What is an SPE (Serum Protein Electrophoresis) pattern?

What is an SPE Pattern?

An SPE (Serum Protein Electrophoresis) pattern is a visual representation of serum proteins separated by electrical charge into distinct zones—albumin, alpha-1, alpha-2, beta, and gamma globulins—used primarily to detect and characterize monoclonal proteins (M-proteins) in plasma cell disorders like multiple myeloma. 1

Normal SPE Pattern Components

The normal SPE pattern displays five distinct protein zones, each representing different protein fractions:

• Albumin zone: The largest peak, representing approximately 60% of total serum protein 2
• Alpha-1 globulin zone: Contains acute phase proteins 2
• Alpha-2 globulin zone: Contains haptoglobin and other inflammatory markers 2
• Beta globulin zone: Contains transferrin and complement proteins 2
• Gamma globulin zone: Contains immunoglobulins (antibodies) produced by plasma cells 2

Abnormal SPE Patterns

Monoclonal Gammopathy Pattern (M-Spike)

The most clinically significant abnormal pattern is a monoclonal spike (M-spike), which appears as a sharp, narrow peak typically in the gamma region, indicating clonal proliferation of plasma cells producing a single type of abnormal antibody. 1, 2

• The M-spike represents a monoclonal protein produced by a single clone of plasma cells and suggests multiple myeloma, Waldenström's macroglobulinemia, or monoclonal gammopathy of undetermined significance (MGUS) 1
• The height and area under the M-spike quantifies the amount of abnormal protein present, with MGUS typically showing M-protein <30 g/L while multiple myeloma typically shows higher levels 1
• M-bands can occasionally appear in the beta region rather than gamma region, which may simulate biclonal gammopathy 3

Critical caveat: Approximately 15-20% of myeloma cases produce only light chains without a visible spike on standard SPE, requiring urine testing or serum free light chain assays for detection 1

Polyclonal Gammopathy Pattern

A polyclonal increase appears as a broad-based elevation in the gamma region (not a sharp spike), representing increased production of multiple immunoglobulin types from many different plasma cell clones in response to chronic inflammation, infection, or autoimmune disease. 4

• Common causes include chronic infections (such as bronchiectasis showing elevated IgG and IgA), autoimmune disorders, and chronic liver disease 4
• This pattern must be distinguished from monoclonal gammopathies through immunofixation electrophoresis when there is clinical suspicion 4

Hypogammaglobulinemia Pattern

• Decreased or absent gamma globulin peak indicates immunodeficiency 5
• Important pitfall: Even with apparently normal SPE and hypogammaglobulinemia, immunofixation can still reveal an M-protein in 9.7% of cases 5
• Predictors of occult M-protein include elevated alpha-2/alpha-1 globulin ratio, low hemoglobin, and elevated creatinine 5

Diagnostic Algorithm Following Abnormal SPE

When SPE shows an M-spike or suspicious pattern, the National Comprehensive Cancer Network mandates the following reflex testing sequence: 1

1. Serum immunofixation electrophoresis (SIFE) to identify the exact immunoglobulin type (IgG, IgA, IgM) and light chain (kappa or lambda) 1
2. Quantitative immunoglobulin levels (IgG, IgA, IgM) 1
3. Serum free light chain assay with kappa/lambda ratio 1
4. 24-hour urine collection for urine protein electrophoresis (UPEP) and urine immunofixation electrophoresis (UIFE) 1

Clinical Action Thresholds

Any detected monoclonal protein on SPE requires referral to a hematologist/oncologist, with urgent referral (within 1-2 weeks) mandated for patients with: 1

• Significant M-protein spike
• Accompanying symptoms (bone pain, fatigue, weight loss)
• Anemia
• Renal dysfunction
• Hypercalcemia

Performance Characteristics in Clinical Practice

• When evaluating radiolucent bone lesions, SPE has 71% sensitivity and 83% specificity for plasma cell neoplasms, with high negative predictive value (94%) but low positive predictive value (47%) 6
• This means SPE is better at ruling out myeloma than ruling it in, and should not be used alone for definitive diagnosis 6
• Machine learning algorithms now achieve 89.9% sensitivity and 99.8% specificity, outperforming human experts 7