Spec-seq unveils transcriptional subpopulations of antibody-secreting cells following influenza vaccination

Neu, Karlynn E.; Guthmiller, Jenna J.; Huang, Min; La, Jennifer; Vieira, Marcos Costa; Kim, Kangchon; Zheng, Na; Cortese, Mario; Tepora, Micah E.; Hamel, Natalie J.; Rojas, Karla Thatcher; Henry, Carole; Shaw, Dustin G.; Dulberger, Charles L.; Pulendran, Bali; Cobey, Sarah; Khan, Aly A.; Wilson, Patrick C.

doi:10.1172/jci121341

Cited by 42 publications

(43 citation statements)

References 50 publications

(88 reference statements)

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“…These results, which hold for relatively short timescales (∼ few months), are consistent with the argument of Wang et al [260] in the case of chronic in-fections. Interestingly, both Murugan et al [261] and Neu et al [262] experimentally found that the majority of SHM do not necessarily improve antigen binding, and that both routes can lead to equally high affinity mutants, which supports the heterogeneity observed by Tas et al [255] in the brainbow experiments. Yet, it is still not clear why some individuals fail to produce any good binding antibodies to the very strong antigenic challenge of malaria.…”

Section: Evolution Of Broadly Neutralizing Antibodiesmentioning

confidence: 67%

“…Both routes (SHM and selecting naive cells) where observed experimentally. Influenza vaccine studies show that, upon secondary immunization (booster vaccines), ∼ 7% of BCR contained no SHM [262] (although these experiments showed strong clonal dominance with ∼ 10% of clones representing ∼ 90% of sequences [259]). The model of Murugan et al [261] predicts that long exposure to small amounts of antigen leads to selecting for antibodies with a lot of SHM, whereas short exposure to high antigen concentrations results in selection on existing naive cell variation, which is confirmed in experiments.…”

Section: Evolution Of Broadly Neutralizing Antibodiesmentioning

confidence: 99%

“…The rate of observed BCRs with SHM also changes from infancy to adulthood [264]. In influenza vaccine studies [262], people born before 1977 show less inter clonal diversity but achieve the same affinity as people born after 1977. Adaptation via the SHM route is less likely in older people, and they are more likely to use their larger existing memory pools than younger people whose antibodies acquire a lot of SHM.…”

Section: Evolution Of Broadly Neutralizing Antibodiesmentioning

confidence: 99%

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Quantitative Immunology for Physicists

Altan‐Bonnet

Mora

Walczak

2019

Preprint

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The adaptive immune system is a dynamical, self-organized multiscale system that protects vertebrates from both pathogens and internal irregularities, such as tumours. For these reason it fascinates physicists, yet the multitude of different cells, molecules and sub-systems is often also petrifying. Despite this complexity, as experiments on different scales of the adaptive immune system become more quantitative, many physicists have made both theoretical and experimental contributions that help predict the behaviour of ensembles of cells and molecules that participate in an immune response. Here we review some recent contributions with an emphasis on quantitative questions and methodologies. We also provide a more general methods section that presents some of the wide array of theoretical tools used in the field. CONTENTS Dissociation ratesAs discussed below, immunological interactions span the range of very short lived interactions, k off > 10 s −1 or τ off = (k off ) −1 < 0.1 s for antibody binding to its target in the initial phase of an immune response, to extremely long-lived interactions, k off < 10 −4 s −1 or τ off > 3 hours for cytokines interacting with their receptors. These estimates set a huge range of time scales the immune system must deal with, even before taking into consideration delays in cellular responses and how these affect the ligand environment on time scales ranging from hours to days. These considerations form the crux of the matter for quantitative immunology at the cellular scale: while the physical chemistry of ligand-receptor interactions is straightforward, the immune system builds a response of devilish complexity from such elementary interactions. Numbers of receptors per cellIn some situations, such as in T cell antigen recognition, where both the T cell receptor and the antigen exist in a membrane-bound form. All dynamics of interactions must be estimated taking into account the surface concentrations of molecules, with proper adjustment for slower diffusion in the association rates.

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Section: Evolution Of Broadly Neutralizing Antibodiesmentioning

confidence: 67%

Section: Evolution Of Broadly Neutralizing Antibodiesmentioning

confidence: 99%

Section: Evolution Of Broadly Neutralizing Antibodiesmentioning

confidence: 99%

See 1 more Smart Citation

Quantitative Immunology for Physicists

Altan‐Bonnet

Mora

Walczak

2019

Preprint

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“…Our comprehension of the way antibody specificities interact with B cell function has remained limited due to the intricacies of polyclonal antibody responses. Neu et al developed the Spec-seq protocol to tackle this challenge ( [92]; Table 1). Spec-seq permits mAb production and transcriptional profiling from the same cell.…”

Section: Personalised Cancer Vaccinesmentioning

confidence: 99%

The Application of Single-Cell RNA Sequencing in Vaccinology

Noé

Cargill

Nielsen

et al. 2020

Journal of Immunology Research

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Single-cell RNA sequencing allows highly detailed profiling of cellular immune responses from limited-volume samples, advancing prospects of a new era of systems immunology. The power of single-cell RNA sequencing offers various opportunities to decipher the immune response to infectious diseases and vaccines. Here, we describe the potential uses of single-cell RNA sequencing methods in prophylactic vaccine development, concentrating on infectious diseases including COVID-19. Using examples from several diseases, we review how single-cell RNA sequencing has been used to evaluate the immunological response to different vaccine platforms and regimens. By highlighting published and unpublished single-cell RNA sequencing studies relevant to vaccinology, we discuss some general considerations how the field could be enriched with the widespread adoption of this technology.

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“…Such datasets have also been used to infer a temporal ordering of dynamic cellular states or cellular trajectories (Saelens et al, 2019). For example, the field of immunology has benefited significantly from the adoption of scRNA-seq in order to characterize cellular states in the context of development and differentiation of distinct innate and adaptive lineages (Kakaradov et al, 2017;Olsson et al, 2016;Yu et al, 2016), including responses to various perturbations (Bossel et al, 2019;Dixit et al, 2016;Neu et al, 2019), as well as in immune system diseases (Lönnberg et al, 2017;Zhang et al, 2019;Zheng et al, 2017). In spite of the tremendous progress, the inference of cellular trajectories from scRNA-seq datasets remains challenging when analyzing heterogeneous cellular compartments with complex dynamics.…”

Section: Introductionmentioning

confidence: 99%

Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer

Reiman

Sonin

et al. 2020

Preprint

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Single cell RNA sequencing (scRNA-seq) can be used to infer a temporal ordering of dynamic cellular states. Current methods for the inference of cellular trajectories rely on unbiased dimensionality reduction techniques. However, such biologically agnostic ordering can prove difficult for modeling complex developmental or differentiation processes. The cellular heterogeneity of dynamic biological compartments can result in sparse sampling of key intermediate cell states. This scenario is especially pronounced in dynamic immune responses of innate and adaptive immune cells. To overcome these limitations, we develop a supervised machine learning framework, called Pseudocell Tracer, which infers trajectories in pseudospace rather than in pseudotime. The method uses a supervised encoder, trained with adjacent biological information, to project scRNA-seq data into a low-dimensional cellular state space. Then a generative adversarial network (GAN) is used to simulate pesudocells at regular intervals along a virtual cell-state axis. We demonstrate the utility of Pseudocell Tracer by modeling B cells undergoing immunoglobulin class switch recombination (CSR) during a prototypic antigen-induced antibody response. Our results reveal an ordering of key transcription factors regulating CSR, including the concomitant induction of Nfkb1 and Stat6 prior to the upregulation of Bach2 expression. Furthermore, the expression dynamics of genes encoding cytokine receptors point to the existence of a regulatory mechanism that reinforces IL-4 signaling to direct CSR to the IgG1 isotype.

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Spec-seq unveils transcriptional subpopulations of antibody-secreting cells following influenza vaccination

Cited by 42 publications

References 50 publications

Quantitative Immunology for Physicists

Quantitative Immunology for Physicists

The Application of Single-Cell RNA Sequencing in Vaccinology

Inferring cellular trajectories from scRNA-seq using Pseudocell Tracer

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