework for unraveling the mechanisms underlying treatment response within the iTME by leveraging CN-based tissue domain identification and SCII-inferred spatial intercellular interactions. The software is designed to be scalable, modular, and user-friendly, making it accessible to a wide range of researchers.

Computational Approaches: A New Frontier in Cancer Research

Cancer is a broad category of disease that can start in virtually any organ or tissue of the body when aberrant cells assault surrounding organs and proliferate uncontrollably. According to the most recent statistics, cancer will be the cause of 10 million deaths worldwide in 2020, accounting for one death out of every six worldwide. The typical approach used in anti-cancer research is highly time-consuming and expensive, and the outcomes are not particularly encouraging. Computational techniques have been employed in anti-cancer research to advance our understanding. Recent years have seen a significant and exceptional impact on anticancer research due to the rapid development of computational tools for novel drug discovery, drug design, genetic studies, genome characterization, cancer imaging and detection, radiotherapy, cancer metabolomics, and novel therapeutic approaches. In this paper, we examined the various subfields of contemporary computational techniques, including molecular docking, artificial intelligence, bioinformatics, virtual screening, and QSAR, and their applications in the study of cancer.

The current status and future of PD-L1 in liver cancer

The application of immunotherapy in tumor, especially immune checkpoint inhibitors (ICIs), has played an important role in the treatment of advanced unresectable liver cancer. However, the efficacy of ICIs varies greatly among different patients, which has aroused people's attention to the regulatory mechanism of programmed death ligand-1 (PD-L1) in the immune escape of liver cancer. PD-L1 is regulated by multiple levels and signaling pathways in hepatocellular carcinoma (HCC), including gene variation, epigenetic inheritance, transcriptional regulation, post-transcriptional regulation, and post-translational modification. More studies have also found that the high expression of PD-L1 may be the main factor affecting the immunotherapy of liver cancer. However, what is the difference of PD-L1 expressed by different types of cells in the microenvironment of HCC, and which type of cells expressed PD-L1 determines the effect of tumor immunotherapy remains unclear. Therefore, clarifying the regulatory mechanism of PD-L1 in liver cancer can provide more basis for liver cancer immunotherapy and combined immune treatment strategy. In addition to its well-known role in immune regulation, PD-L1 also plays a role in regulating cancer cell proliferation and promoting drug resistance of tumor cells, which will be reviewed in this paper. In addition, we also summarized the natural products and drugs that regulated the expression of PD-L1 in HCC.

State-of-the-art mass spectrometry imaging applications in biomedical research

Mass spectrometry imaging has advanced from a niche technique to a widely applied spatial biology tool operating at the forefront of numerous fields, most notably making a significant impact in biomedical pharmacological research. The growth of the field has gone hand in hand with an increase in publications and usage of the technique by new laboratories, and consequently this has led to a shift from general MSI reviews to topic-specific reviews. Given this development, we see the need to recapitulate the strengths of MSI by providing a more holistic overview of state-of-the-art MSI studies to provide the new generation of researchers with an up-to-date reference framework. Here we review scientific advances for the six largest biomedical fields of MSI application (oncology, pharmacology, neurology, cardiovascular diseases, endocrinology, and rheumatology). These publications thereby give examples for at least one of the following categories: they provide novel mechanistic insights, use an exceptionally large cohort size, establish a workflow that has the potential to become a high-impact methodology, or are highly cited in their field. We finally have a look into new emerging fields and trends in MSI (immunology, microbiology, infectious diseases, and aging), as applied MSI is continuously broadening as a result of technological breakthroughs.

Spatial -omics technologies: the new enterprise in 3D breast cancer models

The fields of tissue bioengineering, -omics, and spatial biology are advancing rapidly, each offering the opportunity for a paradigm shift in breast cancer research. However, to date, collaboration between these fields has not reached its full potential. In this review, we describe the most recently generated 3D breast cancer models regarding the biomaterials and technological platforms employed. Additionally, their biological evaluation is reported, highlighting their advantages and limitations. Specifically, we focus on the most up-to-date -omics and spatial biology techniques, which can generate a deeper understanding of the biological relevance of bioengineered 3D breast cancer in vitro models, thus paving the way towards truly clinically relevant microphysiological systems, improved drug development success rates, and personalised medicine approaches.

Clinical Proteomics for Solid Organ Tissues

The evaluation of biopsied solid organ tissue has long relied on visual examination using a microscope. Immunohistochemistry is critical in this process, labeling and detecting cell lineage markers and therapeutic targets. However, while the practice of immunohistochemistry has reshaped diagnostic pathology and facilitated improvements in cancer treatment, it has also been subject to pervasive challenges with respect to standardization and reproducibility. Efforts are ongoing to improve immunohistochemistry, but for some applications, the benefit of such initiatives could be impeded by its reliance on monospecific antibody-protein reagents and limited multiplexing capacity. This perspective surveys the relevant challenges facing traditional immunohistochemistry and describes how mass spectrometry, particularly liquid chromatography-tandem mass spectrometry, could help alleviate problems. In particular, targeted mass spectrometry assays could facilitate measurements of individual proteins or analyte panels, using internal standards for more robust quantification and improved interlaboratory reproducibility. Meanwhile, untargeted mass spectrometry, showcased to date clinically in the form of amyloid typing, is inherently multiplexed, facilitating the detection and crude quantification of 100s to 1000s of proteins in a single analysis. Further, data-independent acquisition has yet to be applied in clinical practice, but offers particular strengths that could appeal to clinical users. Finally, we discuss the guidance that is needed to facilitate broader utilization in clinical environments and achieve standardization.

Location, location, location: mapping the lymphoma tumor microenvironment using spatial transcriptomics

Lymphomas are a heterogenous group of lymphoid neoplasms with a wide variety of clinical presentations. Response to treatment and prognosis differs both between and within lymphoma subtypes. Improved molecular and genetic profiling has increased our understanding of the factors which drive these clinical dynamics. Immune and non-immune cells within the lymphoma tumor microenvironment (TME) can both play a key role in antitumor immune responses and conversely also support lymphoma growth and survival. A deeper understanding of the lymphoma TME would identify key lymphoma and immune cell interactions which could be disrupted for therapeutic benefit. Single cell RNA sequencing studies have provided a more comprehensive description of the TME, however these studies are limited in that they lack spatial context. Spatial transcriptomics provides a comprehensive analysis of gene expression within tissue and is an attractive technique in lymphoma to both disentangle the complex interactions between lymphoma and TME cells and improve understanding of how lymphoma cells evade the host immune response. This article summarizes current spatial transcriptomic technologies and their use in lymphoma research to date. The resulting data has already enriched our knowledge of the mechanisms and clinical impact of an immunosuppressive TME in lymphoma and the accrual of further studies will provide a fundamental step in the march towards personalized medicine.

GammaGateR: semi-automated marker gating for single-cell multiplexed imaging

Motivation

Multiplexed immunofluorescence (mIF) is an emerging assay for multichannel protein imaging that can decipher cell-level spatial features in tissues. However, existing automated cell phenotyping methods, such as clustering, face challenges in achieving consistency across experiments and often require subjective evaluation. As a result, mIF analyses often revert to marker gating based on manual thresholding of raw imaging data.

Results

To address the need for an evaluable semi-automated algorithm, we developed GammaGateR, an R package for interactive marker gating designed specifically for segmented cell-level data from mIF images. Based on a novel closed-form gamma mixture model, GammaGateR provides estimates of marker-positive cell proportions and soft clustering of marker-positive cells. The model incorporates user-specified constraints that provide a consistent but slide-specific model fit. We compared GammaGateR against the newest unsupervised approach for annotating mIF data, employing two colon datasets and one ovarian cancer dataset for the evaluation. We showed that GammaGateR produces highly similar results to a silver standard established through manual annotation. Furthermore, we demonstrated its effectiveness in identifying biological signals, achieved by mapping known spatial interactions between CD68 and MUC5AC cells in the colon and by accurately predicting survival in ovarian cancer patients using the phenotype probabilities as input for machine learning methods. GammaGateR is a highly efficient tool that can improve the replicability of marker gating results, while reducing the time of manual segmentation.

Availability and Implementation

The R package is available at https://github.com/JiangmeiRubyXiong/GammaGateR.