Cells sense, manipulate and respond to their mechanical microenvironment in a plethora of physiological processes, yet the understanding of how cells transmit, receive and interpret environmental cues to communicate with distant cells is severely limited due to lack of tools to quantitatively infer the complex tangle of dynamic cell-cell interactions in complicated environments. We present a computational method to systematically infer and quantify long-range cell-cell force transmission through the extracellular matrix (cell-ECM-cell communication) by correlating ECM remodeling fluctuations in between communicating cells and demonstrating that these fluctuations contain sufficient information to define unique signatures that robustly distinguish between different pairs of communicating cells. We demonstrate our method with finite element simulations and live 3D imaging of fibroblasts and cancer cells embedded in fibrin gels. While previous studies relied on the formation of a visible fibrous ‘band’ extending between cells to inform on mechanical communication, our method detected mechanical propagation even in cases where visible bands never formed. We revealed that while contractility is required, band formation is not necessary, for cell-ECM-cell communication, and that mechanical signals propagate from one cell to another even upon massive reduction in their contractility. Our method sets the stage to measure the fundamental aspects of intercellular long-range mechanical communication in physiological contexts and may provide a new functional readout for high content 3D image-based screening. The ability to infer cell-ECM-cell communication using standard confocal microscopy holds the promise for wide use and democratizing the method. A computational method quantifies long-range cell-cell force transmission through the extracellular matrix by correlating ECM remodeling fluctuations in simulations and live 3D imaging and identifies unique signatures of communicating cell pairs. When embedded in fibrous biological hydrogels, such as collagen or fibrin, cells contract and thereby remodel and densify nearby ECM fibers. Then, in the time-scale of a few hours, these remodeling can form a visible fibrous band of aligned and dense fibers coupling neighboring cells mechanically which can influence the cells’ internal molecular state16 and active response17. This form of long-range cell-cell force transmission through the ECM can be viewed as the imparting or exchanging of information between cells, and thus is aligned with the definition of communication18 termed here cell-ECM-cell communication. This mode of long-range mechanical cell-ECM-cell communication was shown to coordinate various biological processes, including tissue injury17, fibrosis19, vascular assembly, capillary sprouting20, tissue folding21, and cancer invasion and metastasis9. In-vivo, fiber alignment bands can serve as ECM ‘tracks’ for cell migration with potential roles in wound healing, cancer metastasis and fibrosis23.Many types of cells apply considerable traction forces on their surrounding matrix leading to ECM remodeling that can propagate to large distances of tens of cell diameters away26. Most current measurements characterize the structure of the fibrous band extending between mechanically coupled cells to inform on cell–cell mechanical coupling, while relying on the visibility of a band between cells, formed when cell-generated forces are strong enough26. Such measurement lacks the sensitivity to measure the dynamic reciprocal mechanical information transfer between the cells by excluding potential cell-ECM-cell communication in the absence of visible bands, thus hampering our ability to distinguish which cells are actually communicating from the many cells that have the potential to communicate. Bridging this gap will enable tackling long-standing open questions in how tissues develop and diseases progress by enabling us to identify which cells are communicating with each other, and to what extent, in complex environments.Measuring the transfer of forces through the ECM during cell-ECM-cell communication is challenging and has been typically achieved indirectly by measuring changes in the density, alignment or displacement of the remodeled fibrous ECM between the cell pairs resulting from the active contraction of cellsHere, we present a new computational method to quantify the transmitted ECM signal in between neighboring cells by correlating temporal fluctuations of the remodeled matrix. Computational simulations and 3D live imaging of fibroblasts and cancer cells embedded in fibrin gels demonstrate the power of our method in identifying unique ECM remodeling signatures that allow to robustly distinguish between different pairs of communicating cells. Using this method, we were able to measure communication between cell pairs that do not form a visible ‘band’ of densified ECM, and after partial depletion of contractility upon Myosin-II inhibition. These results imply that mechanical signals propagate from one cell to another even in low cell contractility levels.‘cell-ECM’ interaction), and the force transmitted from the second distant cell . Thus, these simulations serve as a minimal mechanical model that enables us to independently tune mechanical parameters , and quantitatively infer their effect on ECM remodeling. These simulations will be used to ask whether ECM remodeling can robustly encode unique ECM signature in communicating cell pairs, forming the basis for our method.During cell-cell mechanical communication, every localized fibrous region in between the neighboring cells is affected by two components: the contractile force of the adjacent cell . In simulations of single contractile cells, we found that the regions next to the cell’s boundary became denser following the application of 50% cell contraction  contraction of 1% and a standard deviation (σ) in the range of 0-0.75% . Even a minimal standard deviation of 0.25% in cell contraction was sufficient to make a clear distinction between communicating and non-communicating cell pairs, which improved with increasing standard deviations , which increased the communication partner’s influence  will lead to variability in the local ECM remodeling around each cell, leading to a unique communication signature between partners. In other words, we thought that heterogeneity in ECM remodeling would enable us to decouple the general ECM remodeling that occurs between any random pair of cells from the specific signal that is transmitted between communicating partners. Indeed, when introducing heterogeneity in simulated cell contraction, we distinguished communicating from non-communicating simulated cell pairs while maintaining the non-communicating correlations around zero Fig. . Cell heons Fig. . This dions Fig. . Moreovence Fig. . Finallynce Fig. .Altogether, these results established the notion that a correlation-based approach can capture force transmission between actively contractile cells, that temporal correlation of local ECM remodeling fluctuations can distinguish between communicating and non-communicating simulated cell pairs, and that contractile heterogeneity is required to make this distinction.z-score fiber intensity normalization in respect to background quantification windows defined at the onset of the experiment at regions that were not influenced by the cells (“Methods” section). While single cells did not show visually apparent fiber densification beyond regions close to the cell, approximately 60% of all cell pairs formed a visible band of increased density extending along the connecting axis between the cells . During this time, cells have already remodeled the fibers around them. These results conclude that the formation of dense fibrous bands between fibroblast cell pairs embedded in 3D fibrin gels are indicative of a mechanical coupling in concurrence with previous studies11.To test the simulated results of correlation in ECM remodeling as a marker of mechanical cell-ECM-cell communication, we used 3D time-lapse confocal microscopy to image live NIH/3T3 fibroblast cells (GFP-Actin) embedded in 3D fluorescently labeled fibrin gels, where fibrin intensity was used as a proxy of fiber density (Methods). Similar to the simulations, we clarify that “communicating pairs” are pairs of cells located next to each other in the fibrous gel and thus are within a pulling distance, whereas “non-communicating cells” are pairs that are not in proximity . To visualize and measure fiber intensity in between cell pairs in 3D, we transformed the microscopy axes to a new 3D coordinate system that is aligned around the connecting axis between the cells’ centers  to multiple non-communicating cells (y-axis), each leading to a different value  located in the exact same distance as the matched pair of communicating cells, and away from other cells  located close to a communicating cell, which we term lls Fig. .Fig. 5QuGiven our ability to distinguish one pair from a different pair of communicating cells, we wondered whether we can match a cell to its true communication partner when considering all other cells in the experiment Fig. , Methods32  Same-versus-different – quantifying the ability to distinguish between ECM remolding of one pair of communicating cells from a different pair of communicating cells, (2) Real-versus-fake – quantifying the confounding factor of non-communication-related local remodeling in the ECM, and (3) Matchmaking – quantitative identification of unique ECM remodeling signatures of communication partners. True communication is characterized by high values in all three measurements.33) correspondingly. A summary of all measurements over all experimental conditions are reported in Table To verify that ECM-remodeling correlations are robust to ECM fluctuations induced by other cells in the gel we included a new set of experiments as negative controls. Severe reduction in all three communication readouts was measured between pairs of fluorescent beads in size comparable to cells, and in experiments with dead cells Fig. , compareXY axis) and axially  in relation to the connecting axis  from the connecting axis between the cells because the higher ECM intensities along the fibrous band confound our ability to sensitively measure the ECM fluctuations as a signature of cell-ECM-cell communication. To test this hypothesis, we calculated the association between fiber density and the sensitivity in measuring communication via same-versus-different pair analysis by correlating these parameters across different locations of the quantification window. This analysis demonstrated a sweet spot for measuring cell-ECM-cell communication  = (1−α)*N + α*Contractionleader (t−1), where t > 1, 0 ≤ α ≤ 1, μ = 1, σ = 0.5 are the mean and standard deviation correspondingly, and Contractionleader is drawn from the normal distribution N. The term α indicates the “followership” magnitude, higher values of α = 0.5, where the follower cell contraction is determined with equal contribution from its intrinsic “decision” and the extrinsic influence by its leader , and introduced β ≥ 1 – the fold increase of the follower’s contraction: We next asked if we could use our approach to quantify asymmetric interactions between the communicating partners. More specifically, can we identify which cell in a communicating pair is more dominant or influential? Our quantitative interpretation of “influential” is that past ECM-remodeling fluctuations of one cell are predictive of the future ECM-remodeling of its communicating partner. Such temporal order defines a leader-follower relation. We performed simulations where a “follower” cell “imitates” the previous contraction of its influential “leader cell”. In other words, the contraction of both cells are determined by the leader cell with a time lag of one simulation step Fig. . The simlls Fig.  and the er Figs.  and S17Brs Figs.  and S17Crs Figs. . Howeverrs Figs. . This inOur work proposes a systematic computational method to quantify cell-ECM-cell communication and demonstrates its applicability in simulations and experiments. The method quantifies the local fiber remodeling dynamics between pairs of communicating cells, located up to a distance of 10 cell-diameters apart, in 2D (simulations) and 3D (experiments) by applying the following key steps. Normalization in relation to the background to enable robust comparison across experiments; Subtracting background remodeling to avoid masking of the communication signal; Detrending to avoid spurious correlations; Systematic evaluation of the location of the quantification window to extract the most informative signal; Measurement of the temporal correlation as a quantitative readout for cell-ECM-cell communication. Our method provides technical advances that will open the door for cell biologists and biophysicists to decipher how cells process mechanical information transmitted through the microenvironment.30 and thus serves as a viable tool to control various parameters independently to test and verify the sensitivity and robustness of our approach. For example, examining the minimal cell contractile heterogeneity required to effectively quantify cell-ECM-cell communication  and a third measurement to decouple non-communication-related local ECM remodeling from real communication . These in silico controls combined with experimental validations verified that our method can sensitively and robustly measure cell-ECM-cell communication in fibroblast and cancer cells located up to a distance of ~8–10 cell-dimeters apart Figs. , and S9.13. Our method does not rely on the existence of a band, rather it correlates local ECM remodeling as a measure for “communication”, the unique mechanical signal propagating from one cell to its communication partner. We demonstrate the sensitivity of correlating ECM fluctuations by establishing that the formation of a denser ECM region between the cells is not required for cell-ECM-cell communication, thus decoupling fiber densification and long-range mechanical intercellular communication. In fact, we find that the fibrous band is a confounder to measure the unique communication signature between a pair of cells  or biological  hydrogels, by embedding tracer particles that will enable the indirect measurement of local ECM remodeling fluctuations. Another exciting venue is deciphering the role of long-range intercellular communication in the more complex and physiological relevant microenvironments in vivo. A third extension of our method would be to apply it to other modes of cell-cell communication. For example, using intracellular molecular fluctuations as the functional readout to infer cell-cell communication, an approach previously taken in38 to reveal signaling ordering at the intracellular scale.Our proof of principle study is an enabler of mechanistic understanding of long-range cell-cell mechanical communication, and sets the ground for potential applications. Having the ability to quantitatively measure cell-ECM-cell communication, could enable to systematically probe the cellular and molecular players for this mode of mechanical communication, naturally starting with the actomyosin cytoskeleton and adhesion complexes39. Another application is high content 3D image-based screening. Image-based phenotypic screening is traditionally applied with 2D imaging, quantifying single cell morphology and distributions of intracellular fluorescent intensities, and is applied for multiple applications including identification and characterization of small molecules in drug discovery40. Recent tools enable high-content 3D image-based cell phenotyping41, providing a more physiologically relevant context for in vivo follow-up studies42. The interplay between 3D cell morphology, the interactions with the environment and the mechanical communication with other cells will likely provide important complementary functional readouts for 3D image-based phenotypic cell phenotyping that are not accessible with current methods. Such phenotyping could be very useful for applications where the cellular microenvironment and the interactions between cells and the ECM are established hallmarks, such as in cancer or fibrosis45.One application where the ability to precisely measure cell-cell communication may be useful is tissue engineering. By controlling the patterning of multiple cell types one could optimize tissue formation according to cell type specific communication capabilities31. We used the finite element software Abaqus/CAE 2017 (Dassault Systèmes Simulia) to model the network mechanics and perform the simulations. The software’s standard\implicit solver was used in all simulations. The cells were modeled as circular voids in the fiber networks. The fibers were represented by discrete one-dimensional elements connected by nodes  and toward homogeneous fiber density. Briefly, we devised a random process to create network geometries, as we previously reported in ref. 12. The process starts from uniformly scattering nodes in a circular domain. The nodes were then connected by fiber elements by considering an objective cost function which controls the fiber length, fiber connectivity  and the angle between fibers connected at each node. The network coordinates spanned from −2 to 2 (AU) in X- and Y-axis . Stiffening was achieved by an exponential increase in the elastic modulus for tensile strains larger than 2%49. In all simulations, the outer boundary of the network was fixed for translations and rotations.Fibers were connected to one another by nodes, which acted as a freely rotating hinge, allowing for a rotation of the fibers without resistance. The fibers were modeled as linear truss elements, undergoing uniaxial tension or compression, without bending. They were characterized by nonlinear behavior typical to ECM fibers (such as collagen), including buckling under compression50.To assess the validity of the method for fibrous ECM networks with varying mechanical properties, we performed additional simulations in which we tested the effect of changing the stiffening behavior to account for a material with a relative linear behavior until 15% strain, representative of fibrin gels Fig. , based oμ) of 1% and varying standard deviations (σ) of 0, 0.25, 0.5, or 0.75, in each simulation step. In all simulations, the network size was set to 50 cell diameters and the cells were placed in its center to prevent boundary effects.Cell contraction was modeled by applying a boundary condition of radial isotropic contractile displacements to all nodes constituting the cell boundaries, reaching up to 50% contraction of the cell radius. To simulate time we consecutively applied 1% cell contraction for 50 steps, reaching a final 50% cell contraction. Heterogeneity in cell contraction was implemented by applying cell contraction selected from a normal distribution with a mean  in 2D without responding to the propagated mechanical signal, is far from realistic biology. Still, this minimal implementation captures the essence of the experimental system in terms of its mechanical aspects (contractile units in fibrous nonlinear networks) and thus serves as a “clean” mechanical framework to examine a process that is inherently mechanical (propagation of forces between cells). Thus, these simulations allow us to obtain proof-of-concept to the ability of a correlation-based method to detect a mechanical signal that propagates in the ECM between two contractile elements. Within this framework, the simulations unambiguously demonstrate the power of correlation in detecting the propagation of a mechanical signal from one cell to the other, as well as the effect of cell-cell distance, cells contraction mismatch and the ability to detect leader-follows. Revisiting the minimal model to include components that are more realistic is not central to the current methodology study, and will be addressed in future studies.t drawn from α is a constant that varies between 0 and 1 and defines the level of leader-follower dependency: α = 0 is the case of independently contracting cells and α = 1 is the case where the follower cell repeats the leader’s contraction with 1 frame delay  contracted with a dependency on the other cell (the “leader”). The leader contraction at each time point was selected from a normal distribution see Fig. . In the Swiss 3T3 fibroblasts stably expressed GFP-actin  were cultured in DMEM supplemented with 10% fetal bovine serum, nonessential amino acids, sodium pyruvate, L-glutamine, 100 units/ml penicillin, 100 µg/ml streptomycin, and 100 µg/ml Neomycin, in a 37 °C humid incubator.51.Murine Hras mutated-transform cells have been generated by infecting normal epithelial cells of the tongue with mutated-HRAS and shTP53 as described in3.Fluorescent beads experiments: 10–14 µm polystyrene beads  were embedded in 20 µl fibrin gels  in concentration of 8 × 103 dead cells were mixed in 20 µl of fibrin .Dead cells experiments: dead cells were prepared by placing cells in a hot water bath at 65 °C for 30 min, 8 × 103 dead cells were mixed with 4 × 103 live cells. After gel polymerization, ethidium homodimer  was added to the medium and incubated for 30 mins at 20–25 °C. This resulted in staining the nuclei of dead cells.Co-culture live and dead cells within the same gel: 4 × 10Blebbistatin experiments: 2 ml medium containing 85 µM or 150 µM blebbistatin were added to cover the polymerized cellular gel.Alexa Fluor 546 carboxylic acid, succinimidyl ester (Invitrogen) was mixed with fibrinogen solution in a 7.5:1 molar ratio for 1 h at room temperature and then filtered through a HiTrap desalting column  packed with Sephadex G-25 resin, to separate the unreacted dye.2.GFP-Actin 3T3 fibroblast cells (8×103 cells) were mixed with 10 µl of a 20 U/ml thrombin solution . Then, 10 µl of a 10 mg/ml fluorescently labeled fibrinogen  suspension was placed in a 35-mm cover-slip bottom dish (MatTek Corporation) and mixed gently with the 10 µl cells suspended in thrombin. The resulting fibrin gel was placed in the incubator for 20 min to polymerize, after which, a warm medium was added to cover the gel. The fibrin gels had an approximate shape of half a sphere, attached to the bottom surface of a cover slip, with a gel height of ~ 2-3 mm, and cells were fully embedded in the 3D gel. From this point on, the gels including the cells embedded in them, were maintained in 37 °C 5% CO2 incubation chamber. Confocal z-stacks were acquired every 5-15 min for about 6 hours from cell seeding. We manually validated that the imaged cells did not undergo division to avoid the enhanced contraction during division. Confocal imaging inherently includes a tradeoff between the temporal resolution, the axial resolution and the number of imaged locations: higher temporal/axial resolution leads to the lower numbers of locations imaged leading to smaller numbers of communicating cell pairs per experiment. Experiments were imaged in three settings: (1) Temporal resolution of 15 mins, with 21 locations of images in resolution of 512 × 512 pixels (0.41 × 0.41 × 2 µm in X′Y′Z′) and 36 Z-slices. (2) Temporal resolution of 5 mins, with 9 locations in each and resolution of 256 × 256 pixels (0.83 × 0.83 × 2 µm in X′Y′Z′) and 40 Z-slices. (3) Temporal resolution of 21 mins, with 7 locations of images in resolution of 512 × 512 pixels (0.41 × 0.41 × 0.53 µm in X′Y′Z′) and 187 Z-slices, where X′, Y′ and Z′ are the microscopy axes. The spatial image resolution used in our study is not sufficient to localize individual fibers, but fine resolution is not required for our method, which uses the time series of the fluorescent intensity accumulated in a quantification window. We prioritized imaging large field of views over better spatial or temporal resolution to increase the number of cell pairs we can image in a single experiment and thus enhancing our statistical power. Moreover, the use of relatively low temporal and spatial resolutions are in fact an advantage. The ability to robustly detect cell-ECM-cell communication even at lower resolutions is yet another indication for our method’s sensitivity and the wide availability of standard confocal imaging in practically any academic institute further highlights the potential of democratizing cell-ECM-cell communication quantification.Pairs of cells were imaged with a Zeiss 880 confocal microscope, equipped with a 40X NA = 1.1 water immersion lens (Zeiss) and a 30 mW argon laser was used to image both the cells (GFP-Actin) and the fluorescently labeled-fibrin matrix with excitation wavelength of 488, and a separated emission spectrum for each. Throughout imaging, the gels with the contained cells were maintained in a 37 °C 5% CO52 “Bleach Correction” on the raw fiber channel with the “Histogram Matching” option. On the actin (cell) channel we applied a median filter (radius = 2) followed by a Gaussian blurring filter (sigma = 2), before segmenting the cells over time using the “3D Objects Counter” Fiji’s plugin53 . The cell’s center coordinates in 3D for each time frame was recorded and used for cell tracking. Custom Python code was used for the cell tracking, by identification of cells to track in the first time frame and simply assigning the nearest cell (in 3D Euclidean distance) in the next time frame to construct the trajectory. Shorter trajectories were recorded for cells that moved beyond the field of view during imaging. This simple approach was sufficient thanks to the sparsity of the cell seeding.We first applied Fiji’sZ axis, parallel to the microscopy axial plane (Z′) and perpendicular to the connecting axis  to slice the images from top to bottom in the XY axis perpendicular to the connecting axis between the cells, with a width of 15 µm, interpolating axial pixel values to match the spatial resolution in XY using bilinear interpolation. Finally, we averaged the pixel intensities across the slices using Fiji’s “Z project” (Projection type = “Average intensity”) to create the 2D visualization. To visualize single cells, we picked an arbitrary XY axis  with the same axial axis (Z). These visualizations were used for all experiments and all manual annotations .To visualize and quantify the 3D band between a cell pair we transformed the image to a new coordinate system that is defined in relation to the spatial relation between the pair. We transformed the image from the microscopy axes  to the following three new axes. The connecting axis, defined by the line connecting the cells’ centers  in the new connecting axis and in the Single cells do not have a preferred axis in 3D since they do not have a communicating partner. Thus, to quantify ECM density near single cells we sampled around each cell in 32 different orientations in 3D. 16 transformations were defined using all paired combinations of four angles , each transformation pair was applied similarly to the transformations in cell pairs. For example, the pair <45,135> implies first rotating the image at 45° in X′, Y′ . Based on previous studies that focused solely on cell pairs with a visible band of increased density between themTo quantify the local ECM density we used a quantification window of the size of a cell diameter in all axes, in 2D simulations (0.08 AU) or 3D in experiments (15 μm). This window size was set to optimize the tradeoff between including sufficient data versus too much irrelevant data within the window Fig. . The numt, t, t.The local fiber density was calculated as the accumulated fiber volume within the quantification window. We assume that the fiber volume is preserved even when the fiber is remodeled. However, this property does not hold in the simulated 2D representation of the fibers where their buckling property reduces the simulated fiber lengths. This is an inherent limitation of simulating a 3D process in 2D. To overcome this limitation we normalize each fiber to its initial length before summing the fiber length in the quantification window. More specifically, we considered two scenarios Fig. . (1) ForThe fiber density within a quantification window was defined as the accumulated fiber volume within it. For single cells the mean fiber density of four windows, above, below, to the left and to the right of the cell was recorded Fig. .The mean fluorescent fibrin channel intensity was used as a proxy of fiber density within the transformed 3D quantification windows (see earlier). Quantification windows with over 5% of pixels extending beyond the image boundaries were marked as “invalid” and were excluded from further analysis. Quantification windows for single cells were calculated similarly to simulations, but in 3D, averaging the mean intensity in 32 orientations (see earlier).z-score - the number of standard deviations away from the mean background fiber density at quantification windows that were not influenced by the cells. Background quantification windows were defined for every location at the onset of simulation/imaging before (simulation) or where minimal (experiments) ECM remodeling occurred. To calculate the mean background fiber density we considered all quantification windows that did not intersect with the quantification window around the cells’ center  was performed on the connecting axis between the cells , the quantification windows were placed adjacent to cell boundaries in 2D (simulations) or 3D (experiments) within the cuboid along the connecting axis between the cells, see Fig. lls Fig. , or the invalid if one of the following criteria holds: (1) Frames with annotated imaging artifacts within the corresponding imaging locations  located above and below (in the Z-axis) the quantification window see Fig. . Each no54 and Augmented Dickey Fuller (ADF)55. The null hypothesis in the KPSS test is time series stationarity, while in the ADF test is time series non-stationarity. Temporal correlations were calculated using Pearson correlation on the derived time series.For correlation-based analysis we considered the longest sub-sequences with continuous valid time frames and mutual timestamps Fig. . We consTo establish that cell-ECM-cell communication of one cell pair can be distinguished from a second cell pair we tested whether the correlation between a cell pair (“same” pair) surpassed the correlation between one cell from that pair and another cell from a different cell pair (“different” pair) Fig. . This coTo control for misinterpreting non-communication related local ECM remodeling correlations as cell-ECM-cell communication we devised a standardized internal computational control that measures whether the correlation between a cell pair  surpassed the correlation between one cell from that pair and another “fake” cell (forming together a “fake” pair). The “fake” cell location was determined by following two constraints. The distance between the “fake” and its “real” partner is equal to the distance between the communicating cell pair, while maximizing the distance to other real cells. The first constraint was defined to allow comparable distance between the “real” and “fake” cells pairs. The second constraint was defined to minimize the effect of other cells in the vicinity. See depiction in Fig. For each cell that takes part in a cell pair we tested our ability to identify its matched communication partner from all the other cells in that experiment Fig. . This waThe contraction of the cells in the gel lead to local ECM correlations in the fibrous network, even in cell-free areas. To further verify that our results in the same-versus-different pair and matchmaking analyses were not merely an artifact of these local ECM correlations in the fibrous network, we compared the correlation of communicating cell pairs to ECM remodeling correlations in quantification windows that were placed in cell-free, fibrous areas. The intuition behind this control experiment was to consider correlations in quantification windows that measure the proximity-component, or the local mutual ECM fluctuations, without the influence of the communicating cells. These controls were performed by manually annotating “fake” cell pairs, and analyzing them while “following” the corresponding “real” cells (Fig. t0, t1 and t2. When choosing t1, for example, the first 3 sampled time frames are To examine the sensitivity of our method to the temporal resolution we performed same-versus-different pair analysis for down-sampled time series. For the sake of completeness, all possible starting time frames were considered when setting the first time frame for sampling. For example, when down-sampling the temporal resolution from 5 to 15 mins per frame, there are 15/5 = 3 possible starting time frames: Each experiment was analyzed independently to avoid erroneous relations stemming from correlating two cells in different fibrous networks. Such correlations would be inherently lower when correlating ECM fluctuations between different networks versus in the same fibrous network, due to global network remodeling. Thus, correlating ECM fluctuations between different experiments will lead to erroneously optimistic results, which we avoided here by considering all possible combinations of “same”/”different”, “real”/”fake”, and matchmaking for each experiment independently. After analyzing each experiment independently we pooled all the results across experiments for statistical assessment. The non-parametric Wilcoxon signed-rank test was used for statistical analysis testing the null hypothesis that a distribution, such as of “same”-”different” correlations, was distributed around zero.Summary of all measurements over all experimental conditions are reported in Table p-values obtained, etc. can be found in the relevant Fig. legend. Processed data is available and can be used for repreducibility.All statistical data such as the test used, number of cells in the test, the distances between the cells, α: μ = 1, σ = 0.5, and α indicates the “followership” magnitude, higher values of α imply that the follower is more influenced by its leader contraction.Finite-element simulations with a predefined leader and follower were examined using cross-correlation analysis, measuring the correlation between two time series under different time lags. We generated simulations where one cell in a pair was predetermined as the “leader” and its communication partner as the “follower”. The “follower” cell “imitated” the “leader” cell contraction in the previous time step, thus the contraction of the “follower” cell is lagging one simulation round behind the “leader”. The magnitude of influence that the leader had on the following was defined with the parameter β: In a second simulation, we tested whether our approach can identify “leader” and “follower” even when the “follower” contracted more than the “leader”, but was still copycating the leader’s contraction with a time lag of 1 simulation round. The fold increase of the “follower’s” contraction was defined using a parameter Cross-correlation analysis was performed by comparing different time-lags to the cells’ time series and evaluating in relation to the simulation ground truth. Cross-correlation was also performed on experimental data, where ground truth was not available, but did not identify any leader/follower relations, either due to lacking temporal resolution or lack of leaders/followers in our dataset.See Tables Further information on research design is available in the Peer Review FileSupplementary FiguresDescription of Additional Supplementary DataTable S1Table S2Table S3Video S1Video S2Video S3Video S4Video S5Video S6Video S7Reporting Summary


Genome duplication occurs through the coordinated action of DNA replication and nucleosome assembly at replication forks. Defective nucleosome assembly causes DNA lesions by fork breakage that need to be repaired. In addition, it causes a loss of chromatin integrity. These chromatin alterations can be restored, even though the mechanisms are unknown. Here, we show that the process of chromatin restoration can deal with highly severe chromatin defects induced by the absence of the chaperones CAF1 and Rtt106 or a strong reduction in the pool of available histones, and that this process can be followed by analyzing the topoisomer distribution of the 2µ plasmid. Using this assay, we demonstrate that chromatin restoration is slow and independent of checkpoint activation, whereas it requires the action of transcription and the FACT complex. Therefore, cells are able to “repair” not only DNA lesions but also chromatin alterations associated with defective nucleosome assembly. Nucleosome position is critical for genome regulation, as it dictates DNA accessibility and therefore the processivity of essential processes like transcription, replication, DNA repair and homologous recombination. Accordingly, chromatin alterations may cause from a severe loss of cell fitness to lethality, and are associated with cancer, neurodevelopmental disorders and aging6. More specifically, chromatin assembly defects can lead to replication fork instability, generating DNA lesions that need to be repaired9.The structural unit of chromatin is the nucleosome, formed by 147-bp of DNA wrapped around an octamer of core histones. Nucleosomes are not randomly distributed on the genome but occupying preferential positions with respect to the DNA sequence. These positions are established by the DNA sequence composition and the action of chromatin remodelers, general regulatory factors (GRFs) and transcription10. Replication-coupled (RC) nucleosome assembly involves the action of histone chaperones that interact with replisome components to incorporate both parental and newly synthesized histones into the nascent strands12. In yeast, the chaperone Asf1 presents newly synthesized H3/H4 dimers to the acetyltransferase Rtt109 for its acetylation at H3K56, a modification that enhances the affinity of the dimer for the histone chaperones CAF1  and Rtt10616. These two chaperones play redundant roles in the deposition of new histones, and only the lack of both complexes strongly affects this process16. However, a double mutant lacking CAF1 and Rtt106 is not significantly affected in cell growth, which is explained by the action of the FACT (facilitates chromatin transcription) complex (formed in yeast by the histone chaperones Spt16 and Pob3)18.Nucleosomes are assembled during S phase through a process that is coupled to DNA synthesis, with the first nucleosome deposited ~ 250-bp behind the replication fork19. These chromatin-disruptive activities are required for the advance of the replisome and facilitated by the process of parental histone recycling21. It was early observed that the newly assembled differed from the bulk chromatin24, reflecting the need to recover the original chromatin features in order to maintain genome functionality. This process, termed chromatin maturation, involves both nucleosome repositioning and recovery of parental histone marks diluted by the incorporation of new histones, and has been studied in the last years in yeast, Drosophila and mouse embryonic stem cells (mESCs) using genome-wide approaches that compare nascent and mature chromatin33. These studies have mostly focused on the chromatin organization of the gene units, as both transcription factors (cis and trans) and transcription activity are major determinants of nucleosome positioning34. A conserved feature across eukaryotes is the presence of a nucleosome-free region (NFR) at promoters flanked by two well positioned nucleosomes (− 1 and + 1), and a regularly spaced nucleosome array downstream the + 1 nucleosome with fuzzier positions as nucleosomes are more distant from the promoter35. Chromatin organization at transcription units is lost at nascent chromatin in all analyzed cells, with gaining of nucleosomes at the NFRs and global loss of nucleosome positioning. This suggests that RC-chromatin assembly outcompetes DNA binding proteins, including transcription factors, offering an opportunity to modify chromatin patterns30. The recovery of matured nucleosome patterns at promoters and enhancers in Drosophila and mESCs takes from 30 to 120 min, and at least in mESCs it requires active transcription except for enhancers with nucleosome destabilizing DNA sequences29. In yeast, this process is faster (less than 5 min), does not require transcription (likely due to the presence of AT-rich DNA sequences at its promoters), and is associated with the binding of GRFs32. In contrast to promoters, chromatin maturation at the gene bodies is slower and requires transcription elongation both in yeast and mESCs32.Paradoxically, building up chromatin at nascent strands during DNA replication is associated with the disruption of parental chromatin ahead of the fork36. This likely reflects the capacity of cells to buffer huge oscillations in gene expression and deal with high levels of genetic instability. However, some of the studies on chromatin maturation have shown that cells are able to partially restore chromatin alterations induced by the absence of CAF1, Asf1 or Rtt10937. Here, we have used a plasmid-based topological assay and genome-wide nucleosome profiling to show that cells are able to restore the highly severe loss of chromatin integrity induced in cac1∆ rtt106∆ and histone-depleted mutant cells. Furthermore, we show that chromatin restoration is facilitated by the action of transcription and the FACT complex.Unexpectedly, yeast cells are viable under conditions that cause severe chromatin integrity defects, as the absence of both CAF1 and Rtt016 or a strong reduction in the pool of available histones38. This loss of negative supercoiling is due to the fact that the assembly of each nucleosome introduces one negative superhelical turn39. This topological change can be detected by analyzing the distribution of topoisomers of a plasmid in chloroquine-containing gels, and has been extensively used to address chromatin alterations in vivo and in vitro44. Specifically, the loss of negative supercoiling in histone-depleted yeast cells can be detected by analyzing the endogenous 2µ plasmid in a strain in which the only source of histone H4 is under control of the doxycycline-regulated tet promoter . These repeats can recombine leading to equal amounts of two plasmids that differ in the orientation of one unique region with respect to the other  . It is worth noting that t::HHF2 and cac1∆ rtt106∆ shared similar chromatin alterations; high accessibility of the nucleosomal DNA and similar modified bands.These results suggest that the alterations in chromatin structure induced by defective histone deposition occur transiently during S phase and are post-replicatively restored. To confirm this, we analyzed the pattern of nucleosomes in the 2µ plasmid by indirect-end labelling of MNaseI-treated cells at different times during the cell cycle. We focused on the chromatin structure of an ter Fig. D. These 48, which allows nucleosomes to be mapped along the whole genome. We analyzed the nucleosomal landscape of cac1∆ rtt106∆ and wild type cells both in G1 and G2 phases (60 min after G1 release) to allow completion of genome replication. The absence of CAF1 and Rtt106 during DNA replication caused severe defects in the distribution of nucleosomes in G2  followed by dynamic analysis of nucleosome position and occupancy by sequencing (DANPOS) G2 Fig. A, consis31. In contrast, the cac1∆ rtt106∆ mutant showed a significant drop in the occupancy of nucleosome − 1 in G1 relative to the wild type strain 53. We observed that a triple mutant cac1∆ rtt106∆ mec1∆, defective in DDC activation, was proficient in the recovery of negative supercoiling 54. Since transcription was essential to exit from G1 . After the shift, cells were maintained at restrictive temperature for 90 min. The absence of transcription post-replication did not affect the pattern of plasmid supercoiling during the cell cycle . The loss of negative supercoiling in the triple mutant cac1∆ rtt106∆ rpb1-1 was less pronounced than in the double mutant cac1∆ rtt106∆ , even though a slight recovery was observed in the triple mutant at later times . Therefore, transcription helps to restore the loss of chromatin integrity associated with defective RC-nucleosome assembly.Transcription activity helps to correctly position nucleosomes G1 Fig. A, cells 55. First, we addressed the role of the HIR complex , which has been involved both in chromatin maturation and restauration of cac1∆-associated chromatin defects28. The absence of the HIR complex in cells lacking its major subunit (Hir1) did not prevent the recovery of plasmid negative supercoiling in cac1∆ rtt106∆ cells  and released at semi-permissive temperature (31 °C) until the following G1 phase, which required different times for each strain. The logic behind is to allow a complete restoration of the cac1∆ rtt106∆-induced chromatin defects. At this semi-permissive temperature, plasmid topology was hardly affected in the spt16-G132D mutant  and Drosophila (Caf1-105 knockdown)37, although the mechanisms of restoration are poorly understood. In yeast, asf1∆ and rtt109∆ mutants delay histone deposition due to a lack of H3K56 acetylation that reduces histone delivery to CAF1 and Rtt106. Accordingly, chromatin is less affected in asf1∆ and rtt109∆ than in cac1∆ and rtt106∆ mutants25. Yet, chromatin assembly defects in cac1∆ and rtt106∆ mutants are buffered by the action of Rtt106 and CAF1, respectively16. Here, we have shown by genome-wide MNase-seq that the severe loss of chromatin integrity induced during S phase by the absence of CAF1 and Rtt106 is largely restored post-replication, and that the process of chromatin restoration can be followed by analyzing the level of plasmid supercoiling along the cell cycle. This provides an alternative to the more expensive and time-consuming MNase-seq assay to screen for genetic requirements of the chromatin restoration process. Using this plasmid topology assay, we have shown that cells are able to restore even the severe chromatin defects induced by a strong reduction in the pool of available histones, and that chromatin restoration is facilitated by the action of transcription and the FACT complex. We have focused on the monomeric and not in the multimeric forms of the 2µ plasmid to study the connection between DNA topology and chromatin alterations, thus minimizing template-specific effects. In any case, chromatin dynamics is influenced by the structural and functional particularities of the analyzed regions, and therefore a deeper characterization will require genome-wide approaches.The efficiency of the nucleosome deposition process during DNA replication eliminates chromatin characteristics, which are recovered through a maturation process that depends on DNA sequence composition, GRFs and chromatin remodeling factors, and transcriptioncac1∆ rtt106∆ cells during DNA replication become restored in G1. However, chromatin was more altered in G2 than in S phase, which is consistent with the severe genome-wide chromatin assembly defects remaining in histone-depleted cells arrested in G2/M57. These results suggest that chromatin restoration is slower than chromatin maturation, which is completed in 5–20 min after replication fork passage28, yet highly efficient even under conditions that strongly disrupt the chromatin landscape as those induced in cac1∆ rtt106∆ and t::HHF2 mutants. The accumulation of chromatin alterations and their “repair” during chromatin restoration can also be detected by following the distribution of plasmid topoisomers in RC-nucleosome assembly mutants during the cell cycle. These mutants display a strong and transient loss of plasmid negative supercoiling during the cell cycle as a consequence of RC-chromatin disruption. In contrast, the population of plasmid topoisomers does not change in the wild type, which reflects the speed and efficiency of the chromatin maturation process. Therefore, this assay allows to specifically follow chromatin restoration.Our genome-wide analysis shows that most chromatin assembly defects generated in 53. We show that this arrest is not required for chromatin restauration, although it likely provides time to coordinate this process with the repair of the DNA lesions that will activate the checkpoints.A common feature of chromatin assembly mutants is a metaphase arrest triggered by the activation of the DDC and/or SAC34, and it is required for chromatin maturation after DNA replication32. We show that transcription facilitates chromatin restauration. The genome-wide chromatin analysis showed that the loss of nucleosome phasing at G2 in the cac1∆ rtt106∆ mutant mainly affected the gene bodies, as previously observed in histone-depleted cells in G2/M57. The analysis of nascent chromatin at early time points in a cac1∆ mutant showed nucleosome defects both at promoters  and gene bodies (loss of phasing)28. Therefore, the promoter architecture of the cac1∆ rtt106∆ mutant is likely first reconstructed to prime active transcription and restore chromatin in the gene body as proposed for chromatin maturation in yeast, where the rapid binding of GRFs at promoters generate molecular landmarks that fix the positions of flanking nucleosomes30. This mechanism is also likely necessary during the restoration of a severely altered chromatin in order to provide a rule for the transcription machinery to properly reposition nucleosomes during elongation. However, it is unlikely that this process resembles chromatin maturation in the initial steps. During chromatin maturation, restructured promoters with bound GRFs are still refractory to RNAPII recruitment31, which explains why transcription is buffered for a while after replication59. In contrast, defective chromatin assembly in asf1∆ and cac1∆ rtt106∆ cells causes a transient accumulation of aberrant coding and non-coding transcripts behind the replication forks37. This effect is more pronounced in the cac1∆ rtt106∆ mutant, likely because of its higher nucleosome deposition defects and the role of Rtt106 in preventing aberrant transcription50. We speculate that transcription from spurious initiation sites may slow the process of chromatin restoration because of the repositioning of nucleosomes without a correctly defined reference.Transcription activity is a major determinant of nucleosome position27. Less clear is the relationship with transcription for the HIR complex27, a chromatin remodeler that participates in replication-independent histone turnover, preferentially at intergenic regions61. The study of bulk nucleosome organization has also pointed to a role for the HIR complex in the restoration of cac1∆-induced nucleosome assembly defects28. Our plasmid topology assay did not reveal any role for the HIR complex in chromatin restoration in the cac1∆ rtt106∆ mutant. Although the difference might be plasmid-specific, it cannot be excluded that the loss of nucleosome phasing in cac1∆ hir1∆ cells reflects an additive effect of the absence of both complexes, as the hir1∆ mutant by itself displayed a reduction in the amplitude of the nucleosomal oscillation on coding regions28.The requirement of transcription elongation for chromatin maturation supports a role for chromatin remodeling complexes traveling with RNAPII like CHD1 and ISW1b. In agreement with this possibility, nascent chromatin-associated alterations persist in the absence of these chromatin remodelers66. We have observed that the spt16-G132D mutant has no defects in the distribution of plasmid topoisomers but prevents the recovery of the negative supercoiling level lost during DNA replication in a cac1∆ rtt106∆ mutant at semi-permissive temperature (31 °C). Therefore, the amount of Spt16 at this temperature seems to be sufficient to avoid a loss of nucleosomes during transcription but not to restore defective chromatin assembly. This suggests that the mechanism by which FACT restores chromatin is either different or requires more Spt16 than the mechanism by which FACT redeposits nucleosomes during transcriptional elongation. FACT is targeted to chromatin by recognizing the surface of disrupted nucleosomes generated mainly—but not exclusively –by transcription68. This observation, together with the ability of FACT to assemble nucleosomes led to Formosa and Winston to propose a role for FACT in the “repair” of disrupted nucleosomes69. It is likely that the dependency on transcription of the chromatin restoration process in cac1∆ rtt106∆ cells reflects the need to disrupt nucleosomes to target FACT, which would be required at higher levels than in the wild type strain to additionally cope with displaced nucleosomes. An alternative but not exclusive possibility for the higher demand of Spt16 during chromatin restoration is that not only the position but also the integrity of some nucleosomes become affected in cac1∆ rtt106∆ cells, targeting FACT in a transcription-independent manner.FACT is a nucleosome remodeler complex with a critical role in nucleosome repositioning during transcription elongation. FACT travels with the RNAPII promoting the redeposition behind RNAPII of the original nucleosomes evicted during elongation through a stepwise mechanism of nucleosome disassembly-assembly that helps to maintain the epigenetic identityIn summary, cells are able to largely restore a severe loss of chromatin integrity induced under conditions of defective nucleosome assembly, providing a mechanism to buffer its impact on cell fitness. In addition, using plasmid topology as an easy and specific assay to study chromatin restoration, we have shown that this process requires the action of both transcription and FACT. This assay may help to uncover additional factors involved in chromatin restoration, as a previous step to a more detailed genome-wide characterization.rpb1-1, spt16 and Gp::CDC6 strains) or supplemented minimal medium (SMM) (rest). For metaphase synchronization, cells were treated with 15 µg/ml nocodazole for 1 h. For G1 synchronization, cells were grown to mid-log-phase and α factor was added twice at 90 min intervals at 0.5 μg/ml, except for t::HHF2 strains (treated with 1 μg/ml) and rpb1-1 strains . Cells were then washed three times and released into fresh medium with 50 μg/ml pronase. For G1 resynchronization, cells released into S phase were washed and resuspended in fresh medium with α-factor at 1 μg/ml (t::HHF2 strains) or 0.5 μg/ml (rest of strains) until G1. To induce nucleosome depletion, t::HHF2 cells growing in the presence of 5 µg/ml doxycycline were shifted to 0.25 μg/ml during G1 synchronization and release. Cdc6 depletion was performed as previously described70. Briefly, Gp::CDC6 cells were synchronized in metaphase in 2% galactose-containing medium with 1% DMSO and 15 µg/ml nocodazole for 2 h, shifted to 2% glucose-containing medium with DMSO and nocodazole for 2 additional hours, synchronized in G1 in 2% glucose-containing medium with α-factor for 2 h, and released into fresh 2% glucose-containing medium with 50 µg/ml pronase for 1 h.Yeast strains used in this study are listed in Table 36. Cells were fixed with 70% ethanol, washed with phosphate-buffered saline (PBS 1X), incubated with 1 mg of RNaseA/ml PBS, and stained with 5 µg/ml propidium iodide. Samples were sonicated to separate single cells and analysed in a FACSCalibur flow cytometer.DNA content analysis was performed by flow cytometry as reported previously36. Briefly, total DNA was extracted using a zymolyase-SDS standard protocol and run into 0.8% TPE 1 × agarose gels containing 4 μg/ml chloroquine for 36 h at 1.6 V/cm with recircularization. Negatively supercoiled topoisomers are resolved at this chloroquine concentration. Gels were blotted onto Hybond™-XL membranes and hybridized with a 32P-labeled FLP1 fragment amplified by PCR from genomic DNA with oligos 5′-tgattacacataacggaaca-3′ and 5′-ttcagcactaccctttagc-3′. Signals were acquired in a Fuji FLA5100 and quantified with the ImageGauge analysis program. The total DNA signal (area under the curve) of the raw topoisomer profiles was equalized to eliminate DNA loading differences.The distribution of topoisomers of the 2µ plasmid was performed as previously described57. Briefly, cells were fixed for 15 min with 1% formaldehyde. Glycine was added to quench the reaction at a final concentration of 125 mM. Cells were sedimented, washed twice with cold PBS and stored at − 80 °C until use. Extracts for MNase digestion were resuspended in 1 M sorbitol/50 mM Tris HCl and digested for 1 h at 30 °C with 4.5 mg of zymoliase 20 T (AmsBio 120491-1) in gentle shaking. Samples were washed first with cold solution I  and then with cold solution I plus 0.1 mM PMSF, then resuspended gently in cold solution II , and finally treated for 20 min at 37 °C with different concentrations of MNase (SIGMA N3755). The reaction was then stopped by adding 3 mM EDTA/4 mM Tris HCl and 10% SDS. To revert crosslinking, samples were incubated for 90 min at 37 °C with 1.5 mg of proteinase K and then overnight at 65 °C. DNA was extracted from samples using a standard phenol–chloroform extraction, treated with RNase A and loaded in a 1% agarose gel to check MNase digestion. MNase digestions used for indirect end labelling were incubated with EcoRI, resolved in 1.5% agarose gels, blotted onto a HybondTM-XL membrane and probed with a 32P-labeled specific PCR fragment located close to one of the EcoRI sites (oligos 5′- ataccaattcctcttcctag-3′ and 5′-tccaaatatacaagtggatc-3′). Signals were acquired in a Fuji FLA5100 with the Image Gauge analysis program.Chromatin analyses by MNaseI and indirect-end labeling were performed as previously described57. Briefly, MNaseI–digested DNA samples from two (G1 and G2) or one (S phase) biological replicates for each yeast strain were obtained as previously indicated for indirect-end labelling. MNase digested samples enriched in mononucleosomes were loaded in a 1% agarose gel, and the DNA corresponding to mononucleosomes was purified with a DNA purification kit . The DNA size and quality was confirmed by an electropherogram analysis . Library construction and sequencing was performed at Genomics Core Facility of CABIMER. DNA libraries were prepared from 10 ng mononucleosome DNA using the TruSeq Chip Library Preparation kit (Illumina), and the size distribution and molarity of each library were analyzed with the Agilent™ DNA High Sensitivity Kit . DNA libraries were sequenced on the NextSeq 500 Sequencing System (Illumina), and raw data were processed for basecalling, filtering and trimming to generate the FASTQ files using the BaseSpace Onsite v3.22.91.158 Software de Illumina. Sequence reads were mapped to S. cerevisiae genome sacCer3 by BowTie271, and potential PCR duplicates were removed by SAM Tools on the Galaxy platform 72. The peak-calling algorithm Dpos function (DANPOS 2.2.0)73 was used for nucleosome occupancy maps and comparative analyses using default parameters. Average nucleosome occupancy patterns flanking transcription start sites (TSS) from one 73.Chromatin analyses by MNase-seq were performed as previously describedone Fig. A or two  G1 and G or one 75 and resolved on a 8% SDS-PAGE. Rad53 was detected by standard western blot analysis with the rabbit polyclonal antibody JDI4876.Yeast protein extracts were prepared using the TCA protocolSupplementary Information.


We aimed to further improve knowledge about volar plate (VP) motion of the finger proximal interphalangeal joint (PIP), by analyzing the dynamic VP shape during a full range of finger flexion using magnetic resonance cinematography of the fingers (MRCF), and to compare the results with anatomical cross sections from cadaver specimens.t-test or rank-sum testing. Anatomical cross sections with differing degrees of PIP joint flexion were obtained from 12 cadaver specimens (fingers) for comparison.The dynamic sagittal VP shape was visualized with MRCF in a total number of 23 healthy volunteers. The length, angle, and thickness as well as the contact length of the VP to the PIP joint base were measured. Statistical analysis included Significant positive correlations between PIP joint flexion angle and VP area, length, depth and the VP contact length were found. This matched histologically to fiber rearrangements especially within the loose third VP layer.Our study analyzed the full range of motion dynamic VP shape of the PIP joint using MRCF. This contributes to a more precise understanding of the complex interaction of the VP with the PIP joint and may facilitate evaluation of clinical cases such as VP avulsion or pulley rupture.The online version contains supplementary material available at 10.1007/s00256-023-04288-6. The volar plate (VP) of the proximal interphalangeal (PIP) joint has utmost importance for the physiological interaction of flexor tendons and joints during finger flexion , 2. It eThe VP integrity may be affected in various everyday injuries such as PIP joint distortion, avulsion, or pulley rupture. Numerous conservative and surgical techniques have been described, including pulley graft and direct pulley suturing for pulley rupture and direct VP suturing with or without the use of bone anchors and the tenodesis technique with flexor digitalis superficialis (FDS) for VP avulsion –11. HoweHistologically, the VP consists of a membranous proximal more flexible part and a cartilaginous thicker and stiffer distal part, which is also referred to as the VP body , 6. SeveStudies analyzing the dynamic VP shape in healthy volunteers during a full range of motion with systematic admission of metric parameters are pending, because no imaging modality could provide the specific requirements for such an analysis. Recently, functional magnetic resonance cinematography of the fingers (MRCF) became available as a new imaging technique . In compA total number of twenty-three consecutive healthy volunteers  were enrolled between May 2017 and July 2018. Four index fingers, 12 middle fingers, and 7 ring fingers were analyzed (one finger from each volunteer). Selection of the examined finger was by random. Exclusion criteria were any history of acute or overuse injuries on the examined hand and prior surgical procedures on the examined upper extremity.Every participant was examined according to full range of motion in finger joints. A 3-Tesla (T) whole-body scanner  with a maximum gradient amplitude of 45 mT/m and a rise time of 225 μs was used. A foot and ankle 8-channel phased array receiving coil  was applied to allow space for the finger movement. A custom-made wooden fingergating device with two individually adjustable sidewalls for finger width adjustment was used as described before . Each inhttps://imagej.nih.gov/ij/), scaled according to the dimensions of the receiving coil, cropped, and finally, magnified (5 ×). Index, middle, and ring finger images were pooled. The analysis included all images of the first complete movement from extension to flexion for each individual. For each image, the flexion angle in the PIP joint was measured with the Fiji ImageJ Angle Tool. Therefore, both the proximal and the middle phalanx were longitudinally marked from the center of the proximal joint line to the center of the distal joint line. The angle between these two lines was considered the PIP joint flexion. Images were separated into 12 ten-degree bins according to their PIP joint flexion angle from 0 to 9.99°, 10 to 19.99°, 20 to 29.99°, 30 to 39.99°, …., 110 to119.99°. The corresponding labeling of the bins was for simplicity “ < 10°,” “ < 20°,” “ < 30°,” …, “ < 120°.”Images were then exported as tagged image file format sequences (.tiff files) and imported in Image-J software , and Feret min (depth). Feret diameters  were measured automatically via ImageJ  , while the Feret max measures the largest distance between the imaginary lines  ) were considered as control group. N = 421 images corresponded to the first full extension–flexion movement of all participants (18.3 ± 8 images per volunteer). All datasets (N = 421/421) were free of relevant artifacts and could be used for analysis. Overall, a total number of 2105 individual measurements were performed. Within the twelve groups of PIP joint flexion angles, the average number of images was 35.1 ± 8.7. Table A total number The area covered by the volar plate in a mid-sagittal plain in various PIP joint flexions is presented in Fig. During flexion, the shape of the VP changed. During this process, it formed various characteristic shapes, which merge smoothly into another. In the sagittal plane evaluated here, the structure assumes an elongated and thin comma shape in extension Fig. A. Up to length and depth distribution of the VP as a function of the flexion position of the PIP joint. Length averages 6.8 ± 0.7 mm and depth averages 2.4 ± 0.5 mm when the finger is extended (< 10° PIP joint flexion). Length was found to remain largely constant throughout the range of motion while depth increased steadily to about 80° of flexion. Above 50 degrees of flexion, depth was found to be significantly larger compared to depth in extension  . We encourage future studies to include new techniques such as MRCF in the evaluation of acute or chronic pathological conditions for a better prediction of postoperative outcome in clinical collectives.In summary, this study is the first metric analysis of the physiological full range dynamic sagittal shape of the VP in real time using new MRCF techniques. This may contribute to a more comprehensive knowledge of the VP interaction with the PIP joint, which also could facilitate understanding of injuries and treatments in clinical cases such as pulley rupture or VP avulsion.Supplementary file1 (MP4 2197 KB)Below is the link to the electronic supplementary material.


Thus, our ability to discern the societal roles of Pleistocene individuals based on their biological sex or genetic ancestry is limited5. Here we report the development of a non-destructive method for the gradual release of DNA trapped in ancient bone and tooth artefacts. Application of the method to an Upper Palaeolithic deer tooth pendant from Denisova Cave, Russia, resulted in the recovery of ancient human and deer mitochondrial genomes, which allowed us to estimate the age of the pendant at approximately 19,000–25,000 years. Nuclear DNA analysis identifies the presumed maker or wearer of the pendant as a female individual with strong genetic affinities to a group of Ancient North Eurasian individuals who lived around the same time but were previously found only further east in Siberia. Our work redefines how cultural and genetic records can be linked in prehistoric archaeology.Artefacts made from stones, bones and teeth are fundamental to our understanding of human subsistence strategies, behaviour and culture in the Pleistocene. Although these resources are plentiful, it is impossible to associate artefacts to specific human individuals A non-destructive DNA isolation method for the stepwise release of DNA trapped in ancient tooth and bone artefacts is developed. Thus, it can be challenging to associate human remains with specific objects. Recent advances in the retrieval of human DNA from sediments8 can be used to connect artefacts with genetic populations. However, precise identification of the specific makers or users of these objects would require the recovery of human DNA directly from the objects themselves, analogous to modern-day forensic investigations. In theory, such analyses are most promising for artefacts made from animal bones or teeth, not only because they are porous and thereby conducive to the penetration of body fluids  but also because they contain hydroxyapatite, which is known to adsorb DNA and reduce its degradation by hydrolysis and nuclease activity10. Ancient bones and teeth may therefore function as a trap not only for DNA that is released within an organism during its lifetime and subsequent decomposition but also for exogenous DNA that enters the matrix post-mortem through microbial colonization11 or handling by humans. However, DNA extraction from ancient skeletal material either requires destructive sampling, or risks alteration of specimens if they are directly submerged in extraction buffer13. Conservation is a primary concern because of the scarcity of bone and tooth artefacts at Pleistocene sites, especially of pendants and other ornaments that were extensively handled or worn in close body contact. We therefore set out to develop a method for DNA isolation from bones and teeth that preserves the integrity of the material, including surface microtopography, and to investigate the possibility of DNA retrieval from bone and tooth artefacts.Palaeolithic assemblages typically contain a multitude of objects that may differ in age by hundreds or thousands of years, even when found in close proximity12, (2) an ethylenediaminetetraacetate (EDTA) solution, which is a decalcifier commonly used in ancient DNA extraction16, (3) a sodium hypochlorite (bleach) solution, which is an oxidizing reagent used to remove surface-exposed contaminant DNA17, and (4) a sodium phosphate buffer supplemented with detergent11, which has been recently shown to enable temperature-controlled DNA release from powdered bone samples18.To identify reagents compatible with non-destructive DNA extraction, we selected ten unmodified faunal remains from the Palaeolithic sites of Quinçay and Les Cottés in France  DNA24. A metagenomic pipeline for assigning sequenced mtDNA fragments to mammalian taxa on the biological family level6 identified 1,628 cervid mtDNA fragments in the 60 °C and 90 °C fractions of object Q10, a reindeer bone As present-day human DNA contamination seemed to be ubiquitous on surfaces of objects that were handled with bare hands during and after excavation, we collected artefacts from ongoing excavations at two Palaeolithic sites, using gloves and facemasks as soon as they became partly exposed to prevent contamination. At Bacho Kiro Cave in Bulgaria, we recovered three Upper Palaeolithic tooth pendants (henceforth ‘BKP1–BKP3’) from layers I, H/I and I/J of niche 1 , as well as directly from the artefacts using the non-destructive method described above. Ancient mammalian mtDNA was detected in all fractions that were analysed, except for some of the water washes and the associated sediment pellets Fig. . The traThe phosphate DNA fractions obtained at 37 °C, 60 °C and 90 °C are dominated by ancient ursid mtDNA fragments for BKP2 and BKP3, and cervid mtDNA fragments for DCP1, in agreement with their morphological identification , but none of the other fractions. For DCP1, human mtDNA fragments with significant evidence for ancient DNA base damage were identified in the first two sediment pellets recovered from the water washes, the first 37 °C and 60 °C fractions, and all three 90 °C fractions , yielded 62-fold average coverage of the human mtDNA genome and a near-complete consensus sequence (Supplementary Information Cervus canadensis), at 635-fold coverage. A tree with eight additional ancient wapiti mtDNA genomes of known age generated in this study , with estimates of present-day human and faunal contamination both below 1%. Comparisons with present-day human populations26 using ƒ3-statistics and D-statistics28 show high affinities to Native Americans 39 layers in the niche 1 area of Bacho Kiro Cave41, as well as on one tooth pendant excavated in 2019 in layer 11 (39–24 ka), square E-3, of the south chamber of Denisova Cave  and Les Cottés (three) 12, samples were completely submerged in 10–15 ml guanidinium thiocyanate buffer  and incubated for 5 days in the dark.Following a protocol16. It is therefore expected to cause severe alterations to the sample material and was included to demonstrate the effect of such alterations on the quantitative 3D-surface texture analysis (3DST) measurements.Incubation in EDTA, supplemented with a detergent , was performed for 15 min at room temperature. EDTA is widely used in bone extraction protocols as a decalcifying agent to dissolve hydroxyapatite, the main mineral component of bones17. Bleach treatment destroys surface-bound DNA and is not suitable for DNA extraction. However, it may be used in later implementations of the method to remove contaminant DNA before non-destructive DNA extraction.Two samples were submerged for 15 min in 0.5% sodium hypochlorite (bleach) solution, a reagent previously used for decontamination of ancient skeletal remains18 by submerging the samples in sodium phosphate buffer . Serial incubations were performed at 21 (room temperature), 37, 60 and 90 °C, each with incubation times of 30 min for a total of three incubations per temperature.Temperature-controlled release of DNA was performed following a modified version of the method by Essel et al. (2021)We evaluated four reagents for their potential use in non-destructive DNA extraction by applying each to two Pleistocene samples comparable with the ones usually transformed into bone or tooth tools and ornaments, preferably choosing one bone and one tooth fragment for each reagent  to ensure complete submergence. Incubations above room temperature were performed in a Heating-ThermoMixer MHR 11 (Hettich Benelux) equipped with inserts for 50-ml Falcon tubes. For the 90 °C incubation in phosphate buffer, the temperature of the device was set to 99 °C to ensure that 90 °C was reached inside the tube by the end of the incubation time. After the treatments, all samples were placed in fresh 50-ml Falcon tubes and incubated in water for 1 h at room temperature to remove residual reagents. Samples were dried at room temperature for 5 days before they were returned to their storage containers.19 before and after extraction following established protocols43. Meshed axiomatic 3D models were generated and the following ISO 25178 parameters were used for statistical testing : mean roughness (Sa), void volume (Vvv), peak curvature (Spc) and peak density (Spd). Further details regarding the 3DST measurements are provided in Supplementary Information Changes in the microtopography of the bone or tooth objects were tracked using quantitative 3DST14C dating , potentially allowing inferences to be drawn about whether these components originate from traces of sediment that may still be adherent to the object, from its surface or its interior. We then applied this protocol to a total of 15 specimens from Quinçay, Bacho Kiro Cave and Denisova Cave. Temperature-controlled non-destructive DNA extraction was performed in an ancient DNA clean-room at the Max Planck Institute for Evolutionary Anthropology in Leipzig, Germany, according to the four steps below  were subsequently subjected to DNA purification (see below).This step was only performed for the freshly excavated material. First, clumps of sediment attached to the specimen were carefully removed by hand using a flexible disposable plastic microspatula. The specimen was then put into a 50-ml Falcon tube, rinsed by pouring between 20 ml and 50 ml of water into the tube, and transferred to a fresh Falcon tube. This procedure was repeated two to three times until no more sediment was released into the water. The tubes with water containing the sediment that had been washed off were then centrifuged at 16,400Each cleaned specimen was put into a 50-ml Falcon tube to which sodium phosphate buffer  was added until the specimen was completely submerged in the reagent (between 5 ml and 50 ml). After 30 min of incubation at room temperature (without agitation), the buffer was transferred to a fresh 50-ml Falcon tube. This step was repeated twice at room temperature  and then three times each at 37 °C, 60 °C and 90 °C (see above for details on the device and temperature settings used). A final incubation in water was performed at room temperature to remove residual reagent and the specimens were dried at room temperature for 5 days.g in a centrifuge with an active cooling unit set to 21 °C. In cases in which the sample volume exceeded 4 ml or 15 ml, the flow-through was discarded and the filter unit was reloaded with remaining sample. Finally, a buffer exchange was performed by adding 4 ml or 15 ml TE buffer  to the concentrated sample on top of the filter unit and spinning for 30 min at 4,000g. The supernatant was filled up to 300 µl with TE buffer  and then transferred into a fresh 1.5-ml Eppendorf low-bind tube and stored at −20 °C until further processing.To facilitate subsequent DNA purification, half of the volumes of the water and phosphate buffer DNA fractions generated in steps (1) and (2) were reduced to between 50 µl and 75 µl by concentrating the DNA using Amicon Ultra-4 Centrifugal Filter Units with Ultracel-3 membranes (Millipore). For this, up to 4 ml or 15 ml of the respective sample was added to a filter unit, which was spun for 90 min at 4,00044. For this, 300 µl concentrated DNA was used as input for DNA purification using binding buffer ‘D’ and the purified DNA was recovered in 50 µl elution buffer. DNA extraction from the sediment samples removed from the artefacts manually or through water washes (sediment pellets) was performed using the same method but different input volumes. In brief, lysates were prepared by transferring up to 130 mg sediment to a 2-ml low-bind Eppendorf tube, adding up to 2 ml lysis buffer  (1 ml for samples of less than 100 mg or 0.5 ml for samples of less than 25 mg) and incubating overnight at 37 °C under rotation. DNA purification was performed using 500 µl or 1,000 µl lysate.DNA was isolated from the concentrated DNA fractions prepared in step (3) using a column-based method for silica-based ancient DNA extraction detailed elsewhereNegative controls containing sodium phosphate buffer or lysis buffer without sample material were carried alongside the samples through all steps of subsequent sample preparation and sequencing. For a subset of samples, DNA was extracted from only the first phosphate buffer fraction produced at each of the four incubation temperatures. Supplementary Data 23. The number of unique library molecules obtained and the efficiency of library preparation were determined using two quantitative PCR assays45. Libraries were then amplified and double-indexed by PCR46. One library was prepared from each DNA fraction, except for the second and third 90 °C phosphate fractions from the DCP1 from which five libraries were prepared, each to maximize the yield of sequence data. Negative controls containing no sample material were carried along each set of extraction and library preparation. Note that there was substantial variation in library preparation efficiency across samples 02023. Th24. In cases in which several libraries were prepared from the same DNA extracts , only the first library was enriched for mammalian mtDNA. In addition, all libraries were enriched specifically for hominin mtDNA using a probe set (‘AA163’) designed in 1-bp tiling based on the revised Cambridge reference sequence of the human mitochondrial genome32. Both types of mtDNA captures were performed using two consecutive rounds of automated capture as detailed in Slon et al. (2017)6 or Zavala et al. (2022)47. An overview of the capture reactions performed is provided in Supplementary Data To determine the taxonomic composition of the DNA recovered from the artefacts, libraries prepared from nearly all extracts generated in this study  were enriched for mammalian mtDNA by hybridization capture with a probe set (‘AA75’) encompassing the mtDNA genomes of 242 mammalian species48. This enrichment was performed using a subset of a previously designed capture probe panel8, named AA204, targeting two groups of SNPs: (1) 59,232 SNPs, randomly selected from set 6 ‘hominin diagnostic sites’8, which represent positions in the genome where primates differ from other mammals and are used to quantify faunal mis-alignments; and (2) 411,492 SNPs selected from the ‘1240k’ panel49 8). This set of SNPs is informative for investigating modern human population histories. Both SNP groups are located in regions of large evolutionary sequence divergence between humans and other mammals8.In addition to mtDNA captures, 11 libraries prepared from the first, second and third 90 °C phosphate fractions of DCP1 were enriched for human nuclear DNA by two consecutive rounds of in-solution capturehttps://github.com/mpieva/leeHom/tree/v.1.1.5)50 was used to trim adapters and, for paired-end data, to merge overlapping paired-end reads.Libraries enriched for mtDNA were combined into pools and sequenced on multiple lanes of a MiSeq sequencer (Illumina Technologies) in paired-end configuration with two index reads (2× 76 + 2× 8 cycles). Libraries enriched for human nuclear DNA were sequenced either on a HiSeq 2500 using the same configuration or on a HiSeq4000 (both Illumina Technologies) in single-read configuration with two index reads (1× 76 + 2× 8 cycles). In addition, one library prepared from the second 90 °C phosphate fraction of the DCP1 was sequenced directly  on one lane of a HiSeq 4000 sequencer (Illumina Technologies) in single-read configuration. Base calling was performed using Illumina’s Bustard tool and sequences were assigned to the library that they originated from, requiring perfect matches to the expected index combinations. LeeHom (6 based on BLAST and MEGAN (version 0.0.12)51, with modifications in data filtering detailed in Vernot et al. (2021)8 53 and to identify ‘diagnostic’ positions in the mtDNA genome, which were used to determine the support for the consensus sequence in each of the DNA fractions recovered from DCP1 8 and data from the libraries with the lowest estimates of modern human contamination (based on AuthentiCT) were merged for further analyses. Principal component analysis including sequence data from present-day and other ancient human individuals was performed using smartpca (from EIGENSOFT package version 8.0.0)55. Using the R package admixr (version 0.7.1)28, ƒ3-statistics were calculated to determine shared genetic drift between DCP1 and a selection of modern and ancient human populations and their relationships were further evaluated using D-statistics. Sex determination for the human DNA component recovered from DCP1 was performed by comparing the coverage of the X chromosome and the autosomes in shotgun data obtained from the second 90 °C phosphate fraction after filtering against faunal mis-mappings8. Further details are provided in Supplementary Information Human DNA capture data were processed as previously describedFurther information on research design is available in the Any methods, additional references, Nature Portfolio reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41586-023-06035-2.Supplementary InformationReporting SummaryPeer Review FileSupplementary Data file 1Overview of DNA lysates, extracts and libraries prepared in this study and results of the mammalian and human mtDNA captures.Supplementary Data file 2Overview and summary statistics of the human nuclear DNA captures targeting 470,724 positions in the genome.Supplementary Data file 3Calculated D-statistics by ADMIXTOOLS via admixr using D where W is a present-day African and X is a present-day non-African. The values in this table are visualized in Supplementary Figure 7.5.Supplementary Data file 4Calculated D-statistics by ADMIXTOOLS via admixr using D where W is a present-day Americans and X is a present-day non-African/American. The values in this table are visualized in Supplementary Figure 7.6.Supplementary Data file 5Calculated D-statistics by ADMIXTOOLS via admixr using D where W is an ancient Siberian and X is an ancient American. The values in this table are visualized in Supplementary Figure 7.7.