CML31 Antibody

What is CD31 and what role does it play in leukemia research?

CD31, also known as Platelet Endothelial Cell Adhesion Molecule (PECAM1), is a protein required for leukocyte transendothelial migration (TEM) and controls macrophage-mediated phagocytosis of leukocytes. This protein is present in viable leukocytes and platelets, typically concentrated at cell-cell junctions with epithelial cells. In the context of leukemia research, CD31 is significant because it is related to pro-inflammatory processes and plays important roles in angiogenesis . Furthermore, CD31 can be found on carcinomas where it modulates immune responses. The presence of CD31/PECAM1 on leukocytes creates a protective mechanism, allowing these cells to avoid macrophage phagocytosis through detachment signaling . This property makes CD31 particularly relevant for studying immune evasion mechanisms in leukemic cells and their interactions with the immune microenvironment.

What are the standard applications for CD31 antibody in hematological research?

CD31 antibody has several validated applications in hematological research, including:

• Western Blot (WB): Typically used at dilutions of 1:500-1:5000 to detect CD31 expression in cell lysates and tissue samples .

• Immunohistochemistry (IHC): Applied at dilutions of 1:50-1:500 to visualize CD31 expression in tissue sections, particularly useful for assessing vascular density in bone marrow biopsies .

• Immunofluorescence (IF): Used at dilutions of 1:50-1:200 for co-localization studies and detailed cellular distribution analysis .

• Flow Cytometry (FC): Employed to quantify CD31 expression on specific cell populations and for sorting CD31-positive cells .

These applications allow researchers to characterize CD31 expression patterns in various hematological malignancies, including CML, and to investigate the protein's role in disease pathogenesis and progression.

How does CD31 relate to other immune markers in CML research?

CD31 functions within a complex network of immune markers relevant to CML research. Studies of immune recognition in CML have revealed interactions between natural killer (NK) cells and leukemic cells via inhibitory pathways such as LGALS9-TIM3 and PVR-TIGIT . While CD31 itself mediates leukocyte migration and helps cells evade phagocytosis, research has shown that CML patients exhibit changes in multiple immunological parameters, including decreased levels of immunoglobulins, complement components, and altered cytokine production . The upregulation of LGALS9 observed in CML cells and the interaction with TIM3 represents one of several immune checkpoint mechanisms that may suppress anti-leukemic immune responses . Understanding CD31's place within this broader immune context is essential for comprehensive CML research, particularly when investigating potential immunotherapeutic approaches that might target multiple pathways simultaneously.

How can CD31 antibody be integrated into single-cell analysis of CML samples?

Integration of CD31 antibody into single-cell analysis protocols provides valuable insights into the heterogeneity of CML samples. Recent research has utilized single-cell profiling to characterize immune responses in CML patients (n=7, N=9), comparing these to healthy controls (n=7) and other cancers (n=28) . To incorporate CD31 antibody effectively:

• Include CD31 in multiparameter flow cytometry panels alongside other endothelial, stem cell, and immune markers to identify and characterize distinct cellular subpopulations.

• For single-cell RNA sequencing studies, use index sorting with CD31 antibody to correlate protein expression with transcriptomic data, focusing on PECAM1 (CD31) expression levels.

• Apply CD31 antibody in mass cytometry (CyTOF) to simultaneously assess multiple markers, enabling high-dimensional analysis of the CML microenvironment.

The advanced single-cell approaches have revealed that NK cells in CML patients display a distinct phenotype compared to healthy controls, with most belonging to an active CD56dim cluster expressing high levels of GZMA/B, PRF1, CCL3/4, and IFNG . By including CD31 in such analyses, researchers can investigate potential correlations between CD31 expression and these active immune cell phenotypes, providing insights into leukemic cell interactions with the bone marrow microenvironment.

What methodological approaches should be used to investigate CD31's role in leukemic stem cell biology?

Investigation of CD31's role in leukemic stem cell (LSC) biology requires sophisticated methodological approaches:

• Co-expression analysis with established LSC markers: Design experiments that examine CD31 alongside known LSC markers such as CD96, TIM-3, CD157, and CD244, which have been identified as potential therapeutic targets .

• Functional assays to assess stem cell properties:

  • Serial transplantation assays using CD31-sorted populations to evaluate self-renewal capacity

  • Colony-forming cell assays to determine progenitor activity

  • Long-term culture-initiating cell assays to assess primitive hematopoietic cell function

• Manipulation of CD31 expression:

  • Use CRISPR/Cas9 gene editing to modulate CD31 expression in primary CML samples

  • Apply neutralizing antibodies against CD31 to evaluate functional effects on LSC survival and proliferation

This methodological framework allows for comprehensive investigation of whether CD31 contributes to LSC maintenance or function. While CD96 has been shown to be expressed on the majority of AML LSCs with minimal expression on normal hematopoietic stem cells (HSCs), and TIM-3 is expressed on LSCs in most types of AML but absent on HSCs , the specific role of CD31 in LSC biology requires further investigation using these approaches.

How can researchers leverage new antibody engineering approaches to target CD31 in CML?

Researchers can apply cutting-edge antibody engineering technologies to develop more effective CD31-targeting strategies for CML:

• Cell-penetrating antibody technologies: Recent breakthroughs with antibodies like 3E10 demonstrate how engineered antibodies can penetrate cells to reach intracellular targets . This approach could be adapted to target CD31-mediated signaling pathways within leukemic cells, potentially disrupting their survival mechanisms.

• Sequence-based antibody design: Utilize computational approaches such as DyAb, which combines language models with regression to predict antibody properties even with limited experimental data . For example:

• Bispecific antibody development: Design bispecific antibodies that simultaneously target CD31 and another leukemia-associated antigen to improve specificity and reduce off-target effects.

• Antibody-drug conjugates (ADCs): Develop CD31-targeting ADCs that can deliver cytotoxic payloads specifically to CD31-expressing leukemic cells.

These advanced engineering approaches expand the therapeutic potential of CD31 antibodies beyond traditional applications. The genetic algorithm approach used in DyAb has demonstrated success in generating antibody variants with improved binding properties, with 84% of designed binders showing enhanced affinity compared to the parent antibody . Similar strategies could be applied to optimize CD31 antibodies for both research and potential therapeutic applications in CML.

What are the optimal sample preparation protocols for CD31 antibody applications in CML research?

Optimal sample preparation is critical for successful CD31 antibody applications in CML research:

For Western Blot analysis:

• Fresh or frozen samples should be lysed in RIPA buffer containing protease inhibitors.

• Use 20-50 μg of total protein per lane.

• Recommend dilution range of 1:500-1:5000, with 1:2000 being optimal for detecting CD31 in human tumor tissues as demonstrated in NCI-H460 tumor samples .

• Include positive controls such as endothelial cell lysates and negative controls lacking CD31 expression.

For Immunohistochemistry:

• Formalin-fixed, paraffin-embedded (FFPE) samples should be subjected to antigen retrieval (citrate buffer, pH 6.0).

• Use dilutions of 1:50-1:500, with optimization required for each specific sample type .

• Include vascular tissues as internal positive controls.

• Compare staining patterns between leukemic and normal bone marrow samples.

For Flow Cytometry:

• Freshly isolated mononuclear cells from peripheral blood or bone marrow should be preserved with minimal processing delay.

• Use multiparameter panels including CD45, CD34, and other relevant markers alongside CD31.

• Include fluorescence minus one (FMO) controls and isotype controls (IgG2a for the CD31 antibody described) .

• Consider fixation and permeabilization requirements depending on whether examining surface or intracellular epitopes.

These protocols should be optimized specifically for CML samples, which may have unique characteristics compared to other hematological malignancies or solid tumors.

How should researchers validate CD31 antibody specificity for CML studies?

Thorough validation of CD31 antibody specificity is essential for generating reliable results in CML research:

• Multiple detection methods verification:

  • Confirm CD31 detection using at least two independent techniques (e.g., WB and IHC)

  • Verify that the antibody recognizes the expected molecular weight band (~130 kDa) in Western blots

  • Ensure staining patterns in IHC/IF correspond to expected cellular localization (cell borders)

• Specificity controls:

  • Use CD31 knockdown/knockout cell lines as negative controls

  • Include cell lines with known CD31 expression levels as positive controls

  • Apply competitive binding assays with purified CD31 protein

  • Test multiple antibody clones targeting different CD31 epitopes

• Epitope mapping and cross-reactivity assessment:

  • Verify the specific epitope recognized by the antibody (e.g., the human CD31 extracellular domain 28-315aa)

  • Test for potential cross-reactivity with other PECAM family members

  • Assess performance in comparison to established reference antibodies

• Reproducibility assessment:

  • Evaluate batch-to-batch consistency

  • Determine optimal storage conditions and stability over time

  • Document sensitivity thresholds for detection of low CD31 expression levels

A well-validated antibody should produce clear, specific staining with minimal background, as demonstrated by the review of a CD31 antibody tested on human NCI-H460 tumor tissue which noted: "The target band is clear and there are no other bands. The antibody has good specificity" .

What experimental controls are essential when studying CD31 in the context of CML immune responses?

When investigating CD31 in relation to CML immune responses, several critical experimental controls must be included:

• Sample-related controls:

  • Matched normal bone marrow/peripheral blood from healthy donors

  • Samples from patients with other myeloproliferative neoplasms for comparison

  • Longitudinal samples from the same CML patients at different disease stages

  • Samples from CML patients before and after tyrosine kinase inhibitor (TKI) therapy

• Technical controls for antibody applications:

  • Isotype controls matching the CD31 antibody (IgG2a for the described antibody)

  • Fluorescence minus one (FMO) controls for flow cytometry

  • Secondary antibody-only controls to assess non-specific binding

  • Positive cell populations with known CD31 expression (e.g., endothelial cells)

• Functional controls for immune response studies:

  • Include assessments of other immunoglobulins and complement components as context for CD31 studies, as CML patients show altered levels of these factors

  • Measure cytomegalovirus (CMV) serostatus, as this affects NK cell populations (90.51% of adaptive NK cells in cluster 3 were from CMV+ donors)

  • Assess cytokine production capacity (IL-2, TNFα)

• Analytical controls:

  • Use standardized gating strategies for flow cytometry

  • Apply consistent thresholds for positive/negative determination

  • Include spike-in controls for single-cell RNA sequencing studies

These comprehensive controls help distinguish CD31-specific effects from general immune alterations in CML and ensure experimental reproducibility across different patient cohorts and technical platforms.

How should researchers interpret CD31 expression patterns in heterogeneous CML samples?

Interpretation of CD31 expression patterns in heterogeneous CML samples requires sophisticated analytical approaches:

• Quantitative assessment across cell populations:

  • Apply hierarchical gating strategies in flow cytometry to identify distinct CD31+ subpopulations

  • Calculate both percentage of positive cells and mean fluorescence intensity (MFI) to capture expression level variations

  • Compare expression in CD34+ progenitor cells versus mature myeloid populations

• Spatial distribution analysis in tissue samples:

  • Assess membrane localization versus cytoplasmic expression

  • Evaluate CD31 expression at cell-cell junctions versus diffuse distribution

  • Quantify CD31+ vascular structures in bone marrow biopsies as reference

• Integration with molecular and clinical data:

  • Correlate CD31 expression with BCR-ABL transcript levels

  • Examine relationships between CD31 patterns and response to TKI therapy

  • Analyze associations with disease progression markers

• Heterogeneity considerations:

  • Acknowledge that CML samples contain multiple cell populations with varying CD31 expression

  • Use single-cell approaches to deconvolute this heterogeneity

  • Consider that leukemia-derived proteins elicit multiple immune responses, as demonstrated by the isolation of eight distinct clones from a CML patient cDNA library

When interpreting results, researchers should recognize that CD31 expression patterns may reflect both direct involvement in leukemic processes and secondary changes in the bone marrow microenvironment. The high-resolution immune cell mapping approach used in recent CML studies revealed distinct NK cell phenotypes , and similar detailed characterization should be applied to CD31 expression analysis.

What statistical approaches are recommended for analyzing CD31 antibody data in CML research?

• Descriptive statistics and visualization:

  • Present CD31 expression as median with interquartile range due to typically non-normal distribution

  • Use box plots, violin plots, or cumulative distribution functions to visualize expression across sample groups

  • Consider dimensionality reduction techniques (t-SNE, UMAP) for multiparameter data visualization

• Comparative analysis between groups:

  • Use non-parametric tests (Mann-Whitney U, Kruskal-Wallis) for comparing CD31 expression between patient cohorts

  • Apply paired tests for before/after treatment comparisons

  • Consider mixed-effects models for longitudinal studies

• Correlation and association analyses:

  • Calculate Spearman correlation coefficients to assess relationships between CD31 and other markers

  • Use multivariate regression to adjust for confounding factors

  • Apply Fisher's exact test for analyzing categorical associations

• Advanced statistical approaches:

  • Utilize survival analysis (Kaplan-Meier, Cox regression) to correlate CD31 expression with clinical outcomes

  • Consider statistical approaches used in similar immunological studies in CML, such as those examining NK cell phenotypes (cluster 3: cells from CMV+ 90.51% and CMV- 9.49%, P < 0.05)

  • Implement multiple testing corrections (Bonferroni, FDR) when analyzing CD31 in high-dimensional datasets

• Sample size and power considerations:

  • Conduct power analysis to determine required sample sizes

  • Report confidence intervals alongside p-values

  • Be transparent about limitations when sample sizes are small, as is common in CML studies (published studies include n=7 diagnostic samples)

The significance level (P < 0.05) should be predetermined and consistently applied across analyses, with exact P-values reported whenever possible.

How can researchers differentiate between CD31 antibody reactivity on normal versus leukemic cells?

Differentiating CD31 antibody reactivity on normal versus leukemic cells requires integrated analytical approaches:

• Multiparameter phenotyping:

  • Combine CD31 antibody with markers that identify leukemic cells (e.g., BCR-ABL FISH probes or surrogate markers)

  • Include lineage markers to distinguish myeloid, lymphoid, and endothelial populations

  • Assess co-expression with stem/progenitor markers (CD34, CD38) to identify potential leukemic stem cells

• Quantitative benchmarking:

  • Establish baseline CD31 expression ranges in healthy donor samples across cell types

  • Calculate fold-change in expression between matched normal and leukemic populations

  • Use statistical methods to define expression thresholds that differentiate normal from abnormal

• Functional correlation:

  • Correlate CD31 expression with functional properties (proliferation, apoptosis resistance)

  • Assess relationship between CD31 levels and response to therapeutic agents

  • Evaluate the association between CD31 expression and immune evasion mechanisms

• Genetic confirmation:

  • Sort CD31+ populations and perform BCR-ABL testing to confirm leukemic origin

  • Consider the approach used in previous studies that identified leukemia-specific antigens, such as constructing expression cDNA libraries from leukemia cells and screening with autologous serum for high-titer IgG antibodies

  • Use single-cell approaches to correlate CD31 expression with genetic abnormalities

The finding that patients with CML have significantly higher prevalence of antibodies against leukemia-derived proteins compared to normal individuals suggests that differential protein expression or modification in leukemic cells may be detectable through careful antibody-based analysis, including CD31 studies.

How is CD31 antibody being integrated with emerging single-cell technologies?

The integration of CD31 antibody with cutting-edge single-cell technologies is opening new frontiers in CML research:

• Single-cell RNA sequencing applications:

  • Index sorting with CD31 antibody enables correlation of protein expression with transcriptomic profiles

  • Trajectory analysis of CD31+ cells reveals developmental relationships between normal and leukemic populations

  • Gene regulatory network analysis identifies transcription factors governing CD31 expression in different cellular contexts

  • Similar approaches have been used to profile immune cells in CML, revealing distinct NK cell clusters with specific expression patterns

• Spatial transcriptomics and proteomics:

  • CD31 antibody staining combined with spatial transcriptomics maps the distribution of CD31+ cells within the bone marrow microenvironment

  • Multiplexed ion beam imaging (MIBI) or Imaging Mass Cytometry (IMC) allows simultaneous visualization of CD31 with dozens of other markers

  • These approaches reveal spatial relationships between CD31+ cells and other components of the leukemic niche

• Single-cell functional assays:

  • CD31 antibody-based cell sorting followed by single-cell functional assays (proliferation, differentiation, drug response)

  • Droplet-based microfluidic platforms for high-throughput assessment of CD31+ cell behaviors

  • Integration with CRISPR screens to evaluate the functional significance of genes co-expressed with CD31

• Computational integration frameworks:

  • Machine learning algorithms to identify CD31+ cell clusters with distinct molecular signatures

  • Multi-omics data integration approaches (MOFA, Seurat, etc.) incorporating CD31 antibody data

  • Pseudotime analysis to track CD31 expression changes during leukemic evolution

These integrated approaches enable unprecedented resolution in understanding CD31 biology in CML, similar to how high-resolution immune mapping has revealed previously unrecognized features of the CML immune microenvironment .

What are the latest developments in using CD31 antibody for monitoring minimal residual disease in CML?

Emerging approaches for using CD31 antibody in minimal residual disease (MRD) monitoring include:

• High-sensitivity flow cytometry applications:

  • Mass cytometry (CyTOF) with CD31 antibody enables detection of rare leukemic populations with aberrant CD31 expression

  • Spectral flow cytometry increases the number of parameters that can be simultaneously assessed with CD31

  • These high-dimensional approaches improve discrimination between residual leukemic cells and regenerating normal hematopoiesis

• Circulating biomarker assessment:

  • Evaluation of soluble CD31 in plasma as a potential biomarker for disease monitoring

  • Analysis of CD31+ microparticles or extracellular vesicles as indicators of residual disease

  • Correlation of these circulating markers with molecular response metrics (BCR-ABL transcripts)

• Integrated MRD assessment frameworks:

  • Combining CD31 antibody-based detection with established MRD methods (qPCR for BCR-ABL, digital PCR)

  • Development of standardized panels incorporating CD31 alongside other markers with demonstrated utility in MRD detection

  • Application of artificial intelligence algorithms to identify subtle CD31 expression pattern changes indicating emerging resistance

• Predictive modeling applications:

  • Longitudinal tracking of CD31+ populations to predict relapse

  • Correlation of CD31 expression dynamics with treatment response metrics

  • Risk stratification based on combined CD31 and molecular markers

While specific CD31-based MRD assays for CML are still under development, the approach mirrors successful immunophenotypic MRD detection strategies in other hematologic malignancies. The evidence that leukemia-derived proteins can elicit immune responses in CML patients suggests that careful characterization of CD31 expression patterns could contribute to improved MRD monitoring.

How might novel antibody engineering approaches enhance CD31-targeted therapies for CML?

Innovative antibody engineering strategies are creating new possibilities for CD31-targeted therapies in CML:

• Cell-penetrating antibody technologies:

  • Adaptation of the 3E10 antibody framework, which has demonstrated ability to penetrate cells and target intracellular molecules

  • Engineering CD31 antibodies to access intracellular signaling domains

  • This approach could overcome limitations of conventional antibodies that only target cell surface epitopes

• Computational antibody design strategies:

  • Application of AI-driven methods like DyAb that combine language models with regression for antibody property prediction

  • Generation of optimized CD31-targeting antibodies with enhanced affinity and specificity

  • The success rate of such approaches is promising, with 85% of DyAb-designed antibodies successfully expressing and binding to target antigens

• Multi-functional antibody formats:

  • Bispecific antibodies linking CD31 with activating receptors on NK cells or T cells

  • Trispecific constructs targeting CD31 and multiple leukemic antigens to improve specificity

  • Antibody-drug conjugates delivering targeted payloads to CD31+ leukemic cells

• Antibody-based immune checkpoint modulation:

  • Engineered CD31 antibodies that disrupt inhibitory signaling pathways

  • Combination approaches targeting CD31 alongside pathways identified in CML immune microenvironment studies, such as LGALS9-TIM3

  • Synergistic application with tyrosine kinase inhibitors

• Therapeutic potential of targeting CD31 in the CML microenvironment:

The considerable progress in antibody engineering techniques, as demonstrated by genetic algorithm approaches achieving 84% improved binding in designed antibodies , provides a promising foundation for developing next-generation CD31-targeted therapies for CML.