Advances in Cell Cycle Analysis

Chromosome segregation, cell division, and all other processes of cell proliferation are central to cancer progression, aging, the success of stem cell therapies, and many other research fields. The cell cycle is highly coordinated and regulated, as well as adaptive and heterogeneous on the single-cell level (Stallaert et al., 2022). Accordingly, computational tools will benefit such lines of inquiry, as in cell cycle transcriptome dynamics (Chervov and Zinovyev, 2022).

“The field of cell cycle analysis is continuously advancing through innovations in technologies and protocols,” explains Sharon Sanderson, Flow Cytometry Application Scientist and Product Manager at Bio-Rad Laboratories. “Single-cell sequencing and high-throughput imaging continue to enhance our understanding of cell cycle progression and its relevance in biological processes and disease, but non-toxic approaches are required for long-term cell proliferation studies.” The pertinence of cell cycle analysis is perhaps best indicated by its global market size: estimated to reach over $16 billion by 2030 (Market Research Future, 2024).

How can cell cycle analysis advance your research program? Here, we provide an overview of representative commercial solutions as well as recent research advances in cell cycle analysis tool and method development.

Flow cytometry remains at the forefront

Flow cytometry remains the workhorse of cell cycle analysis. According to Kamala Tyagarajan, Director of Flow Cytometry Assays and Applications at Cytek Biosciences, “The Cytek Northern Lights full spectrum flow cytometry platform delivers powerful functionality in performing high parametric multiplexed assays as well as the ability to run simpler or conventional low parameter analyses with ease. Full spectrum flow cytometers collect the entire spectrum of light for each fluorophore versus the discrete wavelengths detected by conventional instruments. This provides flexibility in using and combining a wide variety of fluorochromes and reagents, even if the emission peaks overlap.”

“The Cytek Northern Lights system demonstrates high sensitivity and resolution in the detection of populations of interest, which is important for cell cycle analysis,” Tyagarajan adds. “The ability to identify autofluorescence as a separate parameter, and then extract autofluorescence from the fluorescent populations, enhances the resolution of challenging samples and can provide advantages with some cell cycle dyes and fixation conditions. Most importantly, detection based on spectral differentiation allows for multiparametric analysis whether it is with other cell cycle markers or in combination with phenotypic markers.”

Classic cell cycle dyes are compatible with flow cytometry but have substantial limitations, such as incompatibility with live-cell analysis. Cell proliferation indicators or biomarkers are alternative biomolecular imaging targets that can correspond to the state of the cell cycle. “alamarBlue provides a rapid, economical way to quantitatively measure cell proliferation and cytotoxicity in various human and animal cell lines for time course studies, and is a safer alternative to tetrazolium dyes,” notes Sanderson. “alamarBlue does not interfere with cellular respiration and function, and its proprietary buffering agent makes it highly stable. Cells remain fully functional and viable in the presence of the indicator, allowing for a range of research applications including drug susceptibility and metabolism studies, and it is easy to scale-up for high-throughput assays.”

"alamarBlue is a ready-to-use reagent containing the cell permeable and non-toxic indicator dye, resazurin," Sanderon adds. "Resazurin is a redox indicator that both fluoresces and undergoes colorimetric change (from blue to red) in response to cellular metabolic reduction. The reagent is therefore a direct indicator of cell health through the detection of oxidation levels during respiration, quantitatively measuring cell viability and cytotoxicity. These unique properties enable in-depth cell metabolism studies, for example, in identifying the role of advanced glycation end products in complications of diabetes."

Frans Ramaekers, Chief Scientific Officer of Nordic-MUbio, an Absolute Biotech company, emphasizes that flow cytometric analyses are no longer limited to cell surface molecules: “By using FIX&PERM, flow cytometric analysis of intracellular (cytoplasmic and nuclear) antigens has become as easy as surface antigen studies. FIX&PERM solutions can be applied in both an automated as well as a non-automated setting to study peripheral blood cell samples, bone marrow aspirates, mononuclear cell suspensions, or cell suspensions prepared from solid tissue and in vitro cultured cells.”

“State-of-the-art multiple-color flow cytometry, using FIX&PERM combined with Ki-67 and Bcl-2 on the one hand, and extensive panels of bone marrow cell differentiation markers on the other, allows detailed analysis of the proliferative and anti-apoptotic activity in different subtypes of maturing hematopoietic (progenitor) cells in normal bone marrow and bone marrow aspirates from patients with myeloid malignancies,” Ramaekers continues. “This has led to a better understanding of the underlying biology and pathogenesis of myeloid malignancies, and paved the way for integrating these important parameters into clinical practice.”

Modern complements to flow cytometry

Many researchers have combined flow cytometry with other modalities, such as microfluidics, to enhance its utility in clinical practice and basic research (Pang et al., 2023). For example, imaging flow cytometry overcomes the lack of single-cell resolution in flow cytometry and the low-throughput limitation of optical microscopy (Rees et al., 2022). In a preprint, Howell et al. (2023) used Cytek’s technology to evaluate Leishmania mexicana DNA replication, cell shape, and mitotic state. An ongoing challenge is how to optimize automation in data analysis for imaging flow cytometry.

Accordingly, Pozzi et al. (2023) reviewed the data analysis challenges of imaging flow cytometry that can be addressed by artificial intelligence. One, image processing should be conducted accurately on the microsecond scale for cell classification such as senescence state, even when faced with imaging artifacts or other unexpected inputs. Two, assisted data automation will reduce the time required for developing a training dataset, such as morphological indicators of the cell cycle. Three, protocol standardization will be necessary to facilitate data interpretation across laboratories.

Versatility of mass cytometry

Mass cytometry is a variant of mass spectrometry that is increasingly popular in cell cycle research. Instead of fluorescence (optical) detection as in flow cytometry and microscopy, biomarkers are labeled with heavy isotopes that are quantitated after cell nebulization. Tissue sections can also be nebulized and thus provide a pseudo-image of biomarker distribution. Using mass cytometry, one can obtain pertinent indicators of cell cycle state and other metabolic characteristics (Zhao et al., 2022). Currently, up to 50 biomarkers can be analyzed simultaneously, with none of the spectral overlap or background interference in optics-based cytometry (Arnett et al., 2023).

Abdul-Aziz et al. (2023) evaluated the utility of mass cytometry for evaluating cell senescence. On a single-cell level in various cell systems, they quantitated p16 expression (a common target in cancer research) and other biomarkers that correspond to the cell cycle. For example, in WI-38 cells, after 10 days of oxidative stress (2 hours every other day), the S-phase cell population decreased (10% versus 4%) and the G0-phase population increased (17% versus 64%) compared with control cells. Because single-cell mass cytometry can be performed in days, the researchers propose that such assays might facilitate monitoring of hematological cancer therapy, stem cell transplantation, and other medical treatments.

Cell cycle analysis is providing solutions to antimicrobial resistance (Barbuti et al., 2023) and continues to have a global impact. Corresponding data analysis can be challenging, especially for applications that are high-throughput and exhibit substantial intra-sample heterogeneity. However, many tools and protocols—including automation technology—are available for simplifying the task. Speak with an industry specialist to identify the cell cycle analysis workflow that is most appropriate for your application.

References

Abdul-Aziz A, et al. (2023). Mass cytometry as a tool for investigating senescence in multiple model systems. Cells 12(16):2045.

Arnett LP, et al. (2023). Reagents for mass cytometry. Chem. Rev. 123(3):1166–1205.

Barbuti MD, et al. (2023). The cell cycle of Staphylococcus aureus: An updated review. MicrobiologyOpen 12(1):e1338.

Chervov A and Zinovyev A (2022). Computational challenges of cell cycle analysis using single cell transcriptomics. arXiv preprint 2208.05229 (accessed Jan 8, 2024).

Howell J, et al. (2023). The use of imaging flow cytometry for rapid, high-throughput and automated analysis of the Leishmania mexicana promastigote cell cycle provides new insights into cell cycle events of short duration. bioRxiv preprint 2023.07.24.550259 (accessed Jan 8, 2024).

Market Research Future (2024). Cell cycle analysis market research report information by product (consumables, instruments, & others), application (cell identification and others), end-user (academics & research institutions, hospitals & diagnostic laboratories, & others) – Global forecast till 2030. Rep. MRFR/LS/0202-HCR, New York.

Pang K, et al. (2023). Advanced flow cytometry for biomedical applications. J. Biophotonics 16(9):e202300135

Pozzi P, et al. (2023). Artificial intelligence in imaging flow cytometry. Front. Bioinform. 3:1229052.

Rees P, et al. (2022). Imaging flow cytometry. Nat. Rev. Methods Primers 2:86.

Stallaert W, et al. (2022). The structure of the human cell cycle. Cell Syst. 13(3):230–240.

Zhao Y, et al. (2022). Physical cytometry: Detecting mass-related properties of single cells. ACS Sens. 7(1):21–36.