Automated Cell Cycle Analysis of Non-Adherent and Adherent Cells Using a Machine Learning Approach

核心概念

This study presents a comprehensive, automated method for studying the cell cycle in both non-adherent and adherent cells, offering a valuable tool for cellular biology, cancer research, and drug development.

要約

The study introduces a combined approach that allows for the precise measurement of the duration of different cell cycle phases in non-adherent, as well as adherent cells. The method involves a specialized surface to improve cell attachment, the genetically-encoded FUCCI(CA)2 sensor, an automated image processing and analysis pipeline, and a custom machine-learning algorithm.
The key highlights and insights are:

The optimized conditions, using a nanostructured titanium oxide-coated multiwell plate and methylcellulose, enabled the immobilization of non-adherent cells for long time periods (up to 72 hours) without affecting their response to different environmental conditions.

The image processing and data analysis pipelines, utilizing Fiji and R, automated the entire workflow, including cell tracking, filtering of incorrect tracks, and cell cycle phase identification and quantification. This allowed for the analysis of thousands of cells per experimental condition in a fully automated manner.

The machine learning-based approach efficiently assessed the traceability of each cell track, improving the accuracy and reliability of the cell cycle phase assignment.

The method was validated using two different acute myeloid leukemia cell lines, NB4 and Kasumi-1, which have unique and distinct cell cycle characteristics. The impact of different CDK inhibitors on the cell cycle dynamics of the fast-cycling NB4 cells was also measured.

The analysis pipeline was shown to be applicable to both non-adherent and adherent cell types, making it a versatile tool for various cellular biology and cancer research applications.

The entire experimental and analysis workflow was made publicly available, enabling customization and adoption by the research community.

統計

The total average duration of one full cell cycle was quantified as 21.5 h ± 6.5 h (mean ± standard deviation) for NB4 cells and 24.0 h ± 7.8 h for Kasumi-1 cells.
Administration of 50nM Palbociclib extended the G1 phase of the NB4 cells by about 5 hours (from 9.1 h ± 5.1 h to 14.2 h ± 5.7 h in vehicle treated and Palbociclib treated NB4 cells, respectively).

引用

"The capability to track non-adherent cells for up to 72 hours (almost equivalent to three complete cell cycles in rapidly dividing NB4 cells) was achieved by employing SBS-coated glass in combination with the addition of MC to the culture medium."
"Up to about 400 cells in one single experimental condition were quantified, for up to 12 different conditions in a single experiment. This entire analysis process took approximately 2 hours of human involvement, for a total execution time that ranges from 12 to 48 hours, depending on dataset size, that is commissioned to a machine."

抽出されたキーインサイト

Automated workflow for the cell cycle analysis of non-adherent and adherent cells using a machine learning approach

by Hayatigolkha... 場所 www.biorxiv.org 12-21-2023

https://www.biorxiv.org/content/10.1101/2023.12.21.572803v1

深掘り質問

How could this automated cell cycle analysis method be extended to study the effects of different therapeutic agents or environmental factors on the cell cycle dynamics of other cell types, such as primary patient-derived cells or organoids?

This automated cell cycle analysis method can be extended to study the effects of different therapeutic agents or environmental factors on the cell cycle dynamics of various cell types, including primary patient-derived cells or organoids, by adapting the experimental setup and analysis pipeline.

Experimental Setup Modification:

Cell Seeding and Treatment: Adjust the cell seeding density and culture conditions to suit the specific requirements of the cell type under investigation. For primary patient-derived cells, optimization of culture media components and growth factors may be necessary.
Treatment Conditions: Customize the treatment conditions with different therapeutic agents or environmental factors based on the research objectives. Ensure that the concentrations and exposure durations are appropriate for the specific cell type being studied.

Image Acquisition and Analysis:

Fluorescence Markers: Utilize cell cycle markers or genetically encoded probes suitable for the cell type of interest. Ensure compatibility with the imaging system and analysis pipeline.
Image Processing: Tailor the image processing pipeline to account for any unique characteristics of the cell type, such as morphology or fluorescence intensity variations.
Data Analysis: Modify the data analysis workflow to accommodate the specific cell cycle dynamics and phase transitions observed in the new cell type. Adjust thresholds and criteria for cell cycle phase assignment accordingly.

Validation and Optimization:

Validation Studies: Conduct validation experiments to ensure the accuracy and reliability of the automated analysis method for the new cell type.
Optimization: Fine-tune the machine learning model and tracking algorithms to account for any differences in cell behavior or imaging characteristics.

By customizing the experimental setup, image acquisition parameters, and data analysis pipeline, this automated cell cycle analysis method can effectively study the effects of therapeutic agents or environmental factors on the cell cycle dynamics of diverse cell types, including primary patient-derived cells and organoids.

How could the insights gained from this automated cell cycle analysis be integrated with other high-throughput screening approaches, such as drug screening or genetic perturbation studies, to accelerate the discovery of novel cell cycle-targeted therapies?

The insights obtained from automated cell cycle analysis can be integrated with other high-throughput screening approaches, such as drug screening or genetic perturbation studies, to accelerate the discovery of novel cell cycle-targeted therapies through the following strategies:

Combination Screening:

Drug Screening: Combine the automated cell cycle analysis method with high-throughput drug screening assays to evaluate the effects of a large number of compounds on cell cycle progression.
Genetic Perturbation Studies: Integrate genetic perturbation techniques, such as CRISPR-Cas9 or RNA interference, with cell cycle analysis to identify key genes or pathways regulating cell cycle dynamics.

Multi-Omics Integration:

Transcriptomic Analysis: Combine cell cycle analysis data with transcriptomic profiling to correlate gene expression changes with cell cycle phase transitions and identify potential therapeutic targets.
Proteomic and Metabolomic Analysis: Integrate proteomic and metabolomic data to gain a comprehensive understanding of the molecular mechanisms underlying cell cycle regulation and response to perturbations.

Machine Learning and Data Integration:

Machine Learning Models: Develop predictive models that integrate cell cycle analysis data with other high-throughput screening results to identify novel drug candidates or therapeutic combinations.
Data Integration Platforms: Utilize data integration platforms to merge and analyze multi-omics data sets, enabling the discovery of complex interactions and pathways involved in cell cycle regulation.

Validation and Functional Studies:

Follow-up Experiments: Validate the findings from integrated screening approaches through functional studies, such as cell cycle synchronization experiments or in vivo models.
Mechanistic Studies: Conduct mechanistic studies to elucidate the molecular mechanisms underlying the observed effects on cell cycle dynamics and validate potential therapeutic targets.

By integrating insights from automated cell cycle analysis with other high-throughput screening approaches and multi-omics data, researchers can accelerate the discovery of novel cell cycle-targeted therapies and gain a deeper understanding of the complex regulatory networks governing cell division and proliferation.

What are the potential limitations or challenges in applying this approach to study the cell cycle in more complex, heterogeneous cell populations, such as those found in solid tumors?

Applying the automated cell cycle analysis approach to study complex and heterogeneous cell populations, such as those found in solid tumors, presents several challenges and limitations that need to be addressed:

Heterogeneity:

Cell Population Variability: Solid tumors consist of diverse cell types with varying cell cycle dynamics, making it challenging to capture the full spectrum of behaviors with a single analysis method.
Spatial and Temporal Heterogeneity: Tumor microenvironments exhibit spatial and temporal variations in cell cycle progression, requiring advanced imaging techniques and analysis algorithms to account for this heterogeneity.

Technical Challenges:

Imaging Complexity: Solid tumors may have dense and irregular structures, making it difficult to acquire high-quality images for automated analysis. Advanced imaging modalities and sample preparation techniques are needed.
Data Processing: Analyzing heterogeneous cell populations requires sophisticated algorithms to distinguish between different cell types and cell cycle phases accurately.

Biological Complexity:

Cell-Cell Interactions: Interactions between different cell types within solid tumors can influence cell cycle dynamics, necessitating the consideration of paracrine signaling and cell-cell communication in the analysis.
Tumor Evolution: Tumor cells undergo genetic and phenotypic changes over time, leading to clonal evolution and heterogeneity. Longitudinal studies are essential to capture these dynamics.

Validation and Interpretation:

Validation Strategies: Validating automated cell cycle analysis results in heterogeneous cell populations requires robust experimental designs and validation methods to ensure the accuracy of the findings.
Interpretation Challenges: Interpreting the complex interactions and regulatory networks governing cell cycle progression in solid tumors demands interdisciplinary expertise and collaboration between biologists, bioinformaticians, and clinicians.

Ethical and Regulatory Considerations:

Patient-Derived Samples: Working with patient-derived samples raises ethical and regulatory considerations regarding sample acquisition, consent, and data privacy. Compliance with ethical guidelines and regulations is crucial.

Addressing these limitations and challenges in applying automated cell cycle analysis to study heterogeneous cell populations in solid tumors requires a multidisciplinary approach, advanced technologies, and rigorous validation strategies to ensure the reliability and relevance of the findings.