Carmen Gerlach

Heterogeneous Differentiation Patterns of Individual CD8+ T Cells

Dynamic Protection During an immune response, CD8+ T cells are recruited to provide protection. Most cells differentiate into short-lived effectors that help to clear the pathogen, whereas others form long-lived memory cells to protect against reinfection. Gerlach et al. (p. 635, published online 14 March) and Buchholz et al. (p. 630, published online 14 March) used in vivo fate mapping of mouse T cells with a defined specificity during a bacterial infection to show that the dynamics of the single-cell response are not uniform. The response of a particular T cell population is the average of a small number of clones that expand greatly (“large clones”) and many clones that only proliferate at low amounts (“small clones”). The memory pool arises largely from small clones whereas effectors are primarily made up of large clones. The single-cell dynamics as cytotoxic T cells respond to a bacterial infection are analyzed in mice. Upon infection, antigen-specific CD8+ T lymphocyte responses display a highly reproducible pattern of expansion and contraction that is thought to reflect a uniform behavior of individual cells. We tracked the progeny of individual mouse CD8+ T cells by in vivo lineage tracing and demonstrated that, even for T cells bearing identical T cell receptors, both clonal expansion and differentiation patterns are heterogeneous. As a consequence, individual naïve T lymphocytes contributed differentially to short- and long-term protection, as revealed by participation of their progeny during primary versus recall infections. The discordance in fate of individual naïve T cells argues against asymmetric division as a singular driver of CD8+ T cell heterogeneity and demonstrates that reproducibility of CD8+ T cell responses is achieved through population averaging.

Diverse and heritable lineage imprinting of early haematopoietic progenitors

Haematopoietic stem cells (HSCs) and their subsequent progenitors produce blood cells, but the precise nature and kinetics of this production is a contentious issue. In one model, lymphoid and myeloid production branch after the lymphoid-primed multipotent progenitor (LMPP), with both branches subsequently producing dendritic cells. However, this model is based mainly on in vitro clonal assays and population-based tracking in vivo, which could miss in vivo single-cell complexity. Here we avoid these issues by using a new quantitative version of ‘cellular barcoding’ to trace the in vivo fate of hundreds of LMPPs and HSCs at the single-cell level. These data demonstrate that LMPPs are highly heterogeneous in the cell types that they produce, separating into combinations of lymphoid-, myeloid- and dendritic-cell-biased producers. Conversely, although we observe a known lineage bias of some HSCs, most cellular output is derived from a small number of HSCs that each generates all cell types. Crucially, in vivo analysis of the output of sibling cells derived from single LMPPs shows that they often share a similar fate, suggesting that the fate of these progenitors was imprinted. Furthermore, as this imprinting is also observed for dendritic-cell-biased LMPPs, dendritic cells may be considered a distinct lineage on the basis of separate ancestry. These data suggest a ‘graded commitment’ model of haematopoiesis, in which heritable and diverse lineage imprinting occurs earlier than previously thought.

One naive T cell, multiple fates in CD8+ T cell differentiation

The mechanism by which the immune system produces effector and memory T cells is largely unclear. To allow a large-scale assessment of the development of single naive T cells into different subsets, we have developed a technology that introduces unique genetic tags (barcodes) into naive T cells. By comparing the barcodes present in antigen-specific effector and memory T cell populations in systemic and local infection models, at different anatomical sites, and for TCR–pMHC interactions of different avidities, we demonstrate that under all conditions tested, individual naive T cells yield both effector and memory CD8+ T cell progeny. This indicates that effector and memory fate decisions are not determined by the nature of the priming antigen-presenting cell or the time of T cell priming. Instead, for both low and high avidity T cells, individual naive T cells have multiple fates and can differentiate into effector and memory T cell subsets.

Recruitment of Antigen-Specific CD8+ T Cells in Response to Infection Is Markedly Efficient

Preparation for Cell Wars When T cells encounter an infection, they proliferate to create a larger army to fight the invader. The overall magnitude of the T cell response depends on the severity of infection and is determined by the number of T cells of a particular antigen specificity that are initially recruited, as well as the magnitude of the proliferative response. The extent to which these two components contribute to the response is unknown. By using DNA barcoding to track the responses of individual T cells, van Heijst et al. (p. 1265) showed that the recruitment of T cells of a particular antigen specificity is similar and nearly complete, but that the extent of the proliferative response differed, and this determined the overall magnitude of the T cell response. Lymphocyte proliferation, more than recruitment to the site of an infection, determines the success of the immune response. The magnitude of antigen-specific CD8+ T cell responses is not fixed but correlates with the severity of infection. Although by definition T cell response size is the product of both the capacity to recruit naïve T cells (clonal selection) and their subsequent proliferation (clonal expansion), it remains undefined how these two factors regulate antigen-specific T cell responses. We determined the relative contribution of recruitment and expansion by labeling naïve T cells with unique genetic tags and transferring them into mice. Under disparate infection conditions with different pathogens and doses, recruitment of antigen-specific T cells was near constant and close to complete. Thus, naïve T cell recruitment is highly efficient, and the magnitude of antigen-specific CD8+ T cell responses is primarily controlled by clonal expansion.

Intravital Microscopy Through an Abdominal Imaging Window Reveals a Pre-Micrometastasis Stage During Liver Metastasis

An abdominal imaging window allows in vivo visualization of dynamic cellular processes, including liver metastasis and islet cell transplantation. Peering Into Cancer Understanding what goes on inside the body, as it is happening, is an ongoing challenge in medical imaging. Conventional imaging methods are only “snapshots,” unable to truly capture biology in action or the progression of disease. In this study by Ritsma and colleagues, an abdominal imaging window (AIW) proves to be the answer, allowing the authors to visualize and quantify metastatic processes in real time, in vivo in mice. The AIW consisted of a titanium ring with a glass coverslip, which could be tightly secured to the abdominal wall of a mouse. This window stayed in place for an average of 5 weeks, which is long enough to visualize many biological phenomena, including single-cell activity in the small intestine, spleen, pancreas, and kidney, as demonstrated by Ritsma et al. Although able to image many organs and cells, the authors chose to focus on tumor cell metastasis—specifically, the metastasis of mouse colorectal cancer C26 cells to the liver. By tracking fluorescently labeled C26 cells over the course of 2 weeks, the authors were able to confirm that the majority of metastatic growth was clonal (that is, from a single founder cell) rather than synergistic. The authors also noticed that the cancer cells had different phenotypic properties at different time points: At day 3, the cells were motile and diffuse in the liver tissue, whereas, at day 5, the cells stopped moving and were densely packed. The authors called this phenotypic shift a “pre-micrometastatic” state, followed by the “micrometastatic state.” Blocking cell migration in the pre-micrometastatic stage with a small-molecule inhibitor reduced cell growth and formation of subsequent micrometastases. Ristma and coauthors have developed a powerful in vivo imaging tool to track biological events in real time. This will hopefully lend insight into many diseases that affect abdominal organs. Although their preliminary findings suggest a new target for pharmacological inhibition of cancer growth and migration, additional preclinical and clinical studies will be needed to follow up this pre-micrometastatic hypothesis and to further confirm its presence in humans. Cell dynamics in subcutaneous and breast tumors can be studied through conventional imaging windows with intravital microscopy. By contrast, visualization of the formation of metastasis has been hampered by the lack of long-term imaging windows for metastasis-prone organs, such as the liver. We developed an abdominal imaging window (AIW) to visualize distinct biological processes in the spleen, kidney, small intestine, pancreas, and liver. The AIW can be used to visualize processes for up to 1 month, as we demonstrate with islet cell transplantation. Furthermore, we have used the AIW to image the single steps of metastasis formation in the liver over the course of 14 days. We observed that single extravasated tumor cells proliferated to form “pre-micrometastases,” in which cells lacked contact with neighboring tumor cells and were active and motile within the confined region of the growing clone. The clones then condensed into micrometastases where cell migration was strongly diminished but proliferation continued. Moreover, the metastatic load was reduced by suppressing tumor cell migration in the pre-micrometastases. We suggest that tumor cell migration within pre-micrometastases is a contributing step that can be targeted therapeutically during liver metastasis formation.

Dissecting T cell lineage relationships by cellular barcoding

T cells, as well as other cell types, are composed of phenotypically and functionally distinct subsets. However, for many of these populations it is unclear whether they develop from common or separate progenitors. To address such issues, we developed a novel approach, termed cellular barcoding, that allows the dissection of lineage relationships. We demonstrate that the labeling of cells with unique identifiers coupled to a microarray-based detection system can be used to analyze family relationships between the progeny of such cells. To exemplify the potential of this technique, we studied migration patterns of families of antigen-specific CD8+ T cells in vivo. We demonstrate that progeny of individual T cells rapidly seed independent lymph nodes and that antigen-specific CD8+ T cells present at different effector sites are largely derived from a common pool of precursors. These data show how locally primed T cells disperse and provide a technology for kinship analysis with wider utility.

Apoptosis Threshold Set by Noxa and Mcl-1 after T Cell Activation Regulates Competitive Selection of High-Affinity Clones

The adaptive immune system generates protective T cell responses via a poorly understood selection mechanism that favors expansion of clones with optimal affinity for antigen. Here we showed that upon T cell activation, the proapoptotic molecule Noxa (encoded by Pmaip1) and its antagonist Mcl-1 were induced. During an acute immune response against influenza or ovalbumin, Pmaip1(-/-) effector T cells displayed decreased antigen affinity and functionality. Molecular analysis of influenza-specific T cells revealed persistence of many subdominant clones in the Pmaip1(-/-) effector pool. When competing for low-affinity antigen, Pmaip1(-/-) TCR transgenic T cells had a survival advantage in vitro, resulting in increased numbers of effector cells in vivo. Mcl-1 protein stability was controlled by T cell receptor (TCR) affinity-dependent interleukin-2 signaling. These results establish a role for apoptosis early during T cell expansion, based on antigen-driven competition and survival of the fittest T cells.

Mapping the life histories of T cells

The behaviour of T cells is not fixed in the germ line, but is highly adaptable depending on experiences encountered during a T cells life. To understand how different T cell subsets arise and how prior signalling input regulates subsequent T cell behaviour, approaches are required that couple a given T cell state to signals received by the cell, or by one of its ancestors, at earlier times. Here we describe recently developed technologies that have been used to determine the kinship of different T cell subsets and their prior functional characteristics. Furthermore, we discuss the potential value of new technologies that would allow assessment of T cell migration patterns and prior signalling events.

The descent of memory T cells

Our T cell repertoire is shaped by antigen encounter. From a naive T cell pool that contains millions of different T cells with unknown specificities, pathogen infection leads to selection of those T cells that can detect pathogen‐derived antigens. Following clearance of infection, a population of memory T cells remains and protects the individual from severe reinfection. A central question in the field has been how the generation of long‐lived memory T cells, versus short‐lived (“terminally differentiated”) T cells, is controlled. In this review we discuss the models that have been put forward to explain the generation of memory T cells after infection and the experimental evidence supporting these hypotheses. Based on the available data we propose a new model that stipulates that during immune responses T cells do not acquire different fates that determine their subsequent long‐term survival but rather T cells assume different states that simply reflect the likelihood of future survival, states that can still be modulated by external signals.

Single cell behavior in T cell differentiation

Upon primary infection, naïve T cells that recognize their cognate antigen become activated, proliferate, and simultaneously differentiate into various subsets. A long-standing question in the field has been how this cellular diversification is achieved. Conceptually, diverse cellular output may either arise from every single cell or only from populations of naïve cells. Furthermore, such diversity may either be driven by cell-intrinsic heterogeneity or by external, niche-derived signals. In this review, we discuss how recently developed technologies have allowed the analysis of the mechanisms underlying T cell diversification at the single cell level. In addition, we outline the implications of this work on our understanding of the formation of immunological memory, and describe a number of unresolved key questions in this field.