BARCODE: high throughput screening and analysis of soft active materials

Domain: physics
Published: 2025-12-31
Editorial outcome: major_revision

Abstract
Active, responsive, non-equilibrium materials – at the forefront of materials engineering – offer dynamical restructuring, mobility and other complex life-like properties. Yet, this enhanced functionality comes with significant amplification of the size and complexity of the datasets needed to characterize their properties, thereby challenging conventional approaches to analysis. To meet this need, we present BARCODE: Biomaterial Activity Readouts to Categorize, Optimize, Design and Engineer, an open-access software that automates high throughput screening of microscopy video data to enable non-equilibrium material optimization and discovery. BARCODE produces a unique fingerprint or ‘barcode’ of performance metrics that visually and quantitatively encodes dynamic material properties with minimal file size. Using three complementary material-agnostic analysis branches, BARCODE significantly reduces data dimensionality and size, while providing rich, multiparametric outputs and rapid tractable characterization of activity and structure. We analyze a series of datasets of cytoskeleton networks and cell monolayers to demonstrate BARCODE’s abilities to accelerate and streamline screening and analysis, reveal unexpected correlations and emergence, and enable broad non-expert data access, comparison, and sharing.

Introduction
Biological activity manifests through a wide range of mechanical and dynamical features, such as stiffening, restructuring and flow. Examples include migrating slime molds, developing tissues, and the cytoskeleton, a protein network in living cells that undergoes dramatic remodeling during cell division, crawling, and wound healing1–7. Engineered materials can recapitulate many such features, and offer stimuli-responsiveness, patterning, and spatiotemporal control that could, in principle, be harnessed for applications such as self-healing infrastructure, dynamic prosthetics, and self-sensing protective barriers8–19. Like their biological analogs, engineered materials exhibiting such functionalities must be multicomponent and multiphase, with structures and dynamics operating over a broad range of length and time scales. For example, in vitro networks that recapitulate the properties of the cell cytoskeleton, such as contractility, self-organization, and responsivity to external stimuli, comprise filamentous proteins, such as actin and microtubules, enzymatically-active motor proteins, such as myosin and kinesin, and a host of crosslinking proteins12,20–30. Varying the network formulation and intermolecular interactions can tune the material dynamics and structure over decades of spatiotemporal scales20,25,29,31–33. Similarly, cells in vitro exhibit dynamics and restructuring similar to living tissues, such as jamming, flow, and collective patterns emerging on length scales much longer than the individual agents driving the motion34–39. This dynamic structural heterogeneity, foundational to function, challenges classic approaches to material design, characterization and deployment.

Complex active dynamics and restructuring present several challenges to developing predictive relationships between material formulation and performance, and to realizing tractable engineering designs. Video sizes and complexity limit data sharing and use of standard software to process images within manageable timeframes, often forcing valuable information to be ignored, discarded, or siloed. Active materials require a higher dimensionality of characterization metrics, which often emerge in unexpected ways and on spatiotemporal scales that are difficult to predict a priori. Inconsistencies in metrics, definitions, and approaches further hinder the identification of performance intersections among materials. These complexities demand readily accessible material-agnostic algorithms that enable rapid screening of large datasets and characterization of emergent dynamics in a manner that enables data- and physics-driven modeling and rational material design.

To address these challenges, we present BARCODE: Biomaterial Activity Readouts to Categorize, Optimize, Design and Engineer, an open-access software to facilitate the democratized discovery and optimization of non-equilibrium materials. BARCODE automates high throughput (HTP) screening of optical microscopy videos and produces a unique fingerprint that encodes dynamic material properties (Fig. 1). Consisting of three complementary branches that leverage standardized and widely-used image analysis approaches (Fig. 1D), BARCODE produces a unique array or ‘barcode’ of performance metrics for each video, and the collective dataset (Fig. 1E), significantly reducing data dimensionality, complexity and size, while providing rich, multiparametric outputs. Importantly, screening is performed without consideration of material composition or formulation, allowing unexpected correlations between performance metrics or disparate material systems to be rapidly revealed. To produce each barcode, the software also calculates rich reduced data structures (RDS) that enable more detailed understanding of the time-evolving material structures and mechanics, and archives the RDS to enable subsequent hypothesis-driven research. Through these features, BARCODE not only streamlines on-system screening and accelerates analysis but also enables non-expert data access and sharing across different materials and communities.

Results
BARCODE is designed to enable rapid HTP analysis of multi-channel microscopy videos to screen for desirable features of active material systems (Fig. 1). By extracting key parameters that describe material structure and dynamics (Table 1), BARCODE reduces large (~1 TB) and complex datasets (Fig. 1A-C) by up to four orders of magnitude.

BARCODE’s core architecture executes three complementary, yet independent, ‘branches’ in parallel (Fig. 1D). We designed each branch to leverage established image processing tools–image binarization (IB), pixel intensity distribution (ID) analysis, and optical flow (OF)40–for which there is an extensive body of literature describing approaches, implementation, and best practices41–47. Each branch produces distinct metrics selected to encode key structural and dynamic features of active materials, organized in a 1×17 ‘barcode’ that is output both numerically and as a color-coded array (Fig. 1E). We compute all 17 metrics (Table 1) using standardized reduced data structures (RDS) (Fig. 1D)–binarized video images (IB), pixel intensity distributions (ID), and velocity fields (OF)–which are automatically produced and archived to facilitate future downstream analysis and that contain substantially more information than the 17-parameter barcode itself. We envision the barcodes will be used primarily for initial assessment and rapid down-selection, and the standardized outputs of the RDS will facilitate more time-intensive and hypothesis-driven downstream analyses. Based on our extensive analysis of active matter systems, we found that BARCODE’s reduced set of 17 simple metrics captures key dynamic and structural information during rapid screens of large datasets. However, the software is material-agnostic, modular and highly adaptable: branches and metrics can be easily added or removed without impacting other metrics, thus providing flexibility, while allowing for discovery of unexpected correlations or trends.

As fully described in the Supplemental Information (SI, Section 1), BARCODE is a Python-based package that reads .tif and .nd2 video files and converts the data into arrays with dimensions (T, m, n, c), where T is the number of frames, m and n are the number of pixels along the horizontal and vertical axis of each frame, and c is the number of channels (for, e.g., confocal videos with multiple components of a material labeled with distinct fluorophores and recorded in separate detectors). The software has a user-friendly graphical user interface (GUI) and several adjustable parameters that the user can set to tailor and optimize the operations of each branch for their system. We also provide a detailed online tutorial48 to guide users in choosing parameters that best suit their data. BARCODE outputs include three RDS files, one for each branch, for every video, saved as .csv files; and a .csv file and colorized .svg with the 17 BARCODE metrics for the entire dataset (e.g., Figs. 1E, 2E). BARCODE can be executed on dozens, even hundreds of videos to produce a single barcode array (Fig. 1E), in 1-4 min per GB (SI Table S1), that can be used to identify patterns, correlations and trends.

To demonstrate the BARCODE workflow, we analyze two representative videos of motor-driven actin-microtubule composites displaying distinct structures and dynamics (Fig. 2A).

The Image Binarization (IB) branch (Fig. 2B) converts each grayscale image of size m×n pixels of the video of T frames into a binary image of white (1) and black (0) pixels. The threshold pixel intensity for binarization is computed from a user-specified offset that provides a threshold percentage %I above the mean intensity of each image (Fig. 2B, top). This process assigns a unique threshold intensity value for each frame, set by its mean value, which corrects for reduced mean intensities of images over time due to photobleaching and other spurious fluctuations in intensity. We assume in this analysis that there are minimal spatial variations in background intensity across the field of view. To increase processing speed and reduce the data size, the resulting stack of binarized images, which is a saved RDS, can be downsampled in time, by choosing to analyze every kth frame, and spatially, by averaging together pixels within p×p windows to result in a stack of T/k images of m/p × n/p pixels. We have found that %I= 10, k=10 and p= 2-8 provide sufficient resolution and accuracy while maintaining rapid processing times (SI Table S1).

To characterize key structural features of the system, we calculate the areas of connected regions of white pixels, ‘islands’, and black pixels, ‘voids’ in each frame of the binarized RDS. The islands are regions of the field of view (FOV) where the material resides while voids are absent of material, and we compute their areas relative to the FOV area, with values ranging from 0 to 1. We identify the maximum island area I_i and maximum void area V_i in each frame i (Fig. 2B bottom) and report in BARCODE the global maximum island (I) and void (V) area, indicators of the size of the largest features and gaps in the material. In general, to produce a more statistically robust measure of any maximum barcode metric, we compute and report the mean of the highest 10% of all values over all frames. To characterize the initial structure of the time-evolving material, we also compute the average initial maximum and initial secondary maximum island areas in the first X% of frames of each video I_0,1 and I_0,2, where X is a user-defined value with default value X=5. For materials that are primarily space-spanning or connected, I_0,1 will be much larger than I_0,2, while materials that comprise more homogenous distributions of independent entities (e.g., clusters, particles) will have I_0,1 ≈ I_0,2. Another important feature of a material that often dictates its behavior and mechanics is the degree to which it is percolated across dimensions, which we assess by computing ‘connectivity’. The connectivity per frame C_i is defined as C_i=1 (or 0) for images having a continuous path (or not) of white pixels extending from edge to edge along at least one axis. In BARCODE, we report the fraction of frames that are connected C = Σ C_i / T. To assess any time-dependent restructuring of the material, we also provide in BARCODE the relative change in the island and void areas from the first X% of frames to the last X% (ΔI, ΔV), which we compute relative to the initial value, such that changes that are greater than (or less than) one correspond to increased (or decreased) area. We have found X= 5 to be optimal for capturing dynamic information while ensuring sufficient signal to noise. The 7 metrics of the IB branch (Fig. 2E) accurately describe the time-varying structures shown in Fig. 2A. Video 1 remains connected (C=1) with a much smaller maximum void area as compared to maximum island area (I>V) and the void area shrinks over time (ΔV<1), indicative of restructuring to more space-filling composition. Conversely, Video 2 is poorly connected (C=0.13), has comparable maximum island and void sizes (I≈V) and shows a large increase in void area (ΔV>1) over the time course of observation.

The Intensity Distribution (ID) branch (Fig. 2C) evaluates the probability distribution of pixel intensities, which serves as a proxy for mass density, and outputs 6 BARCODE entries that describe the global distribution features, and how these features change from the beginning to the end of the video to report aspects of time-dependent restructuring. The ID branch produces distributions for all T frames in a video, which we output as an RDS. Similar to the IB branch, to accelerate processing speed, users can also choose to only evaluate a subset of T/k frames where the interval k is specified by the user. We have found that an interval of k= 10 provides optimal balance of resolution and speed for the systems we have tested.

To quantitatively characterize and reduce the dimensionality of the distributions, BARCODE computes the median skewness S_1,i, mode skewness S_2,i, and kurtosis K_i for each evaluated frame i (Fig. 2C), which are each metrics that describe the shape of the distributions relative to a Gaussian distribution. S_1,i (S_2,i) is the difference between the median (mode) and mean (Fig. 2C), normalized by the standard deviation of the distribution, with positive values indicating a more pronounced tail of high intensity pixels and/or a peak at lower-than-expected pixel values. These features often correlate with bundling and aggregation which lead to denser (brighter) regions complemented with larger regions of minimal mass, which shift the distribution to having a more pronounced high-intensity tail and lower intensity mode, respectively. Because in skewed distributions, the median is typically closer to the mean than the mode is, the mode skewness S_1,i is often a more sensitive skewness measure. However, the median skewness S_2,i is less susceptible to artifacts of pixel saturation and photobleaching, which can cause modes to be at the highest and lowest pixel values, respectively. Kurtosis, computed as the fourth central moment of the distribution normalized by the squared variance, K = μ_4 / σ^4 - 3, reports the extent to which pixel values are closer to (K<0) or further from (K>0) the mean than expected for a normal distribution (see SI, Section 1). Positive kurtosis values are indicative of de-mixing, coarsening and/or clustering while negative values indicate uniform and/or space-filling materials.

In general, if photobleaching were significant, one would expect the mean of the intensity distribution to decrease with time, shifting the overall distribution left towards zero. However, the shape of the distribution would likely not change substantially, since the underlying structures are not changing. By contrast, material restructuring due to aggregation, phase separation or bundling should change the distribution of mass, and should lead to changes in the distribution shape, which are detected via the kurtosis and skewness outputs of BARCODE. In the case of severe photobleaching, one might expect that eventually some fraction of low intensity pixels would become so dark that their intensity would not be detectable over the static pixel noise which arises from e.g., stray light, dark camera noise, etc. In this limit, we would expect the shape of the intensity distribution, and therefore the kurtosis or skewness, to likely change due to the inability to detect the full distribution (i.e., due to ‘missing events’). To avoid this potential artifact, we flag videos with unusually low intensity and remove these from the analysis; saturated videos are also flagged (see SI Section 1).

Similar to island and void computations in the IB branch, BARCODE reports the maximum of each of these shape metrics across all evaluated frames, denoted as S_1, S_2 and K (Fig. 2E), as well as the difference between the values in the initial and final X% of frames, ΔS_1, ΔS_2 and ΔK, to determine the degree and type of restructuring. These 6 metrics accurately capture the differences between Video 1 and 2 and their time-dependent restructuring. In Video 1, we find that S_2,i is positive and remains relatively constant, indicative of modest bundling seen in the example frames. K_i is likewise positive for all frames but displays a much wider range of values over time and is substantially closer to zero at the end of the video (ΔK<0), indicating a more uniformly bundled material (i.e., a higher density of pixels near the mean).

The Optical Flow (OF) branch (Fig. 2D) outputs 4 BARCODE metrics by calculating velocity vectors for every pixel of every kth frame of a video, and averaging those vectors within each p×p window of the m×n image, resulting in a downsampled field of m/p × n/p velocity vectors for all T/k frames of a video, where k and p are user-defined. From the vector fields, which we save as RDS, we compute, for each field i, the speed v_i, mean flow direction θ_i, and directional spread σ_θ,i, calculated as the circular standard deviation of flow directions across all vectors. Averaging each of these quantities across all fields from a given video provides the global averaged BARCODE metrics, v, θ and σ_θ. We also compute the change in v_i between the final and initial X% of vector fields Δv. To compute direction metrics we use bounds of -π to +π and take care in averaging the vector components to avoid issues with motion that is directed near the ±π boundary (see SI, Section 1). Together these BARCODE entries quantify the magnitude and direction of the material motion, and the extent to which it is directed or randomly oriented and speeding up or slowing down. For example, a material that exhibits fast directed flow in the vertical direction that slows down over time, will report a high v value (fast), Δv < 0 (slowing down), θ ≈ π/2 (vertical direction) and σ_θ ≈ 0 (most vectors are oriented in the same direction). Steady but randomly oriented motion would result in v > 0 (the rate of random fluctuations), Δv ≈ 0 (steady rate), θ ≈ 0 (all directions average to zero in the isotropic case) and σ_θ > 0. Moreover, comparing the OF branch metrics between different channels of the same video reports the degree to which the different components of the material are moving together. OF analysis of the two example videos reveal starkly different dynamics (Fig. 2D). The Video 1 flow field has smaller vectors that have a wider range in orientations compared to Video 2, which can be seen quantitatively by v_i being smaller and σ_θ,i being larger and more variable for Video 1 compared to Video 2. We also see that v_i is relatively constant over time for Video 1 (Δv ≈ 0), while it substantially decreases for Video 2 (Δv < 0). These distinct dynamics are captured in the different colors of the last 4 entries of their respective barcodes (Fig. 2E).

To benchmark BARCODE performance and demonstrate its ability to rapidly screen and discover properties of a broad range of non-equilibrium materials, we present the results of BARCODE analyses performed on microscopy data for four different materials that each have different image sizes, resolution, durations, acquisition parameters, and dataset sizes.

BARCODE accurately measures filament speeds and reveals emergent correlations in active cytoskeleton composites

We first analyze a published set of two-channel confocal videos of composites of entangled actin and microtubules, with and without crosslinkers, that undergo restructuring via kinesin and myosin motors acting on microtubules and actin, respectively (Fig. 3A)20. The dataset includes 48 videos of T ≈ 1000 frames of size m×n = 512×512, each with two channels that separately visualize the actin and microtubules (Fig. 3B). Originally, the videos were analyzed using differential dynamic microscopy (DDM)49,50, an established and powerful, yet labor-intensive, approach to show that all formulations exhibit ballistic motion with speeds spanning roughly three orders of magnitude. DDM analysis also revealed that the data could be categorized into three distinct dynamic classes: ‘fast’ directed flow, ‘slow’ isotropic restructuring or ‘multi-mode’ dynamics that manifested aspects of fast and slow behavior (Fig. 3B). Importantly, the dynamic class was not statistically correlated with the material formulation and the molecular underpinnings remained unclear.

BARCODE analysis of this dataset took ~9 min to complete, with no pre- or post-processing of the videos, compared to ~53 min required to complete the first step of DDM analysis, after which several rounds of subjective fitting and parameter choices requiring expertise are needed to determine dynamics. The resulting barcode matrices for the actin (Fig. 3C) and microtubule (Fig. 3D) channels also provide structural and orientational data for the completed dataset, which was not comprehensively analyzed in the original work20. The 4 OF entries are indistinguishable between the two channels, consistent with the nontrivial finding that actin and microtubules exhibited similar speeds across the entire formulation space20. The other branches show differences, indicating distinct and previously unreported structural features of actin and microtubule networks.

To first benchmark and validate BARCODE, we compare the speeds v computed from BARCODE to the previously reported DDM-computed speeds for the entire dataset, color-coded by the nominal dynamical class (Fig. 3B, E). We find remarkably good agreement over two decades of speeds (Fig. 3E, SI Table S2). Multi-mode class data deviates from the equivalence line more than the other classes because the original DDM analysis resulted in two speeds, which were averaged here, while BARCODE computes a single speed for each condition. BARCODE also quantitatively captures other dynamical features of each class that were only qualitatively described in prior work. Specifically, prior particle image velocimetry (PIV) analysis demonstrated that a single exemplar video for each of the fast and slow classes exhibited unidirectional and randomly oriented motion, respectively. By evaluating correlations between speed v, directional spread σ_θ, and flow direction θ, BARCODE not only reproduces this result but demonstrates its applicability to all data in each class (Fig. 3F).

We next demonstrate the ability of BARCODE to rapidly discover structural properties and correlations. We observe substantially greater median and mode skewness changes, ΔS_1 and ΔS_2, for fast class data compared to slow and multi-mode (Fig. 3G), suggesting more pronounced restructuring. Moreover, the larger range in ΔS_2 values compared to ΔS_1 is a likely indicator of pixel saturation due to largescale aggregation, shifting the mode from a low intensity peak to the highest value (as shown in Fig. 2C). A powerful feature of BARCODE is its ability to directly correlate structural and dynamical features, such as the relationship between directional spread σ_θ and maximum median skewness S_1 (Fig. 3H). Here, we find clear partitioning between the different dynamic classes. Fast data exhibits minimal σ_θ, indicative of directed motion; and a large spread in S_1, suggestive of wide-ranging structures. Conversely, the other classes generally exhibit higher σ_θ, indicative of more randomly oriented motion; and lower and less varied S_1 values, signifying minimal restructuring.

Using BARCODE’s IB branch we compare the initial maximum and secondary maximum island areas, I_0,1 and I_0,2 (Fig. 3I), observing that slow and multi-mode composites exhibit predominantly a single large island, with I_0,2 < 5% for all videos and I_0,1 values up to ~75%. Fast class composites have a comparatively broader spread in initial island sizes and generally smaller I_0,1 and larger I_0,2 values, suggesting that less connected and more heterogeneous networks may more readily facilitate fast flow. Additionally, microtubules generally have smaller island sizes compared to actin, which may reflect increased network heterogeneity and larger mesh sizes.

To corroborate this physical picture, we compare the connectivity and maximum void area for the different classes (Fig. 3J), finding that nearly all slow and multi-mode networks remain fully connected (C ≈ 1) and most void areas are below 50%. Conversely, BARCODE reports a range of fractional (C<1) connectivity values for the fast class networks as well as for microtubules, which also have larger void areas (V>50%) and smaller initial island areas compared to actin.

BARCODE reveals universal features of active cytoskeleton materials

To further validate BARCODE and demonstrate its rapid and robust characterization capabilities, we analyze two additional datasets: a published study that examined the contractile behavior of myosin-driven crosslinked actin10 (Fig. 4A), and previously unpublished data on kinesin-driven composites of actin and microtubules, with and without crosslinking (Fig. 4H). In the first, confocal microscopy videos with separate channels representing labelled myosin and actin were collected. Previously published image analysis performed on the myosin channels revealed three classes of contractile behavior: local, global, and critically connected (Fig. 4A). Local contraction was signified by actin and myosin forming small scale (~10µm) uniformly distributed clusters, whereas global contraction led all components to condense into a single well-defined region. Critically connected networks exhibited distinct reconfiguration across lengthscales, with some regions condensing to large aggregates while others remained more homogeneously distributed (Fig. 4A).

To corroborate these general features of the different classes and discover their dynamical properties and structure-dynamics correlations, we use BARCODE to analyze both actin and myosin channels of the videos (Fig. 4B, C). Upon visual inspection of the barcodes, we find highly similar OF metrics between actin and myosin, suggesting that the two components are highly interacting and moving together. Both channels show larger maximum areas and larger changes for voids as compared to islands (i.e., ΔV>ΔI, V>I). Additionally, the actin channel shows a lower degree of connectivity and increased variability in skewness (S_1, S_2), suggesting more complex structure and reconfiguration, and consistent with the images in Fig. 4A. We next analyze correlations between the quantitative metrics output by BARCODE, color-coding the data by the previously identified contractile classes (Fig. 4D-G).

Comparing the speed v to the speed change Δv (Fig. 4D), we confirm that myosin and actin dynamics are highly correlated (i.e, similar v and Δv values). Locally contractile networks display relatively low speeds with minimal changes, consistent with small-scale local contractions. By contrast, both critically connected and globally contractile networks exhibit higher and more variable speeds, spanning over an order of magnitude in v, with faster speeds showing increased changes in dynamics (Fig. 4D inset).

To determine how the initial structure correlates with contractile class, we compare the initial maximum and secondary maximum island areas for actin and myosin (Fig. 4E). For actin, we find that locally contractile networks exhibit large I_0,1 values (>40%) and nearly zero I_0,2 values, similar to the slow class networks in Fig. 3I, and consistent with prior reports10,20. Conversely, the globally contractile and critically connected networks have smaller I_0,1 and larger I_0,2 values, similar to the fast networks in Fig. 3I, and suggesting the presence of a larger number of structures of similar sizes. In contrast, the myosin data exhibits similar and generally correlated values of I_0,1 and I_0,2, with the critically connected networks having the highest (I_0,1, I_0,2) pairs, consistent with their expected clustering across different scales.

Finally, we relate restructuring to dynamics (Fig. 4F, G), comparing changes in mode skewness ΔS_2 to changes in median skewness ΔS_1 (Fig. 4F) and island area ΔI (Fig. 4G). We observe clear distinctions between the different contractile classes and between actin and myosin. We find that actin exhibits larger skewness changes compared to myosin, as visually indicated by the barcode (Fig. 4B, C), and global and critical contractile networks exhibit larger skewness changes compared to local contraction data, as expected. Interestingly, critical and global data have mostly negative and positive median skewness changes ΔS_1, respectively, indicating large-scale aggregation (brighter pixels) coupled with more network-poor (darker) regions for global data as compared to varied or multiple aggregates (brighter pixels) coupled with sustained network-rich regions (fewer dark pixels) for critically connected networks. These results highlight the power of BARCODE to reveal complex behaviors and correlations in active systems from a remarkably thrifty set of metrics produced in min.

Thus far, we have presented analysis of previously published and well-vetted data to benchmark BARCODE, revealing insights and emergent similarities between two different cytoskeletal materials. To explore potential universality in response, and further demonstrate BARCODE capabilities, we next analyze previously unpublished data on kinesin-driven composites of actin and microtubules, with and without crosslinking of actin or microtubules (Fig. 4H). The system components are similar to those shown in Fig. 3A, but with a lower ratio of microtubules to actin, higher kinesin concentration, and no myosin (see SI Section 2). The actin is also not labeled in this set of 109 videos, so our analysis focuses solely on the structure and dynamics of the microtubule network. Comparing the barcodes for this system (Fig. 4I) to those of the actomyosin system (Fig. 4B), we find similar OF parameters, except Δv appears higher for the kinesin-driven composites, while we observe much more variability in IB metrics. To further investigate similarities and differences between the different cytoskeleton systems, we evaluate similar correlations as in Figs. 3 and 4D-G, color-coding the data by the type of crosslinking: none, microtubule-microtubule, or actin-actin.

We again find that slower speeds correlate with minimal speed changes (Δv ≈ 0) (Fig. 4J), and increasing speed generally correlates with increasing speed change |Δv| (Fig. 4J inset). However, in comparison to actomyosin network dynamics, the speeds are generally an order of magnitude higher and dynamics appear to speed up (Δv > 0) rather slow down. Moreover, uncrosslinked networks generally exhibit faster speeds and greater speed changes compared to crosslinked networks, and crosslinked microtubule networks exhibit the slowest speeds likely because crosslinking provides more rigidity and connectivity thereby slowing contraction. Increased connectivity should lead to larger differences between the initial maximum and secondary maximum island areas, since connected networks should have FOV-spanning islands, which we observe (Fig. 4K). Most of the crosslinked data lies below the equivalency line, indicating I_0,1 > I_0,2, with a roughly inverse relationship between I_0,1 and I_0,2, similar to trends in Fig. 3I and the actin data in Fig. 4E. By contrast, the uncrosslinked network data falls near the equivalency line, indicating an ensemble of similarly sized clusters. Contraction is further confirmed by comparing I_0,1 with the maximum island area I (Fig. 4K inset), which shows that most paired I_0,1 and I values are similar, suggesting that islands are generally shrinking (i.e., contracting) over time.

To provide further insight into the network restructuring, we evaluate correlations between the maximum kurtosis K and the maximum median skewness ΔS_2 (Fig. 4L) and flow directional spread σ_θ (Fig. 4M). We find that K and S_1 are generally inversely related and that most crosslinked networks exhibit lower kurtosis and higher skewness than uncrosslinked networks, which we attribute to increased connectivity and bundling, respectively. Specifically, lower K values and higher S_1 values indicate increased global structural homogeneity and increased local mass density, respectively. Coupling these structural distinctions with dynamical features, we observe that uncrosslinked networks exhibit a wider spread in σ_θ values that extend to much smaller values than for crosslinked networks (Fig. 4M), indicating more variable types of motion and a higher propensity for directed motion in the uncrosslinked case. This behavior is likely enabled by the reduced connectivity, increased structural heterogeneity, and faster speeds that uncrosslinked networks exhibit compared to crosslinked networks. Together, these results demonstrate BARCODE’s ability to quantify and correlate a host of structural and dynamic parameters for different active cytoskeleton network formulations, conditions, acquisition parameters, and spatiotemporal scales to reveal universal behaviors and emergent properties in this foundational class of active matter.

BARCODE characterizes dynamics and structure of active cell monolayers

To further demonstrate the broad applicability of BARCODE, we next analyze two datasets of dynamic cell monolayers. In each, large FOV (>1mm) time-lapse videos are captured over the course of days (69-89 hrs) and the nuclei and cytoplasm are imaged separately, providing two channels for analysis. We first evaluate a previously published dataset34 of videos of weakly interacting spindle-shaped human dermal fibroblasts (hdFs) (Fig. 5A) at two cell densities. The barcodes for each channel of the 72 videos (Fig. 5B) show both expected and nontrivial properties. For example, the entire set of nucleus data, which primarily contains isolated punctate objects that are not expected to significantly change shape or size, has essentially no connectivity, very small maximum and initial island sizes, and large voids. Conversely, the cytoplasm channel has more instances of positive connectivity along with larger maximum island areas and smaller voids. Despite clear structural differences, the dynamic OF metrics (v, Δv, θ, σ_θ) display similar trends between the channels, as expected since the two components comprise the same ensemble of cells. To more closely examine the effects of cell density, we evaluate similar correlations as in Figs. 3 and 4.

Evaluating the speed and its change for each channel and cell density (Fig. 5C), we observe modest slowing for nearly all data, perhaps indicative of jamming as the cell number increases over time due to cell division, in line with the observation that the higher cell density data exhibits generally slower speeds. The cytoplasmic signal is generally slower than the nuclear signal, which we attribute to the more complex shape and intensity changes of the cytoplasm. Comparing the initial maximum and secondary maximum island areas, we find most of the data falling near the I_0,1 ≈ I_0,2 equivalency line. Moreover, the nucleus data is all tightly clustered around I_0,1 ≈ I_0,2 ≈ 1% which is equivalent to ~2000µm2, roughly equivalent to the 2D projected area of the nucleus (Fig. 5A). The cytoplasm data displays more variation in island area and greater deviation from the equivalency line (i.e., I_0,1 > I_0,2), indicating closely packed cells that register as larger multi-cell islands.

Turning to structural changes, quantified by ΔS_2 and ΔV (Fig. 5E), we observe minimal changes in the nucleus channel, as expected, since they are not changing in size, shape or concentration. The cytoplasm data show more variations in both metrics, reflecting changing cell shapes as well as clustering over time. Moreover, ΔS_2 is universally negative, indicative of increased uniformity and reduced spread in pixel intensities, which can be observed in Fig. 5A. Interestingly, we find that the maximum void areas decrease for low density cases and increase for high densities. This suggests that at higher densities cell cluster into large connected multi-cell regions producing larger void areas, while at lower density, cells remain more dispersed, thus producing smaller voids. This physical picture is corroborated by Fig. 5F which shows that lower cell densities result in higher connectivity C and generally higher median skewness S_1, indicating a more connected meshwork of cells at low densities compared to large multi-cell aggregates that come with larger voids and more dark pixels.

To assess the relevance of these findings to other cell systems, we also analyze videos of jamming MCF10A breast epithelial cells (Fig. 5G-L). Here, whole cells and their GFP-labeled nuclei are imaged using phase contrast and epifluorescence, respectively (Fig. 5G). Examining the barcode for each channel (Fig. 5H), we find similarities with the hdFs (Fig. 5B), including minimal connectivity and island areas for the nucleus data and similar OF metrics between the two channels. Evaluating the same correlations as in Fig. 5C-F, we find the both cell types exhibit generally decreasing speeds (i.e., Δv < 0) (Fig. 5C, I) and similar I_0,1 vs I_0,2 correlations (Fig. 5D, J). However, the spread in Δv and v values is less for the MCF10As (Fig. 5I) compared to hdFs (Fig. 5C), suggesting more ordered and correlated motion within epithelia as compared to more motile fibroblasts. Moreover, the mode skewness change is positive (ΔS_2 > 0) for epithelial cells (Fig. 5K) in contrast to hdFs and consistent with increased contrast and sharpness of final versus initial images (Fig. 5G, K), opposite to hdFs. Finally, we observe zero connectivity (C=0) (Fig. 5F, L) and minimal void area change (ΔV ≈ 1) for the nucleus channel and a wider range of connectivity values among the cell boundaries (Fig. 5F, L). Together, our results demonstrate the power of BARCODE to reveal convergent properties, dissect differences between disparate materials systems and accurately characterize material properties using distinct imaging modalities.

Methods
Preparation and imaging of active cytoskeleton networks
Data presented in Figs. 2 and 3 were generated from previously published source videos20. Actomyosin data presented in Fig. 4A-G were generated from previously published source videos10. The cytoskeletal networks and video data presented in Fig. 4H-M were generated as follows.

Proteins. All proteins were purchased as lyophilized powder from Cytoskeleton, Inc, reconstituted, flash-frozen in single-use aliquots, and stored at -80˚C until use. Rabbit skeletal actin monomers (AKL99) and biotin-actin monomers (AB07) were reconstituted in G-buffer (2.0 mM Tris (pH 8), 0.2 mM ATP, 0.5 mM DTT, 0.1 mM CaCl_2). Porcine brain tubulin dimers (T240), HiLyte 647-labeled tubulin dimers (TL670M) and biotin-tubulin dimers (T333P) were reconstituted in PEM-100 buffer (100 mM PIPES (pH 6.8), 2 mM MgCl_2, and 2 mM EGTA).

Biotinylated kinesin-40155,56 was expressed, as described previously20, in Rosetta (DE3) pLysS competent E. coli (Thermo Fisher) grown on selective media plates for 16–18 h at 37°C. Fifteen colonies were added to a 5 mL starter culture of selective LB media and grown for 2 h at 37°C/250 rpm before adding to 400 mL of selective LB media. Cells are grown at 37°C/250 rpm to OD 0.6-0.9 at 600 nm, then induced at 20°C/250 rpm for 18 h with 1 mM Isopropyl β-D-1-thiogalactopyranoside (IPTG), and pelleted at 5000 rpm for 10 min at 4°C before being frozen at -80°C for 1 h. Cells were lysed in lysis binding buffer (50 mM PIPES, 4 mM MgCl_2, 20 mM imidazole, 10 mM β-mercaptoethanol, 50 μM ATP, one protease inhibitor tablet per 10 mL, 1.1 mg/mL PMSF, 1.1 mg/mL lysozyme) via sonication for 3 mins, pulsing every 20 s, then pelleted for 30 mins at 40,000 xg at 4°C, filtered through a 0.22 µM filter, and incubated with 1 mL nickel (Ni-NTA) agarose beads (Qiagen) for 2 h on a rocker at 4°C. The lysate/bead mixture was passed through a chromatography column then washed with 15 mL buffer (50 mM PIPES, 4 mM MgCl_2, 20 mM imidazole, 10 mM β-mercaptoethanol, 50 μM ATP, one protease inhibitor tablet per 10 mL) before 1 mL fractions were eluted in (50 mM PIPES, 4 mM MgCl_2, 20 mM imidazole, 10 mM β-mercaptoethanol, 50 μM ATP, one protease inhibitor tablet per 10 mL, 2 mM DTT, 0.05 mM ATP). An elution dot blot was performed to assess the most concentrated fraction which was run through a 40kD MWCO desalting column for buffer exchange with PEM-100 with 0.1 mM ATP, then mixed with 60% sucrose for a final concentration of 10% sucrose before being aliquoted and flash-frozen in single-use aliquots.

To prepare force-generating kinesin clusters, kinesin-401 dimers were incubated with NeutrAvidin (Thermo Fisher) at a 1.7:1 ratio in PEM-100 to a final concentration of 10 µM supplemented with 4 mM DTT for 30 min at 4°C. Clusters were prepared fresh and used within 24 h.

For composites that incorporate actin or microtubule crosslinking, actin:actin or microtubule:microtubule crosslinked complexes were prepared by combining biotinylated actin monomers or tubulin dimers with NeutrAvidin and free biotin at a ratio of 2:2:1 protein:free biotin:NeutrAvidin and incubating for 90 min57.

Cytoskeletal network preparation. Actin-microtubule composites were prepared by polymerizing a mixture of 5.22 μM actin monomers, 6.06 μM tubulin dimers, 0.32 μM HiLyte 647-labeled tubulin dimers, 5.22 μM phalloidin (Invitrogen) and 5 µM Taxol (Sigma) in PEM-100, supplemented with 0.1% Tween, 4 mM ATP, and 4 mM GTP. For crosslinked composites, a portion of either actin monomers or tubulin dimers was replaced with equivalent crosslinker complexes to achieve the same overall actin and tubulin concentrations and crosslinker:protein ratios of R_A=0.02 for actin or R_T=0.005 for tubulin. Composites were polymerized in the dark for 1 h at 37°C.

To prepare active cytoskeletal composites, 5 μL of the polymerized actin-microtubule composite was combined with the following to a 9 µL final volume: oxygen scavenging system [45 μg/mL glucose, 0.005% β-mercaptoethanol, 43 μg/mL glucose oxidase, 7 μg/mL catalase, 2 mM Trolox (Sigma)] and ATP-regeneration system [26.7 mM phosphoenol pyruvate (Beantown Chemical, 129745) and pyruvate kinase/lactate dehydrogenase (Sigma, P-029)]. Finally, 1 μL of kinesin clusters was added to reach a final concentration of 1 μM, followed by gentle mixing of the sample by pipetting up and down.

Sample chambers. Sample chambers with a volume of ~10 μL were made by placing two strips of parafilm between a No. 1 glass coverslip and microscope slide, followed by heating to fuse them together. To prevent surface adsorption of proteins, the chambers were filled with a 1 mM solution of BSA (bovine serum albumin, Sigma) and incubated for 10 min, after which the solution was flushed out with compressed air. The prepared cytoskeletal network solution was then loaded into the chamber and the open ends were sealed with UV-curable glue.

Imaging. Imaging of HiLyte 647-labeled microtubules was performed using a Nikon A1R laser scanning confocal microscope with a 60× oil-immersion objective (Nikon) and a 640 nm laser with 624±20 nm / 692±20 nm excitation/emission filters. Time-series (videos) of 256 × 256 square-pixel (213 μm × 213 μm) images were collected at 1.33 fps for a minimum of 400 frames (300 s). Imaging began 5 min after the addition of kinesin motors to the sample in the middle plane of the ~100 μm thick sample chamber. Each subsequent video was recorded in a different field of view laterally translated by at least 500 μm. Imaging continued until restructuring or motion was no longer visible (~60-120 min). 5-10 videos were collected for each sample. Each data point shown in Fig. 4 corresponds to a single video. The 5, 7 and 47 data points for the uncrosslinked, actin crosslinked and microtubule crosslinked data, respectively are from 8, 2 and 6 independent replicates, which sufficiently capture the range of material dynamics.

Preparation and imaging of cell monolayers
Data presented in Fig. 5A-F were generated from source videos34 of human dermal fibroblasts (ATCC PCS-201-010), which we further processed as described below. Video data presented in Fig. 5G-J, from experiments similar to previous reports37,38, were graciously shared by Jasmin Di Franco and Roberto Cerbino (University of Vienna, Austria) and prepared as described below.

Cell culture. MCF10A cells (kind gift of J.S. Brugge, Department of Cell Biology, Harvard Medical School, Boston, USA) were cultured in Dulbecco’s Modified Eagle Medium: Nutrient Mixture F-12 supplement with Glutamine (DMEM/F12 GlutaMax) medium (Gibco), supplemented with 5% horse serum (Biowest), 0.5 mg/mL hydrocortisone (Sigma-Aldrich), 100 ng/mL cholera toxin (Sigma-Aldrich), 10 µg/mL insulin (Roche), 1% Penicillin-Streptomycin (HyClone), and 20 ng/mL EGF (Peprotech), the latter being added directly to the culture plates. Cells were maintained at 37°C in a humidified atmosphere with 5% CO₂. Cell identity was verified by fingerprinting by the IFOM (Milan) cell culture facility, and cells were routinely tested for Mycoplasma contamination. Stable expression of GFP-H2B was achieved by lentiviral infection of MCF10A cells with pBABE-puro-GFP-H2B vectors to enable nuclear labeling.

Cell jamming assay. Cells were seeded into six-well plates at a density of 1.5×10⁶ cells per well in complete medium and cultured to form a uniform monolayer (~24 h). Prior to imaging, the cell monolayer was carefully washed with 1X Dulbecco’s Phosphate Buffered Saline (DPBS) to remove floating cells, and the medium was refreshed. Time-lapse images of size 1024 × 1024 square-pixels (1331 µm × 1331 µm) were captured every 5 min over a 72 h period using a Leica Thunder inverted microscope equipped with a 10× objective, both in phase contrast, to image the cell walls and cytoplasm, as well as fluorescence, to image the GFP-labeled nuclei. The assay was conducted in an environmental microscope incubator set to 37°C with 5% CO₂ perfusion. Data presented in Fig. 5G-J are from 20 videos from 5 independent samples, each containing a fluorescence and phase contrast channel, which sufficiently capture the range of material dynamics. For BARCODE analysis, we divided each video channel into four 512 × 512 square-pixel images to increase statistics.

Figure 5A-F video post-processing. Videos34 of size 6318×3546 square-pixels (3702 µm × 2078 µm) and 5088 × 2332 square-pixels (2982 µm × 1367 µm) were divided into smaller FOVs to increase statistics and remove spurious dark spots and sample borders that impact image analysis. Specifically, the data is from 72 videos from 2 independent samples, each containing two channels, which sufficiently capture the range of material dynamics and represent the full published dataset for these cell densities on isotropic subtrates. For BARCODE analysis, we divided each video channel into 30-42 video tiles with tile size of 960×608 square-pixels (562 µm × 35 µm).

Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability
The source data for all plots shown in Figs. 2–5 have been deposited in the Dryad repository and can be accessed at: https://doi.org/10.5061/dryad.pc866t235. Source data are provided with this paper.

Code availability
BARCODE and supporting documentation are available on the GitHub repository58 at https://github.com/softmatterdb/barcode and can be accessed at: https://doi.org/10.5281/zenodo.17585069.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The main challenge addressed in the article, as I see it, is managing large-scale data—both in terms of enabling effective collaboration (a term I personally prefer over \"democratization\") and in extracting meaningful insights despite limited computational capacity due to data volume. This is indeed a critical problem, and addressing it can significantly advance scientific research.\nHowever, my primary concern lies in the selection of parameters. Specifically, I question whether using only 17 parameters is sufficient to capture the complexity of such a large dataset. One potential solution would be to start with a broader set of features and then apply dimensionality reduction techniques. If a larger set of features were used, alternative methods of visualization might also be necessary.\nAdditionally, I would suggest outlining potential next steps in leveraging big data and machine learning techniques. One such step could be the automatic extraction of relevant features or parameters.\nIn summary, I believe the article could benefit from better contextualization for readers who are less familiar with data science. It should also give more critical attention to the implications of representing a massive dataset with a relatively small number of parameters.", "reviewer_2": "This manuscript presents a reasonable straightforward method called BARCODE to characterize several interesting properties of microscopy videos, which can then be used to compare videos from different experiments. The method is flexible as regards to the number of images and their exact dimensions, which is great. Several example data sets are used as demonstrations that the method is powerful. I think this is publishable after the authors clarify some points.\n1. I'm most concerned about the Image Binarization branch. There is one user-defined threshold ('offset' defined in supllemental section 1). It seems that the results will be quite sensitive to this choice; that for example, the connectivity depends on this choice. Of course, BARCODE has far fewer parameters than many competing methods, so this one user-defined parameter is not a fatal flaw. Nonetheless, the authors should comment on how users might pick this threshold, and how sensitive the results can be on this choice. For example, which of the results presented in Figs. 2-5 would change significantly based on the choice of the threshold? The author list is long, so if different authors independently looked at the images and chose thresholds, how much variability occurs? Again: this is not a huge problem, but I suspect that different data will have different sensitivity to these choices, and different people might make different choices, so the ability to compare across data sets and between different research groups would potentially be diminished.\n2. For the Intensity Distribution branch, how does photobleaching affect the results? In the paragraph on page 4 introducing the ID branch, it interprets the decrease of the mean intensity in Fig. 2C as due to the formation of aggregates. In some of my data sets, it would be due to photobleaching, which is not the same science as aggregation. How much is this a problem?\n3. I'm also wondering how much uneven illumination would affect both IB and ID questions. Some days my fluorescent arc lamp is better aligned than others.\n4. For the flow direction theta, it took me a while to understand why this is useful. The point is that if one has a two channel image, if both channels indicate the same flow direction, then the two components being visualized are co-moving. That's great. This should be explained, because if I'm just comparing across multiple experiments, the particular angle theta seems irrelevant. One could change it just by rotating the sample or camera, presumably.\n5. But more importantly for theta, how does the software handle the definition of theta as between 0 and 2 pi? That is, suppose the motion is close to theta = 0. Fluctuations of the direction might mean that sometimes the direction is plus or minus a few degrees; but negative angles will be close to 2pi. The average of 0 and 2pi is pi, the opposite direction of the actual motion. Moreover, the standard deviation (Eqn. 27) will be much larger than it should be. How is this handled for both computing mean theta and computing the standard deviation?\n6. Related to this, Fig. 3F(inset) caption says \"fast flows are directed along plus/minus pi.\" This seems like it must be an artifact -- it depends on the camera orientation -- why would plus or minus pi be special? Although looking closely at the inset, I'm not seeing how \"plus/minus pi\" is deduced. pi is 3.14 and the horizontal axis of the inset ranges from -2 to +2, so does not include plus or minus pi at all. I am really confused.\n7. In Fig. 3B caption it talks about \"temporal colormaps.\" I do not understand what these are, please define.\n8. The supplemental materials are nice, and generally tell exactly how to compute the various quantities. The exception is the kurtosis: please give a formula for how the kurtosis is calculated. The reason I'm asking is that I normally think of skewness as calculated based on moments of the distribution, but BARCODE is instead using \"median skewness\" and \"mode skewness\" which are not based on moments. That's fine, but it makes me wonder if the kurtosis is calculated based on moments. That is, is the kurtosis the fourth standardized moment defined on Wikipedia (https://en.wikipedia.org/wiki/Kurtosis)? And why not use the third standardized moment for defining skewness (https://en.wikipedia.org/wiki/Skewness)? I don't feel strongly about any of this, other than to just simply include an explicit formula for kurtosis in the supplemental materials.", "reviewer_3": "This article describes a new analysis technique called BARCODE. This technique takes large data sets of videos and is able to process it quickly into many different variables that are then tested to see whether they are correlated. The text describes several examples from already analyzed data sets to show the utility of the technique and also shows how BARCODE can be used on new data to find new correlations between variables. This is a really interesting technique that could be impactful especially with the amount of data that can be collected steadlily increasing. The manuscript is written in a way that makes it somewhat hard to understand the technique. As mentioned below, I think using less examples and treating the development of the technique as part of the results and providing a more thorough description in the main text would greatly improve the manuscript. I read the manuscript first and then the SI and it was not until I read the SI that I really started to understand how BARCODE worked. Not all details in the SI need to be added to the main text, but some descriptions of the technique do need to be moved to the main text. This can be done by moving an example or two to the SI. Due to this and other comments provided below, major revisions are required prior to publication in Nature Communications.\nComments, questions and concerns:\n1. The three channels chosen for independent analysis seem to be limiting to the technique. What is the rational for choosing these three channels and what kind of information is excluded by choosing these?\n2. A colorbar should be provided for Fig. 1E, even if it is just qualitative.\n3. There seems to be some redundancy in the first paragraph of the Results, the 3 image processing tools and the reduced data structure, I think are the same but are both listed a sentence apart, are they the same and is this redundant?\n4. How does reducing the data change the accuracy of the results?\n5. It seems that time-dependent data is lost in the data reduction, is that correct? If this is correct, how does this limit the method?\n6. What are the arrows in Figure 3E?\n7. If the changes in the images are not in a single direction, would this method work?\n8. It is hard to follow the examples provided since they are described in terms of the variables and not well connected to the physical results in the movies. The actin-myosin example did a better job of connecting the variables to the physical changes in the videos but some discussion was still very hard to follow. It would help if the variables being described are related to what they physically represent in the videos, that would aid in the readability of the manuscript and better illustrate the power of the technique.\n9. What are the dynamics (time scale) being analyzed in the microtubule work?\n10. A discussion of the limitations of the technique and the advantages of the technique in a separate section would be very helpful.\n11. Several examples are provided of the techniques, although all of these demonstrate the technique it is a bit hard to understand how the technique works since the examples tend to be hard to follow. Refocusing the article more on the technique with fewer examples and provided better descriptions of the technique, including advantages and limitations, would better illustrate BARCODE.\n12. What data is needed to input into BARCODE? Are there limited data types or formats that are needed?\n13. Is there a minimum amount of data needed to provide significant results?\n14. I understand the format of the article and why specifics of BARCODE are put in the SI, but I encourage the authors to move some of this to the main text. Since this is a technique development manuscript, the results are the technique and having this information in main text would make the manuscript more accessible. I would recommend moving this into the results of the main text and moving an example or two into the SI."}, "author_response": "The authors responded satisfactorily to my questions. We appreciate the reviewer’s positive assessment of the potential impact of our work. Below, we respond to each question in detail in blue. We have also included below, and in a marked copy of the manuscript, highlights of the sections we revised in dark orange. Response: We agree that parameter selection is important, and appreciate the opportunity to provide additional details. Our team developed the 17-parameter barcode based on our extensive analysis of a wide range of active matter systems, including contractile protein networks and cell monolayers. We found these 17 metrics, derived from 3 parallel analysis branches, were able to capture key dynamic and structural information with manageable computational complexity, thereby balancing our desire to obtain useful insight into material properties while maintaining the ability to rapidly screen through large datasets. Moreover, we also provide users access to the full reduced data structures (RDS) which are the intermediate level datasets from which the 17 metric barcode is produced. For example, the RDS include the binarized images, not just the island size or connectivity metrics, as well as the full intensity distributions and flow fields, from which the statistical measures (means, standard deviations, etc.) that appear in the barcode are calculated and reported. We added the following text on page 4: We compute all 17 metrics (Table 1) using standardized reduced data structures (RDS) (Fig. 1D) –binarized video images (IB), pixel intensity distributions (ID), and velocity fields (OF) – which are automatically produced and archived to facilitate future downstream analysis and that contain substantially more information than the 17-parameter barcode itself. We envision the barcodes will be used primarily for initial assessment and rapid down-selection, and the standardized outputs of the RDS will facilitate more time-intensive and hypothesis-driven downstream analyses. Based on our extensive analysis of active matter systems, we found that BARCODE’s reduced set of 17 simple metrics captures key dynamic and structural information during rapid screens of large datasets. These RDS are reduced in dimension with respect to the initial video data, but contain substantially more information than the 17 parameter barcode itself. We also enable users to control the level of downsampling, in both space and time, which is used in the calculation of the binarized images in the IB branch and velocity vector fields in the OF branch. We envision the barcode output will be used primarily for initial assessment and rapid downselection, and the standardized output of the RDS will facilitate more time-intensive and hypothesis-driven downstream analyses. We have emphasized these aspects for the reader in the extensively revised section on “Demonstrating BARCODE workflow and utility” that starts on page 5 of the revised manuscript. We refer the reviewer to the marked copy of the manuscript to review these changes in their entirety. We understand that as BARCODE is applied to additional material systems, it is likely that additional metrics may be required, or some of the existing metrics may not prove as useful. To proactively address this issue, we constructed BARCODE in a modular fashion, allowing the expansion of existing branches or even the addition of new branches to enable the functionality and performance of the platform to grow over time. We believe this modularity is a key strength of the method, and we are happy to provide additional details to clarify this for the reader. We include this statement on p. 4 where BARCODE is first introduced: However, the software is material-agnostic, modular and highly adaptable: branches and metrics can be easily added or removed without impacting other metrics, thus providing flexibility, while allowing for discovery of unexpected correlations or trends. Finally, we agree that further dimensionality reduction through machine learning approaches may provide additional benefit, and this is an area of ongoing research in our groups. We have added a statement describing these future directions to the Discussion on page 19. BARCODE is also highly adaptable: branches and metrics can be easily added or removed, providing flexibility to users and enabling the software to meet the future needs of the community. Planned expansions of BARCODE will include additional branches and metrics that quantify diffusive behavior and mechanical properties, including two-point correlation functions, as well as adding user-identified improvements and user-generated extensions in the future. We are also currently investigating the use of machine learning for data classification and to enable data-driven predictions of material behavior. Response: Thank you for the encouragement to add additional details about next steps for this work. As mentioned above, we are already working to apply machine learning approaches to dimension reduction and clustering of BARCODE outputs, which we hope to describe in a future publication soon. We have also started exploring options to extract information directly from the raw video files using data reduction and compression methods which may allow us to identify and extract embedded information automatically, although this work is still in the very early stages and we are not ready to comment on its potential for improvement at this stage. Response: As outlined above, we have modified the discussion section to include more details of the implications of and opportunities provided by the data reduction approaches used here. We have added a new description of the advantages and disadvantages of our method in the Discussion section on pages 18-19. We also included an expanded description of how the data is inputted and reduced, including explicit references to our graphical user interface (GUI) and online tutorial, which provides additional information, on page 4: As fully described in the Supplemental Information (SI, Section 1), BARCODE is a Python-based package that reads .tif and .nd2 video files and converts the data into arrays with dimensions (T,m,n,c), where T is the number of frames, m and n are the number of pixels along the horizontal and vertical axis of each frame, and c is the number of channels (for, e.g., confocal videos with multiple components of a material labeled with distinct fluorophores and recorded in separate detectors). The software has a user-friendly graphical user interface (GUI) and several adjustable parameters that the user can set to tailor and optimize the operations of each branch for their system. We also provide a detailed online tutorial48 to guide users in choosing parameters that best suit their data. BARCODE outputs include three RDS files, one for each branch, for every video, saved as .csv files; and a .csv file and colorized .svg with the 17 BARCODE metrics for the entire dataset (e.g., Figs. 1E, 2E). Response: We appreciate the positive feedback and helpful comments that follow. Below, we respond to each question in detail in blue. We have also included below and in a marked copy of the manuscript highlights of the sections we revised based on reviewer input in dark orange. Response: We agree that the user-defined threshold is an important parameter and are happy to provide additional details. In our initial algorithm designs, we performed a sensitivity analysis to define the default threshold parameter, and determined that using a threshold of 10% above the mean intensity (we term this 10% value the ‘offset’), determined on a per-frame basis, was a good default value that generally worked well for the datasets presented in the manuscript. We chose to use the frame-specific mean intensity to minimize any possible effects of photobleaching. However, we agree that each dataset is unique and may require some user-based optimization. To enable this optimization, we allow users to set their own thresholds, and since BARCODE is designed to run quickly, it is possible for users to perform their own rapid sensitivity analysis, to inform their own choices of offset values. To facilitate this process, we have modified the graphical user interface to enable users to test different threshold values and preview the resulting binarized images. This feature allows users to rapidly assess the threshold sensitivity and choose an offset value that captures the desired structures and dynamics within their own data. We have included additional details describing how users can perform these steps to optimize the value of threshold for their own data in the revised SI and have updated the online tutorial, which is available at the Github site (link: https://github.com/softmatterdb/barcode). The following description was added to page 5: The threshold pixel intensity for binarization is computed from a user-specified offset that provides a threshold percentage %I above the mean intensity of each image (Fig. 2B, top). This process assigns a unique threshold intensity value for each frame, set by its mean value, which corrects for reduced mean intensities of images over time due to photobleaching and other spurious fluctuations in intensity. Response: We appreciate the reviewer’s thoughtful question, and agree that managing the treatment of photobleaching, which is often observed in the visualization of active biomaterials, is important to achieving reliable and interpretable results. We are happy to provide additional details. We used several methods to manage the potential for photobleaching in designing BARCODE. As described above, within the Image Binarization branch, we detect the mean intensity in each frame of the movie, and use the frame-specific mean intensity value to set a frame-specific default threshold value. This ensures that we can reliably detect structures even if the mean (background) intensity changes over time. Separately, we flag videos that we deem too dim for reliable analysis. “Dim” videos are defined as those where the first and last frames have minimum pixel intensity value greater than or equal to 2/e times the mean pixel intensity value for that frame, the video will have a Flag value of 1. Users have the ability to either include these files (which are flagged in the output CSV files) or to ignore them in the analysis. Saturated videos (defined as the case where the mode pixel intensity value is equal to the maximum pixel intensity value for all frames), are also separately flagged. In the Intensity Distribution (ID) branch, photobleaching is accounted for in the determination of skewness and kurtosis, which are measures of distribution symmetry rather than a simple assessment of the distribution mean. In general, one would expect that if photobleaching is the dominant effect that the mean of the intensity distribution would decrease with time, shifting the overall distribution left towards zero. However, the shape of the distribution would likely not change substantially, since the underlying distribution of structures is not changing. By contrast, material restructuring due to aggregation, phase separation or bundling should change the distribution of mass, and therefore intensity, and should lead to changes in the distribution shape, which are detected via the kurtosis and skewness outputs of BARCODE. In the case of severe photobleaching, one might expect that eventually some fraction of low intensity pixels would become so dark that their intensity would not be detectable over the static pixel noise which arises from e.g. stray light, dark camera noise, etc. In this limit, we would expect the shape of intensity distribution, and therefore the kurtosis or skewness, to likely change due to our inability to detect the full distribution (i.e. due to ‘missing events’). A scenario where a substantial fraction of pixels fall within this undetectable limit would therefore be problematic and may lead to unreliable results. This is exactly why we flag frames and videos with low intensity (described above). Additionally, we provide two measures of skewness, based on both the median and mode, which have different sensitivities to a large number of extremal values in the distribution. We have rewritten the description of the IB and ID branch design to include more details about photobleaching in the revised manuscript to clarify this, including this description on page 7. In general, if photobleaching were significant, one would expect the mean of the intensity distribution to decrease with time, shifting the overall distribution left towards zero. However, the shape of the distribution would likely not change substantially, since the underlying structures are not changing. By contrast, material restructuring due to aggregation, phase separation or bundling should change the distribution of mass, and should lead to changes in the distribution shape, which are detected via the kurtosis and skewness outputs of BARCODE. In the case of severe photobleaching, one might expect that eventually some fraction of low intensity pixels would become so dark that their intensity would not be detectable over the static pixel noise which arises from e.g., stray light, dark camera noise, etc. In this limit, we would expect the shape of the intensity distribution, and therefore the kurtosis or skewness, to likely change due to the inability to detect the full distribution (i.e., due to ‘missing events’). To avoid this potential artifact, we flag videos with unusually low intensity and remove these from the analysis; saturated videos are also flagged (see SI Section 1). With regard to the data shown in Fig 2C, we agree that our original description did not accurately capture the distribution features. We appreciate the reviewer’s careful reading. We have corrected this, and substantially rewritten the associated section in the revised manuscript to improve clarity. Response: We encourage users to collect the highest quality data possible and to avoid uneven illumination or other artifacts, as we agree that they could lead to unintended consequences in analysis. We have added the following statement to page 5 to clarify this for the reader. We assume in this analysis that there are minimal spatial variations in background intensity across the field of view. In cases where uneven illumination cannot be avoided, one may need a matrix of threshold values in the binarization branch and a more complex statistical approach to assess changes in the intensity distribution, in order to compensate for poor image quality. While technically possible, this would require significant changes in the calculation of the various BARCODE outputs and/or normalization with a control image(s), which would introduce significantly more computational complexity, which would undermine the HTP aspects of BARCODE for which it was designed. Response: We thank the reviewer for raising this point, and appreciate the opportunity to provide additional details. We agree that flow direction is a parameter that may require some knowledge of the material to interpret, and that flow direction may not be a relevant parameter for all materials. We agree that the identification of co-flowing composite materials is one important outcome. The flow direction metrics also allow us to distinguish directed motion from isotropic motion. As described in more detail in our answer to Question #5 that follows, for directed motion, the flow direction θ indicates the average direction of flow, and the directional spread σ should be approximately zero, since all vectors are pointing in the θ same direction. By contrast, for isotropic motion the flow direction θ should average to zero and the directional spread σ should be >0 . We have included additional information in the θ manuscript in the description of the OF branch outputs on page 8 to address this point, and have updated the SI and tutorial accordingly. In terms of the physical meaning of any particular angle, this is very experiment-specific, and the flow angle can provide physically meaningful information. We have found that many active networks are influenced by the chamber geometry and presence of an aligned side wall of the chamber. For our protein composite work, we often use long channeled chambers, with a large aspect ratio of channel length to width or height, and align this long axis to the microscope stage (and camera) in order to set a physically meaningful angle (in our experiments, this long axis aligns to ) It is also possible to use textured or architected substrates to control 𝜃𝜃 = 𝜋𝜋/2 the directions of active matter flows. These examples are beyond the scope of what we analyzed and presented in this manuscript, but we wanted BARCODE to provide capabilities for this type of analysis. We have expanded the discussion of the data presented in the caption of Figure 3f to the revised manuscript on page 11 to describe this. Response: We appreciate this question. While this was not a scenario we encountered in testing BARCODE, we agree this could be an issue for the motion at or near an extremal value of angle, which range from to . To address this possibility, we have redefined the calculation of −𝜋𝜋 𝜋𝜋 average direction to use circular statistics, which avoid the issue of periodicity at the extremal values of angle. In calculating the mean, we changed the order of operations to take the mean of the x and y components of velocity first, before taking the arctangent. We also implemented use of the two-argument arctan2 function which uses the signs of both the average x and y components of the velocity vector to determine the correct quadrant. We have validated this implementation using simulated flow fields that are aligned to the extremal angle range. Similarly, we now use the circular standard deviation to calculate the directional spread. We have updated the text and SI accordingly. Response: We appreciate the reviewer’s careful reading. As we noted above, active networks are often influenced by the chamber geometry and presence of an aligned side wall of the chamber. For our protein composite work, we often use long-channeled chambers, with a large aspect ratio of channel length to width or height, and align this long axis to the microstage (and camera) in order to set a physically meaningful angle. In our experiments, this long axis aligns to . The plot in Figure 3F is bounded to reflect this preference for fast flows to align to 𝜃𝜃 = 𝜋𝜋/2 1.5 radians; there were no detected values greater than 2 radians or less than – 2 radians in this 𝜃𝜃 = 𝜋𝜋/2 ~ dataset. We have corrected the caption and added the following explanation of the boundary-guided flow alignment . Correlation between𝜃𝜃 = a𝜋𝜋n/d2 flow direction shows fast flows are directed along due to 𝜋𝜋 presence of the cham 𝝈𝝈 b𝜽𝜽er side wall that is a 𝜽𝜽 ligned to that direction, while slow an 𝜽𝜽 d 2 multi- mode videos have no preferred direction. Response: We thank the reviewer for raising this point and we agree that the caption could be improved. Figure 3B shows an array of small panels, representing the three dynamic classes: slow, fast and multi-mode. For each of these three classes, we show three panels, a full-frame 2-channel image (left) showing the actin (green) and microtubule (magenta) structures at the start of the time-series ( ). To the right of this panel, we show temporal colormaps in two half-frames 𝑡𝑡 = 0 of the microtubules only (top right) and actin only (bottom right). For each temporal colormap, images of the network obtained at different times are generated and overlaid, with the image color varying from blue at to red at minutes, as shown in the color legend bar at 𝑡𝑡 = 0 𝑡𝑡 = 6 the top of each temporal colormap pair. With this color-mapping approach, network motions 𝑡𝑡 = 0 𝑡𝑡 = 6 can be clearly identified from the single image, as the colors change from blue to red. Large-scale streaks or lines within this temporal color map indicate regions of large-scale directed displacement over time. Color maps showing many colors but no directed streaks indicate more random, isotropic motion. Color maps with little to no color indicate that the sample lacks dynamic motion over the timescales observed. We updated the caption of Figure 3B to clarify. (B) Multi-channel images of actin (green) and microtubules (magenta), as well as single-channel color-coded temporal projections, in which a 6-min video is collapsed to a single image with features from each frame distinctly colorized according to the time-color scale shown. Response: We apologize for the oversight. We have modified the text on page 6 of the manuscript as copied below, and have updated the SI accordingly. Kurtosis, computed as the fourth central moment of the distribution normalized by the squared variance, , reports the extent to which pixel values are closer to 𝜇𝜇4 ( ) or further from (4 ) the mean than expected for a normal distribution (see 𝐾𝐾 = 𝜎𝜎 −3 SI, Section 1). Positive kurtosis values are indicative of de-mixing, coarsening and/or 𝐾𝐾 < 0 𝐾𝐾 > 0 clustering while negative values indicate uniform and/or space-filling materials. Response: We appreciate the reviewer’s constructive feedback, and are happy to provide additional context. We have extensively modified the manuscript and SI to include more details and descriptions of the techniques we use and the different parameters we compute, and to address each specific point raised by the reviewer. Below, we respond to each question in detail in blue. We have also included below, and in a marked copy of the manuscript, highlights of the sections we revised in dark orange. We also now include much more detail about the physical meaning of the BARCODE parameters and important user considerations, and have streamlined the discussion of the individual examples to address the reviewer’s suggestions. Response: We appreciate the opportunity to provide more details. In selecting these three branches, we focused on how we could leverage established image analysis methods to rapidly access the most important information of material structure and dynamics and enable comparison and downselection for additional screening and analysis of common active materials. We also aimed to improve a user’s ability to more easily share reduced datasets among collaborators and the community, in the form of the 17-metric barcodes and reduced data structures (RDS) that encoded easily understandable information. We did not seek to fully characterize the active materials with BARCODE alone, rather we aimed to provide reliable and actionable information to users to enable decision making with regard to future experiments or analyses, and provide preliminary insights. By using established image analysis methods to generate the RDS, we aim to empower users, including non-experts, to more readily analyze their results and compare their outputs to existing literature values. To clarify this for the reader, we added this sentence to page 4, as well as new references #41-47, as listed in the revied manuscript: We designed each branch to leverage established image processing tools–image binarization (IB), pixel intensity distribution (ID), and optical flow (OF)40–for which there is an extensive body of literature describing approaches, implementation, and best practices41-47. Additionally, we added the following, also on page 4: We compute all 17 metrics (Table 1) using standardized reduced data structures (RDS) (Fig. 1D) –binarized video images (IB), pixel intensity distributions (ID), and velocity fields (OF) – which are automatically produced and archived to facilitate future downstream analysis and that contain substantially more information than the 17-parameter barcode itself. We envision the barcodes will be used primarily for initial assessment and rapid down-selection, and the standardized outputs of the RDS will facilitate more time-intensive and hypothesis-driven downstream analyses. Based on our extensive analysis of active matter systems, we found that BARCODE’s reduced set of 17 simple metrics captures key dynamic and structural information during rapid screens of large datasets. However, the software is material-agnostic, modular and highly adaptable: branches and metrics can be easily added or removed without impacting other metrics, thus providing flexibility, while allowing for discovery of unexpected correlations or trends. In detail, the 17-parameter barcode that is produced from the 3 branches was developed based on our extensive analysis of a wide range of active matter systems, including contractile protein networks and cell monolayers. We found this set of metrics captured key dynamic and structural information with manageable computational complexity, thereby balancing our desire to obtain useful insight into material properties while maintaining the ability to rapidly screen through large datasets. Moreover, the standardized RDS outputs provide additional and richer information, enabling key insights beyond those captured by the 17-metric barcode. The IB branch focuses on structural changes within the material, through analysis of connectivity and the formation of discrete islands (or voids). The ID branch provides information about changes in intensity distribution, which is representative of the redistribution of mass within the material, potentially as a way to assess material compaction or aggregation dynamics. The OF branch assess the speeds and directions of motion of the imaged material. We have emphasized these aspects for the reader in the extensively revised section on “Demonstrating BARCODE workflow and utility” that starts on page 5 of the revised manuscript. We refer to the reviewer to the marked copy of the manuscript to review these changes, which are marked in dark orange in the text. We understand that as BARCODE is applied to the additional material systems, it is likely that additional metrics may be required, and/or some of the existing metrics may not prove as useful. To proactively address this issue, we constructed BARCODE in a modular fashion, allowing the expansion of existing branches or even the addition of new branches to enable the functionality and performance of the platform to grow over time. We believe this modularity is a key strength of the method, and have added more details to the manuscript to make this clearer for the reader – in particular, see p. 4 where BARCODE is first introduced and as copied above. It is difficult to answer the question of what is excluded, as there are many potential paths for analysis that we did not fully explore in this initial work, and enumerating everything not done is not straightforward. We can comment that we are currently testing an additional branch to improve the analysis of diffusive dynamics and anisotropies, leveraging aspects of differential dynamic microscopy and particle tracking. We also plan to expand the scope of the three existing branches to enable two-point correlations between two spatially separated zones, which would begin to allow us to assess how strains, alignment, and even stresses might be correlated over space and time. This would provide new insight into material dynamics and may enable integration of BARCODE outputs into computational or analytical modeling. We are also developing deeper analyses, albeit at lower throughput, of the RDS. All of these expansion efforts are in the early phases, but we have added a few comments to the discussion on pages 18-19 to better describe these future directions for the reader and to emphasize that BARCODE is an open-source platform that we anticipate will expand and evolve over time, based on inputs and innovation from our collaboration and the community, including the following: BARCODE is also highly adaptable: branches and metrics can be easily added or removed, providing flexibility to users and enabling the software to meet the future needs of the community. Planned expansions of BARCODE will include additional branches and metrics that quantify diffusive behavior and mechanical properties, including two-point correlation functions, as well as adding user-identified improvements and user-generated extensions in the future. We are also currently investigating the use of machine learning for data classification and to enable data-driven predictions of material behavior. Response: We originally included a color bar showing the color distribution from the maximum and minimum values of each metric in Figure 2, but agree that including one in Figure 1 would be helpful. We have updated the manuscript to include a similar color bar at the bottom left hand side of Figure 1E. We also included the numeric range of minimum and maximum values for each metric under the barcode output just next to the name of the metric displayed. We have also updated the caption to make this clearer to the reader. Response: We thank the reviewer for raising this point and we agree that there is some redundancy in the paragraph. We have modified this description in the revised text to improve clarity. Response: We agree that the significant dimension reduction through BARCODE, especially in the 17-parameter barcode, inevitably leads to some information loss. However, we have taken care to select metrics that we have found can provide actionable physical insight, even in this limit. Moreover, we have designed BARCODE for use in HTP screening to enable inline experimental decision making and facilitate down-selection for deeper analysis, therefore we have designed for speed and computational simplicity rather than fidelity, accuracy and precision. That said, we have found good agreement between the BARCODE outputs and other published results. In some cases, we have been able to perform quantitative comparisons, for instance of the average speeds of restructuring of active cytoskeletal composites, as shown in Figure 3E. Here, we found excellent agreement between the mean speeds v computed from BARCODE as compared to the previously reported values computed using Dynamic Differential Microscopy (DDM). In analyzing the full published dataset, we also found that the directional spread σ and flow direction θ outputted by BARCODE not only reproduced the published results on a small subset of the data, which were determined using particle image velocimetry (PIV), but also demonstrated the universality of the results for all data in each dynamic class. This result was not previously reported due to the labor-intensive data processing that prevented PIV analysis from being able to be performed on the entire dataset in a tractable time frame (Fig. 3F). We have additionally observed qualitative agreement with previously published results, for example, in assessing connectivity and the (inverse) correlations between percolation and flow dynamics. We have added the following on page 12 to further clarify this for the reader: To corroborate this physical picture, we compare the connectivity and maximum void area for the different classes (Fig 3J), finding that nearly all slow and multi-mode networks remain fully connected (C≈1) and most void areas are below 50%. Conversely, BARCODE reports a range of fractional (C<1) connectivity values for the fast class networks as well as for microtubules, which also have larger void areas (V>50%) and smaller initial island areas compared to actin. Further, to enable users to test the effects of downsampling of raw images to increase throughput, we have added additional details of how downsampling can be executed to balance accuracy and speed on page 5. To increase processing speed and reduce the data size, the resulting stack of binarized images, which is a saved RDS, can be downsampled in time, by choosing to analyze every frame, and spatially, by averaging together windows of pixels to result in a st𝑡𝑡aℎck of images of pixels. We have found that = 10, = 10 and = 2-8 to provide sufficient resolution and accuracy while maintaining rapid processing times (SI Table S1). We have added additional details regarding these design choices and comparisons, in the “Demonstrating BARCODE workflow and utility” section that starts on page 5 and that we have extensively revised. We anticipate that different material systems will demonstrate different sensitivities to dimension reduction, so we hope these additional details will empower users to test for sensitivity to sampling parameters directly in their own data. Response: No, this is not correct, and we appreciate the opportunity to clarify. In fact, many of the barcode parameters assess time dependence by reporting the change in the average value of a metric of interest computed for the last 5% of frames in a time series versus the first 5%. These parameters include: Maximum Island Area Change, Maximum Void Area Change, Kurtosis Change, Mode Skewness Change, Median Skewness Change, and Speed Change. Other metrics, such as the Connectivity and Flow Direction Spread are also sensitive to the entire time trajectory of the video. Additionally, in all branches we provide the RDS as a function of time for the entire time trajectory of each video, not just the first 5% and last 5%. We allow users to downsample the video in space and time to improve throughput; for the systems we describe within this work, we find using every 10th frame generally provides a good balance of information and speed. We view these time-dependent metrics and features of BARCODE to be essential, since capturing time-dependent changes in material structure and dynamics is very important for active systems. We have added additional details emphasizing these features for the reader. We now include this statement on page 2: To produce each barcode, the software also calculates rich reduced data structures (RDS) that enable more detailed understanding of the time-evolving material structures and mechanics, and archives the RDS to enable subsequent hypothesis-driven research. And we added this description to the Discussion on page 19: To increase processing speed and ensure the metrics are applicable to a broad range of materials, we chose to reduce spatial and temporal resolution (downsampling frames and pixels by and ) in calculating the RDS, and to focus on extremal quantities and changes thereof. In general, the metrics that report changes in quantities assess property differences between the beginning and end of each video, so transient changes may not be captured in the lowest dimension barcode matrix. However, the full time-dependent information is saved in the RDS and users can choose to perform advanced analysis on RDS to assess time-dependencies and discern more subtle changes in material structure and dynamics. We have also included more information in the revised section on Demonstrating BARCODE workflow and utility section that starts on page 5 to make this clearer for the reader. We have indicated in the SI what inputs are user-defined in the current implementation of BARCODE. We will also maintain a current list of user-defined quantities in the tutorial, which will capture any changes or additions that are made in the future. Response: The arrows indicate the measured outputs for the networks shown in panel 3B. We have added the following statement to the caption and labeled the arrows to clarify this for the reader. The arrows indicate the data points corresponding to the conditions shown in panel B. Response: Yes. The flow direction metrics allow us to distinguish directed motion from isotropic motion. For directed motion, the flow direction indicates the average direction of flow, and the directional spread should be approximately zero, since all vectors are pointing in the same 𝜃𝜃 direction. By contrast, for isotropic motion the flow direction should average to zero and the directional spread should be > 0 and should increase with increasing disorder. Other flow 𝜃𝜃 types would result in metric values intermediate between these extremal cases. In cases where 𝜎𝜎𝜃𝜃 the barcode is difficult to interpret directly, the OF RDS outputs will allow the user to visualize changes in the flow field on a per-frame basis to improve understanding. We have included additional information in the manuscript in the description of the OF branch outputs on page 9 to address this point, and have updated the SI and tutorial accordingly. Response: We have rewritten the results section of the manuscript to add context and help the reader interpret the BARCODE outputs in light of the physical parameters of the various materials. Newly added text is marked in dark orange in the revised manuscript. Response: The dynamics in the microtubule networks arise from the kinesin motors crosslinking and pulling microtubules relative to another. As we describe in the SI Methods section, each time-series analyzed to produce the results shown in Fig 4H-M was collected at 1.33 frames per second for a duration of 300 s (4000 frames). From BARCODE, we measure average speeds of the microtubules in the range ~10-1 to ~10 µm/s. Response: Thank you for the encouragement to add additional details about the advantages and limitations of the technique. We have rewritten the discussion to include these details, see page 18-19. Response: We appreciate this constructive criticism and have substantially rewritten the manuscript to address these concerns. We have added a new description of the advantages and disadvantages on pages 18-19, and have provided more background and physical interpretation of the data obtained from the various material sets. We appreciate the reviewer’s suggestion to remove examples from the study, but we think it is important to include them all to demonstrate the full power of BARCODE to analyze a wide area of protein- and cell- based active materials. However, to address the reviewer’s suggestion we have substantially simplified and streamlined our discussion of each example, including simplifying the figures; and we have revised the text substantially to provide more description of the technique and contextualization of the results, as described in detail above. New text added in the revision has been marked in dark orange in the revised manuscript. Response: In the current iteration of BARCODE, we can accept tif stacks and .nd2 files as inputs; however, it is straightforward to incorporate other files types in our I/O structures, and we are currently beta testing functions to read and analyze .avi and .czi files as well. We intend to describe all improvements to the code and all versions on our Github site, but have not yet released these for general use and prefer not to describe these changes in detail in this manuscript. To better clarify this for the reader, we have added this description on page 4: As fully described in the Supplemental Information (SI, Section 1), BARCODE is a Python-based package that reads .tif and .nd2 video files and converts the data into arrays with dimensions ( ), where is the number of frames, and are the number of pixels along the horizontal and vertical axis of each frame, and is the number of 𝑇𝑇,𝑚𝑚,𝑛𝑛,𝑐𝑐 𝑇𝑇 𝑚𝑚 𝑛𝑛 channels (for, e.g., confocal videos with multiple components of a material labeled with 𝑐𝑐 distinct fluorophores and recorded in separate detectors). The software has a user-friendly graphical user interface (GUI) and several adjustable parameters that the user can set to tailor and optimize the operations of each branch for their system. We also provide a detailed online tutorial48 to guide users in choosing parameters that best suit their data. Response: In terms of video size, videos of at least 5 frames are required, and the maximum file sizes are limited by the computational power of the analysis hardware and the time-constraints of throughput of the analysis. In terms of time-dependent quantities - the step size used to calculate the RDS for each of the three branches can be adjusted based on number of frames needed to give adequate sampling of the material dynamics. Spatial downsampling is also available. In general, inclusion of longer videos and more videos will enable stronger statistical confidence in the results, as expected. To answer this question in detail requires several assumptions about data quality and material dynamics, which are difficult to justify without a specific material system in mind. For the materials we analyze and present, we find that videos of 256 256 size and 250 frames are generally sufficient to extract useful information. These details were included in Table S1 in the ⨉ SI. Response: We appreciate the suggestion and agree. We have substantially rewritten the manuscript and in particular, the section ‘Demonstrating BARCODE workflow and utility’ to address. New text added in the revision has been marked in dark orange in the revised manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors responded satisfactorily to my questions", "reviewer_2": "I am quite happy with the revised manuscript. I especially like the discussion of photobleaching, and of the circular statistics (a term which I had not heard before, but which I just learned a fair bit about from the internet). I do have two minor concerns which the authors can address if they wish; theser are optional.\n1. I am still a little concerned about the Image Binarization branch. It seems like this is one of the few places in the BARCODE algorithm where there is user discretion. The Discussion section says \"To ensure BARCODE is broadly accessible, we designed it to require minimal subjective inputs...\" and this is true and a strength; nonetheless, results may vary based on those subjective inputs. I'm not even sure my own ability to threshold an image is consistent from day to day. (Although I agree that the 10% default in the BARCODE software is sensible and will ensure consistency for many users.) I would guess the authors are reluctant to add a caveat to the Discussion section or elsewhere in the main text, but I suggest adding some statement to the Supplemental Information that notes the results will depend on the choice of the offset. Or perhaps a recommendation in the software itself that suggests users stick with the default when possible.\n2. I have a minor question about supplement Sec. 2.2. It states \"A high skewness value indicates that the distribution has a long tail on one side, while a low skewness value suggests symmetry in the intensity distribution around the mean.\" This is slightly incorrect. S = 0 suggests symmetry. S < 0 is possible and would suggest asymmetry of the opposite sort of S > 0. I did a quick numeric check. If most pixels are value 1 but 1% are value 0, then S = -9.7. If instead 1% are value 2, then S = +9.7. And to confirm, if 1% are value 0 and another 1% are value 2, then S = 0."}, "author_response": "We thank the reviewer for their positive responses, input and time. We have addressed the two minor concerns below. 1. We appreciate the reviewer’s perspective, and have made the following changes: The description of the binarization threshold (offset) has been updated on our Github page (https://github.com/softmatterdb/barcode/blob/main/README.md; doi: 10.5281/zenodo.17585070) as follows: Controls the threshold percentage of the mean which binarizes the image; offset parameter determines the binarization threshold for a given frame as ( , where represents the mean pixel intensity for frame . Users are cautioned that 1+𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜)𝐵𝐵(𝑖𝑖) changes to the offset value can affect the outputs of the Image Binarization branch. 𝐵𝐵(𝑖𝑖) 𝑖𝑖 The tutorial (available here: https://www.livingbam.org/barcode-tutorial) has been updated to read: The Binarization Threshold is an important parameter that determines what pixels within the image represent material, and should be set to 1, and which are background or noise, and should be set to zero. The Binarization Threshold is calculated for each frame as ((1 + Offset) * Mean Pixel Intensity). The offset is user-defined. By default, BARCODE uses an offset of 0.1. Users should take care when adjusting this value, as it can affect the results of the Binarization Branch. The effect of the threshold offset selection is demonstrated below by transforming the raw image into the following binary datasets based on various threshold values. As the threshold decreases more noise can be seen while as it increases more features are lost. The best offset value for the following examples would be 0.1 or 0.25. 2. We have modified the text as follows: A high positive (or negative) skewness value indicates that the distribution has a long tail of high (or low) intensities relative to the mean, whereas a skewness value near zero indicates that the intensity distribution is more symmetric around its mean."}]

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