A microwell platform for high-throughput longitudinal phenotyping and selective retrieval of organoids.

Organoids are powerful experimental models for studying the ontogeny and progression of various diseases including cancer. Organoids are conventionally cultured in bulk using an extracellular matrix mimic. However, bulk-cultured organoids physically overlap, making it impossible to track the growth of individual organoids over time in high throughput. Moreover, local spatial variations in bulk matrix properties make it difficult to assess whether observed phenotypic heterogeneity between organoids results from intrinsic cell differences or differences in the microenvironment. Here, we developed a microwell-based method that enables high-throughput quantification of image-based parameters for organoids grown from single cells, which can further be retrieved from their microwells for molecular profiling. Coupled with a deep learning image-processing pipeline, we characterized phenotypic traits including growth rates, cellular movement, and apical-basal polarity in two CRISPR-engineered human gastric organoid models, identifying genomic changes associated with increased growth rate and changes in accessibility and expression correlated with apical-basal polarity. A record of this paper's transparent peer review process is included in the supplemental information.

On the dependent recognition of some long zinc finger proteins.

The human genome contains about 800 C2H2 zinc finger proteins (ZFPs), and most of them are composed of long arrays of zinc fingers. Standard ZFP recognition model asserts longer finger arrays should recognize longer DNA-binding sites. However, recent experimental efforts to identify in vivo ZFP binding sites contradict this assumption, with many exhibiting short motifs. Here we use ZFY, CTCF, ZIM3, and ZNF343 as examples to address three closely related questions: What are the reasons that impede current motif discovery methods? What are the functions of those seemingly unused fingers and how can we improve the motif discovery algorithms based on long ZFPs' biophysical properties? Using ZFY, we employed a variety of methods and find evidence for 'dependent recognition' where downstream fingers can recognize some previously undiscovered motifs only in the presence of an intact core site. For CTCF, high-throughput measurements revealed its upstream specificity profile depends on the strength of its core. Moreover, the binding strength of the upstream site modulates CTCF's sensitivity to different epigenetic modifications within the core, providing new insight into how the previously identified intellectual disability-causing and cancer-related mutant R567W disrupts upstream recognition and deregulates the epigenetic control by CTCF. Our results establish that, because of irregular motif structures, variable spacing and dependent recognition between sub-motifs, the specificities of long ZFPs are significantly underestimated, so we developed an algorithm, ModeMap, to infer the motifs and recognition models of ZIM3 and ZNF343, which facilitates high-confidence identification of specific binding sites, including repeats-derived elements. With revised concept, technique, and algorithm, we can discover the overlooked specificities and functions of those 'extra' fingers, and therefore decipher their broader roles in human biology and diseases.

A bead-based method for high-throughput mapping of the sequence- and force-dependence of T cell activation.

Adaptive immunity relies on T lymphocytes that use αß T cell receptors (TCRs) to discriminate among peptides presented by major histocompatibility complex molecules (pMHCs). Identifying pMHCs capable of inducing robust T cell responses will not only enable a deeper understanding of the mechanisms governing immune responses but could also have broad applications in diagnosis and treatment. T cell recognition of sparse antigenic pMHCs in vivo relies on biomechanical forces. However, in vitro screening methods test potential pMHCs without force and often at high (nonphysiological) pMHC densities and thus fail to predict potent agonists in vivo. Here, we present a technology termed BATTLES (biomechanically assisted T cell triggering for large-scale exogenous-pMHC screening) that uses biomechanical force to initiate T cell triggering for peptides and cells in parallel. BATTLES displays candidate pMHCs on spectrally encoded beads composed of a thermo-responsive polymer capable of applying shear loads to T cells, facilitating exploration of the force- and sequence-dependent landscape of T cell responses. BATTLES can be used to explore basic T cell mechanobiology and T cell-based immunotherapies.

Tuning T cell receptor sensitivity through catch bond engineering.

Adoptive cell therapy using engineered T cell receptors (TCRs) is a promising approach for targeting cancer antigens, but tumor-reactive TCRs are often weakly responsive to their target ligands, peptide-major histocompatibility complexes (pMHCs). Affinity-matured TCRs can enhance the efficacy of TCR-T cell therapy but can also cross-react with off-target antigens, resulting in organ immunopathology. We developed an alternative strategy to isolate TCR mutants that exhibited high activation signals coupled with low-affinity pMHC binding through the acquisition of catch bonds. Engineered analogs of a tumor antigen MAGE-A3-specific TCR maintained physiological affinities while exhibiting enhanced target killing potency and undetectable cross-reactivity, compared with a high-affinity clinically tested TCR that exhibited lethal cross-reactivity with a cardiac antigen. Catch bond engineering is a biophysically based strategy to tune high-sensitivity TCRs for T cell therapy with reduced potential for adverse cross-reactivity.

Individual dimers of the mitotic kinesin motor Eg5 step processively and support substantial loads in vitro.

Eg5, a member of the kinesin superfamily of microtubule-based motors, is essential for bipolar spindle assembly and maintenance during mitosis, yet little is known about the mechanisms by which it accomplishes these tasks. Here, we used an automated optical trapping apparatus in conjunction with a novel motility assay that employed chemically modified surfaces to probe the mechanochemistry of Eg5. Individual dimers, formed by a recombinant human construct Eg5-513-5His, stepped processively along microtubules in 8-nm increments, with short run lengths averaging approximately eight steps. By varying the applied load (with a force clamp) and the ATP concentration, we found that the velocity of Eg5 was slower and less sensitive to external load than that of conventional kinesin, possibly reflecting the distinct demands of spindle assembly as compared with vesicle transport. The Eg5-513-5His velocity data were described by a minimal, three-state model where a force-dependent transition follows nucleotide binding.

Stepping and stretching. How kinesin uses internal strain to walk processively.

The ability of kinesin to travel long distances on its microtubule track without dissociating has led to a variety of models to explain how this remarkable degree of processivity is maintained. All of these require that the two motor domains remain enzymatically "out of phase," a behavior that would ensure that, at any given time, one motor is strongly attached to the microtubule. The maintenance of this coordination over many mechanochemical cycles has never been explained, because key steps in the cycle could not be directly observed. We have addressed this issue by applying several novel spectroscopic approaches to monitor motor dissociation, phosphate release, and nucleotide binding during processive movement by a dimeric kinesin construct. Our data argue that the major effect of the internal strain generated when both motor domains of kinesin bind the microtubule is to block ATP from binding to the leading motor. This effect guarantees the two motor domains remain out of phase for many mechanochemical cycles and provides an efficient and adaptable mechanism for the maintenance of processive movement.