Single-Cell Cell-Cycle State & Lineage Dynamic Inference Service
Our service transforms static transcriptomic snapshots into a dynamic, predictive model of cellular behavior. We provide the tools to distinguish between cell-cycle-driven fluctuations and true differentiation programs, allowing you to pinpoint the exact molecular drivers of disease progression or therapeutic resistance. By resolving the temporal "continuum" of your samples, we help you identify rare progenitor states and validate lineage hierarchies with statistical rigor. Creative Biolabs ensures that your data transitions from descriptive observation to mechanistic insight, providing a clear path for target identification.
Background What We Can Offer Workflow Publication Why Choose Us FAQs Customer Review Related Services Contact Us
Introduction to Single-Cell Temporal Dynamics
Single-cell dynamics represent a profound paradigm shift in modern systems biology, moving the field from static classification to predictive modeling. While traditional transcriptomic analysis provides a frozen view of a population, true cellular life exists as a continuous, often stochastic transition between states. By integrating high-dimensional trajectory inference (TI) with clonal lineage tracing (LT) and mechanistic gene regulatory networks (GRNs), researchers can finally decode the complex logic governing cell fate. Recent landmark literature highlights the necessity of utilizing neural ordinary differential equations (ODEs) and optimal transport to model non-equilibrium systems where proliferation or apoptosis occurs. These advanced frameworks allow for the accurate prediction of unobserved intermediate states, offering a comprehensive "continuum" model essential for accelerating 21st-century drug discovery.
Comprehensive Dynamic Inference Solutions
Creative Biolabs delivers a multifaceted suite of analytical tools designed to extract maximum temporal information from single-cell multi-omics data. Our offerings go beyond basic pseudotime analysis by incorporating the physics of cell growth and the logic of genetic regulation.
High-Resolution Cell-Cycle State Mapping
We accurately classify cells into discrete phases (G1, S, G2/M) using Bayesian inference. This allows for the "de-mixing" of cell-cycle signals from differentiation signals, which is critical when studying tissues with high proliferative indices, such as embryonic stem cells or aggressive tumors.
Directional Lineage Inference via RNA Velocity
By analyzing the ratio of unspliced to spliced pre-mRNA, we predict the future state of individual cells. This "vector field" approach provides a definitive direction to cellular trajectories, resolving ambiguities found in traditional distance-based methods.
Population-Aware Optimal Transport
Leveraging unbalanced optimal transport (UOT), we model the "transport plan" of cells between time points. Unlike standard models, our approach accounts for variable cell birth and death rates, providing a realistic representation of non-equilibrium biological systems.
Causal GRN Reconstruction
We identify the underlying regulatory circuitry that drives lineage commitment. By modeling the interactions between transcription factors and their targets using differential equations, we pinpoint "gatekeeper" genes that serve as high-value therapeutic targets.
Bridge the Gap Between Clonal History and Cell State - Inquire for Detailed Technical Specifications
Service Workflow: From Raw Data to Dynamic Insights
Our process is designed to be comprehensive and collaborative, ensuring that the final computational model is grounded in biological reality.
Publication
This publication introduces a novel deep-learning method that leverages single-cell RNA sequencing and RNA velocity to map gene regulation dynamics throughout the cell cycle. By analyzing unspliced and spliced RNA patterns, the approach generates high-resolution transcriptional phases for individual cells across different models. It identifies major waves of transcription during the G1 phase and systematically characterizes cell cycle entry and exit, offering a robust tool to study proliferation and quiescence without external perturbation.
Fig.1 RNA velocity patterns in single-cell transcriptomics.1
Why Choose Us?
Choosing Creative Biolabs means partnering with a pioneer at the absolute intersection of advanced biotechnology and high-performance machine learning. Unlike generic bioinformatics providers that rely on standard, mass-conserving models, our platform is one of the few global services capable of deploying UOT. This allows us to accurately model biological systems where population sizes fluctuate due to variable birth and death rates—a critical requirement for oncology, immunology, and regenerative medicine studies. With over 20 years of experience in lead generation and therapeutic development, we provide the deep biological context and PhD-level interpretation necessary to transform raw data into actionable drug targets. We don't just deliver algorithms; we deliver validated mechanistic insights tailored to your specific project goals.
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FAQs
Can you perform inference on samples with different growth rates?
Yes. Our use of UOT allows us to accurately model populations where mass is not conserved, such as expanding tumor cells or dying progenitor populations.
Is time-series data a requirement?
No. While time-series data is beneficial, our RNA velocity and neural ODE frameworks can often infer temporal directionality from a single asynchronous "snapshot" sample.
What makes your GRN inference better than correlation?
We focus on causal interactions. By modeling gene expression changes over time using differential equations, we identify the actual "drivers" of a transition rather than genes that are simply "along for the ride."
Customer Review
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Enhanced Lineage Resolution
Using Creative Biolabs' inference service in our hematopoietic stem cell research has significantly facilitated the identification of early-priming markers. The inclusion of RNA velocity data provided the directionality we were missing. - Dr. A*son
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Superior GRN Insights
The stochastic GRN modeling helped us find the master regulators driving T-cell exhaustion. The results were published-ready and highly robust. - Prof. M*ld
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How to Contact Creative Biolabs
Creative Biolabs provides the most advanced suite for resolving the temporal complexities of single-cell data. By unifying clonal history with transcriptomic states and mechanistic regulatory networks, we empower your team to discover targets that are invisible to static analysis. Our integration of AI-driven models and deep biological expertise ensures that your research reaches the next milestone with speed and precision.
Empower Your Next Breakthrough - Contact Our PhD-Level Scientists to Discuss Your Custom Project Needs
Reference
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Riba, Andrea, et al. "Cell cycle gene regulation dynamics revealed by RNA velocity and deep-learning." Nature communications 13.1 (2022): 2865. Distributed under Open Access license CC BY 4.0, without modification. https://doi.org/10.1038/s41467-022-30545-8
For Research Use Only | Not For Clinical Use
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