Abstract

   Mice engrafted with components of a human immune system have become
   widely-used models for studying aspects of human immunity and disease.
   However, a defined methodology to objectively measure and compare the
   quality of the human immune response in different models is lacking.
   Here, by taking advantage of the highly immunogenic live-attenuated
   yellow fever virus vaccine YFV-17D, we provide an in-depth comparison
   of immune responses in human vaccinees, conventional humanized mice,
   and second generation humanized mice. We demonstrate that selective
   expansion of human myeloid and natural killer cells promotes
   transcriptomic responses akin to those of human vaccinees. These
   enhanced transcriptomic profiles correlate with the development of an
   antigen-specific cellular and humoral response to YFV-17D. Altogether,
   our approach provides a robust scoring of the quality of the human
   immune response in humanized mice and highlights a rational path
   towards developing better pre-clinical models for studying the human
   immune response and disease.
     __________________________________________________________________

   Humanized mice are an enabling technology to explore human immunity and
   disease. Here, Douam et al. provide an in-depth comparison of immune
   responses to yellow fever vaccine in human vaccinees, conventional and
   second-generation humanized mice and define a workflow to evaluate and
   refine these models.

Introduction

   Much has been learned about how the mammalian immune system functions
   at steady state and during infection using inbred mouse models.
   However, it has become increasingly recognized that the mouse and human
   immune systems differ in numerous important aspects^[46]1, thus
   limiting the predictive value of studies in rodents for human biology.
   Furthermore, the narrow host tropism of many important human-tropic
   pathogens precludes the use of conventional mouse models for analyzing
   the interactions of such pathogens with the mammalian immune
   system^[47]2. The direct study of human immune responses is challenging
   as usually only peripheral blood, but not material from lymphoid organs
   or the site of infection, is readily accessible. Immune responses to
   many pathogens have been studied in patients, but interpreting such
   clinical data is difficult as numerous parameters that could influence
   measured immune response are often unknown. To gain better control of
   these critical factors, immune responses to live-attenuated vaccines,
   including yellow fever^[48]3, flu^[49]4, and smallpox^[50]5, have been
   carefully characterized. These studies have greatly contributed to our
   understanding of human immunity, but intra- and inter-donor
   variability, previous and/or current infections, age or microbiotic
   status still add significant complexity to the data and make analysis
   challenging.

   Humanized mice have emerged as powerful tools for studying a broad
   range of human(-tropic) pathogens. Mice engrafted with components of a
   human hematolymphoid system or human immune system (HIS) have been
   especially useful for dissecting the interactions of human viruses with
   human immune cells^[51]6–[52]10. A variety of mouse strains (reviewed
   in ref. ^[53]11) well-suited for engraftment of human hematolymphoid
   cells have been developed. These recipient strains are usually highly
   immunocompromised to facilitate engraftment of xenogeneic cells.
   Non-obese diabetic (NOD) mice deficient for both the recombinase
   activating gene 1 (Rag1^−/−) and the IL-2 receptor gamma chain
   (IL2Rγ^null) (NRG mice) are commonly used and do not develop functional
   murine B, T, or natural killer (NK) cells^[54]12. NRG mice are also
   deficient in hemolytic complement^[55]13 and harbor a polymorphism in
   the gene encoding murine signal regulatory protein α (SIRPα), which
   reduces phagocytic activity against human cells^[56]14. Injection of
   irradiation-conditioned NRG mice with human hematopoietic stem cells
   (HSCs) leads to de novo hematopoiesis, resulting in stable engraftment
   of human hematolymphoid system components^[57]6,[58]12,[59]15.

   Although there is evidence that the engrafted HIS in such mice becomes
   activated upon microbial challenge, the quality of the immune response
   in conventional models and in other refined models (such as the bone
   marrow–liver–thymus, or BLT model) remains weak or
   uncertain^[60]7,[61]9,[62]16–[63]20. One of the major reasons is the
   underrepresentation of critical human immune cell lineages in these
   models, which are crucial for activating the adaptive immune response.
   In particular, the scarcity of human dendritic cells (DCs) as well as
   other myeloid lineages and NK cells, decreases the functionality of the
   engrafted HIS. The small frequencies of these cell populations can be
   explained, in part, by the limited biological cross-reactivity of the
   non-redundant cytokines that promote lineage differentiation^[64]21.
   Consequently, several new humanized mice models with significant
   reconstitution of myeloid and/or NK cell compartments have been
   recently developed (hereon referred to as second-generation humanized
   mouse models). Indeed, exogenous administration of human interleukin
   (IL) 15 or an IL15/IL15 receptor (R) fusion protein significantly
   increases human NK cell numbers^[65]22. Similarly, injection of
   recombinant cytokines, such as granulocyte-macrophage colony
   stimulating factor (GMCSF), macrophage colony stimulating factor
   (MCSF), IL3 or FMS-like tyrosine kinase 3 ligand (Flt3LG), or their
   expression in engineered xenorecipient strains results in increased
   frequencies of erythro-myeloid lineage cells^[66]15,[67]23–[68]25.
   However, knowledge of how the human immune response in any of these
   novel models compares to those observed in humans remains limited.

   To address this need, we devised an experimental pipeline allowing us
   to quantitatively assess immunity in humanized mice and compare to host
   responses in humans. By probing the cellular, humoral, and
   transcriptomic response to a highly immunogenic common standard, the
   yellow fever virus vaccine YFV-17D^[69]26, we provide here the first
   comprehensive comparison of the human immune response in conventional,
   second-generation humanized mice and human vaccinees. Our results
   highlight that selective expansion of myeloid and NK cells in humanized
   mice induces transcriptomic responses to YFV-17D infection akin to
   those of human vaccinees. The more human-like transcriptomic responses,
   lacking in conventional models, correlated with the development of
   antigen-specific cellular and humoral immunity to YFV-17D in humanized
   mice more robustly engrafted with human NK and various myeloid cells.
   Altogether, our work demonstrates a robust approach for the
   quantitative measurement of immunity in humanized mice, for more
   objective model cross-comparison and consequently, for the rational
   development of better pre-clinical humanized models.

Results

Conventional humanized mice mount limited immunity to YFV-17D

   YFV-17D is one of the most potent vaccines ever developed, and single
   vaccination usually results in protection for at least 10 years^[70]26.
   Existing data on the immune response to YFV-17D in human vaccinees
   could thus serve as a valuable comparator to systematically assess the
   functionality of a transplanted HIS. In contrast to the transient or
   even undetectable viremia observed in human vaccinees^[71]3,[72]5,
   YFV-17D RNA rapidly reached a plateau and persisted in the blood of
   NRG-HIS mice for at least 22 days, suggesting the engrafted HIS cannot
   effectively clear infection (Fig. [73]1a) despite an absence of
   significant mortality (Supplementary Figure [74]1a). In
   YFV-17D-infected conventional NRG-HIS mice, we noticed an overall
   increase in peripheral CD3+ T cells upon YFV-17D infection
   (Fig. [75]1b) without any changes in the ratio of CD4+/CD8+ T cells
   (Supplementary Figure [76]1b). In contrast to reports in patients, the
   frequencies of human CD8+ T cells expressing HLA-DR and CD38—two
   markers to track virus-activated cells within the bulk CD8+ T cell
   population in the peripheral blood of human vaccinees^[77]5—did not
   change in the blood (Fig. [78]1c) or spleen (Supplementary
   Figure [79]1c) of these mice. Downregulation of CCR7 and CD45RA on a
   subset of CD4+ and CD8+ T cells was detectable in the blood and spleen
   of NRG-HIS mice upon YFV-17D infection (Fig. [80]1c; Supplementary
   Figure [81]1c), indicating that the engrafted HIS responded to the
   infection. However, this activation did not correlate with better
   control of viral replication in the periphery (Fig. [82]1a). To enable
   tracking of antigen-specific T cells responses, we infected humanized
   NRG mice expressing transgenically HLA-A2*0201 (NRG-A2-HIS) and
   quantified YFV-specific CD8+ T cells in the blood and spleen of
   NRG-A2-HIS mice using an HLA-A2:YFV NS4B (amino acids 214–222,
   LLWNGPMAV) tetramer^[83]3. Unlike previous studies characterizing
   virus-specific CD8+ T cell responses to HIV, Epstein-Barr virus, dengue
   virus, or adenovirus^[84]9,[85]17,[86]19,[87]27, we did not detect any
   A2:LLWNGPMAV-specific T cells in either the blood or spleen over the
   course of infection, indicating that YFV-17D-specific cells are poorly
   primed in NRG-A2-HIS mice (Supplementary Figure [88]1d). These data
   indicate that while NRG-HIS mice have utility as a challenge
   model^[89]6, they require further refinements to better model human
   immune responses.

Fig. 1.

   [90]Fig. 1
   [91]Open in a new tab

   NRG-HIS mice do not clear YFV-17D infection. a YFV-17D serum viremia in
   the peripheral blood of NRG-HIS mice over the course of infection. (+)
   RNA copies per ml were quantified by RT-qPCR. Limit of detection
   (dotted line) is shown. Horizontal lines represent median viremia at
   each time point (n = 12). ****p ≤ 0.0001, ns non-significant
   (Wilcoxon–Mann–Whitney test). b Percentage of peripheral human CD3+ T
   cells among the total human CD45+ cell population in the blood of
   NRG-HIS mice over the course of YFV-17D infection. Bounds of box and
   whiskers represent the min-to-max fraction of peripheral human CD3+ T
   cell among total human CD45+ at each time point. Medians are indicated
   in each box as center line (n = 5) *p ≤ 0.05, **p ≤ 0.01 (Student’s t
   test). c Fraction of peripheral human CD4+ and CD8+ T cells
   co-expressing HLA-DR and CD38 (red) or lacking expression of both CCR7
   and CD45RA (blue) in the blood of NRG-HIS mice over the course of
   YFV-17D infection. Bounds of box and whiskers represent the min-to-max
   fraction of human CD4+ or CD8+ T cell for each marker combination and
   time point. Medians are indicated in each box as center line (n = 5).
   *p ≤ 0.05, **p ≤ 0.01 (Student’s t test)

   Next, we examined the expression of four genes (MDA5/IFIH1, STAT1,
   IRF7, RSAD2), all reported to be upregulated in PBMCs of human
   vaccinees following vaccination^[92]28, in the human PBMCs of NRG-HIS
   mice following YFV-17D infection. Since we expected the anti-viral
   response in NRG-HIS to be low, we aimed at determining the cumulative
   median expression of these four anti-viral genes by RT-qPCR using
   human-specific primers. Our data revealed that the cumulative median
   expression of these four genes significantly increased at day 11 post
   infection relative to pre-infection levels (day 0) (Fig. [93]2a;
   Supplementary Figure [94]1e). Using RNA-sequencing (RNA-seq), we then
   performed an unbiased quantification of differentially expressed genes
   in human PBMCs of NRG-HIS mice on day 11 post YFV-17D infection.
   Notably, only four genes were significantly differentially expressed
   (DE) (HSP90AA1, HIST1H4C, LRRFIP1, and UGDH-AS1) (Fig. [95]2b–d;
   Supplementary Data [96]1), highlighting the limited and variable
   transcriptomic response of human PBMCs in NRG-HIS mice. Altogether, our
   results indicate that NRG-HIS develop an extremely limited human immune
   response to YFV-17D.

Fig. 2.

   [97]Fig. 2
   [98]Open in a new tab

   Limited transcriptomic response to YFV-17D infection in NRG-HIS mice. a
   Relative expression of a set of four anti-viral genes (green, STAT1;
   blue, MDA5; red, IRF7; and purple, RSAD2) in the PBMCs of NRG-HIS mice
   following infection with YFV-17D. Expression of each gene was assessed
   by RT-qPCR in human peripheral CD45+ cells at different time points
   post infection (day 0, 3, 7, 11, and 22 post infection). Each dot
   represents the average expression of a given gene within a cohort of 4
   NRG-HIS mice. For each time point, the grand median is shown and
   represent the median of the cumulated expression of the four genes.
   Dotted line represents the gene expression level at baseline (n = 2
   cohorts of four NRG-HIS mice each). *p ≤ 0.05, ns non-significant
   (two-way ANOVA). b Schematic representation of the procedure to
   characterize the PBMC transcriptomic signature of NRG-HIS mice
   following YFV-17D infection. c YFV-17D serum viremia at days 0 and 11
   post infection in the NRG-HIS mice used for transcriptomic profiling.
   (+) RNA copies per ml were quantified by RT-qPCR. Red horizontal lines
   represent median viremia at each time point. Limit of detection (dotted
   line) is shown (n = 15). ****p ≤ 0.001 (Wilcoxon–Mann–Whitney test). d
   Number of significantly differentially expressed (DE) genes
   (p[adj] ≤ 0.05) in the PBMCs of NRG-HIS mice following YFV-17D
   infection. The names of the only four DE genes is depicted, followed
   with their respective log[2] fold change

Enhanced human myeloid and NK cell reconstitution in HIS mice

   Impaired immune function in conventional NRG-mice (reviewed in
   ref.^[99]29) can likely be attributed in part to low frequencies of
   critical immune cell subsets, such as myeloid and NK cells, compared to
   humans (Fig. [100]3a) where the myeloid compartment represents 50–80%
   of peripheral leukocytes. Since DCs and NK cells are key effectors of
   the innate immune response and critical in the activation of an
   adaptive response, we hypothesized that selective expansion of these
   cell subsets in humanized mice could promote an enhanced human immune
   response.

Fig. 3.

   [101]Fig. 3
   [102]Open in a new tab

   Selective expansion of human myeloid cells and NK cells in humanized
   mice. a Immune system reconstitution in NRG-HIS mice. Frequency of each
   cell fraction is shown as a percentage of CD45+ cells, with the
   exception of CD4+ and CD8+ T cells, which are displayed as a percentage
   of CD3+ T cells. The frequencies of important myeloid subsets (CD14+
   monocytes and CD11c+ dendritic cells) and CD56+ NK cells are
   highlighted by a red box. Medians are shown for each cell subset
   frequency as horizontal black line (n = 82). b Schematic representation
   of the experimental procedure employed to evaluate the ability of
   NRGF-HIS mice to selectively expand human myeloid cell subsets. 10^11
   AdV-Fluc or 10^10 AdV-Flt3LG particles were injected into NRG-HIS or
   NRGF-HIS mice, and immune cell expansion was examined at day 5 and 10
   post Adv-injection. c, d Expansion of human immune cell subsets in the
   spleen (c), bone marrow (c) or blood (d) of NRG-HIS and NRGF-HIS mice.
   NRG-HIS (blue circle) and NRGF-HIS (red square) mice were injected with
   AdV-Fluc (closed circle/square in panel c) or AdV-Flt3LG (open
   circle/square in panel c). e Expansion of murine cDCs and pDCs in the
   spleen and bone marrow of NRG-HIS and NRGF-HIS mice following injection
   with AdV-Flt3LG or AdV-Fluc. For panels (c–e), medians are shown as
   horizontal black lines for each experimental condition (n = 4 per
   group). *p ≤ 0.05, ns non-significant (Wilcoxon–Mann–Whitney test).
   cDCs conventional dendritic cells, pDCs plasmacytoid dendritic cells,
   NK natural killer cells

   Receptor-type tyrosine-protein kinase FLT3, or fetal liver kinase-2
   (Flk2), is a cell surface receptor broadly expressed on early
   hematopoietic precursors in the bone marrow. Consequently, myeloid cell
   development is severely impaired in Flk2-deficient mice
   (Flk2^−/−)^[103]30,[104]31. However, in xenorecipient mouse strains
   commonly used for human hematopoietic
   engraftment^[105]12,[106]32–[107]34, murine myelopoiesis is largely
   unaffected. Thus, the majority of myeloid cells are still of murine
   origin and putatively interfere with priming of human-specific adaptive
   immune responses upon infection with (human-tropic) pathogens.

   Despite the recent generation of humanized mouse models harboring a
   Flk2 deletion^[108]25,[109]35, the influence of this on the human
   immune response in humanized mice has not yet been described nor
   directly compared to immunity in humans. Therefore, as a
   proof-of-concept, we aimed to quantitatively evaluate the impact of
   this enhancement on the cellular and transcriptomic response to YFV-17D
   infection through cross-comparison with other humanized mouse models
   and human clinical data.

   We thus generated Flk2^−/− mice on the NRG background, yielding NRGF
   mice. While previous work relied on repeated injections of recombinant
   Flt3LG^[110]24,[111]25, we chose a vectored delivery approach,
   constructing a replication-incompetent adenoviral system for sustained
   and stable production of human Flt3LG (AdV-Flt3LG) in HEK293T cells in
   vitro (Supplementary Figure [112]2a) and NRG mice in vivo
   (Supplementary Figure [113]2b). Following injection of NRG-HIS and
   NRGF-HIS mice with AdV-Flt3LG (NRG-HIS/Flt3LG or NRGF-Flt3LG,
   respectively) or with AdV-Fluc (NRG-HIS/Fluc or NRGF-HIS/Fluc,
   respectively) (Fig. [114]3b, Supplementary Figure [115]2c, d), we
   observed a significant expansion of CD33+ myeloid cells, CD33+ CD11c+
   (conventional) DCs (cDCs), and CD123+ BDCA2+ plasmacytoid DCs (pDCs) in
   the spleen and/or bone marrow of NRG-HIS/Flt3LG mice (Fig. [116]3c,
   Supplementary Figure [117]2e, f; Supplementary Figure [118]3).
   Likewise, frequencies of CD56+ CD3− NK cells and CD66+ granulocytes
   increased in the spleen and bone marrow of NRGF-HIS/Flt3LG mice
   (Fig. [119]3c; Supplementary Figure [120]3). Moreover, several cellular
   lineages commonly under-represented in conventional humanized mice,
   including cDCs, monocytes, macrophages, granulocytes, NK cells and CD3+
   CD56+ T cells (which include NK T cells and γδ T cells), were expanded
   in the peripheral blood (Fig. [121]3d; Supplementary Figure [122]2g;
   Supplementary Figure [123]3). Some of these human lineages were also
   more prevalent in NRG-HIS/Flt3LG mice than in NRG-HIS/Fluc mice but
   with greater variability across cohorts (Fig. [124]3c, d; Supplementary
   Figure [125]2f, g; Supplementary Figure [126]3). Importantly, the
   frequencies of various murine myeloid subsets, including cDC, pDC, and
   monocytes, remained largely constant in the spleen and bone marrow of
   NRGF-HIS/Flt3LG mice but were increased in NRG-HIS/Fltl3LG mice
   (Fig. [127]3e; Supplementary Figure [128]2h; Supplementary
   Figure [129]3), consistent with the fact that human Flt3LG is
   biologically cross-reactive and stimulates murine myeloid precursor
   cells^[130]36.

Transcriptomic signatures to YF-17D in NRGF-HIS/Flt3LG mice

   By RNA-seq, NRGF-HIS/Flt3LG mice exhibited a signature of 158 genes
   significantly DE (p ≤ 0.05) while NRG-HIS and NRGF-HIS/Fluc mice
   displayed, respectively, 4 and 9 DE genes (Figs. [131]2d and [132]4a,
   b; Supplementary Figure [133]4a, b; Supplementary Data [134]1). The 158
   genes in the NRGF-HIS/Flt3LG mice contained a panel of 65 upregulated
   genes among which were many immune response-related genes involved in
   macrophage activation/phagocytosis, NK cell cytotoxicity, or type I
   interferon (IFN) anti-viral response (Fig. [135]4c; Supplementary
   Fig. [136]4a). A gene ontology (GO) term enrichment
   analysis^[137]37,[138]38 of the transcriptomic profiles of our
   different mouse models (following replicate analysis) showed that the
   total set of upregulated GO terms of NRGF-HIS/Flt3LG mice was enriched
   for immune-related GO terms (17% of total upregulated GO terms) in
   contrast to NRG-HIS (0%) and NRGF-HIS/Fluc mice (5%) (Fig. [139]4d;
   Supplementary Figure [140]5). In NRGF-HIS/Flt3LG mice, we identified
   upregulated GO terms related to IFN signaling, antigen presentation,
   and cytokine production, consistent with previous findings in
   humans^[141]28,[142]39. Moreover, we observed an enrichment in GO terms
   related to the regulation of B and T cell-mediated immunity, providing
   additional evidence for a more comprehensive immune response to YFV17D
   in NRGF-HIS/Flt3LG mice. In contrast, 20% of the immune-related GO
   terms were significantly downregulated in NRG-HIS mice, further
   underscoring the limited functionality of the HIS in this model. A KEGG
   pathway^[143]40,[144]41 enrichment analysis also confirmed the ability
   of NRGF-HIS/Flt3LG mice to mount an enhanced transcriptional response.
   Among the top five upregulated KEGG pathways identified in each mouse
   model, immune-related pathways were only found in NRGF-HIS/Flt3LG mice
   (Fig. [145]4e). These pathways, related to antigen presentation and NK
   cell activity, were consistent with our findings as well as with
   previous ex vivo and in vivo human
   studies^[146]28,[147]39,[148]42,[149]43.

Fig. 4.

   [150]Fig. 4
   [151]Open in a new tab

   NRGF-HIS/Flt3LG mice display an extensive transcriptomic signature. a
   Schematic representation of the experimental procedure to characterize
   the PBMC transcriptomic signature of NRGF-HIS mice following YFV-17D
   infection. b Number of significantly DE genes (p[adj] ≤ 0.05) at day 11
   post YFV-17D infection (versus day 0, prior infection) in the PBMCs of
   NRG-HIS (red), NRGF-HIS/Fluc (green), and NRGF-HIS/Flt3LG (blue) mouse
   PBMCs upon YFV-17D infection. c Protein–protein network of
   significantly DE (p[adj] ≤ 0.05) in NRGF-HIS mice following YFV-17D
   infection. Each gene is colored based on its log[2]FC (1 < x < 2, red;
   0.5 < x < 1, orange; −1 < x < −0.5, yellow; −2 < x < −1, green). Areas
   enriched with genes related to a specific biological process are
   highlighted by a dotted circle or ellipse. d Frequencies of upregulated
   (red area) or downregulated (blue area) immune-related GO terms among
   all statistically significant GO terms (p ≤ 0.05) in NRG-HIS (red),
   NRGF-HIS/Fluc (green), and NRGF-HIS/Flt3LG (blue) mice following
   replicate analysis. Total count of immune-related GO-terms out of all
   significant GO terms (displayed as immune-related/all) are also
   reported. Dotted lines between the bars symbolize the progressive
   enhancement of human immune functionality across our humanized mice
   models. e KEGG pathway enrichment analysis of the transcriptomes of
   NRG-HIS (red), NRGF-HIS/Fluc (green), and NRGF-HIS/Flt3LG (blue) mouse
   PBMCs following replicate analysis. For each experimental setting or
   mouse model, the top five upregulated KEGG pathways are listed (q
   value ≤ 0.06)

Human-like transcriptomic responses in NRGF-HIS/Flt3LG mice

   Two studies have previously delineated the transcriptomic response to
   YFV-17D in human vaccinees^[152]28,[153]44. Specifically, these studies
   identified sets of genes differentially regulated in PBMCs upon YFV
   vaccination. Hence, we utilized these valuable datasets as a reference
   to quantitatively evaluate how similar transcriptomic responses were in
   our different humanized mouse models in comparison to humans. We
   re-analyzed three datasets (hereon referred to as the Lausanne,
   Montreal, and Emory cohorts) derived from two independent
   studies^[154]28,[155]44 and sorted the list of differentially regulated
   genes (p[adj] ≤ 0.1) at day 7 post vaccination for each human cohort.
   From these three lists of genes, we then generated a global human
   dataset of genes differentially expressed upon YFV-17D vaccination
   using two distinct methods: an unbiased method and a double selection
   method, i.e., composed of genes found in at least one cohort, or in at
   least two cohorts respectively. Using these two distinct human
   reference datasets, we computed a Spearman rho correlation index (see
   Methods for details) for each humanized mouse model (NRG-HIS,
   NRGF-HIS/Fluc, and NRGF-HIS/Flt3LG; day 11 post vaccination) relative
   to human vaccinees, at varying p[adj] thresholds (from 0.01 to 0.1 with
   0.01 increments) for differentially expressed genes. Correlation
   indexes were referred to as r[U] and r[D] for the unbiased and double
   selection method respectively (see Methods for details), for a given
   p[adj] threshold. We chose to use the correlation index value derived
   from gene sets constructed with an p[adj] threshold of 0.05 as our
   definitive r[U] and r[D] correlation indexes (referred to as
   r[U,q][=0.05] and r[D,q][=0.05]; see Methods for details). Independent
   of the method or p[adj] threshold used, NRGF-HIS/Flt3LG always
   displayed the highest correlation index in comparison to the two other
   models (NRGF-HIS/Flt3LG r[U,q=0.05] = 0.075 and r[D,q=0.05] = 0.157
   Fig. [156]5; Supplementary Table [157]1). When limiting the analysis to
   differentially expressed genes at p[adj] ≤ 0.01, the r[U] and r[D]
   correlation indexes of the three mouse cohorts displayed the highest
   differences, with notably r[D] for NRGF-HIS/Flt3LG reaching up to 0.188
   (versus 0.0967 and 0.036 for NRG-HIS and NRGF-HIS/Fluc, respectively).
   Altogether, this analysis demonstrates the superiority of our
   NRGF-HIS/Flt3LG mice over conventional humanized mice for modeling the
   human transcriptomic response to YFV-17D infection. Moreover, our
   approach provides a valuable and relevant methodology for an accurate
   and objective scoring of human immunity in humanized mouse models.

Fig. 5.

   [158]Fig. 5
   [159]Open in a new tab

   Human-like transcriptomic response to YFV-17D infection in
   NRGF-HIS/Flt3LG. Differentially expressed genes from PBMCs of three
   YFV-17D human vaccinee cohorts (Lausanne, n = 11; Montreal, n = 15;
   Emory, n = 25) were used to generate two global human transcriptomic
   datasets: an unbiased dataset (all differentially expressed genes
   (p[adj] ≤ 0.1) found in at least one cohort) and an double-selection
   dataset (all differentially expressed genes (p[adj] ≤ 0.1) found in at
   least two cohorts). For a given human global dataset (unbiased, left;
   double selection, right) and p[adj] threshold (starting at p[adj] ≤ 0.1
   with 0.01 increment), the Spearman rho correlation index was computed
   for each humanized mouse model transcriptomic dataset (NRG-HIS,
   NRGF-HIS/Fluc, and NRGF-HIS/Flt3LG) and plotted as function of the
   p[adj] threshold. The Spearman rho correlation index for the unbiased
   and double selection method are respectively referred as r[U] and r[D].
   The definitive correlation indexes for the unbiased and double
   selection method, referred as r[U] and r[D] at p[adj] = 0.05
   (r[U,q=0.05] and r[D,q=0.05]) respectively, are indicated on each graph
   and for each humanized mouse model. See Methods for more details

Increased control of viral infection in NRGF-HIS/Flt3L mice

   Next, we assessed how increased correlation index between human
   vaccinees and NRGF-HIS/Flt3LG mice related to viral control
   (Fig. [160]6a). While YFV-17D vaccinees are briefly viremic after
   vaccination^[161]3,[162]5,[163]44,[164]45, conventional NRG-HIS mice
   fail to clear the infection (Fig. [165]1a). Although viremia persisted
   in NRG-HIS/Flt3LG mice similarly to NRG-HIS mice, viremia in
   NRGF-HIS/Flt3L mice did not statistically differ from the baseline RNA
   copy number over time (Fig. [166]6b; Supplementary Figure [167]6a).
   This enhanced control of viral infection correlated with a better
   survival rate of NRGF-HIS mice (75% versus 50% in NRG-HIS mice
   survival) over the course of infection (Supplementary Figure [168]6b).
   Of note, the observed mortality among the NRGF-HIS/Flt3LG mice cohort
   (2 out of 8 mice) was not due to lower Flt3LG expression (Supplementary
   Figure [169]6c). Five out of the six surviving NRG-HIS/Flt3LG mice, but
   none of the NRGF-HIS/Flt3L mice, developed significant graft versus
   host disease (GVHD) by day 20 following infection (Supplementary
   Figure [170]6b), which was likely due to the priming of allogeneic T
   cell responses by the Flt3LG-expanded murine DCs activated by YFV-17D.

Fig. 6.

   [171]Fig. 6
   [172]Open in a new tab

   Improved control of infection and T-cell activation in NRGF-HIS/Flt3LG
   mice. a Schematic representation of the NRG-HIS and NRGF-HIS mice time
   course infection experiment. b YFV-17D serum viremia in the peripheral
   blood of NRG-HIS (blue, n = 12) and NRGF-HIS (red, n = 8) mice over the
   course of infection. (+) RNA copies per ml were quantified by RT-qPCR.
   Limit of detection (dotted line) is shown. Horizontal lines represent
   median viremia at each time point. **p ≤ 0.01, ****p ≤ 0.0001, ns
   non-significant (Wilcoxon–Mann–Whitney test). c IP-10 concentration
   fold-change in the serum of NRG-HIS (blue) and NRGF-HIS (red) mice over
   the course of infection. Bounds of box and whiskers represent the
   min-to-max concentration of IP-10 at each time point. Medians are
   indicated in each box as center line (n = 4–5 per group). *p ≤ 0.05,
   ***p ≤ 0.001, ns non-significant (Wilcoxon–Mann–Whitney test). d
   Frequencies of human CD3+ CD8+ HLA-DR+ CD38+ T cells in the blood of
   NRG-HIS (blue) and NRGF-HIS (red) mice over the course of YFV-17D
   infection. Bounds of box and whiskers represent the min-to-max
   frequencies of CD8+ HLA-DR+ CD38+ T cell at each time point. Medians
   are indicated in each box as center line (n = 5 per group). *p ≤ 0.05
   (two-way ANOVA)

   The pro-inflammatory cytokine CXCL10 (or IP-10) is one of the most
   significantly induced cytokines following YFV-17D infection in
   humans^[173]28,[174]46. Consistent with these observations, IP-10 serum
   levels did increase in NRGF-HIS/Flt3LG mice following infection
   (Fig. [175]6c). Several other pro-inflammatory cytokines, including
   MCP-1, IL-6, and IL-18, were also elevated in NRG-HIS/Flt3LG versus
   NRGF-HIS/Flt3LG mice at specific time points (Supplementary
   Figure [176]6d). As these increased cytokine levels did not correlate
   with an enhanced human immune response in the NRG-HIS/Flt3LG mice, they
   likely reflected the severe GVHD conditions in NRG-HIS/Flt3LG. Other
   cytokines, such as IFNγ, IL-23, and GM-CSF, were detected at similar
   levels in both mouse models (Supplementary Figure [177]6e).

   Although an increase in peripheral CD3+ T cells was observed in both
   NRG-HIS/Flt3LG and NRGF-HIS/Flt3LG mice, frequencies of CD8+T cells
   increased in the periphery of only the latter over time (Supplementary
   Figure [178]7a, b). These CD8+ T cells upregulated HLA-DR+ and CD38+ T
   cells, as previously reported in human vaccinees^[179]5 (Fig. [180]6d).
   This distinct phenotypic change of CD8+ T cells and enhanced control of
   peripheral viral replication in NRGF-HIS/Flt3LG mice also correlated
   with higher frequencies of multiple myeloid and NK cell subsets in
   different tissues (Supplementary Figure [181]7c–e).

YFV-specific immunity in HLA-expressing NRGF-HIS/Flt3LG mice

   Since NRGF-HS/Flt3LG mice did control YFV-17D infection, we analyzed
   the virus-specific CD8+ T cell response to ascertain its similarity to
   that of human vaccinees^[182]3,[183]5. We intercrossed NRG-A2 mice with
   NRGF mice, yielding NRGF mice expressing HLA-A2*0201 (NFA2 mice).
   NFA2-HIS mice were then injected with either Adv-Fluc or Adv-Flt3LG 5
   days prior to YFV-17D infection (Fig. [184]7a). Consistent with our
   previous findings in NRG-HIS/Flt3LG and NRGF-HIS/Flt3LG mice
   (Fig. [185]6b), viral replication was better controlled in
   NFA2-HIS/Flt3LG mice compared to NFA2-HIS/Fluc mice over time
   (Fig. [186]7b). Serum viremia in NFA2-HIS/Flt3LG mice peaked between
   days 5 and 10 post infection with the infection ultimately cleared by
   day 20 (Fig. [187]7b). These kinetics mimic those observed in human
   vaccinees with detectable viremia^[188]3,[189]5,[190]47. Lower viremia
   correlated with better survival of the NFA2-HIS/Flt3LG mice (75% versus
   30%) over the 3 weeks of infection (Supplementary Figure [191]8a).
   Neither cohort developed GVHD. We also found no correlation between
   survival and differential human Flt3LG concentration in the serum of
   surviving versus non-surviving NFA2-HIS/Flt3LG mice (Supplementary
   Figure [192]8b).

Fig. 7.

   [193]Fig. 7
   [194]Open in a new tab

   NFA2-HIS/Flt3LG mice mount YFV-specific cellular and humoral response.
   a Schematic representation of the NFA2-HIS mice time course infection
   experiment. b YFV-17D serum viremia in the peripheral blood of
   NFA2-HIS/Fluc (blue, n = 10) and NFA2-HIS/Flt3LG (red, n = 14) mice
   over the course of infection. (+) RNA copies per ml were quantified by
   RT-qPCR. Limit of detection (dotted line) is shown. Horizontal lines
   represent median viremia at each time point. *p ≤ 0.05, ****p ≤ 0.0001,
   ns non-significant (Wilcoxon–Mann–Whitney test). c Absolute cell count
   of peripheral YFV-specific CD8+ T cells (NS4B/A2+) in the blood of
   NFA2-HIS/Fluc and NFA2-HIS/Flt3LG mice over the course of YFV-17D
   infection. Cell counts are shown as per 100 μl of total blood.
   Horizontal lines represent median cell count at each time point
   (n = 4–6). *p ≤ 0.05, **p ≤ 0.01, ns non-significant
   (Wilcoxon–Mann–Whitney test). d Absolute count of YFV-specific CD8+ T
   cells (NS4B/A2+) in the spleen of NFA2-HIS/Fluc and NFA2-HIS/Flt3LG
   mice at day 20 post infection. Negative controls represent one
   non-infected NFA2-HIS/Flt3LG mouse and two infected NRGF-HIS/Flt3LG
   mice (that do not express HLA-A2). Horizontal lines represent median
   cell count (n = 4–11). **p ≤ 0.01 (Wilcoxon–Mann–Whitney test). e, f
   Relative concentration of human anti-YFV-17D IgM (e) and IgG (f)
   antibodies in the serum of NFA2-HIS/Fluc (blue, n = 4) and
   NFA2-HIS/Flt3LG mice (red, n = 4) over a 6-weeks course of infection.
   **p ≤ 0.01, ***p ≤ 0.001, ns non-significant (Wilcoxon–Mann–Whitney
   test). n.a. non applicable as no mice were analyzed at the time of
   serum collection. g, h Correlation between YFV-17D viremia (black line)
   and YFV-IgG relative concentration (colored box and whisker) in the
   serum of NFA2-HIS/Fluc (g) and NFA2-HIS/Flt3LG (h) over a 6-weeks
   course of infection (n = 4 per group). Medians in each box and whisker
   are connected together by a colored line (blue for NFA2-HIS/Fluc and
   red for NFA2-HIS/Flt3LG). Viremia limit of detection (dotted line) is
   shown. n.a. non applicable as no mice in the control group were alive
   at the time of serum collection. For panels (e–h), bounds of box and
   whiskers represent the min-to-max absorbance value at each time point.
   Medians are indicated in each box as center line. i Quantification of
   YFV-neutralizing activity in the serum of NFA2-NRGF-HIS/Flt3LG mice.
   Serum neutralizing activity is represented as percentage of YFV-17D
   infection inhibition (% neutralization). Medians with ranges
   (min-to-max percentage of neutralization) for both serum dilution are
   shown (n = 3). A linear regression (red line) of the average
   neutralization activity is shown and was used to determine the median
   neutralization titer (50% inhibition, red number on the x-axis)

   YFV NS4B-tetramer+ CD8+ T cells were readily detectable in the blood of
   infected NFA2-HIS/Flt3LG mice but not NFA2-HIS/Fluc mice (Fig. [195]7c;
   Supplementary Figure [196]8c). Viremia and the frequencies of
   YFV-specific CD8+ T cells followed similar kinetics in the peripheral
   blood, suggesting an important role for CD8+ T cells in YFV-17D
   infection control and clearance as previously demonstrated in human
   vaccinees^[197]47. Consistent with previously reported YFV-specific
   CD8+ T cell phenotypes from human vaccines^[198]3, a significant
   fraction of YFV NS4B-tetramer+ CD8+ T cells proliferated, as indicated
   by their Ki67 expression, and acquired an HLA-DR+/CD38+ effector
   phenotype (Supplementary Figure [199]8d, e). In contrast, NS4B-tetramer
   negative CD8+ T cells did not demonstrate upregulated Ki-67 expression
   upon YFV-17D infection (Supplementary Figure [200]8f).

   Although the frequency of antigen-specific CD8+ T cells statistically
   decreased to background levels in the blood of NFA2-HIS/Flt3LG mice,
   YFV-specific CD8+ T cells were readily detectable in the spleen by day
   20 post infection but were not detectable in the spleen of
   NFA2-HIS/Fluc mice (Fig. [201]7d, Supplementary Figure [202]8g).
   Absolute cell count and phenotyping of splenic YFV-specific CD8+ T
   cells showed that these cells mostly expressed HLA-DR and CD38 at day
   20 post infection (Supplementary Figure [203]8g–i). They also did not
   show preferential expression of Ki67, suggesting a switch toward a
   memory phenotype. Future studies will be aimed at accurately
   delineating the different phenotypes of these antigen-specific cells.

   Given the important correlation between the induction of a T
   cell-specific response and the clearance of YFV-17D infection in the
   periphery, we conducted a T cell depletion experiment. Prior to YFV-17D
   infection and 5 days post infection, NFA2-HIS/Flt3LG mice were treated
   with anti-CD4 (α-CD4) or anti-CD8 (α-CD8) antibodies (n = 4 per group),
   which have previously been used to deplete CD4+ and CD8+ T cells,
   respectively, in humanized mice^[204]7,[205]9 (Supplementary
   Figure [206]9a). Peripheral CD4+ and CD8+ T cells were efficiently
   depleted in α-CD4 and α-CD8-treated NFA2-HIS/Flt3LG mice, respectively
   (Supplementary Figure [207]9b), prior to infection. In the spleen,
   where T cells are more abundant than in the peripheral blood, we
   observed a more than ten-fold reduction in the number of CD4+ T cells
   in α-CD4-treated mice and a more than 1000-fold reduction in the number
   of CD8+ T cells in α-CD8-treated mice (Supplementary Figure [208]9c).
   Importantly, the counts of myeloid cell populations were unaffected in
   either condition (Supplementary Figure [209]9d). Upon T cell depletion,
   only α-CD8-treated mice exhibited significant mortality upon infection
   (50% survival) (Supplementary Figure [210]9e). However, both
   α-CD4-treated and α-CD8-treated mice were unable to clear viral
   infection in the periphery (Supplementary Figure [211]9f). Thus, these
   results suggest that both CD4+ and CD8+ T cells are important for
   controlling infection in peripheral blood and that CD8+ T cells are
   likely early regulators of such control. Additionally, these results
   are further evidence that the clearance of YFV-17D infection in
   NFA2-HIS/Flt3LG mice is human-, and not murine-, mediated.

   Seroconversion in human vaccinees is the hallmark of YFV-17D potent
   immunogenicity^[212]48. Hence, determined whether the enhanced control
   of infection and T cell-specific response were also associated with an
   improved humoral immune response. We assessed YFV-specific antibody
   concentrations in the sera of NFA2-HIS/Fluc (n = 4) and NFA2-HIS/Flt3LG
   (n = 4) mice over 6-weeks following infection. We detected a
   significant increase in YFV-specific IgM and IgG in the serum of the
   four NFA2-HIS/Flt3LG mice at day 40 post infection (Fig. [213]7e, f;
   Supplementary Figure [214]10a). In contrast, YFV-specific antibodies
   were not detected in the serum of NFA2-HIS/Fluc mice during the first
   30 days following infection, and none of these mice survived till the
   final experimental end-point (day 40 post infection). Notably, we found
   a strong negative correlation between viremia level and YFV-specific
   IgG concentration in the blood of NFA2-HIS/Flt3LG mice but not in
   NFA2-HIS/Fluc mice (Fig. [215]7g, h). Consistently, the serum of three
   NFA2-HIS/Flt3LG mice at day 40 post infection neutralized YFV-17D in
   vitro (median neutralizing titer: 1:33) (Fig. [216]7i). NFA2-HIS/Flt3LG
   mice also displayed a significantly enhanced frequency of multiple B
   cell subpopulations at day 20 post infection in comparison to
   NFA2-HIS/Fluc mice (Supplementary Figure [217]10b, c). Specifically,
   frequencies of follicular B cells, transitional B cells, class-switched
   memory B cells, or plasmablasts were higher in NFA2-HIS/Flt3LG mice,
   suggesting these subpopulations proliferate and differentiate better in
   response to YFV-17D infection in this model.

Enhanced YFV-17D immunity associates with superior HIS complexity

   Finally, we employed Seq-Well^[218]49, a recently developed platform
   for massively parallel single-cell RNA-Seq (scRNA-Seq), to delineate at
   the greatest possible resolution the cellular composition of the
   engrafted HIS that correlated with a superior human immune response to
   YFV-17D. We isolated splenocytes from two NRG-HIS mice and two
   NFA2-HIS/Flt3LG mice at 6-weeks post YFV-17D infection and sorted these
   cells by human CD45 (hCD45) or human CD33 (hCD33) expression (see also
   Supplementary Note [219]1).

   We ran parallel Seq-Well arrays for each sorted population, enabling
   both unbiased characterization of the relative abundances of all
   lymphocytes, as well as a deeper examination of the cellular diversity
   within the myeloid compartment. First, we examined the cell types
   identified in hCD45+single cells in NFA2-HIS/Flt3LG (Fig. [220]8a)
   compared to NRG-HIS (Fig. [221]8b) mice. NFA2-HIS/Fl3LG splenic CD45+
   cells showed a higher diversity of well-resolved subpopulations of
   activated and differentiated cytotoxic lymphocytes, T cells expressing
   known activation and memory markers (CD27, CCR7, STAT1, CD40LG), and
   regulatory T cells (distinguished by high expression of FOXP3 and
   CTLA4). The abundance of myeloid and NK cells within the hCD45+ samples
   was significantly higher in NFA2-HIS/Flt3LG mice, and we were unable to
   resolve a distinct cluster of myeloid cells from the NRG-HIS
   populations when clustered alone.

Fig. 8.

   [222]Fig. 8
   [223]Open in a new tab

   Improved human immune system complexity in NFA2-HIS/Flt3LG. a, b 1297
   and 457 single cells from the CD45+ compartment in NFA2-HIS/Flt3LG (a)
   and NRG-HIS (b) mouse spleens respectively, 6 weeks post infection.
   Single cells are plotted using t-stochastic neighbor embedding (tSNE).
   Significant clusters were defined using a shared nearest neighbor
   modularity based clustering algorithm^[224]67. Clusters identified by
   defining significantly differentially expressed genes in each cluster
   using a likelihood ratio test for single-cell gene expression and
   annotating by literature-supported gene expression programs or
   subpopulation defining genes (see Methods section). c, d
   CD3-CD79-TCR-BCR- single cells from both CD45+ and CD33+ compartments
   in spleens from two NFA2-HIS/Flt3LG (c) and NRG-HIS (d) mice are
   plotted as a tSNE. Clustering and annotation are completed as described
   in (a, b). e, f Cluster-defining genes over CD3-CD79-TCR-BCR- single
   cells from NFA2-HIS/Flt3LG (e) and NRG-HIS (f) mice are represented as
   a projected color scale on the tSNE calculated in (c) and (d),
   respectively. Scaled digital gene expression is represented as a color
   map from light blue (low gene expression) to black (high gene
   expression), and these values are projected onto the single cell point.
   The expression of these genes in NFA2-HIS/Flt3LG mice (e) was compared
   to the expression in NRG-HIS mice (f), and significantly differentially
   expressed genes are shown in (e) (*p ≤ 0.05, ***p ≤ 0.001;
   Benjamini–Hochberg adjusted p-value). Tregs regulatory T cells, NK
   natural killer cells, DCs dendritic cells, cDCs conventional dendritic
   cells, pDCs plasmacytoid dendritic cells

   To compare more directly the myeloid compartments between the NRG-HIS
   mice and the NFA2-HIS mice, we combined both hCD33+ and hCD45+ samples
   from either the NFA2-HIS mice (Fig. [225]8c) or NRG-HIS mice
   (Fig. [226]8d) and computationally gated out all T cells and B cells by
   expression of TCR- or BCR-related genes (full list in Methods section).
   In NFA2-HIS/Flt3LG mice, we identified six distinct clusters of cell
   types corresponding to subpopulations of NK cells, monocytes,
   macrophages, cDCs, pDCs, and cross-presenting DCs (full
   cluster-defining genes in Supplementary Data [227]2). We also directly
   compared the expression of the top subpopulation-defining genes in
   NFA2-HIS/Flt3LG non-T, non-B single cells (Fig. [228]8e) with the
   corresponding single cell gate in NRG-HIS mice (Fig. [229]8f). This
   analysis revealed significant up-regulation of a broad range of myeloid
   and NK cell functional and activation markers (such as HLA-DRA, CD83,
   and CD40 for cDCs, and CCL5, GNLY, GZMA, PRF1, and CD226 for NK cells)
   in NFA2-HIS/Flt3LG mice over NRG-HIS mice.

   When analyzed alone, hCD45+ NRG-HIS single cells did not yield a
   distinct subpopulation that could be annotated as a myeloid type
   cluster. However, when these cells were clustered in concert with
   abundant myeloid cell types from the NFA2-HIS mice, we could resolve
   these cells distinctly. Through this analysis, we confirmed superior
   frequencies of DCs, NK cells and NKT cells in NFA2-HIS/Flt3LG mice in
   comparison to NRG-HIS mice (Fig. [230]9a, b).

Fig. 9.

   [231]Fig. 9
   [232]Open in a new tab

   ScRNA-Seq-based measurement of the myeloid and NK cell engraftment. a,
   b Chart bart displaying the fraction of human cell subsets within the
   hCD45+ compartment in both NRG-HIS and NFA2-HIS/Flt3LG mouse spleens
   (a). The fraction of multiple myeloid and NK subsets is highlighted
   within a second chart bart (b). Cell subsets are colored as in
   Fig. [233]8a, c. c, d Heatmap of all hCD45+ single cells in
   NFA2-HIS/Flt3LG (c) and NRG-HIS (d) mice over myeloid, DC, NK, and
   granulocyte subpopulation-defining genes. Differentially expressed
   genes between NRG-HIS and NFA2-HIS/Flt3LG mice are shown in (c)
   (*p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001; Benjamini–Hochberg adjusted
   p-values). Tregs regulatory T cells, NK natural killer cells, NKT
   natural killer T cells, DCs dendritic cells, cDCs conventional
   dendritic cells, pDCs plasmacytoid dendritic cells

   Finally, we tested differential expression between hCD45+ single cells
   in both mouse models over a curated list of lineage and cell-type
   relevant genes (Fig. [234]9c, d). This analysis revealed significantly
   higher expression of functional markers of activated NK and T cells
   (such as NKG7, PRF1, and GZMA), as well as higher abundance of
   functional mature myeloid cells (notably via ITGAM, ITGAX, CD14, or
   CD83) in NFA2-HIS/Flt3LG mice.

   Altogether, these data provide an in-depth view of the cellular
   composition of the HIS in conventional and second-generation humanized
   mice. They also highlight the enhanced engraftment and functionality of
   the myeloid and NK cell compartment in NFA2-HIS/Flt3LG mice, which is
   likely critical in promoting an enhanced transcriptomic, cellular, and
   humoral response to YFV-17D. A more complete description of our
   experimental design and our scRNA-seq data can be found in
   Supplementary Note [235]1, associated with Supplementary
   Figure [236]10d, Supplementary Figure [237]11, Supplementary
   Figure [238]12, and Supplementary Data [239]3.

Discussion

   Understanding the pathogenesis and immune responses elicited by
   human-tropic pathogens presents considerable challenges. Over the past
   decades, humanized mice have proven susceptible to a large number of
   human-tropic pathogens^[240]6,[241]8,[242]17,[243]27,[244]50,[245]51
   and have emerged as valuable platforms to model human-specific
   infectious processes in vivo. Despite the fact that multiple studies
   have reported that humanized mice can develop immunological features
   resembling those of humans, commonly used humanized mouse models still
   mount an imperfect human immune response^[246]7,[247]9,[248]16,[249]52,
   and it remains incompletely defined how specific refinements of
   xenorecipient strains and/or humanization protocols impact immunity. To
   guide a more directed approach to improving future generations of
   humanized mice, objective metrics are needed to facilitate direct
   comparisons with clinical data. YFV-17D is a highly potent human
   vaccine that induces a strong, polyfunctional immune
   response^[250]3,[251]28,[252]44 as well as long-lasting protective
   immunity (reviewed in ref. ^[253]26). The human immune response to
   YFV-17D has been intensively studied in the peripheral blood of human
   vaccinees^[254]3,[255]5,[256]28,[257]44, and transcriptomic signatures
   in human PBMCs following YFV-17D immunogenicity have been
   delineated^[258]28,[259]44. These features highlight YFV-17D human
   immunity as a powerful comparator for evaluating immune responses in
   humanized mice.

   Here, we provide a comprehensive and quantitative comparison of human
   clinical data with equivalent data sets from conventional and exemplary
   second-generation humanized mouse models. By probing the specific
   cellular, humoral, and transcriptomic response to YFV-17D in different
   humanized mouse models, we examined how a correlation index, built to
   reflect the degree of overlap between the transcriptome of a given
   humanized mouse model and that of human vaccinees upon YFV-17D
   infection, associated with the induction of YFV-specific immunity and
   viral clearance within a given model.

   Despite high levels of human engraftment, the human immune response to
   YFV-17D in NRG-HIS mice was weak and associated with a low correlation
   index. In contrast, enhancement of the NK and myeloid cell compartments
   in NRGF-HIS associated with a significantly higher correlation index in
   comparison to conventional humanized mice, which translated into the
   induction of a specific cellular and humoral response against YFV-17D.
   Hence, our study shows that the transcriptomic correlation index
   represents a powerful and accurate proxy for assessing the quality of
   the human immune response in these—and importantly, other—models.

   Numerous alternative humanization strategies have been realized in
   e.g., so-called BLT or MISTRG models^[260]17,[261]23. The experimental
   pipeline and data sets provided here will undoubtedly be a valuable
   resource to objectively evaluate these strategies in comparison to
   others, as well as for the rational development of advanced humanized
   mice models and improved modeling of human disease in vivo. Refinement
   strategies could include the expression of additional human orthologs
   of non-redundant cytokines, which exhibit limited biological
   cross-reactivity in order to foster development of other
   underrepresented human immune cell types. Currently, most studies
   employ naive, i.e., antigen-inexperienced, mice, which does not take
   into account how immunological history shapes immunity. Thus, exposing
   mice to multiple other vaccines prior to yellow fever vaccination or
   infection with another pathogen may be a valuable approach to further
   enhance immunity. Co-engraftment of NFA2 or other xenorecipient strains
   with additional HSC donor-matched human tissues, such as liver, thymus,
   and/or lymph nodes, could also significantly augment the immune
   response. Such co-engraftments could enhance T and B cell selection,
   intra-hepatic T cell priming^[262]53 as well as liver-mediated
   secretion of key human-immune components^[263]54. Finally, engraftment
   of second-generation humanized mice with a human-like microbiome
   represents another valuable approach to enhance immunity as recently
   suggested^[264]55.

   Taken together, our study demonstrates that correlation of
   transcriptomic signatures provide a relevant and valuable path toward
   the rational refinement of humanized mouse models. Such an approach
   opens avenues for more accurate characterization of human–pathogen
   interaction events in vivo, for the development of innovative
   immunotherapy and vaccine strategies, as well as for the modeling of
   relevant (patho-) physiological processes and auto-immune diseases.

Methods

Cell lines and antibodies

   HEK293, HEK293T cells (both American Type Culture Collection, Manassas,
   VA) and Huh7.5 (kindly provided by Charles Rice, The Rockefeller
   University) were grown in Dulbecco’s modified Eagle’s medium (DMEM)
   supplemented with 10% heat inactivated fetal bovine serum (Thermo
   Scientific, Waltham, MA, USA) and 1% Penicillin Streptomycin (Thermo
   Scientific, Waltham, MA, USA). The following anti-mouse Abs were used:
   From Biolegends (San Diego, CA, USA): CD45-PE-Cy7 clone 30-F11
   (dilution 1/100), CD3-PerCP-Cy5.5 clone 17A2 (dilution 1/100),
   Ly6G/Gr1-PerCP-Cy5.5 HK1.4 (dilution 1/50); From BD Biosciences (San
   Jose, CA, USA): CD11c-allophycocyanin clone HL3 (dilution 1/50); From
   eBiosciences/Thermo Fisher scientific (San Diego, CA, USA):
   CD11b-allophycocyanin-eFluor 780 clone M1/70 (dilution 1/100), F4/80-PE
   clone BM8 (dilution 1/100), CD317-Alexa Fluor 488 clone eBio927
   (dilution 1/50), TER-119-PerCP-Cy5.5 clone TER-119 (dilution 1/50),
   CD19-PerCP-Cy5.5 clone 1D3 (dilution 1/100), NK1.1-PerCP-Cy5.5 clone
   PK136 (dilution 1/100). The following anti-human Abs were used: From BD
   Biosciences (San Jose, CA, USA): CD45-V500 clone HI30 (dilution 1/100),
   CD19-allophycocyanin-Cy7 clone SJ25C1 (dilution 1/100),
   CD4-allophycocyanin clone RPA-T4 (dilution 1/100), CD4-Alexa Fluor 700
   clone RPA-T4 (dilution 1/100); CD8-FITC clone G42-8 (dilution 1/100),
   IgG-FITC clone G18-145 (dilution 1/25), CD138-BV421 clone MI15
   (dilution 1/50); From Life Technologies, Invitrogen (Carlsbad, CA,
   USA); CD3-PE-Cy5 clone 7D6 (dilution 1/100); Ki-67-PE clone 20Raj1
   (dilution 1/50); From Biolegends: CD56-allophycocyanin-Cy7 clone HCD56
   (dilution 1/100), CD45RA-Alexa Fluor 700 clone HI100 (dilution 1/100);
   From Thermo-scientific/eBiosciences (Waltham, MA, USA/San Diego, CA,
   USA): CD3-allophycocyanin-eFluor780 clone UCHT1 (dilution 1/100),
   CD33-PerCP-Cy5.5 clone WM53 (dilution 1/100), CD14-PE-eFluor610 clone
   61D3 (dilution 1/50), CD11c-Alexa Fluor 700 clone 3.9 (dilution 1/50),
   CD68-PE clone Y1/82A (dilution 1/50), HLA-DR-eFluor450 clone L243
   (dilution 1/100), CD279/PD-1-allophycocyanin-eFluor780 clone J43
   (dilution 1/50), CD38-PE-eFluor610 clone HIT2 (dilution 1/50),
   CD197/CCR7-PE clone 3D12 (dilution 1/50), CD1c/BDCA-1-FITC clone L161
   (dilution 1/50), CD123-eFluor450 clone 6H6 (dilution 1/50),
   CD19-PE-eFluor610 clone HIB19 (dilution 1/100), CD33-PE clone WM53
   (dilution 1/100), CD56-FITC clone TULY56 (dilution 1/50), CD27-PE clone
   O323 (dilution 1/50), CD28-PE-eFluor610 clone CD28.2 (dilution 1/50),
   CD127-eFluor450 clone A7R34 (dilution 1/50), CD20-Alexa Fluor 700 clone
   2H7 (dilution 1/50), CD24-eFluor450 clone eBioSN3 (dilution 1/50),
   IgD-PerCP-eFluor710 clone IA6-2 (dilution 1/50), IgM-FITC clone SA-DA4
   (dilution 1/50); From Milteny Biotec (Cambridge, MA, USA):
   CD303/BDCA-2-allophycocyanin clone AC144 (dilution 1/50),
   CD141/BDCA-3-FITC clone REA674 (dilution 1/50); From Novus Biological
   (Littleton, CO, USA): CD66b-Alexa Fluor 700 clone G10F5 (dilution
   1/50); From BioXCell (West Lebanon, NH, USA): CD8α clone OKT-8 (100 µg
   per mouse and per injection), CD4 clone OKT-4 (100 µg per mouse and per
   injection).

Adenovirus constructs

   Adenoviral constructs encoding for the firefly luciferase (Fluc) or
   Flt3LG were created using the AdEasy Adenoviral Vector System (Agilent
   Technologies, Santa Clara, CA, USA) according to the manufacturer’s
   instructions. Briefly, Fluc and human Flt3LG cDNA coding sequence were
   cloned following restriction digest (KpnI-XhoI and KpnI-EcorV,
   respectively) into pShuttle TRE-pAdEasy, leading pShuttle
   TRE-pAdEasy-Fluc and pShuttle TRE-pAdEasy-Flt3LG, respectively.
   Recombinant pShuttle-CMV plasmids were linearized with PmeI and ligated
   to pAdEasy by homologous recombination followed by electroporation into
   BJ5183 cells (Agilent Technologies, Santa Clara, CA, USA). Recombinant
   pShuttle-pAdEasy constructs were identified by PacI restriction
   analysis. All plasmid constructs were verified by DNA sequencing.

Generation of recombinant adenoviruses

   Adenoviral stocks were generated as previously described^[265]56.
   Briefly, adenoviral constructs were transfected into HEK293 cells
   (American Type Culture Collection, Manassas, VA) using the
   calcium-phosphate method. Transfected cultures were maintained until
   cells exhibited full cytopathic effect (CPE), then harvested and
   freeze-thawed. Supernatants were serially passaged two more times with
   harvest at full CPE and freeze thaw was then performed. For virus
   purification, cell pellets were resuspended in 0.01 M sodium phosphate
   buffer pH 7.2 and lysed in 5% sodium-deoxycholate, followed by DNase I
   digestion. Lysates were centrifuged and the supernatant was layered
   onto a 1.2–1.46 g ml^−1 CsCl gradient, then spun at 95,389g on a
   Beckman Optima 100K-Ultra centrifuge using an SW28 spinning-bucket
   rotor (Beckman-Coulter, Brea, CA, USA). Adenovirus bands were isolated
   and further purified on a second CsCl gradient using an SW41Ti
   spinning-bucket rotor. Resulting purified adenoviral bands were
   isolated using a 18.5G needle and twice-dialysed against 4% sucrose.
   Adenovirus concentrations were measured by reading the OD[260] on a
   FLUOstar Omega plate reader (BMG Labtech, Ortenberg, Germany).
   Adenovirus stocks were aliquoted and stored at −80 °C.

Isolation of human CD34+ HSC

   All experiments were performed with authorization from the
   Institutional Review Board and the IACUC at Princeton University. Human
   fetal livers (16–22 weeks of gestational age) were procured from
   Advanced Bioscience Resources (ABR), Inc. (Alameda, CA). Fetal liver
   was homogenized and incubated in digestion medium (HBSS) with 0.1%
   collagenase IV (Sigma-Aldrich, Darmstadt, Germany), 40 mM HEPES, 2 M
   CaCl[2], and 2 U/ml DNAse I (Roche, Basel, Switzerland) for 30 min at
   37 °C. Human CD34+ HSC were isolated using a CD34+ HSC isolation kit
   (Stem Cell Technologies, Vancouver, British Columbia, Canada) according
   to the manufacturer’ protocol. Purification of human CD34+ cells were
   assessed by quantifying by flow cytometry using an anti-human
   CD34+-FITC antibody (dilution 1/100, clone 581, BD Biosciences,
   Franklin Lakes, NJ). Expression of human CD90, CD38, CD45RA was
   assessed among the CD34+ population.

Construction of NRGF and NFA2 mice

   NRG-Flk2−/− mice were generated by backcrossing mice harboring a Flk2
   null allele (Flt3^tm1Irl, kindly provided by Ihor Lemishka, Mount Sinai
   School of Medicine, NY) for 10 generations to NOD Rag1^−/− IL2Rg^null
   mice (NOD.Cg-Rag1^tm1MomIl2rg^tm1Wjl/SzJ, obtained from the Jackson
   Laboratory, catalog number 007799). At each generation, the presence of
   each mutant allele was confirmed with allele-specific primers.
   Resultant NRG-Flk2+/− were intercrossed to produce NRG-Flk2−/− mice.
   NRG-HLA-A2*0201 were generated by intercrossing NSG-A2*0201
   (NOD.Cg-Prkdc^scidIL2rg^tmlWjl/Sz Tg(HLA-A2.1)1Eng/Sz)^[266]7,[267]9
   with NRG mice. The absence of the SCID mutation and the presence of the
   Rag1, IL2Rγ null alleles and the A2 transgene was determined by PCR. To
   generated NRG-Flk2−/− HLA-A*0201 (NFA2) mice, NRGF and NRG-A2 mice were
   intercrossed. NRG, NRG-A2, NRGF, and NFA2 mice were maintained at the
   Laboratory Animal Resource Center at Princeton University.

   All animal experiments described in this study were performed in
   accordance with protocols (number 1930) that were reviewed and approved
   by the Institutional Animal Care and Use and Committee of Princeton
   University.

Generation of human immune system-engrafted mice

   1–5 days old xenorecipient mice were irradiated with 300 cGy and
   1.5–2 × 10^5 human CD34+ HSC were injected intrahepatically 4–6 h after
   irradiation. Male and female mice transplanted with CD34+ HSC derived
   from various human donors were used in this study.

Mouse injections and blood collections

   4–8-month-old NRG-HIS, NRGF-HIS, and NFA2-HIS were infected through
   intravenous injection in the tail with 10^10 recombinant AdV-Flt3LG or
   10^11 recombinant AdV-Fluc particles and/or with 10^6 YFV-17D p.f.u.,
   resuspended in 200 µl of PBS. For experiments involving pre-injection
   of AdV-Fluc or AdV-Flt3LG prior YFV-17D infection, YFV-17D infection
   was always performed 5 days following AdV-injection. 200 µl of blood
   were collected through submandibular bleeding at the indicated
   time-points. Serum was separated from blood cells by centrifugation
   (10 min, 3500 rpm at room temperature) for further quantification of
   serum viremia. For assessment of immune cell expansion and time course
   experiments, all the infected humanized mice displayed a level of
   peripheral humanization ranging from 40 to 60% human CD45+ out of total
   CD45+ cells. For assessment of the YFV-17D transcriptomic signature,
   all humanized mice displayed level of peripheral humanization ranging
   from 50 to 80% human CD45+ out of total CD45+ cells.

Monitoring of clinical symptoms and manifestations

   Clinical manifestations of disease were monitored daily and signs of
   clinical disease progression recorded through weight and clinical
   scoring. All mice succumbing to YFV-17D infection developed severe
   weight loss and displayed several signs of disease such as posture
   hunched, trembling, appearance with ruffled fur and rear leg paralysis
   (at later stage of disease). GVHD was determined by the presence of the
   following clinical symptoms: severe hair loss on a significant portion
   of the body (with visible naked skin), skin rash, and skin
   inflammation.

Quantification of human FLT3LG concentration in mouse serum

   Human Flt3LG concentrations were measured using an in-house sandwich
   ELISA. A rabbit polyclonal capture (Abcam, Cambridge, MA, USA) antibody
   was coated overnight into a 96 well-plate (Thermo Scientific, Waltham,
   MA, USA) at a concentration of 1:500. Following incubation with a
   blocking-buffer (SuperBlock™ Blocking Buffer; Thermo Scientific,
   Waltham, MA, USA), mouse serums were incubated at multiple dilution
   (1:10, 1:100, 1:1000). Captured human Flt3LG was then detected using a
   biotin-conjugated rabbit polyclonal detection antibody (Abcam,
   Cambridge, MA, USA) and a streptavidin-conjugated horseradish
   peroxidase (HRP, Abcam, Cambridge, MA, USA) antibody. A soluble
   recombinant human Flt3LG (initial concentration: 100 ng/ml) was used to
   determine a standard concentration curve. Optical density signals at
   450 nm were then assessed using a TriStar Multimode Microplate reader
   (Berthold Technologies GmbH & Co. KG, Bad Wildbad, Germany).

Quantification of YFV-specific antibodies in mouse serum

   Relative concentration of human YFV-17D-specific IgM and IgG was
   measured by homemade sandwich enzyme-linked immuno-sorbent assay
   (ELISA). 5000 YFV-17D infectious particles were coated overnight in a
   96-well plate (Thermo Scientific, Waltham, MA, USA). Following
   incubation with a blocking-buffer (SuperBlock™ Blocking Buffer; Thermo
   Scientific, Waltham, MA, USA), mouse serums were incubated at 1:20 and
   1:40 dilution. A serum sample from an anonymous human vaccinee was
   diluted from 1:10 to 1:160 in a two-fold dilution manner, and used as a
   positive control. Captured IgG and IgM were then detected using
   anti-human IgG (Thermo Scientific, Waltham, MA, USA; Clone HP6017) or
   IgM (Thermo Scientific, Waltham, MA, USA; Clone HP6083) horse
   peroxidase antibodies. Optical density signals at 450 nm were then
   assessed using a TriStar Multimode Microplate reader (Berthold
   Technologies GmbH & Co. KG, Bad Wildbad, Germany).

YFV-17D infectious clone and in vitro transcription

   pACNR-YFV-17D low-copy number backbone (kindly provided by Charles
   Rice, The Rockefeller University, NY) was transformed and amplified
   using low recombination NEB 5-alpha high efficiency competent
   Escherichia coli (New England Biolabs, Ipswich, MA, USA). Transformed
   bacteria were incubated in LB+ 50 μg/ml Ampicillin (Sigma-Aldrich,
   Darmstadt, Germany) overnight at 30 °C under shaking at 205 rpm.
   Plasmid cDNA was purified using E.Z.N.A. Endonuclease free Maxiprep Kit
   (Omega, Norcross, GA, USA), ethanol precipitated and linearized using
   Afl-II restriction enzyme. Following concentration of linearized DNA by
   ethanol precipitation, viral RNA was transcribed from 1 µg of linear
   template using mMESSAGE mMACHINE SP6 kit (Ambion, Foster City, CA, USA)
   according to manufacturer’s instructions.

Electroporation and production of YFV-17D stocks

   Huh-7.5 cells were washed twice with Opti-MEM Gluta-Max-1 reduced serum
   media (Life Technologies, Invitrogen, Carlsbad, CA, USA) and
   resuspended at a concentration of 1.5 × 10^7 cells/ml in Opti-MEM. 2 µg
   of viral RNA was mixed with 0.4 ml of cell suspension and immediately
   pulsed in a 2 mm cuvette using a BTX ElectroSquare Porator ECM 830
   (860 V, 99 µs, five pulses) (BTX, Holliston, MA, USA). Electroporated
   cells were incubated at room temperature for 10 min and series of 3
   consecutive electroporations (for a total 6 µg for 1.8 × 10^7 cells)
   were then dripped into 25 ml (P150 culture dish) of media. At 24 h post
   electroporation, media was changed and replaced by low-serum
   concentration DMEM (1% FBS). Virus was collected at 48 and 72 h post
   electroporation. At 72 h post electroporation, virus was polled and
   concentrated 40–100-fold using a Millipore 10,000 MWCO spin filter
   columns (Merck Millipore, Darmstadt, Germany) on the last day of
   collection (3000g, 20 min). Viral titer was then assessed using a
   plaque forming unit assay.

Titration of viral stocks and YFV-17D in vitro infections

   To determine the viral titer of the YFV-17D stock, 2.5 × 10^5 Huh7.5
   cells were seeded per well in 6-well plates 24 h post infection. Serial
   dilution from 10^−3 to 10^−12 of the viral stocks were prepared, and
   2 ml of each dilution were incubated with Huh7.5 for 6 h at 37 °C. At
   6 h post infection, media were replaced by a fresh methocell solution
   (DMEM, 10% FBS, 1% Methylcellulose). Four days post infection, cells
   were washed with PBS, fixed with 100% ethanol for 25 min and stained
   with 0.1% (w/vol) crystal violet. The number of plaques (plaque forming
   unit, p.f.u.) for each dilution was then determined.

Organ collection and isolation of immune cells

   Blood (200 µl) was collected through submandibular bleeding and
   transferred into EDTA capillary collection tubes (Microvette 600 K3E,
   Sarstedt, Nümbrecht, Germany). Cells were separated from plasma through
   centrifugation, and red blood cells were lysed with 1× lysis buffer (BD
   Pharm Lyse, BD Biosciences, San Jose, CA, USA) for 15 min at room
   temperature in the dark. Following lysis and quenching with 10% (v/v)
   FBS DMEM media, blood cells were then washed twice with a 1% (v/v)
   FBS–PBS solution before staining. At the indicated endpoints, mice were
   euthanized via exsanguination under ketamine/xylazine anesthesia. To
   generate splenocyte single cell suspensions, spleens were collected and
   individually placed in 15 ml of serum-free DMEM. Spleens were then
   transferred in a 6 cm dish, dissociated using a razor blade and
   digested (0.1% collagenase, Sigma-Aldrich, Darmstadt, Germany; 40 mM
   HEPES; 2 mM CaCl[2]; 2 U/ml DNase1, HBSS, Life Technologies,
   Invitrogen, Carlsbad, CA, USA) for 30 min at 37 °C. Following quenching
   with 10% (v/v) FBS-DMEM media, splenocytes were strained through a
   100 µm cell strainer and washed with 10% (v/v) FBS-DMEM twice.
   Splenocytes were then centrifuged and lysed with 1× lysis buffer (BD
   Pharm Lyse, BD Biosciences, San Jose, CA, USA) for 15 min at room
   temperature in the dark. Cells were then washed twice with a 1% (v/v)
   FBS–PBS solution and counted prior to staining. For bone marrow-derived
   cell isolation, femurs and tibiae of the mice were flushed with PBS
   (Life Technologies, Invitrogen, Carlsbad, CA, USA) in a 10 cm dish.
   Cells were then centrifuged and lysed with 1× lysis buffer (BD Pharm
   Lyse, BD Biosciences, San Jose, CA, USA) for 10 min at room temperature
   in the dark. Cells were then washed twice with a 1% (v/v) FBS–PBS
   solution and counted prior to staining.

YFV-17D single-step RT-quantitative real time PCR

   Viral RNA was isolated from mouse serum using the ZR Viral RNA Kit
   (Zymo, Irvine, CA, USA) according to manufacturer’s instructions. Viral
   RNA was quantified using single-step RT-quantitative real-time PCR
   (SuperScript® III Platinum® One-Step qRT-PCR Kit, Life Technologies,
   Invitrogen, Carlsbad, CA, USA) with primers and TaqMan probes targeting
   a conserved region of the 5′UTR of the 17D genome. Single-step RT-qPCR
   was accomplished in a StepOnePlus Real-Time PCR System (Applied
   Biosystems) using the following thermal cycling: 52 °C for 15 min,
   denaturation at 94 °C for 2 min, 40 cycles of denaturation at 94 °C for
   15 s, annealing at 55 °C for 20 s, and elongation at 68 °C for 20 s. A
   cDNA sequence coding for the 5′UTR was in vitro transcribed and used as
   standard for the absolute quantification of viral RNA. The primers used
   are as follow: YFV-17D sense 1, GCTAATTGAGGTGCATTGGTCTGC; YFV-17D sense
   2, GCTAATTGAGGTGTATTGGTCTGC; YFV-17D antisense 1,
   CTGCTAATCGCTCAACGAACG; YFV-17D antisense 2, CTGCTAATCGCTCAAAGAACG,
   YFV-17D probe, FAM-ATCGAGTTGCTAGGCAATAAACAC-BHQ.

Antibody staining and flow-cytometry analysis

   2–4 × 10^6 PBMCs, splenocytes, or bone marrow cells of human or murine
   origins were isolated as described above and stained for 1 h at 4 °C in
   the dark with the appropriate antibody cocktail. Following washing (1%
   (v/v) FBS in PBS), cells were fixed with fixation buffer (1% (v/v) FBS,
   4% (w/v) PFA in PBS) for 30 min at 4 °C in the dark. Flowcytometric
   analysis was performed using an LSRII Flow Cytometer (BD Biosciences,
   San Jose, CA, USA). Flow cytometry data were analyzed using FlowJo
   software (TreeStar, Ashland, OR). Chimerism of all humanized mice model
   was assessed prior each experiment by quantifying the following human
   populations: Human CD45+, human CD45+ murine CD45−; T-cells, CD45+
   CD3+; CD4+ T cells, CD45+ CD3+ CD4+; CD8+ T cells, CD45+ CD3+ CD8+;
   CD45+ CD16+ leukocytes; B-cells, CD45+ CD19; conventional dendritic
   cells, CD45+ CD11c+; NK/NKT cells, CD45+ CD56+; Monocytes, CD45+ CD14+.
   Mouse immune cell subsets were gated as followed: Murine CD45+, Human
   CD45− Murine CD45+; Conventional dendritic cells, CD45+ CD3− CD19−
   NK1.1− TER119− Ly-6G/Gr1− CD11c+; Plasmacytoid dendritic cells, CD45+
   CD3− CD19− NK1.1− TER119− Ly-6G/Gr1− CD317+; Monocytes, CD45+ CD3−
   CD19− NK1.1− TER119− Ly-6G/Gr1− CD11b+ CD11c− F4/80−; Macrophages,
   CD45+ CD3− CD19− NK1.1− TER119− Ly-6G/Gr1− CD11b+ F4/80+. Human immune
   cell subsets were gated as followed: Human CD45+, human CD45+ murine
   CD45−; T-cells, CD45+ CD3+; CD4+ T cells, CD45+ CD3+ CD4+; CD8+ T
   cells, CD45+ CD3+ CD8+; Myeloid cells, CD45+ CD3− CD19− (CD56+) CD33+;
   Granulocytes, CD45+ CD66b+; B cells, CD45+ CD3− CD19+; Natural Killer
   cells, CD45+ CD3− (CD19−) CD56+; Natural Killer T cells and γδ T cells,
   CD45+ CD3+ (CD19−) CD56+; Conventional dendritic cells, CD45+ CD3−
   CD19− (CD56−) (CD33+) CD11c+ (BDCA1/3+); CD45+ CD3− CD19 CD123+, group
   composed of monocytes, plasmacytoid dendritic cells, basophils and
   myeloid precursors; Plasmacytoid dendritic cells, CD45+ CD3− CD19−
   (CD56−) BDCA-2+ CD123+; Monocytes, CD45+ CD3− CD19− (CD56−) CD14+;
   Macrophages, CD45+ CD3− CD19− (CD56−) CD68+.

   For B-cell phenotyping, human bone marrow-derived CD19+ were analyzed
   for their expression of CD20, CD24, CD38, CD27, CD138, IgD, IgG and
   IgM. Sub-populations were qualified as follow: naive B-cells, CD19+
   IgD+ CD27−; non-classed switched memory B-cells (NCS memB), CD19+ IgD+
   CD27+; Class-switched memory B-cells (CS memB), CD19+ IgD− CD27+ CD20+
   IgM−; Plasmablast cells, CD19+ IgD− CD27+ CD20− IgM− CD38+;
   CD38+/CD138+ B cells, CD19+ IgD− CD27+ CD38+ CD138+; Transitional
   B-cells, CD19+ CD24+ CD38+; Follicular B cells CD19+ IgD+ CD27− CD20+
   CD24+ IgM^low.

   Flow cytometry fluorophor compensation for antibodies was performed
   using AbC™ Anti-Mouse Bead Kit (Life Technologies, Invitrogen, Foster
   City, CA, USA). Counting beads were added to each sample prior
   flow-cytometry analysis (AccuCheck Counting Beads, Life Technologies,
   Invitrogen, Foster City, CA, USA).

Detection and characterization of YFV-specific CD8+ T cells

   PBMCs and splenocytes were isolated and purified as described above.
   2–4 × 10^6 PBMCs or splenocytes were then incubated for 30 min at room
   temperature with a purified recombinant Fc protein (Human BD Fc
   block^TM; BD Biosciences San Jose, CA, USA; 1/10 dilution) in order to
   prevent tetramer non-specific binding to Fc receptor. Cells were then
   incubated for 1 h at room temperature with a HLA-A*02:01 APC-conjugated
   tetramer (MBL International, Woburn, MA, USA; 1/10 dilution) specific
   for the NS4B 214–222 derived epitope (LLWNGPMAV)^[268]44. Cells were
   then incubated with the appropriate antibody cocktail optimized for
   CD8+ T-cell phenotyping and fixed as described above. Flow cytometry
   fluorophore compensation were performed as described above. Counting
   beads were added to each sample prior flow-cytometry analysis
   (AccuCheck Counting Beads, Life Technologies, Invitrogen, Foster City,
   CA, USA) and were used to determine the absolute number of YFV-specific
   CD8+ T cell (also referred NS4B/A2+ CD8+ T cells) per 100 µl of blood.
   Absolute count of splenic YFV-17D-specific CD8+ T cells were determined
   by reporting the number of total splenocytes and YFV-17D-specific CD8+
   T cells processed by flow cytometry with the total count of splenocytes
   determined following tissue digestion. Number of events collected per
   mouse and for a given tissue to determine the absolute count of
   YFV-17D-specific CD8+ T cells ranged from 5 to 10 and from 100 to 700,
   in the blood and spleen, respectively. YFV-17D-specific CD8+ T cells
   were analyzed by flow-cytometry for the expression of HLA-DR, CD38, and
   Ki-67.

T cell depletion experiment

   NFA2-HIS/Flt3LG were injected with 100 µg of anti-CD4 or anti-CD8α
   (clone OKT-4 and OKT-8 respectively, BioXCell, West Lebanon, NH, USA)
   or not. Injections of 100 µg of antibody per mouse (intraperitoneal
   route) were performed for three consecutive days prior YFV-17D
   infection and three days post Adv-Flt3LG injection. A fourth antibody
   injection was performed 5 days post YFV-17D infection. At the day of
   infection, effective T-cell depletion was verified in the blood of
   animals by flow cytometry using antibodies targeting the following
   marker: human CD45+, mouse CD45+, human CD3+, human CD4+, and human
   CD8+. Weight and survival of the animals were monitored over a 20-day
   course of infection. Animals who survived the course of infection were
   sacrificed at day 20 post infection, and serum was collected to
   determine presence or absence of viral infection clearance in
   periphery. At the time of sacrifice, T cell count was determined in the
   blood and spleen of animals to control T cell depletion in mice treated
   with anti-CD4 or anti-CD8α. Additionally, the absolute counts of
   several myeloid cell populations (human cDCs, pDCs, monocytes,
   macrophages) and NK cells in the spleen were determined to ensure the
   specificity of the T-cell depletion.

Cytokine quantification

   Cytokine quantification was realized using the LEGENDplex^TM
   multi-analyte flow assay kit (Biolegend, San Diego, CA, USA). Sera from
   humanized mice were incubated for 2 h at room temperature with
   customized pre-mixed beads and detection antibodies specific for a
   panel of 13 human cytokines (IL-23, IL-12, IFNγ, TNFα, MCP-1, IL1β,
   IP-10, IL6, IFNβ, GM-CSF, IFNα2, IL18, IL33). Samples were then
   incubated for 30 min with SA-PE (Biolegend), wash and resulting
   fluorescent signals were analyzed on a flow cytometer (LSRII, BD
   Biosciences) according to manufacturer’s instructions. Analyte
   concentration was determined using LEGENDplex^TM software (Biolegend,
   San Diego, CA, USA).

Bioluminescence imaging

   NRG-HIS or NRGF-HIS were injected with 10^11 AdV-Fluc particles. Ten
   days after injection, mice were anesthetized with isoflurane and
   injected intraperitoneally with 1.5 mg luciferin (Caliper Lifesciences,
   Waltham, MA, USA). Bioluminescence was quantified using an IVIS Lumina
   II platform (Caliper Lifesciences, Waltham, MA, USA).

Isolation of human CD45+ and RNA extraction

   To isolate human CD45+ cells, humanized mice were bled prior and
   following infection and total blood were collected. Cells were
   centrifuged and lysed with 1× lysis buffer (BD Pharm Lyse, BD
   Biosciences, San Jose, CA, USA) for 10 min at room temperature in the
   dark. Cells were then washed, counted, and resuspended at a
   concentration of 1 × 10^8 cells/ml in a 2% (v/v) FBS–1 mM EDTA-PBS
   solution. Human CD45+ cells were then isolated using a human CD45+
   cells enrichment kit (Stem Cell Technologies, Vancouver, British
   Columbia, Canada) according to the manufacturer’s protocol.
   Purification of human CD45+ cells was assessed by quantifying by flow
   cytometry human and murine CD45+ frequencies prior and after
   purification. Following enrichment, human CD45+ were spined and
   resuspended in RLT lysis buffer (Qiagen, Hilden, Germany). Cellular RNA
   was then extracted using the RNeasy Mini Kit (Qiagen, Hilden, Germany)
   following manufacturer’s instructions. For the transcriptomic
   experiments, mice were assembled as cohorts of 4–5 animals displaying
   humanization levels ranging between 50 and 80%. Following lysis and
   prior to cell resuspension in a 2% FBS–1 mM EDTA-PBS solution, blood
   samples of members of each cohort were pooled together, leading to one
   sample per cohort and time point (day 0 and day 11). Cohorts were
   assembled so the average humanization across cohorts differed only by
   ±5% between cohorts.

Gene expression quantification by one-step RT-qPCR

   Following cellular RNA isolation from human CD45+ cells (as described
   above), human MDA-5, STAT1, MX1, IRF7 and RSAD2 expression were
   quantified by one-step RT-qPCR using an iTaq Universal SYBR One-step
   kit (BioRad, Hercules, CA, USA). Expression was then normalized to the
   expression of human GAPDH. Primer sequences are as follows: MDA-5
   sense, ACCAAATACAGGAGCCATGC; MDA-5 antisense, ACACGTTCTTTGCGATTTCC;
   STAT1 sense, TGGGTTTGACAAGGTTCTT; STAT1 antisense, TATGCAGTGCCACGGAAAG;
   MX1 sense, GTTTCCGAAGTGGACATCGCA; MX1 antisense, CTGCACAGGTTGTTCTCAGC;
   IRF7 sense, CCCACGCTATACCATCTACCT; IRF7 antisense,
   GATGTCGTCATAGAGGCTGTTG; RSAD2 sense, TGGGTGCTTACACCTGCTG; RSAD2
   antisense, GAAGTGATAGTTGACGCTGGTT; GAPDH sense, GAAGGTGAAGGTCGGAGTC;
   GAPDH antisense, GAAGATGGTGATGGGATTTC.

YFV-17D neutralization assay using mouse serum

   Sera isolated from NFA2-NRGF-HIS/Flt3LG mice and identified by ELISA as
   containing YFV-specific antibodies following YFV-17D infection were
   employed for a neutralization assay. For each mouse, sera isolated
   prior (day 0) and after infection (day 40) were diluted at 1:20 and
   1:40 in media containing YFV-17D viral particles (produced as described
   above). The number of viral particles per condition was determined as
   to reach a final m.o.i of 0.004 (200 p.f.u.). Viral particles and serum
   mixtures were incubated for 1 h at room temperature, and mixtures were
   used to infect naive Huh7.5 cells. As controls, we infected cells with
   cell culture media containing YFV-17D particles but no serum (also
   pre-incubated for 1 h at room temperature), or with media containing no
   viral particles and no serum. At 6 h post infection, media were
   replaced by a fresh methocell solution (DMEM, 10% (v/v) FBS, 1% (w/v)
   Methylcellulose). Four days post infection, cells were washed with PBS,
   fixed with 100% (v/v) ethanol for 25 min and stained with 0.1% (w/v)
   crystal violet. The percentage of infection inhibition was determined
   by counting and comparing the number of infectious plaques between the
   pre-infection serum and the post-infection serum condition, for each
   mouse and each given dilution.

cDNA library generation and RNA-sequencing

   The poly-A-containing RNA transcripts in the humanized mice total RNA
   samples were converted to cDNA and amplified following the Smart-seq2
   method^[269]57. Sequencing libraries were made from the amplified cDNA
   samples using the Nextera kit (Illumina, San Diego, CA, USA), assigning
   a unique barcode to each of the libraries to be sequenced together. The
   cDNA samples and libraries were examined on the Bioanalyzer (Agilent
   Technologies, Santa Clara, CA, USA) DNA HS chips for size distribution,
   and quantified by Qubit fluorometer (Life Technologies, Invitrogen,
   Carlsbad, CA, USA). The majority of the cDNA fragments in each sample
   were between 1 and 3 kb, indicating good transcript integrity. The
   RNA-seq libraries from each experiment (i.e., one for each mouse
   strain, NRG-HIS and NRGF-HIS mice) were pooled together in equal
   amounts and sequenced on the Illumina HiSeq 2500 Rapid Flowcell as
   single-end 75nt reads following the standard protocol. Sequencing depth
   was of 10–30 million reads per sample (7–10 samples were loaded on a
   single flow cell). Raw sequencing reads were filtered by Illumina HiSeq
   Control Software and only Pass-Filter (PF) reads were used for further
   analysis.

RNA-Sequencing data analysis

   In the Galaxy platform^[270]58 through Princeton University, single-end
   short reads were mapped to the human reference (version GRCh38) and the
   murine reference (version GRCm38) using TopHat and Bowtie 2 with
   default parameters and read groups specified. Only counts from uniquely
   mapped reads were used in the DESeq 2 analyses.

   Counts were generated by htseq-count^[271]59 version 0.6.1galaxy1 run
   on a Galaxy installation, downloaded, and read into R^[272]60 version
   3.3.1 (2016-06-21) using scripts run in RStudio version
   0.99.489^[273]61. The htseq count data was loaded into R and the DESeq2
   (Galaxy Version 2.11.38) utilized^[274]62, capturing factors for
   cohort, day, and batch, with the sampling day as the experimental
   design. We set up the experimental design for the contrast we were
   interested in (day 0 versus day 11) and normalized log[2] transformed
   transcript counts within DESeq2. Differential gene expression were
   determined for each group of mice (NRG-HIS, NRGF-HIS/Flt3LG or
   NRGF-HIS/Fluc) using the standard DESeq2 filters and nbinomWaldTest.
   Results were extracted from the DESeq2 analysis and annotated using
   Bioconductor’s AnnotationDbi (version 1.32.3) and org.Hs.eg.db packages
   (version 3.2.3). Differential gene expression was considered as
   significant when p[adj] ≤ 0.05. Results are available in Supplementary
   Data [275]1.

   Gene set enrichment analysis was performed in R using the package
   Generally Applicable Gene-set Enrichment (GAGE)^[276]63. Briefly, the
   GAGE method was called with the regularized log[2] fold change values,
   and with the go.sets.hs^[277]37,[278]38 or kegg.sets.hs^[279]40,[280]41
   database. Same.dir was set to true (since we were interested in
   whole-sale up- or down-regulated shifts). The top five resulting KEGG
   pathways (q-value ≤ 0.06) were then identified.

Human vaccinees datasets and measure of correlation index

   For the Emory cohort (n = 25 vaccinees, two independent trials of
   respectively 15 and 10 human vaccinees^[281]28), micro array data were
   downloaded from Gene Expression Omnibus under series accession no.
   [282]GSE13485. For the Lausanne and Montreal cohorts (n = 11 and n = 25
   vaccinees respectively^[283]44), data were downloaded from Gene
   Expression Omnibus under series accession no. [284]GSE13699. For the
   three cohorts, micro array raw data at day 0 and day 7 post vaccination
   were downloaded and we employed GEO2R
   ([285]https://www.ncbi.nlm.nih.gov/geo/geo2r/) to determine the set of
   differentially regulated genes at day 7 post vaccination. For the Emory
   cohort, we identified a set of 193 genes with p[adj] ≤ 0.1
   differentially regulated at day 7 post vaccination. For the Lausanne
   and Montreal cohort, we identified a set of 2674 and 12930 genes with
   p[adj] ≤ 0.1 differentially regulated at day 7 post vaccination,
   respectively.

   To establish our correlation index, we decided to focus on gene
   expression fold-change (FC) as the compared variable, as it is
   adimensional and can therefore be compared across experiments using
   different expression measurement methodologies

   We first sought to establish a global human dataset of genes that could
   be used to measure similarity with humanized mice in a relevant
   fashion. To do so, we used two methods. In the first one (unbiased),
   the gene set included all genes that had been statistically determined
   to be differentially expressed (for a defined Benjamini–Holmes-adjusted
   p-value threshold of q equal or lower than 0.1) in at least one of the
   three human cohorts. This method has the merit of being the most
   unbiased, as it uses no other criteria than statistical relevance.
   However, it is sensitive to false positives (that can occur somewhat
   frequently in transcriptome analyses, especially concerning
   low-expression genes). Moreover, when constructing the gene dataset
   that way, a disproportionate fraction of it was actually determined by
   the Montreal human cohort, as this cohort displayed the highest number
   of differentially regulated genes. To compensate for the over-weight of
   the Montreal cohort in our dataset, we therefore also used a double
   selection method, where we only selected genes that had been
   statistically determined to be differentially expressed (for a defined
   Benjamini–Holmes-adjusted p-value threshold of q equal or lower than
   0.1) in two or more of the three human cohorts. This method ensures the
   biological relevance of the genes selected in the set construction as
   they have been detected in more than one cohort, but at the cost of
   omitting potentially relevant candidates that were only detected in
   only one cohort.

   For both methods, after elimination of duplicates, we averaged the
   fold-changes of the genes in the set across all three human cohorts,
   therefore building two adjusted-p-value-dependent reference vectors:
   the “unbiased” vector U[q] and the “double selection” vector D[q].

   Finally, the Spearman rho correlation statistics to those reference
   vectors were computed for each humanized mouse cohort to build our
   correlation index. For a mouse cohort M, the “unbiased” value vector
   M[U,q] was first built as follows:
   [MATH:
   <msub><mrow><mi>M</mi></mrow><mrow><mi>U</mi><mo>,</mo><mi>q</mi></mrow
   ></msub><mo>=</mo><mfenced close="}" open="{"
   separators=""><mrow><mi>F</mi><msub><mrow><mi>C</mi></mrow><mrow><mi>g<
   /mi></mrow></msub><mo>,</mo><mi>g</mi><mspace
   width="0.3em"></mspace><mi mathvariant="normal">in</mi><mspace
   width="0.3em"></mspace><mi mathvariant="normal">unbiased</mi><mspace
   width="0.3em"></mspace><mi mathvariant="normal">gene</mi><mspace
   width="0.3em"></mspace><mi
   mathvariant="normal">set</mi></mrow></mfenced> :MATH]

   The values in M[U,q] and U[q] were ranked to obtain the vectors
   rgM[U,q]and rgU[q], and the

   unbiased correlation index r[M,U,q] (simply referred to as r[U] in the
   Results section) was then computed as follows:
   [MATH:
   <msub><mrow><mi>r</mi></mrow><mrow><mi>M</mi><mo>,</mo><mi>U</mi><mo>,<
   /mo><mi>q</mi></mrow></msub><mo>=</mo><mi
   mathvariant="normal">cov</mi><mrow><mo>(</mo><mrow><mi
   mathvariant="normal">rg</mi><msub><mrow><mi>M</mi></mrow><mrow><mi>U</m
   i><mo>,</mo><mi>q</mi></mrow></msub><mi
   mathvariant="normal">rg</mi><msub><mrow><mi>U</mi></mrow><mrow><mi>q</m
   i></mrow></msub></mrow><mo>)</mo></mrow><mo>∕</mo><mrow><mo>(</mo><mrow
   ><msub><mrow><mi>σ</mi></mrow><mrow><mi
   mathvariant="normal">rg</mi></mrow></msub><msub><mrow><mi>M</mi></mrow>
   <mrow><mi>U</mi><mo>,</mo><mi>q</mi></mrow></msub><msub><mrow><mi>σ</mi
   ></mrow><mrow><mi
   mathvariant="normal">rg</mi></mrow></msub><msub><mrow><mi>U</mi></mrow>
   <mrow><mi>q</mi></mrow></msub></mrow><mo>)</mo></mrow> :MATH]

   The double selection correlation index r[M,D,q] (simply referred to as
   r[D] in the Results section) was computed in a similar fashion for a
   mouse cohort M.

   It is important to note that the correlation index is dependent on the
   adjusted p-value threshold selected when building the reference vectors
   U[q] and D[q]. As this threshold is raised, more genes will be included
   in building the index, thus affecting the whole process leading to the
   computing of our correlation indexes. To address that question, we
   built several versions of our reference vectors U[q] and D[q] using
   different adjusted p-value thresholds (from 0.01 to 0.1 by 0.01
   increments) and computed r[M,U,q] and r[M,D,q] for each of them. Based
   on the shapes of the r[M,U,q] = f(q) and r[M,D,q] = f(q) curves, which
   all show a steady, linear decline with no sharp discontinuity between
   q = 0.02 and q = 0.08, we chose to use the correlation index value
   derived from gene sets constructed with an adjusted p-value threshold
   of 0.05 as our definitive correlation indexes. For a mouse cohort M,
   the unbiased correlation index to human cohorts r[M,U] is therefore:
   [MATH:
   <msub><mrow><mi>r</mi></mrow><mrow><mi>M</mi><mo>,</mo><mi>U</mi></mrow
   ></msub><mo>=</mo><msub><mrow><mi>r</mi></mrow><mrow><mi>M</mi><mo>,</m
   o><mi>U</mi><mo>,</mo><mi>q</mi></mrow></msub><mo>,</mo><mi>q</mi><mo>=
   </mo><mn>0.05</mn><mrow><mo>(</mo><mrow><mi
   mathvariant="normal">simply</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">referred</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">to</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">as</mi><mspace
   width="0.3em"></mspace><msub><mrow><mi>r</mi></mrow><mrow><mi>U</mi><mo
   >,</mo><mi>q</mi><mo>=</mo><mn>0.05</mn></mrow></msub><mi
   mathvariant="normal">in</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">the</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">Results</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">section</mi></mrow><mo>)</mo></mrow> :MATH]

   And the double selection index r[M,D]:
   [MATH:
   <msub><mrow><mi>r</mi></mrow><mrow><mi>M</mi><mo>,</mo><mi>D</mi></mrow
   ></msub><mo>=</mo><msub><mrow><mi>r</mi></mrow><mrow><mi>M</mi><mo>,</m
   o><mi>D</mi><mo>,</mo><mi>q</mi></mrow></msub><mo>,</mo><mi>q</mi><mo>=
   </mo><mn>0.05</mn><mrow><mo>(</mo><mrow><mi
   mathvariant="normal">simply</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">referred</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">to</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">as</mi><mspace
   width="0.3em"></mspace><msub><mrow><mi>r</mi></mrow><mrow><mi>D</mi><mo
   >,</mo><mi>q</mi><mo>=</mo><mn>0.05</mn></mrow></msub><mi
   mathvariant="normal">in</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">the</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">Results</mi><mspace width="0.3em"></mspace><mi
   mathvariant="normal">section</mi></mrow><mo>)</mo></mrow> :MATH]

Protein–protein interaction network representation

   Network coordinates and protein–protein interactions were downloaded
   via the STRING biological database^[286]64 and rendered using the open
   source software platform for network data integration Cytoscape
   V3.4.0^[287]65.

scRNA-Seq

   Human CD45+ and human CD33+ populations from the spleens of
   NFA2-HIS/Flt3LG mice and NRG-HIS mice were FACS-sorted and loaded onto
   prepared Seq-Well arrays, as previously described. Briefly, 10,000
   single cells were loaded onto one array containing 86,000 barcoded mRNA
   capture beads. Cells and beads were co-confined in microwells on the
   array, and a polycarbonate membrane sealed individual wells to allow
   for isolated single-cell lysis and transcript hybridization prior to
   bead recovery for reverse transcription. Next, each library was treated
   with Exonuclease I to remove excess primers, and PCR amplification was
   performed using KAPA Hifi PCR Mastermix (Thermo Scientific, Waltham,
   MA, USA) to generate final cDNA libraries. Libraries were then
   constructed using the Nextera XT DNA tagmentation method (Illumina, San
   Diego, CA, USA). Tagmented and amplified libraries were subsequently
   purified and sequenced using an Illumina 75 cycle NextSeq500/550v2 kit
   (read 1: 20 12 bp barcode, 8 bp UMI; read 2: 50). Detailed procedures
   for scRNA-Seq data alignment and analysis are available in the Methods
   section.

ScRNA-sequencing library generation

   Frozen splenocytes from NFA2-HIS/Flt3LG and NRG-HIS mice were thawed
   and stained with antibodies for anti-human CD33 or CD45, and calcein
   green as a positive viability marker. Cell populations that were either
   anti-human CD33+ or anti-human CD45+ and calcein green positive were
   sorted into RPMI + 10% FBS. Single cells from each sort gate and each
   animal were loaded onto prepared Seq-Well arrays, as previously
   described^[288]49. Briefly, 10,000 single cells from each sort gate
   were loaded onto one array containing 86,000 barcoded mRNA capture
   beads. Cells and beads were co-confined in microwells on the array, and
   a polycarbonate membrane sealed individual wells to allow for isolated
   single-cell lysis and transcript hybridization prior to bead recovery
   for reverse transcription. Next, each library was treated with
   Exonuclease I to remove excess primers, and PCR amplification with KAPA
   Hifi PCR Mastermix (Thermo Scientific, Waltham, MA, USA) to generate
   final cDNA libraries. Libraries were then constructed using the Nextera
   XT DNA tagmentation method (Illumina, San Diego, CA, USA). Tagmented
   and amplified libraries were subsequently purified and sequenced using
   an Illumina 75 cycle NextSeq500/550v2 kit (30 bp PE reads).

ScRNA-Seq alignment and analysis

   The reads were aligned as described in ref. ^[289]66. Briefly, for each
   NextSeq sequencing run, raw sequencing data was converted to
   demultiplexed FASTQ files using bcl2fastq2 based on Nextera N700
   indices corresponding to individual samples/arrays. Reads were then
   aligned simultaneously to both mm10 and hg19 genomes using the Galaxy
   portal maintained by the Broad Institute for Drop-Seq alignment using
   standard settings of the STAR aligner. Individual reads were tagged
   according to the 12-bp barcode sequenced and the 8-bp UMI contained in
   Read 1 of each fragment. Following alignment, reads were binned onto
   12-bp cell barcodes and collapsed by their 8-bp UMI. Digital gene
   expression matrices for each sample were obtained from quality filtered
   and mapped reads. UMI-collapsed data was utilized as input into R for
   further analysis.

   We first compared the alignment quality between NFA2-HIS/Flt3LG mice
   and NRG-HIS mice in each sorting condition. We confirmed equivalent
   sequencing depth and sample input quality between NFA2-HIS/Flt3LG and
   NRG-HIS mice by observing the total transcripts/single cell detected
   for the mouse single cells that contaminated our sorted populations.
   However, we observed overall lower quality among single cells that
   align to human genomes in the NRG-HIS mice compared to NFA2-HIS/Flt3LG
   mice, which cannot be attributed to differences in sequencing depth. We
   next merged UMI matrices across all genes detected in any hCD45+ sample
   (2 from NFA2-HIS/Flt3LG, 2 from NRG-HIS mice), and eliminated cells
   with fewer than 300 UMI detected and fewer than 300 unique genes
   detected (n = 1297 across 2 NFA2-HIS/Flt3LG mice, n = 457 across 2
   NRG-HIS mice). We next partitioned the matrix into cells originating
   from NFA2-HIS/Flt3LG mice or NRG-HIS mice, and completed all clustering
   and cell calling analyses in parallel. To complete dimensionality
   reduction and data visualization methods, we first identified the top
   variable genes by including all genes with an average normalized and
   scaled expression value greater than 0.32 and dispersion greater than
   0.6. Principal Components Analysis was performed over the list of
   variable genes and all cells (for each experimental group
   individually). We determined the top significant principal components
   using a permutation method as previously described^[290]67. Significant
   principal components were used for tSNE plotting, with perplexity of
   40. We used FindClusters, a clustering algorithm in the R package
   Seurat 1.4.0.1 ([291]https://github.com/satijalab/seurat) to identify
   significant clusters. 10 clusters were found for NFA2-HIS/Flt3LG mice,
   and 6 clusters were found for NRG-HIS mice, using equivalent
   parameters. No cluster was attributed to any single mouse. To identify
   genes that defined each cluster, we performed a likelihood ratio test
   implemented in Seurat. Top marker genes were used to classify cell
   clusters into cell types based on existing biological knowledge.

   To better understand diversity within the non-T cell, non-B cell
   compartment of each humanized mouse type, we merged both hCD45+ and
   hCD33+ arrays for NFA2-HIS/Flt3LG mice, and separately merged hCD45+
   and hCD33+ arrays from NRG-HIS mice. We eliminated low quality cells
   with the same parameters as above, and eliminated any cell with
   expression of the following genes: CD3E, CD3G, CD3D, TRAC*, TRBC*,
   CD79A, CD79B, IGKC, IGLC*, MSA41, CD19 (* indicates any number), to
   gate out any T or B cells. We applied the same protocol as above to
   identify variable genes, identify significant principal components,
   calculate a tSNE representation, find significant cell clusters, and
   annotate cell clusters by identifying biologically meaningful defining
   genes. Genes that define each of these non-T, non-B cell clusters in
   the NFA2-HIS/Flt3LG mice were compared directly to the expression of
   these genes in the NRG-HIS non T, non B cells. Gene differential
   expression was calculated using a likelihood ratio test for zero
   inflated data, implemented in Seurat, with original description
   described^[292]68.

   To directly compare the abundance of each cell type by annotation in
   each humanized mouse dataset, we took all hCD45+ samples and merged
   their UMI matrices. We analyzed this data as described above, and
   calculated the frequency of each cell type within either NRG-HIS mice
   or NFA2-HIS/Flt3LG mice.

Statistical analysis

   Statistical analyses were performed using either a non-parametric
   Wilcoxon–Mann–Whitney test^[293]69 or a parametric Student’ t test
   (GraphPad Prism software V6.0) when appropriate and as indicated in
   each figure legend. A two-way ANOVA test was performed for multiple
   comparison (GraphPad Prism software V6.0). *p ≤ 0.05, **p ≤ 0.01,
   ***p ≤ 0.001, ****p ≤ 0.0001. Information related to the statistical
   analysis performed for RNA-seq and single-cell RNA seq experiments can
   be found in the related sections above.

Electronic supplementary material

   [294]41467_2018_7478_MOESM1_ESM.docx^ (12.2KB, docx)

   Descriptions of Additional Supplementary Files
   [295]Supplementary Information^ (27.2MB, pdf)
   [296]Supplementary Data 1^ (55KB, xlsx)
   [297]Supplementary Data 2^ (33KB, xlsx)
   [298]Supplementary Data 3^ (2.2MB, xlsx)

Acknowledgements