AnnoSpat annotates cell types and quantifies cellular arrangements from spatial proteomics.

Cellular composition and anatomical organization influence normal and aberrant organ functions. Emerging spatial single-cell proteomic assays such as Image Mass Cytometry (IMC) and Co-Detection by Indexing (CODEX) have facilitated the study of cellular composition and organization by enabling high-throughput measurement of cells and their localization directly in intact tissues. However, annotation of cell types and quantification of their relative localization in tissues remain challenging. To address these unmet needs for atlas-scale datasets like Human Pancreas Analysis Program (HPAP), we develop AnnoSpat (Annotator and Spatial Pattern Finder) that uses neural network and point process algorithms to automatically identify cell types and quantify cell-cell proximity relationships. Our study of data from IMC and CODEX shows the higher performance of AnnoSpat in rapid and accurate annotation of cell types compared to alternative approaches. Moreover, the application of AnnoSpat to type 1 diabetic, non-diabetic autoantibody-positive, and non-diabetic organ donor cohorts recapitulates known islet pathobiology and shows differential dynamics of pancreatic polypeptide (PP) cell abundance and CD8+ T cells infiltration in islets during type 1 diabetes progression.

Development of 42 marker panel for in-depth study of cancer associated fibroblast niches in breast cancer using imaging mass cytometry.

Imaging Mass Cytometry (IMC) is a novel, and formidable high multiplexing imaging method emerging as a promising tool for in-depth studying of tissue architecture and intercellular communications. Several studies have reported various IMC antibody panels mainly focused on studying the immunological landscape of the tumor microenvironment (TME). With this paper, we wanted to address cancer associated fibroblasts (CAFs), a component of the TME very often underrepresented and not emphasized enough in present IMC studies. Therefore, we focused on the development of a comprehensive IMC panel that can be used for a thorough description of the CAF composition of breast cancer TME and for an in-depth study of different CAF niches in relation to both immune and breast cancer cell communication. We established and validated a 42 marker panel using a variety of control tissues and rigorous quantification methods. The final panel contained 6 CAF-associated markers (aSMA, FAP, PDGFRa, PDGFRb, YAP1, pSMAD2). Breast cancer tissues (4 cases of luminal, 5 cases of triple negative breast cancer) and a modified CELESTA pipeline were used to demonstrate the utility of our IMC panel for detailed profiling of different CAF, immune and cancer cell phenotypes.

Imaging Mass Cytometry for In Situ Immune Profiling.

The complexities and cellular heterogeneity associated with tissues necessitate the concurrent detection of markers beyond the limitations of conventional imaging approaches in order to spatially resolve the relationships between immune cell populations and their environments. This is a necessary complement to single-cell suspension-based methods to inform a better understanding of the events that may underlie pathological conditions. Imaging mass cytometry is a high-dimensional imaging modality that allows for the concurrent detection of up to 40 protein markers of interest across tissues at subcellular resolution. Here, we present an optimized staining protocol for imaging mass cytometry with modifications that integrate RNAscope. This unique addition enables combined protein and single-molecule RNA detection, thereby expanding the utility of imaging mass cytometry to researchers investigating low abundance or noncoding targets. In general, the procedure described is broadly applicable for comprehensive immune profiling of host-pathogen interactions, tumor microenvironments and inflammatory conditions, all within the tissue contexture.

Integrating AI-Powered Digital Pathology and Imaging Mass Cytometry Identifies Key Classifiers of Tumor Cells, Stroma, and Immune Cells in Non-Small Cell Lung Cancer.

Artificial intelligence (AI)-powered approaches are becoming increasingly used as histopathologic tools to extract subvisual features and improve diagnostic workflows. On the other hand, hi-plex approaches are widely adopted to analyze the immune ecosystem in tumor specimens. Here, we aimed at combining AI-aided histopathology and imaging mass cytometry (IMC) to analyze the ecosystem of non-small cell lung cancer (NSCLC). An AI-based approach was used on hematoxylin and eosin (H&E) sections from 158 NSCLC specimens to accurately identify tumor cells, both adenocarcinoma and squamous carcinoma cells, and to generate a classifier of tumor cell spatial clustering. Consecutive tissue sections were stained with metal-labeled antibodies and processed through the IMC workflow, allowing quantitative detection of 24 markers related to tumor cells, tissue architecture, CD45+ myeloid and lymphoid cells, and immune activation. IMC identified 11 macrophage clusters that mainly localized in the stroma, except for S100A8+ cells, which infiltrated tumor nests. T cells were preferentially localized in peritumor areas or in tumor nests, the latter being associated with better prognosis, and they were more abundant in highly clustered tumors. Integrated tumor and immune classifiers were validated as prognostic on whole slides. In conclusion, integration of AI-powered H&E and multiparametric IMC allows investigation of spatial patterns and reveals tissue relevant features with clinical relevance. SIGNIFICANCE: Leveraging artificial intelligence-powered H&E analysis integrated with hi-plex imaging mass cytometry provides insights into the tumor ecosystem and can translate tumor features into classifiers to predict prognosis, genotype, and therapy response.

Multiplexed Imaging Mass Cytometry Analysis in Preclinical Models of Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancers. PDAC is characterized by a complex tumor microenvironment (TME), that plays a pivotal role in disease progression and resistance to therapy. Investigating the spatial distribution and interaction of TME cells with the tumor is the basis for understanding the mechanisms underlying disease progression and represents a current challenge in PDAC research. Imaging mass cytometry (IMC) is the major multiplex imaging technology for the spatial analysis of tumor heterogeneity. However, there is a dearth of reports of multiplexed IMC panels for different preclinical mouse models, including pancreatic cancer. We addressed this gap by utilizing two preclinical models of PDAC: the genetically engineered, bearing KRAS-TP53 mutations in pancreatic cells, and the orthotopic, and developed a 28-marker panel for single-cell IMC analysis to assess the abundance, distribution and phenotypes of cells involved in PDAC progression and their reciprocal functional interactions. Herein, we provide an unprecedented definition of the distribution of TME cells in PDAC and compare the diversity between transplanted and genetic disease models. The results obtained represent an important and customizable tool for unraveling the complexities of PDAC and deciphering the mechanisms behind therapy resistance.

DNA image cytometry of bronchial washing as a diagnostic adjunct to radial endobronchial ultrasound-guided sampling of peripheral lung lesions: A single center prospective study.

OBJECTIVE: The objective of this study is to study the adjunct role of combining DNA aneuploidy analysis with radial endobronchial ultrasound (R-EBUS)-guided sampling for diagnosis of peripheral lung lesions (PPLs). METHOD: A single-center prospective study was conducted in patients undergoing R-EBUS-guided sampling for PPLs. DNA image cytometry (DNA-ICM) was used to analyze DNA aneuploidy in bronchial washing from the bronchial segment of the PPL. Clinical information, R-EBUS data, pathology, DNA-ICM results, and follow-up data were analyzed. Sensitivity, specificity, and predictive values for R-EBUS-guided sampling, DNA-ICM, and the two methods combined were measured. Binary logistic regression was performed to determine influencing factors on diagnostic positivity rate. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cutoff point for DNA-ICM. RESULTS: A total of 101 patients were enrolled. Sixty-four (63.4%) patients had confirmed malignant tumor, of whom 33 were confirmed by R-EBUS-guided sampling (biopsy and/or bronchial brush and wash cytology), and 31 by surgery or percutaneous lung biopsy. Thirty-seven patients were finally considered to have benign lesions, based on clinical information and 1-year follow-up. The sensitivity for malignant disease was 51.6% by R-EBUS, and specificity was 100%. DNA-ICM had a sensitivity of 67.2% and a specificity of 86.5%. When combining the two methods, sensitivity increased to 78.1% and specificity was 86.5%. Lesion size and whether the R-EBUS probe was located in the lesion were significantly associated with positivity rate of the combined methods. The optimal cutoff point for DNA-ICM was 5c for max DNA content, and 1 for aneuploid cell count (sensitivity 67.2%, specificity 86.5%, accuracy 63.4%). CONCLUSION: In malignant PPLs, DNA-ICM combined with R-EBUS-guided sampling can improve diagnostic positivity compared with either method alone.

Development of a high-throughput image cytometric screening method as a research tool for immunophenotypic characterization of patient samples from clinical studies.

Immunophenotyping has been the primary assay for characterization of immune cells from patients undergoing therapeutic treatments in clinical research, which is critical for understanding disease progression and treatment efficacy. Currently, flow cytometry has been the dominant methodology for characterizing surface marker expression for immunological research. Flow cytometry has been proven to be an effective and efficient method for immunophenotyping, however, it requires highly trained users and a large time commitment. Recently, a novel image cytometry system (Cellaca® PLX Image Cytometer, Revvity Health Sciences, Inc., Lawrence, MA) has been developed as a complementary method to flow cytometry for performing rapid and high-throughput immunophenotyping. In this work, we demonstrated an image cytometric screening method to characterize immune cell populations, streamlining the analysis of routine surface marker panels. The T cell, B cell, NK cell, and monocyte populations of 46 primary PBMC samples from subjects enrolled in autoimmune and oncological disease study cohorts were analyzed with two optimized immunophenotyping staining kits: Panel 1 (CD3, CD56, CD14) and Panel 2 (CD3, CD56, CD19). We validated the proposed image cytometry method by comparing the Cellaca® PLX and the AuroraTM flow cytometer (Cytek Biosciences, Fremont, CA). The image cytometry system was employed to generate bright field and fluorescent images, as well as scatter plots for multiple patient PBMC samples. In addition, the image cytometry method can directly determine cell concentrations for downstream assays. The results demonstrated comparable CD3, CD14, CD19, and CD56 cell populations from the primary PBMC samples, which showed an average of 5% differences between flow and image cytometry. The proposed image cytometry method provides a novel research tool to potentially streamline immunophenotyping workflow for characterizing patient samples in clinical studies.

DNA-ICM as an adjuvant method applied on oral cytological specimens.

OBJECTIVE: This study aimed to evaluate cytology diagnosis accuracy using adjuvant methods in clinical routine for oral cancer. STUDY DESIGN: This prospective study was conducted on 98 patients with clinically potentially malignant or malignant oral cavity lesions. One oral lesion smear was taken from each patient using a cytobrush before biopsy and stored at PreservCyt Thinprep. Samples were cytologically analyzed, and DNA ploidy measurement was performed on the same slide. The diagnostic methods' accuracy was then calculated. RESULTS: In clinical inspection, 61 patients had suspicious lesions for malignancy, whereas 37 had potentially malignant disorders. Cytology associated with DNA image cytometry presented a sensitivity of 81.2% and specificity of 90.9%. When analyzing lesions located in high-risk sites to oral malignancies individually, cytology associated with DNA image cytometry presented a sensitivity of 88.2%, specificity of 100.0%, accuracy of 90.0%, and Kappa value of 0.77 (CI 95%: 0.48-1.00). CONCLUSIONS: Association between cytology and DNA image cytometry is an objective and non-invasive diagnostic method that demonstrated high sensitivity and specificity in diagnosing malignant epithelial squamous cell transformation in the oral cavity.

Dual-modality imaging of immunofluorescence and imaging mass cytometry for whole-slide imaging and accurate segmentation.

Imaging mass cytometry (IMC) is a powerful technique capable of detecting over 30 markers on a single slide. It has been increasingly used for single-cell-based spatial phenotyping in a wide range of samples. However, it only acquires a rectangle field of view (FOV) with a relatively small size and low image resolution, which hinders downstream analysis. Here, we reported a highly practical dual-modality imaging method that combines high-resolution immunofluorescence (IF) and high-dimensional IMC on the same tissue slide. Our computational pipeline uses the whole-slide image (WSI) of IF as a spatial reference and integrates small-FOV IMC into a WSI of IMC. The high-resolution IF images enable accurate single-cell segmentation to extract robust high-dimensional IMC features for downstream analysis. We applied this method in esophageal adenocarcinoma of different stages, identified the single-cell pathology landscape via reconstruction of WSI IMC images, and demonstrated the advantage of the dual-modality imaging strategy.

Characterization of papillary and clear cell renal cell carcinoma through imaging mass cytometry reveals distinct immunologic profiles.

Objective: To characterize and further compare the immune cell populations of the tumor microenvironment (TME) in both clear cell and papillary renal cell carcinoma (RCC) using heavy metal-labeled antibodies in a multiplexed imaging approach (imaging mass cytometry). Materials and methods: Formalin-fixed paraffin-embedded (FFPE) baseline tumor tissues from metastatic patients with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were retrospectively requisitioned from an institutional biorepository. Pretreated FFPE samples from 33 RCC patients (10 ccRCC, 23 pRCC) were accessioned and stained for imaging mass cytometry (IMC) analysis. Clinical characteristics were curated from an institutional RCC database. FFPE samples were prepared and stained with heavy metal-conjugated antibodies for IMC. An 11-marker panel of tumor stromal and immune markers was used to assess and quantify cellular relationships in TME compartments. To validate our time-of-flight (CyTOF) analysis, we cross-validated findings with The Cancer Genome Atlas Program (TCGA) analysis and utilized the CIBERSORTx tool to examine the abundance of main immune cell types in pRCC and ccRCC patients. Results: Patients with ccRCC had a longer median overall survival than did those with pRCC (67.7 vs 26.8 mo, respectively). Significant differences were identified in the proportion of CD4+ T cells between disease subtypes (ccRCC 14.1%, pRCC 7.0%, p<0.01). Further, the pRCC cohort had significantly more PanCK+ tumor cells than did the ccRCC cohort (24.3% vs 9.5%, respectively, p<0.01). There were no significant differences in macrophage composition (CD68+) between cohorts. Our results demonstrated a significant correlation between the CyTOF and TCGA analyses, specifically validating that ccRCC patients exhibit higher levels of CD4+ T cells (ccRCC 17.60%, pRCC 15.7%, p<0.01) and CD8+ T cells (ccRCC 17.83%, pRCC 11.15%, p<0.01). The limitation of our CyTOF analysis was the large proportion of cells that were deemed non-characterizable. Conclusions: Our findings emphasize the need to investigate the TME in distinct RCC histological subtypes. We observed a more immune infiltrative phenotype in the TME of the ccRCC cohort than in the pRCC cohort, where a tumor-rich phenotype was noted. As practical predictive biomarkers remain elusive across all subtypes of RCC, further studies are warranted to analyze the biomarker potential of such TME classifications.

DNA-barcoded signal amplification for imaging mass cytometry enables sensitive and highly multiplexed tissue imaging.

Imaging mass cytometry (IMC) is a highly multiplexed, antibody-based imaging method that captures heterogeneous spatial protein expression patterns at subcellular resolution. Here we report the extension of IMC to low-abundance markers through incorporation of the DNA-based signal amplification by exchange reaction, immuno-SABER. We applied SABER-IMC to image the tumor immune microenvironment in human melanoma by simultaneous imaging of 18 markers with immuno-SABER and 20 markers without amplification. SABER-IMC enabled the identification of immune cell phenotypic markers, such as T cell co-receptors and their ligands, that are not detectable with IMC.

Deciphering the maturation of tertiary lymphoid structures in cancer and inflammatory diseases of the digestive tract using imaging mass cytometry.

Persistent inflammation can promote the development of tertiary lymphoid structures (TLS) within tissues resembling secondary lymphoid organs (SLO) such as lymph nodes (LN). The composition of TLS across different organs and diseases could be of pathophysiological and medical interest. In this work, we compared TLS to SLO in cancers of the digestive tract and in inflammatory bowel diseases. Colorectal and gastric tissues with different inflammatory diseases and cancers from the department of pathology of CHU Brest were analyzed based on 39 markers using imaging mass cytometry (IMC). Unsupervised and supervised clustering analyses of IMC images were used to compare SLO and TLS. Unsupervised analyses tended to group TLS per patient but not per disease. Supervised analyses of IMC images revealed that LN had a more organized structure than TLS and non-encapsulated SLO Peyer's patches. TLS followed a maturation spectrum with close correlations between germinal center (GC) markers' evolution. The correlations between organizational and functional markers made relevant the previously proposed TLS division into three stages: lymphoid-aggregates (LA) (CD20+CD21-CD23-) had neither organization nor GC functionality, non-GC TLS (CD20+CD21+CD23-) were organized but lacked GC's functionality and GC-like TLS (CD20+CD21+CD23+) had GC's organization and functionality. This architectural and functional maturation grading of TLS pointed to differences across diseases. TLS architectural and functional maturation grading is accessible with few markers allowing future diagnostic, prognostic, and predictive studies on the value of TLS grading, quantification and location within pathological tissues in cancers and inflammatory diseases.

scRNA-Seq and imaging mass cytometry analyses unveil iNKT cells-mediated anti-tumor immunity in pancreatic cancer liver metastasis.

Invariant natural killer T (iNKT) cells are innate-like T cells that are abundant in liver sinusoids and play a critical role in tumor immunity. However, the role of iNKT cells in pancreatic cancer liver metastasis (PCLM) has not been fully explored. In this study, we employed a hemi-spleen pancreatic tumor cell injection mouse model of PCLM, a model that closely mimics clinical conditions in humans, to explore the role of iNKT cells in PCLM. Activation of iNKT cells with α-galactosylceramide (αGC) markedly increased immune cell infiltration and suppressed PCLM progression. Via single cell RNA sequencing (scRNA-seq) we profiled over 30,000 immune cells from normal liver and PCLM with or without αGC treatment and were able to characterize the global changes of the immune cells in the tumor microenvironment upon αGC treatment, identifying a total of 12 subpopulations. Upon treatment with αGC, scRNA-Seq and flow cytometry analyses revealed increased cytotoxic activity of iNKT/NK cells and skewing CD4 T cells towards a cytotoxic Th1 profile and CD8 T cells towards a cytotoxic profile, characterized by higher proliferation and reduced exhaustion marker PD1 expression. Moreover, αGC treatment excluded tumor associated macrophages. Lastly, imaging mass cytometry analysis uncovered the reduced epithelial to mesenchymal transition related markers and increased active CD4 and CD8 T cells in PCLM with αGC treatment. Overall, our findings uncover the protective function of activated iNKT cells in pancreatic cancer liver metastasis through increased NK and T cell immunity and decreased tumor associated macrophages.

High-throughput method to analyze the cytotoxicity of CAR-T Cells in a 3D tumor spheroid model using image cytometry.

Solid tumors account for approximately 90% of all adult human cancers. As such, the development of novel cellular therapies has become of increasing importance to target solid tumor malignancies, such as prostate, lung, breast, bladder, colon, and liver cancers. One such cellular therapy relies on the use of chimeric antigen receptor T cells (CAR-T cells). CAR-T cells are engineered to target specific antigens on tumor cells. To date, there are six FDA-approved CAR-T cell therapies that have been utilized for hematologic B cell malignancies. Immune cell trafficking and immunosuppressive factors within the tumor microenvironment increase the relative difficulty in developing a robust CAR-T cell therapy against solid tumors. Therefore, it is critical to develop novel methodologies for high-throughput phenotypic and functional assays using 3D tumor spheroid models to assess CAR-T cell products against solid tumors. In this manuscript, we discuss the use of CAR-T cells targeted towards PSMA, an antigen that is found on prostate cancer tumor cells, the second most common cause of cancer deaths among men worldwide. We demonstrate the use of high-throughput, plate-based image cytometry to characterize CAR-T cell-mediated cytotoxic potency against 3D prostate tumor spheroids. We were able to kinetically evaluate the efficacy and therapeutic value of PSMA CAR-T cells by analyzing the cytotoxicity against prostate tumor spheroids. In addition, the CAR-T cells were fluorescently labeled to visually identify the location of the T cells as cytotoxicity occurs, which may provide more meaningful information for assessing the functionality of the CAR-T cells. The proposed image cytometry method can overcome limitations placed on traditional methodologies to effectively assess cell-mediated 3D tumor spheroid cytotoxicity and efficiently generate time- and dose-dependent results.

Single-cell high-dimensional imaging mass cytometry: one step beyond in oncology.

Solid tumors have a dynamic ecosystem in which malignant and non-malignant (endothelial, stromal, and immune) cell types constantly interact. Importantly, the abundance, localization, and functional orientation of each cell component within the tumor microenvironment vary significantly over time and in response to treatment. Such intratumoral heterogeneity influences the tumor course and its sensitivity to treatments. Recently, high-dimensional imaging mass cytometry (IMC) has been developed to explore the tumor ecosystem at the single-cell level. In the last years, several studies demonstrated that IMC is a powerful tool to decipher the tumor complexity. In this review, we summarize the potential of this technology and how it may be useful for cancer research (from preclinical to clinical studies).

Learning cell identity in immunology, neuroscience, and cancer.

Suspension and imaging cytometry techniques that simultaneously measure hundreds of cellular features are powering a new era of cell biology and transforming our understanding of human tissues and tumors. However, a central challenge remains in learning the identities of unexpected or novel cell types. Cell identification rubrics that could assist trainees, whether human or machine, are not always rigorously defined, vary greatly by field, and differentially rely on cell intrinsic measurements, cell extrinsic tissue measurements, or external contextual information such as clinical outcomes. This challenge is especially acute in the context of tumors, where cells aberrantly express developmental programs that are normally time, location, or cell-type restricted. Well-established fields have contrasting practices for cell identity that have emerged from convention and convenience as much as design. For example, early immunology focused on identifying minimal sets of protein features that mark individual, functionally distinct cells. In neuroscience, features including morphology, development, and anatomical location were typical starting points for defining cell types. Both immunology and neuroscience now aim to link standardized measurements of protein or RNA to informative cell functions such as electrophysiology, connectivity, lineage potential, phospho-protein signaling, cell suppression, and tumor cell killing ability. The expansion of automated, machine-driven methods for learning cell identity has further created an urgent need for a harmonized framework for distinguishing cell identity across fields and technology platforms. Here, we compare practices in the fields of immunology and neuroscience, highlight concepts from each that might work well in the other, and propose ways to implement these ideas to study neural and immune cell interactions in brain tumors and associated model systems.

DNA image cytometry ploidy analysis technique improves the detection rate of pleural effusion cytology.

OBJECTIVE: To explore the clinical diagnostic value of DNA image cytometry (DNA-ICM) ploidy analysis in malignant pleural effusion cancer screening, this study analyzed the effect of exfoliated cell smears (ECSs), cell blocks (CBs), and immunochemistry. METHOD: A total of 830 cases of pleural effusion were considered for the DNA-ICM ploidy analysis. The ECSs were centrifuged, the CBs were formed, and the DNA-ICM ploidy analysis was carried out in the diagnosis of malignant pleural effusion. Immunochemistry and biopsy was applied to differentiate between benign and malignant pleural effusion and to determine the source of the latter. The sensitivity and specificity differences between the three methods alone and in combination were compared. RESULTS: The sensitivity of the DNA-ICM, ECS, and CB methods was 96.28%, 94.93%, and 95.95%, respectively, and the specificity of each method was 86.52%, 87.08%, and 86.14%, respectively. The sensitivity and specificity of the combined diagnosis method were 99.32% and 75.09%, respectively. Among the 22 cases diagnosed as positive in the DNA-ICM ploidy analysis but negative in the ECS and CB analyses, four cases were diagnosed as positive by comprehensive clinical diagnosis. CONCLUSION: The sensitivity and specificity of DNA-ICM ploidy analysis are high; the positive detection rate of pleural fluid cytology is effectively increased, and the missed detection rate of cell pathologies is effectively reduced. The combination of the three methods significantly improves the specificity and sensitivity of the diagnosis of malignant pleural effusion, and immunochemistry with CBs can be used to accurately analyze the primary tumor site.

Imaging Mass Cytometry in Immuno-Oncology.

In situ profiling of the tumor-immune microenvironment (TiME) requires the ability to co-localize and detect multiple proteins simultaneously. Imaging mass cytometry (IMC), using the Hyperion™ imaging system is a novel multiplex imaging modality that currently enables detection of up to 50 markers on fixed tissues at subcellular resolution and thus has the potential to inform both pre-clinical and clinical research by providing investigators with spatially resolved information about the TiME. Here we provide an overview of the IMC workflow from sample fixation to analysis, with a focus on multiplex panel design and tissue staining.

Imaging mass cytometry: High-dimensional and single-cell perspectives on the microenvironment of solid tumours.

Imaging mass cytometry (IMC) is a new technology integrating mass spectrometry, high-resolution laser ablation and immunohistochemistry/cytochemistry. A unique high-dimensional perspective comprehensively and accurately depicts the complex interaction of phenotype, signalling pathway and tumour microenvironment and is widely used in solid tumours. However, the application scenarios of IMC in basic medicine and clinical research in solid tumours lack systematic introduction and classification. This paper reviews the application of IMC in depicting the panorama of the tumour microenvironment, revealing tumour spatial heterogeneity, clarifying tumour pharmacological mechanisms, assisting in new drug development, and dynamically evaluating the efficacy of immunotherapy in solid tumours.