SLAS Technology

Organ-on-chip (OoC) systems are increasingly used to generate human-relevant evidence in drug discovery and preclinical development, yet interpretation can be limited by the interaction of biological variability, device-to-device variability, and technical assay noise. Here, we describe the UstarFlowAI barrier-confidence framework, an in silico assay-readiness approach that quantifies whether a predefined permeability-related effect is resolvable under specified uncertainty. The proposed context of use is decision support for laminar, low-Reynolds-number airway epithelial monolayer barrier OoC assays in which device fabrication, pump performance, flow, wall shear stress, and biological fluctuation may affect permeability-related readouts. Monte Carlo simulations propagate defined manufacturing, biological, systemic, and well-level uncertainty and estimate the minimum detectable shift (MDS) under a prespecified decision rule. In the present proof-of-concept design, the reference workflow was parameterized at 9.4% technical coefficient of variation (CV), whereas the standardized workflow was parameterized at 5.0% CV, corresponding to an approximately 46.8% reduction in the modeled technical-noise component. At n = 48 replicate wells per group and a nominal total assay-noise operating point of 17% CV, a predefined 10% permeability shift exceeded the upper MDS uncertainty bound for the standardized workflow in both best- and worst-case scenarios, whereas it overlapped the 95% MDS interval for the reference workflow and therefore did not meet the prespecified PASS criterion. These findings demonstrate the behavior of the computational framework under the stated assumptions; they do not constitute experimental validation, regulatory qualification, or evidence that a 10% shift is universally meaningful across barrier OoC systems. We therefore propose a prospective wet-lab validation strategy in which model predictions are tested against airway barrier measurements generated under controlled sources of technical and biological variability. The framework is intended to complement, rather than replace, the underlying biological model by making assay uncertainty and decision thresholds explicit and auditable.

Point-of-care (POC) molecular detection has enabled rapid and accessible testing at or near the site of need, facilitating timely decision-making in clinical, field, and industrial settings. Despite these advancements, molecular detection platforms remain limited in their ability to prepare DNA from complex tissue matrices, restricting their applicability to a narrow range of sample types. To address sample preparation inefficiencies, focused ultrasound extraction (FUSE) has been developed for the rapid release of DNA from robust tissue matrices. FUSE disintegrates target tissue and releases DNA by delivering high pressure ultrasound pulses that generate a cavitation bubble cloud. In prior work, FUSE was performed using large, laboratory-based equipment. Here, a miniaturized FUSE device was designed and tested to enable POC DNA sample preparation. Design constraints were defined based on prior FUSE experiments, and based on these metrics, a 750 kHz cylindrical transducer was fabricated. The pressure output was measured, and high-speed optical imaging demonstrated that the transducer generated sustained cavitation within the defined focal region. The feasibility of DNA extraction from piscine and timber tissues was evaluated, and results show that the device can release DNA with quality suitable for PCR-based detection from both tissue types. These results demonstrate the potential of this device to rapidly extract DNA from complex samples and expand the applicability of molecular detection platforms.

At-home sampling can address obstacles to participation in transcriptomics research. We previously developed the homeRNA and homeRNAmax platforms; these custom-designed tubes containing RNA stabilizer interface with a blood tube compatible with upper-arm blood collection devices, allowing for blood collection and RNA stabilization to occur from a participant's home. In this work we introduce homeRNAdrop, an insert for a commercially available blood tube (BD Microtainer) that divides the tube into two compartments: the top compartment holds 200 L blood and the bottom compartment holds ~590 L liquid stabilizer (RNAlater). By combining blood collection and RNA stabilization into the same tube, homeRNAdrop streamlines sample preparation for participants and allows for more compact sample storage for the lab. In a remote pilot study, n=25 participants from across the United States tested the homeRNAdrop kit. The n=23 samples successfully received by the lab show that samples collected using homeRNAdrop are of sufficient RNA quality (all RINs > 6, a common cutoff for analysis) and yield (mean yield=1.44 g) for downstream gene expression analysis. Almost all participants who successfully collected blood using the Tasso Mini device found the homeRNAdrop device easy to use (n=23/24). Overall, this work adds a simplified, user-friendly platform to the growing suite of tools for remote blood RNA stabilization.

Fermentation is a process through which microorganisms are used to transform basic molecules into high value substances in various applications such as biopharma, food production, and biofuel generation. In such processes, monitoring the health of microbial cells is essential, as it is a key factor in the final product yield. Traditional methods of assessing the health of fermenter populations have involved manually extracting samples from the fermenter for benchtop testing, which lacked the accuracy and efficacy of real-time continuous measurements. This has opened the door for inline sensors to be integrated into fermenters for continuous measurement; including microfluidic based Electrochemical Impedance Spectroscopy (EIS). EIS utilizes a range of frequencies to gain insight into the characteristics of microbial populations, such as cell size and membrane capacitance. EIS cytometry, in particular, has emerged as a relevant tool, as it enables single-cell level detection in real-time. However, current EIS cytometry devices cannot be deployed directly within fermenters due to the high cell density conditions. This has limited EIS analysis in fermenters to bulk measurements averaged across a population. Here, we present a microfluidic chip that uses passive cell-flux-partitioning geometry to direct cells from high-density culture through a single-cell-constrained flow path containing integrated impedance-sensing electrodes. The proposed chip combines geometric channel design and embedded electrodes to enable continuous impedance measurements from cell-containing flow while operating at input cell densities on the scale of 10^9 CFU/mL. This work demonstrates a first step toward integrating single-cell-constrained microfluidic handling with impedance sensing for inline monitoring in industrial biomanufacturing.

Functional protein micro/nanofibers integrate high specific surface areas with bioactive architectures but remain hampered low production throughput and severe processing instability. Focused rotary jet spinning (FRJS) shows promising to break these throughput constraints while enabling direct, conformal deposition onto complex, irregular substrates. However, navigating FRJS's narrow processing windows in proteins remains failure-prone without closed-loop experimental guidance. Here, we report FiberPro 1.0, a large language model driven multi agent framework that unites high-throughput spinning with real-time experimental feedback for autonomous design, execution, and optimization. Across three protein systems, FiberPro 1.0 converged on spinnable formulations within an average of two iterations. To demonstrate high-throughput conformal deposition in a translational setting, FiberPro designed a zein-based active packaging system applied directly onto diverse food matrices. The resulting conformal coating combined potent antibacterial activity with real-time freshness monitoring, markedly suppressing Escherichia coli and extending shelf life. This work connects autonomous AI reasoning with high-throughput processing, establishing a verifiable route for scalable manufacturing and conformal coating of functional protein micro/nanofibers.

The modeling of aroma release in food science is a complex, multidisciplinary field integrating mass transfer theory and principles of physical chemistry. While mechanistic models have long been used to understand volatile release, their practical adoption has been hindered because existing implementations are often private, "in-house" tools that lack reproducibility and extensibility. This paper introduces digimouth, an open-source Python framework designed to provide a unified, modular, and reproducible environment for simulating and fitting aroma release kinetics. A key innovation of digimouth is its handling of events and stages. Events represent discrete, instantaneous changes in the system (such as opening a bottle or swallowing), while stages allow for multi-phase simulations where experimental conditions or physical dynamics change over time. To demonstrate the tool's utility, the authors implemented established in vitro convection models and performed sensitivity analyses on parameters such as air-water partition coefficients and mass transfer coefficients. The package also includes an optimization module based on the least-squares method to fit parameters to experimental data. Validation against real-time data from Proton Transfer Reaction-Time of Flight- Mass Spectrometry (PTR-ToF-MS) shows a satisfactory fit, with an average error of approximately 2%. Ultimately, digimouth facilitates robust model development and hypothesis testing, offering the food science community a collaborative tool to better understand the mechanisms of flavor release.

High-throughput enzymology enables quantitative characterization of enzyme function across hundreds to thousands of sequence variants and experimental conditions. Nevertheless, the scale and complexity of these datasets create substantial challenges for analysis and quality control. High-Throughput Microfluidic Enzyme Kinetics (HT-MEK), for example, generates large microscopy datasets that must pass through multiple analytical stages, including image processing, initial-rate fitting, and kinetic modeling. Choices or errors made at any stage can propagate into the final kinetic parameters without being evident from fit statistics alone. Here, we provide a broadly applicable guide for analyzing high-throughput enzymology data from the HT-MEK platform, using Michaelis-Menten kinetics as a representative example. We first outline the conceptual workflow from raw fluorescence measurements to estimates of kcat, KM, and kcat/KM. We then discuss common experimental and analytical failure modes and present a framework for deciding when data should be refitted, filtered, qualified, or repeated. Finally, we provide a step-by-step workflow using Mercury, an open-source Python framework that integrates scalable HT-MEK data processing with traceable quality control and diagnostic visualization. This workflow preserves the connection between reported parameters and their underlying measurements and can be adapted to other kinetic models and biochemical assays.

Influenza A virus (IAV) defective interfering particles (DIPs) are regarded as promising broad-spectrum antivirals due to their high efficacy demonstrated in animal trials. Previously, an OP7 chimera DIP was generated that showed markedly enhanced antiviral efficacy compared to conventional IAV DIPs. In addition, a high-yield cell culture-based production process for OP7 chimera DIPs was established in perfusion mode. However, due to the technical complexity of this technology, we aimed to combine the advantages of perfusion in terms of yield with that of a simple and less complex batch process. In the present work, we established a fed-batch production process for OP7 chimera DIPs that allows a more straightforward implementation into existing production facilities. Using a feeding regime that was able to overcome the cell density effect, we achieved productive virus infection in stirred tank reactors until a cell density of 10E+6 cells/mL. Furthermore, we obtained a high virus titer of 3.8 log10(HAU/100 uL), corresponding to a 3.4-fold increase compared to a reference batch production. Moreover, the produced material demonstrated a significantly increased interfering bioactivity shown by an in vitro potency assay. Interestingly, this efficacy was similarly high as compared to OP7 chimera DIP material previously produced in perfusion mode, despite twofold lower virus titers. Finally, we demonstrate robustness of OP7 chimera DIP seed virus trains, which is significant step towards later pharmaceutical production. Further, corresponding GMP MDCK-PB2(sus) cell and OP7 chimera virus banks are now available for later biomanufacturing further use. In conclusion, the established fed-batch process offers a valid n additional option for pharmaceutical grade OP7 chimera DIP GMP manufacturing production under GMP conditions required for subsequent clinical testing and for later large-scale manufacturing.

Data-driven soft sensors can estimate fermentation product concentrations from routinely recorded process variables, but strong performance during normal operation does not establish reliability during process deviations. This study evaluated current-time penicillin-concentration estimation using 100 simulated IndPenSim batches comprising 90 normal-operation batches and ten documented deviation batches, with 113,935 observations in total. Initial normal-trained models and five follow-up experiments examined complete-batch validation, dependence on batch-progress features, phase-specific error, model-family comparisons, early out-of-distribution warnings and empirical prediction ranges. These analyses motivated a matched comparison between normal-only and fault-inclusive HistGradientBoosting regressors using 36 current and causal history-based inputs. Normal performance was evaluated in five regime-balanced complete-batch folds, and deviation performance by leave-one-fault-batch-out evaluation; every tested deviation batch was excluded from its own model fit. Batch-balanced sample weights were used, with a factor of three assigned to permitted deviation batches in fault-inclusive fitting. On held-out deviation batches, pooled RMSE decreased from 3.195 to 2.564 g/L, a reduction of 19.73%, while MAE decreased from 2.076 to 1.441 g/L and R2 increased from 0.8580 to 0.9085. Normal-operation RMSE was nearly unchanged at 1.981 and 1.983 g/L. Eight of ten deviation batches improved. The mean paired fault-batch RMSE difference was -0.7063 g/L, with a descriptive 95% batch-bootstrap interval of -1.2844 to -0.2194 g/L. Improvement was largest within the first-to-last recorded fault-reference window, but late-stage errors persisted. Batch 100 remained poorly predicted, with fault-inclusive RMSE of 6.631 g/L and R2 of -2.4837. A fault-risk classifier, Isolation Forest OOD detector and empirical error ranges offered incomplete reliability information. Fault-inclusive training therefore improved this benchmark on average, but did not establish generalization to unseen fault mechanisms, calibrated safety warnings or deployment in physical fermentation.

Studying algae requires specialized equipment to characterize culture growth and physiological performance and non-invasive monitoring of these cultures typically relies on optical density (OD) measurements. Currently, OD is often measured using standard spectrometers that suffer from detector saturation at high cell densities, which requires time consuming sample dilutions and lacks compatibility with flat-walled cell culture flasks like Roux bottles. Here we describe a 3D-printable device to measure optically dense algal cultures directly within these cultivation vessels. This instrument combines light attenuation and scattering, resulting in a dual-sensor system that utilizes near-infrared light (950 nm) to precisely quantify biomass without interference from changing photosynthetic pigments. Cultures that reach high cell densities can be assumed to be accurately quantified in situ using the scattered signal, preserving the integrity of the culture and eliminating the need for subsampling. This method is adaptable to the most common geometries of Roux bottles (T-25, T-75, and T-175) and could be used to monitor small-scale batch cultures without contamination risks. A cyanobacteria algal species (Limnospira platensis) and three flask sizes were analysed on light attenuation linearity and scattering detection limits at different cell concentration, demonstrating that the light attenuation module can reliably measure biomass up to an OD of 3.1 (equivalent to >1.5 g/L) without requiring dilution.

The perception of flavor during food consumption is a complex process governed by the dynamic release of aroma compounds, which is influenced by the interaction between the food matrix and human physiology. While mechanistic models have been developed to simulate these phenomena, their practical application has often been limited by the lack of flexible software tools capable of integrating diverse food structures and real-time physiological data. This study addresses this gap by incorporating in vivo mechanistic models for both liquid foods and melting solid matrices in a modular Python-based framework: digimouth. The modeling approach treats the oral, pharyngeal, and nasal cavities as a series of interconnected reactors with mass transfer governed by ordinary differential equations (ODEs). These models explicitly integrate key physiological parameters, including salivary flow, bidirectional respiratory airflow, and discrete events such as mastication and swallowing. For solid foods, the framework incorporates additional mechanisms for matrix dissolution, temperature-dependent melting, and dynamic surface area change. The models were validated using experimental data from 30 subjects who consumed aroma solutions and flavored gelatin gummy discs while monitored by PTR-MS (Proton Transfer Reaction - Mass Spectrometry). The simulations successfully reproduced the main kinetic features and release profiles observed experimentally for both types of matrices. Despite some limitations related to the self-reporting of physiological events and the non-inclusion of initial orthonasal mass transfer in the models, the developed software package proved to be a robust and evolutive tool. It offers a foundation for exploring inter-individual variability and the impact of complex food structures on sensory perception.

Oxygen (O2) transport is a major limitation in high-density cell culture, particularly as three-dimensional (3D) spheroids are increasingly used to better mimic in vivo tissue environments. Here, we developed a poly(4-methyl-1-pentene) (PMP)-based microwell platform, leveraging its lower molecular absorption and greater mechanical stability than the commonly used O2-permeable polydimethylsiloxane (PDMS), while retaining high O2 permeability. We measured O2 permeability in 50-m-thick PMP membranes and found approximately 200-fold greater O2 permeability than the conventional 1-mm polystyrene culture-plate material. PMP membrane-based microwell plates with 350-m-diameter wells maintained spatial separation of pre-formed hepatocyte spheroids while supplying O2 from the plate bottom, synergistically enhancing spheroid oxygenation. Compared with non-O2-permeable plates, the platform reduced localized hypoxia and hypoxia-associated cell injury while improving spheroid viability and growth. Collectively, PMP-based O2-permeable microwell plates provide a promising, scalable, high-throughput platform for spheroid culture with multiple applications in tissue engineering, drug screening and beyond.

Emerging efforts to engineer multicellular kidney tissues for drug discovery, disease modeling, and therapeutic repair require scalable generation of kidney organoids derived from human induced pluripotent stem cells (hiPSCs). Here, we report a method for generating kidney ureteric bud and collecting duct epithelial organoids in stirred tank bioreactors. We demonstrate that extracellular matrix microcarriers facilitate organoid expansion and are compatible with suspension bioreactor methods. The combination of cell-coated matrix microcarriers with stirred bioreactors promotes maturation marker expression compared to static controls comprising matrix-embedded organoids. Our method offers promise for generating epithelial organoids that serve as multicellular models and organ building blocks for scalable biofabrication.

Motivation: Real-time qPCR data are commonly analyzed to obtain a Cq values, which are used to compare the relative abundance of a targeted template. However, improper background subtraction distorts Cq and changing the assigned threshold can alter experimental conclusions. In addition, a Cq values provide no information on the quality of the qPCR for a given sample. Alternatively, global fitting of qPCR data provides agnostic quantification without the use of Cq or thresholds, and the fitting terms provide a useful metric of reaction performance. Current implementations of global fitting are limited to custom in-house programs. Results: qPyCR is comprehensive analysis tool written in Python that performs background signal subtractions, computes a threshold, and reports Cq values in a traditional manner. The program also performs global fitting and reports template abundances along with reaction quality values. The quality values can be used to assign performance standards, which allow researchers to distinguish aberrant reactions from true changes in template abundance. Availability and Implementation: Code and example data are freely available on GitHub (https://github.com/sdmoore-labs/qPyCR) and on Zenodo (https://doi.org/10.5281/zenodo.21115342).

Lack of robustness and high variability in cardiomyocyte (CM) differentiation from human induced pluripotent stem cells (hiPSCs) remain critical challenges for cardiac regenerative medicine and biological research. Guided by developmental principles of lineage specification, we investigated whether early WNT inhibition could expand the process window of CM differentiation and thereby improve robustness. To test this, we compared early WNT inhibition from days 1--5 with standard inhibition from days 3--5 following a 1-day CHIR99021 (CHIR) treatment for WNT activation. Focusing on CHIR concentration as a critical parameter, we found that early WNT inhibition substantially broadened the concentration window yielding [≥]80% differentiation efficiency across distinct hiPSC lines. Extending the analysis to a two-dimensional parameter space incorporating cell density revealed a markedly expanded [≥]80% process window. Temporal analysis of lineage-specific transcription factor expression showed that early WNT inhibition suppressed paraxial mesoderm (PM) specification, likely through MSGN1 suppression, in the range of high initial WNT activation that otherwise favored PM fate, thereby extending the process window. It also suppressed definitive endoderm (DE) specification within the expanded window, promoting a more favorable lateral plate mesoderm/cardiac fate. We further demonstrated substantially reduced variability under parameter perturbations and across wells and batches, accompanied by higher differentiation efficiency, without compromising CM phenotypes. These findings demonstrate early WNT inhibition as an effective strategy for expanding the process window of CM differentiation and improving robustness, while exemplifying the process engineering principle that a broader window can support more robust differentiation, with potential relevance to stem cell--based bioprocesses.

In breast cancer, collagen fiber alignment and the second harmonic generation forward-to-backward ratio (F/B), an optical measurement sensitive to fibril-scale organization within collagen fibers, have each been associated with metastatic outcome. Although both are measured at the tumor-stromal interface, in vitro studies have largely examined them separately, because experimental systems have not allowed the two to be varied independently. To address this technical gap, we used a microfluidic biofabrication approach to generate paired aligned and unaligned regions within 3D collagen hydrogels via extensional strain and varied measured F/B across gels by changing the pre-gel pH. The resulting library covered alignment values reported in breast tumors and F/B values associated with poorer metastatic outcome in invasive ductal carcinoma at matched matrix stiffness, pore diameter, and fiber fraction. In MDA-MB-231 cells, alignment biased migration along the fiber axis without changing migration speed, as expected, but the magnitude of the contact guidance response varied with measured F/B. In contrast, MCF-7 cells showed little overall contact guidance, yet migrated ~30% faster in high F/B than low F/B gels in both aligned and unaligned regions. Together, these results show that cells migrated differently in matrices with similar fiber alignment. Thus, including measured F/B may provide additional information to help anticipate how cells migrate in collagen environments that appear similar based on alignment alone.

The human pancreas secretes digestive fluid that flows through an extensive ductal network before emptying into the duodenum. However, little is known about how hydrodynamic forces influence pancreatic function and development. We hypothesized that pancreatic cells respond to physiological levels of fluid shear stress. In this study, we developed a microfluidic system featuring reversible sealing between the device layer and the cell culture substrate, enabling (1) conventional open-well cell culture prior to fluid shear stress application and (2) selective patterning of proteins and cells for migration studies. Channel dimensions were chosen to support 24-hour perfusion using standard syringe pumps. Using this platform, we investigated effects of physiological shear stress levels representing basal and activated pancreatic function (2, 8 and 33 mPa) on human iPSC-derived pancreatic progenitor cells. Pancreatic progenitor cells exhibit graded sensitivity to shear stress at the transcriptomic level. Specifically, shear stress upregulated expression of cell cycle-related gene sets; endocrine-associated genes including NEUROG3; and Notch receptor ligands (DLL1, DLL3) in a magnitude-dependent manner. The exposure to unidirectional fluid flow also resulted in a reduction in YAP1 protein content, decreased WWTR1 gene expression, and downregulation of YAP/TAZ target genes. Furthermore, barrier-based migration assays under shear stress revealed that cell patches expanded their boundaries both along and against the flow, with greater distance in the direction of flow. Our system and findings may help lay the foundation for understanding biophysical regulation of developmental processes in a physiologically important, yet hard-to-access, epithelial model system.

Organ-on-a-chip (OOC) technology is a valuable tool that can facilitate a faithful replication of target tissues or organs by enabling the application of important physiological cues that cells experience in their native environment. We describe here an endothelium-on-a-chip that was developed for studying endothelial barrier permeability in diabetic and diabetic kidney disease (DKD) milieu under physiologically relevant shear stress. The fluorescently labelled macromolecular tracer flux assay was applied to obtain apparent permeability (Papp) values. We demonstrated the protective effect of shear stress in our EoaC in diabetic and DKD milieu by testing endogenous diabetic and DKD factors individually (hyperglycaemia, pro inflammatory factor TNF[a], and hypertension-related hormone aldosterone) and in combination (aldosterone in hyperglycaemia, as well as a DKD mix consisting of TNF[a] and aldosterone in hyperglycaemia). To assess the applicability of our microfluidic EoaC for drug testing, we added the mineralocorticoid receptor antagonist, finerenone, and observed a statistically significant protective effect of this pharmaceutical on endothelial barrier integrity in DKD milieu in our model system. We conclude that this EoaC can be applied for endothelial barrier permeability evaluation not only in diabetic and DKD milieu, but in various pathophysiological situations. Moreover, it has shown a potential to be used in drug testing targeted to improve endothelial barrier resilience.

Paper-based colorimetric sensors offer a low-cost and accessible platform for point-of-care (POC) analysis, but enzyme activity loss during coating and drying can weaken analytical signals and require high enzyme loadings or complex immobilization procedures. Although surfactants are widely used to improve wettability in paper-based assays, their potential contribution to colorimetric performance beyond these effects remains unclear. Here, we investigated surfactant-assisted colorimetric signal enhancement in a glucose assay implemented on a 96-puddle paper plate (96-PPP) and identified Tween 20 as the most effective surfactant. Its effect on detection performance became more pronounced as glucose oxidase (GOx) loading decreased; at 0.1 mg/mL GOx, Tween 20 lowered the limit of detection (LoD) from 0.113 to 0.034 mg/mL (approximately 3.3-fold) over a working range of 0-5 mg/mL, despite no statistically significant change in the measured contact angle at this loading. Tween 20 had no appreciable effect on the reaction in solution but preserved 95% of the apparent reaction rate constant after drying, compared with 11% without it, and atomic force microscopy (AFM) revealed a more dispersed dried enzyme morphology on mica. Tween 20-containing sensors also showed slower signal decay during repeated wetting-drying cycles and thermal stress, retained 77% (vs 26%) of the response at 400 mM NaCl, and exhibited within-PPP and between-batch coefficients of variation (CVs) below 10% (vs 12.3-19.5%), while maintaining glucose selectivity over potentially interfering molecules. These results indicate that Tween 20 enhances paper-based glucose sensing beyond wettability, in part by retaining enzyme cascade activity during drying, although the contributions of the individual enzymes and the underlying mechanism remain to be established.

A central question in developmental biology is how spatiotemporal embryo morphological events and cell differentiation are precisely coordinated to eventually develop into a mature organism. Previous research has shown cell positions and asymmetric division stages are related to cell lineage specification since morula stage. However, the detailed spatiotemporal dynamics and histories of embryonic cells and their relations with cell lineage specification remain incompletely understood from the onset of embryo development onward. My study on in-vitro embryos showed that continuous live cell tracking mapped community-like patterns in cell origin and differentiation. Cell positions, cell temporal factors (such as cell division order and cell cycle) and their histories intricately interacted over pre- and peri-implantation stages. These interactions of spatiotemporal cellular activities directed embryo morphological events and asymmetric cell lineage origin and differentiation throughout 2-cell to around 100-cell stage. In conclusion, this study provides comprehensive insights into the cellular spatiotemporal dynamics of cell inheritance and differentiation during pre- and peri-implantation morphological events and cell lineage specification. By longitudinally integrating spatial and temporal parameters relating to early embryogenesis, the findings not only bridge divergent explanations stemmed from different studies in the field but also offers a refined foundation for evaluating embryo potential and improving outcomes in assisted reproductive technologies, stem cell research, and regenerative medicine.