The immune system is one of the most dynamic and complex systems in biological systems. As is well known, the very basic function of the immune system is the discrimination between the self and non-self. Thus, the immune system is highly critical in guarding an organism from the invasion of various pathogenic agents as well as tumor cells. However, when immune cells behave abnormally, it could cause serious autoimmune diseases, because the immune system misrecognizes the self as a foreign enemy. To achieve such highly sophisticated functions, the immune system is composed of a large number and variety of cells dynamically regulated in space, time, and cell population. Because the number of immune cells in the human body is in the order of trillions, the complexity of the immune system may be as high as, or even higher than that of human society. Human sociology is an old multidisciplinary science, and its approaches are well developed. Thus, when attempting to tackle immune diseases, we must be prepared to examine the “sociology of the immune cells.” There are various similarities between societies of human and immune cells from a sociological viewpoint; for example, modern human communication systems remind us of cytokine signaling among immune cells. When considering immune cell sociology, we should carefully estimate (i) the variability of single cells in time and population and (ii) the social behavior of cells resulting from reciprocal interactions. Now that a wealth of information regarding cellular and molecular “parts” of immune systems has been amassed, it is time to start seriously considering how to approach the exciting concept of “immune cell sociology.”
In general, the complexity of the system emerges from a variety of interactions among the constituents of the system. It is well known that cell–cell interactions play a pivotal role in the homeostasis of the immune system. Cell–cell interactions are classified into two groups, namely, short- and long-range interactions. A representative example of the short-range interactions is mediated by physical contact between cells via surface molecules (Fig. 1). In contrast, a majority of long-range interactions among cells are via humoral factors, whereas other mechanisms such as membrane nanotube and electric signals also work in some cases. In particular, cytokines serve as a representative set of humoral factors in the immune system, and they function as excellent mediators to convey cellular signals to cells, including those that are distantly positioned, because of their extremely low biologically effective concentration. The aim of this review is to outline current technologies for monitoring secretion processes of cytokines from immune cells, even at the single-cell level, and to raise a provocative discussion as to how the heterogeneity of immune cells in terms of cytokine secretion could affect the “social” behavior as a whole.
IL, interleukin; LPS, lipopolysaccharide; IFN, interferon; CINCA, chronic infantile neurological cutaneous and articular syndrome
Cytokine Profiles of Immune Cells
Comprehensive analysis of secretory proteins, sometimes called “secretome” analysis, has been recently achieved by mass spectrometry (1). However, cytokine amounts are frequently below the practical detection limit of the most advanced proteome approaches based on mass spectrometry. Thus, antibody-based assays remain the gold standard for cytokine quantification. In addition, because an immunoassay system with multiplexity higher than 20 has become commercially available since the last decade, it is feasible to comprehensively monitor cytokine levels simultaneously. While these cytokine profiles of immune cells under various culture conditions must be informative when dealing with immune cell sociology, unfortunately, these data are dispersed in the literature. Thus, we performed cytokine profiling of immune cells by ourselves and made the information available on RefDIC (http://refdic.rcai.riken.jp), a website originally constructed to provide the research community with reference transcriptome/proteome datasets of immune cells. It enables researchers to browse cytokine profiles with mRNA profiles of cytokine and/or cytokine receptor genes in a comparative manner on the same platform (2). Figure 2 shows an example of clustering of cytokine profiles of mouse immune cells under different culture conditions (unpublished results). These data obtained by bulk assays should be very informative to enable the understanding of the state of a cell ensemble. However, it is unclear whether the cell ensemble is composed of a homogeneous population at this stage. To understand complex and dynamic behaviors of the immune system, behaviors of individual cellular agents are very critical from a sociological viewpoint. Although the analysis of protein secretion from single cells used to be highly challenging in the past, the situation has changed drastically, as described below.
Available Methods for Monitoring Protein Secretion from Single Cells
Single-cell analysis is a very active field in technology development (3), and many interesting methods have been developed. For example, single-cell mass cytometry dramatically increases the amounts of information obtained by a single experimental run than by conventional fluorescence-based cytometry, greatly contributing to the understanding of signal transduction systems (4). However, most of the methods are designed for intracellular biomolecules. Single-cell mRNA analysis has been used for estimating the expression of cytokines and/or other secretory proteins. However, as the secretion of some cytokines—particularly proinflammatory cytokines such as those without conventional signal sequences—cannot be estimated from their mRNA levels, cytokine profiling should be done at the protein level. As for protein secretion, several single-cell methods have been also reported. Table 1 lists the methods reported in the relevant literature (5–10). Each method has its own advantages and disadvantages, as shown in Table 1. It is worth noting that all the methods shown here detect the secreted molecules as a snapshot. What kind of new information can be obtained by single-cell secretion assays? Figure 3 displays the typical data of single-cell secretion rates obtained by the microengraving method by our team (Shimura et al., manuscript in preparation). In this case, a macrophage-like cell line (J774.1 cells) was treated with lipopolysaccharide (LPS) and was monitored for the secretion rate of interleukin-6 (IL-6) between 4 and 5 h after the treatment. A conventional bulk assay of IL-6 demonstrated that J774.1 cells started to secrete IL-6 2 h after the treatment and kept increasing the secretion rate till 5 h. The data shown in Fig. 3 revealed that the secretion of IL-6 resulted from the increase in the number of secreting cells and the amounts of secreted IL-6 from each single cell. However, it should be noted that about 40% of the cells did not secrete IL-6 above the detection limit, even 4 h after the LPS stimulation, which cannot be seen from the bulk data. Thus, while many new, unaddressed questions arise from such data, it is evident that the immune cells respond to an external stimulation differently even in an ensemble of cloned, genetically identical cells. In our data, although the secretion profile and the time course of cytokine induction varied from cytokine to cytokine, none of the cytokine profiles became normally distributed.
Real-Time Monitoring of Cytokine Secretion from Single Immune Cells
As described above, the single-cell secretion assay revealed large cell variability in the population context, which is critical for a sociological viewpoint. However, the data are snapshots of the cell ensemble and do not provide us with information on cell dynamics. The single-cell dynamics along time is another important property (11), and it also has great significance in the field of immune cell sociology. While large cell-to-cell variations in cytokine secretion rate were observed on a snapshot in the population context as described above, we cannot know how stable such a biased distribution of cytokine secretion rates is. Just in case the cell heterogeneity of cytokine secretion rates can be averaged within a time range of cellular events, it may not be so critical from a sociological viewpoint. In this regard, we have already developed a real-time monitoring system for antigen–antibody complexes in picoliter-scale microwells (12) and have modified it for monitoring protein secretion. In addition, we considered the following two points in developing the real-time monitoring system: (i) intracellular events can also be monitored, when necessary, in parallel with the detection of secreted molecules and (ii) cells can be retrieved at a specified timing. For these purposes, a new microwell device equipped with total internal reflection illumination was produced (13). While methodological details of this method will be reported later (Shirasaki et al., manuscript in preparation), this new system, for the first time, enables simultaneous monitoring of cytokine secretion and intracellular events (anything seen on the same microscopy platform, such as translocation of transcription factors, membrane intactness, changes in Ca2+ concentration, etc.) in real time, as well as the analysis of mRNA/protein levels of a single cell at the time of interest. In particular, it should be emphasized that the monitoring of protein secretion is absolutely compatible with bioimaging.
By using the real-time monitoring system, we can follow the changes in cytokine secretion along time. Figure 4 shows a snapshot of the assay where the secretion of two cytokines (IL-6 and CCL2) from MC/9 cells, a mast cell-like cell line, were simultaneously monitored, indicating that secretion rates of the two cytokines have a low correlation with each other. Because these two cytokines are induced by treatment with a phorbol ester, MC/9 cells respond differently to the external signal in terms of IL-6 and CCL2 secretion. Because the low correlation of secretion rates of multiple cytokines thought to be under the same induction pathway is frequently observed, it is very likely that cell heterogeneity in cytokine secretion originates from stochasticity. Although it is not evident from Fig. 4, our data indicated that the stochastic differences in cytokine secretion at the single-cell level lasted for more than 8 h. As demonstrated in this example, it now becomes possible to delineate cytokine secretion from single cells not only in a population context but also with time.
How Does Heterogeneity of Cytokine Secretion of Immune Cells Affect Our View of Immune Cell Sociology?
In general, cell heterogeneity could originate from (i) stochasticity, (ii) epigenetic factors, or (iii) genetic factors, while heterogeneity in external conditions and cell cycle state may also affect it to some extent. In experiments using a cloned cell population, cell-to-cell variations are most likely to be derived mainly from the stochasticity of multilayered processes, including transcription, translation, and protein transfer. After a pioneering study by Ko (14), stochasticity in gene expression is being actively explored (15). It is widely accepted that transcription/translation processes are stochastic at the single-cell level, which is no surprise because these processes are governed by a relatively small number of molecules. In other words, cell responses are intrinsically noisy. Thus, an important and obvious question is how the immune system maintains homeostasis when using such noisy cells.
What kind of new insights can we get from these new lines of information? From a viewpoint of immune cell sociology, the cause of cell heterogeneity does not matter but its dynamics does. We have vaguely assumed that such a stochastic effect does not last long; however, our data, as well as those reported by other groups, indicate that it takes relatively long (hours to days) (16, 17). Thus, when we consider immune events responding within a short range of time (i.e., hours), the cell-to-cell variations caused even by stochastic transcription/translation cannot be neglected at all. An analytical framework for linking stochastic dynamics to population distribution was discussed by Friedman et al. (18), and the real situation was described as being more complex, thus necessitating experimental measurements to understand it at the single-cell level.
Rand et al. (19) reported that the key steps of virus-induced signal transduction, interferon (IFN)-beta expression, and the induction of IFN-stimulated genes take place stochastically. According to the authors, the origin of stochasticity seems to be cell-intrinsic noise in transcription and/or translation. Coherent, robust, antiviral protection in spite of multilayered cellular stochasticity is explained to be achieved through intercellular communication, which is likely to be a widely used strategy by mammalian cells to cope with pervasive stochastic signaling and gene expression. However, in this scenario, cell-intrinsic noise is considered disadvantageous for the cell society. Is this always the case? In fact, as long as we consider events that protect cells from destruction, heterogeneity might be undesirable, because the number of cells protected becomes smaller than the coherent cell ensemble. However, if all the cells are subjected to extracellular attacks to induce cell death, heterogeneity might have a positive meaning, because at least a small fraction of cells might survive and save the whole system. Because cytokines play a key role in communication among immune cells, cell-to-cell variations in cytokine secretion could be a strategy to make the “society” flexible and tough in some cases.
The other two reasons, epigenetic and genetic causes, which may generate cell-to-cell variations, are equally or more important than stochasticity from a viewpoint of immune cell sociology. Epigenetic variations are actively explored in their action and regulation at present and will offer us intriguing pieces of information on immune cell sociology. Epigenetic differences frequently result in differences in cell types, which offer a conceptual basis for each immune cell type in conventional immune cell sociology. When epigenetically different cells are functionally distinct, they are conventionally designated as different cell modules (i.e., helper T cells, B cells, NK cells, etc.) in immune cell society. While it has been a relatively macroscopic view, such heterogeneities have been accounted for in immunology.
Genetic variations may be practically more important than stochasticity and genetic factors. A well-known example is found in cancer, where a somatic mutation is first introduced in a very limited number of cells, and the mutant cells are killed, maintained, or expanded under the surveillance by the immune system (20). This is really a matter of immune cell sociology. Another interesting example is found in autoinflammatory disease, a type of primary immunodeficiency disease. Chronic infantile neurological cutaneous and articular syndrome (CINCA), also known as neonatal-onset multisystem inflammatory disease, is characterized by urticarial rash, neurological manifestations, and arthropathy. This dominantly inherited systemic autoinflammatory disease is provoked by somatic mosaicism of gain-of-function NLRP3 mutations as well as a heterozygous germline mutation (21). Furthermore, we recently reported a reliable genetic diagnostic method using massive, parallel DNA sequencing (22). The mutation of NLRP3 activates the inflammasome and is considered to result in the release of a large amount of IL-1β. Thus, in the presence of even a small fraction of mutated cells, CINCA patients might develop a fever. This may be a good example that a small fraction of anomalously behaving cells leads the whole body to a diseased state. Thus, somatic mutation-induced diseases like the CINCA syndrome may serve as a good model to explore how the homeostasis is interrupted or maintained in the presence of such cellular heterogeneity. In this regard, it is interesting to note that periodic fever is observed in CINCA patients, because the oscillating biological events have been actively studies by mathematical modeling for long time (23).
Now that it has become feasible to monitor secretory proteins, as well as intracellular ones at the single-cell level, we can delve into the details of immune cell society at the microscopic level, that is, at the single-cell resolution, if we want. Although such a microscopic analysis might not be always necessary, it must be highly informative, particularly in those cases where the whole system is affected by a small group of heterogeneous immune cells. In such cases, the long-range cellular interactions mediated by cytokines must play a key role. We anticipate that it might unravel a new paradigm of immune cell sociology.
Future Perspectives of the Cell–Cell Interactome at the Single-Cell Level: What are the Implications for Immune Cell Sociology?
The term “cell sociology” was first used in the field of morphogenesis (24). In this review, the term is used for the demonstration of orderliness rather than the dynamic nature of the biological system. Although the term “cell sociology” may be considered an equivalent of “systems biology” in some contexts in this review, we intentionally use it to emphasize the complexity and dynamics of the system. For instance, modern human society has become highly global and dynamic as a consequence of people connecting and interacting via the internet, which is analogous to the situation of the immune system governed by long-range interactions with cytokines. Similarly, the study of how proteins interact with each other and are spatially arranged is designated as “molecular sociology” (25), which also has the sole emphasis on the importance of the complex interactions among common constituents of the “society.” In this regard, short-range interactions mediated by cell–cell contact may be analogous to face-to-face communications in human, which are observed even in primitive society. The issue of how homeostasis is maintained with intrinsically noisy agents is an interesting aspect to address, and it might be analogous to the stabilization of the human society, which comprises considerably heterogeneous individuals from a sociological viewpoint. In both cases, as far as long-range interactions are functioning, individuality can be harnessed such that the system as a whole responds to external perturbations in a harmonious and robust manner.
In this context, to get a comprehensive understanding of the immune system, we propose to take sociological approaches in immunological studies. A good lesson to learn from sociology is that it is a multidisciplinary and multiscale science; many different approaches, ranging from micro level to macro level, are eventually integrated to interpret a variety of social events. In the case of immune cell sociology, regulation of long-range interactions among immune cells must be a critical issue, requiring an understanding at the micro level of individual cell agents, as well as the macro level, where the immune events are explained as a consequence of interactions among immune cellular modules (i.e., functional cellular ensembles with particular cellular designations). While immune cell sociology at the macro level has a long and successful history and is already well developed, advances of single-cell analysis technologies enable us to decipher the interactome of immune cells at a deeper resolution. What is expected is to integrate these multiscale data into a sociological view of the immune system. Although still in infancy, interactome analysis of immune cells at the single-cell level might elucidate unexpected mechanisms underlying the maintenance and destruction of immune homeostasis in the future, which would enable us to develop a new way to tackle various immune diseases.