Characterization of the cell surface markers expressed by the immune cell population is critical for investigating the efficacy of therapeutic treatments and disease progression for clinical studies. The typical immunophenotyping workflow involves the use of high dimensional flow cytometry, which can provide a global view of the immune cell population with large panels of surface markers. After the initial immunophenotyping assays, several key surface markers are identified to continue monitoring patient samples from clinical studies, however, the utilization of flow cytometry in this phase may be tedious and time-consuming. First, flow cytometry requires a start-up and shut-down sequence that may take up to 60 min. Second, analyzing multiple samples usually require several washing steps before acquiring data. Finally, the technical learning curve for flow cytometry can require an experienced user. These barriers to the usage of flow cytometry may cause limitations and accessibility issues that may be difficult to overcome, specifically institutions with financial and personnel restrictions. Therefore, it is important to develop a rapid and high-throughput method that can aid the efficiency of immunophenotyping assays for routine surface marker detection.

In recent years, image cytometry systems have been employed for high-throughput cell-based assays (Magnotti et al., 2020; Maldini et al., 2020; Wang et al., 2020; Bell et al., 2021; Huang et al., 2021; Clair et al., 2023; Zurowski et al., 2023). Recently, a novel image cytometry system, the Cellaca® PLX Image Cytometer was developed to provide streamlined analysis of smaller surface marker panels (Revvity Health Sciences, Inc., an indirect parent company of Nexcelom Bioscience, LLC., Lawrence, MA). Image cytometry can be used to rapidly monitor routine surface marker panels to provide a targeted picture of the immune cell populations of multiple patient samples. In general, image cytometry methodology does not require extensive start-up or shut-down procedures and does not require wash steps between samples due to the lack of fluidics, thus minimizing instrument operation time. Furthermore, the image cytometry system employs a simple user interface and pre-made data analysis templates to increase accessibility and minimize the learning curve of the system.

We have developed and verified a high-precision, high-throughput image cytometric screening method for immunophenotypic characterization of patient samples from clinical studies to analyze peripheral blood mononuclear cell (PBMC) samples from two disease cohorts, multiple myeloma (MM) and rheumatoid arthritis (RA). Multiple myeloma is a rare blood cancer that occurs when cancerous plasma B cells (myeloma cells) accumulate in the bone marrow. This accumulation can result in a variety of complications, including the inability to respond to infections due to a decrease in immune cell activation. On the other hand, rheumatoid arthritis is an autoimmune disease that induces chronic inflammation of the joints. Chronic inflammation caused by RA can result in cartilage damage and bone erosion, with systemic consequences including increased risk for cardiovascular illness (McInnes and Schett, 2011). In patients newly diagnosed with MM, VRd induction therapy, the combination of bortezomib (Velcade), lenalidomide (Revlimid), and dexamethasone, is a common approach to reduce the number of myeloma cells prior to chemotherapy and subsequent allogeneic stem cell transplant (Firer et al., 2021; Sidana et al., 2022). Therapies for RA include Disease-modifying antirheumatic drugs (DMARDS), nonsteroidal anti-inflammatory drugs (NSAIDS) for pain management, and targeted inhibition of activated T and B cells (Edwards et al., 2004; Kholodnyuk et al., 2019). These treatments all impact major PBMC subsets and activation markers including PD-1 (Luo et al., 2018). As such, analyzing surface marker expression could be of prognostic value for patients affected by MM or RA. In particular, the analysis of surface marker expression of immune cells could be utilized as a predictive factor for the development of disease, as well as an indicator of treatment efficacy.

In general, we performed immunophenotyping using two surface marker panels (1) Hoechst/CD3/CD56/CD14 and (2) Hoechst/CD3/CD56/CD19 to demonstrate the capability of Cellaca® PLX Image Cytometer to rapidly characterize and optimize surface marker detection in a high-throughput manner. The CD3 (T cells), CD56 (NK cells), CD14 (Monocytes), CD19 (B cells), and CD3/CD56 (NK T cells) population percentages were directly measured for PBMCs from 23 MM and 23 RA samples, where the results were compared to flow cytometry for verification. The immunophenotyping results were comparable between flow and image cytometry showing a coefficient of determination R2 value of 0.98 for the entire data set. In addition, we demonstrate the ability to use image cytometry for quick surface marker titration optimization showing optimal staining volume for various fluorescent surface markers. While flow cytometry provides the optimal workflow for high dimensional analysis of numerous surface markers, the use of image cytometry provides a more efficient and rapid method for smaller surface marker panels. This method can provide a more targeted approach to analyze the expression levels of key surface markers of interest that require continuous monitoring in patient PBMC samples.