Systems and methods to detect digital t cell receptor interactions with presented antigens

Abstract

Some T-cell receptors (TCRs) require many ligands for activation while others need only a few, functioning in analog or digital modes, correspondingly. Digital performance is best but undiscernible using conventional functional avidity or TCR sequence distance metrics. Correlates of optimal biophysical parameters include magnitudes of ERK phosphorylation, CD3 surface loss, and activation marker upregulation. The disclosed findings underscore the requirement to match TCR performance quality with pMHC copy number, as nature does. Given tumor antigen array paucity, engaging superior digital TCRs appears critical for T-cell adoptive cancer therapies and vaccines. Therefore, disclosed herein are methods for detecting digital T cell receptor interactions with presented antigen and selecting a T cell or antigen based on these methods. Also disclosed are adoptive cell transfer methods for treating a subject with digital T cells identified by the disclosed methods. In addition, disclosed are methods for designing RNA vaccines based on the copy number that elicits a digital T cell response.

Description

SYSTEMS AND METHODS TO DETECT DIGITAL T CELL RECEPTOR INTERACTIONS WITH PRESENTED ANTIGENSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims benefit of U.S. Provisional Application No. 63 / 602,878, filed November 27, 2023, which is hereby incorporated herein by reference in its entirety. STATEMENT OF GOVERNMENT INTEREST

[0002] This invention was made with Government Support under Grant Nos. Al 143565 and Al 136301 awarded by the National Institutes of Health. The Government has certain rights in the invention.SEQUENCE LISTING

[0003] This application contains a sequence listing filed in ST.26 format entitled “222230-2280 Sequence Listing” created on November 22, 2024, and having 2,597 bytes. The content of the sequence listing is incorporated herein in its entirety.BACKGROUND OF THE INVENTION

[0004] Treatment of cancers has improved through immunotherapy, an approach that uses the immune system to control and eliminate cancers. Breakthroughs have been particularly evident in malignant melanoma where treatments with anti-PD1 and / or anti-CTLA4 monoclonal antibodies rejuvenate dormant T lymphocytes within the tumor site by abrogating inhibitory pathways, so-called immune checkpoint blockade therapy (CB). Cancers responsive to CB such as melanoma and a fraction of non-small cell lung cancers (NSCLCs) (20%) display myriad tumor-specific antigens, "neoantigens", to stimulate the immune response. Neoantigens arise most commonly through genetic mutations in a tumor. Cytolytic T lymphocytes (CTLs) are generated naturally against some of these neoantigens, following the priming of naive T cells by professional antigen presenting cells (APCs) that then target the cancer for destruction once the tumor's checkpoint evasion mechanism has been eliminated. However, for many other forms of cancer including the remaining 80% of NSCLCs, almost all ovarian cancers, and brain cancers, to mention a few, the paucity of neoantigens arrayed on the tumor cell prevents the immune system from effectively generating CTLs in the first instance. Unsurprisingly, no positive response is engendered by CB. Mechanisms of tumor evasion that thwart protective immunity are known, including genetic and epigenetic restriction of antigen display, inhibitors of T cell migration and induction of T cell exhaustion via metabolites and / or chronic antigen stimulation in the tumor microenvironment.

[0005] Several approaches are currently being tested to overcome the problem of dysfunctional effectors including the search for additional CB pathways and clinical trials of mAb combinations incorporated in a neoadjuvant setting prior to surgery. In one orthogonal strategy, a chimeric antigen receptor is introduced into T cells (CAR-T) by lentiviral or other retroviral transduction to bind to and destroy tumor cells. CAR-T therapy incorporates an antibody fragment specific for a tumor-specific antigen or tumor-associated antigen (TAA) joined to a CD3 signaling subunit component via a linker and transmembrane (TM) region of another protein. Another strategy to induce killing employs a bispecific antibody fusion protein comprising two single chain variable fragments (scFv), diabodies that engage nearby T cells via CD3 with one arm and a tumor cell target with the other. Each has merit with remarkable successes emerging at various preclinical and / or clinical stages. Notwithstanding, costs for therapies like CAR-T are very high, and benefits to some patients may be transient due either to tumor target antigen loss driven by immune selection or, alternatively, exhaustion of CAR-T function.

[0006] Importantly, ongoing immunotherapy efforts have yet to benefit from recent scientific advances in fundamental T cell receptor (TCR) structural biology and antigen-specific cognate recognition of the a[3 T cell lineage. TCRs with digital performance characteristics (i.e. , requiring only one or a few, countable ligands for activation) directed at physically detected neoantigens can change cancer vaccine and immunotherapy paradigms, by targeting tumors via sparsely as well as luminously arrayed TAAs.SUMMARY OF THE INVENTION

[0007] T lymphocytes leverage a|3 T-cell receptor (TCR) recognition of aberrant peptides bound to major histocompatibility complex molecules (pMHCs) on altered cells to protect the mammalian host from infectious pathogens and cancerous transformations. While adaptive immunity requires efficient TCR performance, comparison metrics are widely limited to force-free assays. Yet, recent data reveal that mechanical load on the TCR-pMHC bond resulting from cellular motions between a T cell and an altered cell conjugate formed during immune surveillance tunes TCR specificity and sensitivity.

[0008] Some TCRs require many ligands for activation while others need only a few, functioning in analog or digital modes, correspondingly. Digital performance is best but undiscernible using conventional functional avidity or TCR sequence distance metrics. Correlates of optimal biophysical parameters include magnitudes of ERK phosphorylation, CD3 surface loss, and activation marker upregulation. The disclosed findings underscore the requirement to match TCR performance quality with pMHC copy number, as nature does. Giventumor antigen array paucity, engaging superior digital TCRs appears critical for T-cell adoptive cancer therapies and vaccines.

[0009] Disclosed herein is a method for detecting a digital T cell receptor interaction. The method first involves contacting an ap T cell receptor (a[3TCR) with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the afSTCR and the pMHC. In some embodiments, the pMHC is presented on a bead or solid support. In some embodiments, the pMHC is on the surface of an antigen presenting cell. Likewise, in some embodiments, the apTCR is presented on a bead or solid support, and in some embodiments, the a£TCR is on the surface of a T cell, such as a chimeric antigen receptor (CAR)-T cell or a tumor infiltrating lymphocyte (TIL).

[0010] The method next involves applying an equilibrium force of 10 to 20 pN, including 12-18 pN, preferably 15 pN, to the catch bond between the a[3TCR and the pMHC to achieve a state where the catch bond or slip bond is hopping / volleying between compact and extended states, wherein a sustained hopping / volleying is an indication of a digital T cell receptor peptide interaction. The term “equilibrium force” refers to the force at which the system has the same probability for being in the compact or extended state. Increasing the force destabilizes the compact state and stabilizes the extended state. When the equilibrium or critical force is reached, the system will be equally likely to be open or closed. At this force the system naturally hops back and forth due to thermal fluctuations. The equilibrium force can be applied using any suitable means, including optical tweezers, magnets, and acoustic force.

[0011] The hopping / volleying can in some cases be energetically driven. Here a force near the equilibrium force, typically slightly above the equilibrium force is applied that drives the system from the closed to extended state. The transition occurs which results in a reduction of the force across the system by for example a factor of the opening distance times the spring stiffness. For example, if the force is applied through an optical trap with a spring stiffness of 0.2 pN / nm and the transition is 10 nm, the force will be reduced by 2pN. This reduction of force toggles the force across the system to a value slightly below the equilibrium force which will favor reclosing to the compact state. The act of reclosing ~10nm pulls on the spring which increases the force to slightly above the equilibrium force favoring re-opening to the extended state. By adjusting the stiffness of the system, one can effectively energetically drive a cycle that goes back and forth between compact and extended states. One can change the spring stiffness to impact the spread of force toggling above and below the equilibrium force to help drive this cycle. One can even adjust dynamically the force on the system from a low to highvalue spanning the equilibrium force by external means. The magnitude of these two values would favor the probability of being in the extended state for the high range and favor closing to the compact state for the low range. The high force would not be set so high as to break the bond or so low as to favor bond dissociation so the range would likely be close to the equilibrium force depending on the catch bond profile. In terms of frequency, the lower end of the active toggling would be similar to the natural hopping lifetime and also bond lifetime. The upper end would be tied to the maximum volleying frequency, likely under 50 Hz. In principle the profile of the force could be binary, at two values or also have a profile resembling a sine wave or triangle wave or any profile that would drive the state between the compact and extended states. This could be implemented by moving the optical trap or changing the optical trap power. It could be implemented by changing a magnetic field by altering the current on an electromagnetic or proximity of a magnet or shield on a magnetic field. For an AFM, cantilever or bioforce probe it could be adjusted by changing the relative position of the cantilever / probe to the system which impacts the force on the system.

[0012] For example, in some cases, the force is applied with a spring stiffness of 0.03 to 2 pN / nm to energetically drive the volleying, such as a spring stiffness of 0.1 to 0.3 pN / nm, including 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1 , 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, or 2.0 pN / nm. In some embodiments, the force is applied at an oscillation frequency of 1 Hz to 50 Hz (e.g. 1Hz to 10Hz, 5 Hz to 10 Hz, 5Hz to 25Hz, 5Hz to 50Hz, 10Hz to 25Hz, 10Hz to 50Hz), and an amplitude of from 0.1 pN to 40 pN (e.g. 0.1pN to 10pN, 1pN to 20pN, 5pN to 10pN, 5pN to 20pN, 5pN to 30pN, 5pN to 40pN) to energetically drive the volleying. As disclosed herein, the term “frequency” refers to the time between peaks (maximum force) and the term “amplitude” refers to the difference between the maximum and minimum force. In some embodiments, the oscillation is a sine wave, square wave, or triangle wave. In other embodiments, the force is applied at a constant magnitude (i.e. amplitude less than 0.1 pN).

[0013] Typically sustained volleying last longer than the peak catch bond lifetime. Lifetime would be a selection criteria where one may want bonds that can volley the longest. Other selection criteria include the hopping / volleying frequency, the hopping / volleying magnitude, and the hopping / volleying force. In some cases, one may want bonds that can transition to hopping / volleying at lower force, e.g. below 15 pN. In cases of T cells in a stiff matrix, it might be advantageous to select bonds that can transition to hopping / volleying at higher forces, e.g. above 15 pN). In some embodiments, a bond that is capable of reversible transitioning to hopping / volleying it is a good bond.

[0014] In some embodiments, the method involves tuning the stiffness change of the tether between the closed and open state. This promotes cycling of the conformational change. In this way, the TCR-pMHC bond can be thought of as attached to a spring. The approximately 10 nm conformational change leads to a change in the spring opening distance such that the spring is not pulled quite as far as it was before the transition. That spring will then relax approximately 10 nm. As a result, the force across the system then reduces by a little that depends on the “stiffness” of the connecting elements (in this case it is two springs in series, the DNA and an optical trap spring). Most of the displacement opening is taken up by reducing the spring of the optical trap. If the trap spring constant is k=0.2 pN / nm and the distance changes 10 nm, then the force reduces by approximately 2pN which will promote re-closing of the TCR- pMHC bond. This promotes volleying. In sum, volleying appears to be favored when the force is higher during the closed state and then reduces during the open state. In some embodiments, a trap stiffness in the range of 0.1-0.3 pN / nm is advantageous.

[0015] In some embodiments, the method involves actively toggling the force from high to low as the system volleys, thereby driving the transition. For example a magnetic force is constant over the transition distance (e.g. 10nm) but if one oscillates it on either side of the force, e.g. 12pN to 17 pN, then they can drive the TCR within this state.

[0016] Also disclosed is a method for selecting a T cell or antigen that involves providing a T cell comprising a ap T cell receptor (a[3TCR) and an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC), wherein either the T cell or the pMHC is tethered to a solid support or bead. The method then involves contacting the candidate T cells with the antigen peptide under suitable conditions to promote a catch bond or slip bond between the afJTCR and the pMHC and then applying a force of 10 to 20 pN to the catch bond or slip bond between the a[3TCR and the pMHC. The cells can then be assayed for T cell activation and activated T cells / antigens can be selected.

[0017] In some embodiments, T cell activation is determined using a reporter of intracellular calcium concentration, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with a high calcium flux.

[0018] In some embodiments, T cell activation is determined by assaying for cell stiffness, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with increased activation-induced T cell stiffening.

[0019] In some embodiments, T cell activation is determined by imaging the cell, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with changes in geometry and / or morphology.

[0020] In some embodiments, the method is used to screen a plurality of T cells and selecting a T cell that has an a[3TCR that interacts with the antigen peptide with digital binding.

[0021] In some embodiments, the method is used to screen a plurality of antigens and apTCRs and selecting an antigen that interacts with an a[3TCR peptide with digital binding.

[0022] The details of one or more embodiments of the invention are set forth in the accompanying drawings and the description below. Other features, objects, and advantages of the invention will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF FIGURES

[0023] FIGs. 1A and 1 B show immune surveillance: an exacting search for a needle in a haystack. FIG. 1A shows a cell expressing approximately 100,000 pMHC complexes of various types (diverse peptides and MHC allele products) shown schematically as blue molecules arrayed on the surface. A magnified view around the boxed region shows a single yellow dot representing a copy of a rare “foreign peptide’’, i.e. , neoantigen or virus-derived peptide that could be targeted by a CTL for destruction. FIG. 1 B shows 2 pMHC complexes that are almost identical except for a focal change at the p4 position of the peptide (RGYVYQGL (SEQ ID NO:1) vs. RGYLYQGL (SEQ ID NO:2)) bound to H-2 Kbthat an N15 TCR distinguishes (see text). Top views of antigen-binding groove of pMHC molecule VSV8 / Kb(Left) and L4 / Kb(Right) are shown from the perspective of an approaching TCR (Upper row). (Left) MHC Kbis shown in surface. VSV8 peptide is in stick representation with its N terminus on the left and C terminus on the right. The Vai residue at p4 is highlighted in magenta. (Right) MHC Kbis shown in surface representation with the heavy chain colored in pale green and [32m in light blue. The L4 peptide is drawn similarly in stick representation. The Leu residue at p4 of the L4 peptide is highlighted in magenta. In the Bottom row, the corresponding side cross-sectional views of the antigenbinding groove of pMHC molecules are offered with peptides in same left to right orientation.

[0024] FIGs. 2A to 2D show ct|3 T cell recognition via mechanosensing. FIG. 2A shows X-ray structures of TCRap recognizing an influenza A virus M1 peptide bound to the HLA-A* 0201 (PDB 1OGA) in comparison to a human immunoglobulin Fab fragment bound to HIV-1 gp120 core (PDB 1GC1). For each, a ribbon diagram of the receptor structure (Left, side view) and a surface model of the respective ligand (Right, 90° rotation) are shown with the foreign protein-derived element in red. The peptide in the pMHC is size-wise a minor component of the interaction surface. N and C mark the amino and carboxy terminus of the foreign peptide. The red peptide chain is barely visible in the side view between the MHC a-helices (cyan). FIG. 2B shows force-bond lifetime curves of two distinct TCRs (1 and 2) interacting with the same pMHC cognate ligand or irrelevant peptide. FIG. 2C shows structural transitions following TCR bindingto pMHC. In this cartoon, only the TCRap variable and constant domains are shown for simplicity. The TCR view in panel A is rotated upside down, which is utilized in subsequent figures. FIG. 2D, Top shows reversible transitioning between extended and compact states with indicated time constants as an example of one mechanotransduction parameter. FIG. 2D, Bottom shows low and high transition rates of two distinct TCRs.

[0025] FIG. 3 shows systems view of the a|3TCR mechanome. Stiffness of the cellular microenvironment modulates mechanical coupling between T cells and APCs (External coupling to the grid shown at the top of the figure). Local compliance ranges two orders of magnitude, about ~1 to 100 kPa, and is substantially altered in inflamed, infected, or diseased tissues. Such changes fine-tune signaling levels during immune surveillance. Differences in compliance of anatomically linked locals as discussed in the text are shown with some key cell types labeled and ECM components represented as wavy lines. The other figure elements depict the mechanochemical pathway that works through the a|3TCR-pMHC bond connecting across the active and load bearing actomyosin machinery in both the APC and T cell as denoted. During surveillance, shear forces exert on cell surfaces, tilting the apTCR-pMHC bond (TCRap alone shown for simplicity). Interfacial organization and catch bond stabilization occurs between pMHC and the variable domains of o[3TCR. Force strengthens the bond, extending the lifetime and energizing the system. This input facilitates a ~10-nm conformational transition that is inaccessible by thermal fluctuation alone. Reversible transitioning agitates the T cell membrane toggling the TCRa transmembrane domain from bent (L) to extended (E) conformations. Such agitation and motion loosen the transmembrane helix organization leading to the release of membrane-sequestered CD3 ITAM domains (omitted for clarity). Internal coupling to the actomyosin machinery maintains the load between the APC and TCR at the proper level for reversible transitioning.

[0026] FIG. 4 shows interdomain motions define an atomistic basis of a[3TCR-pMHC catch bond formation. FIGs. 4A to 4C show nonmatching peptide (FIG. 4A), absence of adequate load (FIG. 4B), or deletion (FIG. 4C) of the Cp FG-loop leads to mismatch between subdomains and allow domain motion, which lead to destabilization of the apTCR-pMHC interface. FIG. 4D shows when the complex with a cognate peptide is under 10-to 20-pN load, fit between domains is achieved, stabilizing the bond. FIG. 4E shows decoupling between the V- and C-modules, as would occur in the extended state of the complex, eliminates the requirement to satisfy the V-C interface, which enhances the fit between the V-module and pMHC. In this illustration, interfacial matches are shown as geometric fit. In reality, it can be more subtle, such as matches in motional behavior. By incorporating “mechanical match,” thepeptide discrimination can be enhanced far beyond chemical and conformational matches in equilibrium.

[0027] FIG. 5 shows single-molecule biophysical parameters defining apTCR-pMHC recognition. (Left column) Single-molecule (SM) measurements probe purified TCRap-pMHC interactions by loading the bond through an optically trapped bead with 0.1-pN force and nm- level spatial resolution. Multiple traces scoring bond lifetimes vs. force populate catch bond curves (example in Fig. 2B) yielding lifetime and force as well as shifts in catch bond profile width. At the critical force, reversible transitions can be tracked to obtain hopping frequency (Fig. 2D), hopping distance, and transition barrier energetics. (Middle) Single-molecule singlecell (SMSC) measurements by tethering pMHC molecules to a bead and presenting the bead to a surface-bound T cell. When the tethered bead is pulled away, it produces a transient that can score the bond lifetime for a given force (catch bond curves), transition magnitudes, and opening probabilities for a given force window. (Right) Single-cell activation requirement (SCAR) measurements by trapping a pMHC-coated bead and facilitating interfacial contact with a T cell containing a fluorescence-based reporter of calcium concentration. Beads of varying pMHC densities ranging from analog to digital are presented to determine the interfacial copy number required for activation with and without force. At limiting pMHC, force is required for activation (digital). In this case the optical trap is not only used to facilitate bead connection with the cell, but force on the bead pulls on the pMHC-TCR bond mimicking a shear load between the T Cell and APC across such a bond through their respective actomyosin machineries. Such force is expected to lead to a conformational change in the TCR including reversible transitioning, which agitates the membrane sequestered ITAM domains releasing them and leading to changes in phosphorylation. Intracellular calcium flux transients show a range of profiles from nonactivating to activating and sustained. A 10-min time course showing activating (Top) and nonactivating (Bottom) cells are shown. Differential interference contrast images are shown on the left with a 1-pm bead and scale bar. For simplicity, the SMSC model lacks CD3 components of the cell surface a[3TCR complex while the SCAR model omits TCRs altogether.

[0028] FIGs. 6A to 6M show TCRs recognizing a high copy number pMHC array on 474 IAV infected cells function in either an analog or digital performance mode. FIG. 6A shows repertoire analysis of NP366-374 / Db-specific TCR. Pie charts show the frequency of individual CDR3a (left) and CDR3[3 (right) clonotypes, with 2 or more cells colored as indicated, and single cell clonotypes in gray. NP34, NP63, and NP41 TCRs utilize the TRV, TRJ, and CDR3 sequences summarized in the bottom table. NP34 and NP63 share the same CDR3 sequence (italic letters) except for letters in boldface. FIG. 6B shows SMSC assay used to measure bondlifetime versus force for NP34 and NP63 TCR transfectants generated by retroviral transduction into BW5147CD8a+TCRap- parental cells. Data are shown as mean ± SEM. Peak lifetimes occur at 15pN, 10 s and at 16 pN, 2 s for NP63 and NP34 respectively. FIG. 6C shows SCAR assay used to measure the magnitude and the longevity of calcium flux in NP34- and NP63-BW cells using either 2,200, and 20,000 NP366-374 / D15copy numbers at the bead-cell interface with or without an optimal vectorial force. The calcium flux signal is indicated as the ratio of maximum fluorescence intensity (lmax) to the initial fluorescence intensity (l0) of the Ca2+-sensitive dye. Each dot in the plot represents a single-cell experiment. The rectangles span SD with mean and median values shown as thick and thin lines, respectively. Pictures to the right are representative DIC (Differential Interference Contrast) images of a cell bead pair in SCAR experiment, and time-lapse images of intracellular free Ca2+release for representative cells. MFI, mean fluorescence intensity. FIG. 6D shows quantification of PMI (Predicted Mean Intensity; the average signal level for triggered cells multiplied by the percentage of cells that trigger in each category) for NP34, NP41 , and NP63 with the indicated interfacial number of pMHC with or without force application. These data were collected separately from FIG. 6C, using a different dual fluorescence OT system with greater fluorescence sensitivity. FIG. 6E shows IL-2 production from the indicated TCRap-transduced BW cells after stimulation with titrated NP366-374 peptide presented on R8 APCs. Peptide 497 concentrations are in ng / ml where Log10 values are shown on the x-axis (0: 1 ng / ml). f, NP366-374 / Db tetramer binding (geometric mean fluorescence intensity, gMFI) to TCR-transfected or untransfected (UT) BW5147CD8a[3+cells following overnight treatment at 37 °C to trigger T cell activation in i and j. Fluorescence intensity was measured by flow cytometry. FIG. 6G shows tetramer dissociation assay.Indicated cells were incubated with WT tetramer and then treated with the 28-14-8 antibody Fab fragment against H-2Db / H-2Ldat indicated time points. FIG. 6H shows Western blot analysis of ERK phosphorylation (p-ERK) in cells after NP366-374 / Db tetramer stimulation. Blots on top show one of three representative results with the p-ERK to ERK ratio at each time point after activation is shown below. FIGs. 6I and 6J shows loss of surface CD3 and concomitant upregulation of CD69 expression after stimulation shown in FIG 6F. The fluorescence intensity of CD3 (FIG. 6I) and CD69 (FIG. 6J) was measured by flow cytometry and normalized using the non-tetramer stimulated value. FIG. 6K shows frequency of NP34 and NP63 Rg T cells in mediastinal LN (mLN) of mixed RgC mice 7 days after PR8 IAV infection. FIG. 6L shows quantification of real-time Rg T cell-mediated killing of PR8-infected LET1 cells over time (h, hours). NP34 and 63 Rg T cells were derived from mLN or lung of RgC mice (dpi 7). m, Representative images of the killing assay for mLN Rg T cells at 20 hours in FIG. 6L. LET1 cellswere visualized by transduced mCherry, apoptosis is in green, and Rg T cells are in blue. For FIG. 6D, data are shown as mean ± SD. For FIGs. 6E-6G and 6I-6M, data are representative of 2-4 independent experiments and are shown as means ± SDs of technical replicates (FIGs. 6E, 6F, 6I, and 6J) and of 6 mice (FIG. 6K). For FIG. 6H, data are shown as means ± SDs of 3 independent experiments. Some error bars are invisible due to small SDs (FIGs. 6E, 6F, 6H, 6I, and 6J). For all data with statistics, ****P <0.0001 , ***P<0.001, **P<0.01 , *P<0.05; ns, not significant. P values were calculated by one-way ANOVA (FIGs. 60 and 6D), comparing slopes of linear regression (FIGs. 6F, 6I, 6J, and 6L), by the Kolmogorov-Smirnov test (FIG. 6G), by regression using trend line analysis models accounting for interexperimental variability (FIG. 6H), or by paired t-test (FIG. 6K).

[0029] FIGs. 7A to 7K show a sparse immunodominant pMHC array exclusively elicits TCRs with digital performance but distinguishable activation features. FIG. 7A shows repertoire analysis of PA224-233 / Db-specific TCRs. Pie charts and the table are as explained in Fig. 6. FIG. 7B shows SMSC measurement of bond lifetime versus force for PA25, PA27, and PA59. Peaks of the catch bond curves occurred at 21 pN and 75 s for PA59, and at 15 pN at 23 s and 13 s for PA29 and PA25, respectively. Data are in mean ± SEM. FIG. 7C shows SCAR assay of indicated transductant with all three PA TCRs triggered by two PA224-233 / Dbmolecules on a bead in conjunction with external force. Other forces indicate outside the optimal range, < 8 pN and > 12 pN for PA25 and PA27, and < 8 pN and > 18 pN for PA59. See Fig. 6C for explanation of plots and images. FIG. 7D shows IL-2 assay for indicated BW transductants after stimulation with titrated PA224-233 peptide presented on R8 cells as explained in Fig. 6E. FIG. 7E shows tetramer binding measured after overnight incubation with WT PA224-233 / Dbtetramer treatment. The fluorescence intensity was measured by flow cytometry. FIG. 7F shows tetramer dissociation assay completed after PA25-, PA27-, and PA59-BW cells were incubated with a PA224-233 / Dbtetramer harboring a CD8BS-MHCI mutation and then treated with 28-14-8 antibody Fab fragment for the indicated time point before FACS analysis. FIG. 7G shows Western blot analysis of p-ERK as described in Fig. 6H. FIGs. 7H and 7I show change of surface CD3 (FIG. 7H) and CD69 (FIG. 7I) expression with increasing concentration of WT tetramer as shown in FIG. 7E. Indicated BW cells were incubated at 37 °C overnight with tetramer, then fluorescence intensity of CD3 (FIG. 7H) and CD69 (FIG. 7I) was measured by flow cytometry and analyzed as in Figs. 6I and 6J. FIGs. 7J and 7K show differential proliferation of BW transductants to WT (FIG. 7J) and CD8BS-mutant (FIG. 7K) tetramers. Measurements were obtained 30 minutes after addition of WT (FIG. 7J) or mutant (FIG. 7K) tetramers. The frequency of proliferation was determined by FSC-A vs. SSC-A plot by flow cytometry and normalized by the non-tetramerstimulation value. For FIGs. 2D-2F and 2H-2K, data are representative of 2-4 independent experiments and are shown as mean ± SD (FIGs. 2D, 2E, 2H, and 2I) and ± SEM (FIGs. 2J and 2K) of replicates. For FIG. 2G, data are shown as mean ± SD of 3 independent experiments. Some error bars are invisible due to small SDs or SEMs (FIGs. 2D, 2E, 2G-2K). For all data with statistics, ****p <0.0001 , ***P<0.001 , **P<0.01, *P<0.05; ns, not significant. P values were calculated by one-way ANOVA (FIG. 2C), comparing slopes of linear regression (FIGs. 2E, 2H, 2I, 2J, and 2K), by the Kolmogorov-Smirnov test (FIGs. 2F), and by regression using trend line analysis models accounting for interexperimental variability (FIGs. 2G).

[0030] FIGs. 8A to 8H show a single molecule dual bead OT system discriminating mechanosensing performance of digital TCRs. FIG. 8A shows SMdb system cartoon. FIG. 8B shows bond lifetime vs. force for PA25 (n= 175), PA27 (n=237), PA59 (n=192), and NP41 (n=57). Lifetimes are binned every 5 pN and are plotted as mean bond lifetime ± SEM. FIG. 8C shows cumulative probability plot for continuous volleying segments. In comparison, the peak lifetime from the catch bond curves for each clone is noted by a vertical dashed line with the corresponding color. Symbols depicted by an X indicate termination by the user. PA25 (n= 10), PA27 (n=8), PA59 (n=8), and NP41 (n=11). PA events were pooled and fit to the function y = (1 - e-x / t). 95% confidence intervals are denoted by magenta (NP41) and grey (pooled PA) shaded areas. FIG. 8D shows representative traces of continuous volleying segments. Multiple extended hopping traces are shown for each clone, separated by a blank space. All traces shown are in 13.8 - 14.5 pN force range. Sample traces from each catch bond curve in the same force range that did not show reversible transitions are shown in the dotted box. FIG. 8E shows zoomed in sections of the black rectangles in Fig. 8D. FIG. 8F shows hopping frequency vs. force in 10-s segments. Markers for NP41 represent segments shorter than 10 seconds due to the lack of sustained volleying. Note that the force indicated is the force applied to the folded state and the force upon opening is 1-1.5 pN smaller. Dashed lines are linear regressions used to guide the eye. Arrowheads with yellow borders indicate elements used in Fig. 8E. Violin plots in the top left insert show pooled frequency for PA’s. FIG. 8G shows transition distance for 10-s segments. The range of the transition is indicated. We consider the major transitions between the distributions of the two major dwell states, excluding small transitions < 5 nm observed within a particular state. FIG. 8H shows example of SM position distributions vs force for the PA25 sample segment in Fig. 8D showing shift in population between two major states as a function of force near the critical force of 14 pN. For all data with statistics, ****p <0.0001 , ***P<0.001, **P<0.01, *P<0.05; ns, not significant. Fits and 95% confidence ranges from the fitparameters are shown as dashed lines and shaded regions, respectively (FIG. 80). P values were calculated by Kruskal-Wallis tests (FIG. 8F insert and 8G).

[0031] FIGs. 9A to 9F show in vivo transcriptomes and expansion of T cells expressing digital TCRs during the acute IAV response. FIG. 9A shows experimental schema to analyze in vivo behaviors of three distinct clonotypic PA224-233 / D15TCRs in the same mice. Rg mice were generated by transferring Rag2'A-derived hematopoietic stem cells (HSC) after transduction of retroviruses containing TCR[3-P2A-TCRa with a fluorescence-protein (FP) gene into irradiated Rag2 - mice (left). Subsequently, mixed RgC mice were generated by adoptively transferring an equal number of naive FP+CD8[3+CD44' T cells from PA25-mCherry, PA27-GFP, and PA59- GFP Rg mice into recipient B6 mice, followed by PR8 infection of the latter 24 hours posttransfer. Rg T cells were analyzed on day 7 post-infection. FIG. 9B shows representative contour plots showing the frequency of Rg T cells in ml_N of mixed RgC mice. PA25 T cells were identified as CD8[3+mCherry+V[37+(TRBV29+) cells, PA27 T cells as CD8p+GFP+V[39+(TRBV17+), and PA59 as CD8+GFP+V[37+(TRBV29+) by flow cytometry. FIG. 9C show quantification of the frequency of Rg T cells in mLN. FIG. 9D shows quantification of the percentage of EdU+Rg T cells. FIG. 9E contains volcano plots of bulk RNA-Seq expression data of FACS isolated clonotypes displaying differentially expressed genes (DEG) as colored dots between PA25 and PA27 Rg T cells (top), PA25 and PA59 (middle), and PA59 and PA27 (bottom) in mLN. Each gene of note is functionally categorized and listed on the right. FIG. 9F contains volcano plots indicating the paucity of DEG between each Rg T cells in lung. For FIGs. 9C and 9D, data are representative of four independent experiments. P values were calculated by paired t-test. **P < 0.01 ; ****p <0.0001. For e and f, DEG are identified as Iog2 Fold-Change >1 (vertical gray lines) and adjusted P value < 0.05 (Wald test with Benjamini-Hochberg correction).

[0032] FIG. 10 shows a depiction of the Bend and Snap Cycle (BSC) for sustained signaling associated with TCR molecular resonance, (left) The TCR-pMHC interaction results in formation of a bond between the cell interface with connectivity and force generated through their respective actomyosin machineries. Force across the TCR-pMHC bond is generated through surveillance motions arising from T cell pulling to the left, coupling to eight a[3TCR membrane associated elements (CD3 ectodomains omitted for clarity) and p HC coupling to the APC when stationary or pulling to the right, (middle) Various idealized states of the cycle near the equilibrium force are shown with mechanical coupling drawn for simplicity as a “spring” in series with the TCR-pMHC bond. In principle, such mechanical connectivity will have both elastic and viscous character. States B and C depict forces slightly higher than equilibrium whilestates A and D are slightly lower than equilibrium. States C and D are extended while states A and B are compact, (right) Energy landscape view of the cycle relative to equilibrium (dashed line) depicting a force slightly higher than equilibrium (F+) for states B and C where the system favors transitioning to the extended state. Similarly, states A and D are slightly lower than equilibrium (F where the closed state is formed. Bend and snap cycle: Force across the bond pulls the system from state A to state B, bending the local membrane shifting the equilibrium to where transitioning to state C is favored. B to C, the TCR snaps open (here shown as an extension of the constant domain) which extends the bond leading to agitation of the membrane. The system immediately adjusts to the new contour length reducing tension on the springs and unbending to state D. The force is now slightly lower than that at equilibrium, reducing tilt of the energy landscape such that the TCR snaps back to state A which retracts the tether agitating the membrane. Cell motions, in turn, pull the bond to state B to allow a repeat of the cycle. Feedback between leftward motions such as the T cell motion indicated (or retrograde flow, not shown) and rightward motion such as from an opposing APC motion (or internal motor activity along the cortical actin, not shown) sustain the cycle. In analogous SMdb assays, changes in the contour length due to TCR-pMHC bond extension reduce force across the tether in the optical trap driving the system between a compact higher force state and extended lower force state. Note that without loading of the TCR-pMHC bond (unloaded curve), the energy barrier is too great to overcome. Under load, in contrast, the forward and reverse distance to transition states are relatively balanced which facilitates the bend and snap cycle.

[0033] FIGs. 11A to 11C is a schematic of cell isolation for single-cell RT-PCR and analysis of NP366-374 / Db- and PA224-233 / Db- specific TCR directed at distinct peptides with divergent copy numbers after IAV infection. FIG. 11 A shows workflow to clone NP366-374 / Db- and PA224-233 / Db- specific TCRs derived from lung resident CD8+T cells. T cells were isolated 5 days post-recall by cell sorting and single cell RT-PCR. FIG. 11 B show sorting strategy to isolate N P366-374 / Db- and PA224-233 / Db- specific T cells. FIG. 11C show copy number of 21 peptide / H-2 MHC complexes on LET 1 cells infected with Influenza A / PR / 8 / 34 virus (PR8) based upon data adapted from Wu et al. Nature communications (2019). The pMHCs were isolated from infected LET1 cells, and then the peptides were eluted from their immunoaffinity-purified Kband DbMHCI molecules and analyzed by LC-MS as described. Results from LET1 are summarized, with NP366-374 / Db- and PA224-233 / Dbpeptide copy numbers adapted from that publication.

[0034] FIGs. 12A to 12G show the greater activation under force of the digital NP63 TCR relative to analog NP34 and NP41. FIG. 12A shows TCR[3 expression on CD8a[3+TCR' BW5147 cell line transduced with NP34, NP41 , and NP63 TCRap after cell sorting to matchTCR expression as defined by H57 anti-CP mAb. FIG. 12B shows single molecule single cell (SMSC) assay design for optical tweezer experiments. Beads functionalized with DNA tethers terminating in pMHC are actively introduced to the cell surface to initiate bond formation, then retracted for bond-loading. Movement of the bead relative to the cell is controlled by a piezo stage that translates the cell relative to a stationary trapping laser. FIG. 12C shows single cell activation requirement (SCAR) assay design for optical tweezer experiments. T cells are introduced to pMHC coated beads, with varying densities of an agonist pMHC (NF D13or PA224-233 / Db) , to promote bond formation at the interface. Moving the piezo stage relative to the T cell applies a vectorial force to the system via the optical trap. FIG. 12D shows force vs. lifetime distributions, comparing NP63 and NP34 obtained with SMSC assay to NP41 obtained with SM assay. FIG. 12E shows tether forming probability in the single-molecule single-cell (SMSC) assay under identical bead and coverslip conditions for each clone. At conditions where sufficient tethers are formed between NP3se / DbpMHC and NP63 or NP34 cells, no tethers are formed in the case of NP41. FIG. 12F shows SCAR assay used to measure the calcium flux in NP63-, NP34-, and NP41-BW cells using either 2 or 20 interfacial copy number of NP366 / Db, conducted on a high-resolution dual fluorescence-OT microscope. Experiments were conducted with and without external force application as indicated. The calcium flux is represented as ratio between the maximum fluorescence intensity (Imax) and the initial fluorescence intensity (Io). Grey triangles represent cells that did not trigger after bead introduction, while filled circles represent cells that did trigger. Some cells are designated as non-triggered cell even though their values are higher than a triggered cell due to the shape of the calcium flux profile. The profile of those non-triggered cells rises and then falls back below baseline in quick succession (within ~1-2 minutes), whereas the lower values considered “triggered” have sustained calcium flux profiles, and thus extended signaling. The width of the rectangles shows the SD with the mean and median as the thick and thin line, respectively. Pie charts show triggering percentage of each population, where colored wedges are the triggering cells. One-way ANOVA used to quantify significant difference in triggering normalized fluorescence means; ***P<0.001 , **P<0.01 , *P<0.05. FIG. 12G shows average fluorescence curves of SCAR data from the high- resolution microscope with the baseline fluorescence subtracted out (solid line). Individual curves from all triggering cells from each respective cell line were pooled (combining interfacial pMHC concentrations of 20 and 2 with force for NP63, NP34 and NP41 and 2 with force for N15) and averaged at time zero. All curves fit to y = A * (1 - e-x / t), where y is the fluorescence intensity, A is the amplitude, t is the rise time constant (s), and x is the time (s) (dashed line). The amplitudes and 95% confidence intervals for NP63, NP34, NP41, and N15 are 4,085 ± 106,2,547 ± 146, 1 ,425 ± 22, and 2,993 ± 47, respectively. The rise-time constants and 95% confidence intervals for NP63, NP34, NP41 , and N15 are 261 ± 14.8, 293 ± 34.1 , 108 ± 6.9, and 185 ± 8.1 seconds, respectively. N15 recognizes VSV8 / Kb, an epitope derived from vesicular stomatitis virus, unlike the NP TCRs which bind NP366 / Db.

[0035] Fig. 13A and 13B show superior activation ability of T cells expressing digital NP63 TCR in response to tetramer stimulation in vitro. FIG. 13A shows tetramer binding of NP3s6-374 / DbWT-tetramer (left) and CD8BS-mutant tetramer (right) to NP34-, 41-, and 63-BW cell lines and BW UT, cells untransduced with a NP-specific TCR. Tetramer treatment was performed at 20 °C for 30 minutes. Data are representative of two independent experiments. ****p <0.0001 , **P<0.01; ns, not significant. P values were calculated by linear regression. FIG. 13B is a histogram of tetramer (top), anti-CD3 (middle), and anti-CD69 binding (bottom) for NP34-, NP41-, and NP63-BW cell lines after stimulation with indicated concentration of NP366-374 / DbWT-tetramer at 37 °C overnight, as shown in Fig. 6F, 6I and 6J, respectively. Data are representative of three replicates in two independent experiments.

[0036] FIGs. 14A to 141 show NP34 and NP63 Rg T cell activation in vivo after IAV infection. FIG. 14A shows representative FACS plots of NP34 (mCherry+) and NP63 (GFP+) T cells in ml_N of mixed RgC mice 7 days after PR8 infection shown in Fig. 6K. Data were derived after first gating on CD8+cells. Control mice were injected with PBS without Rg T cells when the RgC mice were generated. FIGs. 14B and 14C show EdU incorporation in NP34 and NP63 T cells. Representative FACS plots (FIG. 14B) and quantification (FIG. 14C) are shown. FIG. 4D shows original images of ml_N Rg T cell- (top) and lung Rg T cell (bottom)-mediated killing of LET1 cells infected with 4x108EID5o PR8 at 20 hours, as shown in Fig. 6M. Naive splenic T cells were used as control (Naive SP) of uninfected B6 mice (bottom right). Rg T cells were sorted from six pooled RgC mice that received a single RgT cell type (dpi 7) and then cultured on infected LET1 cells. LET1 cells are visualized by transduced mCherry, apoptotic cells are visualized in green using Caspase-3 / 7 Green ReadyProbes, and Rg T cells are stained in blue with Cell Proliferation Dye eFluor 450. Scale bars in main figure panels indicate 1000 pm. Upper left windows are zoomed in views shown in Fig. 6M relative to the larger image with the squares outlined in white dotted lines indicating their position in the original images. Scale bars in white windows indicate 100 pm. FIG. 14E shows time-course of T cell-mediated killing of LET1 cells infected PR8 with indicated doses. mLN and lung Rg T cells were derived from RgC mice adoptively transferred with a single RgT type (dpi 7). FIG. 14F shows intracellular NP protein expression in LET1 cells infected with the indicated dose of PR8 and then analyzed by flow cytometry. FIGs. 14G and 14H shows representative FACS plots (FIG. 14G) and thequantification (FIG. 14H) of NP34 (mCherry+) and NP63 (GFP+) T cells in lungs of mixed RgC mice 7 days after PR8 infection. Intraparenchymal lung T cells shown are identified by lack of anti-CD8a staining after in vivo intravenous staining in conjunction with anti-CD8[3 reactivity following in vitro staining. FIG. 141 shows viral titer in lungs of single NP34- and 63-RgC mice (dpi 7). For FIGs. 14A-14C, 14G, and 14H, data are representative of four independent experiments. For FIGs. 14D-14F and 141, data are representative of two independent experiments and are shown as means ± SDs of six to eight mice. For all data with statistics, ****p <0.0001 , ***P <0.001 , *P<0.05; ns, not significant. P values were calculated by comparing slopes using linear regression analysis (FIG. 14E), paired t-test (FIGs. 14C and 14H), or unpaired t-test (Fig. 141).

[0037] FIGs. 15 shows SCAR assay revealing the prolonged calcium flux in PA27 and the strong magnitude in PA59 under higher force. Time-course of calcium flux signal indicated as the ratio of maximum fluorescence intensity (lmax) to the initial fluorescence intensity (Io) of the Ca2+-sensitive dye for PA25-, 27-, 59-BW cells at 8-12 pN (solid lines) and PA59 at 16-18 pN (dotted line).

[0038] FIGs. 16A to 16G show the greatest proliferation and activation 1290 of PA TCR- expressing cells is observed for PA27 after tetramer stimulation in vitro. FIG. 16A shows CD25 expression on the indicated TCR-transduced BW cells unstimulated (US) or stimulated with 10ug / ml PA224-233 peptide (Pep) overnight. FIG. 16B shows tetramer binding of PA224-233 / Db WT- tetramer (left) and CD8BS mutant-tetramer (right) for the indicated BW cell lines. Tetramer was treated for 30 minutes at 20 °C. FIG. 16C contains representative histograms of PA-tetramer binding (left) as well as CD3 (middle) and CD69 expression (right) for PA25-, 27-, 59-, and untransduced-BW cell lines following overnight stimulation with 10 pg / mL PA224-233 / DbWT- tetramer at 37 °C (bold lines), as shown in FIGs. 7E, 7H, 7I, respectively. Shaded histograms represent control culture without tetramer addition. FIG. 16D shows CD3 expression (left) and tetramer binding (right) on indicated BW cells 30 minutes after PA224-233 / DbWT-tetramer stimulation at indicated temperatures. FIG. 16E contains representative FACS plots of FSC-A and SSC-A for the indicated BW cells cultured for 1 hour at 37 °C with PA224-233 / D15WT-tetramer (3 pg / mL), its CD8BS-mutant tetramer variant (22 pg / mL), or no tetramer. The cells with larger FSC and SSC are identified as proliferating cells shown in Fig. 7J and 7K. FIG. 16F shows proliferation molecule Ki67 expression in PA27-BW cells with or without PA224-233 / DbWT- tetramer stimulation (1 .2 pg / mL) for 1 hour at 37 °C. Top panels show proliferating and resting cells based on SSC-A and FSC-A scatter used for gating to determine Ki67 levels shown in the bottom row. The parentheses show gMFI. FIG. 16G shows proliferation of indicated NP-BWcells with NP366-374 / DbWT-tetramer (left) and CD8BS mutant-tetramer (right) stimulation. Data are normalized by subtracting the percentage of proliferating cells without stimulation from the percentage with stimulation. Data are shown as means ± SEMs of technical replicates. For FIGs. 16A-16C, 16E and 16G, data are representative of two independent experiments. For FIG. 16B and 6G, ****p <0.0001 , ***P <0.001, **P <0.01 , *P<0.05; ns, not significant. P values were calculated by comparing slopes of linear regression.

[0039] FIGs. 17A and 17B shows bin by bin cumulative probability distributions fit a double exponential with long- and short- time constants for dissociation. FIG. 17A shows double exponential fit to cumulative probability distribution of lifetimes within the 15 pN bin for PA25, PA27, and PA59. Equation used was y = A * (1 - C-A / M ) + B * (1 - e-x / t2~). Solid lines show fit with 95% confidence intervals shown by dashed lines. FIG. 17B shows plots comparing time constants from the double exponential fit to the averages from the catch bond curves for each clone, respectively. For time constants, 95% confidence for each parameter are shown. In contrast, catch bond averages are plotted with SEM. In each PA receptor system there is an underlying baseline < 2 seconds of quick dissociation events, and a second population of long lifetime events.

[0040] FIGs. 18A to 18D shows analysis of PA-specific T cells in mixed RgC mice after IAV infection. FIG. 18A shows representative FACS plots of RgT cells after cell sorting and mixing PA25, 27, and 59 RgT cells at a 1 :1 :1 ratio. PA25 is identified as mCherry+V|37+, PA27 is as GFP+Vp9+, PA59 is as GFP+V[37+cells. Those cells are adoptively transferred into recipient B6 mice to generate mixed RgC mice. FIG. 18B shows representative FACS plots of EdU incorporation in PA25, PA27, and PA59 RgT cells in mediastinal LN (mLN) of mixed RgC mice 7 days after PR8 infection, as shown in Fig. 9D. FIGs. 18C and 18D contain representative FACS plots (FIG. 18C) of T cell populations in the lungs of mixed RgC as well as individual Rg populations or controls and their quantification (FIG. 18D) given as the % of PA25, PA27, and PA59 lung resident Rg T cells of mixed RgC mice 7 days after PR8 infection. For FIGs. 18B- 18D, data are representative of four independent experiments. P values were calculated by paired t-test (FIG. 18D). ns, not significant.

[0041] Fig. 19A to 19D show mLN PA27 T cells are strongly transcriptionally activated after IAV infection. FIG. 19A shows experimental schema of a TCR labeling and sorting system for bulk RNA-seq. Different fluorescence-tagged and TCR-expressing PA-Rg T cells were sorted as fluorescence+ CD8[3+CD44-, mixed with at a ratio of 1:1:1 , and then adoptively transferred into recipient B6 mice. These animals then were infected with PR8 24 hours posttransfer. At seven-days post infection, Rg T cells in mLN or lungs of the mixed RgC mice weresorted as fluorescence protein+, intravenous staining of CD8cr, and in vitro staining of CD8+cells and populations used to perform RNA-seq. No anti-TCR mAbs were used for this experiment. FIG. 19B shows Principal Component Analysis (PCA) of ml_N and lung samples for PA25-, PA27-, and PA59-Rg T cells. FIGs. 19C and 19D show Gene Set Enrichment Analysis (GSEA) based on pair-wise gene expression comparison between PA-Rg T cell samples in mLN (FIG. 19C) and lung (FIG. 19D). Statistical significance of gene set enrichment is indicated with asterisks for several thresholds of adjusted P-values (****p < 0.0001 , ***P < 0.001 , **P < 0.01, *P < 0.1).

[0042] FIGs. 20A to 20G show all lung Rg T cells significantly increase various activation genes after IAV infection. FIG. 20A contains Volcano plots showing differentially expressed genes (DEGs) between mLN and lung of PA25 (left), PA27 (middle), and PA59 (right). Significantly up- and down-regulated genes indicated with red and blue dots (fold-change threshold of 2 and adjusted P-value threshold of 0.05). Remarkably expressing-, effector-, cytotoxic-, and TCR signaling-genes are labeled and shown as triangles. FIG. 20B shows GSEA results based on gene expression comparison between mLN and lung of PA25 (left), PA27 (middle), and PA59 (right). Statistical significance is indicated for several thresholds of adjusted P-values with asterisks (****p < 0.0001 , ***P < 0.001 , **P < 0.01 , *P < 0.1). FIG. 20C and 20D show Volcano and GSEA plots similar to (FIG. 20A) and (FIG. 20B), but representing results for the comparisons of aggregated Rg T cells in mLN and lung. FIG. 20E shows indicated gene expression in aggregated Rg T cells in mLN and lung. FIG. 20F shows viral titer in lungs of single PA25-, PA27-, PA59-RgC mice (dpi 7) determined by real-time PCR. Control mice were injected PBS without Rg T cells when RgC mice were generated. For FIGs. 20E and 20F, data are shown as means ± SDs of eight samples (FIG. 20E) or six to eight mice (FIG. 20F). ****p <0.0001 , ***P <0.001 , **P <0.01 , *P<0.05; ns, not significant. P values were calculated by unpaired t-test. FIG. 20G show time-course of T cell-mediated killing of LET 1 cells infected with PR8 at a dose of 4x108EID5. mLN and lung Rg T cells were derived from RgC mice adoptively transferred with the indicated RgT type (dpi 7).

[0043] FIG. 21 is a flowchart illustrating an in silico method for detecting digital vs. analog TCR interactions.

[0044] FIG. 22 is a schematic block diagram illustrating an example of a processing or computing device that can be used for implementation of in silico TCR testing.DETAILED DESCRIPTION

[0045] Before the present disclosure is described in greater detail, it is to be understood that this disclosure is not limited to particular embodiments described, and as such may, ofcourse, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present disclosure will be limited only by the appended claims.

[0046] Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range, is encompassed within the disclosure. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

[0047] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present disclosure, the preferred methods and materials are now described.

[0048] All publications and patents cited in this specification are herein incorporated by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference and are incorporated herein by reference to disclose and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present disclosure is not entitled to antedate such publication by virtue of prior disclosure. Further, the dates of publication provided could be different from the actual publication dates that may need to be independently confirmed.

[0049] As will be apparent to those of skill in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has discrete components and features which may be readily separated from or combined with the features of any of the other several embodiments without departing from the scope or spirit of the present disclosure. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.

[0050] Embodiments of the present disclosure will employ, unless otherwise indicated, techniques of chemistry, biology, and the like, which are within the skill of the art.

[0051] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to perform the methods and use theprobes disclosed and claimed herein. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in °C, and pressure is at or near atmospheric. Standard temperature and pressure are defined as 20 °C and 1 atmosphere.

[0052] Before the embodiments of the present disclosure are described in detail, it is to be understood that, unless otherwise indicated, the present disclosure is not limited to particular materials, reagents, reaction materials, manufacturing processes, or the like, as such can vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting. It is also possible in the present disclosure that steps can be executed in different sequence where this is logically possible.Definitions

[0053] It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.

[0054] The term “subject” refers to any individual who is the target of administration or treatment. The subject can be a vertebrate, for example, a mammal. Thus, the subject can be a human or veterinary patient. The term “patient” refers to a subject under the treatment of a clinician, e.g., physician.

[0055] The term “therapeutically effective” refers to the amount of the composition used is of sufficient quantity to ameliorate one or more causes or symptoms of a disease or disorder. Such amelioration only requires a reduction or alteration, not necessarily elimination.

[0056] The term “pharmaceutically acceptable” refers to those compounds, materials, compositions, and / or dosage forms which are, within the scope of sound medical judgment, suitable for use in contact with the tissues of human beings and animals without excessive toxicity, irritation, allergic response, or other problems or complications commensurate with a reasonable benefit / risk ratio.

[0057] The term “treatment” refers to the medical management of a patient with the intent to cure, ameliorate, stabilize, or prevent a disease, pathological condition, or disorder. This term includes active treatment, that is, treatment directed specifically toward the improvement of a disease, pathological condition, or disorder, and also includes causal treatment, that is, treatment directed toward removal of the cause of the associated disease, pathological condition, or disorder. In addition, this term includes palliative treatment, that is, treatment designed for the relief of symptoms rather than the curing of the disease, pathologicalcondition, or disorder; preventative treatment, that is, treatment directed to minimizing or partially or completely inhibiting the development of the associated disease, pathological condition, or disorder; and supportive treatment, that is, treatment employed to supplement another specific therapy directed toward the improvement of the associated disease, pathological condition, or disorder.

[0058] The term “chimeric antigen receptor” or “CAR,” as used herein, refers to an artificial T cell receptor that is engineered to be expressed on an immune effector cell and specifically bind an antigen. CARs may be used as a therapy with adoptive cell transfer. T cells are removed from a patient and modified so that they express the receptors specific to a particular form of antigen. In some embodiments, the CARs have been expressed with specificity to a tumor associated antigen, for example. CARs may also comprise an intracellular activation domain, a transmembrane domain and an extracellular domain comprising a tumor associated antigen binding region. The specificity of CAR designs may be derived from ligands of receptors (e.g., peptides). In some embodiments, a CAR can target cancers by redirecting the specificity of a T cell expressing the CAR specific for tumor associated antigens.

[0059] The term “T cell” refers to T lymphocytes as defined in the art and is intended to include thymocytes, immature T lymphocytes, mature T lymphocytes, resting T lymphocytes, or activated T lymphocytes. The T cells can be CD4+ T cells, CD8+ T cells, CD4+CD8+ T cells, or CD4-CD8- cells. The T cells can also be T helper cells, such as T helper 1 (TH1), or T helper 2 (TH2) cells, or TH 17 cells, as well as cytotoxic T cells, regulatory T cells, natural killer T cells, naive T cells, memory T cells, or gamma delta T cells.

[0060] The T cells can be a purified population of T cells, or alternatively the T cells can be in a population with cells of a different type, such as B cells and / or other peripheral blood cells. The T cells can be a purified population of a subset of T cells, such as CD4+ T cells, or they can be a population of T cells comprising different subsets of T cells. In another embodiment of the invention, the T cells are T cell clones that have been maintained in culture for extended periods of time. T cell clones can be transformed to different degrees. In a specific embodiment, the T cells are a T cell clone that proliferates indefinitely in culture.

[0061] In some embodiments, the T cells are primary T cells. The term “primary T cells” is intended to include T cells obtained from an individual, as opposed to T cells that have been maintained in culture for extended periods of time. Thus, primary T cells are particularly peripheral blood T cells obtained from a subject. A population of primary T cells can be composed of mostly one subset of T cells. Alternatively, the population of primary T cells can be composed of different subsets of T cells.Mechanical Forces

[0062] Optical and magnetic tweezers have been commonly employed to apply local subcellular forces using functionalized microbeads attached to cell membrane via ligandreceptor binding. Optical tweezer can apply forces typically in the piconewton (pN) range, which is suitable for manipulation of single molecules. Magnetic tweezer can apply local subcellular pulling force as well as twisting stress in the range of pN to nanonewton (nN) by actuating magnetic beads functionalized with specific membrane receptor ligands. Magnetic tweezer has been successfully applied to mammalian cells to regulate gene expression and even stem cell differentiation.

[0063] Acoustic tweezers (also known as acoustical tweezers) are a set of tools that use sound waves to manipulate the position and movement of very small objects. The technology works by controlling the position of acoustic pressure nodes] that draw objects to specific locations of a standing acoustic field. The target object must be considerably smaller than the wavelength of sound used, and the technology is typically used to manipulate microscopic particles. [citation needed] In a standing acoustic field, objects experience an acoustic-radiation force that moves them to specific regions of the field. Depending on an object's properties, such as density and compressibility, it can be induced to move to either acoustic pressure nodes (minimum pressure regions) or pressure antinodes (maximum pressure regions). As a result, by controlling the position of these nodes, the precise movement of objects using sound waves is feasible.

[0064] Atomic force microscopy (AFM) can monitor small forces applied over a surface using a sharp probe mounted on a flexible cantilever, which acts as a spring. The basic components of an atomic force microscope include a piezoelectric scanner, flexible cantilever containing a sharp probe, laser, photodiode detector, and feedback electronics. AFM is based on a simple principle whereby the movements of a flexible cantilever are monitored. The movements of the flexible cantilever can be monitored by changes in laser deflection off of a reflective surface on the backside of the cantilever. A photodiode detector detects the changes in deflection of the laser. In many commercial systems, the sample sits on the piezoelectric scanner, which can move in all three dimensions by applying voltage to the piezoelectric material, while the cantilever remains in a fixed position. In other systems, the piezoelectric scanner is attached to the AFM cantilever holder to directly control the movement of the cantilever while the sample remains stationary. This basic set-up allows for both high-resolution imaging and probing the molecular interactions of biological samples.

[0065] Recent advancements in dynamic force spectroscopy (DFS) techniques have enabled the application and measurement of forces and displacements with high resolutions, providing crucial insights into the mechanical pathways underlying these diseases. Among DFS techniques, the biomembrane force probe (BFP) stands out for its ability to measure bond kinetics and cellular mechanosensing with pico-newton and nano-meter resolutions. A Biomembrane Force Probe (BFP) is a sensitive technique that allows the quantification of single molecular bonds. It is a versatile tool that can be used in a wide range of forces (0.1 pN to 1 nN) and loading rates (1-106pN / s).

[0066] Biomembrane force probe (BFP) stemmed from micropipette aspiration techniques and was intended to investigate the strength of single molecular bonds under sub- microscopic forces at biological interfaces. The conventional BFP setup consists of two opposing micropipettes aligned along their horizontal axis. One micropipette, which is held stationary, aspirates a biotinylated cell. A streptavidin-coated glass microbead, which can be coated with ligands of interest, is attached on the apex of the cell. Another micropipette, controlled by a piezoelectric translator holds the opposing bead or cell bearing complimentary receptors and is driven to impinge the probe bead in a repeated “approach-push-retract-hold- return” test cycle. A third micropipette (termed ‘Helper’) is typically utilized to attach the streptavidin-coated glass bead onto the cell apex. Micropipette aspiration applies a pressure that allows the cell to serve as a hypersensitive force transducer. Typically, the BFP is configured with at least two cameras, an inverted microscope with a dry objective lens, a lamp as a light source, and several video tubes. One camera operates at high speed to track the displacement of the cell-Probe edge, while the other allows real-time visualization of the ongoing experiment. Enabled by fast video processing, the cell-Probe edge is tracked along the pulling direction at a video rate of up to 1600 fps provided the images are reduced to a 24-30- line strip across the bead.

[0067] In some embodiments the force is applied using fluid flow. For example, the cells can be placed in a laminar flow chamber (LFC) apparatus. In this assay, T cells or beads coated with TCR can flow over a low density of pMHC (or visa verse) while a camera records motion. As a result of the anchoring flexibility of the TCR on the bead and the p HC on the surface (or visa versa), the flow velocity resolves into a pulling force along the TCR / pMHC bond axis.

[0068] The adaptive immune system is operating by way of specific interactions between immune cells such as CD8+ or CD4+ T cells and antigen-presenting cells such as dendritic cells, virus-infected cells, and cancer cells. T-lymphocytes (T cells) specifically recognize and bind target cells by interaction of their T cell receptors with MajorHistocompatibility Complexes (MHC) on the target cells. MHC complexes are typically bound to a peptide, and the complexes may then be termed pMHC complexes.

[0069] MHC complexes are found in three variations: MHC class 1 (MHC1), MHC class 2 (MHC2), and MHC-like complexes and proteins.

[0070] For attachment of the pMHCI complex to a Multimer scaffold or other structure or molecule, the N-terminus of the Heavy Chain (HC) may be modified chemically, e.g., it may be fused to the Acceptor Peptide (AP), capable of being biotinylated in vitro or in vivo by biotin ligase (BirA), or it may be fused to the Acid Peptide or Base Peptide, in order to dimerize with the Base Peptide or Acid Peptide, respectively. In another preferred embodiment, the C- terminus of HC or the N- or C-terminus of beta2M may be used to attach the pMHCI complex to a Multimer scaffold or other structure or molecule. Similarly, for attachment of the pMHC2 complex, the N- or C-terminus of the alpha or beta subunits may be modified chemically or may be fused to e.g., the AP-, Acid- or Base Peptide.

[0071] Peptide-receptive MHCI complexes are MHC class I molecules stabilized - in some instances - by a disulfide bond to link the al and a2 helices close to the F pocket and are described in Saini et al., Sci. Immunol. 4, eaau9039 (2019). These disulfide-stabilized MHC class I molecules can be loaded with peptide in the multimerized form allowing for easy binding and display of different peptide epitopes and corresponding formation of functional pMHCI complexes.

[0072] A specific disulfide mutant variant of the human MHC-I protein HLA-A*02:01 has been constructed by introducing two cysteines at positions 84 and 139 (replacing tyrosine and alanine, respectively. This variant had restricted conformational flexibility and a consequently increased stability of the peptide-free state compared with wild-type. Introducing the disulfide bond between the al and a2 helices at positions 84 and 139 eliminated the tendency of the empty binding groove to collapse and keeping the capacity to bind exogenous peptide.

[0073] A similar peptide-receptive MHCI complex has been constructed with human MHC-I molecule HLA-A*24:02.

[0074] Peptide-receptive MHCI complexes can be constructed using other isotypes including but not limited to HLA-A (HLA-A), HLA-B (HLA-B), HLA-C (HLA-C), and some less polymorphic such as HLA-E (HLA-E), HLA-F (HLA-F), HLA-G (HLA-G).

[0075] The peptide of a pMHC complex may comprise 2-1000 amino acid residues, such as 2-4, 5-6, 7-8, 9-10, 11-12, 13-15, 16-20, 21-30, or 30-50 amino acid residues, or more.

[0076] The peptide of a pMHC complex is also called the epitope, neoepitope, peptide epitope, or peptide neoepitope.

[0077] The sequence of the peptide of the pMHC complex may be nonsense, i.e. not originate from any known peptide sequence in Nature, or may be identical to a sequence in the human genome, a virus genome, a bacterial genome, a parasite genome, or a mutant sequence identified in a patient, e.g., in a biopsy from a cancer patient.

[0078] The pMHC can be labelled with any label including DNA oligonucleotides, fluorochromes (e.g., FITC, PE (phycoerythrin), PerCP, APC, GFP, etc.), Lanthanides such as e.g., Lanthanum, Cerium, Praseodymium, Neodymium, Promethium, Samarium, Europium, Gadolinium, Terbium, Dysprosium, Holmium, Erbium, Thulium, Ytterbium, Lutetium, or other chromophores.

[0079] The label may be attached to the pMHC by covalent or non-covalent bond, or by a mechanical bond. For example, filamentous phage M13 may be labelled with anti-M13 phage antibodies carrying a fluorochrome or a chromophore, where the antibodies bind to the coat of the phage. Alternatively, anti- beta2M antibodies or anti-HC antibodies, recognizing beta2M and HC, respectively, carrying fluorochromes or fluorophores may be used.

[0080] pMHC collections ("libraries") may be used for e.g., the screening for e.g., i) disease-specific T cells, ii) cells of a certain haplotype or general ability to bind a certain peptide epitope or T cell receptor or T cell, iii) pairs of T cell receptors and pMHC complexes, iv) or interrecognizing pairs of T cell and antigen-presenting cells; or may be used for e.g., the modification (e.g., activation, stimulation, proliferation, killing or other types of modification) of cells such as antigen-specific T cells or antigen-presenting cells such as dendritic cells; or may be used for the sorting and / or enrichment of certain types of cells such as e.g., (i) antigenspecific T cells, (ii) antigen-presenting cells, and / or (iii) "antigen-specific T cell / antigen- presenting cell pairs".Surrogate markers

[0081] Also disclosed herein is a method for detecting a digital T cell receptor peptide interaction that involves contacting an op T cell receptor (apTCR) on a T cell with an antigen peptide bound to a major histocompatibility complex (MHO) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the apTCR and the pMHC; and assaying the T cell for CD3 and CD69 surface expression, wherein CD3 loss and CD69 upregulation in the T cell is an indication of a digital T cell receptor interaction. CD3 and CD69 expression can be detected using routine methods such as flow cytometery.

[0082] Also disclosed is a method for detecting a digital T cell receptor peptide interaction that involves contacting an op T cell receptor (apTCR) on a T cell with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditionssuitable to promote a catch bond or slip bond between the o[3TCR and the pMHC; and assaying the T cell in a cytotoxicity assay at a 4:1 , 3:1 , 2:1 , 1:1 , 1 :2, 1 :3, 1 :4 T cell to target ratio; wherein a T cell that initiates killing within 1 , 2, 3, 4, 5, 6 hours in the cytotoxicity assay with at least 30%, 40%, 50%, 60% target killing is an indication of a digital T cell receptor interaction. Cytotoxicity (immune cell killing) assays are known in the art and commercially available.Vaccine copy number

[0083] Also disclosed is a method for producing a vaccine encoding a neoantigen or tumor associated antigen to stimulate digital performance T cells directed at a tumor involving generating digital responder T cells through elicitation of an effective immune response directed at the tumor; determining the average copy number of an encoded pMHC epitope in the immune peptidome of an antigen presenting cell by mass spectrometry (generally in the 10-100 copy range / cell) in the good responder subject; and engineering avaccine to produce a comparable number of encoded antigens on antigen presenting cells of a vaccinated subject.

[0084] Mass spectrometry for determining the average copy number of an encoded pMHC epitope in the immune peptidome of an antigen presenting cell are described, for example in Sengupta, et al. Proc Natl Acad Sci U S A. 2022 119(15):e2123406119, which is incorporated by reference in its entirety for the teaching of this method. Briefly, this involves adding a heavy isotope (isotope-labeled) version of the peptide epitope to the samples and them assaying the samples by mass spectrometry.In silico

[0085] Also disclosed herein is an in silico method to test digital vs analog TCRs. This is illustrated in Figure 21 and involves use of an all-atom molecular dynamics simulation of the TCR-pMHC complex under various mechanically loaded states to find the catch bond formation and propensity for volleying. Methods of applying mechanical load include tensional and shear forces as well as the 'staged pulling1where cycles of applying forces and relaxing the system are applied to gauge the propensity for unfolding. A mathematical model is also developed to obtain parameters from experimental volleying traces to quantify signatures of digital vs analog TCRs and relate them with atomistic simulation.

[0086] Initially, atomistic structure of the TCR-pMHC complex obtained either from experiment or generated computationally is used to perform all-atom MD simulation with different levels of loads. Catch bond formation is detected if the interface between TCR and pMHC stabilizes under force ranges corresponding to those present in biological tissues. Next, the TCR is subjected to staged pulling simulations that consist of intervals of pulling with a given force and holding the system steady without pulling. If many cycles of staged pulling or highpulling force is needed (low unfolding propensity), it would be difficult for the TCR to unfold readily and volley. TCRs that are easier to unfold will likely volley more easily (high unfolding propensity), are more likely to be digital. These predictions are validated by analyzing experimental volleying data of the corresponding TCRs using a mathematical model, to extract parameters characterizing volleying behaviors. The mathematical model comprises coupled differential equations that describe probabilities to be in the compact and extended states, and dissociation of the complex. A high dissociation rate corresponds to short bond lifetime. For certain range of parameters that depend on the applied force and intrinsic properties of the TCR-pMHC complex in question, rates of transition between the compact (folded) and extended (unfolded) states can be much larger than dissociation rates, indicative of volleying with prolonged bond lifetime. The experimental volleying data for applying the model are time traces of the force or equivalently, the position of the bead of the optical tweezer. From the time trace, transition rates used for the model can be obtained. Validation of high or low unfolding propensity obtained in atomistic simulations can be made by comparing them with the model parameters obtained from the volleying data.

[0087] For example, an in silico method for detecting a T cell receptor peptide interaction can comprise providing a T cell comprising a op T cell receptor (a[3TCR) and an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC); determining a likelihood of an analog or digital receptor interaction based upon an unfolding propensity of the T cell receptor; and validating the analog or digital receptor interaction based upon model analysis of experimental volleying data of the T cell. The in silico method can comprise subjecting the T cell to staged pulling simulations prior to determining the unfolding propensity of the T cell. The staged pulling simulations can comprise intervals of pulling with a given force and holding steady without pulling. The staged pulling simulations can be performed in response to detection of catch bond formation based upon stabilization of a TCR and pMHC interface under different load levels. The model analysis can comprise determining one or more transition rate between compact and extended states of the T cell based upon the experimental volleying data. The experimental volleying data can comprise time traces of feree. The validation can be based upon comparison of one or more transition rate with the unfolding propensity.

[0088] With reference next to FIG. 22, shown is a schematic block diagram of a processing or computing device 1000. In some embodiments, among others, the computing device 1000 may represent one or more computing devices (e.g., a computer, server, tablet, smart phone, etc.). Each processing or computing device 1000 includes at least one processorcircuit, for example, having a processor 1003 and a memory 1006, both of which are coupled to a local interface 1009. To this end, each processing or computing device 1000 may comprise, for example, at least one server computer or like device, which can be utilized in a cloud-based environment. The local interface 1009 may comprise, for example, a data bus with an accompanying address / control bus or other bus structure as can be appreciated.

[0089] In some embodiments, the processing or computing device(s) 1000 can include one or more network interfaces. The network interface may comprise, for example, a wireless transmitter, a wireless transceiver, and / or a wireless receiver (e.g., Bluetooth®, Wi-Fi, Ethernet, etc.). The network interface can communicate with a remote computing device using an appropriate communications protocol. As one skilled in the art can appreciate, other wireless protocols may be used in the various embodiments of the present disclosure.

[0090] Stored in the memory 1006 are both data and several components that are executable by the processor 1003. In particular, stored in the memory 1006 and executable by the processor 1003 are at least in silico TCR testing application 1012 and potentially other applications and / or programs. Also stored in the memory 1006 may be a data store 1015 and other data. In addition, an operating system 1018 may be stored in the memory 1006 and executable by the processor 1003.

[0091] It is understood that there may be other applications that are stored in the memory 1006 and are executable by the processor 1003 as can be appreciated. Where any component discussed herein is implemented in the form of software, any one of a number of programming languages may be employed such as, for example, C, C++, C#, Objective C, Java®, JavaScript®, Perl, PHP, Visual Basic®, Python®, Ruby, Flash®, or other programming languages.

[0092] A number of software components are stored in the memory 1006 and are executable by the processor 1003. In this respect, the term "executable" means a program or application file that is in a form that can ultimately be run by the processor 1003. Examples of executable programs may be, for example, a compiled program that can be translated into machine code in a format that can be loaded into a random access portion of the memory 1006 and run by the processor 1003, source code that may be expressed in proper format such as object code that is capable of being loaded into a random access portion of the memory 1006 and executed by the processor 1003, or source code that may be interpreted by another executable program to generate instructions in a random access portion of the memory 1006 to be executed by the processor 1003, etc. An executable program may be stored in any portion or component of the memory 1006 including, for example, random access memory (RAM),read-only memory (ROM), hard drive, solid-state drive, USB flash drive, memory card, optical disc such as compact disc (CD) or digital versatile disc (DVD), floppy disk, magnetic tape, or other memory components.

[0093] The memory 1006 is defined herein as including both volatile and nonvolatile memory and data storage components. Volatile components are those that do not retain data values upon loss of power. Nonvolatile components are those that retain data upon a loss of power. Thus, the memory 1006 may comprise, for example, random access memory (RAM), read-only memory (ROM), hard disk drives, solid-state drives, USB flash drives, memory cards accessed via a memory card reader, floppy disks accessed via an associated floppy disk drive, optical discs accessed via an optical disc drive, magnetic tapes accessed via an appropriate tape drive, and / or other memory components, or a combination of any two or more of these memory components. In addition, the RAM may comprise, for example, static random access memory (SRAM), dynamic random access memory (DRAM), or magnetic random access memory (MRAM) and other such devices. The ROM may comprise, for example, a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other like memory device.

[0094] Also, the processor 1003 may represent multiple processors 1003 and / or multiple processor cores and the memory 1006 may represent multiple memories 1006 that operate in parallel processing circuits, respectively, such as multicore systems, FPGAs, GPUs, GPGPUs, spatially distributed computing systems (e.g., connected via the cloud and / or Internet). In such a case, the local interface 1009 may be an appropriate network that facilitates communication between any two of the multiple processors 1003, between any processor 1003 and any of the memories 1006, or between any two of the memories 1006, etc. The local interface 1009 may comprise additional systems designed to coordinate this communication, including, for example, performing load balancing. The processor 1003 may be of electrical or of some other available construction.

[0095] Although the in silico TCR testing application 1012 and other applications / programs, described herein may be embodied in software or code executed by general purpose hardware as discussed above, as an alternative the same may also be embodied in dedicated hardware or a combination of software / general purpose hardware and dedicated hardware. If embodied in dedicated hardware, each can be implemented as a circuit or state machine that employs any one of or a combination of a number of technologies. These technologies may include, but are not limited to, discrete logic circuits having logic gates forimplementing various logic functions upon an application of one or more data signals, application specific integrated circuits (ASICs) having appropriate logic gates, field- programmable gate arrays (FPGAs), or other components, etc. Such technologies are generally well known by those skilled in the art and, consequently, are not described in detail herein.

[0096] Also, any logic or application described herein, including the in silico TCR testing application 1012 and other applications / programs, that comprises software or code can be embodied in any non-transitory computer-readable medium for use by or in connection with an instruction execution system such as, for example, a processor 1003 in a computer system or other system. In this sense, the logic may comprise, for example, statements including instructions and declarations that can be fetched from the computer-readable medium and executed by the instruction execution system. In the context of the present disclosure, a "computer-readable medium" can be any medium that can contain, store, or maintain the logic or application described herein for use by or in connection with the instruction execution system.

[0097] The computer-readable medium can comprise any one of many physical media such as, for example, magnetic, optical, or semiconductor media. More specific examples of a suitable computer-readable medium would include, but are not limited to, magnetic tapes, magnetic floppy diskettes, magnetic hard drives, memory cards, solid-state drives, USB flash drives, or optical discs. Also, the computer-readable medium may be a random access memory (RAM) including, for example, static random access memory (SRAM) and dynamic random access memory (DRAM), or magnetic random access memory (MRAM). In addition, the computer-readable medium may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or other type of memory device.

[0098] Further, any logic or application described herein, including the in silico TCR testing application 1012 and other applications / programs, may be implemented and structured in a variety of ways. For example, one or more applications described may be implemented as modules or components of a single application. The flowchart or diagram of FIG. 21 shows an example of the architecture, functionality, and operation of a possible implementation of the in silico TCR testing application 1012. In this regard, each block can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the blocks may occur out of the order noted in FIG. 21. For example, two blocks shown in succession in FIG. 21 may in fact be executed substantially concurrently or theblocks may sometimes be executed in a different or reverse order, depending upon the functionality involved. Alternate implementations are included within the scope of the preferred embodiment of the present disclosure in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present disclosure. Further, one or more applications described herein may be executed in shared or separate computing devices or a combination thereof. For example, a plurality of the applications described herein may execute in the same processing or computing device 1000, or in multiple computing devices in the same computing environment. Additionally, it is understood that terms such as “application,” “service,” “system,” “engine,” “module,” and so on may be interchangeable and are not intended to be limiting.Digital T cells

[0099] Disclosed herein are digital T cells identified by the methods disclosed herein that can be used in adoptive T cell therapies (e.g., CAR T-cell therapy, a transduced T-cell therapy, and a tumor infiltrating lymphocyte (TIL) therapy).

[0100] Thus, also disclosed are methods for treating an immune system related condition or disease (e.g., cancer) in a subject comprising administering to the subject T cells identified by the methods disclosed herein. Such methods are not limited to a specific type or kind of genetically engineered T cells. In some embodiments, the genetically engineered T cells include, but are not limited to, CAR T cells, genetically engineered TCR expressing T cells, genetically engineered T cells configured for tumor infiltrating lymphocyte (TIL) therapy, genetically engineered T cells configured for transduced T-cell therapy, and / or viral specific T cells reengineered with a TCR or CAR.

[0101] Also disclosed herein is the ex vivo expansion of a population of T cells identified by the methods disclosed herein. Thus, also disclosed are compositions comprising a population of T cells identified by the methods disclosed herein. Also disclosed are kits comprising T cell populations identified by the methods disclosed herein and additional agents (e.g., additional agents useful in expanding T cells) (e.g., additional agents useful in adoptive T cell therapies (e.g., a CAR T-cell therapy, a transduced T-cell therapy, and a tumor infiltrating lymphocyte (TIL) therapy). Such methods are not limited to a specific type or kind of genetically engineered T cells. In some embodiments, the genetically engineered T cells include, but are not limited to, CAR T cells, genetically engineered TCR expressing T cells, genetically engineered T cells configured for tumor infiltrating lymphocyte (TIL) therapy, geneticallyengineered T cells configured for transduced T-cell therapy, and / or viral specific T cells reengineered with a TCR or CAR.

[0102] Such embodiments are not limited to a particular type or kind of an immune system related condition or disease. For example, in some embodiments, the immune system related condition or disease is an autoimmune disease or condition (e.g., Acquired Immunodeficiency Syndrome (AIDS), alopecia areata, ankylosing spondylitis, antiphospholipid syndrome, autoimmune Addison's disease, autoimmune hemolytic anemia, autoimmune hepatitis, autoimmune inner ear disease (AIED), autoimmune lymphoproliferative syndrome (ALPS), autoimmune thrombocytopenic purpura (ATP), Behcet's disease, cardiomyopathy, celiac sprue-dermatitis hepetiformis; chronic fatigue immune dysfunction syndrome (CFIDS), chronic inflammatory demyelinating polyneuropathy (CIPD), cicatricial pemphigoid, cold agglutinin disease, crest syndrome, Crohn's disease, Degos' disease, dermatomyositis-juvenile, discoid lupus, essential mixed cryoglobulinemia, fibromyalgia-fibromyositis, Graves' disease, Guillain-Barre syndrome, Hashimoto's thyroiditis, idiopathic pulmonary fibrosis, idiopathic thrombocytopenia purpura (ITP), IgA nephropathy, insulin-dependent diabetes mellitus, juvenile chronic arthritis (Still's disease), juvenile rheumatoid arthritis, Meniere's disease, mixed connective tissue disease, multiple sclerosis, myasthenia gravis, pernacious anemia, polyarteritis nodosa, polychondritis, polyglandular syndromes, polymyalgia rheumatica, polymyositis and dermatomyositis, primary agammaglobulinemia, primary biliary cirrhosis, psoriasis, psoriatic arthritis, Raynaud's phenomena, Reiter's syndrome, rheumatic fever, rheumatoid arthritis, sarcoidosis, scleroderma (progressive systemic sclerosis (PSS), also known as systemic sclerosis (SS)), Sjogren's syndrome, stiff-man syndrome, systemic lupus erythematosus, Takayasu arteritis, temporal arteritis / giant cell arteritis, ulcerative colitis, uveitis, vitiligo, Wegener's granulomatosis, and any combination thereof).Therapeutic Methods

[0103] Digital T cells identified by the disclosed methods can be used to elicit an antitumor immune response against cancer cells. The anti-tumor immune response elicited by the disclosed Digital T cells may be an active or a passive immune response. In addition, the immune response may be part of an adoptive immunotherapy approach in which Digital T cells induce an immune response specific to a tumor antigen.

[0104] The disclosed digital T cells may be administered either alone, or as a pharmaceutical composition in combination with diluents and / or with other components such as IL-2, IL-15, or other cytokines or cell populations. Briefly, pharmaceutical compositions may comprise a target cell population as described herein, in combination with one or morepharmaceutically or physiologically acceptable carriers, diluents or excipients. Such compositions may comprise buffers such as neutral buffered saline, phosphate buffered saline and the like; carbohydrates such as glucose, mannose, sucrose or dextrans, mannitol; proteins; polypeptides or amino acids such as glycine; antioxidants; chelating agents such as EDTA or glutathione; adjuvants (e.g., aluminum hydroxide); and preservatives. Compositions for use in the disclosed methods are in some embodimetns formulated for intravenous administration. Pharmaceutical compositions may be administered in any manner appropriate treat MM. The quantity and frequency of administration will be determined by such factors as the condition of the patient, and the severity of the patient's disease, although appropriate dosages may be determined by clinical trials.

[0105] When “an immunologically effective amount”, “an anti-tumor effective amount”, “an tumor-inhibiting effective amount”, or “therapeutic amount” is indicated, the precise amount of the compositions of the present invention to be administered can be determined by a physician with consideration of individual differences in age, weight, tumor size, extent of infection or metastasis, and condition of the patient (subject). It can generally be stated that a pharmaceutical composition comprising the T cells described herein may be administered at a dosage of 104to 109cells / kg body weight, such as 105to 106cells / kg body weight, including all integer values within those ranges. T cell compositions may also be administered multiple times at these dosages. The cells can be administered by using infusion techniques that are commonly known in immunotherapy (see, e.g., Rosenberg et al., New Eng. J. of Med. 319:1676, 1988). The optimal dosage and treatment regime for a particular patient can readily be determined by one skilled in the art of medicine by monitoring the patient for signs of disease and adjusting the treatment accordingly.

[0106] In certain embodiments, it may be desired to administer activated digital T cells to a subject and then subsequently re-draw blood (or have an apheresis performed), activate T cells therefrom according to the disclosed methods, and reinfuse the patient with these activated and expanded T cells. This process can be carried out multiple times every few weeks. In certain embodiments, T cells can be activated from blood draws of from 10 cc to 400 cc. In certain embodiments, T cells are activated from blood draws of 20 cc, 30 cc, 40 cc, 50 cc, 60 cc, 70 cc, 80 cc, 90 cc, or 100 cc. Using this multiple blood draw / multiple reinfusion protocol may serve to select out certain populations of T cells.

[0107] The administration of the disclosed compositions may be carried out in any convenient manner, including by injection, transfusion, or implantation. The compositions described herein may be administered to a patient subcutaneously, intradermally, intratumorally,intranodally, intramedullary, intramuscularly, by intravenous (i.v.) injection, or intraperitoneally. In some embodiments, the disclosed compositions are administered to a patient by intradermal or subcutaneous injection. In some embodiments, the disclosed compositions are administered by i.v. injection. The compositions may also be injected directly into a tumor, lymph node, or site of infection.

[0108] In certain embodiments, the disclosed digital T cells are administered to a patient in conjunction with (e.g., before, simultaneously or following) any number of relevant treatment modalities, including but not limited to thalidomide, dexamethasone, bortezomib, and lenalidomide. In further embodiments, the digital T cells may be used in combination with chemotherapy, radiation, immunosuppressive agents, such as cyclosporin, azathioprine, methotrexate, mycophenolate, and FK506, antibodies, or other immunoablative agents such as CAM PATH, anti-CD3 antibodies or other antibody therapies, cytoxin, fludaribine, cyclosporin, FK506, rapamycin, mycophenolic acid, steroids, FR901228, cytokines, and irradiation. In some embodiments, the CAR-modified immune effector cells are administered to a patient in conjunction with (e.g., before, simultaneously or following) bone marrow transplantation, T cell ablative therapy using either chemotherapy agents such as, fludarabine, external-beam radiation therapy (XRT), cyclophosphamide, or antibodies such as OKT3 or CAMPATH. In another embodiment, the cell compositions of the present invention are administered following B-cell ablative therapy such as agents that react with CD20, e.g., Rituxan. For example, in some embodiments, subjects may undergo standard treatment with high dose chemotherapy followed by peripheral blood stem cell transplantation. In certain embodiments, following the transplant, subjects receive an infusion of the expanded immune cells of the present invention. In an additional embodiment, expanded cells are administered before or following surgery.

[0109] The cancer of the disclosed methods can be any cell in a subject undergoing unregulated growth, invasion, or metastasis. In some aspects, the cancer can be any neoplasm or tumor for which radiotherapy is currently used. Alternatively, the cancer can be a neoplasm or tumor that is not sufficiently sensitive to radiotherapy using standard methods. Thus, the cancer can be a sarcoma, lymphoma, leukemia, carcinoma, blastoma, or germ cell tumor. A representative but non-limiting list of cancers that the disclosed compositions can be used to treat include lymphoma, B cell lymphoma, T cell lymphoma, mycosis fungoides, Hodgkin’s Disease, myeloid leukemia, bladder cancer, brain cancer, nervous system cancer, head and neck cancer, squamous cell carcinoma of head and neck, kidney cancer, lung cancers such as small cell lung cancer and non-small cell lung cancer, neuroblastoma / glioblastoma, ovarian cancer, pancreatic cancer, prostate cancer, skin cancer, liver cancer, melanoma, squamous cellcarcinomas of the mouth, throat, larynx, and lung, endometrial cancer, cervical cancer, cervical carcinoma, breast cancer, epithelial cancer, renal cancer, genitourinary cancer, pulmonary cancer, esophageal carcinoma, head and neck carcinoma, large bowel cancer, hematopoietic cancers; testicular cancer; colon and rectal cancers, prostatic cancer, and pancreatic cancer.

[0110] The disclosed digital T cells can be used in combination with any compound, moiety or group which has a cytotoxic or cytostatic effect. Drug moieties include chemotherapeutic agents, which may function as microtubulin inhibitors, mitosis inhibitors, topoisomerase inhibitors, or DNA intercalators, and particularly those which are used for cancer therapy.

[0111] The disclosed digital T cells can be used in combination with a checkpoint inhibitor. The two known inhibitory checkpoint pathways involve signaling through the cytotoxic T-lymphocyte antigen-4 (CTLA-4) and programmed-death 1 (PD-1) receptors. These proteins are members of the CD28-B7 family of cosignaling molecules that play important roles throughout all stages of T cell function. The PD-1 receptor (also known as CD279) is expressed on the surface of activated T cells. Its ligands, PD-L1 (B7-H1 ; CD274) and PD-L2 (B7-DC; CD273), are expressed on the surface of APCs such as dendritic cells or macrophages. PD-L1 is the predominant ligand, while PD-L2 has a much more restricted expression pattern. When the ligands bind to PD-1, an inhibitory signal is transmitted into the T cell, which reduces cytokine production and suppresses T-cell proliferation. Checkpoint inhibitors include, but are not limited to antibodies that block PD-1 (Nivolumab (BMS-936558 or MDX1106), CT-011 , MK- 3475), PD-L1 (MDX-1105 (BMS-936559), MPDL3280A, MSB0010718C), PD-L2 (rHlgM12B7), CTLA-4 (Ipilimumab (MDX-010), Tremelimumab (CP-675,206)), IDO, B7-H3 (MGA271), B7-H4, TIM3, LAG-3 (BMS-986016).

[0112] Human monoclonal antibodies to programmed death 1 (PD-1) and methods for treating cancer using anti-PD-1 antibodies alone or in combination with other immunotherapeutics are described in U.S. Patent No. 8,008,449, which is incorporated by reference for these antibodies. Anti-PD-L1 antibodies and uses therefor are described in U.S. Patent No. 8,552,154, which is incorporated by reference for these antibodies. Anticancer agent comprising anti-PD-1 antibody or anti-PD-L1 antibody are described in U.S. Patent No. 8,617,546, which is incorporated by reference for these antibodies.

[0113] In some embodiments, the PDL1 inhibitor comprises an antibody that specifically binds PDL1 , such as BMS-936559 (Bristol-Myers Squibb) or MPDL3280A (Roche). In some embodiments, the PD1 inhibitor comprises an antibody that specifically binds PD1 , such as lambrolizumab (Merck), nivolumab (Bristol-Myers Squibb), or MEDI4736 (AstraZeneca). Humanmonoclonal antibodies to PD-1 and methods for treating cancer using anti-PD-1 antibodies alone or in combination with other immunotherapeutics are described in U.S. Patent No. 8,008,449, which is incorporated by reference for these antibodies. Anti-PD-L1 antibodies and uses therefor are described in U.S. Patent No. 8,552,154, which is incorporated by reference for these antibodies. Anticancer agent comprising anti-PD-1 antibody or anti-PD-L1 antibody are described in U.S. Patent No. 8,617,546, which is incorporated by reference for these antibodies.

[0114] The disclosed digital T cells can be used in combination with other cancer immunotherapies. There are two distinct types of immunotherapy: passive immunotherapy uses components of the immune system to direct targeted cytotoxic activity against cancer cells, without necessarily initiating an immune response in the patient, while active immunotherapy actively triggers an endogenous immune response. Passive strategies include the use of the monoclonal antibodies (mAbs) produced by B cells in response to a specific antigen. The development of hybridoma technology in the 1970s and the identification of tumor-specific antigens permitted the pharmaceutical development of mAbs that could specifically target tumor cells for destruction by the immune system. Thus far, mAbs have been the biggest success story for immunotherapy; the top three best-selling anticancer drugs in 2012 were mAbs. Among them is rituximab (Rituxan, Genentech), which binds to the CD20 protein that is highly expressed on the surface of B cell malignancies such as non-Hodgkin’s lymphoma (NHL). Rituximab is approved by the FDA for the treatment of NHL and chronic lymphocytic leukemia (CLL) in combination with chemotherapy. Another important mAb is trastuzumab (Herceptin; Genentech), which revolutionized the treatment of HER2 (human epidermal growth factor receptor 2)-positive breast cancer by targeting the expression of HER2.

[0115] In some embodiments, such an additional therapeutic agent may be selected from an antimetabolite, such as methotrexate, 6-mercaptopurine, 6-thioguanine, cytarabine, fludarabine, 5-fluorouracil, decarbazine, hydroxyurea, asparaginase, gemcitabine or cladribine.

[0116] In some embodiments, such an additional therapeutic agent may be selected from an alkylating agent, such as mechlorethamine, thioepa, chlorambucil, melphalan, carmustine (BSNU), lomustine (CCNU), cyclophosphamide, busulfan, dibromomannitol, streptozotocin, dacarbazine (DTIC), procarbazine, mitomycin C, cisplatin and other platinum derivatives, such as carboplatin .

[0117] In some embodiments, such an additional therapeutic agent may be selected from an anti-mitotic agent, such as taxanes, for instance docetaxel, and paclitaxel, and vinca alkaloids, for instance vindesine, vincristine, vinblastine, and vinorelbine.

[0118] In some embodiments, such an additional therapeutic agent may be selected from a topoisomerase inhibitor, such as topotecan or irinotecan, or a cytostatic drug, such as etoposide and teniposide.

[0119] In some embodiments, such an additional therapeutic agent may be selected from a growth factor inhibitor, such as an inhibitor of ErbBI (EGFR) (such as an EGFR antibody, e.g. zalutumumab, cetuximab, panitumumab or nimotuzumab or other EGFR inhibitors, such as gefitinib or erlotinib), another inhibitor of ErbB2 (HER2 / neu) (such as a HER2 antibody, e.g. trastuzumab, trastuzumab-DM I or pertuzumab) or an inhibitor of both EGFR and HER2, such as lapatinib).

[0120] In some embodiments, such an additional therapeutic agent may be selected from a tyrosine kinase inhibitor, such as imatinib (Glivec, Gleevec STI571) or lapatinib.

[0121] Therefore, in some embodiments, a disclosed antibody is used in combination with ofatumumab, zanolimumab, daratumumab, ranibizumab, nimotuzumab, panitumumab, hu806, daclizumab (Zenapax), basiliximab (Simulect), infliximab (Remicade), adalimumab (Humira), natalizumab (Tysabri), omalizumab (Xolair), efalizumab (Raptiva), and / or rituximab.

[0122] In some embodiments, a therapeutic agent for use in combination with digital T cells for treating the disorders as described above may be an anti-cancer cytokine, chemokine, or combination thereof. Examples of suitable cytokines and growth factors include I FNy, IL-2, IL- 4, IL-6, IL-7, IL-10, IL-12, IL-13, IL-15, IL-18, IL-23, IL-24, IL-27, IL-28a, IL-28b, IL-29, KGF, IFNa (e.g., INFa2b), IFN , GM-CSF, CD40L, Flt3 ligand, stem cell factor, ancestim, and TNFa. Suitable chemokines may include Glu-Leu-Arg (ELR)- negative chemokines such as IP-10, MCP-3, MIG, and SDF-la from the human CXC and C-C chemokine families. Suitable cytokines include cytokine derivatives, cytokine variants, cytokine fragments, and cytokine fusion proteins.

[0123] In some embodiments, a therapeutic agent for use in combination with digital T cells for treating the disorders as described above may be a cell cycle control / apoptosis regulator (or "regulating agent"). A cell cycle control / apoptosis regulator may include molecules that target and modulate cell cycle control / apoptosis regulators such as (i) cdc-25 (such as NSC 663284), (ii) cyclin-dependent kinases that overstimulate the cell cycle (such as flavopiridol (L868275, HMR1275), 7-hydroxystaurosporine (UCN-01 , KW-2401), and roscovitine (R- roscovitine, CYC202)), and (iii) telomerase modulators (such as BIBR1532, SOT-095, GRN163 and compositions described in for instance US 6,440,735 and US 6,713,055) . Non-limiting examples of molecules that interfere with apoptotic pathways include TNF-related apoptosisinducing ligand (TRAIL) / apoptosis-2 ligand (Apo-2L), antibodies that activate TRAIL receptors, IFNs, and anti-sense Bcl-2.

[0124] In some embodiments, a therapeutic agent for use in combination with digital T cells for treating the disorders as described above may be a hormonal regulating agent, such as agents useful for anti-androgen and anti-estrogen therapy. Examples of such hormonal regulating agents are tamoxifen, idoxifene, fulvestrant, droloxifene, toremifene, raloxifene, diethylstilbestrol, ethinyl estradiol / estinyl, an antiandrogene (such as flutaminde / eulexin), a progestin (such as such as hydroxyprogesterone caproate, medroxy- progesterone / provera, megestrol acepate / megace), an adrenocorticosteroid (such as hydrocortisone, prednisone), luteinizing hormone-releasing hormone (and analogs thereof and other LHRH agonists such as buserelin and goserelin), an aromatase inhibitor (such as anastrazole / arimidex, aminoglutethimide / cytraden, exemestane) or a hormone inhibitor (such as octreotide / sandostatin).

[0125] In some embodiments, a therapeutic agent for use in combination with digital T cells for treating the disorders as described above may be an anti-cancer nucleic acid or an anticancer inhibitory RNA molecule.

[0126] Combined administration, as described above, may be simultaneous, separate, or sequential. For simultaneous administration the agents may be administered as one composition or as separate compositions, as appropriate.

[0127] In some embodiments, the disclosed digital T cells are administered in combination with radiotherapy. Radiotherapy may comprise radiation or associated administration of radiopharmaceuticals to a patient is provided. The source of radiation may be either external or internal to the patient being treated (radiation treatment may, for example, be in the form of external beam radiation therapy (EBRT) or brachytherapy (BT)). Radioactive elements that may be used in practicing such methods include, e.g., radium, cesium-137, iridium-192, americium-241, gold-198, cobalt-57, copper-67, technetium-99, iodide-123, iodide- 131 , and indium-111.

[0128] In some embodiments, the disclosed digital T cells are administered in combination with surgery.Embodiments

[0129] Embodiment 1. A method for detecting a digital T cell receptor peptide interaction, comprising(a) contacting an a T cell receptor (a[3TCR) with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the a[3TCR and the pMHC; and(b) applying an equilibrium force of 10 pN to 40 pN to the catch bond or slip bond between the a[3TCR and the pMHC to achieve a state where the catch bond or slip bond is volleying between compact and extended states; wherein a sustained volleying is an indication of a digital T cell receptor interaction.

[0130] Embodiment 2. The method of embodiment 1 , wherein the force is applied with a spring stiffness of 0.1 to 0.3 pN / nm to energetically drive the volleying.

[0131] Embodiment 4. The method of embodiment 1 or 2, wherein the force is applied at an oscillation frequency of 1 Hz to 50 Hz and an amplitude of from 0.1 pN to 40 pN to energetically drive the volleying.

[0132] Embodiment 4. The method of embodiment 3, wherein the oscillation is a sine wave, square wave, or triangle wave.

[0133] Embodiment 5. The method of embodiment 1 or 2, wherein the force is applied at a constant magnitude.

[0134] Embodiment 6. The method of any one of embodiments 1 to 5, wherein the force is applied using an optical tweezer.

[0135] Embodiment 7. The method of any one of embodiments 1 to 5, wherein the force is applied using a magnetic force, acoustic force, deformable substrate, atomic force probe or cantilever, or bioforce probe.

[0136] Embodiment 8. The method of any one of embodiments 1 to 7, wherein the pMHC is presented on a bead or solid support.

[0137] Embodiment 9. The method of any one of embodiments 1 to 3, wherein the pMHC is on the surface of an antigen presenting cell.

[0138] Embodiment 10. The method of any one of embodiments 1 to 3, wherein the apTCR is presented on a bead or solid support.

[0139] Embodiment 11. The method of any one of embodiments 1 to 7, wherein the apTCR is on the surface of a T cell.

[0140] Embodiment 12. The method of embodiment 11 , wherein the T cell comprises a chimeric antigen receptor (CAR).

[0141] Embodiment 13. The method of embodiment 11 or 12, wherein the T cell is a tumor infiltrating lymphocyte (TIL).

[0142] Embodiment 14. A method for detecting a digital T cell receptor peptide interaction, comprising(a) contacting an a|3 T cell receptor (apTCR) on a T cell with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the apTCR and the pMHC; and(b) assaying the T cell for CD3 and CD69 surface expression; wherein CD3 loss and CD69 upregulation in the T cell is an indication of a digital T cell receptor interaction.

[0143] Embodiment 15. A method for detecting a digital T cell receptor peptide interaction, comprising(a) contacting an ct|3 T cell receptor (apTCR) on a T cell with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the apTCR and the pMHC; and(b) assaying the T cell in a cytotoxicity assay at a 1 :1 T cell to target ratio; wherein a T cell that initiates killing within 3 hours in the cytotoxicity assay with at least 50% target killing is an indication of a digital T cell receptor interaction.

[0144] Embodiment 16. A method for selecting a T cell or antigen, comprising(a) providing a T cell comprising a ap T cell receptor (a TCR) and an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC), wherein either the T cell or the pMHC is tethered to a solid support or bead;(b) contacting the candidate T cells with the antigen peptide under suitable conditions to promote a catch bond or slip bond between the apTCR and the pMHC;(c) applying a force of 10 to 40 pN to the catch bond or slip bond between the a TCR and the pMHC;(d) assaying the cells for T cell activation, tether forming probability, or a combination thereof; and(e) selecting a T cell or antigen thereof from the candidate T cells that has higher than average activation and / or has a higher than average chance of forming a tether compared to control values.

[0145] Embodiment 17. The method of embodiment 16, wherein T cell activation is determined using a reporter of intracellular calcium concentration, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with a high calcium flux.

[0146] Embodiment 18. The method of embodiment 16, wherein T cell activation is determined by assaying for cell stiffness, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with increased activation-induced T cell stiffening.

[0147] Embodiment 19. The method of embodiment 16, wherein T cell activation is determined by imaging the cell, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with changes in geometry and / or morphology.

[0148] Embodiment 20. The method of any one of embodiments 16 to 19, comprising screening a plurality of T cells and selecting a T cell that has an a[3TCR that interacts with the antigen peptide with digital binding.

[0149] Embodiment 21. The method of any one of embodiments 16 to 20, comprising screening a plurality of antigens and a[3TCRs and selecting an antigen that interacts with an apTCR peptide with digital binding.

[0150] Embodiment 22. The method of any one of embodiments 16 to 21 , wherein the force is applied using an optical tweezer.

[0151] Embodiment 23. The method of any one of embodiments 16 to 21 , wherein the force is applied using a magnetic or acoustic field.

[0152] Embodiment 24. The method of embodiment 23, wherein the force is applied using a magnetic tweezer, acoustic tweezer, deformable substrate, atomic force probe or cantilever, or bioforce probe.

[0153] Embodiment 25. The method of any one of embodiments 16 to 21 , wherein the force is applied using a fluid flow configured to produce a stokes drag.

[0154] Embodiment 26. The method of any one of embodiments 16 to 25, wherein the pMHC is presented on a bead or solid support.

[0155] Embodiment 27. The method of any one of embodiments 16 to 25, wherein the pMHC is on the surface of an antigen presenting cell.

[0156] Embodiment 28. The method of any one of embodiments 16 to 27, wherein the T cell comprises a chimeric antigen receptor (CAR).

[0157] Embodiment 29. The method of any one of embodiments 16 to 28, wherein the T cell is a tumor infiltrating lymphocyte (TIL).

[0158] Embodiment 30. The method of any one of embodiments 16 to 29, wherein the activated T cell has been transduced with a TCR.

[0159] Embodiment 31. The method of any one of embodiments 16 to 30, further comprising adoptively transferring the activated T cell to a subject in need thereof.

[0160] Embodiment 32. A method for producing a vaccine encoding a neoantigen or tumor associated antigen to stimulate digital performance T cells directed at a tumor comprising(a) generating digital responder T cells through elicitation of an effective immune response directed at the tumor;(b) determining the average copy number of an encoded pMHC epitope in the immune peptidome of an antigen presenting cell by mass spectrometry in the good responder subject; and(c) engineering a vaccine to produce a comparable number of encoded antigens on antigen presenting cells of a vaccinated subject.

[0161] Embodiment 33. The method of embodiment 32, wherein the vaccine comprises a promoter operably linked to a gene encoding the antigen, and wherein the promoter is selected based on predicted copy number rate.

[0162] Embodiment 34. An in silico method for detecting a T cell receptor peptide interaction, comprising:(a) providing a T cell comprising a op T cell receptor (apTCR) and an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC);(b) determining a likelihood of an analog or digital receptor interaction based upon an unfolding propensity of the T cell; and(c) validating the analog or digital receptor interaction based upon model analysis of experimental volleying data of the T cell.

[0163] Embodiment 35. The in silico method of embodiment 34, comprising subjecting the T cell to staged pulling simulations prior to determining the unfolding propensity of the T cell.

[0164] Embodiment 36. The in silico method of embodiment 35, wherein the staged pulling simulations comprises intervals of pulling with a given force and holding steady without pulling.

[0165] Embodiment 37. The in silico method of embodiment 35, wherein the staged pulling simulations are performed in response to detection of catch bond formation based upon stabilization of a TCR and pMHC interface under different load levels.

[0166] Embodiment 38. The in silico method of embodiment 34, wherein the model analysis comprises determining one or more transition rate between compact and extended states of the T cell based upon the experimental volleying data.

[0167] Embodiment 39. The in silico method of embodiment 38, wherein the experimental volleying data comprise time traces of force.

[0168] Embodiment 40. The in silico method of embodiment 38, wherein the validation is based upon comparison of one or more transition rate with the unfolding propensity.

[0169] A number of embodiments of the invention have been described. Nevertheless, it will be understood that various modifications may be made without departing from the spiritand scope of the invention. Accordingly, other embodiments are within the scope of the following claims.EXAMPLESExample 1 : Harnessing a[3 T cell receptor mechanobiology to achieve the promise of immuno-oncologyA Fundamental Conundrum: How Can Weak aBTCR Binding Mediate Exquisite In Vivo Specificity and Sensitivity?

[0170] Paucity of tumor target antigens discussed above refers both to the absence of more than 90% of bioinformatically predicted neoantigen candidates on the tumor cell surface and, even when displayed, presentation in limited copy numbers. Consequently, only several molecules per cell are expressed among a sea of normal self-peptides (-100,000). The discernment process from the CTL's perspective (Fig. 1A) is really a search requiring detection of a needle in a haystack. As each CTL carries 20,000 to 40,000 identical clonotypic TCRs, multiple parallel confinement area searches are possible at the CTL-tumor cell interface. Nevertheless, the explanation for how a TCR, a class of receptor with micromolar or weaker affinity for ligand, can accomplish this arduous task was obscure. Additionally perplexing has been the extraordinary selectivity of peptide recognition. For instance, Fig. 1B shows crystal structures of two foreign peptide and MHC ligands (pMHC) that differ at a single p4 residue of the peptide. Only a small number of atoms are distinct and yet a specific TCR can handily identify one from the other with a biological response that diverges 10,000-fold.

[0171] During lymphocyte development, self-reactive cells are eliminated to prevent autoimmunity. Nonetheless, mature T cell recognition of a foreign peptide involves ligation of that peptide bound to a self-MHC (major histocompatibility complex) molecule. As shown in Fig. 2A, the pMHC mosaic comprises a small number of foreign peptide residues embedded within many more “self” MHC residues. This creates a tricky proposition since most of the TCR recognition surface interacts directly with a self-constituent. In contrast, the surface B cell receptor (BCR) and that of its secreted antibodies generally recognize an entirely foreign antigen. Furthermore, B cells undergo somatic hypermutation of their immunoglobulin genes in the lymph node and spleen to enhance affinity for ligand. This refinement mechanism is not operative for TCR genes because it would foster self-MHC reactivity that could result in autoimmunity. Following somatic hypermutation, antibody affinity can achieve equilibrium Kd values of nM-pM while a[3TCR values are orders of magnitude weaker, in the 1 pM to 100 pM range. How then can T cells manifest the specificity and sensitivity required to recognize a single pMHC molecule on the surface of a target cell?

[0172] The answer is that the a TCR is a mechanosensor, a biomolecule that discerns a change in force upon ligation. Force is placed on an individual bond formed between a[3TCR and pMHC consequent to cell motion during immune surveillance. This bond works like a sailor’s bowline knot which is secure under load but easy to work free, untie or slip, when not under tension. Fig. 2B shows a force-bond lifetime curve for two TCRs that recognize the same pMHC. At low or no force, binding to specific and irrelevant pMHC molecules is comparably weak. In contrast, bond lifetime becomes highly specified at a 10-to 15-pN force. Note that these two TCRs interact with the same pMHC but manifest different bond lifetimes. Upon binding to the pMHC each TCR undergoes conformational changes including extensions in the bound state. Initially, a TCR is in a compact state but then transitions to an elongated state while bound to pMHC (Fig. 2C). At this point, the interaction can break (Fig. 2C) or transition back and forth (Fig. 2D) as if in a resonant state. The extension distance, force, and the transition frequency of each TCR can vary. Such motions induced via an optical trap recapitulate the native loading condition generated by the actin-myosin machinery in a T cell in contact with an ARC. Physiological tangential force applied as T cells migrate across stroma and APCs in tissues is exploited for mechanosensing, as seen in optical tweezers experiments where applying forces that are tangential rather than normal to the membrane led to T cell activation. Parameters describing these motions and downstream signaling events are detailed below.

[0173] As a TCR undergoes transitions, it tugs at the membrane of the T cell, delivering energy to generate signal through perturbation of vicinal lipids and facilitating exposure of the cytoplasmic signaling motifs of the CD3 subunits, termed ITAMs (immunoreceptor tyrosinebased activation motifs). Consequent downstream gene transcription occurs through a multicomponent relay (30). One may envision this process as switching between a resting and signaling-competent state driven by an attowatt generator (force*distance*frequency = 15 pN*10nm*1 / s= 1.5*10-19J / s = 0.15 aW). Biophysical distinctions among TCRs, even when directed at the same pMHC ligand, lead to signaling differences. These involve dynamic structural changes in the recognition surface of the TCR and its constant region module as well as the surrounding six invariant CD3 signaling components (CDSey, CD3E5 and CD3 Q associated with the TCRap clonotype (clone-specific heterodimer) that binds pMHC. Unlike soluble antibodies, the a[3TCR transcends simple binding, functioning out of equilibrium in an energized state.Biophysics of TCRs and preTCRs in the aB Lineage vs. TCRs in the vb Lineage

[0174] Catching a Bond in the a / 3 Developmental Pathway. Direct biophysical probing of the aBTCR-pMHC revealed unique mechanosensing features including catch bond profiles and conformational transitions. Measurements performed on cells and purified components with optical tweezers and bioforce probe methods demonstrate a catch bond where the bond lifetime initially increases with load and then weakens, with a peak lifetime occurring at 15-to 20-pN force. During immune surveillance, such forces naturally arise from cell motions that involve the cell’s peripheral actin network. The peak lifetimes generally range ~1 to 10 s depending on the system and ligand potency. Optical tweezer measurements also show discrete conformational transitions under load including small nm-level motions and a large (and reversible) signature conformational change at 15 pN (Fig. 2B-2D).

[0175] Why a catch bond? While the bond lifetime increases with force, it is still modest compared to other high-affinity interactions such as those seen with typical antibody-antigen binding. Thus, it is unlikely that the lifetime per se drives signaling. More importantly, the 15-pN force at the peak catch bond lifetime can drive molecular motions and conformational transitions that would otherwise be inaccessible in a nonenergized state. The energy associated with signature conformational change (15 pN*10 nm) is significant, in fact equivalent in magnitude to the free energy associated with ATP hydrolysis if only half of that transition is utilized (15 pN*5 nm = 75 pN*nm). From a nonequilibrium perspective, the system is normally in a resting, i.e., “off’ state, and it is gated only when force is introduced. Surmounting the barrier through thermal energy alone is 90 million times less likely [exp(-75 / 4.1)] than pulling with an external force through a cognate ligand. Such a change thus requires energy input. A catch bond enables energy delivery and drives the system over the energy barrier to a signaling competent state. This scenario also supports the kinetic proofreading mechanism where the fidelity of signaling is enhanced by combining apparently error-prone kinetic events with the supply of free energy (32).

[0176] The T cell activation process begins with a loaded state spanning the T cell and APC through their respective assembled actomyosin cytoskeletal machineries. On T cells, actin- rich membrane protrusions, microvilli, studded with surface TCRs are facilitative. The attendant motions pulling across the cell-cell interface provide sufficient energy to drive signaling. In contrast, thermal energies are insufficient to surmount this barrier. The load activates the TCR- pMHC catch bond, which in turn induces the conformational change. We believe that the energy associated with the conformational transition drives the TM domain structural rearrangements including a toggle in the a TM domain that leads to dissociation of CD3^ releasing membranesequestered ITAMs. This event appears to be coupled to motor proteins that turn on locally and transport the ligated TCR to follow the force, maintaining an optimal load needed for reversible conformational change and continued signaling. At the same time, unligated TCRs adjacent to the ligated TCR are translocated by motors to initiate immunological synapse (IS) formation as well. This spatial reorganization of individual molecules revealed by pointillist data plausibly promulgates effective signalsome formation and segregates phosphatases away from relevant activating kinases such as Lek for phosphorylation of accessible ITAMs in early T cell activation. In our experiments, at low pMHC interfacial density this load bearing pathway is reproduced from force on the bead through the optical trap. At higher interfacial density, where force is not required for activation, akin to APCs arraying hundreds or more copies of a pMHC at the T cell— APC interface, T cell machinery may be able to organize sufficiently through multiple pMHC locations to pull through the bead and initiate the same downstream signaling pathways.

[0177] The preTCR, which is present during [3 selection, also contains mechanosensing features. The preTCR in many ways is a simpler system since a clonotypic p subunit is paired through its constant domain with preTCR a (pTa), an invariant TCRa surrogate lacking a V domain. The pTa-p heterodimer associates with the same set of CD3 subunits as found in the apTCR. Pulling through pMHC occurs exclusively via the unpaired Vp domain and exhibits a more complex catch bond profile than that of the TCRap paired aVp module and a 10-fold increase in the rate of reversible hopping of the signature conformational transition. By mapping the forward and reverse rates as a function of force, it was determined that the critical force is similar between the preTCR-pMHC and TCRap-pMHC systems implying that the structural transition is associated with the shared p subunit. Hyperactive transitioning and broader peptide specificity of a preTCR (relative to TCRap) during p selection favor signaling to expand the p chain repertoire preceding apTCR expression while limiting cellular plasticity to facilitate normal thymocyte development. These mechanisms anticipate subsequent apTCR-driven winnowing of the mature T cell repertoire linked to thymic positive and negative selection.

[0178] y^TCRs Are Not Mechanosensors. Further insight on the importance of mechanosensing in the T cell lineage came from a study comparing a ydTCR to an apTCR and a VyVd-CaCp chimeric receptor. The latter amalgamated the variable domains of the y5T cells with the constant domains of the apTCR. y5T cells are found in barrier tissues and recognize plentiful nonpeptide ligands induced on stressed cells targeted for destruction. Like antibody Fabs, ydTCRs lack both the extended constant p domain connector between the F and G strands (FG-loop) and a sizeable interfacial contact area between the constant and variable domains, distinguishing features of apTCRs. ydTCR-pMHC interactions were slip bond-like,where the bond lifetime decreases exponentially with applied force. In contrast, the chimera showed a catch bond even though both receptors have the identical V module interacting with the same ligand sulfatide-CD1d, a sulfoglycolipid bound to a class lb MHC molecule. Conformational changes were also readily observed in the chimera but not in the ybTCR.

[0179] Analog vs. Digital Performance. The adaptive immune system must perform effectively with plentiful as well as sparse ligands. To map the chemical and physical requirements for the breadth of response capability, we trapped beads coated with different numbers of a given pMHC molecule and apposed them to a T cell to facilitate activation. A sufficiently large number of pMHC molecules can activate a T cell without any external force. Here, we believe internal cellular machinery / motions stabilize and pull across the bead itself through multiple pMHC bond locations, eliminating the requirement for an external force. We refer to this as an “analog” response. In addition to y5T cells operating under these analog conditions where stress response ligands are plentiful, some ap T cells, including those directed at highly arrayed viral antigens such as the nucleoprotein epitope of influenza A viruses (NP366-374), do not require sparse ligand detection capability. Our bead-facilitated activation study also demonstrates that with an optimal balance of ligand and external force, a subset of T cells can perform “digitally,” triggering reliably with as few as two pMHC molecules at the bead- T cell interface. Such activation requires force from the optical trap, however, and is tightly centered around 15 pN. This digital T cell triggering is tied to the actomyosin machinery that sustains the force in this window where signaling is optimal and in vivo likely induces IS formation through recruitment of nonligated TCRs noted above. Defining those digital-quality TCRs that recognize a given pMHC among an individual’s repertoire of TCRs recognizing the same ligand shall be advantageous for vaccination purposes and adoptive immunotherapy.

[0180] Systems Biomechanics. Cells continually reorganize their internal components, changing shape, and exerting forces on and responding to their surroundings through dynamic interfacial contacts (Fig. 3). This is powered by a network of cellular components, stroma and conduits, collectively a cellular power grid, which delivers energy to drive signaling. The ability of cells to bend various force probes and push / pull on other model APC surfaces demonstrates that sufficient forces are available for individual receptor activation as shown by careful measurements at the single molecule level. Coupling to the grid is also influenced by local stiffness of the microenvironment, which can change dramatically during processes such as inflammation and cancer. Stiffness, a measure of deformation under a given load on a material, can vary 100 fold under physiological (compliant) vs. inflammatory (stiff) conditions in the same tissue, and it varies even more in different tissues such as compliant bone marrow and brain vs.stiff cartilage and bone. The immune system adapts itself to function in the range of organ- related locales of the mammalian organism.

[0181] TCRs may exploit different optimal force parameters when operating on T cells in compliant as opposed to stiff tissues in anatomically linked locales, for example, a mediastinal lymph node draining intraepithelial and subendothelial surfaces of the lung as pictured at the Top of Fig. 3 (Left and Right, respectively). Furthermore, interplay between cancer cells and the tumor microenvironment often increases stiffness of the extracellular matrix (ECM), impacting optimal TCR mechanobiology. T cells produce many mediators which in turn modulate the ECM and its stiffness. For example, TGF-p and TNF-a enhance or inhibit fibroblast collagen biosynthesis, respectively. The quality of a TCR that reacts with a neoantigen and its downstream transcriptome following ligation is likely to differentially regulate responsiveness to the tumor while at the same time modulating ECM mechanics and the tumor microenvironment more broadly, impacting productive T cell activation vs. exhaustion.

[0182] Within the T cell itself, activation is coupled to an intracellular motility system, providing further points of modulation and control. This internal system must work near the activation threshold yet be robust enough to handle a wide range of inputs such as a sparse vs. plentiful pMHC ligand and a stiff vs. a compliant microenvironment. In some cases, internal cytoskeletal machinery may not be preassembled and / or properly connected to the TCR complex, reducing competence to drive receptor inputs. In other cases, organized machinery and coupling may be fully primed at the interface of a mature dendritic cell and a naive T cell within an ideal microenvironment prearranged to foster signaling events linked to relevant biomechanics. There must be a delicate balance between external drivers including dynamic microenvironment stiffness and internal machinery that a T cell integrates.

[0183] How does our immune system dampen T cell activation? If T cells fire too readily in response to ligand, there is excessive noise. Conversely, if activation thresholds are set too high, a rare signal will not be detected. The adaptive T cell response must therefore strike a critical balance between extremes. Perhaps acute inflammation in a microenvironment can shift the set point to where signal levels are being heard even if weak, analogous to a squelch in an electronic circuit that adjusts output based on signal strength. Within a perfect microenvironment and a carefully matched signal frequency such as in the lymph node where naive T cells and APCs prime the immune response, a true digital channel can come in loud and clear. In contrast, chronic inflammation including that found in the tumor microenvironment may pose challenges, which may be overcome by recruitment of high-“acuity” T cells with potent digital responses enabled to function at requisite bioforces mandated by the mechanics of the system.Atomistic Basis of Catch Bond Formation

[0184] Since the mechanosensing action of an apTCR is a nonequilibrium process, static X-ray crystallographic structures of TCRap-pMHC complexes cannot populate all energized states to reveal the clear cognate antigen recognition mechanism. While the activation of a T cell upon recognizing the matching pMHC is a multistep process, mechanosensing starts with the engagement of the TCRap heterodimer by its cognate pMHC, as shown in in vitro single-molecule experiments of isolated TCRap-pMHC complexes without coreceptors. This engagement involves two elements, catch bond formation and conformational transition as noted above. The former is required for the latter and is then followed by pMHC- meditated T cell activation. An example of catch bond being necessary but not sufficient for T cell activation is the experiment where the Cp FG-loop is stabilized by the H57 antibody. It drastically enhanced the peak catch bond and the bond lifetime but blunted the structural transition and associated T cell activation. This study also demonstrated how allostery controls binding between TCRap and pMHC. Further confounding is the fact that TCRap characteristically forms more contacts with the MHC molecule than the antigenic peptide (Fig. 2A), making it difficult to understand how the catch bond behavior is controlled via a handful of peptide contacts, while possessing a very weak equilibrium binding affinity.

[0185] Our all-atom molecular dynamics (MD) simulation study revealed a dynamic mechanism of catch bond formation (Fig. 4). The 4-domain organization in a rhomboidal topology provides room for fine-tuning mechanical properties of the TCRap heterodimer to improve interfacial mismatches. The number and organization of interfacial contacts between the 4 domains (Va-Vp, Va-Ca, Vb-Cp, and Ca-CP) differ. Hence, the most stable binding mode between any two domains in isolation may be incompatible when they are embedded within the TCRap heterodimer. Interfacial mismatch is exacerbated when TCRap binds pMHC, which adds two more interfaces, namely Va-pMHC and Vp-pMHC. Without an adequate load, the mismatched interfaces lead to conformational motion of the individual domains, destabilizing the complex and shortening the bond lifetime. When a 10-to 20-pN load is applied, however, a slight deformation of the complex occurs, resulting in a better fit and stabilization of the interfaces (Fig. 4D). In this mechanism, the dominance of the contacts with the MHC molecule rather than with the peptide per se is required since the MHC must grab and pull Va and vp to suppress the motion. Instead, the peptide organizes the surrounding contacts, screening for the correct fit rather than bearing the load, akin to the teeth of a key that slot into a lock, while the key's stem bears the load when turning the key.

[0186] Allostery can naturally be incorporated into the proposed catch bond mechanism since changes in any of the 4 domains impact the interfacial dynamics. In particular, the Cp FG- loop that is present in jawed vertebrates (gnathostomata) and elongated in mammals, strongly influences stability of the TCRop-pMHC complex. Our simulation showed that Vp-Cp form more extensive interdomain contacts compared to those of Vo-Ca where the Cp FG-loop controls the orientation of vp relative to Cp, in addition to providing structural support for Va in binding to pMHC. In an FG-loop deletion mutant, the orientation and conformational motion of the V- module changes, destabilizing the interface with pMHC when load is applied (Fig. 4C). The proposed catch bond mechanism is also compatible with the 9 to 15-nm conformational transition observed experimentally. In simulations where the C-module was removed, the interface between the V-module and pMHC became more stable than even the high-load case (Fig. 4E) due to absence of the C-module interfacial mismatch, allowing a better fit with pMHC. A partial unfolding of the C-module may occur in the extended state, keeping the V-module engaged with pMHC as the transition occurs. More studies are needed to characterize the structural origin of the conformational transition in TCRap, information that in turn could lead to optimal TCR design for immunotherapy.

[0187] We recently analyzed load-dependence of another set of TCRop-pMHC systems, involving the adult T cell leukemia A6 TCR and agonist and nonstimulatory peptides in complex with HLA-A* 0201 that were studied by X-ray crystallography in the past. Consistent with our previous MD study, the interface was stabilized upon application of physiological-level loads. In contrast, the presence of antagonist peptides led to destabilization of the complex with load, indicative of a slip bond behavior. These results suggest that the catch bond mechanism based on domain motion and interfacial mismatch is general, predicated on the basic organization of domain interfaces. We posit that the interaction with pMHC by preTCRs expressed on DN thymocytes and manifesting ligand-dependent catch bond formation speaks to the fundamental a|3 thymic selection process. During TCR repertoire development, preTCRs may be screened not only for sequence-dependent interaction with self-pMHC, but also for proper mechanical matching.Impact of Force on TCR vs. BCR and CAR-T Systems

[0188] Structural analyses by cryo-electron microscopy of apTCRs (54-56) provide considerable insight into the features that imbue the receptor with its anisotropic (directed) mechanosensing involving the amalgam of CD3 subunits and force transduction architecture. NMR data support that a dynamic TM segment conformational switch of the TCRa subunitimpacts apTCR mechanotransduction as well as dissociation of the CD3 subunits from TCRap. Rapid dissociation of CD3^ is noteworthy, given its prominent role in CAR-T systems.

[0189] B cell receptor (BCR) structures have also been characterized, revealing a TM immunoglobulin molecule noncovalently associated with CD3-like lga / p heterodimeric signaling molecules. BCRs likewise detect force, utilizing cellular energy to discriminate ligand affinity, but without catch bond formation. Separation of ligand binding and signaling functions into distinct subunits and physical dissociation of those components from one another are common to both membrane-bound systems.

[0190] Biophysical measurements of CAR-T cells show markedly different properties than ap T cells interacting with the same pMHC ligands. As CAR-T cells and diabodies lack physiological TCR mechanotransduction features, the sensitivity of such systems is unsurprisingly less than that of digital apTCR mechanosensors. While CARs are strategically facile, they lack participation of the CP loading pathway and the TCRa TM switch. They are also grossly unmatched to the typical lifetime and force ranges found in native TCR-pMHC interactions. Thus, there are opportunities for new CAR-T strategies given the potential for 1) coupling more directly to the same load pathway utilized in ap T cells in fostering conformational transition, 2) matching more appropriately the force window for native T cell activation such that membrane, cytoskeletal machinery, and motors can participate optimally and 3) better mimicking the lifetime profile found in native T cells. Consistent with this notion, a recent study showed that H LA-independent T cell receptors (HITs), chimeric designs utilizing VH-VL module replacement of the Va-Vp module of the apTCR, can target tumors with low antigen density. Similar to the Vyb-Cap chimera, HITs may possess a catch bond, and with the same Capmodule and CD3 signaling subunits as the apTCR complex, that promote conformational transitions and digital sensitivity to the low-density antigen. Further single-molecule (SM) and simulation studies will help to understand the mechanism of HITs.Implications of aPTCR Mechanobioloqy as Related to Tumor Antigen Targeting

[0191] Principles of mechanobiology suggest how to optimize T cell monitoring and craft effective immunotherapies. Force omission in in vitro immunological studies obscures ap T cell recognition of sparse pMHC ligands including neoantigens or certain viral antigens. For effective immuno-oncology approaches, a match must exist between a CTL apTCR acuity, i.e., its ability to recognize a threshold number of pMHC complexes incorporating a neoantigen on a target cell, and the number of copies arrayed on that tumor. Pertinently, certain CTLs manifest digital performance whereas other CTLs need dozens or even hundreds of pMHC copies. If there is such a quantitative mismatch, then no killing of tumor will occur in vivo even if “specific” killing ofpeptide pulsed APCs in vitro is detected. Emphatically, the array on the actual tumor cell is relevant, not a surrogate APC that has been pulsed with micromolar concentrations of peptides yielding thousands of copies of a particular pMHC complex. A variety of evolving methods (microfluidics, smart particles, and optical tweezers) exert force on apTCR-pMHC bonds to foster in vitro TCR performance studies that can gauge acuity. For experimentalists, the challenge is to determine which parameters of a[3TCR mechanotransduction correlate best with TCR activation responses (Fig. 5).

[0192] T cells and their purified receptor-pMHC proteins can be probed using biophysical tools at the SM and SM single-cell (SMSC) levels. Optical trapping facilitates an interaction and provides measurements (detailed in Fig. 5) that directly reveal the strength and properties of the TCRap-pMHC bond. At the SM level, a good TCRap-pMHC pair readily forms interactions and “holds” for a few seconds under load. Bond lifetime distributions typically exhibits a catch bond type profile (rather than irrelevant slip bond) peaking at ~15 pN, a force where one likely observes a conformational change. Good pairs show a clear conformational transition, and many will reversibly hop when held at the right force window. Parameters include lifetime, transition distance, critical force for hopping, and hopping frequency. While purified components provide the highest resolution measurements, similar studies can be performed directly on the surface of a coverslip-bound T cell, eliminating the need for protein expression and purification (SMSC in Fig. 5). Here pMHC is tethered to a bead through a DNA rope and brought in the vicinity of the cell by manipulating the optical trap or cell position. Repeat measurements reveal catch bond curves and even visible transitions. Although the data are “noisier” due to cell motions along the load pathway, one can readily score the ease of forming and maintaining interactions (and frequently identify conformational change) across a panel of cells. Perhaps the best test of cell’s quality against a particular peptide is an assay where one actually mimics activation by proxy through presentation of a bead (and associated force) to determine what physical and chemical thresholds are required for activation [SCAR (Single-cell activation requirement) in Fig. 5], The measurement not only involves mechanical manipulation of the trap but also a simultaneous fluorescence measurement to track calcium flux changes, a marker for early T cell activation. Parameters include chemical threshold for activation, force threshold for activation, calcium flux magnitude and rate, activation probability, and frequency of forming bead-cell adhesion. High-quality T cell-pMHC pairs can be triggered with as few as 2 molecules at the bead-cell interface. As only a few “bits” of information are processed by the cell, we refer to this as a digital activation. Digital T cells readily bind beads and show a steady and sustained rise in calcium in response to bead presentation.

[0193] Defining biomarkers that correlate with the above biophysical parameters of digital performance is a key next step. These signature molecules may include a set of differentially expressed activation molecules measurable using antibodies in conjunction with flow cytometry. Those molecules will likely be brought to light by in vivo single-cell RNAseq gene expression data derived from digital vs. analog CD8 CTL performers directed at the same pMHC. How those biosignatures relate to IS formation and IS topology along with microtubuleorganizing center polarization will also be interesting to discern. Readily measurable markers will circumvent the limited access of immunooncologists currently to detailed mechanobiological measurements. The crucial task of neoantigen identification has been approached in several ways. The recently developed attomole Poisson detection liquid chromatography-data independent acquisition mass spectrometry (LC-DIAMS) method is an important step forward. It captures the entire immune peptidome in a single run from small numbers of tumor cells retrieved by clinical fine needle biopsy. This method uses a Poisson metric to detect a reference fragmentation pattern embedded in a background of ion fragments and mitigates false negative or false-positive detection. It meets the challenge of the complexity, limited sample, and spectral crowding of molecular ions and suits a focus on detecting very low abundance peptides from a very large number of candidate peptides that could mark the cell as infected or transformed. The advance changes the MS calculus, permitting neoantigen search at any point following data collection using existing MS instrumentation in a facile manner to detect both sparse and luminous targets. Both truncal mutations associated with all tumor cells of an individual as well as nontruncal clonal variants are detectable via LC-DIAMS from analysis of individual biopsy sites at one point in time or over the clinical course of a cancer. For CTL-based vaccines to prevent infectious diseases, there is opportunity to target viral, parasitic, or other infectious antigens that appear early in the infectious process after the pathogen’s entry into a host cell.Clinical Benefit Resulting from TCR Mechanobioloqy and Its Modulation

[0194] The matching of TCR mechanosensory performance with distinct neoantigen displays is likely to create a new dawn of immuno-oncology for personalized immunotherapies including targeting of certain high value truncal neoantigens such as TP53 shared by multiple patients. With respect to TAA, although these are not tumor-specific peptides given their expression on normal fetal or adult cell types, the array of certain TAA across tissues and cell types can be very limited in distribution while highly overexpressed on tumors. This differential expression profile creates a therapeutic opportunity. The anaplastic lymphoma kinase (ALK) is an example where TAA epitope-specific T cells can be engendered against ALK without central tolerance. The potential to convert physically defined but immunologically weak TAA intostrongly immunogenic TAA has been documented through clever structural analyses offering additional future therapeutic vaccination opportunities.

[0195] Some investigators have begun to exploit mechanobiology to identify TCRs with enhanced TCR-pMHC catch bonds through site-directed mutagenesis of the specific TCR- ligand interaction surface. This approach seeks to avoid cross-reactivity against off-target antigens as previously resulted in fatal organ immunopathology and occurred through engineering of TCRs for antibody-like high-affinity TAA recognition in the absence of force. It is presently unclear whether enhanced catch bond formation alone shall be broadly successful in avoiding such a danger. Many additional TCR biophysical parameters exist (Figs. 2 and 5), requiring exploration to identify the best surrogates of TCR performance and specificity. Without a nuanced appreciation of mechanobiology, serious errors may follow. Recent usage of an TCRp CAR-T with an unpaired Va touted as an efficient way to achieve TAA targeting would result in significant peptide cross-reactivities akin to a preTCR as evidenced by activation without peptide addition.

[0196] As a more global approach to discern CTL performance predicated on TCR mechanobiology, repertoires of T cells recognizing a single ligand (viral-or tumor-related) should yield important information. The sequence relatedness of highly functional T cells, associations with immune protection, inflammatory pathologies, damaging cross-reactivities with other selfderived epitopes in tissues, and propensities to progress to exhaustion, can all be explored. Not all T cells bearing different TCR clonotypes respond in the same manner against a vi rally infected or transformed cell. Within such a single pMHC-specific repertoire, the determination of TCR sequence distance, differential MD simulation behavior, structural features, biological performance in vivo as well as in vitro, and transcriptomes in response to pMHC ligand shall inform us about the hallmarks of optimal performance to guide adoptive a TCR T cell therapy. In turn, investigators can vet epitopes for incorporation into cancer vaccines to induce T cells arraying optimal cognate recognition receptors.Example 2: Parsing digital or analog TCR 1 performance through piconewton forces

[0197] The vertebrate immune system is comprised of both innate and adaptive cellular components that protect the host from viruses, microbes, toxins, and cancerous transformations. Innate immunity is rapid and non-specific while adaptive immunity is delayed but decisive, incorporating exquisite specificity and manifesting immunological memory. The anamnestic response to pathogen rechallenges is made efficient through rapid clonallymphocyte expansion and deployment in relevant tissues, tailored to precisely target cells perturbed by “foreign” invaders and / or genetic alterations for elimination.

[0198] Adaptive humoral and cellular immunity are mediated through lymphocyte receptors, B cell receptors (BCRs), and T-cell receptors (TCRs), which undergo somatic rearrangements of gene segments encoding their variable domains during lymphoid development This process creates billions of clonotypic structures with the gamut of unique specificities required to recognize diverse pathogens. Without broad repertoire diversity, infectious agents and cancers would overwhelm the mammalian host, as evidenced by pathological sequelae observed in patients with immunodeficiency states. In contrast to BCRs apTCRs are exclusively membrane-bound, lack a genetic mutation mechanism, and manifest weak monomeric 1-200 pM affinities. Ligands recognized by the BCRs are, broadly speaking, foreign in nature, such as envelope proteins from HIV-1 and coronavirus, among other viruses. On the other hand, each a[3TCR recognizes a foreign peptide bound to a self-MHC (major histocompatibility complex) molecule, collectively referred to as a foreign pMHC. Foreign pMHCs are arrayed on the surface of a diseased cell or professional antigen presenting cell (APC) at a relatively low copy number amongst a sea of -100,000 diverse self-pMHCs.

[0199] Given their weak affinities, the strict specificity and sensitivity performance requirements of apTCRs necessary for cytolytic T lymphocytes (CTLs) to eliminate abnormal cells expressing sparse conjoint ligands were enigmatic. Recent studies solved this paradox by revealing that a^TCRs are force-responsive biomolecules, i.e. , mechanosensors that, unlike antibodies, function outside thermal equilibrium. Tensile forces applied to a TCR-pMHC bond increase its lifetime and are referred to as catch bonds. In vivo, piconewton (pN) forces are placed on an individual apTCR-pMHC bond through shear-like tangential cell motions arising between an opposing T lymphocyte and a target APC during immune surveillance, the process through which T lymphocytes move to scan tissues in search of diseased cells. That physical bond load induces conformational changes in the a[3TCR heterodimer, going from a resting, compact state to an elongated, active state with transitions back and forth. These reversible TCR structural transitions locally bend and agitate the T cell membrane and potentially deliver energy to facilitate signaling through perturbation of vicinal membrane lipids and immunoreceptor tyrosine-based activation motifs (ITAMs) exposure in the cytoplasmic tail of the CD3 signaling subunits of the opTCR complex, which stimulates downstream gene transcription. Moreover, the atomistic mechanism for specificity and sensitivity of the mechanosensor involves allosteric control of the conformational motion of the entire TCR framework by force.

[0200] It follows that a[3TCR performance in the absence of mechanical load may not accurately reflect biological function in vivo, particularly given that low or no force application on the apTCR-pMHC bond minimizes ligand specificity. However, for both experimental purposes and clinical monitoring, analysis of TCR function in vitro is at present routinely performed in the absence of feree application. Here we use optical tweezers (OT)-based methods to apply the equivalent of biologically relevant pN load to individual TCR-pMHC bonds, revealing the differential performance of TCRs recognizing the same pMHC ligand. The value of physiological load application and biophysical parameterization relative to immunological metrics like functional avidity or TCR sequence distance measures becomes clear. We posit that those dynamic features of an a TCR that are challenging to measure can be linked to facile biomarkers of adaptive immune recognition performance that will track with protective immunity in a clinically useful manner.ResultsA pipeline of I AV-specific aftTCRs

[0201] To identify a|3TCRs directed at two immunodominant IAV-specific epitopes, NP366-374 / Db and PA224-233 / Db tetramers were used to concurrently isolate CD8 T lymphocytes from lung parenchyma five days post-secondary infection using single cell sorting, RT-PCR molecular cloning and DNA sequencing (Fig. 11A-11B). Of 21 MHC-bound epitopes physically identified by mass spectrometry analysis, the copy number on infected cells for NP366- 374 / Dbis the highest, while PA224-233 / Dbis amongst the lowest (Fig. 11C). Fig. 6A shows a representation of the TCRa (TRA) and TCRp (TRB) clonotypes. The three most prevalent clonotypes with paired TRA and TRB were used for functional analysis below. The NP34 and NP63 utilize the same Va and Vp gene segments, differing by only a single amino acid in Vo CDR3 (Fig. 6A). By contrast, NP41 utilizes entirely different V gene elements encoding a divergent VaVp recognition module.TCR recognition of dense pMHC underload

[0202] Given the near identity of the TCR sequences directed at the same pMHC, we assumed that NP63 and NP34 would yield a similar functional profile with or without load. To our surprise, this was not the case, and clear distinctions emerged. To rule out differences in TCR copy number, adhesion molecules and / or signaling pathways leading to divergent functional outcomes, each TCR was retrovirally transduced into the same parental CD8a[3+TCR- BW5147 recipient cell line and selected by cell sorter for comparable TCRap expression (Fig. 12A). Two key OT-based experimental systems were used to interrogate transfectants (Fig. 12B-12C). Single-molecule single-cell (SMSC) measurements tether pMHC molecules to abead through a DNA rope and present the bead to a surface of a coverslip-bound T cell. As the tethered bead is pulled away, it is displaced from the trap center until the TCR / pMHC bond is broken, revealing the bond lifetime for a given force. Tether formation probability as well as peak lifetime, peak force, and width of catch bonds can also be determined. Single cell activation requirements (SCAR) are performed by trapping a pMHC-coated bead to facilitate interfacial contact with a T cell containing a fluorescence-based reporter of intracellular calcium concentration. An extended calcium flux in these cells is used as an indicator of T cell activation. Beads of varying pMHC densities can be used to judge TCR performance quality and determine the interfacial pMHC copy number required for activation, either with or without force. The tug by the OT mimics a mixture of shear and longitudinal loads between a T cell and an APC (or an infected cell) across such a bond through their respective actomyosin machineries.

[0203] Fig. 6B demonstrates catch bond profiles for NP63 and NP34 TCRs where the bond lifetime peaked at ~15 pN for both, but the bond lifetime of NP63 was 5-fold greater than that of NP34. NP41 had a bond lifetime equivalent to NP34 (Fig. 12D), but its low tethering probability (Fig. 12E) required measurement using a single molecule (SM) assay described subsequently in Fig. 8A as opposed to SMSC. The bond lifetime difference between NP63 and NP34 is further reflected by their differential responsiveness in the SCAR assay (Fig. 6C). Although both NP63 and NP34 induced calcium flux with similar kinetics at high bead copy number (20,000 interfacial pMHCs per bead), only NP63 could be activated by 200 pMHCs arrayed per bead without load. Even 18-23 pN force application did not cause many NP34 T cells to induce calcium flux in this system. In contrast, at 8-10 pN, NP63 was readily activated with as little as two pMHCs per bead, the lowest interfacial ligand concentration achieved in this study.

[0204] To further elucidate activation thresholds, we adapted the SCAR assay to a microscope specifically designed for SM fluorescence detection coupled with a sensitive camera temporally gated with excitation at very low illumination levels. This allowed for improved signal to noise ratio. These conditions virtually eliminated photobleaching and extended facile monitoring of calcium activation to 10 minutes or more. We thereby assayed the activation profiles more thoroughly for NP63, NP34 and NP41 at 20 and 2 interfacial copy numbers with and without force. While every condition revealed an activation capability, the greatest impact observed was on the percentage of activated cells. (Fig. 12F). This percentage combined with the activating signal level reflects the population activation index, or predicted mean intensity (PMI), which revealed a statistically greater performance by NP63 (Fig. 6D, Fig. 12G).Immunological assay comparisons

[0205] Functional avidity measurements using cytokine production as a readout are commonly performed to assess TCR quality through examination of T cell responsiveness to APCs cultured overnight with different peptide concentrations. The assay is technically straightforward, but its interpretation is complex, given myriad cellular components involved including adhesion molecules, co-receptors and kinases affecting cytokine secretion. As shown in Fig. 6E, the functional avidity of all three NP TCRs is comparable. While NP366-374 / Dbtetramer binding and dissociation were comparable for NP34 and NP63 (Fig. 6F and 6G, respectively), the binding to NP41 was the weakest and manifested the fastest dissociation rate. The wild-type (WT) NP366-374 / Dbtetramer (Tet) was used for this dissociation assay because NP41 does not interact with a CD8 binding-site (BS) mutant (Mut) MHC molecule (Fig. 13A). Collectively, these findings reveal that digital vs analog performance amongst TCRs cannot be discerned by the commonly used metrics of functional avidity or pMHC tetramer binding or dissociation.

[0206] Nevertheless, since tetramers can mediate crosslinking of adjacent TCR ectodomains on a T cell, themselves tethered internally to the actin cytoskeleton, we reasoned that mechanical force applied following such in vitro exposure could be used to interrogate downstream activation features. We tested whether differential mechanosensing amongst TCRs elicits divergent signaling responses. As the binding profiles of NP366-374 / Dbtetramer for NP63, NP34 and NP41 were similar at 37 °C (Fig. 6F) and at 20 °C (Fig. 13A), T cell activation at 37 °C following tetramer binding could be readily studied. Rapid phosphorylation of ERK (pERK) was detected within 2 minutes, where the greatest magnitude and persistence were observed for NP63 (Fig. 6H). Furthermore, the same binding leads to a differentially graded CD3E surface loss among the three TCRs (Fig. 6I) and distinctive upregulation of the early C-type lectin activation marker CD69 (Figs. 1J and 13B).

[0207] As these studies involved in vitro assays, we next determined whether in vivo activation of NP63 and NP34 T cells differed upon IAV infection. To this end, we created single or mixed retrogenic T cell (Rg-T) mice bearing each TCR expressing T cell alone or together using FACS sorting of naive retrogenic CD8+CD44_T cells for those adoptive transfer experiments into B6 mice followed by IAV infection, as explained later. We quantified the mediastinal lymph node (mLN) representations at day 7 post infection. As shown, NP63 CD8+T cells expanded significantly more than NP34 in the mixed retrogenic setting and revealed the greatest incorporation of EdU, the nucleoside analog of thymidine, into DNA during the S-phase of cell cycle (Figs. 6K and 14A-14C). In addition, when Rg-T cells were sorted and tested for cytolytic activity against lAV-infected LET1 type I pneumocytes, as monitored continuously over28 hrs ex vivo, mLN NP63 were faster and better at killing than were NP34 T cells (Fig. 6L-6M). This was also the case for lung-derived NP63 T cells (Figs. 14D-14E). The efficacy of IAV infectious doses that supports T-cell mediated killing of LET1 cells roughly correlates with intracellular LET1 NP expression by FACS analysis (Fig. 14F). As shown in Fig. 14G-14I, Rg-T cell numbers in lung and the level of viral titer reduction at day 7 post-lAV infection were comparable for NP34 and NP63, consistent with the notion that the high copy number of the NP3s6-374 / Dbcomplexes allows both digital and analog TCR performance to be effective.Sparse pMHC recognition underload

[0208] Corresponding analysis of PA224-233 / Db-specific TCRs (Fig. 7A) identified two common TCRs termed PA27 and PA59, and a less frequent TCR PA25. They differ in sequence aside from PA25 and PA59 that share a p gene segment and very similar CDR3[3. The SMSC force vs. bond lifetime analysis revealed that all three TCRs exhibited catch bond behavior, like those of the NP series (Fig. 7B and Fig. 6B). However, the PA59 maximal bond lifetime (75 s) was longer, and it occurred at a significantly greater force, 21 pN. Nonetheless, all three PA TCRs manifested digital performance, being triggered by 2 PA224-233 / Dbmolecules per bead-cell interface in the SCAR assay (Fig. 7C). While PA27 and PA25 triggered well in the 8-12 pN range, PA59 required a higher force, i.e. , 16-18 pN, which is consistent with the SMSC result (Fig. 7B). The duration of Ca2+flux was longer for PA27, shortest for PA25, and with the greatest intensity for PA59 at high force (Figs. 7C and 15). Paradoxically, the functional avidity assay indicated that PA27 was the weakest TCR based on ECso (Fig. 7D). Of note, the IL-2Ra expression (CD25) on the BW cell lines after peptide stimulation is comparable (Fig. 16A). Hence, depletion of IL-2 is not the basis of this discrepancy.

[0209] The above results suggest that digital PA receptors are not monolithic, but rather function in distinct ways under force. Nevertheless, WT-tetramer binding assays revealed no substantial differences among these TCRs (Figs. 7E and 16B) while CD8BS-mutated tetramer binding was weaker only for PA25 (Fig. 16B). PA27 and PA59 tetramer dissociation were similar, but both were at a slower rate than PA25 (Fig. 7F). On the other hand, WT-PA224-233 / Db- tetramer activation assays showed that PA27 manifested prolonged Erk mediated activation (Fig. 7G). The downregulation of CD3E was also the largest for PA27 but not statistically distinguishable from PA59 (Figs. 7H and 16C). Differential CD3 surface loss increased at higher temperature but with almost no change in tetramer binding, suggesting an active cellular mechanism of CD3 downregulation likely through tetramer stimulation fostering kinasedependent internalization, and / or dissociation of CD3 dimers from the TCRap clonotype (Fig. 16D). PA27 also showed the greatest differential CD69 upregulation (Figs. 7I and 16C), and thegreatest proliferation response to WT as well as CD8BS- mutant PA224-233 / Db-tetramers (Figs. 7J-7K and 16E-16F). Of note, none of the NP TCRs proliferated following CD8BS-mutated NP356-374 / Dbtetramer stimulation (Fig. 16G).SM OT analysis of digital TCRs

[0210] We next evaluated the performance of the digital PA TCRs using SM analysis. We adapted our SM assay to a geometry where the TCR is bound to a 1.23 pm bead through the anti-leucine zipper mAb 2H11 and pMHC is bound to a second 1 .23 pm bead through a DNA linkage (Fig. 8A). The SM “dual bead” assay (SMdb) was performed by positioning the two traps to initiate tether formation followed by pulling the linkage taught to a predetermined force window. This assay was performed on a LUMICKS m-Trap microscope where microfluidic flow introduces beads to populate the traps and a measurement routine calibrates the system. An advantage of the dual bead geometry is that the bead and tether linkage self-orient forming consistent bond angles, allowing for very precise and controlled bead to bead separation. Many measurements can be performed in a short period of time allowing for greater statistics and analysis.

[0211] PA27 and PA25 showed strong catch bond 255 peaks around 15 pN, while PA59 showed a much broader distribution of lifetimes and broader peak force range. PA27 displayed the longest and narrowest peak lifetime (Fig. 8B). The wide lifetime spread, attributed to an increase in instrument response time capturing short lived interactions for the SMdb assay, and variation in curve shape relative to SMSC (Fig. 7B) prompted a closer look at the lifetime distribution within each force window. A common observation was that the cumulative probability of lifetimes within each bin largely fit a double exponential model, wherein there is one time constant for quick dissociation (< 2 sec) and another for an extended lifetime (Fig. 17A). The peak bin of ~15 pN which spans the critical force for conformational transition, was significantly higher, demonstrating a -45-50% increase in lifetime for PA27 relative to PA25 and PA59. Fit parameters converged to 29.4 ± 5.3 s, 28.2 ± 3.7 for PA25 and PA59, respectively, compared with 42.6 ± 4.4 s for PA27 (Fig. extended data 7b). Overall, PA clones have longer bond lifetimes compared to NP41- NP366-374 / Dbwhich exhibited a much lower catch bond peak lifetime and more difficulty in initiating tether formation in the same SMdb assay (Fig. 8B).

[0212] Since the SMdb assay allows for extremely precise control of the force across the tether, force can be incrementally altered during a measurement by slight adjustment in the trap separation. By actively maintaining tethers at or near a critical force, we were able to observe repeat reversible transitioning between extended and compact states with a corresponding extension of bond lifetime, a state we refer to as volleying. During volleying nearthe critical force, the linkage persisted for several minutes and in some cases more than 5 minutes. To illustrate this, we plot the cumulative frequency of lifetime of volleying segments, which are much longer than the peak average force of the catch bond curve (vertical dashes in Fig. 8C). The population amplitudes remaining for digital PA27, PA59, and PA25 actively held in sustained volley states at this 5-minute mark (300 s in Fig. 8C) were 25%, 12.5% and 10%, respectively, although some traces were artificially ruptured by the user as noted. In contrast, while we were able to achieve volleying of NP41 , the average stretch lasted for only ~5 seconds. NP41 tethers could be nudged back into volleying, but it would sustain only for a short time (shown in Fig. 8C-8E). The cumulative distribution for NP41 fit to a time constant of 6.8 + / - 0.85 seconds. For additional comparison to Fig. 8D in the PA-specific TCRs, we pooled their lifetimes that either terminated naturally or survived to the 5-minute mark, yielding a time constant of 264 + / - 25 seconds which is ~40 fold longer than NP41 (Fig. 8C).

[0213] Long periods of sustained volleys were divided into 10-s segments for further study. Analysis of the transition frequencies vs. force of these sustained volleys showed that PA25, PA27 and PA59 largely behave similarly (Fig. 8F), but that PA27 has potential to transition faster (Fig. 8F insert) and for a slightly longer period of time (Fig. 8C). The average transition distances were similar, ranging from 8-12 nm but with different distributions (Fig. 8G). Note the two distinct PA27 transition differences, for example. All three PA-specific clones had average critical forces for volleying in the 13.7-13.9 pN range, whereas NP41 volleyed on average at 14.3 pN. NP41 generally transitioned at a low frequency compared to the others at the same force, but all showed a spread of frequency spanning 5-30 Hz (Fig. 8F). Average frequencies in Hz ± standard deviation for PA27, PA25 and PA59 were 14.1 ± 10.7, 7.8 ± 6.1 and 9.5 ± 7.0 respectively with PA27 showing significance relative to PA59 and PA25 due to the presence of some high Hz segments (Fig. 8F insert). The critical force for volleying generally centered around ~14 pN below which the population distribution favored the closed state and above favored the extended state (Fig. 8H).In vivo transcriptome of digital TCRs

[0214] To assess the impact of differential digital TCR performance in vivo, we generated Rg mice by transferring Rag2_ / ‘ hematopoietic stem cells retrovirally transduced with each TCRap clonotype and an IRES-linked fluorescence protein into recipient Rag2' / ' mice (Fig. 9A). Subsequently, an equal number of naive Rg-T cells from those Rg mice were adoptively transferred into B6 mice (Rg-chimera mice, RgC mice) followed by PR8 infection (Figs. 9A and 18A). FACS analysis allowed for detection of the Rg-T cells using a combination of the fluorescent protein and antibodies against Vp ( GFP+ V 9+for PA27, GFP+Vp7+for PA59, andmCherry+V[37+for PA25), and revealed that the most dominant Rg-T cells in mLN is PA27, followed by PA59, and then by PA25 at 7-days post-infection (dpi 7) (Figs. 9B-9C). Those data are consistent with increased in vivo EdU incorporation by PA27 relative to PA25 and PA59 that was not significantly different from one another (Fig. 9D and 18B). Of note, percentages of all three Rg T cells relative to CD8[3+T cells were equivalent in lung (Fig. 18C, 18D).

[0215] To exclude the possibility that the binding of mAbs to the TCRs used for cell sorting might have induced TCR signaling and impacted gene expression, we used a third fluorescence protein BFP, for PA27, thus generating “an untouched” TCR labeling and sorting system. Subsequently, we generated mixed RgC mice and performed bulk RNA-seq at 7 dpi (Fig. 19A). Two of the 12 samples, one from PA25 mLN and one PA59 lung were excluded for further analysis due to low RNA quality. Principal component analysis (PCA) shows that each Rg T cell type in mLN is clustered, whereas those in lung are scattered and undistinguishable (Extended Data Fig. 19B). Compared to PA25 and PA59, PA27 T cells differentially upregulate genes (635 and 48 genes, respectively), including those involved in TCR signaling, cytotoxicity, cytokine and chemokine receptors, ribosomes, metabolism, and apoptotic genes (Fig. 9E). Gene set enrichment analysis (GSEA) also revealed that cell cycle pathways are significantly upregulated in PA27 compared to both PA25 and PA59, and the TCR signaling pathway is additionally upregulated in PA27 T cells compared to PA25 (Extended Data Fig. 19C). Compared to PA25, PA59 T cells upregulate 264 genes, including TCR signaling, ribosome, and cell cycle genes (Fig. 9E), although the GSEA was not statistically significant (Extended Data Fig. 19C).

[0216] In contrast to the many differentially expressing genes amongst the three PA-Rg T cells in mLN, there were almost no upregulated genes in those same PA-Rg-T cells in lung, except for a few most prominently displayed in PA27 T cells such as Penk, Mafb, Gpx3 and C3 (Figs. 9F and 19D). We note that the overall comparability of the three types of Rg T cells in lung clearly is not due to their unresponsiveness because all significantly upregulated genes associated with TCR signaling, inflammation, and cytotoxicity compared to those in mLN including Nur77, Zap70, Nfat, Gzmb, Prf1, Il2ra (Fig. 20A-20E). The equivalence of gene expression in the lung is probably a consequence of the collective T cell activation resulting from apTCR triggering in the context of inflammation with attendant cytokine-, as well as chemokine mediated signaling spawning modest but equivalent anti-viral activity in those PA-Rg T cells (Fig. 20F). Of note, ex vivo killing assay shows no PA-Rg T cell-mediated cytotoxicity of LET1 cells (Fig. 20G), most likely because of the virtual absence of PA peptides presented on a major fraction of LET 1 cells compared to, on average, a 5-10 fold higher number on the DC2.4dendritic cell line post infection. Thus, it is likely that digital PA-T cells expand by recognizing A224-233 / Dbpresented on DC in mLN and contribute to virus clearance in lung through production of cytokines and chemokines in a bystander fashion.Discussion

[0217] The a[3 T cell lineage of the mammalian immune system, and that of jawed vertebrates (Gnathostomata) more broadly, utilizes a non-equilibrium mechanosensing modality to detect sparse pMHC ligands, enhancing sensitivity by 1,000-10,000 fold compared to the very same T cells operating in the absence of bioforces. A structural element central to this recognition process is the Cp FG loop, which stabilizes the Vp-Cp domain interaction and supports the binding of the Vp and Vo domains to pMHC. The Cp FG loop allosterically controls the V module’s pMHC interaction surface orientation and thus contributes to TCR-pMHC specificity, sensitivity, and bond lifetime. Load stabilizes interdomain contacts both within the TCRap domains and with pMHC, thereby fostering rapid transitioning between compact and extended TCRap conformations, sustained by external force applied at the interface between the T cell and APC arising from actomyosin machinery. Unsurprisingly, deletion of the Cp FG loop dramatically degrades apTCR-pMHC recognition function. y<5T cells, the second major T cell class using somatic genomic rearrangement for repertoire diversification, but which recognize abundant non-peptidic surface ligands, lack the equivalent of the elongated Cp FG loop. Consequently, y6T cells manifest neither catch bonds nor structural transitions.

[0218] Here we show that with proper force feedback the TCR-pMHC bond can be maneuvered into an unprecedented resonant state revealing lifetimes that are greater than peak catch bond lifetimes by an order of magnitude (Fig. 8). Given the fixed separation between traps, which includes the TCR-pMHC bond, DNA linkage, beads and optical springs (with physiologically relevant stiffnesses in the range of 0.2-0.3 pN / nm), a sudden increase in length of the TCR-pMHC bond results in retraction of the optical springs and corresponding reduction of force. This in turn reduces the force across the system and shifts the energy landscape to just below the equilibrium force favoring transition back to the compact state and a reset of the cycle. Unlike protein unfolding where the distance to the transition state in the forward direction, unfolding, is very short compared to the refolding distance making it energetically unlikely to refold with a sustained load, the forward and reverse TCR-pMHC transition state distances are apparently more balanced given the rapid volleying observed within a narrow force window of the critical force.

[0219] In the cell-cell system, the T cell and APC create a similarly constrained organization that drives bending and unbending of the membrane with lateral agitation as theTCR snaps open and closed (Fig. 10). Each bend and snap cycle represents a means to initiate T cell activation. Thus, what matters for digital T cells isn’t necessarily the presence of a catch bond per se, but rather energetically driven signaling through TCR molecular resonance powered by cell surveillance motions and sustained via cycles of conformational changes. While catch bonds are an indicator of TCR quality, the catch bond itself may serve more as a gating mechanism to exclude unproductive interactions. Bonds that survive this prescreen may then become energized and support sustained molecular resonance with attendant downstream signaling processes. This cycle effectively arises from a simple model of coupled elements and springs within the local region of the TCR-pMHC bond. Local stiffness and other mechanical elements such as the surrounding accessory and adhesion molecules can readily tune the resonant cycle. Considering that a dominant conformational change resides in the Cp domain, the snap open will result in a differential yank at the membrane through pMHC-CD8 linkage(s) with potential to repeatedly and simultaneously drive both inside-out and outside-in signaling.

[0220] That digital versus analog performance can be determined by a single amino acid in CDR3 at the apTCR-pMHC interaction surface (Fig. 6) underscores the impact of the dynamic interactions occurring therein. Consistent with this notion, single amino acid changes in a peptide (i.e., agonist versus antagonist or null) result in disparate activation of T cells expressing the same TCR and whose TCR-pMHC complexes are virtually identical. Nonetheless, differential interdomain-motion and interfacial contacts under load impact the peptide-sensing CDR3 loops, thereby determining mechanical response and peptide discrimination.

[0221] In a digital response, repetitive transitions of single apTCRs can activate motor proteins to foster kinapse initiation and subsequently mature immunological synapse formation. Both OT experiments and pointillist super resolution microscopy experiments revealed that pMHC-ligated apTCRs as well as nearby unligated apTCRs are recruited to initiate TCR clustering. We speculate that in the absence of temporally prolonged repetitive structural transitioning of digital TCRs, analog TCRs benefit from a high density of pMHC ligands to allow for integration of signal or coalescence of smaller TCR clusters as an alternative means to synapse initiation. Regarding TCRs targeting NP366-374 Db, both digital and analog expressing T cells can be engaged in a productive manner given the high copy number of that ligand displayed on IAV infected epithelium. Integration across plentiful individual signals involving multiple TCRs on the T cell can afford sensing across a ligand gradient and is presumably operative in T cells bearing either analog or digital TCRs. By contrast, digital TCRs canrecognize sparse ligands like PA224-233 / Dbwhereas analog TCRs cannot interpret such rare input to drive T cell signaling.

[0222] In the case of the PA TCRs, biophysical performances were distinguishable (Fig. 8), as were their activation responses in different functional assays (Fig. 7). While the PA59 and PA25 V|3 sequences are virtually identical (Fig. 7A), the PA59 and PA25 have related but distinct CDR3a residues on different Va domains. Both TCRs contain a lysine in a flexible CDR3a segment that might interact with the p4E of the SSLENFRAYV peptide under force. Meanwhile, the PA27 is particularly remarkable for its CDR3[3 that contains five acidic residues (Fig. 7A), one or more of which might form a salt bridge with p7R under load in addition to also possessing a CDR3a lysine. Additionally, the PA T-cell transcriptomes were distinct in keeping with the ability of calcium flux and prolonged ERK activation to impact T cell expansion and gene activation. That PA27 has high levels of the antioxidant Gpx3 and proenkephalin (an attenuator of substance P that promotes asthma via the PI3K / Akt / NfKb pathway in bronchial epithelium) is relevant and speaks to a potential protective effect mediated by this T cell in lung. The distinct bond lifetime occurring at higher force for PA59 relative to PA27 and PA25 is noteworthy since local tissue stiffness varies 100-fold in normal versus inflammatory conditions, and even more profoundly in different tissue types. The increased breadth of the PA59 catch bond curve may capitalize on accessing higher force interactions expected in stiffer tissues where a bond captured at ~20 pN can persist long enough to subsequently relax to a resonant state.

[0223] Perhaps PA59 functions best in stiff locales such as intraepithelial sites where certain resident memory T cells (TRM) reside in contrast to other TRM cells found in more compliant and loosely packed lamina propria space beneath the epithelium, airways or bronchus associated airway tissues. That ml_N PA59 T cells express higher CD103 and ltg|37 transcripts than PA27, the products of which heterodimerize to form an oE[37 integrin with its E- cadherin counter- receptor, may facilitate localization to intraepithelial sites50. Spatial gene expression analysis could test this notion in the future. While such a topography is presently conjectural, it is nonetheless evident that there is not a single bioforce load under which every apTCR operates (Fig. 7B). Remarkably, whilst PA25 performance is least ideal of the three PA TCRs examined, its function in the lung in vivo is broadly comparable to that of the others, emphasizing how acute inflammation-related activation cuing fosters productive responses to the benefit of the host.

[0224] Although our investigations are explicitly directed at anti-lAV responses, the discovery of digital-vs-analog performances coupled with distinct bioforce profiles has broadimplications. Parameterization of catch bond curves, conformational transitions, critical force and lifetime of volleying, tether formation, activation threshold, and signaling profiles offer quantitative TCR performance metrics. Beyond the granularity, our findings suggest opportunities for nuanced adoptive T-cell immunotherapies and / or cancer vaccine elicitation of relevant TCR specificities with digital TCR performance requirements mandated by sparse neoantigen arrays on cancer cells. For example, while the bioforces optimal for solid tumors versus hematologic malignancies require further exploration, the stiffness of desmoplastic solid tumors likely necessitates utilizing a greater force-bond lifetime maximum than would compliant hematopoietic tumors. Notwithstanding, defining the atomistic basis of TCR performance is worthy of future study. In turn, utilizing the dynamics data in conjunction with machine learning could achieve, in principle, an aspirational goal of predicting each TCR’s performance within a T-cell repertoire specific for a given pMHC under various physical loads based on primary sequence information.

[0225] Lastly, our attempt to correlate the biophysical parameterization and functional activation assays suggests that it is possible to uncover biomarkers of significance. Of note, surface CD3 loss and CD69 upregulation observed here were shown to be associated with TCR quality in a recent independent study. Furthermore, our transcriptom ic analysis advocates that cytokine receptors, and elements involving the cytotoxicity machinery, ribosomes, RNA regulation and TCR signaling (Fig. 9E) might all be facile biomarker candidates measurable by flow cytometry.

[0226] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of skill in the art to which the disclosed invention belongs. Publications cited herein and the materials for which they are cited are specifically incorporated by reference.

[0227] Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.

Claims

CLAIMS1. A method for detecting a digital T cell receptor peptide interaction, comprising(a) contacting an ap T cell receptor (apTCR) with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the apTCR and the pMHC; and(b) applying an equilibrium force of 10 pN to 40 pN to the catch bond or slip bond between the apTCR and the pMHC to achieve a state where the catch bond or slip bond is volleying between compact and extended states; wherein a sustained volleying is an indication of a digital T cell receptor interaction.

2. The method of claim 1 , wherein the force is applied with a spring stiffness of 0.03 to2 pN / nm to energetically drive the volleying.

3. The method of claim 2, wherein the force is applied with a spring stiffness of 0.1 to 0.3 pN / nm to energetically drive the volleying.

4. The method of claim 1 , wherein the force is applied at an oscillation frequency of 1 Hz to 50 Hz and an amplitude of from 0.1 pN to 40 pN to energetically drive the volleying.

5. The method of claim 4, wherein the oscillation is a sine wave, square wave, or triangle wave.

6. The method of claim 1 , wherein the force is applied at a constant magnitude.

7. The method of claim 1 , wherein the force is applied using an optical tweezer.

8. The method of claim 1 , wherein the force is applied using a magnetic force, acoustic force, deformable substrate, atomic force probe or cantilever, or bioforce probe.

9. The method of claim 1 , wherein the pMHC is presented on a bead or solid support.

10. The method of claim 1 , wherein the pMHC is on the surface of an antigen presenting cell.

11. The method of claim 1 , wherein the apTCR is presented on a bead or solid support.

12. The method of claim 1 , wherein the apTCR is on the surface of a T cell.

13. The method of claim 13, wherein the T cell comprises a chimeric antigen receptor (CAR).

14. The method of claim 13, wherein the T cell is a tumor infiltrating lymphocyte (TIL).

15. A method for detecting a digital T cell receptor peptide interaction, comprising(a) contacting an ap T cell receptor (apTCR) on a T cell with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the apTCR and the pMHC; and(b) assaying the T cell for CD3 and CD69 surface expression;wherein CD3 loss and CD69 upregulation in the T cell is an indication of a digital T cell receptor interaction.

16. A method for detecting a digital T cell receptor peptide interaction, comprising(a) contacting an ct|3 T cell receptor (a[3TCR) on a T cell with an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC) under conditions suitable to promote a catch bond or slip bond between the a[3TCR and the pMHC; and(b) assaying the T cell in a cytotoxicity assay at a 1 :1 T cell to target ratio; wherein a T cell that initiates killing within 3 hours in the cytotoxicity assay with at least 50% target killing is an indication of a digital T cell receptor interaction.

17. A method for selecting a T cell or antigen, comprising(a) providing a T cell comprising a op T cell receptor (apTCR) and an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC), wherein either the T cell or the pMHC is tethered to a solid support or bead;(b) contacting the candidate T cells with the antigen peptide under suitable conditions to promote a catch bond or slip bond between the a|3TCR and the pMHC;(c) applying a force of 10 to 40 pN to the catch bond or slip bond between the apTCR and the pMHC;(d) assaying the cells for T cell activation, tether forming probability, or a combination thereof; and(e) selecting a T cell or antigen thereof from the candidate T cells that has higher than average activation and / or has a higher than average chance of forming a tether compared to control values.

18. The method of claim 17, wherein T cell activation is determined using a reporter of intracellular calcium concentration, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with a high calcium flux.

19. The method of claim 17, wherein T cell activation is determined by assaying for cell stiffness, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with increased activation-induced T cell stiffening.

20. The method of claim 17, wherein T cell activation is determined by imaging the cell, wherein step (e) comprises selecting a T cell or antigen from the plurality of candidate T cells with changes in geometry and / or morphology.

21. The method of claim 17, comprising screening a plurality of T cells and selecting a T cell that has an a[3TCR that interacts with the antigen peptide with digital binding.

22. The method of claim 17, comprising screening a plurality of antigens and afBTCRs and selecting an antigen that interacts with an a[3TCR peptide with digital binding.

23. The method of claim 17, wherein the force is applied using an optical tweezer.

24. The method of claim 17, wherein the force is applied using a magnetic or acoustic field.

25. The method of claim 24, wherein the force is applied using a magnetic tweezer, acoustic tweezer, deformable substrate, atomic force probe or cantilever, or bioforce probe.

26. The method of claim 17, wherein the force is applied using a fluid flow configured to produce a stokes drag.

27. The method of claim 17, wherein the pMHC is presented on a bead or solid support.

28. The method of claim 17, wherein the pMHC is on the surface of an antigen presenting cell.

29. The method of claim 17, wherein the T cell comprises a chimeric antigen receptor (CAR).

30. The method of claim 17, wherein the T cell is a tumor infiltrating lymphocyte (TIL).

31. The method of claim 17, wherein the activated T cell has been transduced with a TCR.

32. The method of claim 17, further comprising adoptively transferring the activated T cell to a subject in need thereof.

33. A method for producing a vaccine encoding a neoantigen or tumor associated antigen to stimulate digital performance T cells directed at a tumor comprising(a) generating digital responder T cells through elicitation of an effective immune response directed at the tumor;(b) determining the average copy number of an encoded pMHC epitope in the immune peptidome of an antigen presenting cell by mass spectrometry in the good responder subject; and(c) engineering a vaccine to produce a comparable number of encoded antigens on antigen presenting cells of a vaccinated subject.

34. The method of claim 32, wherein the vaccine comprises a promoter operably linked to a gene encoding the antigen, and wherein the promoter is selected based on predicted copy number rate.

35. An in silico method for detecting a T cell receptor peptide interaction, comprising:(a) providing a T cell comprising a op T cell receptor (apTCR) and an antigen peptide bound to a major histocompatibility complex (MHC) molecule (pMHC);(b) determining a likelihood of an analog or digital receptor interaction based upon an unfolding propensity of the T cell; and(c) validating the analog or digital receptor interaction based upon model analysis of experimental volleying data of the T cell.

36. The in silico method of claim 35, comprising subjecting the T cell to staged pulling simulations prior to determining the unfolding propensity of the T cell.

37. The in silico method of claim 36, wherein the staged pulling simulations comprises intervals of pulling with a given force and holding steady without pulling.

38. The in silico method of claim 36, wherein the staged pulling simulations are performed in response to detection of catch bond formation based upon stabilization of a TCR and pMHC interface under different load levels.

39. The in silico method of claim 35, wherein the model analysis comprises determining one or more transition rate between compact and extended states of the T cell based upon the experimental volleying data.

40. The in silico method of claim 39, wherein the experimental volleying data comprise time traces of force.

41. The in silico method of claim 39, wherein the validation is based upon comparison of one or more transition rate with the unfolding propensity.

Images