A method and system for evaluating the differentiation potential of cardiac stem cells

Abstract

This invention discloses a method and system for assessing the differentiation potential of cardiac stem cells, relating to the field of biometrics. It is used for efficient and accurate prediction of cell differentiation potential. Under hypoxic conditions, continuous dynamic imaging of the mitochondrial network of a single cardiac stem cell is performed using a live-cell workstation. Morphometric algorithms are used to quantify network connectivity and cristae remodeling status, establishing a single-cell resolution mitochondrial dynamic remodeling atlas. This atlas is then spatiotemporally correlated with synchronously acquired cellular energy metabolome data to identify specific phenotypic patterns with high differentiation potential. Based on these patterns, a multidimensional feature training set is constructed, and a differentiation potential prediction model is established using a random forest algorithm to screen key precursor features. Finally, the prediction model is used to score the cells to be evaluated, and functional verification is performed by combining single-cell mitochondrial genome copy number variation spectra, screening for high-differentiation-potential cardiac stem cell subpopulations, providing a precise solution for evaluating stem cell differentiation potential.

Description

Technical Field

[0001] This invention relates to the field of biometrics, specifically to a method and system for assessing the differentiation potential of myocardial stem cells. Background Technology

[0002] With the continued rise in the incidence of cardiovascular diseases, myocardial tissue regeneration has become an important research direction in regenerative medicine. Cardiac stem cells, possessing the potential to differentiate into mature cardiomyocytes, are considered an important cellular source for repairing damaged myocardium. However, in the practical application of stem cell transplantation and regenerative therapy, myocardial stem cells from different sources and in different states exhibit significant differences in differentiation capacity and functional performance. This heterogeneity severely affects the stability of regenerative therapy and its clinical translation efficiency. Accurately assessing the differentiation potential of myocardial stem cells in the early stages of cell culture is a crucial step in achieving high-quality cell screening and precise regenerative therapy.

[0003] Existing methods for assessing differentiation potential primarily rely on endpoint detection and population-level analysis. For example, common morphological observation methods depend on microscopic images to identify changes in cell morphology, which are highly subjective and fail to reflect early molecular trends in differentiation. Biochemical index detection or gene expression analysis typically involves sampling at fixed time points, making it difficult to capture key turning points in the dynamic process of cells. While some methods attempt to combine metabolomics or proteomics data, they often overlook the synergistic relationship between mitochondrial structural remodeling and metabolic activity, resulting in weak generalization ability of the established assessment models and insufficient accuracy in predicting differentiation potential at the single-cell level. Furthermore, traditional algorithms often employ linear regression or cluster analysis, which cannot fully reveal the nonlinear dependencies in high-dimensional biological data, leading to problems such as model overfitting or poor biological interpretability. This limits the reliability of differentiation potential assessment results in actual stem cell screening and quality control. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method and system for assessing the differentiation potential of myocardial stem cells, thus solving the problems mentioned in the background.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the differentiation potential of cardiomyocyte stem cells, comprising the following steps: S1. Under hypoxia-induced conditions, continuous dynamic imaging of the mitochondrial network morphology of a single cardiomyocyte stem cell is performed using a live-cell workstation, and the degree of network connectivity and the state of mitochondrial cristae remodeling are quantified using morphometric algorithms to establish a mitochondrial dynamic remodeling atlas at single-cell resolution; S2. The mitochondrial dynamic remodeling atlas is spatiotemporally correlated with synchronously acquired cellular energy metabolome data to identify mitochondrial network connectivity enhancement accompanied by cristae remodeling and synchronous... S3. Based on the identified specific phenotypic patterns, a training set containing multidimensional features of mitochondrial morphology-metabolome is constructed. A differentiation potential prediction model is established using the random forest algorithm. The model selects the precursor mitochondrial remodeling features that contribute the most to the differentiation potential prediction by calculating the feature importance weights. S4. The differentiation potential of the cardiomyocytes to be evaluated is assessed based on the differentiation potential prediction model. The high-scoring cell subpopulations are functionally validated by combining the single-cell mitochondrial genome copy number variation spectrum, and a high-differentiation-potential cardiomyocyte subpopulation is selected.

[0006] Furthermore, the specific process of continuous dynamic imaging of the mitochondrial network morphology of a single cardiomyocyte stem cell using a live-cell workstation is as follows: a stable hypoxic environment below atmospheric oxygen partial pressure is maintained in a sealed culture chamber, and live cells are stained using a specific fluorescent probe sensitive to mitochondrial membrane potential; three-dimensional structural images of the mitochondrial network within a single cardiomyocyte are automatically acquired at preset time intervals using a confocal microscope in time-series scanning mode; simultaneously, dissolved oxygen concentration, pH value, and temperature parameters within the culture chamber are monitored and recorded in real time by an environmental controller to ensure the stability of the cell's physiological state during imaging.

[0007] Furthermore, the process of establishing a dynamic mitochondrial remodeling atlas at single-cell resolution was as follows: The acquired temporal 3D images were preprocessed by denoising and segmentation to extract the mitochondrial network skeleton structure at each time point; the network connectivity was quantified by calculating the number of network branches, branch length, and loop closure parameters based on graph theory algorithms; simultaneously, the orientation and complexity features of the mitochondrial cristae were extracted using texture analysis algorithms; and the number of network branches, branch length, loop closure, orientation and complexity features of the mitochondrial cristae were integrated according to the time series to construct a remodeling atlas reflecting the dynamic changes in the mitochondrial network topology and internal cristae remodeling of a single cardiomyocyte in the early differentiation stage.

[0008] Furthermore, the specific process of performing spatiotemporal correlation analysis between the mitochondrial dynamic reconstruction map and the synchronously acquired cellular energy metabolome data is as follows: While performing live cell imaging, a small amount of culture supernatant is collected at regular intervals using a microfluidic chip, and the concentration changes of tricarboxylic acid cycle intermediates are detected using liquid chromatography-mass spectrometry; the metabolite concentration-time curves are time-registered with the mitochondrial dynamic reconstruction map, and a correlation matrix between mitochondrial morphological parameters and metabolite concentrations is established using multivariate statistical analysis methods to identify morphological-metabolic feature combinations that undergo synergistic changes within the same time window.

[0009] Furthermore, the specific process for identifying a specific phenotypic pattern characterized by increased mitochondrial network connectivity, accompanied by cristae remodeling and simultaneous accumulation of tricarboxylic acid cycle intermediates is as follows: Cell populations that simultaneously meet the following three conditions are screened through cluster analysis: the number of mitochondrial network branch points maintains an increasing trend within continuous observation periods and the loop closure reaches a preset standard; cristae texture analysis shows that the complexity characteristic value of the internal membrane structure exceeds the basic threshold; and the concentrations of intracellular citrate, α-ketoglutarate, and succinate show a stable upward trend at the same time points. Cell populations that meet this combination of multimodal characteristics are defined as a specific phenotypic pattern of cardiomyocyte stem cells with high differentiation potential.

[0010] Furthermore, based on the identified specific phenotypic patterns, a training set containing multidimensional features of mitochondrial morphology and metabolome is constructed. The logical process of establishing a differentiation potential prediction model using the random forest algorithm is as follows: The dynamic change curve of the number of mitochondrial network branch points, the evolution trajectory of loop closure, the cristae complexity feature value sequence, and the time-series data of tricarboxylic acid cycle intermediate concentrations are extracted from the validated training samples to form an initial feature pool. The aforementioned mitochondrial dynamic morphological features and energy metabolism features are standardized and feature-engineered to construct a sample-feature matrix. A parallel training mode using a multi-decision tree structure of the random forest algorithm is employed, with node splitting optimized based on the Gini coefficient minimization principle. Through multiple rounds of iterative training, the model learns the nonlinear mapping relationship between early mitochondrial dynamic features and final differentiation potential.

[0011] Furthermore, the specific process of selecting the precursor mitochondrial remodeling features that contribute the most to the prediction of differentiation potential by calculating the feature importance weights is as follows: the contribution of each feature to the splitting of decision tree nodes is calculated based on the out-of-bag error, and the influence of each feature on the prediction accuracy of the model is quantified by the feature ranking importance assessment; the ranking of mitochondrial network connectivity-related features and cristae remodeling parameters in the weight distribution is analyzed, and feature combinations with importance weights exceeding a preset threshold are selected and defined as the precursor mitochondrial remodeling feature set with early prediction value.

[0012] Furthermore, the differentiation potential of the cardiomyocytes in the batch to be evaluated was assessed based on the differentiation potential prediction model, and the functional verification of high-scoring cell subpopulations was performed by combining the single-cell mitochondrial genome copy number variation spectrum. The specific process is as follows: The mitochondrial dynamic reconstruction map and metabolome data of the cells to be evaluated were input into the trained prediction model to obtain the differentiation potential prediction score of each cell and sort them accordingly; the cell subpopulations with the top percentile of the prediction scores were subjected to single-cell mitochondrial genome sequencing, and the genomic stability of high-potential cell subpopulations was verified by analyzing the mitochondrial DNA copy number variation and the integrity of key gene coding regions.

[0013] Furthermore, the logical process for screening high-differentiation potential cardiomyocyte stem cell subpopulations is as follows: A dual screening criterion of prediction score and mitochondrial genome stability is established, and a prediction score threshold and a genome integrity threshold are set; firstly, cell individuals whose prediction scores reach the high-quality threshold are screened to form an initial selection population; in the initial selection population, cells with normal mitochondrial DNA copy numbers and no significant structural variations are further screened; cells that simultaneously meet the prediction score criteria and genome stability criteria are identified with specific markers to form the final high-differentiation potential cardiomyocyte stem cell subpopulation library.

[0014] A differentiation potential assessment system for cardiomyocyte stem cells includes the following modules: a mitochondrial dynamic imaging module, a metabolic correlation analysis module, a differentiation potential modeling module, and a screening and validation module. The mitochondrial dynamic imaging module is used to continuously and dynamically image the mitochondrial network morphology of single cardiomyocyte stem cells under hypoxia-induced conditions using a live-cell workstation, and quantifies the degree of network connectivity and mitochondrial cristae remodeling state using morphometric algorithms to establish a mitochondrial dynamic remodeling atlas at single-cell resolution. The metabolic correlation analysis module is used to perform spatiotemporal correlation analysis between the mitochondrial dynamic remodeling atlas and simultaneously acquired cellular energy metabolome data to identify increased mitochondrial network connectivity, accompanied by… The model identifies specific phenotypic patterns of cristae remodeling and simultaneous accumulation of tricarboxylic acid cycle intermediates. A differentiation potential modeling module constructs a training set containing multidimensional features of mitochondrial morphology and metabolome based on the identified phenotypic patterns. A differentiation potential prediction model is established using a random forest algorithm, where the model selects precursor mitochondrial remodeling features that contribute most to differentiation potential prediction by calculating feature importance weights. A screening and validation module assesses the differentiation potential of cardiomyocytes in the batch to be evaluated based on the differentiation potential prediction model. It also performs functional validation of high-scoring cell subpopulations by combining single-cell mitochondrial genome copy number variation profiles, selecting cardiomyocyte subpopulations with high differentiation potential.

[0015] The present invention has the following beneficial effects:

[0016] (1) A method for assessing the differentiation potential of cardiomyocyte stem cells. This method involves continuous dynamic imaging of individual cardiomyocyte stem cells under hypoxia-induced conditions, combined with morphometric algorithms to quantify mitochondrial network connectivity and cristae remodeling status, establishing a dynamic mitochondrial remodeling atlas at single-cell resolution. This method can capture the continuous changes in mitochondrial morphology under in vivo cell conditions, avoiding information loss caused by traditional fixation staining or endpoint detection, and enabling simultaneous characterization of cellular energy metabolism and structural remodeling processes. Through spatiotemporal correlation analysis with cellular energy metabolome data, this invention reveals the dynamic coupling relationship between mitochondrial morphological remodeling and metabolic pathway activation, achieving accurate identification of key metabolic inflection points in the early stages of differentiation, and providing a high-dimensional, multimodal biological characteristic basis for subsequent potential modeling.

[0017] (2) A differentiation potential assessment system for cardiomyocyte stem cells constructs a multidimensional training set of mitochondrial morphology-metabolome features by identifying specific phenotypic patterns, establishes a differentiation potential prediction model using a random forest algorithm, and screens out precursor mitochondrial remodeling features through feature importance calculation. This model can mine key driving factors affecting differentiation potential in high-dimensional nonlinear data, thereby significantly improving the stability and accuracy of prediction. Finally, functional verification is performed by combining single-cell mitochondrial genome copy number variation spectrum, forming a closed-loop system of prediction-verification, ensuring that the model results have biological interpretability and reproducibility. Overall, this invention realizes a systematic assessment process from cell morphology and dynamics to metabolic state and then to functional verification, which can efficiently screen out cardiomyocyte stem cell subpopulations with high differentiation potential, providing a reliable technical means for stem cell quality control and regenerative medicine applications.

[0018] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0019] Figure 1 This is a flowchart of a method for assessing the differentiation potential of myocardial stem cells according to the present invention.

[0020] Figure 2 This is a flowchart of a system for assessing the differentiation potential of myocardial stem cells according to the present invention. Detailed Implementation

[0021] This application provides a method and system for assessing the differentiation potential of myocardial stem cells, which solves the problems of existing differentiation potential assessment methods, such as single assessment indicators, lack of dynamic monitoring methods, difficulty in establishing morphological and metabolic correlation models, and lack of prediction and verification mechanisms.

[0022] The overall approach of the scheme in this application is as follows: taking mitochondrial dynamic remodeling as the starting point, multimodal coupling analysis of cell morphology and energy metabolism processes is performed. By establishing a mitochondrial morphology-metabolome joint feature space, key dynamic features highly correlated with myocardial differentiation potential are extracted. Then, machine learning algorithms are used to achieve feature importance ranking and potential prediction modeling, thereby forming a quantifiable and verifiable differentiation potential assessment system, providing a basis for the screening and quality control of myocardial regeneration cells.

[0023] Please see Figure 1 This invention provides a technical solution: a method for assessing the differentiation potential of cardiomyocyte stem cells, comprising the following steps: S1. Under hypoxia-induced conditions, continuous dynamic imaging of the mitochondrial network morphology of a single cardiomyocyte stem cell is performed using a live-cell workstation, and the degree of network connectivity and the state of mitochondrial cristae remodeling are quantified using morphometric algorithms to establish a mitochondrial dynamic remodeling atlas at single-cell resolution; S2. The mitochondrial dynamic remodeling atlas is spatiotemporally correlated with synchronously acquired cellular energy metabolome data to identify increased mitochondrial network connectivity, accompanied by cristae remodeling and the simultaneous appearance of tricarboxylic acids. S3. Based on the identified specific phenotypic patterns, a training set containing multidimensional features of mitochondrial morphology-metabolome is constructed. A differentiation potential prediction model is established using the random forest algorithm. The model selects the precursor mitochondrial remodeling features that contribute the most to the differentiation potential prediction by calculating the feature importance weights. S4. The differentiation potential of the cardiomyocytes in the batch to be evaluated is assessed according to the differentiation potential prediction model. The high-scoring cell subpopulations are functionally validated by combining the single-cell mitochondrial genome copy number variation spectrum, and a high-differentiation-potential cardiomyocyte subpopulation is selected.

[0024] In this implementation scheme, S1. Under a hypoxia-induced environment, continuous dynamic imaging of the mitochondrial network morphology of a single cardiomyocyte stem cell is performed using a live-cell workstation. The hypoxia-induced environment refers to controlling the oxygen partial pressure of the culture system at a low level to simulate the hypoxic microenvironment of the myocardium, thereby stimulating the stress-induced metabolic reprogramming response of the stem cells. The live-cell workstation is a comprehensive device with real-time control functions for temperature, gas concentration, and microscopic imaging, enabling long-term dynamic observation without damaging the physiological state of the cells. Morphometric algorithms are used to analyze the imaging results, quantitatively calculating the degree of mitochondrial network connectivity (i.e., the fusion-division state characteristics between mitochondria) and cristae remodeling state (i.e., the remodeling characteristics of the mitochondrial inner membrane folding structure), establishing a mitochondrial dynamic remodeling atlas at single-cell resolution. This step can accurately reveal the precursor manifestations of cellular metabolic dynamics changes at the structural level, providing basic data for subsequent feature correlation. S2. The obtained mitochondrial dynamic remodeling atlas is spatiotemporally correlated with simultaneously acquired cellular energy metabolome data. Metabolomics data refers to the full spectrum of cellular metabolites obtained through mass spectrometry or nuclear magnetic resonance (NMR) techniques, covering intermediates of the tricarboxylic acid cycle (TCA cycle), redox coenzymes, and fatty acid metabolism-related molecules. Using time-series alignment and feature registration algorithms, specific phenotypic patterns appearing during the synchronous phases of increased mitochondrial network connectivity, active cristae remodeling, and accumulation of TCA cycle intermediates are identified. This step aims to reveal the coupling relationship between structural dynamics and energy metabolism, determine which mitochondrial morphological changes significantly co-occur with metabolic activation processes related to myocardial differentiation, and thus extract potential functional precursor patterns. S3. Based on the identified specific phenotypic patterns, a multidimensional training set containing mitochondrial morphology and metabolomics features is constructed, and a differentiation potential prediction model is established using the random forest algorithm. The random forest algorithm is an ensemble learning algorithm based on multiple decision trees. By randomly sampling input features multiple times and training the model, it can effectively avoid overfitting and improve prediction stability. The model calculates the importance weights of each input feature and selects the precursor mitochondrial remodeling features that contribute the most to differentiation potential prediction—that is, the combined structural-metabolic indicators that reflect the differentiation trend in the early stages of stem cell development. The core of this step lies in transforming experimental observations into predictive models, enabling mitochondrial dynamics and metabolic characteristics to be used for machine learning-driven quantitative assessment of potential. S4. Based on the trained differentiation potential prediction model, the potential of the myocardial stem cell samples to be evaluated is scored, and functional validation is performed using single-cell mitochondrial genome copy number variation profiles. Mitochondrial genome copy number variation profiles refer to the statistical patterns of mitochondrial DNA copy number changes at the single-cell sequencing level, reflecting the heterogeneity of mitochondrial biogenesis and energy metabolism activity. Through functional validation of high-scoring cell subpopulations (such as the expression of myocardial-specific markers and detection of electrophysiological characteristics), stem cell subpopulations with high differentiation potential are screened out.This step forms a prediction-validation closed loop, ensuring the interpretability and reliability of the model output in a biological sense.

[0025] Specifically, the process of continuously and dynamically imaging the mitochondrial network morphology of a single cardiomyocyte stem cell using a live-cell workstation is as follows: a stable hypoxic environment below atmospheric oxygen partial pressure is maintained in a sealed culture chamber, and live cells are stained using a specific fluorescent probe sensitive to mitochondrial membrane potential; three-dimensional structural images of the mitochondrial network within a single cardiomyocyte are automatically acquired at preset time intervals using a confocal microscope in time-series scanning mode; simultaneously, dissolved oxygen concentration, pH value, and temperature parameters within the culture chamber are monitored and recorded in real time by an environmental controller to ensure the stability of the cell's physiological state during the imaging process.

[0026] In this implementation scheme, the live-cell workstation is used to simultaneously maintain cellular physiological homeostasis and controllable experimental conditions during imaging. Its core components include a microscopic imaging system, a gas and temperature control module, and an automated sampling control program, enabling long-term dynamic observation without damaging cell viability. A hypoxic environment is used to simulate myocardial ischemia or early developmental physiological states, inducing metabolic remodeling in stem cells and making mitochondrial fusion-division activities more representative, thus reflecting their differentiation readiness. Mitochondrial membrane potential-sensitive fluorescent probes are molecular markers whose fluorescence intensity changes based on the difference in membrane potential between the inner and outer membranes of mitochondria. Their fluorescence signals reflect changes in mitochondrial functional activity and network connectivity. The confocal microscope time-series scanning mode can acquire optical slice images layer by layer in three-dimensional space and continuously scan at preset time intervals, thereby obtaining the dynamic remodeling process of the mitochondrial network. Through reconstruction and morphometric analysis of the time-series images, the network connectivity, morphological complexity, and cristae folding characteristics of mitochondria can be extracted, providing a high-quality data foundation for subsequently establishing a single-cell resolution mitochondrial dynamic remodeling atlas.

[0027] Specifically, the process of establishing a dynamic mitochondrial remodeling atlas at single-cell resolution, using morphometric algorithms to quantify the degree of network connectivity and the remodeling state of mitochondrial cristae, is as follows: The acquired temporal 3D images are preprocessed by denoising and segmentation to extract the mitochondrial network skeleton structure at each time point; the degree of network connectivity is quantified by calculating the number of network branch points, branch length, and loop closure parameters based on graph theory algorithms; simultaneously, the orientation and complexity features of the mitochondrial cristae are extracted using texture analysis algorithms; and the number of network branch points, branch length, loop closure, orientation and complexity features of the mitochondrial cristae are integrated according to the time series to construct a remodeling atlas reflecting the dynamic changes in the mitochondrial network topology and internal cristae remodeling of a single cardiomyocyte in the early stages of differentiation.

[0028] In this implementation scheme, the acquired temporal 3D images are first denoised (e.g., using 3D median filtering or 3D nonlocal mean filtering) to reduce the impact of imaging noise on subsequent segmentation. Adaptive thresholding or Otsu global thresholding is used for initial segmentation of the fluorescence signal. Then, morphological opening and closing operations are used to remove small artifacts and fill tiny holes, obtaining mitochondrial voxels (binary voxels). The original intensity can be preserved during segmentation for subsequent texture analysis. 3D skeletonization is performed on the binary voxels to extract the mitochondrial network skeleton structure (connected branches and nodes), and a corresponding undirected graph G is generated for subsequent graph theory analysis. Network topology metrics include the number of branch nodes. Parameter explanation: N; Total number of nodes in the skeleton graph; ;node Degree (number of connected edges) in the skeleton diagram; The indicator function takes a value of 1 if the condition is true, and 0 otherwise. B reflects the actual number of branches in the network and is a direct measure of the degree of network structure. Average branch length. ; Parameter explanation: Q; The total number of segments on the skeleton identified as independent "branches"; ;No. The length of each branch segment (path length counted in voxels). L describes the typical scale of the branch; the tendency of a mesh network to have more short branches or fewer long branches will be reflected in L. Loop closure (based on loop number normalization) is calculated by first calculating the cycle cardinality of the graph: Then normalized to closure degree: Parameter explanation: E; Total number of edges (connections) in the skeleton graph; V; Total number of vertices (nodes) in the skeleton graph; χ; Number of connected components (connected branches), usually taken as 1 when the network is connected; μ; Cycle cardinality (number of independent loops in the graph); C; Loop closure (the larger the value in the range of 0 to 1, the more closed loops there are relative to the number of edges). C characterizes the closed loop component in the mesh, reflecting the degree of ring mesh structure better than the simple branch count. III. Kurcula Texture and Complexity (Texture Analysis and Fractal Measurement) Kurcula orientation consistency is calculated based on the eigenvalues ​​of the local structure tensor: Parameter explanation: The largest eigenvalue of the structure tensor within a given local window; Minimum eigenvalue within the same window; O; Orientation consistency index (values ​​close to 0 indicate no obvious directionality, values ​​close to 1 indicate high directional consistency). O is used to quantify the directional strength of local fiber / slat arrangement in crenellation texture, reflecting the degree of order in internal crenellation reconstruction. Crenellation complexity (numerical estimation) at scale sets. Upper estimate Parameter explanation: The p-th box scale (pixel / voxel scale); ;by scale Count the number of boxes covering the binary projection / section of the ridge; Number of scale sampling points; An estimate of the fractal dimension (reflecting texture complexity); Log-log linear regression was performed and the slope was taken. F characterizes the self-similarity and complexity of the internal cristae texture; increased complexity is often associated with functional reconstruction. IV. Temporal Integration and Dynamic Characteristics (Discrete-Time Approximation Formula) The set of indicators collected at each time point. (e.g., B(t), L(t), C(t), O(t), F(t)), construct the following dynamic characteristics: mean state and wave average: Parameter explanation: Total number of time points; The r-th sampling time point; The time-series average of index m within the observation window. Variance (volatility): Parameter explanation: The time variance of index m (reflecting the amplitude of fluctuation). The rate of change (early slope), estimated using a linear approximation: Parameter explanation: : Observation time window length (equal to ); :index The overall rate of change. The area under the curve (cumulative response) is approximated by the trapezoidal rule: Parameter explanation: Time series The integral approximation over the observation window (reflecting the cumulative effect). The above time-series features (mean, fluctuation, slope, area) are used as time-domain terms in the training set to characterize early dynamic change patterns. Feature normalization and composite scoring are applied to each original feature. Normalization: Parameter explanation: The minimum value of this feature on the reference training set; The maximum value of this feature on the reference training set; Normalized features, ranging from [0,1]. Composite connectivity score (example): Constructed based on normalized B, L, and C features: Parameter explanation: , , ; these represent the normalized number of branches, average branch length, and loop closure, respectively; , , The corresponding weighting coefficients satisfy... (Normalization constraint); CCS; Composite connectivity score.

[0029] Specifically, the spatiotemporal correlation analysis of the mitochondrial dynamic reconstruction map and the synchronously acquired cellular energy metabolome data is as follows: While performing live cell imaging, a small amount of culture supernatant is collected at regular intervals using a microfluidic chip, and the concentration changes of tricarboxylic acid cycle intermediates are detected using liquid chromatography-mass spectrometry; the metabolite concentration-time curves are time-registered with the mitochondrial dynamic reconstruction map, and a correlation matrix between mitochondrial morphological parameters and metabolite concentrations is established using multivariate statistical analysis methods to identify morphological-metabolic feature combinations that undergo synergistic changes within the same time window.

[0030] In this implementation scheme, the synchronous acquisition of cell morphology changes and metabolite release is achieved through the synchronized control of a microfluidic chip and a live-cell imaging system. A microfluidic chip is an experimental platform that uses micron-level channels to precisely control liquid flow, allowing for the time-varying extraction of minute amounts of supernatant from the culture chamber without interfering with cell growth, thus obtaining high-temporal-resolution data on metabolite changes. The acquired supernatant is analyzed using liquid chromatography-mass spectrometry (LC-MS) to quantitatively detect the changes in the concentration of key intermediates in the tricarboxylic acid cycle (TCA cycle), such as citric acid, α-ketoglutarate, succinic acid, and malic acid, over time. These metabolites reflect changes in the activity of cellular energy metabolism pathways. Subsequently, the metabolite concentration change curves are time-registered with the aforementioned mitochondrial dynamic reconstruction map on the time axis. Specifically, by recording the synchronization signals of the imaging timestamp and the microfluidic sampling timestamp, the two are time-aligned to ensure a one-to-one correspondence between mitochondrial morphological characteristics and corresponding metabolic states within each time window. After time registration, multivariate statistical analysis methods (such as canonical correlation analysis or partial least squares regression) were used to establish a correlation matrix between mitochondrial morphological parameters (e.g., number of network branch points, loop closure, cristae complexity, etc.) and metabolite concentrations. This matrix reflects the linear or nonlinear coupling strength between different mitochondrial structural features and energy metabolism levels. By analyzing highly correlated feature pairs in the correlation matrix, combinations of morphological-metabolic features that undergo synergistic changes within the same time window can be identified. For example, if a simultaneous increase in succinate and malate concentrations is detected along with an increase in mitochondrial network connectivity, it can be inferred that the cell is in a state of high energy metabolism and active mitochondrial remodeling, which typically corresponds to higher myocardial differentiation potential. This step, through spatiotemporal coupling analysis of multi-source data, modeled the correspondence between mitochondrial structural dynamics and cellular metabolic activities, providing crucial correlation information for subsequent feature extraction and prediction of differentiation potential models.

[0031] Specifically, the process of identifying a specific phenotypic pattern characterized by increased mitochondrial network connectivity, accompanied by cristae remodeling and simultaneous accumulation of tricarboxylic acid cycle intermediates is as follows: Cell populations that simultaneously meet the following three conditions are screened through cluster analysis: the number of mitochondrial network branch points maintains an increasing trend within continuous observation periods and the loop closure reaches a preset standard; cristae texture analysis shows that the complexity characteristic value of the internal membrane structure exceeds the basic threshold; and the concentrations of intracellular citrate, α-ketoglutarate, and succinate show a stable upward trend at the same time points. Cell populations that meet this combination of multimodal characteristics are defined as a specific phenotypic pattern of cardiomyocyte stem cells with high differentiation potential.

[0032] In this implementation plan, "identifying specific phenotypic patterns" essentially involves screening highly synergistic cell subpopulations from multimodal data (morphological and metabolic features). The aim of this step is to reveal the typical mitochondrial morphological and metabolic coupling characteristics exhibited by cardiac stem cells in the early differentiation stage, thereby establishing a biomarker pattern that can be used to determine differentiation potential. First, using the aforementioned mitochondrial dynamic reconstruction atlas, time-series analysis is performed on the morphological indicators of each cell. By calculating the changing trend of the number of mitochondrial network branch points over multiple consecutive observation periods, it is determined whether the degree of network integration is continuously increasing. Here, "maintaining a growth trend" means that the derivative of the number of network branch points with time is positive, i.e., dN / dt>0, where N represents the number of network branch points and t represents time. Loop closure, as an indicator of the complexity of the mitochondrial network structure, is compared with empirical standard values ​​to determine whether the network structure has reached a mature connectivity state. Second, for the internal cristae structure of mitochondria, texture analysis algorithms are used to calculate complexity feature values, such as entropy, contrast, and directional consistency based on the gray-level co-occurrence matrix (GLCM). When the combined score of these indicators exceeds a set threshold, it indicates that significant spatial remodeling has occurred in the cristae, suggesting that mitochondria are in a highly active state. The threshold is determined by using the average complexity of a healthy control cell population. with standard deviation Based on the baseline, take the threshold. Where k is an empirical coefficient, its optimal value was determined through cross-validation. Next, the changing trends of three key intermediates—citric acid, α-ketoglutarate, and succinic acid—were analyzed using time-series data of metabolite concentrations. When all three show a continuous upward trend at the same time point, it indicates an increase in the tricarboxylic acid cycle flux and active energy metabolism. Finally, cell populations meeting the above three conditions were subjected to cluster analysis. Specifically, K-means or hierarchical clustering algorithms were used to search for high-density regions in the morphological-metabolic joint feature space to identify cell subpopulations that simultaneously possess enhanced network connectivity, active cristae remodeling, and metabolite accumulation characteristics. This subpopulation exhibited consistent synergistic activation of energy metabolism and mitochondrial structure across multiple modal dimensions and was defined as a high-differentiation potential phenotype pattern.

[0033] Specifically, based on the identified specific phenotypic patterns, a training set containing multidimensional features of mitochondrial morphology and metabolome is constructed. The logical process of establishing a differentiation potential prediction model using the random forest algorithm is as follows: The dynamic change curve of the number of mitochondrial network branch points, the evolution trajectory of loop closure, the cristae complexity feature value sequence, and the time-series data of tricarboxylic acid cycle intermediate concentrations are extracted from the validated training samples to form an initial feature pool. The aforementioned mitochondrial dynamic morphological features and energy metabolism features are standardized and feature-engineered to construct a sample-feature matrix. A parallel training mode using a multi-decision tree structure of the random forest algorithm is employed, with node splitting optimized based on the Gini coefficient minimization principle. Through multiple rounds of iterative training, the model learns the nonlinear mapping relationship between early mitochondrial dynamic features and final differentiation potential.

[0034] In this implementation plan, a biologically validated training sample set is first selected (including several single-cell samples representing high and low differentiation potential). For each sample, raw time-series data items are extracted from previously obtained mitochondrial dynamic reconstruction maps and metabolite time series, such as: the sequence of network branch point number over time, the sequence of loop closure over time, the sequence of cristae complexity over time, and the sequence of concentrations of several tricarboxylic acid cycle intermediates over time. These raw time-series quantities are aggregated by sample to form a raw feature pool for the next step of standardization and feature engineering. The conversion from time series to static / kinetic features is performed for each type of raw time series. (Time series data of mitochondria or metabolites) are transformed into static and dynamic features usable by machine learning through a set of time-domain operators, such as: time-series mean, time-series variance, early rate of change, cumulative response, and number of local peaks. In this way, each sample is mapped from several time-series quantities to a fixed-dimensional feature vector, facilitating the construction of a sample feature matrix. The calculation formula and parameter descriptions are as follows: Let a certain original time series data of a sample be denoted as... The following time series statistics are defined: mean: Parameter explanation: The total number of sampling points in this time series; Sample Index; Original time-series category index; ;sample At any moment The corresponding original timing values; The mean of the time series within the observation window. Variance (volatility): Parameter explanation: Time series The sample variance reflects the magnitude of temporal fluctuations. Early slope (rate of change, slope obtained using least-squares linear fitting): Parameter explanation: ;No. The coordinates of each sampling time point; ; Arithmetic mean at time points; Time series The slope of the linear fit reflects the overall upward or downward trend. Cumulative response (time integral, trapezoidal approximation): Parameter explanation: : The area measure (approximate integral value) of time series x within the observation window. Local peak counts (number of peaks) can be calculated using algorithms and do not require formula expansion here; the result is denoted as... ,in This represents the number of times the peak occurs in the time series of sample u. Standardization (normalization / de-scaling) involves calculating the mean and standard deviation of each feature obtained above on the training set and then performing z-score standardization. Parameter explanation: The k-th original engineered feature of sample u (e.g., the mean or slope of a certain time series); : The mean of the samples for the k-th feature in the training set; : The standard deviation of the k-th feature in the training set; Standardized feature values. (Z-score standardization makes features with different dimensions and magnitudes comparable during model training. A sample-feature matrix is ​​constructed, and the standardized feature vector for each sample is...) Stacked vertically, forming a size of The sample-feature matrix (where Represents the number of training samples. (This represents the feature dimension). It also provides corresponding biological labels for each sample. (For example, binary labels such as "high differentiation potential" / "low differentiation potential" or continuous differentiation scores), as target variables for supervised learning. Total number of training samples; The total number of features contained in each sample; The standardized feature vector of sample u; The label or target score of sample u. Training of individual trees is integrated with forest ensembles, with each tree recursively splitting until a stopping condition is met (e.g., the number of samples per node is below a certain minimum number of samples). Or reach maximum depth After training, the error is estimated using out-of-bag (OOB) samples to obtain the tree's performance metrics. The final prediction of the random forest is the average (regression) or majority vote (classification) of all tree predictions. Parameter explanation (for stopping conditions): Minimum number of samples required for a leaf node; The maximum depth allowed for a single tree. (Number of trees): based on OOB error... Based on the convergence behavior, the minimum value corresponding to the OOB error curve tending to stabilize is selected. This method ensures that the number of trees is stable without excessively wasting computational resources. Feature subset size. Commonly used in classification tasks As an initial value, it is fine-tuned in cross-validation to optimize OOB or cross-validation error. Cross-validation was used to determine the target, aiming to balance bias and variance, avoiding overfitting due to excessive depth or underfitting due to excessive shallowness. Feature importance was assessed and weighted based on the permutation importance of out-of-bag errors for each feature. Calculate the importance of permutations in a trained forest: Parameter explanation: Error of the original model on out-of-bag samples (error rate or AUC-related measure for classification, MSE for regression); ; Features After permutation in the OOB samples, the model's error on the permuted samples; ;feature The permutation importance is determined by the numerical value; a larger value indicates a greater contribution of the feature to the model performance. Importance is then converted into weight coefficients for all features. calculate Then, nonnegation and normalization are performed to obtain the weights. : Parameter explanation: : The weighting coefficients used for composite scoring or ranking after normalization; Feature importance (permutation importance value); The cumulative variable of the feature index. Model validation and stability assurance: Cross-validation and out-of-bag estimation are used to assess model stability, and several independent biological validations are performed: the model is tested to predict scores on new samples outside the training set, and experimental validation is conducted on several representative cells with high predictive differentiation potential (such as expression of myocardial-specific differentiation markers or functional electrophysiological tests). Simultaneously, robustness testing is performed on the model (e.g., adding noise to input features, performing sensitivity analysis to changes in sampling frequency) to ensure that the model is not overly sensitive to small measurement biases.

[0035] Specifically, the process of selecting the precursor mitochondrial remodeling features that contribute the most to the prediction of differentiation potential by calculating the feature importance weights is as follows: the contribution of each feature to the splitting of decision tree nodes is calculated based on the out-of-bag error, and the influence of each feature on the prediction accuracy of the model is quantified by evaluating the importance of feature arrangement; the ranking of mitochondrial network connectivity-related features and cristae remodeling parameters in the weight distribution is analyzed, and feature combinations with importance weights exceeding a preset threshold are selected and defined as the precursor mitochondrial remodeling feature set with early prediction value.

[0036] In this implementation scheme, the contribution of a feature to the model is calculated. A random forest consists of multiple decision trees, each using different features at different nodes for sample classification or regression. When a feature significantly reduces sample impurity (i.e., makes classification or prediction more accurate) at a split node, it indicates a significant contribution to the model results. Here, the Out-of-Bag Error (OOB) approach is adopted: during training, each tree retains some samples not selected for training (out-of-bag samples), which are used to evaluate the tree's predictive performance. By comparing the change in OOB error before and after a feature is randomly shuffled, the importance of that feature can be calculated. The calculation method can be simplified into the following expression: ;in: :feature Importance weights; The total number of decision trees in a random forest; : No. The out-of-bag error of a tree under normal conditions; : in the Tree General Characteristics The out-of-bag error is obtained by randomly shuffling the values ​​of . The larger the value, the more significant the decrease in predictive performance due to disrupting the feature, indicating the greater the importance of the feature. This quantifies the importance distribution of features. It yields the importance distribution of all features. After assigning values, they are normalized so that the importance weights of each feature are distributed within the range of 0 to 1. Then, they are sorted according to their numerical values. This method allows a clear view of which mitochondrial dynamic features (such as the growth rate of network branch points and the slope of loop closure evolution) or metabolic features (such as the concentration change rate of key intermediates in the tricarboxylic acid cycle) play a core role in model evaluation. Key precursor features are then screened. An importance threshold is set. This is used to define which features have predictive significance. The threshold can be determined using the cumulative contribution rate method: when the cumulative contribution rate is... When the sum reaches 85% of the sum of the importance of all features, that position is taken. As .Right now: Make in: Total number of features; The importance value of a feature when its cumulative importance ratio first exceeds 85%. This forms the early prediction feature set. All features that satisfy this condition... The characteristics are defined as those with "predictive value." These characteristics often reflect signs of structural reorganization of the mitochondrial network in the early stages of cell differentiation, such as: a continuous increase in the number of network branches and a tendency to close; an increase in cristae complexity parameters accompanied by the accumulation of energy metabolism intermediates; and a high intensity of synergistic changes between dynamic morphology and metabolism. The significance of this screening step lies in automatically identifying those signals that predict the potential of stem cells before they differentiate from a large amount of morphological and metabolic data, achieving early, non-destructive discrimination.

[0037] Specifically, the process of assessing the differentiation potential of cardiomyocyte stem cells in the batch to be evaluated based on the differentiation potential prediction model, and verifying the functionality of high-scoring cell subpopulations by combining single-cell mitochondrial genome copy number variation profiles, is as follows: The mitochondrial dynamic reconstruction map and metabolome data of the cells to be evaluated are input into the trained prediction model to obtain the differentiation potential prediction score for each cell and sort them accordingly; single-cell mitochondrial genome sequencing is performed on the cell subpopulations with the top percentile predicted scores, and the genomic stability of high-potential cell subpopulations is verified by analyzing the mitochondrial DNA copy number variation and the integrity of key gene coding regions.

[0038] In this implementation plan, model prediction and evaluation are performed. Using the previously constructed and trained differentiation potential prediction model, the mitochondrial dynamic remodeling atlas (i.e., the morphological changes of mitochondria in the time series) and corresponding metabolome data of each cardiomyocyte stem cell to be evaluated are input into the model. Based on the previously learned nonlinear mapping relationship, the model outputs a differentiation potential prediction score. This can be represented by a simplified functional relationship: ;in: Mitochondrial morphological feature vectors (such as network branch point growth rate, loop closure evolution parameters, cristae complexity change rate, etc.); Metabolic feature vectors (such as the concentration trends of key intermediates in the tricarboxylic acid cycle). The random forest prediction function obtained through training. The model output. A higher value indicates that the cell is statistically closer to the characteristic space of cells previously labeled as having "high differentiation potential." This is based on all cells to be evaluated. By sorting the values, the relative distribution pattern of differentiation potential can be obtained. High-potential subpopulation screening. To verify the biological rationale of the model, cell populations with prediction scores in the top percentile (e.g., top 10%) are selected as high-potential candidate subpopulations. This screening strategy reflects the practical value of the model—it does not pursue absolute judgment, but rather prioritizes the target cells with the highest differentiation potential based on probability and feature similarity. Mitochondrial genome validation. Single-cell mitochondrial genome sequencing is performed on the selected high-potential subpopulations. The core idea of ​​this experimental step is to verify whether the "high potential" predicted by the model is consistent with the integrity and stability of the mitochondrial genome. Sequencing data can be used to calculate the copy number and variation of mitochondrial DNA (mtDNA) in each cell. Copy number estimation is usually based on sequencing depth normalization methods and can be expressed as: ;in: : Relative value of mitochondrial DNA copy number; Number of sequencing reads in the mitochondrial genome region; : Mitochondrial genome length; Number of sequencing reads in the nuclear genome reference region; The length of the reference nuclear genome fragment. High-potential cells often exhibit: stable or moderately increased mitochondrial DNA copy number, indicating good energy synthesis capacity; intact gene regions encoding key metabolic enzymes (such as ND, COX, and the ATPase family), without large fragment deletions or high-frequency mutations, indicating healthy genome function. Biological validation and closed loop. When cells with high prediction scores simultaneously exhibit stable mitochondrial genomes, normal copy numbers, and intact functional regions, it indicates that the mitochondrial dynamics identified by the model are consistent with the actual cell differentiation potential. This step is not only validation but also forms a closed loop of data-model-biological experiment: the data level provides quantitative characteristics of morphology and metabolism; the model level learns potential patterns and makes predictions; and the experimental level uses genome stability for biological verification.

[0039] Specifically, the logical process for screening high-differentiation potential cardiomyocyte stem cell subpopulations is as follows: A dual screening criterion of prediction score and mitochondrial genome stability is established, and a prediction score threshold and a genome integrity threshold are set. First, cell individuals whose prediction scores reach the high-quality threshold are screened to form a preliminary selection population. Cells with normal mitochondrial DNA copy numbers and no significant structural variations are further screened within the preliminary selection population. Cells that simultaneously meet both the prediction score criterion and the genome stability criterion are identified with specific markers to form the final high-differentiation potential cardiomyocyte stem cell subpopulation library.

[0040] This implementation plan establishes a dual screening criterion. To ensure that the screening results have both predictive value and biological reliability, this step sets two independent but mutually validating criteria: a prediction score threshold. Differentiation potential prediction score output by the model Determined to reflect a cell's potential differentiation capacity; genome integrity threshold. Gene stability indices calculated based on single-cell mitochondrial genome sequencing results are used to reflect the robustness of cellular mitochondrial functional status. Genome integrity indices can be derived by comprehensively considering mitochondrial DNA copy number stability. With structural variation frequency Represented as: ;in: Mitochondrial genome stability score; : Mean mitochondrial DNA copy number of the reference population; : Weighting coefficient, used to balance the influence of copy number and mutation frequency, can be determined by minimizing the misclassification rate between high-potential and low-potential populations in historical samples; : Frequency of mitochondrial structural variations. (Set) As a criterion for genomic stability. Initial selection based on prediction scores. All cardiac stem cells to be evaluated are selected according to their differentiation potential prediction scores. Sort from highest to lowest and filter out The cells are individual cells. These cells, in the model's view, have already shown significant differentiation potential and are potential high-quality candidates. This stage can be understood as the algorithmic "coarse screening." The third step: secondary screening based on genome stability. In the initially selected population, the integrity of mitochondrial DNA is further examined. Specific requirements include: mitochondrial DNA copy number... Falling within the reference range (avoiding extreme high and low states of energy metabolism disorder); frequency of structural variation Below a set threshold (to avoid functional impairment caused by mutation accumulation); no large deletions or high-frequency missense mutations in the coding regions of key metabolic enzymes. These criteria ensure that the selected cells not only have "high predictive scores" but also have a healthy and stable biological basis, capable of maintaining the continuity of energy metabolism and gene expression required for myocardial differentiation. Step 4: The intersection of the double selection process forms a high-potential subpopulation. Ultimately, only cells that simultaneously meet the following two conditions are retained: and This intersection represents cardiomyocyte stem cell individuals that are both identified as having high differentiation potential by the model and verified as structurally stable by genomic testing. The system automatically assigns specific identifiers (such as batch numbers, cell line codes, or unique index tags) to these cells for subsequent culture, storage, and functional validation, forming a subpopulation of highly differentiated cardiomyocyte stem cells. This subpopulation is the final outcome of the entire method: it is not based on a single phenotype or statistical indicator, but rather a composite set of high-quality cells selected by combining three-dimensional evidence of dynamic morphology, metabolomics, and genomic stability. This "prediction-validation-identification" screening mechanism solves a key problem in the background technology—previous assessments of differentiation potential often relied solely on phenotypes or single biochemical indicators, resulting in a high misjudgment rate; while this scheme, through dual standards, integrates statistical significance with molecular biology, making the screening results more reproducible, interpretable, and applicable.

[0041] Please see Figure 2 A differentiation potential assessment system for cardiomyocyte stem cells includes the following modules: a mitochondrial dynamic imaging module, a metabolic correlation analysis module, a differentiation potential modeling module, and a screening and validation module. The mitochondrial dynamic imaging module is used to continuously and dynamically image the mitochondrial network morphology of single cardiomyocyte stem cells under hypoxia-induced conditions using a live-cell workstation, and quantifies the degree of network connectivity and mitochondrial cristae remodeling state using morphometric algorithms to establish a mitochondrial dynamic remodeling atlas at single-cell resolution. The metabolic correlation analysis module is used to perform spatiotemporal correlation analysis between the mitochondrial dynamic remodeling atlas and synchronously acquired cellular energy metabolome data to identify increased mitochondrial network connectivity, accompanied by… The model identifies specific phenotypic patterns of cristae remodeling and simultaneous accumulation of tricarboxylic acid cycle intermediates. A differentiation potential modeling module constructs a training set containing multidimensional features of mitochondrial morphology and metabolome based on the identified phenotypic patterns. A differentiation potential prediction model is established using a random forest algorithm, where the model selects precursor mitochondrial remodeling features that contribute most to differentiation potential prediction by calculating feature importance weights. A screening and validation module assesses the differentiation potential of cardiomyocytes in the batch to be evaluated based on the differentiation potential prediction model. It also performs functional validation of high-scoring cell subpopulations by combining single-cell mitochondrial genome copy number variation profiles, selecting cardiomyocyte subpopulations with high differentiation potential.

[0042] In this implementation scheme, the mitochondrial dynamic imaging module continuously and dynamically observes and quantifies the mitochondrial network morphology of individual cardiomyocytes, forming a single-cell resolution mitochondrial dynamic reconstruction atlas. Using a live-cell workstation (which can be understood as an experimental platform integrating microscopic imaging, environmental control, and automated operation), the physiological state of the cells is maintained under hypoxia-induced conditions, simulating the microenvironment in the early differentiation stage of cardiomyocytes. Morphometric algorithms are used to quantify the number of branch points, branch length, and loop closure of the mitochondrial network, describing the mitochondrial network connectivity; simultaneously, the texture complexity and orientation characteristics of the mitochondrial cristae are analyzed. The mitochondrial dynamic reconstruction atlas output by the module provides structural phenotypic data for subsequent correlation analysis. Mitochondrial cristae: the folded structure formed by the inner mitochondrial membrane, the main site of oxidative phosphorylation; network connectivity: describes the density of the connected network formed by mitochondria through branches and loops. The metabolic correlation analysis module spatiotemporally correlates the mitochondrial dynamic reconstruction atlas with energy metabolome data to identify specific phenotypic patterns. Microfluidic chips were used to periodically collect trace amounts of culture supernatant, and liquid chromatography-mass spectrometry was used to detect changes in the concentration of tricarboxylic acid cycle intermediates (such as citric acid, α-ketoglutarate, and succinic acid). Metabolite concentration change curves were matched with mitochondrial network dynamic parameters using a time registration method to form a morphological-metabolic coupling matrix. Multivariate statistical analysis or cluster analysis was used to identify cell populations exhibiting synergistic morphological and metabolic changes within the same time window, which displayed specific phenotypic patterns of high differentiation potential. Microfluidic chips enable precise sampling by controlling tiny liquid channels; spatiotemporal correlation analysis considers the correlation between time series and spatial / structural data. A differentiation potential modeling module constructs a multidimensional training set and establishes a differentiation potential prediction model based on the identified specific phenotypic patterns. Mitochondrial morphological features (network branching, loop closure, cristae complexity) are combined with metabolic features (TCA intermediate concentration time series) to form a multidimensional sample feature matrix. A random forest algorithm was used to build a prediction model, and the nonlinear mapping relationship between early mitochondrial dynamic features and differentiation potential was optimized through decision tree node splitting. The importance weights of features are calculated to screen for precursor mitochondrial remodeling features that contribute most to differentiation potential, providing candidate indicators for subsequent functional validation. The random forest algorithm, an integrated decision tree machine learning method, is suitable for handling high-dimensional, multi-feature data; precursor features are early indicators that can predict subsequent biological outcomes. The screening and validation module screens cells based on prediction model scores and validates high-potential subpopulations using single-cell mitochondrial genome analysis. The mitochondrial dynamic remodeling and metabolic data of the cells to be evaluated are input into the model to obtain a differentiation potential prediction score for each cell. Single-cell mitochondrial genome sequencing is performed on the top-ranked cell subpopulations to analyze DNA copy number and the integrity of key gene coding regions, validating genome stability.By employing a dual screening approach combining predictive scoring and genomic stability, a final high-differentiation potential cardiomyocyte stem cell subpopulation library is established, providing reliable cell resources for subsequent culture or clinical applications. Single-cell mitochondrial genome sequencing: Complete sequencing of mitochondrial DNA in individual cells is performed to assess genomic integrity and variation. Dual screening: Combining predictive model output with experimental validation ensures that the selected cells possess both high potential and a stable biological basis.

[0043] In summary, this application has at least the following effects:

[0044] A method and system for assessing the differentiation potential of cardiac stem cells (CSCs) are presented. A mitochondrial dynamic imaging module continuously records the mitochondrial network morphology and cristae remodeling of CSCs under hypoxia-induced conditions, accurately characterizing the structural phenotypes of early-stage differentiated cells and providing a high-resolution data foundation for subsequent analysis. A metabolic correlation analysis module spatiotemporally correlates mitochondrial network dynamic parameters with the concentration of tricarboxylic acid cycle intermediates, identifying specific phenotypic patterns with high differentiation potential and achieving joint assessment of morphological and metabolic characteristics, thus enhancing the biological relevance of potential prediction. A differentiation potential modeling module trains and screens multidimensional mitochondrial morphological-metabolic features based on a random forest algorithm, forming a highly accurate prediction model and identifying precursor mitochondrial remodeling features, providing reliable indicators for early prediction. A screening and validation module combines the prediction model score with single-cell mitochondrial genome integrity, enabling the screening of CSC subpopulations with high differentiation potential and genomic stability, providing high-quality cell resources for clinical applications or subsequent research. The overall method achieves automated acquisition, quantitative analysis, and model prediction of multimodal data, avoiding the limitations of subjective human judgment and single indicators in traditional methods, and can efficiently and accurately evaluate the differentiation potential of CSCs.

[0045] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0046] This invention is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0047] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0048] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0049] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0050] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for assessing the differentiation potential of myocardial stem cells, characterized in that, Includes the following steps: S1. Under hypoxia-induced conditions, the mitochondrial network morphology of a single cardiomyocyte stem cell was continuously and dynamically imaged using a live-cell workstation. The degree of network connectivity and the mitochondrial cristae remodeling state were quantified using morphometric algorithms to establish a dynamic mitochondrial remodeling atlas at single-cell resolution. S2. Spatiotemporal correlation analysis was performed between the mitochondrial dynamic remodeling map and the synchronously acquired cellular energy metabolome data to identify a specific phenotypic pattern characterized by increased mitochondrial network connectivity, accompanied by cristae remodeling and the simultaneous accumulation of tricarboxylic acid cycle intermediates. S3. Based on the identified specific phenotypic patterns, a training set containing multidimensional features of mitochondrial morphology-metabolome is constructed. A differentiation potential prediction model is established using the random forest algorithm. The model selects the precursor mitochondrial remodeling features that contribute the most to the prediction of differentiation potential by calculating the feature importance weights. S4. The differentiation potential of the myocardial stem cells in the batch to be evaluated was assessed based on the differentiation potential prediction model, and the high-scoring cell subpopulation was functionally validated by combining the single-cell mitochondrial genome copy number variation spectrum, and the myocardial stem cell subpopulation with high differentiation potential was screened out. The specific process of establishing a dynamic mitochondrial remodeling atlas at single-cell resolution by quantifying the degree of network connectivity and the mitochondrial cristae remodeling state using morphometric algorithms is as follows: The acquired temporal 3D images were preprocessed by denoising and segmentation to extract the mitochondrial network backbone structure at each time point; The degree of network connectivity is quantified by calculating the number of branch points, branch length, and loop closure parameters of the network using graph theory algorithms. Simultaneously, the orientation and complexity features of the cristae inside the mitochondria are extracted using a texture analysis algorithm; By integrating the number of network branch points, branch length, loop closure, orientation characteristics and complexity characteristics of the mitochondrial inner cristae according to time series, a reconstruction atlas reflecting the dynamic changes in the mitochondrial network topology and internal cristae remodeling of a single cardiomyocyte in the early differentiation stage is constructed.

2. The method for assessing the differentiation potential of myocardial stem cells according to claim 1, characterized in that: The specific process of performing continuous dynamic imaging of the mitochondrial network morphology of a single cardiomyocyte stem cell using a live-cell workstation is as follows: A stable hypoxic environment, lower than the partial pressure of atmospheric oxygen, was maintained in a closed culture chamber, and live cell staining was performed using a specific fluorescent probe sensitive to mitochondrial membrane potential. Three-dimensional structural images of the mitochondrial network within a single cardiomyocyte are automatically acquired at preset time intervals using the time-series scanning mode of confocal microscopy. Meanwhile, the dissolved oxygen concentration, pH value, and temperature parameters in the culture chamber are monitored and recorded in real time by an environmental controller to ensure the stability of the cell physiological state during imaging.

3. The method for assessing the differentiation potential of myocardial stem cells according to claim 1, characterized in that: The specific process of performing spatiotemporal correlation analysis between the mitochondrial dynamic reconstruction map and the simultaneously acquired cellular energy metabolome data is as follows: While performing live cell imaging, a microfluidic chip was used to collect a small amount of culture supernatant at regular intervals, and liquid chromatography-mass spectrometry was used to detect the concentration changes of tricarboxylic acid cycle intermediates. The metabolite concentration time curves were time-registered with the mitochondrial dynamic reconstruction map. A correlation matrix between mitochondrial morphological parameters and metabolite concentrations was established using multivariate statistical analysis to identify morphological-metabolic feature combinations that undergo synergistic changes within the same time window.

4. The method for assessing the differentiation potential of myocardial stem cells according to claim 3, characterized in that: The specific process by which the phenotypic pattern of increased mitochondrial network connectivity, accompanied by cristae remodeling and simultaneous accumulation of tricarboxylic acid cycle intermediates was identified is as follows: Cell populations that meet the following three conditions were selected by cluster analysis: the number of mitochondrial network branch points maintained an increasing trend in continuous observation period and the loop closure reached the preset standard; cristae texture analysis showed that the complexity characteristic value of the internal membrane structure exceeded the basic threshold; and the concentrations of intracellular citrate, α-ketoglutarate and succinic acid showed a stable upward trend at the same time node. Cell populations that meet the above three conditions are defined as a specific phenotype pattern of myocardial stem cells with high differentiation potential.

5. The method for assessing the differentiation potential of myocardial stem cells according to claim 4, characterized in that: Based on the identified specific phenotypic patterns, a training set containing multidimensional features of mitochondrial morphology and metabolome is constructed. The logical process of establishing a differentiation potential prediction model using the random forest algorithm is as follows: The original feature pool is formed by extracting the dynamic change curve of the number of mitochondrial network branch points, the evolution trajectory of loop closure, the sequence of cristae complexity feature values, and the time series data of tricarboxylic acid cycle intermediate concentrations from the validated training samples. The above-mentioned dynamic morphological and energy metabolism features of mitochondria are standardized and transformed by feature engineering to construct a sample-feature matrix. The random forest algorithm employs a parallel training mode with multiple decision tree structures, and optimizes node splitting based on the principle of minimizing the Gini coefficient. Through multiple rounds of iterative training, the model is able to learn the nonlinear mapping relationship between early dynamic characteristics of mitochondria and final differentiation potential.

6. The method for assessing the differentiation potential of myocardial stem cells according to claim 5, characterized in that: The specific process of selecting the precursor mitochondrial remodeling features that contribute the most to the prediction of differentiation potential by calculating feature importance weights is as follows: The contribution of each feature in the splitting of the decision tree node is calculated based on the out-of-bag error, and the influence of each feature on the prediction accuracy of the model is quantified by evaluating the importance of feature ranking. By analyzing the ranking of mitochondrial network connectivity-related features and cristae remodeling parameters in the weight distribution, feature combinations with importance weights exceeding a preset threshold were selected and defined as precursor mitochondrial remodeling feature sets with early predictive value.

7. The method for assessing the differentiation potential of myocardial stem cells according to claim 1, characterized in that: The specific process of assessing the differentiation potential of cardiomyocyte stem cells in the batch to be evaluated based on the differentiation potential prediction model, and performing functional validation of high-scoring cell subpopulations by combining single-cell mitochondrial genome copy number variation profiles, is as follows: The mitochondrial dynamic remodeling map and metabolome data of the cells to be evaluated are input into the trained prediction model to obtain the differentiation potential prediction score of each cell and sort them accordingly. Single-cell mitochondrial genome sequencing was performed on cell subpopulations ranked in the top percentile of predicted scores. By analyzing mitochondrial DNA copy number variation and the integrity of key gene coding regions, the genomic stability of high-potential cell subpopulations was verified.

8. The method for assessing the differentiation potential of myocardial stem cells according to claim 7, characterized in that: The logical process for screening out a subset of cardiac stem cells with high differentiation potential is as follows: Establish a dual screening criterion of prediction score and mitochondrial genome stability, and set prediction score threshold and genome integrity threshold; First, select individual cells whose predicted scores reach the quality threshold to form an initial selection group; Cells with normal mitochondrial DNA copy number and no major structural variations were further screened from the initial population; Cells that simultaneously meet the prediction score criteria and the genomic stability criteria will be identified with specific markers to form the final highly differentiated potential cardiomyocyte stem cell subpopulation pool.

9. A differentiation potential assessment system for cardiac stem cells, applied to the differentiation potential assessment method for cardiac stem cells according to any one of claims 1-8, characterized in that, It includes the following modules: mitochondrial dynamic imaging module, metabolic association analysis module, differentiation potential modeling module, and screening and validation module; The mitochondrial dynamic imaging module is used to continuously and dynamically image the mitochondrial network morphology of a single cardiomyocyte stem cell under hypoxia-induced conditions using a live-cell workstation. It quantifies the degree of network connectivity and the mitochondrial cristae remodeling state using morphometric algorithms to establish a mitochondrial dynamic remodeling atlas at single-cell resolution. The metabolic correlation analysis module is used to perform spatiotemporal correlation analysis on the dynamic mitochondrial remodeling map and the synchronously acquired cellular energy metabolome data to identify specific phenotypic patterns of increased mitochondrial network connectivity, accompanied by cristae remodeling and the simultaneous accumulation of tricarboxylic acid cycle intermediates. The differentiation potential modeling module is used to construct a training set containing multidimensional features of mitochondrial morphology-metabolome based on the identified specific phenotypic patterns. A differentiation potential prediction model is established through the random forest algorithm. The model selects the precursor mitochondrial remodeling features that contribute the most to the differentiation potential prediction by calculating the feature importance weights. The screening and validation module is used to assess the differentiation potential of the myocardial stem cells in the batch to be evaluated based on the differentiation potential prediction model, and to perform functional validation of high-scoring cell subpopulations by combining single-cell mitochondrial genome copy number variation spectrum, thereby screening out myocardial stem cell subpopulations with high differentiation potential. The specific process of establishing a dynamic mitochondrial remodeling atlas at single-cell resolution by quantifying the degree of network connectivity and the mitochondrial cristae remodeling state using morphometric algorithms is as follows: The acquired temporal 3D images were preprocessed by denoising and segmentation to extract the mitochondrial network backbone structure at each time point; The degree of network connectivity is quantified by calculating the number of branch points, branch length, and loop closure parameters of the network using graph theory algorithms. Simultaneously, the orientation and complexity features of the cristae inside the mitochondria are extracted using a texture analysis algorithm; By integrating the number of network branch points, branch length, loop closure, orientation characteristics and complexity characteristics of the mitochondrial inner cristae according to time series, a reconstruction atlas reflecting the dynamic changes in the mitochondrial network topology and internal cristae remodeling of a single cardiomyocyte in the early differentiation stage is constructed.

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