AI-driven stem cell differentiation medium optimization system

Abstract

The invention provides a stem cell differentiation medium optimization system. The system comprises a metabolome / transcriptome monitoring module which is used for collecting metabolite concentration and gene expression data in a stem cell culture process in real time; the AI analysis engine is responsible for establishing a correlation model of a metabolic pathway and a differentiation stage; and the dynamic culture medium blending unit can automatically adjust the growth factor combination and concentration according to the model output. The application of the system achieves the remarkable technical effects that compared with a traditional static culture medium, the stem cell differentiation period is shortened by 50% or above; the consumption of growth factors is reduced by 40-60%; meanwhile, the purity of target cells (such as myocardial cells and nerve cells) can be stably improved to 95% + / -3%.

Description

Technical Field

[0001] This invention relates to the field of stem cell culture technology, specifically to an artificial intelligence-based dynamic optimization system for stem cell differentiation culture medium, which achieves intelligent regulation of growth factors through multi-omics data analysis. Background Technology

[0002] Current stem cell differentiation technology suffers from the following drawbacks: firstly, culture medium formulations heavily rely on expert experience for trial-and-error optimization, a time-consuming process typically requiring 6-12 months; secondly, commonly used static formulations cannot respond to the dynamic changes during cell differentiation; thirdly, key components (such as growth factors) are expensive, accounting for over 60% of the total culture cost; and finally, manual operation leads to significant batch-to-batch variations, with differentiation efficiency fluctuating by ±15-30%. From a technological evolution perspective, stem cell differentiation culture has progressed from the first generation of basic culture media (such as DMEM / F12, with differentiation efficiency <30%), to the second generation of adding pre-optimized growth factor combinations, and finally to the third generation of adding specific factors sequentially. However, these methods still have limitations such as reliance on pre-experiments and inability to respond to cell states in real time. To overcome the core deficiencies of existing technologies (especially static culture medium formulations and pre-programmed factor addition strategies), this invention proposes a dynamic feedback control system based on artificial intelligence. Summary of the Invention

[0003] The purpose of this invention is to establish a real-time dynamic mapping relationship between cell state and culture medium components during stem cell differentiation. Based on this relationship, a precise timing control strategy can be used to effectively compress the ineffective window period during differentiation. Simultaneously, the consumption of high-value biological reagents can be significantly reduced, ultimately achieving a standardized culture medium optimization process applicable across different cell lines. The following is a detailed explanation of the technical solution and its advantages.

[0004] See system architecture Figure 1 .

[0005] Core innovations:

[0006] 1. Dual-signal feedback mechanism

[0007] Metabolic signals: Lactate / pyruvate ratio as a warning sign of differentiation deviation

[0008] Transcription signal: SOX17 / OCT4 expression demarcation phase transition

[0009] 1.1 Metabolic homeostasis signal feedback mechanism

[0010] Cellular energy metabolism and differentiation processes are strictly coupled. This system uses the lactate (Lac) / pyruvate (Pyr) ratio as a core metabolic indicator, with an elevated ratio signifying an abnormal increase in glycolytic flux. When the ratio exceeds a threshold of 5.2 (±0.3), it indicates that the cell is under non-physiological metabolic stress, triggering the following regulatory mechanisms:

[0011] a. Positive regulation: Activation of the AMPK pathway promotes mitochondrial biosynthesis and maintains the flux of the tricarboxylic acid cycle by increasing the supply of 0.5-1.8 mM pyruvate derivatives (such as acetoacetic acid).

[0012] b. Negative regulation: When the concentration ratio Lac / Pyr > 7.0 for more than 6 hours, the BMP4 dose is automatically reduced by 30-50% to prevent premature differentiation.

[0013] This mechanism successfully reduced the batch failure rate caused by metabolic abnormalities from 22.3% in the traditional method to 3.1% (n=120 batches).

[0014] 1.2 Transcription program monitoring mechanism

[0015] The temporal expression of core transcription factors constitutes the molecular clock of the differentiation process. The system achieves precise stage control through three key nodes: monitoring of the pluripotency exit window, confirmation of mesodermal morphology, and determination of the initiation of terminal differentiation.

[0016] 1.2.1 Monitoring of the exit window period for pluripotency

[0017] When the expression level of OCT4 (POU5F1) drops to 35% ± 5% of the baseline and continues to decrease (ΔE / Δt < -0.15h⁻¹), it is determined to be the pluripotency exit critical point. At this time, the Wnt3a gradient addition program should be started immediately (initial concentration 8-12 ng / mL).

[0018] This mechanism precisely defines the critical window for stem cells to leave the undifferentiated state by quantifying the dynamic decay of core pluripotency factors. Its biological basis is based on the coupling effect of epigenetic clock and transcriptional oscillation.

[0019] (1) The selection criteria for molecular markers include:

[0020] a. The master regulator OCT4 (POU5F1) is a core regulatory factor in pluripotency networks.

[0021] Functional threshold: Differentiation program is initiated when expression levels are 35% ± 5% below baseline;

[0022] Dynamic characteristics: Half-life 2.8 ± 0.4 hours (determined by fluorescence recovery bleaching technique);

[0023] b. The auxiliary markers are NANOG and SOX2. The former is a "molecular brake" to maintain pluripotency; the latter forms a positive feedback loop with OCT4 (colocalization frequency >92%).

[0024] Experimental evidence: Chromium immunoprecipitation sequencing (ChIP-seq) showed a 68% reduction in OCT4 binding sites during the withdrawal phase (p<3×10⁻⁶). -9 )

[0025] (2) Real-time monitoring technology solution

[0026] Non-invasive mRNA detection using immobilized molecular beacon probes:

[0027] Probe structure: 5'-FAM-CCGCTAAT(OCT4 complementary sequence)TTAGCG-DABCYL-3'

[0028] Sensitivity: ≥18 copies / cell (single-molecule fluorescence verification)

[0029] The confocal fluorescence scanning parameters were: excitation wavelength 488 nm, emission wavelength 510-550 nm, and spatial resolution 0.8 μm / pixel.

[0030] (3) Logic for determining the window period

[0031] Timing validation rules: Continuous monitoring for ≥3 transcription cycles (approximately 8 hours); exclusion of phototoxic interference (validation of SOX2 synchronous decay).

[0032] Typical case: In the human embryonic stem cell H9 lineage, the window period lasted 4.2±0.7h (n=42).

[0033] (4) Differentiation-initiated regulation strategy

[0034] a. Establishment of Wnt signal gradient

[0035] Time point Wnt3a concentration biological function <![CDATA[t0 (Window opening)]]> 12±2ng / mL Blocking the OCT4 self-sustaining circuit <![CDATA[t0+2h]]> 8±1ng / mL Induced mesodermal gene expression <![CDATA[t0+4h]]> 4±0.5ng / mL Prevent excessive suppression of cardiogenic differentiation

[0036] b. Synergistic metabolic regulation

[0037] Simultaneously increase glucose supply to 15mM (baseline value 5mM) to compensate for the surge in energy demand during the early stages of differentiation.

[0038] (5) Experimental verification and optimization

[0039] a. Window period positioning accuracy verification:

[0040] method Window start error Myocardial differentiation efficiency Morphological observation ±18.2h 61％±7％ OCT4 Immunostaining ±6.5h 79％±5％ This monitoring system ±1.3h 96％±2％

[0041] b. Misjudgment correction mechanism

[0042] The differentiation procedure should be terminated if: SOX17 expression ceases within 24 hours of OCT4 decay, or NANOG rebounds by more than 20% from baseline.

[0043] (6) Molecular mechanism analysis

[0044] The theoretical basis of this monitoring strategy stems from three major findings: 1) Bistable switching characteristics: the concentration of the OCT4-SOX2 complex exhibits a bistable bifurcation point (verified by a kinetic model); 2) Epigenetic memory effect: the window period is accompanied by the deposition of H3K27me3 markers in the OCT4 promoter region (CUT & Tag confirmed a 5.7-fold increase in enrichment); 3) Metabolic-transcriptional coupling: decreased pyruvate dehydrogenase complex (PDC) activity triggers OCT4 deacetylation (mass spectrometry detected an 82% reduction in acetylation at the Lys73 site).

[0045] (7) Biological significance

[0046] This monitoring mechanism achieves three major breakthroughs: 1) Critical point capture: The accuracy of identifying the "irreversible decision point" of pluripotency exit is improved from the hour level to the minute level (response delay <15 minutes); 2) Dynamic adaptability: The threshold is automatically calibrated according to the characteristics of different stem cell lines (e.g., iPSCs require a more stringent decay rate threshold of 0.18h-1); 3) Pathway synergy: The timing of Wnt signal addition is synchronized with the phase of endogenous β-catenin oscillation (phase difference <π / 10rad), avoiding signal conflict.

[0047] Application Case: In cell therapy for Parkinson's disease, this technology increased the differentiation efficiency of dopaminergic neurons from 68% to 94%, and reduced batch-to-batch variability to 4.7% (compared to >25% for traditional methods).

[0048] All signal processing is implemented through analog electronic hardware. This monitoring mechanism achieves precise control of the stem cell differentiation initiation window through a closed-loop architecture of molecular probe-optical detection-electronic decision-making. Its judgment logic and parameter set constitute the core innovation protected by the patent.

[0049] 1.2.2 Confirmation of Mesodermal Characterization

[0050] When the following two indicators are met: ① SOX17 expression level exceeds the threshold of 4500 copies / μg RNA; ② FOXA2 co-expression coefficient r>0.93 (Pearson correlation) is verified simultaneously, and the BMP signaling pathway is shut down within 12 hours.

[0051] 1.2.3 Determination of Terminal Differentiation Initiation

[0052] When the expression level of CTNT during myocardial differentiation reaches a peak of 40% (approximately 1200 copies / cell), and the calcium transient frequency is simultaneously detected to be ≥0.5Hz (via microelectrode array), a pulsed addition of insulin-like growth factor (IGF-1) is triggered (5 min / h).

[0053] 1.3 Signal Cross-validation Logic

[0054] Metabolic and transcriptional signals mutually reinforce each other through the following biological principles:

[0055] (1) Confidence equation for the differentiation process:

[0056] The parameters of the above equations are defined in the table below:

[0057] symbol Mathematical meaning Biological significance C ≥0 The reliability of the differentiation stage assessment is the threshold for effective decision-making. <![CDATA[T index ]]> >0 Transcriptional Coordination Index: Logarithmic transformation of the expression ratio of key markers <![CDATA[M index ]]> <![CDATA[0<M index ≤1]]> Metabolic homeostasis index: Normalized value of energy metabolism pathway activity <![CDATA[Δt trans ]]> Real number (h) Differentiation stage transition time points based on transcription clock prediction <![CDATA[Δt met ]]> Real number (h) Differentiation stage transition time points based on metabolic trajectory prediction k constants > 0 Time tolerance calibration factor (default value 0.1)

[0058] (2) Experimental verification

[0059] Decision-making effectiveness in hepatocyte differentiation models:

[0060] Time synchronization C mean Differentiation success rate ALB+ cell purity High synchronization (<0.5h) 1.24±0.15 97.3％ 94.8％±2.1％ Moderate synchronization (0.5-2 hours) 0.73±0.09 83.6％ 80.1％±5.3％ Desynchronization (>3 hours) 0.31±0.11 38.4％ 49.2％±9.7％

[0061] Decision rule: When C < 0.6, initiate auxiliary marker detection (such as HNF4α, AFP, etc.).

[0062] (3) Scientific basis of the model

[0063] This equation embodies three major biological principles: 1) Molecular network multiplication effect. Transcription factors and metabolic enzymes form positive feedback (e.g., HNF1α activates glucokinase GCK); 2) Time window locking. Metabolic oscillations (period ≈ 12h) and transcriptional oscillations (≈ 24h) are synchronized through frequency coupling. Experiments show that the synchronization efficiency is optimal when m=1 and n=2; 3) Out-of-synchronization amplification mechanism. The design of the time difference being located in the denominator causes the confidence level to decrease hyperbolically when the signal is out of sync, which is consistent with the "critical point" characteristic of cell differentiation.

[0064] This mathematical model provides a quantifiable dynamic decision-making framework for stem cell differentiation by characterizing the coordinated strength of biological signals and quantifying synchronization accuracy through the reciprocal of time difference. All parameters were determined through biological experiments and are not implemented using computer programming.

[0065] When the confidence level is >0.85, the control command is executed; if the confidence level is <0.6, redundancy detection is initiated (adding the detection of 5 auxiliary markers, including Nanog and c-Myc).

[0066] Experimental validation data (human myocardial differentiation model):

[0067] signal type Response delay Decision accuracy Contribution of regulation Metabolic signals alone 4.2±1.1h 83.7％ 42.5％ Transcription signal alone 8.5±2.3h 91.2％ 38.1％ Dual signal coupling 2.7±0.6h 98.6％ 96.3％

[0068] 1.4 Biological Basis

[0069] The establishment of this mechanism relies on the following findings:

[0070] 1) Metabolic-epigenetic coupling: Lactate accumulation leads to histone H3K27ac deacetylation (ChIP-seq verification, p<0.001), directly inhibiting SOX2 enhancer activity.

[0071] 2) The transcriptional oscillation synchronization OCT4 and GLUT1 promoters have a co-regulatory module (fluorescence resonance energy transfer confirms binding distance <10nm), which explains the direct regulation of glucose metabolism on pluripotency factors.

[0072] 3) The Wnt / β-catenin pathway, a crosstalk node in the pathway, regulates the pyruvate dehydrogenase complex through PDK1 phosphorylation, forming a closed-loop feedback loop of metabolism → transcription → re-metabolism (immunoprecipitation verified the interaction strength KD = 4.2 nM).

[0073] This dual-signal system successfully improved the accuracy of differentiation stage transition identification from ±18h in traditional morphological determination to ±2.5h, providing a molecular-level decision-making basis for dynamic culture medium optimization.

[0074] 2. Factor Concentration Optimization Algorithm

[0075] 2.1 Based on the TGF-β / Wnt pathway interaction model

[0076] 1) The formula for the output growth factor concentration function is:

[0077] [GF]=α·Φ(t)+β·ψ(M glc )+γ·Ω(EPOU5F1)

[0078] Where t is the incubation time, M glc The value represents glucose metabolism rate, EPOU5F1 represents OCT4 expression level; α, β, and γ are weighting coefficients.

[0079] 2) Φ(t) is a time-dependent function, and its formula is:

[0080]

[0081] Where λ is the attenuation constant, ω k (This refers to cell cycle frequency parameters);

[0082] 3)ψ(M glc The formula for the glucose metabolism response function is:

[0083]

[0084] Where η is the Hill coefficient and κ is the half-saturation constant;

[0085] 4)Ω(E POU5F1 The formula for the transcription factor regulatory function is:

[0086]

[0087] Where σ represents the expression threshold and θ represents the activation slope.

[0088] 5) Coefficient Fitting

[0089] growth factors α β γ Fitting error BMP4 0.72 0.15 -0.38 ≤8.2％ VEGF 0.31 0.62 0.09 ≤6.5％ Wnt3a -0.54 0.21 -0.17 ≤7.8％

[0090] 2.2 Description of Function Characteristics

[0091] 1) The time-series oscillation term Φ(t) simulates the biological clock rhythm of the differentiation process, with frequency parameter ω. k Synchronized with the contraction cycle of myocardial cells (measured ω1 = 0.26 rad / h, basal biological rhythm);

[0092] 2) Metabolic sensing term ψ(M) glc The S-shaped relationship between energy metabolism and differentiation efficiency (η = 2.1, κ = 1.8) characterizes this relationship.

[0093] 3) Transcription switch term Ω (E) POU5F1 The threshold response for the expression of pluripotency markers was achieved (σ = 0.75, θ = 4500).

[0094] This equation system describes the dynamic interactions of growth factors through a set of differential equations, and its parameter set constitutes the core system of this patent.

[0095] 3. Hardware Integration Solution

[0096] The microfluidic chip enables reagent injection at the <100μL level, and the optical sensor is used to monitor morphological changes in cell clusters.

[0097] 3.1 Microfluidic Precision Transport System

[0098] The miniaturized culture environment built on organ-on-a-chip technology is the core solution to the problem of spatiotemporal precision in growth factor delivery.

[0099] 3.1.1 Multi-level traffic distribution architecture

[0100] Primary distributor: Divides the culture medium stock solution into 8 independent channels at a ratio of 1:100;

[0101] Two-stage microvalve array: driven by piezoelectric ceramics (response time <50ms), it enables the quantitative injection of growth factors at the nano-level (nL).

[0102] Validation data: VEGF injection volume coefficient of variation CV = 1.8% (n = 500 times).

[0103] 3.1.2 Vascular perfusion design

[0104] Fractal geometry flow channels are used (width gradient variation from 200-50μm);

[0105] The wall shear force was maintained at 0.8-1.2 dyne / cm. 2 (Simulating the capillary environment in the body);

[0106] This reduced the difference in oxygen gradient distribution among cell clusters from ±15% in conventional culture to ±3.2%.

[0107] 3.2 Non-invasive sensor networks

[0108] 3.2.1 Metabolite In-situ Monitoring Unit

[0109] Dual-electrode biosensor:

[0110] Working electrode: Carbon nanotube / Prussian blue composite film (sensitivity 18.5 nA / μM·mm) 2 )

[0111] Reference electrode: Miniaturized Ag / AgCl structure (200 μm in diameter)

[0112] 3.2.1.1 Multi-parameter synchronous monitoring mechanism

[0113] a. Integrated sensor array design

[0114] Parallel detection of metabolites is achieved using a "sandwich" stacked architecture:

[0115] hierarchy Detection target Biometric elements Electrochemical methods L1 (Top Floor) glucose Glucose oxidase (GOx) Ampere method (+0.35V) L2 (Middle Layer) lactic acid Lactate oxidase (LOx) Ampere method (+0.60V) L3 (bottom layer) glutamine Glutaminase / Glutamate Oxidase Impedance spectrum (1-100kHz)

[0116] Spatial isolation: Each layer is separated by a 50μm thick hydrophilic membrane to avoid enzyme cross-interference.

[0117] Timing control: Different levels are activated in a rotating sequence every 30 seconds (duty cycle 1:5).

[0118] b. Metabolic Interaction Resolution Algorithm

[0119] Metabolic network equations were constructed based on Michaelis-Menten kinetic constraints.

[0120]

[0121] Among them κ eff =0.18s -1 Determined by the geometric parameters of the microchannel.

[0122] 3.2.1.2 In-situ calibration and reliability assurance

[0123] a. Dual-mode calibration strategy

[0124] Real-time calibration: Inject standard (containing 5mM lactate + 10mM glucose) every 6 hours to correct sensor sensitivity drift based on response value (typical drift rate <0.8% / day);

[0125] Biological calibration: Monitor the physiologically reasonable range (1.5-20) of the lactate / pyruvate ratio (L / PRatio), and automatically trigger optical verification when L / PRatio > 25 (see Section 3.2.2).

[0126] b. Anti-pollution design

[0127] Electrochemical self-cleaning: Applying a -0.2V pulse (lasting 10s) during detection intervals causes protein desorption.

[0128] Fluid shear force: The design of abruptly contracting flow channels (80 μm wide at the narrowest point) generates a wall shear force >50 Pa.

[0129] 3.2.1.3 Validation of Stem Cell Differentiation Applications

[0130] Experimental model: Human iPSC → cardiomyocyte differentiation

[0131] Metabolic trajectory feature extraction:

[0132]

[0133]

[0134] Key findings:

[0135] When lactate production rate remained >0.55 fmol / cell / h during the orientation phase, it indicated abnormal differentiation (CTNT+ cell proportion decreased to 61% ± 9% vs. normal group 93% ± 4%, p < 0.001).

[0136] 3.2.1.4 Quantitative Comparison of Technological Advantages

[0137] Performance indicators Traditional offline biochemical analysis This in-situ monitoring unit Time resolution 2-4 hours / time Continuous real-time (5 minutes / data point) Spatial resolution Average value of petri dish Single-cell cluster scale (region >100μm) Sample consumption 1 mL of culture medium needs to be drawn. Non-invasive (contact only with waste liquid stream) Cellular interference Disruption of the culture environment Maintaining a sterile closed system Multi-parameter synchronization Multiple equipment testing required Integrated single chip completed

[0138] This unit achieves the following through a ternary synergistic design of enzyme-electrode-fluid: 1) Visualization of metabolic dynamics, capturing for the first time the "lactate fluctuation" phenomenon in stem cell differentiation (a brief peak during the orientation phase lasting 3.2±0.8 hours); 2) Early warning value, with abnormally elevated lactate / pyruvate ratio appearing 18±4 hours earlier than morphological changes (n=35 batches); 3) Pathway mechanism analysis, with HIF-1α stability strongly correlated with lactate production rate (r=0.91, p<0.01), revealing a key node in metabolic reprogramming.

[0139] This in-situ metabolite monitoring unit is one of the core innovations of the patent. Its multi-layer sensing architecture, in-situ calibration method, and metabolic early warning rules constitute a complete technical solution. Experimental verification has shown that it can achieve continuous dynamic monitoring for up to 30 days while maintaining a cell viability rate of >97%.

[0140] 3.2.2 Optical Phase Imaging Module

[0141] Quantitative phase microscopy (QPM): Laser wavelength 658nm (penetration depth > 500μm)

[0142] Dry mass measurement accuracy: 0.25 pg / μm 2

[0143] Morphological criteria:

[0144] Differentiation stage Cell cluster dry mass distribution Circularity threshold Multi-energy maintenance <![CDATA[1.8±0.3pg / μm 2 ]]> >0.85 Mesodermal induction <![CDATA[3.2±0.5pg / μm 2 ]]> 0.65-0.75 Terminal differentiation <![CDATA[5.7±0.9pg / μm 2 ]]> <0.50

[0145] 3.3 Dynamic Response Actuator

[0146] 3.3.1 Gradient Concentration Generator

[0147] Performance parameters:

[0148] index numerical values Gradient build-up time <30s Linear range 0.1-100 ng / mL Spatial resolution 50μm / concentration point

[0149] 3.3.2 Temperature control-mechanical stimulation coupling device

[0150] Thermally responsive hydrogel actuator:

[0151] Material: PNIPAM / gelatin composite material (phase transition temperature 32℃)

[0152] Deformation range: 150-300μm (corresponding to 0.5-5kPa stress)

[0153] Applications in myocardial differentiation:

[0154] Stimulation mode Increased proportion of cTNT+ cells Static culture benchmark value Periodic compression (1Hz) +38.2％ Synchronized electrical stimulation (2ms) +27.5％

[0155] 3.4 Biocompatible Material Systems

[0156] 3.4.1 Contact Interface Processing

[0157] Plasma-grafted polypyrrole coating: the contact angle decreased from 78° to 42° (improving hydrophilicity), and the protein adsorption amount decreased by 89% (BSA adsorption experiment).

[0158] 3.4.23D Culture Support Structure

[0159] Mesoporous silicon scaffold parameters:

[0160] characteristic numerical values Aperture distribution 40-120nm Porosity 92.5％±3.1％ elastic modulus 8.7±1.2kPa

[0161] Cell migration speed increased by 2.3 times (compared to Matrigel).

[0162] 3.5 System Integration Verification Data

[0163] Performance testing of iPSCs in neuronal differentiation:

[0164]

[0165] Key innovation: By combining the mechanical precision of hardware (microfluidics) with the sensitivity of biosensors (electrodes / QPM), the timing error of AI decision-making is controlled within ±2 minutes, achieving true closed-loop control.

[0166] In terms of biological significance, this hardware architecture successfully resolves three major contradictions: 1) The conflict between macroscopic and microscopic scales: the microfluidic chip constructs micrometer-scale culture units on a centimeter-scale substrate, enabling compatibility between single-cell resolution monitoring and population culture; 2) The balance between invasiveness and survival rate: non-contact optical detection (QPM) combined with electrodes with a spacing of <100μm reduces the mechanical damage rate from 9.3% to 0.7%; 3) The bottleneck of static-dynamic transition: the gradient generator can establish a new culture medium environment within 90 seconds, which is 50 times faster than traditional medium changes.

[0167] This integrated solution provides an engineerable hardware paradigm for intelligent stem cell culture, and its material selection and structural design parameters constitute the core content of patent protection.

[0168] originality

[0169] 1. Dual-signal dynamic coupling mechanism

[0170] For the first time, real-time cross-validation of metabolomic (lactic acid / pyruvate ratio) and transcriptomic (OCT4 / SOX17 expression) signals was achieved, and a confidence equation was established to quantify the differentiation process, solving the problem of high misjudgment rate in single-signal decision-making (the misjudgment rate was reduced from 16.3% to 1.4%).

[0171] 2. Deep integration of hardware and biological models

[0172] Structural innovation: The microfluidic chip integrates a three-parameter sensing tower (simultaneous detection of glucose / lactic acid / glutamine); the piezoelectric ceramic microvalve enables precise injection of growth factors at the nano-level (nL) (CV<2%).

[0173] Material innovation: Plasma-grafted polypyrrole coating reduces protein adsorption by 89%, maintaining stem cell pluripotency.

[0174] 3. Non-programmable AI decision engine

[0175] Algorithm hardware implementation: Physical calculation of growth factor concentration function is achieved through analog circuits, avoiding software dependence. Knowledge assets: The parameter set is trained and solidified using seven types of stem cell differentiation data, forming a mass-producible black-box module. Beneficial effects (technical advantages)

[0176] 1. Breakthrough improvement in efficiency

[0177] index Traditional technology This invention Improvement rate Differentiation cycle 14-42 days 6.5-19 days 53-55％ Formula optimization time 6-12 months ≤2 weeks 92％↓ Batch-to-batch differences >20％ <5％ 75％↓

[0178] 2. Significant economic benefits

[0179] Cost items Traditional solution This invention Savings rate Growth factor consumption 850μg / batch 320μg / batch 62％↓ Human intervention time 8.5 hours / batch 0.7 hours / batch 92％↓ Losses from failed batches 18,000 / month 1,200 / month 93％↓

[0180] 3. Increased cell mass

[0181]

[0182] 4. Expanding the universality of technology

[0183] Application scenarios Traditional technological limitations Adaptability of the present invention Stem cell types Applicable only to categories 1-2 Covering 7 categories (ESC / iPSC / MSC, etc.) Differentiation lineage Mainly mesodermal Successful verification of the entire trigerm layer system Production scale <![CDATA[Laboratory level (≤ 10 6 cells)]]> Clinical grade (≥10^9 cells)

[0184] 5. Biosafety Assurance

[0185] Risk control: Dual-signal validation increased the detection rate of abnormal differentiation from 71% to 99.2%.

[0186] Ethical advantages: No gene editing involved, complies with FDA / EMA guidelines for cell therapy products.

[0187] Table of the Relationship between Core Innovation and Industrial Value

[0188] Innovation Industry pain point solutions Economic value assessment Metabolic-transcriptional dual-signal decision The industry problem of unstable batch differentiation Reduce quality control costs by 23,000 RMB / month Nano-upgraded microfluidic precision transport Excessive consumption of high-value growth factors Saves 480,000 reagents per year (per 1,000 liters of production). Non-programmable AI hardware engine Cell factory compliance certification barriers Shorten the GMP certification cycle by 6-8 months

[0189] This system is the world's first intelligent stem cell culture platform to achieve a fully closed-loop "monitoring-decision-execution" process, breaking through the core bottleneck of industrial production of regenerative medicine products. It reduces the cost of CAR-T cell preparation from 38,000 to 15,200, promoting the democratization of cell therapy. Attached Figure Description

[0190] Figure 1 System diagram. a is a system architecture diagram; b is a system decision-making process diagram.

[0191] Figure 2 Illustrations related to the metabolic balance signal feedback mechanism.

[0192] Figure 3A Flowchart of logic for determining the exit window period for pluripotency.

[0193] Figure 3B Flowchart of the dynamic decay pattern of transcription factors.

[0194] Figure 4 : Flowchart of dual-signal cross-validation logic.

[0195] Figure 5 : Graph showing the optimization of factor concentration.

[0196] Figure 6 Hardware integration scheme diagram.

[0197] Figure 7 Gradient growth factor control flowchart. Detailed Implementation

[0198] Example 1: Differentiation of human pluripotent stem cells into cardiomyocytes

[0199]

[0200]

[0201] Validation data:

[0202] index control group This invention Improvement rate Differentiation cycle 14 days 6.5 days 53.6％ cTNT+ cell ratio 78％ 96％ +23％ VEGF consumption 850μg 320μg -62.4％

[0203] Example 2: Differentiation of human iPSCs into dopaminergic neurons

[0204] Technical solution

[0205] 1) Key transcriptional markers

[0206] Pluripotency exit: OCT4 < 40% baseline + NURR 1200 copies / μg RNA;

[0207] Neural progenitor conversion: FOXA2 / LMX1A co-expression r = 0.91;

[0208] Terminal maturation: TH expression level 5000 copies / cell + tyrosine hydroxylase activity ≥2.8 U / mg.

[0209] 2) Dynamic factor regulation

[0210]

[0211] Validation data

[0212] index Traditional solution This invention Improvement rate Differentiation cycle 42 days 19 days 54.8％ TH+ cell ratio 68％ 94％ +38.2％ Electrophysiological activity (peak frequency) 2.1Hz 5.3Hz +152％ BDNF consumption 1.2mg 0.3mg -75％

[0213] Example 3: Differentiation of umbilical cord blood stem cells into hepatoid cells

[0214] Technical solution

[0215] 1. Metabolic-transcriptional dual signals

[0216] Metabolic signals: An α-ketoglutarate / succinate ratio of 3.2 predicts bile duct differentiation tendency. Transcriptional signals: HNF4α 800 copies + AFP < 300 copies define hepatocyte maturation. 2. Gradient growth factor control (see...) Figure 7 .

[0217] Validation data

[0218] Functional indicators Traditional solution This invention Urea synthesis 18±3μg / h / 10^6 cells 42±5μg / h / 10^6 cells CYP3A4 activity 0.8 nmol / min / mg 3.5 nmol / min / mg Ammonia removal rate 38％ 89％ Cost-effectiveness 9,200 / batch 3,500 / batch

[0219] Example 4: Differentiation of dental pulp stem cells into vascular endothelial cells

[0220] Technical solution

[0221] 1. Mechano-biochemical signal coupling

[0222] Fluid shear force: 1.5 dyne / cm 2 Activate VEGFR2 internalization

[0223] Transcription threshold: ANGPT1 addition initiated at 1200 vWF copies.

[0224] 2. Dynamic regulation of three-dimensional culture

[0225] Time window Operating parameters Biological effects 0-48h <![CDATA[Hypoxia (5% O2) + VEGF 15 ng / mL]]> Enhance HIF-1α stability CD31 expression <![CDATA[Switch to normoxia + laminar flow (0.8 dyne / cm 2 )]]> Promote tubule formation PECAM1>2000 Add S1P 0.1μM pulse <![CDATA[Enhanced barrier function TEER > 40 Ω·cm 2 >

[0226] Functional verification

[0227] In vivo transplantation: Mouse hindlimb ischemia model

[0228] Group Blood flow recovery rate (14 days) <![CDATA[Neovascular density (number / mm 2 )]]> Traditional differentiated cells 47％±6％ 28±4 Cells of the present invention 83％±5％ 51±7 Positive control (FGF2) 89％±4％ 55±6

[0229] Example 5: Differentiation of adipose-derived stem cells into islet-like cell clusters

[0230] Technical solution

[0231] Metabolic oscillation synchronization

[0232] When the glucose-stimulated insulin secretion (GSIS) index is 2.0

[0233] Simultaneous addition of nicotinamide (10mM) + Exendin-4 (50nM) stage-specific factor

[0234] stage Core signal Dynamic regulation strategy Endoderm induction CXCR4 / SOX17 co-expression Activin A concentration = 0.8 × e^{-0.05t} ng / mL pancreatic precursor PDX1 / NKX6.10.9r FGF10 decreased gradually (100→10 ng / mL / 3 days) β-cell maturation MAFA pulsed expression <![CDATA[Response to intracellular Ca 2+ oscillation frequency (>0.03 Hz)]]>

[0235] Functional verification

[0236] index Traditional solution This invention GSIS Index 1.8±0.3 5.2±0.6 Insulin levels 2.1 μg / 10^6 cells 8.7 μg / 10^6 cells Sugar response delay 8.2±1.1min 2.3±0.4min Transplant survival rate (90 days) 62％ 94％

[0237] Example 6: Differentiation of embryonic stem cells into corneal epithelial cells

[0238] Technical solution

[0239] Optical-Molecular Dual Monitoring

[0240] Optical: A cell refractive index of 1.38 suggests the initiation of keratinization.

[0241] Molecules: KRT3 / KRT12 ratio 1.5 + PAX 63000 copies; surface tension modulation.

[0242] Differentiation stage Interfacial tension control effect Epithelial shaping Maintaining the gas-liquid interface σ = 72 mN / m Promote tight junction formation Terminal differentiation Reduced to σ = 45 mN / m Inhibit fibroblast transdifferentiation

[0243] Clinical-grade validation

[0244] Rabbit corneal injury model:

[0245]

[0246]

[0247] Example 7: Osteogenic Differentiation of Mesenchymal Stem Cells

[0248] Mineralization potential was predicted by the pyruvate / α-KG ratio, and the dexamethasone addition strategy (0.1-10 nM gradient) was dynamically adjusted, which increased the differentiation efficiency from 65% to 92% and the osteocalcin secretion increased by 4.2 times.

[0249] Summary of core advantages across implementation examples

[0250] Universality Improvement

[0251] Covering the three germ layer differentiation lineages (ectoderm / mesoderm / endoderm)

[0252] Applicable stem cell types: embryonic / induced pluripotent / adult stem cells

[0253] Standardization Breakthrough

[0254] parameter coefficient of variation of traditional scheme coefficient of variation of this invention Differentiation cycle 22-35％ <6％ Symbolic expression 18-40％ <8％ Functional indicators 25-50％ <12％

[0255] Cost-effectiveness: Growth factor consumption was reduced by an average of 67±8%; manual intervention time was reduced by 92% (from 8.5h / batch to 0.7h / batch).

[0256] This system achieves precise dynamic control of stem cell differentiation through a dual-engine approach of real-time metabolic flux feedback and transcriptional oscillation phase locking, providing an industrial solution for regenerative medicine.

[0257] Key advantages

[0258] 1. Time cost: Reduce the 6-month formula optimization cycle to 2 weeks.

[0259] 2. Economic efficiency: The cost of reagents for a single differentiation experiment has decreased from 12,000 to 4,800.

[0260] 3. Universality: It has been proven to be applicable to 7 types of stem cells, including iPSCs, MSCs, and HSCs.

[0261] 4. Standardization: Differentiation between batches < 5% (traditional approach > 20%)

[0262] This system provides a quantifiable and replicable intelligent culture platform for regenerative medicine, breaking through the core bottleneck of stem cell industrial application. This solution requires no programming skills from the user; all AI modules are integrated into the device's operating system as pre-trained models, automatically triggering control commands through biosensor data streams.

Claims

1. A dynamic optimization system for stem cell differentiation culture medium, characterized in that... Include: Metabolomics monitoring module: Real-time detection of lactate, glucose and glutamine concentrations in culture medium via immobilized enzyme electrode array, with a detection limit ≤0.1mM and a response time ≤30 seconds. Transcriptome monitoring module: The mRNA expression levels of OCT4, SOX17, and CTNT are quantified in situ using molecular beacon probes with a sensitivity of ≥18 copies / cell. Decision Engine: Based on the Metabolic-Transcription Dual-Signal Confidence Equation Output growth factor regulation instructions, where T index M is the transcriptional coordination index. index This is a metabolic homeostasis index. Dynamic infusion unit: Achieves precise nanoscale injection of growth factors through a microfluidic gradient generator (volume variation coefficient ≤ 2%), with a response delay ≤ 2 minutes.

2. The system as described in claim 1, characterized in that: The metabolomics monitoring module includes: The three-layer enzyme-functionalized electrode (L1: glucose oxidase, L2: lactate oxidase, L3: glutaminase / glutamate oxidase) is physically isolated between each layer by a 50 μm hydrophilic membrane. The potentiostat circuit operates at +0.35V (glucose) and +0.60V (lactic acid), with a background noise ≤50pA.

3. The system as described in claim 1, characterized in that: The confidence equation satisfies: When SOX17 / OCT4 > 8.2 and ΔE| < 15%, T index ≥2.3; Where s = 0.75, τ = 5.

2.

4. The system as described in claim 1, characterized in that: The dynamic infusion unit includes: A piezoelectric ceramic microvalve array (20 μm aperture, response time < 50 ms) drives the injection of growth factor solution at a rate of 0.5-5 nL / pulse. Fractal geometry flow channel (width gradient change 50-200μm), wall shear force maintained at 0.8-1.2 dyne / cm 2 .

5. The system as described in claim 1, characterized in that: The decision engine is implemented in hardware using analog circuitry and includes: Differential calculation circuit for real-time calculation of transcription factor expression change rate Accuracy ±0.01h-1. The multiplier module executes T index ×M index The calculation error is ≤ ±1.5%.

6. The system as described in claim 1, characterized in that: The growth factor regulation instruction includes a concentration function: Where ω1 = 0.26 rad / h (corresponding to a 24-hour circadian rhythm), and the parameter set {α,β,γ,λ,η,κ,σ,θ} is trained and solidified using 7 types of stem cell differentiation data.

7. The system as described in claim 6, characterized in that: The parameter set for cardiomyocyte differentiation takes the following values:

8. A method for stem cell differentiation, characterized in that... The system according to any one of claims 1-7 comprises the following steps: When metabolic signals show Lac / Pyr > 5.2 and persist for > 3 hours, reduce the BMP4 dose by 30-50%. When the transcription signal shows OCT4 < 35% of baseline and Start the Wnt3a gradient addition program (12→4ng / mL / 4h). The control command is executed when the confidence level C = 0.

85. If C < 0.6, the detection of Nanog and c-Myc auxiliary markers is added.

9. The method as described in claim 8, characterized in that: The stem cells include human pluripotent stem cells, mesenchymal stem cells, and hematopoietic stem cells, and the target cells for differentiation include cardiomyocytes, neurons, hepatocytes, and pancreatic β cells.

10. A regenerative medicine product, characterized in that: The cell products prepared by the method described in claims 8-9 have a purity of ≥92%, functional activity that is ≥35% higher than that of conventional methods, and batch-to-batch variation of <5%.

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