Recent advances in spatial and single-cell omics have significantly revolutionized biomarker discovery in tumor immunotherapy by addressing critical challenges such as tumor heterogeneity, immune evasion, and variability within the tumor microenvironment (TME). Immunotherapeutic strategies, including immune checkpoint inhibitors and adoptive T-cell transfer, have demonstrated promising clinical outcomes; however, their efficacy is limited by low response rates and the incidence of immune-related adverse events (irAEs). Therefore, the identification of reliable biomarkers is essential for predicting treatment efficacy, minimizing irAEs, and facilitating patient stratification. Spatial omics integrates molecular profiling with spatial localization, thereby providing comprehensive insights into the cellular organization and functional states within the TME. By elucidating the spatial patterns of immune cell infiltration and tumor heterogeneity, this approach enhances the prediction of therapeutic responses. Similarly, single-cell omics enables high-resolution analysis of cellular heterogeneity by capturing transcriptomic, epigenomic, and metabolic signatures at the single-cell level. The integrated application of spatial and single-cell omics has enabled the identification of previously undetected biomarkers, including rare immune cell subsets implicated in resistance mechanisms. In addition to spatial transcriptomics (ST), this technological landscape also includes spatial proteomics (SP) and spatial metabolomics, which further facilitate the study of dynamic tumor-immune interactions. Multi-omics integration provides a comprehensive overview of biomarker landscapes, while the rapid evolution of artificial intelligence (AI)-based approaches enhances the analysis of complex, multidimensional datasets to ultimately enhance predictive potential and clinical utility. Despite substantial progress, several challenges remain in the context of standardization, data integration, and real-time monitoring. Nevertheless, the incorporation of spatial and single-cell omics into biomarker research holds transformative potential for advancing personalized cancer immunotherapy. These emerging strategies pave the way for the development of innovative diagnostic and therapeutic interventions, thereby enabling precision oncology and improving treatment outcomes across a wide range of tumor profiles.

This review aims to provide a comprehensive overview of the integration of spatial omics with single-cell omics in the discovery of biomarkers for tumor immunotherapy. Specifically, it examines the strategies by which these emerging technologies address the challenges related to tumor heterogeneity, immune evasion, and the dynamic nature of the TME. By elaborating on the principles, applications, and clinical potential of these technologies, this review also critically evaluates their limitations, challenges, and the current gaps in clinical translation.

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Objective

The concentration of bilirubin in blood or serum is useful for assessing liver function as well as monitoring treatment. This study evaluates the clinical performance of a novel point-of-care (PoC) device for the detection of bilirubin in serum. The PoC device incorporates an integrated miniature optoelectronic sensing module and a microfluidic test cartridge.

Methods

Patients’ serum total bilirubin concentrations, ranging from 2 ​μmol/L to 480 ​μmol/L, were measured using the PoC device and the standard laboratory method (n=20). Bland-Altman analysis and regression analysis using Passing-Bablok method were used to benchmark the PoC device against the standard laboratory measurements. The diagnostic capability of the PoC device in categorising the serum samples within clinically relevant bilirubin concentration thresholds of 200, 300, and 450 ​μmol/L was assessed using receiver operating characteristic (ROC) analysis.

Results

The mean difference between the PoC device and the standard laboratory method was −5.6 ​μmol/L, with a 95% confidence interval (CI) of −45.1 ​μmol/L to 33.9 ​μmol/L. The coefficient of determination (R²) was 0.986. The PoC device achieved a detection sensitivity of 90% and specificity of 97% in categorising bilirubin concentrations within bands used in clinical decision-making.

Conclusions

This study demonstrates that the proposed PoC device is capable of measuring bilirubin levels in patient samples with clinically acceptable accuracy.