Resolving heterogeneity in Lymph Node Stromal Cells using high-dimensional analysis of non-optimized flow cytometry
Mikala E Heon1,2, Eduardo Rosa-Molinar1,2,3,4,5,6
1The University of Kansas, Lawrence, KS, United States.
Insights
Lymph Node Stromal Cells (LNSCs) are diverse and crucial for immune responses, but their heterogeneity complicates *in vitro* replication. Machine learning offers a robust solution to identify changing LNSC populations and overcome flow cytometry analysis challenges.
Area of Science:
- Immunology
- Cell Biology
- Bioinformatics
Background:
- Lymph Node Stromal Cells (LNSCs) are vital for maintaining lymph node structure and function, including immune response regulation.
- LNSCs hold potential for replicating lymph node functions *in vitro* due to their critical roles.
- The inherent heterogeneity of LNSCs presents significant challenges for their study and application.
Purpose of the Study:
- To demonstrate the difficulties in analyzing heterogeneous cell populations like LNSCs, particularly concerning changing population ratios and marker expression.
- To present a machine learning-based approach for more accurate and unbiased assessment of heterogeneous cell populations.
- To overcome limitations in flow cytometry analysis caused by experimental constraints and similar marker profiles.
Main Methods:
- Analysis of heterogeneous cell populations, focusing on Lymph Node Stromal Cells (LNSCs).
- Application of machine learning algorithms to identify and track cell population dynamics over time.
- Utilizing flow cytometry data while addressing challenges posed by similar marker expression and non-optimized controls.
Main Results:
- Demonstrated challenges in flow cytometry analysis of heterogeneous LNSCs due to population variability and marker overlap.
- Successfully applied machine learning to identify changing cell populations, reducing user bias in gating.
- Increased confidence in population identification by integrating multiple algorithms to overcome individual limitations.
Conclusions:
- Machine learning provides a robust method for analyzing complex, heterogeneous cell populations like LNSCs.
- This approach enhances the reliability of flow cytometry data analysis and reduces experimental bias.
- The findings support future research directions for understanding LNSC functions and *in vitro* applications.
Abstract:
Lymph Node Stromal Cells (LNSCs) are a diverse population of cells responsible for maintaining the lymph node environment and regulating the immune response. Given these roles, they have the potential to help replicate lymph node functions invitro. However, LNSCs are challenging to work with due to their high heterogeneity. Here, we demonstrate the challenges of working with heterogeneous cell populations, where ratios between populations can change over time. We show how similar marker expression profiles between populations, along with non-optimized controls due to experimental limitations, can make flow cytometry analysis difficult. To better assess this heterogeneous population, we demonstrate how to use machine learning algorithms to identify changing populations while overcoming the limitations of any single algorithm. This approach reduces the effects of user bias when placing gates while also increasing confidence in population identification. This analysis method is robust, utilizes existing tools, and provides information that can inform various directions of future studies.
