Global life expectancy continues to rise, and now stands at an average of 73 years. [1] It is in these later years that poor blood cell formation can result in myelodysplastic syndromes (MDS), one of the most common blood cancers. It is characterized by a vast range of symptoms, from fatigue and paleness to more severe complications, such as anemia and thrombocytopenia. [2] More disconcertingly, MDS patients show a propensity for leukemic evolution. [3] Despite ongoing efforts, the underlying causes of MDS onset have not been fully uncovered.

Diagnostics relying solely on cytologic and histologic evaluation of peripheral blood and bone marrow cells are often insufficient in disease subtyping. More recently, researchers started exploring the impact of point mutations through whole-genome sequencing, which enhanced precision in predicting disease phenotype outcomes. [4] However, it has also been revealed that the majority of somatic point mutations are not MDS-specific, as they were also detected in other myeloid disorders and even healthy individuals with normal blood cell counts. [5]

MDS is also characterized by rapid disruptions of hematopoiesis – the production of mature blood cells from stem cells – which increases the apoptotic rate of blood cell precursors. Research suggests that immune dysregulation may play an instrumental role in driving apoptosis in MDS. [6] Furthermore, many MDS patients responded positively to immunosuppressive therapies targeting dysregulation, which is believed to be a contributor to MDS. [7] Subsequent research points to a number of immune mechanisms associated with MDS. For example, hematopoietic stem cell growth and differentiation in the bone marrow were shown to be suppressed by T-cell mediated response. [8] Furthermore, inflammatory cytokines [9] and innate immune activators [10] are significantly overexpressed in MDS patients.

Despite these findings, the correlation between immune dysfunction and MDS phenotype has not been fully established, partly due to the heterogeneity of MDS patient groups. The immune development and genetic makeup can greatly vary among patients, leading to different MDS profiles and treatment responses. A personalized approach is needed to determine patient-specific immune profiles and risk factors, which can enable accurate prediction of disease progression and tailor treatment strategies per individual.

Current input for MDS risk assessment involves data, such as patient demographics and comorbidity, blood count, and genomic profiling. Immune profiling can significantly add predictive value to already existing methods.

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Immunophenotyping by Flow Cytometry
Flow cytometry is regarded as the gold standard in immunophenotyping (11), as it can classify mixed cell populations based on multiple parameters. Using an antibody panel, researchers can identify and sort immune cells in a sample based on differential expression of antigens. This helps to identify the key abnormalities common to MDS patients as well as patient-specific aberrations. Therefore, flow cytometry holds immense potential for rapid quantitation of immune markers in MDS patients.

Academic research began to delineate immunophenotypic abnormalities in MDS in the early 2000s. The 2001 study by Stetler-Stevenson et al. was among the first to employ flow cytometry for monitoring immune cells, bone marrow cells, and red blood cells to detect indicators of dysplasia. [12] Further studies applied flow cytometry workflows to evaluate the maturation of neutrophils and monocytes in MDS. These studies revealed abnormal expression levels in various cluster-of-differentiation (CD) antigens, significantly impacting the maturation states of monocytic cells, which are part of the myeloid family [13].

Pain Points and the Need for Standardization
Identifying immune dysfunction markers does not suffice to predict the exact immune phenotype, as each biomarker contributes differently to malignancy and leukemic evolution. This creates a gap between academic insights and outcomes.

In addition, variations in antibody kits, laboratory protocols, data collection, and analysis methods can lead to significant data variability and jeopardize diagnostic validity. That’s why translating flow cytometry into immunophenotyping is only possible through standardization of the procedures.

How Dry Antibody Panels Can Transform Reagent Reliability
One of the factors in flow cytometry standardization is reagent reliability. Monoclonal antibodies (mAbs) are widely used in diagnostics because of their ease of production and high antigen specificity. However, research facilities use a cocktail of mAbs produced in different laboratories under varying conditions and protocols, leading to a variance in the quality of flow cytometry data.

To minimize variability, staff seek must seek dry antibodies produced at a central commercial manufacturer, whereby reagents are dried during the manufacturing process adhering to good manufacturing practices (GMP) protocols. The resulting reagents are stable at room temperature and do not require refrigeration, significantly simplifying storage and shipping.

Antibody panels manufactured adhering to good manufacturing practices (GMP) significantly reduce time to final results and massively reduce variability due to manual preparation. Dry versions should provide similar quality data when compared to liquid, but not always for lyophilized. Commercial manufacturers can also customize antibody panels according to the flow cytometry configuration of the laboratory, the antigens of interest, and the fluorochrome used. Another benefit of dry antibody panels, such as DURAclone panels, is their durability and stability, which enables storage of antibody panels under various conditions without the risk of denaturation or contamination. This allows multicentric and longitudinal monitoring of immune cells to track changes over subsequent years.

Automation in Flow Cytometry Workflows
Another area for improving the quality of immunophenotyping is the automation of manual steps, from sample preparation to data analysis. The use of dry antibody panels solves the sample preparation problem, as laboratory staff no longer must manually mix mAbs only to end up with a non-uniform mixture. Flow cytometry workflows that are expected to deliver high-throughput results in a limited time especially benefit from harnessing the power automation can bring to the lab.

Manual pipetting is highly labor-intensive and error-prone and can be replaced with automated liquid handlers that dispense the reagents into plates. Instrument manufacturers can offer innovative designs, such as load-and-go flow cytometers that congregate sample preparation and analysis in the same platform. This can be especially helpful for laboratories with limited space. It also ensures the technician can handle various reagent types with minimum health risk and human error. Traceability also improves thanks to the ability to barcode reagents.

Flow cytometry data management can also be improved through advanced optics and laboratory information system (LIS) solutions. Next-generation flow cytometry tools contain cutting-edge laser technologies and multi-parameter capabilities to improve accuracy in cell population clustering. In addition, by integrating digital data management software, labs can streamline data entry, sample tracking, and data export.

How Flow Cytometry Standardization Could Transform Research Flow Cytometry
These innovations are making a difference in research efforts around the world. Matteo Giovanni Della Porta, Professor of Hematology at Humanitas University, Milan Italy, has been conducting large-scale studies to define immunological status markers from peripheral blood samples. His  team is also interested in exploring patient responses to hypomethylating reagents to determine which subgroups respond better to treatment.

“To have a simple solution for the analysis of complex data is crucial in terms of providing reliable research results from the experiment,” he said. They collaborated with Beckman Coulter Life Sciences using dry antibody panels to generate immunophenotyping output with very low inter-operator variability.

To generate reproducible immunophenotyping data and confirm validity, the team had to compare statistical results from three different operators performing two runs per day, two repeats per run over five days using two Navios Flow Cytometers. The variability of the specificities for 119 data points in this study is reported in the table below:

“Their DURAClone technology specifically reduced variability and improved the standardization of the results,” Dr. Della Porta stated. “So overall in our personal experience, the implementation of this new technology is associated with a significant improvement of the overall quality of our work and the quality of our laboratory results.”

The company also assisted the research team in optimizing their immunophenotypic panel as well as flow cytometry instrumentation and data analysis. Dr. Della Porta noted that the standardized flow cytometry technologies and technical support from the company significantly improved the quality of their results.

“They provided a significant effort to first assist us to define the best design for our

research panel,” he said. “Secondly, they helped employ data standardization tools to assess the output from the flow cytometric and reagent technologies. The training was very short because we were working with an effective and user-friendly technology.”

According to Dr. Della Porta, collaborations with flow cytometry experts are crucial to propelling flow cytometric immune monitoring into routine practice. “Having concrete support by experts is crucial to becoming familiar with the new technology and to understanding how to effectively employ this technology to address scientific questions across different projects. This kind of collaboration always led to a great confidence in our studies and especially in understanding the methodology for flow cytometric immunophenotyping.”

MDS has various risk factors that require an integrative diagnostic approach. The factors behind MDS-associated immune dysfunctions must be studied thoroughly to improve the understanding of complex MDS onset. By utilizing automation and standardization, laboratories will be able to focus on the basic biology of MDS and immunophenotyping through genomic, morphological, and immunophenotypic analyses to predict disease progression better, inform treatment decisions, and improve MDS patient outcomes in a personalized manner.

[1] World Health Organization (2020) World Health Statistics. Online. Last accessed on April 17, 2024.
[2] Corey SJ, Minden MD, Barber DL, Kantarjian H, Wang JC, Schimmer AD. Myelodysplastic syndromes: the complexity of stem-cell diseases. Nat Rev Cancer. 2007 Feb;7(2):118-29. doi: 10.1038/nrc2047. PMID: 17251918.
[3] Glenthøj A, Ørskov AD, Hansen JW, Hadrup SR, O’Connell C, Grønbæk K. Immune Mechanisms in Myelodysplastic Syndrome. Int J Mol Sci. 2016 Jun 15;17(6):944. doi: 10.3390/ijms17060944. PMID: 27314337; PMCID: PMC4926477.
[4] Glenthøj A, Ørskov AD, Hansen JW, Hadrup SR, O’Connell C, Grønbæk K. Immune Mechanisms in Myelodysplastic Syndrome. Int J Mol Sci. 2016 Jun 15;17(6):944. doi: 10.3390/ijms17060944. PMID: 27314337; PMCID: PMC4926477.
[5] Kwok B, Hall JM, Witte JS, Xu Y, Reddy P, Lin K, Flamholz R, Dabbas B, Yung A, Al-Hafidh J, Balmert E, Vaupel C, El Hader C, McGinniss MJ, Nahas SA, Kines J, Bejar R. MDS-associated somatic mutations and clonal hematopoiesis are common in idiopathic cytopenias of undetermined significance. Blood. 2015 Nov 19;126(21):2355-61. doi: 10.1182/blood-2015-08-667063. Epub 2015 Oct 1. PMID: 26429975; PMCID: PMC4653764.
[6] Kwok, Brian, et al. “MDS-associated somatic mutations and clonal hematopoiesis are common in idiopathic cytopenias of undetermined significance.” Blood, The Journal of the American Society of Hematology 126.21 (2015): 2355-2361.
[7] Olnes MJ, Sloand EM. Targeting immune dysregulation in myelodysplastic syndromes. JAMA. 2011 Feb 23;305(8):814-9. doi: 10.1001/jama.2011.194. PMID: 21343581.
[8] Vercauteren SM, Starczynowski DT, Sung S, McNeil K, Salski C, Jensen CL, Bruyere H, Lam WL, Karsan A. T cells of patients with myelodysplastic syndrome are frequently derived from the malignant clone. Br J Haematol. 2012 Feb;156(3):409-12. doi: 10.1111/j.1365-2141.2011.08872.x. PMID: 25289412; PMCID: PMC4191868.
[9] Kitagawa, M., et al. “Overexpression of tumor necrosis factor (TNF)-α and interferon (IFN)-γ by bone marrow cells from patients with myelodysplastic syndromes.” Leukemia 11.12 (1997): 2049-2054.
[10] Starczynowski DT, Kuchenbauer F, Argiropoulos B, Sung S, Morin R, Muranyi A, Hirst M, Hogge D, Marra M, Wells RA, Buckstein R, Lam W, Humphries RK, Karsan A. Identification of miR-145 and miR-146a as mediators of the 5q- syndrome phenotype. Nat Med. 2010 Jan;16(1):49-58. doi: 10.1038/nm.2054. Epub 2009 Nov 8. PMID: 19898489.
[11] Gerstner AO, Mittag A, Laffers W, Dähnert I, Lenz D, Bootz F, Bocsi J, Tárnok A. Comparison of immunophenotyping by slide-based cytometry and by flow cytometry. J Immunol Methods. 2006 Apr 20;311(1-2):130-8. doi: 10.1016/j.jim.2006.01.012. Epub 2006 Feb 20. PMID: 16527301. [PubMed]
[12] Stetler-Stevenson, Maryalice, et al. “Diagnostic utility of flow cytometric immunophenotyping in myelodysplastic syndrome.” Blood, The Journal of the American Society of Hematology 98.4 (2001): 979-987.
[13] Craig FE, Foon KA. Flow cytometric immunophenotyping for hematologic neoplasms. Blood. 2008 Apr 15;111(8):3941-67. doi: 10.1182/blood-2007-11-120535. Epub 2008 Jan 15. PMID: 18198345.

Featured image: Hypogranular neutrophil with a pseudo-Pelger-Huet nucleus in MDS.

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