Spatial transcriptomics reveals distinct and conserved tumor core and edge architectures that predict survival and targeted therapy response

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

The extracellular matrix in solid tumor immunotherapy

The extracellular matrix (ECM) of solid tumors impacts the antitumor activities of CD8+ T and natural killer (NK) cells in a variety of ways. Cell motility is restricted by the tumor ECM which creates physical barriers. The tumor ECM directly alter the phenotypes and functions of cytotoxic lymphocytes, and indirectly influences immunological synapse-mediated interactions between cytotoxic lymphocytes and cancer cells. Therefore, strategies to improve solid tumor immunotherapy should be established by considering complex ternary interactions between cytotoxic lymphocytes, cancer cells, and the tumor ECM. Novel bioengineering tools approximating key characteristics of the tumor ECM, such as in vitro reconstituted 3D ECMs and microfluidics are valuable from a fundamental study viewpoint and from a translational perspective, aiming to enable systematic screening approaches.

The spatial distribution of intermediate fibroblasts and myeloid-derived cells dictate lymph node metastasis dynamics in oral cancer

BACKGROUND

Oral cancer poses a significant health challenge due to limited treatment protocols and therapeutic targets. We aimed to investigate the invasive margins of gingivo-buccal oral squamous cell carcinoma (GB-OSCC) tumors in terms of the localization of genes and cell types within the margins at various distances that could lead to nodal metastasis.

METHODS

We collected tumor tissues from 23 resected GB-OSCC samples for gene expression profiling using digital spatial transcriptomics. We monitored differential gene expression at varying distances between the tumor and its microenvironvent (TME), and performed a deconvulation study and immunohistochemistry to identify the cells and genes regulating the TME.

RESULTS

We found that the tumor-stromal interface (a distance up to 200 µm between tumor and immune cells) is the most active region for disease progression in GB-OSCC. The most differentially expressed apex genes, such as FN1 and COL5A1, were located at the stromal ends of the margins, and together with enrichment of the extracellular matrix (ECM) and an immune-suppressed microenvironment, were associated with lymph node metastasis. Intermediate fibroblasts, myocytes, and neutrophils were enriched at the tumor ends, while cancer-associated fibroblasts (CAFs) were enriched at the stromal ends. The intermediate fibroblasts transformed into CAFs and relocated to the adjacent stromal ends where they participated in FN1-mediated ECM modulation.

CONCLUSION

We have generated a functional organization of the tumor-stromal interface in GB-OSCC and identified spatially located genes that contribute to nodal metastasis and disease progression. Our dataset might now be mined to discover suitable molecular targets in oral cancer.

Single-cell transcriptome profiles the heterogeneity of tumor cells and microenvironments for different pathological endometrial cancer and identifies specific sensitive drugs

Endometrial cancer (EC) is a highly heterogeneous malignancy characterized by varied pathology and prognoses, and the heterogeneity of its cancer cells and the tumor microenvironment (TME) remains poorly understood. We conducted single-cell RNA sequencing (scRNA-seq) on 18 EC samples, encompassing various pathological types to delineate their specific unique transcriptional landscapes. Cancer cells from diverse pathological sources displayed distinct hallmarks labeled as immune-modulating, proliferation-modulating, and metabolism-modulating cancer cells in uterine clear cell carcinomas (UCCC), well-differentiated endometrioid endometrial carcinomas (EEC-I), and uterine serous carcinomas (USC), respectively. Cancer cells from the UCCC exhibited the greatest heterogeneity. We also identified potential effective drugs and confirmed their effectiveness using patient-derived EC organoids for each pathological group. Regarding the TME, we observed that prognostically favorable CD8+ Tcyto and NK cells were prominent in normal endometrium, whereas CD4+ Treg, CD4+ Tex, and CD8+ Tex cells dominated the tumors. CXCL3+ macrophages associated with M2 signature and angiogenesis were exclusively found in tumors. Prognostically relevant epithelium-specific cancer-associated fibroblasts (eCAFs) and SOD2+ inflammatory CAFs (iCAFs) predominated in EEC-I and UCCC groups, respectively. We also validated the oncogenic effects of SOD2+ iCAFs in vitro. Our comprehensive study has yielded deeper insights into the pathogenesis of EC, potentially facilitating personalized treatments for its varied pathological types.

Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities

Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future.

Open-ST: High-resolution spatial transcriptomics in 3D

Spatial transcriptomics (ST) methods unlock molecular mechanisms underlying tissue development, homeostasis, or disease. However, there is a need for easy-to-use, high-resolution, cost-efficient, and 3D-scalable methods. Here, we report Open-ST, a sequencing-based, open-source experimental and computational resource to address these challenges and to study the molecular organization of tissues in 2D and 3D. In mouse brain, Open-ST captured transcripts at subcellular resolution and reconstructed cell types. In primary head-and-neck tumors and patient-matched healthy/metastatic lymph nodes, Open-ST captured the diversity of immune, stromal, and tumor populations in space, validated by imaging-based ST. Distinct cell states were organized around cell-cell communication hotspots in the tumor but not the metastasis. Strikingly, the 3D reconstruction and multimodal analysis of the metastatic lymph node revealed spatially contiguous structures not visible in 2D and potential biomarkers precisely at the 3D tumor/lymph node boundary. All protocols and software are available at https://rajewsky-lab.github.io/openst.

EBF1-COX4I2 signaling axis promotes a myofibroblast-like phenotype in cancer-associated fibroblasts (CAFs) and is associated with an immunosuppressive microenvironment

Immunotherapy has limited response rates in colorectal cancer (CRC) due to an immunosuppressive tumor microenvironment (TME). Combining transcriptome sequencing, clinical specimens, and functional experiments, we identified a unique group of CAF subpopulations (COX4I2 + ) with inhibited mitochondrial respiration and enhanced glycolysis. Through bioinformatics predictions and luciferase reporter assays, we determined that EBF1 can upstreamly regulate COX4I2 transcription. COX4I2 + CAFs functionally and phenotypically resemble myofibroblasts, are important for the formation of the fibrotic TME, and are capable of activating the M2 phenotype of macrophages. In vitro experiments demonstrated that COX4I2 + CAFs promote immunosuppressive TME by blocking CD8 + T cell infiltration and inducing CD8 + T cell dysfunction. Using multiple independent cohorts, we also found a strong correlation between the immunotherapy response rate of CRC patients and COX4I2 expression in their tumors. Our results identify a CAF subpopulation characterized by activation of the EBF1-COX4I2 axis, and this group of CAFs can be targeted to improve cancer immunotherapy outcomes.

Assessment of Concentration KRT6 Proteins in Tumor and Matching Surgical Margin from Patients with Head and Neck Squamous Cell Carcinoma

Head and neck squamous cell carcinomas (HNSCCs) are one of the most frequently detected cancers in the world; not all mechanisms related to the expression of keratin in this type of cancer are known. The aim of this study was to evaluate type II cytokeratins (KRT): KRT6A, KRT6B, and KRT6C protein concentrations in 54 tumor and margin samples of head and neck squamous cell carcinoma (HNSCC). Moreover, we examined a possible association between protein concentration and the clinical and demographic variables. Protein concentrations were measured using enzyme-linked immunosorbent assay (ELISA). Significantly higher KRT6A protein concentration was found in HNSCC samples compared to surgical margins. An inverse relationship was observed for KRT6B and KRT6C proteins. We showed an association between the KRT6C protein level and clinical parameters T and N in tumor and margin samples. When analyzing the effect of smoking and drinking on KRT6A, KRT6B, and KRT6C levels, we demonstrated a statistically significant difference between regular or occasional tobacco and alcohol habits and patients who do not have any tobacco and alcohol habits in tumor and margin samples. Moreover, we found an association between KRT6B and KRT6C concentration and proliferative index Ki-67 and HPV status in tumor samples. Our results showed that concentrations of KRT6s were different in the tumor and the margin samples and varied in relation to clinical and demographic parameters. We add information to the current knowledge about the role of KRT6s isoforms in HNSCC. We speculate that variations in the studied isoforms of the KRT6 protein could be due to the presence and development of the tumor and its microenvironment. It is important to note that the analyses were performed in tumor and surgical margins and can provide more accurate information on the function in normal and cancer cells and regulation in response to various factors.

Development and validation of a novel disulfidptosis-related lncRNAs signature in patients with HPV-negative oral squamous cell carcinoma

Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan-Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

STimage-1K4M: A histopathology image-gene expression dataset for spatial transcriptomics

Recent advances in multi-modal algorithms have driven and been driven by the increasing availability of large image-text datasets, leading to significant strides in various fields, including computational pathology. However, in most existing medical image-text datasets, the text typically provides high-level summaries that may not sufficiently describe sub-tile regions within a large pathology image. For example, an image might cover an extensive tissue area containing cancerous and healthy regions, but the accompanying text might only specify that this image is a cancer slide, lacking the nuanced details needed for in-depth analysis. In this study, we introduce STimage-1K4M, a novel dataset designed to bridge this gap by providing genomic features for sub-tile images. STimage-1K4M contains 1,149 images derived from spatial transcriptomics data, which captures gene expression information at the level of individual spatial spots within a pathology image. Specifically, each image in the dataset is broken down into smaller sub-image tiles, with each tile paired with 15,000 - 30,000 dimensional gene expressions. With 4,293,195 pairs of sub-tile images and gene expressions, STimage-1K4M offers unprecedented granularity, paving the way for a wide range of advanced research in multi-modal data analysis an innovative applications in computational pathology, and beyond.

Spatial transcriptomics in cancer research and potential clinical impact: a narrative review

Spatial transcriptomics (ST) provides novel insights into the tumor microenvironment (TME). ST allows the quantification and illustration of gene expression profiles in the spatial context of tissues, including both the cancer cells and the microenvironment in which they are found. In cancer research, ST has already provided novel insights into cancer metastasis, prognosis, and immunotherapy responsiveness. The clinical precision oncology application of next-generation sequencing (NGS) and RNA profiling of tumors relies on bulk methods that lack spatial context. The ability to preserve spatial information is now possible, as it allows us to capture tumor heterogeneity and multifocality. In this narrative review, we summarize precision oncology, discuss tumor sequencing in the clinic, and review the available ST research methods, including seqFISH, MERFISH (Vizgen), CosMx SMI (NanoString), Xenium (10x), Visium (10x), Stereo-seq (STOmics), and GeoMx DSP (NanoString). We then review the current ST literature with a focus on solid tumors organized by tumor type. Finally, we conclude by addressing an important question: how will spatial transcriptomics ultimately help patients with cancer?

Spatial Transcriptomics: A Powerful Tool in Disease Understanding and Drug Discovery

Recent advancements in modern science have provided robust tools for drug discovery. The rapid development of transcriptome sequencing technologies has given rise to single-cell transcriptomics and single-nucleus transcriptomics, increasing the accuracy of sequencing and accelerating the drug discovery process. With the evolution of single-cell transcriptomics, spatial transcriptomics (ST) technology has emerged as a derivative approach. Spatial transcriptomics has emerged as a hot topic in the field of omics research in recent years; it not only provides information on gene expression levels but also offers spatial information on gene expression. This technology has shown tremendous potential in research on disease understanding and drug discovery. In this article, we introduce the analytical strategies of spatial transcriptomics and review its applications in novel target discovery and drug mechanism unravelling. Moreover, we discuss the current challenges and issues in this research field that need to be addressed. In conclusion, spatial transcriptomics offers a new perspective for drug discovery.

Insights and Opportunity Costs in Applying Spatial Biology to Study the Tumor Microenvironment

SUMMARY

The recent development of high-dimensional spatial omics tools has revealed the functional importance of the tumor microenvironment in driving tumor progression. Here, we discuss practical factors to consider when designing a spatial biology cohort and offer perspectives on the future of spatial biology research.

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.

Review of Molecular Technologies for Investigating Canine Cancer

Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.

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.

The Reign of Follistatin in Tumors and Their Microenvironment: Implications for Drug Resistance

Follistatin (FST) is a potent neutralizer of the transforming growth factor-β superfamily and is associated with normal cellular programs and various hallmarks of cancer, such as proliferation, migration, angiogenesis, and immune evasion. The aberrant expression of FST by solid tumors is a well-documented observation, yet how FST influences tumor progression and therapy response remains unclear. The recent surge in omics data has revealed new insights into the molecular foundation underpinning tumor heterogeneity and its microenvironment, offering novel precision medicine-based opportunities to combat cancer. In this review, we discuss these recent FST-centric studies, thereby offering an updated perspective on the protean role of FST isoforms in shaping the complex cellular ecosystem of tumors and in mediating drug resistance.

SRT-Server: powering the analysis of spatial transcriptomic data

BACKGROUND

Spatial resolved transcriptomics (SRT) encompasses a rapidly developing set of technologies that enable the measurement of gene expression in tissue while retaining spatial localization information. SRT technologies and the enabled SRT studies have provided unprecedent insights into the structural and functional underpinnings of complex tissues. As SRT technologies have advanced and an increasing number of SRT studies have emerged, numerous sophisticated statistical and computational methods have been developed to facilitate the analysis and interpretation of SRT data. However, despite the growing popularity of SRT studies and the widespread availability of SRT analysis methods, analysis of large-scale and complex SRT datasets remains challenging and not easily accessible to researchers with limited statistical and computational backgrounds.

RESULTS

Here, we present SRT-Server, the first webserver designed to carry out comprehensive SRT analyses for a wide variety of SRT technologies while requiring minimal prior computational knowledge. Implemented with cutting-edge web development technologies, SRT-Server is user-friendly and features multiple analytic modules that can perform a range of SRT analyses. With a flowchart-style interface, these different analytic modules on the SRT-Server can be dragged into the main panel and connected to each other to create custom analytic pipelines. SRT-Server then automatically executes the desired analyses, generates corresponding figures, and outputs results-all without requiring prior programming knowledge. We demonstrate the advantages of SRT-Server through three case studies utilizing SRT data collected from two common platforms, highlighting its versatility and values to researchers with varying analytic expertise.

CONCLUSIONS

Overall, SRT-Server presents a user-friendly, efficient, effective, secure, and expandable solution for SRT data analysis, opening new doors for researchers in the field. SRT-Server is freely available at https://spatialtranscriptomicsanalysis.com/ .

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

Deciphering head and neck cancer microenvironment: Single-cell and spatial transcriptomics reveals human papillomavirus-associated differences

Human papillomavirus (HPV) is a major causative factor of head and neck squamous cell carcinoma (HNSCC), and the incidence of HPV- associated HNSCC is increasing. The role of tumor microenvironment in viral infection and metastasis needs to be explored further. We studied the molecular characteristics of primary tumors (PTs) and lymph node metastatic tumors (LNMTs) by stratifying them based on their HPV status. Eight samples for single-cell RNA profiling and six samples for spatial transcriptomics (ST), composed of matched primary tumors (PT) and lymph node metastases (LNMT), were collected from both HPV- negative (HPV- ) and HPV-positive (HPV+ ) patients. Using the 10x Genomics Visium platform, integrative analyses with single-cell RNA sequencing were performed. Intracellular and intercellular alterations were analyzed, and the findings were confirmed using experimental validation and publicly available data set. The HPV+ tissues were composed of a substantial amount of lymphoid cells regardless of the presence or absence of metastasis, whereas the HPV- tissue exhibited remarkable changes in the number of macrophages and plasma cells, particularly in the LNMT. From both single-cell RNA and ST data set, we discovered a central gene, pyruvate kinase muscle isoform 1/2 (PKM2), which is closely associated with the stemness of cancer stem cell-like populations in LNMT of HPV- tissue. The consistent expression was observed in HPV- HNSCC cell line and the knockdown of PKM2 weakened spheroid formation ability. Furthermore, we found an ectopic lymphoid structure morphology and clinical effects of the structure in ST slide of the HPV+ patients and verified their presence in tumor tissue using immunohistochemistry. Finally, the ephrin-A (EPHA2) pathway was detected as important signals in angiogenesis for HPV- patients from single-cell RNA and ST profiles, and knockdown of EPHA2 declined the cell migration. Our study described the distinct cellular composition and molecular alterations in primary and metastatic sites in HNSCC patients based on their HPV status. These results provide insights into HNSCC biology in the context of HPV infection and its potential clinical implications.

DDX5 Functions as a Tumor Suppressor in Tongue Cancer

DEAD-box polypeptide 5 (DDX5), a DEAD-box RNA helicase, is a multifunctional protein that plays important roles in many physiological and pathological processes. Contrary to its documented oncogenic role in a wide array of cancers, we herein demonstrate that DDX5 serves as a tumor suppressor in tongue cancer. The high expression of DDX5 is correlated with better prognosis for clinical tongue cancer patients. DDX5 downregulates the genes associated with tongue cancer progression. The knockdown of DDX5 promotes, while the overexpression of DDX5 inhibits, tongue cancer proliferation, development, and cisplatin resistance. Furthermore, the expression of DDX5 in tongue cancer is associated with immune cell infiltration in the tumor microenvironment. Specifically, the expression of DDX5 is associated with the reduced infiltration of M2 macrophages and increased infiltration of T cell clusters, which may contribute to anticancer effects in the tumor microenvironment. In this study, we establish DDX5 as a valuable prognostic biomarker and an important tumor suppressor in tongue cancer.

Macrophage-driven cardiac inflammation and healing: insights from homeostasis and myocardial infarction

Early and prompt reperfusion therapy has markedly improved the survival rates among patients enduring myocardial infarction (MI). Nonetheless, the resulting adverse remodeling and the subsequent onset of heart failure remain formidable clinical management challenges and represent a primary cause of disability in MI patients worldwide. Macrophages play a crucial role in immune system regulation and wield a profound influence over the inflammatory repair process following MI, thereby dictating the degree of myocardial injury and the subsequent pathological remodeling. Despite numerous previous biological studies that established the classical polarization model for macrophages, classifying them as either M1 pro-inflammatory or M2 pro-reparative macrophages, this simplistic categorization falls short of meeting the precision medicine standards, hindering the translational advancement of clinical research. Recently, advances in single-cell sequencing technology have facilitated a more profound exploration of macrophage heterogeneity and plasticity, opening avenues for the development of targeted interventions to address macrophage-related factors in the aftermath of MI. In this review, we provide a summary of macrophage origins, tissue distribution, classification, and surface markers. Furthermore, we delve into the multifaceted roles of macrophages in maintaining cardiac homeostasis and regulating inflammation during the post-MI period.