Clonal expansion under the microscope: studying lymphocyte activation and differentiation using live‐cell imaging

Independent and temporally separated dynamics for RORγt and Foxp3 during Th17 differentiation

T helper 17 and Regulatory T cells (Th17 and Treg, respectively) are two well-described lymphocyte subsets with opposing actions. The divergent fates of Th17 and Treg cells are accounted for, at least in part, by molecular antagonism that occurs between their respective specific transcription factors, RORγt and Foxp3. An imbalance between Th17 and Treg cells may lead to tissue inflammation and is associated with certain types of autoimmunity. In order to understand the heterogeneity and dynamics of the differentiation process, we studied Th17/Treg cell differentiation of naïve cells in vitro, using RORγtGFPFoxp3RFP dual-reporter mouse. Flow cytometry revealed the consistent emergence of a population of double positive RORγt+Foxp3+ (DP) cells during the early stages of Th17 cell differentiation. These DP cells are closely related to RORγt+ single positive (SPR) cells in terms of global gene expression. Nevertheless, for some genes, DP cells share an expression pattern with Foxp3+ single positive (SPF) Treg cells, most importantly by reducing IL17 levels. Using time-lapse microscopy, we could delineate the expression dynamics of RORγt and Foxp3 at a clonal level. While the RORγt expression level elevates early during differentiation, Foxp3 rises later and is more stable upon environmental changes. These distinct expression profiles are independent of each other. During differentiation and proliferation, individual cells transit between SPR, DP, and SPF states. Nevertheless, the differentiation of sister cells within a clone progeny was highly correlated. We further demonstrated that sorted SPR and DP populations were not significantly affected by changes in their environment, suggesting that the correlated fate decision emerged at early time points before the first division. Overall, this study provides the first quantitative analysis of differentiation dynamics during the generation of DP RORγt+Foxp3+ cells. Characterizing these dynamics and the differentiation trajectory could provide a profound understanding and be used to better define the distinct fates of T cells, critical mediators of the immune response.

Time-dependent cell-state selection identifies transiently expressed genes regulating ILC2 activation

The decision of whether cells are activated or not is controlled through dynamic intracellular molecular networks. However, the low population of cells during the transition state of activation renders the analysis of the transcriptome of this state technically challenging. To address this issue, we have developed the Time-Dependent Cell-State Selection (TDCSS) technique, which employs live-cell imaging of secretion activity to detect an index of the transition state, followed by the simultaneous recovery of indexed cells for subsequent transcriptome analysis. In this study, we used the TDCSS technique to investigate the transition state of group 2 innate lymphoid cells (ILC2s) activation, which is indexed by the onset of interleukin (IL)-13 secretion. The TDCSS approach allowed us to identify time-dependent genes, including transiently induced genes (TIGs). Our findings of IL4 and MIR155HG as TIGs have shown a regulatory function in ILC2s activation.

Best practices for single-cell analysis across modalities

Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.

Advances in antibody discovery from human BCR repertoires

Antibodies make up an important and growing class of compounds used for the diagnosis or treatment of disease. While traditional antibody discovery utilized immunization of animals to generate lead compounds, technological innovations have made it possible to search for antibodies targeting a given antigen within the repertoires of B cells in humans. Here we group these innovations into four broad categories: cell sorting allows the collection of cells enriched in specificity to one or more antigens; BCR sequencing can be performed on bulk mRNA, genomic DNA or on paired (heavy-light) mRNA; BCR repertoire analysis generally involves clustering BCRs into specificity groups or more in-depth modeling of antibody-antigen interactions, such as antibody-specific epitope predictions; validation of antibody-antigen interactions requires expression of antibodies, followed by antigen binding assays or epitope mapping. Together with innovations in Deep learning these technologies will contribute to the future discovery of diagnostic and therapeutic antibodies directly from humans.

Dissecting the dynamic transcriptional landscape of early T helper cell differentiation into Th1, Th2, and Th1/2 hybrid cells

Selective differentiation of CD4+ T helper (Th) cells into specialized subsets such as Th1 and Th2 cells is a key element of the adaptive immune system driving appropriate immune responses. Besides those canonical Th-cell lineages, hybrid phenotypes such as Th1/2 cells arise in vivo, and their generation could be reproduced in vitro. While master-regulator transcription factors like T-bet for Th1 and GATA-3 for Th2 cells drive and maintain differentiation into the canonical lineages, the transcriptional architecture of hybrid phenotypes is less well understood. In particular, it has remained unclear whether a hybrid phenotype implies a mixture of the effects of several canonical lineages for each gene, or rather a bimodal behavior across genes. Th-cell differentiation is a dynamic process in which the regulatory factors are modulated over time, but longitudinal studies of Th-cell differentiation are sparse. Here, we present a dynamic transcriptome analysis following Th-cell differentiation into Th1, Th2, and Th1/2 hybrid cells at 3-h time intervals in the first hours after stimulation. We identified an early bifurcation point in gene expression programs, and we found that only a minority of ~20% of Th cell-specific genes showed mixed effects from both Th1 and Th2 cells on Th1/2 hybrid cells. While most genes followed either Th1- or Th2-cell gene expression, another fraction of ~20% of genes followed a Th1 and Th2 cell-independent transcriptional program associated with the transcription factors STAT1 and STAT4. Overall, our results emphasize the key role of high-resolution longitudinal data for the characterization of cellular phenotypes.

Heritable changes in division speed accompany the diversification of single T cell fate

Rapid clonal expansion of antigen-specific T cells is a fundamental feature of adaptive immune responses. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naive CD8⁺ T cell throughout up to ten divisions of activation-induced proliferation. Bayesian inference of tree-structured data reveals that clonal expansion is divided into a homogenously fast burst phase encompassing two to three divisions and a subsequent diversification phase during which T cells segregate into quickly dividing effector T cells and more slowly cycling memory precursors. Our work highlights cell cycle speed as a major heritable property that is regulated in parallel to key lineage decisions of activated T cells.

Rapid clonal expansion of antigen-specific T cells is a fundamental feature of adaptive immune responses. It enables the outgrowth of an individual T cell into thousands of clonal descendants that diversify into short-lived effectors and long-lived memory cells. Clonal expansion is thought to be programmed upon priming of a single naive T cell and then executed by homogenously fast divisions of all of its descendants. However, the actual speed of cell divisions in such an emerging “T cell family” has never been measured with single-cell resolution. Here, we utilize continuous live-cell imaging in vitro to track the division speed and genealogical connections of all descendants derived from a single naive CD8⁺ T cell throughout up to ten divisions of activation-induced proliferation. This comprehensive mapping of T cell family trees identifies a short burst phase, in which division speed is homogenously fast and maintained independent of external cytokine availability or continued T cell receptor stimulation. Thereafter, however, division speed diversifies, and model-based computational analysis using a Bayesian inference framework for tree-structured data reveals a segregation into heritably fast- and slow-dividing branches. This diversification of division speed is preceded already during the burst phase by variable expression of the interleukin-2 receptor alpha chain. Later it is accompanied by selective expression of memory marker CD62L in slower dividing branches. Taken together, these data demonstrate that T cell clonal expansion is structured into subsequent burst and diversification phases, the latter of which coincides with specification of memory versus effector fate.

A Single-Cell Perspective on Memory T-Cell Differentiation

Memory differentiation of CD4 and CD8 T-cell populations has been extensively studied and many key molecular players and transcriptional networks have been identified. But how regulatory principles, identified on this population level, translate to immune responses that originate from single antigen-specific T cells is only now being elucidated. Here, we provide a short summary of the approaches used for mapping the fate of individual T cells and their progeny in vivo. We then highlight which major questions, with respect to memory T-cell differentiation, have been addressed by studying the development of single-cell-derived T-cell families during infection or vaccination. We discuss how fate decisions of single T cells are modulated by the affinity of their TCR and further shaped through a coregulation of T-cell differentiation and T-cell proliferation. These current findings indicate the early segregation into slowly dividing T central memory precursors (CMPs) and rapidly dividing non-CMPs, as a key event that separates the developmental paths of long- and short-lived T cells.

Assay and Isolation of Single Proliferating CD4+ Lymphocytes Using an Automated Microraft Array Platform

OBJECTIVE

While T lymphocytes have been employed as a cancer immunotherapy, the development of effective and specific T-cell-based therapeutics remains challenging. A key obstacle is the difficulty in identifying T cells reactive to cancer-associated antigens. The objective of this research was to develop a versatile platform for single cell analysis and isolation that can be applied in immunology research and clinical therapy development.

METHODS

An automated microscopy and cell sorting system was developed to track the proliferative behavior of single-cell human primary CD4+ lymphocytes in response to stimulation using allogeneic lymphoblastoid feeder cells.

RESULTS

The system identified single human T lymphocytes with a sensitivity of 98% and specificity of 99% and possessed a cell collection efficiency of 86%. Time-lapse imaging simultaneously tracked 4,534 alloreactive T cells on a single array; 19% of the arrayed cells formed colonies of ≥2 cells. From the array, 130 clonal colonies were isolated and 7 grew to colony sizes of >10,000 cells, consistent with the known proliferative capacity of T cells in vitro and their tendency to become exhausted with prolonged stimulation. The isolated colonies underwent ELISA assay to detect interferon-γ secretion and Sanger sequencing to determine T cell receptor β sequences with a 100% success rate.

CONCLUSION

The platform is capable of both identification and isolation of proliferative T cells in an automated manner.

SIGNIFICANCE

This novel technology enables the identification of TCR sequences based on T cell proliferation which is expected to speed the development of future cancer immunotherapies.

Stochastic Inheritance of Division and Death Times Determines the Size and Phenotype of CD8+ T Cell Families

After antigen stimulation cognate naïve CD8⁺ T cells undergo rapid proliferation and ultimately their progeny differentiates into short-lived effectors and longer-lived memory T cells. Although the expansion of individual cells is very heterogeneous, the kinetics are reproducible at the level of the total population of cognate cells. After the expansion phase, the population contracts, and if antigen is cleared, a population of memory T cells remains behind. Different markers like CD62L, CD27, and KLRG1 have been used to define several T cell subsets (or cell fates) developing from individual naïve CD8⁺ T cells during the expansion phase. Growing evidence from high-throughput experiments, like single cell RNA sequencing, epigenetic profiling, and lineage tracing, highlights the need to model this differentiation process at the level of single cells. We model CD8⁺ T cell proliferation and differentiation as a competitive process between the division and death probabilities of individual cells (like in the Cyton model). We use an extended form of the Cyton model in which daughter cells inherit the division and death times from their mother cell in a stochastic manner (using lognormal distributions). We show that this stochastic model reproduces the dynamics of CD8⁺ T cells both at the population and at the single cell level. Modeling the expression of the CD62L, CD27, and KLRG1 markers of each individual cell, we find agreement with the changing phenotypic distributions of these markers in single cell RNA sequencing data. Retrospectively re-defining conventional T-cell subsets by “gating” on these markers, we find agreement with published population data, without having to assume that these subsets have different properties, i.e., correspond to different fates.

Multiplexed Division Tracking Dyes for Proliferation-Based Clonal Lineage Tracing

The generation of cellular heterogeneity is an essential feature of immune responses. Understanding the heritability and asymmetry of phenotypic changes throughout this process requires determination of clonal-level contributions to fate selection. Evaluating intraclonal and interclonal heterogeneity and the influence of distinct fate determinants in large numbers of cell lineages, however, is usually laborious, requiring familial tracing and fate mapping. In this study, we introduce a novel, accessible, high-throughput method for measuring familial fate changes with accompanying statistical tools for testing hypotheses. The method combines multiplexing of division tracking dyes with detection of phenotypic markers to reveal clonal lineage properties. We illustrate the method by studying in vitro-activated mouse CD8⁺ T cell cultures, reporting division and phenotypic changes at the level of families. This approach has broad utility as it is flexible and adaptable to many cell types and to modifications of in vitro, and potentially in vivo, fate monitoring systems.

Induction of CD4 T cell memory by local cellular collectivity

Cell differentiation is directed by signals driving progenitors into specialized cell types. This process can involve collective decision-making, when differentiating cells determine their lineage choice by interacting with each other. We used live-cell imaging in microwell arrays to study collective processes affecting differentiation of naïve CD4+ T cells into memory precursors. We found that differentiation of precursor memory T cells sharply increases above a threshold number of locally interacting cells. These homotypic interactions involve the cytokines interleukin-2 (IL-2) and IL-6, which affect memory differentiation orthogonal to their effect on proliferation and survival. Mathematical modeling suggests that the differentiation rate is continuously modulated by the instantaneous number of locally interacting cells. This cellular collectivity can prioritize allocation of immune memory to stronger responses.

Modeling Cell-to-Cell Communication Networks Using Response-Time Distributions

Cell-to-cell communication networks have critical roles in coordinating diverse organismal processes, such as tissue development or immune cell response. However, compared with intracellular signal transduction networks, the function and engineering principles of cell-to-cell communication networks are far less understood. Major complications include: cells are themselves regulated by complex intracellular signaling networks; individual cells are heterogeneous; and output of any one cell can recursively become an additional input signal to other cells. Here, we make use of a framework that treats intracellular signal transduction networks as "black boxes" with characterized input-to-output response relationships. We study simple cell-to-cell communication circuit motifs and find conditions that generate bimodal responses in time, as well as mechanisms for independently controlling synchronization and delay of cell-population responses. We apply our modeling approach to explain otherwise puzzling data on cytokine secretion onset times in T cells. Our approach can be used to predict communication network structure using experimentally accessible input-to-output measurements and without detailed knowledge of intermediate steps.

Nongenetic origins of cell-to-cell variability in B lymphocyte proliferation

Rapid antibody production in response to invading pathogens requires the dramatic expansion of pathogen-derived antigen-specific B lymphocyte populations. Whether B cell population dynamics are based on stochastic competition between competing cell fates, as in the development of competence by the bacterium Bacillus subtilis, or on deterministic cell fate decisions that execute a predictable program, as during the development of the worm Caenorhabditis elegans, remains unclear. Here, we developed long-term live-cell microscopy of B cell population expansion and multiscale mechanistic computational modeling to characterize the role of molecular noise in determining phenotype heterogeneity. We show that the cell lineage trees underlying B cell population dynamics are mediated by a largely predictable decision-making process where the heterogeneity of cell proliferation and death decisions at any given timepoint largely derives from nongenetic heterogeneity in the founder cells. This means that contrary to previous models, only a minority of genetically identical founder cells contribute the majority to the population response. We computationally predict and experimentally confirm nongenetic molecular determinants that are predictive of founder cells' proliferative capacity. While founder cell heterogeneity may arise from different exposure histories, we show that it may also be due to the gradual accumulation of small amounts of intrinsic noise during the lineage differentiation process of hematopoietic stem cells to mature B cells. Our finding of the largely deterministic nature of B lymphocyte responses may provide opportunities for diagnostic and therapeutic development.

In vitro tracking and intracellular protein distribution in immunology

New imaging techniques have enabled major advances in understanding how immune reactions are initiated, coordinated and controlled. Imaging methods, which were previously mostly descriptive and supplementary to more quantitative approaches, have now reached sufficient precision and throughput that they are becoming integral to almost all aspects of immunology research. Imaging methodologies that increase the resolution and sensitivity of detection, alongside an ever-expanding range of fluorescent reporters of molecular and cellular activity, and vastly improved analysis methods, have all facilitated this transformation. In this review, we will discuss how advances in imaging are changing the way we view immune activation and control using T cells as the model immune system. We will describe how imaging has transformed our knowledge of molecular and signalling events in T-cell activation, and the impact of these molecular events on the behaviour of T cells.

Autonomous TNF is critical for in vivo monocyte survival in steady state and inflammation

Monocytes are circulating mononuclear phagocytes, poised to extravasate to sites of inflammation and differentiate into monocyte-derived macrophages and dendritic cells. Tumor necrosis factor (TNF) and its receptors are up-regulated during monopoiesis and expressed by circulating monocytes, as well as effector monocytes infiltrating certain sites of inflammation, such as the spinal cord, during experimental autoimmune encephalomyelitis (EAE). In this study, using competitive in vitro and in vivo assays, we show that monocytes deficient for TNF or TNF receptors are outcompeted by their wild-type counterpart. Moreover, monocyte-autonomous TNF is critical for the function of these cells, as TNF ablation in monocytes/macrophages, but not in microglia, delayed the onset of EAE in challenged animals and was associated with reduced acute spinal cord infiltration of Ly6C^hi effector monocytes. Collectively, our data reveal a previously unappreciated critical cell-autonomous role of TNF on monocytes for their survival, maintenance, and function.