High-throughput single cell data analysis - A tutorial

White blood cells protect the body against disease but may also cause chronic inflammation, auto-immune diseases or leukemia. There are many different white blood cell types whose identity and function can be studied by measuring their protein expression. Therefore, high-throughput analytical instruments were developed to measure multiple proteins on millions of single cells. The information-rich biochemistry information may only be fully extracted using multivariate statistics. Here we show an overview of the most essential steps for multivariate data analysis of single cell data. We used white blood cells (immunology) as a case study, but a similar approach may be used in environment or biotech research. The first step is analyzing the study design and subsequently formulating a research question. The three main designs are immunophenotyping (finding different cell types), cell activation and rare cell discovery. When preparing the data it is essential to consider the design and focus on the cell type of interest by removing all unwanted events. After pre-processing, the ten-thousands to millions of single cells per sample need to be converted into a cellular distribution. For immunophenotyping a clustering method such as Self-Organizing Maps is useful and for cell activation a model that describes the covariance such as Principal Component Analysis is useful. In rare cell discovery it is useful to first model all common cells and remove them to find the rare cells. Finally discriminant analysis based on the cellular distribution may highlight which cell (sub)types are different between groups.

Inability of monoclonal anti-light chain antibody to detect clonal B-cells in a patient with follicular lymphoma by multicolor flow cytometry

BACKGROUND

Our recent publication "Inability of a monoclonal anti-light chain antibody to detect clonal plasma cells in a patient with multiple myeloma by multicolor flow cytometry," underlined the importance of choice of antibodies to detect cytoplasmic light chains. Our present study extends this finding for detection of surface immunoglobulin (SIg) light chains on clonal B-cells.

METHODS

Multicolor flow cytometry was used for analyzing bone marrow (BM) from a patient with a CD10-positive follicular lymphoma for infiltrating clonal B-cells.

RESULTS

In the BM aspirate, B cells could be identified expressing CD19, CD10, and high levels of CD20. No SIg light chain expression was found on this population of B cells employing monoclonal antibodies. Re-analysis using polyclonal antibodies against SIg light chains, revealed presence of lambda light chains on the CD10positive B-cells.

CONCLUSIONS

These data illustrate when antibodies against SIg light chains are employed for B-cell clonality assessment, polyclonal antibodies are preferred over monoclonal antibodies.