Aptamer-Based Multiparameter Analysis for Molecular Profiling of Hematological Malignancies

Area of Science:

• Biotechnology and Biomedical Engineering
• Molecular Biology and Genetics
• Computational Biology and Bioinformatics

Background:

• Hematological malignancies (HM) exhibit significant clinical heterogeneity despite minimal molecular differences between subtypes.
• Current diagnostic methods struggle to differentiate HM subtypes with subtle surface protein marker variations.
• Accurate phenotyping is crucial for tailoring treatment regimens and improving patient outcomes in HM.

Purpose of the Study:

• To develop high-affinity aptamer probes capable of discriminating subtle surface protein differences among HM subtypes.
• To establish an aptamer-based multiparameter analysis strategy for precise HM cell classification.
• To validate the clinical applicability of the developed aptamer probes for HM phenotyping.

Main Methods:

• Utilized Cell-SELEX technology to generate a panel of aptamer probes targeting HM cell surface proteins.
• Characterized aptamer affinity with apparent dissociation constants (Kd) below 10 nM.
• Integrated aptamer recognition patterns with a partial least-squares discriminant analysis (PLS-DA) machine learning model for classification.

Main Results:

• Generated aptamer probes with high affinity (Kd < 10 nM) exhibiting unique recognition patterns on different HM subtypes.
• Achieved 100% classification accuracy for differentiating HM cells using the combined aptamer-based and machine learning approach.
• Preliminary validation demonstrated accurate classification of complex clinical HM samples.

Conclusions:

• Aptamer-based multiparameter analysis is a potent molecular tool for precise HM phenotyping.
• This strategy effectively differentiates HM subtypes with subtle surface protein profile variations.
• The developed method shows promise for advancing precision diagnosis and personalized treatment in hematological malignancies.