immunology.sciencemag.org/cgi/content/full/4/41/eaay6125/DC1

Supplementary Materials for

Incomplete genetic reconstitution of B cell pools contributes to prolonged

immunosuppression after measles

Velislava N. Petrova*, Bevan Sawatsky, Alvin X. Han, Brigitta M. Laksono, Lisa Walz, Edyth Parker, Kathrin Pieper, Carl A. Anderson, Rory D. de Vries, Antonio Lanzavecchia, Paul Kellam, Veronika von Messling,

Rik L. de Swart, Colin A. Russell*

*Corresponding author. Email: vp5@sanger.ac.uk (V.N.P.); c.a.russell@amc.uva.nl (C.A.R.)

Published 1 November 2019, Sci. Immunol. 4, eaay6125 (2019)

DOI: 10.1126/sciimmunol.aay6125

The PDF file includes:

Text. Evaluation of technical accuracy of the obtained BCR repertoires from individuals B35 and B42. Fig. S1. Example of FACS strategy for stratification of lymphocyte populations. Fig. S2. Repertoire sampling across time points. Fig. S3. Changes in clonotype composition of the B naïve compartment.

Other Supplementary Material for this manuscript includes the following: (available at immunology.sciencemag.org/cgi/content/full/4/41/eaay6125/DC1)

Table S1. Sampling points and clinical features of measles cohorts (in Excel spreadsheet). Table S2. FACS-sorted lymphocyte counts and frequencies across sampling points (in Excel spreadsheet). Table S3. Read processing information and accession numbers for BCR sequencing data (in Excel spreadsheet). Table S4. Primer sequence information for BCR sequencing (in Excel spreadsheet). Table S5. Raw data in Excel spreadsheet.

Text. Evaluation of technical accuracy of the obtained BCR repertoires from

individuals B35 and B42.

Measles virus infected individuals B35 and B42 showed an extreme phenotype of repertoire

restriction in the B naïve compartment. To ensure that the observed phenotype is not a

technical artefact, we evaluated each stage of processing of these samples and considered the

different possible sources of technical variation:

1. FACS sorting failure or operator bias

The four samples (two PRE/POST pairs) from these two individuals were sorted on two

different days. The PRE/POST sample pair for individual B35 was sorted on 21/01/2016

together with sample B16, while the B42 pair - on 08/02/2016 together with samples B71 and

B78. All samples were sorted by the same operator under the same gating and compensation

conditions as shown in Fig. S1. No FACS failures were reported on these dates and the same

immunological effect is not observed in individuals B16, B71 or B78 suggesting that the

genetic restriction does not result from a compromised cell gating or FACS sorting.

2. Low frequency of sorted B cells

The post measles B naive samples of individuals B35 and B42 had 98,748 and 65,600 cells in

the CD19+CD27

- B naïve compartment which is higher than the median and the average

sorted cell counts for this compartment across all tested individuals (median cell count -

36,518; average cell count - 58,868 respectively). The memory compartment for individuals

B35 and B42 was also sampled at sufficient depth with 40,567 cells (B35) and 15,646 (B42)

sorted B memory cells compared to median and average of 6,246 and 10,883 cells for the

remaining samples. Therefore, we do not see any evidence of specific biological

undersampling of the naïve or memory B cell compartment that can drive the observed

genetic diversity in individuals B35 and B42 post measles.

3. Sequencing failure or insufficient sequencing depth

The four samples of individuals B35 and B42 were sequenced on the same lane together with

samples B16, B25, B27 and B54. The derived paired reads for the post-measles naïve B cell

samples were 668,013 for B35 and 665,797 for B42 which were consistent with the average

read count for the rest of the samples - 627,911. Therefore, we do not see any evidence of

lower read depth driving the observed restriction in the naïve BCR repertoire of individuals

B35 and B42.

Fig. S1. Example of FACS strategy for stratification of lymphocyte populations.

Peripheral blood mononuclear cells were first sorted based on size and viability (using DAPI

staining). T and B lymphocytes were stratified based on the expression of CD3 and CD19

cell surface markers, respectively. T naive and T memory populations were sorted from the

CD3+ lymphocyte population based on expression of CD45RA and CD45RO surface

markers, respectively. B naive cells were sorted from the CD19+ cell population based on low

expression of CD27 marker. B memory cells and plasmablasts were sorted from the CD27+

population based on low and high expression of CD38 marker respectively. The scheme

below is based on 20,000 sorting events. The raw counts of sorted lymphocyte populations

are shown in table S2.

Lymphocytes

CD38 Neg CD38 Pos

B/T cells T naive/memory

B memory/plasmablastsB naive

Single cells Live/Dead cells

Fig. S2. Repertoire sampling across time points.

A. Number of FACS sorted B naive (CD19+CD27

-) and B memory (CD19

+CD27

+) B cells

for individuals from each cohort across sampling timepoints. Gating is based on the strategy

presented in fig.S1. B. Number of IGHV gene-matched BCR reads with correct ORF for

individuals from each cohort across sampling timepoints. Significance of the differences in

cell counts and read depth across timepoints was calculated for each cohort using Wilcoxon

rank-sum test. No significant differences (i.e. P < 0.05) were identified for any of the cohorts.

Fig. S3. Changes in clonotype composition of the B naïve compartment.

Kernel density distributions of the clonotype frequencies before (red) and after (green)

infection or vaccination. Clonotypes were defined as B cell receptors with identical IGHV,

IGHJ gene and CDR3 length. Only individuals whose clonotype distribution is significantly

different across timepoints are shown. Significance was determined using Wilcoxon rank-

sum test using FDR correction q-value < 0.05). Since changes in clonotype frequency can

have the same absolute value with different direction of the effect, repertoires undergone

significant shift in clonotype distribution can appear to have similar kernel densities (e.g.

B52). Thus, we have provided the individual clonotype frequency distributions across all

individuals in Supplementary Data File 1 and all clonotype frequency files used as input in

our GitHub repository (See Materials and Methods).