Multiplexed single-cell morphometry for hematopathology diagnostics.

Area of Science:

• Hematology
• Computational Biology
• Biotechnology

Background:

• Accurate diagnosis of lymphomas and leukemias relies on integrating cell morphology and surface molecule expression.
• Current diagnostic methods can be time-consuming and require extensive specialized training.

Purpose of the Study:

• To develop a high-throughput, single-cell assay that merges morphological and molecular profiling for diagnosing hematopoietic malignancies.
• To empower mass cytometry with the ability to 'see' cellular morphology through antibody-measurable components.

Main Methods:

• Quantifying cell morphological features using antibody-measurable molecular components.
• Applying single-cell morphometric profiling to diverse clinical samples.
• Utilizing machine learning for visualization and analysis of cellular distribution and differentiation.

Main Results:

• Identification of robust and distinct morphometric marker patterns for major cell types.
• Lamin B1 identified acute leukemias; lamin A/C distinguished normal from neoplastic T cells; VAMP-7 correlated with light-cytometric side scatter.
• The approach demonstrated superiority to flow cytometry and comparability to expert microscopy for blast enumeration.

Conclusions:

• Morphometric profiling combined with machine learning enables sensitive and automated diagnosis of complex hematopoietic diseases.
• This novel approach contextualizes traditional markers within morphometric frameworks for enhanced diagnostic accuracy.
• The assay has the potential to bypass years of specialized training for hematopathologists.