Producing Ag-specific immune responses constrained to target tissues or cells that can be engaged or disengaged at will is predicated on understanding the network of genes governing immune cell function, defining the rules underlying Ag specificity, and synthesizing the tools to engineer them. The successes and limitations of chimeric Ag receptor (CAR) T cells emphasize this goal, and advances in high-throughput sequencing, large-scale genomic screens, single-cell profiling, and genetic modification are providing the necessary data to bring it to fruition—including a broader application into the treatment of autoimmune diseases. In this review, we delve into the implementation of these developments, survey the relevant works, and propose a framework for generating the next generation of synthetic T cells informed by the principles learned from these systems approaches.

The recent proliferation and success of mAbs and genetically modified T cells targeting cancer and autoimmune disease, although worthy of celebration, belie a significant gap between our knowledge of the immune system and our ability to harness it. Case in point, despite knowledge of the immunologically important epitopes driving many conditions (1), effective strategies to engender a long-lasting Ag-specific reaction that minimizes injury to other tissues continue to elude us. The reasons for the aforementioned gap are numerous. They have been argued to partly stem from inertially driven approaches to enumerate the function of every individual player thought to be involved in the immune response—be they proteins or unique cells—largely independent of the networks in which they operate (24). Technological advances in the form of high-throughput sequencing, facile methods for genetic modification, and comprehensive single-cell profiling promise to shorten this divide. But technological approaches represent only one arm of this ongoing effort. Philosophical shifts centered around characterizing networks and the forces that shape them, fueled by an abundance of data and methods to analyze them, are expected to yield improved models for how the immune system functions in homeostasis and disease.

Yet T cell biology imposes nontrivial challenges upon efforts to dissect their role in immune responses. How do we make sense of the complexity of genes and pathways controlling T cell differentiation and function? How do we overcome the genetic architecture of the TCR and MHC restriction of Ag recognition to determine T cell specificity for a cell population? How do we harness this information to design cell-based therapies that have shown success in targeting hematologic malignancies for the treatment of autoimmune disease? We will gradually expose a framework (Fig. 1) for generating the next generation of synthetic T cells motivated by solutions to these very questions that we strive to answer below. Specifically, we focus on key developments and experimental considerations that render such an endeavor possible.

FIGURE 1.

A scheme for the generation and optimization of synthetic T cells cytotoxic or regulatory T cells targeting molecular signatures of cells driving autoimmunity are infused into patients and serially queried using functional genomic screens and phenotypic profiling. Genetic modifications, informed by these analyses, are then introduced to engender target tissue- or cell-specific immune responses, the outcome of which can be used to alter experimental conditions for further screens.

FIGURE 1.

A scheme for the generation and optimization of synthetic T cells cytotoxic or regulatory T cells targeting molecular signatures of cells driving autoimmunity are infused into patients and serially queried using functional genomic screens and phenotypic profiling. Genetic modifications, informed by these analyses, are then introduced to engender target tissue- or cell-specific immune responses, the outcome of which can be used to alter experimental conditions for further screens.

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Functional genomic screens (5), when optimally executed, offer a practical avenue for tackling the complexity of inflammatory and regulatory pathways involved in an immune response. In a single, internally controlled experiment (Fig. 2), they allow for the testing of thousands of gene knockout assays under identical experimental conditions. Leveraging gene synthesis technology (6), robust methods for gene knockout generation (5, 7, 8), and high-throughput sequencing readouts (9), their economical and time-saving value is unparalleled. They also enable hypothesis-agnostic research pursuits, which not only have the potential to reveal previously unappreciated roles for genes but are also unencumbered by the bias of decades of work on limited model systems and commonly studied pathways (10). In our schema (Fig. 1), these experiments serve dual roles: they identify pathways critical to maintain a given phenotype, and they allow us to sample new phenotypes that could be further selected for if proven advantageous. However, proper execution of these assays is fraught with technical issues that warrant addressing in greater detail.

FIGURE 2.

Functional genomic screens in T cell libraries of gene-specific nucleic acids (short hairpin RNAs [shRNAs] or CRISPR/Cas9-associated guide RNAs [gRNAs]) are introduced into viral vectors, which are subsequently transduced into T cells. These cells are then simultaneously tested for function either in mouse models or in vitro expression assays. High-throughput sequencing of DNA barcodes (typically the gene-specific nucleic acids) from the output of the function experiments allow for the matching of phenotype to targeted gene and undergo further statistical analysis. Acquisition of interpretable and meaningful data from these screens requires consideration and optimization of key experimental variables, denoted in red text, at each step of the experiment.

FIGURE 2.

Functional genomic screens in T cell libraries of gene-specific nucleic acids (short hairpin RNAs [shRNAs] or CRISPR/Cas9-associated guide RNAs [gRNAs]) are introduced into viral vectors, which are subsequently transduced into T cells. These cells are then simultaneously tested for function either in mouse models or in vitro expression assays. High-throughput sequencing of DNA barcodes (typically the gene-specific nucleic acids) from the output of the function experiments allow for the matching of phenotype to targeted gene and undergo further statistical analysis. Acquisition of interpretable and meaningful data from these screens requires consideration and optimization of key experimental variables, denoted in red text, at each step of the experiment.

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These approaches typically subscribe to the following structure: generation of library of targets, introduction into cells of interest, and measurement of phenotype. Whole genome libraries (11) or those with a narrower scope [for example, cell-surface proteins (12) or kinases (13)] are typically synthesized from custom oligonucleotide arrays or obtained from gene repositories such as Addgene (https://www.addgene.org/). In experiments using clustered regularly interspaced short palindromic repeat (CRISPR)–associated Cas9 nucleases, each oligonucleotide encodes a guide RNA, or single-guide RNA (sgRNA), targeting a unique gene. These are subsequently introduced into diverse types of viral vectors, including lentiviral (1416), retroviral (17), and adenoviral (12), each successfully used in murine and human cells and with their own purported benefits [for example, capacity to deliver CRISPR/Cas9 machinery using adeno-associated viruses (12)]. T cells transduced with the libraries are then employed in dropout screens or selection experiments that either couple a phenotype to the expression of a reporter gene or wherein preferential cell survival/proliferation is directly ascertained by detecting the abundance of a genetic barcode unique to a library target using high-throughput sequencing. In CRISPR screens, that barcode is the genomically integrated guide RNA itself.

Successful interpretation of quality data hinges on critical variables at each step of the experiment. Depending on the efficiency of the transduction step, the number of cells tested in the assay, and the stringency of the selection/screen, the size of the library that can be tested in single experiments requires optimization (typically trimming) to ensure that the signal from the sequencing output—detection, or lack thereof, of the library target—is not overwhelmed by experimental or statistical noise. Determining library size also needs to consider both the fact that libraries in these experiments typically employ three to five sgRNAs per gene to ensure knockout robustness and the establishment of an acceptable threshold for library encoding (for a library with a degeneracy of 1 × 107 unique targets all equally represented, the number of successfully transduced cells must exceed this number by roughly 16 times to achieve a 99% probability of having a complete library). This also needs to be balanced with the risk of transducing cells with multiple sgRNAs targeting different genes that can obfuscate actual phenotypes, although carrying out transduction replicates and using multiple sgRNAs per gene are helpful in identifying this phenomenon.

Finally, optimizing assay parameters and calibrating experimental effects to physiologically relevant measurements represent the most critical part of the entire endeavor (18). Certain assays, for example, may require the creation of synthetic circuits to link phenotype to cellular abundance (19). Controls with previously defined effects can help in establishing a dynamic range for the assay or even in deriving a mathematical mapping from sequencing data to function (1922). Experimental replicates, having distinct methods for orthogonal verification, and comparisons to other similar datasets are requisite steps in the process of establishing data validity and extracting meaning from these experiments. The shape that meaning takes can depend on whether assays are designed to be qualitative (as in dropout screens) or quantitative (as in selection experiments) or whether the investigator is interested in pursuing top hits or larger patterns in the data. Assumptions underlying methods for statistical analyses and dimension reduction impose additional obstacles to data interpretation. Executing the experiment in different model systems is insightful not simply because doing experiments in human cells or tissues renders the results more likely to be clinically relevant (10). In addition, imposing the additional constraint of conservation (in this review, the invariance of phenotype across species) is likely to reveal evolutionarily important targets that reflect the organization of the pathway being perturbed (23) rather than idiosyncrasies of the experiment.

Application of genomic screens using RNA-guided CRISPR/Cas9 systems in T cells has shed light on diverse immunological processes. Investigating factors intrinsic to T cells necessary for HIV entry and replication, Park et al. (14) identified potential therapeutic targets in genes involved in CCR5 sulfation and in cellular aggregation that impaired HIV infection, but not cell viability, when knocked out in a CD4+ T cell line model. Their efforts involved assaying a library of nearly 190,000 sgRNAs, yielding five hits and confirming results with genome editing in primary human T cells. Ye et al. (12) developed a hybrid system of adeno-associated viral vector delivery and Sleeping Beauty transposon sgRNA integration to reveal genes encoding membrane-bound proteins on T cells that altered the immune response to glioblastoma. They used this system to target a more limited set of 1700 genes and identified 33 targets of which a subset underwent validation using alternate assays and single knockout testing. Diffuse genome integration was detected in intergenic and intronic regions, although how it altered the effect of each knockout via epistatic effects is uncertain. Shifrut et al. (24) optimized electroporation delivery of CRISPR/Cas9 machinery into primary human T cells in which lentiviral transduction is known to be less efficient. They assayed over 75,000 sgRNAs in an in vitro experiment of T cell proliferation, identifying both positive and negative regulators of this process. Their results correlated well with a prior study that employed a short hairpin RNA screen in mice (13), and the effects of higher-ranked hits on T cell gene expression were further probed using single-cell transcriptomics. Henriksson et al. (17) used a retroviral delivery system in CD4+ murine T cells to determine regulators of Th2 cell differentiation, employing a fluorescent reporter assay and direct Ab binding to differentiation markers. They correlated these data with chromosome accessibility and transcriptomic information to construct a network of genes involved in this process. Dong et al. (16) screened a library of nearly 130,000 sgRNAs in a model using transgenic CD8+ T cells and tumor cells ectopically expressing the corresponding Ag; they identified a novel repressor of T cell function whose role they elucidated using functional assays and transcriptomic profiling. Collectively, these efforts have unequivocally demonstrated that targeting the entirety of the genome is feasible in the context of a diversity of phenotypes in in vitro, ex vivo, and in vivo settings. However, their contribution is fundamentally encapsulated in a list of genes (of which only a subset is independently validated) that is associated with a diversity of cellular processes often too complex to synthesize into a narrative. More work is needed to determine the nature of the distribution of functional effects. Is it surprising that certain pathways did not play a more frequent role? Were the findings believed to be idiosyncratic to the contrived experimental conditions or arbitrary thresholds for statistical significance? Is there a recurrence of the same networks? Admittedly, these are difficult questions to answer, particularly if the objective is to identify novel regulators of the processes under scrutiny. Comparisons among experiments represent a logical next step in the effort to determine the existence of patterns. Linking these patterns to clinically relevant observations or using them to guide cellular engineering efforts can allow the experiment to offer more than descriptive characterizations.

These genetic screens clearly represent a source of substantial insight. In addition to identifying potential therapeutic targets, sampling the spectrum of possible phenotypic effects by targeting every gene in the genome yields an intuition for cellular behaviors that could be harnessed. This same information could also be used to reparameterize the genome into groups of genes with similar phenotypic effects upon perturbation. This can, in turn, suggest pathway cross-talk or potentially identify genes or pathways that must be dually targeted to have a robust, clinical effect. The above efforts have already established a template for future endeavors aimed at revealing the structure of the genetic networks governing T cell function through pairwise and higher-order gene knockout experiments (2529). The implicit assumption driving much of this work is that this level of knowledge will ultimately inform disease-specific or even personalized interventions to entities such as autoimmune disease and cancer, whose treatment algorithms continue to rely on systemic immunotherapies with a nonnegligible risk of toxicity (3033).

MHC restriction and the heterodimeric architecture of the TCR impose the two significant challenges in determining T cell specificity. In the past, assessing the clonality of an immune response entailed isolating individual T cells, a process that was often limited by commercially available or difficult to synthesize reagents (MHC tetramers or Vα/Vβ Abs), individually sequencing the TCR α and β segment genes, reconstituting them into transgenic mice or cells, and then retesting them for reactivity to a specific Ag (34, 35). This entire process has been streamlined. Characterizing T cell specificity is an effort that can be divided into the determination of the TCR sequence (TCR α and TCR β subunits) and the space of possible peptide–MHC complexes they bind to.

The genomic separation of the TCR subunits complicates efforts to reconstruct individual TCRs using in vitro amplification of bulk T cell DNA, an obstacle that was not mitigated even with the advent of high-throughput sequencing. It was not until the development of single-cell genomics that linkage of the sequences of the two chains could be reliably achieved. Indeed, there are numerous techniques (exhaustively reviewed in Refs. 3638) that allow for the provenance of any sequence to be determined in multiplexed sequencing experiments, enabling the TCR profiling (even the entire transcriptome) of thousands of individual T cells simultaneously. TCR repertoire analysis has shed light on signatures specific to cancers, identifying links between TCR diversity and tumor mutational load (39), T cell subset specificity, including the finding of nonoverlapping repertoires between regulatory T cells and other tumor infiltrating CD4+ T cells (40), unique TCR repertoires in distinct autoimmune diseases (41), and tracking responses to infection, revealing, for example, that repertoires skew toward lower-affinity receptors late in chronic infection (42).

Methods for mapping the specificity of B cells, using peptides through bacterial (43) or phage display (44) and protein microarrays (45, 46) or intact proteins via ribosome display (47, 48) and mass spectrometry (49) have matured to the point of clinical utility in diagnostics (50). Similar efforts in T cells are hampered by the context in which they recognize Ags (as peptides loaded onto MHC scaffolds) and the lower affinity characterizing these interactions. There are other variables, including the density of TCR–MHC complexes and other signaling proteins present at the immunological synapse that render it so that TCR binding does not always result in T cell activation (51). Two key studies have made progress in this regard. Birnbaum et al. (52) synthesized a yeast-display platform in which library-encoded peptides are presented on MHC molecules on the surface of yeast and are assayed against binding to T cells. They were able to generate specificity profiles of TCRs and used structural information to define specificity, and therefore the basis of TCR cross-reactivity, as a space of peptide sequences linked by conserved and covarying positions that interact with CDR loops and diverging at positions outside the TCR binding interface and contacting MHC. This space, however, is explicitly defined by binding and not physiologic relevance (that is, functional outcome). Kula et al. (53) addressed this concern by implementing a system using APCs with native Ag–processing capabilities and selection of functional binding using an activity assay. Their platform was capable of determining the sequence determinants of an epitope important for function (namely the CDR-contacting positions) and could be used to screen libraries of T cells against an epitope library to elucidate the predominant epitopes during an immune response.

The application of these approaches to the diagnosis of autoimmune diseases where Ab profiling has proven unrevealing (54) is clear. In addition, the continued mapping of the sequence determinants underlying T cell specificity will eventually enable the prediction of the space of possible TCR epitopes based on sequence features alone (55, 56), working in tandem with tools predicting peptide binding to specific HLA alleles (57). With the information produced by these tools, the prospect of synthetically stimulating a reaction against immunodominant epitopes in an individual is close to materializing.

The ability of T cells to integrate and process environmental information to produce a diversity of specific behaviors, their longevity, their adaptability, and their intrinsic compatibility with hosts renders them ideal interventions to treat a variety of conditions (58). Chimeric Ag receptor (CAR) T cells epitomize this concept. These cells encode a synthetic receptor consisting of an extracellular binding domain (typically a single chain fragment derived from an Ab) and intracellular signaling domains (59) enhanced to elicit effector function upon Ag recognition in an MHC-independent manner (60). These cells are currently approved for the treatment of various hematologic malignancies with remarkable efficacy (61).

Optimization efforts have focused on improving their function and minimizing toxicity. They take advantage of the modular architecture of the CAR to design constructs with unique binding domains (including cytokines or their receptors) and signaling domains (from unique costimulatory or even inhibitory receptors) to potentiate or attenuate their activation (59). Synthetic biology approaches to limiting toxicity employ the serial (62) or combinatorial (63) recognition of multiple tumor-associated Ags to enhance specificity or the introduction of off switches in the form of ectopic expression of proteins targetable by Abs (59). Ectopic expression of chemokine receptors is an adjunctive approach (59). Dampening overactive T cell responses, including cytokine release syndrome, is often achieved by the addition of anti-cytokine Abs or steroids during or after the administration of the CAR T cells (33, 64), but direct modification of the circuitry leading to production of inflammatory proteins, informed by efforts to characterize the signaling networks underlying them, is under active investigation (59).

Recognition of pathogen-infected cells or abnormal ones in tumors represents only one key function of T cells. Inhibiting autoimmune responses, carried out by various subsets of regulatory T cells, is another active area of research in which genetically modified T cells have high potential. They have been shown in a myriad of model systems to effectively reduce or eliminate autoimmunity (65, 66) and are now in clinical trials for conditions as diverse as transplantation (67), type 1 diabetes (68), multiple sclerosis (69), Crohn disease (70), and amyotrophic lateral sclerosis (71). They, too, benefit from work carried out in cytotoxic CAR T cells, especially given the recognition that Ag-specific, rather than polyclonal, regulatory T cells are more effective at reducing autoimmunity (72). There is ongoing work optimizing the generation and maintenance of these cells (72), involving ectopic expression of transcription factors (73), incubation with cytokines or costimulatory molecules (74), to support their wide adoption.

The delicate balance that plagues checkpoint-inhibitor (32, 75) and CAR T cell (33) therapies—the dichotomy between an effective immune response against a pathogen or tumor cell and the risk of autoimmunity—naturally begs the prospect of hijacking the similar targets and methods to foment the diametrically opposed outcome to treat autoimmune disease. The approaches run the gamut from exploiting the aforementioned regulatory T cells to interfering with pathways involving inhibitory receptors, such as PD-1 and CTLA4 (76, 77). So although deletion of PD1 in cancer-specific T cells is associated with increased cytotoxicity and cytokine production (78), overexpression of these receptors or their ligands has been shown to ameliorate autoimmune disease in various models, including experimental autoimmune encephalitis (79) and lupus (80).

B cell depletion using anti-CD19 or anti-CD20 Abs such as rituximab is used to treat a variety of autoimmune diseases (8183) both to drive down autoantibody production and decrease a prevalent population of cytokine-producing APCs (84). The adoption of B cell–specific CAR T cells used in cancer therapy to achieve this goal, although effective in achieving disease remission in experimental conditions such as murine lupus (85) is limited by the inability to discriminate between pathogenic B cells and those with the potential to combat infection. Toward overcoming this, Ellebrecht et al. (86) designed a chimeric autoantibody receptor consisting of the primary Ag in pemphigus vulgaris fused to intracellular signaling domains to focus T cells harboring this construct to effectively eliminate self-reactive B cells. So long as ectopic expression of the Ag does not disrupt endogenous signaling, this approach is promising as a tactic against the small population of self-reactive B cells that drive disease that would likely avoid immunosuppression and potential toxicity.

Directly targeting clonal, self-reactive T cells is complicated by the lack of easily identifiable neoantigens—with the exception of the unique TCR. Strategies to exploit this have included T cell vaccination (87, 88) (which, rather than stimulating anti-idiotypic T cells specific for a unique TCR peptide, likely expand regulatory T cells) and T cells with synthetic receptors composed of MHC–peptide complexes that interact with autoreactive TCRs (S. Kobayashi, M.A. Thelin, H.L. Parrish, N.R. Deshpande, M.S. Lee, A. Karimzadeh, M.A. Niewczas, T. Serwold, and M.S. Kuhns, manuscript posted on bioRxiv, DOI: 10.1101/2020.01.24.916932). The latter have been shown to reduce the consequences of self-reactive T cells in a mouse model of type 1 diabetes (S. Kobayashi et al., manuscript posted on bioRxiv, DOI: 10.1101/2020.01.24.916932).

These approaches benefit from ongoing work identifying Ags and the dominant effectors of the autoimmune response. They represent the next path forward in exploiting the main advantages of the adaptive immune system: specificity and memory. Indeed, fully functional genetically modified T cells have been isolated from patients who underwent CAR T cell therapy years after infusion (89, 90).

The availability of methods to decode the Ag specificity of TCRs, introduce these into T cells, genetically perturb cells, and decipher the gene expression changes that maintain a particular phenotype furnish clear strategies toward engineering cells to target autoimmune disease (Fig. 1). At least two approaches are envisioned: in the first, cytotoxic T cells targeting specific Ags of cells driving self-injury can be generated. These Ags take the form of autoreactive T or B cell receptors (idiotypes), immunodominant peptide–MHC complexes, or cell-surface markers of activated cells. Transgenic T cells bearing TCRs or CAR targeting these markers are then infused into patients using disease-optimized protocols. The second approach uses engineered regulatory T cells with similar antigenic specificity and enhanced with tissue-specific homing capabilities (for example, ectopically expressed chemokine receptors). In principle, these two approaches can be implemented in series to direct an immune response in which target cell destruction is followed by induction of tolerance. High-throughput technologies to monitor therapeutic efficacy lie at the core of this approach. In the case of autoimmunity in the CNS as in multiple sclerosis, clinical and serological responses, not limited to imaging, biomarkers, serum, and cerebrospinal fluid cell profiling, can be measured. Ex vivo flow cytometry, cytokine expression analysis, and transcriptomics of isolated cells from the serum and cerebrospinal fluid can also inform on the activity of the engineered cells. This, in turn, can motivate specific gene perturbations or unbiased genome-wide screens to alter phenotypes to titrate or skew the pathogenicity of the cells for subsequent rounds of infusion (Fig. 1).

In their current state, high-throughput technologies continue to generate data all the while undergoing optimization largely independently from one another (91). This situation perpetuates criticisms against large-scale data gathering that boil down to our inability to properly exploit the data to extract meaningful biological conclusions (92). We believe that the solution does not lie in the development of sophisticated analytical methods but rather in a framework that acknowledges the limitations of said experiments, focuses on reduction of complexity, and uses results iteratively to inform new rounds of experimentation. This forms the basis for our proposed roadmap for translating these omics approaches into viable clinical workflows. The tools to put it into practice, which we have summarized above, are in our grasp.

Controlling the immune response, realizing what Richard Feynman famously said, “What I cannot create, I do not understand,” would materialize as follows: enabling the generation of T cells specific to certain Ags that are active in certain tissues, that carry out defined, titratable functions in those tissues, and that can persist or be readily turned off (Fig. 2). This is the implicit goal of all of the experimental efforts reviewed in this review. The requirement of massive amounts of data are a recognition of the complexity of interacting components and the assumption that patterns simplifying this apparent complexity exist and can be identified. The pursuit of this level of understanding is already underway.

Abbreviations used in this article:

CAR

chimeric Ag receptor

CRISPR

clustered regularly interspaced short palindromic repeat

sgRNA

single-guide RNA.

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The authors have no financial conflicts of interest.