1. Immunology and Inflammation
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Capturing change in clonal composition amongst single mouse germinal centers

  1. Daniel J Firl
  2. Soren E Degn
  3. Timothy Padera
  4. Michael C Carroll  Is a corresponding author
  1. Cleveland Clinic Lerner College of Medicine, United States
  2. Boston Children’s Hospital, United States
  3. Howard Hughes Medical Institute, United States
  4. Aarhus University, Denmark
  5. Massachusetts General Hospital, United States
  6. Harvard Medical School, United States
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Cite this article as: eLife 2018;7:e33051 doi: 10.7554/eLife.33051

Abstract

Understanding cellular processes occurring in vivo on time scales of days to weeks requires repeatedly interrogating the same tissue without perturbing homeostasis. We describe a novel setup for longitudinal intravital imaging of murine peripheral lymph nodes (LNs). The formation and evolution of single germinal centers (GCs) was visualized over days to weeks. Naïve B cells encounter antigen and form primary foci, which subsequently seed GCs. These experience widely varying rates of homogenizing selection, even within closely confined spatial proximity. The fluidity of GCs is greater than previously observed with large shifts in clonality over short time scales; and loss of GCs is a rare, observable event. The observation of contemporaneous, congruent shifts in clonal composition between GCs within the same animal suggests inter-GC trafficking of memory B cells. This tool refines approaches to resolving immune dynamics in peripheral LNs with high temporospatial resolution and minimal perturbation of homeostasis.

https://doi.org/10.7554/eLife.33051.001

Introduction

The advent of intravital imaging techniques allowing sequential, continuous imaging of tissues in the live organism for up to 8–12 hr has significantly advanced several fields over the past decade, not least within immunology (Allen et al., 2007; Miller et al., 2002; Roozendaal et al., 2009; Schwickert et al., 2007). Initially confined to peripherally accessible tissues, the scope of targetable locations has recently expanded notably to include for example the spleen in the peritoneal cavity (Arnon et al., 2013), and even the heart within the thoracic cavity (Vinegoni et al., 2015). However, a significant gap has remained between the capability for continuous imaging of individual animals on time scales of less than half a day, and the statistically based analysis of timed cohorts of experimental animals on longer time scales. This gap has narrowed in the field of neurobiology by the advent of techniques such as the cranial (Holtmaat et al., 2009) and the thinned skull (Yang et al., 2010) windows, and similar methodologies have been harnessed for long-term imaging of tumors, such as the mammary window (Kedrin et al., 2008), and the abdominal imaging window (Ritsma et al., 2013). Transecting the fields of tumor biology and immunology, a discontinuous longitudinal imaging model was recently applied to study dissemination of lymphoma in the murine lymph node (LN) (Ito et al., 2012), and the lack of angiogenesis in the growth of LN metastasis was investigated using a modification of the chronic mammary fat pad window model (Jeong et al., 2015). A model of LN transplantation to the murine ear and subsequent imaging has also been reported (Gibson et al., 2012). Notwithstanding these efforts, a full-fledged setup for longitudinal imaging of complex cellular dynamics in LNs in living animals has been lacking. Here we present such a setup, and its application to the central immunological question of initiation and clonal evolution of single germinal centers (GCs) in response to foreign, as well as self-antigen.

Adaptive immune responses are crucial to long-lived immunological memory, providing more rapid and effective clearance of previously encountered pathogens (Kurosaki et al., 2015; McHeyzer-Williams et al., 2011). Following antigen drainage and transport to the LN, cognate naïve follicular B cells recognize antigen and become activated through B cell receptor (BCR) cross-linking and signaling (Barnett et al., 2014; De Silva and Klein, 2015). Concomitantly, cognate T cells recognize antigen-derived peptides presented by DCs and they take on the characteristics of T follicular helper cells (Tfh), allowing them to provide a necessary second signal to begin an adaptive immune response (Barnett et al., 2014). Following antigen engagement and T cell help, cognate B cells seed early GCs (De Silva and Klein, 2015; Heesters et al., 2014). In the GCs they interact with follicular dendritic cells (FDCs), stromal cells that act as long-lived reservoirs of antigen (Heesters et al., 2013) and may contribute cytokines to shape the response (Das et al., 2017), and with Tfh (Shulman et al., 2014). B cell clones which successfully take up and present antigen to Tfh receive survival and proliferation signals. Establishment of the GC response involves expansion of dozens to hundreds of cognate B cell centroblasts, which cycle between light and dark zones (Tas et al., 2016; Victora et al., 2010). In an iterative process of division and diversification through somatic hypermutation (SHM) in the dark zone (DZ), and affinity-dependent antigen probing and presentation in the light zone (LZ), Darwinian selection drives the process of affinity maturation (De Silva and Klein, 2015; Heesters et al., 2014; Hauser et al., 2010; Beltman et al., 2011).

During commitment to the GC phenotype, B cells express activation-induced cytidine deaminase (Aicda), which is the central enzyme responsible for the process of SHM (Allen et al., 2007; De Silva and Klein, 2015; Heesters et al., 2014; Victora et al., 2010; Beltman et al., 2011). In recent years, numerous genetic multicolor systems have been developed for lineage tracing studies, broadly referred to as ‘Brainbow’ or ‘Confetti’ systems (Weissman and Pan, 2015; Bonaguidi et al., 2011; Centanin et al., 2014). Recently, an Aicda-CreERT2 driver line was combined with the Confetti system, allowing visualization of GC selection in vivo (Tas et al., 2016). Combining time point analyses with population-modeling, it was found that GC responses are started by as little as a few hundred naïve B cells reacting to the challenging antigen and that GCs proceed towards pauciclonality at differential rates. Here, we harness the power of this experimental system by using longitudinal intravital imaging and further explore the mechanisms behind GC formation and shifts in clonality.

Hypothesizing that GC dynamics can be better resolved by following single GC over time, increasing the power of observations exponentially, we combined microsurgical implantation of an imaging window onto the inguinal LN (iLN) with gentle multiphoton imaging and the power of the Confetti reporter. The early events of GC initiation were followed by tracking B cells migrating to and between early GC and primary foci. We also measured the dynamics of B cell clonality at the single GC level over time.

Results

The Chronic lymph node window (CLNW) model

To allow longitudinal studies of cellular dynamics in the LN, we adopted a modification of the chronic mammary fat pad window model referred to as the chronic lymph node window (Jeong et al., 2015). Using sterile surgical technique, window chambers were surgically implanted over the iLN, and a small incision made in the skin to expose the underlying node (Figure 1). The chambers were sealed with a coverslip and O-ring (Figure 1D) to prevent contamination of the field by external contaminants, further mice received one week of perioperative antibiotics. Following implantation, mice were periodically anaesthetized and imaged using multi-photon microscopy with a customized setup (Materials and methods and Figure 1E).

Figure 1 with 1 supplement see all
Design and surgical implantation of titanium CLNW.

(A) CAD schematic detailing the dimensions of the titanium plates opposed to fit a chronic lymph node window (CLNW). (B) Following shaving the surgical site, window plates are briefly overlaid for surgical planning and prior to sterile preparation. (C) Following surgical implantation of the CLNW, transillumination confirms the node centered appropriately within the imaging range. (D) Photomicrograph of completed CLNW demonstrating coverglass, incision closure, and LN placement within the center of viewing range. (E) Imaging setup demonstrating anesthetized mouse on a heated H2O reservoir with clamp fixation of the window minimizing motion artifact.

https://doi.org/10.7554/eLife.33051.002

Homeostatic conditions are maintained in the CLNW model

To ensure that the surgery and window implantation did not significantly affect any observations made, several control experiments were performed. The percent change in body weight was followed in a group of CLNW implanted mice and a control group that was sham operated (skin opened, tissue over the node removed and then skin closed, recovered as standard) but did not receive the window implantation. As seen in Figure 2A, CLNW bearing mice experienced a slightly greater initial weight loss following the surgical procedure than the controls. However, by Day 3, they had rebounded to preoperative weight, and subsequently followed the same weight course development as control animals (p=0.107 for treatment in two-way ANOVA) (Figure 2A). We also verified intact and sufficient lymphatic drainage to the implanted node by injecting and measuring PE-IC complexes, either before or after window implantation (Figure 2—figure supplement 1). Absence of fluid accumulation and swelling in the surrounding tissue confirmed adequate lymphoid drainage from the site. Following conclusion of the window implantation (14 days), mice were sacrificed, and the window implanted iLN was compared with the contralateral iLN. No gross histological differences (size, shape, vascularization) were noted (representative examples in Figure 2B). Absolute numbers of immune cell subsets were quantified by flow cytometric analyses of spleen (Figure 2C), mesenteric LN (mLN, Figure 2D), the contralateral iLN (Figure 2E), and the CLNW implanted iLN (Figure 2F), as well as the brachial LN (bLN) (Figure 2G). There was a decrease in relative T cell composition (CD4 and CD8) with increase in B cell (B220) composition. A trend towards an opposite effect was observed in the brachial LN which may indicate an effect on systemic lymphatic flow as opposed to true inflammatory processes. Additionally, there were slight increases in Gr1 positive cells, representing both Ly6C (monocyte) and Ly6G (neutrophil) subsets in the spleen and window implanted iLN, and a small increase in GC B cells (Figure 2C and F). These changes could potentially be attributed to a low-level sterile inflammation associated with disruption of the overlying tissue and exposure of the node, whereas they appeared too mild to be caused by contamination of the window site and unlikely related to antibiotics as sham operated mice also received them. We concluded that CLNW implantation did not cause a dramatic perturbation of homeostatic conditions.

Figure 2 with 1 supplement see all
CLNW implantation does not dramatically perturb homeostasis.

(A) Daily weight monitoring of sham operated and window implanted mice demonstrates a slightly more dramatic post-operative decline followed by correction to the level of controls by day 3. Experiments conducted on more than three independent occasions. (B) Gross morphological assessment following 2 weeks within the CLNW. The sham operated LNs [I; two vertical nodes at left] were found to be similar to imaged nodes [ii; two vertical nodes at right] on the basis of size, vascularity, and consistency. (C) Flow cytometric quantification of numbers of live, CD45+ gate for Gr-1+, CD4+, CD8+, B220+, and GL7hiCD38lo GC B cells in spleen compared between sham operated and CLNW implanted mice (n = 3 per group). Mean ± SEM indicated. P values obtained from chi squared analysis of proportions of live CD45+ cells. (D) Similar to (C) except for mesenteric lymph nodes. (E) Similar to (C) except for the contralateral (opposite operated side) inguinal lymph nodes. (F) Similar to (C) except for CLNW implanted inguinal lymph nodes. (G) Similar to (C) except for brachial lymph nodes.

https://doi.org/10.7554/eLife.33051.004

Initiation of germinal centers

To investigate the initiation of GCs, naïve B cells were purified from spleen and lymph nodes of B1-8hi CFP donors and transferred into CCL19-Cre EYFP mice (displaying YFP labeling of fibroblastic reticular cells (FRCs) as a ‘counterstain’ [Cremasco et al., 2014]) at Day −2. Then recipients were immunized with 15 µg NP-CGG subcutaneously in the groin and footpad bilaterally on Day 0, and window implantation was performed on Day 2. Mice were imaged every 24 hr starting from 48 hr post immunization, and the frequency of CFP+ cells per follicle was quantified (Figure 3A and B). Paracortical CFP+ cells were seen as early as 48–72 hr in very small numbers (Figure 3C) and small primary foci were observable around 72–96 hr (Figure 3D). Interestingly, on more than one occasion between 72–120 hr, CFP+ cells were seen to traffic between the core, high density GC and the paracortical clusters (Figure 3—video 1). This may demonstrate simple continuation of primary foci efflux or early trafficking between the primary foci and GCs. This movement ceased as the paracortical clusters dissipated 120–168 hr into the response. By 120 hr, GC were seeded, and within these, CFP+ cells were seen to expand at an exponential rate over the subsequent days.

Figure 3 with 2 supplements see all
Observing the establishment of GC to foreign antigen using the CLNW.

(A) Schematic outlining the adoptive transfer of B-18hi cells into CCL19-CreEYFP recipients on day −2, the immunization with NP-CGG bilaterally on day 0 and surgical implantation of the CLNW on day two with subsequent daily imaging. Experiments conducted on two independent preparations with two independent mice. (B) Graphical quantification of CFP+ cells within longitudinally observed single follicles from days 2–7 demonstrating an average exponential increase in cellularity with a surprising level of variability. Individual black lines with different symbols for each Fo as in the legend, blue overlay is the average fit line. (C) Following 7 days of imaging, a single CLNW is observed at constant depth (~150 um) and images ‘stitched’ to form a GC map. *represent the location of GC, of varying intensity due to depth. Second harmonics generation from collagen rendered in blue, YFP+ cells rendered in green and CFP+ cells rendered in blue. (D) Follicle (i) from empty through evolution to a germinal center over the 48–168 hr observation, shown in (C), white arrowhead demonstrates location of extafollicular CFP+ cells. (E) Follicle (ii) from empty through evolution to a germinal center over the 48–168 hr observation, shown in (C), white arrowhead demonstrates location of extafollicular CFP+ cells.

https://doi.org/10.7554/eLife.33051.008

The B1-8hi CFP+GC initiation in response to NP-CGG was modelled by fitting exponential growth functions to the observed data (Table 1). The CFP+ cells in five out of eight GC conformed to the predicted response dynamics (Figure 3—video 2), displaying doubling times on the order of 6–12 hr, in line with what has been previously reported (Fo1, 2, 4, 6, 7) (Anderson et al., 2009). CFP+ cells in the remaining three GC (Fo3, 5, 8) displayed much longer doubling times, reflective either of their failure to establish a GC altogether or to become GC winners. Notably these also expressed poor fit statistics. The average of the GCs observed conformed to the expected results based on earlier investigations, but the longitudinal approach allowed identification of a fraction (Fo3, 5, 8; 3/8, 37.5%) of non-conforming events, which would not have been observable with traditional time-point analyses. The observation of follicles in which CFP+ cells were present early on, expanded until around 132 hr, but then subsequently waned, was remarkable. As indicated, this could be caused by competition with a dark clone (from the endogenous repertoire) or, perhaps less likely, a failure to establish productive GC. The former possibility would suggest very early competition between B cell clones that occurs already before or during seeding of GCs.

GC evolution is accurately reflected in the CLNW model

Although our data suggested that overall homeostasis was maintained in the CLNW model, the question remained whether longer term GC evolution could be perturbed. To investigate this, we employed a novel chimeric model of spontaneous autoreactive GC formation (the ARTEMIS model, [Degn et al., 2017]) or foreign-antigen NP-CGG immunization (imm). Of note, although these models are very different, one reflecting autoreactive responses to complex antigen, the other a response to a less complex foreign antigen, they were found to have overall similar clonal evolution kinetics by extensive conventional time-point analyses using the Confetti model (Degn et al., 2017). For autoreactive GC, following reconstitution, chimeric mice (n = 2) were tamoxifen treated to switch on the confetti reporter, and then received CLNW implants. For foreign antigen GC, Confetti mice (n = 2) were immunized subcutaneously with NP-CGG, were tamoxifen treated, and then received CLNW implants (Figure 4A). At specified time points, the window implanted iLNs were imaged, and then the mice were sacrificed and the contralateral iLN explanted and imaged. For each paired window iLN and contralateral explanted iLN, the average of the frequency of most dominant clone (Figure 4B–C), sum of most and second-most dominant clones (Figure 4D), and divergence index (Figure 4E), were calculated and correlation plots were generated. Example paired images can be seen in Figure 4F–K for window (top) and explant (bottom) LNs. Although depth resolution and image quality was inferior in vivo, compared to explant, color dominance was accurately quantifiable. As can be seen, based on all three parameters, windows and explants correlated well, with the slope approximating 1. For example, for the NP-CGG response, we found similar mean clonality at day 11 and day 25 post tamoxifen treatment regardless of whether GCs were analyzed via explant or by longitudinal imaging (Day 11 post imm 44.8% vs 42.8% and Day 25 post imm 77.7% vs 80.3%, respectively; p=0.890). Therefore, the CLNW was not found to adversely affect the GC dynamics and clonal evolution.

Establishing fidelity of clonal development in CLNW observed GC.

(A) Schematic overview of experimental approach for generation and analysis of CD45.2 Aicda-Cre-Confetti and CD45.2 Aicda-Cre-Confetti 564 mixed BM chimeras. Experiments conducted on two independent preparations with two independent mice each. (B) Overview of fidelity of the window imaging model. The frequency of most (first) dominant colors observed in individual mice between GCs in the CLNW as compared to the explanted contralateral LN observed ex vivo. These observations are shown in both the average (bar with standard error) as well as individual values plotted per the key for both NPCGG and 564 at early (day 11/12) and later (day 24/25) times. (C) Comparing the frequency of most (first) predominant colors observed in individual mice between GCs in the CLNW as compared to the contralateral LN observed ex vivo. Both NP-CGG and 564 comparisons are included and a linear regression is fitted, demonstrating a beta coefficient overlapping with unity, 1.04 (0.99–1.08). The mean of an average of 4.1 GC per LN for each mouse at each time point, for both NP-CGG and 564, is indicated by each point. (D) Similar to (C) except comparing the frequency of the most and second most predominant colors as observed ex vivo from the contralateral LN and in vivo in the CLNW. Regression line with beta coefficient overlapping unity, 0.99 (0.96–1.02). (E) Similar to (C) except comparing the divergence index for color composition by GC as observed ex vivo from the contralateral and in vivo in the CLNW. Regression line with beta coefficient overlapping unity, 0.92 (0.55–1.30). (F–G) Representative images used for quantification of color between CLNW observed (top) and explant imaged (bottom) within the same mouse. (H–I) As in (F–G) but a separate mouse. (J–K) As in (F–G) but a separate mouse.

https://doi.org/10.7554/eLife.33051.012

Single GC evolution in response to foreign antigen

To analyze single GC dynamics in response to foreign antigen, Confetti mice were immunized subcutaneously with NP-CGG, were tamoxifen treated, then received CLNW implants, and were followed over time (Figure 5A). For each time point, the frequency of the most dominant clone (Figure 5B) and the divergence index (Figure 5C) for every GC was quantified. The overall evolution towards pauciclonality was also quantifiable using a third metric, which we coined the meander index, and which is for all practical purposes the integral of the modulus for derivative of the divergence index, and could thereby also be considered a measure of distance traveled (Figure 5D). In agreement with previous observations, GC were found to evolve towards pauciclonality at widely varying rates (Tas et al., 2016). However, whereas the earlier work arrived at this conclusion based on population averages and variation within these averages across timed cohorts, the CLNW allowed resolution of individual GC over time, revealing additional information (examples in Figure 5E–H, please note that the representative pictures do not necessarily fully reflect the quantification plots, as the latter are based on several z-slices throughout the depth of the GCs). One GC, GC H, displayed a prominent double clonal inversion event (Figure 5H). GC H was found already by day seven to be >60% single-colored (RFP), indicating early clonal expansion. The expanded RFP clone(s) subsequently stagnated, dropped by day 13, and was supplanted by a YFP clone(s), which in turn was supplanted by a YFP+ mCFP clone(s) by day 19. Although one color does not necessarily signify a single clone in the present setup, and we therefore cannot exclude that one or several of the clonal events observed were driven by multiple clones, it appears highly unlikely that two consecutive clonal inversion events within the same GC were caused by such chance. Furthermore, prominent clonal inversion was also observed in GC F and G (Figure 5F and G), supporting the validity of the observation. One potential concern could be a phototoxicity issue, whereby the imaging itself could influence the predominant clones adversely, leading to outgrowth of minor clones. However, in agreement with previous observations, despite great heterogeneity, on average GCs were seen to evolve steadily towards pauciclonality at a rate comparable to that previously reported in time-point analyses (Tas et al., 2016), suggesting that clonal evolution was unperturbed in the present setup. In summary, the single-GC temporal resolution unmasked great heterogeneity in the evolution of GC over time. Our observations revealed that clonal inversion, although a rare event, does occur, and reveals a much greater GC fluidity than previously appreciated, underscoring the power of the longitudinal approach.

Following the clonal composition of individual GC responding to foreign antigen over days to weeks.

(A) Schematic overview of experimental approach for NP-CGG immunization (imm), tamoxifen treatment, surgical implantation of the CLNW, and multiphoton intravital microscopy in CD45.2 Aicda-Cre-Confetti mice. Data represent one independent experiment with three mice each. (B) Frequency of most dominant color for each individual GC as observed from 7 to 19 days post-imm in 3 mice. (C) Divergence Index for each individual GC as observed from 7 to 19 days post-imm in 3 mice. (D) Meander Index for each individual GC as observed from 7 to 19 days post-imm in 3 mice. (E) GC labeled E as in (B), (C), and (D) with pie chart to elaborate quantification below at each observation from days 7–19. (F) GC labeled F as in (B), (C), and (D) with pie chart to elaborate quantification below at each observation from days 7–19. Prominent clonal inversion observed from days 11–15. (G) GC labeled G as in (B), (C), and (D) with pie chart to elaborate quantification below at each observation from days 7–19. (H) GC labeled H as in (B), (C), and (D) with pie chart to elaborate quantification below at each observation from days 7–19. Double clonal inversion observed from days 11–13 and days 17–19. Please note that in E-H, representative images and quantification charts do not display 1:1 correspondence, since the latter are based on multiple z-planes throughout the volume of each of the GCs.

https://doi.org/10.7554/eLife.33051.014

Single GC evolution in an autoimmune setting

We recently developed a novel chimeric model displaying spontaneous, autoreactive GCs (Degn et al., 2017). Time-point analyses indicated that the autoreactive GCs in this model evolved towards pauciclonality at a similar rate as foreign antigen GCs. Using the CLNW, we reevaluated the kinetics using the longitudinal analysis (Figure 6). Mixed bone marrow chimeras were set up, and following reconstitution, chimeric mice were tamoxifen treated to switch on the Confetti reporter, and then received CLNW implants (Figure 6A). Again, the frequency of the most dominant color (Figure 6B), the divergence index (Figure 6C), and the meander index (Figure 6D) were determined, and representative examples are shown in Figure 6E (again, representative images and quantification plots do not necessarily fully correlate, as the latter are based on multiple z-slices through the depth of the GCs). The findings in the autoreactive GC setting recapitulated those in the foreign-antigen scenario, and confirmed the possibility of clonal inversion (e.g., RFP clone(s) in Figure 6E, second row).

Following the clonal composition of individual autoimmune GC over days to weeks.

(A) Schematic overview of experimental approach for CD45.2 Aicda-Cre-Confetti 564 mixed BM chimeras elaborating tamoxifen treatment, surgical implantation of the CLNW, and multiphoton intravital microscopy. Data represent two independent experiments with three mice each. (B) Frequency of most dominant color for each individual GC as observed from 4 to 26 days post-tamoxifen induction in individual GC from 6 mice. (C) Divergence Index for each individual GC as observed from 4 to 26 days post-tamoxifen in 6 mice. (D) Meander Index for each individual GC as observed from 4 to 26 days post-tamoxifen in 6 mice. (E) Four representative GC labeled with pie chart to elaborate quantification below at each observation from days 4–26. Second row down, prominent double inversion event comparing days 12–14 and 20–22. Third row down, white arrow for marking the site of a cluster of extra-GC YFP+ cluster which compete in the RFP +dominant GC on days 24 and 26. Please note that the representative images and quantification charts do not display 1:1 correspondence, since the latter are based on multiple z-planes throughout the volume of each of the GCs.

https://doi.org/10.7554/eLife.33051.016

We additionally made the peculiar observation of a ‘satellite cluster’ of YFP cells outside an almost completely homogenized (RFP) GC by day 22 (third row, Figure 6E, see white arrow), followed by outgrowth of YFP cells in this GC. Whether these were residual YFP+ cells, regaining traction in the GC, or whether they were clones entering from outside this GC, was unclear. In several instances, we also observed that clonal bursts within one GC were followed by emergence of clones of similar color in neighboring GCs, raising the possibility of reentry of output memory B cells.

Modeling GCs as ecological communities

Data analysis revealed similar GC dynamics for both NP-CGG immunized mice and mice reacting to self-antigen. However, we made the anecdotal observation that several adjacent GCs within the same animals had similar color composition or shifts in color composition later in the response; greater than would be expected to occur at random. Numerous prior studies have indicated that GC B cells do not traffic between GC. However, it has been demonstrated that output memory B cells can reenter GCs and rediversify. Thus, we sought to test whether there was evidence of synchrony between GCs using classical methods from ecology. In principal components analysis (PCA), large datasets with complex variable sets can be deconstructed to dimensions of variance. These ‘principal components’ where observations are stripped of their relatedness to x variable, but rather vectors of the covariance matrix, allow an exploration of population dynamics, since it provides insight into the internal structure of the data. It is important to note that it approaches the question of variance rather than a specific hypothesis and as such is exploratory in nature. Following principal components development, data were examined first visually and then statistically for clustering by mouse along the variant axes (Figure 7A–C). There were several clusters of GC observations, which were clearly distinguishable from the larger dataset and which turned out to be derived from three of the nine mice, termed A, B, and C. Following this discovery, the GCs observed within these mice were plotted individually to examine the trends in the dominant colors by time (Figure 7D–F). Whereas Mouse A demonstrated RFP+ dominance with increasing penetration across all three GC, Mouse B was striking for a contemporaneous spike in the relative abundance of CFP+ cells. Finally Mouse C appeared to have similar composition of largely YFP+ and RFP+ cells in both GCs. Upon regression analysis, as factor variables, each of Mouse A, B, and C were statistically significantly associated with PC1, PC2, and PC3; despite adjustment for time and color confounders. Although these data do not provide a direct observation of synchrony between GCs, they support statistically the observation that within the same animal, in different GCs, similar colors had a tendency to move together contemporaneously, suggesting the possibility of reentry of output memory B cell clones to ongoing GCs.

Principal component analysis reveals global mouse association with variance in color relative frequency for adjacent GC.

(A) Following generation of principal components, GC data points are shown by principal component 1 (PC1) and principal component 2 (PC2). Data points are colored as Mouse A (blue), Mouse B (red), Mouse C (gold), and the remainder of GC (gray). Statistical association for PC1 was p<0.0001 for A, p<0.0001 for B, and p=0.5861 for C. For PC2 A was p=0.142, B was p<0.0001, and C was p<0.0001. For PC3 A was p=0.002, B was was p=0.005 and C was p=0.002. (B) As in (A) but for PC1 and PC3. (C) As in (A) but for PC2 and PC3. (D) Relative frequencies of the four most prevalent colors in all observed GC (#1–3) within the window of Mouse A. (E) As in (D) but for Mouse B. (F) As in (D) but for Mouse C.

https://doi.org/10.7554/eLife.33051.018

Discussion

Our development of the present protocol has been prevised by several elegant studies. Based on static and dynamic imaging data, Jenkins and colleagues generated a numerically, spatially, and temporally scaled simulation of the first 50 hr of the primary T cell-dependent immune response (Catron et al., 2004). Ito et al. employed an inguinal lymph node window chamber to conduct serial, but discontinuous, imaging of cancer cell dissemination in vivo (Ito et al., 2012). Padera and colleagues developed a chronic lymph node window model to investigate the growth and spread of lymph node metastases (Jeong et al., 2015; Meijer et al., 2017). Here we present longitudinal B cell imaging covering the first 150 hr of the primary T cell-dependent immune response to foreign antigen, from initial formation of primary foci to seeding of follicular GCs. We followed the clonal evolution of foreign antigen elicited GCs for up to three weeks; and we have similarly followed the clonal evolution of autoreactive GCs on a three-week time frame.

Follow-up of early time events has previously been hard to achieve, as classical MP-IVM approaches have been limited to time-frames of 8–12 hr maximum. While our results were overall in agreement with prior findings and with the simulation of Catron et al (Catron et al., 2004)., some additional insights could be gleaned from the longitudinal time resolution. First and not surprisingly, high-affinity B cells specific for the immunized antigen appeared in the paracortical region around 72 hr after immunization and then over the course of the next 12–24 hr migrated into the follicle where GCs developed. Whereas two of eight observed GCs (Fo1 and 4) demonstrated a neat exponential growth phenomenon with doubling time closely approximating the shorter end of past work using mathematical modeling following BrdU labeling (Table 1, Figure 3—video 1 and Figure 3—video 2 and [Anderson et al., 2009]), a further three demonstrated lower but still reasonable doubling time around ~12 hr (Fo2, 6, and 7) and the final three demonstrated longer doubling times > 12 hr (Fo3, 5, and 8). In silico analysis of the early response has previously been performed using average rates of division and cell death across multiple explanted GCs for fixed low and high affinity clones (Anderson et al., 2009). In contrast, this study relies on the affinity of the B1-8hi clone in the GC to estimate dynamics. Specifically, in GCs where the B1-8hi clones dominate, the doubling time observed will most closely approximate the ‘true’ underlying doubling rate as more of the GC is observed. However, in GCs with more competitive clones that are dark, the observed rate may deviate from the true GC dynamics since a greater proportion is unobserved. As competitive factors and/or GC viability factors begin to affect the dominance of the CFP+ clones, the estimates of doubling time will be artificially elevated, that is slower, as it has been shown that cell death as opposed to cell division is the primary effector of affinity-driven selection (Anderson et al., 2009). Thus GCs where the CFP+ clones are dying at relatively greater rates will have estimates with slower doubling times since the rest of the GC is unmeasured. Despite this caveat, within the same lymph node and within the same animal subjected to timed antigen exposure, expansion of primary foci and seeding of GCs, even in adjacent follicles, was found to occur at highly variable rates (Table 1).

Table 1
Modeling* the B1-8hi CFP+ GC Initiation to NP-CGG.
https://doi.org/10.7554/eLife.33051.020
Y0KTauDoubling time (hours)R2
Fo12.5 × 10−6 (-−8.5 × 10−6 – 1.4 × 10−5)0.110 (0.085–0.136)9.06 (7.35–11.81)6.28 (5.10–8.18)0.9980
Fo20.022 (−0.022–0.066)0.053 (0.042–0.065)18.73 (15.32–24.09)12.98 (10.62–16.70)0.9890
Fo31.469 (−3.945–6.883)0.018 (−0.006–0.0422)56.49 (23.68-inf)39.15 (16.41-INF)0.5811
Fo40.001 (0.001–0.001)0.072 (0.069–0.753)13.82 (13.28–14.40)9.58 (9.21–9.99)0.9998
Fo50.120 (−0.147–0.387)0.046 (0.033–0.060)21.66 (16.74–30.67)15.01 (11.61–21.26)0.9781
Fo60.005 (−0.005–0.015)0.055 (0.043–0.060)18.13 (14.91–23.13)12.57 (10.34–16.03)0.9895
Fo70.010 (−0.016–0.036)0.055 (0.039–0.071)18.26 (14.11–25.88)12.66 (9.78–17.94)0.9820
Fo80.472 (−0.873–1.817)0.014 (−0.005–0.034)69.67 (29.58-inf)48.29 (20.50-INF)0.5779
Average0.011 (0.006–0.016)0.056 (0.053–0.059)17.86 (17.08–18.70)12.38 (11.84–12.96)0.9996

However, several GCs that appeared to have adequate ‘seeding’ (by comparing relative infiltration of CFP+ cells) later went dark. This black-out phenomenon was observed in three of eight Fo (Fo3, 5, 8), which is surprising due to the significantly greater baseline affinity of the B1-8hi clones compared to the resting repertoire and may indicate (i) failure to establish a productive GC, (ii) entry of an endogenous dark clone with similar or superior affinity for the antigen and subsequent ‘winning-out’ of the GC leading to loss of CFP+, or (iii) that early competition took place and SHM led to the generation of a dark clone with greater affinity than the B1-8hi clones adoptively transferred prior to immunization. Although more difficult to assess due to complex technical limitations, (excitation wavelengths and spectral overlap of multiple fluorochromes with the multiphoton microscopy approach as well as genetic limitations) access to FDCs and to T-cell help may play a role in the described variation. Nonetheless this observation in multiple GCs followed across time in separate animals deserves further exploration.

Combining our intravital imaging approach with the Aicda-Confetti reporter of Tas et al., the process of clonal evolution could be followed over time with single-GC resolution. Again, our overall findings were in agreement with previous observations, but the longitudinal resolution allowed for additional layers of detail to be uncovered. The average clonal evolution towards pauciclonality was found to mask complex underlying processes entailing both clonal bursts, as previously reported, but also clonal ‘inversion events’, whereby a seemingly winning color was suddenly seen to lose competitive momentum allowing for emergence of one or several new colors. In the case of a single-color take-over, this event could of course be reflective of a clonal burst occurring in the new winning color, whereas this explanation fails to account for multi-color take-overs, since simultaneous clonal bursts are unlikely. Additionally, we cannot account for a drift in antigenic reactivities over time. It was recently reported that complex antigens drive permissive clonal selection in GCs (Kuraoka et al., 2016). Although we employed NP-CGG immunizations, where NP would be expected to be the immunodominant epitope on the B cell side, the CGG is a more complex mixture of chicken gamma-globulins, which may cater to multiple layers of T follicular helper cells, which could potentially skew responses over time. In this context, it is important to note that chiefly B cells responding to the same antigen or catering to the same T helper cells, would be able to enter an ongoing GC (Schwickert et al., 2007; Schwickert et al., 2009). This scenario is vastly more complex in the ARTEMIS model, which involves a very broad autoreactive repertoire, with increasing epitope spreading over time (Degn et al., 2017).

Of note, the GC that displayed the prominent sequential dual clonal inversion event, GC H (Figure 5H), was at the outset relatively small, and the final take-over by a YFP +mCFP clone coincided with a decrease in GC size coincident with the latest stages of the GC response. It is unclear what determines the size of GCs, and the notable variability in GC size within for example a single lymph node of an individual. One could speculate that it is related to the degree of successful affinity maturation, and in turn that clonal inversion is a more likely event in smaller, less successful, GC. This is supported by studies in both mice and humans that demonstrate larger GC structures in AID deficient subjects who have less SHM and affinity maturation (Fagarasan et al., 2002; Revy et al., 2000). However, this supposition is contraindicated in the present dataset by the clonal inversion event observed in GC G (Figure 5G), which was a massive GC.

Conversely, one could speculate that GCs that homogenize quickly do not ‘travel far’, and may be suggestive of less affinity matured clones as compared to GC which ‘churn’ through many clones with mutations accumulating on the way to clonality. However, elucidating this question would require an additional in-depth analysis of the mutational landscape across the observed clonal populations.

Finally, our studies were extended to the recently described novel ARTEMIS model for spontaneous autoreactive GCs (Degn et al., 2017) in combination with the Confetti reporter. This revealed that autoreactive GC responses were largely reflective of foreign antigen elicited GC clonal dynamics, and confirmed our insights from the foreign antigen GC scenario in an independent model.

It has previously been reported that GCs are open and dynamic structures (Schwickert et al., 2007), and that newly activated B cells can enter ongoing GCs (Schwickert et al., 2007). Whether this extends to short-term memory cells, allowing reseeding of GCs in a chronic GC setting remains an open question. However, secondary remodeling of BCRs of memory B cells has been observed (McHeyzer-Williams et al., 2015). In several instances, we observed that clonal bursts within one GC were followed by emergence of clones of similar color in neighboring GCs, suggestive of some level of inter-GC communication at the B cell level (Figure 7). Integrating the above-mentioned considerations derived from the available literature, this would not occur directly at the GC B cell level, but rather by exit and subsequent reentry of (short-term) memory B cells. At the time-scales presented here, this would only be expected to occur due to high (auto)antigen levels driving immediate re-engagement of the BCR following GC exit. Although this remains at present inference, future studies employing an Aicda-driven reporter coupled with CD40L-blockade to ablate all ongoing GCs and a subsequent investigation of reporter B cell reentry in reemergent GCs could provide greater clarity.

Along the same lines, our finding of trafficking between the high-density CFP+ core of dividing cells in the GC and the paracortical clusters (presumably primary foci) between 72–120 hr after immunization (Figure 3—video 1), may demonstrate a mechanism by which early, pre-GC, competition may influence the clonal composition of the GC and may contribute to the ‘black-out’ phenomenon described above.

The validity of the CLNW model was ascertained in experiments comparing weight curves between sham-operated and window-implanted animals, as well as basic cellular parameters of secondary lymphoid tissues, including window-imaged and contralateral inguinal lymph nodes. The most notable differences between sham and window animals were the increase in Gr1 cells in the spleen and the slight, but statistically significant increase in GC B cells in the window node (Figure 2). The latter finding in particular could be cause for concern in relation to the investigations of clonal dynamics presented here. Two factors could contribute to these differences: 1) the sterile, local inflammation driven by disruption of the overlying tissue, however gentle, and exposure of the underlying tissues; and 2) a non-sterile inflammation driven by exposure of the chamber to ambient skin commensals and opportunistic pathogens. Naturally, the former cannot completely be avoided, whereas the latter would be a greater cause for concern. We employed broad spectrum perioperative antibiotics in both sham and CLNW animals an effort to reduce the risk of meaningful perturbation further. Additionally, the comparison of clonal evolution in window and contralateral lymph nodes within the same animals, presented in Figure 4, confirmed that this process was not affected by the CLNW. Any potential effect might in part also be alleviated by the experimental setup, whereby immunization (foreign antigen GC) or establishment of autoreactive GCs in the chimeras, and tamoxifen-induced activation of the reporter precedes surgical placement of the window. Therefore, any potential GC response associated with the surgical procedure or subsequent contamination of the chamber (although none such were noted by us in the experiments presented), would be driven by unlabeled clones, which would not directly perturb results. Indirectly, outgrowth of a significant body of unlabeled, ‘dark’ clones, targeting ambient antigen (rather than the immunogen NP-CGG, or autoantigen in the autoreactive GC) would instead lead to GCs going dark. However, we did not observe any GCs going dark for the duration of the imaging experiments presented here, and the GCs that were followed displayed a consistent and robust labeling density, indicating that this was not an issue in the presented setup.

Therefore, with the present setup, cellular immune responses can now be followed at a temporal resolution of hours and days and weeks per experimental question at hand.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or referenceIdentifiersAdditional
information
AntibodyRabbit polyclonal
anti-B-Phycoerythrin
Rockland
Immunochemicals
Cat#200-401-099;
RRID: AB_10893993
AntibodyRabbit polyclonal
anti-C3b-A633
This paperN/A
AntibodyRat monoclonal
anti-mouse/human
GL7 antigen-PacBlue
(clone GL7)
BiolegendCat#144613;
RRID: AB_2563291
AntibodyRat monoclonal anti-mouse
/human CD45R/B220-PerCP/Cy5.5
(clone RA3-6B2)
BiolegendCat#103235;
RRID: AB_893356
AntibodyMouse monoclonal anti-mouse
IgMb-FITC (clone AF6-78)
BiolegendCat#406205;
RRID: AB_315038
AntibodyMouse monoclonal anti-mouse
IgMa-PE (clone MA-69)
BiolegendCat#408608;
RRID: AB_940545
AntibodyMouse monoclonal anti-mouse
CD45.1-FITC (clone A20)
BiolegendCat#110705;
RRID: AB_313494
AntibodyMouse monoclonal anti-mouse
CD45.2-APC (clone 104)
BiolegendCat#109813;
RRID: AB_389210
AntibodyRat monoclonal anti-mouse
IgD-PacBlue (clone 11–26 c.2a)
BiolegendCat#405711;
RRID: AB_1937245
AntibodyRat monoclonal anti-mouse
CD21/CD35 (CR2/CR1)-PE
(clone 7E9)
BiolegendCat#123409;
RRID: AB_940411
AntibodyRat monoclonal anti-mouse
CD138 (Syndecan-1)-PE
(clone 281–2)
BiolegendCat#142503;
RRID: AB_10915989
AntibodyRat monoclonal anti-mouse
CD38-PE/Cy7 (clone 90)
BiolegendCat#102717;
RRID: AB_2072892
AntibodyRat anti-mouse CD31-A647
(clone 390)
BiolegendCat#102415
RRID: AB_493411
AntibodyMouse monoclonal anti-mouse
CD157 (BST-1)-PE (clone BP-3)
BiolegendCat#140203;
RRID: AB_10643273
AntibodyMouse monoclonal anti-mouse
CD95 (APO-1/Fas)-PE
(clone 15A7)
eBioscienceCat#12-0951-81;
RRID: AB_465788
AntibodyRabbit polyclonal anti-Goat
IgG (H + L) Cross
-adsorbed-A488
ThermoFisher ScientificCat#A-11078;
RRID: AB_2534122
AntibodyGoat polyclonal anti-Mouse
IgG2c, Human adsorbed-AP
Southern BiotechCat#1079–04;
RRID: AB_2692321
AntibodyGoat polyclonal anti-Mouse
IgG2a, Human adsorbed-AP
Southern BiotechCat#1080–04;
RRID: AB_2692322
AntibodyRabbit polyconal anti-Mouse
Immunoglobulins-biotin
DAKOCat#E035401-2;
RRID: AB_2722694
AntibodyMouse monoclonal
anti-idiotype (clone 9D11)
Chatterjee et al. (2013)N/A
OtherCustom ChamberThis paperN/A
OtherMicrosurgical InstrumentsFine Science Tools,
Foster City, California,
USA
N/A
Other5–0 ethilon and 5–0
stainless steel suture
Ethicon, Somerville,
New Jersey, USA
N/A
Chemical
compound, drug
TamoxifenSigmaCat#T5648
Chemical
compound, drug
Imject Alum AdjuvantThermoFisher ScientificCat#77161
Chemical
compound, drug
Np-OsuBioresearch technologiesCat#N-1010–100
Chemical
compound, drug
Fixable Viability Dye
eFluor 780
eBioscienceCat#65-0865-14
Chemical
compound, drug
Hoechst 33342ThermoFisher ScientificCat#H3570
Chemical
compound, drug
DAPI
(4’,6-Diamidino-2-Phenylindole,
Dihydrochloride)
ThermoFisher ScientificCat#D1306
Peptide,
recombinant protein
B-PhycoerythrinThermoFisher ScientificCat#P800
Peptide,
recombinant protein
Streptavidin-PE/Cy7BiolegendCat#405206
Peptide,
recombinant Protein
Phalloidin-A568ThermoFisher ScientificCat#A12380
Peptide,
recombinant protein
Chicken gamma globulinRockland ImmunochemicalsCat#D602-0100
Peptide,
recombinant protein
Europium-labeled
streptavidin
Perkin ElmerCat#1244–360
Biological
sample (Mus musculus)
Mouse: Aicda-CreERT2 EYFP:
Aicda-CreERT2
flox-stop-flox-EYFP
Dogan et al. (2009)N/A
Biological
sample (mouse)
Mouse: PA-GFP: B6.Cg-Ptprca
Tg(UBC-PA-GFP)1Mnz/J
The Jackson LaboratoryJAX: 022486
Biological
sample (mouse)
Mouse: 564Igi: 564 HiKiBerland et al. (2006)N/A
Biological
sample (mouse)
Mouse: Aicda-CreERT2 Confetti:
Aicda-CreERT2-Rosa26Confetti
Tas et al. (2016)N/A
Software,
algorithm
ImageJRasband WS, ImageJ, NIH,
Bethesda, Maryland, USA
https://imagej.nih.gov/ij/
Software,
algorithm
STATAStatacorp, College Station,
Texas, USA
https://stata.com

Mice and reagents

C57BL/6 were obtained from Jackson Labs (Bar Harbor, ME) and maintained in our SPF animal facility under the supervision of Harvard Medical School IACUC (protocol numbers IS00000095 and IS00000111). B1-8hi CFP donors for adoptive transfers were kindly provided by Gabriel Victora (Whitehead Institute, MIT, Cambridge). Aicda-CreERT2-Confetti mice were previously generated (Tas et al., 2016). These mice are heterozygous carriers of tamoxifen-regulated Cre under the control of the Aicda-gene, and harbor biallelic knock-in of a stop-floxed expression cassette containing 4 XFPs (mCFP, nGFP, cYFP and cRFP) flanked by mutually exclusive loxP sites. Expression of Cre in the presence of tamoxifen allows stochastic recombination of a single, or less frequently, both alleles, to express any one XFP or combination of two independent XFPs, yielding 10 possible color combinations. Cre activity ceases following the pharmacokinetic clearance of tamoxifen, somewhere between 3 and 4 days for a single oral gavage administration, and at this point recombined cells are locked in. Thus, the strain can be utilized in pulse-chase experiments where a temporal population of germinal center B cells (expressing AID) and all their future progeny can be committed to genetically hard-wired expression of one out of 10 possible XFP combinations. It is subsequently possible to track the clonal dynamics of GC B cells.

Quantification was performed using manual counting. Antibodies against CD45, Gr-1, CD4, CD8, B220, GL7, and CD38, were obtained from Biolegend (San Diego, CA) and used between 1/200-1/500 dilutions from 1 mg/ml starting concentrations per our experience. Flow cytometry was performed using a FACS Canto II (for non-Confetti controls) or FACS ARIA SORP (for Confetti analysis and cell sorting) (BD Biosciences, San Jose, CA).

Bone marrow chimeras

Recipients were irradiated with 950 rad, then immediately placed on water containing antibiotics (sulfamethoxazole/trimethoprim) to prevent opportunistic infections during the reconstitution phase. Femurs and tibia were extracted from bone marrow donors, mechanically cleaned and rinsed through several rounds of sterile filtered HBSS containing 10 mM HEPES, pH 7.2, 1 mM EDTA and 2% heat-inactivated FBS (BM buffer). The bones were subsequently crushed in a mortar, and the cell extract was passed through a 70 micrometer cell strainer (Corning). An aliquot was subjected to RBC lysis and counted in a standard hemacytometer (Neubauer chamber). Based on cell counts, appropriate ratios of mixed bone marrow were calculated to achieve final desired donor ratios. Cells were pelleted by centrifugation (200 g, 5 min) and resuspended at 1 * 108 cells per ml, and 100 µl was injected i.v. into each irradiated recipient ~10–12 hr post irradiation.

Adoptive transfers

B1-8hi CFP donor mice were sacrificed by isofluorane overdose followed by cervical dislocation, and spleens were harvested into ice-cold FACS buffer (PBS, 2% FCS, 1 mM EDTA), mechanically dissociated, then passed through a 70 µm cell strainer, followed by centrifugation at 200 g for 5 min. The supernatant was decanted and the pellet resuspended in RBC lysis buffer (155 mM NH4Cl, 12 mM NaHCO3, 0.1 mM EDTA), incubated for 2–3 min then spun down as before, washed with PBS, and resuspended in FACS buffer containing a cocktail of antibodies for untouched B cell purification (biotinylated antibodies from BD Biosciences: CD11b, CD11c, CD69, CD3ε, TCRβ, Ter119). Cells were incubated with staining mix on ice for 30 min, then washed, and streptavidin magnetic beads (Pierce) were added in FACS buffer. After another 15 min incubation on ice, cells were again washed, then resuspended in FACS buffer and loaded on a prewetted MACS LD column. The flow-through containing untouched purified B cells was collected and cells were immediately transferred to recipients. Recipients were anaesthetized with isofluorane and cell suspensions were injected i.v. through the retroorbital sinus. Flow cytometric analysis of sample cell suspensions estimated ~50,000 B1-8hi CFP+ cells per transfer.

Immunizations

A hapten immunization scheme utilizing 4-hydroxy-3-nitrophenylacetyl-chicken gamma globulin (NP-CGG) was chosen to mimic responses to a model foreign antigen. Immunization was conducted peripherally at the hock and groin bilaterally (20 µg/injection) in equal volume alum adjuvant. Immunization was conducted 3 days prior to tamoxifen administration to generate primitive GCs specific for NP-CGG prior to labeling the GC compartment. Past work in our lab has established a recombination efficiency between 45% and 50% using a single tamoxifen gavage. Mice were longitudinally imaged following implantation of a surgical chamber (as described below) or were sacrificed at various time points following NP-CGG immunization (explant analysis) and GC read out by multiphoton microscopy, FACS, or imaging cytometry.

Surgical chambers and supplies

Surgical chambers were developed previously (Jeong et al., 2015) to follow tumor metastasis and custom milled for our purposes. They consist of a top and bottom plate of titanium containing a circular cut out for placement of an 11.7 mm circumference coverslip (Figure 1). These two plates are superimposed in a mirror configuration with a section of skin brought to the lateral border of the plates which is then sutured in place. Surgical supplies were those used for basic microsurgical technique, fine microsurgical instruments (Fine Science Tools, Foster City, CA), 5–0 ethilon and 5–0 stainless steel sutures (Ethicon, Somerville, NJ).

Microsurgical technique

Mice were used around 8 weeks of age. Although use of older mice was technically feasible, the age-associated increase in subcutaneous adipose tissue was found to negatively impact the process of locating the node with minimal dissection. Conversely, the use of younger mice was challenging due to their smaller size, rendering window implantation risky due to excessive skin stretching and limiting chamber lifespan. Instruments and window implant components were washed to remove any macroscopic contamination and then autoclaved to yield sterile surgical tools. Prior to surgery, both sham and CLNW implanted mice received one-week perioperative broad spectrum antibiotics, sulfamethoxazole-trimethoprim, in their drinking water (SMZ/TMP oral suspension, Hi-Tech Pharmacal, Amityville NY). Mice were prepared using standard surgical technique, the area to receive the window implant was shaved (approximately from the right costal margin down to the inguinal fossa medially and iliac crest laterally) and then prepped using alternating washes of ethanol and betadine. Following site preparation and draping, the surgical instruments were opened in a sterile fashion and the surgeon donned sterile gloves. Next the inguinal/subiliac LN was palpated, typically ~4–6 millimeters cranial to the iliac crest and ~3 millimeters lateral to the fifth or inferior inguinal nipple. The chamber to be implanted was briefly overlaid over the site to visualize the location of the node and necessary incisions for the anchor points, then removed from the field again. Incisions were made cranially and caudally to allow for one screw anchor at each location. The top plate with two screw anchors inserted was placed into the incision, and the screw anchors were carefully passed through the connective tissue underlying the dermis and out on the other side where they were gently tightened into the corresponding holes of the bottom plate. Upon correct placement, skin was seen to extend >75% of the way across the interior cut out area without any manipulation, as less was found to result in excessive stretching leading to early failure. Skin anchors were placed with 5–0 sterile suture; using ethilon for shorter duration and stainless steel for longer duration of the chamber. Three skin anchors were secured at the lateral margin, bringing skin across the entire interior of the chamber. Using a surgical stereoscope with 10-25X magnification, an incision was made above the location nearest approximating the LN through the epidermis only, taking care to avoid the underlying dermal fascia containing small vessels. After incision, several avascular planes were appreciable through which the subdermal fascia could be entered; a plane was chosen that was parallel to the direction of the LN and the space was entered using blunt dissection. The fascia was gently retracted on each side and the incision was extended towards the LN. Once the node was visualized, 2–3 layers of connective tissue overlying the perilymphatic adipose tissue and then a single layer just superior to the capsule was gently taken down, in order to expose a few millimeters of the node. The exposed LN was kept irrigated to prevent it from desiccating until the chamber was closed with a coverslip. The coverslip was placed over a pool of sterile irrigation overlying the node and gently pushed to force this fluid out, creating a vacuum. A flexible, stainless steel O-ring was placed into the groove securing the coverslip on the top plate. Finally, stay sutures were placed on the medial anchor points on both the top and bottom plates. A coverslip and O-ring was placed on the back plate chamber to protect the exposed posterior skin and to prevent exterior contamination of the environment inside. Mice received typical buprenorphine s.c. injections perioperatively for the first 48 hr. CLNW implanted mice were noted to remain less mobile for the first 24–48 hr post-op than sham controls, however, they were remarkably agile thereafter with minimal, if any, restrictions due to pain or discomfort (including climbing upside down in the cages). Notably, mice were single-housed post-operatively as is typical.

Multiphoton microscopy

Multiphoton intravital microscopy (MP-IVM) was utilized to visualize fluorescently labeled cells within the lymph node. All imaging was performed on an upright Olympus FV1200 MPE multiphoton system microscope fitted with either a 20 × 0.95 NA Plan water-immersion objective or a 25 × 1.05 NA Plan IR optimized water-immersion objective, a MaiTai HP DeepSee Ti-Sapphire laser (Spectraphysics), and four non-descanned detectors (2 GaAsP and two regular PMTs). Imaging of Confetti alleles was performed using λ = 940 nm excitation. Fluorescence emission was collected in three channels, using the following filter sets: a pair of CFP (480/40 nm) and YFP (525/50 nm) filters, separated by a 505 nm dichroic mirror, for CFP/GFP/YFP detection, and a third filter (605/70 nm) for RFP detection. Prior to imaging, the mice were anesthetized using an isofluorane vaporizer; with 1.5–2% isofluorane for induction and 0.5–1% for maintenance. Animals were maintained on an H2O reservoir which was kept at ~34 degC to maintain body temperature. Following induction of a stable plane of anesthesia, mice were placed into a custom-built Plexiglas fixture and the chamber grasped firmly by fixable alligator grips to stabilize the imaging plane. The microscope was centered on the node and focused on the top of the node using transmitted light and then convert to dark room operations for imaging. Mice were continuously monitored during imaging for vital signs (respiratory rate and pulse) and adequate plane of anesthesia (toe pinch reflex). Imaging was performed either daily or on alternating days using 640 × 640 pixel resolution without Kalman line correction to avoid phototoxicity.

Statistics and quantitative analysis

Flow cytometry data was quantified using FlowJo (ver. 8, Ashland, OR). BM chimeras using the Confetti allele were quantified manually following acquisition. Observers were blinded to animal status and interobserver reliability confirmed by independent counts of 15 GC with concordance >95% between two observers. Counting was performed on 3–4 z planes from stacks acquired at 5 µm steps through the GC. Slices were at least 15–20 µm apart to preclude possibility of double counting. Very sparse, <50% occupation GC were discarded prior to analysis as they are considered to have at least one expanded dark clone. Typically, greater than 250 GC B cells were quantified for a single GC per time point. Raw counts were converted to relative frequencies for each color possibility to simplify analysis. Clonal dominance was calculated as the frequency of the most dominant clone/color at a given time point. The meander score is mathematically the integral of the relative velocities for each color shown below:

Meanderscore(t)=0ti=110(freqcolor(i)(t)freqcolor(i)(t1))2

Clonal divergence score was reported previously (Tas et al., 2016) and is calculated at a given time point by comparing the observed distribution of colors at time t to the expected frequencies if the distribution was randomly recombined. Expected frequencies for each of 10 possible colors was derived using explant data from day three post tamoxifen treatment where color distribution most closely mimics random recombination and prior to any selection events:

Clonaldivergencescore(t)=i=110|observedfreqcolor(i)expectedfreqcolor(i)|

Descriptors of GC behavior and clonality were graphed using GraphPad Prism 6 (GraphPad Software Inc, La Jolla, CA). Statistical inference was tested using chi squared or one-way or two-way ANOVA as appropriate depending on the number of groups. Principal components analysis (PCA) was performed in the standard fashion and resulting dimensions tested for association with individual mice after adjusting for time and treatment. All observations were repeatedly in at least two independent experiments and which is indicated in the respective presentation of the data. As this was an exploratory analysis of longitudinal variation we did not perform sample size calculations in advance of the study design. Group sample sizes were based on technical and biological variation observed in prior studies and emphasis was placed on confidence intervals when interpreting variance to give better contextualization of all data points. All statistical inference utilized Stata version 13 (StataCorp, College Station, TX).

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Decision letter

  1. Michael L Dustin
    Reviewing Editor; University of Oxford, United Kingdom
  2. Michel C Nussenzweig
    Senior Editor; The Rockefeller University, United States

In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.

Thank you for submitting your article "Capturing Change in Clonal Composition amongst Single Germinal Centers" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Michael L Dustin as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen Michel Nussenzweig as the Senior Editor.

The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Summary:

Your manuscript presents a modification of an imaging preparation to permit longitudinal studies in lymph nodes. The authors use it to address imaging of germinal centers over the time course of three weeks. This is a helpful development, with many potential applications in immunology.

They demonstrate that the clonal populations in GCs are more dynamic and heterogeneous in their evolution than had previously been reported by imaging of individual time points, and that GCs can "devolve" – i.e., a highly represented clone can be replaced by another clone. This finding is important and interesting, but "devolution" is not the only way to conceptualize this and based on the level of evidence presented in this tools/resources article, other interpretations should be discussed and if the method could eventually resolve different possibilities. There is also some concern about the scope of evidence that preparation is not altering the function of the lymph node and the rigour with which results and reported and discussed, which should be addressed prior to publication. The revision may present additional data to address some points, but more generally the revision should address, as quantitatively as possible, the potential of the method to address important issues raised in a definitive manner. The highest priority is to make sure the tool/resource described is suitable to definitively address some of the issues raised by the initial results collected while validating the technical approach.

Essential revisions:

1) One of the most important applications of the method is the idea that B cell clones may move between GC, which is thought not to take place. You note that in some cases neighbouring GCs exhibit similar color changes at the same time and suggest that this may be due to GC trafficking. The proposed mechanism is unclear – do the authors have evidence that GC cells are found outside of a GC? Or are they suggesting that a memory B cell leaving one GC may re-enter a nearby GC without leaving the LN? It is unclear how the authors would distinguish GCs seeded by a memory B cell that has not left the LN from a GC seeded by recirculating memory B cells. You suggest that "PA-GFP activation and sequencing could provide greater clarity", but it is unclear how this could distinguish between these two possibilities. Sequencing provides definitive proof of clonal relationships between cells but is inherently incompatible with further longitudinal imaging. PA-GFP has a short half-life in rapidly dividing cells, and to demonstrate that one labeled cell productively seeds a GC, it must not simply be observed inside a given GC but must be observed to divide in this GC. You should clarify which mechanism(s) you believe to be responsible for this phenomenon and why. While it may be beyond the scope of the current paper to fully solve this issue, is there a way forward statistically or through direct observation to resolve what is happening using this method?

2) You note that some GCs exhibit a clonal "devolution", in which a relatively monoclonal GC becomes less monoclonal. "Devolution" or "stagnation" are problematic, misleading ways to think about this process, as they do not consider the function of the GC: to generate high affinity antibodies. The finding that GCs do not monotonically progress towards monoclonality is important but reflects in this case simply an early clone being outcompeted by a clone with a higher affinity for antigen, not a regressive process.

It is also possible that over the course of a chronic immune reaction, additional antigens may be presented on FDCs. B cells recognizing these new antigens would also compete for T cell help with B cells recognizing the "original" antigens. Changes in the Tfh populations inside the GC over the course of a chronic immune reaction would be unsurprising. All of these would lead to replacement of one population of cells by another, independent of the prevalence of the major clone inside the GC.

Taken together, without measuring and documenting a decrease in antibody affinity (ideally from individual cells), it is misleading to talk about "devolution" or "stagnation".

3) You should show that the afferent and efferent lymphatic drainage is unaffected by the implantation of the chronic imaging window. This could be done by injection of fluorescent dextrans and imaging their appearance in the implanted LN, and in the lymphatic vasculature.

4) As the imaging window implantation causes inflammation, absolute cell counts per LN are a more appropriate comparison than percentages. The authors should provide absolute cell counts too. Further, the gross morphological assessment (i.e. size, weight) should be presented quantitatively, not anecdotally. Additional information about lymphocyte phenotype would also be informative (e.g. expression of CD44, CD62L, and CCR7).

5) You examined granulocytes and lymphocytes, but not monocyte/macrophages and dendritic cells in implanted versus control and sham-implanted LNs. These cells would be expected to be recruited by inflammation – i.e. the implanted LN – and could affect the subsequent kinetics of any immune reaction. You should demonstrate that these cells are not recruited and should present both percentages and absolute cell counts.

6) The authors acknowledge subtle changes in lymphocyte populations in implanted, as compared to sham-operated LNs (decrease in CD4+, CD8+ T cells, increase in B220+ cells, increased GC B cells). However, they do not adequately explain what could be responsible for these changes, and how these processes could also affect the germinal center reaction.

7) The data presented in Figure 4 to show the fidelity of clonal development in the CLNW mice are somewhat unclear. First, two groups of two mice are small, and would require a dramatic difference to show statistical nonequivalence p < 0.05. The authors should calculate what the power of this number of animals is, i.e. what difference they are capable of detecting.

8) It is further not clear to me that the BM chimera ARTEMIS model and the NP-CGG model are similar enough that they can be grouped together as a total n=4, instead of considered independently in separate panels, each with n=2. They should certainly be color-coded separately in Figure 4B-D. This problem is compounded by plotting one number per mouse. The authors should plot individual GCs, denoting which GCs belong to which mouse, in order to increase the number of plotted data points. This would demonstrate the variance between individual GCs in the same animal, which is valuable and informative information. Also, as the divergence is calculated along both X and Y axes (for window LN and explant LN), shouldn't there also be error bars along the X axis for the divergence in the window LN?

https://doi.org/10.7554/eLife.33051.023

Author response

Essential revisions:

1) One of the most important applications of the method is the idea that B cell clones may move between GC, which is thought not to take place. You note that in some cases neighbouring GCs exhibit similar color changes at the same time and suggest that this may be due to GC trafficking. The proposed mechanism is unclear – do the authors have evidence that GC cells are found outside of a GC? Or are they suggesting that a memory B cell leaving one GC may re-enter a nearby GC without leaving the LN? It is unclear how the authors would distinguish GCs seeded by a memory B cell that has not left the LN from a GC seeded by recirculating memory B cells. You suggest that "PA-GFP activation and sequencing could provide greater clarity", but it is unclear how this could distinguish between these two possibilities. Sequencing provides definitive proof of clonal relationships between cells but is inherently incompatible with further longitudinal imaging. PA-GFP has a short half-life in rapidly dividing cells, and to demonstrate that one labeled cell productively seeds a GC, it must not simply be observed inside a given GC but must be observed to divide in this GC. You should clarify which mechanism(s) you believe to be responsible for this phenomenon and why. While it may be beyond the scope of the current paper to fully solve this issue, is there a way forward statistically or through direct observation to resolve what is happening using this method?

We thank the reviewer for this very elegant consideration, which we admittedly had not discussed in the manuscript. In response to this point, we have added a discussion of the potential of the described method to resolve these important questions. Recent work by Gabriel Victora’s group, and earlier work by numerous other groups, has indeed indicated that GC B cells are unlikely to move between GC. Therefore, we have focused the discussion on the likelihood of auto-reactive B memory cells returning to GC for further maturation, as previously reported by McHeyzer-Williams for continued diversification of B memory cells specific for foreign antigen. In this context, it is relevant to note that the autoreactive GC ‘never rests’, as self-antigen is ubiquitous and can never be fully cleared. In line with this notion, in our mixed chimeric model (Degn et al., 2017) we have not seen resting long-term memory cells in the bone marrow (Degn and Carroll, unpublished results). As a general approach to address the question of cyclic B memory cell exit and reentry in GCs, we propose employing anti-CD40L blockade of GCs in tamoxifen-pulsed aid-YFP 564 mice and studying reemergence of YFP+ GC B cells after clearance of the anti-CD40L antibody and reformation of GCs. An alternative approach could be through adoptive transfer of sorted aid-YFP memory cells to 564 mice. However, these approaches would not answer whether such reentry occurs without the memory B cell leaving the node first, as would be suggested by our specific observation of ‘color synchrony’ between neighboring GCs. To address this particular question, we would suggest employing a strategy similar to that used by Gabriel Victora and Michel Nussenzweig’s groups to elucidate T follicular helper cell dynamics in GCs (Shulman et al., 2013), combined with our new window approach. Shulman et al. successfully demonstrated that Tfh can traffic between GCs, using conventional intravital lymph node imaging and photoactivation, followed by closure of the incision and follow-up by explant analyses of the same nodes. Immediately after photoactivation, PA-GFP+ Tfh cells were restricted to the targeted region, but ~20 hours after activation, around one third of photoactivated Tfh cells were found outside the original GC. This trafficking only occurred to a significant extent between GCs within the same node on the relatively short time scales analyzed. Only a very small number of photoactivated cells could be detected in the blood or pooled distal lymphoid organs of mice examined 36 hours after photoactivation. One advantage in that study was that Tfh cells are in G1 phase and hence do not dilute out PA-GFP by division. Of note, the half-life of photoactivated PA-GFP in naive B cells was estimated to be 30 hours (Victora et al., 2010), indicating that the protein itself is indeed stable enough on these time-scales. A disadvantage to the application presented here is of course that dark zone GC B cells are rapidly proliferating. However, these cells would not be expected to directly give rise to short-term memory B cells (or other output cells), as mutated GC B cells have to undergo an affinity selection step in the light zone. Thus, we would expect the (progenitor) memory B cells to exit non-concomitantly with division, minimizing concerns of dilution of PA-GFP. Of course, should these memory B cells be reactivated and participate in the GC, they would begin dividing and diluting out the PA-GFP. These processes can be followed longitudinally locally with the new window setup and combining this with explants of distal lymphoid tissue would address whether significant numbers of memory B cells exit GCs.

2) You note that some GCs exhibit a clonal "devolution", in which a relatively monoclonal GC becomes less monoclonal. "Devolution" or "stagnation" are problematic, misleading ways to think about this process, as they do not consider the function of the GC: to generate high affinity antibodies. The finding that GCs do not monotonically progress towards monoclonality is important but reflects in this case simply an early clone being outcompeted by a clone with a higher affinity for antigen, not a regressive process.

It is also possible that over the course of a chronic immune reaction, additional antigens may be presented on FDCs. B cells recognizing these new antigens would also compete for T cell help with B cells recognizing the "original" antigens. Changes in the Tfh populations inside the GC over the course of a chronic immune reaction would be unsurprising. All of these would lead to replacement of one population of cells by another, independent of the prevalence of the major clone inside the GC.

Taken together, without measuring and documenting a decrease in antibody affinity (ideally from individual cells), it is misleading to talk about "devolution" or "stagnation".

We really appreciate the reviewer’s thoughts regarding devolution, stagnation, and the nature of GC evolution. We agree that the complex interplay between antigen libraries and presentation, somatic hypermutation and possible reseeding, as well as the availability of Tfh, all contribute to the dynamic GC population and peculiar kinetics we have observed. Further, we think the continuous observation of clonal dynamics within a given GC as reflective of the sum of intrinsic B cell and GC environmental processes is very interesting and an improvement in terms of thinking of what is being measured in changes in clonality. The intent of “devolution” in the manuscript was not to imply a decreased affinity per se but rather to describe the ‘regression’ from mono-/oligo-clonality (as inferred by color, which is admittedly a rough approximation) towards increasing ‘color diversity’. As such we agree with the reviewer and a more appropriate term may be ‘inversion’. We have edited the manuscript accordingly, omitting the term "devolution", and have included a discussion of the above point. In this connection, we have also discussed recent elegant work by Garnett Kelsoe’s group on responses to complex antigen and put this into the perspective of Michel Nussenzweig’s papers on the ‘open nature’ of GCs, where he demonstrated that mainly B cells responding to the same antigen or catering to the same T helper cells, would be able to enter an ongoing GC. This suggests that a GC elicited in response to antigen A is rarely taken over by clones responding to antigen B, if A and B are unrelated. Of course, the chronic scenario may differ from the transient scenario, as also indicated by the reviewer.

3) You should show that the afferent and efferent lymphatic drainage is unaffected by the implantation of the chronic imaging window. This could be done by injection of fluorescent dextrans and imaging their appearance in the implanted LN, and in the lymphatic vasculature.

This is also an excellent point. In the preparation of the B1-8hi early GC experiment we actually conducted several control experiments. We injected rabbit-anti-PE antibody 24 hours prior to the surgery, and then injected PE either just prior to surgical implantation or following surgical implantation of the chamber. In either case, intact afferent lymphatics are required for robust labeling of FDCs in the inguinal nodes. We noted robust and similar staining of FDC networks regardless of whether we injected prior to, or just after surgical implantation of the window chamber. There was only a minimal and statistically non-significant difference between pre- and post-implant injection of PE 6 hours after surgery, and no difference 24 hours after surgery. Along with the fact that technically (surgical approach) there is minimal perturbation of the deep and peripheral afferent lymphatics these data support intact afferent drainage to operated nodes. Lack of peripheral edema in any of dozens of surgeries is strong support of lack of injury to the efferent lymphatic drainage. We have included these additional data as Figure 2—figure supplement 1 and a discussion of this important point in the manuscript.

4) As the imaging window implantation causes inflammation, absolute cell counts per LN are a more appropriate comparison than percentages. The authors should provide absolute cell counts too. Further, the gross morphological assessment (i.e. size, weight) should be presented quantitatively, not anecdotally. Additional information about lymphocyte phenotype would also be informative (e.g. expression of CD44, CD62L, and CCR7).

This is another good point. Ideally, we should have included counting beads for absolute quantification, but this was not done. However, we have the volumes of FACS samples and the total numbers of spleen cells available, which has allowed us to give a close estimate of absolute counts instead of relative percentages. We unfortunately do not have weights or height/widths of the nodes, as we noted minimal perturbation to LN morphology throughout these experiments, and hence did not consider it for a more comprehensive analysis. Please also refer to our considerations regarding point 5, below.

5) You examined granulocytes and lymphocytes, but not monocyte/macrophages and dendritic cells in implanted versus control and sham-implanted LNs. These cells would be expected to be recruited by inflammation – i.e. the implanted LN – and could affect the subsequent kinetics of any immune reaction. You should demonstrate that these cells are not recruited and should present both percentages and absolute cell counts.

We appreciate this point, and again we must concede that we have not done all potentially interesting analyses. However, in the analyses we did perform, we actually used Gr1, which recognizes both Ly6C (mainly monocytes) and Ly6G (neutrophils), and therefore should provide a reasonable estimate of inflammatory cell recruitment. Indeed, as is evident from the relative percentages and now cell counts presented in the present version of the manuscript, there is a slight influx of these cells into the window node, and a small upregulation in the spleen. As for above, we have of course provided cell counts instead of relative frequencies to give a more accurate depiction of this effect in agreement with the reviewer’s comment. We cannot argue that there is not a low-level degree of inflammation in the lymph node associated with the window implantate, either as an effect of sterile inflammation due to the opening of the overlying tissue or due to the increased exposure to skin-associated commensals at the incision site, or both. Unfortunately, it is an inherent problem to any procedure breaking the skin barrier, but we have sought to minimize it as much as possible and based on our results we do not think that it impacts our ability to assess antigen-specific clonal responses in germinal centers. In the updated manuscript, we have included the cell counts for the Gr1+ cells and explain the subsets represented by this marker and present the above considerations.

6) The authors acknowledge subtle changes in lymphocyte populations in implanted, as compared to sham-operated LNs (decrease in CD4+, CD8+ T cells, increase in B220+ cells, increased GC B cells). However, they do not adequately explain what could be responsible for these changes, and how these processes could also affect the germinal center reaction.

Granted, there is some low-level inflammation, as the window cannot with certainty be 100% sterile and there may be some possibility of increased antigen diversity. We have now included a discussion of the relevant distinction between ‘sterile inflammation’ (tissue damage and exposure) versus non-sterile inflammation (skin commensals and whatever else could potentially access the chamber), where the former might not be as critical as the latter. However, based on our studies of clonality in the NP-CGG and 564 models, this does not dramatically impact evolution of the GCs. Please also refer to our response to point 7, below.

7) The data presented in Figure 4 to show the fidelity of clonal development in the CLNW mice are somewhat unclear. First, two groups of two mice are small, and would require a dramatic difference to show statistical nonequivalence p < 0.05. The authors should calculate what the power of this number of animals is, i.e. what difference they are capable of detecting.

We have added a new panel (Figure 4B), displaying raw data for GC color dominance on a per-GC, per-mouse basis. We also added x-dimension error bars to Figure 4C-E. Regarding the question of statistical non-equivalence, this is rather based on the total number of observed GC than the number of mice. Increasing the number of mice would perhaps allow us to determine the frequency of mice in which color dominance would deviate between window implant and contralateral side, but this would merely be an expression of the frequency of failure of the surgery/chamber implant (causing perturbation of homeostasis in the implanted node), and as such refers more to a technical parameter (this would vary depending on the experience and proficiency of the surgeon, but would not necessarily reflect on the adequacy of the method itself). Having a relatively high number of observed GCs within each mouse allows a statistically sound interpretation of whether implanted and contralateral node GCs deviate. Increasing this number would only be predicted to decrease noise/variation in individual GC color dominance (as estimated based on the large set of explants analyzed in Degn et al., 2017). Therefore, we strongly believe that our statistical analysis is sound. Please also see the response to point 8, below, regarding pooling ARTEMIS and NP-CGG mice. Furthermore, based on the following considerations, we do not believe that a power analysis will change this conclusion.

We appreciate the reviewer’s concern in distinguishing between true negatives and false negatives. It is an important consideration alongside any statistical analysis. Unfortunately, a post-hoc power analysis would not provide additional power in the presented analysis, since p-values are directly related to the observed power (please refer Figure 1, Hoenig and Heisey, 2001). From this, we can see that there is always low observed power associated with non-significant (p>0.05) tests. We believe that the emphasis on conclusive interpretation of statistical tests disregards the hypothesis-based nature of these tests. That is, a p-value of 0.1 is not “stronger” than 0.3 despite the fact that the observed power would necessarily be greater, and this is independent of sample size. The cut-off of 0.05 is merely a convention, and we would argue for placing relatively greater emphasis on our confidence intervals for effect sizes.

In the case of our Figure 5C-E (most relevant to the current discussion), we believe that even if the “true” β coefficients were at the extremes of the confidence intervals, this would not be great cause for concern, for the following reasons. For both Figure 5C and D it seems unlikely that a deviation within 4-8% of true color domination represents a meaningful departure. That is to say, in the event we increased the sample size, it is unlikely that these intervals would depart from unity towards the extremes. In the case of Figure 5E, the interval could be more concerning. A true deviation of ~30-40% from the unperturbed scenario would significantly reduce our confidence in this technique. However, it is important to note the large variation in the divergence index (as compared to the other measures of GC dynamics). The divergence index can be significantly skewed as a result of rarer color combinations (such as YFP/CFP or GFP) becoming dominant clones in GC (Degn et al., 2017, and Tas et al., 2016). In this case it is more likely that the width of the confidence interval reflects the underlying variation associated with the divergence index rather than a true observation of failure of this technique to recapitulate homeostatic conditions. This notion is supported by Figure 5C and D, and the other lines of evidence presented, which suggest that homeostasis is not significantly perturbed.

8) It is further not clear to me that the BM chimera ARTEMIS model and the NP-CGG model are similar enough that they can be grouped together as a total n=4, instead of considered independently in separate panels, each with n=2. They should certainly be color-coded separately in Figure 4B-D. This problem is compounded by plotting one number per mouse. The authors should plot individual GCs, denoting which GCs belong to which mouse, in order to increase the number of plotted data points. This would demonstrate the variance between individual GCs in the same animal, which is valuable and informative information. Also, as the divergence is calculated along both X and Y axes (for window LN and explant LN), shouldn't there also be error bars along the X axis for the divergence in the window LN?

As outlined above, we have added a new panel (Figure 4B), displaying raw data for GC color dominance on a per-GC, per-mouse basis. We also added x-dimension error bars to Figure 4C-E. Regarding the question of whether the ARTEMIS and NP-CGG models are similar enough to pool them for the analysis, we note that although these models are very different, one reflecting autoreactive responses to complex antigen, the other a response to a less complex foreign antigen, they were demonstrated to have overall similar clonal evolution kinetics by traditional time-point analyses using the Confetti model (Degn et al., 2017). As suggested by the reviewer, for clarity we have indicated in Figure 4C-E which group the mice were derived from.

https://doi.org/10.7554/eLife.33051.024

Article and author information

Author details

  1. Daniel J Firl

    1. Cleveland Clinic Lerner College of Medicine, Cleveland, United States
    2. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, United States
    3. Howard Hughes Medical Institute, Maryland, United States
    Contribution
    Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7993-6167
  2. Soren E Degn

    1. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, United States
    2. Department of Biomedicine, Aarhus University, Aarhus, Denmark
    Contribution
    Conceptualization, Supervision, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5409-045X
  3. Timothy Padera

    Edwin L. Steele Laboratories for Tumor Biology, Department of Radiation Oncology, Massachusetts General Hospital, Boston, United States
    Contribution
    Resources, Writing—review and editing
    Competing interests
    No competing interests declared
  4. Michael C Carroll

    1. Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, United States
    2. Department of Pediatrics, Harvard Medical School, Boston, United States
    Contribution
    Resources, Supervision, Funding acquisition, Project administration, Writing—review and editing
    For correspondence
    michael.carroll@childrens.harvard.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3213-4295

Funding

Howard Hughes Medical Institute

  • Daniel J Firl

Marie Curie International Fellowships

  • Soren E Degn

Alfred Benzon Foundation

  • Soren E Degn

National Institutes of Health (R21AI117737)

  • Michael C Carroll

National Institutes of Health (R01AI039246)

  • Michael C Carroll

Alliance for Lupus Research

  • Michael C Carroll

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful to Luka Mesin and Gabriel Victora from Rockefeller University for the B1-8hi CFP and confetti allele animals and for related technical consultation and Eelco Meijer from the MGH for the window chambers. We finally thank Harry Leung of the Optical Microscopy Core at the PCMM for technical assistance. DJF was supported by a Howard Hughes Medical Institute, Medical Research Fellowship. SED was supported by the Benzon Foundation and by a Marie Curie International Outgoing Fellowship within the seventh European Community Framework Programme. This research was supported by NIH (R21AI117737, R01AI039246) (MCC), and the Alliance for Lupus Research (MCC).

Ethics

Animal experimentation: Animal studies supervised by the Institutional Animal Care and Use Committee of Harvard Medical School (protocol numbers IS00000095 and IS00000111) as described in the manuscript.

Senior Editor

  1. Michel C Nussenzweig, The Rockefeller University, United States

Reviewing Editor

  1. Michael L Dustin, University of Oxford, United Kingdom

Publication history

  1. Received: October 24, 2017
  2. Accepted: July 1, 2018
  3. Version of Record published: August 1, 2018 (version 1)

Copyright

© 2018, Firl et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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