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How do T-cells determine which cells they've already inspected?

How do T-cells determine which cells they've already inspected?


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From what I understand, T-cells are constantly traveling in the body, inspecting cells by looking for antigens. If they're self antigens, then the T-cell doesn't attack, whereas if they're non-self, they attack. My question is how does a T-cell know when it just inspected a cell? Does the T-cell leave something behind on the cell to mark it as checked or does the cell itself present something on its surface to indicate that it has just been checked? If there is no such system, then what prevents the T-cells from being stuck in a loop, and just inspecting the same cell over and over?


Your question spans two activities of T-cells that are related to each other: migration and activation. T-cells that usually stay in lymphoid organs migrate to non-lymphoid organs with different mechanisms for each T-cell subtype. When migrated to non-lymphoid organ, the T-cells move through the organ looking for infected cells.

Migration

As you can see in the image below, there are many mechanisms for moving T-cells across the endothelial layer.

The main idea for all three mechanisms is that the tethering of the T-cell to the endothelial layer is weak. Because of the weak interaction, the blood flow causes the T-cell to move across the endothelial layer while still being attached to the endothelial layer. This is known as rolling. Other stimuli (usually chemokines) such as CCL21, CCL25, or ICAM 1 are needed to induce T-cell to migrate across the endothelial layer. This is important because these chemokines are expressed where there is inflammation. For example, there is a correlation between CCL25 level in the gut and inflammation in the area.

T-cells express α4β7 integrin or CCR7 that bind T-cell to the endothelial layer. Central memory T-cells and naive T-cells express CCR7 and CD62L and thus reside preferentially within the secondary lymphoid organs. Effector memory T-cells bind to gut endothelium with α4β7 integrin, CCR9, and LFA-1.

CCL21 is expressed by both stromal cells of lymph node paracortex and endothelium of lymphatic vessels to assist migration of activated dendritic cells and naive and central memory T-cells to lymph node respectively. Dendritic cells are professional antigen-presenting cells (APCs) that migrate to lymph node where it can interact with T-cells more efficiently.

T-cell activation

One important concept to take away from T-cell activation is that there needs to be direct contact between MHC complex and T-cell receptor (TCR).

T-cells that have moved into the lymph node interact with dendritic cells, which have high MHC complex concentration on their cell surface. This leads to subsequent activation of the T-cell leading to release of cytokines such as IL-2 leading to proliferation of activated T-cells.

The activated T-cells now are able to freely move across the endothelial layer of non-lymphoid organs to infiltrate and look for infected sites. (Naive T-cells enter endothelial layer of non-lymphoid organs as well but this is chemokine independent nad thus not a reaction to inflammation).

Sources:

Difference between naive T-cells and memory T-cells: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1782715/

Dendritic cell's role in immune response: http://lab.rockefeller.edu/steinman/dendritic_intro/immuneResponse

Memory vs naive T-cell migration: http://www.nature.com/icb/journal/v86/n3/full/7100132a.html

Dendritic cell and role of CCL21: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3078419/

Naive T-cell non-lymphoid organ infiltration: http://onlinelibrary.wiley.com/doi/10.1002/eji.200535539/full

CCL25 and inflammation relation: http://www.sciencedirect.com/science/article/pii/S0896841116300014


T-cell does not inspect any cell until the cell shows a piece of the "non-self" antigen on it by the MHC.

The helper T-cell connects with the antigen which is on the cell's surface. The T-cell sorts the cytokines that activate the cytotoxic T-cell to divide and form a colony of cytotoxic cells. The colony of cytotoxic cells then attack the "non-self" antigen expressing cell.


Single-cell analysis of CD4+ T-cell differentiation reveals three major cell states and progressive acceleration of proliferation

Differentiation of lymphocytes is frequently accompanied by cell cycle changes, interplay that is of central importance for immunity but is still incompletely understood. Here, we interrogate and quantitatively model how proliferation is linked to differentiation in CD4+ T cells.

Results

We perform ex vivo single-cell RNA-sequencing of CD4+ T cells during a mouse model of infection that elicits a type 2 immune response and infer that the differentiated, cytokine-producing cells cycle faster than early activated precursor cells. To dissect this phenomenon quantitatively, we determine expression profiles across consecutive generations of differentiated and undifferentiated cells during Th2 polarization in vitro. We predict three discrete cell states, which we verify by single-cell quantitative PCR. Based on these three states, we extract rates of death, division and differentiation with a branching state Markov model to describe the cell population dynamics. From this multi-scale modelling, we infer a significant acceleration in proliferation from the intermediate activated cell state to the mature cytokine-secreting effector state. We confirm this acceleration both by live imaging of single Th2 cells and in an ex vivo Th1 malaria model by single-cell RNA-sequencing.

Conclusion

The link between cytokine secretion and proliferation rate holds both in Th1 and Th2 cells in vivo and in vitro, indicating that this is likely a general phenomenon in adaptive immunity.


REVIEW article

  • 1 Sir William Dunn School of Pathology, University of Oxford, Oxford, United Kingdom
  • 2 Radcliffe Department of Medicine, Medical Research Council Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, United Kingdom

The immune system serves as a crucial line of defense from infection and cancer, while also contributing to tissue homeostasis. Communication between immune cells is mediated by small soluble factors called cytokines, and also by direct cellular interactions. Cell-cell interactions are particularly important for T cell activation. T cells direct the adaptive immune response and therefore need to distinguish between self and foreign antigens. Even though decades have passed since the discovery of T cells, exactly why and how they are able to recognize and discriminate between antigens is still not fully understood. Early imaging of T cells was very successful in capturing the early stages of conjugate formation of T cells with antigen-presenting cells upon recognition of peptide-loaded major histocompatibility complexes by the T cell receptor (TCR). These studies lead to the discovery of a “supramolecular activation cluster” now known as the immunological synapse, followed by the identification of microclusters of TCRs formed upon receptor triggering, that eventually coalesce at the center of the synapse. New developments in light microscopy have since allowed attention to turn to the very earliest stages of T cell activation, and to resting cells, at high resolution. This includes single-molecule localization microscopy, which has been applied to the question of whether TCRs are pre-clustered on resting T cells, and lattice light-sheet microscopy that has enabled imaging of whole cells interacting with antigen-presenting cells. The utilization of lattice light-sheet microscopy has yielded important insights into structures called microvilli, which are small membrane protrusions on T cells that seem likely to have a large impact on T cell recognition and activation. Here we consider how imaging has shaped our thinking about T cell activation. We summarize recent findings obtained by applying more advanced microscopy techniques and discuss some of the limitations of these methods.


1. Introduction

The adaptive immune response depends on T cell interactions with dendritic cells (DCs) in the paracortex, or T cell zone, of lymph nodes (LNs). The rate at which naïve T cells sample DCs determines how fast the immune system can mount a response to infection (1). The development of imaging methods such as two-photon microscopy (2PM) and histocytometry have enabled direct observation of cell locations in tissues. Many studies showing the relative location of T cells and DCs suggest that they are both positioned in the LN to maximize the likelihood of T:DC interactions (2, 3). Despite advances in the ability to image and observe T cells in LNs, few studies make direct quantitative comparisons of how closely T cells associate with multiple other cells types in LNs.

T cells enter the paracortex of the LN from small post-capillary blood vessels termed high endothelial venules (HEVs). T cells, DCs, and fibroblastic reticular cells (FRCs) occupy this region along with blood vessels (BVs). T cells move among DCs, FRCs, and other T cells to interact with DCs presenting antigen. FRCs are stromal cells that encapsulate a collagen fiber conduit network which allows for transport of lymph fluid carrying soluble antigen and chemokines (4𠄷). FRCs produce the chemokine CCL21, which has an established role in naïve T cell homing into the paracortex from blood vessels (8, 9). FRCs also provide structural support required for efficient T cell activation (10). Bajenoff et al. showed the FRC network is closely associated with naïve T cells moving within the paracortex, suggesting that FRCs may provide a network on which T cells migrate (11).

There are several hypotheses regarding the role of individual cell types in mediating T:DC interactions. HEVs are the entry points for T cells entering the LN. Girard et al. suggests that DCs gather near HEVs to maximize their contact rate with incoming T cells (12). Others have suggested that DCs may congregate at the intersections of the FRC network, allowing T cells that travel along the edges of the network to encounter DCs at an increased rate (13�). Spatial interactions between T cells and blood vessels, FRCs, and DCs are important if they change how T cells move through the paracortex and the timing of encounters with antigen-presenting DCs, the key step in T cell activation and the initiation of the adaptive immune response.

In addition to structural and cellular cues, chemical mediators, including chemokines, contribute to T cell motion and T:DC contacts in the LN. For example, the signaling molecule LPA produced by FRCs has been shown to mediate rapid T cell motion in LNs (17). In addition, C𠄼 chemokine receptor type 7 (CCR7), the receptor recognizing CCL21, is important for high speed T cell motility in the LN (18, 19). While CCR7 increases T cell movement speed in LNs, whether CCR7 impacts T:DC contacts has not been investigated.

Understanding the contribution of cellular and structural LN components to T cell localization requires a quantitative metric that allows direct comparisons of spatial associations of multiple cell types. Several other groups have reported spatial relationships between cells and structures using methods such as visual inspection (12, 20) and comparison of turning angles of T cell movements with structures (11, 21). However, none of these directly compare associations between multiple cell types or structures with a consistent quantitative metric.

In this study, we use both the Pearson correlation coefficient [PCC (22, 23)] as well as Mutual Information [MI (24)] to compare the spatial association of multiple cell types and structures. PCC measures the covariance of homologous pixel intensities, and has been often used to determine colocalization, particularly of fluorescent proteins, in multiple biological systems including the study of T cells (25, 26). PCC and MI can be calculated without the need to identify individual cell boundaries which can be difficult for 2PM images.

MI is an application of Shannon entropy (which measures the amount of uncertainty about the value of a random variable in bits) originally defined to understand limitations on signal processing and communication (27). MI quantifies the reduction in uncertainty about one variable when one knows the value of another variable. In analyzing spatial associations, we measure the reduction in uncertainty about the location of one cell type given the location of another cell type. MI has been successfully used in other biomedical image processing applications, particularly in measuring image similarity in X-rays and MRIs for automated image registration (28�). Furthermore, MI and other information theoretic measures are increasingly recognized as powerful tools for analysis of non-linear complex systems, including complex biological systems such as the immune system (32, 33). In this article, we use MI to quantify the spatial association of T cells with other cell types (e.g., DCs or FRCs). We use MI as a measure of spatial association that is independent of specific types of cells or structures. In addition, MI is theoretically insensitive to coarse graining (34). Thus, MI can measure the amount of spatial dependence of one fluorescent marker on another while minimizing observational bias. MI, unlike distance measures such as nearest-neighbor analysis, is parsimonious, since it does not require extensive image processing to remove photon noise and determine cell boundaries. Instead, MI can operate on the image directly without the introduction of thresholds. In preliminary work we used MI to quantify the association of T cells and DCs and found less correspondence between T cell and DCs than expected (35).

However, MI is not comparable across images with different sizes and amounts of fluorescence. In this study, we use NMI to normalize MI to be between 0 and 1 (36�), which allows quantitative comparisons of spatial associations between cells fluorescing in one color channel and another cell type fluorescing in a different color channel across experiments. Since PCC and NMI are both pixel-based methods that do not correspond to cell sizes, we create regions within the images that match cellular scales and apply PCC and NMI. Analyzing regions as well as pixels allows these methods to capture associations at biologically relevant scales. Both regional PCC and NMI analyses show T cells associate much less with their ultimate targets, DCs, than with FRCs. Our results also show that CCR7 does not increase T cell association with DCs.


Biomarker for chronic fatigue syndrome identified

Stanford scientists devised a blood-based test that accurately identified people with chronic fatigue syndrome, a new study reports.

Ron Davis is the senior author of a paper that describes a blood test that may be able to identify chronic fatigue syndrome.
Steve Fisch

People suffering from a debilitating and often discounted disease known as chronic fatigue syndrome may soon have something they’ve been seeking for decades: scientific proof of their ailment.

Researchers at the Stanford University School of Medicine have created a blood test that can flag the disease, which currently lacks a standard, reliable diagnostic test.

“Too often, this disease is categorized as imaginary,” said Ron Davis, PhD, professor of biochemistry and of genetics. When individuals with chronic fatigue syndrome seek help from a doctor, they may undergo a series of tests that check liver, kidney and heart function, as well as blood and immune cell counts, Davis said. “All these different tests would normally guide the doctor toward one illness or another, but for chronic fatigue syndrome patients, the results all come back normal,” he said.

The problem, he said, is that they’re not looking deep enough. Now, Davis Rahim Esfandyarpour, PhD, a former Stanford research associate and their colleagues have devised a blood-based test that successfully identified participants in a study with chronic fatigue syndrome. The test, which is still in a pilot phase, is based on how a person’s immune cells respond to stress. With blood samples from 40 people — 20 with chronic fatigue syndrome and 20 without — the test yielded precise results, accurately flagging all chronic fatigue syndrome patients and none of the healthy individuals.

The diagnostic platform could even help identify possible drugs to treat chronic fatigue syndrome. By exposing the participants’ blood samples to drug candidates and rerunning the diagnostic test, the scientists could potentially see whether the drug improved the immune cells’ response. Already, the team is using the platform to screen for potential drugs they hope can help people with chronic fatigue syndrome down the line.

A paper describing the research findings was published online April 29 in the Proceedings of the National Academy of Sciences. Davis is the senior author. Esfandyarpour, who is now on the faculty of the University of California-Irvine, is the lead author.

Providing the proof

The diagnosis of chronic fatigue syndrome, when it actually is diagnosed, is based on symptoms — exhaustion, sensitivity to light and unexplained pain, among other things — and it comes only after other disease possibilities have been eliminated. It is also known as myalgic encephalomyelitis and designated by the acronym ME/CFS. It’s estimated that 2 million people in the United States have chronic fatigue syndrome, but that’s a rough guess, Davis said, and it’s likely much higher.

For Davis, the quest to find scientific evidence of the malady is personal. It comes from a desire to help his son, who has suffered from ME/CFS for about a decade. In fact, it was a biological clue that Davis first spotted in his son that led him and Esfandyarpour to develop the new diagnostic tool.

The approach, of which Esfandyarpour led the development, employs a “nanoelectronic assay,” which is a test that measures changes in miniscule amounts of energy as a proxy for the health of immune cells and blood plasma. The diagnostic technology contains thousands of electrodes that create an electrical current, as well as chambers to hold simplified blood samples composed of immune cells and plasma. Inside the chambers, the immune cells and plasma interfere with the current, changing its flow from one end to another. The change in electrical activity is directly correlated with the health of the sample.

The idea is to stress the samples from both healthy and ill patients using salt, and then compare how each sample affects the flow of the electrical current. Changes in the current indicate changes in the cell: the bigger the change in current, the bigger the change on a cellular level. A big change is not a good thing it’s a sign that the cells and plasma are flailing under stress and incapable of processing it properly. All of the blood samples from ME/CFS patients created a clear spike in the test, whereas those from healthy controls returned data that was on a relatively even keel.

“We don’t know exactly why the cells and plasma are acting this way, or even what they’re doing,” Davis said. “But there is scientific evidence that this disease is not a fabrication of a patient’s mind. We clearly see a difference in the way healthy and chronic fatigue syndrome immune cells process stress.” Now, Esfandyarpour and Davis are expanding their work to confirm the findings in a larger cohort of participants. Recruitment for the larger project, which aims to further confirm the success of the diagnostic test, is being done on a rolling basis. Those who are interested in participating should contact clinical research coordinator Anna Okumu.

Doubling up

In addition to diagnosing ME/CFS, the researchers are also harnessing the platform to screen for drug-based treatments, since currently the options are slim. “Using the nanoelectronics assay, we can add controlled doses of many different potentially therapeutic drugs to the patient’s blood samples and run the diagnostic test again,” Esfandyarpour said.

If the blood samples taken from those with ME/CFS still respond poorly to stress and generate a spike in electrical current, then the drug likely didn’t work. If, however, a drug seems to mitigate the jump in electrical activity, that could mean it is helping the immune cells and plasma better process stress. So far, the team has already found a candidate drug that seems to restore healthy function to immune cells and plasma when tested in the assay. The drug, while successful in the assay, is not currently being used in people with ME/CFS, but Davis and Esfandyarpour are hopeful that they can test their finding in a clinical trial in the future.

All of the drugs being tested are either already approved by the Food and Drug Administration or will soon be broadly accessible to the public, which is key to fast access and dissemination should any of these compounds pan out.

Other Stanford authors of the study are research scientists Mohsen Nemat-Gorgani and Julie Wilhelmy and research assistant, Alex Kashi.

The study was funded by the Open Medicine Foundation. Davis is the director of the foundation’s scientific advisory board.

Stanford’s departments of Genetics and of Biochemistry also supported the work.


Introduction

Stress is defined as a process in which environmental demands strain an organism’s adaptive capacity resulting in both psychological demands as well as biological changes that could place at risk for illness (1). Things that cause us stress are called stressors. Stress affects everyone, young and old, rich and poor. Life is full of stress. Stress is an every fact of life that we must all deal with. It comes in all shapes and sizes even our thoughts can cause us stress and make the human body more susceptible to illness. There are three theories or perspectives regarding stress environmental stress, psychological (emotional) stress and biological stress (1). The environmental stress perspective emphasizes assessment of environmental situations or experiences that are objectively related to substantial adaptive demands. The psychological stress perspective emphasizes people’s subjective evaluations of their ability to cope with demands presented to them by certain situations and experiences. Finally, the biological stress perspective emphasizes the function of certain physiological systems in the body that are regulated by both psychologically and physically demanding conditions.

The relationship between stress and illness is complex. The susceptibility to stress varies from person to person. An event that causes an illness in a person may not cause illness in other person. Events must interact with a wide variety of background factors to manifest as an illness. Among the factors that influenced the susceptibility to stress are genetic vulnerability, coping style, type of personality and social support. When we are confronted with a problem, we assess the seriousness of the problem and determine whether or not we have the resources necessary to cope with problem. If we believe that the problem is serious and do not have the resources necessary to cope with the problem, we will perceive ourselves as being under stress (2). It is our way of reacting to the situations that makes a difference in our susceptibility to illness and our overall well-being.

Not all stress has negative effect. When the body tolerates stress and uses it to overcome lethargy or enhance performance, the stress is positive, healthy and challenging. Hans Selye (3), one of the pioneers of the modern study of stress, termed this eustress. Stress is positive when it forces us to adapt and thus to increase the strength of our adaptation mechanisms, warns us that we are not coping well and that a lifestyle change is warranted if we are to maintain optimal health. This action-enhancing stress gives the athlete the competitive edge and the public speaker the enthusiasm to project optimally. Stress is negative when it exceeds our ability to cope, fatigues body systems and causes behavioral or physical problems. This harmful stress is called distress. Distress produces overreaction, confusion, poor concentration and performance anxiety and usually results in sub par performance. Figure 1 illustrates this concept.

Eustress is the action-enhancing stress that give athletes the competitive edge

There is a growing concern about the increasing cost and prevalence of stress-related disorders especially in relation to work place. “Worked to death, drop death, work until you drop” are highlighted “work-related death” in the 21 st century. Countries renowned for their long working hours know this well enough Japan and China each have a word for death by overwork – karoshi and guolaosi respectively. Both Japan and Korea recognize suicide as an official and compensatable work-related condition (4). The estimated prevalence of stress and stress-related conditions in the United Kingdom rose from 829 cases per 100,000 workers in 1990 to 1,700 per 100,000 in 2001/2002. In that year, 13.4 million lost working days were attributed to stress, anxiety or depression, with an estimate 265,000 new cases of stress. The latest HSE (Health and Safety Executive) analysis of self-reported illnesses rate revealed that stress, depression or anxiety affects 1.3% of the workforce (5). It is estimated that 80% to 90% of all industrial accidents are related to personal problem and employees’ inability to handle stress (6). The European Agency for Safety and Health at work reported that about 50% of job absenteeism is caused by stress (7).

The morbidity and mortality due to stress-related illness is alarming. Emotional stress is a major contributing factor to the six leading causes of death in the United States: cancer, coronary heart disease, accidental injuries, respiratory disorders, cirrhosis of the liver and suicide. According to statistics from Meridian Stress Management Consultancy in the U.K, almost 180,000 people in the U.K die each year from some form of stress-related illness (7). The Centre for Disease Control and Prevention of the United States estimates that stress account about 75% of all doctors visit (7). This involves an extremely wide span of physical complaints including, but not limited to headache, back pain, heart problems, upset stomach, stomach ulcer, sleep problems, tiredness and accidents. According to Occupational Health and Safety news and the National Council on compensation of insurance, up to 90% of all visits to primary care physicians are for stress-related complaints.

Stress and the immune system

Our immune system is another area which is susceptible to stress. Much of what we know about the relationship between the brain, the nervous system, and the immune response has come out of the field of psychoneuroimmunology (PNI). PNI was developed in 1964 by Dr. Robert Ader, the Director of the Division of Behavioral and Psychosocial Medicine at the University of Rochester. Psychoneuroimmunology is the study of the intricate interaction of consciousness (psycho), brain and central nervous system (neuro), and the body’s defence against external infection and aberrant cell division (immunology) (8). More specifically it is devoted to understanding the interactions between the immune system, central nervous system and endocrine system. Although a relatively new medical discipline, the philosophical roots of the connection between physical health, the brain and emotions can be traced to Aristotle.

Immune responses are regulated by antigen, antibody, cytokines and hormones. Lymphocytes are most responsible for orchestrating the functions of the immune system. The immune system has about 1 trillion lymphocytes. Lymphocytes that grow and mature in the thymus are called T cells other lymphocytes are called B cells. B cells secrete antibodies, chemicals that match specific invaders called antigens (humoral immunity). T cells do not secrete antibodies but act as messengers and killers, locating and destroying invading antigens (cellular immunity). Some T cells, called helpers, help activate the production of other T and B cells. Other T cells, called suppressors, stop the production of antigens, calling off the attack. The number of T and B cells must be balanced for them to perform effectively. When the ratio of T to B cells is out of balance, the immune response is compromised and does not work effectively. Other key chemicals that are produced by the immune systems are macrophages, monocytes and granulocytes. These chemicals envelop, destroy and digest invading microorganisms and other antigens. Known generally as phagocytes, they team up with more than 20 types of proteins that make up the immune system’s complement system. This system is triggered by antibodies that lock onto antigens, which cause inflammatory reactions.

Cytokines are non-antibody messenger molecules from a variety of cells of the immune system. Cytokines stimulate cellular release of specific compounds involved in the inflammatory response. They are made by many cell populations, but the predominant producers are helper T cells (Th) and macrophages. Th1 and Th2 cytokines inhibit one another’s production and function: Th1 cells stimulate cellular immunity and suppress humoral immunity, while Th2 cytokines have opposite effect. Cytokines is a general name other specific name includes lymphokines (cytokines produced by lymphocytes), chemokines (cytokines with chemotactic activities), interleukin (IL) (cytokines made by one leukocyte and acting on other leukocytes) and interferon (IFN) (cytokines release by virus-invaded cell that prompt surrounding cell to produce enzymes that interfere with viral replication).

Cytokines are produced de novo in response to an immune stimulus. They generally act over short distances and short time spans and at very low concentration. They act by binding to specific membrane receptors, which then signal the cell via second messenger, often tyrosine kinases, to alter its behaviour (gene expression). Responses to cytokines include increasing or decreasing expression of membrane proteins (including cytokines receptors), proliferation and secretion of effectors molecules. The largest group of cytokines stimulates immune cell proliferation and differentiation. Some common bacterial antigens activate complement and stimulate macrophages to express co-stimulatory molecules. Antigens stimulate adaptive immune responsiveness by activating lymphocytes, which in turn make antibody to activate complement and cytokines to increase antigen elimination and recruit additional leukocytes.

Several studies have shown that chronic stress exerts a general immunosuppressive effect that suppresses or withholds the body’s ability to initiate a prompt, efficient immune reaction (9,10). This has been attributed to the abundance of corticosteroids produced during chronic stress, which produces an imbalance in corticosteroid levels and weakens immunocompetence. This weakening of immune function is thought to be associated with general strain on the various body parts associated with the production and maintenance of the immune system. For example, atrophy of the thymus or shrinking of the thymus results in its inability to produce T cells or the hormones needed to stimulate them. This can lead to an imbalance and inefficiency of the entire immune response. This is consistence with the finding that as we get elder, we are prone to suffer from infection, cancer, hypersensitivity and autoimmunity.

In a meta-analysis of 293 independent studies reported in peer-reviewed scientific journal between 1960 and 2001 with some 18,941 taking part, it is confirmed that stress alters immunity (11). Short-term stress actually boosts the immune system as it readies itself to meet and overcome a challenge such as an adaptive response preparing for injury or infection but long-term or chronic stress causes too much wear and tear, and the system will break down especially if the individual has little control over events. The analyses (11) revealed that the most chronic stressors which change people’s identities or social roles, are more beyond their control and seem endless–were associated with the most global expression of immunity almost all measures of immune function dropped across the board. Duration of stress also plays a role. The longer the stress, the more the immune system shifted from potentially adaptive changes (such as those in fight-or-flight response) to potentially detrimental changes, at first in cellular immunity and then in broader immune function. They also found that the immune systems of people who are older or already sick are more prone to stress-related change.

The link between stress and illness

The critical factor associated with stress is its chronic effect over time. Chronic stressors include daily hassles, frustration of traffic jams, work overload, financial difficulties, marital arguments or family problems. There are, of course, many more things that can cause stress, but these are the stressors commonly encountered in daily life. The pent-up anger we hold inside ourselves toward any of these situations, or the guilt and resentment we hold toward others and ourselves, all produce the same effects on the hypothalamus. Instead of discharging this stress, however, we hold it inside where its effects become cumulative.

Research shows that almost every system in the body can be influenced by chronic stress. When chronic stress goes unreleased, it suppresses the body’s immune system and ultimately manifests as illness. One can only wonder what would happen to the body if it remained in the fight-or-flight response. Fortunately, under normal circumstances, three minutes after a threatening situation is over and the real or imagined danger is removed, the fight-or-flight response subsides and the body relaxes and returns to its normal status. During this time heart rate, blood pressure, breathing, muscle tension, digestion, metabolism and the immune system all return to normal. If stress persists after the initial fight-or-flight reaction, the body’s reaction enters a second stage. During this stage, the activity if the sympathetic nervous system declines and adrenaline secretion is lessened, but corticosteroid secretion continues at above normal levels. Finally, if stress continues and the body is unable to cope, there is likely to be breakdown of bodily resources.

Medical illnesses

In asthma, both external and internal factors are involved it is the internal factor that is most affected by acute effects of psychological stressors. Family therapy is widely incorporated in the management of asthmatic children. The improvement is attributed to minimizing the interaction with parents that produced frequent stressful situation. Additionally, asthmatics exposed to a harmless substance that they thought they were allergic would elicit a severe attack (12). A study by Gauci et al. (13) demonstrated significant positive correlations between a few of Minnesota Multiphasic Personality Inventory (MMPI) distressed-related scales and skin reactivity in response to allergens. Collectively, these data provide evidence for a clear association between stress, immune dysfunction and clinical activity of atopic and asthmatic disease. For further reference, Liu et al. (14) provided excellent evidence that stress can enhance allergic inflammatory response.

Gastrointestinal diseases such as peptic ulcer (PU) and ulcerative colitis (UC) are known to be greatly influenced by stress. PU occurs twice as often in air traffic controllers as in civilian copilots, and occurs more frequently among air traffic controllers at high-stress centers (Chicago O’Hare, La Guardia, JFK and Los Angeles International Airport) than low-stress centers (airports in less-populated cities in Virginia, Ohio, Texas and Michigan). Although stress is a risk factor in PU, more than 20 other factors are thought to be associated as well: blood type, sex, HLA antigen type, alcoholic cirrhosis, hypertension, chronic obstructive pulmonary disease, cigarette smoking, and even consumption of coffee, carbonated beverage or milk during college (12). Certain stressful life events have been associated with the onset or symptom exacerbation in other common chronic disorders of the digestive system such as functional gastrointestinal disorders (FGD), inflammatory bowel disease (IBD) and gastro-esophageal reflux disease (GERD). Early life stress in the form of abuse also plays a major role in the susceptibility to develop FGD as well as IBD later in life (15).

Ulcers are caused by excessive stomach acid, and studies of patients with gastric fistulas have shown that anger and hostility increase stomach acidity, while depression and withdrawal decrease it. Other theory correlating the effects of stress on the development of ulcers linked to the mucous coating that lines the stomach. The theory states that, during chronic stress, noradrenaline secretion causes capillaries in the stomach lining to constrict. This in turn, results in shutting down of mucosal production, and the mucous protective barrier for the stomach wall is lost. Without the protective barrier, hydrochloric acid breaks down the tissue and can even reach blood vessels, resulting in a bleeding ulcer (16). However, it has recently been discovered that many cases of ulcers are caused by a bacterial called Helicobacter pylori (H. pylori) (17). Although the exact mechanism by which it causes ulcers is unknown, it is believed that H. pylori inflames the gastrointestinal lining, stimulates acid production or both.

Coronary Heart disease (CHD) has long been regarded as a classical psychosomatic illness in that its onset or course was influenced by a variety of psychosocial variables. Psychosocial aspects of CHD had been studied extensively and there is strong evidence that psychological stress is a significant risk factor for CHD and CHD mortality (18,19,20,21). Tennant (19) found a positive relationship between life stress and cardiac infarction and sudden death while study by Rosengren et al. (20) reported that CHD mortality was increased two folds for men experiencing three or more antecedent life events. The INTERHEART study (21) revealed that people with myocardial infarction reported higher prevalence of four stress factors: stress at work and at home, financial stress and major life events in the past year.

Although the evidences supporting an association between type A behaviour (aggressive, competitive, work-oriented and urgent behaviour) and CHD were conflicting (22) some studies found that type A individuals generate more stressful life events and were more likely than others to interpret encountered life event in an emotionally adverse way (23, 24). If type A is a risk factor it may not operate by way of long-term physiological dysfunction (leading to atherogenesis), but by way of acute life events provoking severe strain on the heart. One of the components of Type A behaviour is hostility, which may be correlated with CHD risk. Some studies (25, 26) noted that clinical CHD events are predicted by hostility and this seems to independent of other risk factors. Hostility was also found to be related to atherosclerosis in some angiography studies (27,28). Other studies found suppression of anger was associated with CHD event (29) and atherosclerosis (27,28). In review of these findings, Tennant (30) concluded that the possibility emerges that hostility (or its suppression) may have some role in CHD, although the mechanism is unclear.

The three major risk factors commonly agreed to be associated with CHD are hyper cholesterolemia, hypertension and cigarette smoking. In attempt to determine the causes of increased levels of serum cholesterol Friedman et al. (31) conducted one of the early investigations of the relationship between stress and serum cholesterol. They found that stress is one of the causes of increased levels of serum cholesterol. Other researchers who studied the medical students facing the stress of exam (32), and military pilot at the beginning of their training and examination period (33) verified the findings. Since blood pressure and serum cholesterol increases during stress, the relationship between stress and hypertension has long been suspected emotional stress is generally regarded as a major factor in the etiology of hypertension (34). One of the early evidence of this relationship came from the massive study of 1,600 hospital patients by Dunbar (35). He found that certain personality traits were characteristic of hypertensive patients for example they were easily upset by criticism or imperfection, possessed pent-up anger and lack self-confidence. Recognizing this relationship, educational programs for hypertensive patients have included stress management.

It appears that some people are hereditarily susceptible to rheumatoid arthritis (RA). Approximately half of the sufferers of this condition have a blood protein called the rheumatoid factors (RF), which is rare in non-arthritic people. Since RA involves the body turning on itself (an autoimmune response), it was hypothesized that a self-destructive personality may manifest itself through this disease (16). Although the evidence to support this hypothesis is not conclusive, several investigators have found personality differences between RA sufferers and others. Those affected with this disease have been found to be perfectionists and are self-sacrificing, masochistic, and self-conscious. Female patients were found to be nervous, moody and depressed, with a history of being rejected by their mothers and having strict fathers. It has been suggested that people with the RF who experience chronic stress become susceptible to RA (16). Their immunological system malfunctions and genetic predisposition to RA results in their developing of the condition.

Migraine headaches are the result of constriction and dilatation of the carotid arteries of one side of the head. The constriction phase, called the prodrome, is often associated with light or noise sensitivity, irritability and a flushing or pallor of the skin. When the dilatation of the arteries occurs, certain chemicals stimulate adjacent nerve endings, causing pain. Diet may precipitate migraine headaches for some people. However, predominant thought on the cause of migraine pertains to emotional stress and tension. Feeling of anxiety, nervousness, anger or repressed rages are associated with migraine. An attack may be aborted when the individual gives vent to underlying personality (8). A typical migraine sufferer is a perfectionist, ambitious, rigid, orderly, excessively competitive and unable to delegate responsibility.

There is also evidence that emotionally stressful experience is associated with endocrine disorder such as diabetes mellitus (36). Physical or psychological stressors can alter insulin needs stressors may often be responsible for episodes of loss of control, especially in diabetic children. Type II diabetes is most often affected by stress, as it tends to occur in overweight adults and is a less severe form of diabetes (12). Additionally, children who had stressful life events stemming from actual or threatened losses within the family and occurring between the ages of 5 and 9 had a significantly higher risk of type I diabetes.

Acute stress can suppress the virus-specific antibody and T cell responses to hepatitis B vaccine (37). People who show poor responses to vaccines have higher rate of illness including influenza virus infection. There are several other studies which demonstrated a relationship between psychological stress and susceptibility to several cold viruses (38,39). This is not surprising, as stress does suppress the immune system latent viruses then have an easier time resurging since the body cannot defend itself any more. Attempts to find an association between stress and disease progression in patients with acquired immunodeficiency syndrome (AIDS) have met with conflicting results (40). Analysis of the Multicentre AIDS Cohort Study failed to observe an association between depression and the decline of CD4+ T lymphocytes, disease progression or death (41), but others have found significant association between immunological parameters reflective of HIV progression and psychosocial factors, particularly denial and distress (42), and concealment of homosexual identity (43).

Psychiatric illness

A large body of research in the past four decades has provided evidence that recent life events contribute to the onset of psychiatric illness (44). The association between stressful life events and psychiatric illness is stronger than the association with physical or medical illness. Vincent and Roscenstock (45) found that prior to hospitalization, patients with psychiatric disorders had suffered more stressful event than those with physical disorders. Meanwhile, Andrew and Tennant (46) failed to find the association between stress and physical illness. Although the exact relationship between stress and psychiatric illness is not clear, the final pathway is biochemical. As with medical illness, the appropriate model is one of multifactorial causations. Most life event research indicates a limit of 6 months to consider a stress having significant effect on illness. After that, the effect of stress diminishes with time.

Recent life events held to have a major etiological role in neuroses, a formative role in the onset of neurotic depression (mixed depressive illness) and a precipitating role in schizophrenic episodes (47). In other words, the association of stress with psychiatric illness is the strongest in neuroses, followed by depression and schizophrenia is the least. The correlation between neuroses and schizophrenia with stress is clearer. The weak association between stressful life events and onset of psychotic illness, particularly schizophrenia had been demonstrated in a few studies (48,49,50), in contrast with strong association between stress and neuroses (51,52, 53,54). However the degree of relationship between depressive illness and neuroses in relation to stress is rather controversial. Neither Paykel (55) nor Brown et al. (56) found the relationship between life event stress and illness is greater for neurotic depression than unipolar (endogenous) depression.

Bebbington et al. (57) found that there is an excess of life events preceding the onset of all types of psychoses, particularly in the first 3 months. In the study of recent onset of schizophrenia, schizophreniform disorder and hypomania, Chung et al. (49) found that threatening life events were significantly related to the onset of schizophreniform psychosis but not schizophrenia. They also found that threatening events might precipitate hypomanic episodes. Other study (50) found that individuals with schizophrenia do not experience more stressful life events than normal controls, but they reported greater subjective stress. A study that investigated the relationship between recent life events and episodes of illness in schizophrenia found that, initial or early episodes of schizophrenia are more likely to be associated with recent life events than are later episodes (48).

On the other hand, bipolar disorders have received less study than unipolar. In bipolar disorder, the effect of life events is generally weaker than unipolar however major life events may be important in first onset (58). Causative factors in bipolar disorders are multifactorial and complex, and genetic factor seems to influence life events exposure. Those with greater genetic loading, there were fewer stressful life events before the first episode and they had the earlier onset of the disease. A number of studies have shown that the onset of depression is often preceded by stressful life events (59,60). Stressful life events along with recent minor difficulties have also been identified as predictors of an episode of depression in a monozygotic female twin study. Kessler (61) who came with the same conclusion added that there is evidence that concomitant chronic stress enhances the effect of major life events on depression.

Cooper and Sylph (51) documented the role of life events in the causation of neurotic illness. They found that neurotic group reported 50% more stressful events than the control group. McKeon et al. (52) found that patients with obsessive-compulsive neuroses who have abnormal personality traits (obsessional, anxious and self-conscious) experienced significantly fewer life events than those without such traits. Zheng and Young (53) in comparing live event stress between neurotic patients and normal control found that neurotic patients had significantly higher level of stress and experienced more life event changes than the control group. Rajendran et al. (54) who compared the neurotic executives with healthy executives as a control group, found significant differences between normal and neurotic groups in terms of the frequency of the life events as well as the stress they experienced due to those life events.

Stress and cancer

The relationship between breast cancer and stress has received particular attention. Some studies have indicated an increased incidence of early death, including cancer death among people who have experienced the recent loss of a spouse or loved one. A few studies of women with breast cancer have shown significantly high rate of disease among those women who experienced traumatic life events and loses within several years before their diagnosis. However, most cancers have been developing for many years and are diagnosed only after they have been growing in the body for a long time. Thus, this fact argues against an association between the death of a loved one and the triggering of cancer. There is no scientific evidence of a direct cause-and-effect relationship between these immune systems changes and the development of cancer. It has not been shown that stress-induced changes in the immune system directly cause cancer. However, more research is needed to find if there is a relationship between psychological stress and the transformation of normal cells into cancerous cells. One area that is currently being studied is whether psychological interventions can reduce stress in the cancer patients, improve immune function and possibly even prolonged the survival.

Studies in animals, mostly rats, revealed the link between stress and progression of cancerous tumors. Chronic and acute stress, including surgery and social disruptions, appear to promote tumor growth. It is easy to do such research in animal, but it is harder with humans. Furthermore, the interactions of many systems that affect cancer, from the immune system to the endocrine system, along with environment factors that are impossible to control for, make sorting out the role of stress extremely difficult. In addition, researchers cannot expose people to tumour cells as they do with animals. A recent study (62) found that there was a link between stress, tumour development and a type of white blood cells called natural killer (NK) cells. Of all the immune systems cells, NK cells have shown the strongest links to fighting certain forms of the disease, specifically preventing metastasis and destroying small metastases. Although the result of this study is not definitive, it is indicates that stress acts by suppressing NK-cell activity. Other preliminary study showed the evidence of a weakened immune system in breast cancer patients who feel high level of stress compared to those experiencing less stress.

A new study shows stress and social supports are important influences in a man’s risk for developing prostate cancer. Researchers (63) at State University of New York at Stony Brook’s medical school found men with high level of stress and a lack of satisfying relationships with friends and family had higher levels of Prostate-Specific Antigen (PSA) in their blood, a marker for an increased risk of developing prostate cancer. Based on the results, the risk of having an abnormal PSA was three times higher for men with high levels of stress. Likewise, men who had felt they had low levels of support from friends and family were twice as likely to have an abnormal PSA. The findings raise the possibility that a man’s psychological state can have a direct impact on prostate disease.


Better Together

In 2001, shortly after arriving in New York, Formenti attended a talk by Sandra Demaria, a pathologist also at Weill Cornell. Demaria was studying slivers of breast tumors removed from patients who had received chemotherapy and had found that in some patients, chemotherapy caused immune cells to flood the tumors. This made Formenti wonder if the same thing could happen after radiation therapy.

In addition to fighting off illness-causing pathogens, part of the immune system’s job is to keep tabs on cells that could become cancerous. For example, cytotoxic T cells kill off any cells that display signs of cancer-related mutations. Cancer cells become troublesome when they find ways to hide these signs or release proteins that dull T cells’ senses. “Cancer is really a failure of the immune system to reject [cancer-forming] cells,” Formenti says.

Formenti and Demaria, a fellow Italian native, quickly joined forces to determine whether the immune system was driving the abscopal response. To test their idea, their team injected breast cancer cells into mice at two separate locations, causing individual tumors to grow on either side of the animals’ bodies. Then they irradiated just one of the tumors on each mouse. Radiation alone prevented the primary tumor from growing, but didn’t do much else. Yet when the researchers also injected a protein called GM-CSF into the mice, the size of the second tumor was also controlled .

GM-CSF expands the numbers of dendritic cells, which act as T cells’ commanding officers, providing instructions about where to attack. But the attack couldn’t happen unless one of the tumors was irradiated. “Somehow radiation inflames the tumor and makes it interesting to the immune system,” Formenti says.

Formenti and Demaria knew that if their findings held up in human studies, then it could be possible to harness the abscopal effect to treat cancer that has metastasized throughout the body.

“The abscopal response is not common, but we see it, and it’s pretty remarkable when it happens.”

Although radiation therapy is great at shrinking primary tumors, once a cancer has spread, the treatment is typically reserved for tumors that are causing patients pain. “Radiation is considered local therapy,” says Michael Lim, a neurosurgeon at Johns Hopkins University in Baltimore who is studying ways to combine radiotherapy with immunotherapy to treat brain tumors. But, he adds, “if you could use radiation to kindle a systemic response, it becomes a whole different paradigm.”

When Demaria and Formenti first published their results in 2004, the concept of using radiation to activate immunity was a hard sell. At the time, research into how radiation affected the immune system focused on using high doses of whole-body irradiation to knock out the immune systems of animal models. It was counterintuitive to think the same treatment used locally could activate immunity throughout the body.

That perspective, however, would soon change. In 2003 and 2004, James Hodge, an immunologist at the National Cancer Institute and his colleagues published two mouse studies showing that after radiation, tumor cells displayed higher levels of proteins that attract and activate cancer-killing T cells . It was clear radiation doesn’t just kill cancer cells, it can also make those that don’t die more attractive to immune attack, Hodge says.

This idea received another boost in 2007 when a research team from Gustave Roussy Institute of Oncology near Paris reported that damage from radiation caused mouse and human cancer cells to release a protein that activates dendritic cells called HMGB1. They additionally found that women with breast cancer who also carried a mutation preventing their dendritic cells from sensing HMGB1 were more likely to have metastases in the two years following radiotherapy. In addition to making tumors more attractive to the immune system, Hodge says, the damage caused by radiation also releases bits of cancer cells called antigens, which then prime immune cells against the cancer, much like a vaccine.

In some ways, Barker says, oncologists have always sensed that radiation works hand-in-hand with the immune system. For example, when his patients ask him where their tumors go after they’ve been irradiated, he tells them that immune cells mop up the dead cell debris. “The immune system acts like the garbage man,” he says.

Now, immunologists had evidence that the garbage men do more than clean up debris: they are also part of the demolition team, and if they could coordinate at different worksites, they could generate abscopal responses. With radiation alone, this only happened very rarely. “Radiation does some of this trick,” Formenti says. “But you really need to help radiation a bit.”

Formenti and Demaria had already shown in mice that such assistance could come in the form of immunotherapy with GM-CSF, and in 2003 they set out to test their theory in patients. They treated 26 metastatic cancer patients who were undergoing radiation treatment with GM-CSF. The researchers then used CT scans to track the sizes of non-irradiated tumors over time. Last June, they reported that the treatment generated abscopal responses in 20% of the patients. Patients with abscopal responses tended to survive longer, though none of the patients were completely cured.

As the Weill Cornell team was conducting their GM-CSF study, a new generation of immunotherapeutic drugs arrived on the scene. Some, like imiquimod, activate dendritic cells in a more targeted way than GM-CSF does. Another group, the checkpoint inhibitors, release the brakes on the immune system and T cells in particular, freeing the T cells to attack tumors.

In 2005, Formenti and her team found that a particular checkpoint inhibitor worked better with radiotherapy than alone and later reported that the same combination produces abscopal responses in a mouse model of breast cancer.

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An Image-Based Method to Detect and Quantify T Cell Mediated Cytotoxicity of 2D and 3D Target Cell Models

Authors: Brad Larson, Agilent Technologies, Inc. Wini Luty, Courtney Noah, BioreclamationIVT Olivier Donzé, AdipoGen Life Sciences Glauco R. Souza, University of Texas Health Science Center at Houston and Nano3D Biosciences, Inc.

Abstract

T cell mediated cytotoxicity plays an important role in a suite of new methods being developed with the goal of boosting a patient&rsquos immune system to combat cancer. In order to evaluate and optimize adoptive T cell immunotherapies, sensitive in vitro methods must be included in the testing process. In the procedure described here, phenotypic and quantitative assessments of 2D and 3D target cell necrotic induction were made using automated live cell imaging. It was found that direct activation of T cells produced a significantly greater cytotoxic effect than general activation suggesting that T cells can be "taught" to target and destroy specific target cells.

Introduction

CD3+CD8+ cytotoxic T lymphocytes (CTL) are the effector cells responsible for T cell mediated cytotoxicity that can act by cell-to-cell contact either by releasing granzymes and perforin or through Fas ligand mediated toxicity. 1 As part of the adaptive immune system, these cells mount targeted attacks to rid the body of a variety of compromised cells, such as cancer cells, without harming healthy cells. Counteracting this natural defense is the widely known fact that tumors develop multiple methods to avoid immune detection and create a level of tolerance against the immune cells designed to seek out and destroy cells containing foreign antigens. 2 For many years, the development of treatments avoided use of a patient&rsquos immune system to kill cancer cells, as immunotherapy-based treatments met with multiple clinical failures. Developing methods offer renewed hope for cancer patients. Adoptive immunotherapy techniques activates a patient&rsquos T cells ex vivo against tumor antigens before infusing the activated T cells back into the patient to target and destroy tumor cells selectively. 3

The most popular in vitro method to monitor CTL effect on target cells is the cell mediated cytotoxicity (CMC) assay where T cells and target cells are added to a microplate well as a coculture. Traditionally toxicity was measured using chromium ( 51 Cr) release from preloaded target cells. Due to problems with radioactivity disposal, and low sensitivity due to spontaneous release of the isotope from target cells 4 , newer methods were developed using microplate-based optical methods generating luminescence or fluorescence. These techniques were optimized to detect the signal from target cells plated in a uniform two-dimensional (2D) monolayer in microplate wells. With increasing adaptation of cells aggregated into a three-dimensional (3D) configuration to create a more in vivo-like model, cells are no longer evenly spread throughout the bottom of a well. Through the incorporation of microscopic imaging and cellular analysis, sensitive detection of induced cytotoxicity from 2D and 3D plated target cells, as well as visualization of the interplay between CTL and target cells, can be achieved.

Here, we demonstrate an automated research method to monitor and measure CTL cell mediated cytotoxicity kinetically using digital widefield microscopy. Cocultured target MDA-MB-231 breast cancer and fibroblast cells were plated in 2D and 3D format and dosed with a live cell apoptosis/necrosis reagent. T cells, activated using general or directed methods and stained with a far red tracking dye, were then added in ratios of 20, 10, 5, or 0:1 to the target cells. The plates were then added to an automated incubator and shuttled to the digital widefield microscope, using a robotic arm, every four hours where brightfield and fluorescent images were captured for a total of seven days. Visual observation of the kinetic images enabled monitoring of CTL:target cell interactions for 2D and 3D cultured cells, while cellular image analysis allowed for calculation of CTL induced cytotoxicity during the entire incubation period.

Materials and methods

Cells and media
MDA-MB-231 epithelial breast adenocarcinoma cells (part number HTB-26) were obtained from ATCC (Manassas, VA). Human Neonatal Dermal Fibroblast cells stably expressing RFP (part number cAP-0008RFP) were purchased from Angio-Proteomie (Boston, MA). Human purified CD3+ T cells, isolated via negative selection from peripheral blood mononuclear cells (part number HM-PBMC-TCELLCD3-M) were donated by BioreclamationIVT (Westbury, NY). Advanced DMEM (part number 12491-015), RPMI 1640 medium (part number 11875-093), Fetal bovine serum, (part number 10437-036), and penicillin-streptomycinglutamine (100X) (part number 10378-016) were purchased from ThermoFisher Scientific (Waltham, MA).

Assay and experimental components
IL-2 Superkine (Fc) (part number AG-40B-0111-C010), anti-CD3 (human), mAb (UCHT1) (part number ANC-144-020) and anti-CD28 (human), mAb (ANC28.1/5D10) (part number ANC-177-020) were donated by AdipoGen Life Sciences (San Diego, CA). SCREENSTAR 190 µm cycloolefin filmbottom 384-well microplates (GBO part number 789836), CELLSTAR µClear 384-well cell-repellent surface microplates (GBO part number 781976) and the 384-Well BiO Assay Kit (GBO part number 781846, consisting of 2 vials NanoShuttle-PL, 6-Well Levitating Magnet Drive, 384-Well Spheroid and Holding Magnet Drives (2), 96-Well Deep Well Mixing Plate, 6-Well and 384-Well Clear Cell Repellent Surface Microplates), prototype 384-Well Ring Drive, and additional Cell Repellent Surface 6-Well (GBO part number 657860) were donated by Nano3D Biosciences, Inc., and Greiner Bio-One, Inc., (Monroe, NC). The Kinetic Apoptosis Kit (Microscopy) (part number ab129817) was donated by Abcam (Cambridge, MA). CellTracker Deep Red Dye (part number C34565) was purchased from ThermoFisher Scientific (Waltham, MA).

Cytation 5 is a modular multimode microplate reader combined with an automated digital microscope. Filter- and monochromator-based microplate reading are available, and the microscopy module provides up to 60x magnification in fluorescence, brightfield, color brightfield and phase contrast. The instrument can perform fluorescence imaging in up to four channels in a single step. With special emphasis on live cell assays, Cytation 5 features shaking, temperature control to 65 °C, CO2/O2 gas control and dual injectors for kinetic assays, and is controlled by integrated Agilent BioTek Gen5 microplate reader and imager software, which also automates image capture, processing and analysis. The instrument was used to kinetically monitor CTL:target cell interactions as well as cytotoxicity induction within the 2D and 3D plated target cells.

Agilent BioTek BioSpa 8 automated incubator
The BioSpa 8 automated incubator links Agilent BioTek readers or imagers together with Agilent BioTek washers and dispensers for full workflow automation of up to eight microplates. Temperature, CO2/O2 and humidity levels are controlled and monitored through the Agilent BioTek BioSpa software to maintain an ideal environment for cell cultures during all experimental stages. Test plates were incubated in the BioSpa to maintain proper atmospheric conditions for a period of seven days and automatically transferred to the Cytation 5 every four hours for brightfield and fluorescent imaging.

This work uses three workflows which are depicted pictorially in Figure 1.

Figure 1. T Cell activation and cell mediated cytotoxicity assay workflow.

The first workflow on the left side of Figure 1 involves T cell activation where CD3+ T cells are exposed to the MDA-MB-231 target cells which have been bioprinted into spheroids using magnetic fields (see "3D target cell preparation" for further detail.) The activated T cells are then stained with CellTracker Deep Red dye and used in either a bioprinted 3D spheroid-based cytototoxicity assay or another using plated cells. The CellTracker dye allows visualization of the T cells attacking the target cells, while propidium iodide dye allows for quantification of target cell death associated with plasma membrane rupture.

3D target cell preparation

T-75 flasks of MDA-MB-231 or fibroblast cell cultures were cultured to 80% confluence, then as illustrated in Figure 2, treated with 600 &muL NanoShuttle-PL overnight at 37 °C/5% CO2. After incubation, cells were trypsinized for 3 to 5 minutes at 37 °C/5% CO2.

Figure 2. Bioprinting procedure used to create 3D spheroids for T cell activation.


Cells were removed from the flasks and added to the 6-well cell repellent plate at a concentration of 1.2 × 106 cells/well. A 6-well magnet drive was placed atop the well plate to levitate the cells, where aggregation and extracellular matrix (ECM) formation took place during an eight-hour incubation at 37 °C/5% CO2. After incubation, the cells and ECM were broken up, resuspended, and combined together at equal concentrations in complete advanced DMEM medium.

For T cell activation, 3D spheroids were bioprinted in a 24-well cell repellant microplate using a 384-well spheroid magnet drive (see "Directed and general T cell activation").

A modified procedure associated with Figure 2 was used to prepare 3D spheroids for the cytotoxicity assay. The procedure was the same until the spheroid bioprinting was conducted. Instead of bioprinting in a 24-well plate as for T cell activation, the assay used 384-well plates such that a single spheroid was bioprinted in each well. To each well of the 384-well cell repellent microplate, a total of 2,000 cells (1,000 MDA-MB-231 and 1,000 fibroblasts) were added. The microplate was incubated at 37 °C/5% CO2 for 48 hours to allow the cells to aggregate into cocultured tumoroids within each well.


2D target cell preparation

T-75 flasks of MDA-MB-231 or fibroblast cell cultures were cultured to 80% confluence. Cells were then trypsinized for 3-5 minutes at 37 ºC/5% CO2 and removed from the flasks. Following centrifugation, the cells were resuspended and combined together at equal concentrations in complete advanced DMEM medium. A total of 2000 cells (1000 MDA-MB-231 and 1000 fibroblasts) were added to wells of a 384-well TC treated microplate intended for 2D cell culture (Figure 2). The microplate was incubated at 37 °C/5% CO2 overnight to allow the cells to attach to the wells.

Directed and general T cell activation

A total of 10,000 target cells and media were added to 24-well cell repellent plate wells for each experimental condition as follows (Figure 2). Directed activation: (A) 100% MDA-MB-231 (B) 75% MDA-MB-231 and 25% fibroblasts (C) 50% MDA-MB-231 and 50% fibroblasts general activation: (D) no cells. Total volume was 1 mL for wells in each test condition. The 24-well plate was then placed atop a 384-well spheroid magnet drive and incubated at 37 ºC/5% CO2 for four days where the cells aggregated into multiple 3D spheroids within each well (Figure 3). Note that the magnet drive is designed for 384-well densities, such that the expanded size of a 24-well plate well provides nine (9) separate spheroids/well.

Figure 3. 24-well plate well showing coculture of T cells and bioprinted magnetized 3D target spheroids prior to commencement of directed activation. T cells added in a 10:1 ratio to target cells previously aggregated into 3D spheroids (

Following spheroid aggregation, T cells were prepared at a concentration of 100,000 cells/mL in RPMI medium containing 100 ng/mL IL-2 Superkine (Fc) (superkine) along with 250 ng/mL each of anti-CD3 and anti-CD28 antibodies. Spent media was then aspirated while the plate remained on the magnet drive to secure the spheroids, and replaced with fresh media containing the T cells, antibodies, and superkine as previously described. The plate was then placed back into the BioSpa to incubate for six days. The BioSpa was preprogrammed to capture a 12 × 10 image montage from each test well every six hours. Manual exchange of media, IL-2 Superkine, and antibodies was performed after 72 hours. The directed activation procedure over the six days serves to not only activate the T cells, but also teaches them to recognize target cell antigens allowing for targeted cytotoxicity. General activation follows the same procedure, but uses no target cells, thus there should be no targeted cytotoxicity. 5

T cell staining and addition

Upon completion of the activation process, the 24-well plate containing the T cells and magnetized target cells was placed back on the 384-well magnet drive. The T cells were then removed from each well and transferred to a separate 15 mL conical tube for staining with the CellTracker Deep Red dye allowing for differentiation from the target cells during the cytotoxicity experiment. Dye, at a concentration of 1 µM, was added to the tubes and incubated at 37 °C/5% CO2 for 45 minutes. The tubes were then centrifuged for 15 minutes at 200 RCF. Media containing the excess dye was then removed and replaced with fresh RPMI medium. Stained T cells from each activation condition were then diluted in RPMI medium containing 10 µL/mL of the propidium iodide necrosis probe from the Kinetic Apoptosis kit. The cells were then added to the 384-well 2D or 3D cell culture plates, already containing a total of 2,000 target cells, in concentrations equaling 40,000 cells/ well, 20,000 cells/well, or 10,000 cells/well (Figure 1). These concentrations created ratios of 20:1, 10:1, or 5:1 T cells to target cells in each well. Untreated negative control wells were also included to examine basal target cell cytotoxicity levels over time. Table 1 illustrates the final plate layout.

Table 1. 2D and 3D cell-mediated cytotoxicity assay plate layout.


Cell-mediated cytotoxicity assay automated procedure

Figure 4. Agilent BioTek BioSpa live cell imaging system, including Agilent BioTek BioSpa 8 and Agilent BioTek Cytation 5.


2D and 3D assay plates, containing T cells and target cells, were added to the BioSpa 8, as part of the BioSpa live cell imaging system (Figure 4), with atmospheric conditions previously set to 37 °C/5% CO2. Water was also added to the pan to create a humidified environment, which was monitored. The Agilent BioTek BioSpa software was set such that the plates were automatically transferred to the Cytation 5 for brightfield and fluorescent imaging of the test wells every four hours for a total of seven days. Table 2 explains the imaging carried out with each channel. For 2D plated cells, a single 4x magnification image was taken with each channel to capture a representative population of cells per well. Laser autofocus was incorporated to ensure proper focusing on the target cell layer as well as the most efficient focusing procedure. For 3D plated cells, since the cells within the 3D target cell spheroids existed on multiple z-planes, a z-stack consisting of five slices was captured with each channel. Laser autofocus was again incorporated. Two images each were taken below and above the decided upon focal plane.

Table 2. Cell imaged per imaging channel.


2D and 3D image processing

Following capture, 2D and 3D images were processed prior to analysis. 2D images underwent preprocessing to remove background signal from each channel using the settings in Table 3.

Table 3. 2D image preprocessing parameters.


For 3D images, first a z-projection of the images captured in the z-stack was carried out to create a final image containing only the most in-focus information (Table 4).

Table 4. 3D Z-projection criteria


Preprocessing of the projected image was then performed to again remove background signal from each channel (Table 5).

Table 5. 3D image preprocessing parameters.


Cellular analysis of 2D and 3D processed images

Cellular analysis was carried out on the processed images to determine the total signal emanating from necrotic target cells using the criteria in Table 6.

Table 6. Necrotic cell identification criteria.


An additional image analysis step was performed on the 3D images to determine the extent to which target cell spheroids disintegrated following T cell treatment (Table 7).

Table 7. Spheroid disintegration criteria.

Results and discussion

Image-based detection of cocultured cell interaction

T cells, activated using direct and general activation procedures, were added to the target cells in concentrations equaling 20:1, 10:1, 5:1 and 0:1 to start the cell-mediated cytotoxicity assay. To monitor the interaction of the cocultured cells, plates were imaged immediately following T cell addition and every four hours subsequent throughout the entire seven-day incubation period.

As assay incubation times increase, it is apparent that activated T cells (red fluorescence) seek out and cluster around the antigen presenting target cells through antigen-receptor binding in both 2D and 3D formats (Figure 5A). This T cell aggregation is in marked contrast to the more even distribution of red fluorescence at time 0.

Figure 5. Brightfield/CY5 imaging of cellular interaction. 4x brightfield and CY5 images showing T cell clustering and binding to (A) 2D or (B) 3D target cells. Time = 24 hours.

When images from the PI channel are overlaid with those from the brightfield channel one can observe that yellow fluorescent signal from the propidium iodide necrotic cell probe originates from the same target cells with bound T cells (Figure 6). This confirms the downstream cytotoxic effect of T cell binding to the target cells.

Figure 6. Brightfield/PI imaging of cellular interaction. 4x brightfield and PI images showing necrotic (A) 2D or (B) 3D target cells in response to T cell binding. Time = 24 hours.


Kinetic imaging of T cell-mediated target cell cytotoxicity induction

In order to determine the kinetics of cytotoxicity induction within the target cells, imaging must be carried out at regular intervals throughout the entire incubation period. As the full cytotoxic effect may not be reached until days after T cell addition, it is also essential that cells be allowed to interact for multiple days. The environmental controls of the Cytation 5 and BioSpa 8, as well as automatic transfer of test plates from incubator to imager, allow kinetic analysis to be completed without compromising cell health. In the experiments performed here, brightfield and fluorescent images were captured every four hours for a total of seven days. Figures 7 and 8 demonstrate the iterative cytotoxic effect that T cells, directly activated in the presence of 100% MDA-MB-231 cells and added at a 20:1 ratio, have on 2D and 3D cultured target cells, respectively.

Figure 7. CY5/PI imaging of 2D cytotoxic target cell induction. 4x overlaid CY5 and PI images showing stained T cells and signal from propidium iodide necrotic cell probe following (A) 0 (B) 48 (C) 96 and (D) 168 hour coculture incubation periods.

Figure 8. Brightfield/CY5/PI imaging of 3D cytotoxic target cell induction. 4x overlaid brightfield, CY5, and PI images showing stained T cells and signal from propidium iodide necrotic cell probe following (A) 0 (B) 48 (C) 96 and (D) 168 hour coculture incubation periods.


Quantification of target cell cytotoxicity

Following image capture, the level of T cell-induced target cell cytotoxicity was then quantified.

Figure 9. Cellular analysis of target cell cytotoxicity. 4x images showing fluorescence from propidium iodide necrotic cell probe following 96-hour incubation. Object masks (in blue) placed around (A) 2D and (B) 3D cultured target cells meeting cellular analysis criteria.

Using the optimized image analysis criteria described in Table 6, object masks were placed around cells meeting minimum threshold signal criteria from the PI necrotic cell probe (Figure 9). As T cells have a smaller size compared to the target cells in either 2D or 3D format, the minimum object size cutoff value was set such that single necrotic T cells were not included in the analysis. This can be seen in Figures 9A and 9B.

A phenomenon also observed in the kinetic images of the 3D CMC assay is that the tumoroid began to disintegrate in response to increasing cytotoxicity, releasing groups of cells into the surrounding media. While smaller than the intact tumoroid body, these aggregates remain larger than individual T cells and also emit signal from the PI necrotic cell probe, therefore are included in the final analysis (Figure 9B).

From the analysis performed, the number of necrotic cells per image was calculated for 2D cultured target cells. When cultured in 3D, cells within the tumoroid and smaller aggregates exist on multiple z-planes. Therefore, to quantify induced cytotoxicity with the greatest level of accuracy, the total PI signal within all object masks per image was quantified. The values (cell count or total PI signal) calculated at each timepoint were then automatically divided by the value calculated at time 0 in Gen5 software. In this way small variances between replicates were normalized. Following analysis, the results were plotted to evaluate whether differences were seen in induced target cell cytotoxicity between test conditions. The graphs in Figure 10 show the calculated data for T cells added to test wells in a 20:1 ratio, activated in the presence of 100%, 75%, 50% or 0% MDA-MB-231 cells, compared to unactivated T cells.

From Figure 10 it is evident that T cell-induced cytotoxicity increases in terms of the degree of directed cell activation in both 2D and 3D cell models. T cells activated in the presence of 100% MDA-MB-231 cells elicit the highest level of cytotoxicity, while those activated only in the presence of antibodies and superkine elicit the lowest increase in necrotic cell numbers per image over basal necrotic cell numbers. The models differ in their kinetic responses, however. In the 2D model (Figure 10A), T cell-mediated cytotoxicity peaks at about 24 hours after addition of the activated T cells, as witnessed by the ratio of necrotic cells from wells containing T cells to necrotic cell numbers from negative control wells. Any further necrosis beyond about 3 days is due to the limitations of the 2D model as noted by the increased necrosis over time evident in the negative control. Conversely, in the 3D model (Figure 10B), necrotic ratios of total signal from the PI probe continue to increase or plateau over the course of the kinetic run due to the fact that cell health is much better retained in the untreated 3D cell model.

Analysis of necrotic cell induction was then performed on wells containing T cells directly activated in the presence of 100% MDA-MB-231 cells and then added to 2D and 3D plated target cells for the CMC assay in ratios of 20:1, 10:1, 5:1. A negative control was also included where target cells were untreated.

Figure 10. Activation protocol cytotoxicity induction analysis. Comparison of cytotoxic target cell induction by T cells activated in the presence of anti-CD3 and anti-CD28 antibodies, superkine, and 100% MDA-MB-231 cells, 75% MDA-MB-231/25% fibroblast cells, 50% MDA-MB-231/50% fibroblast cells, or no cells. Data for unactivated T cells also included and plotted using left y-axis. Necrotic cell count or total PI signal over time from untreated negative control target cells plotted on right y-axis. Results shown for T cells incubated with (A) 2D cultured target cells or (B) 3D cultured target cells for seven days.

It is evident from Figure 11 that kinetic responses of T cell-mediated cytotoxicity for different ratios of T cell to target cell for both 2D and 3D models are obtained over time. These findings are consistent with the previous results from the activation protocol comparison (Figure 10), as well as results reported with in vivo testing.

Figure 11. Effect of T cell concentration. Comparison of cytotoxic target cell induction by T cells added to wells at concentrations of 40,000 cells/ well (20:1 ratio), 20,000 cells/well (10:1 ratio), 10,000 cells/well (5:1 ratio), and 0 cells/well (negative control). Results shown for T cells incubated with (A) 2D or (B) 3D cultured target cells for seven days.

Finally, the effects of directed activation can also be measured using the brightfield channel when target-cells are cultured in 3D. This is due to the fact that in response to the cytotoxic T cell effect, tumoroids break apart over time, or explode, releasing cells and ECM within the well. Using the confluence measurement capabilities of Gen5 and the optimized metrics in Table 7, the extent of tumoroid disintegration can then be quantified. Only pixels within each image with a brightfield signal below the upper threshold criteria are included in the percent confluence calculation. When viewed in Gen5, outlier pixels are seen as white (Figure 12).

Figure 12. Image confluence determination using brightfield signal. 4x brightfield images following image analysis and % confluence determination. Pixels not included in confluence calculation appear white. Images shown after cell interaction and binding with a 10:1 T cell to target cell ratio for (A) 72 (B) 116 (C) 136 and (D) 168 hour incubation periods.

Percent confluence values can then be plotted over time to visualize the kinetics of tumoroid disintegration in response to increasing T cell to target cell ratios.

The curves in Figure 13 illustrate how plotting confluence over time explains the kinetics of the final effect. As would be expected, higher concentrations of activated T cells destroy the tumoroid faster than lower concentrations. Untreated tumoroids also show little change in confluence due to the fact that little to no cellular toxicity is seen (Figure 10B) allowing the tumoroid to remain intact during the seven day incubation period.

Figure 13. Kinetic percent image confluence quantification. Plot of kinetic brightfield image percent confluence due to 3D tumoroid disintegration.

Conclusion

It was found that direct activation of T cells, where they were exposed to target cells over extended periods in vitro, produced a significant increase in cytotoxicity compared to general activation using no target cells. Furthermore, a diminishing effect was evident if the target cells were cocultured with fibroblasts in the activation process: the greater the ratio of fibroblasts, the less cytotoxicity evident. This suggests that T cells can be inctructed in the activation process to seek out and destroy target cells.

The 3D cell model was far superior to the 2D cell model as cell health was maintained throughout the long kinetic runs. Cytotoxicity could be quantified using propidium iodide that measure plasma membrane rupture or with brightfield (label-free) that measured confluence increase by spheroid disaggregation.

The Agilent BioTek BioSpa system, comprised of an automated CO2 incubator shuffling microplates to the Agilent BioTek cell imaging reader, allows for walk-away automation of the 7-day kinetic cytotoxicity assay.


Results

Generation of an Nlrc5-stop flox transgenic mouse as a tool for conditional expression of MHC class I molecules

It was previously shown that the expression of MHC II on DP thymocytes promotes the development of MHC II-restricted thymocyte-selected CD4 T cells (T-CD4) with an NKT-like phenotype 21 , 22 , 24 , 29 . Therefore, we hypothesized that MHC I expression on DP thymocytes could have a similar effect by selecting MHC I-restricted NKT-like T cells (Fig.  1a ). Normally, murine thymocytes at the DP stage of their development do not express classical MHC I or MHC II molecules 19 . In search of a suitable approach to drive stable MHC I expression in DP, we selected the transcription factor Nlrc5 (also known as CITA) as a top candidate with such potential function. Nlrc5 is crucial for transcription not only of MHC I genes but also of components necessary for MHC I processing and peptide loading such as 㬢m, Tap1, and Lmp2 25 , 28 . In addition, Nlrc5 expression is heavily repressed at the DP thymocyte stage (Fig.  1b data from ImmGen 30 ). To test whether Nlrc5 could drive higher MHC I expression in vitro, we cloned the coding sequence (CDS) of the murine Nlrc5 gene into a lentiviral expression vector. Indeed, transduced HEK 293 cell samples showed that Nlrc5 was sufficient to drive higher MHC I expression in comparison to control samples (Fig.  1c ).

a Schematic representation of positive selection of innate-like T cells on DP thymocytes. b mRNA expression level of murine Nlrc5 in different T-cell subsets from B6 WT mice. Data were obtained from ImmGen. c Flow cytometry analysis of HEK 293 cells transduced with lentivirus expression vector encoding the murine Nlrc5 coding sequence (CDS) or with empty virus as a control. As additional controls, non-transduced cells and isotype control staining are shown. Cells are analyzed 48 h post transduction. d Structure of the wild-type (WT) Rosa26 and the targeted allele of the transgenic mouse (Nlrc5-stop flox ). e Flow cytometry evaluation of MHC I and MHC II expression on T cells from WT, CD4-Cre × Nlrc5-stop flox (T-MHC I), and Plck-CIITA (T-MHC II) transgenic mice. Data are representative of five independent experiments, n ≥𠂕 mice per experimental group. Source data are provided as a Source Data file.

To test the role of Nlrc5 in vivo, we generated an inducible transgenic knock-in mouse where Nlrc5 expression can be upregulated in a tissue-specific manner, in cells that express Cre recombinase. In brief, a construct with a loxP-flanked stop cassette in front of the murine Nlrc5 CDS was inserted into the Rosa26 locus (Fig.  1d ). This design allows Nlrc5 expression once Cre recombinase is present in the cell. This mouse line was crossed to the CD4-Cre mouse initiating Cre expression at the DP stage. We observed that MHC I (H-2Db and H-2Kb) was highly expressed on DP thymocytes in these mice, compared to controls, which normally do not express it (Fig.  1e and Supplementary Fig.  1a ). Single-positive (SP) thymocytes and splenocytes, which normally express high levels of MHC I, displayed only slightly higher levels in these mice. Of note, MHC II expression was not affected by the Nlrc5 transgene (Fig.  1e ).

The non-classical MHC Ib alleles that present peptides, Qa-1, Qa-2, and H2-M3, showed the same expression pattern on thymocytes as the classical MHC Ia alleles H2-Kb and H2-Db (i.e., low on DP thymocytes) (Supplementary Fig.  1d ). Nlrc5 overexpression led to their upregulation as well (Supplementary Fig.  1b ). In contrast, gene expression of non-peptide presenting molecules CD1d and MR1 is high in DP thymocytes (Supplementary Fig.  1e ) and was not altered by overexpression of Nlrc5 (Supplementary Fig.  1c ). This suggests that peptide presentation through MHC Ia/Ib molecules, in particular, is absent in cortical DP thymocytes due to the absence of Nlrc5 expression, whereas lipid and metabolite presentation are preserved.

As an additional control, we obtained plck-CIITA transgenic mice 21 , which overexpress MHC II on T cells starting from the DP thymocyte stage (Fig.  1e ). In the following sections, both strains were analyzed in parallel. For simplicity, we refer to CD4-Cre/Nlrc5-stop flox as “T-MHC I” mouse and pLck-CIITA as “T-MHC II” mouse.

MHC I expression on DP thymocytes resulted in an increase in PIL T cells

The thymus architecture and the general subset distribution in the thymus showed no striking alterations between wild type (WT) and T-MHC I littermates (Fig.  2a𠄼 and Supplementary Fig.  2 ). There were no significant differences in the frequency of DN, DP, CD4 SP T cells, γδ T cells, or γδ NKT cells (defined as PLZF +  γδ T cells). T-MHC II mice show an increased CD8 SP frequency due to the induction of “memory phenotype” CD8 T cells, as previously described 31 . In contrast, T-MHC I mice showed, if anything, a slight reduction in CD8 SP T-cell number and frequency, and a small increase in MAIT cell number (Fig.  2a𠄼 and gating in Supplementary Fig.  3a𠄼 ). iNKT cells were approximately twofold reduced in number and frequency in both T-MHC I and T-MHC II mice. The reduction in iNKT cell number and frequency was even more prominent in the spleen and the same reduction trend was present in the liver as well (Supplementary Fig.  4a, b ). There were no significant differences in the MAIT cell and γδ NKT cell number in the spleen (Supplementary Fig.  4d, e ). Surprisingly, Treg cell frequency was not affected in T-MHC II mice but was increased in the thymus and spleen of T-MHC I mice. In the spleen, this difference was also statistically significant in cell number (Supplementary Fig.  4b lower panel). Overall, the T-MHC I transgenic mouse did not show major lymphocyte development alterations, except for a modest increase in Treg and a decrease in iNKT cell frequency and numbers.

a Representative flow cytometry plots of total thymocytes from WT, T-MHC I, and T-MHC II mice. b Data quantification according to the gating strategy displayed in a. Cell frequencies are plotted on the left axis and numbers on the right axis. c Total cell counts of MAIT cells and γδNKT cells in the thymus of WT and T-MHC I mice. d Representative plots of flow cytometry strategy for the identification of PIL cells in WT thymus. e Representative flow cytometry plots of staining for PIL cells comparing WT with T-MHC I and T-MHC II mice from the thymus and spleen. f Frequency (plotted on the left axis) and number (plotted on the right axis) of PIL cells from WT, T-MHC I, and T-MHC II mice defined by the flow cytometry strategy depicted in d. b, c, f Each point represents one animal: n =� animals per group (WT and T-MHC I groups) and n =𠂘 animals (T-MHC II group) in b, n =𠂔 animals per group in c and n =𠂙 animals per group in f. Data are representative of five in a, b and eight in df independent experiments. One experiment was performed in c. Unpaired two-tailed Mann–Whitney test was performed in b, c, f) p ≥𠂐.01 are not depicted, **p <𠂐.01, ***p <𠂐.001, and ****p <𠂐.0001. Data are presented as mean values ± SD. Source data are provided as a Source Data file.

Next, we sought to determine whether cells with NKT-like phenotype were expanded in the T-MHC I transgenic mouse similar to that seen with T-MHC II mice (Fig.  1a ). PLZF is the major transcription factor endowing innate-like features to the iNKT cell lineage 14 . Therefore, we established a gating strategy that allowed identification of peptide-specific PIL T cells, by gating on TCRβ + PLZF + cells and excluding other subsets known to express PLZF, such as iNKT cells, γδ T cells, and MAIT cells (Fig.  2d ). With this strategy, we identified a small fraction of PIL T cells present in WT littermates, increasing by fourfold in T-MHC I mice (Fig.  2e, f ). Nonetheless, this change was less robust than the 10�-fold increase of PIL T cells in the T-MHC II.

PIL T-cell expansion is SAP dependent and not mediated by Nlrc5 in a cell-intrinsic way

Even though we did not observe upregulation of surface CD1d in response to Nlrc5 expression in DP thymocytes, it is formally possible that PIL T cells are CD1d dependent. Therefore, we crossed T-MHC I mice to Cd1d −/− mice. Deficiency of CD1d abrogated iNKT cell development, as expected, but if anything, increased PIL T-cell numbers in T-MHC I mice (Fig.  3a, b ). Thus, most PIL T cells are likely MHC Ia/Ib restricted, as their numbers are dependent on MHC I expression in DP thymocytes, yet not dependent on CD1d.

a Representative flow cytometry plots of total thymocytes from Cd1d −/− and Cd1d −/− T-MHC I mice. b Thymic iNKT and PIL cell frequency and number comparison between WT and Cd1d −/− , and T-MHC I and Cd1d −/− T-MHC I mice. c Representative flow cytometry plots from a set of unequal bone marrow (BM) chimeric mice 8 weeks post transplantation. WT CD45.1 + mice were lethally irradiated and transplanted with a mix of WT CD45.1/2 + and T-MHC I CD45.2 + bone marrow at a ratio of 1 :�. This experimental group is depicted as WT + T-MHC I group. In the control group, mice were transplanted with a mix of WT CD45.1/2 + and WT CD45.2 + bone marrow at a ratio of 1 :�. This experimental group is depicted as WT + WT group. Shown are representative flow cytometry plots displaying the gating strategy from WT + WT group (in the left two panels). In the right four panels (in green) are shown representative plots displaying PIL cell frequencies (gated on WT CD45.1/2 + ) from the WT + WT group (on the left) and WT + T-MHC I group (on the right). PIL cell frequency evaluation is shown in d. e Representative flow cytometry plots of total thymocytes from Sh2d1a/− (SAP deficient) and Sh2d1a −/− T-MHC I mice. iNKT, PIL, and Treg cell frequency are shown in f. b, d, f Each point represents one animal: n =� animals per group (WT and T-MHC I groups), n =𠂗 animals (CD1d −/− group), n =𠂓 animals (CD1d −/− T-MHC I group), n =𠂘 animals (WT + WT group), n =𠂙 animals (WT + T-MHC I group), n =𠂕 animals (Sh2d1a −/− group), and n =𠂖 animals (Sh2d1a −/− T-MHC I group). Data are representative of four in a, b, e, f and two in c, d independent experiments. Unpaired two-tailed Mann–Whitney test was performed in b, d and an unpaired two-tailed Student’s t-test was performed in f ns, not significant (p ≥𠂐.05), *p <𠂐.05, **p <𠂐.01, ***p <𠂐.001, and ****p <𠂐.0001. Data are presented as mean values ± SD. Source data are provided as a Source Data file.

Next, we investigated whether MHC I was required on neighboring DP thymocytes for PIL T-cell development, or if it is due to an unknown cell-intrinsic effect of Nlrc5 overexpression. To this end, we established a set of unequal BM chimeric mice, where WT recipients were lethally irradiated and transplanted with a mix of WT and T-MHC I BM at a ratio 1 :� from different congenic mice. With this setup, 90% of the neighboring thymocytes would express MHC I and is thus capable of inducing PIL T-cell selection in WT progenitors. A control group received a mix of WT and WT BM from different congenic mice. Indeed, 8 weeks post transplantation, a significant increase in PIL T cells was present among WT BM-derived cells when neighboring thymocytes were expressing MHC I, in comparison to the control group (Fig.  3c, d ). A significant increase in PIL T-cell frequency was present also in the spleen. Therefore, this result disfavors the possibility that forced expression of Nlrc5 promotes PILs development in a cell-intrinsic way and confirms the hypothesis that PIL T cells expand because of MHC I upregulation on neighboring DP thymocytes. Notably, the frequency of Treg cells did not increase among the WT BM-derived cells but was only observed among the T-MHC I-derived cells (Supplementary Fig.  5a, b ). This indicates that the observed expansion of Treg cells in the T-MHC I mouse is mediated by Nlrc5 expression in a cell-intrinsic way.

A crucial requirement for iNKT cell development is a triggering of SLAM-SAP signaling pathways during agonist selection 13 . Therefore, we sought to determine whether SAP deficiency has the same impact on PIL T cell as on iNKT cell development. Expectedly, lack of SAP completely abrogated iNKT cell development. It also abrogated the generation of PIL T cells in both the thymus (Fig.  3e, f ) and spleen (Supplementary Fig.  5c, d ). This indicates that PIL T cells and iNKT cells employ similar signaling pathways during their selection and development.

PIL T cells are found in the same effector subsets as iNKT cells

Thymic iNKT cells exist in three well-defined effector subsets (iNKT1, 2, and 17), analogous to peripherally activated helper T cells 32 . Using transcription factor staining, we found that PIL T cells also segregate into three similar subsets, which we name as PIL1 (PLZF lo Tbet + RORγt - ), PIL2 (PLZF hi RORγt - ), and PIL17 (PLZF int RORγt + ) cells (Fig.  4a ). The expression of NK1.1 on PIL1, CD4 on PIL2, and CD138 on PIL17 cells was also similar to that of iNKT cell subsets (Supplementary Fig.  6a ). Hence, this is suggestive that each PIL fraction might exhibit similar functional properties to the corresponding iNKT cell subset. Several independent studies have shown that the three NKT subsets have quite distinct gene expression programs 33 – 35 . Indeed, expression of a large panel of functionally relevant molecules, including CD122, CXCR3, CD69, PD1, CCR6, and CD25 was similar among PIL and iNKT subsets (Supplementary Fig.  6b ), as was the production of the cytokines interferon-γ (IFNγ), interleukin (IL)-4, and IL-17A after in vitro stimulation (Supplementary Fig.  6c ) 32 , 33 . Taken together, these data infer that analogous transcriptional and functional programs operate in PIL and iNKT cells.

a Akin to iNKT cells, PIL T cells segregate into three subsets defined by expression pattern of transcription factors PLZF and RORγt. Shown are representative flow cytometry plots comparing PIL T-cell subsets from WT, T-MHC I, and T-MHC II mice. b Comparisons of PIL T-cell subset frequencies (upper row) and numbers (lower row). c Representative flow cytometry plots of staining for CD4 and CD8α on PIL cells from WT, T-MHC I, and T-MHC II mice. d Flow cytometry assessment of the TCR Vβ chain repertoire of thymic PIL T cells from WT, T-MHC I, and T-MHC II mice in comparison to conventional T cells. e Exemplary plots showing staining for TCRβ on iNKT cell subsets compared to PIL T-cell subsets from WT, T-MHC I, and T-MHC II mice. All analyzed animals are F1 generation with BALB/c mice (further characterized in Fig.  6 ). f CD44 and NK1.1 expression pattern on (from left to right) iNKT, WT PIL, T-MHC I PIL, and T-MHC II PIL T cells. b Each point represents one animal: n =𠂘 animals per group (WT and T-MHC II groups) and n =𠂙 animals (T-MHC I group). Data are representative of seven in ac and three in df independent experiments. Unpaired two-tailed Mann–Whitney test was performed in b ns, not significant (p ≥𠂐.05), *p <𠂐.05, **p <𠂐.01, ***p <𠂐.001, and ****p <𠂐.0001. Data are presented as mean values ± SD. Source data are provided as a Source Data file.

Notably, both MHC I- and MHC II-restricted PIL T cells were differentiated into three subsets (Fig.  4a, b ). However, MHC II-restricted PIL T cells in T-MHC II animals showed a strong skewing toward the PIL2 effector phenotype, at the expense of PIL17. This is consistent with the well-described overproduction of IL-4 by PLZF + T-CD4s in pLck-CIITA mice 31 . However, the three subsets were present in similar proportions in WT and T-MHC I mice (Fig.  4a, b ).

As mentioned above, PIL T cells might be phenotypically and functionally similar to iNKT cells. Yet, in contrast to iNKT cells, they are selected on classical MHC molecules. Therefore, we inspected their TCR co-receptor expression and TCR Vβ chain usage. As previously reported, PIL T cells developing in T-MHC II mice were found to be largely CD4 SP with a small fraction of DN cells (Fig.  4c ). Surprisingly, PIL T cells in T-MHC I mice did not exclusively express CD8, but segregated into CD4 SP, CD8 SP, and DN, similar to those in WT mice, suggesting that PIL T cells in WT mice may be more closely related to MHC I-restricted cells (Fig.  4c ). Although we did not observe a severe bias in TCR Vβ chain usage by PIL T cells, there was a noticeable increase in frequency of V㬣 + and V㬥.1/V㬥.2 + cells within the PIL T-cell pool from WT and T-MHC I mice. In addition, a small increase in frequency of V㬦 + cells was present within the PIL T cells from T-MHC II mice (Fig.  4d ).

It was previously shown that thymic iNKT cell subsets display different levels of TCR on their surface, a trait that is more prominent on the BALB/c background 33 , 36 . TCR levels are highest on NKT2 cells, lowest on NKT1 cells, and intermediate on NKT17 cells. Moreover, these differences in TCR expression levels, and thus the presumed signaling strength, are reported to be pivotal for iNKT cell subset commitment and differentiation 37 – 39 . However, surface TCR did not differ significantly between PIL T-cell subsets (Fig.  4e ), suggesting that factors other than TCR expression level may dictate subset commitment and differentiation in these cells.

Historical studies in the field of iNKT cell biology used CD44 and NK1.1 as markers to assess iNKT cell maturity and stage of development 40 . Using these markers, a higher proportion of PIL T cells were found to be CD44 low compared to iNKT cells, indicating a less mature stage (Fig.  4f ).

Memory phenotype CD8 T-cell development requires CD4 co-receptor engagement in PIL T cells

As previously reported, the frequency of CD8 SP T cells was increased in the thymus of T-MHC II mice (Fig.  2b ) 21 , 22 , 31 . This was shown to be caused by IL-4 produced by PIL T cells (called T-CD4 cells in that report), which mediates development of memory phenotype CD8 T (TMP) cells through the induction of eomesodermin (Eomes) 41 . Thus, a large proportion of CD8 SP T cells in T-MHC II mice express Eomes (Fig.  5a ) and have a memory phenotype 31 . To our surprise, there was no increase in Eomes + CD8 TMP in T-MHC I mice (Fig.  5b ). As interaction of the TCR with MHC II can invoke CD4 co-receptor signaling, we reasoned that the induction of IL-4 production by PIL T cells and, consequently, the increase in CD8 TMP cells might be the result of CD4 co-receptor signaling. To test this hypothesis, we crossed T-MHC I mice with CD8.4 transgenic mice. In these mice, the cytoplasmic tail of the endogenous Cd8a gene was substituted with the cytoplasmic tail of CD4 42 . The CD8.4 transgene did not cause an increase in the total number of PIL T cells in T-MHC I mice (Fig.  5c ), but it shifted the proportion of PIL T cells in favor of PLZF hi PIL2 cells (Fig.  5d ), which produce IL-4. Consistent with this, the number of CD8 TMP was markedly increased in CD8.4 T-MHC I mice (Fig.  5e ). A similar trend was seen in the spleen (Supplementary Fig.  7 ). Thus, the particular co-receptor involved in sensing MHC ligands on DP thymocytes influences effector subset differentiation of PIL T cells and has secondary effects of CD8 TMP development.

a Representative flow cytometry plots of intracellular staining for Eomes on CD8 SP thymocytes from WT, T-MHC I, and T-MHC II mice. b Number (plotted on the right axis) and frequency (plotted on the left axis) of CD8 TMP cells among CD8 SP cells in the thymus from WT, T-MHC I, and T-MHC II mice, defined by the gating strategy shown in a. c Representative flow cytometry plots of thymocytes comparing PIL T-cell frequency from T-MHC I mice with CD8.4 Tg/+  T-MHC I mice. Shown are summary evaluations for PIL T-cell number and frequency (right two panels). d Representative flow cytometry plots and summary evaluation for frequency of PIL2 T-cell subset among thymocytes from T-MHC I and CD8.4 Tg/+  T-MHC I mice. Both groups are compared to WT mice. e Exemplarity flow cytometry plots of CD8 TMP cell frequency among CD8 SP cells in the thymus from T-MHC I mice with CD8.4 Tg/+ T-MHC I mice (left two panels) and summary evaluation of CD8 TMP cell number (right panel). Each point represents one animal: n =𠂙 animals per group (WT and T-MHC II groups) in a, b, n =𠂙 animals (T-MHC I group) in c, d, n =𠂗 animals (T-MHC I group) in b, e, and n =𠂔 animals (CD8.4 Tg/+ T-MHC I group) in ce. Data are representative of 7 in a, b and 2 in ce independent experiments. Unpaired two-tailed Mann–Whitney test was performed in be ns, not significant (p ≥𠂐.05), *p <𠂐.05, **p <𠂐.01, and ****p <𠂐.0001. Data are presented as mean values ± SD. Source data are provided as a Source Data file.

PIL T cells compete with iNKT cells for a cellular niche within the thymus

Similar to what have been previously reported with T-MHC II mouse 43 , we observed a two- to threefold reduction in the number of lipid-specific iNKT cells in mice with increased numbers of PIL T cells due to MHC I or MHC II expression on DP thymocytes (Fig.  2b ). Of note, the pooled total cell count of PILs + iNKT cells was not significantly different between WT and T-MHC I mice (data not shown). As both peptide and lipid-specific T cells require SAP signaling from the surface SLAM family co-receptors to develop into PIL T cells or iNKT cells, respectively, this suggests that the two cell types may be in competition with each other for a cellular niche within the thymus. To further test this notion, we examined B6xBALB/c F1 mice, which have a slightly larger (twofold) iNKT cell niche 32 , 44 . If a similar niche regulates PIL T cells, then we might expect more PIL T cells in F1 mice. Indeed, both the portion and number of PIL T cells in T-MHC I and T-MHC II mice were higher in animals on the F1 background compared to the B6 background (Fig.  6a, b and Supplementary Fig.  8a, b ) inferring an existence of a bigger niche for PIL T-cell development on the F1 background. We observed an inverse relationship between the number of iNKT cells and PIL T cells in both B6 and F1 mice (Fig.  6c and Supplementary Fig.  8c ). Taken together, with the expansion of PIL T cells in mice that lack iNKT cells (Fig.  3b ), these data strongly suggest that PIL T cells and iNKT cells compete for the same cellular niche. As an aside, the reported skewing of iNKT cells towards the PFZF hi NKT2 subset in F1 mice 32 was also observed in PIL T cells (Fig.  6d and Supplementary Fig.  8d ). This suggests that the same factors control iNKT and PIL T-cell effector subset skewing in different strains, which are more likely to be environmental cytokines, cell-intrinsic factors, or co-stimulatory molecules rather than specific self-antigens.

a B6 WT, T-MHC I, and T-MHC II mice were crossed to BALB/c mice and F1 generation littermates (labeled as F1, F1 T-MHC I, and F1 T-MHC II) were analyzed by flow cytometry for frequency of iNKT and PIL T cells in the thymus. b Summary evaluation of iNKT and PIL T-cell frequency (left panel) and number (right panel) from WT, T-MHC I, and T-MHC II mice on B6 and F1 background. c Inverse correlation between number of iNKT cells and the number of PIL T cells in the thymus from WT (black dots), T-MHC I (red triangles), and T-MHC II mice (blue inverse triangles) on B6 (left panel) and F1 (right panel) background. d Shown are representative flow cytometry plots comparing iNKT and PIL T-cell subsets from WT, T-MHC I, and T-MHC II mice on B6 (upper row) and F1 (lower row) background. Each point represents one animal: n =� animals per group (WT and T-MHC I groups), n =𠂘 animals (T-MHC II group), n =𠂙 animals (F1 WT group), n =𠂖 animals (F1 T-MHC I group), and n =𠂗 animals (F1 T-MHC II group) in ad. Data are representative of seven independent experiments. Unpaired two-tailed Mann–Whitney test was performed in b ns, not significant (p ≥𠂐.05), ***p <𠂐.001, and ****p <𠂐.0001. R 2 -values and p-values in c were calculated by fitting nonlinear regression and performing a Goodness-of-Fit test and extra-sum-of-squares F test. Data are presented as mean values ± SD. Source data are provided as a Source Data file.


Stimulation of Human Peripheral Blood Mononuclear Cells

Human peripheral blood mononuclear cells (PBMCs) are routinely isolated from blood samples and then used in several fields of research including autoimmune disorders, infectious diseases, vaccine development and cancers. The ELISpot Assay monitors ex vivo cellular immune responses to antigenic stimuli. Here we use the Agilent BioTek Cytation 7 cell imaging multimode reader in conjunction with Agilent BioTek Gen5 microplate reader and imager software to quantitate changes in cytokine secretion in PBMCs using the colorimetric ELISpot assay format.

Introduction

Human peripheral blood mononuclear cells (PBMCs) are differentially stimulated to secrete a number of cytokines as a result of a receptor mediated cascade based on the cell type and the stimuli. The response of this diverse group of cells to different stimuli offers insights into their role in disease and the development of treatment modalities.

PBMCs are peripheral blood cells that have a round nucleus. 1 These cells consist of lymphocytes (T-, B-, and NK-cells) as well as monocytes. Other peripheral blood cells either have no nuclei (erythrocytes and platelets) or have multi-lobed nuclei (neutrophils, basophils, and eosinophils). In humans, lymphocytes make up the majority of the PBMC population, followed by monocytes, and only a small percentage of dendritic cells. 2

Cytokines are small molecular weight proteins or peptides secreted by many cell types (particularly immune system cells) that regulate the duration and intensity of the immune response. The cytokine interleukin 2 (IL-2) is a pleiotropic cellular regulatory molecule that is produced by lymphoid cells in response to several stimuli. It plays a role in preventing autoimmune diseases by promoting differentiation of immature T cells into regulatory T cells. 3 In addition, IL-2 causes the differentiation of T cells into effector T cells and memory T cells when the original T cell was stimulated by an antigen. 4 Interferon gamma (IFN-&gamma), is a cytokine critical for innate and adaptive immunity against infections. IFN-&gamma is produced predominantly by natural killer (NK) and natural killer T (NKT) cells as part of the innate immune response, and by cytotoxic T lymphocyte (CTL) effector T cells once antigen-specific immunity develops. 5 The importance of IFN-&gamma in the immune system stems in part from its ability to inhibit viral replication directly, and from its immune-stimulatory and immunomodulatory effects. Aberrant IFN-&gamma expression is associated with a number of auto-inflammatory and autoimmune diseases.

T-cell activation is normally initiated by the interaction of a cell surface receptor to its specific ligand molecule along with a costimulatory molecule. 6 This binding event triggers the rapid hydrolysis of inositol phospholipids to diacylglycerol and inositol phosphates by phospholipase C (PLC).

Diacylglycerol is an allosteric activator of protein kinase C (PKC). PKC activation and inositol phosphates, which trigger Ca 2+ release and mobilization, result in a cascade of additional cellular responses mediating T cell activation (Figure 1). Two of these cellular responses are the production and secretion of IL-2 and INF-&gamma. Triptolide is a diterpene triepoxide that is a potent immunosuppressant and anti-inflammatory (Figure 2). Triptolide has been shown to inhibit the expression of IL-2 in activated T cells at the level of purine-box/nuclear factor and NF-&kappaB mediated transcription activation. 7

Figure 1. Schematic of signal cascade for stimulation of IL-2 and INF-&gamma secretion.

Figure 2. Structure of triptolide.


While some PBMCs are known to produce IL-2 and INF-&gamma, under normal growth conditions little is produced. Only after stimulation will substantial amounts of the cytokines be expressed. 8 Phytohemagglutinin (PHA) is a lectin that binds to the sugars on glycosylated surface proteins, including the T cell receptor (TCR), and nonspecifically binds them. The result is the low level stimulation of the signal cascade required for IL-2 or INF-&gamma secretion. 9 Likewise, Phorbol myristate acetate (PMA) is a small organic compound, which has a structure analogous to diacylglycerol, that diffuses through the cell membrane into the cytoplasm where it directly activates Protein Kinase C (PKC). When used in combination with ionomycin, a calcium ionophore, which triggers calcium release, it results in a moderate level of cytokine release. However, when PMA and a costimulator, such as PHA, stimulate PBMC cells concurrently, cytokine production is strongly enhanced. 10

The ELISpot assay procedure is very similar to that of a conventional ELISA. The plates are first coated with the appropriate capture antibody. Cultured secreting cells are added to the wells along with any interested experimental mitogen or antigen. Cells are maintained for a period of time after which they are removed. The secreted analyte remains bound to the capture antibodies in close proximity to the location on the plate where the cell that produced the analyte was situated. After removal of the cells and any unbound materials, a detection antibody (usually biotinylated) is added followed by an enzyme conjugate with an incubation to allow binding and a wash to remove unbound materials after each step. As the substrate is converted by the conjugate enzyme to colored compounds, spots on the plate membrane bottom at the locations of the original analyte capture are formed. The resultant spots are then analyzed/counted using image analysis. (Figure 3).

Figure 3. ELISpot stain procedure.

Experimental

Human IL-2 ELISpot colorimetric kit was obtained from U-CyTech biosciences (Utrecht, The Netherlands) and a two color human IFN-&gamma/IL-2 ELISpot kit was from Cellular Technology Limited (Cleveland, OH). Phorbol 12-myristate (PMA), and triptolide (part number T3652) were purchased from Millipore-Sigma. Ionomycin (part number 407952) was from EMD-Millipore. Human PBMCs were obtained from Astarte Biologicals (Bothell, WA). White PVDP membrane 96-well (part number MSIP4W10) were from Millipore-Sigma.

Cell culture: Purified human PBMCs were received and maintained frozen until needed. After rapid thawing cells were immediately diluted 1:10 in RPMI-1640 plus 10% FBS supplemented with 2 mM glutamine, penicillin and streptomycin. Cells were centrifuged at 300 g for 10 minutes and the supernatant removed. Cells were resuspended in 10 mL of fresh RPMI media, counted and diluted as needed to provide a density of 5 × 10 4 cells/well.

Plate coating: Either a human IL-2 ELISpot kit from U-CyTech Biosciences or a 2-color human INF-&gamma/IL-2 kit from CTL were used for these experiments. PVDF membrane plates are first coated with the appropriate concentration of capture antibody (anti-IL-2 or anti-FTN-&gamma) and allowed to absorb overnight at 4 °C. The unbound antibody is aspirated and the plate is manually washed 3x with PBS. The wells are then filled with a blocking solution (200 µL) and allowed to incubate for at least 1 hour at room temperature. Blocking buffer is aspirated without washing immediately before the addition of cells.

Cell seeding: Unless otherwise indicated, cells were plated in 96-well membrane plates previously coated with antibody at a density of 5 × 104 /well. PBMCs were stimulated to secrete IL-2 with a PMA (50 ng/mL), ionomycin (1 µg/mL) mixture. Typical experiments used a volume of 100 µL for cells followed by the addition of 100 µL of stimulant mixture at a 2x concentration.

Triptolide inhibition: PBMCs were plated at 5 × 10 4 /well in 50 µL volume of complete RPMI media. After allowing cells to recover for 1 hour at 37 °C, in a humidified 5% CO2 environment, triptolide treatment was added in complete RPMI media at 4x of final concentration to each well in 50 µL. IL-2 stimuli mixture (2x) was then added in 100 µL for a final volume of 200 µL.

One-color ELISpot assay: The assays were performed according to the U-Cytech BioSciences kit instructions. After seeding, cells were incubated for 24 hours, at 37 °C in a humidified 5% CO2 environment plates and then assayed using an ELISpot kit. Briefly, cells were removed by washing 5x with 250 µL PBS-Tween 0.05% using an Agilent BioTek MultiFlo FX multimode dispenser. A biotinylated detection antibody (100 µL) is added to the well and allowed to incubate for 60 minutes at 37 °C or overnight at 5 °C, after which unbound detection antibody was removed by washing. A streptavidin-HRP conjugate was then added (100 µL) and incubated at 37 °C for 60 minutes. Again, unbound conjugate is removed by washing. Next a two-part AEC substrate was added that deposits dye onto the well membrane bottom. Reactions were halted after 30 minutes at RT by washing with deionized water (250 µL) 3x using the MultiFlo FX and allowed to dry in the dark. Entire wells were then imaged.

Two-color ELISpot development: The assays were performed according to the C.T. L. Immunospot 2-color ELISpot kit instructions. After seeding, cells were incubated for 24 hours, at 37 °C in a humidified 5% CO2 environment plates were then assayed using an ELISpot kit. Briefly, cells removed by washing 5x with 250 µL PBS-Tween 0.05% using a MultiFlo FX multimode dispenser. A detection antibody solution (80 µL/well) was added to the well and allowed to incubate at room temperature (RT) for 120 minutes, after which unbound detection antibody is removed by washing. Tertiary solution (80 µL/well) was added and allowed to incubate for 60 minutes at RT. Unreacted reagents were removed by washing 2x with PBS-Tween, followed by 2 washes with dH2O and then allowed to air dry in the dark. Blue developer solution was then added (80 µL/well) and incubated for 15 minutes at RT. Reaction was stopped by washing 3x with dH2O. Red developer solution was then added (80 µL/well) and incubated at RT for 7 minutes. Plate was the washed 3x with dH2O. Plate is air dried in the dark for at least 2 hours prior to imaging.

Plate Washing

Plates were washed according to the assay kit instructions using a MultiFlo Washer Dispenser (BioTek Instruments. Wash buffer consisted of PBS (NaCl 137 mM, KCl 2.7 mM, Na2HPO4 10 mM, KH2PO4 7.4 mM) supplemented with 0.05% Tween 20. Unless specifically indicated, plates were washed five times with 250 &muL buffer per well.

Plate Imaging: Prepared microplates were imaged using a Cytation cell imaging multimode reader configured with an upright color camera. The imager uses a white LED light source in conjunction with a color digital camera. A series of images were taken with the 2x lens in order to image the entire well in a single frame. Once the focal plane and camera exposure were determined manually, images were captured automatically using a fixed focal height routine using reflected light in Gen5.

Table 1. Image capture and preprocessing parameters.

Table 2. Image analysis parameters.

Results and discussion

Initial experiments demonstrate the specificity of the ELISpot reaction. PBMCs that have been stimulated with a combination of PMA/ionomycin produce numerous spots, while unstimulated cells produce few if any. Treatment alone without PBMCs does not produce any spots.

Figure 4. Specificity of IL-2 ELISpot reaction. Images of ELISpot wells containing PBMCs treated with or without PMA (1 ng/mL), Ionomycin (1 µg/mL). Negative control that lacks cells, but received stimulant.


Correct sizing of the identified objects is necessary for accurate determinations. The intent of the ELISpot assay is to identify and quantitate the number of cells responding to specific stimuli. The antibody-coated plate captures its specific target rather than the actual secretory cell. While most of the secreted analyte will be captured in the area immediately surrounding the position of the cell, some of the analyte will diffuse into the media and be captured elsewhere. The high concentration of analyte near the cell will result in a spot as large, or larger, than the physical size of the cell, while dispersed analyte will result in very small intense deposits. Figure 5 demonstrates the number of spots present in a typical ELISpot well. Only those spots exceeding 25 µm in size are designated as true spots.

Figure 5. Scatterplot of object size vs red density. All spots achieving a green threshold of 7000 greater than were plotted against their designation number. The size threshold of 25 µm is indicated with a blue vertical line.

The number of recorded spots produced from stimulated cells is proportional to the number of secreting cells. When a titration of PBMCs are exposed to a fixed concentration of stimulant the number of counted spots is proportional to the cell number. As demonstrated in Figure 6, increasing cell number in a well results in an increase in spots counted. Cell counts above 50,000 per well resulted in the spots coalescing together. Subsequent experiments used 5,000 cells per well.

Figure 6. Cell titration. PBMC were seeded at various concentration into an ELISpot plate and stimulated with 50 ng/mL PMA, 1 µg/mL ionomycin for 24 hours. The ELISpot plate was then assayed for IL-2 secretion. Data points represent the mean of 8 determinations.


Stimulation of IL-2 secretion by a mixture of PMA and ionomycin is dose dependent. As observed in Figure 7, increasing concentration of PMA produces more spots.

Figure 7. Titration of PMA stimulate. PBMCs (5000 cells/well) were stimulated with various dilutions of PMA and 1µg/mL Ionomycin for 24 hours in an ELISpot plate coated with IL-2 antibody. After stimulation IL-2 secretion was assessed and spots counted. Data points represent the mean of 7 determinations.

Pretreating PBMCs with Triptolide for 1 hour prior to stimulation reduces IL-2 secretion in a dose dependent manner. As demonstrated in Figure 8, increasing concentrations of triptolide result in fewer spots indicative of an IL-2 secreting cell. In these experiments, a stimulatory dose that was 80% of maximal was employed. The IC50 under these conditions was determined to be 40 nM, which is similar to reports in the literature 8 .

Figure 8. Inhibition of IL-2 secretion by triptolide. PBMCs (5000 cells/ well) were pre-incubated for 60 minutes with various concentrations of triptolide were stimulated with 6 ng/mL PMA, 1 µg/mL Ionomycin, to secrete IL-2. After 24-hours ELISpot plate was assayed for IL-2 secretion. Data represents the mean of 7 data points.

Multiplex ELISpot assays are available to quantitate a number of different analytes simultaneously. While there are several fluorescence-based assays that provide information for up to 4 analytes in a single well, colorimetric ELISpot assays are limited to two analytes per well. Initial experiments using a two-color ELISpot specific to human IL-2 and IFN-&gamma demonstrated the specificity of the assay to specifically identify IL-2 or IFN-&gamma secreting cells. In this assay, cells secreting IL-2 can be visualized by the formation of blue spots, while those secreting IFN-&gamma form red spots. As observed in the control experiment (Figure 9), wells coated with anti-IL-2 antibodies only form blue spots, while wells coated with only anti-IFN-&gamma antibodies only form red spots. Wells receiving both coating antibodies formed both red and blue spots, while cells lacking PBMCs or PMA stimulation failed to form any spots.

Figure 9. Specificity of two-analyte ELISpot detection. Images of ELISpot wells that have PBMC that have been treated with or without PMA (10 ng/mL). Well membranes were coated with both IL-2 and IFN-&gamma specific antibodies and color developed for either IL-2 or IFN-&gamma or both. Negative control that lacks cells, but received stimulant.

Discrimination between red and blue colored spots can be achieved using the differences in red and blue densities of the spots. The histogram plots in Figure 10 demonstrate differences in the calculated red/blue density ratio between red only and blue only control wells. The mean of the red/blue ratio plus two times its standard deviation can be used as the upper limit for red-only spots. Likewise, the mean minus two standard deviations of the blue spot controls defines the lower limit of the red/blue ratio for blue spots. Spots with ratio values between these two thresholds are considered to be both blue and red.

Figure 10. Frequency histogram analysis of red-blue ELISpot intensity ratio values. The frequency of red-blue ratio values from 8 red only and blue only control wells. The mean and the mean plus or minus 2 times the standard deviation of the population are indicated.

These threshold values can be used to quantitate single color reactions where only IL-2 or IFN-&gamma reactions are developed. As shown in Figure 11, both IL-2 and IFN-&gamma cytokines are secreted when PBMCs are stimulated with PMA. The stimulation occurs in a concentration dependent fashion with the EC50 values being very similar (EC50=0.05 ng/mL). Interestingly, twice as many PBMCs, as measured by the spot count, are likely to secrete IFN-&gamma as compared to IL-2.

Figure 11. Comparison of IL-2 and IFN-&gamma secretion by PBMCs after stimulation with PMA. PBMCs were stimulated with PMA in a PVDF membrane ELISpot plate coated with both IL-2 and IFN-&gamma capture antibodies. After 24 hours plates were processed and colors were developed in parallel wells. Spots (Red and Blue) were quantitated and plotted as a function of PMA concentration. Data represents the mean and standard deviation of duplicate wells.

Multiplex, 2-color analysis in the same well can be performed when both colors are developed using the same criteria. A frequency histogram of the data from several separate wells where both colors were developed in the wells is depicted in Figure 12. When the images are examined by eye, most spots in the wells have spots that are visibly either red or blue, with a smaller percentage that appear a mixture. These observations are corroborated by the frequency histogram depicted in Figure 12 that demonstrates that the identified spots have a spectrum of red/blue ratio values. There are two obvious peaks based on the red/ blue ratio that correspond to the red and blue spots observed when only one color has been developed. In between their respective cutoff values is a significant number of spots that have an intermediate red/blue ratio.

Figure 12. Frequency histogram of ELISpot red-blue ratio values. The red-blue ratio of ELISpot spots from 8 wells of a two-color ELISpot assay plate were plotted as a function of frequency. Subpopulations analysis based on cut off values for red, blue or red and blue spots are indicated by color.

These visibly correspond to spots that appear purple (i.e. a mixture of red and blue). The relative number of spots identified as red or blue is similar to the numbers identified when a single color was developed. When one analyzes the data with a scatter plot that compared the red/blue ratio to spot size, two loose clusters of spots that correspond to red or blue spots are observed along with a number of intermediate ratio spots (Figure 13). While all three subpopulations of spots have the same range in size, red spots tend to be more numerous and smaller in size than spots identified as blue.

Figure 13. Scatter plot of ELISpot red-blue ratio values. The redblue ratio of ELISpot spots from 8 wells of a two-color ELISpot assay plate were plotted as a function of size. Subpopulations analysis based on cut off values for red, blue or red and blue spots are indicated.

This multiplex analysis can be used on individual wells with different experimental conditions. Figure 14 demonstrates the response of PBMCs to PMA stimulation where both blue (IL-2) and red (IFN-&gamma) colors are developed in the same well. As with separate color development, PMA stimulated cytokine secretion on PBMCs in a concentration dependent manner. Also, more PBMCs secreted IFN-&gamma than IL-2, with equivalent EC50 values. If one compares the total number of cells that secrete IFN-&gamma (red only spots plus red and blue spots) or the total number of cells that secrete IL-2 (blue only spots plus red and blue spots) the numbers are consistent with wells where only one color was developed.

Figure 14. Comparison of IL-2 and IFN-&gamma secretion in stimulated PBMCs. PBMCs were stimulated with various concentrations of PMA using a PVDF membrane 96-well plate that was pre-coated with both anti-IL-2 and anti-IFN-&gamma antibodies. After ELISpot processing, the plate wells were imaged and the images analyzed using Gen5. Subpopulation analysis defined spots that were either red, blue or a mix of red and blue. The number of each spot subpopulation was plotted against PMA concentration. Data represents the mean and standard deviation of 4 determinations.

Discussion

These data demonstrate the utility of the Agilent BioTek Cytation 7 cell imaging multimode reader in conjunction with Agilent BioTek Gen5 microplate reader and imager software to image and analyze colorimetric PVDF ELISpot assay plates. The combination of a PMA/ionomycin has been shown to markedly stimulate IL-2 secretion in PBMCs. Without stimulation, IL-2 is virtually absent. The ability of triptolide, a known transcription inhibitor, to prevent IL-2 secretion suggests that new protein synthesis is required after stimulation. 11

ELISpot is a sensitive assay to monitor the ex vivo cellular immune response at the single cell level by detecting secreted proteins released by cells. This technique has been derived from the sandwich enzymelinked immunosorbent assay (ELISA) to accommodate the use of whole cells to identify the frequency of the secreting cells. As such, there are a number of critical parameters that need to be optimized in order for experiments to be successful. Depending on the degree of cellular secretion, developed spots can be quite large. The expected number of positive cells is of greater importance than the total number of cells used initially. The presence of too many secreting cells results in the individual spots coalescing making a numerical determination difficult. For example, an investigation of a relatively rare secreting event would require a greater number of cells to be seeded as compared to a more common event. Timing of the response relative to the stimulation and/or the inhibition is important. Receptor mediated events often will take longer to elicit a response than a stimulatory molecule that can interact within the cell directly. It is important that appropriate interval between stimulation and measurement be utilized. The testing of inhibitors still requires a stimulating agent to be present. In these experiments, it is important that a less than maximal concentration of the stimulatory agent be used, lest it mask any inhibitory affects.

The Cytation 7 is an ideal platform to interpret colorimetric PVDF membrane ELISpot assays. The imager supports digital top-down color imaging with 2x, 4x and 8x microscope objectives that are factory installed. The 2x objective can capture the entire well in a single image, making it ideal for 96-well ELISpot determination. If desired, higher resolution can be obtained by using a higher magnification objective and a montage of the well. Using this camera both reflected or transmitted light can be used for optimal imaging. While this research only used the upright top-down camera with PVDF membrane plates, the imager also supports bright field imaging using an inverted camera for silver stain ELISpot assays. In addition, the inverted microscope supports fluorescence-based microscopy with LED and filter cubes. Gen5 microplate reader and imager softrware, besides controlling reader function, can be used to automatically perform stitch of separate montage image tiles, perform background subtraction and mask off regions outside the well prior to analysis.