Single-cellular dynamic mechanical analysis of live 3D organoids under light-sheet fluorescence microscopy

Abstract

A system and method for analyzing mechanical properties of biological tissues combines light-sheet fluorescence microscopy with mechanical perturbation capabilities. The system integrates a MEMS-based compression device, high-speed light-sheet scanner, and digital image correlation analysis to characterize viscoelastic properties of three-dimensional cellular structures. The system enables quantification of spatially-resolved mechanical characteristics and cellular responses through volumetric strain analysis, correlating mechanical properties with fluorescence-based cellular markers. Implementation with maternal-fetal interface models demonstrates capabilities in analyzing tissue mechanics during cellular invasion processes. The system provides applications in studying mechanical interactions in biological processes including embryonic development, cancer metastasis, and wound healing. The disclosed methodology enables unprecedented analysis of mechanical property dynamics at cellular interfaces in three-dimensional tissue environments.

Description

[0001]Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 SINGLE-CELLULARDYNAMICMECHANICALANALYSISOFLIVE3D ORGANOIDSUNDERLIGHT-SHEETFLUORESCENCEMICROSCOPY CROSS-REFERENCE TO RELATED APPLICATIONS Thisapplication claims the benefit of U.S. Provisional Patent Application No. 63 / 618,716, filed January 8, 2024, which is incorporated by reference in its entirety. GOVERNMENTFUNDING Thisinvention was made with government support under 2223957, and 1942518 awarded by the National Science Foundation. The government has certain rights in the invention. FIELD Thepresentdisclosurerelatesgeneralytosystemsandmethodsforanalyzing mechanicalpropertiesofbiologicaltissues,andmoreparticularlytointegratedmicroscopy systemsforcharacterizingviscoelasticpropertiesofthree-dimensionalcelularstructuresand tissueinterfaces. BACKGROUND Phase transitions in close-packed systems, such as glass transitions in amorphous materials, the flowing and clogging of particles, and viscoelastic transitions in biological tissues, have been of broad interest. Transitions often refered to as ‘cel jamming and unjamming’ are analogous to the colective behaviors of inert particles, such as a pile of beans or grains. Although the term is intuitive, viscoelastic transitions in tissues involve essential biological phenomena not found with inert particles.Tissues are composed of heterogeneous cel types that orchestrate essential physiological, developmental, and pathological events. Viscoelastic interactions between three-dimensionaly aranged distinct functional regions define the structural characteristics of tissues and inform tissue integrity, deviations from which can lead to pathology. It is crucial to evaluate the interactions of individual cels to understand the colective functions of tissues. The transitions between fluid-like (viscous) and solid-like (elastic) behaviors permit cels to undergo diferent modes of single-cel and colective-cel migration. Notable processes include progression of development through the interaction of dermal layers in gastrulation, sprouting of blood vessels, invasion at the maternal-fetal interface, and desmoplastic reaction to cancer. Epithelial- Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 mesenchymal transition (EMT) and mesenchymal-epithelial transition (MET) play critical roles in embryonic development, tumorigenic process, and wound healing.However, there has not been a method capable of assessing the structural characteristics of single cels in 3D tissues. Most common cytometry methods observe suspended or fixed cels and cannot evaluate their structural functions, therefore, there is an unmet need for systemsandmethodsforcharacterizingviscoelastic propertiesofthree-dimensionalcelularstructuresandtissueinterfaces. SUMMARY The present disclosure provides, in at least one aspect, systems for analyzing biological tissue, comprising, an imaging system configured to acquire three-dimensional images of a sample, a mechanical perturbation device configured to apply force to the sample, and a processor configured to analyze deformation of the sample based on images acquired during application of the force. In some embodiments, the imaging system comprises a light-sheet microscope. In some embodiments, the light-sheet microscope comprises, a light-sheet scanner configured to generate a moving light sheet, at least one ilumination objective for directing the light sheet to the sample and an imaging objective positioned orthogonaly to the ilumination objective. In some embodiments, the light-sheet scanner comprises a miror mounted to at least one piezoelectric bimorph actuator, electrostatic or electromagnetic actuator, and a position sensor configured to monitor position of the light sheet. In some embodiments, the mechanical perturbation device comprises a piezoelectric actuator a compression plate coupled to the piezoelectric actuator, and a load cel configured to measure force applied to the sample. In some embodiments, the processor is configured to generate a three-dimensional model of the sample comprising a plurality of segments representing individual cel, track displacement of nodes within the segments during application of the force, and calculate strain distribution within the sample based on the tracked displacement. In some embodiments, the processor is configured to calculate strain tensors for elements within the segments, and determine von Mises strain for individual cels based on the strain tensors. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 The present disclosure provides, in at least another aspect, methods for analyzing biological tissue, comprising, providing a sample comprising multiple cel types, applying force to the sample, acquiring three-dimensional images of the sample during application of the force andanalyzing deformation of the sample based on the acquired images. In some embodiments, the method further comprises embedding the sample in a gel matrix prior to applying force. In some embodiments, acquiring three-dimensional images comprisesiluminating the sample with a light sheet anddetecting fluorescence emission with an objective oriented orthogonaly to the light sheet. In some embodiments, analyzing deformation comprisesgenerating a three-dimensional model of the sample by segmenting the acquired images into regions coresponding to individual celstracking displacement of nodes distributed throughout the model andcalculating strain within regions of the model based on the tracked displacement. In some embodiments, calculating strain comprisesgenerating tetrahedral elements between the nodescalculating strain tensors for the tetrahedral elements anddetermining von Mises strain for individual cels based on the strain tensors. In some embodiments, the method further comprises corelating calculated strain with fluorescence intensity for individual cels within the sample. In some embodiments, providing the sample comprises forming a three-dimensional organoid by combining diferent cel types labeled with distinct fluorescent markers. The present disclosure provides, in at least another aspect,systemsfor analyzing tissue interfaces, comprisingan imaging system configured to acquire three-dimensional images, a force application device,anda processor configured toanalyze deformation of a tissue sample during force application and corelate the deformation with spatial distribution of diferent cel types within the sample. In some embodiments, the processor is configured tosegment acquired images into regions coresponding to individual cels, calculate strain tensors for elements within the segmented regions,anddetermine viscoelastic properties of diferent regions based on the calculated strain tensors. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 In some embodiments, the imaging system comprisesdualilumination objectives positioned to iluminate the sample from opposite sidesanddual cameras configured to simultaneously acquire images in diferent wavelength channels. In some embodiments, wherein the force application device is configured to apply cyclic compression at a defined frequency. In some embodiments, the processor is configured toidentify regions exhibiting coordinated versus random motion paterns,andcorelate the identified paterns with cel type distribution. In some embodiments, the processor is configured togenerate a three-dimensional computational model of the sample comprising tetrahedral elements andcalculate strain distribution within the elements during force application. In some embodiments, the processor is configured to corelate calculated strain with fluorescence intensity on a cel-by-cel basis. The present disclosure provides, in at least another aspect, methodsof analyzing cel- cel interactions in three-dimensional tissue constructs, comprisingforming a three-dimensional tissue construct comprising multiple labeled cel types, embedding the tissue construct in a gel matrix, applying force to the gel matrix while acquiring three-dimensional images, and analyzing relative motion between diferent cel types based on the acquired images. In some embodiments, forming the three-dimensional tissue construct comprises combining epithelial cels and stromal cels in a defined ratio,andculturing the combined cels under conditions promoting self-organization. In some embodiments, analyzing relative motion comprisestracking displacement vectors for individual cels,andidentifying regions exhibiting coordinated versus random motion paterns. In some embodiments, the method further comprises calculating strain tensors for elements within the tissue construct,andcorelating calculated strain with spatial distribution of diferent cel types. In some embodiments, acquiring three-dimensional images comprisesscanning a light sheet through the tissue construct while detecting fluorescence emission with an objective oriented orthogonaly to the light sheet. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 In some embodiments, the method further comprises analyzing changes in relative motion paterns in response to biochemical treatment of the tissue construct. In some embodiments, the force comprises cyclic compression at a defined frequency. The present disclosure provides, in at least another aspect,methodsof analyzing tissue mechanical properties, comprisingacquiring time-series three-dimensional images of a tissue sample during application of force, generating a computational model comprising segments coresponding to individual cels, tracking displacement within the segments anddetermining mechanical properties based on the tracked displacement. In some embodiments, the method further comprises correlating determined mechanical properties with celular markers on a cel-by-cel basis. In some embodiments, generating the computational model comprisesapplying three- dimensional watershed segmentation to the images. In some embodiments, determining mechanical properties comprises:calculating strain tensors for elements within the computational model,and determining viscoelastic properties based on the calculated strain tensors. In some embodiments, the method further comprises analyzing spatial distribution of mechanical properties relative to tissue interfaces within the sample. In some embodiments, acquiring time-series three-dimensional images comprises iluminating the sample with a scanning light sheet,anddetecting fluorescence emission with an objective oriented orthogonaly to the light sheet. In some embodiments, the method further comprises analyzing changes in mechanical properties in response to biochemical treatment of the tissue sample. The present embodiment provides, in at least another aspect,methodsof analyzing tissue interfaces, comprisingproviding a three-dimensional tissue construct comprising multiple cel types, applying cyclical force to the tissue construct while acquiring three-dimensional images, generating a computational model of the tissue construct,andanalyzing relative deformation between different regions of the tissue construct based on the computational model. In some embodiments, providing the three-dimensional tissue construct comprises combining diferent cel types labeled with distinct markers. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 In some embodiments, the method further comprises calculating strain tensors for elements within the computational model,andcorelating calculated strain with spatial distribution of diferent cel types. In some embodiments, analyzing relative deformation comprisesidentifying regions exhibiting coordinated versus random motion paterns. In some embodiments, the method further comprises analyzing changes in relative deformation paterns in response to biochemical treatment of the tissue construct. BRIEFDESCRIPTIONOFTHEDRAWINGS FIGS.1A-1C: Analysis method. (A) Comparison between a conventional DMA analysis and the presently disclosed 3D DMA analysis. (B) Force diagram, The stress distribution can be found within agar from image analysis because its viscoelastic characteristics are pre-calibrated. The photograph shows a multi-celular spheroid in agar. Scale bar = 0.5 mm. (C) Dynamic FEM analysis conducted by the PT when sinusoidal actuation is applied, points in the model, as A and B, respond with diferent amplitude and phase. FIGS.2A-2C: (A) Core-shel hydrogel for sample holding. (B-C) 3D cel migration assay. FIGS.3A-3D: System overview. (A) Shows a live biosample embedded in an agarose pilar that is simultaneously compressed and scanned. (B) Is an image of a biosample embedded in an agarose pilar pushed out onto the μ-force sensor from the capilary tube. (C) is an FPGA system diagram. (D) Is a control signal diagram showing the laser scan, liquid lens focusing, micro indentation, and sCMOS fluorescence camera exposure trigger signals, respectfuly, from top to botom, for an acquisition sequence. FIGS.4A-4E: Top a), b), and c) show the load cel. Botom d) and e) show a COMSOL FEA simulation of load cel deformation. FIGS.5A-5B: (a) Optical diagram of excitation and (b) detection axes for LSM imaging. FIG.6: Light-sheet scanner. FIG.7: Typical control and output signals of the micro load cel and the light-sheet scanner. FIG.8: Opticaly tracked compression of sample in an agarose pilar. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 FIG.9: Opticaly tracked fluorescence beads in agarose showing a phase delay. FIGS.10A-10E: (A) Agarose compression testing experimental setup, (B) input strain control signal and output measured force, (C) compression frequency vs. phase delay plot, (D) compression frequency vs. RMS amplitude plot, and (E) opticaly tracked fluorescent bead displacement from fluorescence images ploted against control signals across 1-6 Hz compression frequencies. FIG.11: 3D modeling of an organoid. FIG.12: shows how the image data is processed. The compression sequence is recorded as a 4D image. Usingthe image, a computational model of the 3D tissue sampleis built. Optical tracking is then used to find the displacement of nodal points. Based on the 3D model and the nodal displacement, (i) displacement of individual cels and (i) deformation (strain) within individual celsmay be found. FIGS.13A-13B: (a) The top ilustration overviews the light-sheet microscopy experimental setup. The micromechanical perturbation device applies compressive strain to the sample embedded in an agarose pilar. The botom photograph shows the perturbation device and an organoid embedded in agarose (scale bar = 1 mm). (b) Fluorescence 4D images on top showing a compression sequence of a 3D organoid composed of extravilous trophoblasts, and endometrial stromal fibroblasts (scale bar = 50 µm). The 4D digital image corelation (DIC) algorithm tracked the displacement of ~20,000 nodal points as shown in the botom graphs (scale bar = 50 µm). FIGS.14A-14C: (a) Internal celular displacement vectors under compression. Scale bar = 50 µm. (b) Cels in 3D organoids show diferent local paterns, including solid-like wel-aligned deformation (top) and fluid-like local backward shearing (middle) and rotational (botom) motion. Scale bars = 20 µm. (c) Using the displacement of ~20,000 nodal points, the von Mises strains were calculatedto evaluate the randomness of the deformation. Scale bar = 50 µm. FIGS.15A-15F: Structural analysis and gene expression analysis supported the transitions in endometrial stromal fibroblast (ESF) structural characteristics in response to EVT interaction. (a) Comparison of von Mises strains in the regions occupied by ESFs. Decidualized dESFs are stifer than ESFs and show smaler von Mises strains in the core, while dESFs treated with conditioned medium from HTR8 (dESFscond) showed partialy-reversed stiffness with von Mises strains significantly larger than those of dESFs. (b) PCA plot showing global gene expression in ESFs, decidual ESFs (dESFs), and dESFs treated with conditioned medium from Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 HTR8 (dESFcond); n = 3 biological replicates. (c) Relative activation of gene ontologies (GOs) associated with myofibroblast activation during decidualization (pink outline), and after HTR8 co- culture (blue outline); size of bars shows activation score of respective GOs, fil-color: p-value. (d) GSEA plot showing negative enrichment of “Myotube diferentiation” in dESFs in response to HTR8 conditioning; gene expression of leading-edge genes shown in heatmap. (e) Heatmap showing z-score for expression for top activated genes in GO: actomyosin assembly in ESFs, dESFs, and dESFcond. (f) Activation score for upstream ligand regulator based on diferential gene expression induced in decidualization (x-axis), and conditioning with HTR8 (y-axis); fil- color: p-value; significant transcriptional regulators are labeled. FIGS.16A-16H: Single-celular 3D strain analysis revealed a clear contrast in viscoelastic properties between (a-d) an ESF organoid and (e-f) a dESF organoid. Regions of interestare highlightedby hiding other cels (see panels (a-b) and (e-f). In the ESF organoid, fibroblast cels (indicated in red, #513, #565, #573 and #651 in (c) show the large strains in (d). On the other hand, in the dESF organoid, trophoblast cels (#1108, #1163, and #1247 in (g)) experience the largest von Mises strains in (h). These quantitative observations corespond wel to the wel-studied structural changes in endometrial fibroblast decidualization. FIGS.17A-17D: (a) Spatial plots of celular displacement vectors in 3D composite organoids. Trophoblast celstend to move in alignment with neighboring trophoblast cels in a solid-like motion. Fibroblast cels in ESF, dESFcond, and dESFHB-EGF, show fluid-like random motion, causing non-symmetric oragnoiddeformation. Eddy-like motion with local swirling and reverse displacement was observed in diferent scales. (b) Calculated von Mises strain to assess randomness in celular motion. (c) To evaluate fluorescence strain correlation, the strain is ploted against 2D fluorescence, similar to flow cytometry. dESF showed larger strains in the trophoblast region near fibroblasts. In ESF, dESFcond, and dESFHB-EGF, larger strains were observed in the fibroblast region near trophoblasts. (d) The mean locations of the segments with the top 5% strain are found to be significantly diferent from the mean locations of randomly chosen 5% segments through two-dimensional Monte Carlo permutation tests (N=109). Both channels show clear reversed characteristics in dESFcondand dESFHB-EGF. FIGS.18A-18D: Steps of creating a 3D model. (a) A slice of the raw 3D image. (b) The 3D volume is low-pass and high-pass filtered to find cel features. (c) The 3D watershed technique is used to divide the 3D volume into ~500-600 segments representing single cels. Eachsegment Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 is numbered for further analysis. (d) Atotal of ~20,000 nodal points were distributed for meshing and optical tracking (digital image correlation). Each segment is meshed to 100-200 tetrahedral elements. Scale bars = 50 µm. FIGS.19A-19B: Evaluation of the digital image corelation (DIC) accuracy. An organoid sample was given 6 steps (total 7 timeframes including the initial image) of 5 µm translation in x direction without compression, and the displacement was measured by a 4D DIC algorithm. (a) Nodal displacement of 9638 nodes. The deviation from the linear fit (R2 = 0.99999) was 0.73 µm at 29.6 µm indentation. (b) Segmental displacement measured for 385 segments. The deviation from the linear fit (R2 = 0.99999) was 0.18 µm at 29.6 µm indentation, which was smaler than the optical resolution defined by the Rayleigh criterion (0.61 × λ) / NA for the fluorescence wavelengths (517 nm and 602 nm). FIGS.20A-20C: (a) Four types of ESFswere prepared, namely, ESF, dESF, dESFcond, and dESFHB-EGF. (b) Organoids were prepared by counting 300 trophoblast and 300 ESFs using a hemocytometer and mixing them in a 96 U-botom wel plate. (c) Samples for imaging were embedded in agarose suspended from a glass. FIG.21: Data flow chart. The experimental data captured by the lightsheet microscope software is imported into MATLAB for subsequent analysis, including 3D modeling, image tracking, structural analysis, and statistical analysis. FIG.22: Example implementation of a micro compression device for compression and shear. FIGS.23A-23B: Evaluation of fluorescence intensity. (a) Laser photobleaching decays the fluorescence intensity over imaging steps. (b) The special intensity distribution is negligible when a proper middle section is chosen. FIGS.24A-24H: Example ilustration ofcelular displacement and strain analysis in control and MRTFA knockdown conditions. DETAILEDDESCRIPTION Viscoelasticpropertieswithintissuesandatinterfacesbetweenadjacenttissuesfunction crucialyinphysiological,developmental,andpathologicalprocesses.Themeasurementof micromechanicalcharacteristicsoftissuesin3D,particularlyatmechanicalynon-uniform Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 interfaces,presentstechnicalchalenges.Amicroscopicanalysissystemcombinesthefunctions oflight-sheetfluorescencemicroscopy(LSM)anddynamicmechanicalanalysis(DMA), integratingthreecomponents:(i)aMEMSdynamiccompressiondevicewithaμ-forcesensor,(i) ahigh-speed3Dlight-sheetscannerandimager,and(ii)animage-based3Dmodelingalgorithm. The correlation between mechanical properties of cels and tissues and their behavior, development, and functionality can be characterized. Multicelular organisms comprise multiple cel types aranged spatialy to construct functional 3D tissues, forming juxtaposed interfaces. Physical interactions between distinct tissues are critical in physiological, developmental, and pathological processes, including development progression through dermal layer interaction in gastrulation, blood vessel sprouting, maternal-fetal interface invasion, and desmoplastic reaction to cancer. During carcinoma growth and basal lamina disruption, stromal fibroblasts activate and undergo myofibroblast transition, characterized by increased celular contractility and extracelular matrix remodeling, regulating cancer dissemination. Spheroids and organoids serve as three-dimensional models for such studies, bridging single cels and complex tissues. Viscoelastic properties at interfaces maintain tissue homeostasis, wherein deviation from physiological values can result in complex phenotypes. Transitions between viscous and elastic behaviors enable various modes of celular migration. Epithelial- mesenchymal transition and mesenchymal-epithelial transition influence embryonic development, tumorigenic processes, and wound healing, involving transitions in celular viscoelastic properties. Cels, acting as contractile agents, determine tissue viscoelastic characteristics, with biophysical parameters responding to microenvironmental factors. Tissue viscoelasticity arises from the combined properties of individual cel rigidity, cel shape, cel-cel adhesion, cel-matrix adhesion, and cel density.Both celular motion and celular deformation contribute to the tissue's colective characteristics.Investigating these properties at the single-cel leveldecomposesindividual cels' contributionandofers a comprehensive understanding of the structural characteristics and functional dynamics of tissues. Methods for mechanical property analysis of spheroids and organoids include cavitation rheology for elastic modulus determination, magnetic tweezers, Brilouin microscopy, atomic force microscopy nanoindentation, traction force microscopy, and compression testing, varying with applied force magnitude. Dynamic mechanical analysis enables characterization of time- varying forces in nonhomogeneous, viscoelastic celular structures. Additionaly, smal tissue Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 pieces smaler than about 2-3 mm can be analyzed, including whole animal embryos and ex vivo tissue pieces such as tumor biopsy fragments, organ slices, blood vessel segments, skin explants, cartilage chips, corneal fragments, lymph node samples, muscle micro-biopsies, placental tissue fragments, and bone microsections. Light-sheet microscopy enables three-dimensional imaging of biological samples with high temporal and spatial resolution. Laser beam sheet ilumination, positioned orthogonaly to observation direction, provides three-dimensional optical sectioning capability, reduced phototoxicity, and rapid acquisition compared to confocal microscopy. Three-dimensional image construction occurs through iluminated planar image assembly at various sample sections during light-sheet movement. A system integrating mechanical perturbation devices with light-sheet microscopy enables comprehensive mechanical characterization. This system resolves three-dimensional tissue structural characteristics through volumetric analysis of micro-mechanical strain distribution. This approach is particularly useful for investigating embryonic development, organ growth, tissue regeneration, and drug response analysis. Validation includes characterization of viscoelastic properties in maternal-fetal interface models during human placentation, a process sharing features with cancer metastasis, wound healing, and gastrulation. Thesystemenablescharacterizationofviscoelasticinteractionsthroughlight-sheet microscopyintegratedwithaninsitumicro-mechanicalperturbationdevice.Volumetricanalysis ofmicro-mechanicalperturbationthrough4Ddigitalimagecorelation(DIC)revealsbothsolid- likewel-aligneddisplacementandliquid-likerandommotionincel-celinteractions,enabling corelationofviscoelasticpropertiesandmulti-channelfluorescencecel-by-cel.Thesystem providesspatialy-resolvedmechanicalcharacterizationofviscoelasticmaterialsunderhigh- resolution3Dfluorescencemicroscopy. Analysisofa3Dmodelofmaternal-fetalinterfacedemonstratesthatendometrial stromalfibroblasts(ESFs)exhibitincreasedstifnessinresponsetodecidualization.Thesystem andgeneexpressionanalysisdemonstratethatinteractionwithplacentalextraviloustrophoblasts (EVTs)modifiesthecontractilestifnessofESFs,indicatingEVTscancountermaternaldefense againstinvasionin3Denvironments.Thedemonstrateddecidua-trophoblastinteractionshares characteristicswithviscoelasticdynamicsatotherstroma-epitheliainterfaces,includingtumor invasionandwoundhealing,indicatingbroadapplicabilityofthesystem. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 The present disclosure relates to systems and methods for analyzing biological tissue, and more particularly to systems and methods for characterizing mechanical properties and cel- cel interactions in three-dimensional tissue constructs through combined imaging and mechanical perturbation approaches. In one embodiment, a system for analyzing biological tissue includes an imaging system configured to acquire three-dimensional images of a sample, a mechanical perturbation device configured to apply force to the sample, and a processor configured to analyze deformation of the sample based on images acquired during application of the force. The imaging system may comprise various types of microscopes capable of three-dimensional imaging, including but not limited to light-sheet microscopes, confocal microscopes, multiphoton microscopes, or any combinations thereof. In anembodiment, the imaging system comprises a light-sheet microscope. The light- sheet microscope includes a light-sheet scanner configured to generate a moving light sheet, at least one ilumination objective for directing the light sheet to the sample, and an imaging objective positioned orthogonaly to the ilumination objective. This orthogonal arangement minimizes detection of scatered excitation light while maximizing colection of fluorescence emission from the iluminated plane. The light-sheet scanner may comprise various mechanisms for generating and moving the light sheet, including but not limited to galvanometer mirors, acousto-optic deflectors, piezoelectric actuatorselectrostatic actuators, electromagnetic actuators, or any combinations thereof. In one implementation, the light-sheet scanner comprises a miror mounted to at least one piezoelectric bimorph actuator, electrostatic actuator, electromagnetic actuator, or any combinations thereof;and a position sensor configured to monitor position of the light sheet. In an embodiment, the light-sheet scanner comprises a miror mounted to at least one electrostatic actuator. In an embodiment, the light-sheet scanner comprises a miror mounted to at least one electromagneticactuator. The piezoelectric bimorph actuator enables precise control of miror position and scanning speed. The position sensor may comprise various types of sensors including but not limited to capacitive sensors, strain gauges, or optical sensors. Feedback from the position sensor enables closed-loop control of light sheet position and scanning parameters. The mechanical perturbation device is configured to apply controled forces to the sample during imaging. In one embodiment, the mechanical perturbation device comprises a Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 piezoelectric actuator, a compression plate coupled to the piezoelectric actuator, and a load cel configured to measure force applied to the sample. In one embodiment, the mechanical perturbation device comprises an electrostatic actuator, a compression plate coupled to the piezoelectric actuator, and a load cel configured to measure force applied to the sample. In one embodiment, the mechanical perturbation device comprises an electromagnetic actuator, a compression plate coupled to the piezoelectric actuator, and a load cel configured to measure force applied to the sample. Alternative embodiments may utilize other force application mechanisms including but not limited to stepper motors, or pneumatic actuators. The processor is configured to analyze sample deformation through various computational approaches. In one embodiment, the processor generates a three-dimensional model of the sample comprising a plurality of segments representing individual cels, tracks displacement of nodes within the segments during application of force, and calculates strain distribution within the sample based on the tracked displacement. For strain analysis, the processor may calculate strain tensors for elements within the segments and determine von Mises strain for individual cels based on the strain tensors. The strain tensors may be calculated using various methods including but not limited to finite element analysis, particle tracking, or digital volume correlation. Alternative embodiments may utilize other strain metrics including but not limited to principal strains, shear strains, or volumetric strain. The three-dimensional imaging may be performed using various modalities, with light- sheet microscopy being particularly advantageous due to its reduced photobleaching and rapid acquisition capabilities. In one implementation, acquiring three-dimensional images comprises iluminating the sample with a light sheet and detecting fluorescence emission with an objective oriented orthogonaly to the light sheet. Alternative imaging approaches may include but are not limited to confocal microscopy, multiphoton microscopy, or optical coherence tomography. The analysis of sample deformation may include generating a three-dimensional model of the sample by segmenting the acquired images into regions coresponding to individual cels, tracking displacement of nodes distributed throughout the model, and calculating strain within regions of the model based on the tracked displacement. In one implementation, calculating strain comprises generating tetrahedral elements between the nodes, calculating strain tensors for the tetrahedral elements, and determining von Mises strain for individual cels based on the strain Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 tensors. The tetrahedral mesh may be generated using various approaches including but not limited to Delaunay triangulation or advancing front methods. The systems and methods described herein are particularly useful for analyzing tissue interfaces. In one embodiment, a system for analyzing tissue interfaces includes an imaging system configured to acquire three-dimensional images, a force application device, and a processor configured to analyze deformation of a tissue sample during force application and corelate the deformation with spatial distribution of diferent cel types within the tissue sample. The imaging system may incorporate dual ilumination objectives positioned to iluminate the sample from opposite sides and dual cameras configured to simultaneously acquire images in diferent wavelength channels. This configuration enables simultaneous imaging of multiple fluorescently- labeled cel populations and reduces shadowing artifacts.Multiple cameras and channels may be used to further reduce shadowing artifacts. Conventional flow cytometry involves suspending cels in a liquid medium and passing them through a narow capilary, where laser-based detection measures fluorescence and light scatering. However, this approach does not reflect how cels interact within intact tissues, as cel behavior, particularly structural and functional characteristics, can differ significantly in a suspension. Cels dynamicaly adapt and modify their properties in response to interactions with neighboring cels, which are critical for their natural function within tissues. The method described herein, capable of quantifying single-cel viscoelastic characteristics on a cel-by-cel basis, enables the study of how individual cels behave and interact with other cels within real 3D tissues. By corelating these mechanical properties with multi- channel fluorescence intensities, it reveals diverse molecular and functional characteristics that cannot be observed using conventional flow cytometry as discussed above. These characteristics include protein expression, intracelular signaling dynamics, metabolic activity, cel cycle states, membrane potential, cel-cel or cel-matrix adhesion, celular polarity and orientation. This capability facilitates a deeper understanding of key biological phenomena, including embryonic development, organ growth, tissue regeneration, wound healing, drug responses, and cancer invasion and metastasis. The method can serve as an assessment tool in fundamental biology, cancer research, drug development, tissue engineering and regenerative medicine, and personalized medicine. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 The method can be used to find diseased sites in embryos, biopsy, and engineered tissues. Long-term time-lapse imaging enhances the capabilities of the method. Imaging tissue structural changes over time provides valuable insights into cel diferentiation, cel migration, tissue formation, viscoelastic transitions, drug responses, and other time-dependent processes in fundamental biology, cancer research, drug development, tissue engineering and regenerative medicine, and personalized medicine. To achieve long-term imaging over hours or days, the sample can be immersed in cel culture media under a tissue culture condition (about 37 ºC, about 5% CO2). Microscope-compatible cel culture chambers, which include a temperature controler, CO2sensor, and a controlable valve, are commercialy available. A consideration is the agarose structural and transport properties, which can impact the growth of embedded samples. For stable 3D imaging, agarose can be suficiently solid (typ. >10 kPa), making it stifer than commonly used hydrogels for 3D cel culture(typ. <1 kPa). This rigidity has caused significant growth defects in zebrafish embryonic development. Functional hydrogels, widely studied for cel 3D printing, may ofer suitable experimental conditions for future imaging analyses. Cel viability withinhydrogels, such as calcium alginate or PEG, is crucial in regenerative medicine, particularly in 3D bioprinting. The matrix stifness influences the growth and activities of 3D cultures. Therefore, selecting appropriate hydrogel for embedding samples can be implemented, while evaluation of its impact on celular activities can be conducted. A solution involves using a core-shel hydrogel structure for 3D bioprinting. It utilizes the extrusion of hydrogels through coaxial needles. The outside shel provides structural integrity suitable for compression imaging, while the inside core provides desired microenvironment for tissues.See FIG.2A. Additionaly, spatialy paterned objects in distinct paterns and gradients within hydrogels with tunable mechanical properties can be created using photopolymerization and 3D stereo lithography. Gradients in elasticity or the concentration of molecules afect the migration or growth of cels. Cel-matrix interaction can be studied to determine how cels react to micro- paterns with diferent elasticities Electric field induction in the hydrogel can be implemented to provide electric stimuli. One example embodiment includes applying a voltage between the top and the botom part of the compression device. A paterned conductive polymer can be utilized to create electrodes within the hydrogel. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 A concentration gradient can be generated by exposing one opening side of the core shel hydrogel to a high concentration molecule. Microfluidic channels can be integrated into the hydrogel to provide molecules localy. Now refering to FIGS.2B-2C, creating an obstacle (wal with pores) in the middle enables study of the invasiveness of cancer cels. This approach bears similarity to the method known as cel invasion assay. A key diferentiating feature of the described method is the capability to study real-time elasticity changes in live cels through application of compression. The force application device may be configured to apply various types of mechanical stimuli including but not limited to static compression, cyclic compression at a defined frequency, tension, or shear forces.In embodiments utilizing cyclic compression, the frequency may be varied from approximately 0.1 Hz to 10 Hz, although other frequencies may be used depending on the specific application. The amplitude and waveform of the applied force may also be varied, with options including but not limited to sinusoidal, square wave, or triangularwaveforms. The viscosity at each node, element, or cel can be calculated as the phase delay between the applied compression and resulting displacement. Viscoelastic materials exhibit both elastic and viscous behaviors. Elasticity is defined as the ratio of stress (force) to strain (deformation). For a viscous material, the stress is proportional to the rate of the strain (speed of deformation). When sinusoidal stress is applied to a viscoelastic sample as the input, the output manifests as a delayed strain observed in the material. Due to the damping of the viscous component, the strain in a viscoelastic material appears with a phase delay from the stress. For homogeneous material (see FIG.1Aleft), the relationship between stress φ and strain ε can be expressed as: ^(^)=^∙exp(^^^), ^(^)=^∙exp(^(^^−^)) wherein the real parts of σ(t) and ε(t) are observed in the measurement, and φ is the phase delay. The elastic modulus becomes a complex number: ∗ ^(^) ^ ^= = wherein E' is the storage and E' is the loss modulus. Image analysis can be utilized to determine the amplitude ε_i and the phase delay φ_i for local regions (see FIG.1Aright). In this model, neighboring elements are linked to each other, necessitating computational methods to solve the behavior of this complex system. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 The stress induced in the agarose can be determined using the strain measured from the image analysis, based on the known material properties of the agarose which embeds the sample. The stress measured in the agarose can serve as the input to the sample (see FIG.1B). A viscoelastic FEM model can be constructed for agarose-embedded, heterogeneous soft organ tissues. Each element can be modeled as a standard linear solid model comprising three parameters (ke, kv, and cv). In a viscoelastic material, an instantaneous elastic response upon loading is folowed by a slow and continuous change due to the viscous component. This delayed deformation enables viscoelastic materials to maintain a record of response history. FIG.1Cshows FEM analysis of a viscoelastic sphere embedded in a cylinder with a diferent viscoelasticity, representing the spheroid embedded in agarose. Upon application of sinusoidal compression, each element in the model responds with a certain amplitude andphase delay, as indicated in the figure. Images can be captured over multiple cycles and the obtained data points can be processed through lock-in amplification to determine the phase delay and the amplitude for each element. Lock-in amplification provides robust noise resistance and enables accurate determination of phase delay between the reference and the sample displacement from image analysis. The processor may be configured to perform various analyses including segmenting acquired images into regions coresponding to individual cels, calculating strain tensors for elements within the segmented regions, and determining viscoelastic properties of diferent regions based on the calculated strain tensors. Additional analysis capabilities may include identifying regions exhibiting coordinated versus random motion paterns and corelating the identified paterns with cel type distribution. This analysis may utilize various mathematical approaches including but not limited to corelation analysis, principal component analysis, or optical flow methods. The processor may also generate a three-dimensional computational model of the sample comprising tetrahedral elements and calculate strain distribution within the elements during force application. The calculated strain may be corelated with fluorescence intensity on a cel-by-cel basis, enabling investigation of relationships between mechanical properties and celular phenotype. For analyzing cel-cel interactions in three-dimensional tissue constructs, methods may include forming a three-dimensional tissue construct comprising multiple labeled cel types, embedding the tissue construct in a gel matrix, applying force to the gel matrix while acquiring Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 three-dimensional images, and analyzing relative motion between diferent cel types based on the acquired images. The tissue construct may be formed using various approaches, including but not limited to combining epithelial cels and stromal cels in a defined ratio and culturing the combined cels under conditions promoting self-organization.The gel may be three-dimensionaly paterned to provide functions, such asholding a sample in a desired position and orientation, providing mechanical, electrical,or molecular gradient(oxygen / nutrients / signaling molecules)and include fluidic channels, scafolds, wals, and pores, to mimic tissue microenvironment. The analysis of relative motion between different cel types may comprise tracking displacement vectors for individual cels and identifying regions exhibiting coordinated versus random motion paterns. Displacement vectors may be calculated using various tracking algorithms including but not limited to particle tracking, optical flow, or feature-based tracking methods. Tissue remodeling often involves localized detachment and rearangement of cels, leading to a transient, more liquid-like state in the affected area. This local tissue fluidization results from reduced cel-cel adhesion, increased celular motility, and ECM reorganization. During tissue remodeling, observation ofchanges in viscoelastic characteristics in the local area, driven by interactions among smal groups of celsis expected. Certain spots found may indicate physiological event such as cel division, migration, or remodeling, which involves transitions in celular structural characteristics. Strain analysis may include calculating strain tensors for elements within the tissue construct and corelating calculated strain with spatial distribution of diferent cel types. Alternatively, velocity gradient tensor may also be used.The strain tensors may be used to derive various mechanical metrics including but not limited to principal strains, deviatoric strains, or strain energy density. Alternative approaches may analyze deformation using diferent mathematical frameworks including but not limited to continuum mechanics or discrete element methods.In the present disclosure, a tetrahedron element has been used as the primary finite element for modeling and analysis. Other finite elements commonly used in Finite Element Method (FEM) analysis, including, hexahedron elements, pentahedron elements, wedge elements, quadrilateral elements, and triangular elements, may also be utilized. Calculating strain tensors is an approach with solid mechanics. Because the tissue is viscoelastic, the randomnessmay be calculatedusing the fluid dynamics approach. For example, Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 calculating the velocity gradient tensor and evaluating the vorticity of the flow provides a result similar to the von Mises strain. For analyzing tissue mechanical properties, methods may include acquiring time-series three-dimensional images of a tissue sample during application of force, generating a computational model comprising segments coresponding to individual cels, tracking displacement within the segments, and determining mechanical properties based on the tracked displacement. The computational model generation may utilize various segmentation approaches, with three-dimensional watershed segmentation being particularly useful for separating densely packed cels. Alternative segmentation approaches may include but are not limited to level set methods, region growing, or deep learning-based segmentation.In some embodiments, cel nuclei may be fluorescently labeled by a fluorescent marker, such as DAPI, and imaged to identify and locate individual celsaccurately. The determination of mechanical properties may comprise calculating strain tensors for elements within the computational model and determining viscoelastic properties based on the calculated strain tensors. The viscoelastic analysis may incorporate variousmathematical models including but not limited to spring-dashpot models, power law models, or fractional derivative models. The analysis may further include examining spatial distribution of mechanical properties relative to tissue interfaces within the sample. The imaging of tissue samples may be performed using various implementations of light-sheet microscopy. In one embodiment, acquiring time-series three-dimensional images comprises iluminating the sample with a scanning light sheet and detecting fluorescence emission with an objective oriented orthogonaly to the light sheet. The light sheet may be generated using various optical arangements including but not limited to cylindrical lenses, scanned Gaussian beams, or Bessel beams. The thickness of the lightsheet may be optimized for diferent applications, typicaly ranging from approximately 1-10 micrometers, although other thicknesses may be used depending on the specific requirements. The analysis of tissue interfaces may comprise providing a three-dimensional tissue construct comprising multiple cel types, applying cyclical force to the tissue construct while acquiring three-dimensional images, generating a computational model of the tissue construct, and analyzing relative deformation between diferent regions of the tissue construct based on the computational model. The three-dimensional tissue construct may be prepared by combining Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 diferent cel types labeled with distinct fluorescent markers. Suitable fluorescent markers include but are not limited to fluorescent proteins, fluorescent beads,organic dyes, or quantum dots, provided they are spectraly distinct and stable during imaging.Autofluorescence or light scatering may be used to avoid the process of labeling. This is particularly useful when imaging some types of live tissues, such as embryos or regenerated organs. The computational model may incorporate various elements for mechanical analysis, including but not limited to finite elements, discrete particles, or continuum representations. The model may account for diferent material properties of various tissue components, including but not limited to elastic moduli, or viscosity. The analysis of relative deformation may utilize various mathematical approaches including but not limited to tensor analysis, continuum mechanics, or statistical mechanics. Alternative embodiments of the force application device may incorporate diferent mechanical stimulation paterns. These may include but are not limited to uniaxial stretch, biaxial stretch, shear flow, or hydrostatic pressure. The force application may besynchronized with image acquisition using various timing strategies, including but not limited to triggered acquisition, continuous recording, or stroboscopic imaging. Data analysis methods may include various approaches for characterizing tissue mechanical properties and celular behavior. The strain tensor calculations may utilize diferent mathematical frameworks including but not limited to infinitesimal strain theory, finite strain theory, or rate-dependent formulations. Alternative embodiments may incorporate additional measurement modalities synchronized with the mechanical testing and imaging. These may include but are not limited to electrical measurements, chemical sensing, or metabolic monitoring. The integration of multiple measurement modalities may provide complementary information about tissue structure and function. Quality control procedures may incorporate various methods for ensuring data reliability and reproducibility. Image quality assessment may include but is not limited to evaluating signal- to-noise ratio, spatial resolution, photobleaching rates, or motion artifacts. Mechanical testing quality control may include monitoring force measurement stability, evaluating load cel drift, or assessing mechanical coupling between the force application device and sample.Some image analysis methods, such as optical flowcalculation,use the brightness constancy assumption, where Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 the intensity of a point does not change over time. Evaluating fluorescence decay overtime is may be importantfor image corection. Data management approaches may include methods for organizing and analyzing large datasets generated during experiments. These may include but are not limited to structured file hierarchies, metadata tracking, or database systems for storing experimental parameters and results. The data management system may incorporate various tools for data validation, including but not limited to automated eror checking, consistency verification, or outlier detection. The imaging protocols may be optimized for diferent experimental requirements, including but not limited to maximizing temporal resolution, minimizing photodamage, or optimizing spatial coverage. The acquisition parameters may be adjusted based on variousfactors including but not limited to sample thickness, fluorophore brightness, or required spatial resolution. Alternative imaging modes may include but are not limited to multi-angle imaging, multi-color imaging, or sequential imaging of diferent sampleregions. In some embodiments, imaging a larger sample may be accomplished by applyingabout 50 µm (peak to peak) sinusoidal indentation to the agarose, which provides the maximum of~5% strain in the sample. The limiting factor to define the frequency range of the dynamic analysis is the frame rate of image acquisition. To obtain dynamic properties of a larger volume, dynamic responses wil be acquired for thin sub-volumes and stack them to form a thick 3D volume. Neighboring sublayers have overlaps on both sides and share the same data (i.e., the last few layers of one sub-volume are also used as the first few layers of the next sub-volume) so the multiple sub-volumes may be smoothly stitched together. System integration may incorporate various approaches for coordinating imaging, force application, and data acquisition. The integration may utilize diferent synchronization strategies including but not limited to hardware triggers, software timing, or hybrid control systems. The integrated system may incorporate various feedback mechanisms including but not limited to force feedback, position feedback, or image-based feedback for maintaining experimental conditions. Data visualization approaches may include various methods for representing complex datasets. These may include but not limited to three-dimensional rendering, time-series visualization, or multi-parameter ploting. The visualization tools may incorporate various features for interactive data exploration, including but not limited to dynamic filtering, region-of-interest selection, or parameter adjustment. Alternative visualization approaches may include but are not Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 limited to virtual reality interfaces, augmented reality displays, or web-based visualization platforms. The mechanical analysis may utilize various constitutive models for characterizing tissue behavior. These may include but are not limited to linear elastic models, hyperelastic models, or models incorporating multiple time scales or length scales. The model parameters may be fited using various optimization approaches including but not limited to least squares fiting, Bayesian inference, or genetic algorithms. Calibration procedures may include various methods for ensuring accurate measurement of mechanical and optical parameters. The optical calibration may include but is not limited to point spread function measurement, chromatic aberation corection, or field flatness assessment. The mechanical calibration may incorporate various reference materials including but not limited to calibrated springs, standardized hydrogels, or materials with known mechanical properties. Eror analysis may utilize various approaches for quantifying measurement uncertainty and systematic errors. These may include but are not limited to propagation of uncertainties, statistical bootstrapping, or Monte Carlo simulation. The eror analysis mayaccount for various sources of uncertainty including but not limited to mechanical noise, optical distortions, or numerical approximations in the analysis methods. Alternative embodiments may incorporate additional measurement capabilities for characterizing sample properties. These may include but are not limited to spectroscopic measurements, chemical sensors, or electrical measurements. The integration of multiple measurement modalities may enable comprehensive characterization of tissue properties and celular responses to mechanical stimulation. The system may include various safety features and monitoring capabilities. These may include but are not limited to temperature monitoring, sample position tracking, or automated shutdown procedures in case of system malfunction. The safety features may incorporate various feedback mechanisms including but not limited to limit switches, overflow sensors, or power monitoring systems. System optimization approaches may include methods for improving performance across diferent operational parameters. The optical system optimization may include but is not limited to minimizing photobleaching, reducing light scatering, or improving detection eficiency. The mechanical system optimization may incorporate various approaches including but not limited Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 to minimizing mechanical noise, improving force application uniformity, or enhancing temporal response. Alternative embodiments may incorporate diferent approaches for sample manipulation and analysis. These may include but are not limited to microfluidic devices for applying shear forces, acoustic devices for applying mechanical stimulation, or magnetic systems for remote force application. The integration of diferent manipulation approaches may enable investigation of various mechanical stimuli and their efects on tissue behavior. Data management systems may incorporate various approaches for handling complex experimental datasets. These may include but are not limited to hierarchical data structures, metadata tracking systems, or automated data backup procedures. The data organization may utilize various standardized formats including but not limited to HDF5, TIFF stacks, or custom binary formats optimized for specific data types. Quality assurance procedures may include various methods for validating experimental results. These may include but are not limited to automated quality metrics, statistical validation tests, or reference measurements using standardized samples. The validation procedures may incorporate various controls including but not limited to positive and negative controls, internal standards, or cross-validation measurements. Specific experimental protocols may be developed for diferent sample types and research objectives. Sample preparation protocols may include but are not limited to methods for labeling specific cel populations, techniques for generating defined tissue geometries, or approaches for controling initial tissue organization. The protocols may incorporate various steps for ensuring reproducibility including but not limited to standardized reagent preparation, defined environmental conditions, or controled timing of experimental procedures. The analysis methods may incorporate various approaches for characterizing dynamic tissue behavior. These may include but are not limited to methods for analyzing viscoelastic responses, techniques for characterizing strain rate dependence, or approaches for evaluating mechanical memory efects. The dynamic analysis may utilize various mathematical frameworks including but not limited to fractional calculus, non-linear dynamics, or statistical mechanics. The system may include capabilities for automated experiment execution and control. These may include but are not limited to automated sample positioning, programmed force application sequences, or adaptive imaging protocols. The automation may incorporatevarious Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 feedback mechanisms including but not limited to image-based focusing, force feedback control, or automated sample tracking. System maintenance procedures may incorporate various approaches for ensuring reliable operation. These may include but are not limited to regular calibration schedules, component lifetime tracking, or preventive maintenance protocols. The maintenance procedures may address various system aspects including but not limited to optical alignment, mechanical component wear, or electronic system performance. Troubleshooting procedures may include systematic approaches for identifying and resolving system issues. These may include but are not limited to diagnostic tests, performance validation measurements, or component-specific verification procedures. The troubleshooting protocols may incorporate various tools including but not limited to system logs, eror tracking, or automated diagnostics. Specific analysis workflows may be optimized for diferent experimental objectives. For analyzing cel-cel interactions, workflows may include but are not limited to methods for quantifying celular connectivity, analyzing force transmission between cels, or evaluating colective cel behavior. The analysis may incorporate various mathematical approaches including but not limited to network analysis, force chain identification, or colective motion metrics. The system may incorporate various methods for data validation and quality control. These may include but are not limited to automated outlier detection, measurement uncertainty quantification, or systematic eror corection. The validation procedures may utilize various statistical approaches including but not limited to confidence interval estimation, hypothesis testing, or bootstrap analysis. Alternative embodiments may include various modifications for specific applications. These may include but are not limited to specialized sample chambers for particular tissue types, modified force application devices for diferent loading conditions, or adapted imaging configurations for specific experimental requirements. The modifications may address various experimental needs including but not limited to extended imaging duration, specialized environmental control, or unique sample geometry requirements. Specific implementations may incorporate various optimizations for diferent experimental conditions. The imaging system implementation may include but is not limited to methods for reducing photobleaching, minimizing phototoxicity, or improving signal-to-noise Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 ratio. These may be achieved through various approaches including but not limited to optimized ilumination paterns, smart exposure control, or adaptive imaging strategies. Optimization strategies may address various aspects of system performance. These may include but are not limited to methods for improving temporal resolution, enhancing spatial resolution, or increasing measurement accuracy. The optimization may utilize various approaches including but not limited to hardware improvements, software optimization, or refined experimental protocols. Experimental considerations may include various factors affecting measurement quality. These may include but are not limited to sample preparation variables, environmental conditions, or measurement timing. The experimental design may incorporate various controls including but not limited to method validation studies, reproducibility assessments, or systematic parameter variation. The system may include various approaches for data interpretation and analysis. These may include but are not limited to methods for identifying mechanical paterns, characterizing tissue heterogeneity, or evaluating mechanical adaptation. The analysis mayincorporate various computational approaches including but not limited to patern recognition algorithms, texture analysis, or feature extraction methods. Additional capabilities may include methods for relating mechanical measurements to biological function. These may include but are not limited to approaches for corelating mechanical properties with gene expression, analyzing relationships between mechanical stimuli and cel signaling, or evaluating mechanobiological feedback mechanisms. The analysis may utilize various experimental approaches including but not limited to time-resolved measurements, multiplexed analysis, or integrated multi-modal characterization. System configuration may include various options for diferent experimental requirements. These may include but are not limited to diferent optical configurations, force application mechanisms, or sample mounting approaches. The configuration options may address various experimental needs including but not limited to diferent sample sizes, tissue types, or measurement requirements. Data processing workflows may incorporate various steps for converting raw measurements into meaningful results. These may include but are not limited to image preprocessing, mechanical data analysis, or integrated multi-modal analysis. The workflows may Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 utilize various computational approaches including but not limited to automated processing pipelines, interactive analysis tools, or batch processing capabilities. Advanced analytical methods may include various approaches for extracting complex information from experimental data. These may include but are not limited to machine learning methods for patern recognition, statistical approaches for heterogeneity analysis, or modeling techniques for mechanical behavior prediction. The analytical methods may incorporate various mathematical frameworks including but not limited to deep learning, probabilistic modeling, or physics-based simulation. The system may include various methods for data export and sharing. These may include but are not limited to standardized file formats, automated report generation, or database integration. The data sharing capabilities may incorporate various features including but not limited to data compression, encryption, or selective access control. Implementation considerations may include various practical aspects of system operation. These may include but are not limited to user interface design, operator training requirements, or system documentation. The implementation may incorporate various features for improving usability including but not limited to automated operation sequences, eror prevention mechanisms, or integrated help systems. Sectionheadingsasusedinthissectionandtheentiredisclosurehereinaremerelyfor organizationalpurposesandarenotintendedtobelimiting. 1.Definitions Unlessotherwisedefined,altechnicalandscientifictermsusedhereinhavethesame meaningascommonlyunderstoodbyoneofordinaryskilintheart.Incaseofconflict,thepresent document,includingdefinitions,wilcontrol.Preferredmethodsandmaterialsaredescribed below,althoughmethodsandmaterialssimilarorequivalenttothosedescribedhereincanbeused inpracticeortestingofthepresentdisclosure.Alpublications,patentapplications,patentsand otherreferencesmentionedhereinareincorporatedbyreferenceintheirentirety.Thematerials, methods,andexamplesdisclosedhereinareilustrativeonlyandnotintendedtobelimiting. Asnotedherein,thedisclosedembodimentshavebeenpresentedforilustrative purposesonlyandarenotlimiting.Otherembodimentsarepossibleandarecoveredbythe disclosure,whichwilbeapparentfromtheteachingscontainedherein.Thus,thebreadthand Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 scopeofthedisclosureshouldnotbelimitedbyanyoftheabove-describedembodimentsbut shouldbedefinedonlyinaccordancewithclaimssupportedbythepresentdisclosureandtheir equivalents.Moreover,embodimentsofthesubjectdisclosuremay includemethods, compositions,systemsandapparatuses / devicesthatmayfurtherincludeanyandalelementsfrom anyotherdisclosedmethods,compositions,systems,anddevices.Inotherwords,elementsfrom oneoranotherdisclosedembodimentsmaybeinterchangeablewithelementsfromotherdisclosed embodiments.Moreover,somefurtherembodimentsmayberealizedbycombiningoneand / or anotherfeaturedisclosedhereinwithmethods,compositions,systemsanddevices,andoneor morefeaturesthereof,disclosedinmaterialsincorporatedbyreference.Inaddition,oneormore features / elementsofdisclosedembodimentsmayberemovedandstilresultinpatentablesubject mater(andthus,resultinginyetmoreembodimentsofthesubjectdisclosure).Furthermore,some embodimentscorespondtomethods,compositions,systems,anddeviceswhichspecificalylack oneand / oranotherelement,structure,and / orsteps(asapplicable),ascomparedtoteachingsofthe priorart,andthereforerepresentpatentablesubjectmaterandaredistinguishabletherefrom(i.e. claimsdirectedtosuchembodimentsmaycontainnegativelimitationstonotethelackofoneor morefeaturespriorartteachings). Whendescribingthemoleculardetectingmethods,systemsanddevices,termssuchas linked,bound,connect,atach,interact,andsoforthshouldbeunderstoodasreferringtolinkages thatresultinthejoiningoftheelementsbeingreferedto,whethersuchjoiningispermanentor potentialyreversible.Thesetermsshouldnotbereadasrequiringaspecificbondtypeexceptas expresslystated. Theindefinitearticles“a”and“an,”asusedhereininthespecificationandintheclaims, unlessclearlyindicatedtothecontrary,shouldbeunderstoodtomean“atleastone.” Thephrase“and / or,”asusedhereininthespecificationandintheclaims,shouldbe understoodtomean“eitherorboth”oftheelementssoconjoined,i.e.,elementsthatare conjunctivelypresentinsomecasesanddisjunctivelypresentinothercases.Multipleelements listedwith“and / or”shouldbeconstruedinthesamefashion,i.e.,“oneormore”oftheelements soconjoined.Otherelementsmayoptionalybepresentotherthantheelementsspecificaly identifiedbythe“and / or”clause,whetherrelatedorunrelatedtothoseelementsspecificaly identified.Thus,asanon-limitingexample,areferenceto“Aand / orB”,whenusedinconjunction withopen-endedlanguagesuchas“comprising”canrefer,inoneembodiment,toAonly Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 (optionalyincludingelementsotherthanB);inanotherembodiment,toBonly(optionaly includingelementsotherthanA);inyetanotherembodiment,tobothAandB(optionaly includingotherelements);etc. Asusedhereininthespecificationandintheclaims,“or”shouldbeunderstoodtohave thesamemeaningas“and / or”asdefinedabove.Forexample,whenseparatingitemsinalist,“or” or“and / or”shalbeinterpretedasbeinginclusive,i.e.,theinclusionofatleastone,butalso includingmorethanone,ofanumberorlistofelements,and,optionaly,additionalunlisteditems. Onlytermsclearlyindicatedtothecontrary,suchas“onlyoneof”or“exactlyoneof”or,when usedintheclaims,“consistingof,” wilrefertotheinclusionofexactlyoneelementofanumber orlistofelements.Ingeneral,theterm“or”asusedhereinshalonlybeinterpretedasindicating exclusivealternatives(i.e.“oneortheotherbutnotboth”)whenprecededbytermsofexclusivity, suchas“either,”“oneof”“onlyoneof”or“exactlyoneof.”“Consistingessentialyof,” when usedintheclaims,shalhaveitsordinarymeaningasusedinthefieldofpatentlaw. Asusedhereininthespecificationandintheclaims,thephrase“atleastone,”in referencetoalistofoneormoreelements,shouldbeunderstoodtomeanatleastoneelement selectedfromanyoneormoreoftheelementsinthelistofelements,butnotnecessarilyincluding atleastoneofeachandeveryelementspecificalylistedwithinthelistofelementsandnot excludinganycombinationsofelementsinthelistofelements.Thisdefinitionalsoalowsthat elementsmayoptionalybepresentotherthantheelementsspecificalyidentifiedwithinthelist ofelementstowhichthephrase“atleastone”refers,whetherrelatedorunrelatedtothoseelements specificalyidentified.Thus,asanon-limitingexample, “atleastoneofAandB”(or, equivalently,“atleastoneofAorB,”or,equivalently“atleastoneofAand / orB”)canrefer,in oneembodiment,toatleastone,optionalyincludingmorethanone,A,withnoBpresent(and optionalyincludingelementsotherthanB);inanotherembodiment,toatleastone,optionaly includingmorethanone,B,withnoApresent(andoptionalyincludingelementsotherthanA); inyetanotherembodiment,toatleastone,optionalyincludingmorethanone,A,andatleastone, optionalyincludingmorethanone,B(andoptionalyincludingotherelements);etc. Intheclaims,aswelasinthespecificationabove,altransitionalphrasessuchas “comprising,” “including,” “carying,” “having,” “containing,” “involving,” “holding,” “composedof,”andthelikearetobeunderstoodtobeopen-ended,i.e.,tomeanincludingbutnot limitedto.Onlythetransitionalphrases“consistingof”and“consistingessentialyof”shalbe Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 closedorsemi-closedtransitionalphrases,respectively,assetforthintheUnitedStatesPatent OficeManualofPatentExaminingProcedures,Section2111.03. The terms “about” or “approximately,” as used herein, is inclusive of the stated value and means within an acceptable range of deviation for the particular value as determined by one of ordinary skil in the art, considering the measurement in question andthe eror associated with measurement of the particular quantity (i.e., the limitations of the measurement system). For example, “about” can mean within one or more standard deviations, or within ± 10% or 5% of the stated value. Recitation of ranges of values are merely intended to serve as a shorthand method of refering individualy to each separate value faling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individualy recited herein. Al ranges disclosed herein include both end points as discrete values as wel as al integers and fractions specified within the range. For example, a range of 0.1-2.0 includes 0.1, 0.2, 0.3, 0.4...2.0. Al methods described herein can be performed in a suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and al examples, or exemplary language (e.g., “such as”), is intended merely to beter ilustrate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention as used herein. Asusedherein,theterm“functionalycoupled” generalyreferstothebinding,joining, orcombiningoftwoormoremoleculessuchthattheresultantcombinationcomprisesatleastone functionalproperty.Insomecases,theresultantcombinationofthetwoormoremolecules maintainsoneormorefunctionalpropertiesofoneormoreoftheuncoupledmolecules.Inother cases,theresultantcombinationyieldsafunctionalpropertynotpreviouslypresentinthe individual,uncoupledmolecules.Insomecases,“functionalcoupling”includesthebinding, joining,orcombiningoftwoormoremoleculesviachemicalmeans(e.g.,covalentornon-covalent interaction).Asdescribedfurtherherein,insomecases,the“functionalcoupling”oftwoormore moleculesresultsintheformationofajunctionhavingcertainelectricaland / orbiochemical properties. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 2.OverviewofDevices,Systems,andMethods Themechanicalpropertiesoftissuesserveacentralroleindeterminingcel specification,development,morphogenesis,andpathogeneses.Viscoelasticpropertiesoftissues constitutecriticaldeterminantsoftissuefunctions,withmeasurementofthesemechanical atributespresentingtechnicalchalenges,particularlyinthree-dimensionalcontextsandat heterogeneoustissueinterfaces.Themechanicaldiferencesbetweenadjoiningtissuescan determinemorphologicalcharacteristicsoforganisms,includingdermallayerfolding,dermis wrinkling,keloidformation,andepithelial-stromalinvasionpaterns. Amicroscopicanalysissystemenablesdynamicimagingthroughfluorescentbead embeddinginviscoelasticmaterial.When finding the viscoelastic properties of the tissue sample, the agarose surounding the sample may serve as the reference, because its viscoelastic properties can be measured with a conventional DMA tool. Beads embedded agarose may serve as a built-in force sensor to measure the force surounding the sample. Thesystemcomprisesalight-sheetfluorescencemicroscopyplatformincorporatinga high-speedpiezoelectriclight-sheetscanner,MEMSmicro-compressiondevice,andstructural modelingalgorithmforimageacquisitionandanalysis.Thissystemprovidesquantificationof spatialy-resolvedcharacteristicsofviscoelasticcel-cel interactioninthree-dimensional homogeneousandcompositeorganoidmodels,enablingspatialanalysisofviscoelasticproperties withinnaturalcelularenvironments,bothinbulktissueandatinter-tissueinterfaces. Implementationofthesystem withmaternal-fetalinterfacemodelsdemonstrates characteristicsofinterfaceevolutionandadaptation.Analysisindicatesdecidualizationof endometrialstromalfibroblasts(ESFs)increasestissuestifness,whereindESFsexhibitsolid-like characteristics.Interactionwithfetaltrophoblastsmodifiesthischaracteristic,withdESFs displayingmoreliquid-likeproperties.RNAseqanalysisofdESFstreatedwithHTR8EVTcel lineconditionedmediumdemonstratesmodificationingenesetsassociatedwithactomyosin contractilityandforcegeneration. Thesystem implementsdigitalimagecorelation(DIC)techniqueswiththree- dimensionalmicroscopyfortissuestructuralcharacterization.DICenablescomputationofsample shape,motion,anddeformationinformationthroughdigitalimagefeatureanalysis.Resolution enhancementbeyonddigitalimagepixelresolutioncanbeachievedthroughspatial-domain Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 interpolationorfrequencydomainanalysis.Advancementindigitalphotographyandmicroscopy technologiesenablesenhancedmaterialscharacterizationcapabilities. Thesystemprovidesapplicationsinanalysisofplacental-endometrialinvasionand cancer-stromaldisseminationinterfaces.Thetechnologyenablesmechanisticanalysisoftissue mechanicsunderlyingepithelia-stromainterfaceregulationacrossmultiplecontexts.Applications extendtobiomedicalstudiesincludingembryonicbodystructuraldevelopmentevaluation,tumor metastasischaracterizationinthree-dimensionalcancermodels,andwoundhealingand regenerationobservationinthree-dimensionalcultures. 3. Examples Itwilbereadilyapparenttothoseskiledintheartthatothersuitablemodificationsand adaptationsofthemethodsofthepresentdisclosuredescribedhereinarereadilyapplicableand appreciable,andmaybemadeusingsuitableequivalentswithoutdepartingfromthescopeofthe presentdisclosureortheaspectsandembodimentsdisclosedherein.Havingnowdescribedthe presentdisclosureindetail,thesamewilbemoreclearlyunderstoodbyreferencetothefolowing examples,whicharemerelyintendedonlytoilustratesomeaspectsandembodimentsofthe disclosure,andshouldnotbeviewedaslimitingtothescopeofthedisclosure.Thedisclosuresof aljournalreferences,U.S.patents,andpublicationsreferedtohereinareherebyincorporatedby referenceintheirentireties. Thepresentdisclosurehasmultipleaspects,ilustratedbythefolowingnon-limiting examples. Example1 SystemOverview.FIG.3depictsthesystemoverview.Theorganoidsamplecanbe embeddedinanagarosepilarwithadiameterof1.4mmandelasticmoduliof4.3kPa.TheMEMS dynamiccompressiondevice,drivenbyapiezoelectricactuator,appliessinusoidalcompressional straintothesampleembeddedintheagarosepilar,whileamicroloadcelmeasurestheμ-force. Displacementcanbeadjustedtoachievedesiredstrainlevels. Ahigh-speed3Dlight-sheetscannerenablessamplescanningatfrequencieshigherthan indentationfrequencies.Amicroscopeobjectivewithaliquidlensfolowsthelight-sheet scanning.Amirorwithappropriatedimensionscanbescannedbypiezoelectricbimorphactuators Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 thatprovidesheetlaserscancapabilitiesatvariousdrivingvoltages.Laserpositiondetermination occursthroughalong-passdichroicmirorandposition-sensitivedetectorsystem. MEMSCompressionDevice.Themicro-compressiondevicecomprisesanylonframe producedthroughselectivelasersintering(SLS),anamplifiedpiezoelectricactuatorwithflexure mountAPF710,andanacrylicloadcel,asilustratedinFIG.4.Thesystemutilizesapiezo actuatorforglasscapilarymovementandamechanicalstageformetalrodpositioning.Aglass capilarypositionsadjacenttotherod,withaflatplateatachedtothescrewthreadatthemetal rodterminus.Theloadcelpositioncanbeadjustedtoestablishcontactwiththeagarosegelpilar base.Duringmechanicaltesting,thepiezoactuatorcompressestheagarosegelpilarandinternal sampleontheloadcel.Theloadcelcantileverbeamincorporatesspecificdimensions,withhalf- bridgesemiconductorstraingauges. Loadcelmanufacturingcanbeaccomplishedthroughmicro-CNCmilingprocesses. Strainanddisplacementvaluesoftheacrylicloadcelcanbesimulatedusingappropriatemodeling andfiniteelementanalysistechniques.Analysisparametersmayincludematerialpropertiessuch aselasticmodulusandPoisson’sratio,meshconfigurations,andboundaryandloadconditions. OpticalDesign.FIG.5ilustratestheopticaldiagramoftheexcitationanddetection axes.Thesystemcanincorporatemultiplelaserconfigurations,includingpilotandexcitation lasersofvariouswavelengths.Wavelengthmanagementoccursthroughlong-passdichroicmiror elements.Theopticalpathincludesbeamexpansiontelescopes,verticalslitcapabilitiesforsheet laserwidthcontrol,andcylindricallenselementsforlightsheetcreation.Positionmonitoringcan beachievedthroughpilotlaserreflectiontoaposition-sensitivedetector. PSDoutputtransductionoccursthroughlinearoperatingcircuitry.Signalprocessing fromPSDregionsenablesaccuratelaserpositiondetermination.Circuitboardconfigurationscan accommodatemountingtoopticalcagesystems. Thedetectionaxisincorporatesmultiplecamerasforfluorescenceandbrightfield imaging.Wavelengthseparationoccursthroughlong-passdichroicmirorelements,with fluorescencefiltersenablingspecificemissionwavelengthtransmission.Focalplaneadjustment basedonsheetlaserpositioncanbeaccomplishedthroughliquidlensimplementation. Light-SheetScanner.FIG.6depictsthelightsheetscannerconfigurationutilizing piezoelectricbimorphactuatorsconnectedtoamirrorelement.Time-varyingsignalapplicationto theactuatorsenablesbeamreflectionmodulationforsamplescanning,asshowninFIG.5A. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 Three-dimensionalimageformationcanbeaccomplishedthroughlightsheetmovementacross stationarysamples.Thescannersystem enablescoordinationbetweenscanningamplitude, frequency,andcompressionsignals.FIG.7ilustratestypicalcontrolandoutputsignalsofthe light-sheetscannerandloadcelsystem. ImageAcquisition.ImageacquisitionwasperformedusingMicro-Manager,anopen- sourcemicroscopycontrolplatform.Exposuretimewassetto30ms.Themultidimensional acquisitionfeaturewasusedtoimagecyclesofacompressionsequence.FIG.8showstheopticaly trackedcompressionofasampleinanagarosepilar. Wethenembeddedfluorescentbeads(Cospheric,Somis,CA,USA)inanagarosepilar andconductedacompressionanalysisat0.5Hztovisualizetheviscoelasticcharacteristicsofthe agarose.Thepositionofbeadswere tracked intwolocations(FIG.9)withrespecttotimeto demonstrateaphasedelayof0.2radwithrespecttothecompressionsignal. 3DModelingAlgorithm.A3Dmodelingalgorithmwas created tocreatea3Dmodel from3Dfluorescenceimagestacks.Thefluorescentimagesofsamplesareprocessedtoa3D tetrahedralmeshmodelusingacustomMATLABprogram.A3Dwatershedtechniquewas used toseparatetheorganoidvolume(~160µmindiameter)into~1000segments.Eachsegmentis numberedforfurtherdetailedsinglecelularanalysis.FIG.11showsanexampleofa3Dmodel createdusingthealgorithm. Example2 4DImageAcquisitionandProcessing.Theimageacquisitionsystemimplementation utilizestheZeissLightsheet7microscope(FIG.13A top).Thesystemincorporatesaminiaturized roboticmicro-perturbationdevice,whichappliescompressivestraintothespecimenembeddedin agarosehangingfromaglasscapilary.FIG.13A botomdepictsaphotographofamicrotissue sampleinagaroseonthecompressiontable.Aminiaturesteppermotordisplacesthetableby~5 µmforeachcompressionstep.Asthetablemovesandappliesstepwisecompressiontothe agarose,theLSMmicroscopeacquiresvolumetricimagesofsampledeformation.Thesystem employstwolaserlightsources(488nmand561nm)anddualiluminationobjectives(10xNA 0.2)forlightsheetexcitationfrombothsides.Animagingobjective(20xNA1.0),dichroicfilters, andtwosCMOScameras(PCO.edge4.2)enablesimultaneoustwo-channelfluorescence acquisition.Theorganoiddeformationinresponsetocompressionrepresentsatime-dependent Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 viscoelasticprocess.Eachstagedisplacementincludes~10sec(typ.)ofwaittimebeforeimaging. Withthetimeconstantofaspheroidcompressionreportedtobelessthan5sec,thesamplereaches mechanicalequilibriumwhenimaged.Foreachcompressionstep,thesystemacquiresthe3D volume(1,600(x)×1,600(y)×~360(typ.))atarateof100ms / zlayer(50msforeachofleft andrightexcitation),resultingin~60sec / stepincludingthewaittime.Thetotalimageacquisition timefortheentiremicro-compressionprocess(7indentationsteps)typicalyspans~7mins.FIG. 13A topilustratesanacquiredindentationsequenceofa3Dorganoidmodelofmaternal-fetal interfacewithplacentalextraviloustrophoblasts(EVTs)andendometrialstromalfibroblasts (ESFs).Thespatialy-resolveddeformationanalysisutilizesacustom4Ddigitalimagecorelation (DIC)programimplementedinMATLAB(FIG.13B botom).Theanalysisencompassesthe hemisphericalhalfofanorganoidclosertothemicroscopeobjectiveastheregionofinterest.The implementationincorporatesanalgorithmmodifiedtoseparatethewholeregionofinterestinto 500-600segmentsrepresentingsinglecels.Nodalpointsdistributedevenlyonthesegment boundariesandinsidethesegmentswith10pixel(~4µm)distancesresultinatotalof~20,000 nodalpoints.The4Ddigitalimagecorrelationimplementationutilizesanalgorithmmodifiedfrom totrackthe3Dcoordinatesofalnodesalongtheindentationsteps. StructuralAnalysis.Thenodaldisplacementmapobtainedthroughthe4DDIC analysisprovidesspatialyresolvedcelulardisplacementanddeformation.FIG.14B depictsan exampleofthecelulardisplacementvectorundercompressivestress.Thecelulardisplacement vectorsaredeterminedbyaveragingthoseofnodalpointsincludedineachcel.Eachcelreceives anumberdesignationforfurthercelularanalysis.Multiplecelsexhibitcorelativedisplacement alongwithneighboringcelsasexpectedinanelasticbody(seeFIG.14B topmarked‘aligned’). Theanalysisadditionalyrevealsregionswithliquid-likedeformation,wheredisplacementvectors demonstrateaneddy-likepaternwithbackwardorrotationalmotion(seeFIG.14B middleand botom).Influidmechanicsstudies,theintensityofshearstressservesasanindicatorofrandomly movingfinite-sizedfluidparticlesinaturbulentflow.Utilizingasimilarapproach,thecelular shearingmotionevaluationinthe3DorganoidemploysthevonMisesstrain,whichincorporates shearstrainintotheanalysis.Thestraincomputationutilizesthe~20,000nodalpointstogenerate ameshwith100,000-200,000tetrahedralelements.Thenumberoftetrahedralelementstypicaly exceedsthenumberofnodalpoints.Theseelementsundergosegmentationinto500-600segments forcelularstrainanalysis. Thestraintensorcalculationencompasseseachtetrahedralelement, Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 withthesegmentalstraintensorderivedfromthevolumetricaverageofelementalstrains.Each segment,representingasinglecel,typicalycomprisesseveralhundredelements.FIG.14C ilustratesthevonMisesstrainmapcalculatedforthesamesampleasinFIG.14A.Thelocalareas exhibitinglargervonMisesstrainindicatemoreintensedistortionenergythansurounding regions,suggestingenhancedliquid-likebehaviors. 3D CompositeOrganoid ModelofMaternal-Fetal(Endometrial-Placental) interface.Thematernal-fetalinterfaceinmammalsrepresentsauniquephysiologywithtissuesof geneticalydistinctoriginsphysicalyopposedtoeachother.Thisuniquejuxtapositionoftissues withdiferentgeneticbackgroundgivesrisetoanevolutionaryphenomenoncaled“genetic conflict.” Amonghemochorialplacentalmammals,suchasgreatapes,whichincludehumans,and rodents,placentaltrophoblastsoffetalorigininvadedeeplyintothematernalendometrium. Amonghumans,inanticipationofimplantation,thematernalendometrialstromalfibroblasts (ESFs)diferentiateintodecidualizedESFs(dESFs),aprocessevolutionarilyderivedfrom myofibroblast-liketransformation.Decidualization,amongmanyotherchanges,substantialy increasesthecontractileforcegenerationandcolagendepositionbyESFs.Researchdemonstrates thatrecentlyevolvedextraviloustrophoblasts(EVT)intheplacentaofgreatapescanpartialy reversethedecidualcontractileforcegeneration,promotingtheirowninvasionintothematernal endometrium.EVTsarecharacterizedbyaggressiveinvasionintothematernaltissues;however, thephenomenonbywhichEVTsreversematernalcontractiledefensesrequiresvalidationin3D, andthemechanisticcausallinkbetweenthealteredviscoelasticpropertiesofdecidualESFsand theirresistancetotrophoblastinvasionrequiresfurtherdemonstration.Themeasurementof celularviscoelasticpropertiesatthetissueinterfacein3Dutilizesacompositeorganoidmodelof theplacental-deciduainterface. Transcriptomicanalysisdemonstratesthatconditionedmediumfromextravilous trophoblastsreversesdecidualcontractility:Decidualizationrepresentsbothanadaptive responsetoprovidesustenancetotheplacenta,aswelasanevolvedresponsetolimitexcessive invasionbytrophoblasts.Decidualizationhasevolvedfromthefibroblastactivationresponse, whichactivatesduetotheinjurycausedbyimplantationintheendometrialluminalepithelium.A 2DmodeldemonstratesthatdecidualizationofESFssignificantlyincreasestheircontractileforce generation,increasingtheirresistancetotrophoblastinvasion.Researchindicatesthatco-culture withHTR8,acellinederivedfromEVTs,reversestheforcegenerationcapabilityofdecidualized Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 ESFs(dESFs).TheinvestigationexamineswhethertheHTR8inducedreversalintheincreased contractileforcegenerationcapabilityinESFsacquiredbydecidualizationoccursthrough paracrinesignals.TheanalysisutilizesRNAseqdataforhumanESFs,dESFs,aswelasdatafor dESFstreatedwithconditionedmediumfromHTR8(dESFscond).Themethodologyincludes progesterone(MPA)additionintheHTR8conditionedmediumtopreventdediferentiationof dESFsthroughwithdrawalofdecidualstimulus. FIG.15 summarizestheaveraged(bulk)structuralcharacteristicsofESFsin3D organoidsanddetailedgeneexpressionanalysis,bothdemonstratingthemyofibroblastactivation indecidualization,anditsreversalbyparacrinesignalsfrom EVTs.Theexperimental methodologyutilizesorganoidscontaininghumanendometrialstromalfibroblasts(ESFs)and placentalextraviloustrophoblastHTR8cels.Theanalysisencompassesthreeconditionsof stromalfibroblasts:undiferentiated(ESF),decidualized(dESF),andEVT conditioned decidualized(dESFcond)fibroblasts.Eachorganoidcomprises600-800celswithadiameter rangingbetween160-180µm.Acrossalconditions(ESF,dESF,anddESFcond),fibroblastcels spontaneouslyformacoreinthe3Dorganoid,accountingfor~50%ofthetotalvolume.The fibroblaststifnesscomparisoninvolvescalculationofthevonMisesstrainofthecoreregions (50%ofthetotalvolume)asshowninFIG.15A.TheanalysisrevealsthatdESFsexhibit significantlylowerlevelsofstrain,whichundergoespartialandsignificantrestorationthrough treatmentwithEVTconditionedmedium.Geneexpressionanalysiscoroboratesthestructural analysisfindings.Principalcomponentanalysis(PCA)forESFs(FIG.15B)revealswel-separated clustersforsamplesfromalthreeconditions.Thechangesinducedbydecidualizationdemonstrate near-reversalinthePC2axis,indicatingthatHTR8conditioningpartialyreversesgeneexpression changescausedbydecidualization.Theanalysisidentifies1794geneswithsignificantincreases fromESFtodESF,with1116demonstratingreversalbyHTR8conditioning.Additionaly,ofthe 1946genesshowingsignificantreversalbydecidualization,1389genesexhibitsignificant increasesthroughHTR8conditioning,establishingthatEVTsreversedecidualizationofESFs. TheanalysisexaminestheeffectsofconditionedmediumfromHTR8ongene-sets associatedwithcelularcontractility,actomyosinassembly,andotherontologiesassociatedwith celularforcegeneration(FIG.15C).Formostofthekeycontractility-relatedgeneontologies (GOs),decidualizationresultsinsignificantactivation,whileconditionedmediumreversesthe trend.NotableGOsincludemuscle-contraction,myotube-diferentiation,variousontologies Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 associatedwithcontractileactinfilamentassembly,andceladherensjunctions.Theanalysis revealsthatHTR8conditioningresultsinincreasedactivationofmyosin-V-binding,aswelas GOsassociatedwithcel-celadherensjunction.TheunconventionalmyosinV,atubulin associatedmotorprotein,doesnotparticipateincelularcontractility,butfunctionsincargo transportinyeastandmammaliancels.ThedatademonstratesthatHTR8reversesthe decidualizationinducedactivationofpathwaysinvolvedinincreasedcelularforcegeneration,and cel-matrixinteractions. FurtherexaminationofarepresentativeGOusingGeneSetEnrichmentAnalysis (GSEA)demonstratesthatHTR8conditioningresultsinnegativeenrichmentfor“Myotube diferentiation,” withleadingedgegenesincludingMEF2CencodingMyocyteenhancerfactor 2C,akeytranscriptionalregulatorformyofibroblasttransition,andakeyregulatorofCa2+ signalingassociatedwithforcegenerationinmuscles.Theanalysisidentifiesadditionalgenes encodingcalciumchannelscrucialintriggeringintracelularforcegenerationandactomyosin activation,particularlyincardiomyocytes(FIG.15D).TheseincludeRYR1,encodingRyanodine receptor1,aCa2+releasechannelintheendoplasmicreticulum,andKCNH1encodingK+voltage gatedchannel.Z-scorecalculationsforkeygenesassociatedwithcontractilefibermaturationin fibroblastsdemonstrateexpectedincreasesduringdecidualizationandtrendreversalinresponse toHTR8conditioning.Multiplecardiac-relatedgenesassociatedwithactomyosinassemblage featureintheanalysisshownin(FIG.15E).TheseincludeMYL9encodingmyosinlightchain9, thekeyhomologueinfibroblastactivation,whichexhibitsphosphorylationindESFresultingin co-localizationwithactinfilaments,ACTC1encodingactincardiacmuscle1,presentinairway smoothmusclecels,DMDencodingdystrophin,akeygeneinskeletalmusclefibers,TPM2 encodingtropomyosin2,LDB3encodingaLIM domainbindingcardiacprotein,PARVA encodinganactinbindingprotein,andACTN4encodingactinin4.Researchdemonstratesactinin- 4’scriticalroleinfibroblastcontractility,whilePARVAshowsupregulationinactivated fibroblastsinpancreaticcancer.Similarly,TPM2servesasaprognosticmarkerincancer- associatedfibroblastsincoloncarcinoma.Thedataanalysisconfirmsthatconditionedmedium fromHTR8reducesthedecidualizationassociatedmyofibroblastactivationinESFs. ThesuficiencyofHTR8conditionedmediumtocausechangeindecidualphenotype indicatesthatparacrinesignalingfromHTR8reprogramsdESFs.Thecomputationofpotential upstreamregulatorymolecularspeciesexplainsthediferentialgeneexpressionbetweenthe Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 conditions.Theidentificationofgenesencodingsecretedligandswithintheseregulatorsreveals severalinflammatorycytokineencodinggenescontributingtothediferentialexpressionpaterns inbothcomparisons(FIG.15F).TheseincludeinterleukinsencodingIL1A,IL1B,andIL15,as welasTNFencodingtumornecrosisfactor.TheanalysisidentifiesTGFB1andTGFB3with predictedactivationindecidualization,aligningwithpreviousfindingsoftheprocesshaving evolvedfromamoreancestralstromalactivationprocess.ResearchdemonstratesthatHTR8 secretesheparinbindingepidermalgrowthfactor(HB-EGF),withpredictionsindicating contributionstogeneexpressiondiferencescausedbyHTR8co-culture,andmoderate contributionstodecidualization,consistentwithotherreportsofHB-EGF’simportanceindecidual transformation.LigandencodinggenescontributingexclusivelytoHTR8responseindESFs encompassHGF,CSF2,andAREG.Tractionforcemicroscopyin2DdemonstratesthatdESFHB- EGFresultsinashiftintheassociationofSerumResponseFactor(SRF)associationwithMRTF (MyocardinRelatedTFs)tomoremitogenicTCF,reducingthedecidualizationinducedMRTF activation,anddownstreamexpressionofgenesassociatedwithactomyosinmaturation.These findingsundergoevaluationinthe3DcompositeorganoidmodelofdESF-HTR8interface. Themethodologyenablesviscoelasticanalysisatcelularresolutionin3D.Tissues presentremarkableheterogeneityintheircelularstates,whichcaninformboththeaggregate behavior,aswelasemergenceofnewphenotypes.Invasivephenomenalikecancerdissemination orplacentationareessentialyspatialyheterogeneousprocesses,andlocalmechanicalproperties inform theprogressionoftheinvasivephenotype.Asmuchasdisseminationrepresentsa mechanicalphenotype,itfunctionsrelativetothehighlylocalizedviscoelasticpropertyofeach cel,anditsimmediateconnectivetissuemilieu.Cancercelsandlikelytrophoblastsharnesslocal heterogeneityintheviscoelasticpropertiesofthemilieutodisseminate.Themeasurementof mechanicalviscoelasticpropertiesoftissuesatsinglecelresolutionpresentssignificanttechnical chalenges. Themethodologyinvolvesindexingindividualcelsinanorganoidandobservingcel- celstructuralinteractionbetweenthecelsofinterest.FIGS.16A-16D demonstrateazoom-in observationandanalysisofcelsrepresentingatypicaldeformationpaternintheESFsample. FIGS.16E-16H ilustrateatypicalpaterninthedESFsample.Theregionofinterestbecomes highlightedthroughcelularocclusion(SeeFIGS.16A-16B and16E-16F).FIGS.16C and16G displaytheindexnumbersassignedtothecels.FIGS.16D and16H presentthevonMisesstrain, Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 utilizedtoevaluatethesheardeformation.IntheESFsample,largestrainsmanifestamong fibroblastcels.Conversely,thedESFsampleexhibitslargestrainsamongtrophoblastcels.This contrastbetweenthetwosamplesindicatesthatthedecidualizedfibroblastcelsdemonstrate increaseddeformationresistancecomparedtoundiferentiatedESFs,aligningwiththeestablished changesindecidualization,whereendometrialfibroblasts(ESFs)undergomorphologicaland functionalchangeswithincreasedcelularcontractilityinanticipationofimplantation. Spatialcorrelationofstrainwithcelulardisplacementvalidatesrecentinnovations inthematernal-fetalconflict.ThestructuralanalysesconductedforESF,dESF,anddESFcond spheroidsdemonstratethatwhilethevonMisesstrainincreasesdramaticalyafterdecidualization, treatmentwithEVTsreversesthetrend.RecentresearchimplicatesthatHB-EGFsecretedby EVTsrebalancestheassociationofSRF(serum responsefactor)withmyocardinrelated transcriptionfactor(MRTF)towardsmitogenicMAPKsignaling.Theconsequenceofthis rebalancingmanifestsinthereductionofMRTFinducedgenes,manyofwhichcontributeto contractileactomyosinmachinerywithindESFs.TheinvestigationexamineswhetherHB-EGF treatmentelicitsasimilareffectondESFsin3D,andifthisefectresultsinadiferentspatial viscoelasticresponseatthedESF-HTR8interface. Theanalysisrevealsthatinalcases,self-organizationofamixedESF-HTR8ensemble resultsinalayeroftrophoblastcelsenvelopingthefibroblastcels(red),suggestingthesurface orinterfacialtensionofthetrophoblastcelsmeasureslowerthanthefibroblastcels.Thegene analysisoftheincreasedcel-celadherensjunctionintreatedsamplesindicatesthattrophoblast- fibroblastpolaritymaintainsconsistencyafterthetreatment. FIG.17A ilustratestheinternalcelulardisplacementvectorsrelativetothevolume centeroftheorganoid.Thecelulardisplacementrepresentstheaveragedisplacementofalnodes ineachcel.Themeasurementaccuracyachieves0.18µmatanaverageof29.6µmdisplacement. EVTsdemonstratedeformationcharacteristicscommonlyobservedinasolidelasticmaterial.The displacementvectorsexhibitalignmentwiththoseofneighboringtrophoblastcels,demonstrating typicalcharacteristicsofepithelialcels.Incontrast,endometrialstromalfibroblastsexhibit displacementvectorsshowingamoreliquid-likerandomspatialmap.DecidualizationofESFs resultsinadramaticchangeinthedisplacementvectormap,showingamoresolid-like deformationpaternthroughouttheorganoidsamples.Treatmentwithconditionedmediumfrom HTR8revertsthesolid-likedeformationpaternofdESFstowardsamorerandom,liquid-likestate. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 HB-EGFtreatmentindESFsreplicatesthistransformation.dESFsHB-EGFexhibitsliquid-like randomdisplacement,causingnon-symmetricdeformationoforganoids.ThevonMisesstrain calculationforthefoursamples(FIG.17B)revealssignificantlylargerstrains(p=2.4×10-24) thantheestimatederorcausedbyerorsinnodaldisplacementtrackingthrough4DDIC.As anticipated,dESFexhibitsthesmaleststrains. Themethodologyenablescorelationofmechanicalstrainandfluorescenceof individualysegmentedcels.Fluorescencerepresentsanintegraltoolinquantifiablemeasurement ofcelularsignaling,themethodologycorelatesmechanicalpropertieswiththeintracelular signalingstateofcelsinheterogeneous3Densembles.Combinedwithspatialdisplacement measurement,themethodologyenablesunprecedentedmultifactorialassessmentofcelswithin their3Dtissuecontext.Thefunctionalitydemonstrationutilizesfluorescenceofindividualcelsin ESF-EVTensembleswithvisualizationsimilartoflowcytometryplots,enablingmeasurementof viscoelasticcharacteristicsofindividualcelsalongwithfluorescenceinformation,applicableto cel-surfacemarkerorsignalingreadoutdetermination.Themethodologyprovidesfunctional readoutsformechanicalpropertiesofcelsincorrelationwithotherpotentialsignalingreadoutsor celmarkerassignmentsin3Denvironments.Themethoddeterminesrelativecorelationbetween strainanddistancefromthedESF-HTR8interfaceforthe4conditions(FIG.17C).Theanalysis revealsthatdESFexhibitslargerstrainsinregionswithstrongergreensignals,indicatingmore deformationinEVTs,whileothersamples(ESF,dESFcond,anddESFHB-EGF)exhibitlargerstrains inregionswithstrongerredsignals,indicatingdeformationinfibroblastcels.Thistrendconfirms reverseddecidualstifnessin3D.FIG.17D ilustratesmeanlocationsofsegmentswithtop5% strain(straincenter,largeredpoints)andmeanlocationsofrandomlychosen5%segments (fluorescencemean,largebluepoints)throughtwo-dimensionalMonteCarlopermutationtests. ThroughN=109permutationsforeachcondition,thestraincenterdemonstratessignificant diferencefromrandomlychosengroupsforalcaseswithp-valuesp=5.0×10-7,p=1.4×10-3,p =7.0×10-7andp=1.0×10-9,respectively.Statisticaldistancesforredandgreensignalsundergo calculationthroughpermutationanalysis.ThedESForganoidexhibitsthestraincenteronthe strongersideofthegreensignalandcloserproximitytothefluorescencecenterintheredsignal (indicatedbytwoarrowsinthefigure).ThedESFcondanddESFHB-EGForganoidsdemonstrate reversedcharacteristicssimilartoESF. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 OtherDescriptions Creating3Dmodels.FIG.18 ilustratesthe3Dmodelingprocedures.Themodel creationutilizestheinitial3Dimagebeforeindentation(i.e.,thefirsttimeframe).FIG.18A depicts asliceofaraw3Dvolume,typicalycomprising~360zlayersof1600×1600pixelxyimages. ThemethodologyimplementsGaussian3Dlow-passandhigh-passfilteringofthevolumeto removebackgroundvariationsandsmalnoises.Thefilterbandwidthcut-offrequencies accommodatetypicalcelsizes(5-15µm).Post-filteringrevealscelsasbrightspots(FIG.18B). TheMATLABfunction‘watershed’ enablesseparationofthesecelsinto3Dsegments(FIG. 18C).In the present disclosure, 3D cel segmentation was performed using the watershedding method. However, it should be understood that any other suitable segmentation methods commonly used in 3D imaging and analysis, such as thresholding, edge detection, region growing, or machine learning-based approaches, may also be employed.Thestrainanalysisencompasses thehemisphericalregionclosertothemicroscopeobjectivetooptimizeimagingquality.Each segmentreceivesanumericaldesignationforfurthersinglecelularanalysis.Theregiontypicaly contains~500-600segments.Themethodologyincorporatesevenlydistributednodalpointson segmentboundariesandwithinsegments,maintainingdistancesof~10pixels(~4µm)between points.Thesenodalpointsservebothfor4Ddigitalimagecorelation(DIC)andtetrahedralmesh creationusingtheMATLAB‘alphaShape’ function.Theanalysistypicalyencompasses~20,000 nodalpointsand~100,000-200,000tetrahedralelements.Eachsegmentcontains~100-200 tetrahedralelementsand~50nodalpoints.Thequantityoftetrahedralelementsexceedsthenode countduetonodestypicalybeingsharedbymultipleelements.Nodesonsegmentboundaries maintainsharedstatusacrossmultiplesegments.Individualelementsmaintainexclusive associationwithsinglesegmentscontainingthem. Displacementandstrainanalysisthrough4Ddigitalimagecorrelation.Thedigital imagecorelation (DIC)analysisemploysspatial-domain volumetricpatern matching methodology.Thetrackingprocessencompassesavolumetricregionof30pixels×30pixels×14 pixels(=11.6µm×11.6µm×7.0µm)suroundingeachnodalpointthroughouttheindentation steps.Thedisplacementcalculationinrealnumbersbeyonddigitizedpixelresolutionutilizes interpolationtechniques,enablingmeasurementsbeyondinteger-basedpixelresolution. Theaccuracyevaluationof4Ddigitalimagecorrelation(DIC)utilizestrackingofan organoidsubjectedtotranslationalmovementwithoutcompressionorstretching.FIG.19A Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 ilustratesthemeasureddisplacementof9638nodesacross6indentationsteps(R=0.99999).The averagedisplacementafter6stepsmeasures29.6±0.73µm(127.6±3.2pixels).Thecelular displacementcalculationincorporatestheaveragedisplacementofalnodeswithineachsegment. FIG.19B demonstratesthedisplacementmeasurementforthesametranslationalmovement(R= 0.99999).Theaveragedisplacementafter6stepsmeasures29.6±0.18µm(127.6±0.8pixels).For comparativereference,themicroscopeopticalresolutionsdefinedbytheRayleighcriterion(0.61 ×λ) / (N.A.)forthefluorescencewavelengthsof517nmand602nmmeasure0.32µmand0.37 tm,respectively.ThenodaldisplacementerrorsgeneratenoiseinthevonMisesstraincalculation. Theanalysisrevealsanoisecomponentof0.18±0.07(N=385)atanaveragetranslational displacementof29.6µm,whilecompressiontestingwithacomparable30.4µm average displacementexhibitsanaveragevonMisesstrainof0.22±0.09(N=631),demonstrating significantlylargervaluesthanthetranslation(p=7.0×10-16). FIG.20 outlinestheprotocoloforganoidcultureandimagingsamplepreparation.The detailedmethodologyencompasses: Celtreatments.Thepreparationinvolvesorganoidscontaininghumanendometrial stromalfibroblasts(ESFs)andplacentalextraviloustrophoblastcels(EVTs).TheESFsderive from primarycelsisolatedfrom anormalpatientattheauthors’ institution.TheEVT implementationutilizesHTR8-SV-Neo(HTR8),acellinerepresentingextraviloustrophoblasts withpartialmesenchymalproperties,obtainedfromATCC(CRL-3271).ESFmaintenanceoccurs inDMEM / HamsF-1250 / 50Mix(phenol-redfree),supplementedwith10%charcoal-stripped fetalbovineserum(CS-FBS)and1Xantibiotic-antimycotic.ThedecidualizedESFs(dESFs) preparationinvolvestreatingESFsat80% confluencywith0.5mM 8-B-cAMPand1mM medroxyprogesteroneacetate(MPA)forfourdaysinDMEM / F12(withphenol-red)supplemented with2% fetalbovineserum (FBS).Thismedium forceldiferentiationisdesignatedas “diferentiationmedia.” TheconditioneddESFs(dESFcond)preparationinvolvestreatingpre- decidualizeddESFsfor2dayswithconditionedmediumfromHTR8cels.Theconditionedmedia acquisitioninvolvesculturingHTR8celsinSIGMARPMI-1640supplementedwith10%(FBS) and1Xantibiotic-antimycotic.ColectionoftheconditionedmediafromtheHTR8flaskoccurs afteraminimumoftwodaysofgrowth,folowedby1:1dilutionwithdiferentiationmedia,and placementontodESFcelsfortwodays.ThemethodologyincludesESFstreatedwithHeparin- bindingepidermalgrowthfactor(HB-EGF)ataconcentrationof300ng / mL.Thetreatmentmedia Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 preparationcombinesdiferentiationmediaandadditiontodESFcelsfortwodaysbefore organoidpreparation,designatedasdESFHB-EGF. 3Dorganoidculture.UponcompletionofESFtreatmentimmediatelybefore3D culture,cellabelingutilizesceltrackerdyes.HTR8cellabelingemploysCelTrackerTMGreen CMFDA(5-chloromethylfluoresceindiacetate)(ThermoFisherScientific,Waltham,MA)ata 1:1000ratio,dilutedin1xphosphate-bufferedsaline(PBS)for14minutesinacelincubatorwith gentleshakingevery5minutes.TheendometrialfibroblastcellabelingutilizesInvitrogenby CelTrackerTMRedCMTPX(ThermoFisherScientific,Waltham,MA)ata1:500dilutionin1x PBSfor35minutes,withgentleshakingevery5minutes.Labelingeficacyverificationinvolves observationunderaninvertedfluorescentmicroscope.Theprotocolincludesthreewasheswith 1XPBSandceldetachmentusingGibco0.25%trypsin-EDTA. TheorganoidpreparationinvolvescountingandmixingEVTsandESFsusinga hemocytometer.CelseedingoccursinaU-botomplate,folowedbycentrifugationfor5minutes at350g.Thecelmixturecultureutilizesasolutionof1Xmethylcelulosedilutedin diferentiationmedia,excluding8-B-cAMPduetoHTR8celtoxicity.Experimentaloptimization indicatesoptimalimagingsizefororganoidsseededfrom300trophoblastand300ESFs.The protocolmaintainsplatestorageinahumidifiedincubatorat37ºCfor48hours.HTR8andESFs demonstratespontaneousspatialsortingin3D,withESFsinthecoresuroundedbyHTR8layers, creatingaplacenta-deciduainterface.OrganoidtransferfromtheU-botomplatetoa35mmby 10mmpetridishcontaining1XPBSutilizesa1000pipete.Sampleselectionfocuseson specimensbetween160-180μmindiameter. Preparationofsamplesforimaging.The3D imagingmethodologyinvolves embeddingorganoidsinlowmeltingpointagarosegel(MiliporeSigma,Burlington,MA).The protocolutilizesagarosegelpreparedat0.8%concentration,liquifiedona250ºChotplate.The methodologytransfers2000μLofheatedagaroseintoa35mm×10mmpetridish.Additionof3 μLofInvitrogenTM FluoSpheresTM Carboxylate-ModifiedMicrospheres,0.2μm (Crimson) (ThermoFisherScientific,Waltham,MA)totheagarosefolows,withpre-sonicationofbeadsfor 10+minutes.TemperaturemonitoringemploysaFluke62Max+InfraredHandheldThermometer, withorganoidadditionoccuringat40ºC.Organoidtransferfromthepetridishutilizesa1000μL Eppendorfmicropipetesetto100μL.ThemethodologyincorporatesorganoidandPBSmedia mixingintothecooledmeltedagarose.SamplepreparationemploysaWiretrol®I50&100μL Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 glasscapilary(I.D.1.4mm)withawireplunger(DrummondScientific,Broomal,PA)for agarosecontainment.Theprotocolpositionstheorganoidattheagarosemidpointinthecapilary, folowedbycoolingto26ºC.Post-gelcuring,excessagaroseremovalutilizesascalpel,ensuring aclean,straightcut.Samplepreservationduringtransferinvolvesmaintainingthecapilarytipin a15mLconicaltubecontainingPBS. RNAsequencingandGeneSetEnrichmentAnalysis.Themethodologyemployscel lysisandRNAisolationutilizingRNeasyMiniKit(Qiagen).RNAintegrityevaluationutilizes Bioanalyzer2100(Agilent),withsamplesdemonstratingRIN~8advancingtolibrarypreparation. LibrarypreparationandRNAsequencingservicesareperformedbyNovogeneInc.TheRNAread alignmentprocessutilizesNCBIGRCh38genomeassemblythroughtheHISAT2pipeline.Read countingimplementsHTSeq,whileDESeq2facilitatesp-valueandfold-changeestimationfor diferentialexpressionanalysisbetweenundiferentiated,decidualized,andconditionedsamples. Thediferentialanalysismethodologyincorporatesmoderatedlog2foldchange.Diferential expressionp-valuecalculationsemploytheWaldtest.Geneontology(G.O.)andpathway(Kegg) activationanalysesutilizetheFisherexacttestfortermoverrepresentationcalculationthrough hypergeometrictestingfolowedbymultipletestingcorections.Transcriptionfactor(T.F.) activationscoredeterminationimplementsIngenuityPathwayAnalysis. Image analysis data flow. The experimental data contains the raw 4D (x, y, z, and time step) data and metadata, including the x, y, and z scaling (pixels to meters conversion). The data is converted into a .tif format using the ImageJ software and import it to MATLAB for data analysis. FIG.21shows the flowchart of data analysis. 4D Digital image correlation.The digital image corelation (DIC) analysis was based on spatial-domain volumetric patern matching. The volumetric region of 30 pixels ×30 pixels × 14 pixels (= 11.6 µm × 11.6 µm × 7.0 µm) around each nodal point Ni(x, y, z, t) was chosen from the 3D volume V(t) at time point t, and the next volume V(t+1) was searched for the closest patern to find the next coordinate, Ni(x, y, z, t+1). The same processwas repeatedthroughout al the indentation steps for al the nodal points. Alocalized searchwas usedbecause the target patern only moves by a few microns between consecutive time points. In V(t+1), the search window S was slid near the previous location of Ni(x, y, z, t), limiting the search area considering the expected maximum displacement of typicaly 10 pixels. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 To find the closest patern for N (30 pixels × 30 pixels × 14 pixels) in V(t+1), the similarity between N and S was computed using the normalized Frobenius inner product calculated in MATLAB as:similarity = inner_product / (norm_N * norm_S);= sum(N(:) .* S(:) / ( sqrt(sum(N(:.)̂2) * sqrt(sum(S(:.)̂2) ) After calculating the similarity for al possible positions, the sub-volume Smaxwith the highest similaritywas identified. This gives the best discrete match in V(t+1).Since the displacement is discrete due to the digitized nature of the volumes, the displacement found from this analysis is an integer. For each Smax, the similarity values were mapped around it and picked the three closest points in each of x, y, and z components, and fited a second-order polynomial to estimate the displacement in real numbers beyond digitized pixel resolution given in integers. Alternatively, thefrequency domain cross-correlationmay be usedto find the best matched paterns. Optical flow (vector flow of optical paterns) may also be used to find the nodal displacement. Structural analysisequations.Thestrain tensor for a tetrahedral elementmay be computedfrom the nodal coordinates and the corresponding displacements: (x1, y1, z1), (x2, y2, z2), (x3, y3, z3), and (x4, y4, z4) (u1, v1, w1), (u2, v2, w2), (u3, v3, w3), and (u4, v4, w4). The global coordinates (x, y, z) are related to the local coordinates (X, Y, Z) in a tetrahedral element using shape functions N1-N4: x = N1* x1 + N2* x2 + N3* x3 + N4* x4; y = N1* y1 + N2* y2 + N3* y3 + N4* y4; z = N1* z1 + N2* z2 + N3* z3 + N4* z4; Similarly, the displacements (u, v, w) are: u = N1* u1 + N2* u2 + N3* u3 + N4* u4; v = N1* v1 + N2* v2 + N3* v3 + N4* v4; w = N1* w1 + N2* w2 + N3* w3 + N4* w4; The shape functions provide the linear interpolation among the four nodes and are given as: N1= 1 -X -Y -Z; N2= X; N3= Y; Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 N4= Z. The Jacobian matrix (J) represents the relationship between local and global coordinates. It is defined as: J = [ ∂x / ∂X, ∂x / ∂Y, ∂x / ∂Z; ∂y / dX, ∂y / ∂Y, ∂y / ∂Z; ∂z / ∂X, ∂z / ∂Y, ∂z / ∂Z ]. By calculating ∂Ni / ∂X, ∂Ni / ∂Y, and ∂Ni / ∂Z, the Jacobian becomes: J = [ x2-x1, x3-x1, x4-x1; y2-y1, y3-y1, y4-y1; z2-z1, z3-z1, z4-z1 ]. The displacement gradient in local coordinates (X, Y, Z) is writen as: ∂u / ∂X = [∂u / ∂X, ∂u / ∂Y, ∂u / ∂Z; ∂v / ∂X, ∂v / ∂Y, ∂v / ∂Z; ∂w / ∂X, ∂w / ∂Y, ∂w / ∂Z]. Since u, v, and w are given as linear combinations of N1, N2, N3, and N4, onecan find ∂u / ∂X from ∂Ni / ∂X, ∂Ni / ∂Y, and ∂Ni / ∂Z. The displacement gradient in global coordinates (x, y, z) is: ∂u / ∂x = [∂u / ∂x, ∂u / ∂y, ∂u / ∂z; ∂v / ∂x, ∂v / ∂y, ∂v / ∂z; ∂w / ∂x, ∂w / ∂y, ∂w / ∂z]. The relationship between the local and global displacement gradients is given by: ∂u / ∂x = inv(J) * ∂u / ∂X, where inv(J) is the inverse of the Jacobian matrix. We calculated the strain tensor from the global displacement gradient elements: εxx = ∂u / ∂x εyy= ∂v / ∂y εzz= ∂w / ∂z εxy= 0.5 * (∂u / ∂y + ∂v / ∂x) εxz= 0.5 * (∂u / ∂z + ∂w / ∂x) εyz= 0.5 * (∂v / ∂z + ∂w / ∂y) The von Mises strain is: Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 ε = (0.5 * ( 2 2 2 2 2 2 VM εxx-εyy)+ (εyy-εzz)+ (εzz-εxx)+ 6 * (εxy+ εyz+ εzx)) Alternative calculation.Since the tissue is methods used in fluid displacement vector (u, v, w) is considered a flow velocity vector field. One method of evaluating randomness in a flow is the calculation of vorticity. The vorticity vector in a flow vector field is defined as: ω = [∂w / ∂y -∂v / ∂z; ∂u / ∂z -∂w / ∂x; ∂v / ∂x -∂u / ∂y] The magnitude of vorticity is | ω | = sqrt(∂w / ∂y -∂v / ∂z)2+ (∂u / ∂z -∂w / ∂x)2+ (∂v / ∂x -∂u / ∂y)2) Vorticity is typicalyanalyzed across diferent scale lengths. To study local behavior, we compute the vorticity within specific groups of cels, such as a center cel and its directly neighboring cels in contact with it.Since vorticity represents the local spinning motion of a fluid, it can also be evaluated using the rotational moment of a group of cels. This rotational moment is calculated as the sum of the products of each cel's mass and the cross product of its positionrelative to the center of rotationand its velocity. Example of micro-compression device.Example of micro compressio achieved by multi-dimensional piezo positioning systemsis provided in FIG.22. A miniature puley system rotates the capilary. This example also shows a sample chamber that controls the temperature and CO2concentration to create cel culture condition.The compression system sits above the sample chamber and controls the motion of the robotic arm which applies dynamic compressive and shear deformation to the sample. The robot arm is made of 3D-printed titanium aloy (Ti6Al4V). The present disclosure usesthree piezoelectric actuators (P-602.5SL, PI, Auburn, MA) to provide controled displacement and deform samples in multiple dimensions of y+ (arm up), y-(capilary down), and x+ (arm shear). Another actuator may be added to control a plunger inside the capilary. Each actuator is capable of providing 500 µm displacement at a 35 nm repeatability. The resonant frequency of the actuator is 230 Hz, which is suficient to provide the target sinusoidal displacement of 500 µm (peak -peak) at up to 4 Hz. Coarse motion wil be achieved by a miniature manual mechanical stage. Having two actuators (such as y+ and y-) is preferred, but not required, because it can minimize the sample translational displacement by bringing the center of compression or stretching within the field of view.The mechanical perturbation device may also serve as a tool Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 to apply mechanical stimulation. Long-term stretching or compression affects the growth of cels. Such stimulation is especialy important for the growth of muscle cels.Alternatively, utilizing the mechanical force created by the sample itself is also an alternative. Examples include the force created by sample muscle contraction, such as heartbeat, or volume changes by tissue growth. Fluorescence decay and intensity distribution.Because of the laser photobleaching, the intensity of the fluorescencedecays over indentation steps.FIG23(A) shows the decay imaged over 8 steps of 3D imaging of indentation. The DIC algorithm uses normalized images for matching, and this decay does not deteriorate the tracking accuracy. The tissue thickness may also cause non-uniform intensity distribution. However, when a proper middle point of the sample is chosen, fluorescence intensitycan be consideredto be practicaly uniform, as shown in FIG.23B. Cancer dissemination model example.The tumor-stroma model comprisedcancer cels (MCF10CA1) and two types (control and gene-edited) of fibroblast cels.In this example, MRTFA gene was knocked down through CRISPR / Cas9 gene editing using Lipofectamine CRISPRMAX transfection reagent (Invitrogen). MRTFA encodes MKL1, which regulates G- actin availability, and thereby actin cytoskeletal organization. Now referencing FIG.24, the celular displacement vectors in panels (a)and (e)show diferent paterns of celular motion (arows); the control shows large displacements in a few spots in the cancer cel region, while MRTFA knockdown clearly showed large displacements in the fibroblast region. A closer look in areas with larger displacements shows swirling, random behaviors of moving cels. The von Mises strain plots in (b)and (f)quantitatively indicate the areas with larger celular displacements. A comparison of the cytometry plots in (c)and (g) shows that cels with a large strain moved to the red cel (fibroblasts) side after the knockdown. The statistical analysis in (d)shows that thestrain center (defined as the average location of the cels with the top 5% strain)showedsignificantly weak green fluorescence (p = 5.2 ×10-6), while red fluorescence is not significantly diferent (p = 0.22) from the fluorescence mean. This suggests that the large deformation occurs near the green-red interface, but not in the red region. Viscoelastic deformation mainly occurs in the green, cancer cel region. On the other hand, in (h), the strain center is significantly shifted toward the red cel region with much smaler p- values both in the red (p < 1.0-9) and green (p < 1.0-9) channels. This analysis shows that Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 knocking down the MRTFA gene made the fibroblast cels a more liquid-like state. It is important to note that each dot in the scater plot represents a cel that has been identified in the 3D model. Other Embodiments Those skiled in the art wil recognize or be able to ascertain using no more than routine experimentation many equivalents to the specific embodiments described herein. The scope of the present embodiments described herein is not intended to be limited tothe above Description, but rather is as set forth in the appended claims. For reasons of completeness, various aspects of the disclosure are set out in the folowing numbered clauses: Clause 1. A system for analyzing biological tissue, comprising:an imaging system configured to acquire three-dimensional images of a sample;a mechanical perturbation device configured to apply force to the sample; anda processor configured to analyze deformation of the sample based on images acquired during application of the force. Clause2. The system of clause 1, wherein the imaging system comprises a light-sheet microscope. Clause3. The system of clause 2, wherein the light-sheet microscope comprises:a light- sheet scanner configured to generate a moving light sheet;at least one ilumination objective for directing the light sheet to the sample; andan imaging objective positioned orthogonaly to the ilumination objective. Clause4. The system of clause 3, wherein the light-sheet scanner comprises:a miror mounted to at least one piezoelectric bimorph actuator,electrostaticactuator,electromagnetic actuator, or any combinations thereof; anda position sensor configured to monitor position of the light sheet. Clause5. The system of clause 1, wherein the mechanical perturbation device comprises:a piezoelectric actuator;a compression plate coupled to the piezoelectric actuator; and a load cel configured to measure force applied to the sample. Clause6. The system of clause 1, wherein the processor is configured to:generate a three-dimensional model of the sample comprising a plurality of segments representing individual Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 cels;track displacement of nodes within the segments during application of the force; and calculate strain distribution within the sample based on the tracked displacement. Clause7. The system of clause 6, wherein the processor is configured to: calculate strain tensors for elements within the segments; anddetermine von Mises strain for individual cels based on the strain tensors. Clause8. A method for analyzing biological tissue, comprising:providing a sample comprising multiple cel types;applying force to the sample;acquiring three-dimensional images of the sample during application of the force; andanalyzing deformation of the sample based on the acquired images. Clause9. The method of clause 8, further comprising:embedding the sample in a gel matrix prior to applying force. Clause10. The method of clause 8, wherein acquiring three-dimensional images comprises:iluminating the sample with a light sheet; anddetecting fluorescence emission with an objective oriented orthogonaly to the light sheet. Clause11. The method of clause 8, wherein analyzing deformation comprises: generating a three-dimensional model of the sample by segmenting the acquired images into regions coresponding to individual cels;tracking displacement of nodes distributed throughout the model; andcalculating strain within regions of the model based on the tracked displacement. Clause12. The method of clause 11, wherein calculating strain comprises:generating tetrahedral elements between the nodes;calculating strain tensors for the tetrahedral elements; and determining von Mises strain for individual cels based on the strain tensors. Clause13. The method of clause 8, further comprising:corelating calculated strain with fluorescence intensity for individual cels within the sample. Clause14. The method of any one of clauses 8 or 9, wherein providing the sample comprises forming a three-dimensional organoid by combining diferent cel types labeled with distinct fluorescent markers. Clause15. A system for analyzing tissue interfaces, comprising:an imaging system configured to acquire three-dimensional images;a force application device; anda processor configured to:analyze deformation of a tissue sample during force application; andcorelate the deformation with spatial distribution of diferent cel types within the sample. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 Clause16. The system of clause 15, wherein the processor is configured to:segment acquired images into regions coresponding to individual cels;calculate strain tensors for elements within the segmented regions; anddetermine viscoelastic properties of diferent regions based on the calculated strain tensors. Clause17. The system of clause 15, wherein the imaging system comprises:dual ilumination objectives positioned to iluminate the sample from opposite sides; anddual cameras configured to simultaneously acquire images in diferent wavelength channels. Clause18. The system of clause 15, wherein the force application device is configured to apply cyclic compression at a defined frequency. Clause19. The system of any one of the clauses 15 or 16, wherein the processor is configured to:identify regions exhibiting coordinated versus random motion paterns; and corelate the identified paterns with cel type distribution. Clause20. The system of clause 15, wherein the processor is configured to:generate a three-dimensional computational model of the sample comprising tetrahedral elements; and calculate strain distribution within the elements during force application. Clause21. The system of clause 20, wherein the processor is configured to corelate calculated strain with fluorescence intensity on a cel-by-cel basis. Clause22. A method of analyzing cel-cel interactions in three-dimensional tissue constructs, comprising:forming a three-dimensional tissue construct comprising multiple labeled cel types;embedding the tissue construct in a gel matrix;applying force to the gel matrix while acquiring three-dimensional images; andanalyzing relative motion between diferent cel types based on the acquired images. Clause23. The method of clause 22, wherein forming the three-dimensional tissue construct comprises:combining epithelial cels and stromal cels in a defined ratio; andculturing the combined cels under conditions promoting self-organization. Clause24. The method of clause 22, wherein analyzing relative motion comprises: tracking displacement vectors for individual cels; andidentifying regions exhibiting coordinated versus random motion paterns. Clause25. The method of any one of the clauses 22 or 24, further comprising: calculating strain tensors for elements within the tissue construct; andcorrelating calculated strain with spatial distribution of diferent cel types. Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 Clause26. The method of clause 22, wherein acquiring three-dimensional images comprises:scanning a light sheet through the tissue construct while detecting fluorescence emission with an objective oriented orthogonaly to the light sheet. Clause27. The method of any one of the clauses 22, 24, or 25, further comprising: analyzing changes in relative motion paterns in response to biochemical treatment of the tissue construct. Clause28. The method of any one of the clauses 22 or 24, wherein the force comprises cyclic compression at a defined frequency. Clause29. A method of analyzing tissue mechanical properties, comprising:acquiring time-series three-dimensional images of a tissue sample during application of force;generating a computational model comprising segments coresponding to individual cels;tracking displacement within the segments; anddetermining mechanical properties based on the tracked displacement. Clause30. The method of clause 29, further comprising:corelating determined mechanical properties with celular markers on a cel-by-cel basis. Clause31. The method of clause 29, wherein generating the computational model comprises:applying three-dimensional watershed segmentation to the images. Clause32. The method of clause 29, wherein determining mechanical properties comprises:calculating strain tensors for elements within the computational model; and determining viscoelastic properties based on the calculated strain tensors. Clause33. The method of any one of the clauses 29 or 32, further comprising:analyzing spatial distribution of mechanical properties relative to tissue interfaces within the sample. Clause34. The method of clause 29, wherein acquiring time-series three-dimensional images comprises:iluminating the sample with a scanning light sheet; anddetecting fluorescence emission with an objective oriented orthogonaly to the light sheet. Clause35. The method of any one of the clauses 29, 32 or 33, further comprising: analyzing changes in mechanical properties in response to biochemical treatment of the tissue sample. Clause36. A method of analyzing tissue interfaces, comprising:providing a three- dimensional tissue construct comprising multiple cel types;applying cyclical force to the tissue construct while acquiring three-dimensional images;generating a computational model of the Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 tissue construct; andanalyzing relative deformation between different regions of the tissue construct based on the computational model. Clause37. The method of clause 36, wherein providing the three-dimensional tissue construct comprises:combining diferent cel types labeled with distinct markers. Clause38. The method of any one of the clauses 36 or 37, further comprising: calculating strain tensors for elements within the computational model; andcorelating calculated strain with spatial distribution of diferent cel types. Clause39. The method of clause 36, wherein analyzing relative deformation comprises: identifying regions exhibiting coordinated versus random motion paterns. Clause40. The method of any one of the clauses 36 or 38, further comprising:analyzing changes in relative deformation paterns in response to biochemical treatment of the tissue construct. Those of ordinary skil in the art wil appreciate that various changes and modifications to this description may be made without departing from the spirit or scope of the present invention, as defined in the folowing claims.

Claims

Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 CLAIMS What is claimed is:

1. A system for analyzing biological tissue, comprising: an imaging system configured to acquire three-dimensional images of a sample; a mechanical perturbation device configured to apply force to the sample; and a processor configured to analyze deformation of the sample based on images acquired during application of the force.

2. The system of claim 1, wherein the imaging system comprises a light-sheet microscope.

3. The system of claim 2, wherein the light-sheet microscope comprises: a light-sheet scanner configured to generate a moving light sheet; at least one ilumination objective for directing the light sheet to the sample; and an imaging objective positioned orthogonaly to the ilumination objective.

4. The system of claim 3, wherein the light-sheet scanner comprises: a miror mounted to at least one piezoelectric bimorph actuator, electrostatic actuator, electromagnetic actuator, or any combinations thereof; and a position sensor configured to monitor position of the light sheet.

5. The system of claim 1, wherein the mechanical perturbation device comprises: a piezoelectric actuator; a compression plate coupled to the piezoelectric actuator; and a load cel configured to measure force applied to the sample.

6. The system of claim 1, wherein the processor is configured to: generate a three-dimensional model of the sample comprising a plurality of segments representing individual cels; track displacement of nodes within the segments during application of the force; andClient Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 calculate strain distribution within the sample based on the tracked displacement.

7. The system of claim 6, wherein the processor is configured to: calculate strain tensors for elements within the segments; and determine von Mises strain for individual cels based on the strain tensors.

8. A method for analyzing biological tissue, comprising: providing a sample comprising multiple cel types; applying force to the sample; acquiring three-dimensional images of the sample during application of the force; and analyzing deformation of the sample based on the acquired images.

9. The method of claim 8, further comprising: embedding the sample in a gel matrix prior to applying force.

10. The method of claim 8, wherein acquiring three-dimensional images comprises: iluminating the sample with a light sheet; and detecting fluorescence emission with an objective oriented orthogonaly to the light sheet.

11. The method of claim 8, wherein analyzing deformation comprises: generating a three-dimensional model of the sample by segmenting the acquired images into regions coresponding to individual cels; tracking displacement of nodes distributed throughout the model; and calculating strain within regions of the model based on the tracked displacement.

12. The method of claim 11, wherein calculating strain comprises: generating tetrahedral elements between the nodes; calculating strain tensors for the tetrahedral elements; and determining von Mises strain for individual cels based on the strain tensors.

13. The method of claim 8, further comprising:Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 corelating calculated strain with fluorescence intensity for individual cels within the sample.

14. The method of any one of claims 8 or 9, wherein providing the sample comprises forming a three-dimensional organoid by combining diferent cel types labeled with distinct fluorescent markers.

15. A system for analyzing tissue interfaces, comprising: an imaging system configured to acquire three-dimensional images; a force application device; and a processor configured to: analyze deformation of a tissue sample during force application; and corelate the deformation with spatial distribution of diferent cel types within the sample.

16. The system of claim 15, wherein the processor is configured to: segment acquired images into regions coresponding to individual cels; calculate strain tensors for elements within the segmented regions; and determine viscoelastic properties of diferent regions based on the calculated strain tensors.

17. The system of claim 15, wherein the imaging system comprises: dual ilumination objectives positioned to iluminate the sample from opposite sides; and dual cameras configured to simultaneously acquire images in diferent wavelength channels.

18. The system of claim 15, wherein the force application device is configured to apply cyclic compression at a defined frequency.

19. The system of any one of the claims 15 or 16, wherein the processor is configured to: identify regions exhibiting coordinated versus random motion paterns; and corelate the identified paterns with cel type distribution.

20. The system of claim 15, wherein the processor is configured to:Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 generate a three-dimensional computational model of the sample comprising tetrahedral elements; and calculate strain distribution within the elements during force application.

21. The system of claim 20, wherein the processor is configured to corelate calculated strain with fluorescence intensity on a cel-by-cel basis.

22. A method of analyzing cel-cel interactions in three-dimensional tissue constructs, comprising: forming a three-dimensional tissue construct comprising multiple labeled cel types; embedding the tissue construct in a gel matrix; applying force to the gel matrix while acquiring three-dimensional images; and analyzing relative motion between different cel types based on the acquired images.

23. The method of claim 22, wherein forming the three-dimensional tissue construct comprises: combining epithelial cels and stromal cels in a defined ratio; and culturing the combined cels under conditions promoting self-organization.

24. The method of claim 22, wherein analyzing relative motion comprises: tracking displacement vectors for individual cels; and identifying regions exhibiting coordinated versus random motion paterns.

25. The method of any one of the claims 22 or 24, further comprising: calculating strain tensors for elements within the tissue construct; and corelating calculated strain with spatial distribution of diferent cel types.

26. The method of claim 22, wherein acquiring three-dimensional images comprises: scanning a light sheet through the tissue construct while detecting fluorescence emission with an objective oriented orthogonaly to the light sheet.

27. The method of any one of the claims 22, 24, or 25, further comprising:Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 analyzing changes in relative motion paterns in response to biochemical treatment of the tissue construct.

28. The method of any one of the claims 22 or 24, wherein the force comprises cyclic compression at a defined frequency.

29. A method of analyzing tissue mechanical properties, comprising: acquiring time-series three-dimensional images of a tissue sample during application of force; generating a computational model comprising segments coresponding to individual cels; tracking displacement within the segments; and determining mechanical properties based on the tracked displacement.

30. The method of claim 29, further comprising: corelating determined mechanical properties with celular markers on a cel-by-cel basis.

31. The method of claim 29, wherein generating the computational model comprises: applying three-dimensional watershed segmentation to the images.

32. The method of claim 29, wherein determining mechanical properties comprises: calculating strain tensors for elements within the computational model; and determining viscoelastic properties based on the calculated strain tensors.

33. The method of any one of the claims 29 or 32, further comprising: analyzing spatial distribution of mechanical properties relative to tissue interfaces within the sample.

34. The method of claim 29, wherein acquiring time-series three-dimensional images comprises: iluminating the sample with a scanning light sheet; and detecting fluorescence emission with an objective oriented orthogonaly to the light sheet.

35. The method of any one of the claims 29, 32 or 33, further comprising:Client Ref. No. UConn 24-051 Aty. Docket No. UCONN 44234.601 analyzing changes in mechanical properties in response to biochemical treatment of the tissue sample.

36. A method of analyzing tissue interfaces, comprising: providing a three-dimensional tissue construct comprising multiple cel types; applying cyclical force to the tissue construct while acquiring three-dimensional images; generating a computational model of the tissue construct; and analyzing relative deformation between diferent regions of the tissue construct based on the computational model.

37. The method of claim 36, wherein providing the three-dimensional tissue construct comprises: combining diferent cel types labeled with distinct markers.

38. The method of any one of the claims 36 or 37, further comprising: calculating strain tensors for elements within the computational model; and corelating calculated strain with spatial distribution of diferent cel types.

39. The method of claim 36, wherein analyzing relative deformation comprises: identifying regions exhibiting coordinated versus random motion paterns.

40. The method of any one of the claims 36 or 38, further comprising: analyzing changes in relative deformation paterns in response to biochemical treatment of the tissue construct.

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