Skip to main content
. 2015 May 8;4:e07218. doi: 10.7554/eLife.07218

Figure 2. Atypical and classical MBCs are similar in isotype distribution, replication history and Ig repertoire characteristics.

(A) Flow cytometry data showing the distribution of IgM+ and IgG+ cells as a percentage of each B cell subpopulation in Malian adults (n = 7 subjects). (B) Purity of B cell subpopulations as a percentage of mature B cells following FACS sorting. (C) Average number of in vivo replications determined by KREC assay for each B cell subpopulation in Malian adults (n = 6 subjects). (D) Somatic hypermutation rates of unswitched and switched B cell subpopulations based on IgH sequence analysis. Shown is average of 4 Malian adults. (E) Clonality of B cell subpopulations determined by IgH sequence analysis and displayed by size-ranking clonal lineages from top (largest) to bottom (smallest) along the y-axis representing 50% of all sequences from the largest clones. X-axis is the normalized lineage size of each clone (percentage of the total number of sequences). Shown is data from 4 Malian adults. (F) Percentages of IgH sequences in both unswitched and switched B cell subpopulations that are shared by other subpopulations. Each bar represents a different subject (n = 4 subjects). Horizontal bars and whiskers represent means and SD, respectively. Horizontal bars on (D) represent means. p values determined by ANOVA with Tukey's multiple comparisons test (A and C).

DOI: http://dx.doi.org/10.7554/eLife.07218.004

Figure 2.

Figure 2—figure supplement 1. Flow cytometry gating strategy to determine purity of naïve B cells (CD10−CD19+CD20+CD21+CD27−) (A), classical MBCs (CD10−CD19+CD20+CD21+CD27+) (B), and atypical MBCs (10−CD19+CD20+CD21−CD27−) (C) after flow cytometry sorting.

Figure 2—figure supplement 1.

Shown are representative dot-plots of a Malian adult.
DOI: http://dx.doi.org/10.7554/eLife.07218.005
Figure 2—figure supplement 2. Bioinformatics analysis of NGS data.

Figure 2—figure supplement 2.

IgH repertoire was analyzed using a bioinformatics pipeline written and assembled by Dr Alex Rosenberg and Chris Fucile from University of Rochester School of Medicine. Steps for processing data are presented with individual parameters to the right. The pipeline makes use of publicly available software (fastq-joiner), public resources (IMGT/HighV-Quest) and custom software written in Perl and Matlab. After raw reads are joined, the sequences are run through a strict quality filter that removes any sequences less that 200 bp and/or containing more than 0, 10, and 15bp below Q10, Q20, and Q30 quality thresholds, respectively. The isotype of each sequence is determined based on the identified constant region primer and then 100,000 sequences is randomly selected to be aligned to VH germline genes using IMGT/HighV-Quest. Results of the alignment are filtered for unknown or unproductive sequences and then custom software clusters sequences into clones based on identical VH, JH, HCDR3 length, and 85% HCDR3 similarity. The data obtained from IMGT/HighV-Quest is retained through the process and used for other data analyses.
DOI: http://dx.doi.org/10.7554/eLife.07218.006