Journal of Cancer Diagnosis
Open Access

Hematologic cancer diagnosis; Single-cell RNA sequencing in oncology; Flow cytometry-based classification; Universal cancer diagnostic tool; Integrative single-cell analysis; Machine learning for leukemia detection; Immunophenotyping with scRNA-seq; Diagnostic precision in blood cancers; High-dimensional cell profiling; Data fusion in cancer diagnostics; Early detection of hematologic malignancies

Introduction

Hematologic malignancies represent a significant global health burden, with millions of new cases diagnosed annually [1]. These malignancies are complex and multifactorial, comprising diverse diseases such as acute and chronic leukemias, lymphomas, myelodysplastic syndromes (MDS), and multiple myeloma. Despite decades of research and technological innovation, achieving early, accurate, and reproducible diagnoses remains a persistent challenge in hematopathology [2]. Flow cytometry is a widely used tool in clinical laboratories for immunophenotyping, allowing for high-throughput analysis of cell surface and intracellular markers. It provides rapid and cost-effective insights into cell lineage and maturation status [3]. However, flow cytometry has limitations in resolving the full transcriptomic complexity and often requires expert interpretation, especially in ambiguous cases or those with overlapping immunophenotypes [4]. Over the past decade, the rise of single-cell transcriptomics (scRNA-seq) has transformed our ability to understand disease biology at an unprecedented level of detail. Meanwhile, flow cytometry continues to be an indispensable diagnostic tool in clinical hematology, offering rapid immunophenotypic profiling of cells in suspension [5]. While each of these platforms offers critical insights, their individual limitations can hinder precise diagnostics. Flow cytometry, though rapid and accessible, lacks transcriptomic depth, while scRNA-seq, though powerful, remains computationally and logistically demanding.

To bridge these complementary strengths and limitations, researchers and clinicians are exploring the development of a universal diagnostic algorithm that integrates flow cytometry data with single-cell transcriptomic profiles [6]. Such a model would offer a unified view of both surface phenotypes and gene expression programs, enabling robust classification and subtyping of hematologic malignancies [7]. Leveraging machine learning and multi-omics integration techniques, this approach could revolutionize hematopathology by making diagnostics more accurate, data-rich, and adaptable across clinical settings [8]. Integrating flow cytometry with scRNA-seq data in a unified diagnostic algorithm can offer both phenotypic and molecular insights bridging the gap between classical immunophenotyping and modern genomics. This article explores the conceptual framework, design considerations, advantages, challenges, and clinical relevance of developing a universal diagnostic algorithm that combines these two powerful modalities.

Diagnostic limitations in current hematologic practice

Current diagnosis of hematologic malignancies typically involves a multi-step process:

• Morphological analysis (blood smear or bone marrow biopsy)
• Immunophenotyping via flow cytometry
• Cytogenetics and fluorescence in situ hybridization (FISH)
• Molecular diagnostics (e.g., PCR, next-generation sequencing)
• Subjectivity in interpretation
• Difficulty in detecting rare clones or precursor cells
• Ambiguity in immunophenotypic overlaps (e.g., MPAL)
• Delayed diagnosis due to multi-platform testing
• Inadequate molecular characterization in some low-resource settings

A universal algorithm that synergizes the speed and phenotypic clarity of flow cytometry with the transcriptional depth of scRNA-seq offers a more holistic view of disease biology, potentially streamlining diagnosis and guiding treatment earlier.

Conclusion

The integration of flow cytometry and single-cell transcriptomics into a unified diagnostic algorithm represents a transformative advance in the diagnosis of hematologic malignancies. By capturing both phenotypic surface markers and the underlying molecular programs of individual cells, this approach holds the potential to vastly improve diagnostic precision, reduce ambiguity, and guide targeted therapies more effectively than conventional methods alone. While significant hurdles remain—particularly related to cost, infrastructure, and interpretability—ongoing advancements in technology, machine learning, and data sharing frameworks are rapidly closing these gaps. The future of hematologic oncology diagnostics lies in multi-dimensional, data-driven systems that can decipher the complex language of cancer biology at single-cell resolution.

As these integrated algorithms evolve from research tools to clinical utilities, they promise to usher in an era of universal, personalized, and predictive diagnostics, bringing hope for earlier detection, better treatment outcomes, and improved survival for patients worldwide.